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Advances in the...

Advances in the management of chronic kidney disease

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Peer review
Teresa K Chen , assistant professor 1 ,
Melanie P Hoenig , associate professor 2 ,
Dorothea Nitsch , professor 3 ,
Morgan E Grams , professor 4
1 Kidney Health Research Collaborative and Division of Nephrology, Department of Medicine, University of California San Francisco; and San Francisco VA Health Care System, San Francisco, CA, USA
2 Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
3 Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK
4 Department of Medicine, New York University Langone School of Medicine, New York, NY, USA
Correspondence to: M E Grams Morgan.Grams{at}nyulangone.org

Chronic kidney disease (CKD) represents a global public health crisis, but awareness by patients and providers is poor. Defined as persistent abnormalities in kidney structure or function for more than three months, manifested as either low glomerular filtration rate or presence of a marker of kidney damage such as albuminuria, CKD can be identified through readily available blood and urine tests. Early recognition of CKD is crucial for harnessing major advances in staging, prognosis, and treatment. This review discusses the evidence behind the general principles of CKD management, such as blood pressure and glucose control, renin-angiotensin-aldosterone system blockade, statin therapy, and dietary management. It additionally describes individualized approaches to treatment based on risk of kidney failure and cause of CKD. Finally, it reviews novel classes of kidney protective agents including sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, non-steroidal selective mineralocorticoid receptor antagonists, and endothelin receptor antagonists. Appropriate, widespread implementation of these highly effective therapies should improve the lives of people with CKD and decrease the worldwide incidence of kidney failure.

Introduction

Chronic kidney disease (CKD) affects approximately 10% of the world’s population and is associated with substantial morbidity and mortality. 1 Risks of kidney failure, acute kidney injury, heart failure, cardiovascular disease, and hospital admissions are all heightened in people with CKD. 2 The Global Burden of Disease Consortium projects that CKD will be in the top five conditions contributing to years of life lost by 2040. 3 However, CKD remains under-recognized by both patients and providers. 1 A diverse entity, CKD is most commonly attributed to diabetes or high blood pressure, but many other causes exist, from genetic causes to adverse effects of drugs to autoimmune processes. 2 In this review, we summarize the evidence for current paradigms of disease identification and classification, discuss new equations developed for estimating glomerular filtration rate (GFR) and harmonizing different measures of albuminuria, report major progress in individualized risk estimation of kidney failure and other adverse outcomes both for CKD in general and within specific disease entities, and describe longstanding and novel treatment strategies. Notable advances have been made in both general and cause specific therapies, including sodium-glucose cotransporter-2 (SGLT-2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, non-steroidal selective mineralocorticoid receptor antagonists (MRA), and endothelin receptor antagonists. Finally, we describe major guidelines in CKD and highlight common themes as well as differences in their recommendations.

Sources and selection criteria

We searched PubMed for peer reviewed articles in the English language from 1 January 2010 to 14 July 2023 using the keywords listed in the web appendix. We additionally reviewed reference lists of selected articles, prioritizing randomized controlled trials, systematic reviews, and meta-analyses when possible but also including observational studies and reviews that were of high quality. We included older articles if we deemed them to be of high importance. Finally, we reviewed guidelines from websites of professional societies and advisory committees (for example, the National Institute for Health and Care Excellence (NICE), Kidney Disease: Improving Global Outcomes (KDIGO), US Centers for Disease Control and Prevention, US Department of Health and Human Services, and International Society of Hypertension).

Epidemiology

CKD is a global public health crisis. Recent estimates suggest that more than 700 million people have CKD, with greater burdens in low income and middle income countries. 1 4 Determining the global, regional, and national burden of disease is challenging owing to inconsistent use of estimating equations for GFR, laboratory assay standardization, and albuminuria testing. Despite this, some important observations can still be made. The prevalence of CKD increases with age and is greatest in people over 70 years. 2 In the US, compared with White people, Black people have substantially higher rates of kidney failure, followed by Native Americans, people of Hispanic ethnicity, and people of Asian descent. 5

The most commonly reported risk factors for CKD are diabetes mellitus and hypertension. 6 7 Social determinants of health are also important and likely contribute to racial disparities in kidney disease. Specific genetic variants increase risk of CKD, including variants in the APOL1 and HBB genes that are present in far greater proportions among people of African ancestry. 8 9 10 11 In Central America, Sri Lanka, Egypt, and Central India, defined geographic areas exist where many cases of CKD of unknown cause have been identified. 12 Some experts postulate that heat stress or pesticides may contribute.

Whereas the incidence of CKD is difficult to estimate, reliant as it is on testing for GFR and albuminuria, the incidence of kidney failure with the receipt of replacement therapy (KFRT) is more readily captured. Many countries have developed national registries of patients with kidney failure, allowing comparison of incidence across ages and countries. 13 For example, the countries with the highest incidence of treated kidney failure in 2020 were Taiwan, the US, and Singapore, whereas the countries with the highest prevalence were Taiwan, the Republic of Korea, and Japan. 5

Definition and classification of CKD: cause, GFR, and albuminuria staging

CKD is defined as persistent abnormalities in kidney structure or function for more than three months, manifest as either low GFR or presence of a marker of kidney damage. 2 Specifically, diagnosis requires one or more of the following: albuminuria, defined as an albumin-to-creatinine ratio (ACR) ≥30 mg per gram of creatinine (approximately ≥3 mg/mmol) or albumin excretion of ≥30 mg/day; GFR <60 mL/min/1.73 m 2 ; abnormalities on urine sediment, histology, or imaging; electrolyte or other abnormalities attributed to tubular disorders; or history of kidney transplantation. The KDIGO heat map helps with understanding of overall risk (low, moderately increased, high, and very high) of patients according to level of albuminuria (A category), level of GFR (G category), and cause of disease ( fig 1 ), such that people with normal estimated GFR but higher albuminuria have a similar risk to people with moderately reduced estimated GFR and no albuminuria.

Kidney Disease: Improving Global Outcomes heat map with guidance on monitoring. 2 Numbers in boxes indicate recommended frequency of monitoring (number of times per year). Colors denote risk as follows: green (low risk), yellow (moderately increased risk), orange (high risk), and red (very high risk). CKD=chronic kidney disease; GFR=glomerular filtration rate

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Clinical manifestations of CKD

Albuminuria.

Albuminuria is often the first sign of kidney damage, and its detection drives many treatment decisions. 2 The prevalence of albuminuria in people with diabetes or hypertension is estimated to be 32% and 22%, respectively. 14 However, only a minority of patients receive urine screening tests. 14 15 For example, the mean albuminuria screening rates across health systems in the US were 35% among adults with diabetes and 4% among adults with hypertension. 14

The gold standard for assessing albuminuria is either a sample collected mid-stream from an early morning urine void or a 24 hour urine collection; however, in situations where this is not possible, a spot collection is reasonable. 2 Quantification of albumin is preferred over that of total protein. 2 16 This preference is because the sensitivity of the total protein assay to different protein components can vary by laboratory, as well as the fact that proteinuria assessments do not easily discriminate A1 and A2 categories. Both urine albumin and urine protein are typically indexed to urine creatinine to account for differences in dilution, as urine ACR or urine protein-to-creatinine ratio (PCR). Dipstick protein assessment is generally more economical than both methods; however, like PCR, dipstick assessment can be insensitive in A1 and A2 categories. Although conversion calculators exist to aid in the harmonization of ACR and PCR measures; they do not work well at lower ranges of albuminuria. 17 18

The second axis for CKD classification focuses on GFR. 2 The gold standard for assessing GFR is direct measurement from clearance of an exogenous filtration marker such as iohexol or iothalamate; however, this is relatively cumbersome and rarely done in clinical practice. Instead, GFR is usually estimated by using plasma or serum concentrations of endogenous filtration markers, such as creatinine and cystatin C, and demographic variables. Early equations for adults, such as Modification of Diet in Renal Disease (MDRD) and CKD Epidemiology Collaboration (CKD-EPI) 2009 equations, used filtration markers along with age, sex, and race (Black versus non-Black) to estimate GFR. 19 20 21 The newer European Kidney Function Consortium equation, which allows for seamless GFR evaluation from infancy to old age, uses a population specific divisor to adjust creatinine values (for example, separate values for Black European and White European populations). 22 However, the use of race in GFR estimation has faced strong criticism and, in 2021, the US based American Society of Nephrology-National Kidney Foundation Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease recommended immediate adoption of the race-free CKD-EPI 2021 estimating equations, which exist for creatinine alone (eGFRcr) as well as for creatinine and cystatin C (eGFRcr-cys). 23 24 25 Cystatin C has distinct confounders (non-GFR determinants) of its relation with GFR compared with creatinine ( fig 2 ). 2 26 Thus, eGFRcr-cys is a more accurate estimate of GFR than eGFRcr alone, irrespective of equation used, in most scenarios, including those in which large differences exist between eGFRcr and that estimated solely using cystatin C (eGFRcys). 25 27 28 However, the newest GFR estimating equations have not been tested extensively in Asian populations. 29 30

Common non-glomerular filtration rate (GFR) determinants of blood concentrations of creatinine and cystatin C. 2 26 eGFR=estimated glomerular filtration rate

The third axis for classification is cause of CKD, which is generally ascertained through imaging, assessment of extrarenal manifestations and biomarkers, or kidney biopsy. 2 Classification of cause typically hinges on the presence or absence of systemic disease (for example, obesity, diabetes, hypertension, systemic autoimmune disease) and the specific location of the kidney pathology (for example, glomeruli, tubulointerstitium, vasculature, or cystic/congenital abnormality). Unfortunately, the cause of CKD is often unknown, limiting its utility. Molecular phenotyping and genetic testing are increasingly being used to assign cause of disease. Targeted gene panels offered commercially may have high diagnostic yields in select populations, such as patients with glomerular disease, nephrotic syndrome, or congenital anomalies of the kidney and urinary tract. 31 One study suggested that for appropriately selected patients, 34% had disease either reclassified or assigned on the basis of genetic testing, thus changing clinical management. 32 The European Renal Association and the European Rare Kidney Disease Reference Network have issued a joint statement providing recommendations for how to provide genetic testing, including specific settings in which it may be considered ( box 1 ). 33

European Renal Association and European Rare Kidney Disease Reference Network recommendations for settings in which genetic testing might be considered 33

Most tubulopathies

Glomerulopathies:

Congenital nephrotic syndrome

Nephrotic syndrome refractory to standard steroid therapy

Multi-organ phenotypes suggestive of syndromic steroid resistant nephrotic syndrome

Complement disorders:

Immune complex mediated membranoproliferative glomerulonephritis

C3 glomerulopathy

Atypical hemolytic uremic syndrome

Renal ciliopathies

Congenital anomalies of the kidney and urinary tract

Patients aged <50 years with severe CKD of unknown cause

Patients aged >50 years with adult onset CKD and family history of CKD

CKD=chronic kidney disease

Individualized prognosis and treatment

Identifying the cause of CKD is critical as different causes of CKD carry different prognoses and can have distinct treatments. 2 For example, autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic cause of CKD and is typically associated with faster progression than other disease entities. 32 34 Individualized prognosis is often determined by using disease specific risk classification or calculators (for example, the Mayo classification or the ADPKD Prognostic Tool), and screening and treatment recommendations such as increased fluid intake and tolvaptan are unique to this entity. 35 36 37 38 IgA nephropathy, the most common type of glomerulonephritis worldwide, particularly in East Asian and Pacific Asian countries, 39 has its own prognostic aids, such as the International IgA Nephropathy Prediction Tool, 40 41 and treatments specific to IgA nephropathy are in various stages of development. 42 The APOL1 high risk genotypes confer about twofold higher risk of kidney failure in the general population and are common in people of African ancestry. 8 43 44 45 A recently published phase 2A study of targeted therapy for APOL1 related disease showed promising reductions in albuminuria; the phase 3 study is ongoing. 46 Other disease specific therapies are increasingly available, such as belimumab in lupus nephritis and lumasiran for primary hyperoxaluria type 1. 47 48

Individualized risk prediction is also available for more general populations of patients with CKD. The most widely known and validated is the kidney failure risk equation (KFRE), which is used in patients with GFR <60 mL/min/1.73 m 2 . 49 Tested in more than 30 countries and 700 000 people, the tool provides probabilities of kidney failure at two years and five years based on age, sex, and estimated GFR and albuminuria levels. 50 Like all risk equations, the KFRE may perform better with recalibration to absolute risk levels of local populations, but the discriminatory ability (that is, distinguishing people at high risk from those at low risk) has been extremely consistent across all studies. The KFRE has also been validated in recipients of kidney transplants. 51 52 Although the KFRE does not explicitly take into account the competing risk of death, estimates are quite accurate except among the members of the oldest segments of the population at the highest risk. 53 One study suggested that the KFRE provides more accurate prediction of kidney failure than both patients and providers. 54 Even within categories of GFR and urine ACR, the KFRE provides a wide estimate of risk prediction, which can be helpful in the counseling and referral of patients ( fig 3 ). Some centers will refer patients with a two year risk of kidney failure greater than 20-40% for vascular access and kidney transplantation evaluation, on the basis that tools that incorporate albuminuria provide more accurate and unbiased time to kidney failure than does estimated GFR alone. 55 Studies suggest that the KFRE is robust to different GFR equations (specifically, CKD-EPI 2009 and CKD-EPI 2021) and that many patients value being counseled using this information. 53 56

Range of predicted risk of kidney failure using the kidney failure risk equation (KFRE) within G and A categories of chronic kidney disease (CKD). The KFRE ( ckdpcrisk.org/kidneyfailurerisk ) was used to estimate two year risk of kidney failure in 350 232 patients with estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m 2 from the Optum Laboratories Data Warehouse (OLDW). OLDW is a longitudinal, real world data asset with deidentified administrative claims and electronic health record data. Patients with eGFR and albuminuria (urine albumin-to-creatinine ratio (ACR), protein-to-creatinine ratio, or dipstick protein) within a two year window were included in this analysis. Different measures of albuminuria were harmonized to ACR levels for A categories ( ckdpcrisk.org/pcr2acr )

Other risk equations exist to predict the risk of cardiovascular disease and death in CKD; some of these do consider the competing risk of death ( www.ckdpcrisk.org ). For example, the advanced CKD risk tool provides simultaneous estimates of kidney failure, cardiovascular disease, and death for patients with estimated GFR <30 mL/min/1.73 m 2 , which can inform decisions on access placement and reinforce the importance of cardiovascular risk reduction. 57 Estimating risks of cardiovascular disease is particularly relevant given that many more patients with CKD have cardiovascular disease events than need KFRT. 58 Other efforts incorporate estimated GFR and albuminuria into existing tools, such as SCORE2 and the pooled cohort equation for the prediction of cardiovascular disease. 59 60

Patient specific prognostic clues may stem from discrepant estimated GFR values between eGFRcr and eGFRcys. 61 62 63 When eGFRcys is substantially lower than eGFRcr, the risk for kidney related laboratory abnormalities (for example, anemia, hyperuricemia, and hyperphosphatemia) and subsequent adverse outcomes (for example, kidney failure, heart failure, and mortality) is higher. 61 64 65 By contrast, having a lower eGFRcr than eGFRcys is associated with lower risk of adverse outcomes. 66 Risk factors for having a discrepancy between eGFRcr and eGFRcys include older age, female sex, higher body mass index, recent weight loss, and smoking.

General principles of management

The mainstays of therapy for patients with CKD include treating the underlying cause if known, and correcting risk factors (for example, albuminuria) for CKD progression and other CKD related complications ( fig 4 ). 2

Comprehensive care of patients with chronic kidney disease (CKD), irrespective of cause

Blood pressure targets

The three major studies for evaluating the optimal blood pressure target in CKD were the Modification of Diet in Renal Disease Study (MDRD), African American Study of Kidney Disease and Hypertension (AASK), and Systolic Blood Pressure Intervention Trial (SPRINT). 67 68 69 In both MDRD and AASK, intensive blood pressure control did not slow GFR decline overall. 67 68 However, in MDRD, participants with baseline proteinuria of ≥3 g/day seemed to benefit from intensive blood pressure control, with slower mean rates of GFR decline compared with their counterparts in the usual blood pressure control group. 67 Among SPRINT participants with baseline CKD (n=2646), aiming for a systolic blood pressure goal of <120 mm Hg versus <140 mm Hg did not significantly reduce the risk for a composite kidney outcome that included a ≥50% reduction in estimated GFR, long term dialysis, or kidney transplant. 69 70 However, benefits of intensive blood pressure control were seen with respect to prevention of the composite cardiovascular outcome (defined as myocardial infarction, acute coronary syndrome, stroke, heart failure, or death from cardiovascular causes—hazard ratio 0.75, 95% confidence interval 0.64 to 0.89) and all cause mortality (hazard ratio 0.73, 0.60 to 0.90), regardless of CKD status. 69 Blood pressure control can also reduce albuminuria, as shown in the Chlorthalidone in Chronic Kidney Disease (CLICK) trial of chlorthalidone in advanced CKD. 71

Glycemic targets

Among patients with diabetes and CKD, glycemic control is an important component of comprehensive care. 72 The Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) was the largest trial of intensive glucose control to enroll patients with CKD. 73 Among the 11 140 trial participants, 19% had an estimated GFR <60 mL/min/1.73 m 2 and 31% had albuminuria at baseline. 74 Compared with standard glucose control, intensive glucose control was associated with 9% (hazard ratio 0.91, 0.85 to 0.98), 30% (0.70, 0.57 to 0.85), and 65% (0.35, 0.15 to 0.83) lower risks of developing new onset ACR 30-300 mg/g, ACR >300 mg/g, and end stage kidney disease (ESKD), respectively.

Specific classes of therapy

Angiotensin converting enzyme inhibitors and angiotensin receptor blockers.

When choosing antihypertensive agents, those that act by inhibiting the renin-angiotensin-aldosterone system (RAAS) have particular relevance in CKD. A 2001 meta-analysis of 11 studies suggested that, for non-diabetic CKD, the use of angiotensin converting enzyme (ACE) inhibitors resulted in a 30% reduction in risk of KFRT or doubling of serum creatinine. 75 Clinical trials in populations with CKD and diabetes (for example, IDNT, RENAAL) have also shown benefit of angiotensin receptor blockers (ARB) in preventing CKD progression ( table 1 ). 77 78 RAAS inhibition also plays a role in prevention of cardiovascular disease. The Heart Outcomes Prevention Evaluation (HOPE) study showed that ACE inhibitors reduced the risks of myocardial infarction, stroke, and cardiovascular death in populations at high risk for cardiovascular disease, including those with diabetes and albuminuria. 80 The Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET) showed that ACE inhibitors and ARB were generally equivalent in the prevention of cardiovascular events. 81 Because of the increased risk of hyperkalemia and acute kidney injury, dual therapy with both an ACE inhibitor and an ARB is typically avoided. 82

Landmark randomized clinical trials on angiotensin converting enzyme inhibitors or angiotensin receptor blockers in chronic kidney disease

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When GFR declines, providers often grapple with whether RAAS inhibitors should be continued. The Benazepril in Advanced CKD study showed that benazepril reduced the risk of the primary composite kidney endpoint by 43% compared with placebo, thus suggesting that RAAS inhibitors are beneficial even in advanced CKD (baseline serum creatinine 3.1-5.0 mg/dL). 79 Three recent reports further explored this question, also examining the benefits in prevention of death and cardiovascular events associated with continuation of RAAS inhibitors. 83 84 85 A retrospective, propensity score matched study of patients with estimated GFR <30 mL/min/1.73 m 2 showed higher risk of all cause mortality and major adverse cardiovascular events in those who stopped RAAS inhibitors compared with those who continued them, 83 as did a Swedish trial emulation study. 84 The risk of kidney replacement therapy associated with cessation of RAAS inhibitors was not statistically significant in the first study and lower in the second study. 83 84 In an open label randomized trial, cessation of RAAS inhibitors did not show significant between group differences in long term decline in estimated GFR or initiation of kidney replacement therapy, providing reassurance that RAAS inhibitors can be safely continued as GFR declines. 85

SGLT-2 inhibitors

One of the biggest advancements in CKD management over the past decade was the discovery that SGLT-2 inhibitors have robust protective effects on the heart and kidneys in patients with and without diabetes. Recent trials showed an approximate 30% reduction in risk for diverse kidney outcomes among patients with baseline estimated GFR values as low as 20 mL/min/1.73 m 2 ( table 2 ). 86 88 89 91 Importantly, the three trials designed with primary kidney outcomes (Canagliflozin and Renal Events in Diabetes and Established Nephropathy Clinical Evaluation (CREDENCE), Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease (DAPA-CKD), and Study of Heart and Kidney Protection with Empagliflozin (EMPA-KIDNEY)) were terminated early because pre-specified efficacy criteria were met, with median follow-up times ranging from 2.0 to 2.6 years. 88 89 91 The overwhelming majority of trial participants were taking an ACE inhibitor or ARB before randomization, showing that the benefits of SGLT-2 inhibitors on slowing CKD progression are additive to those of RAAS inhibitors. One simulation study estimated that a 50 year old adult with non-diabetic albuminuric CKD would have seven extra years free from doubling of serum creatinine, kidney failure, or all cause mortality if treated with an SGLT-2 inhibitor and RAAS inhibitor. 92

Landmark randomized clinical trials on sodium-glucose co-transporter 2 inhibitors in chronic kidney disease (CKD)

Subgroup analyses of the DAPA-CKD and EMPA-KIDNEY trials have provided additional insights on the wide range of patients who are likely to benefit from SGLT-2 inhibitors. 89 91 In DAPA-CKD, dapagliflozin was favored over placebo in all pre-specified subgroups by baseline age, sex, race, diabetes status, systolic blood pressure, estimated GFR (<45 v ≥45 mL/min/1.73 m 2 ), and ACR (≤1000 v >1000 mg/g or ≤113 v >113 mg/mmol). 89 Similarly, in EMPA-KIDNEY, empagliflozin was associated with lower risk of the primary composite outcome compared with placebo regardless of baseline diabetes status or estimated GFR (<30 v ≥30 mL/min/1.73 m 2 to <45 v ≥45 mL/min/1.73 m 2 ). 91 The risk of the primary outcome was not lower among patients with ACR ≤300 mg/g (approximately ≤30 mg/mmol). In exploratory analyses, however, empagliflozin was associated with slower annual rates of decline in estimated GFR compared with placebo among participants with ACR between 30 and 300 mg/g (approximately 3-30 mg/mmol) and slower chronic slope (from two months to the final follow-up visit) among all ACR subgroups.

The DAPA-CKD trial also showed that the kidney protective effects of SGLT-2 inhibitors extend to patients with IgA nephropathy and perhaps also those with focal segmental glomerulosclerosis (FSGS). 93 94 Among 270 participants with IgA nephropathy (mean estimated GFR 44 mL/min/1.73 m 2 ; median ACR 900 mg/g (102 mg/mmol)), dapagliflozin was associated with a 71% lower risk of developing the primary outcome and a 70% lower risk of ESKD compared with placebo. 93 Among the 104 participants with FSGS (mean estimated GFR 42 mL/min/1.73 m 2 ; median ACR 1248 mg/g (141 mg/mmol)), dapagliflozin was not associated with a lower risk of the primary composite outcome, although this analysis was limited in power (only 11 events). 94 In exploratory analyses, dapagliflozin was associated with slower chronic decline in estimated GFR in the FSGS population. Investigations on the use of SGLT-2 inhibitors in other patient populations, such as those with polycystic kidney disease and kidney transplant recipients, are ongoing (clinicaltrials.gov).

SGLT-2 inhibitors, which act at the level of the proximal tubule to block the reabsorption of glucose and sodium, 95 are generally safe to use in patients with CKD. Early signals of heightened risks of volume depletion, serious genital infections, bone fractures, and need for limb amputation in the Canagliflozin Cardiovascular Assessment Study (CANVAS) were not observed in subsequent studies—CREDENCE, DAPA-CKD, and EMPA-KIDNEY—thus assuaging these concerns ( table 3 ). 86 88 89 91 A pooled analysis of 15 081 participants with type 2 diabetes and CKD G3-4 showed similar rates of serious adverse events for empagliflozin versus placebo, with a higher rate only of mild genital infections with the SGLT-2 inhibitor. 96 A real world study of patients receiving SGLT-2 inhibitors compared with dipeptidyl peptidase-4 (DPP-4) inhibitors found no increased risk of outpatient urinary tract infections or severe urinary tract infection events requiring hospital admission. 97

Adverse effects of SGLT-2 inhibitors * in CANVAS, CREDENCE, DAPA-CKD, and EMPA-KIDNEY trials

GLP-1 receptor agonists

GLP-1 receptor agonists have also been shown to improve kidney outcomes among patients with type 2 diabetes, albeit in trials that were designed for primary cardiac outcomes ( table 4 ). 98 99 100 101 102 103 104 105 106 107 108 109 The reduction in risk of kidney outcomes, which included albuminuria, ranged from 15% to 36%. A large meta-analysis of approximately 44 000 participants from the six trials in table 4 reported that use of GLP-1 receptor agonists was associated with a 21% lower risk of developing the composite kidney outcome, defined as new onset albuminuria >300 mg/g, doubling of serum creatinine, ≥40% decline in estimated GFR, kidney replacement therapy, or death due to kidney causes, compared with placebo. 100 This risk reduction seemed to be driven by the reduction in incident albuminuria >300 mg/g; associations between GLP-1 receptor agonists and CKD progression and kidney failure were not statistically significant. However, results were more promising in A Study Comparing Dulaglutide with Insulin Glargine on Glycemic Control in Participants with Type 2 Diabetes and Moderate or Severe Chronic Kidney Disease (AWARD-7), a clinical trial designed to evaluate change in glycated hemoglobin. 110 Among 577 adults with type 2 diabetes and CKD G3-4 randomized to open label dulaglutide 1.5 mg once weekly, dulaglutide 0.75 mg once weekly, or insulin glargine daily, both dulaglutide groups had slower estimated GFR declines compared with the insulin glargine group; among participants with baseline albuminuria >300 mg/g, dulaglutide was associated with greater ACR reductions in a dose dependent manner over the one year follow-up.

Landmark randomized clinical trials on associations of glucagon-like peptide-1 (GLP-1) receptor agonists with secondary kidney outcomes among patients with type 2 diabetes mellitus

Exact mechanisms by which the GLP-1 receptor agonists slow decline in estimated GFR and/or reduce albuminuria are not entirely clear, but proposed mechanisms include improved glycemic control, weight loss, increased natriuresis, and reduced inflammation and oxidative stress. 111 112 113 Adverse effects observed with this class of drugs have included diarrhea, nausea, and vomiting. 103 104 107 109 110

Mineralocorticoid receptor antagonists

Several MRAs are available and can be useful adjuncts to RAAS inhibitors, particularly among populations with albuminuria and/or diabetes. Two common steroidal non-selective MRAs, spironolactone and eplerenone, both lower albuminuria. 72 In a meta-analysis of 372 participants from seven trials, combination therapy with a non-selective MRA and an ACE inhibitor and/or ARB was associated with a significant reduction in proteinuria, albeit with a higher risk of hyperkalemia. 114 Finerenone, a non-steroidal selective MRA, was also recently approved. 115 Compared with the steroidal non-selective MRAs, finerenone has a stronger selectivity for the mineralocorticoid receptor, a shorter half life, less of a blood pressure lowering effect, and a more favorable side effect profile, as well as potentially greater anti-inflammatory and antifibrotic effects. 115 116 117 The Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease (FIDELIO-DKD) trial and the Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial were two complementary phase 3 clinical trials designed to investigate the kidney and cardiovascular benefits of finerenone, respectively, in people with albuminuria levels ≥30 mg/g and type 2 diabetes ( table 5 ). 116 118 Both trials included patients taking maximally tolerated ACE inhibitor or ARB, with participants in FIDELIO-DKD generally having more severe baseline CKD. In a pooled analysis of the two trials, finerenone was associated with a 15-23% lower risk of developing the kidney composite outcomes and a 32% lower mean change in ACR from baseline to four months. 119 Hyperkalemia was more frequent among patients randomized to finerenone (14%) compared with placebo (7%). In pre-specified analyses, baseline SGLT-2 inhibitor use (n=877) or GLP-1 receptor agonist use (n=944) did not modify the beneficial effect of finerenone on the kidney composite outcome, thus suggesting a potential role for dual therapy (for example, finerenone plus SGLT-2 inhibitor or GLP-1 receptor agonist) among patients with type 2 diabetes and CKD.

Landmark randomized clinical trials on finerenone in chronic kidney disease

Endothelin receptor antagonists

Endothelin receptor antagonists have emerged as novel treatments for a variety of kidney diseases. The Study of Diabetic Nephropathy with Atrasentan (SONAR) evaluated the effect of atrasentan on a composite kidney outcome (defined as a doubling of serum creatinine or ESKD) among adults with type 2 diabetes, estimated GFR 25-75 mL/min/1.73 m 2 , and urine ACR 300-5000 mg/g taking a stable dose of ACE inhibitor or ARB. 120 After a six week enrichment period during which all participants received atrasentan 0.75 mg daily (n=5517), those who responded (defined as a ≥30% reduction in urine ACR without the development of substantial fluid retention or increase in serum creatinine by >0.5 mg/dL and 20% from baseline; n=2648) were randomized to receive atrasentan or placebo. Over a median follow-up of 2.2 years, the atrasentan group had a 35% lower risk of developing the composite kidney outcome compared with the placebo group, although fluid retention and anemia were more frequent in the former. Of note, the frequency of hyperkalemia was low (1%) in both treatment groups. Sparsentan, a dual endothelin and angiotensin II receptor antagonist, is also being investigated as a treatment for FSGS and IgA nephropathy. 121 122 In a phase 2, randomized, double blind, active control trial, 109 adults with biopsy proven FSGS (estimated GFR >30 mL/min/1.73 m 2 and urine PCR ≥1 g/g) received varying doses of sparsentan (200, 400, or 800 mg daily) or irbesartan 300 mg daily. 121 At eight weeks, participants receiving sparsentan had greater reductions in urine PCR compared with those receiving irbesartan. In an interim analysis of the PROTECT phase 3 trial, adults with biopsy proven IgA nephropathy (urine PCR ≥1 g/day) randomized to sparsentan 400 mg daily had a 41% greater reduction in urine PCR over 36 weeks and threefold higher odds of achieving complete remission of proteinuria at any point compared with their counterparts who were randomized to irbesartan 300 mg daily. 122 Based in part on the results of this study, the US Food and Drug Administration (FDA) granted accelerated approval for the use of this drug in adults with primary IgA nephropathy considered to be at risk of rapid disease progression. 123

Endothelin 1 has been implicated in the pathogenesis of kidney disease via various mechanisms including vasoconstriction, vascular hypertrophy, endothelial and podocyte injury, inflammation, cell proliferation, extracellular matrix accumulation, and fibrosis. 124 Systemic and local kidney production of endothelin 1 is augmented in CKD.

Other nephroprotective and cardiovascular risk reduction strategies

A bidirectional association exists between CKD and cardiovascular disease: cardiovascular disease is both a risk factor for CKD and a common outcome in patients with CKD. 125 126 Thus, patients with CKD are likely to benefit from efforts at cardiovascular risk reduction including administration of a statin as well as the gamut of lifestyle changes. 2 127

Lipid management

The Study of Heart and Renal Protection (SHARP) trial evaluated the efficacy of ezetimibe and simvastatin combination therapy in patients with moderate to severe CKD (33% on dialysis; 67% not on dialysis with mean estimated GFR of 27 mL/min/1.73 m 2 ). 128 Treatment with these low density lipoprotein (LDL) cholesterol lowering agents led to a 17% risk reduction for development of a first major atherosclerotic event compared with placebo, although this benefit was seen only in the patients not requiring maintenance dialysis. Those at very high risk (for example, with previous major atherosclerotic cardiovascular disease events) may benefit from additional therapies to lower LDL cholesterol, including evolocumab. 129 Evolocumab is a monoclonal antibody for proprotein convertase subtilisin/kexin type 9, which increases LDL cholesterol receptors and hence clearance of LDL; this novel therapy also seems to be safe and efficacious in patients with CKD. 129 130

Physical activity

Exercise has been shown to benefit patients with CKD. Several small, randomized trials have reported that exercise training programs in patients with moderate to severe CKD are safe, feasible, and effective in improving physical activity levels, cardiorespiratory fitness, and quality of life. 131 132 133 134 135 Whether these interventions also slow CKD progression remains to be determined, as many of these studies were underpowered for this outcome.

For patients with obesity, weight loss may reduce the risk of CKD progression, whether it comes from intensive lifestyle intervention such as in the Look AHEAD (Action for Health in Diabetes) trial or, as in observational studies, from bariatric surgery. 136 137 138 Micronutrient and macronutrient composition of diets may also matter. 139

Traditional recommendations about diet in the setting of CKD have focused on limiting protein and dietary acid intake. Experimental evidence suggests that protein intake can increase intraglomerular pressure and cause glomerular hyperfiltration. 140 141 142 Observational data from large cohort studies suggest that the type of protein may be important; a diet high in animal protein may increase risk, whereas protein from plant sources may be better tolerated. 143 144 For example, an observational study in Singapore found a strong correlation between red meat intake and risk of ESKD. 145 Little clinical trial evidence for protein restriction exists. The MDRD study randomized patients to different levels of protein restriction but found no statistically significant difference in the rate of GFR decline. 67

A second line of investigation has been into the benefits of increasing nutritional alkali intake, with a body of open label trials suggesting benefits on kidney function and prevention of starting dialysis. 146 A phase 3 double blinded, placebo controlled trial reported that veverimer (a potent acid binder that acts in the intestine) was effective in raising or normalizing serum bicarbonate among patients with CKD and chronic metabolic acidosis. 147 Other double blinded studies using veverimer suggested that treating acidosis in CKD improves quality of life and overall physical function. 148 However, a recent trial evaluating veverimer in slowing progression of CKD was negative. 149

Although patients with CKD are prone to hyperkalemia, potassium intake has a beneficial effect on blood pressure, cardiovascular disease, and death independent of and opposite to that of sodium intake. 150 151 152 153 One large randomized controlled trial suggested that substituting 25% of sodium chloride intake with potassium chloride reduced the risk of major adverse cardiovascular events by 13% in the general population. 154 Similarly, small studies suggest that diets rich in potassium may be beneficial in CKD. A feeding trial in people with CKD G3 observed that 100 mmol compared with 40 mmol of dietary potassium per day increased serum potassium by 0.21 mmol/L, 155 similar to the increase seen with finerenone. 156 Many dietary studies have evaluated patterns of diet rather than potassium alone: for example, plant based diets tend to be rich in not only potassium but also alkali and fiber. Observational data from prospective cohorts suggest that plant based diets are associated with less CKD progression. 143 157 158 Evidence is also emerging to suggest that increasing fiber intake benefits the gut microbiome, decreases inflammation, and possibly slows CKD progression. 159

Appropriate drug dosing and nephrotoxin avoidance

An important component of care for patients with CKD is avoidance of additional insults. Many drugs are cleared by glomerular filtration or tubular secretion by the kidney, and reduced GFR can lead to accumulation of the drug or its metabolites resulting in adverse effects. 160 Careful estimation of GFR is generally a first step in determining dosage for renally excreted drugs. 161 The US FDA guidance to industry suggests that estimated GFR based on serum creatinine may be used in pharmacokinetic studies. 162 If drugs are dosed on the basis of estimated GFR (rather than estimated creatinine clearance from the Cockcroft-Gault equation, an equation that is known to be flawed), estimated GFR must be “de-indexed” by multiplying the standardized estimated GFR by the individual’s calculated body surface area and dividing by 1.73 m 2 . 163 164 165 This is because drug clearance is thought to be proportional to a person’s GFR and not the GFR standardized to body surface area. Antibiotics and antiviral agents, direct oral anticoagulants, drugs for diabetes mellitus, and chemotherapeutic agents are the most common drugs that require attention to dosing in CKD. 2 160 164

Some drugs should be avoided or minimized in CKD because of their potential to worsen kidney function. For example, non-steroidal anti-inflammatory drugs (NSAIDs) can exacerbate hypertension, cause fluid retention, and contribute to the risk of acute kidney injury. 166 Particularly when used with RAAS inhibitors and diuretics, NSAIDs are ideally avoided. 167 In select patients with CKD, however, some clinicians will prescribe an abbreviated course of NSAIDs given that the most common alternative, opioids, also have significant adverse effects. 168 Proton pump inhibitors can lead to acute or chronic interstitial nephritis and have been associated with incident CKD, progression of CKD, and ESKD. 169 170 Although the mechanism by which proton pump inhibitors contribute to CKD remains unclear, most experts agree that these agents should be used judiciously.

Emerging treatments

Many phase 3-4 clinical trials are ongoing to evaluate emerging treatments for kidney disease (clinicaltrials.gov). These include, but are not limited to, investigations on the use of dapagliflozin in advanced CKD (for example, estimated GFR <25 mL/min/1.73 m 2 , on maintenance dialysis with residual daily urine output of >500 mL, and kidney transplant recipients with estimated GFR ≤45 mL/min/1.73 m 2 ; NCT05374291 ); finerenone in non-diabetic CKD ( NCT05047263 ); and monteluklast ( NCT05362474 ) and pentoxyifylline ( NCT03625648 ) in diabetic CKD. Several therapies are also being tested for rarer causes of kidney disease: obinutuzumab ( NCT04629248 ), zanubrutinib ( NCT05707377 ), and SNP-ACTH (1-39) gel ( NCT05696613 ) in membranous nephropathy; voclosporin ( NCT05288855 ), atacicept ( NCT05609812 ), anifrolumab ( NCT05138133 ), inanalumab ( NCT05126277 ), secukinumab ( NCT04181762 ), obinutuzumab ( NCT04221477 ), and ACTHar gel ( NCT02226341 ) in lupus nephritis; VX-147 in APOL1 related kidney disease ( NCT05312879 ); imlifidase in antiglomerular basement membrane disease ( NCT05679401 ); sparsentan in focal segmental glomerulosclerosis ( NCT03493685 ); and pegcetacoplan ( NCT05067127 ) in immune complex glomerulonephritis. IgA nephropathy, in particular, is an area of high interest, as recent work suggests that disease activity may be driven by the overproduction of galactose deficient IgA antibodies that are recognized as autoantigens, triggering glomerular deposition of immune complexes. 171 Monoclonal antibodies to signaling molecules that enhance IgA production are in phase 3 trials, as are immunosuppressive and non-immunosuppressive agents (for example, those acting on the endothelin-1 and angiotensin II pathways): budesonide ( NCT03643965 ), sparsentan ( NCT03762850 ), atrasentan ( NCT04573478 ), LNP023 ( NCT04578834 ), RO7434656 ( NCT05797610 ), atacicept ( NCT04716231 ), and sibeprenlimab ( NCT05248646 ; NCT05248659 ).

Major guidelines in CKD are issued by the international KDIGO group ( https://kdigo.org/ ), and locally in the UK by NICE ( www.nice.org.uk/guidance/ng28/chapter/Recommendations#chronic-kidney-disease ), with the most recent issuances primarily from 2023 (currently in public review) and 2021, respectively. KDIGO publishes guidelines on the evaluation and management of patients with CKD in general, as well as myriad other aspects (for example, diabetes, blood pressure, lipids, anemia, mineral and bone disease, hepatitis C, ADPKD, glomerular diseases). With the expansion of therapeutic options, both organizations are updating recommendations frequently. Other guideline producing organizations such as the American College of Cardiology, the American Heart Association, the European Society of Cardiology, the European Society of Hypertension, the International Society of Hypertension, and the American Diabetes Association (ADA) provide more limited statements of recommendation for the specific aspects of the management of patients with CKD. 172 173 174 175

Annual screening for CKD (including testing for albuminuria) is widely recommended in people with diabetes. 72 174 175 176 177 Guidelines in hypertension are less clear. 178 The 2020 Global Hypertension Practice Guideline from the International Society of Hypertension is a notable exception and now recommends routine assessment of albuminuria in addition to estimated GFR in people with hypertension. 173 KDIGO and NICE also recommend testing anyone who is at risk for CKD, which includes those with hypertension, cardiovascular disease, diabetes, and previous acute kidney injury, along with multiple other, less common conditions. 179 For CKD, the KDIGO guidelines recommend at least annual albuminuria testing with greater frequency in higher risk categories ( fig 1 ). 2 The NICE guidelines, on the other hand, recommend annual ACR testing with individualization based on clinical characteristics, risk of progression, and whether a change in ACR would lead to a change in management. 16

KDIGO guidelines and those from NICE differ slightly on staging CKD. KDIGO recommends using a validated equation for GFR estimation and suggests that using “race as a distinct variable in the computation of GFR” is not appropriate. 179 NICE recommends using the CKD-EPI 2009 equation, which did include race, but using the computed value for non-Black people for everyone, a position that is also endorsed by other European groups. 16 180 181 The KDIGO guidelines recommend staging CKD by eGFRcr-cys when cystatin C is available, as well as when precise estimates of GFR are needed for clinical decision making. 2 179 The NICE guidelines recommend direct measurement of GFR rather than the use of cystatin C in clinical situations requiring additional precision. 16

Both KDIGO and NICE emphasize the importance of risk assessment in patients with CKD. The NICE guidelines suggest that primary care providers should counsel patients using the KFRE five year risk estimate, with referral to a specialist if risk is greater than 5%. 16 KDIGO 2023 additionally suggests that the two year risk estimate can drive referral for multidisciplinary care (>10%) and preparation for kidney replacement therapy, including vascular access planning and referral for transplantation (>40%). 179 The KDIGO 2023 guidelines also emphasize the importance of cardiovascular risk assessment using equations developed in people with CKD or that encompasses estimated GFR and albuminuria and the use of disease specific tools in IgA nephropathy and ADPKD. 179

Multiple guidelines comment on target blood pressures in the setting of CKD. The NICE guidelines recommend a target of <140/90 mm Hg, or <130/80 mm Hg if ACR is ≥70 mg/mmol (approximately 700 mg/g). 16 Guidelines from the American College of Cardiology, American Heart Association, European Society of Cardiology, and European Society of Hypertension recommend a systolic blood pressure target of <130 mm Hg as a best practice target, with the European Society of Cardiology and European Society of Hypertension specifically advising against lower targets. 172 The KDIGO guidelines on hypertension in CKD advocate for a systolic blood pressure goal of <120 mm Hg, as assessed using standardized office measurements. 182 This recommendation is based largely on data from SPRINT and the observed benefits in cardiovascular endpoints and survival rather than benefits in kidney endpoints. 70

Of note, disparate guideline recommendations may reflect different emphasis on standardized blood pressure measurement techniques, which can result in measured blood pressure that is substantially lower than measurement in an uncontrolled setting. 183 Joint statements from several international groups including KDIGO stress the importance of proper technique when assessing blood pressure. 184 Both NICE and KDIGO recommend RAAS inhibitors (either ACE inhibitor or ARB) as first line antihypertensive treatment for people without diabetes but with albuminuria (NICE: urine ACR >70 mg/mmol; KDIGO: A3) as well as those with diabetes and CKD G1-G4, A2-A3. 16 182 KDIGO 2023 suggests continuation of RAAS inhibitors even when estimated GFR is <30 mL/min/1.73 m 2 . 179

For patients with diabetes and CKD not treated with dialysis, KDIGO recommends a hemoglobin A 1c target ranging from <6.5% to <8%. 72 NICE does not provide specific recommendations for people with CKD, instead emphasizing shared decision making but a general goal of hemoglobin A 1c <7% for people with diabetes treated with drugs associated with hypoglycemia and <6.5% for people with diabetes managed by lifestyle or a single drug not associated with hypoglycemia. 185

KDIGO and ADA guidelines recommend SGLT-2 inhibitors as first line drug therapy for all people with type 2 diabetes, CKD, and an estimated GFR ≥20 mL/min/1.73 m 2 ( fig 5 ). 72 174 175 179 The NICE guidelines recommend that an SGLT-2 inhibitor should be offered when ACR is >30 mg/mmol (approximately >300 mg/g) and considered when ACR is between 3 and 30 mg/mmol (approximately 30 to 300 mg/g) in patients with type 2 diabetes and CKD who are already taking an ACE inhibitor or ARB and meet estimated GFR thresholds. 185 The NICE guidelines further specify that dapagliflozin should also be considered in people with estimated GFR 25-75 mL/min/1.73 m 2 and ACR ≥22.6 mg/mmol (approximately 200 mg/g) regardless of diabetes status 186 ; KDIGO is broader and recommends SGLT-2 inhibitors in general in people with ACR ≥200 mg/g and estimated GFR ≥20 mL/min/1.73 m 2 , as well as in those with CKD and heart failure. 179 KDIGO further specifies that once started, a SGLT-2 inhibitor can be continued even if the estimated GFR drops below 20 mL/min/1.73 m 2 , as long as it is tolerated and kidney replacement therapy has not yet been started. 72 179 The KDIGO and ADA guidelines recommend the use of GLP-1 receptor agonists in patients with type 2 diabetes and CKD who are unable to tolerate metformin or an SGLT-2 inhibitor or do not meet their individualized glycemic target with these drugs. 72 174 175 179

Kidney Disease: Improving Global Outcomes/American Diabetes Association recommendations on the management of diabetes in populations with chronic kidney disease. 72 174 ACR=albumin-to-creatinine ratio; ASCVD=atherosclerotic cardiovascular disease; BP=blood pressure; CCB=calcium channel blocker; CVD=cardiovascular disease; eGFR=estimated glomerular filtration rate; GLP-1 RA=glucagon-like peptide-1 receptor agonist; HTN=hypertension; MRA=mineralocorticoid receptor antagonist; PCSK9i=proprotein convertase subtilisin/kexin type 9 inhibitor; RAS=renin-angiotensin system; SGLT2i=sodium-glucose cotransporter-2 inhibitor

In patients with diabetes and CKD, the KDIGO and ADA guidelines recommend that finerenone should be used as add-on therapy to maximally tolerated ACE inhibitor or ARB if ACR is ≥30 mg/g (approximately ≥3 mg/mmol) and potassium is within normal limits (that is, ≤4.8 mmol/L based on trial and ≤5.0 mmol/L as per FDA). 72 174 175 179 More specifically, the starting dose should be 10 mg daily when estimated GFR is 25-59 mL/min/1.73 m 2 and 20 mg daily when it is ≥60 mL/min/1.73 m 2 . The guidelines also recommend that potassium concentration should be checked at four weeks after starting treatment, with each dose change, and routinely during treatment. If potassium is >5.5 mmol/L, the drug should be stopped and restarted at the lower dose of 10 mg daily when potassium is ≤5.0 mmol/L. Additionally, finerenone need not be stopped when estimated GFR falls below 25 mL/min/1.73 m 2 as long as the patient is normokalemic. 174 175

With respect to cardiovascular risk reduction, the KDIGO guidelines suggest that all patients aged over 50 with CKD G3-G5 but not treated with chronic dialysis or kidney transplantation should be treated with a statin, irrespective of cholesterol concentrations or a statin/ezetimide combination. 179 187 The NICE recommendation is broader, recommending starting atorvastatin 20 mg for all people with CKD. 188 KDIGO recommends regular physical activity for people with CKD, for at least 150 minutes a week of moderate intensity exercise. 179 NICE simply suggests providing lifestyle advice, including encouragement of exercise, maintenance of healthy weight, and smoking cessation, and specifically recommends against offering low protein diets (defined as dietary protein intake <0.8 g/kg/day). 16 KDIGO recommends maintaining sodium intake <2 g/day and a protein intake of 0.8 g/kg/day but no higher than 1.3 g/kg/day. 179

People with CKD face high risks of many adverse outcomes, including requirement for kidney replacement therapy, cardiovascular events, and death. Fortunately, major advances have been made in the field of CKD over the past decade. Estimating equations for GFR and ACR have evolved for more precise classification of disease. Individualized risk prediction tools exist to assist in the counseling, referral, and treatment of patients. Novel therapies build on the fundamentals—a healthy lifestyle, blood pressure and glucose control, and statin therapy and RAAS blockade—to provide effective preventive strategies for CKD progression and cardiovascular events.

Glossary of abbreviations

ACE—angiotensin converting enzyme

ACR—albumin-to-creatinine ratio

ADA—American Diabetes Association

ADPKD—autosomal dominant polycystic kidney disease

ARB—angiotensin receptor blockers

CKD—chronic kidney disease

CKD-EPI—CKD Epidemiology Collaboration

DPP-4—dipeptidyl peptidase-4

eGFRcr—estimated glomerular filtration rate using creatinine

eGFRcr-cys—estimated glomerular filtration rate using creatinine and cystatin C

eGFRcys—estimated glomerular filtration rate using cystatin C

ESKD—end stage kidney disease

FDA—Food and Drug Administration

FSGS—focal segmental glomerulosclerosis

GFR—glomerular filtration rate

GLP-1—glucagon-like peptide-1

KDIGO—Kidney Disease: Improving Global Outcomes

KFRE—kidney failure risk equation

KFRT—kidney failure with replacement therapy

LDL—low density lipoprotein

MDRD—Modification of Diet in Renal Disease

MRA—mineralocorticoid receptor antagonists

NICE—National Institute for Health and Care Excellence

NSAID—non-steroidal anti-inflammatory drug

PCR—protein-to-creatinine ratio

RAAS—renin-angiotensin-aldosterone system

SGLT-2—sodium-glucose cotransporter-2

Questions for future research

How do the race-free estimating equations perform in global populations?

Where can genetic testing add value in patient care?

Can cause of chronic kidney disease be incorporated into risk prediction tools?

How can medical therapy be best tailored for the individual patient with chronic kidney disease?

Patient perspective

Increasing awareness of chronic kidney disease is key to empowering patients to make lifestyle changes and seek treatments to improve their health outcomes. We are pleased to offer our perspective as husband and wife, and as physicians, who have been affected by kidney disease. Roberta M Falke is a patient with autosomal dominant polycystic kidney disease (ADPKD), a kidney transplant recipient, and a retired hematologist-oncologist. Andrew S Levey is a kidney donor and a nephrologist. Our knowledge of Roberta’s family history enabled early diagnosis and treatment. 189 Although we have benefited from our training and positions in the healthcare system, all patients can benefit from early diagnosis.

RMF —My ADPKD was diagnosed when I developed pyelonephritis at age 22 years. Thereafter, I had prophylaxis and prompt treatment of recurrent urinary tract infections and, as the disease progressed, complications of kidney and liver cysts, hypertension, hyperparathyroidism, vitamin D deficiency, acidosis, hyperkalemia, and ultimately kidney failure, with fatigue, dietary restrictions, and a long list of medications to take every day. I had always known that living donor kidney transplantation would be the best treatment for my kidney failure. Over time, family members without ADPKD donated to others, and when I was ready at age 60 years no family members were available. Fortunately, Andy stepped up. I felt better immediately after the transplant, and in the 13 years since then I have continued to take medications daily but have had few complications. I am grateful to all those who have cared for me for many years and enabled me to make the best choices I could to help myself, and I’m especially grateful to Andy who gave me the gift of life.

ASL —I knew that Roberta would develop kidney failure and hoped that a living kidney donor would be available for her. I wanted to donate, but our blood group incompatibility was an obstacle, so it was exciting when paired donor exchange was conceived and implemented in our region. I believe that kidney donors benefit from donation, not only by fulfilling their spirit of altruism but by improving their own lives. In my case, donating has been life changing. Roberta and I have been able to have an active, fulfilling life for more than a decade after the transplant, without the demands and complications of kidney failure or dialysis. I hope that we will have many more years together. I am also grateful to all those who enabled me to achieve my goal and to Roberta, who always takes full responsibility for caring for her kidney disease.

Acknowledgments

We thank Andrew S Levey and Roberta M Falke for providing both their perspective as patients affected by kidney disease and their input on the manuscript itself. We also acknowledge Alix Rosenberg and Yingying Sang for their help with the boxes and figures.

Series explanation: State of the Art Reviews are commissioned on the basis of their relevance to academics and specialists in the US and internationally. For this reason they are written predominantly by US authors

Contributors: All authors were involved in the conception, writing, and revision of the manuscript. MEG is the guarantor.

Funding: TKC is supported by NIH/NIDDK K08DK117068; MEG is supported by NIH/NIDDK R01DK108803, R01DK100446, R01DK115534, R01DK124399, and NIH/NHLBI K24HL155861.

Competing interests: We have read and understood the BMJ policy on declaration of interests and declare the following interests: TKC and MEG received an honorarium from the American Society of Nephrology (nephSAP).

Patient involvement: We invited a husband and wife, Andrew S Levey and Roberta M Falke, who are affected by chronic kidney disease, to write a patient perspective together. They also reviewed and provided input on the penultimate draft of the paper.

Provenance and peer review: Commissioned; externally peer reviewed.

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Chronic Kidney Disease

Affiliations.

1 Sydney School of Public Health, University of Sydney, NSW, Australia; Centre for Transplant and Renal research, Westmead Hospital, Westmead, NSW, Australia. Electronic address: [email protected].
2 Renal Section, Department of Internal Medicine, Ghent University Hospital, Ghent, Belgium.
3 NHMRC Clinical Trials Centre, University of Sydney, Camperdown, NSW, Australia.
4 Department of Renal Medicine, Royal Infirmary of Edinburgh, Edinburgh, Scotland, UK.
PMID: 27887750
DOI: 10.1016/S0140-6736(16)32064-5

The definition and classification of chronic kidney disease (CKD) have evolved over time, but current international guidelines define this condition as decreased kidney function shown by glomerular filtration rate (GFR) of less than 60 mL/min per 1·73 m 2 , or markers of kidney damage, or both, of at least 3 months duration, regardless of the underlying cause. Diabetes and hypertension are the main causes of CKD in all high-income and middle-income countries, and also in many low-income countries. Incidence, prevalence, and progression of CKD also vary within countries by ethnicity and social determinants of health, possibly through epigenetic influence. Many people are asymptomatic or have non-specific symptoms such as lethargy, itch, or loss of appetite. Diagnosis is commonly made after chance findings from screening tests (urinary dipstick or blood tests), or when symptoms become severe. The best available indicator of overall kidney function is GFR, which is measured either via exogenous markers (eg, DTPA, iohexol), or estimated using equations. Presence of proteinuria is associated with increased risk of progression of CKD and death. Kidney biopsy samples can show definitive evidence of CKD, through common changes such as glomerular sclerosis, tubular atrophy, and interstitial fibrosis. Complications include anaemia due to reduced production of erythropoietin by the kidney; reduced red blood cell survival and iron deficiency; and mineral bone disease caused by disturbed vitamin D, calcium, and phosphate metabolism. People with CKD are five to ten times more likely to die prematurely than they are to progress to end stage kidney disease. This increased risk of death rises exponentially as kidney function worsens and is largely attributable to death from cardiovascular disease, although cancer incidence and mortality are also increased. Health-related quality of life is substantially lower for people with CKD than for the general population, and falls as GFR declines. Interventions targeting specific symptoms, or aimed at supporting educational or lifestyle considerations, make a positive difference to people living with CKD. Inequity in access to services for this disease disproportionally affects disadvantaged populations, and health service provision to incentivise early intervention over provision of care only for advanced CKD is still evolving in many countries.

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Introduction
Conclusions
Article Information

A, Unadjusted prevalence. B, Prevalence adjusted by age, sex, and race/ethnicity.

ACE indicates angiotensin-converting enzyme; ARB, angiotensin receptor blocker; NSAID, nonsteroidal anti-inflammatory drug; PPI, proton pump inhibitor; and SGLT2, sodium-glucose cotransporter 2.

eFigure. CURE-CKD STROBE Diagram, Participant Groups by CKD and At-Risk Categories

eTable 1. Inclusion Criteria for the CURE-CKD Registry

eTable 2. Characteristics of Adults at Risk of CKD in the CURE-CKD Registry

eTable 3. Characteristics of Adults With CKD and Diabetes or Prediabetes With or Without Hypertension in the CURE-CKD Registry

eTable 4. Characteristics of Adults at Risk of CKD With Diabetes or Prediabetes With or Without Hypertension in the CURE-CKD Registry

eTable 5. Medications Prescribed to Adults With CKD in the CURE-CKD Registry

eTable 6. Medications Prescribed to Adults at Risk of CKD in the CURE-CKD Registry

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Tuttle KR , Alicic RZ , Duru OK, et al. Clinical Characteristics of and Risk Factors for Chronic Kidney Disease Among Adults and Children : An Analysis of the CURE-CKD Registry . JAMA Netw Open. 2019;2(12):e1918169. doi:10.1001/jamanetworkopen.2019.18169

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Clinical Characteristics of and Risk Factors for Chronic Kidney Disease Among Adults and Children : An Analysis of the CURE-CKD Registry

1 Providence St Joseph Health, Providence Medical Research Center, Spokane, Washington
2 University of Washington School of Medicine, Seattle
3 Division of General Internal Medicine, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles
4 Division of Nephrology, University of California, Los Angeles
5 Elson S. Floyd College of Medicine, Washington State University, Spokane
6 College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane

Question What are the clinical characteristics of and major risk factors for chronic kidney disease among patients in 2 large US health care systems?

Findings In this cohort study of the Center for Kidney Research, Education, and Hope (CURE-CKD) registry, more than 2.6 million adults and children had chronic kidney disease or were at risk. Albuminuria or proteinuria was tested in approximately one-eighth of adults with chronic kidney disease, renin-angiotensin system inhibitors were prescribed to one-fifth, and nonsteroidal anti-inflammatory agents or proton pump inhibitors were prescribed to more than one-third.

Meaning Despite common occurrence of chronic kidney disease, rates of identification and use of kidney protective agents were low, while use of potential nephrotoxins was widespread.

Importance Chronic kidney disease (CKD) is serious and common, yet recognition and public health responses are limited.

Objective To describe clinical features of, prevalence of, major risk factors for, and care for CKD among patients treated in 2 large US health care systems.

Design, Setting, and Participants This cohort study collected data from the Center for Kidney Disease Research, Education, and Hope (CURE-CKD) registry, an electronic health record–based registry jointly curated and sponsored by Providence St Joseph Health and the University of California, Los Angeles. Patients were adults and children with CKD (excluding end-stage kidney disease) and adults at risk of CKD (ie, with diabetes, hypertension, or prediabetes) identified by laboratory values, vital signs, prescriptions, and administrative codes. Data were collected from January 2006 through December 2017, with analyses performed from March 2019 through November 2019.

Exposures Diabetes, hypertension, and prediabetes.

Main Outcomes and Measures Clinical and demographic characteristics, prevalence, and prescribed medications.

Results Of 2 625 963 adults and children in the sample, 606 064 adults (23.1%) with CKD had a median (interquartile range [IQR]) age of 70 (59-81) years, with 338 785 women (55.9%) and 434 474 non-Latino white individuals (71.7%). A total of 12 591 children (0.4%) with CKD had a median (IQR) age of 6 (1-13) years, with 7079 girls (56.2%) and 6653 non-Latino white children (52.8%). Median (IQR) estimated glomerular filtration rate was 53 (41-61) mL/min/1.73 m 2 among adults and 70 (50-95) mL/min/1.73 m 2 in children. Prevalence rates for CKD in adults were 4.8% overall (606 064 of 12 669 700) with 1.6% (93 644 of 6 011 129) during 2006 to 2009, 5.7% (393 455 of 6 903 084) during 2010 to 2013, and 8.4% (683 574 of 8 179 860) during 2014 to 2017 ( P < .001). A total of 226 693 patients (37.4%) had category 3a CKD; 100 239 (16.5%), category 3b CKD; 39 125 (6.5%), category 4 CKD; and 20 328 (3.4%), category 5 CKD. Among adults with CKD, albuminuria and proteinuria assessments were available in 52 551 (8.7%) and 25 035 (4.1%) patients, respectively. A renin-angiotensin system inhibitor was prescribed to 124 575 patients (20.6%), and 204 307 (33.7%) received nonsteroidal anti-inflammatory drugs or proton pump inhibitors. Of 1 973 258 adults (75.1%) at risk, one-quarter had diabetes or prediabetes (512 299 [26.0%]), nearly half had hypertension (955 812 [48.4%]), and one-quarter had both hypertension and diabetes or prediabetes (505 147 [25.6%]).

Conclusions and Relevance This registry-based cohort study revealed a burgeoning number of patients with CKD and its major risk factors. Rates of identification and use of kidney protective agents were low, while potential nephrotoxin use was widespread, underscoring the pressing need for practice-based improvements in CKD prevention, recognition, and treatment.

Chronic kidney disease (CKD) is a serious and common disease, and it eventuates in multiple complications, including premature mortality and end-stage kidney disease (ESKD). 1 - 3 An estimated 1 in 7 to 10 adults worldwide have CKD, with only approximately 10% surviving to ESKD and only half of survivors receiving dialysis or a kidney transplant because of lack of access or high costs. 3 From 1990 to 2016, the prevalence of CKD increased by 90%, and related deaths, mainly due to cardiovascular diseases and infections, nearly doubled in the United States and globally. 4 - 6 In high-income countries, 2% to 3% of annual health care costs are devoted to the 0.03% of the population with ESKD. 7

The increasing prevalence of CKD is closely tied to the increase of at-risk populations with diabetes, hypertension, and prediabetes. Indeed, diabetes is the leading cause of CKD and a global health emergency, with 425 million individuals affected worldwide in 2017 and a projected 629 million individuals affected by 2045. 8 - 10 Hypertension is the second most frequent cause of CKD, affecting nearly one-third of US adults and 1.13 billion people globally in 2015. 11 , 12 The estimated population size for prediabetes was 78.5 million among adults in the United States between 2011 and 2014, and nearly one-tenth have been reported with CKD. 13 Even so, awareness of CKD and its major risk factors remains strikingly low among health care professionals and patients alike. 14 - 16

The Advancing American Kidney Health initiative was recently launched by a US executive order calling for new approaches to prevent and treat CKD, with a goal of reducing ESKD incidence 25% by 2030. 17 Real-world data from electronic health records (EHRs) provide an ideal platform to answer this call by improving CKD detection, tracking, and public health responses. The Center for Kidney Disease Research, Education, and Hope (CURE-CKD) registry contains detailed patient-level EHR data from more than 2.6 million adults and children with CKD and at risk of CKD during 12 inclusive years. 18 The objective of this study was to describe baseline clinical features of, prevalence of, major risk factors for, and care for CKD based on data from the CURE-CKD registry.

The study was approved by Providence St Joseph Health (PSJH; Washington, Montana, Oregon, Alaska, and California) and the University of California, Los Angeles (UCLA; California) Health institutional review boards, which determined that written informed consent was not required for this limited data set. Data use agreements between PSJH and UCLA Health formed the framework for data sharing, stewardship, and security. This study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for cohort studies. 19

Detailed methodology for CURE-CKD has been previously published. 18 The formation of CURE-CKD was supported by institutional funding from PSJH and UCLA Health. Both health care systems use Epic EHRs (Epic Systems). The first phase of CURE-CKD created a data repository with patient information from EHRs with at least 1 measure indicating CKD, diabetes, prediabetes, or hypertension based on patient-level laboratory values, vital signs, prescription medications, and administrative codes from January 1, 2006, to December 31, 2017. Electronic health record data for these patients were extracted from ambulatory and inpatient encounters. Unstructured data from the EHRs were not extracted. The total number of patients with encounters and serum creatinine measures for the health care systems was also recorded. Repository updates are performed annually.

The second phase crafted an EHR-based registry of participants with CKD and at risk for CKD derived from the repository. The first 90 days a patient was included in the registry were considered the baseline period. Registry criteria were based on established clinical practice guidelines for CKD (eTable 1 in the Supplement ). Adults (ie, aged ≥18 years) were included with 2 or more of the following laboratory measurements recorded at least 90 days apart: estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m 2 , calculated from serum creatinine levels using the Chronic Kidney Disease Epidemiology equation; urine albumin to creatinine ratio greater than 30 mg/g; and urine protein to creatinine ratio greater than 150 mg/g. 20 , 21 Children (ie, aged <18 years) with CKD were identified using the same criteria, except the bedside Schwartz equation was used to calculate eGFR from serum creatinine levels. 22 We identified CKD categories 1 and 2 by an administrative code, urine albumin to creatinine ratio greater than 30 mg/g, and/or urine protein to creatinine ratio greater than 150 mg/g. We identified CKD categories 3 to 5 based on eGFR and/or administrative code. Patients with ESKD treated with dialysis or kidney transplant were excluded. Participants with diabetes, prediabetes, and hypertension were identified by clinical practice guidelines and published criteria for EHR identification 23 - 25 (eTable 1 in the Supplement ).

Data analyses were performed from March 2019 through November 2019. Continuous variables are reported as mean and SD or as median and interquartile range (IQR) for skewed or kurtotic distributions. Categorical variables are reported as frequencies and percentages. The Pearson χ 2 test for independence was used to determine differences between categorical variables. Prevalence rates for CKD among adults are presented as a combined data set from PSJH and UCLA Health and by each system. To address sources of bias in CKD prevalence rates, data were analyzed as proportions based on the 3 following definitions for CKD: (1) CURE-CKD entry criteria, (2) 2 measurements of eGFR less than 60 mL/min/1.73 m 2 at least 90 days apart; and (3) 1 measurement of eGFR less than 60 mL/min/1.73 m 2 . Serial prevalence rates of CKD overall, by categories, and prescription medication use during 3 periods (ie, 2006-2009, 2010-2013, and 2014-2017) were analyzed by logistic regression models. Prevalence was adjusted for age, sex, and race/ethnicity in the models using repository data (ie, CKD by 1 or 2 eGFR measurements). Adjustments could not be made for CKD prevalence with the denominator based on the total number of patients with encounters because the institutional review board approvals did not include data extraction for age, sex, and race/ethnicity from the total populations in the health care system.

To reduce risk of type I error, a 2-tailed P < .001 was the a priori threshold for statistical significance because of the large sample size and resultant high level of statistical power. Because overall CKD participant characteristics, except distribution of geolocation, were similar between PSJH and UCLA Health, findings other than prevalence are presented from a jointly curated data set. Descriptive statistics and the Pearson χ 2 test were conducted with SQL Server Management Studio 2012 version 11.0.2100.60 (Microsoft Corp); tests for normality and logistic regression were completed using SPSS statistical software version 23 (IBM Corp).

A total of 2 625 963 adults and children were included in the sample. The cohort of adults with CKD included 606 064 individuals (23.1%), including 338 785 women (55.9%), 434 474 non-Latino white individuals (71.7%), 17 625 Latino individuals (2.9%), 29 974 black individuals (4.9%), 32 850 Asian individuals (5.4%), 5461 American Indian and Alaska Native individuals (0.9%), and 3899 Hawaiian and Pacific Islander individuals (0.6%) ( Table 1 ). The median (IQR) age among adults was 70 (59-81) years. The proportions with CKD were highest among those aged 60 to 89 years (401 541 [66.3%]). A total of 12 591 children (0.4%) with CKD included 7079 girls (56.2%) and 6653 non-Latino white children (52.8%) ( Table 2 ). The median (IQR) age of children with CKD was 6 (1-13) years, and CKD was comparably distributed across age groups (2545 [20.2%] aged <1 year; 2241 [17.8%], 1-3 years; 1515 [12.0%], 4-6 years; 1863 [14.8%], 7-10 years; 1916 [15.2%], 11-14 years; and 2511 [19.9%], 15-17 years). The cohort of participants at risk for CKD included 1 973 258 adults (75.1%). Among them, 955 812 (48.4%) had hypertension alone, while 505 147 (25.6%) had diabetes or prediabetes with hypertension, and 512 299 (26.0%) had diabetes or prediabetes alone. Those at risk for CKD included 1 014 847 women (51.4%), 1 308 036 non-Latino white individuals (66.3%), 60 201 Latino individuals (3.1%), 92 403 black individuals (4.9%), 114 400 Asian individuals (5.8%), 19 820 American Indian and Alaska Native individuals (1.0%), and 11 420 Hawaiian and Pacific Islander individuals (0.6%) (eTable 2 in the Supplement ). Proportions of participants at risk for CKD were highest among those aged 50 to 69 years (866 528 [43.9%]).

Comparing adults with CKD with those at risk for CKD, women were more frequently represented in the cohort with CKD than in the cohort at risk for CKD (338 785 [55.9%] vs 1 014 847 [51.4%]; P < .001). Non-Latino white individuals (434 474 [71.7%] vs 1 308 036 [66.3%]; P < .001) and individuals aged 70 years or older (315 397 [52.0%] vs 386 364 [19.6%]; P < .001) were also more common among participants with CKD vs those at risk. There was a higher proportion with rural geolocation within PSJH vs UCLA Health (287 622 [17.2%] vs 6918 [1.8%]; P < .001).

A total of 243 635 adults with CKD (40.2%) were identified by eGFR, 163 375 (27.0%) by administrative codes, and 151 794 (25.0%) by both eGFR and administrative codes. Various combinations of laboratory measurements and administrative codes accounted for the remainder of adult CKD identification. More than half of adults with CKD were in category 3 (3a, 226 693 [37.4%]; 3b, 100 239 [16.5%]) ( Table 1 ). Decreases in prevalence were observed for CKD category 4 (39 125 [6.5%]) and category 5, not dialyzed (20 328 [3.4%]). Median (IQR) eGFR was 53 (41-61) mL/min/1.73 m 2 , and measurements of albuminuria and proteinuria were recorded in 52 511 (8.7%) and 25 035 (4.1%) patients, respectively. Mean (SD) systolic and diastolic blood pressure values were 129 (18) mm Hg and 72 (11) mm Hg, respectively. When participants with CKD and diabetes or prediabetes were assessed separately, higher proportions of patients with diabetes than those with prediabetes had CKD category 4 or 5 (9790 [18.4%] vs 3724 [13.2%]; P < .001), and higher levels of albuminuria or proteinuria were present in the group with diabetes compared with the group with prediabetes (5555 [10.4%] vs 965 [3.4%]; P < .001) (eTable 3 in the Supplement ).

Most children (10 841 [86.1%]) were identified exclusively through CKD administrative codes. Among 8145 children (64.7%), CKD was not categorized ( Table 2 ). Median (IQR) eGFR was 70 (50-95) mL/min/1.73 m 2 . Mean (SD) systolic and diastolic blood pressure were 104 (16) mm Hg and 61 (11) mm Hg, respectively. Measurements of albuminuria and proteinuria were available in 520 (4.1%) and 798 (6.4%) children, respectively.

Median (IQR) eGFR in adults at risk of CKD was 90 (77-103) mL/min/1.73 m 2 , and albuminuria and proteinuria measurements were recorded in 51 470 (2.6%) and 10 285 (0.5%), respectively (eTable 2 in the Supplement ). Mean (SD) systolic and diastolic blood pressure values were 135 (18) mm Hg and 79 (12) mm Hg, respectively. When participants with diabetes or prediabetes who were at risk for CKD were analyzed separately, frequency of ascertainment for albuminuria or proteinuria was 7% or less in all groups (eg, among 317 648 patients with diabetes and hypertension, albumin to creatine ratio measurements were available for 21 697 patients [6.8%]; among 187 499 patients with prediabetes and hypertension, protein to creatine ratio measurements were available in 907 [0.5%]) (eTable 4 in the Supplement ).

A total of 12 669 700 patients received care at PSJH (10 793 550 [85.2%]) and UCLA Health (1 876 150 [14.8%]) between January 1, 2006, and December 31, 2017 (eFigure in the Supplement ). During this period, 606 064 adults (4.8%) met the CURE-CKD registry entry criteria for CKD. However, when CKD was determined by at least 2 eGFR measurements of less than 60 mL/min/1.73 m 2 at least 90 days apart, unadjusted prevalence among adults was 26.1% (420 678 of 1 612 737), and adjusted CKD prevalence was 22.6%. When determined by 1 eGFR measure, unadjusted CKD prevalence was 34.4% (873 642 of 2 542 393), and adjusted prevalence was 32.9% ( Table 3 ). Diagnostic coding for CKD was recorded among 171 011 patients (40.7%) with CKD determined by 2 eGFR measurements at least 90 days apart and among 240 630 patients (27.5%) with CKD determined by 1 eGFR measurement.

Temporal trends in CKD prevalence were determined for the 3 following periods: 2006 to 2009, 2010 to 2013, and 2014 to 2017. CKD prevalence rates by CURE-CKD registry criteria increased over time (2006-2009, 93 644 of 6 011 129 [1.6%]; 2010-2013, 393 455 of 6 903 084 [5.7%]; and 2014-2017, 683 574 of 8 179 860 [8.4%]). Prevalence rates adjusted for age, sex, and race/ethnicity and based on eGFR classification alone were higher and stable over time among patients with 2 or more eGFR measurements at least 90 days apart (20.8%, 22.6%, and 21.2%, respectively), while increasing adjusted prevalence was observed among patients with 1 eGFR measurement (22.3%, 27.8%, and 28.5%, respectively). Rates of administrative coding for CKD increased progressively at both PSJH and UCLA Health ( Table 3 ). For example, among patients with 2 eGFR measurements of less than 60 mL/min/1.73 m 2 at least 90 days apart, 2766 of 87 225 (3.2%) were identified by administrative code during 2006 to 2009 and 124 897 of 238 750 (52.3%) were identified by administrative code during 2014 to 2017. When CKD categories were analyzed by at least 2 eGFR measurements at least 90 days apart, unadjusted prevalence rates and prevalence rates adjusted by age, sex, and race/ethnicity showed progressive increases for categories 3a and 3b with declines in categories 4 and 5 (eg, category 3a: 2006-2009, 22 805 [prevalence, 26.1%; adjusted prevalence 26.1%]; 2014-2017, 96 449 [prevalence 40.4%; adjusted prevalence, 38.2%]; category 4: 2006-2009, 22 338 [prevalence, 25.7%, adjusted prevalence, 19.4%], 2014-2017, 42 065 [prevalence, 17.6%; adjusted prevalence, 16.1%]) ( Figure 1 ).

Angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) were prescribed to 127 574 adults (20.5%) with CKD, with slightly higher use of these agents among those with CKD and hypertension (112 449 of 434 657 [25.9%]) (eTable 5 in the Supplement ). By contrast, 204 307 participants (33.7%) with CKD had prescriptions for nonsteroidal anti-inflammatory drugs (NSAIDs) or proton pump inhibitors (PPIs). Statins and aspirin were prescribed to 107 445 (17.7%) and 110 335 (18.2%) individuals, respectively. The most commonly prescribed antihyperglycemic agents among patients with CKD and diabetes or prediabetes were insulin (38 278 [10.0%]), metformin (30 393 [7.9%]), and sulfonylureas (16 989 [4.4%]). Medications prescribed among the cohort of participants at risk of CKD were generally similar to the CKD cohort, except for more common use of insulin (83 363 [16.3%]) among those with diabetes and of NSAIDs (701 493 [35.5%]) and PPIs (295 804 (15.0%]) overall (eTable 6 in the Supplement ).

Temporal trends in prescription medications were determined for participants with CKD determined by 2 eGFR measurements of less than 60 mL/min/1.73 m 2 at least 90 days apart for the 3 periods. Use rates of ACE inhibitors, ARBs, NSAIDs, and PPIs across CKD categories 3a to 5 all increased (ACE inhibitors: 2006-2009, 5654 [2.0%]; 2010-2013, 46 921 [5.1%]; 2014-2017, 81 601 [7.6%]; ARBs: 2006-2009, 2461 [0.9%]; 2010-2013, 21 791 [2.4%]; 2014-2017, 47 233 [4.4%]; NSAIDs: 2006-2009, 7009 [2.4%]; 2010-2013, 57 705 [6.3%]; 2014-2017, 113 251 [11.0%]; PPIs: 2006-2009, 5331 [1.8%]; 2010-2013, 44 362 [4.9%]; 2014-2017, 83 340 [7.7%]) ( Figure 2 ). Sodium-glucose cotransporter 2 inhibitors were rarely prescribed, but use increased over time (2006-2009, 0; 2010-2013, 22 [0.002%]; 2014-2017, 1002 [0.093%]).

More than 2.6 million adults and children who received care at PSJH and UCLA Health from 2006 to 2017 had CKD or were at risk of CKD. Overall, CKD prevalence among adults in the health care systems was 4.8%, as determined by a combination of eGFR, albuminuria and proteinuria measures, and administrative code criteria. However, adult CKD prevalence adjusted for age, sex, and race/ethnicity was 22.6% based on persistently low eGFR alone. Adults with CKD were more likely to be older, women, and non-Latino white individuals. In this study, CKD category 3 was most frequent, with a clear drop-off in prevalence at more advanced categories. Kidney protective agents (ie, renin-angiotensin system inhibitors) were prescribed to approximately one-fifth of adults with CKD, while potential nephrotoxins (ie, NSAIDs and PPIs) were prescribed to more than one-third of adults with CKD. Albuminuria and proteinuria testing for CKD assessment was rarely reported.

The CURE-CKD registry is among the most comprehensive CKD registries worldwide. A unique feature is the extensive amount of patient-level data on laboratory measures, prescriptions, and vital signs, combined with administrative codes, to identify CKD and major risk factors according to guideline-based criteria. 21 , 24 , 25 Previous registries were restricted by containing primarily administrative data, ESKD, primary care practices, single health care systems, older adults, or men. 4 , 26 - 32 In contrast, CURE-CKD participants represent the life span, from children to adults, and include women and men and a wide spectrum of races and ethnicities across an expansive region of the western United States that has not been previously involved in large-scale epidemiologic studies of CKD. Moreover, PSJH and UCLA Health care for patients in a variety of settings that include academic, primary care, and specialty practices as well as community health and safety-net systems. Rural patients were well represented in the geography covered by PSJH. Thus, CURE-CKD provides in-depth identification of patients with and at risk for CKD in contemporary US health care systems.

In CURE-CKD, the progressive increase in adult CKD prevalence was largely driven by diagnostic coding. Among adults with persistently low eGFR, use of CKD administrative codes increased from 3.2% to 52.3% between the periods of 2006 to 2009 and 2014 to 2017, while overall CKD prevalence estimates, adjusted for age, sex, and race/ethnicity, were essentially stable between 20.8% and 22.6%. Although the upward trend in CKD recognition represents a clinically meaningful improvement, nearly one-half of patients with low eGFR remained undiagnosed in the most recent period. The present findings from CURE-CKD point to the critical need for quality improvement and research at the point of care.

Although nearly two-thirds of the adults with CKD had diabetes, hypertension, or prediabetes, rates of laboratory testing for albuminuria or proteinuria and of prescribing ACE inhibitors or ARBs were low. Potentially nephrotoxic agents (ie, NSAIDs and PPIs) were used more commonly than renin-angiotensin system inhibitors. Given the most common cause of death in CKD is cardiovascular disease, the low use of preventive agents, such as statins and aspirin, is also concerning. 33 , 34 Compared with participants in the National Health and Nutrition Examination Survey, patients with CKD in CURE-CKD received ACE inhibitors or ARBs much less often during approximately the same period. 35 Although CURE-CKD found an increase in uptake of renin-angiotensin system inhibitors in adults with CKD categories 3a to 5, NSAID and PPI use also increased over time. However, these prescription rates were lower than in the overall CKD cohort, perhaps because of concerns about adverse effects with more advanced CKD. While this may seem counterintuitive for renin-angiotensin system inhibitors, these agents may be avoided because of fear of complications such as hyperkalemia or acute kidney injury, especially in acute care settings. In Ontario, Canada, primary care practices reported ACE inhibitor or ARB use in three-fourths of patients with CKD, but the metric was confined to those with diabetes and albuminuria or adults older than 66 years. 30 , 31 Nevertheless, rates of albuminuria testing in the overall CKD population were comparably low, although avoidance of NSAIDs was better among patients in Canada than in CURE-CKD. 30 A recent Canada-wide study 32 from an EHR-based surveillance system in primary care found that only 4 of 12 quality indicators for CKD care were met, with ACE inhibitor or ARB use among approximately one-third of patients with diabetes or proteinuria. Contrasts exist between reports from health care systems, community screenings, primary care practices, and countries, but they consistently illuminate major gaps in CKD care and the need for more comprehensive surveillance to uncover actionable trends.

In comparison with patients treated at PSJH and UCLA Health, the Kidney Early Evaluation Program and the National Health and Nutrition Examination Survey have reported lower frequencies of individuals at risk for CKD in community screenings. 36 Moreover, associations between risk factors and CKD are remarkably complex. For example, although a primary contributor to CKD is diabetes, CKD in patients with diabetes greatly amplifies cardiovascular risks. 37 Additionally, nearly one-fifth of patients with CKD in the CURE-CKD registry had prediabetes. The prediabetes phenotype of CKD appears less severe than the diabetes phenotype of CKD, as reflected by fewer patients with advanced CKD categories, albuminuria, or proteinuria and prediabetes. Nevertheless, consistent with findings from the National Health and Nutrition Examination Survey, findings from CURE-CKD support the observation that subdiabetic hyperglycemia may contribute to kidney damage before overt diabetes ensues. 13 The CURE-CKD registry contains abundant longitudinal data that will be invaluable for elucidating CKD incidence among individuals at risk as well as progression and complications in those with CKD. Given its vast scope, CURE-CKD is also ideally suited to generate and validate CKD risk prediction models. 38

Strengths of the CURE-CKD registry include the large sample size, long observation duration, and curated patient-level data from 2 US health care systems. However, this study has limitations. First, CURE-CKD is limited by differences in documentation methods across and between health care systems and varying attrition rates based on insurance, socioeconomic factors, and geolocation. Variation in platforms even within a common EHR system also presents a limitation to the creation of interinstitutional registries, highlighting the importance of collaboration in identifying data elements, structures, and synchronization. Lack of information on over-the-counter medications underestimates the usage rates for NSAIDs, PPIs, and other potential nephrotoxins. Data in CURE-CKD on sodium-glucose cotransporter 2 inhibitor use, recently recommended for diabetes and CKD, came from an era before this new indication. It will be important to follow this trend to ensure sodium-glucose cotransporter 2 inhibitors are delivered to patients who may benefit. Another limitation of EHR-based registries is undercoding and miscoding. To mitigate this limitation, patient-level data for laboratory values, vital signs, and prescriptions were used to classify CURE-CKD registry participants and their care, which allowed for the use of guideline-based criteria for persistence of low eGFR or elevated albuminuria or proteinuria levels. Although CURE-CKD produced a lower range estimate of overall CKD prevalence compared with other US reports, this prevalence rate is similar to that found in Canadian primary care. 4 , 32 A higher range estimate for CKD based solely on eGFR could be because of more frequent testing in patients with higher risk who were treated by both specialty and primary care practices at PSJH and UCLA Health. Ascertainment bias is an inherent limitation of EHR-based registries, and information about CKD will also be missed from patients receiving care elsewhere or not receiving testing. The actual prevalence of overall CKD likely lies between the low (4.8%) and high (22.6%) range estimations from CURE-CKD. Nevertheless, these detailed prevalence estimates are strengths that represent the complexity and composition of patients treated in typical US health care systems.

In conclusion, the CURE-CKD registry reveals a burgeoning number of patients with CKD and major risk factors of diabetes, hypertension, and prediabetes. Rates of identification and use of kidney protective agents were low, while nephrotoxin use was widespread, underscoring the pressing need for practice-based improvement in CKD prevention, recognition, and treatment. These real-world data lay the groundwork for the development of more effective strategies to deliver care that enhances wellness and survival for patients with and at risk for CKD.

Accepted for Publication: November 4, 2019.

Published: December 20, 2019. doi:10.1001/jamanetworkopen.2019.18169

Corresponding Author: Katherine R. Tuttle, MD, Providence St Joseph Health, Providence Medical Research Center, 105 W Eighth Ave, Ste 6050 W, Spokane, WA 99204 ( [email protected] ).

Author Contributions: Dr Tuttle had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Tuttle, Jones, Daratha, Nicholas, McPherson, Bell, Mangione, Norris.

Acquisition, analysis, or interpretation of data: Tuttle, Alicic, Duru, Jones, Daratha, McPherson, Neumiller, Bell, Norris.

Drafting of the manuscript: Tuttle, Alicic, Jones, Nicholas, McPherson, Norris.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Tuttle, Jones, Daratha, McPherson, Bell.

Obtained funding: Tuttle, Magione, Norris.

Administrative, technical, or material support: Tuttle, Alicic, Daratha, McPherson, Neumiller, Bell, Norris.

Supervision: Tuttle, Magione, Norris.

Conflict of Interest Disclosures: Dr Tuttle reported receiving personal fees from Eli Lilly and Co, Boehringer Ingelheim, AstraZeneca, Gilead Sciences, Goldfinch Bio, and Novo Nordisk outside the submitted work. Dr Nicholas reported receiving grants from Goldfinch Bio, Bayer, the US Centers for Disease Control and Prevention, and Terasaki Research Institute; serving as national leader of a phase 3 clinical trial for the George Clinical Institute of Global Health; and receiving consulting fees from Janssen Pharmaceuticals and Amgen outside the submitted work. Dr McPherson reported receiving grants from the US Department of Health and Human Services and US Centers for Disease Control and Prevention during the conduct of the study. Dr Norris reported receiving grants from the National Institutes of Health during the conduct of the study. No other disclosures were reported.

Funding/Support: The CURE-CKD registry was supported by institutional funding from Providence St Joseph Health and the University of California, Los Angeles. Dr Tuttle is supported by grants 4UL1TR00426-10, 1U2CDK114886-01, 1U54DK083912, and 2UC4DK101108-02 from the National Institutes of Health. Drs Tuttle and Alicic are supported by grants 5UM1DK100846-03 and 2U01DK10086-07 from the National Institutes of Health. Drs Tuttle, Alicic, Jones, Daratha, and McPherson are supported by grant 75D301-19-Q-69877 from the US Centers for Disease Control and Prevention. Drs Duru, Bell, Mangione, and Norris are supported by grant UL1TR000124 from the National Institutes of Health. Drs Duru, Mangione, and Norris are supported by grant P30AG021684-15S2 from the National Institutes of Health. Dr Nicholas is supported by grant UL1TRR001881 from the National Center for Advancing Translational Science. Dr McPherson is supported by grants P20MD006871, UG1DA013714, R01EY027476, N44DA162246, R01AA022070, R01AA020248, P60AA026112, R41AA026793, N44DA171210, and R01AG042467 from the National Institutes of Health and grant I01HX002518 from the US Department of Veterans Affairs. Dr Mangione is supported by the Barbara A. Levey and Gerald S. Levey Endowed Chair.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: Carol Miceli, BS, Robert Follett, BS, and Theona Tacorda, MS (Providence St Joseph Health), extracted data from electronic health records, and Art Gongora (Providence St Joseph Health) provided administrative support. They were compensated for their time.

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Global health inequalities of chronic kidney disease: a meta-analysis

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Rachael Duff, Omodolapo Awofala, Muhammad Tahir Arshad, Emilie Lambourg, Peter Gallacher, Neeraj Dhaun, Samira Bell, Global health inequalities of chronic kidney disease: a meta-analysis, Nephrology Dialysis Transplantation , Volume 39, Issue 10, October 2024, Pages 1692–1709, https://doi.org/10.1093/ndt/gfae048

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Chronic kidney disease (CKD) is a significant contributor to global morbidity and mortality. This study investigated disparities in age, sex and socio-economic status in CKD and updated global prevalence estimates through systematic review and meta-analysis.

Five databases were searched from 2014 to 2022, with 14 871 articles screened, 119 papers included and data analysed on 29 159 948 participants. Random effects meta-analyses were conducted to determine overall prevalence, prevalence of stages 3–5 and prevalence in males and females. Influences of age, sex and socio-economic status were assessed in subgroup analyses and risk of bias assessment and meta-regressions were conducted to explore heterogeneity.

The overall prevalence of CKD was 13.0% [95% confidence interval (CI) 11.3–14.8] and 6.6% (95% CI 5.6–7.8) for stages 3–5. The prevalence was higher in studies of older populations (19.3% for stages 1–5, 15.0% for stages 3–5) and meta-regression demonstrated an association of age, body mass index, diabetes and hypertension with prevalence of stages 3–5. The prevalence of CKD stages 1–5 was similar in males and females (13.1% versus 13.2%), but the prevalence of stages 3–5 was higher in females (6.4% versus 7.5%). Overall prevalence was 11.4%, 15.0% and 10.8% in low-, middle- and high-income countries, respectively; for stages 3–5, prevalence was 4.0%, 6.7% and 6.8%, respectively. Included studies were at moderate–high risk of bias in the majority of cases (92%) and heterogeneity was high.

This study provides a comprehensive assessment of CKD prevalence, highlighting important disparities related to age, sex and socio-economic status. Future research should focus on targeted screening and treatment approaches, improving access to care and more effective data monitoring, particularly in low- and middle-income countries.

What was known:

The burden of CKD is increasing.

Concerns about disparities in age, sex and socio-economic status exist.

A clearer understanding of these elements is crucial for targeted research and interventions.

This study adds:

This is the largest systematic review of CKD prevalence.

Females and older age groups had a higher prevalence, with disparities across countries of differing socio-economic status.

Compared with a previous systematic review, this study has a greater number of participants, with most included studies using the Chronic Kidney Disease Epidemiology Collaboration estimate.

Potential impact:

Demonstration of significant disparities in age, sex and socio-economic status should encourage further research into understanding what is driving these differences.

Research that considers these elements along with examining important outcomes such as mortality and quality of life will be instrumental in driving positive changes in global health.

Chronic kidney disease (CKD) is a significant contributor to the healthcare burden globally. The Global Burden of Disease Study (2017) demonstrated a CKD prevalence of 9.1%, accounting for 35.8 million disability-adjusted life years (DALYs), with all-age mortality increasing by 41.5% from 1990 to 2017 and much of the burden concentrated in areas of lower socio-economic status [ 1 ]. Research shows healthcare costs of cardio-renal events are higher than those for atherosclerotic events and that CKD costs in excess of $114 billion in the USA and £1.45 billion in the UK annually [ 2 , 3 ].

Age, sex and socio-economic status are considered influential in the development, progression and outcomes of CKD. Age is a well-established risk factor for developing CKD, but understanding the extent of the burden associated with an ageing population is crucial for effective screening and management. The influence of sex is less clear, with research indicating a higher prevalence of CKD in females but a greater prevalence of end-stage kidney disease (ESKD) in males. Defining whether this is reflected globally is important to further understand gender gaps in CKD care. Lower socio-economic countries are also thought to have a greater burden of CKD, although there are challenges in assessing this due to limited access to data sources. Obtaining a comprehensive understanding of prevalence in countries of varying economic status would provide valuable insights into the level of work needed to bridge any disparities that exist.

The aim of this study was to update systematic review data [ 4 ] to determine current global prevalence estimates for CKD. A primary focus was to examine disparities associated with CKD, focusing on age, sex and socio-economic status. A better understanding of these factors will play a crucial role in guiding healthcare professionals, policymakers and the public in identifying priorities for intervention and research. By focusing efforts in these areas, the overall burden imposed by CKD can be reduced and the quality of life of patients can be improved.

This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 5 ]. The protocol was registered on PROSPERO (CRD42022311032) [ 6 ].

Searches were carried out using MEDLINE, PubMed, Embase, Cochrane Controlled Register of Trials (CENTRAL), CINAHL and Web of Science, with search strategy developed with the assistance of a librarian. The search was carried out on 28 September 2022, limited to studies published since 2014 to avoid duplicating papers in a previous systematic review [ 4 ]. The search strategy consisted of free-text words and Medical Subject Headings terms ( Supplementary Table S1 ). References of associated systematic reviews and included studies were searched, along with grey literature. Contact was made with authors if there was difficulty sourcing the full text or if details required clarification. References were screened using title and abstract by three reviewers (R.D., O.A. and M.A.) using Rayyan (Rayyan, Boston, MA, USA) [ 7 ]. Conflict was resolved by a fourth reviewer (S.B.). Following initial review, full-text assessment of all potentially suitable studies was carried out against pre-determined inclusion/exclusion criteria ( Supplementary Table S2 ).

Studies in English or French that reported the prevalence of CKD or allowed prevalence to be calculated in participants ≥18 years of age and carried out in the general population were included. CKD was defined as the presence of albuminuria/proteinuria and/or an estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m 2 . Definitions of CKD stages are provided in Supplementary Table S3 . Studies were excluded if they only described CKD stages 1 and 2, were performed in a specialist population, included participants <18 years of age or did not describe CKD prevalence. Qualitative papers, case reports, case series and opinion pieces were excluded.

Data analysis

Data were fully extracted by three reviewers (R.D., O.A. and M.A.). Studies reported in French were translated and the data extracted by E.L. Key elements of the PICO (patient/population/problem, intervention, comparison and outcome) criteria are given in Supplementary Table S4 . Conflict was resolved by a fourth reviewer (S.B.).

Risk of bias was assessed using criteria published by Stanifer et al . [ 8 ]. This tool was designed for assessing risk of bias in studies of CKD prevalence. It considers subject sampling, sampling technique, response and exclusion rates and the determination of kidney disease, enabling an overall assessment ( Supplementary Methods ). Assessment was carried out by three reviewers (R.D., O.A. and M.A.) and conflicts were resolved by a fourth reviewer (S.B.). All studies were included irrespective of their risk of bias.

Small-study effect was assessed using funnel plots. Logit-transformed prevalence was used against the standard error. Asymmetry was tested using Egger's [ 9 ] linear regression and Begg's [ 10 ] rank correlation tests. If there was evidence of publication bias, the trim and fill method [ 11 ] was used to calculate a corrected estimate.

Meta-analyses were conducted to determine overall CKD prevalence, prevalence of CKD stages 3–5, overall CKD prevalence in men/women and prevalence of CKD stages 3–5 in men/women. Meta-regressions were carried out to determine the association between the prevalence of CKD and population characteristics [age, sex, year, body mass index (BMI), diabetes, hypertension, smoking and obesity]. All meta-regressions were planned a priori.

For articles reporting multiple different prevalences of CKD using different methods of GFR estimation, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) estimates were used if available. For articles reporting crude and adjusted prevalence, crude prevalence was used. If an article reported CKD prevalence in multiple populations, the populations were considered as separate estimates. Between-study heterogeneity was assessed by the Higgins and Thompson [ 12 ] I 2 , with a value >75% representing a high level of heterogeneity.

Sensitivity analyses looked at the impact of outliers, influential articles and studies with a high risk of bias. Subgroup analyses were planned a priori, examining differences between regions, studies that looked at a limited age group, the difference in prevalence depending on a country's income status according to the World Bank [ 13 ] and studies of different quality. Subgroup analyses that assessed differences between articles that tested for chronicity, different methodologies, different definitions and crude versus adjusted prevalence were data driven. A sensitivity analysis looking at the prevalence of kidney replacement therapy (KRT) was planned but was not carried out due to insufficient data.

Statistical analyses were carried out using R (version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria). Details of the R packages used are provided in Supplementary Methods . A random effects model was used for all meta-analyses. Models were built using a generalised linear mixed model (GLMM) [ 14 ]. A maximum likelihood estimator was used and a Q profile estimated tau and its confidence interval (CI) [ 15 ]. The Hartung–Knapp [ 16 ] method was used to adjust the CI of the overall estimate. Heterogeneity was explored with sensitivity analyses that used a basic outlier removal defined by Viechtbauer and Cheung [ 17 ], as well as the leave-one-out method sorted by effect size and I 2 .

The search yielded 14 871 studies (Fig. 1 ) and 119 [ 2 , 18–135 ] met the inclusion criteria. Of these, 12 studies [ 18 , 21 , 27 , 41 , 45 , 48 , 50 , 69 , 94 , 101 , 107 , 112 ] used the same sources of data (National Health and Nutrition Examination Survey, Korea National Health and Nutrition Examination Survey, German Health Interview and Examination Survey for Adults 1 and Age, Gene/Environment Susceptibility Reykjavik Study) and subsequently one study covering each source of data was included [ 27 , 48 , 69 , 101 ]. These studies were selected as they had already covered the data presented in the other studies [ 27 , 101 ], had a larger sample size [ 48 ] or had presented the same data with a more comprehensive analysis [ 69 ]. The study by Bragg-Gresham et al. [ 94 ] was included, but only data from the Punjab survey was used within the meta-analysis. Five studies [ 2 , 24 , 39 , 109 , 122 ] had divided their population, and the separate populations were included as separate estimates, resulting in 127 estimates, comprising 29 159 948 participants. A total of 54 different countries provided data, with China providing the largest number of included studies ( n = 14, number of participants 916 825) and low- and middle-income countries (LMICs) comprising a significant proportion of included studies but a small number of participants overall ( n = 77, number of participants 1 141 602). The total number of studies and data obtained are detailed in Supplementary Table S5 . Risk of bias was high across the included studies.

PRISMA flow chart.

The prevalence of CKD stages 1–5 was 13.0% (95% CI 11.3–15.0) (Fig. 2 ). Prevalence estimates ranged from 2.3 to 47.7%, and heterogeneity was high ( I 2 = 100%). The prevalence of CKD stages 3–5 was 6.6% (95% CI 5.6–7.8) (Fig. 3 ). The prevalence of specific CKD stages was also calculated where possible: stage 1, 3.0% (95% CI 2.1–4.3); stage 2, 2.9% (95% CI 2.2–3.8), stage 3A, 4.1% (95% CI 3.0–5.5); stage 3B, 1.3% (95% CI 1.0–1.8); stage 4, 0.4% (95% CI 0.3–0.5) and stage 5, 0.1% (95% CI 0.1–0.2).

Pooled prevalence of CKD stages 1–5. GLMM.

Pooled prevalence of CKD stages 3–5. GLMM.

The prevalence of CKD (stages 1–5) in males and females was 13.1% (95% CI 11.2–15.3) and 13.2% (95% CI 11.4–15.2), respectively (Fig. 4 ), whereas the prevalence of stages 3–5 was 6.4% (95% CI 4.9–8.3) and 7.5% (95% CI 5.8–9.8) in males and females, respectively (Fig. 5 ). Within a multivariable analysis for CKD stages 1–5, a higher proportion of females within the population was associated with an increased prevalence of CKD (Table 1 ).

Pooled prevalence of CKD stages 1–5 in (a) females and (b) males.

Pooled prevalence of CKD stages 3–5 in (a) females and (b) males.

There was a significant difference in studies that looked at an older population versus studies within the general population (Tables 2 and 3 ). For CKD stages 1–5, prevalence in studies carried out in the general population was 12.4% (95% CI 10.7–14.4), compared with 19.3% (95% CI 13.5–26.8) in studies carried out in participants ≥60 years of age (Table 2 ). Similarly, for stages 3–5, prevalence in studies carried out in the general population was 5.9% (95% CI 5.0–7.0), compared with 15.0% (95% CI 9.9–22.2) in those ≥60 years of age (Table 3 ). The mean age was significant in the multivariable analysis for stages 1–5 and the univariable analysis for stages 3–5 (Table 1 ). Subgroup analyses were also carried out looking at differences in the prevalence of specific CKD stages in general and older populations, with findings presented in Table 4 .

Meta-regression results.

Predictors .	Age .	Sex .	Study period .	Mean BMI .	Diabetes .	HTN .	Smoking .	Obesity .
Stages 1–5
Univariable	= .12	= .14	= .24	= .96	= .49	= .15	= .35	= .78
Multivariable	= .02	= .04	= .34	= .69	= .44	= .64	= .91	= .89
Stages 3–5
Univariable	< .0001	= .86	= .37	= .02	= .003	= .004	= .94	= .63
Multivariable	= .0002	= .46	= .07	= .09	= .87	= .88	= .99	= .56

Predictors .	Age .	Sex .	Study period .	Mean BMI .	Diabetes .	HTN .	Smoking .	Obesity .
Stages 1–5
Univariable	= .12	= .14	= .24	= .96	= .49	= .15	= .35	= .78
Multivariable	= .02	= .04	= .34	= .69	= .44	= .64	= .91	= .89
Stages 3–5
Univariable	< .0001	= .86	= .37	= .02	= .003	= .004	= .94	= .63
Multivariable	= .0002	= .46	= .07	= .09	= .87	= .88	= .99	= .56

HTN: hypertension.

Multivariable analysis adjusted for age and sex.

Bubble plots for significant findings are shown in Supplementary Fig. S1 .

There was a variation in prevalence for both stages 1–5 and stages 3–5 in countries of differing socio-economic status. For stages 1–5, the prevalence was 10.8% (95% CI 8.3–13.8) in high-income countries, 15.0% (95% CI 12.6–17.9) in middle-income countries and 11.4% (95% CI 7.3–17.5) in low-income countries. For stages 3–5, the prevalence was 6.8% (95% CI 5.1–9.1) in high-income countries, 6.7% (95% CI 5.6–8.1) in middle-income countries and 4.0% (95% CI 0.8–17.2) in low-income countries (Fig. 6 ). The prevalence in different regions was also determined, as shown in Fig. 7 . The results of full subgroup and sensitivity analyses are provided in Tables 2 and 3 .

Pooled prevalence of CKD according to income status for (a) stages 1–5 and (b) stages 3–5.

Pooled prevalence of CKD according to region for (a) stages 1–5 and (b) stages 3–5.

CKD stages 1–5 subgroup analyses.

Analysis .	Subgroup .	Prevalence .	95% CI .	(%) .	Subgroup difference .
Region	Africa	13.4	9.1–19.1	99.2	Q = 6.5 = .26
	Asia	15.5	12.8–18.7	100
	Australia/Oceania	12.9	5.7–26.3	99.8
	Central/South America	11.2	6.2–19.5	98.6
	Europe	10.0	7.1–14.1	100
	North America and Canada	12.6	4.4–31.2	100
Risk of bias	High	13.0	10.5–16.0	100	Q = 1.5 = .48
	Moderate	13.5	10.9–16.6	99.7
	Low	10.5	6.3–17.1	99.6
Test for chronicity	No	14.4	12.3–16.8	99.8	Q = 6.1 = .01
	Yes	9.9	7.5–13.0	100
Population income status	High	10.8	8.3–13.8	100	Q = 5.8 = .06
	Middle	15.0	12.6–17.9	99.5
	Low	11.4	7.3–17.5	97.8
Methodology used	Random sampling	13.4	11.3–15.7	99.7	Q = 36.3 .0001 Q = 2.2 = .33
	Routine dataset	10.6	7.3–15.1	100
	Health camp recruitment	25.2	7.3–59.1	63.8
	Other	14.6	9.1–22.8	99.5
Population age	General	12.4	10.7–14.4	100	Q = 6.7 = .01
	≥60 years	19.3	13.5–26.8	99.7
Adjusted prevalence	No	12.6	10.8–14.7	100	Q = 0.7 = .40
	Yes	14.8	10.2–20.9	99.8
eGFR estimating equation	MDRD	13.1	9.4–17.8	99.5	Q = 3.6 = .16
	CKD-EPI	14.0	11.9–16.4	99.9
	Other	8.8	5.0–14.8	100

Analysis .	Subgroup .	Prevalence .	95% CI .	(%) .	Subgroup difference .
Region	Africa	13.4	9.1–19.1	99.2	Q = 6.5 = .26
	Asia	15.5	12.8–18.7	100
	Australia/Oceania	12.9	5.7–26.3	99.8
	Central/South America	11.2	6.2–19.5	98.6
	Europe	10.0	7.1–14.1	100
	North America and Canada	12.6	4.4–31.2	100
Risk of bias	High	13.0	10.5–16.0	100	Q = 1.5 = .48
	Moderate	13.5	10.9–16.6	99.7
	Low	10.5	6.3–17.1	99.6
Test for chronicity	No	14.4	12.3–16.8	99.8	Q = 6.1 = .01
	Yes	9.9	7.5–13.0	100
Population income status	High	10.8	8.3–13.8	100	Q = 5.8 = .06
	Middle	15.0	12.6–17.9	99.5
	Low	11.4	7.3–17.5	97.8
Methodology used	Random sampling	13.4	11.3–15.7	99.7	Q = 36.3 .0001 Q = 2.2 = .33
	Routine dataset	10.6	7.3–15.1	100
	Health camp recruitment	25.2	7.3–59.1	63.8
	Other	14.6	9.1–22.8	99.5
Population age	General	12.4	10.7–14.4	100	Q = 6.7 = .01
	≥60 years	19.3	13.5–26.8	99.7
Adjusted prevalence	No	12.6	10.8–14.7	100	Q = 0.7 = .40
	Yes	14.8	10.2–20.9	99.8
eGFR estimating equation	MDRD	13.1	9.4–17.8	99.5	Q = 3.6 = .16
	CKD-EPI	14.0	11.9–16.4	99.9
	Other	8.8	5.0–14.8	100

a This subgroup analysis was repeated by excluding health camp recruitment papers ( n = 2) due to the significant difference in prevalence and subsequent influence on subgroup analysis.

CKD stages 3–5 subgroup analyses.

Analysis .	Subgroup .	Prevalence .	95% CI .	(%) .	Subgroup difference .
Region	Africa	5.7	3.5–9.2	98.3	Q = 2.0 = .85
	Asia	6.3	4.9–8.0	99.9
	Australia/Oceania	8.1	3.0–20.4	100
	Central/South America	7.3	5.0–10.6	99.1
	Europe	7.2	4.8–10.6	99.9
	North America and Canada	6.0	1.4–22.0	99.7
Risk of bias	High	7.0	5.6–8.8	100	Q = 0.6 = .76
	Moderate	6.4	5.0–8.2	99.8
	Low	5.8	3.0–11.1	99.8
Test for chronicity	No	6.6	5.2–8.0	99.9	Q = 0 = .99
	Yes	6.6	5.2–8.3	99.9
Population income status	High	6.8	5.1–9.1	99.9	Q = 0.7 = .70
	Middle	6.7	5.6–8.1	99.8
	Low	4.0	0.8–17.2	97.3
Methodology used	Random sampling	6.1	5.1–7.4	99.7	Q = 4.8 = .19
	Routine dataset	7.7	5.4–10.9	100
	Health camp recruitment	10.5	0.01–94.4	94.2
	Other	10.4	4.4–22.4	99.9
Population age	General	5.9	5.0–7.0	99.8	Q = 19.4 < .0001
	≥60 years	15.0	9.9–22.2	99.9
Adjusted prevalence	No	6.6	5.5–7.9	99.9	Q = 0.6 = .43
	Yes	5.4	2.9–9.6	99.0
eGFR estimating equation	MDRD	6.9	4.8–9.9	99.8	Q = 0.1 = .96
	CKD-EPI	6.5	5.4–7.9	99.9
	Other	6.7	2.9–14.4	99.9

Analysis .	Subgroup .	Prevalence .	95% CI .	(%) .	Subgroup difference .
Region	Africa	5.7	3.5–9.2	98.3	Q = 2.0 = .85
	Asia	6.3	4.9–8.0	99.9
	Australia/Oceania	8.1	3.0–20.4	100
	Central/South America	7.3	5.0–10.6	99.1
	Europe	7.2	4.8–10.6	99.9
	North America and Canada	6.0	1.4–22.0	99.7
Risk of bias	High	7.0	5.6–8.8	100	Q = 0.6 = .76
	Moderate	6.4	5.0–8.2	99.8
	Low	5.8	3.0–11.1	99.8
Test for chronicity	No	6.6	5.2–8.0	99.9	Q = 0 = .99
	Yes	6.6	5.2–8.3	99.9
Population income status	High	6.8	5.1–9.1	99.9	Q = 0.7 = .70
	Middle	6.7	5.6–8.1	99.8
	Low	4.0	0.8–17.2	97.3
Methodology used	Random sampling	6.1	5.1–7.4	99.7	Q = 4.8 = .19
	Routine dataset	7.7	5.4–10.9	100
	Health camp recruitment	10.5	0.01–94.4	94.2
	Other	10.4	4.4–22.4	99.9
Population age	General	5.9	5.0–7.0	99.8	Q = 19.4 < .0001
	≥60 years	15.0	9.9–22.2	99.9
Adjusted prevalence	No	6.6	5.5–7.9	99.9	Q = 0.6 = .43
	Yes	5.4	2.9–9.6	99.0
eGFR estimating equation	MDRD	6.9	4.8–9.9	99.8	Q = 0.1 = .96
	CKD-EPI	6.5	5.4–7.9	99.9
	Other	6.7	2.9–14.4	99.9

Subgroup analyses determining differences in prevalence in all CKD stages in general versus older (≥60 years) populations.

CKD stage .	Population .	Prevalence (%) .	95% CI .	(%) .	Subgroup difference .
1	General	3.2	2.2–4.5	99.8	Q = 0.6 = .44
	Older	2.0	0.3–11.4	99.4
2	General	2.8	2.0–3.8	99.8	Q = 4.6 = .03
	Older	4.1	3.0–5.5	97.1
3A	General	3.7	2.7–5.0	99.9	Q = 8.0 = .0047
	Older	10.7	3.6–28.2	99.8
3B	General	1.2	0.9–1.6	99.7	Q = 7.1 = .0076
	Older	3.9	1.0–13.6	99.7
3	General	4.7	3.7–6.0	99.9	Q = 8.9 = .0029
	Older	11.8	5.9–22.1	99.9
4	General	0.4	0.3–0.5	99.0	Q = 0.4 = .518
	Older	0.5	0.2–1.2	98.1
5	General	0.1	0.1–0.2	95.7	Q = 4.2 = .0397
	Older	0.1	0.1–0.1	61.6

CKD stage .	Population .	Prevalence (%) .	95% CI .	(%) .	Subgroup difference .
1	General	3.2	2.2–4.5	99.8	Q = 0.6 = .44
	Older	2.0	0.3–11.4	99.4
2	General	2.8	2.0–3.8	99.8	Q = 4.6 = .03
	Older	4.1	3.0–5.5	97.1
3A	General	3.7	2.7–5.0	99.9	Q = 8.0 = .0047
	Older	10.7	3.6–28.2	99.8
3B	General	1.2	0.9–1.6	99.7	Q = 7.1 = .0076
	Older	3.9	1.0–13.6	99.7
3	General	4.7	3.7–6.0	99.9	Q = 8.9 = .0029
	Older	11.8	5.9–22.1	99.9
4	General	0.4	0.3–0.5	99.0	Q = 0.4 = .518
	Older	0.5	0.2–1.2	98.1
5	General	0.1	0.1–0.2	95.7	Q = 4.2 = .0397
	Older	0.1	0.1–0.1	61.6

A total of 8% of studies had a low risk of bias, 48% had a high risk and 44% had a moderate risk. The response rate was not reported in 56% of the studies, 32% did not state their exclusion rate, 53% were not considered to be a representative sample, 36% were not recruited at random ( Supplementary Figs. S2 and S3 ) and 20% tested for chronicity of kidney impairment. Subgroup and sensitivity analysis looked at the risk of bias and chronicity assessment. There was no difference between low, moderate and high risk of bias for either stages 1–5 or 3–5 ( P = .48 and P = .76), although there was a difference between studies that tested for chronicity when looking at stages 1–5 ( P = .01) (Fig. 8 ). For CKD stages 1–5, removing articles with a high risk of bias had little effect on the overall pooled prevalence [13.1% (95% CI 10.8–15.8)]. Similar results were also noted for CKD stages 3–5 when excluding studies with a high risk of bias [6.3% (95% CI 5.0–8.3)] ( Supplementary Table S7 ). Chronicity assessment did not change the prevalence estimates for CKD stages 3–5 (Fig. 8 ).

Pooled prevalence of CKD according to chronicity assessment for (a) stages 1–5 and (b) stages 3–5.

Heatmap of global CKD prevalence for (a) stages 1–5 and (b) stages 3–5. Grey: no data available.

Funnel plots assessing publication bias are shown in Supplementary Figs. S8 and S9 . There was asymmetry when examining stages 1–5 using both the Egger's test ( P = .005) and Begg's test ( P = .001). There was no evidence of publication bias in the analysis for stages 3–5. Trim and fill results for stages 1–5 are detailed in Supplementary Table S8 .

The burden of CKD is significant, with a prevalence of 13.0% for stages 1–5 and 6.6% for stages 3–5. Females had a higher prevalence of later-stage CKD compared with males (7.5% versus 6.4%), and studies that only investigated older participants (≥60 years) found a significantly higher prevalence of CKD. This was as high as 19.3% for CKD stages 1–5 and 15.0% for CKD stages 3–5. The highest prevalence of CKD stages 1–5 was in Asia (15.5%), whereas the highest prevalence of CKD stages 3–5 was in Australia and Oceania (8.1%). Europe has the lowest prevalence of CKD stages 1–5 (10.0%), while Africa had the lowest prevalence of CKD stages 3–5 (5.7%). High-income countries had the lowest prevalence of CKD stages 1–5 (10.8%), whereas low-income countries had the lowest prevalence of CKD stages 3–5 (4.0%). The risk of bias was significant and heterogeneity was high and there was evidence of publication bias. However, traditional methods to determine publication bias are not designed for observational studies of single proportions [ 136 , 137 ].

CKD prevalence is similar between males and females, but later stage CKD is more common in females. Pre-dialysis CKD is more prevalent in females, but males make up a greater proportion of dialysis patients [ 138 , 139 ]. Although a lower eGFR was previously considered less of a risk factor for CKD progression in females than in males, a meta-analysis found the risk of ESKD to be equal [ 140 ]. There were insufficient data to define the prevalence of males and females on KRT, but the fact that females have a higher prevalence of CKD stages 3–5 demonstrates there may be a disparity between a higher risk of CKD and treatment in females. However, GFR estimating equations may overdiagnosis CKD in females, and the fact that they have a longer life expectancy may also explain these findings [ 138 ]. CKD is thought to progress faster in men, but women are less likely to be screened for CKD, have less access to specialist care and are more likely to choose conservative management [ 139 , 141 ]. Addressing differences in mortality, overall comorbidities and discrepancies in access to KRT for males and females would help quantify whether this represents a greater burden of disease in females, and further research into psychosocial and behavioural elements that may be driving differences will guide what should be done to reduce this gap.

CKD prevalence was higher in older age groups. There was a higher prevalence of stages 3A and 3B in older populations, with no significant difference in stages 4 and 5. These findings are important given the greater risk of poorer outcomes in older patients with CKD stage 3B in particular [ 142 ]. The fact that stages 4 and 5 are similar in both general and older populations may also suggest a greater risk of poorer outcomes in older people at earlier stages of CKD. It is also possible that these findings could reflect overdiagnosis, and it is important to interpret these findings in the context of concerns regarding the validity of eGFR thresholds in older people [ 143 ]. A decreasing GFR occurs during the normal ageing process, and consensus on whether eGFR thresholds and their interpretation should be adapted for older people is important for differentiating normal ageing from true kidney damage [ 143 , 144 ]. Changing thresholds may result in different prevalence estimates of CKD in older populations, which is important for optimising management and helping identify those at greatest risk. Mortality data are important, but increased screening in older populations and exploration of age-specific interventions to delay and ideally prevent the development and progression of CKD should also be considered.

There were geographical variations and disparities between low-, middle- and high-income countries. Low-income countries had the lowest overall prevalence of CKD stages 3–5 (4%), whereas high-income countries had the lowest prevalence of CKD overall (11%). Notably, the ages of these populations differed, with a mean age of 56.4 years in high-income countries, 49.3 years in middle-income countries and 38.3 years in low-income countries. The highest prevalence of CKD overall was in Asia, with Australia/Oceania having the highest prevalence of later-stage CKD. Australian data suggest that the high prevalence of CKD is likely due to an ageing population and increased survival of patients with ESKD receiving KRT [ 145 ]. Similarly, a lower prevalence of elderly people, as well as lower rates of testing, in low-income countries could explain the lower prevalence of CKD stages 3–5. It may also reflect the fact that people with advanced CKD in LMICs have a greater mortality, as they are unable to access KRT and other forms of treatment. Future work assessing differences in CKD mortality between high-income countries and LMICs would help quantify this further. Certain environmental factors may also contribute to CKD prevalence, but these are poorly understood [ 146 ].

A limitation of this review is the risk of bias within included studies. There was no difference in prevalence noted between studies at low-, moderate- and high-risk of bias, but the predominance of a moderate–high risk of bias within studies means that results must be interpreted with caution. The studies included were designed to represent the general population, but only half were considered truly representative, and there were concerns regarding reporting of the sampling techniques. CKD prevalence estimates at a population level are also influenced by variations in screening methods, eGFR measurement, CKD definitions and limitations of resources in some areas, thus pooled estimates and reported differences according to demographic factors should be interpreted with this in mind.

Despite attempts to standardise defining CKD, there continues to be variation. There were nine different GFR estimating equations used within the included studies ( Supplementary Table S5 ), and even in studies using the CKD-EPI estimate, there were multiple variations of this, making comparability challenging. Standardising GFR estimating equations and any modifications used within published research is crucial for ensuring consistency, and this would be an important element to include within core outcomes expected to be reported in CKD prevalence studies. Other important outcomes should include reporting of key demographic data such as race and standardised reporting of prevalence by age. Race and ethnicity are associated with differences in measured and estimated GFR, but only 22 studies provided data on the race of participants, and there was no consistency in the reporting of CKD prevalence by age, which meant that a more accurate assessment in this review was not possible.

Only 20% of articles assessed chronicity, and there was a notable difference in prevalence when comparing CKD stages 1–5 in studies that considered this versus those that did not. An expectation that researchers include a chronicity assessment is crucial. The vast majority of studies were cross-sectional, which can limit understanding of the chronic nature of CKD. Routine datasets such as primary care databases, laboratory databases and renal registries provide valuable sources of data that are capable of generating large sample sizes and should be utilised to allow researchers to distinguish CKD from acute kidney injury. Enhancing funding for national screening programs that are designed to ensure chronicity is assessed would also be valuable. Improved collaboration on an international scale and consideration of how these methods of data monitoring can be extended to areas with more limited resources is crucial. Greater investment in training of healthcare professionals, researchers and community workers in LMICs to understand the risks of CKD and the value of accurate monitoring in their population is needed, along with improved public health campaigns, better integration with primary care and consideration of low-cost point-of-care testing to enable monitoring in areas that are harder to engage. This would improve the quality of global surveillance networks and enable a greater focus on longitudinal assessment of CKD.

The prevalence of CKD overall was not different from that in a previous systematic review [ 4 ], although the prevalence of stages 3–5 was notably lower in the current study. This may be due to the higher proportion of CKD-EPI eGFRs used. Sensitivity analysis did not demonstrate a difference between studies using Modification of Diet in renal Disease (MDRD) estimates of GFR and those using CKD-EPI. However, this analysis is limited by only 29 of the 127 population estimates included having used the MDRD definition. Of note, the previous review showed a higher prevalence of CKD in females, and it is concerning that in the almost 10 years between these reviews, this discrepancy remains.

This review extends previous work in several ways. It provides the most up-to-date global prevalence estimate for CKD, with most studies making use of the CKD-EPI estimate of GFR. It demonstrated significant health inequalities affecting CKD prevalence, and addressing these concerns should be a priority for policymakers. A large number of studies were included, covering prevalence across all worldwide regions. Compared with a previous systematic review [ 4 ], this review was able to demonstrate the prevalence of all CKD stages, including separate estimates for 3A and 3B, which are important to differentiate given the greater risk associated with stage 3B [ 142 ]. The search strategy was comprehensive and articles published in both English and French were included to reduce bias caused by including only studies reported in English.

Global CKD prevalence remains high, with significant gaps across age, sex and socio-economic status. Future research that focuses on understanding the reasons for these disparities and considers interventions that are needed to drive improvements will be important in reducing the overall burden from this condition.

None declared.

S.B. conceived and supervised the study. R.D., O.A. and M.A. screened the studies, extracted data and completed the risk of bias assessment. E.L. translated articles in French. R.D. carried out the statistical analysis and wrote the first draft of the manuscript. R.D., S.B., E.L., P.G. and N.D. interpreted the findings. All authors critically reviewed the manuscript and approved the final version.

All data were extracted from published studies and most data used are supplied in Supplementary Table S5 . Access to the full set of data will be made available upon request.

R.D., O.A., M.A., E.L., P.D. and N.D. report no conflicts of interest. S.B. reports consultancy fees from AstraZeneca, Bayer and GSK.

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Published: 27 September 2024

Nonlinear association between atherogenic index of plasma and chronic kidney disease: a nationwide cross-sectional study

Bo Wang 1 ,
Chunqi Jiang 2 ,
Yinuo Qu 2 ,
Jun Wang 2 ,
Chuanzhu Yan 2 &
Xin Zhang 3

Lipids in Health and Disease volume 23 , Article number: 312 ( 2024 ) Cite this article

Metrics details

The interplay between metabolic disorders and chronic kidney disease (CKD) has been well-documented. However, the connection between CKD and atherogenic index of plasma (AIP) remains understudied. This research delves into the correlation between these two factors, aiming to shed new light on their potential association.

The relationship between AIP and CKD was evaluated using a weighted multivariate logistic regression model, and the curvilinear relationship between AIP and CKD was explored through smooth curve fitting. We engaged a recursive partitioning algorithm in conjunction with a two-stage linear regression model to precisely determine the inflection point. By conducting stratified analyses, the heterogeneity within subpopulations was explored.

In the regression model that accounted for all covariates, ORs (95% CI) for the association between CKD and AIP were 1.12 (0.91, 1.36), indicating no significant association between AIP and CKD. However, sensitivity analyses suggested that the relationship between them may be non-linear. Smooth curve analysis confirmed the non-linear relationship between AIP and CKD, identifying an inflection point at -0.55. Below this threshold, AIP exhibited a significant inverse correlation with CKD. Conversely, above this threshold, a pronounced positive correlation was detected. Stratified analyses elucidated that a non-linear association between AIP and CKD was observed among female participants and those aged 50 and above.

We found a curvilinear relationship between chronic kidney disease and atherogenic index of plasma.

Introduction

Chronic kidney disease (CKD) is a condition characterized by irreversibility and a progressive course of development, which significantly contributes to increased mortality from cardiovascular death and diabetes [ 1 ]. CKD imposes a substantial burden on global public health. An estimated 850 million individuals globally are afflicted with kidney disorders [ 2 ]. Risk factors for CKD include proteinuria, metabolic syndrome, diabetes, hypertension, advanced age, nephrotoxic drugs, etc. [ 3 ]. In recent years, there has been a growing recognition of the role that dyslipidemia plays in the pathogenesis of chronic kidney disease. Evidence is mounting that dyslipidemia significantly contributes to the development and progression of nephropathies. It is becoming clear that perturbations in lipid metabolism are associated with a deterioration of renal function [ 4 ]. Renal disorders,in turn, can intensify metabolic imbalances. The studies conducted by Vaziri ND and colleagues have elucidated that nephrotic syndrome results in profound disruptions to lipid metabolism [ 5 ]. Early identification and intervention of these risk factors play a crucial role in preventing CKD and improving renal outcomes.

The plasma atherosclerosis index (AIP) was introduced by Dobiasova and Frohlich in 2001, not only reflecting the ratio of pro-atherosclerotic lipids to protective lipids in plasma but also indicating particle size and esterification rate of HDL-C particles [ 6 ]. Studies have demonstrated associations between AIP and various conditions. A recent systematic review and meta-analysis has reported notably higher AIP levels in individuals with Obstructive Sleep Apnea (OSA) [ 7 ]. Additionally, elevated AIP values have been found to be independently associated with coronary artery disease (CAD) [ 8 ]. The AIP also stands out as a trustworthy biomarker for diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) [ 9 ].

Preliminary explorations into the relationship between AIP and the onset of CKD have been conducted. Evidence suggests that elevated AIP levels are correlated with an increased risk of CKD among adults with metabolic disorders [ 10 ]. Particularly, the risk of diabetic kidney disease (DKD) escalates significantly with higher AIP values, proposing its potential as a biomarker for early renal impairment [ 11 ]. Nonetheless, the current body of research exploring the connection between AIP and the initiation or progression of CKD remains scarce, resulting in a constrained comprehension of their association. Consequently, leveraging data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2003 to 2020 in the United States, the present study embarked on a cross-sectional investigation to elucidate the association between AIP and CKD. The objective was to yield fresh evidence that could inform lipid management interventions for the treatment of CKD.

Materials and methods

Study participants.

Our data comes from the NHANES database in the United States. The data covers nine periods from 2003 to 2020. Demographic, lifestyle, and blood biochemical data on the U.S population were collected through home visits, mobile screening centers (MECs), and laboratory tests. This is an open database that researchers can access without any permission. The survey protocol was approved by the National Center for Health Statistics Research Ethics Review Board, and written informed consent was obtained from all participants involved. To safeguard the privacy of the participants, all personal identifying information was de-identified. In data processing, we excluded 38,416 subjects < 18 years of age, 6447 subjects lacking CKD data, 26639 subjects lacking TG or HDL-C data, 482 subjects pregnant women, leaving 23888 subjects to participate in our study (Fig. 1 ).

Flow chart of sample selection from the 2003-2020

Study variables

Definition of chronic kidney disease.

CKD definition adheres to the guidelines provided by the Kidney Disease: Improving Global Outcomes (KDIGO) Glomerular Diseases Work Group. A diagnosis of CKD is warranted with the fulfillment of one or more of the following conditions for a period of three months or more: 1. An estimated glomerular filtration rate (eGFR) that is persistently below 60 mL/min/1.73 m2, as determined by the CKD Epidemiology Collaboration (CKD-EPI) creatinine equation. 2. A urine albumin-to-creatinine ratio (UACR) that surpasses the threshold of 30 mg/g [ 12 ].

Definition of atherogenic index of plasma

AIP represents a quantitative metric for evaluating lipid profiles, calculated from the ratio of fasting triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C) following logarithmic transformation. The AIP was determined using the formula: AIP = Log [TG (mmol/L) / HDL-C (mmol/L)] [ 13 , 14 ]. HDL-C and TC were meticulously assessed by specialists, who were trained in accordance with the rigorous standards set by the National Center for Health Statistics (NCHS) and executed under the guidance of the Centers for Disease Control and Prevention (CDC) protocols. The blood lipid levels were measured from peripheral blood samples collected in the morning after at least 8 h of fasting. Enzymatic methods were used to determine the serum levels of triglycerides. High-density lipoprotein cholesterol (HDL-C) in serum is quantified using either direct immunoassay or precipitation techniques.

Assessment of other variables

CDC systematically and comprehensively collected demographic, lifestyle, self-reported health, physical measurements, and biochemical data from participants in the form of personal interviews using computer assistance. In our study, the main demographic variables needed were participants' age, sex, race, education, marriage, and income-poverty ratio; The lifestyle variables we took into account were participants' smoking status, drinking status and recreational activities; Self-reported health conditions included diabetes, hypertension and cardiovascular disease. Physical variables included body mass index (BMI); Biochemical detection variables included hemoglobin, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), serum uric acid (SUA), blood urea nitrogen (BUN), serum creatinine (SCR), urinary albumin (on-site collection). Smoking status: Never: By now, participants had smoked fewer than 100 cigarettes; Former: Participants had a history of smoking but did not currently smoke; "Now": Participants are still smoking [ 15 ]. Engage in recreational activities: “Yes” and “no.” Diabetes diagnosis (Including pre-diabetes): At least one of the following is present.1. Fasting blood glucose (FPG > 7.0 mmol/L). 2. Glycosylated hemoglobin HbA1c (> = 6.5%). 3. Random blood glucose (> = 11.1 mmol/ L). 4. Two hours OGTT blood glucose (> = 11.1 mmol/ L). 5. Doctor tells you have diabetes. 6.IFG (6.11 mmol/L < = FPG < = 7.0 mmol/L) or IGT (7.7 mmol/L < = OGTT < = 11.1 mmol/L). High blood pressure: At least one of the following occurs. 1. Systolic blood pressure ≥ 140 mmHg. 2. Diastolic blood pressure ≥ 90 mmHg. 3. Taking blood pressure medications 4. Self-report hypertension. Alcohol consumption: heavy (women: ≥ 3 drinks per day or ≥ 4 drinks on the same occasion; For men: ≥ 4 cups per day or ≥ 5 cups on the same occasion; Binge drinking ≥ 5 days per month); Moderate (women: ≥ 2 cups per day; For men: ≥ 3 cups per day; Binge drinking ≥ 2 days per month);Light (women: 1 cup per day; Men: 2 cups per day); never (< 12 drinks in a lifetime);Former (Participants had a history of drinking but did not currently drink). CVD was identified through a medical status questionnaire that recorded whether participants had been diagnosed by a physician with conditions such as coronary artery disease, congestive heart failure, or heart attack [ 16 ].

Statistical analysis

The study data were appropriately weighted to ensure representation. Participants were grouped based on the presence or absence of chronic kidney disease, according to their baseline characteristics. Data presentation was structured as follows: continuous variables were described using means ± standard errors, and categorical variables were represented as percentages. Weighted logistic regression analyses were used to explore the relationship between CKD and AIP. We used odds ratios (ORs) and 95% confidence interval (95% CI) to report results. A linear trend test was used to investigate the stability of the relationship between AIP and CKD. The nonlinear relationship between CKD and AIP was evaluated by restricted cubic spline analysis. Recursive algorithm and a two-stage linear regression model were utilized to calculate the curve inflection point. Stratified analysis was employed to assess the presence of specific populations.

Baseline characteristics

Table 1 presents the demographic and clinical profiles of the enrolled participants. Comparatively, participants diagnosed with CKD manifest higher levels of age, BMI, BUN, SCR, SUA, and AIP, while exhibiting reduced income levels, hemoglobin, and LDL-C concentrations. Among female participants, participants with no recreational activities, diabetes, hypertension, and heart disease had a higher proportion of CKD.

Association between AIP and CKD

In Model 1, with no covariate adjustments, ORs (95%CI) were 2.00 (1.81, 2.20), signifying a doubling of the risk for CKD with each unit increment in AIP (Table 2 ). Upon covariate adjustment for age, sex, and ethnicity in Model 2, the OR (95% CI) escalated to 2.18 (1.94, 2.43), denoting a 118% elevation in CKD risk for every unit increase in AIP. In Model 3, after comprehensive adjustment for sex, age, ethnicity, BMI, educational attainment, poverty income ratio, marital status, smoking behavior, recreational activities, hemoglobin levels, LDL-C, SUA, SCR, BUN, as well as diabetes, hypertension, and cardiovascular diseases, the OR (95% CI) moderated to 1.12 (0.91, 1.36), suggesting a lack of significant association between AIP and the risk of CKD. Sensitivity analyses, contrasting the reference quartile Q1, yielded ORs (95% CI) of 0.91 (0.78, 1.07), 0.92 (0.78, 1.08), and 1.10 (0.93, 1.31) for quartiles Q2, Q3, and Q4, respectively. These findings intimate a potential non-linear relationship between AIP levels and the incidence of CKD.

Stratified analyses by sex and age, with all covariates accounted for, revealed a significant positive correlation between AIP and CKD uniquely within the cohort aged less than 50 years. Conversely, stratified analyses across other age and sex did not demonstrate a significant association between AIP and CKD.

We conducted an extensive analysis to elucidate the non-linear correlation between AIP and CKD, after rigorously controlling for a spectrum of confounding variables. These included age, sex, ethnicity, poverty income ratio, educational level, marital status, smoking behavior, recreational activities, BMI, LDL-C, SUA, SCR, BUN, hypertension, diabetes, and CVD. Our findings, illustrated in Fig. 2 , reveal a non-linear association between AIP and CKD, with a significant inflection point identified at -0.55. Below this threshold, OR (95% CI) were 0.14 (0.04, 0.54), signifying a pronounced negative correlation. Above the inflection point, the ORs (95% CI) were 1.25 (1.01, 1.55), indicating a substantial positive correlation, as detailed in Table 3 .

The association between AIP and CKD

Stratified analyses by sex and age demarcated distinct patterns of association. In female participants and individuals over 50 years of age, a non-linear relationship was observed, while a linear relationship was noted in men and participants under 50 years old (Figs. 3 and 4 ). Female participants exhibited an inflection point at -0.57, with ORs (95% CI) of 0.15 (0.03, 0.86), suggesting an 85% decrease in CKD risk per unit increase in AIP below this threshold. Post-inflection, the ORs (95% CI) was 1.38 (1.01, 1.89), reflecting a 38% increase in CKD risk for each unit increment in AIP. In participants over 50, the inflection point was -0.55, with ORs (95% CI) of 0.05 (0.01, 0.27), indicating a 95% reduction in CKD risk per unit increase in AIP below this point. Beyond the inflection, the ORs (95% CI) was 1.08 (0.83, 1.42), which suggests a not significant association between AIP and CKD risk.

Association between AIP and CKD stratified by sex (a female; b male). In the subgroup analysis stratified by sex, the model is not adjusted for sex

Association between AIP and CKD stratified by age (a age > = 50 ;b age < 50). In the subgroup analysis stratifed by age, the model is not adjusted for age

This extensive study encompassed 23888 participants from the United States. We discovered a non-linear relationship between CKD and the plasma atherosclerosis index, with an inflection point at -0.55. Before this inflection point, a significant negative correlation was observed between the plasma atherosclerosis index and CKD; however, following the inflection point, a significant positive correlation was noted. As far as we are aware, our research stands as the most comprehensive study of its kind to date.

Although research on the correlation between AIP and CKD is limited, the association between lipids and chronic kidney disease has been extensively studied. A study from China by Oh et al. found a nonlinear relationship between the two [ 10 ]. They found that the critical threshold for the atherosclerosis index to be associated with the composite kidney outcome is 0.51. They observed that as the AIP exceeded 0.51, the risk ratio for composite renal outcomes significantly increased with higher AIP levels. A retrospective analysis by Toth PP and colleagues has identified hypertriglyceridemia as a significant potential risk factor for the development of kidney disease [ 17 ]. A retrospective investigation led by Jairoun AA and colleagues has revealed that statin therapy potentially arrests the progression of renal function deterioration, thus providing additional validation to our conclusions from an alternative vantage point [ 18 ].

The potential pathogenic mechanisms linking AIP with CKD remain unclear. The negative correlation observed below the inflection point potentially links to the adverse impacts of malnutrition and systemic inflammation on renal function [ 19 ]. The positive correlation observed beyond the inflection point may be attributed to several potential mechanisms. Firstly, genetic factors are pivotal. For instance, the SNP rs12951387 site predisposes individuals to obesity, which in turn triggers a cascade of metabolic disruptions, including the abnormal accumulation of lipids, resistance to insulin, imbalances in adipokine function, and the activation of inflammatory signaling pathways [ 20 ]. Secondly, an overabundance of caloric intake can lead to the anomalous deposition of lipids within the kidneys, inciting inflammatory responses within these tissues [ 21 ]. Inflammation and metabolic disorders are intrinsically linked in the complex relationship between AIP and CKD. The inflammatory process is mediated through the adipose-kidney axis [ 20 ]. In the context of metabolic dysregulation, adipose tissue hypertrophy and the aggregation of M1 macrophages can induce insulin resistance, leading to adipokine dysfunction and dyslipidemia. These conditions further propagate lipotoxicity, insulin resistance, and inflammatory responses. Studies have demonstrated that the buildup of TG can trigger the generation of reactive oxygen species, which in turn causes an increase in the permeability of the glomerular filtration barrier, resulting in renal tubular injury and interstitial fibrosis [ 22 ]. Concurrently, the lipotoxicity of TG may induce mitochondrial dysfunction within cells, which in turn can lead to an exacerbation of lipid deposition [ 23 ]. Adipose tissue-derived factors such as leptin, Tumor Necrosis Factor (TNF), and angiotensin II contribute to oxidative stress, inflammation, and the progression of fibrotic scarring, culminating in damage to both the glomeruli and renal tubules [ 24 ]. Moreover, Endothelial activation and systemic endothelial dysfunction are pivotal in the interplay between kidney disease and AIP. These endothelial changes can precipitate lipid transport impairments and instigate oxidative stress and inflammation, thereby aggravating the injury to glomeruli and tubules [ 25 ]. Studies have demonstrated that sustained endothelial alterations can identify patients with Minimal Change Disease (MCD) or Secondary Nephrotic Syndrome (SSNS) who are at an elevated risk of progression to Focal Segmental Glomerulosclerosis (FSGS) or the advanced stages of chronic kidney disease [ 26 ]. Additionally, CKD can reciprocally elevate levels of TG and decrease HDL-C. Hypertriglyceridemia (HTG) arises from impaired metabolism of Very-Low-Density Lipoprotein (VLDL) and diminished activity of Lipoprotein Lipase (LPL). The reduction in HDL-C is correlated with a decrease in Lecithin Cholesterol Acyltransferase (LCAT). This reduction in LCAT activity impacts the expression of the liver’s apolipoprotein A-I (apoA-I) gene and LCAT mRNA, resulting in diminished HDL-C levels [ 27 ].

Upon stratification by sex and age, distinct variations were observed in the association between AIP and CKD across different groups. In the sex-specific analysis, a nonlinear relationship was evident between AIP and CKD among female participants, whereas a straightforward linear association was noted among male participants. Furthermore, age-stratified analysis has demonstrated a direct linear correlation between AIP and CKD in participants aged 50 years or younger. Above this age threshold, a significant non-linear association has emerged, with an inflection point identified at -0.55. Existing research has consistently revealed pronounced disparities in lipid metabolism between the sexes. A cross-sectional study conducted by Yadegar A et al. revealed that HDL-C levels significantly decrease with the progression of CKD stages in males, while no such change is observed in females [ 28 ]. Dyslipidemia has been shown to induce inflammation, endoplasmic reticulum stress, and consequently, insulin resistance [ 29 ]. Inflammatory status, oxidative stress, and insulin resistance show pronounced disparities across genders and age groups [ 30 ]. These factors could potentially explain the noted differences.

Our research has delineated a sophisticated curvilinear relationship between AIP and CKD, underscoring the pivotal regulatory effects of gender and age on their dynamic interaction. The clinical relevance of our study results underscores the essential role of individualized lipid management in the trajectory of CKD.

Our data is derived from NHANES database, renowned for its stringent data collection protocols and extensive sample sizes, which lend considerable credibility and reliability to our findings. Utilizing stratified and subgroup analyses, we have delved into the nexus between AIP and CKD, as well as scrutinized the variations in this relationship across diverse demographic groups. Nevertheless, our research is not without its inherent limitations. Firstly, as a cross-sectional observational study, it does not establish a causal link between CKD and AIP, underscoring the necessity for longitudinal research to ascertain causality and the chronological order of events. Secondly, despite accounting for a range of covariates, there may remain unmeasured confounding factors influencing the relationship between AIP and CKD, including personal lifestyle choices and genetic propensities. Thirdly, disparities in socioeconomic status and healthcare access could affect the outcomes of the study. Urgently needed are robust longitudinal studies to substantiate our findings and clarify the long-term implications of the AIP-CKD relationship, essential for enhancing predictive modeling and tailoring clinical preventive and therapeutic approaches.

This investigation, drawing on data from NHANES between 2003 and 2020, has revealed a nonlinear association between AIP and CKD, characterized by an inflection point at -0.55. Below this threshold, there was a significant negative correlation between AIP and CKD. In contrast, above this inflection point, a significant positive correlation was observed. This finding carries profound implications for clinical practice, especially in the meticulous management of lipid profiles in CKD patients. The revealed nonlinear relationship between AIP and CKD emphasizes the critical role of individualized lipid management strategies in curbing the disease's progression, highlighting the need for precise lipid regulation.Our study enriches the scientific comprehension of the AIP-CKD nexus and offers actionable insights for clinical decision-making. The ultimate objective is to prevent or decelerate the advance of CKD, aiming to enhance patient outcomes and improve the quality of care in the management of this debilitating condition.

Availability of data and materials

The datasets generated and analysed during the current study are publicly available from the National Center for Health Statistics: wwwn.cdc.gov/nchs/nhanes/Default.aspx .

Data availability

No datasets were generated or analysed during the current study.

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The authors express their gratitude to the NHANES database for their uploading valuable datasets.

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XZ: Responsible for project development and research design, overseeing the entire research process, coordinating all tasks, and finalizing the review and editing. BW and CQJ: The primary contributors to data analysis and the writing of the manuscript. JW and YNQ: Collected and organized the data, securing the required information for our research. GZ: In charge of data analysis and interpretation. All authors have read and approved the final manuscript.

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Wang, B., Jiang, C., Qu, Y. et al. Nonlinear association between atherogenic index of plasma and chronic kidney disease: a nationwide cross-sectional study. Lipids Health Dis 23 , 312 (2024). https://doi.org/10.1186/s12944-024-02288-6

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Diabetic kidney disease (DKD) is the leading cause of kidney failure worldwide and regardless of intensive treatments, the prevalence of DKD remains high. Hyperglycemia is the primary etiological factor responsible for the development of diabetic kidney disease. Once hyperglycemia becomes established, multiple pathophysiological disturbances contribute to the onset and progression of diabetic kidney disease. As part of the journal’s mission to address key subjects related to the challenging complications of Diabetes Mellitus, Frontiers in Clinical Diabetes and Healthcare is organizing a Research Topic focused on the 'Pathophysiology of Diabetic Kidney Disease'. Led by an expert team of specialists, the Research Topic will accept manuscripts within this area of research, with the aim of giving the reader an overview of the latest discoveries and new findings, analysis of previously published data, new opinions and perspectives, and methods and protocols (when relevant). For that reason, all article types accepted by Frontiers are encouraged. Sub-themes for this Research Topic may include, but are not limited to: - Hemodynamic factors relating to the pathophysiology of DKD - Metabolic factors relating to the pathophysiology of DKD - Signaling pathways involved in the onset and development of DKD - The role of oxidative stress in the pathophysiology of DKD - The role of autophagy in the pathophysiology of DKD - The role of epigenetics in the pathophysiology of DKD - The role of inflammation in the pathophysiology of DKD - The role of fibrosis in the pathophysiology of DKD - Renoprotective effects of antidiabetics beyond glucose control

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Frontier role of extracellular vesicles in kidney disease

Bei Li 1 na1 ,
Chen Qi 2 na1 ,
Yifan Zhang 3 ,
Linru Shi 1 ,
Jiahui Zhang 1 ,
Hui Qian 1 &
Cheng Ji 1

Journal of Nanobiotechnology volume 22 , Article number: 583 ( 2024 ) Cite this article

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Kidney diseases represent a diverse range of conditions that compromise renal function and structure which characterized by a progressive deterioration of kidney function, may ultimately necessitate dialysis or kidney transplantation as end-stage treatment options. This review explores the complex landscape of kidney diseases, highlighting the limitations of existing treatments and the pressing need for innovative strategies. The paper delves into the role of extracellular vesicles (EVs) as emerging biomarkers and therapeutic agents in the context of kidney pathophysiology. Urinary extracellular vesicles (uEVs), in particular, offer a non-invasive means of assessing renal injury and monitoring disease progression. Additionally, mesenchymal stem cell-derived EVs (MSC-EVs) are examined for their immunomodulatory and tissue repair capabilities, presenting a promising avenue for novel therapeutic interventions. And discusses the potential of engineering EVs to enhance their targeting and therapeutic efficacy. This paper systematically integrates the latest research findings and aims to provide a comprehensive overview of the role of EVs in kidney disease, providing cutting-edge insights into their potential as a diagnostic and therapeutic tool.

Introduction

Kidney diseases pose a significant challenge to public health departments worldwide, directly impacting global morbidity and mortality rates [ 1 , 2 ]. Research statistics indicate that approximately 10–15% of adults globally are affected by kidney diseases of varying severity [ 3 , 4 ].The early symptoms of kidney disease are often inconspicuous, but as the disease progresses, patients may experience a reduction in urine output, frothy urine, hematuria, edema, anemia, and other symptoms. Prolonged illness can have a profound impact on patients’ quality of life and lead to a range of serious complications, including cardiovascular diseases, diabetes, and hypertension [ 5 ]. If not treated promptly, the disease may advance to renal failure, requiring alternative treatments such as dialysis or kidney transplantation [ 6 ]. While dialysis and kidney transplantation are effective for treating renal failure, they demand long-term patient commitment and carry certain risks and complications [ 7 ].

Extracellular vesicles (EVs) are nanoscale lipid-membrane-enclosed structures secreted by a variety of cell types and serve as pivotal agents in intercellular communication [ 8 ]. The EVs are primarily categorized into two major classes: exosomes and ectosomes [ 9 ]. Ectosomes arise from the outward budding of the cell membrane, encompassing a size spectrum from 50 nanometers to 1 micrometer, which includes microvesicles, microparticles, and larger vesicles [ 8 , 9 ]. In contrast, exosomes are a subset of EVs that are derived from the endosomal compartment, typically exhibiting diameters ranging from 40 to 160 nanometers, with an average size of approximately 100 nanometers [ 8 , 9 ]. EVs are capable of encapsulating a diverse array of cellular constituents, such as DNA, RNA, lipids, metabolites, and proteins, which are sourced from both the cytoplasm and the cell membrane (Fig. 1 ). This cargo reflects the specific physiological state and functional profile of the originating cell [ 10 ]. As natural transporters in the biological milieu, EVs exhibit several advantageous characteristics, including stability, biocompatibility, low immunogenicity, low toxicity, and the capacity to traverse biological barriers [ 11 ]. These attributes render EVs highly promising in the realm of drug delivery. Their inherent biocompatibility is complemented by an appropriate size distribution and intrinsic cellular targeting capabilities, which confer unique benefits for the delivery of therapeutic agents [ 12 , 13 ].

In recent years, the role of EVs in nephrology has garnered considerable attention [ 14 , 15 , 16 ]. In the context of kidney injury, extracellular vesicles originating from different parts of the kidney play a propelling role in the progression of disease by mediating intercellular communication among different cell types within the nephron [ 17 , 18 ]. Concurrently, EVs possess unique compositions specific to their origins, and significant changes in their quantity and components can reflect the physiological or pathological state of their source, thus endowing them with the value of being disease biomarkers [ 19 , 20 ]. Moreover, in-depth research on EVs, especially MSC-EVs, and the bioactive molecules they carry, has unveiled their immense potential in the treatment of kidney diseases [ 21 , 22 ]. Therefore, the thorough investigation of the mechanisms of EVs in kidney diseases aids in deepening our understanding of the pathogenesis of these conditions. Moreover, it holds significant research importance and practical value for the improvement of diagnostic techniques and the development of novel therapeutic approaches.

EVs classification, biogenesis, and uptake. Based on their biogenesis and size, EVs are typically divided into two categories: exosomes and ectosomes. Exosomes are a subset of EVs that originate from the endosomal compartment, while ectosomes arise from budding of the cell membrane, including microvesicles, microparticles, and larger vesicles. EVs possess a phospholipid bilayer that can encapsulate a variety of cellular components, such as DNA, RNA, lipids, metabolites, and proteins. EVs can be internalized by recipient cells through mechanisms such as membrane fusion, phagocytosis, pinocytosis, and clathrin-mediated endocytosis

EVs in the pathology and diagnosis of kidney disease

Development and pathological changes of renal disease.

Extracellular vesicles play a crucial role in renal pathology, being involved in the development process from the initial stages of kidney disease. The progression of kidney disease is a multifaceted process characterized by dynamic shifts in renal function. Acute kidney injury (AKI) and chronic kidney disease (CKD) represent the two principal stages of this continuum [ 23 ]. AKI typically arises from abrupt injury or illness, precipitating a swift deterioration in kidney function, common inciting factors include infections [ 24 ], sepsis [ 25 ], ischemia [ 26 ], and exposure to nephrotoxic drugs [ 27 ].Rapid decline in renal function, electrolyte disturbance and acid-base imbalance are considered typical symptoms of AKI [ 28 ]. Conversely, CKD is distinguished by a gradual and sustained deterioration of renal function attributable to enduring or recurrent kidney damage or pathology. Notably, among the prevalent etiologies are diabetes, hypertension, and glomerulonephritis, with IgA nephropathy being particularly significant [ 4 , 29 ].

From a pathological standpoint, the hallmarks of AKI and CKD diverge. AKI is primarily associated with tubular necrosis, interstitial edema, inflammation, and vascular alterations [ 30 ], which are often reversible with prompt intervention, thus offering the potential for renal function recovery. In contrast, the pathological features of CKD are more intricate, encompassing glomerulosclerosis, tubular atrophy, interstitial fibrosis, renal ischemia, and capillary loss [ 31 ], which are generally irreversible and contribute to a progressive decline in renal function.

It is crucial to recognize that there is not an absolute dichotomy between AKI and CKD, there exists a degree of overlap and the potential for interconversion [ 32 , 33 ]. Patients with AKI, if not treated promptly and effectively, may evolve into CKD; likewise, individuals with CKD may manifest AKI characteristics under certain conditions [ 28 ]. A profound investigation into the mechanisms driving the progression and pathological changes of kidney disease is of paramount importance for unraveling the complexities of these conditions and for informing diagnostic and therapeutic strategies.

Pathological effects of EVs in kidney disease

EVs secreted by damaged kidney cells play an indispensable role in the complex multi-dimensional pathological processes associated with kidney diseases [ 34 , 35 ]. Data shows that under damaged or stressed conditions, extracellular vesicle-mediated long-distance communication between different regions and different cells in the kidney can amplify kidney damage [ 36 ]. This process involves key aspects such as inflammatory response, oxidative stress, apoptosis, and tissue fibrosis in the kidney (Fig. 2 ). These factors can interfere with normal cellular signaling pathways, leading to the disruption of intracellular homeostasis and increased sensitivity to damage [ 37 , 38 ]. At the same time, these factors not only participate in the initial stage of kidney damage through interaction but may also continuously exacerbate the damage through a variety of mechanisms, thereby affecting the long-term health of the kidney [ 39 , 40 ].

EVs-mediated intercellular communication in the pathology of kidney disease. Under conditions of injury or stress, extracellular vesicle-mediated long-distance communication between different regions of the kidney and various kidney cells can exacerbate kidney damage by inducing key aspects such as inflammatory responses, oxidative stress, apoptosis and tissue fibrosis of kidney cells

The role of EVs in inflammation and immune modulation

Kidney disease involves a complex, multistage inflammatory process orchestrated by a complex network of cytokines/chemokines, growth factors, adhesion molecules and signal transduction pathways [ 41 ]. The injury to renal tubular epithelial cells (TECs) and macrophage infiltration remain the pivotal mechanisms determining the extent of inflammatory damage [ 42 ]. There is a complex interplay between macrophages and TECs, for example, the infiltration of macrophages can exacerbate TECs injury, oxidative stress, and apoptosis [ 43 ]. Studies have indicated that in cisplatin-induced acute kidney injury (CP-AKI), macrophage-derived EVs (Mφ-EVs) facilitate communication between autophagy-deficient macrophages and TECs by transferring miR-195a-5p. This transfer leads to mitochondrial damage in TECs and the activation of inflammatory cells in the kidney [ 42 ]. In diabetic kidney disease(DKD), Mφ-EVs induced by high glucose levels, carrying miR-7002-5p, target autophagy-related gene 9B (Atg9b) to suppress autophagy in renal tubular epithelial cells, inducing dysfunction, autophagy inhibition, and inflammation [ 44 ].

TECs-EVs also have a powerful role in driving persistent inflammation. Research has found that the secretion of TEC-EVs induced by bovine serum albumin (BSA) has been proven to promote the expression of miR-26a-5p. This upregulation of miR-26a-5p targets the cationic amino acid transporter regulator homolog 1/the nuclear factor kappa B (CHAC1/NF-κB) pathway, exacerbating TEC inflammatory injury [ 45 ]. TECs-EVs containing miR-19b-3p [ 46 ] and miR-374b-5p [ 47 ], as well as Mφ-EVs containing miR-155 [ 48 ], can target the NF-κB/suppressor of cytokine signaling-1 (SOCS-1) pathway to participate in the interaction between renal tubular epithelial cells and macrophages. This interaction significantly activates M1-type macrophages, thereby exacerbating renal tubular interstitial inflammation. Furthermore, it has been discovered that miR-199a-5p within exosomes from human serum albumin (HSA)-stimulated HK-2 cells promotes M1 polarization by targeting the Klotho/toll-like receptor 4 (TLR4) signaling pathway, potentially accelerating the progression of DKD [ 49 ]. Proteinuria is a widely recognized indicator of renal dysfunction and plays a key role in renal tubular interstitial inflammation [ 50 ]. Studies have shown that in proteinuric kidney disease, TECs-EVs rich in chemokine ligand 2(CCL2) mRNA. These mRNAs are transferred to macrophages via EVs, activating their function and exacerbating albumin-induced renal tubular interstitial inflammation [ 51 ]. Furthermore, in patients with DKD exhibiting significant albuminuria, researchers have found that TEC-EVs treated with HSA can promote macrophage glycolytic activation by stabilizing hypoxia-inducible factor 1α(HIF-1α), thereby inducing renal fibrosis and inflammation [ 52 ]. Hypoxia is a potent inducer of inflammation within the tubulointerstitial inflammation. HIF-1α serves as the central regulator of the hypoxic response. The HIF-1α-driven release of these miRNA-23a-enriched EVs from hypoxic TECs can be internalized by recipient macrophages. Consequently, this internalization leads to the suppression of the ubiquitin editor A20 expression and the promotion of tubulointerstitial inflammation [ 53 ].

Additionally, research has demonstrated that kidney injury molecule-1(KIM-1), a protein expressed by injured renal tubules, can recognize phosphatidylserine (PS) exposed on the surface of apoptotic cells. The interaction between KIM-1 and PS facilitates the uptake of EVs, thereby enhancing hypoxia-induced tubulointerstitial inflammation [ 54 ].Recent studies have also revealed that the disruption of gut microbiota and its derived outer membrane vesicles (OMVs) play a significant role in tubulointerstitial inflammation of DKD. An increase in OMVs due to gut microbiota dysbiosis is transferred to the tubulointerstitial inflammation via the intestinal barrier. This transfer induces cell inflammation and renal tubular interstitial injury by activating the caspase-11 pathway triggered by lipopolysaccharide [ 55 ]. These findings enhance our understanding of the influence of gut microbiota and its released OMVs on the development and progression of kidney disease.

The role of EVs in the formation of oxidative stress

EVs related to oxidative stress can have beneficial or detrimental effects. They have the capacity to transport a range of molecules, including antioxidants, enzymes, and oxidized species that can generate reactive oxygen species (ROS) [ 56 ]. In a study conducted at the Royal Brisbane and Women’s Hospital in Australia, researchers observed that hypoxic TEC-derived EVs, through the transfer of specific miRNAs and other bioactive molecules, intensify oxidative stress and tissue damage in the kidney [ 57 ]. This process potentially drives the transition from AKI to CKD. Furthermore, an increase in platelet-derived EVs during sepsis has been shown to aggravate septic AKI via the release of ADP-ribosylation factor 6(ARF6), which in turn, stimulates inflammation, apoptosis, and oxidative stress [ 58 ]. The proposed mechanism involves the activation of the extracellular signal-regulated kinase (ERK)/Smad3/p53 signaling pathway by ARF6. Our findings may offer potential therapeutic targets for the management of septic AKI.

The role of EVs in the formation of regulating apoptosis

Apoptosis is a form of programmed cell death that plays a crucial role in maintaining the balance of cell numbers and the normal development and renewal of tissues under normal physiological conditions [ 59 ]. However, in kidney diseases, abnormal apoptosis may accelerate the progression of the disease [ 60 ]. Apoptosis of TECs plays a significant role in kidney diseases [ 61 ]. Excessive apoptosis can disrupt the structure and function of the renal tubules, leading to impaired reabsorption and secretion functions, which further exacerbates kidney damage. For instance, in DKD, TECs release lipotoxic exosomes rich in leucine-rich α-2-glycoprotein 1 (LRG1). And the LRG1/TGFβR1 signaling pathway enhances the expression of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) in macrophages and TRAIL-positive macrophage-derived exosomes, promoting TECs apoptosis and establishing a feedback loop in DKD [ 62 ]. Additionally, platelet microvesicles secrete miR-191, which induces TECs apoptosis by targeting Cystathionine-β-synthase (CBS) [ 63 ]. After high glucose (HG) stimulation, podocyte-derived EVs undergo specific changes, with downregulation of mmu-miR-1981-3p, mmu-miR-3474, mmu-miR-7224-3p, and mmu-miR-6538, and upregulation of mmu-let-7f-2-3p, also promoting apoptosis of TECs [ 64 ]. The crosstalk pathways between the glomeruli and renal tubules have also been carefully studied. Results confirming that endothelial cells damaged by podocytes release EVs containing specific microRNAs, especially miR-424 and miR-149, which may activate the p38 MARK signaling pathway, inducing apoptosis in renal tubular epithelial cells [ 65 ].

Furthermore, mesangial cells (MCs) maintain the structure and function of the glomerulus, and increased apoptosis of MCs can lead to impaired glomerular filtration function, affecting the normal filtering and excretory functions of the kidney. Studies have found that HG upregulates urinary EVs miR-15b-5p, which directly binds to the target BCL-2 in DKD, causing MCs apoptosis [ 66 ].

The role of EVs in the formation of fibrosis

Renal fibrosis, with a particular focus on tubulointerstitial fibrosis, represents a prevalent terminal pathway for the majority of progressive chronic kidney diseases [ 67 , 68 ]. The pathogenesis of this fibrotic process is exceedingly intricate, implicating a broad spectrum of both resident and infiltrating renal cell populations. The morphological characteristics of renal fibrosis include glomerulosclerosis, tubular atrophy, interstitial chronic inflammation, fibrosis itself, and vascular rarefaction [ 69 ].

Intercellular communication is pivotal in the development of renal fibrosis, with injured renal tubular cells participating in disease progression through extensive communication with interstitial fibroblasts. Research indicates that TEC-EVs carrying the sonic hedgehog (Shh) signaling ligand are upregulated during renal injury. The Shh ligand is then transported to interstitial fibroblasts via exosomes, facilitating their transition into myofibroblasts and perpetuating the fibrotic cascade [ 70 ].Similarly, TECs directly regulate the activation and proliferation of fibroblasts through exosome-mediated miR-150 [ 71 ]. Furthermore, miR-196b-5p [ 72 ] and miR-21 [ 73 ], miR-216a [ 74 ] derived from TEC-EVs mediate crosstalk between TECs and fibroblasts during the development of renal fibrosis. Respectively, they target the signal transducer and activator of transcription 3/SOCS2(STAT3/SOCS2) and phosphatase and tensin homolog (PTEN)/AKT signaling pathways. In fibroblast-derived EVs(Fibro-EVs), studies have shown that miR-153-3p contained within microvesicles (MVs) released by renal interstitial fibroblasts is transferred to proximal renal tubular epithelial cells through the damaged tubular basement membrane [ 75 ]. This transfer induces apoptosis in TECs by suppressing B-cell lymphoma-2(Bcl-2) levels, thereby exacerbating renal interstitial fibrosis (RIF). It has been reported that macrophages, in addition to participating in the inflammatory response of kidney disease, are also involved in the process of fibrosis formation. For example, exosomal miRNA-34a from TECs has been shown to target and inhibit the PPARGC1A gene, promoting the activation of M1 macrophages and the fibrosis of renal tubular cells [ 76 ].

In the context of DKD, the HNRNPA1-mediated exosome sorting mechanism transports miR-483-5p from TECs to urine. This process diminishes the inhibitory effect of miR-483-5p on mitogen-activated protein kinase 1(MAPK1) and tissue Inhibitor of metalloproteinases 2(TIMP2) mRNAs, and accelerating the progression of DKD-induced renal interstitial fibrosis [ 77 ]. Podocyte-derived EVs have also been shown to be involved in the fibrotic development of kidney disease. In the pathogenesis of DKD, podocyte-derived EVs containing miR-221 exacerbate renal cell injury through the Wnt/β-catenin signaling pathway, thereby further promoting the progression of renal cell injury and fibrosis [ 78 ].Additionally, a new study has elucidated the mechanism of interaction between podocyte microparticles and CD36 on TECs, activating the p38MAPK/transforming growth factor-β receptor signaling pathway, thereby promoting fibrotic responses [ 79 ].

EVs as diagnostic biomarkers for kidney diseases

Accurate diagnostic markers are essential for the early detection and precise treatment of kidney diseases, which have a complex and dynamic pathological progression. Renal biopsy is a powerful tool for diagnosing and classifying glomerular diseases, but it is limited by sampling errors and the difficulty of obtaining subsequent renal biopsies to track the progress of treatment. Considerable effort is being expended to discover non-invasive biomarkers for both acute and chronic kidney injury. Urinary extracellular vesicles (uEVs) are a rich source of biomarkers because they are released from every part of the nephron [ 80 ], are easily accessible. It can comprehensively and directly reflect the real-time status of tissue inflammation and kidney injury [ 81 , 82 ],making them an ideal non-invasive source of potential biomarkers for kidney diseases (Fig. 3 ).

Urinary extracellular vesicles (uEVs) serve as biomarkers for kidney diseases. Proteins and nucleic acids derived from uEVs hold promise as potential biomarkers for various types of kidney diseases. Abundant in source and easily accessible, they are ideal non-invasive biomarkers

Protein cargos of EVs for the diagnosis of kidney diseases

Ischemia/reperfusion aki (i/r-aki).

Studies have demonstrated that uEVs AQP1 levels decrease within 6 h post-injury and remain low for up to 96 h, without a significant increase in plasma creatinine levels [ 83 ]. This suggests that uEVs AQP1 may serve as a sensitive biomarker for detecting renal cell status throughout the I/R injury timeline, indicating its potential for early I/R injury detection. Additionally, researchers have identified potential biomarker candidates for the diagnosis of AKI through proteomic analysis of uEVs. Simultaneously, they have detected that uEVs Fetuin-A may serve as a biomarker for the detection of I/R-AKI [ 84 ].In I/R-AKI, the level of uEVs Na/H exchanger isoform 3 (NHE3) protein is elevated compared to the control group, a result that has also been confirmed in four other AKI models. This suggests that uEVs NHE3 could serve as a diagnostic biomarker for AKI [ 85 ].

Sepsis-AKI (S-AKI)

uATF3 [ 86 ], typically undetectable in sepsis without AKI, may qualify as a qualitative indicator for sepsis-AKI diagnosis. In contrast, the role of neutrophil gelatinase-associated lipocalin (NGAL) in sepsis-AKI remains debated, particularly regarding its critical value and baseline levels. The presence of transcription factor uATF3 in uEVs is emerging as a potential biomarker for sepsis-associated AKI.

Diabetic kidney disease (DKD

Researchers have meticulously examined the proteomic changes within EVs at different stages of DKD. Utilizing proteomic approaches, key proteins MASP2, CALB1, S100A8, and S100A9 were identified as potential biomarkers for the diagnosis and monitoring of DKD [ 87 ].In diabetic patients with normal albuminuria, uEVs containing alpha-1-antitrypsin (α1-AT) were significantly increased and elevated with the progression of DKD, indicating their potential as biomarkers for early diagnosis of DKD [ 88 ]. Similarly, another study found that NGAL within exosomes is an early biomarker for DKD, and it outperforms free NGAL in the DKD evaluation in children and adolescents with type 1 diabetes mellitus [ 89 ].Additionally, researchers investigated the differential expression of actin cytoskeleton regulatory factors in uEVs, and the upregulation of uEVs proteins PAK6 and EGFR suggests that they may become new biomarkers for the diagnosis of DKD [ 90 ].It has been reported that pathological changes in podocytes play a key role in the development of DKD. Studies have found that the presence of Elf3 protein in the uEVs of patients with DKD indicates irreversible damage in podocytes. This suggests that Elf3-positive urinary extracellular vesicles (Elf3 + uEVs) can serve as a new biomarker for podocyte injury in patients with type 2 diabetes mellitus (T2DM), especially in those with significant proteinuria [ 91 ]. Additionally, Wilms’ tumor protein (WT1) in uEVs is predominantly found in the exosomes of diabetic patients. Detection has revealed that its expression levels increase with the decline of renal function, making it another potential biomarker for podocyte injury [ 92 ].

End-stage renal disease (ESRD)

In ESRD patients, the absence of uEVs expressing the CD133 marker contrasts sharply with healthy subjects, where such EVs are readily detectable [ 93 ]. The presence of urinary CD133 EV may reflect ongoing nephron homeostatic processes, suggesting a potential role in ESRD diagnosis.

IgA nephropathy (IgAN)and thin basement membrane nephropathy (TBMN)

Urinary exosome proteomic analysis has identified several proteins, such as aminopeptidase N, vasorin precursor, α-1-antitrypsin, and ceruloplasmin. These proteins differentiate early IgA nephropathy from tubulointerstitial nephritis and normal conditions, indicating their potential as biomarkers [ 94 ].

Lupus nephritis (LN)

Urinary podocyte-derived microparticles (MPS) levels are positively correlated with clinical indicators of SLE activity [ 95 ], including the SLE disease activity index (SLEDAI) score and anti-dsDNA antibody titer, suggesting their potential as non-invasive biomarkers for lupus nephritis diagnosis and monitoring.

In essential hypertension (EH), increased levels of urinary podocyte-specific EVs and peritubular capillary (PTC) endothelial microparticles(EMPs) may indicate renal microcirculation damage [ 96 ], suggesting their potential as new biomarkers for renal capillary loss. Urinary PTC-EMP levels are directly related to renal histological PTC counts and fibrosis [ 97 ], further supporting their role as a novel biomarker for renal intracapillary loss.

RNA cargos of EVs for the diagnosis of kidney diseases

Diabetic kidney disease (dkd).

A study involving 103 patients with diabetic kidney disease (DKD), 100 patients with diabetes without kidney disease, and 53 healthy controls. Results showed that the levels of WT1 mRNA and ACE mRNA in circulating EVs were elevated in DKD patients, suggesting that they may serve as potential early diagnostic markers for DKD [ 98 ]. Another study found that the enriched expression of uEVs miRNA-4534, which targets BCL2 interacting protein 3(BNIP3) and is involved in the forkhead box O(FOXO) signaling pathway, may become a new biomarker for the progression of DKD in type 2 diabetes [ 99 ]. Additionally, research has found that the progression of proteinuria is paralleled by an increase in the levels of miR145 and miR126 in uEVs. And they are simultaneously elevated in renal epithelial cells undergoing epithelial-mesenchymal transition (EMT), highlighting their potential as biomarkers for the progression of diabetic nephropathy and the occurrence of proteinuria [ 100 ]. Analysis of miRNA in uEVs from patients with T2DM revealed that in patients with microalbuminuria (MIC), the levels of let-7i-3p, miR-24-3p, and miR-27b-3p were increased, while the level of miR-15b-5p was decreased. In patients with macroalbuminuria (MAC), the concentration of miR-30a-5p in uEVs was specifically modified, but not in MIC patients, indicating that miR-30a-5p may be associated with severe renal damage [ 101 ].Additionally, it has been found that in type 2 diabetic patients with kidney damage, the enrichment of uEVs miR-21-5p and the reduction of miR-30b-5p may represent candidate biomarkers for renal injury in type 2 diabetes [ 102 ].Furthermore, data indicate that uEVs miR-615-3p is positively correlated with various renal injury markers. And the diagnostic efficacy of miRNA-615-3p in combination with albumin-to-creatinine Ratio(ACR) is higher than that of ACR alone, suggesting that it can serve as a more stable and sensitive diagnostic biomarker for DKD [ 103 ].

IgA nephropathy (IgAN)

In the context of IgAN, EV-associated CCL2 has been found to be correlated with the estimated glomerular filtration rate (eGFR) and associated with kidney inflammation and C3 deposition. Elevated CCL2 levels are also linked to the progression of renal dysfunction, implying that uEVs and exosomal CCL2 mRNA could serve as biomarkers reflecting IgAN activity and the deterioration of renal function [ 104 ]. In patients with IgAN and DKD, researchers have observed a decrease in the expression of miR-200b in uEVs as fibrosis progresses [ 105 ], suggesting its potential as a diagnostic biomarker. Furthermore, researchers have explored the differences in the uEVs microRNA expression profiles between patients with IgAN and healthy controls. The significant upregulation of uEVs miRNAs, such as miR-215-5p and miR-378i [ 106 ], miR-4639 and miR-210 [ 107 ], and the significant downregulation of miR-29c and miR-205-5p [ 106 ] may represent new non-invasive biomarkers for IgAN. These findings may aid in diagnosis, assessment of severity, and evaluation of disease progression in IgAN.

In lupus nephritis (LN), the regulatory effects of uEVs miRNAs, including miR-135b-5p, miR-107, and miR-31-5p, have been demonstrated to be potential early biomarkers for the disease [ 108 ]. In the study of LN renal fibrosis, researchers have found that miR-29c is correlated with the chronicity of kidney disease [ 109 ]. Moreover, this level of variation is independent of renal function, indicating that it can serve as a non-invasive biomarker for the early progression to fibrotic processes in LN patients. In recent years, due to the insufficiency of a single biomarker in achieving adequate sensitivity and specificity in clinical diagnosis. Another study has demonstrated that a multi-biomarker panel composed of uEVs miRNAs, including miR-29c, miR-150, and miR-21, can be used to detect early renal fibrosis and predict the progression of LN [ 110 ].

Renal cell carcinoma(RCC)

In mice with the transgenic PrCC-TFE3 gene, uEVs miR-204-5p levels surge during precancerous and tumor development stages, indicating its potential as a diagnostic biomarker for xp11 translocation renal cell carcinoma [ 111 ].In clear cell renal cell carcinoma (ccRCC), researchers have detected that the expression levels of exosomal miR-210 and miR-1233 in ccRCC patients are significantly higher than those in healthy individuals. Circulating microRNAs, miR-210 and miR-1233, may potentially serve as biomarkers for the future diagnosis and monitoring of ccRCC [ 112 ].

Early renal injury in hypertension

In hypertensive patients, increased levels of uEVs miR-146a are significantly correlated with urinary albumin excretion, with a decrease observed in patients with proteinuria [ 113 ], indicating its potential as a non-invasive biomarker for early renal injury in hypertension.

BKV nephropathy (BKVN)

The BK virus (BKV) is an important pathogen causing nephropathy in renal transplant recipients. Recent studies have shown that BKV-associated microRNAs are significantly enriched in the uEVs fraction of patients with BKV nephropathy (BKVN). The diagnostic role of specific microRNAs such as BKV-miR-B1-5p, which has been found to be consistent with the assessment of blood and urine BK viral loads, suggests that uEVs microRNAs may become valuable biomarkers for the diagnosis and monitoring of BKVN [ 114 ].

In conclusion, the exploration of EVs as biomarkers in kidney diseases is a rapidly evolving field, presenting new opportunities for early diagnosis, disease monitoring, and personalized therapeutic strategies. Future research is warranted to validate the clinical potential of these biomarkers and to elucidate their mechanisms of action in the context of various kidney diseases.

EVs-based therapy in kidney diseases

The treatment of kidney diseases has always faced many challenges. Existing therapeutic methods, such as drug therapy and dialysis, can control the condition to a certain extent, but find it difficult to completely reverse the damage. Drug therapy often only targets specific aspects, and struggles to address all pathological issues comprehensively. Moreover, the self-repair capacity of kidney tissue is relatively weak. Once severe damage occurs, it is extremely challenging to restore its normal structure and function. Although dialysis can temporarily replace some of the kidney’s functions, long-term dialysis may lead to cardiovascular complications, malnutrition, and psychological issues for patients.

In the realm of kidney disease research and therapeutics, EVs have emerged as a promising avenue, exhibiting distinct properties and therapeutic potential based on their cellular origins. Notably, EVs derived from mesenchymal stem cells (MSC-EVs), renal tubular epithelial cells (TEC-EVs), and other sources have each demonstrated unique contributions to the field. The following sections will delve into the advancements and applications of these diverse EVs in the treatment of renal pathologies.

Stem cell- derived extracellular vesicles

MSCs possess unique abilities for self-renewal and differentiation, and they have a rich variety of sources, including umbilical cord, placenta, bone marrow, adipose tissue, and Urinary stem cells, among others. In recent years, MSC-EVs have been extensively studied as therapeutic molecules for kidney diseases in both in vitro cellular models and preclinical animal models (Fig. 4 ).

Therapeutic effect of MSC-EVs on kidney diseases. MSC-EVs, carrying a variety of bioactive molecules, target different molecular mechanisms, alleviating kidney damage by inhibiting inflammatory responses, fibrosis, oxidative stress, and other pathological changes in the kidney

Ischemia/Reperfusion AKI (I/R-AKI)

Studies have found that human umbilical cord mesenchymal stem cell-derived extracellular vesicles (hucMSC-EVs) can improve the outcomes of I/R-AKI and promote the repair and regeneration of renal tubular cells [ 115 ]. In AKI, exosomes derived from mesenchymal stem cells, specifically miR-125b-5p, adhere to TECs via very late antigen-4(VLA-4) and lymphocyte function-associated antigen-1(LFA-1), targeting the miR-125b-5p/p53 signaling pathway in TECs. This targeting induces cell cycle arrest and apoptosis, and mediates kidney repair in AKI [ 116 ]. Similarly, exosomal miR-148b-3p from hucMSCs can suppress apoptosis in I/R injury by downregulating the expression of pyruvate dehydrogenase kinase 4(PDK4), activating the activating transcription factor 6(ATF-6) pathway, and inducing endoplasmic reticulum stress [ 117 ]. Research has also demonstrated that pyroptosis is associated with AKI, and hucMSC-Exos can improve AKI by inhibiting pyroptosis and reducing kidney damage [ 118 ]. These studies further elucidate the mechanisms by which protective cells hucMSC-EVs resist acute kidney injury.

Cisplatin-induced AKI (CP-AKI)

Research indicates that the synergistic effect of pulsed focused ultrasound (PFUS) and hucMSC-EVs can inhibit the heat shock protein70/90 (HSP70/90). This inhibition leads to a reduction in the expression of the NLRP3 inflammasome and downstream pro-inflammatory cytokines, ultimately improving renal function [ 119 ]. The 14-3-3ζ protein carried by hucMSC-ex can also induce autophagy levels in HK-2 cells, thereby protecting HK-2 cells from the toxic damage of cisplatin [ 120 ]. Similarly, pre-incubation with hucMSC-Ex can significantly reduce CP-induced injury in NRK cells, which may be achieved by upregulating Bcl-2 and inhibiting the expression of apoptotic markers, thereby enhancing cell survival and suppressing apoptosis [ 121 ].

Glycerin-induced AKI

Platelet-rich plasma (PRP) can promote the proliferation of MSCs by inducing the nuclear expression of yes-associated protein (YAP), maintaining and enhancing their stemness. Furthermore, PRP promotes the secretion of hucMCS-EX that inhibit apoptosis of renal tubular cells and repair glycerol-induced AKI through the activation of the AKT/Rab27 pathway in a paracrine manner [ 122 ].

Sepsis-AKI(S-AKI)

Treatment with hucMSC-Ex upregulates the levels of miR-146b, leading to a reduction in the expression of interleukin-1 receptor-associated Kinase 1 (IRAK1), which in turn suppresses the activity of NF-κB. This ultimately alleviates S-AKI and improves the survival rate of septic mice. This suggests that hucMSC-Ex could serve as a novel therapeutic agent for reducing S-AKI [ 123 ].

Unilateral ureteral obstruction (UUO)

In CKD, hucMSCs release exosomes carrying miR-186-5p, which reduce the expression of Smad5 by directly binding to its 3’-untranslated region (3’-UTR). This action leads to a decrease in the accumulation of extracellular matrix (ECM) proteins, inhibition of EMT and apoptosis, and alleviation of renal fibrosis [ 124 ]. Similarly, hucMSC-Ex can also inhibit the activation of YAP by promoting its ubiquitination and degradation through the delivery of casein kinase 1δ (CK1δ) and β-transducin repeat-containing protein (β-TrCP). This results in reduced collagen deposition and alleviation of mechanical stress-induced renal fibrosis [ 125 ]. Furthermore, hucMSC-EVs inhibit apoptosis and promote cell proliferation by suppressing the ROS -activated p38MAPK/ERK signaling pathway. This reduces renal tubular damage and interstitial fibrosis, protecting the UUO kidney from oxidative stress-induced injury [ 126 ].Another study has confirmed that the exosomes from hucMSCs carry miR-874-3P, which targets receptor-interacting serine/threonine-protein kinase 1 (RIPK1) to regulate necroptosis, reduces the expression of Phosphoglycerate mutase family member 5 (PGAM5), and promotes the dephosphorylation of the Drp1 gene at the S637 site. This maintains mitochondrial function homeostasis, alleviates renal injury, and promotes repair [ 127 ].

Studies have shown that a single injection of bone marrow stem cell (BMSC)-purified MVs at the time of UUO can prevent epithelial-mesenchymal transition (EMT) [ 128 ], this protective effect is observed in in vivo experiments using TGF-β1 induced in HK2 cells, indicating that curtailing initial damage can forestall subsequent kidney injury.

Furthermore, recent research has found that BMSC-EVs provides therapeutic approaches for kidney diseases by targeting binding immunoglobulin protein (BIP), semaphorin 3 A(Sema3A), the mammalian target of rapamycin(mTOR) signaling pathway, krüppel-like factor 6(KLF6)/NF-κB, and phosphofructokinase (PFKM) in renal tubular epithelial cells. Specifically, these targets are modulated through miR-199a-5p [ 129 ], miR-199a-3p [ 130 ],miRNA-122a [ 131 ],miR-181d [ 132 ], and miR-21a-5p [ 133 ], respectively. Exosomes derived from BMSCs can prevent kidney damage and inhibit renal fibrosis both in vitro and in vivo by regulating the klotho protein in rats that have undergone 5/6 nephrectomy [ 134 ] and by modulating the Smurf2/Smad7 pathway [ 135 ].

Adipose-derived mesenchymal stem cell-derived extracellular vesicles(ADSC-EVs) [ 136 ] that have demonstrated efficacy in improving kidney damage within experimental paradigms of chronic metabolic syndrome complicated by renal artery stenosis (MetS + RAS). Furthermore, ADSC-EVs convey miR-26a-5p [ 137 ] and miR-342-5p [ 138 ], which ameliorate the pathological symptoms of DKD and AKI by aiming at TLR4 and TLR9, respectively. Simultaneously, ADSC-derived EVs manifest protective influences in S-AKI, potentially through the modulation of sirtuin 1(SIRT1) on the NF-κB signaling pathway, thereby diminishing inflammatory responses and apoptosis [ 139 ].

Hepatic MSC-derived extracellular vesicles (HP-MSC-EVs) [ 140 ], for instance, concentrate in renal tubules during renal I/R injury. They stimulate the recuperation of kidney function by invoking the kelch-like ECH-associated protein 1(Keap1)-nuclear factor erythroid 2 related factor 2(Nrf2) signaling pathway and bolstering the mitochondrial function of TECs.

Hepatocyte-like stem cell-derived EVs (HLSC-EVs) [ 141 ] have evinced reparative traits, forestalling interstitial fibrosis and renal tubular necrosis while advocating kidney regeneration and attenuating the inflow of inflammatory immune cells in a CKD mouse model.

Induced MSC-derived EVs (iMSC-EV) [ 142 ] shield against cisplatin-provoked kidney injury by curbing apoptosis, immune cell infiltration, and inflammation.

Exosomes derived from PSC-MSCs [ 143 ], through the upregulation of SIRT6, have been demonstrated to effectively mitigate endothelial cell injury, attenuate inflammatory responses, preserve renal function, and decelerate the progression of renal fibrosis.

Urinary stem cells (USCs) [ 144 ] are capable of averting kidney IRI through exosomal miR-146a-5p, which homes in on the 3’UTR of IRAK1, subsequently suppressing the ignition of the NF-κB pathway and the encroachment of inflammatory cells, thus nurturing kidney function.

Human Wharton’s jelly MSC-derived EVs (hWJMSC-EV) can amplify the expression of miR-30b/c/d in renal tubular cells, alleviate the activation of dynamin-related protein 1(DRP1) and mitochondrial fragmentation, thus assuming an anti-apoptotic role [ 145 ]. Furthermore, hWJ-MSC-EV can rehabilitate AKI induced by I/R and assist in balancing the oxidative stress/antioxidant equilibrium by invigorating Nrf2/antioxidant response element(ARE) activation, offering new vistas into the therapeutic mechanisms of MSC-EVs in renal regenerative medicine [ 146 ].

Non-stem cell- derived extracellular vesicles

TEC-EVs have emerged as significant contributors to the renal repair process following ischemia-reperfusion injury (IRI). Evidence suggests that the systemic administration of EVs from preconditioned renal tubular cells, particularly those subjected to hypoxic preconditioning, can substantially ameliorate severe I/R-AKI. The therapeutic infusion of these EVs within a critical 24 to 48-hour window post-IRI has been shown to enhance renal function, mitigate renal tubular damage, oxidative stress, inflammatory infiltration, fibrosis, and microvascular structural alterations [ 147 ].EVs derived from hypoxia-preconditioned renal tubular epithelial cells ameliorate renal IRI via the HIF-1α/Rab22 pathway, while hypoxia-preconditioned hepatocyte EVs may offer renal protection through differential expression of EV-miRNAs [ 148 ].Furthermore, miR-590-3p is transferred between renal tubular epithelial cells via exosomes, modulating autophagy by targeting TRAF6. This paracrine miRNA transfer suggests that augmenting miR-590-3p levels in exosomes may bolster autophagy and protect against renal IRI [ 149 ].

Exosomal miR-122 derived from cisplatin-treated HK2 cells has been demonstrated to inhibit pyroptosis in surrounding cells, with miR-122 targeting embryonic lethal abnormal vision (ELAVL1) to suppress pyroptosis and AKI, offering a potential therapeutic target for AKI [ 150 ].

Antimycin A-induced AKI(AMA-AKI)

Intrinsic renal scattered tubular cells (STC-like cells) have demonstrated protective effects on AMA-impaired TECs in vitro, with exosomes potentially transferring mitochondria or mitochondrial fragments to rejuvenate the mitochondrial function of recipient cells. Systemic delivery of mitochondria-laden exosomes may integrate into ischemic renal tubules, improving mitochondrial function and mitigating chronic kidney injury [ 151 ].

The combined therapy of artemisinin and hydroxychloroquine has shown renoprotective effects in IgAN by inhibiting exosomal NF-κB signaling and NLRP3 inflammasome activation, presenting a novel therapeutic strategy for IgAN by modulating exosome release and NF-κB/NLRP3 signaling [ 152 ].

Endothelial progenitor cell-EVs (EPC-EVs)

Investigations have uncovered that miR-21-5p, contained within endothelial progenitor cell (EPC)-derived exosomes, possesses the potential to mitigate S-AKI. This is achieved by downregulating the expression of runt-related transcription factor 1(RUNX1), thereby introducing a fresh strategy for renal endothelial preservation in the context of S-AKI [ 153 ].

Circulating extracellular vesicles

Circulating exosomal miR-1-3p, identified in the aftermath of myocardial infarction, has been demonstrated to suppress cardiomyocyte (CM)-induced apoptosis and autophagy of renal tubular epithelial cells. It achieves this by targeting autophagy-related protein 13(ATG13) and activating the AKT signaling pathway, thereby enhancing renal function. Post-cardiac injury, these exosomal miR-1-3p are swiftly released into the kidney, where they exert a positive influence on renal function by directly targeting ATG13 [ 154 ].

Human amniotic epithelial cells-EVs (hAEC-EVs)

Recent research outcomes highlight human amniotic epithelial cells (hAECs) and their derived exosomes can markedly alleviate cisplatin-induced nephrotoxicity. They reduce mortality and serum creatinine levels and decrease renal tubular damage. The renal protective mechanism involves the inhibition of the TNF-α/MAPK and caspase signaling pathways [ 155 ].

Macrophage-derived EVs(Mφ-EVs)

A study’s data indicate that exosomal miR-25-3p derived from M2 macrophages protects podocytes from HG-induced injury by activating autophagy in podocytes through the inhibition of dual specificity phosphatase 1(DUSP1) expression [ 156 ]. This confirms the importance of paracrine communication via exosomes between M2 macrophages and HG-induced podocytes, and identifies a new potential target for the treatment of diabetic nephropathy.

In summary, different extracellular vesicles (including hucMSC-EVs, BMSC-EVs, ADSC-EVs, TEC-EVs, EPC-EVs, Mφ-EVs, and Circulating EVs) play a diversified role in the treatment of kidney diseases through the bioactive substances they carry and the signals they transmit. EVs can deliver survival signals to injured kidney cells, inhibiting apoptosis. They promote the proliferation and differentiation of damaged cells, accelerating tissue repair and regeneration. For instance, EVs derived from specific cell types, such as MSC-EVs, can carry miR125b-5p, which promotes renal tubular repair and inhibits apoptosis, thus playing a role in the treatment of AKI [ 116 ]. Additionally, extracellular vesicles can transfer molecules with anti-inflammatory effects. For example, IL-10-loaded EVs (IL-10 EVs) produced by engineered macrophages can enhance the stability of IL-10 and its targeting to damaged kidneys. A large number of IL-10 EVs located in the renal tubular interstitial macrophages induce a significant shift in the polarization of renal macrophages from the M1 to the M2 phenotype, ultimately significantly improving renal tubular damage and inflammation caused by ischemia/reperfusion injury [ 157 ]. Furthermore, EVs can inhibit the activation and proliferation of fibroblasts, reducing the excessive deposition of extracellular matrix. They modulate fibrotic-related signaling pathways and reverse the fibrotic process. For example, fibrosis-related macrophages promote the differentiation of mesangial cells into myofibroblasts by activating the TGF-β1/Smad2/3/YAP axis, while MSC-EVs antagonize the fibrotic niche in DKD by mediating the degradation of YAP through CK1δ/β-TRCP, alleviating the progression of DKD. These collectively illustrate that the precise regulation of EVs’ biosynthesis and function may pave the way for innovative therapeutic interventions in kidney diseases [ 158 ] (Table 1 ).

Engineered extracellular vesicles of kidney diseases

Given the preliminary achievements of EVs in the treatment of kidney diseases, researchers are committed to overcoming their limitations through engineering approaches to achieve more optimized therapeutic strategies. Engineered EVs are gradually becoming the focus of research due to their great potential as multifunctional drug delivery systems in the field of biomedicine.

The design of engineered EVs focuses on improving their delivery capabilities by means of strategic surface modifications or the inclusion of functional ligands. These modifications enable the conjugation of a variety of molecules, such as endogenous and exogenous substances, drugs, proteins, or nucleic acids, to either the surface or the interior of the vesicles [ 159 ]. Furthermore, this engineering facilitates the precise targeting of specific cell types or tissues, which is essential for directed therapeutic interventions [ 160 ].This approach not only expands the therapeutic potential of EVs, but also introduces a multifaceted enhancement of their capabilities. The ability to selectively deliver cargo to intended sites while minimizing off-target effects is particularly beneficial [ 161 ]. The implications of these advancements are significant, as they open the door for a new generation of clinical applications of EVs, providing tailored treatments with enhanced efficacy and safety profiles.

Engineering strategies for EVs can be categorized into three main approaches: cargo loading, surface modification, and genetic engineering. Each engineering strategy has its unique strengths and limitations. In practical applications, it may be necessary to consider the appropriate engineering strategy based on the specific type of kidney disease, therapeutic objectives, and the individual patient’s condition. Combining multiple strategies may be required to achieve the best therapeutic outcomes.

Cargo loading

Cargo loading involves incorporating therapeutic agents, such as small molecule drugs, proteins, nucleic acids, etc., into EVs through methods like electroporation, extrusion, sonication, and incubation, to achieve the purpose of drug delivery. Strategies for cargo loading include endogenous loading and exogenous loading. Endogenous loading is an engineering loading method based on the parental cells, which involves modifying the source cells to introduce the target molecules, allowing them to be incorporated into the vesicles during the production of EVs. Exogenous loading, on the other hand, involves using membrane penetration or other loading strategies to directly load the cargo into the isolated EVs [ 8 , 162 ].

The extrusion method is a commonly used strategy for engineering EVs. For example, by mixing purified neutrophil membranes with recombinant human IL-37 protein and then preparing N-MV@IL-37 through the extrusion method. This approach not only enhances the stability of IL-37 but also enables it to be targeted to injured renal endothelial cells via PSGL-1 on the neutrophil membrane. This enhances the therapeutic effect of IL-37 on renal IRI [ 163 ]. Electroporation loading technology primarily involves encapsulating small RNA molecules into the interior of EVs. Studies have shown that by using electroporation to load exogenous microRNA from Caenorhabditis elegans (cel-miR-39) into MSC-EVs, one can protect podocytes from damage, reduce cell death, and decrease albumin permeability [ 164 ].

To enhance the efficacy of EVs, various stimuli can be used to bioengineer the originating cells. For instance, exosomes derived from melatonin-stimulated mesenchymal stem cells (Exocue) can reduce the gene expression of miRNAs associated with the severity of CKD, increase the levels of aquaporins AQP2 and AQP5, and decrease blood urea nitrogen (BUN) and creatinine levels. Thereby alleviating the severity of CKD and modulating chronic inflammation and fibrosis [ 165 ]. Integrins are a type of protein found on the surface of EVs, and the RGD peptide has a strong binding affinity for integrins. Research indicates that supramolecular nanofibers containing the arginine-glycine-aspartic acid (RGD) peptide can enhance MSC-EVs carrying let-7a-5p miRNA. By targeting the CASP3 and RragD genes, these nanofibers can reduce apoptosis and activate autophagy [ 166 ].

Klotho is a single-pass transmembrane protein that is essential for renal tissue regeneration. By using the Exo-Fect exosome transfection reagent to load recombinant Klotho protein into EVs, it has been found that in an AKI mouse model, engineered EVs expressing recombinant human Klotho exhibit stronger renal protective effects compared to the same dosage of soluble Klotho [ 167 ].In another study, by transfecting MSCs with let-7i-5p antagomir, which is an antagonist targeting let-7i-5p, it is possible to suppress the levels of let-7i-5p in MSC-EVs. This suppression increases the anti-fibrotic activity, reduces the deposition of extracellular matrix, and mitigates epithelial-mesenchymal transition [ 168 ].

The advantage of cargo loading is that the type and dose of therapeutic substances (e.g., drugs, nucleic acids, etc.) encapsulated in EVs can be precisely controlled, thus facilitating targeted therapy. It can load some large molecule drugs to improve their stability and bioavailability. However, the loading efficiency of this method may be affected by various factors, such as the nature of the cargo and the loading method.

Surface modification

The surface of EVs is enriched with various proteins and polysaccharides, which can influence the targeting, stability, and immunogenicity of EVs. By modifying the surface molecules of EVs, one can confer cell and tissue targeting specificity, thereby enhancing their targeting efficiency and therapeutic efficacy. Methods for surface modification include chemical modification, bio-fusion expression, and liposome fusion, among others [ 169 ]. Chemical modification is key in ensuring that the biological activity and stability of EVs are not compromised while achieving the desired engineered characteristics. For instance, by using copper-free click chemistry, the LTH peptide can be conjugated to the surface of red blood cell-derived extracellular vesicles (REVs) through a reaction between azadibenzocyclooctyne (DBCO) and an azide. The targeting effect of the Kim-1 binding peptide LTH effectively reduces the expression of P-P65 and Snail1 in injured renal tubular cells, inhibiting ischemia/reperfusion injury and unilateral ureteral obstruction-induced kidney inflammation and fibrosis in mice, thus delaying the progression from acute kidney injury (AKI) to chronic kidney disease (CKD) [ 170 ].In another study, researchers covalently linked a P-selectin binding peptide (PBP) to a polyethylene glycol-derivatized phospholipid (DMPE-PEG) and then anchored this complex to the surface of EVs. It was found that these PBP-EVs could competitively bind to P-selectin on damaged endothelial cells, inhibiting the invasion of inflammatory cells and thereby reversing the pro-fibrotic renal microenvironment [ 171 ].

Furthermore, after achieving efficient expression of the CHIP protein by transducing MSCs with the CHIP gene using lentiviral transduction technology, the authors surface-modified the isolated MSC-EVs-CHIP with superparamagnetic iron oxide nanoparticles (SPION). The results indicated that SPION-EVS-CHIP had a good targeting effect on kidney injury in rats with unilateral ureteral obstruction (UUO). Compared to traditional MSC-EVs, SPION-EVS-CHIP significantly reversed collagen deposition and inhibited the inflammatory response mediated by renal tubular injury by inducing ubiquitination of renal tubular cells and degradation of Smad2/3169 [ 172 ].

Surface modification offers the advantage of significantly improving the targeting of EVs to kidney lesion tissues or cells by conjugating targeting ligands or specific molecules on the EV surface, thereby enhancing therapeutic effects. It can improve the stability and bioavailability of EVs. However, the process of surface modification can be quite complex, requiring precise chemical or biological reaction conditions. Modification might alter the natural characteristics of EVs, potentially triggering immune responses or other adverse effects.

Genetic engineering

The gene engineering strategy in engineered EVs refers to the modification of the genes of EVs to express specific proteins or RNAs to achieve particular functions. This process typically involves the specific insertion, deletion, or modification of target genes in the genome, followed by the isolation of EVs containing engineered genetic material or therapeutic agents [ 173 ]. Transfection of plasmids can achieve the effect of engineered EVs by introducing plasmids containing specific genes into cells, causing the cells to express the gene and package it into EVs. For example, transfecting RAW 264.7 macrophages with a plasmid encoding mouse IL-10 to produce EVs loaded with interleukin-10 (IL-10-EV). This process can promote mitochondrial autophagy and the polarization of renal tubular interstitial macrophages towards an anti-inflammatory M2 phenotype, effectively treating ischemic acute kidney injury (AKI) [ 157 ]. In another study, the authors transfected mouse renal tubular epithelial cells (TEC) with a plasmid encoding mouse VEGF-A to prepare EVs expressing high levels of VEGF-A (sEV-VEGF-A). It was found that these vesicles could treat ischemic renal injury by promoting the repair of the peritubular capillary (PTC) [ 174 ].

Lentiviral transduction is a commonly used method for gene delivery, utilizing a modified lentivirus vector to effectively integrate the gene of interest (such as reporter genes, functional genes, or gene-editing tools) into the host cell’s genome. In a study, the authors transduced mesenchymal stem cells (MSCs) with an expression plasmid of miR-let7c via a lentiviral vector. The genetically engineered mesenchymal stem cells with high expression of miR-let7c (miR-let7c-MSCs) could selectively deliver to the damaged kidneys, reducing fibrosis in vivo and alleviating the injury to renal cells stimulated by transforming growth factor-β1 [ 175 ].Additionally, by using the lentiviral transfection system to transduce the GDNF gene into human adipose-derived mesenchymal stem cells (ADSCs), one can obtain GDNF-modified exosomes from human adipose-derived mesenchymal stem cells (GDNF-AMSC-exos). Research findings have shown that GDNF-AMSC-exos can enhance the preservation of peritubular capillaries and activate the post-injury angiogenesis program to improve renal fibrosis by activating the SIRT1/eNOS signaling pathway [ 176 ].In addition, the use of an inducible lentiviral vector containing the FOXP3 gene to transduce CD4 + T cells allows for the construction of engineered T cells (Foe-Th). From these T cells, genetically engineered EVs containing specific FOXP3 transcripts are isolated, known as FOE-TEV. These vesicles can improve the inhibition coefficient under secondary lymphatic drainage by suppressing Th1 cell polarization, inhibiting the production of donor-specific antibodies (DSA). And obstructing complement activation, effectively alleviating allograft rejection after renal transplantation [ 177 ].Researchers have utilized a lentiviral vector to insert the human EPO gene into Kidney Mesenchymal Stem Cells (KMSCs), resulting in the generation of engineered KMSCs that express EPO (EPO (+) –KMSCs). They found that the EVs secreted by these cells can transfer EPO mRNA to target cells, alleviating anemia in rats with chronic kidney disease (CKD) [ 178 ]. Additionally, by using a lentiviral vector to transfer the OCT-4 gene into human mesenchymal stem cells, overexpression of OCT-4 was achieved. Subsequently, EVs enriched with Oct-4 mRNA, termed EVs + Oct-4, were isolated. It was found that EVs + Oct-4 exhibited a greater inhibitory effect on the expression of Snail1 and enhanced the anti-apoptotic and proliferative effects on renal cells [ 179 ].

Unlike lentiviral transduction, adenoviral transfection is typically used for transient expression of the gene of interest because they do not integrate into the host cell genome but exist in the host cell as free circular DNA. In a study, the authors transfected an adenoviral vector expressing mouse CD26 (VirusCD26 + ) into a renal tubular epithelial cell line (TCMK-1). And then found that the isolated exosomes with overexpressed CD26 (Exo CD26 + ) could treat ischemia-reperfusion acute kidney injury (IR-AKI) by maintaining cell proliferation and reducing inflammation [ 180 ].

Genetic engineering can regulate the production and function of EVs at the genetic level, enabling them to continuously express specific therapeutic gene products. It allows for long-term, stable modification of the intrinsic composition and function of EVs. However, compared to other engineering techniques, genetic engineering is relatively complex to operate, and there are potential risks associated with gene editing and ethical issues. Controlling gene expression is challenging and may result in unpredictable gene expression changes.

In summary, significant strides have been made in the field of kidney disease research through the strategic engineering of EVs. Enhancements in renal targeting and therapeutic efficacy have been achieved by employing techniques such as cargo loading, surface modification, and genetic engineering. These approaches have endowed EVs with the ability to more precisely deliver therapeutic agents and modulate immune responses within the renal microenvironment (Fig. 5 ). As the field progresses, ongoing research is expected to further elucidate the mechanisms of EVs action in renal pathophysiology and expand their application in the therapeutic arsenal for kidney diseases. The continued development of these nanoscale therapeutics holds promise for the future, potentially offering patients a range of more precise, effective, and personalized diagnostic and treatment options.

Engineering strategies for EVs. Three distinct engineering strategies—cargo loading, surface modification, and genetic engineering—are utilized to enhance the therapeutic efficacy and targeting specificity of extracellular vesicles derived from various sources

Perspectives and challenges

EVs are emerging as promising agents in the advancement of kidney disease diagnostics and therapeutics. However, translating EVs research from the bench to clinical practice is not without its challenges. Researchers are actively engaged in surmounting these hurdles and are exploring innovative approaches to harness the full therapeutic potential of EVs in renal pathologies. In the quest to standardize EVs research, a primary focus is the development of protocols for EVs production, characterization, storage, and clinical assessment. A significant challenge lies in the heterogeneity of EVs [ 181 ]; those derived from different cellular origins can exhibit considerable variability in size, composition, and functionality. This diversity complicates processes such as isolation, characterization, and quantification. Current methodologies, including ultracentrifugation, size exclusion chromatography, ultrafiltration, immunocapture, precipitation, and microfluidic technologies, are not without their drawbacks, including low yields and impurities, which can impact subsequent analyses and applications [ 182 ]. Establishing standardized methods for EVs isolation and analysis is essential for ensuring the reproducibility and comparability of research outcomes.

Additionally, there is an imperative need to validate techniques for the detection and quantification of specific EVs subpopulations, particularly those from unique cell types or bearing distinct molecular cargo [ 183 ]. At present, there are several mature single EV analysis technologies, including nanoflow cytometry, the ExoView platform, super-resolution fluorescence imaging, surface plasmon resonance (SPR) technology, and single-particle dark-field imaging [ 184 ]. In addition, researchers from the University of Gothenburg in Sweden have proposed a method that uses enzymatic treatment, differential centrifugation, and density gradient separation, followed by characterization with electron microscopy and RNA profiling, to directly isolate up to six different EV subpopulations from tissues [ 185 ]. The research team from Zhongshan Hospital affiliated with Fudan University in Shanghai has developed HNCIB (High-throughput Nano-bio Chip Integrated System for Liquid Biopsy), a technology capable of simultaneously detecting and analyzing multiple biomarkers on the surface and within EVs [ 186 ]. Professor Zheng Lei’s team at Nanfang Hospital, Southern Medical University, has respectively constructed a fluorescent aptamer sensor based on aptamer-functionalized metal-organic frameworks and cholesterol-triggered signal amplification-EV-ANCHOR [ 187 ], and a single-vesicle membrane protein expression profile analysis technology based on droplet digital immuno-PCR (ddiPCR) [ 188 ]. These can be used for the separation and detection of PD-L1EVs and the quantitative detection analysis of specific EV subpopulations, providing a new strategy for the clinical diagnosis of cancer with EV subpopulations. The development of these technologies has provided new avenues and possibilities for the study and application of extracellular vesicles. With the continuous advancement and optimization of technology, it is expected that in the future, more efficient and precise research and applications of extracellular vesicles will be realized.

The biological functions and mechanisms of action of EVs in kidney diseases are not yet fully elucidated. While numerous roles have been identified, the underlying signaling pathways and molecular mechanisms necessitate further exploration [ 189 ]. This deeper understanding will be pivotal in revealing the integral roles of EVs in the etiology and progression of renal diseases, thereby providing a robust theoretical foundation for the development of novel therapeutic interventions. The utilization of EVs as biomarkers also presents its own set of challenges. For example, in studies employing uEVs as biomarkers, the protocols for urine collection and preservation, as well as the methodologies for the isolation of urinary EVs and the elimination of contaminants, are critical [ 190 ]. Furthermore, while certain studies suggest that specific molecules within EVs could serve as diagnostic indicators for kidney diseases, the specificity and sensitivity of these biomarkers require additional validation. The establishment of standardized detection methods and reference ranges is imperative to ensure their reliability and reproducibility in clinical settings.

The therapeutic application of EVs is an area that requires further exploration. Although preliminary studies indicate that EVs can function as drug delivery systems or therapeutic agents, their safety and efficacy must be comprehensively evaluated through preclinical and clinical trials. Currently, there is a dearth of extensive clinical trials for EVs. Rigorous clinical trials are indispensable for corroborating existing research findings and for the transformation of EVs into viable clinical therapies [ 15 , 191 ]. Optimizing the loading and delivery of EVs is another critical challenge. The selection of appropriate loading methods, enhancement of drug or bioactive molecule loading efficiency within EVs, and the optimization of delivery routes and targeting are all focal points of research. Advances in the engineering of EVs offer new opportunities to augment the therapeutic efficacy of EVs in renal diseases [ 159 , 192 , 193 ]. Techniques such as gene editing and synthetic biology provide avenues for modifying EVs to improve their efficacy and targeting specificity. While preclinical studies have demonstrated the potential benefits of EVs in AKI and CKD, the long-term efficacy and durability of EVs therapy remain uncertain. Further research is essential to elucidate the in vivo lifespan of EVs, the duration of drug efficacy, and the safety and efficacy of repeated administrations.

The application of EVs in kidney disease holds great promise but is also confronted with significant challenges, including the development of accurate diagnostic markers, in-depth exploration of therapeutic mechanisms, optimization of targeted delivery systems, long-term safety and efficacy evaluation, translational research and regulatory approval for market launch, etc. To effectively tackle these challenges, it is imperative to sustain ongoing research efforts and to foster technological innovation. Interdisciplinary collaboration is pivotal in propelling the study of EVs in kidney disease. The study of EVs spans fields such as biology, medicine, chemistry, and physics, necessitating a collaborative approach among experts from diverse disciplines to address the issues and challenges effectively. In conclusion, as an emerging field, the study of extracellular vesicles offers novel perspectives and methodologies for the diagnosis and treatment of kidney diseases. Despite the challenges, with ongoing technological advancements and in-depth research, the prospects for the application of EVs in renal medicine are expected to expand significantly (Table 2 ).

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Extracellular vesicles

Acute Kidney Injury

Diabetic Kidney Disease

Macrophage-derived EVs

Autophagy-related gene 9B

Cationic amino acid transporter regulator homolog 1

Suppressor of cytokine signaling-1

Toll-like receptor 4

Hypoxia-inducible factor 1α

Phosphatidylserine

Sonic hedgehog

Phosphatase and tensin homolog

Microvesicles

Renal interstitial fibrosis

Tissue Inhibitor of metalloproteinases 2

Tumor necrosis factor-related apoptosis-inducing ligand

High glucose

Reactive oxygen species

ADP-Ribosylation Factor 6

Ischemia/Reperfusion AKI

Na/H exchanger isoform 3

Alpha-1-antitrypsin

Wilms’ tumor protein 1

IgA Nephropathy

Lupus Nephritis

SLE disease activity index

Peritubular capillary

Epithelial-mesenchymal transition

Macroalbuminuria

Renal Cell Carcinoma

Very late antigen-4

Pyruvate dehydrogenase kinase 4

Heat shock protein

Platelet-rich plasma

Interleukin-1 receptor-associated kinase 1

Extracellular matrix

β-transducin repeat-containing protein

Receptor-interacting serine/threonine-protein kinase 1

Binding immunoglobulin protein

Mammalian target of rapamycin

Phosphofructokinase

Kelch-like ECH-associated protein 1

Dynamin-related protein 1

Embryonic lethal abnormal vision

Cardiomyocyte

Human amniotic epithelial cells

Blood urea nitrogen

Superparamagnetic iron oxide nanoparticles

Kidney Mesenchymal Stem Cells

Forkhead box O

Mesenchymal stem cells-EVs

Chronic Kidney Disease

Renal tubular epithelial cells

Cisplatin-induced AKI

Bovine serum albumin

Nuclear Factor kappa B

Human serum albumin

Chemokine Ligand 2

Kidney injury molecule-1

Outer membrane vesicles

Signal transducer and activator of transcription 3

Fibroblast-derived EVs

B-cell lymphoma-2

Mitogen-activated protein kinase 1

Leucine-rich α-2-glycoprotein 1

Cystathionine-β-synthase

Mesangial cells

Extracellular signal-regulated kinase

Urinary extracellular vesicles

Neutrophil gelatinase-associated lipocalin

Type 2 Diabetes Mellitus

End-Stage Renal Disease

Thin basement membrane nephropathy

Podocyte-derived microparticles

Essential hypertension

Endothelial microparticles

Microalbuminuria

Albumin-to-creatinine ratio

BK virus nephropathy

Lymphocyte function-associated antigen-1

Activating transcription factor 6

Pulsed focused ultrasound

Yes-associated protein

3’-Untranslated region

Casein kinase 1δ

Unilateral ureteral obstruction

Phosphoglycerate mutase family member 5

Semaphorin 3 A

Krüppel-like factor 6

Nuclear factor erythroid 2 related factor 2

Antioxidant response element

Runt-related transcription factor 1

Autophagy-related protein 13

Dual specificity phosphatase 1

Arginine-glycine-aspartic

Donor-specific antibodies

BCL2 interacting protein 3

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (82172102), China Postdoctoral Science Foundation (2023M731376, 2023M741431), the Jiangsu Province’s Major Project in Research and Development (BE2021689), the Natural Science Foundation of Jiangsu Province (BK20220527), Zhenjiang City Science and Technology Innovation Fund (SH2023048).

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Bei Li and Chen Qi contributed equally to this work.

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Jiangsu Key Laboratory of Medical Science and Laboratory Medicine, Department of laboratory Medicine, School of Medicine, Jiangsu University, Zhenjiang, Jiangsu, 212013, China

Bei Li, Linru Shi, Jiahui Zhang, Hui Qian & Cheng Ji

Department of Clinical Laboratory, Suzhou Municipal Hospital of Anhui Province, Anhui, 234000, China

College of Medical Imaging, Dalian Medical University, Dalian, Liaoning, 116000, China

Yifan Zhang

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L.B. and Q.C. wrote the main manuscript text and design picture, Z.YF, S. LR. and Z.JH.prepared Figs. 1 , 2 , 3 , 4 and 5 . J.C and Q.H participated in the overall idea design and text proofreading of the article. All authors reviewed the manuscript.

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Li, B., Qi, C., Zhang, Y. et al. Frontier role of extracellular vesicles in kidney disease. J Nanobiotechnol 22 , 583 (2024). https://doi.org/10.1186/s12951-024-02852-3

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Magnus F.G. Grøndahl , Andreas H. Lange , Malte P. Suppli , Jonatan I. Bagger , Mira Thing , Lise L. Gluud , Dea H. Kofod , Mads Hornum , Gerrit van Hall , Samuel A.J. Trammell , Trisha J. Grevengoed , Bolette Hartmann , Jens J. Holst , Tina Vilsbøll , Mikkel B. Christensen , Asger B. Lund , Filip K. Knop; Glucagon Clearance Is Decreased in Chronic Kidney Disease but Preserved in Liver Cirrhosis. Diabetes 20 September 2024; 73 (10): 1641–1647. https://doi.org/10.2337/db24-0305

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It is not completely clear which organs are responsible for glucagon elimination in humans, and disturbances in the elimination of glucagon could contribute to the hyperglucagonemia observed in chronic liver disease and chronic kidney disease (CKD). Here, we evaluated kinetics and metabolic effects of exogenous glucagon in individuals with stage 4 CKD ( n = 16), individuals with Child-Pugh A–C cirrhosis ( n = 16), and matched control individuals ( n = 16), before, during, and after a 60-min glucagon infusion (4 ng/kg/min). Individuals with CKD exhibited a significantly lower mean metabolic clearance rate of glucagon (14.0 [95% CI 12.2;15.7] mL/kg/min) compared with both individuals with cirrhosis (19.7 [18.1;21.3] mL/kg/min, P < 0.001) and control individuals (20.4 [18.1;22.7] mL/kg/min, P < 0.001). Glucagon half-life was significantly prolonged in the CKD group (7.5 [6.9;8.2] min) compared with individuals with cirrhosis (5.7 [5.2;6.3] min, P = 0.002) and control individuals (5.7 [5.2;6.3] min, P < 0.001). No difference in the effects of exogenous glucagon on plasma glucose, amino acids, or triglycerides was observed between groups. In conclusion, CKD, but not liver cirrhosis, leads to a significant reduction in glucagon clearance, supporting the kidneys as a primary site for human glucagon elimination.

This study was undertaken to gain insights into the elimination of glucagon in humans, as this is an important, yet not fully uncovered, part of glucagon physiology.

We investigated whether individuals with chronic kidney disease or liver cirrhosis were characterized by an altered elimination of glucagon.

We found that individuals with stage 4 chronic kidney disease exhibit a significantly reduced elimination of glucagon, while patients with liver cirrhosis have a preserved glucagon elimination.

Our findings support the kidneys as the prevailing site of glucagon elimination in humans and shed light on glucagon-related pathophysiology in liver and kidney diseases.

Graphical Abstract

Elevated levels of glucagon, hyperglucagonemia, can increase the risk of detrimental hyperglycemia and are reported to play a role in several diseases, including obesity, liver diseases, chronic kidney disease (CKD), and type 2 diabetes ( 1–6 ).

The causes of hyperglucagonemia are not fully elucidated. Most research has focused on hyperglucagonemia as a consequence of disturbances in glucagon secretion, whereas less attention has been paid to potentially altered elimination of glucagon ( 7 , 8 ). Glucagon is thought mainly to be eliminated in the kidneys and the liver; however, the relative role of each of these organs has not been fully determined, and the existence of interspecies differences in glucagon handling and conflicting evidence in humans have left human glucagon metabolism unresolved ( 9–11 ). In humans, hyperglucagonemia is frequently observed in both acute and chronic kidney failure, and fasting glucagon levels are inversely correlated with kidney function, suggesting the kidneys as a likely site of glucagon clearance ( 3 , 4 ). In early- and late-stage liver diseases, hyperglucagonemia has also been demonstrated, but controversies regarding the hepatic handling of glucagon exist ( 12 , 13 ). Hyperglucagonemia in various disease states has also been at least partly attributed to cross-reactivity of glucagon assays with other products of proglucagon processing (e.g., proglucagon [1–61] and glicentin), especially in individuals with CKD where such moieties may accumulate ( 14 ). Recent development of sandwich ELISA-based glucagon analyses with limited cross-reactivity has decreased the risk of unspecific interference ( 14 ).

Here, we conducted a glucagon infusion study in individuals with either CKD or liver cirrhosis and in matched healthy control individuals, measuring glucagon by ELISA, to describe the importance of the liver and the kidneys for the clearance of glucagon.

The study was approved by the Danish Scientific Ethics Committees for the Capital Region of Denmark (ID: H–16043802), registered with ClinicalTrials.gov (ID: NCT05056584), and performed in accordance with the Declaration of Helsinki.

Participants

The study included three groups of 16 participants: individuals with CKD, individuals with liver cirrhosis, and healthy control individuals. Following informed consent, eligibility was evaluated including assessment of kidney and liver function by routine biochemistry ( Table 1 ) and transient elastography (FibroScan). Body composition was estimated by bioelectrical impedance analysis (mBCA 515; SECA). Inclusion criteria for the CKD group were stage 4 CKD with an eGFR between 15 and 30 mL/min/1.73 m 2 , normal liver function, and normal transient elastography. Inclusion criteria for the cirrhosis group were verified liver cirrhosis (clinically and/or histologically) without hepatic encephalopathy, and normal kidney function. Inclusion criteria for the control group were normal kidney function and liver function and normal transient elastography. Exclusion criteria for all participants included any form of diabetes, severe anemia, and treatment with drugs known to affect glucagon physiology. The participants were recruited to obtain an equal distribution of sex, age, body weight, and BMI across the three groups.

Baseline characteristics

.	Cirrhosis group .	CKD group .	Control group .
(female)	16 (6)	16 (6)	16 (6)
Age (years)	64.8 [60.5;69.1]	59.8 [53.5;65.8]	61.2 [56.3;66.1]
Weight (kg)	80.2 [69.8;90.9]	87.6 [82.5;92.8]	82.9 [76.7;89.1]
BMI (kg/m )	25.6 [23.3;27.9]	28.5 [26.9;30.1]	27.1 [25.9;28.4]
Lean body mass (kg)	50.5 [43.8;57.3]	57.1 [51.2;63.1]	55.9 [51.0;60.8]
Skeletal muscle mass (kg)	22.3 [18.6;26]	25.1 [20.2;30]	24.5 [20.3;28.8]
Fat mass (kg)	28.5 [23.2;33.8]	31.3 [27.5;35.3]	27.1 [24.2;30.0]
ALT (units/L)	29.5 [22.2;39.2]**	18.9 [16.7;21.3]**	22.5 [18.9;26.9]
AST (units/L)	48.1 [31.8;64.3]**§§	21.5 [18.7;24.3]**	24.1 [21.1;27.0]§§
eGFR (creatinine, mL/kg/min)	96.0 [88.3;103]***	26.3 [24.3;28.3]***###	86.8 [80.6;92.9]###
eGFR (cystatin C–creatinine, mL/kg/min)	83.0 [75.1;91.7]***	27.9 [26.2;29.7]***###	93.3 [86.2;101]###
Fibrosis 4 score	3.06 [2.17;4.32]***§§§	1.24 [0.94;1.64]***	1.21 [1.00;1.46]§§§
HbA (%)	5.3 [5.1;5.5]	5.4 [5.2;5.6]	5.2 [5.1;5.4]
HbA (mmol/mol)	34.3 [32.1;36.4]	35.9 [33.7;38.2]	33.8 [32.0;35.6]
Plasma glucose (mmol/L)	5.4 [5.2;5.7]	5.2 [5.0;5.4]	5.2 [5.0;5.4]
Plasma insulin (pmol/L)	79.4 [55.6;113]§	52.5 [43.09;63.92]	38.9 [30.6;49.5]§
Plasma C-peptide (pmol/L)	853 [633;1.07 × 10 ]	996 [874;1.12 × 10 ]###	513 [417;609]###
HOMA-IR	3.6 [2.2;4.93]*§	1.9 [1.5;2.2]*	1.5 [1.1;1.9]§
UACR	4 [0;12.3]***	309 [31.2;928]***	2.5 [0;3.05]###
CAP (dB/m)	272 [235;309]	241 [215;268]	233 [204;263]
TE (kPa)	20 [14;30]***§§§	3.6 [3.3;4.6]***	4.1 [3.7;4.7]§§§

.	Cirrhosis group .	CKD group .	Control group .
(female)	16 (6)	16 (6)	16 (6)
Age (years)	64.8 [60.5;69.1]	59.8 [53.5;65.8]	61.2 [56.3;66.1]
Weight (kg)	80.2 [69.8;90.9]	87.6 [82.5;92.8]	82.9 [76.7;89.1]
BMI (kg/m )	25.6 [23.3;27.9]	28.5 [26.9;30.1]	27.1 [25.9;28.4]
Lean body mass (kg)	50.5 [43.8;57.3]	57.1 [51.2;63.1]	55.9 [51.0;60.8]
Skeletal muscle mass (kg)	22.3 [18.6;26]	25.1 [20.2;30]	24.5 [20.3;28.8]
Fat mass (kg)	28.5 [23.2;33.8]	31.3 [27.5;35.3]	27.1 [24.2;30.0]
ALT (units/L)	29.5 [22.2;39.2]**	18.9 [16.7;21.3]**	22.5 [18.9;26.9]
AST (units/L)	48.1 [31.8;64.3]**§§	21.5 [18.7;24.3]**	24.1 [21.1;27.0]§§
eGFR (creatinine, mL/kg/min)	96.0 [88.3;103]***	26.3 [24.3;28.3]***###	86.8 [80.6;92.9]###
eGFR (cystatin C–creatinine, mL/kg/min)	83.0 [75.1;91.7]***	27.9 [26.2;29.7]***###	93.3 [86.2;101]###
Fibrosis 4 score	3.06 [2.17;4.32]***§§§	1.24 [0.94;1.64]***	1.21 [1.00;1.46]§§§
HbA (%)	5.3 [5.1;5.5]	5.4 [5.2;5.6]	5.2 [5.1;5.4]
HbA (mmol/mol)	34.3 [32.1;36.4]	35.9 [33.7;38.2]	33.8 [32.0;35.6]
Plasma glucose (mmol/L)	5.4 [5.2;5.7]	5.2 [5.0;5.4]	5.2 [5.0;5.4]
Plasma insulin (pmol/L)	79.4 [55.6;113]§	52.5 [43.09;63.92]	38.9 [30.6;49.5]§
Plasma C-peptide (pmol/L)	853 [633;1.07 × 10 ]	996 [874;1.12 × 10 ]###	513 [417;609]###
HOMA-IR	3.6 [2.2;4.93]*§	1.9 [1.5;2.2]*	1.5 [1.1;1.9]§
UACR	4 [0;12.3]***	309 [31.2;928]***	2.5 [0;3.05]###
CAP (dB/m)	272 [235;309]	241 [215;268]	233 [204;263]
TE (kPa)	20 [14;30]***§§§	3.6 [3.3;4.6]***	4.1 [3.7;4.7]§§§

Data are mean with 95% CI in brackets unless otherwise specified. Baseline characteristics of participants in the CKD group ( n = 16), the cirrhosis group ( n = 16), and the healthy control group ( n = 16) were obtained in the fasting state. Causes of CKD were hypertension (7 of 16), systemic lupus erythematosus (1 of 16), previous malignancy (2 of 16), amyloidosis (2 of 16), sarcoidosis (1 of 16), hyperparathyroidism (1 of 16), chronic granulomatous disease (1 of 16), and hereditary kidney disease (1 of 16). Causes of cirrhosis were alcohol-induced cirrhosis (13 of 16), metabolic dysfunction–associated steatotic liver disease with increased alcohol intake (2 of 16), and hemochromatosis-induced cirrhosis (1 of 16). The individuals in the cirrhosis group comprised 14 individuals with Child-Pugh grade A (10 of whom had experienced previous episodes of decompensation), 1 subject with Child-Pugh grade B, and 1 subject with Child-Pugh grade C. Significant differences are highlighted with the following symbols: *between the CKD group and the cirrhosis group, #between the CKD group and the control group, and §between the cirrhosis group and the control group. A single symbol denotes P < 0.05; a double symbol denotes P < 0.01; a triple symbol denotes P < 0.001. CAP, controlled attenuation parameter; HOMA-IR, HOMA for insulin resistance; TE, transient elastography; UACR, urine-albumin-creatinine ratio. 1 Log-normalized values. 2 UACR and alcohol intake are displayed as median with interquartile range in brackets.

Study Design

Participants were studied after an overnight fast (10 h) including abstinence from medicine, tobacco, and liquids. Participants were instructed not to consume alcohol or exercise vigorously in the 3 days leading up to the experiment. Intravenous cannulas were inserted into a cubital vein in both arms of the resting participants. At time −120 min, a primed infusion of isotopically stable [6,6-D 2 ]-glucose tracer was initiated and continued for the entirety of the experiment (priming bolus dose of 26.4 µmol/kg, infusion rate of 0.6 µmol/kg/min). At time point 0 min, an infusion of recombinant human glucagon was initiated (4 ng/kg/min) (GlucaGen; Novo Nordisk A/S) and continued for 60 min followed by a 60-min washout period. From the contralateral arm, venous blood samples were frequently drawn throughout the 240 min. A heating pad (∼40°C) was applied to the forearm from which blood was sampled, to promote arterialization of the venous blood. Urine was collected at baseline and end of the study. Vital signs were assessed throughout the study ( Supplementary Fig. 4 ).

Analyses of Blood Samples

Plasma glucagon concentrations were quantified using both a sandwich ELISA (Mercodia AB, Uppsala, Sweden) and a radioimmunoassay (RIA) directed against the C-terminal end of the glucagon molecule (in-house, antibody code 4305) ( 14 ). ELISA-based glucagon results are reported unless otherwise stated. An overview of secondary end point analyses is provided in the Supplementary Material .

Statistical Methodology

The primary end point was the metabolic clearance rate (MCR) of glucagon. Secondary end points included glucagon apparent volume of distribution (V d ), glucagon plasma half-life ( t 1/2 ), glucose rate of appearance (R a ) and disappearance (R d ), and plasma/serum levels of amino acids, insulin, and triglycerides, both fasting and in response to the glucagon infusion. The calculation of MCR, V d , and t 1/2 has been described in detail previously ( 15 ). Change from baseline in response to the glucagon infusion was evaluated as incremental area under the curve. Differences between the three groups were assessed using ANOVA. Individual comparisons between any two groups were made using t tests. Normally distributed data are presented as arithmetic mean with 95% CI in brackets unless otherwise stated. Skewed data were logarithmically transformed prior to analysis and presented as geometric mean with 95% CI in brackets unless otherwise stated. A two-sided P value <0.05 was chosen to indicate significance. The false discovery rate method by Benjamini and Hochberg was used to correct for multiple testing of the secondary end points. Uncorrected P values and full overview of calculations are presented in the Supplementary Material .

Data and Resource Availability

The data sets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. No applicable resources were generated or analyzed during the current study.

Between February 2021 and March 2023, 48 participants were enrolled, and all successfully completed the study. An equal distribution of age, sex, weight, BMI, and body composition was obtained between the three groups ( Table 1 ). Individuals in the CKD group all had stage 4 CKD with a markedly reduced mean eGFR while exhibiting normal markers of liver function, similar to those in the control group. The cirrhosis group displayed significantly elevated liver enzymes and liver stiffness/fibrosis markers and normal markers of kidney function, not differing from the control group. No differences in FibroScan-assessed hepatic fat content, fasting plasma glucose, or HbA 1c were observed between the three groups.

Fasting Glucagon

No differences in fasting glucagon concentrations were observed between groups when assessed by ELISA (cirrhosis group 6.68 [5.19;8.6] pmol/L, CKD group 4.82 [3.48;6.67] pmol/L, and control group 5.96 [3.82;9.32] pmol/L) ( Table 2 and Supplementary Fig. 1 ). However, interestingly, the RIA analysis showed elevated glucagon concentrations in the two disease groups, reaching statistical significance between the CKD group and the control group (15.5 [11.9;19.1] vs. 6.6 [3.3;9.9] pmol/L, P = 0.004).

.	Cirrhosis group .	CKD group .	Control group .
Baseline ELISA (pmol/L)	6.68 [5.19;8.6]	4.82 [3.48;6.67]	5.96 [3.82;9.32]
Baseline RIA (pmol/L)	12.4 [7.9;20.1]	15.5 [11.9;19.1]##	6.6 [3.3;9.9]##
Steady state (pmol/L)	56.7 [51.0;62.5]***	80.6 [71.6;89.7]***###	56.9 [51.1;62.7]###
MCR (mL/kg/min)	19.7 [18.1;21.3]***	14.03 [12.2;15.7]***###	20.4 [18.1;22.7]###
(min)	5.7 [5.2;6.3]**	7.5 [6.9;8.2]**###	5.7 [5.2;6.3]###
V (mL/kg)	210 [168;252]	178 [152;205]	179 [161;197]

.	Cirrhosis group .	CKD group .	Control group .
Baseline ELISA (pmol/L)	6.68 [5.19;8.6]	4.82 [3.48;6.67]	5.96 [3.82;9.32]
Baseline RIA (pmol/L)	12.4 [7.9;20.1]	15.5 [11.9;19.1]##	6.6 [3.3;9.9]##
Steady state (pmol/L)	56.7 [51.0;62.5]***	80.6 [71.6;89.7]***###	56.9 [51.1;62.7]###
MCR (mL/kg/min)	19.7 [18.1;21.3]***	14.03 [12.2;15.7]***###	20.4 [18.1;22.7]###
(min)	5.7 [5.2;6.3]**	7.5 [6.9;8.2]**###	5.7 [5.2;6.3]###
V (mL/kg)	210 [168;252]	178 [152;205]	179 [161;197]

Data are mean with 95% CI in brackets. Glucagon levels in the fasting state and during steady-state conditions in response to an exogenous glucagon infusion (4 ng/kg/min) in participants with CKD and with cirrhosis and healthy matched control participants ( n = 16 in each group). Steady state, MCR, t 1/2 , and V d are all calculated from ELISA glucagon measurements. Significant differences are highlighted with the following symbols: *between the CKD group and the cirrhosis group, #between the CKD group and the control group, and §between the cirrhosis group and the control group. A single symbol denotes P < 0.05; a double symbol denotes P < 0.01; a triple symbol denotes P < 0.001.

1 Log-normalized values.

Glucagon Levels in Response to Infusion of Glucagon

Glucagon levels quickly rose in response to the glucagon infusion ( Fig. 1 and Table 2 ), with the CKD group reaching significantly higher glucagon steady state concentrations (80.6 [71.6;89.7] pmol/L) compared with the concentrations measured in the cirrhosis group (56.7 [51.0;62.5] pmol/L, P = 0.001) and control group (56.9 [51.1;62.7] pmol/L, P = 0.001).

Glucagon levels before, during, and after a 1-h exogenous glucagon infusion (4 ng/kg/min, shaded area) in participants with liver cirrhosis ( n = 16) and CKD ( n = 16) and matched healthy control participants ( n = 16). Data are plotted as mean ± SEM. ***Significant difference ( P = 0.001) in steady-state glucagon levels between the CKD group and the two other groups.

Glucagon Kinetics

The MCR of glucagon was markedly lower in the CKD group (14.0 [12.2;15.7] mL/kg/min) compared with both the cirrhosis group (19.7 [18.1;21.3] mL/kg/min, P < 0.001) and the control group (20.4 [18.1;22.7] mL/kg/min, P < 0.001) ( Table 2 ). There were no differences in glucagon V d between the three groups. Consequently, the t 1/2 of glucagon was prolonged in the CKD group (7.5 [6.9;8.2] min) compared with the t 1/2 in both the cirrhosis group (5.7 [5.2;6.3] min, P = 0.002) and the control group (5.7 [5.2;6.3] min, P < 0.001).

No differences in fasting insulin levels were observed between the CKD group and the two other groups, whereas fasting insulin levels were significantly higher in the cirrhosis group compared with the control group ( Fig. 2A and Table 1 ). Insulin change from baseline was similar in all groups in response to the glucagon infusion ( Fig. 2A and Supplementary Table 1 ).

Plasma insulin ( A ), C-peptide ( B ), glucose ( C ), glucose R a ( D ), glucose R d ( E ), total amino acids ( F ), and triglycerides ( G ) before, during, and after a 1-h exogenous glucagon infusion (4 ng/kg/min, shaded area) in participants with liver cirrhosis ( n = 16) and chronic kidney disease ( n = 16) and healthy matched control participants ( n = 16). Data are plotted as means ± SEM.

All participants had fasting plasma glucose values within the normal range, with no differences between the groups ( Table 1 ). Plasma glucose levels rose in response to the glucagon infusion, with no differences in change from baseline between groups (mean increase 1.2 mmol/L, range 0.4–2.8 mmol/L) ( Fig. 2C ). No differences in change from baseline in glucose R a and R d were observed between the groups ( Fig. 2D and E and Supplementary Table 1 ).

Amino Acids

Total fasting amino acid levels did not differ among groups, but baseline differences in several individual amino acids were observed ( Supplementary Table 2 ). Total amino acids decreased similarly and approximately 10% in all groups in response to glucagon infusion ( Fig. 2F ), with no significant differences between the three groups at the level of individual amino acids ( Supplementary Fig. 3 ).

Triglycerides

No differences in triglyceride levels were observed between the three groups ( Fig. 2G and Supplementary Table 1 ).

Here, we evaluated kinetics and effects of exogenous glucagon in individuals with CKD, individuals with liver cirrhosis, and healthy control individuals to provide insights into human glucagon physiology and the hyperglucagonemia reportedly associated with CKD and liver cirrhosis. We show that individuals with stage 4 CKD have significantly reduced glucagon elimination, clearly demonstrated by an approximately 30% lower MCR and a markedly longer t 1/2 , compared with both individuals with liver cirrhosis and healthy control individuals. In contrast, the individuals with liver cirrhosis exhibited a preserved ability to eliminate glucagon. Together, the kidneys appear to be the most important organ for glucagon clearance in humans.

Our study adds novel insights both by the direct comparison between CKD and liver cirrhosis and by the use of the newer sandwich ELISA. In previous RIA-based studies ( 15 , 16 ), and in a recent ELISA-based study ( 17 ), glucagon MCR of healthy participants was similar to that of our control group. Of note, Laurenti et al. report slightly shorter t 1/2 and smaller V d , which may be due to technical differences between the studies ( 17 ). ELISA-based studies evaluating glucagon kinetics in CKD and liver cirrhosis are lacking, but our results agree with previous RIA-based studies, showing glucagon clearance to be decreased in end-stage renal disease ( 4 , 18 ), and preserved in liver cirrhosis ( 12 , 19 ), while contradicting others who report signs of hepatic glucagon uptake and clearance ( 20–22 ). In our study, the fasting hyperglucagonemia previously observed in CKD and liver cirrhosis could only be reproduced using RIA, and not ELISA, likely because of increased cross-reactivity between the RIA and other proglucagon derivatives (e.g., proglucagon 1–61 [ 23 ]) in the groups with advanced organ disease. Notably, the absence of fasting hyperglucagonemia in the CKD group despite a significantly reduced MCR of glucagon suggests a compensatory reduction in pancreatic glucagon secretion and questions the presence of actual hyperglucagonemia in isolated CKD.

We further investigated whether potentially altered glucagon kinetics and/or compromised organ function could result in altered effects of glucagon, as previously reported ( 24 ). We uncovered robust differences in fasting levels of several amino acids, including tyrosine, tryptophane, leucine, and serine, but observed no significant differences in the glucagon-induced changes in amino acid levels between the groups. Together with the comparable effects on plasma glucose, glucose kinetics, and triglycerides, our results suggest that the participants with liver cirrhosis and CKD have a preserved metabolic response to the glucagon infusion, contrary to our expectations. Notably, insulin levels should be considered when evaluating these effects, and the observed elevated insulin levels in the cirrhosis group—likely due to both increased insulin resistance and decreased insulin clearance—could influence our results ( 25 ).

Our study has limitations. The participants in the cirrhosis group had predominantly Child-Pugh A cirrhosis, and we cannot exclude the possibility that our results would have been different if more individuals with clinical impairment had been included. On the other hand, 10 individuals with Child-Pugh A had previous decompensation episodes, suggesting that some degree of impairment of liver function could exist. Similarly, participants in the CKD group did not have end-stage renal disease. Our relatively small sample size limits the power of our secondary statistical analyses. We did not clamp endogenous insulin and glucagon concentrations and thus cannot disentangle the endogenous effects of these on our results. Finally, we cannot deduce exact single-organ clearance from our study, as we only measured whole-body clearance.

An important strength of our study is the comparison of three groups who, outside their primary disease state, were well matched on BMI, sex, age, hepatic fat content, and body composition, limiting bias by confounding factors. To our knowledge, this is the first direct comparison of these three groups, using a highly sensitive and specific sandwich ELISA for glucagon quantification.

Taken together, we conclude that glucagon clearance is decreased in CKD, while glucagon clearance is preserved in liver cirrhosis, highlighting the kidneys as a prevailing organ for glucagon elimination in humans.

Clinical trial reg. no. NCT05056584 , ClinicalTrials.gov

This article contains supplementary material online at https://doi.org/10.2337/figshare.26317105 .

This article is featured in a podcast available at diabetesjournals.org/diabetes/pages/diabetesbio .

Acknowledgments. The authors thank the participants for their time spent on this project. The authors are also grateful for the assistance of the recruiting clinicians and the laboratory expertise of Brian Jensen, Center for Clinical Metabolic Research, Copenhagen University Hospital Herlev and Gentofte, Hellerup, Denmark.

Funding. The present work was funded by the Novo Nordisk Foundation and the Helen and Ejnar Bjørnow Foundation.

The funding parties have not been involved in the design, data collection, data analysis, data interpretation, writing, or publication of the study.

Duality of Interest. A.B.L. is on the speaker’s bureau of and/or has received research support from Sanofi, Boehringer Ingelheim, Novo Nordisk, and AstraZeneca. B.H. is a cofounder of Bainan Biotech. J.I.B. has received lecture fees from Novo Nordisk. J.J.H. was on the advisory panel of, consultant for, in the speaker’s bureau of, and/or has received research support from AstraZeneca, GlaxoSmithKline, Hamni, Intarcia, Merck Sharp & Dohme, Novartis, Novo Nordisk, Sanofi, and Zealand Pharma. F.K.K. has been on the advisory panel of, consultant for, in the speaker’s bureau of, and/or received research support from 89bio, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Gubra, Novo Nordisk, Merck Sharp & Dohme, Sanofi, Structure Therapeutics, Zealand Pharma, and Zucara, and is cofounder of and minority shareholder in Antag Therapeutics and currently employed by Novo Nordisk. M.F.G.G. has received lecture fees and travel compensation from Novo Nordisk Denmark A/S, is a minority shareholder of Zealand Pharma A/S, and is cofounder of Medvaegt ApS. M.H. has received speaker and consultancy fees from AstraZeneca, Bayer, Boehringer Ingelheim, Novo Nordisk, Vifor, and GSK within the last 3 years. T.V. is on the advisory panel for, consultant for, in the speaker’s bureau of, and/or has received research support from AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, Gilead, GSK, Merck Sharp & Dohme, Novartis, Novo Nordisk, Sanofi, and Sun Pharmaceuticals. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. M.F.G.G., M.B.C., A.B.L., and F.K.K. conceived and designed the study. L.L.G., M.H., and T.V. contributed to the design of the study. M.T. and D.H.K. recruited participants. M.F.G.G. and A.H.L. collected the clinical data. G.v.H., S.A.J.T., T.J.G., B.H., and J.J.H. provided analyses, data acquisition, and data interpretation. M.F.G.G. performed the statistical analyses. M.P.S. and J.I.B. researched data. M.F.G.G. drafted the manuscript. All authors reviewed and approved the manuscript. M.F.G.G. and F.K.K. are the guarantors of this work and, as such, had full access to all data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

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Chronic Kidney Disease and Its Complications

Robert thomas.

1 Department of Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH

2 MetroHealth System Campus; the CWRU Center for the Study of Kidney Disease and Biology, School of Medicine, Case Western Reserve University, Cleveland, OH

Abbas Kanso

John r. sedor.

3 Department of Physiology and Biophysics, School of Medicine, Case Western Reserve University, Cleveland, OH

Chronic kidney disease (CKD) is a complex disease impacting more than twenty million individuals in the United States. Progression of CKD is associated with a number of serious complications, including increased incidence of cardiovascular disease, hyperlipidemia, anemia and metabolic bone disease. CKD patients should be assessed for the presence of these complications and receive optimal treatment to reduce their morbidity and mortality. A multidisciplinary approach is required to accomplish this goal.

Introduction

Chronic kidney disease (CKD) is recognized as a major health problem affecting approximately 13% of the United States population 1 . Numbers of prevalent CKD patients will continue to rise, reflecting the growing elderly population and increasing numbers of patients with diabetes and hypertension. As numbers of CKD patients increase, primary care practitioners will be confronted with management of the complex medical problems unique to patients with chronic renal impairment. As well documented in the literature, the nephrologist rarely manages the medical needs of CKD patients until renal replacement therapy is required. In this chapter we will define CKD staging and discuss five complications associated with CKD: anemia, hyperlipidemia, nutrition, osteodystrophy, and cardiovascular risk.

CKD Classification/Staging

CKD is defined as the presence of kidney damage, manifested by abnormal albumin excretion or decreased kidney function, quantified by measured or estimated glomerular filtration rate (GFR), that persists for more than three months 2 ; 3 . Although creatinine clearances can be calculated from urine creatinine concentration measured in a 24 hour urine collection and a concomitant serum creatinine concentration, a more practical approach in the office is to estimate GFR (estimated GFR or eGFR) from the serum creatinine concentration, using either the Cockcroft-Gault or the Modification of Diet in Renal Disease (MDRD) Study estimating equations. Web based tools are available for both estimating equations (MDRD eGFR: http://www.nkdep.nih.gov/professionals/gfr_calculators/index.htm ; Cockcroft-Gault eGFR: http://www.mdcalc.com/cockcroftgault ). Both complications and likelihood of progression to end-stage renal disease requiring renal replacement therapy are more likely to occur in patients with severe CKD. In addition, early intervention will more commonly reduce serious CKD sequelae and slow CKD progression. To facilitate assessment of CKD severity and, the National Kidney Foundation developed criteria, as part of its Kidney Disease Outcomes Quality Initiative (NKF KDOQI™), stratify CKD patients 4 :

Stage 1: normal eGFR ≥ 90 mL/min per 1.73 m 2 and persistent albuminuria
Stage 2: eGFR between 60 to 89 mL/min per 1.73 m 2
Stage 3: eGFR between 30 to 59 mL/min per 1.73 m 2
Stage 4: eGFR between 15 to 29 mL/min per 1.73 m 2
Stage 5: eGFR of < 15 mL/min per 1.73 m 2 or end-stage renal disease

The prevalence of these stages of CKD in the US population is as follows: 1.8% for stage 1, 3.2% for stage 2, 7.7% for stage 3 and 0.35 % for stages 4 and 5. Patients with stage 3 or 4 disease progress to end stage renal disease or stage 5 at a rate of 1.5% per year. Stage 1 or 2 CKD patients progress to more advanced stages at approximately 0.5% per year 5 . In addition, the NKF KDOQI provides evidence-based, clinical practice guidelines for all stages of chronic kidney disease to optimize management of related complications. Twelve sets of guidelines have been published and are available on the NKF web site ( http://www.kidney.org/professionals/KDOQI/ ). Each of the complications discussed in this article is addressed by the NKF KDOQI guidelines.

Chronic kidney disease-associated anemia

Anemia is defined as a reduction in one or more of the major red blood cell measurements; hemoglobin concentration, hematocrit, or red blood cell count. The World Health Organization defines anemia as a hemoglobin level less than 13 g/dL in men and post-menopausal women, and less than 12 g/dL in pre-menopausal women 6 . The NKF defines anemia as a hemoglobin of less than 13.5 g/dL in men and less than 12.0 g/dL in women 7 .

A normochromic, normocytic anemia usually accompanies progressive CKD 8 , and the overall prevalence of CKD-associated anemia is approximately 50% 9 . Although anemia may be diagnosed in patients at any stage of CKD, there is a strong correlation between the prevalence of anemia and the severity of CKD. One quarter of stage 1 CKD patients, half of those stratified to CKD stages 2, 3, and 4 and three quarters of CKD patients starting dialysis suffer from anemia 10 . Therefore, primary care providers play an important role in diagnosing and managing anemia in CKD patients.

While anemia in CKD can result from multiple mechanisms (iron, folate, or vitamin B12 deficiency; gastrointestinal bleeding; severe hyperparathyroidism, systemic inflammation, and shortened red blood cell survival), decreased erythropoietin synthesis is the most important and specific etiology causing CKD-associated anemia. Erythropoietin is a glycoprotein secreted by the kidney interstitial fibroblasts 11 and is essential for the growth and differentiation of red blood cells in the bone marrow. In CKD, tubular atrophy generates tubulointerstitial fibrosis, which compromises renal erythropoietin synthetic capacity and results in anemia.

The anemia of CKD increases morbidity and mortality from cardiovascular complications (angina, left ventricular hypertrophy (LVH) and worsening heart failure) 8 , which may lead to further deterioration of renal function and the establishment of a vicious cycle termed the “cardiorenal anemia syndrome”. The presence LVH is associated with decreased survival of patients on dialysis. In fact, end stage renal disease patients with LVH have a 30% lower five-year survival rate than individuals lacking LVH 12 . In addition, anemia is an independent predictor of death in stable coronary artery disease patients with CKD 13 .

The anemia of CKD is treated via recombinant human erythropoietin (epo). This intervention has replaced transfusions as the mainstay of treatment and improved the survival of anemic CKD patients 14 . The target level of Hgb in patients with CKD has changed as more studies have been reported. Normalization of hemoglobin levels is no longer considered the goal of therapy since these target levels have been associated with higher mortality 15 . The CHOIR trial studied the outcomes of anemia treatment in over 1400 CKD patients (MDRD eGFR between 15 to 50 mL/min per 1.73 m 2 ), who had a hemoglobin < 11 g/dL at entry. Enrolled subjects were randomly assigned to epo therapy treatment protocols designed to achieve a target hemoglobin levels of either 13.5 (n=715) or 11.3 g/dL (n= 717). The study was terminated prematurely due to higher mortality rates and adverse events in the group with higher targeted Hgb levels. 16 Consequently, the United States Food and Drug Administration (FDA) issued an alert recommending a target Hgb level between 11 and 12 g/dL in CKD patients, although more data will be needed to determine the optimal Hgb level to maximize quality of life and reduce excess mortality from anemia-related complications. In summary, despite the clear benefit from treatment of anemia on morbidity and morality in CKD patients, a significant proportion of anemic CKD patients do not receive adequate treatment prior to dialysis to achieve current FDA recommended targets 17 , and half of all CKD patients with anemia do not receive treatment with erythropoietin 18 . The precise target level for Hgb has not been definitively determined but following FDA recommendations is prudent.

CKD-associated Mineral and Bone Disorders

The term “CKD-associated mineral and bone disorders” comprises abnormalities in bone and mineral metabolism and/or extra-skeletal calcification secondary to CKD pathophysiology 19 ; 20 . Renal osteodystrophy is the spectrum of histological changes, which occur in bone architecture of patients with CKD. The kidney is the primary site for phosphate excretion and 1-α-hydroxylation of vitamin D. CKD patients develop hyperphosphatemia as a result of inadequate 1, 25 dihydroxy-vitamin D levels that reflect reduced synthesis from parenchymal scarring. In addition, renal phosphate excretion is reduced. Together both processes cause, serum calcium levels to fall resulting in increased secretion of parathyroid hormone (secondary hyperparathyroidism). Parathyroid hormone has a phosphaturic effect. It also increases the calcium levels by increasing bone resorption and promoting 1-α-hydroxylation of 25-hydroxy vitamin D synthesized by the liver (limited effect because of reduced kidney reserve from scarring). Rising phosphorus levels are almost universally observed in stage 3 CKD patients. However, secondary hyperparathyroidism often begins to distort bone architecture earlier before serum phosphorus is noted to be abnormal, indicating that phosphate binder therapy needs to be initiated when eGFRs have declined below 50 mL/min per 1.73 m 2 .

Changes in bone architecture can be caused by either a high bone turnover state or a low bone turnover state. Four types of bone phenotypes (renal osteodystrophy) can be diagnosed in CKD patients: osteitis fibrosa cystica (high bone turnover with secondary hyperparathyroidism), osteomalacia (low bone turnover and inadequate mineralization, primarily related to diminished vitamin D synthesis), adynamic bone disorder (low bone turnover from excessive suppression of the parathyroid glands), and mixed osteodystrophy (with elements of both high and low bone turnover). The predominant type of renal osteodystrophy and CKD-mineral and bone disorder differs between pre-dialysis and end stage renal disease patients. In pre-dialysis patients, high bone turnover bone disease is most prevalent. In contrast, low bone turnover predominates in dialysis patients. Patients with low turnover disease represent the majority of cases of renal osteodystrophy 21 . The cause of this prevalent bone phenotype results from oversuppression of parathyroid hormone and high calcium dialysate concentrations 22 . Acidosis, the suppressive effect of phosphate retention on renal synthesis of 1, 25 dihydroxy-vitamin D synthesis, and absence of the physiologic inhibitory effect of vitamin D on parathormone secretion are also minor factors that contribute to the low turnover bone disease in CKD patients 23

CKD-associated mineral bone disorders significantly increase mortality in CKD patients. In fact, hyperphosphatemia is one of the most important risk factors associated with cardiovascular disease in CKD patients 24 . The exact mechanism underlying this association remains unclear. It is believed to be related to hyperparathyroidism 25 and vascular calcification, which results from high phosphorus levels 26 . Use of calcium based binders and excessive vitamin D therapy 27 may also contribute to the vascular calcification and its attendant cardiovascular mortality. Patients on hemodialysis who have a plasma phosphorus level above the K/DOQI guideline target levels have a 40% higher mortality rate when compared to those having target levels 28 .

The principle goal of the treatment of CKD-associated bone and mineral disorders is phosphorous level reduction 1 . Initial treatment restricts dietary phosphorus intake when phosphate or parathyroid hormone levels begin to rise. According to K/DOQI guidelines http://www.kidney.org/professionals/KDOQI/guidelines_bone/index.htm ), serum phosphorus levels should be maintained between 2.7 and 4.6 mg/dL in patients with stages 3 and 4 CKD, and between 3.5 and 5.5 mg/dL in individuals with stage 5 CKD. Different classes of phosphate binders can be used to accomplish this goal. For chronic therapy, calcium-based formulations for management of CKD-associated hyperphosphatemia are the most widely used clas of phosphate binders and have supplanted aluminum-based phosphate binders since aluminum-associated toxicities have been recognized. However, calcium-based phosphate binders can induce hypercalcemia, which increases the tissue calcium deposition, especially in the presence of hyperphosphatemia. If indicated (e.g., a CKD patient with hypercalemia), short term usage of aluminum-based phosphate binders remains appropriate, although alternative calcium-free, phosphate have been developed, such as the nonabsorbable agent sevelamer. This agent has the advantage of lacking calcium or aluminum.

In addition to phosphate binders, several other classes of drugs have been developed to manage CKD-associated mineral disorder. Given the reduced 1-hydroxylation of vitamin D by the failing kidney, vitamin D and its related compounds may be needed to raise the serum calcium concentration sufficiently to suppress parathyroid hormone secretion. Patients can also be given calcimimetics, agents which increase the calcium sensitivity of the calcium-sensing receptor expressed by the parathyroid gland, down-regulating parathyroid hormone secretion and reducing hyperplasia of the parathyroid gland. The KDOQI guidelines provide specific management recommendations for use these agents and the interested reader is referred to the web link provided above for details.

Cardiovascular Risk

The increased cardiovascular risk associated with end stage renal disease has been well established, and estimated cardiovascular mortality rates are ten to one hundred fold higher among dialysis patients than age- and sex-matched individuals in the general population. 29 The cardiovascular risk associated with renal impairment increases earlier in the course of kidney disease progression than was initially hypothesized. More specifically, there is evidence that even mild to moderate degrees of renal impairment are associated with increased cardiovascular risk. Many traditional cardiovascular risk factors, documented in the general population, contribute to cardiovascular risk in CKD patients. In fact, many Framingham risk factors are more prevalent among individuals with CKD than those with normal renal function. In addition, non-traditional risk factors, specific to CKD patients, also contribute to the burden of cardiovascular disease (discussed below).

Hypertension is a traditional cardiovascular risk factor which contributes to the cardiovascular risk associated with CKD. Szcech and colleagues demonstrated that patients with hypertension are at increased risk for new or recurrent cardiovascular events in individuals with stage 2–3 CKD 30 . Systolic blood pressure is more strongly associated with cardiovascular death in dialysis patients than either pulse or diastolic pressure 31 . However, a U-shaped relationship exist between systolic blood pressure and mortality in which high or low systolic blood pressures appear to be associated with increased mortality rates in stage 5 CKD patients. Low systolic pressures may identify a sicker group of patients rather than being an etiology for excess mortality. KDOQI guidelines recommend target blood pressure less than 130/85 mm Hg for all patients with kidney disease and less than 125/75 mmHg for patients with urinary protein excretion greater than 1g/24h. Detailed treatment recommendations are beyond the scope of this review. Given the renal protective effects of angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers, this class of agents are optimal first-line agents in patients with proteinuric (> 1 gm/24h), progressive diabetic and nondiabetic renal disease.

Diabetes is associated with adverse outcomes in all stages of CKD 32 . Moreover, lower fasting plasma glucose and /or glycated hemoglobin levels are associated with lower risk of all cause mortality and reduced cardiovascular death of borderline significance in patients with moderate to severe renal impairment. The presence of left ventricular hypertrophy (LVH), a complication which increases in relation to progressively lower levels of eGFR, is also a cardiovascular risk determinant in CKD patients. Anemia and hypertension, are two CKD associated complications hypothesized to play a role in the development of LVH 12 . In a prospective cohort of 2,423 patients with stage 3–4 CKD, investigators noted an independent risk of LVH for the composite endpoint of myocardial infarction and fatal coronary heart disease. Patients were followed over a period of 102 months. In adjusted analysis, LVH was associated with increased risk for composite and cardiac outcomes hazard ratio (HR 1.67; 95% CI 1.34 to 2. 07).Tobacco use is also associated with increased mortality and incidence of heart failure among patients with stage 5 CKD 33 .

Several cardiovascular risk factors associated with CKD are unique to patients with this disease (non-traditional risk factors). Anemia, which has been discussed above, is a risk factor for adverse cardiovascular outcomes in CKD patients. Abnormal serum phosphate levels, calcium-phosphate ion product, and parathyroid hormone levels are independent cardiovascular risk factors in the setting of stage 5 CKD 34 . Higher calcium–phosphate products and the cumulative dose of oral calcium-based phosphate binders correlate with the extent and progression of arterial calcification in dialysis 35 and stage 3 or 4 CKD patients Interestingly, serum phosphate levels were associated with increased rates of death and myocardial infarction in patients with stage 3 or 4 CKD 36 , 37 . This suggests that arterial calcification results in clinical morbidity and mortality in this patient population. Poorly controlled metabolic bone disease contributes to vascular calcification, which promotes arteriolosclerosis and increases vascular wall stiffness. Aortic stiffness is an independent predictor of total and cardiovascular mortality, CAD, and fatal stroke in patients with hypertension. One study of 96 patients, aged 18–70 with a creatinine clearances ranging from 15–90 ml/min per 1.73m 2 , found coronary calcification in 64%, and severe calcification present in 23% patients 38 .

Inflammation is a non-traditional risk factor believed to play a role in mediating cardiovascular risk in CKD. Markers of inflammation are often elevated in CKD patients and are predictive of cardiovascular risk in this population. Some, but not all studies, have found serum C-reative protein (CRP) levels predicts cardiovascular outcomes in CKD patients. Menon et al. analyzed samples obtained from Modification of Diet in Renal Disease study patients (all had stage 3, 4 or 5 CKD at enrollment), measuring CRP concentration and analyzing its relationship to long-term outcomes 39 . With a 10 year median follow-up period, all-cause mortality was 20% and cardiovascular mortality was 10%. High CRP was an independent predictor of all cause and cardiovascular mortality after investigators adjusted for confounding variables. The authors concluded that elevated CRP is useful for predicting outcomes in CKD patients.

Proteinuria, a hallmark of renal impairment, is associated with an increased risk for cardiovascular disease and early cardiovascular mortality in patients with and without diabetes and hypertension 40 ; 41 . This association was first demonstrated by the Framingham Heart Study investigators. More recently, Gerstein et al. in a cohort of more than 9000 individuals enrolled in the Heart Outcomes Prevention Evaluation (HOPE) trial noted an increased relative risk in the primary aggregate outcome of myocardial infarction, stroke and cardiovascular death in microalbuminuric (urine albumin excretion 30 mg/ 24hr) subjects with and without diabetes (1.97 and 1.61 respectively). The risk associated with the presence of microalbuminuria increased progressively with increasing absolute levels of microalbuminuria 42 .

CKD patients are more likely to develop congestive heart failure (CHF). Bibbins et al evaluated the association between CKD and new-onset CHF in African and Caucasians Americans 43 . In the study, enrollees were stratified by cystatin C- and serum creatinine-based measurements of renal function. Investigators noted that risk for developing CHF correlated with the degree of renal impairment. A meta analysis (16 studies, which included 80,098 hospitalized and non hospitalized patients with CHF) evaluated the prevalence and mortality risk associated with the presence of CKD in patients with CHF 44 . The eGFR was < 90ml/min in 63% of patients included in the analysis. Approximately 30% of these patients were found to have moderate to severe renal impairment. In 11 of the 16 studies reporting all-cause mortality rates for follow up ≥ 1 year (range 1–11.7 years), 26% of patients without renal impairment, 42% with any renal impairment and 51% with moderate to severe impairment died. A combined unadjusted mortality risk of RR= 1.48, 95% CI 1.45 to 1.52, P < 0.001 was noted in patients with any renal impairment and RR = 1.81, 95% CI 1.76 TO 1.86, P < 0.001 in patients with moderate to severe impairment. The authors concluded that renal impairment confers a clinically significant risk for excess mortality in patients with heart failure and the magnitude of the increased mortality risk is comparable to that associated with traditional prognostic indicators in heart failure such as ejection fraction.

Progression of CKD is associated with a number of serious health complications, including increased incidence of cardiovascular disease ( Figure 1 ). Treating both traditional and non-traditional cardiovascular risk factors in individuals with CKD involves a multidisciplinary approach to care. Involvement of nurses, dieticians, educators and surgeons increases optimization of care. Controlling blood pressure control using KDOQI guidelines (BP goal <130/85, <125/75 with proteinuria, <130/85 in the setting of diabetes), use of ACE inhibitor and/or angiotensin receptor blockers to reduce proteinuria, titrating insulin and statin therapy to achieve appropriate glycated hemoglobin and serum cholesterol levels (<100 mg/dL), respectively will reduce cardiovascular risk and prevent or slow the progression of kidney failure. Additional randomized trials are needed to establish treatment goals for cardioprotective therapies in this population of patients.

An external file that holds a picture, illustration, etc.
Object name is nihms54692f1.jpg

Interplay of processes secondary to chronic kidney disease leading to cardiovascular disease and death. Red arrows: Pathogenetic pathways; black arrow: Feedback loop; kidney disease worsened by heart failure

Dyslipidemia

Dyslipidemia is a major risk factor for cardiovascular morbidity and mortality and is common among patients with CKD. Lipid profiles vary widely in these patients, reflecting the level of kidney function and the degree of proteinuria 45 . In general, the prevalence of hyperlipidemia increases as renal function declines, with the degree of hypertriglyceridemia and elevation of LDL cholesterol being proportional to the severity of renal impairment.

Several factors contribute to the development dyslipidemia associated with chronic renal impairment. Patients with CKD have a reduction in the activity of lipoprotein lipase and hepatic triglyceride lipase. This interferes with uptake of triglyceride-rich, apolipoproteinB-containing lipoproteins by the liver and in peripheral tissue, yielding increased circulation of these atherogenic lipoproteins. Hypercholesterolemia in nephrotic syndrome is thought to be due to increased production and decreased catabolism of lipoproteins. The degree of lipoprotein abnormality is roughly proportional to the amount of proteinuria and inversely proportional to serum albumin levels. However, infusions of albumin or dextran both normalize lipoprotein concentrations, suggesting that oncotic pressure changes rather than hypoalbuminemia signals increased lipoprotein synthesis by the liver. Additional data supporting this hypothesis is derived from in-vitro experiments demonstrating direct stimulation of increased hepatic apolipoprotein-B gene transcription in cells exposed to reduced oncotic pressure 46 . Studies also suggest that hyperparathyroidism and the accumulation of calcium in pancreatic islet cells likely contribute to dyslipidemia of CKD as well 47 .

Clinical trials in the general population have demonstrated that coronary heart disease mortality decreases proportional to LDL-cholesterol level reduction. Evidence for benefit of statins in reducing cardiovascular risk (i.e., composite outcomes) in CKD patients is less definitive. Recently, the largest clinical trial of statins in patients with stage 5 CKD (4D trial) was conducted in Germany, In this study, atorvastatin did not to reduce death from fatal stroke, nonfatal myocardial infarction, or nonfatal stroke in 200 patients with diabetes and stage 5 CKD 48 . The results of the Study of Heart and Renal Protection (SHARP) will be available in 2008 and should provide further insight into the role of cholesterol lowering therapy in reducing cardiovascular events in kidney disease patients. SHARP is a prospective, randomized trial in which 9,000 patients with CKD and 3,000 dialysis patients without coronary artery disease have been enrolled to assess the effects of lowering LDL-cholesterol with the combination of simvastatin and ezetimibe, with the primary outcome measure being the time to a first “major vascular event” defined as non-fatal myocardial infarction or cardiac death, non-fatal or fatal stroke, or an arterial revascularization procedure.

A relationship between total cholesterol levels and coronary heart disease (CHD) mortality as the primary outcome also has not been clearly established. In fact, several observational studies of stage 5 kidney disease patients suggest that lower total cholesterol levels are associated with higher mortality rate. For example, in a recent 10 -year prospective study the importance of total cholesterol levels on mortality was evaluated in 1,167 stage 5 kidney disease patients 49 . Hypercholesterolemia (total cholesterol levels >200) was associated with increased all cause mortality rate. Further studies are needed to evaluate whether low cholesterol identifies a subgroup of more severely ill patients or whether inflammation and /or malnutrition were confounding variables in these studies.

A complete fasting lipid profile with assessment of total, LDL and HDL cholesterol and triglyceride levels should be included in the evaluation of patients with CKD and hyperlipidemia. Individuals with elevated cholesterol or other forms of hyperlipidemia should undergo evaluation for secondary dyslipidemias before initiation of lipid lowering therapy 50 . KDOQI guidelines recommend that all stages of CKD be considered a CHD-risk equivalent. Thus, patients with CKD are viewed as being in the highest risk group for CHD and LDL-cholesterol levels should be lowered below 100 mg/dl (2. 6 mmol /L). CKD patients may achieve LDL goals via implementation of lifestyle modification (dietary modification with dietitian consultation, increased physical activity, moderate alcohol intake, and smoking cessation). All adults with CKD should be evaluated for lipid abnormalities. In CKD patients with nephrotic syndrome the primary goal is induce remission of the disease 51 . When this is not possible any reduction in urinary protein excretion will be beneficial. In addition, nephrotic patients with elevated lipid levels should be treated with a lipid lowering diet, which may aid in reducing total cholesterol and LDL cholesterol levels.

Specific K/DOQI guidelines on the management of hyperlipidemia include:

For patients with LDL cholesterol levels between 100 and 129 mg/dL (2.57 to 3.34mmol /L) lifestyle changes may be the initial therapy. If target LDL levels are not achieved (LDL < 100 mg/dL [2.57 mmol/L]), low-dose statin therapy can be instituted.
For patients with LDL ≥ 130 mg/dL (3.36 mmol/L), lifestyle changes alone are likely to be ineffective. Statins can used as initial therapy and the dose titrated to achieve target LDL < 100 mg/dL (2.57 mmol/L).
For patients with TG ≥ 200 mg/dL (3.36 mmol/L), the goal is to achieve non-HDL cholesterol ≤ 130 mg/dL. Initial treatment comprises lifestyle changes plus a low dose statin which is increased as needed to achieve target levels.

In summary, patients with CKD have a higher burden of dyslipidemia in comparison to the general population and are at increased risk for cardiovascular morbidity and mortality. This disproportionate cardiovascular disease burden places CKD patients in the highest risk category, as defined by the Adult Treatment Panel III (ATPIII) treatment guidelines. Identification of these patients and intervention via lifestyle and /or pharmacologic therapy is a sound, initial clinical approach. Ongoing randomized trials will provide more definitive data on the risk and benefits of lipid lowering therapy in this population of patients.

Nutritional Issues

As patients progress through the stages of CKD, nutritional requirements are altered and metabolism of protein, water, salt, potassium, and phosphorous are affected 52 . These changes lead to ineffective energy generation despite adequate intake of protein and carbohydrate substrates. In more extreme manifestations, these alterations in nutrient utilization cause “uremic malnutrition,” a syndrome that is distinct from malnutrition caused by inadequate nutrient intake. Both inadequate nutrient intake and ineffective nutrient utilization can contribute to nutritional disorders in CKD patients and we will not distinguish between these etiologies in our discussion. The association between uremic malnutrition and outcomes in the early stages of CKD has not been investigated. However, there is adequate evidence to suggest that a poor predialysis, nutritional status increases patient morbidity and mortality after initiation of renal replacement therapy 53 . Maintenance of neutral nitrogen balance is important for preservation of nutritional health in patients with chronic renal impairment. Treatment goals in this setting should be to establish and maintain optimal nutritional status, minimize uremic symptoms and signs as renal impairment declines, and to establish a nutritional plan that is acceptable to the patient. To accomplish these goals, involvement of a dietician in the care of these patients is often necessary.

The ability of the generalist to assess nutritional status in the setting of CKD is important in addressing the nutritional needs of individuals with CKD. Several nutritional markers can be used to assess nutritional status. Serum albumin is the most extensively studied nutritional marker in all patient populations due to its easy availability and strong association with hospitalization and risk of death 54 . Low levels of serum albumin are highly predictive of poor clinical outcomes in all stages of CKD, and therefore, serum albumin is considered a reliable marker of general clinical status 55 . Clinical guidelines recommend maintenance of a value of 4.0 g/dL or greater for serum albumin in stage 5 CKD patients 56 . Non-nutritional causes of hypoalbuminemia, such as tissue injury, hepatic disease, gastrointestinal disorders, and volume overload, can affect the specificity of this marker 57 . Moreover, given the fact that serum albumin is a negative acute-phase reactant, its levels decrease in response to inflammatory stimuli such as burns, infection, or trauma 58 . Serum prealbumin is a sensitive marker for assessing subtle changes in visceral protein stores given its low body pool and fairly rapid turnover of 2–3 days. Levels less than 30 mg/dL suggest protein depletion. 59 Low serum creatinine concentrations are associated with poor clinical outcome in maintenance of stage 5 CKD. Patients with serum creatinine concentration less than 10 mg/dL should be evaluated for muscle wasting due to poor nutrition. Serum cholesterol concentration is an independent predictor of mortality in chronic dialysis patients, and low levels can suggest low dietary and energy intake. Serum cholesterol concentrations less than 150 mg/dL also warrant careful evaluation of nutritional status. Utilization of Subjective Global Assessment (SGA) as a nutritional assessment tool for various stages of CKD is growing in both clinical and research settings 60 . Studies have demonstrated that SGA can adequately assess nutritional status in the setting of peritoneal and hemodialysis. 61

Prevention and treatment are as important as identifying inadequate nutritional status in CKD patients. Therapy varies with the severity of CKD and no single treatment approach will alleviate the adverse consequences associated with uremic malnutrition. 62 In cases in which low protein and energy intake (as noted in patients on unrestricted diets), a dietary protein intake of less than .75 g/kg/d is an early warning sign for the development of uremic malnutrition. For many CKD patients, poor nutrition may warrant initiation of hemodialysis or be an indication for transplant. Several studies have suggested better outcomes with early initiation of hemodialysis in this setting. Additional signs that suggest need for early hemodialysis initiation include: energy intake less than 20 kcal/kg/d, serum albumin concentration of less than 4.0 g/dL, and decrements in other nutritional indices such as transferrin, prealbumin, insulin growth factor-1, and lean body mass. Alternative interventions may be necessary in cases when dietary counseling alone fail to optimize dietary intake. Enteral delivery of nutrition may be necessary, including oral protein, amino acid and/or energy supplements; feeding through nasogastric tubes or percutaneous endoscopic gastroscopy or jejunostomy tubes or institution of intradialytic parental nutrition. Evidence supporting these approaches is limited however. Only a few studies evaluating the efficacy of oral nutrition supplementation in stage 5 CKD patients have been published. For example, Eustace et al , found that oral amino acid supplementation improved serum albumin concentration in CKD 5 patients 63 . Caglar and colleagues noted that intradialytic oral nutritional supplementation improved several nutritional parameters in a subgroup of malnourished stage 5 CKD patients 64 . However, the role of supplemental enteral nutrition in patients with advanced CKD or in dialysis patients remains controversial, and a primary care provided should consider expert consultation before initiating any of these therapies

In conclusion, uremic malnutrition is very prevalent in CKD patients, and several studies have established a correlation between malnutrition and poor clinical outcome. Management of nutrition in CKD and dialysis patients can be difficult and involvement of dieticians with experience in the treatment of kidney disease patients is recommended.

Patients with CKD present several complex management issues to health care providers. The staging system introduced in 2002 by the National Kidney Foundation is a significant accomplishment, which stratifies patients according to disease severity. In addition, the K/DOQI guidelines are an excellent tool for management of CKD and dialysis patients and recommend treatments according to disease stage. These interventions may reduce morbidity and mortality in these patients. With early identification and treatment of anemia, renal osteodystrophy, uremic malnutrition, hyperlipidemia and cardiovascular disease, primary care physicians and nephrologists together are making significant strides toward extending and improving the lives of patients with chronic renal disease. Table 1 briefly summarizes current treatment and preventive measures.

Mainstay of Treatment in Chronic Kidney Disease Complications

Complication	Treatment
Osteodystrophy	Vitamin D Supplements	Calcium Supplements	Intestinal Phosphate Binder
Anemia	Recombinant Erythropoietin		Transfusion in Urgent Cases
Cardiovascular	Statins	Blood Pressure Control via ACE Inhibitor and/or Angiotensin Receptor Blockers	Specific CAD Interventions
Dyslipidemia	Statins		Fibrates

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Published: 18 September 2024

Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis

Samar Elbedwehy 1 ,
Esraa Hassan 2 ,
Abeer Saber 3 &
Rady Elmonier 4

Scientific Reports volume 14 , Article number: 21740 ( 2024 ) Cite this article

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Kidney diseases
Radiography

Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Utilizing a comprehensive dataset of 12,446 CT whole abdomen and urogram images, this study developed an advanced AI-driven diagnostic system specifically tailored for kidney disease classification. The innovative approach of this study combines the strengths of traditional convolutional neural network architecture (AlexNet) with modern advancements in ConvNeXt architectures. By integrating AlexNet’s robust feature extraction capabilities with ConvNeXt’s advanced attention mechanisms, the paper achieved an exceptional classification accuracy of 99.85%. A key advancement in this study’s methodology lies in the strategic amalgamation of features from both networks. This paper concatenated hierarchical spatial information and incorporated self-attention mechanisms to enhance classification performance. Furthermore, the study introduced a custom optimization technique inspired by the Adam optimizer, which dynamically adjusts the step size based on gradient norms. This tailored optimizer facilitated faster convergence and more effective weight updates, imporving model performance. The model of this study demonstrated outstanding performance across various metrics, with an average precision of 99.89%, recall of 99.95%, and specificity of 99.83%. These results highlight the efficacy of the hybrid architecture and optimization strategy in accurately diagnosing kidney diseases. Additionally, the methodology of this paper emphasizes interpretability and explainability, which are crucial for the clinical deployment of deep learning models.

Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography

Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet++

Diagnostic ability of deep learning in detection of pancreatic tumour

Introduction.

Kidney diseases have emerged as a significant global health concern, with chronic kidney disease affecting over 10% of the world's population. This condition, predicted to rise to the fifth leading cause of death by 2040, underscores the pressing need for effective control measures. Among the prevalent kidney ailments impeding normal renal function, kidney cysts, nephrolithiasis (kidney stones), and renal cell carcinoma (kidney tumor) pose substantial threats 1 . Kidney cysts, fluid-filled pockets on the kidney's surface, and nephrolithiasis, involving crystal concretion formation, impact approximately 12% of the global population. Renal cell carcinoma is identified as one of the top ten most common cancers worldwide. There are different types of data that researchers handled it; Text and images. Text datasets Also often contain valuable information derived from medical records, pathology reports, and patient histories, which can be leveraged to train machine learning models 2 , 3 , 4 , 5 .

Also, diagnostic tools such as X-ray, computed tomography (CT), B-ultrasound, and magnetic resonance imaging (MRI) play crucial roles in conjunction with pathology tests for accurate kidney disease diagnosis. CT scans, particularly valuable for their three-dimensional insights and detailed slice-by-slice imaging, offer a comprehensive understanding of kidney anatomy 6 .

Recognizing the urgency of addressing these challenges, the advancement of deep learning in vision tasks presents a compelling opportunity. Building artificial intelligence (AI) models capable of efficiently detecting kidney radiological findings has become imperative to assist medical professionals and alleviate the suffering of individuals affected by kidney diseases. While some studies have explored this domain, the scarcity of publicly available datasets remains a hindrance. Furthermore, past research has often relied on traditional machine learning algorithms, focusing on the classification of single disease classes, such as cysts, tumors, or stones, and occasionally utilizing ultrasound images. In light of these considerations, there is a growing need to expand the scope of AI applications, leveraging deep learning advancements for a more comprehensive approach to kidney disease detection.

Feature concatenation

Feature concatenation plays a crucial role in enhancing the effectiveness of deep learning models, especially in tasks such as image classification. By combining different types of features extracted from diverse sources, feature concatenation enables the creation of a more comprehensive and informative representation of the input data. This process allows the model to leverage complementary information embedded in various aspects of the data, such as color, texture, or spatial features. Unlike traditional single-feature approaches, feature concatenation enables the model to capture a richer set of characteristics, potentially improving its ability to generalize and make accurate predictions. Moreover, this technique facilitates the integration of information from different modalities or feature extraction methods, leading to a more robust and nuanced representation. In essence, feature concatenation serves as a powerful tool for refining the input representation, contributing to the model's overall performance and its capacity to handle complex patterns and relationships within the data.

The main contributions of this study are as follows

Novel classification method: the paper proposes a new approach for classifying kidney diseases that demonstrates robust performance across various datasets, emphasizing the importance of interpretability and explainability for clinical applications.

Advanced integration of neural networks: this study integrates features from AlexNet and ConvNexT to create a comprehensive and informative feature representation. This fusion leverages the strengths of both architectures, resulting in superior performance compared to individual models.

Enhanced model performance: By combining AlexNet and ViT, the paper achieved improved discriminative ability, capturing a broader range of visual features and surpassing the performance of the individual models.

Optimized training process: this study introduced a custom optimization technique based on Adam that dynamically adjusts the step size according to the gradient norm, leading to more efficient convergence in training the merged AlexNet and ConvNexT models.

The rest of the paper is organized as follows; in the next section; literature reviews. In Sect. " Motivation ", the motivation. In Sect. " Proposed methodology ", the proposed methodology is used in this paper, followed by Sect. “ Experiments and results ”. Finally, in Sect. " Conclusions "; the paper is concluded with future work.

Literature reviews

The classification of kidney diseases is a pivotal area of research that holds significant implications for clinical diagnosis, treatment planning, and patient management. As the understanding of renal disorders continues to evolve, there has been a growing body of literature dedicated to exploring various methodologies and techniques for accurate and efficient kidney disease classification. This literature review seeks to provide a comprehensive overview of the existing research landscape, delving into the diverse approaches employed in the classification of kidney conditions. From traditional methods to the latest advancements in machine learning and deep learning, this review aims to distill key insights and trends, shedding light on the progress made in enhancing diagnostic accuracy and paving the way for more effective therapeutic interventions. Through a systematic exploration of relevant studies, this literature review endeavors to offer a synthesis of knowledge that not only underscores the current state of kidney disease classification but also identifies potential avenues for future research and technological innovation in this critical domain. Parakh et al. 7 proposed the initial convolutional neural network (CNN) was responsible for delineating the urinary tract's extent, while the second CNN focused on identifying the presence of stones. The authors created nine model variations by combining different training data sources (S1, S2, or both, denoted as SB) with pre-trained CNNs using ImageNet and GrayNet, as well as without pretraining (Random). The accuracy of GrayNet-SB, at 95%, surpassed that of ImageNet-SB (91%) and Random-SB (88%).

The research of Kuo et al. 8 aims to enhance the prediction of kidney function and chronic kidney disease (CKD) through kidney ultrasound imaging, develop a model integrating the ResNet architecture, pre-trained on the ImageNet dataset, to estimate the glomerular filtration rate (eGFR) and CKD status from 4505 labeled kidney ultrasound images. The model demonstrated a strong correlation (Pearson coefficient of 0.741) between AI-based and creatinine-based GFR estimations and achieved 85.6% accuracy in classifying CKD status, outperforming experienced nephrologists (60.3%–80.1%).

Sudharson et al. 9 utilized an ensemble technique, amalgamating diverse pre-trained Deep Neural Networks (DNNs) such as ResNet-101, ShuffleNet, and MobileNet-v2. The ultimate predictions were determined through the majority voting technique, resulting in a peak classification accuracy of 96.54% during testing with high-quality images and 95.58% during testing with noisy images.

Aksakallı et al. 10 proposed the examination encompassed diverse machine learning approaches, including Decision Trees (DT), Random Forest (RF), Support Vector Machines (SVC), Multilayer Perceptron (MLP), K-Nearest Neighbor (kNN), Naive Bayes (BernoulliNB), and deep neural networks employing Convolutional Neural Network (CNN). The experimental outcomes revealed that the Decision Tree Classifier (DT) yielded the most favorable classification results. Specifically, this method attained the highest F1 score, achieving a success rate of 85.3% when employing the S + U sampling method.

Liu et al. 11 focuse on making deep learning techniques more accessible for clinical users in the field of microscopic image classification by developing AIMIC, out-of-the-box software that requires no programming knowledge. AIMIC integrates advanced deep learning methods and data preprocessing techniques, allowing users to train new networks and infer unseen samples seamlessly. The platform was evaluated on four benchmark microscopy image datasets, demonstrating its effectiveness in selecting suitable algorithms for entry-level practitioners. Notably, the ResNeXt-50–32 × 4d model achieved the highest performance with an average accuracy of 96.83% and an average F1-score of 96.82%, making it the preferred choice for microscopic image classification. Additionally, MobileNet-V2 provided a good balance between accuracy (95.72%) and computational cost, with an inference time of 0.109 s per sample, making it a viable option for scenarios with limited computing resources.

Srivastava et al. 12 used machine learning models (SVM, KNN, Random Forest, Decision Tree, AdaBoost) with the normalized dataset with an accuracy of 98.75%. Baygin et al. 13 proposed a novel transfer learning-based image classification method called ExDark19. This method utilized iterative neighborhood component analysis (INCA) to select the most informative feature vectors, which were then input into a k nearest neighbor (kNN) classifier for kidney stone detection. Their results achieved an accuracy of 99.22% with a ten-fold cross-validation strategy and 99.71% using the hold-out validation method.

Nazmul Islam et al. 14 employed a total of six machine learning models, with three being founded on advanced variants of Vision Transformers, namely EANet, CCT, and Swin Transformers. The remaining three models were based on deep learning architectures, ResNet, VGG16, and Inception v3, with adjustments made to their final layers. Despite commendable performances from the VGG16 and CCT models, the Swin Transformer emerged as the top performer in terms of accuracy, achieving an impressive accuracy rate of 99.30 percent. In this investigation, diverse physiological parameters were considered alongside the application of various machine learning (ML) techniques. Different ML models, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest, Decision Tree, and AdaBoost, were trained using a normalized dataset, resulting in an impressive accuracy of 98.75%, perfect sensitivity (100%), high specificity of 96.55%, and a notable f1 score of 99.03%.

Subedi et al. 15 explore the potential of a novel model called Vision Transformer (ViT), which was initially designed for natural language processing (NLP) tasks but shows promise for medical image classification. ViT’s capabilities are further enhanced by coupling it with Fully Connected Networks (FCN). This combination merges the feature extraction capabilities of ViT with the classification ability of FCN, ultimately overcoming the challenge of detecting kidney-related issues with greater accuracy and reliability with an accuracy of 99.64%.

Asif et al. 16 introduced "StoneNet” which is based on MobileNet using depthwise separable convolution, offering a low-cost solution compared to existing models with drawbacks such as high computational costs and lengthy training times. Their model achieved accuracy at 97.98%, with short training and testing times of 996.88 s and 14.62 s, respectively. Qadir et al. 17 focused on the Densenet-201 model for feature extraction with Random Forest being the chosen method. They achieved an accuracy rate of 99.719%. Table 1 presents the related work for the kidney classification.

Sasikaladevi et al. 18 address the critical need for early and automatic detection of chronic kidney disease (CKD) from radiology images using deep learning techniques. The dataset used contains 12,446 unique CT scan images. Deep features were extracted from these images, and hyperedges were generated to construct hypergraphs representing the renal images. These hypergraphs were then used in a hypergraph convolutional neural network for representational learning. The model was validated using a hold-out dataset, and deep learning metrics including precision, recall, accuracy, and F1 score were used to evaluate its performance. The proposed model demonstrated a superior validation accuracy of 99.71%, outperforming other state-of-the-art algorithms. This robust digital-twin model facilitates early diagnosis of kidney diseases and aids nephrologists in better prognosis of kidney-related abnormalities.

The urgent need to improve patient care and medical diagnostics in the field of renal health is the driving force behind the kidney classification paper. Kidney illnesses are a major global health concern, encompassing both acute and chronic ailments. Timely and accurate categorization of these ailments is essential for efficient treatment strategy development and patient supervision.

Several factors contribute to the motivation for kidney classification research:

Clinical Importance: Diagnosing kidney disorders accurately can be challenging due to their wide range of etiologies and symptoms. Enhancing classification techniques helps medical professionals better comprehend various kidney disorders and customize treatment plans based on individual disease profiles.

Early Identification and Intervention: It's critical to identify kidney disorders early to launch prompt interventions that can halt the disease's progression and enhance patient outcomes. Classification models can help detect kidney function issues early on, which can result in more proactive and focused medical interventions.

Application of Advanced Technologies: The development of complex models for the classification of renal disease is made possible by advances in machine learning, deep learning, and image processing techniques. Making use of these technologies has the potential to completely transform how accurate and effective diagnostic procedures are.

Proposed methodology

The paper discusses the impact of the concatenating features for enhancing the accuracy of kidney disease classification using the merging of Alex-Net 19 with other models such as (ViT 20 , Swin 21 , and ConvNexT 22 ) and also the impact with using the modified Adam optimizer “Custom-Adam” instead of the popular optimizer “Adam”.

The paper compared its performance with more recent architectures such as VGG and ResNet. The results show that the pre-trained VGG and ResNet models achieved accuracies of 91.73% and 94.63%, respectively. In contrast, more advanced models such as Vision Transformer (ViT), Swin Transformer, and ConvNexT achieved higher accuracies of 98.71%, 96.44%, and 96.44%, respectively. These findings highlight the superior performance of these newer architectures over Alex-Net. While Alex-Net has a well-established reputation in image classification tasks as its architecture is known for efficient feature extraction, which is crucial for accurately classifying kidney diseases from medical images.

Transformer models which include ViT and Swin have demonstrated remarkable performance in various computer vision tasks, particularly in capturing long-range dependencies and spatial relationships within images. For example, the main purpose for using the ViT model is self-attention mechanism allows it to capture global contextual information in images, enabling it to identify complex patterns and long-range relationships. But Swin optimizes the attention computation in Vision Transformers by limiting self-attention to non-overlapping local windows. This shifted window approach reduces the normally quadratic complexity of ViT to linear complexity concerning image size, making Swin more computationally efficient. Also, Swin is a hierarchical vision transformer that progressively merges adjacent patches as the network deepens. This hierarchical structure enables the model to manage features at various scales, enhancing the learning of robust and discriminative features compared to convolutional neural networks. But with ConvNexT model, incorporates modern techniques like hierarchical design and larger kernel sizes, enhancing its ability to handle diverse image features while maintaining the simplicity of traditional CNNs. The paper included these models to explore their potential to extract relevant features from medical images, which could contribute to improving diagnostic accuracy.

On the other side, changing the optimizer can significantly impact model accuracy, convergence speed, generalization ability, and overall stability. Therefore, choosing the right optimizer is crucial for optimizing machine learning models. The paper compared the effect of Adam 23 and Custom_Adam optimizer on the dataset to find the Custom_Adam is better in most cases while the primary difference between the standard Adam optimizer and the Custom_Adam lies in the additional calculation and utilization of the gradient norm in the custom version. Specifically, Custom_Adam computes the norm of the gradient (denoted as norm_value) for each parameter $\theta$ with a non-None gradient:

This norm is then used in the custom update rule. The _update_rule method in Custom_Adams incorporates this norm_value along with the parameter $\theta$ , gradient ${g}_{t}$ , and state during the update process, which can be expressed as:

The parameter update in the standard Adam is as:

Additionally, Custom_Adam overrides the step method to include the gradient norm calculation and the call to the _update_rule, whereas the standard Adam optimizer utilizes its default step method without these extra computations. This enhancement allows Custom_Adam to adapt the learning rate based on the gradient's scale, potentially improving optimization performance. See the algorithm as the following .

Algorithm: Custom_Adam

To accomplish this, the two actions listed can be taken: first; compare using four single vision models (ViT, Alex-Net, Swin, and ConvNexT) for extracting the features from images by using the optimizer Adam and the Custom_Adam. The second is to improve the extracting feature process using the concatenating features from the four vision models with the best optimizer that got from the first action; the vision models are (“Swin + ConvNexT”, “Alex-Net + ViT”, “Alex-Net + Swin” and “Alex-Net + ConvNexT” ). The paper finds as in Fig. 3 that concatenating the models Alex-Net with ConvNexT with Custom_Adam optimizer is the best value in accuracy 99.85% with metrics used for the evaluation such as average precision, recall, and specificity, reaching 99.89%, 99.95%, and 99.83% respectively.

The methodology of this study for kidney classification involves several steps as in Fig. 1

The methodology of this study for Kidney classification.

Image loading from the directory then applied using T.Compose to augment the training data, these transformations include random horizontal and vertical flips, random color jitter, resizing to 256 * 256 pixels, center cropping to 224 * 224 pixels, conversion to a PyTorch tensor, normalization using ImageNet mean and standard deviation, and random erasing with a probability of 0.1.

Load pre-trained models (AlexNet and ConvNexT) then freeze the parameters of the loaded models and create a new model by concatenating the output features of the two models and then adding a classifier layer.

Define a custom optimizer class that inherits from Adam with the modifications.

Define functions to get data loaders for training and validation then implement data loading and augmentation for the training set and the validation set.

Define the training loop using the optimizer and the loss “CrossEntropyLoss”.

Evaluate the model using the confusion matrix and the learning curve for the loss and the accuracy.

The paper used the dataset that originated from various hospitals in Dhaka, Bangladesh, where patients had previously received diagnoses related to kidney tumors, cysts, normal conditions, or stone findings. The gathered data from the Picture Archiving and Communication System (PACS), incorporating both Coronal and Axial cuts from contrast and non-contrast studies covering the entire abdomen and urogram. Subsequently, patient information and metadata were excluded from the Dicom images, and the images were converted to a lossless jpg format. To ensure accuracy, each image finding underwent verification by both a radiologist and a medical technologist after the conversion process 14 . The dataset contains 12,446 unique data within it which the cyst contains 3709, normal 5077, stone 1377, and tumor 2283. As shown in Fig 2 . The sample of the dataset used.

Sample images from the dataset.

Experiments and results

This study used the assembled and annotated 12,446 CT 14 whole abdomen and urogram images that contained four classes Cyst, Normal, Stone, and Tumor as in Fig. 2 . The paper divided the dataset into training and validation using augmentation to overcome the overfitting problem such as RandomHorizontalFlip, RandomVerticalFlip, CenterCrop, and Normalize the images. After augmentation training dataset be 19,450 instead of 9725 and the validation be 5442 instead of 2721.

The hyperparameter settings for the best model (Alex-NeT + ConvNexT with custom-Adam optimizer) are as follows: learning rate with 1e-4, Epochs = 100, loss = CrossEntropyLoss, Optimizer = custom-Adam and batch_size = 32. The paper trained the models using pytorch with a laptop with one GPU (2060 RTX). Figures 3 and 4 show the (training and validation loss) and (training and validation accuracy) respectively while Fig. 5 shows the Precision, Recall, and F1-score for the model.

Training and Validation Loss.

Training and Validation Accuracy.

Precision, Recall, and F1-score for Alex-NeT + ConvNexT with custom-Adam optimizer.

Performance evaluation methods

The evaluation of the eight models involves an analysis based on parameters such as accuracy in training, sensitivity (or recall), and precision (or positive predictive value - PPV). To calculate precision, and Recall, the paper utilizes true positive (TP), false positive (FP), true negative (TN), and false negative (FN) samples. Recall, also known as sensitivity, is determined by dividing the number of true positives by the sum of true positives and false negatives. In medical diagnosis, high recall is imperative for accurately identifying individuals with the disease, as overlooking the positive category can result in serious consequences like misdiagnosis and treatment delays. Precision (PPV) becomes crucial when assessing the proportion of predicted positive examples that are genuinely positive. Precision is calculated by dividing the number of true positives by the sum of true positives and false positives. In the realm of medical imaging, achieving high precision is highly desirable. The F1 score for all models is derived from the sensitivity and precision values. The provided formulas are applied to calculate accuracy, precision, sensitivity, and the F1 score 24 .

where, i=class of the kidney (Cyst or Normal or Stone or Tumor), TP= True Positive, FN= False Negative, TN=True Negative.

Table 2 shows the comparison between single vision models using the Adam optimizer and custom_Adam optimizer for the four classes of kidney diseases with some factors such as; accuracy, precision, recall, f-score, and the average for the four classes.

The presented table summarizes the performance of various models, each employing different optimizers, in distinguishing between four classes: Cyst, Normal, Stone, and Tumor. Notably, Vision Transformer (ViT) models, both with Adam and Custom_Adam optimizers, consistently demonstrate robust accuracy, precision, and recall across the specified classes, showcasing their effectiveness in image classification tasks. Swin and ConvNexT models also exhibit commendable performance, with high accuracy and stable precision-recall metrics. Alex-Net models, while slightly lagging in accuracy, still demonstrate competitive results. The ViT model with Adam optimizer consistently demonstrates high accuracy across all classes, making it a strong contender. Precision and recall are often critical in medical imaging; the balance between the two might be preferred.

Here, the study presents the best confusion matrix for the four individual vision models utilizing Adam and custom_Adam, which demonstrates improved results in Figs. 6 , 7 , 8 , and 9 .

ViT with Adam optimizer model.

Alex-Net with custom_Adam optimizer.

Swin with custom_Adam optimizer.

ConvNexT with Adam optimizer.

Visualizing results using class-wise error rates is also essential for the evaluation of image classification models. This approach provides a detailed view of the model's performance across different categories. Unlike overall accuracy metrics, which aggregate performance across all classes, class-wise error rates highlight disparities in classification performance. It can offer a comprehensive understanding of model efficiency. Here is the class-wise error rate for the best four models the paper used in Figs. 10 , 11 , 12 , and 13 .

Class-wise error rate for ViT with Adam optimizer model.

Class-wise error rate for Alex-Net with custom_Adam optimizer.

Class-wise error rate for Swin with custom_Adam optimizer.

Class-wise error rate for ConvNexT with Adam optimizer.

The summarized comparison of the class-wise error rate between the best four models in Fig. 14

Class-wise error rate for the best four models.

As in Fig. 14 , all models consistently achieve near-perfect performance, with the second model (Swin with custom_Adam optimizer) achieving perfect classification. The error rates vary, with the third model (Alex-Net with custom_Adam optimizer) showing higher error rates, while the final model (ViT with Adam optimizer) shows the best performance. All models demonstrate strong performance with low error rates, with the second and fourth models showing the best performance. The best overall model appears to be the "ViT with Adam optimizer model", as it achieves the lowest error rates across most classes, demonstrating consistent and strong performance in classifying 'Cyst', 'Normal', 'Stone', and 'Tumor' samples.

Table 3 shows the comparison between concatenated vision models using Adam and custom_Adam optimizer for the four classes of kidney diseases with some factors such as; accuracy, precision, recall, f-score, and the average for the four classes.

Table 3 presents the effect of the concatenated features between the models. Alex-Net + ConvNexT with the custom_Adam stand out with the highest accuracy of 99.85%. On the other hand, the model with the lowest accuracy among those provided, Swin + ConvNexT with the custom_Adam optimizer with an accuracy of 98.75% has the lowest accuracy but its balanced precision and recall suggest effectiveness across various classes. But Alex-Net + ConvNext with the custom_Adam stands out with consistently high average precision (0.9989) and recall (0.9995) values, indicating robust performance across all classes. Among the provided models, the custom_Adam optimizer consistently outperforms the standard Adam optimizer in terms of accuracy, precision, recall, and F1-score in all concatenated models specifically the Alex-Net model with any Transformer model with the dynamic adjustment of the step size based on the norm of the gradient except of the Swin + ConvNexT model which give the less result with the custom_Adam and the Adam optimizer which may because the different architectures that make the model more complexity. Also if the gradient flow between Swin and ConvNexT is not well-aligned, the gradients might not propagate effectively during training, leading to convergence challenges.

Here, the study presents the best confusion matrix for the four concatenated vision models utilizing Adam and custom_Adam, which demonstrates the best results in Figs. 15 , 16 , 17 , and 18 .

Swin + ConvNexT with Adam optimizer.

Alex-Net + Swin with custom_Adam optimizer.

Alex-Net + ViT with custom_Adam optimizer.

Alex-Net + ConvNexT with custom_Adam optimizer.

Here is the class-wise error rate of the best concatenated models in Figs. 19 , 20 , 21 , and 22 .

Class-wise error rate for Swin + ConvNexT with Adam optimizer.

Class-wise error rate for Alex-Net + Swin with custom_Adam optimizer.

Class-wise error rate for Alex-Net + ViT with custom_Adam optimizer.

Class-wise error rate for Alex-Net + ConvNexT with custom_Adam optimizer.

The summarized comparison for the class-wise error rate between the best four concatenated models in Fig. 23

Class-wise error rate for the best four concatenated models.

As in Fig. 23 , all models perform well in classifying 'Cyst' samples, with the final model (Alex-Net + ConvNexT with Adam Optimizer) showing perfect performance. Also, all models consistently achieve near-perfect or perfect performance in classifying 'Normal' samples, with multiple models achieving perfect performance. The error rates vary slightly, but all models generally perform well, with the second model (Alex-Net + Swin with custom_Adam Optimizer) showing the best improvement. All models demonstrate strong performance, with very low error rates across the board. The second model (Alex-Net + Swin with custom_Adam Optimizer) and the final model (Alex-Net + ConvNexT with Adam Optimizer) show perfect or near-perfect performance. The best overall model appears to be the "Alex-Net + ConvNexT with Adam Optimizer Model", as it achieves perfect classification in the 'Cyst' and 'Normal' classes, very low error rates in the 'Stone' class, and almost perfect performance in the 'Tumor' class. This model consistently demonstrates strong performance across all classes, making it the most reliable and effective model in this comparison.

No. parameters of different models

One essential feature that greatly affects a neural network model's capacity, efficiency, and flexibility is the number of parameters. Deep learning models consist of several layers, each of which has weights and biases that add to the total number of parameters. Greater representational capacity is often possessed by larger, more parameterized models, which allows them to learn complex characteristics and relationships in data. Conversely, more compact models with fewer parameters could be less prone to overfitting and more computationally efficient, which makes them appropriate for jobs requiring sparse data. As shown in Table 4 , the total number of parameters and trainable parameters for the single models and the concatenated models used in this paper. It's generally more meaningful to focus on "Trainable parameters" rather than "Total number of parameters." because not all parameters in a model may be trainable, as some might be fixed or non-trainable. As in Table 4 , the model with the least parameters is Swin, and the model with the most parameters is Alex-Net + ConvNexT. Larger parameter counts are often associated with better model accuracy, so the progression from the model with the least parameters to the most parameters could represent an increase in model capacity and, potentially, accuracy as in Fig. 24 .

Trainable parameters for the models used in the paper.

Time evaluation

For each of the 8 tested single models, the study compared the time taken for training for each model to get the less time that was taken. As shown in Fig. 25 , Alex-Net with Adam optimizer was the fastest in training as it took the least training time (50 minutes) with an accuracy of 96.32 followed by Swin with Adam optimizer which took 59 minutes with an accuracy of 96.44 then Alex-Net+ custom_Adam that took 66 minutes with accuracy 96.91 while the best one which is ViT with Adam optimizer took 5 hours approximately with accuracy 98.71 while the ConvNexT with custom_Adam optimizer took the longest time with around 10 hours with an accuracy of 96.62.

Time evaluation for training the eight single models.

As shown in Fig. 26 , the concatenated models for each of the 8 tested concatenated models, the paper also compared the time taken for training for each model to get the less time that taken., Swin + Alex-Net with custom_Adam optimizer was the fastest in training as it took the least training time (2 h and 30 min) with an accuracy of 99.78 followed by Swin + ConvNexT with Adam optimizer which took around 3 h and a half with an accuracy of 99.12 while ConvNexT + Alex-Net, Swin + Alex-Net with Adam optimizer and Swin + ConvNexT with custom_Adam optimizer took the same time around 4 h and a half with accuracies 99.63, 99.45 and 98.75 respectively. The best one which is ConvNexT + Alex-Net with cuatom_Adam optimizer took 6 h approximately with an accuracy of 99.85. While the Alex-Net + ViT with custom_Adam optimizer took the longest time around 7 h with an accuracy of 99.74.

Time evaluation for training the eight concatenated models.

Conclusions

This study explored the impact of feature concatenation and optimizer selection on neural network performance. The experimental results reveal that concatenating features, such as Alex-Net + ConvNexT, in combination with the custom_Adam optimizer, achieved an impressive accuracy of 99.85%. This highlights the benefits of integrating diverse model architectures and optimizing strategies to capture complex patterns and correlations in data. The custom_Adam optimizer demonstrated superior performance compared to the standard Adam optimizer across all concatenated models, excelling in accuracy, precision, recall, and F1-score. Particularly notable was its effect when paired with Transformer models, where dynamic step size adjustments based on gradient norms contributed to consistently high average recall and accuracy. The trade-off between model capacity and efficiency was evident, with the Swin model, despite its fewer parameters, performing competitively. This underscores its utility in scenarios where computational efficiency and reduced overfitting are critical. While larger models like Alex-Net + ConvNexT exhibited higher accuracy, the Swin + Alex-Net combination offered a balanced approach with a training duration of 2 h and 30 min and an accuracy of 99.78%. Conversely, the Alex-Net + ViT configuration, though achieving 99.74% accuracy, required the longest training time of approximately 7 h.

Data availability

The data that support the findings of this study are available from https://www.kaggle.com/datasets/nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone .

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Samar Elbedwehy

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Esraa Hassan

Department of Information Technology, Faculty of Computers and Artificial Intelligence, Damietta University, Damietta, 34517, Egypt

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Elbedwehy, S., Hassan, E., Saber, A. et al. Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis. Sci Rep 14 , 21740 (2024). https://doi.org/10.1038/s41598-024-71410-6

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