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Lifestyle and the Prevention of Type 2 Diabetes: A Status Report

Karla i galaviz , phd, msc, k m venkat narayan , md, msc, mba, felipe lobelo , md, phd, faha, mary beth weber , phd, mph.

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Karla I. Galaviz, MSc, PhD, Emory Global Diabetes Research Center, Hubert Department of Global Health, Emory University, No. 1518 Clifton Rd, Atlanta, GA 30322; e-mail: [email protected] .

Received 2015 Aug 3; Revised 2015 Nov 3; Accepted 2015 Nov 4; Collection date 2018 Jan-Feb.

Diabetes is a costly disease affecting 387 million individuals globally and 28 million in the United States. Its precursor, prediabetes, affects 316 and 86 million individuals globally and in the United States, respectively. People living with elevated blood glucose levels are at high risk for all-cause mortality and numerous cardiometabolic ailments. Fortunately, diabetes can be prevented or delayed by maintaining a healthy lifestyle and a healthy body weight. In this review, we summarize the literature around lifestyle diabetes prevention programs and provide recommendations for introducing prevention strategies in clinical practice. Overall, evidence supports the efficacy and effectiveness of lifestyle diabetes prevention interventions across clinical and community settings, delivery formats (eg, individual-, group-, or technology-based), and implementers (eg, clinicians, community members). Evidence-based diabetes prevention strategies that can be implemented in clinical practice include brief behavior change counseling, group-based education, community referrals, and health information technologies. These strategies represent opportunities where practitioners, communities, and health care systems can work together to provide individuals with education, support and opportunities to maintain healthy, diabetes-free lifestyles.

Keywords: impaired glucose tolerance, prediabetes, physical activity, diet, clinical practice

‘Obesity and physical inactivity lead to insulin resistance by increasing the nonphysiological deposition of fat in visceral, hepatic, and muscle tissues and by intracellular sequestration of glucose transporter-4 (GLUT-4) in unexercised muscle.’

Type 2 diabetes mellitus is a costly disease, affecting individuals, health care systems, economies, and whole societies worldwide. Diabetes and its precursor, prediabetes, affect 8% and 7% of the world’s population, respectively. 1 , 2 Type 2 diabetes mellitus (hereafter called diabetes) makes up 95% of all diabetes cases. 2 Overwhelming evidence shows that lifestyle intervention programs promoting healthy diets, physical activity, and modest body weight reductions can prevent or delay the onset of diabetes among high-risk populations, such as those with impaired glucose tolerance (IGT). 3 - 5 In this review, we briefly define diabetes and prediabetes and discuss their worldwide burden, describe the role physical activity and obesity play in the pathophysiology of diabetes, summarize the evidence around the effectiveness of lifestyle interventions for preventing diabetes, and provide evidence-based recommendations for health care practitioners looking to promote healthy lifestyles among their patients at risk of diabetes.

Defining Glucose Intolerance

Hyperglycemia is a major contributor to cardiovascular mortality and morbidity worldwide, 6 and it manifests in the form of prediabetes or diabetes. Prediabetes is a state of hyperglycemia where glucose levels are higher than normal but lower than diabetes thresholds and includes both impaired fasting glucose (IFG) and IGT. IFG is defined as fasting plasma glucose (FPG) levels of 100 to 125 mg/dL, whereas IGT is diagnosed when plasma glucose levels after an oral glucose tolerance test (OGTT) reach 140 to 199 mg/dL. 7 Elevated blood glucose levels represent defects in insulin secretion, insulin action, or both, where IFG is a reflection of impaired β-cell function and IGT of insulin resistance. Glycated hemoglobin (HbA1c) reflects chronic hyperglycemia, and values between 5.7% and 6.4% are also clinically used to define prediabetes.

Hyperglycemia is a strong risk factor for diabetes, and many adults with prediabetes will develop diabetes within the next 10 years, unless lifestyle changes are made. 8 People with IFG/IGT progress to diabetes when they reach FPG ≥126 mg/dL, plasma glucose levels ≥200 mg/dL after an OGTT, or an HbA1c value ≥6.5 (see Figure 1 ). 7 In some cases (eg, patients with hyperglycemia symptoms), a random plasma glucose ≥200 mg/dL can also be used to detect diabetes. Overall, HbA1c is regarded as a more convenient and more stable method than IFG and OGTT, although it is more expensive and not available in some settings. 9

Figure 1.

Prediabetes and Diabetes FBG, OGTT, and HbA1c Cutoff Points According to ADA 2015. 7

Abbreviations: ADA, American Diabetes Association; FBG, fasting blood glucose; OGTT, oral glucose tolerance test; HbA1c, hemoglobin A1c.

The Diabetes Burden

Diabetes is an independent risk factor for premature illness and mortality, mainly because of cardiovascular disease (CVD) 10 , 11 but can also lead to nephropathy, retinopathy, and neuropathy, 12 representing a major global public health burden. The International Diabetes Federation (IDF) reports that in 2014, diabetes prevalence among adults between 20 and 79 years old reached 8.3%, with 77% of the global cases living in low- and middle-income countries. 2 The diabetes prevalence is higher in urban than in rural populations, whereas the prevalence seems to be similar among men and women. 2 Overall, China, India, and the United States have the largest numbers of diabetes cases, with 92, 62, and 24 million people affected, respectively. 13 - 15

Because diabetes can be asymptomatic and remain undetected for long periods of time, around 46% of global diabetes cases are undiagnosed, 16 suggesting that affected persons are not receiving the appropriate treatment to prevent complications. Diabetes has become an important cause of disability and mortality globally, accounting for 46 823 disability-adjusted life years (per 1000 population) in 2010 17 and 4.9 million global deaths in 2014. 2 Given global population growth and ageing trends, 18 the world prevalence of diabetes is expected to escalate and reach 55% by 2035. 1

In the United States, 9.3% of adults older than 20 years were affected by diabetes in 2012, and approximately 28% of diabetes cases were undiagnosed. 19 Together, undiagnosed and diagnosed diabetes affect 12.3% of American adults, with men showing a higher prevalence than women (13.6% vs 11.2%, respectively) and adults >65 years showing the highest prevalence (25.6%). Diabetes, particularly affects minorities, with American Indians and Alaska Natives having the highest prevalence (16%), followed by non-Hispanic blacks (13%), Hispanics (13%), and Asian Americans (9%). Conversely, the prevalence of diabetes among non-Hispanic whites is the lowest in the country (7.6%). 19

The financial burden diabetes poses to individuals and societies is also alarming. The global health expenditure on diabetes reached $612 billion in 2014. 2 The United States has the highest diabetes expenditure in the world, accounting for about 53% of the global diabetes expenditure. 20 In 2012, diabetes costs reached $245 billion in the United States, with $176 billion spent in direct medical costs and $69 billion spent in reduced productivity. 21 The costs of undiagnosed diabetes alone have been estimated at $33 billion, 22 suggesting that direct and indirect diabetes costs may be underestimated. Overall, Americans with diagnosed diabetes have 2.3 times higher medical expenditures than their healthy counterparts, creating a financial burden that affects individuals and societies through higher insurance premiums and taxes, reduced earnings, and reduced standard of living ( Table 1 ). 21

Summary of the Burden of Prediabetes and Diabetes, the Evidence on Lifestyle Prevention and Recommendations.

Abbreviations: IGT, impaired glucose tolerance; BMI, body mass index; IFG, impaired fasting glucose.

The Prediabetes Burden

Prediabetes is associated with all-cause mortality and has been shown to increase CVD risk by almost 2-fold. 23 - 25 The IDF reports that the global prevalence of prediabetes reached 7% in 2013 and is expected to rise to 8% by 2035. 2 About 70% of the prediabetes cases are found in low- and middle-income countries and among adults younger than 50 years. 2 The prevalence of prediabetes is higher in Africa and European Regions and lower in the South-East Asia Region. 2 Slightly higher levels of prediabetes have been observed in rural than in urban areas, and IGT seems to be more common among women than men. 13 , 26 Differences in prevalence between ethnic groups have also been observed, even between groups from the same country, as observed in India and China. 13 , 14

In the United States, 37% of adults older than 20 years were affected by prediabetes in 2012, 19 and it is estimated that approximately 90% of prediabetes cases are undiagnosed ( Table 1 ). 27 These estimates are similar for non-Hispanic whites (35%), non-Hispanic blacks (39%), and Hispanics (38%), 19 although recent studies have reported differences in prediabetes prevalence and susceptibility between ethnic groups. For instance, African Americans have shown a higher prevalence of prediabetes than their white counterparts, 28 whereas Asian Indians seem to be particularly susceptible to developing prediabetes. 29 The economic burden of prediabetes in the United States reached $44 billion in direct health care costs in 2012. 22 Prediabetes represents a substantial economic burden in its own right, 30 but given that IFG and IGT are likely to progress to diabetes, 23 , 31 it also represents a potential future increase in new diabetes cases and associated costs.

Energy Balance and Diabetes Development

Energy balance plays an important role in the development of diabetes. The key elements in the energy balance equation are energy intake and energy expenditure, the former directly linked to diet and the latter to physical activity. High energy intake levels have been shown to increase diabetes risk by 11% to 26%, whereas adequate levels of physical activity reduce risk by 8% to 30%. 32 A positive energy balance, where energy intake is higher than energy expenditure, leads to overweight or obesity, further increasing diabetes risk. 32 , 33

Obesity and physical inactivity lead to insulin resistance by increasing the nonphysiological deposition of fat in visceral, hepatic, and muscle tissues 34 and by intracellular sequestration of glucose transporter-4 (GLUT-4) in unexercised muscle. 35 The pathological deposition of fat is a major contributor to insulin resistance. Visceral, particularly intrahepatic fat, is associated with insulin resistance 36 and with lipid accumulation in muscle cells. 37 The excess adipose tissue interferes with glucose transport signaling, mainly by sending toxic messages in the form of free fatty acids, cytokines, and oxidative stress, which impair insulin’s ability to regulate glucose production by the liver and glucose uptake by the muscle. 38 , 39

In contrast, physical activity reduces insulin resistance directly by promoting free fatty acid oxidation and reducing lipotoxicity in skeletal muscle and liver 40 and indirectly by reducing visceral fat. 34 Furthermore, exercise seems to improve serum levels of adiponectin, 41 a hormone that promotes insulin sensitivity 42 and is reduced in the presence of obesity. 43 Physical activity represents a physiological stressor that triggers changes in glucose transport and disposal to satisfy energy demands. The muscle is the main site for insulin-stimulated glucose disposal and, together with the adipose tissue, stores around 90% of GLUT-4, the main insulin-responsive glucose transporter. 35 In the absence of insulin or other stimuli (eg, physical activity), GLUT-4 remains sequestered intracellularly, impairing its ability to transport glucose into the muscle cell. Physical activity leads to GLUT-4 translocation from the intracellular storage vesicles to the plasma membrane, where it can transport glucose into the cell in a noninsulated mediated pathway. 35 Exercise increases glucose uptake by the working muscle 7 to 20 times above the basal rate, with improvements in insulin sensitivity lasting up to 3 days. 44 Among diabetes patients, exercise can increase nonoxidative glucose disposal, which in turn can improve whole-body glucose utilization. 45

β-Cell dysfunction and mass loss result in impaired insulin secretion. Loss in β-cell mass through apoptosis of β-cells has been tightly correlated with diabetes. 46 Studies have found reduced β-cell mass, ranging from 40% among prediabetes patients to 60% among diabetes patients. 47 Reductions in β-cell mass contribute to β-cell dysfunction, where the insulin secretion burden of the remaining cells increases, leading to chronic β-cell stress and ultimately impairing functional insulin secretion. 48 Although the mechanisms are not fully understood, evidence suggests that a variable combination of β-cell mass loss and β-cell dysfunction are involved in the pathogenesis of diabetes. 48 The influence of lifestyle changes on β-cell mass and function has not been fully understood, but preliminary animal and human studies suggest that physical activity improves β-cell function by upregulating insulin signaling pathways and β-cell mass by stimulating proliferation and preventing apoptosis. 49

Diabetes develops progressively as a result of the complex interaction between insulin resistance and β-cell dysfunction. Insulin resistance triggers a compensatory response, where the β-cells increase insulin secretion to maintain glucose homeostasis. If an adequate compensatory insulin secretion response is given, hyperglycemia can be offset, and blood glucose levels can regress to normal. Conversely, if the β-cells fail to cover the insulin demand triggered by insulin resistance, hyperglycemia will remain and potentially progress to IGT and ultimately to diabetes. 50 This pathway results in reductions in β-cell mass, further impairing the body’s ability to sustain normal glucose levels. 51 Figure 2 depicts the diabetes development process.

Figure 2.

Diabetes development process (adapted from Stumvoll et al 50 ).

Abbreviation: IGT, impaired glucose tolerance.

In sum, the degree of insulin resistance and the extent of β-cell dysfunction influence the development of glucose intolerance and progression to diabetes. Given the complex interaction among the physiological factors discussed above, people with insulin resistance will not necessarily develop glucose intolerance, nor will all people with prediabetes necessarily progress to diabetes. However, it is important to note that physical inactivity increases diabetes risk by 20%, 52 and each additional kilogram of weight gained translates into a 4.5% increase in diabetes risk. 53 Considering the high obesity and physical inactivity rates and poor diets among US adults, promoting lifestyle changes among at-risk adults before they develop the disease is imperative.

A Lifestyle Prescription for Diabetes Prevention

Overwhelming evidence shows that lifestyle changes—namely, improvements in physical activity and diet, leading to weight loss—reduce diabetes risk significantly. 54 , 55 Randomized controlled trials have shown that lifestyle interventions focused on physical activity, healthy diets, and weight loss can reduce diabetes risk by 58% in people with IGT. 56 , 57 Evidence also shows that lifestyle changes may help mitigate the effects of genes on diabetes risk. For instance, in the US Diabetes Prevention Program (DPP), the association between susceptible genotypes and progression to diabetes was attenuated in individuals receiving a lifestyle intervention. 58 Similarly, a study among Swedish adults showed that the genetic predisposition to diabetes may be offset by physically active lifestyles. 59

Weight loss is the main driver of diabetes prevention in populations with high mean BMI, such as Americans. 3 In the US DPP for instance, weight loss was the strongest predictor of reduced diabetes incidence; a 5-kg weight loss explained an incidence reduction of 58%. 60 More specifically, for every kilogram participants lost, a 16% reduction in diabetes risk was observed. 60 Similarly, only participants who lost 8-17% of their weight in the Finish Diabetes Prevention Study (DPS) achieved significant improvements in insulin sensitivity. 61 Changes in fat distribution have also been linked to improved insulin sensitivity, particularly changes in visceral fat mass and liver fat content. 62 Based on this, a weight loss of 7% of total body weight has been recommended for the primary prevention of diabetes.

Because obesity is in part an outcome of positive energy balance, targeting physical activity and diet in weight loss–driven diabetes prevention efforts is essential. Physical activity involves any bodily movement produced by skeletal muscles that requires energy expenditure and includes leisure time physical activity, transportation (eg, walking or cycling), occupational (ie, work), household chores, play, games, and sports or planned exercise (see Table 2 ). 63 A range of physical activities and intensities are associated with 20% to 30% diabetes risk reduction, especially among high-risk individuals. 64 For instance, ≥2.5 h/wk of moderate-intensity brisk walking is associated with a diabetes risk reduction of 27%, independent of BMI. 65 Furthermore, moderate- to vigorous-intensity physical activity has been linked to enhanced β-cell function and glucose regulation, independent of obesity. 66 Structured exercise training has been found to reduce HbA1c by 67% among diabetes patients 67 and to improve β-cell function and insulin sensitivity among high-risk patients. 40 , 68 These effects are comparable or superior to those achieved with common antidiabetic drugs. 68 , 69 Overall, international guidelines recommend that adults 18 years and older engage in 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity aerobic physical activity (or a combination of these) per week, accumulated in bouts lasting at least 10 minutes with muscle-strengthening activities involving major muscle groups performed on 2 or more days a week. 63

Physical Activity Definitions According to the World Health Organization 2010. 63

Absolute intensity can be determined by the rate of work being performed (eg, milliliters per kilogram per minute of oxygen consumed), whereas relative intensity is determined as a percentage of an individual’s maximum heart rate or aerobic capacity (VO 2max ).

Caloric intake and diet quality are important drivers of obesity and diabetes. Overall, a diet rich in fiber and whole grains and low in saturated fat is associated with reduced risk of obesity and diabetes. 70 , 71 Prospective studies have consistently shown that consumption of cereal fiber or mixtures of whole grains and bran reduce diabetes risk by 18% to 40%. 70 Furthermore, with a 2 serving/d increment in whole-grain intake, diabetes risk can be reduced up to 21%. 72 It has also been shown that a saturated fat intake less than 7% of the total energy intake is associated with reduced diabetes risk. 71 Because people who drink more than 1 sugar-sweetened beverage per day have a 26% increase in diabetes risk compared with those who drink less than 1 per month, 73 reducing sugar-sweetened beverage consumption may also decrease risk. Regarding dietary strategies, low to moderate fat (10%-45%), high protein, low carbohydrate, and low glycemic index diets have been shown to effectively reduce weight and improve diabetes risk factors. 74 High-carbohydrate and low glycemic index diets seem to improve β-cell function among those with IGT. 75 The ADA recommends that high-risk individuals are encouraged to increase their fiber intake and consume whole grain foods. 9 Behavioral counseling and ongoing support should be used to help patients, 74 whereas individualized nutrition counseling should be provided by a registered dietitian (see Table 1 ).

The extent to which lifestyle changes can prevent or delay the onset of diabetes is associated with the degree of β-cell dysfunction and hyperglycemic nature. For example, in persons with mild β-cell dysfunction, who exhibit IGT as a result of peripheral insulin resistance, lifestyle changes can improve insulin sensitivity and ultimately restore normoglycemia. Conversely, in patients with moderate β-cell dysfunction, who present with isolated IFG, lifestyle changes may not restore normoglycemia but may help regulate glucose levels. 76 In other words, lifestyle changes may have a small effect on the progression to diabetes in individuals with isolated IFG concentrations but they are highly beneficial among those with IGT. 76

Other lifestyle behaviors and mental health factors such as smoking, sedentary behavior (time spent seating), sleeping patterns, and stress have also been linked to diabetes risk, independent of sociodemographic factors, physical activity, and obesity. 77 - 85 Meta-analyses have shown that smoking significantly increases diabetes risk, with active smokers showing a 44% increased risk. 78 A diabetes risk 1 to 2 times higher has been observed among men who watch television for more than 20 hours per week, compared to those who do it for less than 2 hours per week. 79 Similarly, a 3.4% increase in diabetes risk for each additional hour spent watching television was observed among DPP participants. 80 Sleeping disturbances have also been linked to abnormal glucose metabolism, where trouble sleeping, sleep apnea, and sleep loss have been shown to significantly increase diabetes risk. 81 - 83 Likewise, psychosocial stress predicts diabetes incidence in men 84 and abnormal glucose metabolism among women. 85 Because these behaviors are out of the scope of the present review, we invite interested readers to review the articles cited in this paragraph and other articles available elsewhere.

Efficacy of Lifestyle Diabetes Prevention

Five highly controlled, large-scale studies assessing the efficacy of lifestyle DPPs have been implemented across the globe. 57 , 86 - 91 In the US DPP, adults with IGT were randomly assigned to receive a placebo, metformin (a glucose-lowering drug), or a lifestyle program consisting of 24 individual counseling sessions on diet and physical activity. After 2.8 years, diabetes risk was reduced by 58% in the lifestyle group and by 31% in the metformin group. Lifestyle participants also achieved a significant weight loss, which was higher than that of controls (5.6 vs 0.1 kg, respectively). 86 At the end of the original DPP, participants in all groups were offered a group-based lifestyle program, and 15 years after original randomization, diabetes incidence was reduced by 27% among lifestyle participants and 18% among metformin participants. 92

In Finland, the DPS showed that patients receiving a 4-year intervention comprising individualized dietary counseling and free access to gym-based supervised exercise sessions achieved a diabetes risk reduction of 58%. 93 Likewise, intervention patients showed greater weight reductions, better glycemic and lipid levels, and healthier diets than patients receiving usual care. 87 After 13 years of randomization, diabetes incidence was reduced by 43% among intervention patients while improvements in body weight, FBG and 2-hour blood glucose, and dietary habits were maintained. 94

Three studies in Asia, the Da Qing IGT and Diabetes Study, the Indian DPP (IDPP) and the Japanese DPP also showed the benefits of lifestyle intervention. The Da Qing IGT and Diabetes Study implemented in China showed that compared with control participants, those receiving individualized dietary prescription combined with physical activity counseling over 6 years had a 51% lower diabetes incidence at the end of the intervention and a 43% lower incidence over a 20-year follow-up period. 88 , 89 In the IDPP, participants randomized to receive monthly in-person advice on physical activity and diet to prevent diabetes achieved a 28.5% relative risk reduction compared with the control group. This risk reduction was similar to that achieved among participants assigned to a metformin or a lifestyle plus metformin group (28% and 26%, respectively). 90 Finally, the Japanese DPP showed that participants receiving frequent individual instructions and lifestyle support from medical staff over 3 years achieved a diabetes risk reduction of 44%. 91

Not only can lifestyle programs significantly reduce diabetes incidence among people with prediabetes, but they can also improve the CVD risk profile. Numerous randomized controlled trials have shown that lifestyle interventions can reduce FPG (from −3 to −19 mm/dL), 93 , 95 - 102 2-hour post-load plasma glucose (from −6 to −29 mm/dL), 93 , 96 , 98 - 101 , 103 , 104 systolic blood pressure (from −1.6 to −11 mm Hg), 96 , 98 , 103 , 105 , 106 total cholesterol (from −3 to −15 mg/dL), 96 , 98 , 99 , 105 - 107 triglycerides (from −2 to −15 mm/dL), 96 , 98 , 101 , 105 HbA1c (from −0.09% to −19%), 100 , 102 , 103 , 106 and body weight (from −2 to −10 kg). 95 , 96 , 98 , 108 , 109 Furthermore, a meta-analytic synthesis showed that lifestyle interventions among individuals with IGT achieve a standardized mean reduction of −0.27 (95% CI = −0.38 to −0.15) on FPG and −0.56 (95% CI = −1.01 to −0.10) on 2-hour post-load glucose levels, with effects varying between intervention strategies (eg, diet vs physical activity). 5 Finally, lifestyle interventions have also been shown to promote regression to normoglycemia ( Table 1 ). 95 , 98 , 102 , 109 , 110

Effectiveness of Lifestyle Diabetes Prevention

The efficacy-driven interventions discussed above are resource intensive, directed at homogeneous populations, implemented in highly controlled settings, and often delivered by health care professionals, thereby limiting their real-world applicability and impact. Thus, researchers have focused on investigating low-cost strategies that can be implemented in real-world practice, utilizing group-based formats and lay community members or technology, and implemented in a variety of community settings where heterogeneous populations can be reached. For instance, the 16-week original DPP curriculum has been adapted for group-based delivery and has been implemented in YMCAs, churches, primary care clinics, and other community settings, by trained staff, physicians, lay community members, and technology-assisted devices (eg, Internet, text messages). 111 - 123 The achieved effects include weight reductions ranging from −1.9 to −8.7 kg 112 , 113 , 115 , 116 , 118 - 122 , 124 and improvements in fasting glucose levels ranging from −2.1 to −9 mm/dL. 113 , 115 , 119 , 121 , 125 - 127

A recent meta-analysis of 26 intervention studies aimed at translating the DPP in the United States found that lifestyle interventions achieved a pooled weight reduction of 4%, regardless of whether the intervention was delivered by health care professionals or lay community educators. 128 Another meta-analysis summarizing the evidence on the effectiveness of 22 translational DPPs from 11 countries showed that lifestyle interventions achieved a mean weight loss of 2.3 kg (95% CI = −2.9 to −1.7 kg), although effects varied widely across studies. 129 Factors associated with heterogeneity in weight change effects include intervention dose (eg, number of sessions delivered, adherence to guidelines), the delivery agent (eg, health care provider, community member), and study design and follow-up length. 128 , 129

In light of this, the Community Preventive Services Task Force, the ADA, the IDF, and the World Health Organization recommend combined diet and physical activity counseling and education promotion programs for people at increased risk of diabetes across a range of intervention intensities, settings, and implementers. 9 , 130 - 132

Lifestyle Diabetes Prevention in Clinical Practice

Lifestyle diabetes prevention in clinical practice is effective and feasible, leading to the adaptation of large prevention trials for the clinical setting. 113 , 120 , 126 , 133 - 135 A meta-analytic review showed that dietary and physical activity counseling for diabetes prevention in routine clinical practice promotes weight and waist circumference reductions in high-risk patients. 54 Implementing lifestyle counseling in clinical practice has been shown to be feasible and cost-effective 3 , 54 , 136 - 139 and acceptable among practitioners and patients. 140 This has led to the conclusion that lifestyle-related health care provider services should be widely implemented and potentially reimbursed. 141

Based on this, a number of clinical recommendations have been released. The US Preventive Services Task Force 2014 recommendations support offering or referring adults at risk of CVD to physical activity and nutrition counseling, a strategy that under the Affordable Care Act must be included in new health plans. 142 , 143 The American Heart Association recommends using behavior change techniques such as goal setting and self-monitoring for promoting lifestyle change, 144 and the ADA promotes referring high-risk patients to programs that target weight loss and increased physical activity. 9 Despite this, the adoption and implementation of lifestyle diabetes prevention strategies in regular clinical practice remains a challenge.

The US Department of Health and Human Services reports that in 2010, 32.4% of adults and 56% of diabetes patients who had seen a health professional in the past year were advised to engage in physical activity. 145 The barriers to lifestyle counseling practitioners face—mainly lack of time, training, and organizational resources 146 —as well as the disconnection between clinical practice and community health programs 147 contribute to these low rates. Furthermore, clinic-level factors such as organizational characteristics and capacity/resources may hinder the adoption of diabetes preventive practices. Recognizing this, strategies to integrate lifestyle diabetes prevention in clinical practice have been identified. Collaborating with community-based lifestyle programs, establishing patient referrals to such programs, creating multidisciplinary teams to provide patient education and follow-up, training physicians on effective brief counseling, and using health information technologies to promote behavior change represent the most promising strategies. 141 , 148

From Evidence to Practice

The IDF has proposed a simple 3-step plan for the prevention of diabetes in high-risk individuals that includes (1) identification of those who may be at increased risk, (2) risk evaluation, and (3) intervention to prevent diabetes. 149 Following these steps, we now outline the evidence-based diabetes prevention strategies that health care practitioners can implement in their practice (see Table 3 ).

Steps Outlined by IDF 149 for the Prevention of Diabetes in High-Risk Individuals.

Abbreviations: IDF, International Diabetes Federation; BMI, body mass index; HbA1c, hemoglobin A1c; FBG, fasting blood glucose; OGTT, oral glucose tolerance test; CVD, cardiovascular disease.

Identification

The IDF recommends that high-risk individuals should be identified through opportunistic screening by physicians, nurses, and pharmacists and through self-screening. 149 Simple, noninvasive diabetes risk questionnaires exist 150 - 152 and are useful tools for identifying patients at high risk. According to ADA 2015 criteria, patients of any age with overweight or obesity (BMI ≥25 kg/m 2 or ≥23 kg/m 2 in Asian Americans) and who have one or more additional risk factors (ie, family history of diabetes, history of gestational diabetes, high risk race/ethnicity [African American, Latino, Native American, Asian American, Pacific Islander]) are at increased risk. 9 The opportunistic screening of high-risk people could help lower undiagnosed diabetes rates.

Measurement

According to ADA, high-risk patients of any age (see criteria above) and low-risk patients ≥45 years of age should be tested for glucose intolerance. HbA1c, FPG, or OGTT are appropriate methods for identifying prediabetes as well as diabetes cases. If tests are normal, testing should be repeated at least once every 3 years. In people with prediabetes, testing should be repeated yearly. 9 Although this is still under debate, the US Preventive Services Task Force’s 2015 recommendations and others support the testing of glucose intolerance among individuals who are at increased risk. 30 , 153 , 154 Among patients with prediabetes, screening for and treatment of modifiable CVD risk factors is recommended, 9 and preexisting CVD should be treated. 149

Lifestyle Intervention

The objective of an intervention should be helping patients lose body weight (7% is recommended, but patient’s ethnicity should be considered), increase moderate-intensity physical activity levels to at least 150 min/wk, and increase fiber intake. 9 In terms of intervention strategies, individual or group (or a combination of both) dietary and physical activity counseling from health care professionals, group educational sessions, and tailoring of diet or physical activity plans can be used. 4 , 54 For promoting lifestyle behavior change, helping patients set lifestyle goals, providing information on the health consequences of current behaviors, and using follow-up prompts and plans have been shown to be effective behavior change techniques. 144 , 155 Similarly, empathy, nonjudgmental interactions, and specific personalized recommendations have been identified as essential components of weight loss counseling. 156 Regarding “dose,” a minimum of 4 to 6 months of weekly weight loss education sessions and consistent follow-up for 1 year are necessary to achieve a clinically meaningful weight loss. 157 High-intensity counseling strategies (>360 minutes of total patient time) show greater effects on lifestyle and health outcomes than medium-intensity counseling (31-360 minutes). 158 Overall, DPPs have been shown to be effective across a range of counseling intensities, settings, and implementers, 4 and evidence-based strategies to help integrate lifestyle promotion in clinical practice are available.

Clinician-Based Strategies

Health care professionals interested in providing individual lifestyle counseling to their patients can use existing evidence-based tools such as the 5-As (Assess, Advice, Assist, Agree, and Arrange) model and Brief Action Planning. The 5-As model is a 5-step tool designed to assist health care providers in their lifestyle counseling efforts. The model can help health care professionals improve their lifestyle and weight loss counseling, which can in turn improve patient behavior. 159 , 160 Using diet as an example, the practitioner first assesses patient’s dietary behavior, then advices the patient to increase fiber intake, agrees on a collaboratively set goal (eg, increasing dietary fiber 20-30 g/d), follows with assisting the patient on identifying barriers to their goal and strategies to overcome these, and finally arranges follow-up visits and support (see Table 4 ).

Steps Outlined in the 5-As Model for Lifestyle Counseling With Corresponding Tools and Behavior Change Strategies.

Abbreviations: 5-As, Assess, Advice, Assist, Agree, and Arrange; PAVS, physical activity vital sign; ADA, American Diabetes Association; BCT, behavior change technique; WHO, World Health Organization; ACSM, American College of Sports Medicine.

To provide more in-depth behavior change counseling, practitioners can turn to brief action planning. 161 This is a highly structured, self-management support technique that includes 3 questions and 5 skills focused on helping patients set goals and form action plans (ie, when, where, and how to enact a goal-directed behavior) to change their behavior. With training, using interactive Web-based tools and guidelines, brief action planning can be learned by health care practitioners and used in clinical settings. 161 However, brief action planning takes about 20 minutes to complete, whereas the 5-As model can take 3 to 5 minutes, which challenges the successful incorporation of individual lifestyle counseling in busy clinical settings. 148 , 156 Recognizing that many health care professionals may not have time to provide individual counseling, other strategies such as group-based, lifestyle referrals and health information technology have been proposed.

Group and Referral Strategies

The implementation of group-based DPPs in the clinical setting has been successful in reducing diabetes risk while avoiding placing time burdens on health care practitioners. Examples include DPPs consisting of 1 to 6 educational group sessions on physical activity and healthful eating delivered by health care proressionals. 162 , 163 Furthermore, the DPP has been adapted for implementation in clinical settings, 120 , 133 and course curriculum, program materials, and training are available to support implementation. 164 , 165 If implementing group-based lifestyle programs is not possible, collaborating with existing community-based programs and resources is a feasible strategy for supporting lifestyle change in clinical settings. 148

Lifestyle referral strategies in primary care are cost-effective, 166 whereas practical collaborative approaches among health care practitioners (eg, physicians, nurses, dietitians) show promising weight outcomes. 156 A great example is the physical activity referral scheme, which has been widely tested and shown to promote small, short-term improvements in patients’ physical activity. 167 Moreover, lifestyle referral schemes have the potential to support patient behavior change and maintenance while also promoting the creation of clinical-community linkages. 168 In line with this, the ADA recommends referring high-risk patients to weight loss and physical activity programs as well as to educational programs for lifestyle change support. 9 Because referring patients to lifestyle programs and resources may not be enough to promote behavior change, 169 the collaborative effort between clinicians, organizations, and community settings is warranted to provide patients with healthy eating and physical activity opportunities in all settings.

Health Information Technology

The use of health information technology to facilitate behavior change has been identified as a promising strategy for promoting lifestyle change in the clinical context. 148 Electronic health records, online tools and websites, electronic physical activity monitors, and mobile technologies (eg, smart phones and applications) are among technologies that have been developed to assist patients in their self-management efforts. 170 , 171 Health information technology used in primary care represents a feasible, cost-effective, and acceptable strategy to deliver or augment standardized interventions, improve patient self-management and access to resources, and provide flexibility of care services. 172 - 174

A major example of such technologies is the electronic health record, which has been strongly supported by the US federal government to improve diabetes prevention and quality of care. 175 The usefulness of electronic health records for identification of obesity and the delivery of interventions has also been emphasized. 156 Through electronic health records, the physical activity vital sign, a 2-question tool to screen for physical inactivity, has been implemented in the United States, achieving favorable changes in physician practice and patient metabolic outcomes. 176

Health information technologies facilitate the delivery of effective interventions. Physical activity electronic monitors for instance have been used to implement behavior change strategies such as self-monitoring, feedback, and environmental change 177 and for physical activity counseling in clinical and community settings. 178 Likewise, telephone-based strategies have been used to deliver dietary and physical activity counseling, which have been shown to have a wide patient reach 162 and promote behavior change. 179 Other strategies include DVD and Internet program delivery, both of which have shown promising impact on weight loss. 120 , 133 Although current evidence supporting the use of health information technologies for diabetes prevention is lacking, such an approach has the potential to facilitate the delivery of targeted interventions to people with prediabetes, accelerate the diffusion of new evidence, and generate automatic referrals to lifestyle intervention programs. 180

A Case Study

One example of a comprehensive lifestyle intervention that combines the elements described above is Exercise is Medicine, a program aimed at making physical activity part of the standard medical care in the US and global health care systems. 147 Using a multisector, multilevel approach, Exercise is Medicine is focused on integrating physical activity assessment within health care systems, creating clinical-community linkages through physical activity referrals, and advancing health information technologies to support patient behavior change. 147 Pilot studies are promising and support the feasibility and effectiveness of integrating physical activity assessment, counseling, and referrals in clinical practice. 181 , 182 Comprehensive tools, customized physical activity prescriptions for several diseases, and resources to help health care practitioners and systems integrate physical activity promotion in their practice are available on the Exercise is Medicine website. 183

The Exercise is Medicine model can be used to guide lifestyle diabetes prevention efforts in clinical practice. Comprising behavior assessment, counseling, and referral steps, the clinical component of the model outlines strategies that can help patients improve their diabetes-related behaviors. 147 First, the patient’s physical activity and dietary behaviors can be assessed using available tools such as the physical activity vital sign 176 and brief dietary and obesity-related behaviors questionnaires 184 - 186 or simply by asking patients about their lifestyle behaviors. Second, brief lifestyle counseling can be provided, where health care practitioners can turn to the evidence-based tools previously described (ie, 5-As model, brief action planning) or implement behavior change techniques such as informing patients on the risks of their current behaviors, helping them set behavioral goals, and providing support and follow-up. 144 , 155 , 187 Here, health care practitioners can offer the patient a verbal or written prescription that summarizes the patient’s goals or that can be based on weight loss, physical activity, and dietary recommendations for diabetes prevention. 9 , 63 Finally, health care practitioners can refer patients to self-management resources (eg, websites, electronic physical activity monitors, diet and exercise tracking apps), 177 , 178 , 188 , 189 individual counseling with physical activity and nutrition experts, 139 , 190 or existing group-based DPPs and resources in the community. 112 , 120 , 191 The community and health information technology components of the model can be consulted elsewhere. 147

Affecting individuals, health care systems, economies, and whole societies worldwide, diabetes has become a major public health threat. Lifestyle intervention programs promoting healthy diets, physical activity, and modest body weight reductions can prevent or delay the onset of diabetes among high-risk populations. Evidence supports the efficacy and effectiveness of such interventions across clinical and community settings, delivery formats, and implementers. At-risk individuals need education, access to ongoing support, and an adequate environment to engage in health-enhancing behaviors. Health care practitioners and systems can contribute by providing individual counseling, establishing referral systems and linkages with community programs and resources, or by introducing health information technologies to screen for unhealthy behaviors or deliver interventions. These strategies represent opportunities where practitioners, communities, and health care systems can work together to provide individuals with education, support, and opportunities to maintain healthy, diabetes-free lifestyles.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval: Not applicable, because this article does not contain any studies with human or animal subjects.

Informed Consent: Not applicable, because this article does not contain any studies with human or animal subjects.

Trial Registration: Not applicable, because this article does not contain any clinical trials.

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  • Review Article
  • Published: 06 June 2022

The burden and risks of emerging complications of diabetes mellitus

  • Dunya Tomic   ORCID: orcid.org/0000-0003-2471-2523 1 , 2 ,
  • Jonathan E. Shaw   ORCID: orcid.org/0000-0002-6187-2203 1 , 2   na1 &
  • Dianna J. Magliano   ORCID: orcid.org/0000-0002-9507-6096 1 , 2   na1  

Nature Reviews Endocrinology volume  18 ,  pages 525–539 ( 2022 ) Cite this article

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  • Diabetes complications
  • Type 1 diabetes
  • Type 2 diabetes

The traditional complications of diabetes mellitus are well known and continue to pose a considerable burden on millions of people living with diabetes mellitus. However, advances in the management of diabetes mellitus and, consequently, longer life expectancies, have resulted in the emergence of evidence of the existence of a different set of lesser-acknowledged diabetes mellitus complications. With declining mortality from vascular disease, which once accounted for more than 50% of deaths amongst people with diabetes mellitus, cancer and dementia now comprise the leading causes of death in people with diabetes mellitus in some countries or regions. Additionally, studies have demonstrated notable links between diabetes mellitus and a broad range of comorbidities, including cognitive decline, functional disability, affective disorders, obstructive sleep apnoea and liver disease, and have refined our understanding of the association between diabetes mellitus and infection. However, no published review currently synthesizes this evidence to provide an in-depth discussion of the burden and risks of these emerging complications. This Review summarizes information from systematic reviews and major cohort studies regarding emerging complications of type 1 and type 2 diabetes mellitus to identify and quantify associations, highlight gaps and discrepancies in the evidence, and consider implications for the future management of diabetes mellitus.

With advances in the management of diabetes mellitus, evidence is emerging of an increased risk and burden of a different set of lesser-known complications of diabetes mellitus.

As mortality from vascular diseases has declined, cancer and dementia have become leading causes of death amongst people with diabetes mellitus.

Diabetes mellitus is associated with an increased risk of various cancers, especially gastrointestinal cancers and female-specific cancers.

Hospitalization and mortality from various infections, including COVID-19, pneumonia, foot and kidney infections, are increased in people with diabetes mellitus.

Cognitive and functional disability, nonalcoholic fatty liver disease, obstructive sleep apnoea and depression are also common in people with diabetes mellitus.

As new complications of diabetes mellitus continue to emerge, the management of this disorder should be viewed holistically, and screening guidelines should consider conditions such as cancer, liver disease and depression.

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Introduction.

Diabetes mellitus is a common, albeit potentially devastating, medical condition that has increased in prevalence over the past few decades to constitute a major public health challenge of the twenty-first century 1 . Complications that have traditionally been associated with diabetes mellitus include macrovascular conditions, such as coronary heart disease, stroke and peripheral arterial disease, and microvascular conditions, including diabetic kidney disease, retinopathy and peripheral neuropathy 2 (Fig.  1 ). Heart failure is also a common initial manifestation of cardiovascular disease in patients with type 2 diabetes mellitus (T2DM) 3 and confers a high risk of mortality in those with T1DM or T2DM 4 . Although a great burden of disease associated with these traditional complications of diabetes mellitus still exists, rates of these conditions are declining with improvements in the management of diabetes mellitus 5 . Instead, as people with diabetes mellitus are living longer, they are becoming susceptible to a different set of complications 6 . Population-based studies 7 , 8 , 9 show that vascular disease no longer accounts for most deaths among people with diabetes mellitus, as was previously the case 10 . Cancer is now the leading cause of death in people with diabetes mellitus in some countries or regions (hereafter ‘countries/regions’) 9 , and the proportion of deaths due to dementia has risen since the turn of the century 11 . In England, traditional complications accounted for more than 50% of hospitalizations in people with diabetes mellitus in 2003, but for only 30% in 2018, highlighting the shift in the nature of complications of this disorder over this corresponding period 12 .

figure 1

The traditional complications of diabetes mellitus include stroke, coronary heart disease and heart failure, peripheral neuropathy, retinopathy, diabetic kidney disease and peripheral vascular disease, as represented on the left-hand side of the diagram. With advances in the management of diabetes mellitus, associations between diabetes mellitus and cancer, infections, functional and cognitive disability, liver disease and affective disorders are instead emerging, as depicted in the right-hand side of the diagram. This is not an exhaustive list of complications associated with diabetes mellitus.

Cohort studies have reported associations of diabetes mellitus with various cancers, functional and cognitive disability, liver disease, affective disorders and sleep disturbance, and have provided new insights into infection-related complications of diabetes mellitus 13 , 14 , 15 , 16 , 17 . Although emerging complications have been briefly acknowledged in reviews of diabetes mellitus morbidity and mortality 11 , 17 , no comprehensive review currently specifically provides an analysis of the evidence for the association of these complications with diabetes mellitus. In this Review, we synthesize information published since the year 2000 on the risks and burden of emerging complications associated with T1DM and T2DM.

Diabetes mellitus and cancer

The burden of cancer mortality.

With the rates of cardiovascular mortality declining amongst people with diabetes mellitus, cancer deaths now constitute a larger proportion of deaths among this population in some countries/regions 8 , 9 . Although the proportion of deaths due to cancer appears to be stable, at around 16–20%, in the population with diabetes mellitus in the USA 7 , in England it increased from 22% to 28% between 2001 and 2018 (ref. 9 ), with a similar increase reported in Australia 8 . Notably, in England, cancer has overtaken vascular disease as the leading cause of death in people with diabetes mellitus and it is the leading contributor to excess mortality in those with diabetes mellitus compared with those without 9 . These findings are likely to be due to a substantial decline in the proportion of deaths from vascular diseases, from 44% to 24% between 2001 and 2018, which is thought to reflect the targeting of prevention measures in people with diabetes mellitus 18 . Over the same time period, cancer mortality rates fell by much less in the population with diabetes mellitus than in that without diabetes 9 , suggesting that clinical approaches for diabetes mellitus might focus too narrowly on vascular complications and might require revision 19 . In addition, several studies have reported that female patients with diabetes mellitus receive less-aggressive treatment for breast cancer compared with patients without diabetes mellitus, particularly with regard to chemotherapy 20 , 21 , 22 , suggesting that this treatment approach might result in increased cancer mortality rates in women with diabetes mellitus compared with those without diabetes mellitus. Although substantial investigation of cancer mortality in people with diabetes mellitus has been undertaken in high-income countries/regions, there is a paucity of evidence from low-income and middle-income countries/regions. It is important to understand the potential effect of diabetes mellitus on cancer mortality in these countries/regions owing to the reduced capacity of health-care systems in these countries/regions to cope with the combination of a rising prevalence of diabetes mellitus and rising cancer mortality rates in those with diabetes mellitus. One study in Mauritius showed a significantly increased risk of all-cause cancer mortality in patients with T2DM 23 , but this study has yet to be replicated in other low-income and middle-income countries/regions.

Gastrointestinal cancers

Of the reported associations between diabetes mellitus and cancer (Table  1 ), some of the strongest have been demonstrated for gastrointestinal cancers.

Hepatocellular carcinoma

In the case of hepatocellular carcinoma, the most rigorous systematic review on the topic — comprising 18 cohort studies with a combined total of more than 3.5 million individuals — reported a summary relative risk (SRR) of 2.01 (95% confidence interval (CI) 1.61–2.51) for an association with diabetes mellitus 24 . This increased risk of hepatocellular carcinoma with diabetes mellitus is supported by the results of another systematic review that included case–control studies 25 . Another review also found that diabetes mellitus independently increased the risk of hepatocellular carcinoma in the setting of hepatitis C virus infection 26 .

Pancreatic cancer

The risk of pancreatic cancer appears to be approximately doubled in patients with T2DM compared with patients without T2DM. A meta-analysis of 36 studies found an adjusted odds ratio (OR) of 1.82 (95% CI 1.66–1.89) for pancreatic cancer among people with T2DM compared with patients without T2DM 27 (Table  1 ). However, it is possible that these findings are influenced by reverse causality — in this scenario, diabetes mellitus is triggered by undiagnosed pancreatic cancer 28 , with pancreatic cancer subsequently being clinically diagnosed only after the diagnosis of diabetes mellitus. Nevertheless, although the greatest risk (OR 2.05, 95% CI 1.87–2.25) of pancreatic cancer was seen in people diagnosed with T2DM 1–4 years previously compared with people without T2DM, those with a diagnosis of T2DM of more than 10 years remained at increased risk of pancreatic cancer (OR 1.51, 95% CI 1.16–1.96) 27 , suggesting that reverse causality can explain only part of the association between T2DM and pancreatic cancer. Although T2DM accounts for ~90% of all cases of diabetes mellitus 29 , a study incorporating data from five nationwide diabetes registries also reported an increased risk of pancreatic cancer amongst both male patients (HR 1.53, 95% CI 1.30–1.79) and female patients (HR 1.25, 95% CI 1.02–1.53) with T1DM 30 .

Colorectal cancer

For colorectal cancer, three systematic reviews have shown a consistent 20–30% increased risk associated with diabetes mellitus 31 , 32 , 33 . One systematic review, which included more than eight million people across 30 cohort studies, reported an incidence SRR of 1.27 (95% CI 1.21–1.34) of colorectal cancer 31 , independent of sex and family history (Table  1 ). Similar increases in colorectal cancer incidence in patients with diabetes mellitus were reported in a meta-analysis of randomized controlled trials (RCTs) and cohort studies 32 and in a systematic review that included cross-sectional studies 33 .

Female-specific cancers

Endometrial, breast and ovarian cancers all occur more frequently in women with diabetes mellitus than in women without diabetes mellitus.

Endometrial cancer

For endometrial cancer, one systematic review of 29 cohort studies and a combined total of 5,302,259 women reported a SRR of 1.89 (95% CI 1.46–2.45) and summary incidence rate ratio (IRR) of 1.61 (95% CI 1.51–1.71) 34 (Table  1 ). Similar increased risks were found in two systematic reviews incorporating cross-sectional studies 35 , 36 , one of which found a particularly strong association of T1DM (relative risk (RR) 3.15, 95% CI 1.07–9.29) with endometrial cancer.

Breast cancer

The best evidence for a link between diabetes mellitus and breast cancer comes from a systematic review of six prospective cohort studies and more than 150,000 women, in which the hazard ratio (HR) for the incidence of breast cancer in women with diabetes mellitus compared with women without diabetes mellitus was 1.23 (95% CI 1.12–1.34) 32 (Table  1 ). Two further systematic reviews have also shown this increased association 37 , 38 .

The association of diabetes mellitus with breast cancer appears to vary according to menopausal status. In a meta-analysis of studies of premenopausal women with diabetes mellitus, no significant association with breast cancer was found 39 , whereas in 11 studies that included only postmenopausal women, the SRR was 1.15 (95% CI 1.07–1.24). The difference in breast cancer risk between premenopausal and postmenopausal women with diabetes mellitus was statistically significant. The increased risk of breast cancer after menopause in women with diabetes mellitus compared with women without diabetes mellitus might result from the elevated concentrations and increased bioavailability of oestrogen that are associated with adiposity 40 , which is a common comorbidity in those with T2DM; oestrogen synthesis occurs in adipose tissue in postmenopausal women, while it is primarily gonadal in premenopausal women 41 . Notably, however, there is evidence that hormone-receptor-negative breast cancers, which typically carry a poor prognosis, occur more frequently in women with breast cancer and diabetes mellitus than in women with breast cancer and no diabetes mellitus 42 , indicating that non-hormonal mechanisms also occur.

Ovarian cancer

Diabetes mellitus also appears to increase the risk of ovarian cancer, with consistent results from across four systematic reviews. A pooled RR of 1.32 (95% CI 1.14–1.52) was reported across 15 cohort studies and a total of more than 2.3 million women 43 (Table  1 ). A SRR of 1.19 (95% CI 1.06–1.34) was found across 14 cohort studies and 3,708,313 women 44 . Similar risks were reported in meta-analyses that included cross-sectional studies 45 , 46 .

Male-specific cancers: prostate cancer

An inverse association between diabetes mellitus and prostate cancer has been observed in a systematic review (RR 0.91, 95% CI 0.86–0.96) 47 , and is probably due to reduced testosterone levels that occur secondary to the low levels of sex hormone-binding globulin that are commonly seen in men with T2DM and obesity 48 . Notably, however, the systematic review that showed the inverse association involved mostly white men (Table  1 ), whereas a systematic review of more than 1.7 million men from Taiwan, Japan, South Korea and India found that diabetes mellitus increased prostate cancer risk 49 , suggesting that ethnicity might be an effect modifier of the diabetes mellitus–prostate cancer relationship. The mechanisms behind this increased risk in men in regions of Asia such as Taiwan and Japan, where most study participants came from, remain unclear. Perhaps, as Asian men develop diabetes mellitus at lower levels of total adiposity than do white men 50 , the adiposity associated with diabetes mellitus in Asian men might have a lesser impact on sex hormone-binding globulin and testosterone than it does in white men. Despite the reported inverse association between diabetes mellitus and prostate cancer in white men, however, evidence suggests that prostate cancers that do develop in men with T2DM are typically more aggressive, conferring higher rates of disease-specific mortality than prostate cancers in men without diabetes mellitus 51 .

An assessment of cancer associations

As outlined above, a wealth of data has shown that diabetes mellitus is associated with an increased risk of various cancers. It has been argued, however, that some of these associations could be due to detection bias resulting from increased surveillance of people with diabetes mellitus in the immediate period after diagnosis 52 , or reverse causality, particularly in the case of pancreatic cancer 53 . However, neither phenomenon can account for the excess risks seen in the longer term. An Australian study exploring detection bias and reverse causality found that standardized mortality ratios (SMRs) for several cancer types in people with diabetes mellitus compared with the general population fell over time, but remained elevated beyond 2 years for pancreatic and liver cancers 54 , suggesting that diabetes mellitus is a genuine risk factor for these cancer types.

A limitation of the evidence that surrounds diabetes mellitus and cancer risk is high clinical and methodological heterogeneity across several of the large systematic reviews, which makes it difficult to be certain of the effect size in different demographic groups. Additionally, many of the studies exploring a potential association between diabetes mellitus and cancer were unable to adjust for BMI, which is a major confounder. However, a modelling study that accounted for BMI found that although 2.1% of cancers worldwide in 2012 were attributable to diabetes mellitus as an independent risk factor, twice as many cancers were attributable to high BMI 55 , so it is likely that effect sizes for cancer risk associated with diabetes mellitus would be attenuated after adjustment for BMI. Notably, however, low-income and middle-income countries/regions had the largest increase in the numbers of cases of cancer attributable to diabetes mellitus both alone and in combination with BMI 55 , highlighting the need for public health intervention, given that these countries/regions are less equipped than high-income countries/regions to manage a growing burden of cancer.

As well as the cancer types outlined above, diabetes mellitus has also been linked to various other types of cancer, including kidney cancer 56 , bladder cancer 57 and haematological malignancies; however, the evidence for these associations is not as strong as for the cancers discussed above 58 . Diabetes mellitus might also be associated with other cancer types such as small intestine cancer, but the rarity of some of these types makes it difficult to obtain sufficient statistical power in analyses of any potential association.

Potential aetiological mechanisms

Several aetiological mechanisms that might be involved in linking diabetes mellitus to cancer have been proposed, including hyperinsulinaemia, hyperglycaemia, inflammation and cellular signalling mechanisms.

Hyperinsulinaemia

Most cancer cells express insulin receptors, through which hyperinsulinaemia is thought to stimulate cancer cell proliferation and metastasis 59 . Hyperinsulinaemia might also promote carcinogenesis through increased local levels of insulin-like growth factor 1 (IGF1), which has potent mitogenic and anti-apoptotic activities 60 , owing to decreased levels of insulin-like growth factor binding proteins. As outlined above, people with diabetes mellitus show a strong risk of pancreatic and liver cancers; this increased risk might occur because insulin is produced by pancreatic β-cells and transported to the liver via the portal vein 61 , thereby exposing the liver and pancreas to high levels of endogenous insulin 59 .

Hyperglycaemia and inflammation

Hyperglycaemia can induce DNA damage 62 , increase the generation of reactive oxygen species 63 and downregulate antioxidant expression 64 , all of which are associated with cancer development. Inflammatory markers, including cytokines such as IL-6, appear to have an important role in the association between diabetes and cancer 65 .

Cellular signalling mechanisms

Several cellular signalling components are common to the pathogenesis of T2DM and cancer. These include the mechanistic target of rapamycin (mTOR), a central controller of cell growth and proliferation; AMP-activated protein kinase, a cellular energy sensor and signal transducer 66 ; and the phosphatidylinositol 3-kinase (PI3K)–AKT pathway, which transduces growth factor signals during organismal growth, glucose homeostasis and cell proliferation 67 . Dysregulation of any of these cellular signalling components or pathways could contribute to the development of cancer and metabolic disorders, including T2DM, and glucose-lowering drugs such as metformin have been associated with a reduction in cancer cell proliferation through effective inhibition of some of these components 68 .

Diabetes mellitus and infections

Infection-related complications.

Although infection has long been recognized as a complication of diabetes mellitus, an association between diabetes mellitus and infection has not been well documented in epidemiological studies 69 . Only in the past decade have major studies quantified the burden of infection-related complications in people with diabetes mellitus and explored the specific infections accounting for this burden. In a US cohort of 12,379 participants, diabetes mellitus conferred a significant risk of infection-related hospitalization, with an adjusted HR of 1.67 (95% CI 1.52–1.83) compared with people without diabetes mellitus 70 (Table  2 ). The association was most pronounced for foot infections (HR 5.99, 95% CI 4.38–8.19), with significant associations also observed for respiratory infection, urinary tract infection, sepsis and post-operative infection, but not for gastrointestinal infection, a category that included appendicitis and gastrointestinal abscesses but not viral or bacterial gastroenteritis. Interestingly, a report from Taiwan demonstrated an association between the use of metformin and a lower risk of appendicitis 71 .

In an analysis of the entire Hong Kong population over the period 2001–2016, rates of hospitalization for all types of infection remained consistently higher in people with diabetes mellitus than in those without diabetes mellitus 72 . The strongest association was seen for hospitalization due to kidney infections, for which the adjusted RR was 4.9 (95% CI 3.9–6.2) in men and 3.2 (95% CI 2.8–3.7) in women with diabetes mellitus compared with those without diabetes mellitus in 2016 (Table  2 ). Diabetes mellitus roughly doubled the risk of hospitalization from tuberculosis or sepsis. The most common cause of infection-related hospitalization was pneumonia, which accounted for 39% of infections across the study period, while no other single cause accounted for more than 25% of infections across the same period. Pneumonia-related hospitalization rates increased substantially from 2001 to 2005, probably as a result of the 2003 severe acute respiratory syndrome (SARS) epidemic and the decreased threshold for pneumonia hospitalization in the immediate post-epidemic period. Rates for hospitalization for influenza increased from 2002 to 2016, possibly because of changes in the virus and increased testing for influenza. Declining rates of hospitalization for tuberculosis, urinary tract infections, foot infections and sepsis could be due to improvements in the management of diabetes mellitus.

Infection-related mortality rates were found to be significantly elevated among 1,108,982 Australians with diabetes mellitus studied over the period 2000–2010 compared with rates in people without diabetes mellitus 73 . For overall infection-related mortality, SMRs were 4.42 (95% CI 3.68–5.34) for T1DM and 1.47 (95% CI 1.42–1.53) for people with T2DM compared with those without diabetes mellitus (Table  2 ). Substantially higher infection-related mortality rates were seen in people with T1DM compared with those with T2DM for all infection types, even after accounting for age. Hyperglycaemia is thought to be a driver of infection amongst people with diabetes mellitus (see below) 73 , which might explain the higher SMRs amongst people with T1DM, in whom hyperglycaemia is typically more severe, than in those with T2DM. The highest SMRs were seen for osteomyelitis, and SMRs for septicaemia and pneumonia were also greater than 1.0 for both types of diabetes mellitus compared with those without diabetes mellitus.

Post-operative infection

Post-operative infection is also an important complication of diabetes mellitus. In a meta-analysis, diabetes mellitus was found to be associated with an OR of 1.77 (95% CI 1.13–2.78) for surgical site infection across studies that adjusted for confounding factors 74 (Table  2 ). The effect size appears to be greatest after cardiac procedures, and one US study of patients undergoing coronary artery bypass grafting found diabetes mellitus to be an independent predictor of surgical site infection, with an OR of 4.71 (95% CI 2.39–9.28) compared with those without diabetes mellitus 75 . Risks of infection of more than threefold were reported in some studies of gynaecological 76 and spinal surgery 77 in people with diabetes mellitus compared with those without diabetes mellitus. Increased risks of infection among people with diabetes mellitus were also observed in studies of colorectal and breast surgery and arthroplasty, suggesting that the association between diabetes mellitus and post-operative infection is present across a wide range of types of surgery 74 .

Respiratory infections

The incidence of hospitalizations due to respiratory infections among people with diabetes mellitus was increasing substantially even before the onset of the coronavirus disease 2019 (COVID-19) pandemic, probably owing to increased life expectancy in these patients as well as an increased likelihood of them being hospitalized for conditions such as respiratory infections, which occur mostly in older age 12 . This rising burden of respiratory infection, in combination with the rising prevalence of diabetes mellitus, highlights the importance of addressing the emerging complications of diabetes mellitus to minimize impacts on health-care systems in current and future global epidemics.

Although diabetes mellitus does not appear to increase the risk of becoming infected with COVID-19 (ref. 78 ), various population-based studies have reported increased risks of COVID-19 complications among people with diabetes mellitus. In a study of the total Scottish population, people with diabetes mellitus were found to have an increased risk of fatal or critical care unit-treated COVID-19, with an adjusted OR of 1.40 (95% CI 1.30–1.50) compared with those without diabetes mellitus 79 (Table  2 ). The risk was particularly high for those with T1DM (OR 2.40, 95% CI 1.82–3.16) 79 . Both T1DM and T2DM have been linked to a more than twofold increased risk of hospitalization with COVID-19 in a large Swedish cohort study 80 . In South Korean studies, T2DM was linked to intensive care unit admission among patients with COVID-19 infection 81 , and diabetes mellitus (either T1DM or T2DM) was linked to a requirement for ventilation and oxygen therapy 82 in patients with COVID-19. Diabetes mellitus appears to be the primary predisposing factor for opportunistic infection with mucormycosis in individuals with COVID-19 (ref. 83 ). The evidence for diabetes mellitus as a risk factor for post-COVID-19 syndrome is inconclusive 84 , 85 . Interestingly, an increase in the incidence of T1DM during the COVID-19 pandemic has been reported in several countries/regions 86 , and some data suggest an increased risk of T1DM after COVID-19 infection 87 , but the evidence regarding a causal effect is inconclusive.

Pneumonia, MERS, SARS and H1N1 influenza

The data regarding diabetes mellitus and COVID-19 are consistent with the published literature regarding other respiratory infections, such as pneumonia, for which diabetes mellitus has been shown to increase the risk of hospitalization 88 and mortality 88 , with similar effect sizes to those seen for COVID-19, compared with no diabetes mellitus. Diabetes mellitus has also been also linked to adverse outcomes in people with Middle East respiratory syndrome (MERS), SARS and H1N1 influenza 89 , 90 , 91 , 92 , suggesting that mechanisms specific to COVID-19 are unlikely to be responsible for the relationship between diabetes mellitus and COVID-19. Unlike the case for COVID-19, there is evidence that people with diabetes mellitus are at increased risk of developing certain other respiratory infections, namely pneumonia 93 and possibly also MERS 94 .

The mechanisms that might link diabetes mellitus and infection include a reduced T cell response, reduced neutrophil function and disorders of humoral immunity.

Mononuclear cells and monocytes of individuals with diabetes mellitus secrete less IL-1 and IL-6 than the same cells from people without diabetes mellitus 95 . The release of IL-1 and IL-6 by T cells and other cell types in response to infection has been implicated in the response to several viral infections 96 . Thus, the reduced secretion of these cytokines in patients with diabetes mellitus might be associated with the poorer responses to infection observed among these patients compared with people without diabetes mellitus.

In the context of neutrophil function, hyperglycaemic states might give rise to reductions in the mobilization of polymorphonuclear leukocytes, phagocytic activity and chemotaxis 97 , resulting in a decreased immune response to infection. Additionally, increased levels of glucose in monocytes isolated from patients with obesity and/or diabetes mellitus have been found to promote viral replication in these cells, as well as to enhance the expression of several cytokines, including pro-inflammatory cytokines that are associated with the COVID-19 ‘cytokine storm’; furthermore, glycolysis was found to sustain the SARS coronavirus 2 (SARS-CoV-2)-induced monocyte response and viral replication 98 .

Elevated glucose levels in people with diabetes mellitus are also associated with an increase in glycation, which, by promoting a change in the structure and/or function of several proteins and lipids, is responsible for many of the complications of diabetes mellitus 99 . In people with diabetes mellitus, antibodies can become glycated, a process that is thought to impair their biological function 100 . Although the clinical relevance of this impairment is not clear, it could potentially explain the results of an Israeli study that reported reduced COVID-19 vaccine effectiveness among people with T2DM compared with those without T2DM 101 .

Diabetes mellitus and liver disease

Nonalcoholic fatty liver disease.

The consequences of nonalcoholic fatty liver disease (NAFLD) make it important to recognize the burden of this disease among people with diabetes mellitus. NAFLD and nonalcoholic steatohepatitis (NASH; an advanced form of NAFLD) are major causes of liver transplantation in the general population. In the USA, NASH accounted for 19% of liver transplantations in 2016 — second only to alcoholic liver disease, which was the cause of 24% of transplantations 102 . In Australia and New Zealand, NAFLD was the primary diagnosis in 9% of liver transplant recipients in 2019, only slightly below the figure for alcoholic cirrhosis of 13% 103 . In Europe, NASH increased as the reason for transplantations from 1% in 2002 to more than 8% in 2016, in parallel with the rising prevalence of diabetes mellitus 104 .

NAFLD is highly prevalent among people with T2DM. In a systematic review of 80 studies across 20 countries/regions, the prevalence of NAFLD among 49,419 people with T2DM was 56% 105 , while the global prevalence of NAFLD in the general population is estimated to be 25% 106 . In a Chinese cohort study of 512,891 adults, diabetes mellitus was associated with an adjusted HR of 1.76 (95% CI 1.47–2.16) for NAFLD compared with no diabetes mellitus 107 (Table  3 ). Another smaller longitudinal Chinese study also reported an increased risk of developing NAFLD among those with T2DM compared with those without T2DM 108 . However, most evidence regarding the association between NAFLD and diabetes mellitus is from cross-sectional studies 109 , 110 , 111 .

NASH and fibrosis

Diabetes mellitus appears to enhance the risk of NAFLD complications, including NASH and fibrosis. An analysis of 892 people with NAFLD and T2DM across 10 studies showed that the prevalence of NASH was 37% (ref. 105 ); figures for the prevalence of NASH in the general population with NAFLD vary greatly across different study populations, ranging from 16% to 68% 112 . Amongst 439 people with T2DM and NAFLD in seven studies, 17% had advanced fibrosis 105 . An analysis of 1,069 people with NAFLD in a US study found that diabetes mellitus was an independent predictor for NASH (OR 1.93, 95% CI 1.37–2.73) and fibrosis (3.31, 95% CI 2.26–4.85) 113 .

Bidirectional relationship between diabetes mellitus and liver disease

The relationship between diabetes mellitus and NAFLD is bidirectional, as NAFLD is associated with an increased risk of developing T2DM 114 . There is also a notable bidirectional relationship between diabetes mellitus and liver cirrhosis. The prevalence of diabetes mellitus in people with liver cirrhosis has been reported as 20–63%, depending on the severity of liver damage, aetiology and diagnostic criteria 115 . In an Italian study of 401 participants with cirrhosis, 63% of those with decompensated liver disease had diabetes mellitus compared with 10% of those with well-compensated liver disease 116 , suggesting that diabetes mellitus is more common in severe cases of liver damage. The association between diabetes mellitus and cirrhosis also varies according to the cause of liver disease. In a US study of 204 people with cirrhosis, the prevalence of diabetes mellitus was 25% among those with cirrhosis caused by hepatitis C virus, 19% among those with cirrhosis from alcoholic liver disease and only 1% among those with cirrhosis due to cholestatic liver disease 117 . Among the causes of cirrhosis, haemochromatosis has the strongest association with diabetes mellitus, with diabetes mellitus mainly resulting from the iron deposition that is characteristic of haemochromatosis 118 .

Several factors have been implicated in the aetiology of liver disease in people with diabetes mellitus, with insulin resistance being the most notable 119 .

Insulin resistance

Insulin resistance causes lipolysis, thereby increasing the circulating levels of free fatty acids, which are then taken up by the liver as an energy source 120 . These fatty acids overload the mitochondrial β-oxidation system in the liver, resulting in the accumulation of fatty acids and, consequently, NAFLD 121 . Of those individuals with NAFLD, 2–3% develop hepatic inflammation, necrosis and fibrosis, which are the hallmarks of NASH 122 . The exact mechanisms leading to steatohepatitis are unclear, although dysregulated peripheral lipid metabolism appears to be important 14 .

Ectopic adipose deposition

Excessive or ectopic deposition of adipose tissue around the viscera and in the liver might be an important mechanism underlying both T2DM and liver disease, particularly NAFLD 123 . Dysfunction of long-term adipose storage in white adipose tissue is known to lead to ectopic adipose deposition in the liver. In this state, increased levels of fatty acyl-coenzyme As, the activated form of fatty acids, might lead to organ dysfunction, including NAFLD 124 . Ectopic adipose deposition leading to organ-specific insulin resistance has emerged as a major hypothesis for the pathophysiological basis of T2DM, and ectopic adipose in the pancreas could contribute to β-cell dysfunction and, thus, the development of T2DM 125 .

Diabetes mellitus and affective disorders

The prevalence of depression appears to be high among people with diabetes mellitus. The strongest evidence for an association comes from a systematic review of 147 studies among people with T2DM, which revealed a mean prevalence of depression of 28% 126 , while the global prevalence of depression in the general population is estimated at around 13% 127 . For T1DM, a systematic review reported a pooled prevalence of depression of 12% compared with only 3% in those without T1DM 128 . The risk of depression among people with diabetes mellitus appears to be roughly 25% greater than the risk in the general population, with consistent findings across several meta-analyses (Table  4 ). A 2013 study found an adjusted RR of 1.25 (95% CI 1.10–1.44) for incident depression among people with diabetes mellitus compared with those without diabetes mellitus 129 . Another systematic review of people with T2DM reported a near identical effect size 130 .

Anxiety and eating disorders

Evidence exists for an association of diabetes mellitus with anxiety, and of T1DM with eating disorders. In a systematic review involving 2,584 individuals with diabetes mellitus, a prevalence of 14% was found for generalized anxiety disorder and 40% for anxiety symptoms, whereas the prevalence of generalized anxiety disorder in the general population is estimated as only 3–4% 131 . People with diabetes mellitus had an increased risk of anxiety disorders (OR 1.20, 95% CI 1.10–1.31) and anxiety symptoms (OR 1.48, 95% CI 1.02–1.93) compared with those without diabetes mellitus in a meta-analysis 132 (Table  4 ), although these findings were based on cross-sectional data. Across 13 studies, 7% of adolescents with T1DM were found to have eating disorders, compared with 3% of peers without diabetes mellitus 133 .

Broader psychological impacts

There is a substantial literature on a broad range of psychological impacts of diabetes mellitus. Social stigma 134 can have profound impacts on the quality of life of not only people with diabetes mellitus, but their families and carers, too 135 . In a systematic review, diabetes mellitus distress was found to affect around one-third of adolescents with T1DM, which was consistent with the results of studies of adults with diabetes mellitus 136 . Diabetes mellitus burnout appears to be a distinct concept, and is characterized by exhaustion and detachment, accompanied by the experience of a loss of control over diabetes mellitus 137 .

Diabetes mellitus and depression appear to have common biological origins. Activation of the innate immune system and acute-phase inflammation contribute to the pathogenesis of T2DM — increased levels of inflammatory cytokines predict the onset of T2DM 138 — and there is growing evidence implicating cytokine-mediated inflammation in people with depression in the absence of diabetes mellitus 139 . Dysregulation of the hypothalamic–pituitary–adrenal axis is another potential biological mechanism linking depression and diabetes mellitus 140 . There have been numerous reports of hippocampal atrophy, which might contribute to chronic activation of the hypothalamic–pituitary–adrenal axis, in individuals with T2DM as well as those with depression 141 , 142 . A meta-analysis found that, although hypertension modified global cerebral atrophy in those with T2DM, it had no effect on hippocampal atrophy 143 . This suggests that, although global cerebral atrophy in individuals with T2DM might be driven by atherosclerotic disease, hippocampal atrophy is an independent effect that provides a common neuropathological aetiology for the comorbidity of T2DM with depression. There is a lack of relevant information regarding the potential aetiological mechanisms that link diabetes to other affective disorders.

Diabetes mellitus and sleep disturbance

Obstructive sleep apnoea.

Obstructive sleep apnoea (OSA) is highly prevalent among people with diabetes mellitus. In a systematic review of 41 studies of adults with diabetes mellitus, the prevalence of OSA was found to be 60% 144 , whereas reports for OSA prevalence in the general population range from 9% to 38% 145 . In a UK study of 1,656,739 participants, T2DM was associated with an IRR for OSA of 1.48 (95% CI 1.42–1.55) compared with no T2DM 146 . A population-based US study reported a HR of 1.53 (95% CI 1.32–1.77) for OSA in people with T2DM compared with those without diabetes mellitus 147 . However, the association in this latter report was attenuated after adjustment for BMI and waist circumference (1.08, 95% CI 1.00–1.16), suggesting that the excess risk of OSA among people with diabetes mellitus might be mainly explained by the comorbidity of obesity. Although most studies on OSA have focused on T2DM, a meta-analysis of people with T1DM revealed a similar prevalence of 52% 148 ; however, this meta-analysis was limited to small studies. The association between T2DM and OSA is bidirectional: the severity of OSA was shown to be positively associated with the incidence of T2DM, independent of adiposity, in a large US cohort study 149 .

The mechanism by which T2DM might increase the risk of developing OSA is thought to involve dysregulation of the autonomic nervous system leading to sleep-disordered breathing 150 . Conversely, the specific mechanism behind OSA as a causative factor for T2DM remains poorly understood. It has been suggested that OSA is able to induce insulin resistance 151 , 152 and is a risk factor for the development of glucose intolerance 152 . However, once T2DM has developed, there is no clear evidence that OSA worsens glycaemic control, as an RCT of people with T2DM found that treating OSA had no effect on glycaemic control 153 .

Diabetes mellitus and cognitive disability

Dementia and cognitive impairment.

Dementia is emerging as a major cause of mortality in both individuals with diabetes mellitus and the general population, and is now the leading cause of death in some countries/regions 9 . However, compared with the general population, diabetes mellitus increases the risk of dementia, particularly vascular dementia. The association is supported by several systematic reviews, including one of eight population-based studies with more than 23,000 people, which found SRRs of 2.38 (95% CI 1.79–3.18) for vascular dementia and 1.39 (95% CI 1.16–1.66) for Alzheimer disease comparing people with diabetes mellitus with those without diabetes mellitus 154 (Table  4 ). Similar results, as well as a RR of 1.21 (95% CI 1.02–1.45) for mild cognitive impairment (MCI), were reported across 19 population-based studies of 44,714 people, 6,184 of whom had diabetes mellitus 155 . Two meta-analyses of prospective cohort studies have shown increased risks of all-cause dementia in people with diabetes mellitus compared with those without diabetes mellitus 156 , 157 , and T2DM has been shown to increase progression to dementia in people with MCI 158 .

The boundaries between Alzheimer disease and vascular dementia remain controversial, and these conditions are often difficult to differentiate clinically 159 . Consequently, vascular dementia might have been misdiagnosed as Alzheimer disease in some studies investigating diabetes mellitus and dementia, resulting in an overestimation of the effect size of the association between diabetes mellitus and Alzheimer disease. Although a cohort study found a significant association between diabetes mellitus and Alzheimer disease using imaging 160 , autopsy studies have failed to uncover an association between diabetes mellitus and Alzheimer disease pathology 161 , 162 , suggesting that vascular mechanisms are the key driver of cognitive decline in people with diabetes mellitus.

Another important finding is a 45% prevalence of MCI among people with T2DM in a meta-analysis, compared with a prevalence of 3–22% reported for the general population 163 . Notably, however, the prevalence of MCI in individuals with T2DM was similar in people younger than 60 years (46%) and those older than 60 years (44%), which is at odds with previous research suggesting that MCI is most common in older people, particularly those aged more than 65 years 164 However, another meta-analysis found cognitive decline in people with T2DM who are younger than 65 years 165 , suggesting that a burden of cognitive disease exists among younger people with diabetes mellitus.

Although there is solid evidence that links diabetes mellitus to cognitive disability, our understanding of the underlying mechanisms is incomplete. Mouse models suggest a strong association between hyperglycaemia, the advanced glycation end products glyoxal and methylglyoxal, enhanced blood–brain barrier (BBB) permeability and cognitive dysfunction in both T1DM and T2DM 166 . The BBB reduces the access of neurotoxic compounds and pathogens to the brain and sustains brain homeostasis, so disruption to the BBB can result in cognitive dysfunction through dysregulation of transport of molecules between the peripheral circulation and the brain 167 . There appears to be a continuous relationship between glycaemia and cognition, with associations found between even high-normal blood levels of glucose and cognitive decline 168 . Another hypothetical mechanism involves a key role for impaired insulin signalling in the pathogenesis of Alzheimer disease. Brain tissue obtained post mortem from individuals with Alzheimer disease showed extensive abnormalities in insulin and insulin-like growth factor signalling mechanisms compared with control brain tissue 169 . Although the synthesis of insulin-like growth factors occurred normally in people with Alzheimer disease, their expression levels were markedly reduced, which led to the subsequent proposal of the term ‘type 3 diabetes’ to characterize Alzheimer disease.

Diabetes mellitus and disability

Functional disability.

Disability (defined as a difficulty in functioning in one or more life domains as experienced by an individual with a health condition in interaction with contextual factors) 170 is highly prevalent in people with diabetes mellitus. In a systematic review, lower-body functional limitation was found to be the most prevalent disability (47–84%) among people with diabetes mellitus 171 The prevalence of difficulties with activities of daily living among people with diabetes mellitus ranged from 12% to 55%, although most studies were conducted exclusively in individuals aged 60 years and above, so the results are not generalizable to younger age groups. A systematic review showed a significant association between diabetes mellitus and falls in adults aged 60 years and above 172 . A 2013 meta-analysis 173 showed an increased risk of mobility disability, activities of daily living disability and independent activities of daily living disability among people with diabetes mellitus compared with those without diabetes mellitus (Table  4 ). Although this analysis included cross-sectional data, results were consistent across longitudinal and cross-sectional studies, suggesting little effect of reverse causality. However, people with functional disabilities that limit mobility (for example, people with osteoarthritis or who have had a stroke) might be more prone to developing diabetes mellitus owing to physical inactivity 174 .

Workplace productivity

Decreased productivity while at work, increased time off work and early dropout from the workforce 175 are all associated with diabetes mellitus, probably partly due to functional disability, and possibly also to comorbidities such as obesity and physical inactivity 176 . Given that young-onset diabetes is becoming more common, and most people with diabetes mellitus in middle-income countries/regions are less than 65 years old 177 , a pandemic of diabetes mellitus-related work disability among a middle-aged population does not bode well for the economies of these regions.

The mechanisms by which diabetes mellitus leads to functional disability remain unclear. One suggestion is that hyperglycaemia leads to systemic inflammation, which is one component of a multifactorial process that results in disability 154 . The rapid loss of skeletal muscle strength and quality seen among people with diabetes mellitus might be another cause of functional disability 178 (Box  1 ). In addition, complications of diabetes mellitus, including stroke, peripheral neuropathy and cardiac dysfunction, can obviously directly cause disability 179 .

Box 1 Diabetes mellitus and skeletal muscle atrophy

Individuals with diabetes mellitus exhibit skeletal muscle atrophy that is typically mild in middle age and becomes more substantial with increasing age.

This muscle loss leads to reduced strength and functional capacity and, ultimately, increased mortality.

Skeletal muscle atrophy results from a negative balance between the rate of synthesis and degradation of contractile proteins, which occurs in response to disuse, ageing and chronic diseases such as diabetes mellitus.

Degradation of muscle proteins is more rapid in diabetes mellitus, and muscle protein synthesis has also been reported to be decreased.

Proposed mechanisms underlying skeletal muscle atrophy include systemic inflammation (affecting both protein synthesis and degradation), dysregulation of muscle protein anabolism and lipotoxicity.

Mouse models have also revealed a key role for the WWP1/KLF15 pathway, mediated by hyperglycaemia, in the pathogenesis of muscle atrophy.

See refs 195 , 196 , 197 , 198 .

Diabetes management and control

Although a detailed discussion of the impacts of anti-diabetes mellitus medications and glucose control on emerging complications is beyond the scope of this Review, their potential effect on these complications must be acknowledged.

Medications

Anti-diabetes mellitus medications and cancer.

In the case of cancer as an emerging complication, the use of medications for diabetes mellitus was not controlled for in most studies of diabetes mellitus and cancer and might therefore be a confounding factor. People taking metformin have a lower cancer risk than those not taking metformin 180 . However, this association is mainly accounted for by other factors. For example, metformin is less likely to be administered to people with diabetes mellitus who have kidney disease 181 , who typically have longer duration diabetes mellitus, which increases cancer risk. A review of observational studies into the association between metformin and cancer found that many studies reporting significant reductions in cancer incidence or mortality associated with metformin were affected by immortal time bias and other time-related biases, casting doubt on the ability of metformin to reduce cancer mortality 182 . Notably, the use of insulin was associated with an increased risk of several cancers in a meta-analysis 183 . However, in an RCT of more than 12,000 people with dysglycaemia, randomization to insulin glargine (compared with standard care) did not increase cancer incidence 184 . Furthermore, cancer rates in people with T1DM and T2DM do not appear to vary greatly, despite substantial differences in insulin use between people with these types of diabetes mellitus.

Anti-diabetes mellitus medications and other emerging complications

Anti-diabetes medications appear to affect the onset and development of some other emerging complications of diabetes mellitus. Results from RCTs suggest that metformin might confer therapeutic effects against depression 185 , and its use was associated with reduced dementia incidence in a systematic review 186 . In an RCT investigating a potential association between metformin and NAFLD, no improvement in NAFLD histology was found among people using metformin compared with those given placebo 187 . An RCT reported benefits of treatment with the glucagon-like peptide 1 receptor agonist dulaglutide on cognitive function in a post hoc analysis 188 , suggesting that trials designed specifically to test the effects of dulaglutide on cognitive function should be undertaken.

Glucose control

Another important consideration is glycaemic control, which appears to have variable effects on emerging complications. A meta-analysis found no association of glycaemic control with cancer risk among those with diabetes mellitus 189 , and an RCT found no effect of intensive glucose lowering on cognitive function in people with T2DM 190 . However, glycaemic control has been associated with improved physical function 191 , decreased COVID-19 mortality 192 and a decreased risk of NAFLD 193 in observational studies of patients with diabetes mellitus; notably, no RCTs have yet confirmed these associations.

Conclusions

With advances in the management of diabetes mellitus and associated increased life expectancy, the face of diabetes mellitus complications is changing. As the management of glycaemia and traditional complications of diabetes mellitus is optimized, we are beginning instead to see deleterious effects of diabetes mellitus on the liver, brain and other organs. Given the substantial burden and risk of these emerging complications, future clinical and public health strategies should be updated accordingly. There is a need to increase the awareness of emerging complications among primary care physicians at the frontline of diabetes mellitus care, and a place for screening for conditions such as depression, liver disease and cancers in diabetes mellitus guidelines should be considered. Clinical care for older people with diabetes mellitus should target physical activity, particularly strength-based activity, to reduce the risk of functional disability in ageing populations. Ongoing high-quality surveillance of diabetes mellitus outcomes is imperative to ensure we know where the main burdens lie. Given the growing burden of these emerging complications, the traditional management of diabetes mellitus might need to broaden its horizons.

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Acknowledgements

D.T. is supported by an Australian Government Research Training Program (RTP) Scholarship and Monash Graduate Excellence Scholarship. J.E.S. is supported by a National Health and Medical Research Council Investigator Grant. D.J.M. is supported by a National Health and Medical Research Council Senior Research Fellowship.

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Dunya Tomic, Jonathan E. Shaw & Dianna J. Magliano

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D.T. researched data for the article and wrote the article. J.E.S and D.J.M. contributed substantially to discussion of the content. D.T., J.E.S. and D.J.M reviewed and/or edited the manuscript before submission.

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Tomic, D., Shaw, J.E. & Magliano, D.J. The burden and risks of emerging complications of diabetes mellitus. Nat Rev Endocrinol 18 , 525–539 (2022). https://doi.org/10.1038/s41574-022-00690-7

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research paper on diabetes 2

Type 2 diabetes

Affiliations.

  • 1 Diabetes Research Centre, University of Leicester and the Leicester NIHR Biomedical Research Centre, Leicester General Hospital, Leicester, UK.
  • 2 Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea.
  • 3 Family Medicine Department, Korle Bu Teaching Hospital, Accra Ghana and Community Health Department, University of Ghana Medical School, Accra, Ghana.
  • 4 Diabetes Research Centre, University of Leicester and the Leicester NIHR Biomedical Research Centre, Leicester General Hospital, Leicester, UK. Electronic address: [email protected].
  • PMID: 36332637
  • DOI: 10.1016/S0140-6736(22)01655-5

Type 2 diabetes accounts for nearly 90% of the approximately 537 million cases of diabetes worldwide. The number affected is increasing rapidly with alarming trends in children and young adults (up to age 40 years). Early detection and proactive management are crucial for prevention and mitigation of microvascular and macrovascular complications and mortality burden. Access to novel therapies improves person-centred outcomes beyond glycaemic control. Precision medicine, including multiomics and pharmacogenomics, hold promise to enhance understanding of disease heterogeneity, leading to targeted therapies. Technology might improve outcomes, but its potential is yet to be realised. Despite advances, substantial barriers to changing the course of the epidemic remain. This Seminar offers a clinically focused review of the recent developments in type 2 diabetes care including controversies and future directions.

Copyright © 2022 Elsevier Ltd. All rights reserved.

Publication types

  • Diabetes Mellitus, Type 2* / drug therapy
  • Diabetes Mellitus, Type 2* / epidemiology
  • Pharmacogenetics
  • Precision Medicine
  • Young Adult

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