research papers on diabetes mellitus

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• World J Diabetes
• v.6(6); 2015 Jun 25

Diabetes mellitus: The epidemic of the century

Correspondence to: Akram T Kharroubi, PhD, Associate Professor of Biochemistry and Endocrinology, Dean of Faculty of Health Professions, Department of Medical Laboratory Sciences, Faculty of Health Professions, Al-Quds University, P.O. Box 51000, Abed Elhamaid Shoman Street, Beit Hanina-Jerusalem, Jerusalem 91000, Palestine. [email protected]

Telephone: +972-2-2791243 Fax: +972-2-2791243

The epidemic nature of diabetes mellitus in different regions is reviewed. The Middle East and North Africa region has the highest prevalence of diabetes in adults (10.9%) whereas, the Western Pacific region has the highest number of adults diagnosed with diabetes and has countries with the highest prevalence of diabetes (37.5%). Different classes of diabetes mellitus, type 1, type 2, gestational diabetes and other types of diabetes mellitus are compared in terms of diagnostic criteria, etiology and genetics. The molecular genetics of diabetes received extensive attention in recent years by many prominent investigators and research groups in the biomedical field. A large array of mutations and single nucleotide polymorphisms in genes that play a role in the various steps and pathways involved in glucose metabolism and the development, control and function of pancreatic cells at various levels are reviewed. The major advances in the molecular understanding of diabetes in relation to the different types of diabetes in comparison to the previous understanding in this field are briefly reviewed here. Despite the accumulation of extensive data at the molecular and cellular levels, the mechanism of diabetes development and complications are still not fully understood. Definitely, more extensive research is needed in this field that will eventually reflect on the ultimate objective to improve diagnoses, therapy and minimize the chance of chronic complications development.

Core tip: Diabetes mellitus is rising to an alarming epidemic level. Early diagnosis of diabetes and prediabetes is essential using recommended hemoglobin A1c criteria for different types except for gestational diabetes. Screening for diabetes especially in underdeveloped countries is essential to reduce late diagnosis. Diabetes development involves the interaction between genetic and non-genetic factors. Biomedical research continues to provide new insights in our understanding of the mechanism of diabetes development that is reviewed here. Recent studies may provide tools for the use of several genes as targets for risk assessment, therapeutic strategies and prediction of complications.

DEFINITION OF DIABETES MELLITUS

Diabetes mellitus is a group of metabolic diseases characterized by chronic hyperglycemia resulting from defects in insulin secretion, insulin action, or both. Metabolic abnormalities in carbohydrates, lipids, and proteins result from the importance of insulin as an anabolic hormone. Low levels of insulin to achieve adequate response and/or insulin resistance of target tissues, mainly skeletal muscles, adipose tissue, and to a lesser extent, liver, at the level of insulin receptors, signal transduction system, and/or effector enzymes or genes are responsible for these metabolic abnormalities. The severity of symptoms is due to the type and duration of diabetes. Some of the diabetes patients are asymptomatic especially those with type 2 diabetes during the early years of the disease, others with marked hyperglycemia and especially in children with absolute insulin deficiency may suffer from polyuria, polydipsia, polyphagia, weight loss, and blurred vision. Uncontrolled diabetes may lead to stupor, coma and if not treated death, due to ketoacidosis or rare from nonketotic hyperosmolar syndrome[ 1 - 3 ].

CLASSIFICATION OF DIABETES MELLITUS

Although classification of diabetes is important and has implications for the treatment strategies, this is not an easy task and many patients do not easily fit into a single class especially younger adults[ 1 , 4 - 6 ] and 10% of those initially classified may require revision[ 7 ]. The classical classification of diabetes as proposed by the American Diabetes Association (ADA) in 1997 as type 1, type 2, other types, and gestational diabetes mellitus (GDM) is still the most accepted classification and adopted by ADA[ 1 ]. Wilkin[ 8 ] proposed the accelerator hypothesis that argues “type 1 and type 2 diabetes are the same disorder of insulin resistance set against different genetic backgrounds”[ 9 ]. The difference between the two types relies on the tempo, the faster tempo reflecting the more susceptible genotype and earlier presentation in which obesity, and therefore, insulin resistance, is the center of the hypothesis. Other predictors of type 1 diabetes include increased height growth velocity[ 10 , 11 ] and impaired glucose sensitivity of β cells[ 12 ]. The implications of increased free radicals, oxidative stress, and many metabolic stressors in the development, pathogenesis and complications of diabetes mellitus[ 13 - 18 ] are very strong and well documented despite the inconsistency of the clinical trials using antioxidants in the treatment regimens of diabetes[ 19 - 21 ]. The female hormone 17-β estradiol acting through the estrogen receptor-α (ER-α) is essential for the development and preservation of pancreatic β cell function since it was clearly demonstrated that induced oxidative stress leads to β-cell destruction in ER-α knockout mouse. The ER-α receptor activity protects pancreatic islets against glucolipotoxicity and therefore prevents β-cell dysfunction[ 22 ].

TYPE 1 DIABETES MELLITUS

Autoimmune type 1 diabetes.

This type of diabetes constitutes 5%-10% of subjects diagnosed with diabetes[ 23 ] and is due to destruction of β cells of the pancreas[ 24 , 25 ]. Type 1 diabetes accounts for 80%-90% of diabetes in children and adolescents[ 2 , 26 ]. According to International Diabetes Federation (IDF), the number of youth (0-14 years) diagnosed with type 1 diabetes worldwide in 2013 was 497100 (Table ​ (Table1) 1 ) and the number of newly diagnosed cases per year was 78900[ 27 ]. These figures do not represent the total number of type 1 diabetes patients because of the high prevalence of type 1 diabetes in adolescence and adults above 14 years of age. One reported estimate of type 1 diabetes in the United States in 2010 was 3 million[ 28 , 29 ]. The number of youth in the United States younger than 20 years with type 1 diabetes was estimated to be 166984 in the year 2009[ 30 ]. The prevalence of type 1 diabetes in the world is not known but in the United States in youth younger than 20 years was 1.93 per 1000 in 2009 (0.35-2.55 in different ethnic groups) with 2.6%-2.7% relative annual increase[ 26 , 31 ]. Type 1 diabetes is mainly due to an autoimmune destruction of the pancreatic β cells through T-cell mediated inflammatory response (insulitis) as well as a humoral (B cell) response[ 25 ]. The presence of autoantibodies against the pancreatic islet cells is the hallmark of type 1 diabetes, even though the role of these antibodies in the pathogenesis of the disease is not clear. These autoantibodies include islet cell autoantibodies, and autoantibodies to insulin (IAA), glutamic acid decarboxylase (GAD, GAD65), protein tyrosine phosphatase (IA2 and IA2β) and zinc transporter protein (ZnT8A)[ 32 ]. These pancreatic autoantibodies are characteristics of type 1 diabetes and could be detected in the serum of these patients months or years before the onset of the disease[ 33 ]. Autoimmune type 1 diabetes has strong HLA associations, with linkage to DR and DQ genes. HLA-DR/DQ alleles can be either predisposing or protective[ 1 ]. This autoimmune type 1 diabetes is characterized by the absence of insulin secretion and is more dominant in children and adolescents.

Number of subjects with type 1 diabetes in children (0-14 years), with diabetes in adults (20-79 years) and with hyperglycemia (type 2 or gestational diabetes) in pregnancy (20-49 years)

Data extracted from International Diabetes Federation Diabetes Atlas, 6th ed, 2013.

In addition to the importance of genetic predisposition in type 1 diabetes, several environmental factors have been implicated in the etiology of the disease[ 9 , 33 ]. Viral factors include congenital rubella[ 34 , 35 ], viral infection with enterovirus, rotavirus, herpes virus, cytomegalovirus, endogenous retrovirus[ 36 , 37 ] and Ljungan virus. Other factors include low vitamin D levels[ 38 ], prenatal exposure to pollutants, improved hygiene and living conditions decreased childhood infections in countries with high socioeconomic status leading to increased autoimmune diseases (hygiene hypothesis), early infant nutrition such as using cow’s milk formula instead of breast feeding[ 39 ] in addition to insulin resistance in early childhood due to obesity or increased height growth velocity. The role of environmental factors remains controversial[ 40 ]. Recent evidence supported the causative effect of viral infections in diabetes[ 41 - 43 ].

Type 1 diabetes often develops suddenly and can produce symptoms such as polydipsia, polyuria, enuresis, lack of energy, extreme tiredness, polyphagia, sudden weight loss, slow-healing wounds, recurrent infections and blurred vision[ 27 ] with severe dehydration and diabetic ketoacidosis in children and adolescents. The symptoms are more severe in children compared to adults. These autoimmune type 1 diabetes patients are also prone to other autoimmune disorders such as Graves’ disease, Hashimoto’s thyroiditis, Addison’s disease, vitiligo, celiac sprue, autoimmune hepatitis, myasthenia gravis, and pernicious anemia[ 1 ]. The complete dependence on insulin of type 1 diabetes patients may be interrupted by a honeymoon phase which lasts weeks to months or in some cases 2-3 years. In some children, the requirement for insulin therapy may drop to a point where insulin therapy could be withdrawn temporarily without detectable hyperglycemia[ 44 ].

Idiopathic type 1 diabetes

A rare form of type 1 diabetes of unknown origin (idiopathic), less severe than autoimmune type 1 diabetes and is not due to autoimmunity has been reported. Most patients with this type are of African or Asian descent and suffer from varying degrees of insulin deficiency and episodic ketoacidosis[ 45 ].

Fulminant type 1 diabetes

This is a distinct form of type 1 diabetes, first described in the year 2000, and has some common features with idiopathic type 1 diabetes being non-immune mediated[ 46 , 47 ]. It is characterized by ketoacidosis soon after the onset of hyperglycemia, high glucose levels (≥ 288 mg/dL) with undetectable levels of serum C-peptide, an indicator of endogenous insulin secretion[ 48 ]. It has been described mainly in East Asian countries and accounted for approximately 20% of acute-onset type 1 diabetes patients in Japan (5000-7000 cases) with an extremely rapid and almost complete beta-cell destruction resulting in nearly no residual insulin secretion[ 48 , 49 ]. Both genetic and environmental factors, especially viral infection, have been implicated in the disease. Anti-viral immune response may trigger the destruction of pancreatic beta cells through the accelerated immune reaction with no detectable autoantibodies against pancreatic beta cells[ 48 , 50 ]. Association of fulminant type 1 diabetes with pregnancy has also been reported[ 51 ].

TYPE 2 DIABETES MELLITUS

The global prevalence of diabetes in adults (20-79 years old) according to a report published in 2013 by the IDF was 8.3% (382 million people), with 14 million more men than women (198 million men vs 184 million women), the majority between the ages 40 and 59 years and the number is expected to rise beyond 592 million by 2035 with a 10.1% global prevalence. With 175 million cases still undiagnosed, the number of people currently suffering from diabetes exceeds half a billion. An additional 21 million women are diagnosed with hyperglycemia during pregnancy. The Middle East and North Africa region has the highest prevalence of diabetes (10.9%), however, Western Pacific region has the highest number of adults diagnosed with diabetes (138.2 millions) and has also countries with the highest prevalence (Figure ​ (Figure1 1 )[ 27 ]. Low- and middle-income countries encompass 80% of the cases, “where the epidemic is gathering pace at alarming rates”[ 27 ]. Despite the fact that adult diabetes patients are mainly type 2 patients, it is not clear whether the reported 382 million adults diagnosed with diabetes also include type 1 diabetes patients.

Comparative prevalence of diabetes in adults (20-79 years) in countries with high prevalence (≥ 10%). Data extracted from International Diabetes Federation Diabetes Atlas, 6th ed, 2013.

More than 90%-95% of diabetes patients belong to this type and most of these patients are adults. The number of youth (less than 20 years) with type 2 diabetes in the United States in the year 2009 was 0.46 in 1000 and accounted for approximately 20% of type 2 diabetes in youth[ 26 ]. The increased incidence of type 2 diabetes in youth is mainly due to the change in the lifestyle of the children in terms of more sedentary life and less healthy food. Obesity is the major reason behind insulin resistance which is mainly responsible for type 2 diabetes[ 52 - 54 ]. The ADA recommends screening of overweight children and adolescence to detect type 2 diabetes[ 55 , 56 ]. The prevalence of obesity in children in on the rise[ 6 ] which is probably the main reason for the increased incidence of type 2 diabetes in the young (30.3% overall increase in type 2 diabetes in children and adolescence between 2001 and 2009)[ 26 ].

Insulin resistance in type 2 diabetes patients increases the demand for insulin in insulin-target tissues. In addition to insulin resistance, the increased demand for insulin could not be met by the pancreatic β cells due to defects in the function of these cells[ 18 ]. On the contrary, insulin secretion decreases with the increased demand for insulin by time due to the gradual destruction of β cells[ 57 ] that could transform some of type 2 diabetes patients from being independent to become dependent on insulin. Most type 2 diabetes patients are not dependent on insulin where insulin secretion continues and insulin depletion rarely occurs. Dependence on insulin is one of the major differences from type 1 diabetes. Other differences include the absence of ketoacidosis in most patients of type 2 diabetes and autoimmune destruction of β cells does not occur. Both type 1 and type 2 diabetes have genetic predisposition, however, it is stronger in type 2 but the genes are more characterized in type 1 (the TCF7L2 gene is strongly associated with type 2 diabetes)[ 58 ]. Due to the mild symptoms of type 2 diabetes in the beginning, its diagnosis is usually delayed for years especially in countries where regular checkup without symptoms is not part of the culture. This delay in diagnosis could increase the incidence of long-term complications in type 2 diabetes patients since hyperglycemia is not treated during this undiagnosed period.

In addition to diabetes, insulin resistance has many manifestations that include obesity, nephropathy, essential hypertension, dyslipidemia (hypertriglyceridemia, low HDL, decreased LDL particle diameter, enhanced postprandial lipemia and remnant lipoprotein accumulation), ovarian hyperandrogenism and premature adrenarche, non-alcoholic fatty liver disease and systemic inflammation[ 6 , 54 ]. The presence of type 2 diabetes in children and adolescence who are not obese[ 59 - 61 ], the occasional severe dehydration and the presence of ketoacidosis in some pediatric patients with type 2 diabetes[ 55 ] had led to the misclassification of type 2 to type 1 diabetes.

Some patients with many features of type 2 diabetes have some type 1 characteristics including the presence of islet cell autoantibodies or autoantibodies to GAD65 are classified as a distinct type of diabetes called latent autoimmune diabetes in adults (LADA)[ 62 ]. People diagnosed with LADA do not require insulin treatment. In a recent study, Hawa et al[ 63 ] reported 7.1% of European patients with type 2 diabetes with a mean age of 62 years, tested positive for GAD autoantibodies and the prevalence of LADA was higher in patients diagnosed with diabetes at a younger age. This classification of LADA as a distinct type of diabetes is still controversial[ 6 , 64 - 66 ].

Insulin resistance and signaling

Defects in the insulin-dependent substrate proteins IRS-1 and IRS-2 mediated signaling pathway are implicated in the development of metabolic disorders, mainly diabetes. This pathway mediates the cellular response to insulin and involves a large array of insulin-stimulated protein kinases including the serine/threonine kinase AKT and protein kinase C (PKC) that phosphorylate a large number of Ser/Thr residues in the insulin receptor substrate (IRS) proteins involved in the metabolic response to insulin[ 67 ]. In addition, other non-insulin dependent kinases including the AMP-activated protein kinase, c-Jun N-terminal protein kinase and G protein-coupled receptor kinase 2 that are activated under various conditions can phosphorylate the two insulin responsive substrates[ 67 - 71 ]. Disruption in the AKT and PKC kinases is central to the development of diabetes[ 72 ] and is associated with all major features of the disease including hyperinsulinemia, dyslipidemia and insulin resistance[ 73 ]. Replacing the wild type IRS-1 with a mutant version of the protein having alanine instead of tyrosine in three locations using genetic knock-in approach provided evidence to the central role of IRS-1 phosphorylation in the development of insulin resistance[ 74 ]. Using a similar approach to generate IRS-1 mutant with a single mutation involving a specific tyrosine residue, confirmed the role of IRS-1 phosphorylation in the development of insulin resistance pathogenesis[ 75 ]. The large cumulative evidence indicates a complex array of factors including environmental factors[ 76 ] and a wide range of cellular disturbances in glucose and lipid metabolism in various tissues[ 77 ] contribute to the development of insulin resistance. This condition generates complex cellular metabolic changes in a variety of tissues, mainly liver and muscles, that include the inability of the liver to transport and dispose glucose, control glucose production via gluconeogenesis, impaired storage of glucose as glycogen, de novo lipogenesis and hypertriglyceridemia[ 77 ]. Among the factors implicated in the development of insulin resistance, obesity is the most predominant risk factor leading to insulin insensitivity and diabetes which involves several mechanisms that participate in the pathogenesis of the disease[ 78 ]. Obesity-induced insulin resistance is directly linked to increased nutrient flux and energy accumulation in tissues that directly affect cell responsiveness to insulin[ 77 ]. However, it seems that other insulin-independent mechanisms are involved in the overall metabolic disturbances of glucose homeostasis and diabetes including activities in extra-hepatic tissues in addition to the central role of liver.

OTHER TYPES OF DIABETES MELLITUS

Monogenic diabetes.

Characterization of the genetic etiology of diabetes enables more appropriate treatment, better prognosis, and counseling[ 79 ]. Monogenic diabetes is due to a genetic defect in single genes in pancreatic β cells which results in disruption of β cell function or a reduction in the number of β cells. Conventionally, monogenic diabetes is classified according to the age of onset as neonatal diabetes before the age of six months or Maturity Onset Diabetes of the Young (MODY) before the age of 25 years. However, certain familial defects are manifested in neonatal diabetes, MODY or adult onset diabetes[ 2 , 9 , 80 ]. Others believe that classification of diabetes as MODY and neonatal diabetes is obsolete and monogenic diabetes is currently used relating specific genetic etiologies with their specific treatment implications[ 79 ]. Beta cell differentiation depends on the expression of the homeodomain transcription factor PDX1 where mutation in the gene results in early onset diabetes (MODY) and its expression decreases before the onset of diabetes[ 81 ]. The angiopoietin-like protein 8 (ANGPTL8) may represent a potential “betatrophin” that acts to promote the proliferation of beta cells, however, studies using mice lacking the ANGPTL8 active gene or overexpressed protein indicated that it did not seem to play a role in beta cells proliferation[ 82 ].

Mitochondrial diabetes is due to a point mutation in the mitochondrial DNA associated with deafness and maternal transmission of the mutant DNA can result in maternally-inherited diabetes[ 1 , 83 ].

Mutations that result in mutant insulin or the inability to convert proinsulin to insulin result in glucose intolerance in some of these cases. Genetic defects in the insulin receptor or in the signal transduction pathway of insulin have been demonstrated to result in hyperinsulinemia and modest hyperglycemia to severe diabetes[ 1 ].

Disease of the exocrine pancreas

Damage of the β cells of the pancreas due to diffused injury of the pancreas can cause diabetes. This damage could be due to pancreatic carcinoma, pancreatitis, infection, pancreatectomy, and trauma[ 1 ]. Atrophy of the exocrine pancreas leads to progressive loss of the β cells[ 84 ]. Accumulation of fat in the pancreas or pancreatic steatosis could lead to diabetes due to decreased insulin secretion but may require a long time before the damage to β cells occurs[ 85 ]. In most cases, extensive damage of the pancreas is required before diabetes occurs and the exocrine function of the pancreas is decreased in these patients[ 86 ]. Cirrhosis in cystic fibrosis may contribute to insulin resistance and diabetes[ 2 ].

Hormones and drugs

Diabetes has been found in patients with endocrine diseases that secrete excess hormones like growth hormone, glucocorticoids, glucagon and epinephrine in certain endocrinopathies like acromegaly, Cushing’s syndrome, glucagonoma, and pheochromocytoma, respectively[ 1 ]. Some of these hormones are used as drugs such as glucocorticoids to suppress the immune system and in chemotherapy and growth hormone to treat children with stunted growth.

Genetic syndromes

Diabetes has been detected in patients with various genetic syndromes such as Down syndrome, Klinefelter syndrome, Turner syndrome and Wolfram syndrome[ 1 ].

PREDIABETES

Individuals with prediabetes do not meet the criteria of having diabetes but are at high risk to develop type 2 diabetes in the future. According to the ADA Expert Committee, individuals are defined to have prediabetes if they have either impaired fasting plasma glucose (IFG) levels between 100-125 mg/dL (5.6-6.9 mmol/L) or impaired glucose tolerance test (IGT) with 2-h plasma glucose levels in the oral glucose tolerance test (OGTT) of 140-199 mg/dL (7.8-11.0 mmol/L). The World Health Organization (WHO) still adopts the range for IFG from 110-125 mg/dL (6.1-6.9 mmol/L). Prediabetes has been shown to correlate with increased cardiovascular mortality[ 87 , 88 ] and cancer[ 89 ]. The definition of prediabetes with the indicated cut off values is misleading since lower levels of glucose in the normal range are still correlated with cardiovascular disease in a continuous glycemic risk perspective[ 90 ]. In accordance with the recommendation of the ADA in 2009 to use hemoglobin A1c (HbA1c) to diagnose diabetes, ADA also recommended the use of an HbA1c (5.7%-6.4%) to diagnose prediabetes[ 91 ]. The number of people with IGT according to IDF was 316 million in 2013 (global prevalence 6.9% in adults) and is expected to rise to 471 million in 2030[ 27 ]. According to a report in 2014 by the Center for Disease Control and Prevention, 86 million Americans (1 out of 3) have prediabetes[ 92 ]. Four of the top ten countries with the highest prevalence of prediabetes are in the Middle East Arab States of the Gulf (Kuwait, Qatar, UAE and Bahrin with prevalence of 17.9%, 17.1%, 16.6% and 16.3%, respectively)[ 27 ]. The number of people diagnosed with prediabetes is different according to the method and criteria used to diagnose prediabetes. The number of people with prediabetes defined by IFG 100-125 mg/dL is 4-5 folds higher than those diagnosed using the WHO criteria of 110-125 mg/dL[ 93 ]. Diabetes and prediabetes diagnosed using an HbA1c criteria give different estimates compared to methods using FPG or OGTT. Higher percentages of prediabetes were diagnosed using HbA1c compared to FPG[ 94 - 96 ]. Prediabetes is associated with metabolic syndrome and obesity (especially abdominal or visceral obesity), dyslipidemia with high triglycerides and/or low HDL cholesterol, and hypertension[ 97 ]. Not all individuals with prediabetes develop diabetes in the future, exercise with a reduction of weight 5%-10% reduces the risk of developing diabetes considerably (40%-70%)[ 98 ]. Individuals with an HbA1c of 6.0%-6.5% have twice the risk of developing diabetes (25%-50%) in five years compared to those with an HbA1c of 5.5%-6.0%[ 99 ].

DIAGNOSTIC CRITERIA FOR DIABETES MELLITUS

Diabetes mellitus is diagnosed using either the estimation of plasma glucose (FPG or OGTT) or HbA1c. Estimation of the cut off values for glucose and HbA1c is based on the association of FPG or HbA1c with retinopathy. Fasting plasma glucose of ≥ 126 mg/dL (7.0 mmol/L), plasma glucose after 2-h OGTT ≥ 200 mg/dL (11.1 mmol/L), HbA1c ≥ 6.5% (48 mmol/mol) or a random plasma glucose ≥ 200 mg/dL (11.1 mmol/L) along with symptoms of hyperglycemia is diagnostic of diabetes mellitus. In addition to monitor the treatment of diabetes, HbA1c has been recommended to diagnose diabetes by the International Expert Committee in 2009[ 100 ] and endorsed by ADA[ 101 ], the Endocrine Society, the WHO[ 102 ] and many scientists and related organizations all over the world. The advantages and disadvantages of the different tests used to diagnose diabetes have been reviewed by Sacks et al[ 103 ]. The advantages of using HbA1c over FPG to diagnose diabetes include greater convenience and preanalytical stability, lower CV (3.6%) compared to FPG (5.7%) and 2h OGTT (16.6%), stronger correlation with microvascular complications especially retinopathy, and a marker for glycemic control and glycation of proteins which is the direct link between diagnosis of diabetes and its complications[ 104 - 109 ]. It is recommended to repeat the HbA1c test in asymptomatic patients within two weeks to reaffirm a single apparently diagnostic result[ 110 ].

A cut off value for HbA1c of ≥ 6.5% (48 mmol/mol) has been endorsed by many countries and different ethnic groups, yet ethnicity seems to affect the cut off values to diagnose diabetes[ 111 , 112 ]. Cut-off values of 5.5% (37 mmol/mol)[ 113 ] and 6.5% (48 mmol/mol)[ 114 ] have been reported in a Japanese study, 6.0% (42 mmol/mol) in the National Health and Nutrition Examination Survey (NHANES III), 6.2% (44 mmol/mol) in a Pima Indian study, 6.3% (45 mmol/mol) in an Egyptian study as reported by Davidson[ 105 ]; and three cut-off values for Chinese[ 112 ]. The Australians recommended the use of two cut-off values: ≤ 5.5% to “rule-out” and ≥ 7.0% to “rule-in” diabetes[ 115 ]. Variations in the prevalence of diabetes[ 94 , 116 - 119 ] and prediabetes[ 120 ] due to ethnicity have been documented. Most studies diagnosed less subjects with diabetes using HbA1c compared to FPG or OGTT[ 121 - 123 ]. Yet, other studies reported more subjects diagnosed with diabetes using HbA1c[ 96 , 124 - 126 ].

GESTATIONAL DIABETES

Hyperglycemia in pregnancy whether in the form of type 2 diabetes diagnosed before or during pregnancy or in the form gestational diabetes has an increased risk of adverse maternal, fetal and neonatal outcome. Mothers with gestational diabetes and babies born to such mothers have increased risk of developing diabetes later in life. Hyperglycemia in pregnancy is responsible for the increased risk for macrosomia (birth weight ≥ 4.5 kg), large for gestational age births, preeclampsia, preterm birth and cesarean delivery due to large babies[ 127 ]. Risk factors for gestational diabetes include obesity, personal history of gestational diabetes, family history of diabetes, maternal age, polycystic ovary syndrome, sedentary life, and exposure to toxic factors[ 3 ].

Diagnosis of type 2 diabetes before or during pregnancy is based on criteria mentioned before. Fasting plasma glucose ≥ 126 mg/dL (7.0 mmol/L) or 2-h plasma glucose ≥ 200 mg/dL (11.1 mmol/L) after a 75 g oral glucose load. However, gestational diabetes has been diagnosed at 24-28 wk of gestation in women not previously diagnosed with diabetes using two approaches: the first approach is based on the “one-step” International Association of the Diabetes and Pregnancy Study Groups (IADPSG) consensus[ 128 ] and recently adopted by WHO[ 129 ]. Gestational diabetes is diagnosed using this method by FPG ≥ 92 mg/dL (5.1 mmol/L), 1-h plasma glucose after a 75 g glucose load ≥ 180 mg/dL (10.0 mmol/L) or 2-h plasma glucose after a 75 g glucose load ≥ 153 mg/dL (8.5 mmol/L). This criteria is derived from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study[ 127 ] even though the HAPO study showed a continuous relationship between hyperglycemia and adverse short-term pregnancy outcome with no threshold reported[ 130 ]. The second approach is used in the United States and is based on the “two-step” NIH consensus[ 131 ]. In the first step 1-h plasma glucose after a 50 g glucose load under nonfasting state ≥ 140 mg/dL (7.8 mmol/L) is followed by a second step under fasting conditions after a 100 g glucose load for those who screened abnormal in the first step. The diagnosis of gestational diabetes is made when at least two of the four plasma glucose levels are met. The four plasma glucose levels according to Carpenter/Coustan criteria are: FPG ≥ 95 mg/dL (5.3 mmol/L); 1-h ≥ 180 mg/dL (10.0 mmol/L); 2-h ≥ 155 mg/dL (8.6 mmol/L); and 3-h ≥ 140 mg/dL (7.8 mmol/L)[ 1 ].

The use IADPSC criteria in comparison with the Carpenter/Coustan criteria was associated with a 3.5-fold increase in GDM prevalence as well as significant improvements in pregnancy outcomes, and was cost-effective[ 132 ]. In another retrospective cohort study of women diagnosed with gestational diabetes, Ethridge et al[ 133 ] have shown that newborns of women diagnosed with gestational diabetes by IADPSG approach have greater measures of fetal overgrowth compared with Carpenter-Coustan “two-step” approach neonates. A strategy of using fasting plasma glucose as a screening test and to determine the need for OGTT is valid[ 134 , 135 ]. According to Sacks[ 136 ], correlation of glucose concentrations and the risk of subsequent complications will eventually lead to universal guidelines.

The use of ADA/WHO cut off value of HbA1c ≥ 6.5% (48 mmol/mol) to diagnose gestational diabetes is not recommended by the “one step” IADPSC criteria or the “two-step” NIH criteria. Further investigation is required in light of recent reports on HbA1c in combination with OGTT and its usefulness to predict adverse effect of gestational diabetes or obviate the use OGTT in all women with gestational diabetes[ 137 - 141 ].

DIABETES AND GENETICS

Diabetes is a complex disease that involves a wide range of genetic and environmental factors. Over the past several years, many studies have focused on the elucidation of the wide spectrum of genes that played a role in the molecular mechanism of diabetes development[ 142 - 144 ]. However, despite the vast flow of genetic information including the identification of many gene mutations and a large array of single nucleotide polymorphisms (SNPs) in many genes involved in the metabolic pathways that affect blood glucose levels, the exact genetic mechanism of diabetes remains elusive[ 145 , 146 ]. Evidently, a major complication is the fact that a single gene mutation or polymorphism will not impose the same effect among different individuals within a population or different populations. This variation is directly or indirectly affected by the overall genetic background at the individual, family or population levels that are potentially further complicated by interaction with highly variable environmental modifier factors[ 147 , 148 ].

Molecular genetics and type 2 diabetes

One of the major focuses of biomedical research is to delineate the collective and broad genetic variants in the human genome that are involved in the development of diabetes. This major effort will potentially provide the necessary information to understand the molecular genetics of the different forms of diabetes including type 1, type 2 and monogenic neonatal diabetes among individuals of all populations and ethnic groups. Despite the fact that linkage and association studies allowed the identification and characterization of many candidate genes that are associated with type 2 diabetes[ 144 , 149 , 150 ], however, not all of these genes showed consistent and reproducible association with the disease[ 151 ]. Genome wide association studies (GWAS) in various populations identified 70 loci associated with type 2 diabetes and revealed positive linkage of many mutations and SNPs that influence the expression and physiological impact of the related proteins and risk to develop type 2 diabetes. One study involved several thousand type 2 diabetes patients and control subjects from the United Kingdom allowed the identification of several diabetes putative loci positioned in and around the CDKAL1 , CDKN2A/B , HHEX/IDE and SLC30A8 genes in addition to the contribution of a large number of other genetic variants that are involved in the development of the disease[ 152 ]. Two similar studies from the Finns and Swedish populations and the United States resulted in the identification of similar single nucleotide variants[ 153 ] that are linked to the risk of acquiring type 2 diabetes[ 154 , 155 ]. The study in the United States population included in addition to type 2 diabetes, the association of the identified SNPs with the level of triglycerides in the tested subjects[ 155 ]. These SNPs are located near several candidate genes including IGFBP2 and CDKAL1 and other genes in addition to several other variants that are located near or in genes firmly associated with the risk of acquiring type 2 diabetes. Other GWAS analysis studies were performed in the Chinese, Malays, and Asian-Indian populations which are distinct from the European and United States populations in addition to meta-analysis of data from other populations in the region revealed relevant findings among patients with European ancestry[ 156 ]. The results of the combined analysis showed significant association of SNPs in the CDKAL1 , CDKN2A/B , HHEX , KCNQ1 and SLC30A8 genes after adjustment with gender and body mass index. More recently, meta-analysis of GWAS data involving African American type 2 diabetes patients identified similar loci to the previous studies with the addition of two novel loci, HLA-B and INS-IGF[ 157 ]. These results provide strong evidence of common genetic determinants including common specific genes that are linked to diabetes. A small list of specific genetic markers seem strongly associated with the risk of developing type 2 diabetes including the TCF7L2 [ 158 ] and CAPN10 [ 159 , 160 ] genes which also play a significant role in the risk and pathogenesis of the disease[ 158 , 159 ]. The association of TCF7L2 gene variants with type 2 diabetes and its mechanism of action received special attention by several investigators[ 161 , 162 ]. Over expression of the protein was shown to decrease the sensitivity of beta islet cells to secrete insulin[ 163 , 164 ] and was more precisely involved in the regulation of secretary granule fusion that constitute a late event in insulin secretion pathway[ 165 ]. The role of TCF7L2 in insulin secretion was partially clarified[ 166 ] that involves modifying the effect of incretins on insulin secretion by lowering the sensitivity of beta cells to incretins. Several other genes have been found to be significantly associated with the risk of developing type 2 diabetes including a specific SNP in a hematopoietically-expressed homeobox ( HHEX ) gene[ 167 ]. The islet zinc transporter protein (SLC30A8)[ 168 ] showed positive correlation with the risk of developing type 2 diabetes where variant mutations in this gene seem protective against the disease which provides a potential tool for therapy[ 169 ]. More recently, a low frequency variant of the HNF1A identified by whole exome sequencing was associated with the risk of developing type 2 diabetes among the Latino population and potentially may serve as a screening tool[ 170 ]. Genetic variants and specific combined polymorphisms in the interleukin and related genes including interlukin-6 ( IL-6 ), tumor necrosis factor-α and IL-10 genes were found to be associated with greater risk of developing type 2 diabetes[ 171 ], in addition to genetic variants in the genes for IL12B , IL23R and IL23A genes[ 172 ]. In a study involving the hormone sensitive lipase responsible for lipolysis in adipose tissues, a deletion null mutation, which resulted in the absence of the protein from adipocytes, was reported to be associated with diabetes[ 173 ]. Nine specific rare variants in the peroxisome proliferator-activated receptor gamma ( PPARG ) gene that resulted in loss of the function of the protein in adipocytes differentiation, were significantly associated with the risk of developing type 2 diabetes[ 174 ]. In addition, certain SNPs in the alpha 2A adrenergic receptor ( ADRA2A ) gene, involved in the sympathetic nervous system control of insulin secretion and lipolysis, were found to be associated with obesity and type 2 diabetes[ 175 ]. Link analysis between the melatonin MT2 receptor ( MTNR1B ) gene, a G-protein coupled receptor, identified 14 mutant variants from 40 known variants revealed by exome sequencing, to be positively linked with type 2 diabetes[ 176 ]. The authors suggested that mutations in the MT2 gene could provide a tool with other related genes in modifying therapy for type 2 diabetes patients based on their specific genetic background to formulate personalized therapies which potentially may ensures the optimum response. Interestingly, mutations in the clock[ 177 , 178 ] and Bmal1 [ 179 ] transcription factor genes which are involved in beta cells biological clock affecting growth, survival and synaptic vesicle assembly in these cells, resulted in reduced insulin secretion and diabetes. Evidently, prominent metabolic functions involve the production of specific reactive metabolites, leading to oxidative stress, which affect lipids, proteins and other biological compounds leading to serious damage in various tissues and organs. Mutations and SNPs in the antioxidant genes, including superoxide dismutase, catalase and glutathione peroxidase, that decrease their activity are implicated in the risk and pathogenesis of type 2 diabetes[ 180 ]. The metabolic syndrome was shown to be associated with the development of type 2 diabetes in a population that is described as highly endogenous especially in individuals over 45 years of age[ 181 ]. Since consanguinity marriages is high in this population, screening for this syndrome among families could provide an informative marker on the risk of developing type 2 diabetes[ 181 ].

Molecular genetics of type 1 diabetes

Even though type 1 diabetes is basically described as an autoimmune disease that results in the destruction of pancreatic beta cells, however, single gene mutations and SNPs have been found to be associated with the susceptibility to this type of diabetes. Initially, two gene mutations were linked to the development of type 1 diabetes including the autoimmune regulator ( AIRE ) gene which affect the immune tolerance to self antigens leading to autoimmunity[ 182 ] and the FOXP3 gene which results in defective regulatory T cells[ 183 ]. In addition, a mutation in the histone deacetylase SIRTI gene predominantly expressed in beta cells involved in the regulation of insulin secretion[ 184 ] and played a role in modulating the sensitivity of peripheral tissues to insulin[ 185 ] was detected in type 1 diabetes patients[ 186 ]. Recently, additional mutations and SNPs in the CTLA-4 +49A/G and HLA-DQB1 and INS gene VNTR alleles were found to be associated with type 1 diabetes, which have the advantage of differentiating between Latent autoimmune type 1 diabetes and type 2 diabetes[ 187 ]. The HLA-DQB1, in combination with HLA-DR alleles and a polymorphism in PTPN22 gene seem to be associated with the age onset of late type 1 diabetes[ 188 , 189 ]. Two specific polymorphisms in the promoter region of a transmembrane protein (DC-SIGN) gene expressed in macrophages and played an important role of T- cell activation and inflammation were found to be protective against type 1 diabetes[ 190 ]. An innovative non-parametric SNP enrichment tool using summary GWAS DATA allowed the identification of association between several transcription factors and type 1 diabetes and are located in a type 1 diabetes susceptibility region[ 191 ]. Nine SNP variants in several genes associated with type 1 diabetes, not including the major histocompatibility gene region, were identified using extensive GWAS analysis[ 192 ]. Furthermore, several novel SNPs in a region in chromosome 16 located in the CLEC16A gene were shown to be associated with type 1 diabetes and seem to function through the reduced expression of DEX1 in B lymphoblastoid cells[ 193 ]. Since more than 40 regions in the human genome were identified to be associated with the susceptibility to type 1 diabetes[ 194 - 196 ], a weighted risk model was developed utilizing selected genes SNPs could be used for testing infants for these genetic markers that could provide insights in the susceptibility to type 1 diabetes development or safe prevention of the disease among young children[ 197 ].

Molecular genetics of monogenic diabetes

A large array of genes were identified to be involved in the development of monogenic diabetes[ 80 ] which represent about 2%-5% of diabetes patients. Monogenic diabetes results primarily from gene defects that lead to a decrease in beta cell number or function. Monogenic diabetes genes were identified using linkage studies or code for proteins that directly affected glucose homeostasis. The majority of genes responsible for monogenetic diabetes code for either transcription factors that participate in the control of nuclear gene expression or proteins that are located on the cell membrane, cytoplasm and endoplasmic reticulum, proteins involved in insulin synthesis and secretion, exocrine pancreatic proteins and autoimmune diabetes proteins[ 80 ]. The collective function of these proteins is their participation in glucose metabolism at different levels. Evidently, the hierarchy of a specific gene in the overall glucose metabolism pathway determines the onset of diabetes in the patient and whether it is neonataly expressed or have late onset expression (adulthood). Consequently, molecular defects in the structure and function of these genes lead to the disturbance of plasma glucose level, the primary pathological sign of diabetes. The molecular mechanism of permanent neonatal diabetes mellitus (PNDP) in addition to MODY explains the observed phenotype of monogenetic diabetes that involves loss of function of the expressed mutant protein. The first gene implicated in monogenic diabetes was the glucokinase ( GCK ) gene[ 198 ] which functions as a pancreatic sensor for blood glucose where more than 70 mutations in the gene were identified that affected its activity[ 199 ]. A recent study on GCK gene mutations causing neonatal and childhood diabetes showed that the majority of mutations resulted in the loss of the enzyme function primarily due to protein instability[ 148 , 150 ]. Two hepatocytes nuclear factor genes that code for the HNF4A and HNF1A transcription factors were closely associated with MODY1 and MODY2[ 148 , 149 ]. Definitely, a whole list of other genes involved in monogenic diabetes are either overlooked or included in the genetic determinants of type 1 and type 2 diabetes which will be identified and clarified through more careful future studies.

MOLECULAR GENETICS OF DIABETES COMPLICATIONS

In addition to the genetic determinants of diabetes, several gene mutations and polymorphisms have been associated with the clinical complications of diabetes. The cumulative data on diabetes patients with a variety of micro- and macrovascular complications support the presence of strong genetic factors involved in the development of various complications[ 200 ]. A list of genes have been reported that are associated with diabetes complications including ACE and AKR1B1 in nephropathy, VEGF and AKRB1 in retinopathy and ADIPOQ and GLUL in cardiovascular diseases[ 200 ]. A study on Chinese patients revealed a single SNP in the promoter region of the smooth muscle actin ( ACTA2 ) gene correlates with the degree of coronary artery stenosis in type 2 diabetes patients[ 201 ]. Furthermore, the alpha kinase 1 gene ( ALPK1 ) identified as a susceptibility gene for chronic kidney disease by GWAS[ 202 ], was demonstrated in type 2 diabetes patients[ 203 ]. Three additional genes have been strongly correlated with this risk of diabetic retinopathy (DR) including the vascular endothelial growth receptor, aldose reductase and the receptor for advanced glycation products genes[ 204 ] where specific polymorphisms in these genes seem to increase the risk of DR development in diabetes patients[ 204 ]. A significant differential proteome (involving 56 out of 252 proteins) is evident that characterizes vitreous samples obtained from diabetes patients with the complication in comparison to diabetes patients without the complication and control individuals[ 205 ]. Interestingly, a large portion of these proteins (30 proteins) belong to the kallikrein-kinin, coagulation and complement systems including complement C3, complement factor 1, prothrombin, alpha-1-antitrypsin and antithrombin III that are elevated in diabetic patients with retinopathy[ 205 ]. In addition, 2 single nucleotides polymorphisms in the human related B7-I gene seem to mediate podocyte injury in diabetic nephropathy[ 206 ]. Furthermore, increased concentration of the ligand of B7-1 correlates with the progression of end-stage renal disease (ESRD) in diabetes patients[ 206 ]. These results indicate that B7-I inhibition may serve as a potential target for diabetes nephropathy prevention and/or treatment. Recently, it was shown that direct correlation is evident between circulating levels of tumor necrosis factors 1 and 2 and increased risk of ESRD in American Indian patients[ 207 ]. The link between diabetes and proper bone development and health is evident. Studies using animal models with major significant reduction in insulin receptor (IR) in osteoprogenitor cells resulted in thin and rod-like weak bones with high risk of fractures[ 208 ]. Similar findings were observed in animal models with bone-specific IR knockdown animals which points to the central role of IR in the proper development of bones[ 208 ]. Type 2 diabetes is also associated with mitochondrial dysfunction in adipose tissues. Using knockout animal models of specific mitochondrial genes led to significant reduction in key electron transport complexes expression and eventually adipocytes death[ 209 ]. These animals exhibited Insulin resistance in addition to other complications that can potentially lead to cardiovascular disease[ 209 ].

Diabetes mellitus is the epidemic of the century and without effective diagnostic methods at an early stage, diabetes will continue to rise. This review focuses on the types of diabetes and the effective diagnostic methods and criteria to be used for diagnosis of diabetes and prediabetes. Evidently, diabetes is a complex disease with a large pool of genes that are involved in its development. The precise identification of the genetic bases of diabetes potentially provides an essential tool to improve diagnoses, therapy (more towards individualized patient targeted therapy) and better effective genetic counseling. Furthermore, our advanced knowledge of the association between medical genetics and the chronic complications of diabetes, will provide an additional advantage to delay or eradicate these complications that impose an immense pressure on patient’s quality of life and the significantly rising cost of health-care services.

Conflict-of-interest: The authors declare that there is no conflict of interest associated with this manuscript.

Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

Peer-review started: November 23, 2014

First decision: February 7, 2015

Article in press: April 14, 2015

P- Reviewer: Hegardt FG, Surani S, Traub M S- Editor: Gong XM L- Editor: A E- Editor: Wang CH

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• 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

44k Accesses

219 Citations

56 Altmetric

Metrics details

• 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.

Similar content being viewed by others

Type 2 diabetes mellitus in older adults: clinical considerations and management

Srikanth Bellary, Ioannis Kyrou, … Clifford J. Bailey

The cross-sectional and longitudinal relationship of diabetic retinopathy to cognitive impairment: a systematic review and meta-analysis

Robert N. F. Chan, Ziqi Tang, … Carol Y. Cheung

Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025

Xiling Lin, Yufeng Xu, … Peng-Fei Shan

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 .

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.

Zimmet, P., Alberti, K. G. M. M. & Shaw, J. Global and societal implications of the diabetes epidemic. Nature 414 , 782–787 (2001).

Article   CAS   PubMed   Google Scholar  

Fowler, M. J. Microvascular and macrovascular complications of diabetes. Clin. Diabetes 26 , 77–82 (2008).

Article   Google Scholar  

Shah, A. D. et al. Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1·9 million people. Lancet Diabetes Endocrinol. 3 , 105–113 (2015).

Article   PubMed   PubMed Central   Google Scholar  

Bertoni, A. G. et al. Heart failure prevalence, incidence, and mortality in the elderly with diabetes. Diabetes Care 27 , 699–703 (2004).

Article   PubMed   Google Scholar  

Gregg, E. W. et al. Changes in diabetes-related complications in the United States, 1990–2010. N. Engl. J. Med. 370 , 1514–1523 (2014).

Gregg, E. W., Sattar, N. & Ali, M. K. The changing face of diabetes complications. Lancet Diabetes Endocrinol. 4 , 537–547 (2016).

Gregg, E. W. et al. Trends in cause-specific mortality among adults with and without diagnosed diabetes in the USA: an epidemiological analysis of linked national survey and vital statistics data. Lancet 391 , 2430–2440 (2018).

Harding, J. L., Shaw, J. E., Peeters, A., Davidson, S. & Magliano, D. J. Age-specific trends from 2000–2011 in all-cause and cause-specific mortality in type 1 and type 2 diabetes: a cohort study of more than one million people. Diabetes Care 39 , 1018–1026 (2016).

Pearson-Stuttard, J. et al. Trends in predominant causes of death in individuals with and without diabetes in England from 2001 to 2018: an epidemiological analysis of linked primary care records. Lancet Diabetes Endocrinol. 9 , 165–173 (2021).

Einarson, T. R., Acs, A., Ludwig, C. & Panton, U. H. Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovasc. Diabetol. 17 , 83 (2018).

Pearson-Stuttard, J., Buckley, J., Cicek, M. & Gregg, E. W. The changing nature of mortality and morbidity in patients with diabetes. Endocrinol. Metab. Clin. North Am. 50 , 357–368 (2021).

Pearson-Stuttard, J. et al. Trends in leading causes of hospitalisation of adults with diabetes in England from 2003 to 2018: an epidemiological analysis of linked primary care records. Lancet Diabetes Endocrinol. 10 , 46–57 (2022).

Pearson-Stuttard, J., Blundell, S., Harris, T., Cook, D. G. & Critchley, J. Diabetes and infection: assessing the association with glycaemic control in population-based studies. Lancet Diabetes Endocrinol. 4 , 148–158 (2016).

Tolman, K. G., Fonseca, V., Dalpiaz, A. & Tan, M. H. Spectrum of liver disease in type 2 diabetes and management of patients with diabetes and liver disease. Diabetes Care 30 , 734–743 (2007).

Chatterjee, S. et al. Type 2 diabetes as a risk factor for dementia in women compared with men: a pooled analysis of 2.3 million people comprising more than 100,000 cases of dementia. Diabetes Care 39 , 300–307 (2016).

Tsilidis, K. K., Kasimis, J. C., Lopez, D. S., Ntzani, E. E. & Ioannidis, J. P. Type 2 diabetes and cancer: umbrella review of meta-analyses of observational studies. BMJ 350 , g7607 (2015).

Harding, J. L., Pavkov, M. E., Magliano, D. J., Shaw, J. E. & Gregg, E. W. Global trends in diabetes complications: a review of current evidence. Diabetologia 62 , 3–16 (2019).

Unal, B., Critchley, J. A. & Capewell, S. Explaining the decline in coronary heart disease mortality in England and Wales between 1981 and 2000. Circulation 109 , 1101–1107 (2004).

Pearson-Stuttard, J., Ezzati, M. & Gregg, E. W. Multimorbidity — a defining challenge for health systems. Lancet Public. Health 4 , e599–e600 (2019).

Lee, L., Cheung, W. Y., Atkinson, E. & Krzyzanowska, M. K. Impact of comorbidity on chemotherapy use and outcomes in solid tumors: a systematic review. J. Clin. Oncol. 29 , 106–117 (2011).

Srokowski, T. P., Fang, S., Hortobagyi, G. N. & Giordano, S. H. Impact of diabetes mellitus on complications and outcomes of adjuvant chemotherapy in older patients with breast cancer. J. Clin. Oncol. 27 , 2170–2176 (2009).

Gross, C. P., McAvay, G. J., Guo, Z. & Tinetti, M. E. The impact of chronic illnesses on the use and effectiveness of adjuvant chemotherapy for colon cancer. Cancer 109 , 2410–2419 (2007).

Harding, J. L. et al. All-cause cancer mortality over 15 years in multi-ethnic Mauritius: the impact of diabetes and intermediate forms of glucose tolerance. Int. J. Cancer 131 , 2385–2393 (2012).

Wang, C. et al. Increased risk of hepatocellular carcinoma in patients with diabetes mellitus: a systematic review and meta-analysis of cohort studies. Int. J. Cancer 130 , 1639–1648 (2012).

El-Serag, H. B., Hampel, H. & Javadi, F. The association between diabetes and hepatocellular carcinoma: a systematic review of epidemiologic evidence. Clin. Gastroenterol. Hepatol. 4 , 369–380 (2006).

Desbois, A. C. & Cacoub, P. Diabetes mellitus, insulin resistance and hepatitis C virus infection: a contemporary review. World J. Gastroenterol. 23 , 1697–1711 (2017).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Huxley, R., Ansary-Moghaddam, A., Berrington De González, A., Barzi, F. & Woodward, M. Type-II diabetes and pancreatic cancer: a meta-analysis of 36 studies. Br. J. Cancer 92 , 2076–2083 (2005).

Gullo, L. et al. Diabetes and the risk of pancreatic cancer. N. Engl. J. Med. 331 , 81–84 (1994).

Gershell, L. Type 2 diabetes market. Nat. Rev. Drug Discov. 4 , 367–368 (2005).

Carstensen, B. et al. Cancer incidence in persons with type 1 diabetes: a five-country study of 9,000 cancers in type 1 diabetic individuals. Diabetologia 59 , 980–988 (2016).

Jiang, Y. et al. Diabetes mellitus and incidence and mortality of colorectal cancer: a systematic review and meta-analysis of cohort studies. Eur. J. Epidemiol. 26 , 863–876 (2011).

De Bruijn, K. M. J. et al. Systematic review and meta-analysis of the association between diabetes mellitus and incidence and mortality in breast and colorectal cancer. Br. J. Surg. 100 , 1421–1429 (2013).

Deng, L., Gui, Z., Zhao, L., Wang, J. & Shen, L. Diabetes mellitus and the incidence of colorectal cancer: an updated systematic review and meta-analysis. Dig. Dis. Sci. 57 , 1576–1585 (2012).

Liao, C., Zhang, D., Mungo, C., Andrew Tompkins, D. & Zeidan, A. M. Is diabetes mellitus associated with increased incidence and disease-specific mortality in endometrial cancer? A systematic review and meta-analysis of cohort studies. Gynecol. Oncol. 135 , 163–171 (2014).

Saed, L. et al. The effect of diabetes on the risk of endometrial cancer: an updated a systematic review and meta-analysis. BMC Cancer 19 , 527 (2019).

Friberg, E., Orsini, N., Mantzoros, C. S. & Wolk, A. Diabetes mellitus and risk of endometrial cancer: A meta-analysis. Diabetologia 50 , 1365–1374 (2007).

Anothaisintawee, T. et al. Risk factors of breast cancer: a systematic review and meta-analysis. Asia-Pac. J. Public Health 25 , 368–387 (2013).

Larsson, S. C., Mantzoros, C. S. & Wolk, A. Diabetes mellitus and risk of breast cancer: a meta-analysis. Int. J. Cancer 121 , 856–862 (2007).

Boyle, P. et al. Diabetes and breast cancer risk: a meta-analysis. Br. J. Cancer 107 , 1608–1617 (2012).

Rinaldi, S. et al. Anthropometric measures, endogenous sex steroids and breast cancer risk in postmenopausal women: a study within the EPIC cohort. Int. J. Cancer 118 , 2832–2839 (2006).

Michels, K. B. et al. Type 2 diabetes and subsequent incidence of breast cancer in the nurses’ health study. Diabetes Care 26 , 1752–1758 (2003).

Bronsveld, H. K. et al. Diabetes and breast cancer subtypes. PLoS ONE 12 , e0170084 (2017).

Article   PubMed   PubMed Central   CAS   Google Scholar  

Zhang, D., Li, N., Xi, Y., Zhao, Y. & Wang, T. Diabetes mellitus and risk of ovarian cancer. A systematic review and meta-analysis of 15 cohort studies. Diabetes Res. Clin. Pract. 130 , 43–52 (2017).

Weng, L., Wang, L., Zhang, J., Wang, B. & Liu, H. Association between diabetes mellitus and subsequent ovarian cancer in women: a systematic review and meta-analysis of cohort studies. Medicine 96 , e6396 (2017).

Wang, L., Zhong, L., Xu, B., Chen, M. & Huang, H. Diabetes mellitus and the risk of ovarian cancer: a systematic review and meta-analysis of cohort and case-control studies. BMJ Open 10 , e040137 (2020).

Lee, J. Y. et al. Diabetes mellitus and ovarian cancer risk: a systematic review and meta-analysis of observational studies. Int. J. Gynecol. Cancer 23 , 402–412 (2013).

Bonovas, S., Filioussi, K. & Tsantes, A. Diabetes mellitus and risk of prostate cancer: a meta-analysis. Diabetologia 47 , 1071–1078 (2004).

Shikata, K., Ninomiya, T. & Kiyohara, Y. Diabetes mellitus and cancer risk: review of the epidemiological evidence. Cancer Sci. 104 , 9–14 (2013).

Long, X. J., Lin, S., Sun, Y. N. & Zheng, Z. F. Diabetes mellitus and prostate cancer risk in Asian countries: a meta-analysis. Asian Pac. J. Cancer Preven. 13 , 4097–4100 (2012).

Rhee, E. J. Diabetes in Asians. Endocrinol. Metab. 30 , 263–269 (2015).

Article   CAS   Google Scholar  

Bensimon, L., Yin, H., Suissa, S., Pollak, M. N. & Azoulay, L. Type 2 diabetes and the risk of mortality among patients with prostate cancer. Cancer Causes Control. 25 , 329–338 (2014).

Johnson, J. A., Bowker, S. L., Richardson, K. & Marra, C. A. Time-varying incidence of cancer after the onset of type 2 diabetes: evidence of potential detection bias. Diabetologia 54 , 2263–2271 (2011).

Johnson, J. A. et al. Diabetes and cancer (1): evaluating the temporal relationship between type 2 diabetes and cancer incidence. Diabetologia 55 , 1607–1618 (2012).

Harding, J. L., Shaw, J. E., Peeters, A., Cartensen, B. & Magliano, D. J. Cancer risk among people with type 1 and type 2 diabetes: disentangling true associations, detection bias, and reverse causation. Diabetes Care 38 , 264–270 (2015).

Pearson-Stuttard, J. et al. Worldwide burden of cancer attributable to diabetes and high body-mass index: a comparative risk assessment. Lancet Diabetes Endocrinol. 6 , e6–e15 (2018).

Larsson, S. C. & Wolk, A. Diabetes mellitus and incidence of kidney cancer: a meta-analysis of cohort studies. Diabetologia 54 , 1013–1018 (2011).

Xu, X. et al. Diabetes mellitus and risk of bladder cancer: a meta-analysis of cohort studies. PLoS ONE 8 , e58079 (2013).

Gong, I. Y. et al. Association between diabetes and haematological malignancies: a population-based study. Diabetologia 64 , 540–551 (2021).

Giovannucci, E. et al. Diabetes and cancer: a consensus report. Diabetes Care 33 , 1674–1685 (2010).

Weinstein, D., Simon, M., Yehezkel, E., Laron, Z. & Werner, H. Insulin analogues display IGF-I-like mitogenic and anti-apoptotic activities in cultured cancer cells. Diabetes Metab. Res. Rev. 25 , 41–49 (2009).

Najjar, S. M. & Perdomo, G. Hepatic insulin clearance: mechanism and physiology. Physiology 34 , 198–215 (2019).

Lorenzi, M., Montisano, D. F., Toledo, S. & Barrieux, A. High glucose induces DNA damage in cultured human endothelial cells. J. Clin. Invest. 77 , 322–325 (1986).

Robertson, R., Zhou, H., Zhang, T. & Harmon, J. S. Chronic oxidative stress as a mechanism for glucose toxicity of the beta cell in type 2 diabetes. Cell Biochem. Biophys. 48 , 139–146 (2007).

Turturro, F., Friday, E. & Welbourne, T. Hyperglycemia regulates thioredoxin-ROS activity through induction of thioredoxin-interacting protein (TXNIP) in metastatic breast cancer-derived cells MDA-MB-231. BMC Cancer 7 , 96 (2007).

Wu, Y., Liu, Y., Dong, Y. & Vadgama, J. Diabetes-associated dysregulated cytokines and cancer. Integr. Cancer Sci. Ther. 3 , 370–378 (2016).

PubMed   PubMed Central   Google Scholar  

Inoki, K., Kim, J. & Guan, K. L. AMPK and mTOR in cellular energy homeostasis and drug targets. Annu. Rev. Pharmacol. Toxicol. 52 , 381–400 (2012).

Huang, X., Liu, G., Guo, J. & Su, Z. Q. The PI3K/AKT pathway in obesity and type 2 diabetes. Int. J. Biol. Sci. 14 , 1483–1496 (2018).

Zhao, Y. et al. Metformin is associated with reduced cell proliferation in human endometrial cancer by inbibiting PI3K/AKT/mTOR signaling. Gynecol. Endocrinol. 34 , 428–432 (2018).

Knapp, S. Diabetes and infection: is there a link?-A mini-review. Gerontology 59 , 99–104 (2013).

Fang, M. et al. Diabetes and the risk of hospitalisation for infection: the atherosclerosis risk in communities (ARIC) study. Diabetologia 64 , 2458–2465 (2021).

Tseng, C.-H. Metformin use is associated with a reduced risk of acute appendicitis in Taiwanese patients with type 2 diabetes mellitus. Sci. Rep. 11 , 12400 (2021).

Luk, A. O. Y. et al. Temporal trends in rates of infection-related hospitalisations in Hong Kong people with and without diabetes, 2001–2016: a retrospective study. Diabetologia 64 , 109–118 (2021).

Magliano, D. J. et al. Excess risk of dying from infectious causes in those with type 1 and type 2 diabetes. Diabetes Care 38 , 1274–1280 (2015).

Martin, E. T. et al. Diabetes and risk of surgical site infection: a systematic review and meta-analysis. Infect. Control. Hosp. Epidemiol. 37 , 88–99 (2016).

Trussell, J. et al. Impact of a patient care pathway protocol on surgical site infection rates in cardiothoracic surgery patients. Am. J. Surg. 196 , 883–889 (2008).

Coleman, J. S. et al. Surgical site infections after hysterectomy among HIV-infected women in the HAART era: a single institution’s experience from 1999–2012. Am. J. Obstet. Gynecol. 210 , 117.e111–117.e117 (2014).

Friedman, N. D., Sexton, D. J., Connelly, S. M. & Kaye, K. S. Risk factors for surgical site infection complicating laminectomy. Infect. Control. Hosp. Epidemiol. 28 , 1060–1065 (2007).

Apicella, M. et al. COVID-19 in people with diabetes: understanding the reasons for worse outcomes. Lancet Diabetes Endocrinol. 8 , 782–792 (2020).

McGurnaghan, S. J. et al. Risks of and risk factors for COVID-19 disease in people with diabetes: a cohort study of the total population of Scotland. Lancet Diabetes Endocrinol. 9 , 82–93 (2021).

Rawshani, A. et al. Severe COVID-19 in people with type 1 and type 2 diabetes in Sweden: a nationwide retrospective cohort study. Lancet Reg. Health Eur. 4 , 100105 (2021).

You, J. H. et al. Clinical outcomes of COVID-19 patients with type 2 diabetes: a population-based study in Korea. Endocrinol. Metab. 35 , 901–908 (2020).

Moon, S. J. et al. Independent impact of diabetes on the severity of coronavirus disease 2019 in 5,307 patients in South Korea: a nationwide cohort study. Diabetes Metab. J. 44 , 737–746 (2020).

Aranjani, J. M., Manuel, A., Razack, H. I. A. & Mathew, S. T. Covid-19–associated mucormycosis: evidence-based critical review of an emerging infection burden during the pandemic’s second wave in India. PLoS. Negl. Trop. Dis. 15 , e0009921 (2021).

Crankson, S., Pokhrel, S. & Anokye, N. K. Determinants of COVID-19-related length of hospital stays and long COVID in Ghana: a cross-sectional analysis. Int. J. Environ. Res. Public Health 19 , 527 (2022).

Bellan, M. et al. Respiratory and psychophysical sequelae among patients with covid-19 four months after hospital discharge. JAMA Netw. Open 4 , e2036142 (2021).

Gottesman, B. L., Yu, J., Tanaka, C., Longhurst, C. A. & Kim, J. J. Incidence of new-onset type 1 diabetes among US children during the COVID-19 global pandemic. JAMA Pediatr. 176 , 414–415 (2022).

Barrett, C. E. et al. Risk for newly diagnosed diabetes <30 days after SARS-CoV-2 infection among persons aged >18 years-United States, March 1, 2020-June 28, 2021. Morb. Mortal. Wkly. Rep. 71 , 59–65 (2022).

Kornum, J. B. et al. Diabetes, glycemic control, and risk of hospitalization with pneumonia: a population-based case-control study. Diabetes Care 31 , 1541–1545 (2008).

Matsuyama, R., Nishiura, H., Kutsuna, S., Hayakawa, K. & Ohmagari, N. Clinical determinants of the severity of Middle East respiratory syndrome (MERS): a systematic review and meta-analysis. BMC Public Health 16 , 1203 (2016).

Badawi, A. & Ryoo, S. G. Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis. Int. J. Infect. Dis. 49 , 129–133 (2016).

Badawi, A. & Ryoo, S. G. Prevalence of diabetes in the 2009 influenza A (H1N1) and the middle east respiratory syndrome coronavirus: a systematic review and meta-analysis. J. Public. Health Res. 5 , 130–138 (2016).

Yang, J. K. et al. Plasma glucose levels and diabetes are independent predictors for mortality and morbidity in patients with SARS. Diabet. Med. 23 , 623–628 (2006).

Ehrlich, S. F., Quesenberry, C. P. Jr, Van Den Eeden, S. K., Shan, J. & Ferrara, A. Patients diagnosed with diabetes are at increased risk for asthma, chronic obstructive pulmonary disease, pulmonary fibrosis, and pneumonia but not lung cancer. Diabetes Care 33 , 55–60 (2010).

Alraddadi, B. M. et al. Risk factors for primary middle east respiratory syndrome coronavirus illness in humans, Saudi Arabia, 2014. Emerg. Infect. Dis. 22 , 49–55 (2016).

Geerlings, S. E. & Hoepelman, A. I. M. Immune dysfunction in patients with diabetes mellitus (DM). FEMS Immunol. Med. Microbiol. 26 , 259–265 (1999).

Velazquez-Salinas, L., Verdugo-Rodriguez, A., Rodriguez, L. L. & Borca, M. V. The role of interleukin 6 during viral infections. Front. Microbiol. 10 , 1057 (2019).

Joshi, N., Caputo, G. M., Weitekamp, M. R. & Karchmer, A. W. Infections in patients with diabetes mellitus. N. Engl. J. Med. 341 , 1906–1912 (1999).

Codo, A. C. et al. Elevated glucose levels favor SARS-CoV-2 infection and monocyte response through a HIF-1α/Glycolysis-dependent axis. Cell Metab. 32 , 437–446.e435 (2020).

Miyazawa, T., Nakagawa, K., Shimasaki, S. & Nagai, R. Lipid glycation and protein glycation in diabetes and atherosclerosis. Amino Acids 42 , 1163–1170 (2012).

Peleg, A. Y., Weerarathna, T., McCarthy, J. S. & Davis, T. M. E. Common infections in diabetes: pathogenesis, management and relationship to glycaemic control. Diabetes Metab. Res. Rev. 23 , 3–13 (2007).

Barda, N., Dagan, N. & Balicer, R. D. BNT162b2 mRNA Covid-19 vaccine in a nationwide mass vaccination setting. reply. N. Engl. J. Med. 384 , 1970 (2021).

PubMed   Google Scholar  

Cholankeril, G. & Ahmed, A. Alcoholic liver disease replaces hepatitis C virus infection as the leading indication for liver transplantation in the United States. Clin. Gastroenterol. Hepatol. 16 , 1356–1358 (2018).

Fink, M. & Byrne, M. Australia and New Zealand Liver and Intestinal Transplant Registry Annual Report 2019 (Melbourne, Victoria, Australia, 2019).

Haldar, D. et al. Outcomes of liver transplantation for non-alcoholic steatohepatitis: a European liver transplant registry study. J. Hepatol. 71 , 313–322 (2019).

Younossi, Z. M. et al. The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: a systematic review and meta-analysis. J. Hepatol. 71 , 793–801 (2019).

Younossi, Z. M. et al. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 64 , 73–84 (2016).

Pang, Y. et al. Diabetes, plasma glucose, and incidence of fatty liver, cirrhosis, and liver cancer: a prospective study of 0.5 million people. Hepatology 68 , 1308–1318 (2018).

Li, Y. et al. Bidirectional association between nonalcoholic fatty liver disease and type 2 diabetes in Chinese population: evidence from the Dongfeng-Tongji cohort study. PLoS ONE 12 , e0174291 (2017).

Mansour-Ghanaei, F. et al. Prevalence of non-alcoholic fatty liver disease in patients with diabetes mellitus, hyperlipidemia, obesity and polycystic ovary syndrome: a cross-sectional study in north of Iran. Diabetes Metab. Syndr. 13 , 1591–1596 (2019).

Leite, N. C., Salles, G. F., Araujo, A. L., Villela-Nogueira, C. A. & Cardoso, C. R. Prevalence and associated factors of non-alcoholic fatty liver disease in patients with type-2 diabetes mellitus. Liver Int. 29 , 113–119 (2009).

Singh, S. P. et al. Risk factors associated with non-alcoholic fatty liver disease in Indians: a case-control study. J. Clin. Exp. Hepatol. 5 , 295–302 (2015).

Dufour, J.-F. et al. The global epidemiology of nonalcoholic steatohepatitis (NASH) and associated risk factors–a targeted literature review. Endocr. Metab. Sci. 3 , 100089 (2021).

Loomba, R. et al. Association between diabetes, family history of diabetes, and risk of nonalcoholic steatohepatitis and fibrosis. Hepatology 56 , 943–951 (2012).

Anstee, Q. M., Targher, G. & Day, C. P. Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis. Nat. Rev. Gastroenterol. Hepatol. 10 , 330–344 (2013).

Holstein, A., Hinze, S., Thießen, E., Plaschke, A. & Egberts, E. H. Clinical implications of hepatogenous diabetes in liver cirrhosis. J. Gastroenterol. Hepatol. 17 , 677–681 (2002).

Del Vecchio Blanco, C., Gentile, S., Marmo, R., Carbone, L. & Coltorti, M. Alterations of glucose metabolism in chronic liver disease. Diabetes Res. Clin. Pract. 8 , 29–36 (1990).

Zein, N. N., Abdulkarim, A. S., Wiesner, R. H., Egan, K. S. & Persing, D. H. Prevalence of diabetes mellitus in patients with end-stage liver cirrhosis due to hepatitis C, alcohol, or cholestatic disease. J. Hepatol. 32 , 209–217 (2000).

Niederau, C. et al. Survival and causes of death in cirrhotic and in noncirrhotic patients with primary hemochromatosis. N. Engl. J. Med. 313 , 1256–1262 (1985).

Larter, C. Z. & Farrell, G. C. Insulin resistance, adiponectin, cytokines in NASH: which is the best target to treat? J. Hepatol. 44 , 253–261 (2006).

Marchesini, G. et al. Association of nonalcoholic fatty liver disease with insulin resistance. Am. J. Med. 107 , 450–455 (1999).

Angulo, P. Medical progress: nonalcoholic fatty liver disease. N. Engl. J. Med. 346 , 1221–1231 (2002).

Porepa, L., Ray, J. G., Sanchez-Romeu, P. & Booth, G. L. Newly diagnosed diabetes mellitus as a risk factor for serious liver disease. CMAJ 182 , E526–E531 (2010).

Stefan, N. Causes, consequences, and treatment of metabolically unhealthy fat distribution. Lancet Diabetes Endocrinol. 8 , 616–627 (2020).

Stefan, N., Häring, H. U. & Cusi, K. Non-alcoholic fatty liver disease: causes, diagnosis, cardiometabolic consequences, and treatment strategies. Lancet Diabetes Endocrinol. 7 , 313–324 (2019).

Sattar, N. & Gill, J. M. R. Type 2 diabetes as a disease of ectopic fat? BMC Med. 12 , 123 (2014).

Harding, K. A. et al. Depression prevalence in type 2 diabetes is not related to diabetes–depression symptom overlap but is related to symptom dimensions within patient self-report measures: a meta-analysis. Diabet. Med. 36 , 1600–1611 (2019).

Lim, G. Y. et al. Prevalence of depression in the community from 30 countries between 1994 and 2014. Sci. Rep. 8 , 2861 (2018).

Roy, T. & Lloyd, C. E. Epidemiology of depression and diabetes: a systematic review. J. Affect. Disord. 142 , S8–S21 (2012).

Rotella, F. & Mannucci, E. Diabetes mellitus as a risk factor for depression. A meta-analysis of longitudinal studies. Diabetes Res. Clin. Pract. 99 , 98–104 (2013).

Nouwen, A. et al. Type 2 diabetes mellitus as a risk factor for the onset of depression: a systematic review and meta-analysis. Diabetologia 53 , 2480–2486 (2010).

Grigsby, A. B., Anderson, R. J., Freedland, K. E., Clouse, R. E. & Lustman, P. J. Prevalence of anxiety in adults with diabetes a systematic review. J. Psychosom. Res. 53 , 1053–1060 (2002).

Smith, K. J. et al. Association of diabetes with anxiety: a systematic review and meta-analysis. J. Psychosom. Res. 74 , 89–99 (2013).

Young, V. et al. Eating problems in adolescents with type1 diabetes: a systematic review with meta-analysis. Diabet. Med. 30 , 189–198 (2013).

Schabert, J., Browne, J. L., Mosely, K. & Speight, J. Social stigma in diabetes: a framework to understand a growing problem for an increasing epidemic. Patient 6 , 1–10 (2013).

Barnard, K. D., Speight, J. & Skinner, T. C. Quality of life and impact of continuous subcutaneous insulin infusion for children and their parents. Pract. Diabetes Int. 25 , 278–283 (2008).

Hagger, V., Hendrieckx, C., Sturt, J., Skinner, T. C. & Speight, J. Diabetes distress among adolescents with type 1 diabetes: a systematic review. Curr. Diabetes Rep. 16 , 1–14 (2016).

Abdoli, S. et al. New insights into diabetes burnout and its distinction from diabetes distress and depressive symptoms: a qualitative study. Diabetes Res. Clin. Pract. 169 , 108446 (2020).

Pickup, J. C. & Crook, M. A. Is type II diabetes mellitus a disease of the innate immune system? Diabetologia 41 , 1241–1248 (1998).

Dantzer, R., O’Connor, J. C., Freund, G. G., Johnson, R. W. & Kelley, K. W. From inflammation to sickness and depression: when the immune system subjugates the brain. Nat. Rev. Neurosci. 9 , 46–56 (2008).

Prestele, S., Aldenhoff, J. & Reiff, J. [The HPA-axis as a possible link between depression, diabetes mellitus and cognitive dysfunction]. Fortschr. Neurol. Psychiatr. 71 , 24–36 (2003).

Cole, J., Costafreda, S. G., McGuffin, P. & Fu, C. H. Hippocampal atrophy in first episode depression: a meta-analysis of magnetic resonance imaging studies. J. Affect. Disord. 134 , 483–487 (2011).

Gold, S. M. et al. Hippocampal damage and memory impairments as possible early brain complications of type 2 diabetes. Diabetologia 50 , 711–719 (2007).

Moulton, C. D., Costafreda, S. G., Horton, P., Ismail, K. & Fu, C. H. Y. Meta-analyses of structural regional cerebral effects in type 1 and type 2 diabetes. Brain Imaging Behav. 9 , 651–662 (2015).

Khalil, M., Power, N., Graham, E., Deschênes, S. S. & Schmitz, N. The association between sleep and diabetes outcomes – systematic review. Diabetes Res. Clin. Pract. 161 , 108035 (2020).

Senaratna, C. V. et al. Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep. Med. Rev. 34 , 70–81 (2017).

Subramanian, A. et al. Risk of incident obstructive sleep apnea among patients with type 2 diabetes. Diabetes Care 42 , 954–963 (2019).

Huang, T. et al. A population-based study of the bidirectional association between obstructive sleep apnea and type 2 diabetes in three prospective U.S. Cohorts. Diabetes Care 41 , 2111–2119 (2018).

Reutrakul, S. et al. Sleep characteristics in type 1 diabetes and associations with glycemic control: systematic review and meta-analysis. Sleep. Med. 23 , 26–45 (2016).

Nagayoshi, M. et al. Obstructive sleep apnea and incident type 2 diabetes. Sleep. Med. 25 , 156–161 (2016).

Ficker, J. H. et al. Obstructive sleep apnoea and diabetes mellitus: the role of cardiovascular autonomic neuropathy. Eur. Respir. J. 11 , 14–19 (1998).

Young, T., Peppard, P. E. & Taheri, S. Excess weight and sleep-disordered breathing. J. Appl. Physiol. 99 , 1592–1599 (2005).

Ip, M. S. M. et al. Obstructive sleep apnea is independently associated with insulin resistance. Am. J. Respir. Crit. Care Med. 165 , 670–676 (2002).

Shaw, J. E. et al. The effect of treatment of obstructive sleep apnea on glycemic control in type 2 diabetes. Am. J. Respir. Crit. Care Med. 194 , 486–492 (2016).

Lu, F. P., Lin, K. P. & Kuo, H. K. Diabetes and the risk of multi-system aging phenotypes: A systematic review and meta-analysis. PLoS ONE 4 , e4144 (2009).

Cheng, G., Huang, C., Deng, H. & Wang, H. Diabetes as a risk factor for dementia and mild cognitive impairment: a meta-analysis of longitudinal studies. Intern. Med. J. 42 , 484–491 (2012).

Li, X. Y. et al. Midlife modifiable risk factors for dementia: a systematic review and meta-analysis of 34 prospective cohort studies. Curr. Alzheimer Res. 16 , 1254–1268 (2019).

Article   PubMed   CAS   Google Scholar  

Xue, M. et al. Diabetes mellitus and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 144 prospective studies. Ageing Res. Rev. 55 , 100944 (2019).

Pal, K., Mukadam, N., Petersen, I. & Cooper, C. Mild cognitive impairment and progression to dementia in people with diabetes, prediabetes and metabolic syndrome: a systematic review and meta-analysis. Soc. Psychiatry Psychiatr. Epidemiol. 53 , 1149–1160 (2018).

Biessels, G. J., Staekenborg, S., Brunner, E., Brayne, C. & Scheltens, P. Risk of dementia in diabetes mellitus: a systematic review. Lancet Neurol. 5 , 64–74 (2006).

Peila, R., Rodriguez, B. L. & Launer, L. J. Type 2 diabetes, APOE gene, and the risk for dementia and related pathologies: the Honolulu-Asia aging study. Diabetes 51 , 1256–1262 (2002).

Abner, E. L. et al. Diabetes is associated with cerebrovascular but not Alzheimer’s disease neuropathology. Alzheimer’s Dement. 12 , 882–889 (2016).

Matioli, M. N. P. S. et al. Association between diabetes and causes of dementia: evidence from a clinicopathological study. Dement. Neuropsychol. 11 , 406–412 (2017).

You, Y. et al. The prevalence of mild cognitive impairment in type 2 diabetes mellitus patients: a systematic review and meta-analysis. Acta Diabetol. 58 , 671–685 (2021).

Langa, K. M. & Levine, D. A. The diagnosis and management of mild cognitive impairment: a clinical review. JAMA 312 , 2551–2561 (2014).

Pelimanni, E. & Jehkonen, M. Type 2 diabetes and cognitive functions in middle age: a meta-analysis. J. Int. Neuropsychol. Soc. 25 , 215–229 (2019).

Rom, S. et al. Hyperglycemia and advanced glycation end products disrupt BBB and promote occludin and claudin-5 protein secretion on extracellular microvesicles. Sci. Rep. 10 , 7274 (2020).

Hussain, B., Fang, C. & Chang, J. Blood–brain barrier breakdown: an emerging biomarker of cognitive impairment in normal aging and dementia. Front. Neurosci. 15 , 688090 (2021).

Anstey, K. J., Sargent-Cox, K., Eramudugolla, R., Magliano, D. J. & Shaw, J. E. Association of cognitive function with glucose tolerance and trajectories of glucose tolerance over 12 years in the AusDiab study. Alzheimers Res. Ther. 7 , 48 (2015).

Steen, E. et al. Impaired insulin and insulin-like growth factor expression and signaling mechanisms in Alzheimer’s disease - Is this type 3 diabetes? J. Alzheimer’s Dis. 7 , 63–80 (2005).

Leonardi, M., Bickenbach, J., Ustun, T. B., Kostanjsek, N. & Chatterji, S. The definition of disability: what is in a name? Lancet 368 , 1219–1221 (2006).

Lisy, K., Campbell, J. M., Tufanaru, C., Moola, S. & Lockwood, C. The prevalence of disability among people with cancer, cardiovascular disease, chronic respiratory disease and/or diabetes: a systematic review. Int. J. Evid. Based Healthc. 16 , 154–166 (2018).

Yang, Y., Hu, X., Zhang, Q. & Zou, R. Diabetes mellitus and risk of falls in older adults: a systematic review and meta-analysis. Age Ageing 45 , 761–767 (2016).

Wong, E. et al. Diabetes and risk of physical disability in adults: a systematic review and meta-analysis. Lancet Diabetes Endocrinol. 1 , 106–114 (2013).

Havercamp, S. M., Scandlin, D. & Roth, M. Health disparities among adults with developmental disabilities, adults with other disabilities, and adults not reporting disability in North Carolina. Public. Health Rep. 119 , 418–426 (2004).

Herquelot, E., Guéguen, A., Bonenfant, S. & Dray-Spira, R. Impact of diabetes on work cessation: data from the GAZEL cohort study. Diabetes Care 34 , 1344–1349 (2011).

Virtanen, M. et al. Work disability among employees with diabetes: latent class analysis of risk factors in three prospective cohort studies. PLoS ONE 10 , e0143184 (2015).

Cho, N. H. et al. IDF Diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res. Clin. Pract. 138 , 271–281 (2018).

Seok, W. P. et al. Accelerated loss of skeletal muscle strength in older adults with type 2 diabetes: the health, aging, and body composition study. Diabetes Care 30 , 1507–1512 (2007).

Stratton, I. M. et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. Br. Med. J. 321 , 405–412 (2000).

DeCensi, A. et al. Metformin and cancer risk in diabetic patients: A systematic review and meta-analysis. Cancer Prev. Res. 3 , 1451–1461 (2010).

Inzucchi, S. E., Lipska, K. J., Mayo, H., Bailey, C. J. & McGuire, D. K. Metformin in patientswith type 2 diabetes and kidney disease a systematic review. JAMA 312 , 2668–2675 (2014).

Suissa, S. & Azoulay, L. Metformin and the risk of cancer: time-related biases in observational studies. Diabetes Care 35 , 2665–2673 (2012).

Karlstad, Ø. et al. Use of insulin and insulin analogs and risk of cancer-systematic review and meta-analysis of observational studies. Curr. Drug. Saf. 8 , 333–348 (2013).

Bordeleau, L. et al. The association of basal insulin glargine and/or n-3 fatty acids with incident cancers in patients with dysglycemia. Diabetes Care 37 , 1360–1366 (2014).

Guo, M. et al. Metformin may produce antidepressant effects through improvement of cognitive function among depressed patients with diabetes mellitus. Clin. Exp. Pharmacol. Physiol. 41 , 650–656 (2014).

CAS   PubMed   Google Scholar  

Campbell, J. M. et al. Metformin use associated with reduced risk of dementia in patients with diabetes: a systematic review and meta-analysis. J. Alzheimer’s Dis. 65 , 1225–1236 (2018).

Haukeland, J. W. et al. Metformin in patients with non-alcoholic fatty liver disease: a randomized, controlled trial. Scand. J. Gastroenterol. 44 , 853–860 (2009).

Cukierman-Yaffe, T. et al. Effect of dulaglutide on cognitive impairment in type 2 diabetes: an exploratory analysis of the REWIND trial. Lancet Neurol. 19 , 582–590 (2020).

Johnson, J. A. & Bowker, S. L. Intensive glycaemic control and cancer risk in type 2 diabetes: a meta-analysis of major trials. Diabetologia 54 , 25–31 (2011).

Launer, L. J. et al. Effects of intensive glucose lowering on brain structure and function in people with type 2 diabetes (ACCORD MIND): a randomised open-label substudy. Lancet Neurol. 10 , 969–977 (2011).

Jia, Y. et al. Associations of the glycaemic control of diabetes with dementia and physical function in rural-dwelling older Chinese adults: a population-based study. Clin. Interv. Aging 16 , 1503–1513 (2021).

Lesniak, C. et al. Inpatient glycemic control and outcome of COVID-19 patients: a retrospective cohort. SAGE Open. Med. 9 , 20503121211039105 (2021).

Afolabi, B. I. et al. The relationship between glycaemic control and non-alcoholic fatty liver disease in Nigerian type 2 diabetic patients. J. Natl Med. Assoc. 110 , 256–264 (2018).

Nouwen, A. et al. Longitudinal associations between depression and diabetes complications: a systematic review and meta-analysis. Diabet. Med. 36 , 1562–1572 (2019).

Perry, B. D. et al. Muscle atrophy in patients with Type 2 diabetes mellitus: roles of inflammatory pathways, physical activity and exercise. Exerc. Immunol. Rev. 22 , 94–109 (2016).

Hirata, Y. et al. Hyperglycemia induces skeletal muscle atrophy via a WWP1/KLF15 axis. JCI Insight 4 , e124952 (2019).

Article   PubMed Central   Google Scholar  

Bassil, M. S. & Gougeon, R. Muscle protein anabolism in type 2 diabetes. Curr. Opin. Clin. Nutr. Metab. Care 16 , 83–88 (2013).

Meex, R. C. R., Blaak, E. E. & van Loon, L. J. C. Lipotoxicity plays a key role in the development of both insulin resistance and muscle atrophy in patients with type 2 diabetes. Obes. Rev. 20 , 1205–1217 (2019).

Download references

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.

Author information

These authors jointly supervised this work: Jonathan E. Shaw and Dianna J. Magliano.

Authors and Affiliations

Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia

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

School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia

You can also search for this author in PubMed   Google Scholar

Contributions

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.

Corresponding author

Correspondence to Dianna J. Magliano .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Reviews Endocrinology thanks Emily Gallagher, Norbert Stefan and Assaad Eid for their contribution to the peer review of this work.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Fundamental skills required to independently care for oneself such as eating, bathing and mobility.

Activities that allow an individual to live independently in a community.

The error in estimating the association between an exposure and an outcome that results from misclassification or exclusion of time intervals.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

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

Download citation

Accepted : 06 May 2022

Published : 06 June 2022

Issue Date : September 2022

DOI : https://doi.org/10.1038/s41574-022-00690-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Insights and implications of sexual dimorphism in osteoporosis.

• Yuan-Yuan Zhang
• Zhisen Shen

Bone Research (2024)

Fingerprinting hyperglycemia using predictive modelling approach based on low-cost routine CBC and CRP diagnostics

• Kashif Asghar
• Safee Ullah Chaudhary

Scientific Reports (2024)

An eye on equity: faricimab-driven health equity improvements in diabetic macular oedema using a distributional cost-effectiveness analysis from a UK societal perspective

• Aurelie Meunier
• Oyin Opeifa
• Richard P. Gale

Ti3C2/Ni/Sm-based electrochemical glucose sensor for sweat analysis using bipolar electrochemistry

• Zahra Damirchi
• Ali Firoozbakhtian
• Mohammad Reza Ganjali

Microchimica Acta (2024)

Structural Analysis and Novel Mechanism of Enteromorpha prolifera Sulfated Polysaccharide in Preventing Type 2 Diabetes Mellitus

• Wenxiang Wang

Plant Foods for Human Nutrition (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Recent Advances

ADA-funded researchers use the money from their awards to conduct critical diabetes research. In time, they publish their findings in order to inform fellow scientists of their results, which ensures that others will build upon their work. Ultimately, this cycle drives advances to prevent diabetes and to help people burdened by it. In 2018 alone, ADA-funded scientists published over 200 articles related to their awards!

Identification of a new player in type 1 diabetes risk

Type 1 diabetes is caused by an autoimmune attack of insulin-producing beta-cells. While genetics and the environment are known to play important roles, the underlying factors explaining why the immune system mistakenly recognize beta-cells as foreign is not known. Now, Dr. Delong has discovered a potential explanation. He found that proteins called Hybrid Insulin Peptides (HIPs) are found on beta-cells of people with type 1 diabetes and are recognized as foreign by their immune cells. Even after diabetes onset, immune cells are still present in the blood that attack these HIPs.

Next, Dr. Delong wants to determine if HIPs can serve as a biomarker or possibly even targeted to prevent or treat type 1 diabetes. Baker, R. L., Rihanek, M., Hohenstein, A. C., Nakayama, M., Michels, A., Gottlieb, P. A., Haskins, K., & Delong, T. (2019). Hybrid Insulin Peptides Are Autoantigens in Type 1 Diabetes. Diabetes , 68 (9), 1830–1840.

Understanding the biology of body-weight regulation in children

Determining the biological mechanisms regulating body-weight is important for preventing type 2 diabetes. The rise in childhood obesity has made this even more urgent. Behavioral studies have demonstrated that responses to food consumption are altered in children with obesity, but the underlying biological mechanisms are unknown. This year, Dr. Schur tested changes in brain and hormonal responses to a meal in normal-weight and obese children. Results from her study show that hormonal responses in obese children are normal following a meal, but responses within the brain are reduced. The lack of response within the brain may predispose them to overconsumption of food or difficulty with weight-loss.

With this information at hand, Dr. Schur wants to investigate how this information can be used to treat obesity in children and reduce diabetes.

Roth, C. L., Melhorn, S. J., Elfers, C. T., Scholz, K., De Leon, M. R. B., Rowland, M., Kearns, S., Aylward, E., Grabowski, T. J., Saelens, B. E., & Schur, E. A. (2019). Central Nervous System and Peripheral Hormone Responses to a Meal in Children. The Journal of Clinical Endocrinology and Metabolism , 104 (5), 1471–1483.

A novel molecule to improve continuous glucose monitoring

To create a fully automated artificial pancreas, it is critical to be able to quantify blood glucose in an accurate and stable manner. Current ways of continuously monitoring glucose are dependent on the activity of an enzyme which can change over time, meaning the potential for inaccurate readings and need for frequent replacement or calibration. Dr. Wang has developed a novel molecule that uses a different, non-enzymatic approach to continuously monitor glucose levels in the blood. This new molecule is stable over long periods of time and can be easily integrated into miniaturized systems.

Now, Dr. Wang is in the process of patenting his invention and intends to continue research on this new molecule so that it can eventually benefit people living with diabetes.

Wang, B. , Chou, K.-H., Queenan, B. N., Pennathur, S., & Bazan, G. C. (2019). Molecular Design of a New Diboronic Acid for the Electrohydrodynamic Monitoring of Glucose. Angewandte Chemie (International Ed. in English) , 58 (31), 10612–10615.

Addressing the legacy effect of diabetes

Several large clinical trials have demonstrated the importance of tight glucose control for reducing diabetes complications. However, few studies to date have tested this in the real-world, outside of a controlled clinical setting. In a study published this year, Dr. Laiteerapong found that indeed in a real-world setting, people with lower hemoglobin A1C levels after diagnosis had significantly lower vascular complications later on, a phenomenon known as the ‘legacy effect’ of glucose control. Her research noted the importance of early intervention for the best outcomes, as those with the low A1C levels just one-year after diagnosis had significantly lower vascular disease risk compared to people with higher A1C levels.

With these findings in hand, physicians and policymakers will have more material to debate and determine the best course of action for improving outcomes in people newly diagnosed with diabetes.

Laiteerapong, N. , Ham, S. A., Gao, Y., Moffet, H. H., Liu, J. Y., Huang, E. S., & Karter, A. J. (2019). The Legacy Effect in Type 2 Diabetes: Impact of Early Glycemic Control on Future Complications (The Diabetes & Aging Study). Diabetes Care , 42 (3), 416–426.

A new way to prevent immune cells from attacking insulin-producing beta-cells

Replacing insulin-producing beta-cells that have been lost in people with type 1 diabetes is a promising strategy to restore control of glucose levels. However, because the autoimmune disease is a continuous process, replacing beta-cells results in another immune attack if immunosorbent drugs are not used, which carry significant side-effects. This year, Dr. Song reported on the potential of an immunotherapy he developed that prevents immune cells from attacking beta-cells and reduces inflammatory processes. This immunotherapy offers several potential benefits, including eliminating the need for immunosuppression, long-lasting effects, and the ability to customize the treatment to each patient.

The ability to suppress autoimmunity has implications for both prevention of type 1 diabetes and improving success rates of islet transplantation.

Haque, M., Lei, F., Xiong, X., Das, J. K., Ren, X., Fang, D., Salek-Ardakani, S., Yang, J.-M., & Song, J . (2019). Stem cell-derived tissue-associated regulatory T cells suppress the activity of pathogenic cells in autoimmune diabetes. JCI Insight , 4 (7).

A new target to improve insulin sensitivity

The hormone insulin normally acts like a ‘key’, traveling through the blood and opening the cellular ‘lock’ to enable the entry of glucose into muscle and fat cells. However, in people with type 2 diabetes, the lock on the cellular door has, in effect, been changed, meaning insulin isn’t as effective. This phenomenon is called insulin resistance. Scientists have long sought to understand what causes insulin resistance and develop therapies to enable insulin to work correctly again. This year, Dr. Summers determined an essential role for a molecule called ceramides as a driver of insulin resistance in mice. He also presented a new therapeutic strategy for lowering ceramides and reversing insulin resistance. His findings were published in one of the most prestigious scientific journals, Science .

Soon, Dr. Summers and his team will attempt to validate these findings in humans, with the ultimate goal of developing a new medication to help improve outcomes in people with diabetes.

Chaurasia, B., Tippetts, T. S., Mayoral Monibas, R., Liu, J., Li, Y., Wang, L., Wilkerson, J. L., Sweeney, C. R., Pereira, R. F., Sumida, D. H., Maschek, J. A., Cox, J. E., Kaddai, V., Lancaster, G. I., Siddique, M. M., Poss, A., Pearson, M., Satapati, S., Zhou, H., … Summers, S. A. (2019). Targeting a ceramide double bond improves insulin resistance and hepatic steatosis. Science (New York, N.Y.) , 365 (6451), 386–392.

Determining the role of BPA in type 2 diabetes risk

Many synthetic chemicals have infiltrated our food system during the period in which rates of diabetes has surged. Data has suggested that one particular synthetic chemical, bisphenol A (BPA), may be associated with increased risk for developing type 2 diabetes. However, no study to date has determined whether consumption of BPA alters the progression to type 2 diabetes in humans. Results reported this year by Dr. Hagobian demonstrated that indeed when BPA is administered to humans in a controlled manner, there is an immediate, direct effect on glucose and insulin levels.

Now, Dr. Hagobian wants to conduct a larger clinical trial including exposure to BPA over a longer period of time to determine precisely how BPA influences glucose and insulin. Such results are important to ensure the removal of chemicals contributing to chronic diseases, including diabetes.

Hagobian, T. A. , Bird, A., Stanelle, S., Williams, D., Schaffner, A., & Phelan, S. (2019). Pilot Study on the Effect of Orally Administered Bisphenol A on Glucose and Insulin Response in Nonobese Adults. Journal of the Endocrine Society , 3 (3), 643–654.

Investigating the loss of postmenopausal protection from cardiovascular disease in women with type 1 diabetes

On average, women have a lower risk of developing heart disease compared to men. However, research has shown that this protection is lost in women with type 1 diabetes. The process of menopause increases rates of heart disease in women, but it is not known how menopause affects women with type 1 diabetes in regard to risk for developing heart disease. In a study published this year, Dr. Snell-Bergeon found that menopause increased risk markers for heart disease in women with type 1 diabetes more than women without diabetes.

Research has led to improved treatments and significant gains in life expectancy for people with diabetes and, as a result, many more women are reaching the age of menopause. Future research is needed to address prevention and treatment options.

Keshawarz, A., Pyle, L., Alman, A., Sassano, C., Westfeldt, E., Sippl, R., & Snell-Bergeon, J. (2019). Type 1 Diabetes Accelerates Progression of Coronary Artery Calcium Over the Menopausal Transition: The CACTI Study. Diabetes Care , 42 (12), 2315–2321.

Identification of a potential therapy for diabetic neuropathy related to type 1 and type 2 diabetes

Diabetic neuropathy is a type of nerve damage that is one of the most common complications affecting people with diabetes. For some, neuropathy can be mild, but for others, it can be painful and debilitating. Additionally, neuropathy can affect the spinal cord and the brain. Effective clinical treatments for neuropathy are currently lacking. Recently, Dr. Calcutt reported results of a new potential therapy that could bring hope to the millions of people living with diabetic neuropathy. His study found that a molecule currently in clinical trials for the treatment of depression may be valuable for diabetic neuropathy, particularly the type affecting the brain.

Because the molecule is already in clinical trials, there is the potential that it can benefit patients sooner than later.

Jolivalt, C. G., Marquez, A., Quach, D., Navarro Diaz, M. C., Anaya, C., Kifle, B., Muttalib, N., Sanchez, G., Guernsey, L., Hefferan, M., Smith, D. R., Fernyhough, P., Johe, K., & Calcutt, N. A. (2019). Amelioration of Both Central and Peripheral Neuropathy in Mouse Models of Type 1 and Type 2 Diabetes by the Neurogenic Molecule NSI-189. Diabetes , 68 (11), 2143–2154.

ADA-funded researcher studying link between ageing and type 2 diabetes

One of the most important risk factors for developing type 2 diabetes is age. As a person gets older, their risk for developing type 2 diabetes increases. Scientists want to better understand the relationship between ageing and diabetes in order to determine out how to best prevent and treat type 2 diabetes. ADA-funded researcher Rafael Arrojo e Drigo, PhD, from the Salk Institute for Biological Studies, is one of those scientists working hard to solve this puzzle.

Recently, Dr. Arrojo e Drigo published results from his research in the journal Cell Metabolism . The goal of this specific study was to use high-powered microscopes and novel cellular imaging tools to determine the ‘age’ of different cells that reside in organs that control glucose levels, including the brain, liver and pancreas. He found that, in mice, the cells that make insulin in the pancreas – called beta-cells – were a mosaic of both old and young cells. Some beta-cells appeared to be as old as the animal itself, and some were determined to be much younger, indicating they recently underwent cell division.

Insufficient insulin production by beta-cells is known to be a cause of type 2 diabetes. One reason for this is thought to be fewer numbers of functional beta-cells. Dr. Arrojo e Drigo believes that people with or at risk for diabetes may have fewer ‘young’ beta-cells, which are likely to function better than old ones. Alternatively, if we can figure out how to induce the production of younger, high-functioning beta-cells in the pancreas, it could be a potential treatment for people with diabetes.

In the near future, Dr. Arrojo e Drigo’s wants to figure out how to apply this research to humans. “The next step is to look for molecular or morphological features that would allow us to distinguish a young cell from and old cell,” Dr. Arrojo e Drigo said.

The results from this research are expected to provide a unique insight into the life-cycle of beta-cells and pave the way to novel therapeutic avenues for type 2 diabetes.

Watch a video of Dr. Arrojo e Drigo explaining his research!

Arrojo E Drigo, R. , Lev-Ram, V., Tyagi, S., Ramachandra, R., Deerinck, T., Bushong, E., … Hetzer, M. W. (2019). Age Mosaicism across Multiple Scales in Adult Tissues. Cell Metabolism , 30 (2), 343-351.e3.

Researcher identifies potential underlying cause of type 1 diabetes

Type 1 diabetes occurs when the immune system mistakenly recognizes insulin-producing beta-cells as foreign and attacks them. The result is insulin deficiency due to the destruction of the beta-cells. Thankfully, this previously life-threatening condition can be managed through glucose monitoring and insulin administration. Still, therapies designed to address the underlying immunological cause of type 1 diabetes remain unavailable.

Conventional approaches have focused on suppressing the immune system, which has serious side effects and has been mostly unsuccessful. The American Diabetes Association recently awarded a grant to Dr. Kenneth Brayman, who proposed to take a different approach. What if instead of suppressing the whole immune system, we boost regulatory aspects that already exist in the system, thereby reigning in inappropriate immune cell activation and preventing beta-cell destruction? His idea focused on a molecule called immunoglobulin M (IgM), which is responsible for limiting inflammation and regulating immune cell development.

In a paper published in the journal Diabetes , Dr. Brayman and a team of researchers reported exciting findings related to this approach. They found that supplementing IgM obtained from healthy mice into mice with type 1 diabetes selectively reduced the amount of autoreactive immune cells known to target beta-cells for destruction. Amazingly, this resulted in reversal of new-onset diabetes. Importantly, the authors of the study determined this therapy is translatable to humans. IgM isolated from healthy human donors also prevented the development of type 1 diabetes in a humanized mouse model of type 1 diabetes.

The scientists tweaked the original experiment by isolating IgM from mice prone to developing type 1 diabetes, but before it actually occurred. When mice with newly onset diabetes were supplemented with this IgM, their diabetes was not reversed. This finding suggests that in type 1 diabetes, IgM loses its capacity to serve as a regulator of immune cells, which may be contribute to the underlying cause of the disease.

Future studies will determine exactly how IgM changes its regulatory properties to enable diabetes development. Identification of the most biologically optimal IgM will facilitate transition to clinical applications of IgM as a potential therapeutic for people with type 1 diabetes.    Wilson, C. S., Chhabra, P., Marshall, A. F., Morr, C. V., Stocks, B. T., Hoopes, E. M., Bonami, R.H., Poffenberger, G., Brayman, K.L. , Moore, D. J. (2018). Healthy Donor Polyclonal IgM’s Diminish B Lymphocyte Autoreactivity, Enhance Treg Generation, and Reverse T1D in NOD Mice. Diabetes .

ADA-funded researcher designs community program to help all people tackle diabetes

Diabetes self-management and support programs are important adjuncts to traditional physician directed treatment. These community-based programs aim to give people with diabetes the knowledge and skills necessary to effectively self-manage their condition. While several clinical trials have demonstrated the value of diabetes self-management programs in terms of improving glucose control and reducing health-care costs, whether this also occurs in implemented programs outside a controlled setting is unclear, particularly in socially and economically disadvantaged groups.

Lack of infrastructure and manpower are often cited as barriers to implementation of these programs in socioeconomically disadvantaged communities. ADA-funded researcher Dr. Briana Mezuk addressed this challenge in a study recently published in The Diabetes Educator . Dr. Mezuk partnered with the YMCA to evaluate the impact of the Diabetes Control Program in Richmond, Virginia. This community-academic partnership enabled both implementation and evaluation of the Diabetes Control Program in socially disadvantaged communities, who are at higher risk for developing diabetes and the complications that accompany it.

Dr. Mezuk had two primary research questions: (1) What is the geographic and demographic reach of the program? and (2) Is the program effective at improving diabetes management and health outcomes in participants? Over a 12-week study period, Dr. Mezuk found that there was broad geographic and demographic participation in the program. The program had participants from urban, suburban and rural areas, most of which came from lower-income zip codes. HbA1C, mental health and self-management behaviors all improved in people taking part in the Greater Richmond Diabetes Control Program. Results from this study demonstrate the value of diabetes self-management programs and their potential to broadly improve health outcomes in socioeconomically diverse communities. Potential exists for community-based programs to address the widespread issue of outcome disparities related to diabetes.  Mezuk, B. , Thornton, W., Sealy-Jefferson, S., Montgomery, J., Smith, J., Lexima, E., … Concha, J. B. (2018). Successfully Managing Diabetes in a Community Setting: Evidence from the YMCA of Greater Richmond Diabetes Control Program. The Diabetes Educator , 44 (4), 383–394.

Using incentives to stimulate behavior changes in youth at risk for developing diabetes

Once referred to as ‘adult-onset diabetes’, incidence of type 2 diabetes is now rapidly increasing in America’s youth. Unfortunately, children often do not have the ability to understand how everyday choices impact their health. Could there be a way to change a child’s eating behaviors? Davene Wright, PhD, of Seattle Children’s Hospital was granted an Innovative Clinical or Translational Science award to determine whether using incentives, directed by parents, can improve behaviors related to diabetes risk. A study published this year in Preventive Medicine Reports outlined what incentives were most desirable and feasible to implement. A key finding was that incentives should be tied to behavior changes and not to changes in body-weight.

With this information in hand, Dr. Wright now wants to see if incentives do indeed change a child’s eating habits and risk for developing type 2 diabetes. She is also planning to test whether an incentive program can improve behavior related to diabetes management in youth with type 1 diabetes. Jacob-Files, E., Powell, J., & Wright, D. R. (2018). Exploring parent attitudes around using incentives to promote engagement in family-based weight management programs. Preventive Medicine Reports , 10 , 278–284.

Determining the genetic risk for gestational diabetes

Research has identified more than 100 genetic variants linked to risk for developing type 2 diabetes in humans. However, the extent to which these same genetic variants might affect a woman’s probability for getting gestational diabetes has not been investigated.

Pathway to Stop Diabetes ® Accelerator awardee Marie-France Hivert, MD, of Harvard University set out to answer this critical question. Dr. Hivert found that indeed genetic determinants of type 2 diabetes outside of pregnancy are also strong risk factors for gestational diabetes. This study was published in the journal Diabetes .

The implications? Because of this finding, doctors in the clinic may soon be able to identify women at risk for getting gestational diabetes and take proactive steps to prevent it. Powe, C. E., Nodzenski, M., Talbot, O., Allard, C., Briggs, C., Leya, M. V., … Hivert, M.-F. (2018). Genetic Determinants of Glycemic Traits and the Risk of Gestational Diabetes Mellitus. Diabetes , 67 (12), 2703–2709.

Donate Today and Change Lives!