Your activity: 265 p.v.
your limit has been reached. plz Donate us to allow your ip full access, Email: sshnevis@outlook.com

Pathogenesis of type 2 diabetes mellitus

Pathogenesis of type 2 diabetes mellitus
Authors:
R Paul Robertson, MD
Miriam S Udler, MD, PhD
Section Editor:
David M Nathan, MD
Deputy Editor:
Katya Rubinow, MD
Literature review current through: Dec 2022. | This topic last updated: Dec 14, 2021.

INTRODUCTION — Type 2 diabetes mellitus is characterized by hyperglycemia, insulin resistance, and relative impairment in insulin secretion. Although the diagnostic criteria rely solely on measures of elevated glycemia without explicit knowledge of the underlying pathophysiology, type 2 diabetes is a heterogeneous disease with patients experiencing varying contributions of defective insulin secretion and insulin action (insulin resistance). Insulin resistance may play an important role in the genesis of other abnormalities in type 2 diabetes including inflammation, lipoprotein abnormalities, hypertension, and myriad other metabolic abnormalities.

Type 2 diabetes is a common disorder with a prevalence that rises markedly with increasing degrees of obesity (figure 1) [1] and age. The prevalence of type 2 diabetes has risen alarmingly in the past decade [2,3], in large part linked to the trends in obesity and sedentary lifestyle [4].

This topic will present an overview of our evolving understanding of the pathophysiology of type 2 diabetes from both human physiological studies and genetic studies. The diagnosis, evaluation, and management of type 2 diabetes are reviewed separately.

(See "Clinical presentation, diagnosis, and initial evaluation of diabetes mellitus in adults".)

(See "Overview of general medical care in nonpregnant adults with diabetes mellitus".)

(See "Initial management of hyperglycemia in adults with type 2 diabetes mellitus".)

PATHOPHYSIOLOGY

General principles — The pathogenesis of type 2 diabetes is multifactorial [5]. Patients typically present with a combination of varying degrees of insulin resistance and defective insulin secretion (beta cell dysfunction), neither of which is routinely measured in the clinical setting. Both contribute to type 2 diabetes, with heightened demand for insulin action mediated by resistance that is not matched by insulin secretion [6-8].

Insulin resistance – The underlying cause of the insulin resistance has traditionally been attributed to predominantly "environmental" factors related to overeating, sedentary lifestyle, and resulting overweight and obesity, with less prominent contributions from aging and genetics.

Impaired insulin secretion – The defective insulin secretion is largely a result of genetic influences and the programming of the beta cell mass and function in utero. Additionally, hyperglycemia itself can impair pancreatic beta cell function and exacerbate insulin resistance ("glucotoxicity"), leading to a vicious cycle of hyperglycemia causing a worsening metabolic state [8,9].

Type 2 diabetes is often accompanied by other conditions, including hypertension, dyslipidemia (raised triglycerides and lowered high-density lipoprotein [HDL] cholesterol), and central obesity. This constellation of clinical conditions is referred to as the metabolic syndrome [10]. Insulin resistance may play an important role in the genesis of these abnormalities. Increased free fatty acid levels, inflammatory cytokines from fat, and oxidative factors have all been implicated in the pathogenesis of metabolic syndrome, type 2 diabetes, and their cardiovascular complications [11]. (See "Metabolic syndrome (insulin resistance syndrome or syndrome X)" and "Insulin resistance: Definition and clinical spectrum", section on 'Metabolic syndrome'.)

Impaired insulin secretion and insulin resistance — The relative importance of impaired insulin release and insulin resistance in the pathogenesis of type 2 diabetes has been evaluated in several studies [12-14]. As an example, in a prospective study of over 6500 British civil servants without diabetes at baseline, 505 subjects were diagnosed with diabetes during 9.7 years (median) of follow-up [15]. In those who developed diabetes compared with those who did not, there was a marked decrease in insulin sensitivity during the five years prior to diagnosis. Beta cell function (insulin secretion) increased three to four years prior to diagnosis, likely as a compensatory mechanism, and then decreased until diagnosis. In addition, a seven-year prospective study of 714 Mexican Americans without diabetes suggested that decreased insulin secretion and insulin resistance were independent risk factors for type 2 diabetes [13]. Among Pima Indians, in whom the frequency of diabetes is very high, the transition from normal glucose tolerance to impaired glucose tolerance to diabetes is characterized by concomitant decreases in insulin-stimulated glucose disposal and glucose-stimulated insulin secretion [14]. Finally, the study of the high-risk "prediabetic" population in the Diabetes Prevention Program (DPP) showed that the combination of decreased baseline insulin sensitivity (resistance) and reduced insulin secretion acted additively to increase risk for diabetes development over time [16].

Beta cell dysfunction – There is evidence that primary defects in beta cell function can occur early in disease pathogenesis. As an example, beta cell function is demonstrably abnormal with fasting glucose levels >100 mg/dL (figure 2), although one of the diagnostic criteria for type 2 diabetes is development of fasting plasma glucose levels >126 mg/dL (7 mmol/L) [17]. Since this is true in lean individuals, as well as in people with diabetes and obesity, this indicates that primary defects in beta cell function can occur before the development of obesity and insulin resistance. Studies have shown that this lack of insulin response is a glucose-specific defect as insulin responses in the same people are relatively intact when isoproterenol or arginine are used to stimulate insulin secretion (figure 3) [18,19].

Insulin resistance – Insulin resistance alone is not a reliable predictor of type 2 diabetes [6,7]. It is possible that insulin resistance becomes more severe with increasing age and weight, thereby unmasking an underlying defect in beta cell function in susceptible subjects to cause impaired glucose tolerance and eventually overt hyperglycemia. Insulin resistance may, at least in part, be related to substances secreted by adipocytes ("adipokines," including leptin, adiponectin, tumor necrosis factor [TNF]-alpha, and resistin). (See 'Factors released from adipose tissue' below.)

The importance of the combination of genetic and environmental factors is suggested by another study of offspring (without diabetes) of two parents with type 2 diabetes [7]. Their insulin sensitivity was similar to that of normal subjects with no first-degree relatives with type 2 diabetes at near ideal body weight; with increasing degrees of obesity, however, the progressive decrease in insulin sensitivity was much more pronounced in those with a family history of type 2 diabetes (figure 4) [7].

Impaired insulin processing — Normal insulin production involves cleavage of insulin from proinsulin; 10 to 15 percent of secreted insulin is proinsulin and its conversion intermediates. In contrast, the proportion of immunoreactive insulin that is proinsulin in type 2 diabetes is increased considerably in the basal state (>40 percent) [20]. The difference between individuals with and without diabetes becomes even more pronounced after stimulation with arginine or glucagon [21]. The increase in proinsulin secretion persists after matching for degree of obesity, suggesting that it represents beta cell dysfunction and not merely the response to the increased secretory demand imposed by the insulin resistance of obesity [21]. These findings suggest that the processing of proinsulin to insulin in the beta cells is impaired in type 2 diabetes or that there is insufficient time for granules to mature properly so that they release more proinsulin.

Role of islet amyloid polypeptide — Islet amyloid polypeptide (amylin) is stored in insulin secretory granules in the pancreatic beta cells. It is cosecreted with insulin, resulting in serum concentrations approximately one-tenth those of insulin, and is present in increased amounts in the pancreas of many patients with type 2 diabetes [22]. Serum insulin and amylin concentrations are lower in patients with impaired glucose tolerance compared with patients with normal glucose tolerance, and the concentrations are very low in patients with type 2 diabetes [23].

High concentrations of amylin decrease glucose uptake/signaling in the islets and inhibit endogenous insulin secretion, suggesting that amylin may be directly involved in the pathogenesis of type 2 diabetes [24]. However, administration of physiologic amounts of amylin has no acute effect on insulin secretion or insulin action in humans [25]. On the other hand, the administration of an amylin antagonist to rats results in a fall in blood glucose and an increase in insulin secretion, suggesting that amylin may tonically inhibit insulin secretion [26].

GENETIC SUSCEPTIBILITY — Type 2 diabetes is a polygenic disease, with likely thousands of genetic factors contributing to disease risk together with complex interaction with environmental factors.

Observations that demonstrate a genetic influence on the development of type 2 diabetes include:

Thirty-nine percent of patients with type 2 diabetes have at least one parent with the disease [27].

Among monozygotic twin pairs with one affected twin, approximately 90 percent of unaffected twins eventually develop the disease [28].

The lifetime risk for a first-degree relative of a patient with type 2 diabetes is 5 to 10 times higher than that of age- and weight-matched subjects without a family history of diabetes [29].

More than 500 distinct genetic signals have been robustly associated with type 2 diabetes from large-scale genome-wide association studies, including over 300 newly discovered in one of the largest multi-ancestral meta-analyses involving 1.4 million human participants (figure 5) [30]. There have been advances using genetics for prediction of risk of developing diabetes as well as for gaining insights into disease pathophysiology.

Genetic risk prediction for type 2 diabetes — In contrast to monogenic causes of diabetes, where inheriting a given causative mutation imparts a substantial risk of developing disease (typically odds ratios [ORs] >10), the genetic variants associated with type 2 diabetes each have small impact on risk (typically ORs <1.2) [31]. Common genetic variants can be combined together to generate polygenic risk scores that allow greater prediction of disease risk than individual common variants.

Polygenic risk scores for type 2 diabetes have incorporated thousands of genetic markers, including those not reaching the stringent "genome-wide" statistical significance required to indicate a genetic variant as robustly associated with disease. One type 2 diabetes polygenic risk score captured approximately 20 percent of the variance in individual predisposition to type 2 diabetes (approximately one-half the total estimated heritability) [32]. Using such a type 2 diabetes polygenic risk score, individuals with the top 2.5 to 5 percent scores can be identified as having approximately threefold increased risk compared with the population mean [32,33]. The risk of diabetes conferred to individuals with type 2 diabetes polygenic risk scores in the top 1 percent remains significantly lower than that conferred by the rare variants causing monogenic diabetes [31]. (See "Classification of diabetes mellitus and genetic diabetic syndromes", section on 'Monogenic diabetes (formerly called maturity onset diabetes of the young)'.)

Another way to consider information conferred by a test is to calculate the area under the receiver operator characteristic curve (the AUROC, or C-statistic), which provides a measure of the proportion of times such a test will correctly assign disease state between a pair of individuals, one who has the disease of interest and another who does not. The AUROC for type 2 diabetes polygenic risk scores are 0.73 when adjusting for age and sex [32,33], indicating some utility, but not reaching levels >0.80 typically required for clinical implementation.

Among groups with increased genetic risk for diabetes, environmental factors still play a major role in the development of diabetes. The Diabetes Prevention Program (DPP) demonstrated that in a population at high risk for diabetes (based on rising subdiabetic glucose levels and overweight or obesity), even those at very high genetic risk had substantial reductions in risk when "environmental" factors (including overweight, obesity, and a sedentary lifestyle) were ameliorated [34].

Genetic insights into pathophysiology — The wealth of genome-wide associations with type 2 diabetes offer an opportunity to improve understanding of the pathophysiology of type 2 diabetes; however, since the majority of these genetic signals reside in nonprotein-coding regions of the genome, determining the mechanisms underlying genetic signals has proved challenging. As a result, the disease mechanisms underlying the majority of association signals for type 2 diabetes remain unknown.

Insights from genome-wide association studies into disease mechanism have generally come from:

Signals residing near genes with known relevance to diabetes

or

Extensive laboratory-based investigation of the chromosomal regions containing type 2 diabetes association signals

Type 2 diabetes association signals that have been mapped to genes already implicated in diabetes pathophysiology include signals near PPARG, HNF1A, HNF4A, HNF1B, KCNJ11/ABCC8, WSF1, and GCKR. Such findings also demonstrate a genetic continuum that exists between monogenic and polygenic diabetes (see "Classification of diabetes mellitus and genetic diabetic syndromes", section on 'Monogenic diabetes (formerly called maturity onset diabetes of the young)'). For example, a protein-coding variant in the gene HNF1A (p.E508K) was identified in Latino populations conferring approximately fourfold increased risk of type 2 diabetes [35]. This variant is almost exclusively seen in Latino populations, where its population frequency is approximately 0.3 percent. HNF1A is a known cause of monogenic diabetes, where patients have defective beta cell insulin secretion. In experimental assays, the HNF1A protein encoding the p.E508K mutant demonstrated transactivation activity that was reduced compared with wild-type protein, but greater than HNF1A proteins encoding HNF1A mutations known to cause monogenic diabetes. Analysis of protein coding variants in over 40,000 individuals demonstrated that individuals with type 2 diabetes carry more rare variants in monogenic diabetes genes than would be expected by chance [36], further supporting a genetic continuum existing between type 2 diabetes and monogenic diabetes.

For several signals arising from type 2 diabetes genetic association studies, extensive laboratory-based investigation has elucidated shared disease mechanisms. For example, investigation of the CILP2/TM6SF2 locus [37] implicated two amino acid-altering variants in TM6SF2 as driving the association signal [38]. One of these genetic variants was also found to be associated with nonalcoholic fatty liver disease in an independent analysis, with the same allele associated with increased risk of type 2 diabetes and nonalcoholic fatty liver disease, as well as with higher circulating levels of alanine transaminase (a marker of liver injury) and with lower levels of triglycerides [39]. Functional experiments knocking down Tm6sf2 in mice [39] as well as TM6SF2 in human hepatoma cell lines [40] suggest that reduced gene expression increases liver triglyceride content and decreases secretion of triglyceride-rich lipoproteins from liver tissue, thus implicating TM6SF2 in liver fat metabolism. Defective liver triglyceride secretion results in increased hepatic fat content and hepatic insulin resistance [41]. These findings indicate a disease pathway of primary liver tissue origin leading to increased risk of type 2 diabetes.

SUBTYPES OF DIABETES BASED ON PATHOGENESIS — As individual patients with type 2 diabetes may have varying degrees of impaired beta cell function (insulin deficiency) and insulin action (insulin resistance), studies have attempted to identify more homogeneous subtypes of type 2 diabetes. The two main approaches have been:

Subclassification of patients using clinical measures

Subclassification of genetic risk factors for type 2 diabetes to identify driving genetic pathways

These two approaches have provided further support for the roles of both impaired insulin secretion and impaired insulin action in pathogenesis of disease. At the present time, these subtypes are not routinely employed in clinical practice.

Subclassification based on clinical parameters – In a Scandinavian study, six diabetes-related clinical parameters (measured at the time of diagnosis in a Scandinavian population with new-onset diabetes) were used to cluster patients with adult-onset diabetes into different subtypes [42]. The clinical parameters included:

Glutamic acid decarboxylase (GAD) antibody

Age

Body mass index (BMI)

Glycated hemoglobin (A1C)

Homeostatic model assessments of beta cell function (HOMA2-B)

Insulin resistance (HOMA2-IR) based on C-peptide concentrations

By clustering individuals based on similarity of these six parameters, five reproducible subgroups of patients were identified, including a severe autoimmune form (capturing type 1 diabetes and latent autoimmune diabetes of adults). The other four subgroups represented different forms of type 2 diabetes:

Severe insulin deficiency

Severe insulin resistance

Mild obesity related

Mild age related

Importantly, the subgroups of type 2 diabetes differed with regard to initiation of insulin therapy and diabetes-related complications. For example, compared with other subgroups, the severe insulin-deficient subgroup progressed most rapidly to use of insulin, and the severe insulin resistance group had increased risk of chronic kidney disease and fatty liver disease. The results have been replicated in other populations [43].

Subclassification of genetic risk factors – An alternative strategy to connect type 2 diabetes genetic association signals to mechanistic pathways comes from physiological informed analysis of the variants and their associations with multiple diabetes-related traits. Two such independent analyses studying an overlapping set of 94 type 2 diabetes risk variants converged on a shared set of five clusters of genetic variants, two representing mechanisms of beta cell dysfunction and three representing mechanisms of insulin resistance [38,44].

The two clusters of type 2 diabetes risk variants relate to pancreatic development and beta cell function:

The first set includes variants at SLC30A8, TCF7L2, ADCY5, HNF1A, HNF1B, and MTNR1B, several of which have been previously connected to beta cell dysfunction. This set of variants has alleles associated with both increased type 2 diabetes risk and increased fasting proinsulin levels adjusted for insulin levels, but decreased fasting insulin levels, therefore likely representing defective insulin processing and secretion.

The second beta cell-related cluster contains variants including those at ARAP1, IGFBP2, and CCND2. These alleles are also associated with increased type 2 diabetes risk and decreased fasting insulin levels, but decreased fasting proinsulin levels adjusted for insulin levels, potentially indicative of defective insulin synthesis.

The three clusters of loci represent mechanisms of insulin resistance. They contain type 2 diabetes risk alleles associated with increased fasting insulin in addition to other defining phenotypes and genetic loci:

Obesity mediated (increased BMI and waist circumference; FTO, MC4R, NRXN3 genetic loci)

"Lipodystrophy-like" fat distribution (reduced BMI, adiponectin, and high-density lipoprotein (HDL) cholesterol, and increased triglycerides; PPARG, GRB14, IRS1, FLF14 loci)

Disrupted liver lipid metabolism (reduced serum triglycerides; GCKR, TMSF2 loci)

These genetic clusters underscore the genetic contributions of beta cell dysfunction and insulin resistance to type 2 diabetes. The clusters have been used to generate "partitioned" polygenic scores that can be applied to identify individuals at highest risk of a given genetic mechanism. Individuals with type 2 diabetes and elevated risk of each cluster have phenotypic features that distinguish them from others with type 2 diabetes (eg, those in the two beta cell-related clusters had lower C-peptide levels) [44]; however, the effect sizes were small and not currently useful for clinical application.

ROLE OF DIET, OBESITY, AND INFLAMMATION

Obesity and insulin resistance — The prevalence of impaired glucose tolerance and type 2 diabetes has increased dramatically in the United States population in the past two decades [45]. The most striking features in these groups and of most patients who develop type 2 diabetes are increased weight gain (figure 1) and decreased physical activity, each of which increases the risk of diabetes (figure 6) [4]. Upper-body obesity, so-called "central adiposity," has a much greater association with insulin resistance and impaired glucose tolerance than lower-body obesity (figure 7). (See "Type 2 diabetes mellitus: Prevalence and risk factors", section on 'Fat distribution' and "Overweight and obesity in adults: Health consequences", section on 'Diabetes mellitus'.)

Obesity causes peripheral resistance to insulin-mediated glucose uptake [11,46,47] and may also decrease the sensitivity of the beta cells to glucose [47]. The causal nature of these defects is demonstrable as they are largely reversed by weight loss, leading to a fall in blood glucose concentrations toward normal with remission of diabetes [48] (see "Nutritional considerations in type 2 diabetes mellitus"). Although not as effective as weight loss, an exercise regimen also may improve glucose tolerance and prevent the development of overt diabetes. (See "Prevention of type 2 diabetes mellitus", section on 'Exercise'.)

The mechanism by which obesity induces insulin resistance is poorly understood. The pattern of fat distribution and perhaps a genetic abnormality in the beta-3-adrenergic receptor, as described above, appear to contribute. The c-Jun amino-terminal kinase (JNK) pathway may be an important mediator of the relationship between obesity and insulin resistance as JNK activity is increased in obesity, an effect that can interfere with insulin action. In animal models of obesity, absence of JNK1 results in decreased adiposity and enhanced insulin sensitivity [49]. (See "Obesity: Genetic contribution and pathophysiology".)

Inflammation — Many studies have focused on the role of inflammation as a common mediator linking obesity to both the pathogenesis of diabetes and atherosclerosis [50,51]. The incidence of type 2 diabetes has been correlated with increased levels of markers of inflammation, including C-reactive protein, interleukin (IL) 6, plasminogen activator inhibitor 1 (PAI-1) [52], tumor necrosis factor (TNF)-alpha [53,54], chemokines (chemotactic proinflammatory cytokines) [55], and white cell count [56-59]. Adipokines (factors released from adipose tissue) stimulate inflammatory activity, which correlates with insulin resistance [60]. Intensive lifestyle interventions have been shown to decrease markers of inflammation [61].

Anti-inflammatory properties of medications including thiazolidinediones and statins [62] may contribute therapeutic benefit beyond their activity to lower glucose and cholesterol levels, respectively. Among patients with rheumatoid arthritis or psoriasis, use of anti-inflammatory, disease-modifying antirheumatic drugs (such as TNF inhibitors and hydroxychloroquine) is associated with a lower incidence of diabetes than other agents [63]. However, no trials of anti-inflammatory agents have shown a convincing reduction in the development of diabetes or amelioration of hyperglycemia [64].

Factors released from adipose tissue

Leptin – Leptin is produced by adipocytes and is secreted in proportion to adipocyte mass. It signals the hypothalamus about the quantity of stored fat. Studies in humans and animals have shown that leptin deficiency and leptin resistance are associated with obesity and insulin resistance. (See "Physiology of leptin".)

Adiponectin – Adiponectin, an adipocyte-derived cytokine, reduces levels of blood free fatty acids and has been associated with improved lipid profiles, better glycemic management, and reduced inflammation in patients with diabetes [65]. Adiponectin has also been inversely associated with risk for diabetes in populations without diabetes [66-68].

Adiponectin and adiponectin receptors may become an important target in the management of diabetes. Two studies have suggested that dietary cereal fiber and reduced glycemic load can increase adiponectin in males and females with diabetes [69,70]. In a study of subjects with insulin resistance, administration of thiazolidinediones, which has been shown to decrease the development of diabetes, increased serum adiponectin concentrations without affecting body weight [71].

In addition to its strong association with type 2 diabetes risk, preliminary data suggest that adiponectin may be moderately associated with cardiovascular morbidity and mortality. (See "Overview of established risk factors for cardiovascular disease", section on 'Obesity' and "Predictors of survival in heart failure with reduced ejection fraction", section on 'Weight loss and body mass index'.)

Resistin – In diet-induced or genetic obesity in mice, adipocytes secrete a signaling molecule named resistin. Administration of resistin decreases while neutralization of resistin increases insulin-mediated glucose uptake by adipocytes [72]. Hypothalamic administration of resistin also enhances glucose production, independent of changes in glucoregulatory hormones [73]. Thus, resistin may be a hormone that links obesity to diabetes [74].

Retinol-binding protein 4 – Retinol-binding protein 4 (RBP4), another protein released from adipocytes, correlates with the degree of insulin resistance in patients with obesity, impaired glucose tolerance, or type 2 diabetes, as well as in normal-weight subjects with [75] or without [76] a strong family history of type 2 diabetes. Exercise training reduced RBP4 levels in patients whose insulin resistance improved with exercise. In a mouse model, mice lacking adipocyte glucose transporter 4 (GLUT4) had increased levels of RBP4, and RBP4 was shown to cause insulin resistance in mouse muscle and liver [77]. An inverse relationship between GLUT4 in adipocytes and serum RBP4 was demonstrated in the human study, as well [75]. Whether RBP4 in humans causes, or is correlated with, insulin resistance has not been determined.

Obestatin – Obestatin, a hormone that was initially isolated from rat stomach, is encoded by the ghrelin gene and opposes the effects of ghrelin on food intake (see "Ghrelin"). Circulating obestatin concentrations are decreased in individuals with diabetes and impaired glucose tolerance compared with normal glucose tolerance [78]. In addition, expression of the obestatin receptor in adipose tissue is downregulated in obesity-associated type 2 diabetes, but not in normoglycemic subjects with obesity, suggesting that obestatin may play a role in glucose regulation and development of type 2 diabetes independent of obesity [79,80].

ROLE OF INTRAUTERINE DEVELOPMENT

Low birth weight — The presence of insulin resistance in obesity and type 2 diabetes led to the theory of the "thrifty" genotype in which insulin resistance might improve survival during states of caloric deprivation but would lead to diabetes in states of caloric excess or even adequacy. However, other observations have suggested an in utero variation of the hypothesis: the thrifty genotype might be induced by malnutrition during fetal and early life. In particular, intrauterine growth restriction leading to low birth weight may be associated with an increased risk in adulthood of insulin resistance, glucose intolerance, type 2 diabetes, dyslipidemia, and hypertension [81-89]. (See "Possible role of low birth weight in the pathogenesis of primary (essential) hypertension".)

The inverse relationship between birth weight and diabetes mellitus was illustrated in an analysis from the Nurses' Health Study of over 69,000 women [90]. The relative risk of type 2 diabetes compared with a reference group by ascending birth weight categories decreased progressively from 1.8 for a birth weight less than 2.3 kg to 0.8 for a birth weight greater than 4.5 kg. Adjustment for ethnicity, childhood socioeconomic status, and adult lifestyle factors did not substantially alter this association. A meta-analysis of 30 studies, including the Nurses' Health Study, confirmed the inverse association between birth weight and type 2 diabetes (adjusted odds ratio [OR] of diabetes 0.80, 95% CI 0.72-0.89 for each 1 kg increase in birth weight) [91].

Thus, thinness at birth and in adult life have opposing effects on insulin resistance, such that subjects who were underweight at birth but who become overweight in middle age have the most severe insulin resistance and the greatest risk for type 2 diabetes (figure 8) [81]. Even among infants with a normal birth weight (≥3.5 kg), those who had slow growth in length in the first three months after birth were more likely to develop diabetes later in life, suggesting that the critical period for pancreatic beta cell development extends beyond the intrauterine period [86].

High birth weight — Higher birth weight (>4.0 kg) may also be associated with an increased risk of diabetes [92]. A meta-analysis of 14 studies (involving 132,180 individuals) of birth weight and subsequent risk of type 2 diabetes demonstrated a U-shaped relationship between birth weight and diabetes risk [93]. High birth weight was associated with increased risk of diabetes in later life to the same extent as low birth weight (ORs 1.36 versus 1.47).

The association between high birth weight and risk of type 2 diabetes may be related to maternal hyperglycemia during pregnancy. Prenatal exposure to hyperglycemia may increase the risk of type 2 diabetes, independent of genetic predisposition. This was demonstrated in a study of 31 adults without diabetes, 15 of whom were exposed to a diabetic environment in utero (mothers with type 1 diabetes) and 15 who had not been exposed but whose fathers had type 1 diabetes (controls). The exposed subjects had an increased risk of impaired glucose tolerance (5 of 15 versus 0 of 16) and a defective insulin secretory response when compared with control subjects [94]. The mechanism of this association is unknown. (See "Infants of women with diabetes".)

Prematurity — Children born prematurely, whether they were appropriate or small for gestational age, may also be at increased risk for type 2 diabetes and other diseases of adulthood associated with insulin resistance. This was illustrated in a study of 50 healthy children ages 4 to 10 years who had been born prematurely (<32 weeks gestation; 38 with a birth weight that was appropriate for gestational age and 12 who were small for gestational age) and 22 control subjects (≥37 weeks gestation with a normal birth weight) [95]. A similar reduction in insulin sensitivity (as measured by paired insulin and glucose values on an intravenous glucose tolerance tests) was seen in both groups of children born prematurely (normal or small for gestational age) when compared with the control group. The alteration in insulin dynamics appears to be present at birth. However, measurements in neonates/infants are limited.

In a prospective birth cohort of 1358 children (418 born preterm), there was an inverse association between gestational age (regardless of birth weight for gestational age) and elevated plasma insulin levels at birth [96]. Plasma insulin levels in early childhood were also inversely associated with gestational age, but the association was attenuated after adjustment for rapid weight gain in the first year of life. In another study, the reduction in insulin sensitivity associated with preterm birth persisted into adulthood [97]. The implications for future development of type 2 diabetes require further study.

DRUG-INDUCED HYPERGLYCEMIA — A large number of drugs can impair glucose tolerance; they act by decreasing insulin secretion, increasing hepatic glucose production, or causing resistance to the action of insulin (table 1). Commonly used medications included in this list are glucocorticoids, several classes of antihypertensive drugs such as beta blockers, thiazide diuretics, nicotinic acid, statins, protease inhibitors used for the treatment of HIV infection, gonadotropin-releasing hormone (GnRH) agonists used for the treatment of prostate cancer, tacrolimus, sirolimus, and cyclosporine used primarily to prevent transplant rejection, and some of the atypical antipsychotic agents [98,99].

Androgen deprivation therapy for prostate cancer treatment – (See "Side effects of androgen deprivation therapy", section on 'Body composition and metabolism'.)

Antipsychotics – In patients with preexisting diabetes, the initiation of atypical or typical antipsychotic agents has been associated with worsening hyperglycemia [100]. In addition, some of the atypical antipsychotic agents, in particular, clozapine and olanzapine, have been associated with weight gain, obesity, hypertriglyceridemia, and development of diabetes mellitus [101-103]. The mechanism(s) by which they cause the metabolic syndrome have not been defined. An American Diabetes Association (ADA) consensus panel concluded that data on risperidone and quetiapine show an increased risk for weight gain but conflicting data on diabetes and dyslipidemia risk [104]. The panel also concluded that patients taking ziprasidone and aripiprazole are not at increased risk for developing diabetes or dyslipidemia. (See "First-generation antipsychotic medications: Pharmacology, administration, and comparative side effects", section on 'Metabolic syndrome' and "Second-generation antipsychotic medications: Pharmacology, administration, and side effects", section on 'Metabolic syndrome'.)

Glucocorticoids – (See "Major side effects of systemic glucocorticoids", section on 'Metabolic and endocrine effects' and "Major side effects of inhaled glucocorticoids", section on 'Other potential concerns'.)

HIV antiretrovirals – (See "Overview of antiretroviral agents used to treat HIV", section on 'PI class characteristics'.)

Immune checkpoint inhibitors – Checkpoint inhibitors are immunomodulatory antibodies that are used to enhance the immune response against tumor cells. They are associated with a unique spectrum of immune-related adverse events, including checkpoint inhibitor-associated diabetes [105]. Patients treated with checkpoint inhibitors may present with new-onset hyperglycemia requiring exogenous insulin or with new insulin requirements in the setting of well-controlled type 2 diabetes. Forty to 60 percent may present with diabetic ketoacidosis (DKA) and low or undetectable C-peptide levels. The abrupt presentation usually occurs within a week or two of starting checkpoint inhibitor therapy, but it may occur after numerous cycles of treatment. Some patients may also have evidence of acute exocrine pancreatitis. Patients who develop checkpoint inhibitor-associated diabetes usually have persistent insulin deficiency and need for chronic insulin treatment following therapy [105]. (See "Toxicities associated with checkpoint inhibitor immunotherapy", section on 'Type 1 diabetes mellitus'.)

Although currently unknown, the mechanisms underlying checkpoint inhibitor-associated diabetes may be related directly to the therapeutic effects of the checkpoint inhibitor agents to potentiate T cell activity. In the tumor milieu, antigen-presenting cells such as macrophages and dendritic cells as well as tumor cells themselves express programmed cell death ligand 1 (PD-L1), which blocks T cell responses by producing inhibitory signals through the programmed cell death receptor 1 (PD-1) pathway. PD-1 and PD-L1 inhibitors overcome this immune suppression, activating cytotoxic T cells. Cytotoxic T-lymphocyte associated protein 4 (CTLA-4) acts in lymph nodes to prevent T cell activation and provoke regulatory T cells to suppress cytotoxic T cells. CTLA-4 inhibitors release this suppressive control of cytotoxic T cells. Thus, checkpoint inhibitor agents permit an exuberant cytotoxic T cell response that has a potent anti-tumor effect, but may also promote inflammatory destruction of a range of endocrine tissues including the pancreatic islets [105]. (See "Toxicities associated with checkpoint inhibitor immunotherapy", section on 'Endocrinopathies'.)

Lipid-lowering agents – (See "Statins: Actions, side effects, and administration", section on 'Diabetes mellitus' and "Low-density lipoprotein cholesterol lowering with drugs other than statins and PCSK9 inhibitors", section on 'Nicotinic acid (niacin)'.)

T-cell immunosuppressive drugsTacrolimus, sirolimus, and cyclosporin may cause glucose intolerance and diabetes. (See "Pharmacology of cyclosporine and tacrolimus", section on 'Metabolic abnormalities' and "Pharmacology of mammalian (mechanistic) target of rapamycin (mTOR) inhibitors", section on 'Metabolic effects'.)

Thiazide diuretics – Treatment with thiazide diuretics is associated with a small increase in fasting plasma glucose and risk of developing type 2 diabetes [106,107]. A substantial increase in fasting plasma glucose is unusual, even in patients with type 2 diabetes, with the recommended regimen of low-dose thiazide therapy (eg, 12.5 to a maximum of 25 mg of hydrochlorothiazide) (figure 9) [108-110]. Concurrent hypokalemia appears to play an important role, as evidenced by a small study showing no change in glucose tolerance if urinary losses are replaced by potassium supplements [111]. Subsequent analyses of larger trials confirmed the association between hypokalemia and a higher probability of developing type 2 diabetes [112,113]. As an example, in the Systolic Hypertension in Elderly Program trial, the risk of diabetes with use of a thiazide (chlorthalidone) was significantly attenuated when adjusted for changes in serum potassium [112]. Each 0.5 mEq/L decrease in serum potassium was associated with a 45 percent higher risk of new diabetes. The putative mechanism for this association is a failure of potassium channels to close in response to rising plasma glucose concentrations, with a resultant decrease in insulin secretion. (See "Treatment of hypertension in patients with diabetes mellitus".)

INFORMATION FOR PATIENTS — UpToDate offers two types of patient education materials, "The Basics" and "Beyond the Basics." The Basics patient education pieces are written in plain language, at the 5th to 6th grade reading level, and they answer the four or five key questions a patient might have about a given condition. These articles are best for patients who want a general overview and who prefer short, easy-to-read materials. Beyond the Basics patient education pieces are longer, more sophisticated, and more detailed. These articles are written at the 10th to 12th grade reading level and are best for patients who want in-depth information and are comfortable with some medical jargon.

Here are the patient education articles that are relevant to this topic. We encourage you to print or e-mail these topics to your patients. (You can also locate patient education articles on a variety of subjects by searching on "patient info" and the keyword(s) of interest.)

Basics topics (see "Patient education: Type 2 diabetes (The Basics)")

Beyond the Basics topics (see "Patient education: Type 2 diabetes: Overview (Beyond the Basics)")

SUMMARY

Pathophysiology – The pathogenesis of type 2 diabetes is multifactorial. Patients typically present with a combination of varying degrees of insulin resistance and defective insulin secretion (beta cell dysfunction). Both contribute to type 2 diabetes, with heightened demand for insulin action mediated by resistance that is not matched by insulin secretion. Its occurrence most likely represents a complex interaction among many genes and environmental factors, which are different among different populations and individuals. (See 'Pathophysiology' above.)

Genetic susceptibility – More than 500 genetic variants have been robustly associated with type 2 diabetes and related to pathways of beta cell function and insulin action (figure 5). There are ongoing studies of using genetics for prediction of risk of developing diabetes as well as for gaining insights into disease pathophysiology. (See 'Genetic susceptibility' above.)

Role of environment – The most striking environmental risk factors in most patients who develop type 2 diabetes are increased weight gain and decreased physical activity, each of which increases the risk of diabetes. (See 'Role of diet, obesity, and inflammation' above.)

The mechanism by which obesity induces insulin resistance is poorly understood. Inflammation may be the common mediator linking obesity to the pathogenesis of diabetes.

Role of intrauterine development – Low or high birth weight as well as prematurity are associated with an increased risk of developing type 2 diabetes. (See 'Role of intrauterine development' above.)

Drug-induced hyperglycemia – A large number of drugs can impair glucose tolerance. They act by decreasing insulin secretion, increasing hepatic glucose production, or causing resistance to the action of insulin (table 1). (See 'Drug-induced hyperglycemia' above.)

ACKNOWLEDGMENT — The UpToDate editorial staff acknowledges David McCulloch, MD, who contributed to an earlier version of this topic review.

  1. Harris MI. Impaired glucose tolerance in the U.S. population. Diabetes Care 1989; 12:464.
  2. Engelgau MM, Geiss LS, Saaddine JB, et al. The evolving diabetes burden in the United States. Ann Intern Med 2004; 140:945.
  3. Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2019; 157:107843.
  4. Sullivan PW, Morrato EH, Ghushchyan V, et al. Obesity, inactivity, and the prevalence of diabetes and diabetes-related cardiovascular comorbidities in the U.S., 2000-2002. Diabetes Care 2005; 28:1599.
  5. Stumvoll M, Goldstein BJ, van Haeften TW. Type 2 diabetes: principles of pathogenesis and therapy. Lancet 2005; 365:1333.
  6. Beck-Nielsen H, Groop LC. Metabolic and genetic characterization of prediabetic states. Sequence of events leading to non-insulin-dependent diabetes mellitus. J Clin Invest 1994; 94:1714.
  7. Kahn CR. Banting Lecture. Insulin action, diabetogenes, and the cause of type II diabetes. Diabetes 1994; 43:1066.
  8. Robertson RP. Antagonist: diabetes and insulin resistance--philosophy, science, and the multiplier hypothesis. J Lab Clin Med 1995; 125:560.
  9. Li Y, Xu W, Liao Z, et al. Induction of long-term glycemic control in newly diagnosed type 2 diabetic patients is associated with improvement of beta-cell function. Diabetes Care 2004; 27:2597.
  10. Alberti KG, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009; 120:1640.
  11. DeFronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care 1991; 14:173.
  12. Chen KW, Boyko EJ, Bergstrom RW, et al. Earlier appearance of impaired insulin secretion than of visceral adiposity in the pathogenesis of NIDDM. 5-Year follow-up of initially nondiabetic Japanese-American men. Diabetes Care 1995; 18:747.
  13. Haffner SM, Miettinen H, Gaskill SP, Stern MP. Decreased insulin secretion and increased insulin resistance are independently related to the 7-year risk of NIDDM in Mexican-Americans. Diabetes 1995; 44:1386.
  14. Weyer C, Bogardus C, Mott DM, Pratley RE. The natural history of insulin secretory dysfunction and insulin resistance in the pathogenesis of type 2 diabetes mellitus. J Clin Invest 1999; 104:787.
  15. Tabák AG, Jokela M, Akbaraly TN, et al. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet 2009; 373:2215.
  16. Kitabchi AE, Temprosa M, Knowler WC, et al. Role of insulin secretion and sensitivity in the evolution of type 2 diabetes in the diabetes prevention program: effects of lifestyle intervention and metformin. Diabetes 2005; 54:2404.
  17. Brunzell JD, Robertson RP, Lerner RL, et al. Relationships between fasting plasma glucose levels and insulin secretion during intravenous glucose tolerance tests. J Clin Endocrinol Metab 1976; 42:222.
  18. Robertson RP, Porte D Jr. The glucose receptor. A defective mechanism in diabetes mellitus distinct from the beta adrenergic receptor. J Clin Invest 1973; 52:870.
  19. Robertson RP, Bogachus LD, Oseid E, et al. Assessment of β-cell mass and α- and β-cell survival and function by arginine stimulation in human autologous islet recipients. Diabetes 2015; 64:565.
  20. Kahn SE, Halban PA. Release of incompletely processed proinsulin is the cause of the disproportionate proinsulinemia of NIDDM. Diabetes 1997; 46:1725.
  21. Røder ME, Dinesen B, Hartling SG, et al. Intact proinsulin and beta-cell function in lean and obese subjects with and without type 2 diabetes. Diabetes Care 1999; 22:609.
  22. Westermark P, Johnson KH, O'Brien TD, Betsholtz C. Islet amyloid polypeptide--a novel controversy in diabetes research. Diabetologia 1992; 35:297.
  23. Mäkimattila S, Fineman MS, Yki-Järvinen H. Deficiency of total and nonglycosylated amylin in plasma characterizes subjects with impaired glucose tolerance and type 2 diabetes. J Clin Endocrinol Metab 2000; 85:2822.
  24. Hull RL, Westermark GT, Westermark P, Kahn SE. Islet amyloid: a critical entity in the pathogenesis of type 2 diabetes. J Clin Endocrinol Metab 2004; 89:3629.
  25. Wilding JP, Khandan-Nia N, Bennet WM, et al. Lack of acute effect of amylin (islet associated polypeptide) on insulin sensitivity during hyperinsulinaemic euglycaemic clamp in humans. Diabetologia 1994; 37:166.
  26. Bennet WM, Beis CS, Ghatei MA, et al. Amylin tonally regulates arginine-stimulated insulin secretion in rats. Diabetologia 1994; 37:436.
  27. Klein BE, Klein R, Moss SE, Cruickshanks KJ. Parental history of diabetes in a population-based study. Diabetes Care 1996; 19:827.
  28. Barnett AH, Eff C, Leslie RD, Pyke DA. Diabetes in identical twins. A study of 200 pairs. Diabetologia 1981; 20:87.
  29. Bennett, PH. Epidemiology of diabetes mellitus. In: Ellenberg and Rifkin's Diabetes Mellitus, Rifkin, H, Porte, D Jr (Eds), Elsevier, New York 1990. p.363.
  30. Vujkovic M, Keaton JM, Lynch JA, et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet 2020; 52:680.
  31. Goodrich JK, Singer-Berk M, Son R, et al. Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes. Nat Commun 2021; 12:3505.
  32. Mahajan A, Taliun D, Thurner M, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet 2018; 50:1505.
  33. Khera AV, Chaffin M, Aragam KG, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet 2018; 50:1219.
  34. Hivert MF, Jablonski KA, Perreault L, et al. Updated genetic score based on 34 confirmed type 2 diabetes Loci is associated with diabetes incidence and regression to normoglycemia in the diabetes prevention program. Diabetes 2011; 60:1340.
  35. SIGMA Type 2 Diabetes Consortium, Estrada K, Aukrust I, et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA 2014; 311:2305.
  36. Flannick J, Mercader JM, Fuchsberger C, et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 2019; 570:71.
  37. Morris AP, Voight BF, Teslovich TM, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 2012; 44:981.
  38. Mahajan A, Wessel J, Willems SM, et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat Genet 2018; 50:559.
  39. Kozlitina J, Smagris E, Stender S, et al. Exome-wide association study identifies a TM6SF2 variant that confers susceptibility to nonalcoholic fatty liver disease. Nat Genet 2014; 46:352.
  40. Mahdessian H, Taxiarchis A, Popov S, et al. TM6SF2 is a regulator of liver fat metabolism influencing triglyceride secretion and hepatic lipid droplet content. Proc Natl Acad Sci U S A 2014; 111:8913.
  41. Musso G, Cipolla U, Cassader M, et al. TM6SF2 rs58542926 variant affects postprandial lipoprotein metabolism and glucose homeostasis in NAFLD. J Lipid Res 2017; 58:1221.
  42. Ahlqvist E, Storm P, Käräjämäki A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 2018; 6:361.
  43. Ahlqvist E, Prasad RB, Groop L. Subtypes of Type 2 Diabetes Determined From Clinical Parameters. Diabetes 2020; 69:2086.
  44. Udler MS, Kim J, von Grotthuss M, et al. Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis. PLoS Med 2018; 15:e1002654.
  45. https://www.cdc.gov/diabetes/data/statistics-report/index.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fdiabetes%2Fdata%2Fstatistics%2Fstatistics-report.html (Accessed on October 15, 2020).
  46. Friedman JE, Dohm GL, Leggett-Frazier N, et al. Restoration of insulin responsiveness in skeletal muscle of morbidly obese patients after weight loss. Effect on muscle glucose transport and glucose transporter GLUT4. J Clin Invest 1992; 89:701.
  47. Henry RR, Scheaffer L, Olefsky JM. Glycemic effects of intensive caloric restriction and isocaloric refeeding in noninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab 1985; 61:917.
  48. Riddle MC, Cefalu WT, Evans PH, et al. Consensus Report: Definition and Interpretation of Remission in Type 2 Diabetes. Diabetes Care 2021.
  49. Hirosumi J, Tuncman G, Chang L, et al. A central role for JNK in obesity and insulin resistance. Nature 2002; 420:333.
  50. Shoelson SE, Lee J, Goldfine AB. Inflammation and insulin resistance. J Clin Invest 2006; 116:1793.
  51. Vandanmagsar B, Youm YH, Ravussin A, et al. The NLRP3 inflammasome instigates obesity-induced inflammation and insulin resistance. Nat Med 2011; 17:179.
  52. Kanaya AM, Wassel Fyr C, Vittinghoff E, et al. Adipocytokines and incident diabetes mellitus in older adults: the independent effect of plasminogen activator inhibitor 1. Arch Intern Med 2006; 166:350.
  53. Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose expression of tumor necrosis factor-alpha: direct role in obesity-linked insulin resistance. Science 1993; 259:87.
  54. Zinman B, Hanley AJ, Harris SB, et al. Circulating tumor necrosis factor-alpha concentrations in a native Canadian population with high rates of type 2 diabetes mellitus. J Clin Endocrinol Metab 1999; 84:272.
  55. Chavey C, Lazennec G, Lagarrigue S, et al. CXC ligand 5 is an adipose-tissue derived factor that links obesity to insulin resistance. Cell Metab 2009; 9:339.
  56. Duncan BB, Schmidt MI, Pankow JS, et al. Low-grade systemic inflammation and the development of type 2 diabetes: the atherosclerosis risk in communities study. Diabetes 2003; 52:1799.
  57. Pradhan AD, Manson JE, Rifai N, et al. C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA 2001; 286:327.
  58. Vozarova B, Weyer C, Lindsay RS, et al. High white blood cell count is associated with a worsening of insulin sensitivity and predicts the development of type 2 diabetes. Diabetes 2002; 51:455.
  59. de Rekeneire N, Peila R, Ding J, et al. Diabetes, hyperglycemia, and inflammation in older individuals: the health, aging and body composition study. Diabetes Care 2006; 29:1902.
  60. Jaganathan R, Ravindran R, Dhanasekaran S. Emerging Role of Adipocytokines in Type 2 Diabetes as Mediators of Insulin Resistance and Cardiovascular Disease. Can J Diabetes 2018; 42:446.
  61. Haffner S, Temprosa M, Crandall J, et al. Intensive lifestyle intervention or metformin on inflammation and coagulation in participants with impaired glucose tolerance. Diabetes 2005; 54:1566.
  62. Muhlestein JB, May HT, Jensen JR, et al. The reduction of inflammatory biomarkers by statin, fibrate, and combination therapy among diabetic patients with mixed dyslipidemia: the DIACOR (Diabetes and Combined Lipid Therapy Regimen) study. J Am Coll Cardiol 2006; 48:396.
  63. Solomon DH, Massarotti E, Garg R, et al. Association between disease-modifying antirheumatic drugs and diabetes risk in patients with rheumatoid arthritis and psoriasis. JAMA 2011; 305:2525.
  64. Tsalamandris S, Antonopoulos AS, Oikonomou E, et al. The Role of Inflammation in Diabetes: Current Concepts and Future Perspectives. Eur Cardiol 2019; 14:50.
  65. Mantzoros CS, Li T, Manson JE, et al. Circulating adiponectin levels are associated with better glycemic control, more favorable lipid profile, and reduced inflammation in women with type 2 diabetes. J Clin Endocrinol Metab 2005; 90:4542.
  66. Li S, Shin HJ, Ding EL, van Dam RM. Adiponectin levels and risk of type 2 diabetes: a systematic review and meta-analysis. JAMA 2009; 302:179.
  67. Kadowaki T, Yamauchi T, Kubota N, et al. Adiponectin and adiponectin receptors in insulin resistance, diabetes, and the metabolic syndrome. J Clin Invest 2006; 116:1784.
  68. Weyer C, Funahashi T, Tanaka S, et al. Hypoadiponectinemia in obesity and type 2 diabetes: close association with insulin resistance and hyperinsulinemia. J Clin Endocrinol Metab 2001; 86:1930.
  69. Qi L, Rimm E, Liu S, et al. Dietary glycemic index, glycemic load, cereal fiber, and plasma adiponectin concentration in diabetic men. Diabetes Care 2005; 28:1022.
  70. Qi L, Meigs JB, Liu S, et al. Dietary fibers and glycemic load, obesity, and plasma adiponectin levels in women with type 2 diabetes. Diabetes Care 2006; 29:1501.
  71. Maeda N, Takahashi M, Funahashi T, et al. PPARgamma ligands increase expression and plasma concentrations of adiponectin, an adipose-derived protein. Diabetes 2001; 50:2094.
  72. Steppan CM, Bailey ST, Bhat S, et al. The hormone resistin links obesity to diabetes. Nature 2001; 409:307.
  73. Muse ED, Lam TK, Scherer PE, Rossetti L. Hypothalamic resistin induces hepatic insulin resistance. J Clin Invest 2007; 117:1670.
  74. Emamalipour M, Seidi K, Jahanban-Esfahlan A, Jahanban-Esfahlan R. Implications of resistin in type 2 diabetes mellitus and coronary artery disease: Impairing insulin function and inducing pro-inflammatory cytokines. J Cell Physiol 2019; 234:21758.
  75. Graham TE, Yang Q, Blüher M, et al. Retinol-binding protein 4 and insulin resistance in lean, obese, and diabetic subjects. N Engl J Med 2006; 354:2552.
  76. Gavi S, Stuart LM, Kelly P, et al. Retinol-binding protein 4 is associated with insulin resistance and body fat distribution in nonobese subjects without type 2 diabetes. J Clin Endocrinol Metab 2007; 92:1886.
  77. Yang Q, Graham TE, Mody N, et al. Serum retinol binding protein 4 contributes to insulin resistance in obesity and type 2 diabetes. Nature 2005; 436:356.
  78. Qi X, Li L, Yang G, et al. Circulating obestatin levels in normal subjects and in patients with impaired glucose regulation and type 2 diabetes mellitus. Clin Endocrinol (Oxf) 2007; 66:593.
  79. Catalán V, Gómez-Ambrosi J, Rotellar F, et al. The obestatin receptor (GPR39) is expressed in human adipose tissue and is down-regulated in obesity-associated type 2 diabetes mellitus. Clin Endocrinol (Oxf) 2007; 66:598.
  80. Green BD, Grieve DJ. Biochemical properties and biological actions of obestatin and its relevence in type 2 diabetes. Peptides 2018; 100:249.
  81. Phillips DI, Barker DJ, Hales CN, et al. Thinness at birth and insulin resistance in adult life. Diabetologia 1994; 37:150.
  82. Phillips DI, Hirst S, Clark PM, et al. Fetal growth and insulin secretion in adult life. Diabetologia 1994; 37:592.
  83. Valdez R, Athens MA, Thompson GH, et al. Birthweight and adult health outcomes in a biethnic population in the USA. Diabetologia 1994; 37:624.
  84. Forsén T, Eriksson J, Tuomilehto J, et al. The fetal and childhood growth of persons who develop type 2 diabetes. Ann Intern Med 2000; 133:176.
  85. Bhargava SK, Sachdev HS, Fall CH, et al. Relation of serial changes in childhood body-mass index to impaired glucose tolerance in young adulthood. N Engl J Med 2004; 350:865.
  86. Eriksson JG, Forsen TJ, Osmond C, Barker DJ. Pathways of infant and childhood growth that lead to type 2 diabetes. Diabetes Care 2003; 26:3006.
  87. Burke JP, Forsgren J, Palumbo PJ, et al. Association of birth weight and type 2 diabetes in Rochester, Minnesota. Diabetes Care 2004; 27:2512.
  88. Lawlor DA, Davey Smith G, Clark H, Leon DA. The associations of birthweight, gestational age and childhood BMI with type 2 diabetes: findings from the Aberdeen Children of the 1950s cohort. Diabetologia 2006; 49:2614.
  89. BIRTH-GENE (BIG) Study Working Group, Huang T, Wang T, et al. Association of Birth Weight With Type 2 Diabetes and Glycemic Traits: A Mendelian Randomization Study. JAMA Netw Open 2019; 2:e1910915.
  90. Rich-Edwards JW, Colditz GA, Stampfer MJ, et al. Birthweight and the risk for type 2 diabetes mellitus in adult women. Ann Intern Med 1999; 130:278.
  91. Whincup PH, Kaye SJ, Owen CG, et al. Birth weight and risk of type 2 diabetes: a systematic review. JAMA 2008; 300:2886.
  92. Dyck RF, Klomp H, Tan L. From "thrifty genotype" to "hefty fetal phenotype": the relationship between high birthweight and diabetes in Saskatchewan Registered Indians. Can J Public Health 2001; 92:340.
  93. Harder T, Rodekamp E, Schellong K, et al. Birth weight and subsequent risk of type 2 diabetes: a meta-analysis. Am J Epidemiol 2007; 165:849.
  94. Sobngwi E, Boudou P, Mauvais-Jarvis F, et al. Effect of a diabetic environment in utero on predisposition to type 2 diabetes. Lancet 2003; 361:1861.
  95. Hofman PL, Regan F, Jackson WE, et al. Premature birth and later insulin resistance. N Engl J Med 2004; 351:2179.
  96. Wang G, Divall S, Radovick S, et al. Preterm birth and random plasma insulin levels at birth and in early childhood. JAMA 2014; 311:587.
  97. Hovi P, Andersson S, Eriksson JG, et al. Glucose regulation in young adults with very low birth weight. N Engl J Med 2007; 356:2053.
  98. Luna B, Feinglos MN. Drug-induced hyperglycemia. JAMA 2001; 286:1945.
  99. Jain V, Patel RK, Kapadia Z, et al. Drugs and hyperglycemia: A practical guide. Maturitas 2017; 104:80.
  100. Lipscombe LL, Lévesque L, Gruneir A, et al. Antipsychotic drugs and hyperglycemia in older patients with diabetes. Arch Intern Med 2009; 169:1282.
  101. Henderson DC, Cagliero E, Gray C, et al. Clozapine, diabetes mellitus, weight gain, and lipid abnormalities: A five-year naturalistic study. Am J Psychiatry 2000; 157:975.
  102. Henderson DC. Clozapine: diabetes mellitus, weight gain, and lipid abnormalities. J Clin Psychiatry 2001; 62 Suppl 23:39.
  103. Gianfrancesco FD, Grogg AL, Mahmoud RA, et al. Differential effects of risperidone, olanzapine, clozapine, and conventional antipsychotics on type 2 diabetes: findings from a large health plan database. J Clin Psychiatry 2002; 63:920.
  104. American Diabetes Association, American Psychiatric Association, American Association of Clinical Endocrinologists, North American Association for the Study of Obesity. Consensus development conference on antipsychotic drugs and obesity and diabetes. Diabetes Care 2004; 27:596.
  105. Quandt Z, Young A, Anderson M. Immune checkpoint inhibitor diabetes mellitus: a novel form of autoimmune diabetes. Clin Exp Immunol 2020; 200:131.
  106. ALLHAT Officers and Coordinators for the ALLHAT Collaborative Research Group. The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial. Major outcomes in high-risk hypertensive patients randomized to angiotensin-converting enzyme inhibitor or calcium channel blocker vs diuretic: The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). JAMA 2002; 288:2981.
  107. Kostis JB, Wilson AC, Freudenberger RS, et al. Long-term effect of diuretic-based therapy on fatal outcomes in subjects with isolated systolic hypertension with and without diabetes. Am J Cardiol 2005; 95:29.
  108. Carlsen JE, Køber L, Torp-Pedersen C, Johansen P. Relation between dose of bendrofluazide, antihypertensive effect, and adverse biochemical effects. BMJ 1990; 300:975.
  109. Passmore AP, Whitehead EM, Crawford V, et al. The antihypertensive and metabolic effects of low and conventional dose cyclopenthiazide in type II diabetics with hypertension. Q J Med 1991; 81:919.
  110. Harper R, Ennis CN, Heaney AP, et al. A comparison of the effects of low- and conventional-dose thiazide diuretic on insulin action in hypertensive patients with NIDDM. Diabetologia 1995; 38:853.
  111. Helderman JH, Elahi D, Andersen DK, et al. Prevention of the glucose intolerance of thiazide diuretics by maintenance of body potassium. Diabetes 1983; 32:106.
  112. Shafi T, Appel LJ, Miller ER 3rd, et al. Changes in serum potassium mediate thiazide-induced diabetes. Hypertension 2008; 52:1022.
  113. Zillich AJ, Garg J, Basu S, et al. Thiazide diuretics, potassium, and the development of diabetes: a quantitative review. Hypertension 2006; 48:219.
Topic 1810 Version 30.0

References