Alternative titles; symbols
Other entities represented in this entry:
SNOMEDCT: 44054006; ICD10CM: E11; DO: 9352;
| Location | Phenotype |
Phenotype MIM number |
Inheritance |
Phenotype mapping key |
Gene/Locus |
Gene/Locus MIM number |
|---|---|---|---|---|---|---|
| 2q24.1 | {Type 2 diabetes mellitus, susceptibility to} | 125853 | Autosomal dominant | 3 | GPD2 | 138430 |
| 2q31.3 | {Type 2 diabetes mellitus, susceptibility to} | 125853 | Autosomal dominant | 3 | NEUROD1 | 601724 |
| 2q36.3 | {Type 2 diabetes mellitus, susceptibility to} | 125853 | Autosomal dominant | 3 | IRS1 | 147545 |
| 3p25.2 | {Diabetes, type 2} | 125853 | Autosomal dominant | 3 | PPARG | 601487 |
| 3q26.2 | {Diabetes mellitus, noninsulin-dependent} | 125853 | Autosomal dominant | 3 | SLC2A2 | 138160 |
| 3q27.2 | {Diabetes mellitus, noninsulin-dependent, susceptibility to} | 125853 | Autosomal dominant | 3 | IGF2BP2 | 608289 |
| 4p16.1 | {Diabetes mellitus, noninsulin-dependent, association with} | 125853 | Autosomal dominant | 3 | WFS1 | 606201 |
| 5q34-q35.2 | {Diabetes mellitus, noninsulin-dependent} | 125853 | Autosomal dominant | 2 | NIDDM4 | 608036 |
| 6p21.31 | {Type 2 diabetes mellitus, susceptibility to} | 125853 | Autosomal dominant | 3 | HMGA1 | 600701 |
| 6q23.2 | {Diabetes mellitus, non-insulin-dependent, susceptibility to} | 125853 | Autosomal dominant | 3 | ENPP1 | 173335 |
| 7p15.3 | {Type 2 diabetes mellitus} | 125853 | Autosomal dominant | 3 | IL6 | 147620 |
| 7p13 | Diabetes mellitus, noninsulin-dependent, late onset | 125853 | Autosomal dominant | 3 | GCK | 138079 |
| 7q31.1 | Insulin resistance, severe, digenic | 125853 | Autosomal dominant | 3 | PPP1R3A | 600917 |
| 7q32.1 | Diabetes mellitus, type 2 | 125853 | Autosomal dominant | 3 | PAX4 | 167413 |
| 8q24.11 | {Diabetes mellitus, noninsulin-dependent, susceptibility to} | 125853 | Autosomal dominant | 3 | SLC30A8 | 611145 |
| 10q25.2-q25.3 | {Diabetes mellitus, type 2, susceptibility to} | 125853 | Autosomal dominant | 3 | TCF7L2 | 602228 |
| 11p15.1 | {Diabetes mellitus, type 2, susceptibility to} | 125853 | Autosomal dominant | 3 | KCNJ11 | 600937 |
| 11p15.1 | Diabetes mellitus, noninsulin-dependent | 125853 | Autosomal dominant | 3 | ABCC8 | 600509 |
| 11p11.2 | {Diabetes mellitus, noninsulin-dependent} | 125853 | Autosomal dominant | 3 | MAPK8IP1 | 604641 |
| 11q14.3 | {Diabetes mellitus, type 2, susceptibility to} | 125853 | Autosomal dominant | 3 | MTNR1B | 600804 |
| 12q24.31 | {Diabetes mellitus, noninsulin-dependent, 2} | 125853 | Autosomal dominant | 3 | HNF1A | 142410 |
| 13q12.2 | {Diabetes mellitus, type II, susceptibility to} | 125853 | Autosomal dominant | 3 | PDX1 | 600733 |
| 13q34 | {Diabetes mellitus, noninsulin-dependent} | 125853 | Autosomal dominant | 3 | IRS2 | 600797 |
| 15q21.3 | {Diabetes mellitus, noninsulin-dependent} | 125853 | Autosomal dominant | 3 | LIPC | 151670 |
| 17q12 | Type 2 diabetes mellitus | 125853 | Autosomal dominant | 3 | HNF1B | 189907 |
| 19p13.2 | {Diabetes mellitus, noninsulin-dependent, susceptibility to} | 125853 | Autosomal dominant | 3 | RETN | 605565 |
| 19p13.2 | {Hypertension, insulin resistance-related, susceptibility to} | 125853 | Autosomal dominant | 3 | RETN | 605565 |
| 19q13.2 | Diabetes mellitus, type II | 125853 | Autosomal dominant | 3 | AKT2 | 164731 |
| 20q13.12 | {Diabetes mellitus, noninsulin-dependent} | 125853 | Autosomal dominant | 3 | HNF4A | 600281 |
| 20q13.13 | {Insulin resistance, susceptibility to} | 125853 | Autosomal dominant | 3 | PTPN1 | 176885 |
A number sign (#) is used with this entry because of evidence that more than one gene is involved in the causation of type 2 diabetes (T2D), also known as noninsulin-dependent diabetes mellitus (NIDDM).
Type 2 diabetes mellitus is distinct from maturity-onset diabetes of the young (see 606391) in that it is polygenic, characterized by gene-gene and gene-environment interactions with onset in adulthood, usually at age 40 to 60 but occasionally in adolescence if a person is obese. The pedigrees are rarely multigenerational. The penetrance is variable, possibly 10 to 40% (Fajans et al., 2001). Persons with type 2 diabetes usually have an obese body habitus and manifestations of the so-called metabolic syndrome (see 605552), which is characterized by diabetes, insulin resistance, hypertension, and hypertriglyceridemia.
Genetic Heterogeneity of Susceptibility to Type 2 Diabetes
Susceptibility to T2D1 (601283) is conferred by variation in the calpain-10 gene (CAPN10; 605286) on chromosome 2q37. The T2D2 locus (601407) on chromosome 12q was found in a Finnish population. The T2D3 locus (603694) maps to chromosome 20. The T2D4 locus (608036) maps to chromosome 5q34-q35. Susceptibility to T2D5 (616087) is conferred by variation in the TBC1D4 gene (612465) on chromosome 13q22.
A mutation has been observed in hepatocyte nuclear factor-4-alpha (HNF4A; 600281.0004) in a French family with NIDDM of late onset. Mutations in the NEUROD1 gene (601724) on chromosome 2q32 were found to cause type 2 diabetes mellitus in 2 families. Mutation in the GLUT2 glucose transporter was associated with NIDDM in 1 patient (138160.0001). Mutation in the MAPK8IP1 gene, which encodes the islet-brain-1 protein, was found in a family with type 2 diabetes in individuals in 4 successive generations (604641.0001). Polymorphism in the KCNJ11 gene (600937.0014) confers susceptibility. In French white families, Vionnet et al. (2000) found evidence for a susceptibility locus for type 2 diabetes on 3q27-qter. They confirmed the diabetes susceptibility locus on 1q21-q24 reported by Elbein et al. (1999) in whites and by Hanson et al. (1998) in Pima Indians. A mutation in the GPD2 gene (138430.0001) on chromosome 2q24.1, encoding mitochondrial glycerophosphate dehydrogenase, was found in a patient with type 2 diabetes mellitus and in his glucose-intolerant half sister. Mutations in the PAX4 gene (167413) have been identified in patients with type 2 diabetes. Triggs-Raine et al. (2002) stated that in the Oji-Cree, a gly319-to-ser change in HNF1-alpha (142410.0008) behaves as a susceptibility allele for type 2 diabetes. Mutation in the HNF1B gene (189907.0007) was found in 2 Japanese patients with typical late-onset type 2 diabetes. Mutations in the IRS1 gene (147545) have been found in patients with type 2 diabetes. A missense mutation in the AKT2 gene (164731.0001) caused autosomal dominant type 2 diabetes in 1 family. A (single-nucleotide polymorphism) SNP in the 3-prime untranslated region of the resistin gene (605565.0001) was associated with susceptibility to diabetes and to insulin resistance-related hypertension in Chinese subjects. Susceptibility to insulin resistance has been associated with polymorphism in the TCF1 (142410.0011), PPP1R3A (600917.0001), PTPN1 (176885.0001), ENPP1 (173335.0006), IRS1 (147545.0002), and EPHX2 (132811.0001) genes. The K121Q polymorphism of ENPP1 (173335.0006) is associated with susceptibility to type 2 diabetes; a haplotype defined by 3 SNPs of this gene, including K121Q, is associated with obesity, glucose intolerance, and type 2 diabetes. A SNP in the promoter region of the hepatic lipase gene (151670.0004) predicts conversion from impaired glucose tolerance to type 2 diabetes. Variants of transcription factor 7-like-2 (TCF7L2; 602228.0001), located on 10q, have also been found to confer risk of type 2 diabetes. A common sequence variant, rs10811661, on chromosome 9p21 near the CDKN2A (600160) and CDKN2B (600431) genes has been associated with risk of type 2 diabetes. Variation in the PPARG gene (601487) has been associated with risk of type 2 diabetes. A promoter polymorphism in the IL6 gene (147620) is associated with susceptibility to NIDDM. Variation in the KCNJ15 gene (602106) has been associated with T2DM in lean Asians. Variation in the SLC30A8 gene (611145) has been associated with susceptibility to T2D. Variation in the HMGA1 gene (600701.0001) is associated with an increased risk of type 2 diabetes. Mutation in the MTNR1B gene (600804) is associated with susceptibility to type 2 diabetes.
Protection Against Type 2 Diabetes Mellitus
Protein-truncating variants in the SLC30A8 (611145) have been associated with a reduced risk for T2D.
In 3 families with MODY and 7 with 'common' type 2 diabetes mellitus, O'Rahilly et al. (1992) excluded linkage to the INS locus (176730). Exclusive of the mendelian forms of NIDDM represented by MODY, the high incidence of diabetes in certain populations and among first-degree relatives of type 2 diabetic patients, as well as the high concordance in identical twins, provides strong evidence that genetic factors underlie susceptibility to the common form of NIDDM which affects up to 6% of the United States population. Although defects in both insulin secretion and insulin action may be necessary for disease expression in groups with a high incidence of NIDDM, such as offspring of type 2 diabetic parents and Pima Indians, insulin resistance and decreased glucose disposal can be shown to precede and predict the onset of diabetes (Martin et al., 1992; Bogardus et al., 1989). In both of these groups, relatives and Pima Indians, there is evidence of familial clustering of insulin sensitivity. Thus, insulin resistance appears to be a central feature of NIDDM and may be an early and inherited marker of the disorder.
Martinez-Marignac et al. (2007) analyzed and discussed the use of admixture mapping of type 2 diabetes genetic risk factors in Mexico City. Type 2 diabetes is at least twice as prevalent in Native American populations as in populations of European ancestry. The authors characterized the admixture proportions in a sample of 286 unrelated type 2 diabetes patients and 275 controls from Mexico City. Admixture proportions were estimated using 69 autosomal ancestry-informative markers (AIMs). The average proportions of Native American, European, and West African admixture were estimated as 65%, 30%, and 5%, respectively. The contributions of Native American ancestors to maternal and paternal lineages were estimated as 90% and 40%, respectively. In a logistic model with higher educational status as dependent variable, the odds ratio for higher educational status associated with an increase from 0 to 1 in European admixture proportions was 9.4. This association of socioeconomic status with individual admixture proportion showed that genetic stratification in this population is paralleled, and possibly maintained, by socioeconomic stratification. The effective number of generations back to unadmixed ancestors was 6.7, from which Martinez-Marignac et al. (2007) could estimate the number of evenly distributed AIMs required to localize genes underlying disease risk between populations of European and Native American ancestry, i.e., about 1,400. Sample sizes of about 2,000 cases would be required to detect any locus that contributed an ancestry risk ratio of at least 1.5.
Kong et al. (2009) found 3 SNPs at 11p15 that had association with type 2 diabetes and parental origin specific effects; These were rs2237892, rs231362, and rs2334499. For rs2334499 the allele that confers risk when paternally inherited (T) is protective when maternally inherited.
A subgroup of patients diagnosed with type 2 diabetes have circulating antibodies to islet cell cytoplasmic antigens, most frequently to glutamic acid decarboxylase (see GAD2; 138275). Among 1,122 type 2 diabetic patients, Tuomi et al. (1999) found GAD antibody in 9.3%, a significantly higher prevalence than that found in patients with impaired glucose tolerance or in controls. The GADab+ patients had lower fasting C-peptide concentration, lower insulin response to oral glucose, and higher frequency of the high-risk HLA-DQB1*0201/0302 (see 604305) genotype (though significantly lower than in patients with type I diabetes) when compared with GADab- patients. Tuomi et al. (1999) suggested the designation latent autoimmune diabetes in adults (LADA) to define the subgroup of type 2 diabetes patients with GADab positivity (greater than 5 relative units) and age at onset greater than 35 years.
Both defective insulin secretion and insulin resistance have been reported in relatives of NIDDM subjects. Elbein et al. (1999) tested 120 members of 26 families containing an NIDDM sib pair with a tolbutamide-modified, frequently sampled intravenous glucose tolerance test to determine the insulin sensitivity index (SI) and acute insulin response to glucose (AIRglucose). Both SI x AIRglucose and SI showed strong negative genetic correlations with diabetes (-85 +/- 3% and -87 +/- 2%, respectively, for all family members), whereas AIRglucose did not correlate with diabetes. The authors concluded that insulin secretion, as measured by SI x AIRglucose, is decreased in nondiabetic members of familial NIDDM kindreds; that SI x AIRglucose in these high-risk families is highly heritable; and that the same polygenes may determine diabetes status and a low SI x AIRglucose. They also suggested that insulin secretion, when expressed as an index normalized for insulin sensitivity, is more familial than either insulin sensitivity or first-phase insulin secretion alone, and may be a very useful trait for identifying genetic predisposition to NIDDM.
Li et al. (2001) assessed the prevalence of families with both type I and type 2 diabetes in Finland and studied, in patients with type 2 diabetes, the association between a family history of type 1 diabetes, GAD antibodies (GADab), and type I diabetes-associated HLA-DQB1 genotypes. Further, in mixed type 1/type 2 diabetes families, they investigated whether sharing an HLA haplotype with a family member with type I diabetes influenced the manifestation of type 2 diabetes. Among 695 families with more than 1 patient with type 2 diabetes, 100 (14%) also had members with type 1 diabetes. Type 2 diabetic patients from the mixed families more often had GADab (18% vs 8%) and DQB1*0302/X genotype (25% vs 12%) than patients from families with only type 2 diabetes; however, they had a lower frequency of DQB1*02/0302 genotype compared with adult-onset type I patients (4% vs 27%). In the mixed families, the insulin response to oral glucose load was impaired in patients who had HLA class II risk haplotypes, either DR3(17)-DQA1*0501-DQB1*02 or DR4*0401/4-DQA1*0301-DQB1*0302, compared with patients without such haplotypes. This finding was independent of the presence of GADab. The authors concluded that type I and type 2 diabetes cluster in the same families. A shared genetic background with a patient with type I diabetes predisposes type 2 diabetic patients both to autoantibody positivity and, irrespective of antibody positivity, to impaired insulin secretion. Their findings also supported a possible genetic interaction between type 1 and type 2 diabetes mediated by the HLA locus.
Fonseca et al. (1998) studied the effects of troglitazone monotherapy on glycemic control in patients with NIDDM in 24 hospital and outpatient clinics in the U.S. and Canada. Troglitazone 100, 200, 400, or 600 mg, or placebo, was administered once daily with breakfast to 402 patients with NIDDM and fasting serum glucose (FSG) greater than 140 mg/dL, glycosylated hemoglobin (HbA1c) greater than 6.5%, and fasting C-peptide greater than 1.5 ng/mL. Patients treated with 400 and 600 mg troglitazone had significant decreases from baseline in mean FSG (-51 and -60 mg/dL, respectively) and HbA1c (-0.7% and -1.1%, respectively) at month 6 compared to placebo-treated patients. In the diet-only subset, 600 mg troglitazone therapy resulted in a significant (P less than 0.05) reduction in HbA1c (-1.35%) and a significant reduction in FSG (-42 mg/dL) compared with placebo. Patients previously treated with sulfonylurea therapy had significant (P less than 0.05) decreases in mean FSG with 200 to 600 mg troglitazone therapy compared with placebo (-48, -61, and -66 mg/dL, respectively). The authors concluded that troglitazone monotherapy significantly improves HbA1c and fasting serum glucose, while lowering insulin and C-peptide in patients with NIDDM.
Chung et al. (2000) studied the effect of HMG-CoA reductase inhibitors on bone mineral density (BMD) of type 2 diabetes mellitus by a retrospective review of medical records. In the control group, BMD of the spine significantly decreased after 14 months. In the treatment group, BMD of the femoral neck significantly increased after 15 months. In male subjects treated with HMG-CoA reductase inhibitors, there was a significant increase in BMD of the femoral neck and femoral trochanter, but in female subjects, only BMD of the femoral neck increased. The authors concluded that HMG-CoA reductase inhibitors may increase BMD of the femur in male patients with type 2 diabetes mellitus.
Aljada et al. (2001) investigated the effect of troglitazone on the proinflammatory transcription factor NF-kappa-B (see 164011) and its inhibitory protein I-kappa-B (see 164008) in mononuclear cells (MNC) in obese patients with type 2 diabetes. Seven obese patients with type 2 diabetes were treated with troglitazone (400 mg/day) for 4 weeks, and blood samples were obtained at weekly intervals. NF-kappa-B binding activity in MNC nuclear extracts was significantly inhibited after troglitazone treatment at week 1 and continued to be inhibited up to week 4. On the other hand, I-kappa-B protein levels increased significantly after troglitazone treatment at week 1, and this increase persisted throughout the study. The authors concluded that troglitazone has profound antiinflammatory effects in addition to antioxidant effects in obese type 2 diabetics, and that these effects may be relevant to the beneficial antiatherosclerotic effects of troglitazone at the vascular level.
In a multicenter, double-blind trial, Garber et al. (2003) enrolled patients with type 2 diabetes who had inadequate glycemic control (glycosylated hemoglobin A1C greater than 7% and less than 12%) with diet and exercise alone to compare the benefits of initial therapy with glyburide/metformin tablets versus metformin or glyburide monotherapy. They randomized 486 patients to receive glyburide/metformin tablets, metformin, or glyburide. Changes in A1C, fasting plasma glucose, fructosamine, serum lipids, body weight, and 2-hour postprandial glucose after a standardized meal were assessed after 16 weeks of treatment. Glyburide/metformin tablets caused a superior mean reduction in A1C from baseline versus metformin and glyburide monotherapy. Glyburide/metformin also significantly reduced fasting plasma glucose and 2-hour postprandial glucose values compared with either monotherapy. The final mean doses of glyburide/metformin were lower than those of metformin and glyburide. The authors concluded that first-line treatment with glyburide/metformin tablets provided superior glycemic control over component monotherapy, allowing more patients to achieve American Diabetes Association treatment goals with lower component doses in drug-naive patients with type 2 diabetes.
The GoDARTs and UKPDS Diabetes Pharmacogenetics Study Group and Wellcome Trust Case Control Consortium 2 (2011) performed a genomewide association study for glycemic response to metformin in 1,024 Scottish individuals with type 2 diabetes with replication in 2 cohorts including 1,783 Scottish individuals and 1,113 individuals in the UK Prospective Diabetes Study. In a combined metaanalysis, the consortia identified a SNP, rs11212617, associated with treatment success (n = 3,920, P = 2.9 x 10(-9), OR = 1.35, 95% CI 1.22-1.49) at a locus containing the ATM gene (607585). In a rat hepatoma cell line, inhibition of ATM with KU-55933, a selective ATM inhibitor, attenuated the phosphorylation and activation of AMP-activated protein kinase (see 602739) in response to metformin. The consortia concluded that ATM, a gene known to be involved in DNA repair and cell cycle control, plays a role in the effect of metformin upstream of AMP-activated protein kinase, and variation in this gene alters glycemic response to metformin.
Yee et al. (2012) commented on the GoDARTS and UKPDS paper and examined the inhibitory effect of KU-55933 on metformin in H4IIE cells and in HEK293 cells stably expressing OCT1. They demonstrated in both cases that KU-55933 inhibits metformin uptake via inhibition of OCT1 and that the attenuation of metformin-induced AMPK phosphorylation is a result of its inhibition of metformin uptake into the cells. This effect is independent of ATM. Yee et al. (2012) demonstrated that ATM does not have a detectable effect on OCT1 activity. Woods et al. (2012) also found that in hepatocytes lacking AMPK activity (see Woods et al., 2011), metformin still has the ability to reduce hepatic glucose output. Woods et al. (2012) argued that the SNP rs11212617 maps to a locus on chromosome 11q22 that encodes a number of genes and that no direct evidence had been found that ATM acts upstream of AMPK; Woods et al. (2012) concluded that other genes within this locus should be considered as candidates responsible for the reduced therapeutic effect of metformin action. Zhou et al. (2012) concurred with the comments of Yee et al. (2012) and Woods et al. (2012) that all genes surrounding rs11212617 should be examined.
In 66 patients with T2D, Ferrannini et al. (2014) studied the effects of the selective SGLT2 (SLC5A2; 182381) inhibitor empagliflozin. Empagliflozin-induced glycosuria improved beta-cell function and insulin sensitivity, thus lowering fasting and postprandial glycemia, after 1 dose, despite a decrease in insulin secretion and tissue glucose disposal and a rise in endogenous glucose production. Chronic dosing shifted substrate utilization from carbohydrate to lipid. Bonner et al. (2015) observed an increase in glucagon secretion in human pancreatic islet cells after siRNA-mediated SLC5A2 knockdown. Treatment of human islets with dapagliflozin, a selective and potent SGLT2 inhibitor, prevented induction of SLC5A2 mRNA at high glucose concentrations and concomitantly increased glucagon mRNA levels. Acute inhibition of active SGLT2 glucose transport using dapagliflozin at glucose concentrations of 6 mM resulted in a marked increase of glucagon secretion without affecting glucagon content or insulin secretion. Commenting on the findings by Ferrannini et al. (2014) and Bonner et al. (2015), Hattersley and Thorens (2015) cautioned that the increased glucagon secretion that results from SGLT2 inhibition means that the glucose level will fall less than would be expected given the degree of urinary glucose loss, which is significant because it is glycosuria that causes most of the symptoms of diabetes, including polyuria, polydipsia, weight loss, and genitourinary infection.
Piatti et al. (2000) compared resistance to insulin-mediated glucose disposal and plasma concentrations of nitric oxide (NO) and cGMP in 35 healthy volunteers with, or 27 without, at least 1 sib and 1 parent with type 2 diabetes. The mean insulin sensitivity index (ISI) was significantly greater in those without a family history as compared with nondiabetic volunteers with a family history of type 2 diabetes, whether they had normal glucose tolerance or impaired glucose tolerance. In addition, basal NO levels, evaluated by the measurement of its stable end products (i.e., nitrite and nitrate levels, NO2-/NO3-) were significantly higher, and levels of cGMP, its effector messenger, were significantly lower in those with a family history, irrespective of their degree of glucose tolerance, when compared with healthy volunteers without a family history of type 2 diabetes. Furthermore, when the 62 volunteers were analyzed as 1 group, there was a negative correlation between ISI and NO2-/NO3- levels and a positive correlation between ISI and cGMP levels. The authors concluded that alterations of the NO/cGMP pathway seem to be an early event in nondiabetic individuals with a family history of type 2 diabetes, and that these changes are correlated with the degree of insulin resistance. To investigate how insulin resistance arises, Petersen et al. (2003) studied 16 healthy, lean elderly aged 61 to 84 and 13 young participants aged 18 to 39 matched for lean body mass (BMI less than 25) and fat mass assessed by DEXA (dual energy X-ray absorptiometry) scanning, and activity level. Elderly study participants were markedly insulin-resistant as compared with young controls, and this resistance was attributable to reduced insulin-stimulated muscle glucose metabolism. These changes were associated with increased fat accumulation in muscle and liver tissue, assessed by NMR spectroscopy, and with an approximately 40% reduction in mitochondrial oxidative and phosphorylation activity, as assessed by in vivo NMR spectroscopy. Petersen et al. (2003) concluded that their data support the hypothesis that an age-associated decline in mitochondrial function contributes to insulin resistance in the elderly.
Petersen et al. (2004) performed glucose clamp studies in healthy, young, lean, insulin-resistant offspring of patients with type 2 diabetes and insulin-sensitive subjects matched for age, height, weight, and physical activity. The insulin-stimulated rate of glucose uptake by muscle was approximately 60% lower in insulin-resistant subjects than in controls (p less than 0.001) and was associated with an increase of approximately 80% in intramyocellular lipid content (p less than 0.005). The authors attributed the latter increase to mitochondrial dysfunction, noting a reduction of approximately 30% in mitochondrial phosphorylation (p = 0.01 compared to controls). Petersen et al. (2004) concluded that insulin resistance in the skeletal muscle of insulin-resistant offspring of patients with type 2 diabetes is associated with dysregulation of intramyocellular fatty acid metabolism, possibly because of an inherited defect in mitochondrial oxidative phosphorylation.
Do et al. (2005) assessed the correlation between persistent diabetic macular edema and hemoglobin A1c. Patients with type 2 diabetes and persistent clinically significant macular edema had higher HbA1c at the time of their disease than patients with resolved macular edema. Patients with bilateral disease had more elevated HbA1c than those with unilateral disease.
Foti et al. (2005) reported 4 patients with insulin resistance and type 2 diabetes in whom cell-surface insulin receptors were decreased and INSR (147670) gene transcription was impaired, although the INSR genes were normal. In these individuals, expression of HMGA1 (600701) was markedly reduced; restoration of HMGA1 protein expression in their cells enhanced INSR gene transcription and restored cell-surface insulin receptor protein expression and insulin-binding capacity. Foti et al. (2005) concluded that defects in HMGA1 may cause decreased insulin receptor expression and induce insulin resistance.
Increases in the concentration of circulating glucose activate the hexosamine biosynthetic pathway and promote the O-glycosylation of proteins by O-glycosyl transferase (OGT; 300255). Dentin et al. (2008) showed that OGT triggered hepatic gluconeogenesis through the O-glycosylation of the transducer of regulated cAMP response element-binding protein (CREB) 2 (TORC2 or CRTC2; 608972). CRTC2 was O-glycosylated at sites that normally sequester CRTC2 in the cytoplasm through a phosphorylation-dependent mechanism. Decreasing amounts of O-glycosylated CRTC2 by expression of the deglycosylating enzyme O-GlcNAcase (604039) blocked effects of glucose on gluconeogenesis, demonstrating the importance of the hexosamine biosynthetic pathway in the development of glucose intolerance.
In an autosomal genome screen in 363 nondiabetic Pima Indians at 516 polymorphic microsatellite markers, Pratley et al. (1998) found a suggestion of linkage at several chromosomal regions with particular characteristics known to be predictive of NIDDM: 3q21-q24, linked to fasting plasma insulin concentration and in vivo insulin action; 4p15-q12, linked to fasting plasma insulin concentration; 9q21, linked to 2-hour insulin concentration during oral glucose tolerance testing; and 22q12-q13, linked to fasting plasma glucose concentration. None of the linkages exceeded a lod score of 3.6 (a 5% probability of occurring in a genomewide screen).
In 719 Finnish sib pairs with type 2 diabetes, Ghosh et al. (2000) performed a genome scan at an average resolution of 8 cM. The strongest results were for chromosome 20, where they observed a weighted maximum lod score of 2.15 at map position 69.5 cM from pter, and secondary weighted lod score peaks of 2.04 at 56.5 cM and 1.99 at 17.5 cM. The next largest maximum lod score was for chromosome 11 (maximum lod score = 1.75 at 84.0 cM), followed by chromosomes 2, 10, and 6. When they conditioned on chromosome 2 at 8.5 cM, the maximum lod score for chromosome 20 increased to 5.50 at 69.0 cM.
Watanabe et al. (2000) reported results from an autosomal genome scan for type 2 diabetes-related quantitative traits in 580 Finnish families ascertained for an affected sib pair and analyzed by the variance components-based quantitative-trait locus linkage approach. In diabetic individuals, the strongest results were observed on chromosomes 3 and 13. Integrating genome scan results of Ghosh et al. (2000), they identified several regions that may harbor susceptibility genes for type 2 diabetes in the Finnish population.
In a genomewide scan of 359 Japanese individuals with type 2 diabetes from 159 families, including 224 affected sib pairs, Mori et al. (2002) found suggestive linkage at chromosome 11p13-p12, with a maximum lod score of 3.08. Analysis of sib pairs who had a BMI of less than 30 revealed suggestive linkage at chromosomes 7p22-p21 and 11p13-p12 (lod scores of 3.51 and 3.00, respectively). Analysis of sib pairs who were diagnosed before the age of 45 revealed suggestive linkage at chromosome 15q13-q21, with a maximum lod score of 3.91.
Demenais et al. (2003) applied the genome search metaanalysis (GSMA) method to genomewide scans conducted with 4 European type 2 diabetes mellitus cohorts comprising a total of 3,947 individuals, 2,843 of whom were affected. The analysis provided evidence for linkage of type 2 diabetes to 6 regions, with the strongest evidence on chromosome 17p11.2-q22 (p = 0.0016), followed by 2p22.1-p13.2 (p = 0.027), 1p13.1-q22 (p = 0.028), 12q21.1-q24.12 (p = 0.029), 6q21-q24.1 (p = 0.033), and 16p12.3-q11.2 (p = 0.033). Linkage analysis of the pooled raw genotype data generated maximum lod scores in the same regions as identified by GSMA; the maximum lod score for the 17p11.2-q22 region was 1.54.
Using nonparametric linkage analyses, Van Tilburg et al. (2003) performed a genomewide scan to find susceptibility loci for type 2 diabetes mellitus in the Dutch population. They studied 178 families from the Netherlands, who constituted 312 affected sib pairs. Because obesity and type 2 diabetes mellitus are interrelated, the dataset was stratified for the subphenotype BMI, corrected for age and gender. This resulted in a suggestive maximum multipoint lod score of 2.3 (single-point P value, 9.7 x 10(-4); genomewide P value, 0.028) for the most obese 20% pedigrees of the dataset, between marker loci D18S471 and D18S843. In the lowest 80% obese pedigrees, 2 interesting loci on chromosome 2 and 19 were found, with lod scores of 1.5 and 1.3.
Shtir et al. (2007) performed ordered subset analysis on affected individuals from 2 sets of families ascertained on affected sib pairs with type 2 diabetes mellitus and found that 33 families with the lowest average fasting insulin (606035) showed evidence for linkage to a locus on chromosome 6q (maximum lod score of 3.45 at 128 cM near D6S1569, uncorrected p = 0.017) that was coincident with QTL linkage results for fasting and 2-hour insulin levels in family members without type 2 diabetes mellitus.
The Wellcome Trust Case Control Consortium (2007) described a joint genomewide association study using the Affymetrix GeneChip 500K Mapping Array Set, undertaken in the British population, which examined approximately 2,000 individuals and a shared set of approximately 3,000 controls for each of 7 major diseases. Case-control comparisons identified 3 significant independent association signals for type 2 diabetes, at rs9465871 on chromosome 6p22, rs4506565 on chromosome 10q25, and rs9939609 on chromosome 16q12.
In a genomewide association study of 1,363 French type 2 diabetes cases and controls, Sladek et al. (2007) confirmed the known association with rs7903146 of the TCF7L2 gene (602228.0001) on chromosome 10q25.2 (p = 3.2 x 10(-17)). They also found significant association between T2D and 2 SNPs on chromosome 10q23.33 (rs1111875 and rs7923837), located near the telomeric end of a 270-kb linkage disequilibrium block containing the IDE (146680), HHEX (604420), KIF11 (148760) genes. Sladek et al. (2007) stated that fine mapping of the HHEX locus and biologic studies would be required to identify the causative variant.
The Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes for BioMedical Research (2007) analyzed 386,731 common SNPs in 1,464 patients with type 2 diabetes and 1,467 matched controls, each characterized for measures of glucose metabolism, lipids, obesity, and blood pressure. With collaborators Finland-United States Investigation of NIDDM Genetics (FUSION) and Wellcome Trust Case Control Consortium/United Kingdom Type 2 Diabetes Genetics Consortium (WTCCC/UKT2D), this group identified and confirmed 3 loci associated with type 2 diabetes--in a noncoding region near CDKN2A (600160) and CDKN2B (600431), in an intron of IGF2BP2 (608289), and in an intron of CDKAL1 (611259)--and replicated associations near HHEX and SLC30A8 (611145) by recent whole-genome association study. The Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes for BioMedical Research (2007) identified and confirmed association of a SNP in an intron of glucokinase regulatory protein (GCKR; 600842) with serum triglycerides (see 613463). The authors concluded that the discovery of associated variants in unsuspected genes and outside coding regions illustrates the ability of genomewide association studies to provide potentially important clues to the pathogenesis of common diseases.
Onuma et al. (2010) analyzed the GCKR SNP rs780094 in 488 Japanese patients with type 2 diabetes and 398 controls and found association between a reduced risk of T2DM and the A allele (odds ratio, 0.711; p = 4.2 x 10(-4)). A metaanalysis with 2 previous association studies (Sparso et al., 2008 and Horikawa et al., 2008) confirmed the association of rs780094 with T2D susceptibility. In the general Japanese population, individuals with the A/A genotype had lower levels of fasting plasma glucose (see 613463), fasting plasma insulin, and HOMA-IR than those with the G/G genotype (p = 0.008, 0.008, and 0.002, respectively); conversely, those with the A/A genotype had higher triglyceride levels than those with the G/G genotype (p = 0.028).
Adopting a genomewide association strategy, Scott et al. (2007) genotyped 1,161 Finnish type 2 diabetes cases and 1,174 Finnish normal glucose tolerant controls with greater than 315,000 SNPs and imputed genotypes for an additional greater than 2 million autosomal SNPs. Scott et al. (2007) carried out association analysis with these SNPs to identify genetic variants that predispose to type 2 diabetes, compared to their type 2 diabetes association results with the results of 2 similar studies, and genotyped 80 SNPs in an additional 1,215 Finnish type 2 diabetes cases and 1,258 Finnish normal glucose tolerant controls. Scott et al. (2007) identified type 2 diabetes-associated variants in an intergenic region of chromosome 11p12, contributed to the identification of type 2 diabetes-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirmed that variants near TCF7L2, SLC30A8, HHEX, FTO (610966), PPARG (601487), and KCNJ11 (600937) are associated with type 2 diabetes risk. Scott et al. (2007) concluded that this brings the number of type 2 diabetes loci now confidently identified to at least 10.
Starting from genomewide genotype data for 1,924 diabetic cases and 2,938 population controls generated by the Wellcome Trust Case Control Consortium, Zeggini et al. (2007) set out to detect replicated diabetes association signals through analysis of 3,757 additional cases and 5,346 controls and by integration of their findings with equivalent data from other international consortia. Zeggini et al. (2007) detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed associations at HHEX/IDE and at SLC30A8. Zeggini et al. (2007) concluded that their findings provided insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect. The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.
Van Vliet-Ostaptchouk et al. (2008) genotyped 501 unrelated Dutch patients with type 2 diabetes and 920 healthy controls for 2 SNPs in strong linkage disequilibrium near the HHEX gene, rs7923837 and rs1111875, and found that for both SNPs, the risk for T2D was significantly increased in carriers of the major alleles (OR of 1.57 and p = 0.017; OR of 1.68 and p = 0.003, respectively). Assuming a dominant genetic model, the population-attributable risks for diabetes due to the at-risk alleles of rs7923837 and rs1111875 were estimated to be 33% and 36%, respectively.
Gudmundsson et al. (2007) found that the A allele of rs4430796 in the HNF1B gene (189907) was associated with a protective effect against type 2 diabetes in a study of 1,380 Icelandic patients and 9,940 controls, and in 7 additional type 2 diabetes case-control groups of European, African, and Asian ancestry (p = 2.7 x 10(-7) and odds ratio of 0.91, for the combined results). This SNP is also associated with prostate cancer risk (see HPC11, 611955).
Prokopenko et al. (2008) reviewed advances in identifying common genetic variants that contribute to complex multifactorial phenotypes such as type 2 diabetes (T2D), particularly the ability to perform genomewide association studies in large samples. They noted that the 2 most robust T2D candidate-gene associations previously reported, for common polymorphisms in PPARG and KCNJ11, have only modest effect sizes, with each copy of the susceptibility allele increasing the risk of disease by 15 to 20%. In contrast, microsatellite mapping detected an association with variation in the TCF7L2 gene that has a substantially stronger effect, with the 10% of Europeans who are homozygous for the risk allele having approximately twice the odds of developing T2D compared to those carrying no copies of the risk allele. Prokopenko et al. (2008) stated that about 20 common variants had been robustly implicated in T2D susceptibility to date, but noted that for most of the loci, causal variants had yet to be identified with any certainty.
The Wellcome Trust Case Control Consortium (2010) undertook a large direct genomewide study of association between copy number variants (CNVs) and 8 common human diseases involving approximately 19,000 individuals. Association testing and follow-up replication analyses confirmed association of CNV at the TSPAN8 (600769) locus with type 2 diabetes.
At the time of the report of Fuchsberger et al. (2016), the variants associated with T2D that had been identified by genomewide association studies, although common, explained only a minority of observed T2D heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from 5 ancestry groups. To increase statistical power, Fuchsberger et al. (2016) expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with T2D after sequencing were overwhelmingly common and most fell within regions previously identified by genomewide association studies. Fuchsberger et al. (2016) concluded that, although comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to T2D.
Flannick et al. (2019) performed exome sequencing analysis of 20,791 individuals with type 2 diabetes (T2D) and 24,440 nondiabetic control participants from 5 ancestries, and identified gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exomewide significance, including a series of more than 30 SLC30A8 (611145) alleles that conveys protection against T2D, and in 12 gene sets, including those corresponding to T2D drug targets (p = 6.1 x 10-(3)) and candidate genes from knockout mice (p = 5.2 x 10(-3)). Within their study, the strongest T2D gene-level signals for rare variants explained at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that were observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exomewide significance.
Association with Variation in KCNQ1
Yasuda et al. (2008) carried out a multistage genomewide association study of type 2 diabetes mellitus in Japanese individuals, with a total of 1,612 cases and 1,424 controls and 100,000 SNPs. The most significant association was obtained with SNPs in KCNQ1 (607542), and dense mapping within the gene revealed that rs2237892 in intron 15 showed the lowest p value (6.7 x 10(-13), odds ratio = 1.49). The association of KCNQ1 with type 2 diabetes was replicated in populations of Korean, Chinese, and European ancestry as well as in 2 independent Japanese populations, and metaanalysis with a total of 19,930 individuals (9,569 cases and 10,361 controls) yielded a p value of 1.7 x 10(-42) (odds ratio = 1.40; 95% confidence interval = 1.34-1.47) for rs2237892. Among control subjects, the risk allele of this polymorphism was associated with impairment of insulin secretion according to the homeostasis model assessment of beta-cell function or the corrected insulin response.
Unoki et al. (2008) conducted a genomewide association study using 207,097 SNP markers in Japanese individuals with type 2 diabetes and unrelated controls, and identified KCNQ1 to be a strong candidate for conferring susceptibility to type 2 diabetes. Unoki et al. (2008) detected consistent association of a SNP in KCNQ1 (rs2283228) with the disease in several independent case-control studies (additive model p = 3.1 x 10(-12); odds ratio = 1.26, 95% confidence interval = 1.18-1.34). Several other SNPs in the same linkage disequilibrium block were strongly associated with type 2 diabetes. The association of these SNPs with type 2 diabetes was replicated in samples from Singaporean and Danish populations.
Gaulton et al. (2015) performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. They identified 49 distinct association signals at these 39 loci, including 5 mapping in or near KCNQ1 (607542). Gaulton et al. (2015) found 5 SNPs in the region flanking KCNQ1 with modest effect on diabetes risk, with the weakest association at rs458069 (p = 1.0 x 10(-6), OR 1.06, 95% CI 1.04-1.09) and the strongest association at rs74046911 (p = 9.6 x 10(-26), OR 1.29, 95% CI 1.23-1.35).
Association with Variation in SHBG
Ding et al. (2009) analyzed levels of sex hormone-binding globulin (see SHBG; 182205) in 359 women newly diagnosed with type 2 diabetes and 359 female controls and found that higher plasma levels of SHBG were prospectively associated with a lower risk of type 2 diabetes, with multivariable odds ratios ranging from 1.00 for the lowest quartile of plasma levels to 0.09 for the highest quartile; the results were replicated in an independent cohort of men (p less than 0.001 for results in both women and men). Ding et al. (2009) identified an SHBG SNP, rs6259, that was associated with a 10% higher plasma level of SHBG, and another SNP, rs6257, that was associated with a 10% lower plasma level of SHBG; variants of both SNPs were also associated with a risk of type 2 diabetes in directions corresponding to their associated SHBG levels. In mendelian randomization analyses, the predicted odds ratio of type 2 diabetes per standard deviation increase in plasma level of SHBG was 0.28 among women and 0.29 among men. Ding et al. (2009) suggested that variation in the SHBG gene on chromosome 17p13-p12 may have a causal role in the risk of type 2 diabetes.
Kong et al. (2009) identified a differentially methylated CTCF binding site at 11p15 and demonstrated correlation of rs2334499 with decreased methylation of that site. The CTCF-binding site is OREG0020670 and its 2-kb region located 17 kb centromeric to the type 2 diabetes marker rs2334499.
Perry et al. (2010) genotyped 27,657 type 2 diabetes patients and 58,481 controls from 15 studies at the SHBG promoter SNP rs1799941 that is strongly associated with serum levels of SHBG. The authors used data from additional studies to estimate the difference in SHBG levels between type 2 diabetes patients and controls. The rs1799941 variant was associated with type 2 diabetes (OR, 0.94; 95% CI, 0.91-0.97; p = 2 x 10(-5)), with the SHBG-raising A allele associated with reduced risk of type 2 diabetes, the results were very similar in men and women. There was no evidence that rs1799941 was associated with diabetes-related intermediate traits, including several measures of insulin secretion and resistance.
Association with Variation in RBP4
Serum levels of RBP4 (180250), a protein secreted by adipocytes, are increased in insulin-resistant states. Experiments in mice suggested that elevated RBP4 levels cause insulin resistance (Yang et al., 2005). Graham et al. (2006) found that serum RBP4 levels correlated with the magnitude of insulin resistance in human subjects with obesity (601665), impaired glucose tolerance, or type 2 diabetes and in nonobese, nondiabetic subjects with a strong family history of type 2 diabetes. Elevated serum RBP4 was associated with components of the metabolic syndrome, including increased body mass index (BMI), waist-to-hip ratio, serum triglyceride levels, and systolic blood pressure and decreased high-density lipoprotein cholesterol levels. Exercise training was associated with a reduction in serum RBP4 levels only in subjects in whom insulin resistance improved. Adipocyte GLUT4 protein (138190) and serum RBP4 levels were inversely correlated. Graham et al. (2006) concluded that RBP4 is elevated in serum before the development of frank diabetes and appears to identify insulin resistance and associated cardiovascular risk factors in subjects with varied clinical presentations. They suggested that these findings provide a rationale for antidiabetic therapies aimed at lowering serum RBP4 levels.
Aeberli et al. (2007) studied serum RBP4, serum retinol (SR), the RBP4-to-SR molar ratio, and dietary vitamin A intakes in seventy-nine 6- to 14-year-old normal-weight and overweight children and investigated the relationship of these variables to insulin resistance, subclinical inflammation, and the metabolic syndrome. Only 3% of children had low vitamin A status. Independent of age, vitamin A intakes, and C-reactive protein (see 123260), BMI, body fat percentage, and waist-to-hip ratio were significant predictors of RBP4, serum retinol, and RBP4/SR. Aeberli et al. (2007) concluded that independent of subclinical inflammation and vitamin A intakes, serum RBP4 and the RBP4-to-SR ratio are correlated with obesity, central obesity, and components of the metabolic syndrome in prepubertal and early pubertal children.
Mutation in PPAR-Gamma
Altshuler et al. (2000) confirmed an association of the common pro12-to-ala polymorphism in PPAR-gamma (601487.0002) with type 2 diabetes. They found a modest but significant increase in diabetes risk associated with the more common proline allele (approximately 85% frequency). Because the risk allele occurs at such high frequency, its modest effect translates into a large population-attributable risk--influencing as much as 25% of type 2 diabetes in the general population.
Savage et al. (2002) described a family, which they referred to as a 'Europid pedigree,' in which several members had severe insulin resistance. The grandparents had typical late-onset type 2 diabetes with no clinical features of severe insulin resistance. Three of their 6 children and 2 of their grandchildren had acanthosis nigricans, elevated fasting plasma insulin levels. Hypertension was also a feature. By mutation screening, Savage et al. (2002) identified a heterozygous frameshift resulting in a premature stop mutation of the PPARG (601487.0011) gene which was present in the grandfather, all 5 relatives with severe insulin resistance, and 1 other relative with normal insulin levels. Further candidate gene studies revealed a heterozygous frameshift/premature stop mutation in PPP1R3A (600917.0003) which was present in the grandmother, in all 5 individuals with severe insulin resistance, and in 1 other relative. Thus, all 5 family members with severe insulin resistance, and no other family members, were double heterozygotes with respect to frameshift mutations. (Although the article by Savage et al. (2002) originally stated that the affected individuals were compound heterozygotes, they were actually double heterozygotes. Compound heterozygosity is heterozygosity at the same locus for each of 2 different mutant alleles; double heterozygosity is heterozygosity at each of 2 separate loci. The use of an incorrect term in the original publication was the result of a 'copy-editing error that was implemented after the authors returned corrected proofs' (Savage et al., 2002).)
Association with Insulin Receptor Substrate-2
Mammarella et al. (2000) genotyped 193 Italian patients with type 2 diabetes and 206 control subjects for the insulin receptor substrate-2 G1057D polymorphism (600797.0001). They found evidence for a strong association between type 2 diabetes and the polymorphism, which appears to be protective against type 2 diabetes in a codominant fashion.
Association with Adiponectin
For a discussion of an association between variation in the ADIPOQ gene (605441) on chromosome 3q27 and type 2 diabetes, see ADIPQTL1 (612556).
Association with Mitochondrial DNA Variation
A common mtDNA variant (T16189C) in a noncoding region of mtDNA was positively correlated with blood fasting insulin by Poulton et al. (1998). Poulton et al. (2002) demonstrated a significant association between the 16189 variant and type 2 diabetes in a population-based case-control study in Cambridgeshire, UK (n = 932, odds ratio = 1.61; 1.0-2.7, P = 0.048), which was greatly magnified in individuals with a family history of diabetes from the father's side (odds ratio = infinity; P less than 0.001). Poulton et al. (2002) demonstrated that the 16189 variant had arisen independently many times and on multiple mitochondrial haplotypes. They speculated that the 16189 variant may alter mtDNA bending and hence could influence interactions with regulatory proteins which control replication or transcription.
Mohlke et al. (2005) presented data supporting previous evidence for association of 16189T-C with reduced ponderal index at birth and also showed evidence for association with reduced birth weight but not with diabetes status. This study suggested that mitochondrial genome variants may play at most a modest role in glucose metabolism in the Finnish population studied. Furthermore, the data did not support a reported maternal inheritance pattern of type 2 diabetes mellitus but instead showed a strong effect of recall bias.
Because mitochondria play pivotal roles in both insulin secretion from the pancreatic beta cells and insulin resistance of skeletal muscles, Fuku et al. (2007) performed a large-scale association study to identify mitochondrial haplogroups that may confer resistance against or susceptibility to type 2 diabetes mellitus. The study population comprised 2,906 unrelated Japanese individuals, including 1,289 patients with type 2 diabetes mellitus and 1,617 controls, and 1,365 unrelated Korean individuals, including 732 patients with type 2 diabetes and 633 controls. The genotypes for 25 polymorphisms in the coding region of the mitochondrial genome were determined, and the haplotypes were classified into 10 major haplogroups. Multivariate logistic regression analysis with adjustment for age and sex revealed that the mitochondrial group N9a was significantly associated with resistance against type 2 diabetes mellitus (P = 0.0002) with an odds ratio of 0.55 (95% confidence interval 0.40-0.75). Even in the modern environment, which is often characterized by satiety and physical inactivity, this haplotype might confer resistance against type 2 diabetes mellitus. The N9a haplogroup found to be associated with reduced susceptibility to type 2 diabetes mellitus by Fuku et al. (2007) consisted of a synonymous SNP in ND2 (516001), 5231G-A; a missense change in ND5 (516005), thr8 to ala; and a synonymous change also in ND5, 12372G-A.
Mutation in PAX4
Shimajiri et al. (2001) scanned the PAX4 gene (167413) in 200 unrelated Japanese probands with type 2 diabetes and identified an arg121-to-tyr mutation (R121W; 167413.0001) in 6 heterozygous patients and 1 homozygous patient (mutant allele frequency 2.0%). The mutation was not found in 161 nondiabetic subjects (p = 0.01). Six of 7 patients had a family history of diabetes or impaired glucose tolerance, and 4 of 7 had transient insulin therapy at the onset. One of them, a homozygous carrier, had relatively early-onset diabetes and slowly fell into an insulin-dependent state without an autoimmune-mediated process.
Association with TFAP2B
Maeda et al. (2005) performed a genomewide, case-control association study using gene-based SNPs in Japanese patients with type 2 diabetes and controls and identified several variations within the TFAP2B gene (601601) that were significantly associated with type 2 diabetes: an intron 1 VNTR (p = 0.0009), intron 1 +774G-T (p = 0.0006), and intron 1 +2093A-C (p = 0.0004). The association of TFAP2B with type 2 diabetes was also observed in a U.K. population. Maeda et al. (2005) suggested that the TFAP2B gene may confer susceptibility to type 2 diabetes.
Mutation in ABCC8
Babenko et al. (2006) screened the ABCC8 gene (600509) in 34 patients with permanent neonatal diabetes (606176) or transient neonatal diabetes (see 601410) and identified heterozygosity for 7 missense mutations in 9 patients (see, e.g., 600509.0017-600509.0020). The mutation-positive fathers of 5 of the probands with transient neonatal diabetes developed type 2 diabetes mellitus in adulthood; Babenko et al. (2006) proposed that mutations of the ABCC8 gene may give rise to a monogenic form of type 2 diabetes with variable expression and age at onset.
Association with WFS1
Sandhu et al. (2007) conducted a gene-centric association study for type 2 diabetes in multiple large cohorts and identified 2 SNPs located in the WFS1 gene, rs10010131 (606201.0021) and rs6446482 (602201.0022), that were strongly associated with diabetes risk (P = 1.4 x 10(-7) and P = 3.4 x 10(-7), respectively, in the pooled study set). The risk allele was the major allele for both SNPs, with a frequency of 60% for both. The authors noted that both are intronic, with no obvious evidence for biologic function.
Association with IL6
Mohlig et al. (2004) investigated the IL6 -174C-G SNP (147620.0001) and development of NIDDM. They found that this SNP modified the correlation between BMI and IL6 by showing a much stronger increase of IL6 at increased BMI for CC genotypes compared with GG genotypes. The -174C-G polymorphism was found to be an effect modifier for the impact of BMI regarding NIDDM. The authors concluded that obese individuals with BMI greater than or equal to 28 kg/m2 carrying the CC genotype showed a more than 5-fold increased risk of developing NIDDM compared with the remaining genotypes and, hence, might profit most from weight reduction.
Illig et al. (2004) investigated the association of the IL6 SNPs -174C-G and -598A-G on parameters of type 2 diabetes and the metabolic syndrome in 704 elderly participants of the Kooperative Gesundheitsforschung im Raum Augsburg/Cooperative Research in the Region of Augsburg (KORA) Survey 2000. They found no significant associations, although both SNPs exhibited a positive trend towards association with type 2 diabetes. Illig et al. (2004) also found that circulating IL6 levels were not associated with the IL6 polymorphisms; however, significantly elevated levels of the chemokine monocyte chemoattractant protein-1 (MCP1; 158105)/CC chemokine ligand-2 (CKR2; 601267) in carriers of the protective genotypes suggested an indirect effect of these SNPs on the innate immune system.
Association with KCNJ15
Okamoto et al. (2010) identified a synonymous SNP (rs3746876, C566T) in exon 4 of the KCNJ15 (602106) that showed significant association with type 2 diabetes mellitus affecting lean individuals in 3 independent Japanese sample sets (p = 2.5 x 10(-7); odds ratio, 2.54) and with unstratified T2DM (p = 6.7 x 10(-6); OR, 1.76). The diabetes risk allele frequency was, however, very low among Europeans and no association between the variant and T2DM could be shown in a Danish case-control study. Functional analysis in HEK293 cells demonstrated that the risk T allele increased KCNJ15 expression via increased mRNA stability, which resulted in higher expression of protein compared to the C allele.
Mutation in and Association with MTNR1B
Bonnefond et al. (2012) performed large-scale exon resequencing of the MTNR1B gene (600804) in 7,632 Europeans, including 2,186 individuals with type 2 diabetes mellitus, and identified 36 very rare variants associated with T2D. Among the very rare variants, partial or total loss-of-function variants but not neutral ones contributed to T2D (odds ratio, 5.67; p = 4.09 x 10(-4)). Genotyping 4 variants with complete loss of melatonin-binding and signaling capabilities (A42P, 600804.0001; L60R, 600804.0002; P95L, 600804.0003; and Y308S, 600804.0004) as a pool in 11,854 additional French individuals, including 5,967 with T2D, demonstrated their association with T2D (odds ratio, 3.88; p = 5.37 x 10(-3)). Bonnefond et al. (2012) concluded that their study established a firm functional link between MTNR1B and T2D risk.
Gaulton et al. (2015) performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 (600288) chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. Gaulton et al. (2015) confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells and observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression.
Mutation in SLC16A11
The SIGMA Type 2 Diabetes Consortium (2014) analyzed 9.2 million SNPs in each of 8,214 Mexicans and other Latin Americans, 3,848 with type 2 diabetes and 4,366 nondiabetic controls. In addition to replicating previous findings, the SIGMA Type 2 Diabetes Consortium (2014) identified a novel locus associated with type 2 diabetes at genomewide significance spanning the solute carriers SLC16A11 (615765) and SLC16A13 (p = 3.9 x 10(-13); odds ratio = 1.29). The association was stronger in younger, leaner people with type 2 diabetes, and replicated in independent samples (p = 1.1 x 10(-4); odds ratio = 1.20). The risk haplotype carries 4 missense SNPs, all in SLC16A11: V113I (rs117767867), D127G (rs13342692), G40S (rs75418188), and P443T (rs75493593). This haplotype is present at 50% frequency in Native American samples and approximately 10% in East Asian, but is rare in European and African samples. Each haplotype copy is associated with a 20% increased risk of type 2 diabetes. Analysis of an archaic genome sequence indicated that the risk haplotype introgressed into modern humans via admixture with Neanderthals. The SIGMA Type 2 Diabetes Consortium (2014) concluded that, despite type 2 diabetes having been well studied by genomewide association studies in other populations, analysis in Mexican and Latin American individuals identified SLC16A11 as a novel candidate gene for type 2 diabetes with a possible role in triacylglycerol metabolism.
Association with STARD10
Using pancreatic samples from nondiabetic individuals and patients with type 2 diabetes mellitus, Carrat et al. (2017) performed cis-expression quantitative trait locus (eQTL) analysis of islet transcriptomes and observed that carriers of T2DM risk alleles at 11q13 displayed reduced levels of STARD10 (617382) mRNA. In addition, beta cell-selective deletion of Stard10 in mice resulted in impaired glucose-stimulated Ca(2+) dynamics and insulin secretion, and recapitulated the pattern of improved proinsulin (see 176730) processing observed in human carriers of the risk alleles, whereas overexpression of Stard10 in the adult beta cell improved glucose tolerance in high-fat-fed mice. Carrat et al. (2017) suggested that T2DM risk associated with variation at this locus is mediated through reduction in STARD10 expression in the beta cell.
Reclassified Variants
The D76N variant in the IPF1/PDX1 gene (600733.0002) that was identified by Macfarlane et al. (1999) in patients with type 2 diabetes has been reclassified as a variant of unknown significance.
Diabetes mellitus is a recognized consequence of hereditary hemochromatosis (HFE; 235200). To test whether common mutations in the HFE gene (613609) that associate with this condition and predispose to increases in serum iron indices are overrepresented in diabetic populations, Halsall et al. (2003) determined the allele frequencies of the C282Y (613609.0001) and H63D (613609.0002) HFE mutations among a cohort of 552 patients with typical type 2 diabetes mellitus. There was no evidence for overrepresentation of iron-loading HFE alleles in type 2 diabetes mellitus, suggesting that screening for HFE mutations in this population is of no value.
Meigs et al. (2008) genotyped SNPs at 18 loci associated with diabetes in 2,377 participants of the Framingham Offspring Study. They created a genotype score from the number of risk alleles and used logistic regression to generate C statistics indicating the extent to which the genotype score can discriminate the risk of diabetes when used alone and in addition to clinical risk factors. There were 255 new cases of diabetes during 28 years of follow-up. The mean (+/- standard deviation) genotype score was 17.7 +/- 2.7 among subjects in whom diabetes developed and 17.1 +/- 2.6 among those in whom diabetes did not develop (P = less than 0.001). The sex-associated odds ratio for diabetes was 1.12 per risk allele (95% confidence interval, 1.07 to 1.17). The C statistic was 0.534 without the genotype score and 0.581 with the score (P = 0.01). In a model adjusted for age, sex, family history, body mass index, fasting glucose level, systolic blood pressure, high-density lipoprotein cholesterol level, and triglyceride level, the C statistic was 0.900 without the genotype score and 0.901 with the score, not significantly different. The genotype score resulted in the appropriate risk reclassification of, at most, 4% of the subjects. Meigs et al. (2008) concluded that a genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly better prediction of risk than knowledge of common risk factors alone.
Lyssenko et al. (2008) genotyped 16 SNPs and examined clinical factors in 16,061 Swedish and 2,770 Finnish subjects. Type 2 diabetes developed in 2,201 (11.7%) of these subjects during a median follow-up period of 23.5 years. Strong predictors of diabetes were a family history of the disease, increased body mass index, elevated liver enzyme levels, current smoking status, and reduced measures of insulin secretion action. Variants in 11 genes were significantly associated with the risk of type 2 diabetes independently of clinical risk factors; variants in 8 of these genes were associated with impaired beta cell function. The addition of specific genetic information to clinical factors slightly improved the prediction of future diabetes, with a slight increase in the area under the receiver-operating-characteristic (also known as C statistics) curve from 0.74 to 0.75; however, the magnitude of the increase was significant (P = 1.0 x 10(-4)). Lyssenko et al. (2008) concluded that as compared with clinical risk factors alone, common genetic variants associated with the risk of diabetes had a small effect on the ability to predict the future development of type 2 diabetes. The value of genetic factors increased with an increasing duration of follow-up.
The most widely used animal model of nonobese NIDDM is the Goto-Kakizaki (GK) rat. Galli et al. (1996) mapped 3 independent loci involved in the disease. Thus, NIDDM in the rat is polygenic. The 3 NIDDM loci were found to have distinct physiologic effects. One affected postprandial but not fasting hyperglycemia, whereas the other 2 affected both. Gauguier et al. (1996) mapped up to 6 independently segregating loci predisposing to hyperglycemia, glucose intolerance, or altered insulin secretion in the GK rat. Both Galli et al. (1996) and Gauguier et al. (1996) identified a locus implicated in body weight. The close similarity between diabetes-related phenotypes in the GK rat and human NIDDM suggested to the authors that similar patterns of genetic heterogeneity may underlie the disease in humans and that the results in rats may be useful in understanding the human disease.
Fakhrai-Rad et al. (2000) mapped the NIDDM1B locus in the GK rat to a 1-cM region by genetic and pathophysiologic characterization of new congenic substrains for the locus. The gene encoding insulin-degrading enzyme (IDE; 146680) was also mapped to this 1-cM region, and 2 amino acid substitutions (H18R and A890V) were identified in the GK allele which reduced insulin-degrading activity by 31% in transfected cells. However, when the H18R and A890V variants were studied separately, no effects were observed, suggesting a synergistic effect of the 2 variants on insulin degradation. No effect on insulin degradation was observed in cell lysates, suggesting that the effect may be coupled to receptor-mediated internalization of insulin. Congenic rats with the IDE GK allele displayed postprandial hyperglycemia, reduced lipogenesis in fat cells, blunted insulin-stimulated glucose transmembrane uptake, and reduced insulin degradation in isolated muscle. Analysis of additional rat strains demonstrated that the dysfunctional IDE allele was unique to GK rats. The authors concluded that IDE plays an important role in the diabetic phenotype in GK rats.
Bruning et al. (1997) created a polygenic (or at least digenic) model of NIDDM in mice. The model reproduced the characteristics of the human disease, namely insulin resistance in muscle, fat, and liver, followed by failure of pancreatic beta-cells to compensate adequately for this resistance despite increased insulin secretion. Mice doubly heterozygous for null alleles in the insulin receptor (147670) and insulin receptor substrate-1 (IRS1; 147545) genes exhibited the expected reduction by approximately 50% in expression of these 2 proteins, but a synergism at the level of insulin resistance with 5- to 50-fold elevated plasma insulin levels and comparable levels of beta-cell hyperplasia. At 4 to 6 months of age, 40% of these doubly heterozygote mice became overtly diabetic. Thus, diabetes arose in an age-dependent manner from an interaction between 2 genetically determined, subclinical defects in the insulin signaling cascade, demonstrating the role of epistatic interactions in the pathogenesis of common diseases with nonmendelian genetics.
Terauchi et al. (1997) likewise created a polygenic model of NIDDM by heterozygous knockout of the IRS1 gene with heterozygous knockout of the beta-cell GCK gene. They found that the genetic abnormalities, each of which was nondiabetogenic by itself, caused overt diabetes if they coexisted.
The Zucker diabetic fatty (ZDF) rat is another animal model of human adipogenic NIDDM. Shimabukuro et al. (1998) demonstrated in islets of obese ZDF rats a pathway of lipotoxicity leading to diabetes. Elevated levels of circulating free fatty acids (Lee et al., 1994) and lipoproteins transport to islets of obese ZDF rats far more free fatty acids than can be oxidized. Because fa/fa islets exhibit a markedly increased lipogenic capacity and a decreased oxidative capacity, unused free fatty acids in islets are esterified and over time an excessive quantity is deposited (Lee et al., 1997). This is associated with an increase in ceramide, inducible NOS expression, and NO production, which causes apoptosis. That troglitazone, an agent that reduces islet fat in ZDF rats (Shimabukuro et al., 1997) and prevents their diabetes (Sreenan et al., 1996), is equally efficacious in human NIDDM suggests a comparable pathway of lipotoxicity to diabetes in humans.
Hart et al. (2000) showed that FGF receptors 1 and 2 (136350, 176943), together with ligands FGF1 (131220), FGF2 (134920), FGF4 (164980), FGF5 (165190), FGF7 (148180), and FGF10 (602115), are expressed in adult mouse beta cells, indicating that FGF signaling may have a role in differentiated beta cells. When Hart et al. (2000) perturbed signaling by expressing dominant-negative forms of the receptors, FGFR1C and FGFR2B, in the pancreas, they found that mice with attenuated FGFR1C signaling, but not those with reduced FGFR2B signaling, developed diabetes with age and exhibited a decreased number of beta cells, impaired expression of glucose transporter 2 (138160), and increased proinsulin content in beta cells owing to impaired expression of prohormone convertases 1/3 and 2. These defects are all characteristic of patients with type 2 diabetes. Mutations in the homeobox gene IPF1/PDX1 (600733) are linked to diabetes in both mouse and human. Hart et al. (2000) showed that IPF1/PDX1 is required for the expression of FGFR1 signaling components in beta cells, indicating that IPF1/PDX1 acts upstream of FGFR1 signaling in beta cells to maintain proper glucose sensing, insulin processing, and glucose homeostasis.
Yuan et al. (2001) demonstrated that high doses of salicylates reverse hyperglycemia, hyperinsulinemia, and dyslipidemia in obese rodents by sensitizing insulin signaling. Activation or overexpression of IKBKB (603258) attenuated insulin signaling in cultured cells, whereas IKKB inhibition reversed insulin resistance. Thus, Yuan et al. (2001) concluded that IKKB, rather than the cyclooxygenases (see 600262), appears to be the relevant molecular target. Heterozygous deletion (IKKB +/-) protected against the development of insulin resistance during high fat feeding and in obese Lep (ob/ob) (see 164160) mice. Yuan et al. (2001) concluded that their findings implicate an inflammatory process in the pathogenesis of insulin resistance in obesity and type 2 diabetes mellitus and identified the IKKB pathway as a target for insulin sensitization.
Scheuner et al. (2005) studied glucose homeostasis in mice with a ser51-to-ala substitution at the phosphorylation site of the translation initiation factor eIF2-alpha (see 603907) and observed that heterozygous mutant mice became obese and diabetic on a high-fat diet. Profound glucose intolerance resulted from reduced insulin secretion accompanied by abnormal distention of the ER lumen, defective trafficking of proinsulin, and a reduced number of insulin granules in beta cells. Scheuner et al. (2005) proposed that translational control couples insulin synthesis with folding capacity to maintain ER integrity and that this signal is essential to prevent diet-induced type 2 diabetes.
In Hmga1 (600701)-deficient mice, Foti et al. (2005) observed decreased insulin receptor expression in muscle, fat, and liver, largely impaired insulin signaling, and severely reduced insulin secretion, causing a phenotype characteristic of human type 2 diabetes.
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