Search

Leptin influences food intake by informing the brain about the status of body fat stores. Rare LEP mutations associated with congenital leptin deficiency cause severe early-onset obesity that can be mitigated by administering leptin. However, the role of genetic regulation of leptin in polygenic obesity remains poorly understood. We performed an exome-based analysis in up to 57,232 individuals of diverse ancestries to identify genetic variants that influence adiposity-adjusted leptin concentrations. We identify five novel variants, including four missense variants, in LEP, ZNF800, KLHL31, and ACTL9, and one intergenic variant near KLF14. The missense variant Val94Met (rs17151919) in LEP was common in individuals of African ancestry only, and its association with lower leptin concentrations was specific to this ancestry (P = 2 × 10-16, n = 3,901). Using in vitro analyses, we show that the Met94 allele decreases leptin secretion. We also show that the Met94 allele is associated with higher BMI in young African-ancestry children but not in adults, suggesting that leptin regulates early adiposity.

Genome-wide association studies (GWAS) have found few common variants that influence fasting measures of insulin sensitivity. We hypothesized that a GWAS of an integrated assessment of fasting and dynamic measures of insulin sensitivity would detect novel common variants. We performed a GWAS of the modified Stumvoll Insulin Sensitivity Index (ISI) within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium. Discovery for genetic association was performed in 16,753 individuals, and replication was attempted for the 23 most significant novel loci in 13,354 independent individuals. Association with ISI was tested in models adjusted for age, sex, and BMI and in a model analyzing the combined influence of the genotype effect adjusted for BMI and the interaction effect between the genotype and BMI on ISI (model 3). In model 3, three variants reached genome-wide significance: rs13422522 (NYAP2; P = 8.87 × 10(-11)), rs12454712 (BCL2; P = 2.7 × 10(-8)), and rs10506418 (FAM19A2; P = 1.9 × 10(-8)). The association at NYAP2 was eliminated by conditioning on the known IRS1 insulin sensitivity locus; the BCL2 and FAM19A2 associations were independent of known cardiometabolic loci. In conclusion, we identified two novel loci and replicated known variants associated with insulin sensitivity. Further studies are needed to clarify the causal variant and function at the BCL2 and FAM19A2 loci.

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.

ObjectiveProinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology.Research design and methodsWe have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates.ResultsNine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets.ConclusionsWe have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis.

HNF1α (TCF1) is a key transcription factor that is essential for pancreatic β-cell development and function. Rare mutations of HNF1α cause maturity-onset diabetes of the young. A common variant, G319S, private to the Oji-Cree population, predisposes to type 2 diabetes, but the role of common HNF1α variation in European populations has not been comprehensively assessed. We determined the linkage disequilibrium and haplotype structure across the HNF1α gene region using 29 single nucleotide polymorphisms (SNPs). Eight tagging SNPs (tSNPs) that efficiently capture common haplotypes and the amino acid-changing variant, A98V, were genotyped in 5,307 subjects (2,010 type 2 diabetic case subjects, 1,643 control subjects, and 1,654 members of 521 families). We did not find any evidence of association between the tSNPs or haplotypes and type 2 diabetes. We could exclude odds ratios (ORs) > 1.25 for all tSNPs. The rare V98 allele (∼3% frequency) showed possible evidence of association with type 2 diabetes (OR 1.23 [95% CI 0.99-1.54], P = 0.07), a result that was supported by meta-analysis of this and published studies (OR 1.31 [1.08-1.59], P = 0.007). Further studies are required to investigate this association, demonstrating the difficulty of defining the role of rare (<5%) alleles in type 2 diabetes risk. Diabetes 54:2487-2491, 2005 [ABSTRACT FROM AUTHOR]

The gene encoding insulin receptor substrate-1 (IRS1) represents a strong biological candidate for a contributory role in type 2 diabetes susceptibility. Indeed, functional studies have implicated the G971R variant, and a recent meta-analysis of 27 association studies suggested that carriage of 971R was associated with a 25% increase in disease risk. However, this association has not been evaluated in large samples. The present study genotyped the P512A and G971R IRS1 variants in 971 U.K. type 2 diabetic subjects ascertained for strong family history and/or early onset, as well as 1,257 control subjects matched by ethnicity. There was no evidence for association with type 2 diabetes for either variant. (For example, the odds ratio [OR] for carriage of 971R was 1.11 [95% CI 0.86-1.44, P = 0.44]) An updated meta-analysis (31 studies: 5,104 case and 7,418 control subjects) remained significant for the G971R association (P = 0.025), albeit with a diminished OR (1.15 [95% CI 1.02-1.31]). Additional studies of IRS1 variation will be required to obtain a robust estimate of the overall contribution of IRS1 variation to type 2 diabetes susceptibility, but the current study suggests that previous studies have overestimated the magnitude of this effect. [ABSTRACT FROM AUTHOR]

Additional information on genetic susceptibility effects relevant to type 2 diabetes pathogenesis can be extracted from existing genome scans by extending examination to related phenotypes such as age at disease onset. In this study, we report the reanalysis of data from 573 U.K. sibships ascertained for multiplex type 2 diabetes, using age at onset (assessed by the proxy measure of age at diagnosis) as the phenotype of interest. Genome-wide evidence for linkage to age at diagnosis was evaluated using both variance components and Haseman-Elston (HECOM) regression approaches, with extensive simulations to derive empirical significance values. There was broad agreement across analyses with six regions of interest (logarithm of odds [LOD] >/==" BORDER="0">1.18) identified on chromosomes 1qter, 4p15-4q12, 5p15, 12p13-12q13, 12q24, and 14q12-14q21. The strongest empirically "suggestive" evidence for linkage comes from regions on chromosome 12. The first region (12p13-12q13), peaking at D12S310 (variance components LOD [LOD(VC)] = 2.08, empirical pointwise P = 0.0007; HECOM LOD [LOD(HECOM)] = 2.58, P = 0.0010) seems to be novel. The second (12q24) peaking between D12S324 and D12S1659 (LOD(VC) = 1.87, P = 0.0016; LOD(HECOM) = 1.93, P = 0.0027) overlaps a region showing substantial prior evidence for diabetes linkage. These data provide additional evidence that genes mapping to these chromosomal regions are involved in the susceptibility to, and/or development of, type 2 diabetes. [ABSTRACT FROM AUTHOR]

The orphan receptor small heterodimer partner (SHP, NR0B2) modulates the transcription activity of the MODY1 gene HNF4a. Mutations in SHP were found in 7% of Japanese obese young-onset type 2 diabetic patients and were associated with moderate obesity and increased birth weight. We investigated SHP in 1927 U.K. subjects, examining relationships with type 2 diabetes, obesity, and birth weight. Sequencing of the coding region of SHP in 122 obese, young-onset type 2 diabetic patients detected the polymorphism G171A. The polymorphism was not associated with diabetes in case control or familial association studies. The A allele (frequency 0.07) was not associated with obesity in type 2 diabetic subjects (n = 348), their parents (n = 272), or young nondiabetic adults (n = 925). However, the rare (<1%) AA homozygotes had a raised BMI in each cohort; this was significant when all cohorts were combined (Z score = 0.67 AA vs. -0.05 G/x, P = 0.02). There was no association with corrected birth weight in 382 normal babies, but the only AA baby was 4,069 g. Our study suggests that genetic variation in SHP is unlikely to be common in the predisposition to diabetes, obesity, or increased birth weight in U.K. Caucasians. [ABSTRACT FROM AUTHOR]

Variation in the calpain-10 gene (CAPN10) has been shown to be associated with type 2 diabetes in Mexican-Americans and in at least three Northern European populations. Studies in nondiabetic Pima Indians showed that one of the at-risk DNA polymorphisms, single-nucleotide polymorphism (SNP)-43, in CAPN10 was associated with insulin resistance, and individuals with the G/G-genotype had significantly higher fasting plasma glucose and 2-h insulin concentrations after a 75-g oral glucose tolerance test (OGTT). We have examined the effect of variation in CAPN10 on plasma glucose and insulin levels in a group of 285 nondiabetic British subjects after a 75-g OGTT. The results showed that subjects with G/G genotype at SNP-43 had higher 2-h plasma glucose levels than the combined G/A + A/A group (P = 0.05). We also examined the SNP-43, -19, and -63 haplotype combination 112/121, which is associated with an approximately threefold increased risk of diabetes. Subjects with the 112/121 haplotype combination (n = 29) had increased fasting (P = 0.004) and 2-h plasma glucose levels (P = 0.003) compared with the rest of the study population after correction for age, sex, and BMI. The 112/121 haplotype combination was also associated with a marked decrease in the insulin secretory response, adjusted for the level of insulin resistance (P = 0.002). We conclude that genetic variation in the CAPN10 gene influences blood glucose levels in nondiabetic British subjects and that this is due, at least in part, to the effects of calpain-10 on the early insulin secretory response. [ABSTRACT FROM AUTHOR]

Studies the primary molecular events underlying non-insulin dependent diabetes (NIDDM) and obesity. Individual susceptibility to interrelated conditions under genetic influence; Processing of proinsulin to mature insulin; Evidence for linkage between the gene region and NIDDM.