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Literature record

Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.

PMID 24296717 | PMCID PMC4030103 | DOI 10.2337/db13-0949 · Diabetes · 2014

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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.

Validated evidence

TypeEntitySource evidenceConfidenceExtractor
genePPARG“The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity.”0.98hgnc_dict_v1
geneKLF14“The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity.”0.98hgnc_dict_v1
geneIRS1“The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity.”0.98hgnc_dict_v1
geneGCKR“The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity.”0.98hgnc_dict_v1
geneMTNR1B“The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia.”0.98hgnc_dict_v1
geneGCK“The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia.”0.98hgnc_dict_v1
geneARAP1“ARAP1 constituted a third cluster characterized by defects in insulin processing.”0.98hgnc_dict_v1
geneTCF7L2“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.”0.98hgnc_dict_v1
geneSLC30A8“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.”0.98hgnc_dict_v1
geneHHEX“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.”0.98hgnc_dict_v1
geneIDE“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.”0.98hgnc_dict_v1
geneCDKAL1“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.”0.98hgnc_dict_v1
geneCDKN2A“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.”0.98hgnc_dict_v1
phenotypediabetes mellitus“Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.”0.93phenotype_alias_lexicon_v2
populationPopulation“We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures.”0.80saudi_context_rules_v1