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Träfflista för sökning "WFRF:(Knowles Joshua W) srt2:(2014)"

Sökning: WFRF:(Knowles Joshua W) > (2014)

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1.
  • Yaghootkar, Hanieh, et al. (författare)
  • Genetic evidence for a normal-weight "metabolically obese" phenotype linking insulin resistance, hypertension, coronary artery disease and type 2 diabetes.
  • 2014
  • Ingår i: Diabetes. - : American Diabetes Association. - 1939-327X .- 0012-1797. ; 63:12, s. 4369-77
  • Tidskriftsartikel (refereegranskat)abstract
    • The mechanisms that predispose to hypertension, coronary artery disease (CAD) and type 2 diabetes (T2D) in individuals of normal weight are poorly understood. In contrast, in monogenic primary lipodystrophy - a reduction in subcutaneous adipose tissue - it is clear that it is adipose dysfunction that causes severe insulin resistance (IR), hypertension, coronary artery disease and type 2 diabetes. We aimed to test the hypothesis that common alleles associated with insulin resistance also influence the wider clinical and biochemical profile of monogenic insulin resistance. We selected 19 common genetic variants associated with fasting insulin based measures of insulin resistance. We used hierarchical clustering and results from genome wide association studies of 8 non-disease outcomes of monogenic insulin resistance, to group these variants. We analysed genetic risk scores against disease outcomes including 12,171 T2D cases, 40,365 CAD cases and 69,828 individuals with blood pressure measurements. Hierarchical clustering identified 11 variants associated with a metabolic profile consistent with a common, subtle, form of lipodystrophy. A genetic risk score consisting of these 11 IR risk alleles was associated with higher triglycerides (ß=0.018; p=4x10(-29)), lower HDL cholesterol (ß=-0.020; p=7x10(-37)), greater hepatic steatosis (ß=0.021; p=3x10(-4)) higher alanine transaminase (ß=0.002; p=3x10(-5)), lower SHBG (ß=-0.010; p=9x10(-13)) and lower adiponectin (ß=-0.015; p=2x10(-26)). The same risk alleles were associated with lower BMI (per-allele ß=-0.008; p=7x10(-8)), and increased visceral-to-subcutaneous adipose tissue ratio (ß=-0.015; p=6x10(-7)). Individuals carrying >= 17 fasting insulin raising alleles (5.5% population) were slimmer (0.30 kgm(-2)) but at increased risk of T2D (odds ratio [OR] 1.46, per-allele p=5x10(-13)), CAD (OR 1.12, per-allele p=1x10(-5)), and increased blood pressure (systolic and diastolic blood pressure of 1.21 mmHg (per-allele p=2x10(-5)), and 0.67 mmHg (per-allele p=2x10(-4)), respectively, compared to individuals carrying <=9 risk alleles (5.5% population). Our results provide genetic evidence for a link between the three diseases of the "metabolic syndrome" and point to reduced subcutaneous adiposity as a central mechanism.
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2.
  • Dimas, Antigone S, et al. (författare)
  • Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.
  • 2014
  • Ingår i: Diabetes. - : American Diabetes Association. - 1939-327X .- 0012-1797. ; 63:6, s. 2158-2171
  • Tidskriftsartikel (refereegranskat)abstract
    • Patients with established type 2 diabetes display both beta-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 non-diabetic subjects with basal measures, and 17,327 with dynamic measures. We employed 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 (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 (including TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without detectable change in fasting glucose. The final group contained twenty 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.
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