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      Combined effects of 17 common genetic variants on type 2 diabetes risk in a Han Chinese population.

      Diabetologia
      Aged, Alleles, Asian Continental Ancestry Group, genetics, Diabetes Mellitus, Type 2, Female, Genetic Predisposition to Disease, Genetic Variation, Genotype, Humans, Hyperglycemia, Logistic Models, Male, Middle Aged, Odds Ratio, Polymorphism, Single Nucleotide, ROC Curve, Risk

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          Abstract

          The recent advent of genome-wide association studies has considerably accelerated the identification of type 2 diabetes loci. We aimed to investigate the combined effects of multiple genetic variants, alone or in combination with conventional risk factors, on type 2 diabetes and diabetes-related traits in Han Chinese. We genotyped 17 variants in 17 loci in a population-based Han Chinese cohort including 3,210 unrelated individuals. A genetic risk score (GRS) was calculated on the basis of these variants. The discriminatory ability was assessed by the area under the receiver operating characteristics curve. The odds ratio for type 2 diabetes and hyperglycaemia with each GRS point (per risk allele) was 1.18 (95% CI 1.12-1.23, p = 1.3 x 10(-12)) and 1.12 (95% CI 1.09-1.16, p = 7.5 x 10(-14)), respectively. Compared with participants with GRS < or =11.0 (7.63%), those with GRS > or =19.0 (8.87%) had a 4.58-fold higher risk (95% CI 2.49-8.42) of type 2 diabetes. The GRS also showed a significant association with lower beta cell function estimated by HOMA of beta cell function (p = 8.4 x 10(-10)). In addition, we observed significant interactive effects between GRS and BMI on fasting glucose and HbA(1c) levels (p = 0.04 and p = 0.03 for interaction, respectively). Discrimination of diabetes risk was improved (p < 0.001) when the GRS was added to a model including clinical risk factors. The AUCs were 0.62 and 0.77, respectively, for the GRS and conventional clinic risk factors alone, and 0.79 when the GRS was added. In this Han Chinese population, the GRS of 17 combined variants modestly but significantly improved discrimination of the conventional risk factors for type 2 diabetes.

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