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Diabet Med. 2018 May;35(5):602-611. doi: 10.1111/dme.13602. Epub 2018 Mar 12.

Predictive performance of a genetic risk score using 11 susceptibility alleles for the incidence of Type 2 diabetes in a general Japanese population: a nested case-control study.

Author information

1
Epidemiology and Prevention Group, Centre for Public Health Sciences, National Cancer Centre, Tokyo.
2
Department of Endocrinology and Diabetes, Saitama Medical University, Saitama.
3
Department of Diabetes and Endocrinology, JCHO Tokyo Yamate Medical Centre, Tokyo.
4
Department of Metabolic Disorder, Diabetes Research Centre, National Centre for Global Health and Medicine, Tokyo, Japan.
5
Department of Epidemiology and Prevention, National Centre for Global Health and Medicine, Tokyo, Japan.

Abstract

AIMS:

To assess the predictive ability of a genetic risk score for the incidence of Type 2 diabetes in a general Japanese population.

METHODS:

This prospective case-control study, nested within a Japan Public Health Centre-based prospective study, included 466 participants with incident Type 2 diabetes over a 5-year period (cases) and 1361 control participants, as well as 1463 participants with existing diabetes and 1463 control participants. Eleven susceptibility single nucleotide polymorphisms, identified through genome-wide association studies and replicated in Japanese populations, were analysed.

RESULTS:

Most single nucleotide polymorphism loci showed directionally consistent associations with diabetes. From the combined samples, one single nucleotide polymorphism (rs2206734 at CDKAL1) reached a genome-wide significance level (odds ratio 1.28, 95% CI 1.18-1.40; P = 1.8 × 10-8 ). Three single nucleotide polymorphisms (rs2206734 in CDKAL1, rs2383208 in CDKN2A/B, and rs2237892 in KCNQ1) were nominally significantly associated with incident diabetes. Compared with the lowest quintile of the total number of risk alleles, the highest quintile had a higher odds of incident diabetes (odds ratio 2.34, 95% CI 1.59-3.46) after adjusting for conventional risk factors such as age, sex and BMI. The addition to the conventional risk factor-based model of a genetic risk score using the 11 single nucleotide polymorphisms significantly improved predictive performance; the c-statistic increased by 0.021, net reclassification improved by 6.2%, and integrated discrimination improved by 0.003.

CONCLUSIONS:

Our prospective findings suggest that the addition of a genetic risk score may provide modest but significant incremental predictive performance beyond that of the conventional risk factor-based model without biochemical markers.

PMID:
29444352
DOI:
10.1111/dme.13602

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