Goto A, Noda M, Goto M, Yasuda K, Mizoue T, Yamaji T, Sawada N, Iwasaki M, Inoue M, Tsugane S. 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.
Diabet Med 2018;
35:602-611. [PMID:
29444352 DOI:
10.1111/dme.13602]
[Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/09/2018] [Indexed: 01/05/2023]
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.
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