European Genomic Institute for Diabetes, FR 3508, Lille, France,
Diabetologia. 2014 Aug;57(8):1601-10. doi: 10.1007/s00125-014-3277-x. Epub 2014 Jun 4.
AIMS/HYPOTHESIS: Genome-wide association studies have firmly established 65 independent European-derived loci associated with type 2 diabetes and 36 loci contributing to variations in fasting plasma glucose (FPG). Using individual data from the Data from an Epidemiological Study on the Insulin Resistance Syndrome (DESIR) prospective study, we evaluated the contribution of three genetic risk scores (GRS) to variations in metabolic traits, and to the incidence and prevalence of impaired fasting glycaemia (IFG) and type 2 diabetes.
Three GRS (GRS-1, 65 type 2 diabetes-associated single nucleotide polymorphisms [SNPs]; GRS-2, GRS-1 combined with 24 FPG-raising SNPs; and GRS-3, FPG-raising SNPs alone) were analysed in 4,075 DESIR study participants. GRS-mediated effects on longitudinal variations in quantitative traits were assessed in 3,927 nondiabetic individuals using multivariate linear mixed models, and on the incidence and prevalence of hyperglycaemia at 9 years using Cox and logistic regression models. The contribution of each GRS to risk prediction was evaluated using the C-statistic and net reclassification improvement (NRI) analysis.
The two most inclusive GRS were significantly associated with increased FPG (β = 0.0011 mmol/l per year per risk allele, p GRS-1 = 8.2 × 10(-5) and p GRS-2 = 6.0 × 10(-6)), increased incidence of IFG and type 2 diabetes (per allele: HR GRS-1 1.03, p = 4.3 × 10(-9) and HR GRS-2 1.04, p = 1.0 × 10(-16)), and the 9 year prevalence (OR GRS-1 1.13 [95% CI 1.10, 1.17], p = 1.9 × 10(-14) for type 2 diabetes only; OR GRS-2 1.07 [95% CI 1.05, 1.08], p = 7.8 × 10(-25), for IFG and type 2 diabetes). No significant interaction was found between GRS-1 or GRS-2 and potential confounding factors. Each GRS yielded a modest, but significant, improvement in overall reclassification rates (NRI GRS-1 17.3%, p = 6.6 × 10(-7); NRI GRS-2 17.6%, p = 4.2 × 10(-7); NRI GRS-3 13.1%, p = 1.7 × 10(-4)).
CONCLUSIONS/INTERPRETATION: Polygenic scores based on combined genetic information from type 2 diabetes risk and FPG variation contribute to discriminating middle-aged individuals at risk of developing type 2 diabetes in a general population.
目的/假设:全基因组关联研究已经确定了 65 个与 2 型糖尿病相关的独立欧洲来源的位点和 36 个与空腹血浆葡萄糖(FPG)变化相关的位点。利用来自流行病学胰岛素抵抗综合征研究(DESIR)前瞻性研究的个体数据,我们评估了三种遗传风险评分(GRS)对代谢特征变化的影响,以及对空腹血糖受损(IFG)和 2 型糖尿病发生率和患病率的影响。
在 4075 名 DESIR 研究参与者中分析了三种 GRS(GRS-1,65 个与 2 型糖尿病相关的单核苷酸多态性[SNP];GRS-2,GRS-1 与 24 个升高 FPG 的 SNP 相结合;GRS-3,升高 FPG 的 SNP 单独)。在 3927 名非糖尿病个体中,使用多元线性混合模型评估 GRS 对纵向定量特征变化的影响,使用 Cox 和逻辑回归模型评估 9 年内高血糖的发生率和患病率。使用 C 统计量和净重新分类改善(NRI)分析评估每个 GRS 对风险预测的贡献。
两个最全面的 GRS 与 FPG 升高显著相关(每个风险等位基因每年每 mmol/l 增加 0.0011,p GRS-1 = 8.2×10(-5)和 p GRS-2 = 6.0×10(-6)),IFG 和 2 型糖尿病的发生率增加(每个等位基因:HR GRS-1 1.03,p = 4.3×10(-9)和 HR GRS-2 1.04,p = 1.0×10(-16)),9 年的患病率(OR GRS-1 1.13 [95%CI 1.10,1.17],p = 1.9×10(-14),仅用于 2 型糖尿病;OR GRS-2 1.07 [95%CI 1.05,1.08],p = 7.8×10(-25),用于 IFG 和 2 型糖尿病)。未发现 GRS-1 或 GRS-2 与潜在混杂因素之间存在显著交互作用。每个 GRS 都导致整体重新分类率的适度但显著提高(NRI GRS-1 17.3%,p = 6.6×10(-7);NRI GRS-2 17.6%,p = 4.2×10(-7);NRI GRS-3 13.1%,p = 1.7×10(-4))。
结论/解释:基于 2 型糖尿病风险和 FPG 变化的遗传信息综合的多基因评分有助于区分一般人群中发生 2 型糖尿病风险的中年个体。