Xiaolu Chen1, Congcong Liu1, Shucheng Si1, Yunxia Li1, Wenchao Li1, Tonghui Yuan1, Fuzhong Xue2,3. 1. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, No.44 Wenhuaxi Road, Jinan, 250012, People's Republic of China. 2. Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, No.44 Wenhuaxi Road, Jinan, 250012, People's Republic of China. xuefzh@sdu.edu.cn. 3. Institute for Medical Dataology, Shandong University, No.12550 Erhuandong Road, Jinan, 250002, People's Republic of China. xuefzh@sdu.edu.cn.
Abstract
AIMS: Type 2 diabetes (T2D) is affected by a combination of genetic and environmental factors. However, the comprehensive genomic risk scores (GRSs) for T2D prediction have not been evaluated. METHODS: Using a meta-scoring approach, we developed a metaGRS for T2D; T2D-related traits consist of 1,692 genetic variants in the UK Biobank training set (n = 40,423 + 7,558 events) and evaluate this score in the validation set (n = 303,053). RESULTS: The hazard ratio (HR) for T2D was 1.32 (95% confidence interval [CI]: 1.29-1.35) per standard deviation of metaGRS and was larger than previously published T2D-GRS. Individuals, in the top 25% of metaGRS, have an HR of 2.08 (95%CI: 1.93-2.23) compared with those in the bottom 25%. The addition of metaGRS to all conventional risk factors significantly increased the AUC (P < 0.001). Adding metaGRS to all conventional risk factors significantly improved the reclassification accuracy (continuous net reclassification improvement = 11.8%, 95%CI: 9.2%-14.2%). All analyses adjusted for age, sex, and 10PCs. CONCLUSIONS: The metaGRS significantly improves T2D prediction ability.
AIMS: Type 2 diabetes (T2D) is affected by a combination of genetic and environmental factors. However, the comprehensive genomic risk scores (GRSs) for T2D prediction have not been evaluated. METHODS: Using a meta-scoring approach, we developed a metaGRS for T2D; T2D-related traits consist of 1,692 genetic variants in the UK Biobank training set (n = 40,423 + 7,558 events) and evaluate this score in the validation set (n = 303,053). RESULTS: The hazard ratio (HR) for T2D was 1.32 (95% confidence interval [CI]: 1.29-1.35) per standard deviation of metaGRS and was larger than previously published T2D-GRS. Individuals, in the top 25% of metaGRS, have an HR of 2.08 (95%CI: 1.93-2.23) compared with those in the bottom 25%. The addition of metaGRS to all conventional risk factors significantly increased the AUC (P < 0.001). Adding metaGRS to all conventional risk factors significantly improved the reclassification accuracy (continuous net reclassification improvement = 11.8%, 95%CI: 9.2%-14.2%). All analyses adjusted for age, sex, and 10PCs. CONCLUSIONS: The metaGRS significantly improves T2D prediction ability.
Entities:
Keywords:
Competing risk model; Genomic risk scores; Net reclassification improvement; Risk factors; Type 2 diabetes
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