| Title: | Genomic risk score provides predictive performance for type 2 diabetes in the UK biobank |
| Journal: | Acta Diabetologica |
| Published: | 3 Jan 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/33392712/ |
| DOI: | https://doi.org/10.1007/s00592-020-01650-1 |
| Citations: | 13 (8 in last 2 years) as of 8 Aug 2024 |
Publication 3790
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Abstract
AimsType 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.MethodsUsing 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).ResultsThe 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.ConclusionsThe metaGRS significantly improves T2D prediction ability.</p>
7 Authors
- Xiaolu Chen
- Congcong Liu
- Shucheng Si
- Yunxia Li
- Wenchao Li
- Tonghui Yuan
- Fuzhong Xue
1 Application
| Application ID | Title |
|---|---|
| 51470 | Physical measurement, blood biochemistry, lifestyle, environmental exposure: causality, gene-environment interaction in relation to metabolic diseases and cancer risk. |
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