| Application: | 51470, Physical measurement, blood biochemistry, lifestyle, environmental exposure: causality, gene-environment interaction in relation to metabolic diseases and cancer risk. |
| Title: | Genomic risk score provides predictive performance for type 2 diabetes in the UK biobank |
| Size: | 530 B |
| Archived: | 6 Sep 2021 |
| Stability: | Complete |
| Personal: | No individual-level data |
Return 3789
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Notes
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.
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.
Application 51470
Physical measurement, blood biochemistry, lifestyle, environmental exposure: causality, gene-environment interaction in relation to metabolic diseases and cancer risk.
Cancer and metabolic diseases account for a large proportion of the global burden of disease. Epidemiological association studies reported a variety of environmental risk factors and risk genes for these diseases. However, limited studies have definite causal effects on metabolic disease or cancer, except for few widely recognized risk factors such as smoking for lung cancer and high-calorie diet for obesity. In fact, the effects of these exposures and genes on the diseases are very complicated including direct effects, indirect effects, mediating effects, gene-environment interactions, gene pleiotropy, effect modification and so on. Meanwhile, routine observational studies inevitably suffer from confounding or reverse causality. Besides, metabolic diseases and cancer may also share common risk factors with each other, however, the causal effect and pathogenic mechanism of these factors may be highly heterogeneous. Therefore, for each potential risk factor, if the causal association with a specific disease was determined, we could intervene the exposure levels of risk factors promptly to reduce the morbidity or mortality of metabolic diseases and cancer. So we aimed to explore the potential causal effect of specific exposure or pathway (physical measurement, blood biochemistry, lifestyle, environment, genes, interactions) on chronic metabolic diseases or cancer and to provide evidence for intervention and prevention. Then we also expect to develop some novel causal inference methods and effective disease prediction methods. We intend to perform our research for the duration of three years. The study may have certain practicality values for public health and further research if our results are supported. Study on both genes and environmental exposures would provide strong evidence to clarify the relationship between various exposures and diseases. Causal findings may contribute to identify novel biological pathways for disease prevention, diagnosis and treatment or provide suitable prediction indicators. This is of great significance to public health for cancer and metabolic diseases controlling. Additionally, we may also provide new analytical strategies and methods, which may have some application values for further research.
| Lead investigator: | Professor Fuzhong Xue |
| Lead institution: | Shandong University |
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| 3795 | 51470 | Genetically Determined Chronic Low-Grade Inflammation and Hundreds of Health Outcomes in the UK Biobank and the FinnGen Population: A Phenome-Wide Mendelian Randomization Study | 6 Sep 2021 |
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1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 3790 | Genomic risk score provides predictive performance for type 2 diabetes in the UK biobank | Chen X et al. | 2021 | Acta Diabetol |
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