Genome‐wide polygenic risk score, cardiometabolic risk factors, and type 2 diabetes mellitus in the Chinese population

Author:

Huang Ninghao1,Xiao Wendi1,Lv Jun12,Yu Canqing12ORCID,Guo Yu34,Pei Pei2,Yang Ling56,Millwood Iona Y.56,Walters Robin G.56,Chen Yiping56,Du Huaidong56,Avery Daniel6ORCID,Ou Tingting7,Chen Junshi8,Chen Zhengming6,Huang Tao1910ORCID,Li Liming12,

Affiliation:

1. Department of Epidemiology and Biostatistics, School of Public Health Peking University Beijing China

2. Peking University Centre for Public Health and Epidemic Preparedness & Response Beijing China

3. Fuwai Hospital, Chinese Academy of Medical Sciences Beijing China

4. National Center for Cardiovascular Diseases Beijing China

5. Medical Research Council Population Health Research Unit at the University of Oxford Oxford UK

6. Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health University of Oxford Oxford UK

7. Noncommunicable Diseases Prevention and Control Department Hainan Centers for Disease Control and Prevention Hainan China

8. China National Centre for Food Safety Risk Assessment Beijing China

9. Centre for Intelligent Public Health, Academy for Artificial Intelligence Peking University Beijing China

10. Key Laboratory of Epidemiology of Major Diseases (Peking University) Ministry of Education Beijing China

Abstract

AbstractObjectiveType 2 diabetes (T2D) is caused by both genetic and cardiometabolic risk factors. However, the magnitude of the genetic predisposition of T2D in the Chinese population remains largely unknown.MethodsThis study included 93,488 participants from the China Kadoorie Biobank, and multiple polygenic risk scores (PRS) were calculated. A common cardiometabolic risk score (CRS) using smoking, alcohol consumption, physical activity, diet, obesity, blood pressure, and blood lipids was constructed to investigate the effects of cardiometabolic risk factors on T2D. Furthermore, an equation based on ideal PRS, CRS, and their interaction was established to explore the combined effects on T2D.ResultsAn ideally fitting PRS model (variance explained, R2 = 7.6%) was reached based on multiple PRS calculation methods. An additive interaction between PRS and CRS (coefficient = 28%, 95% CI: 0.20–0.36, p < 0.001) was found. The R2 of the T2D predictive model could increase to 8.3% when CRS and the interaction terms of PRS × CRS were considered. In the etiological composition of T2D, the ratio of genetic risk effect, cardiometabolic risk effect, and interaction between genetic and cardiometabolic factors was 67:16:17.ConclusionsThis study identified an ideally fitting PRS model for identifying and predicting the risk of T2D suitable for the Chinese population. The quantified proportional structure of genetic risk factors, cardiometabolic risk factors, and their interaction was detected, which elucidated the critical effect of genetic factors.

Funder

Department of Science and Technology for Social Development

National Key Research and Development Program of China

National Natural Science Foundation of China

Wellcome Trust

Publisher

Wiley

Subject

Nutrition and Dietetics,Endocrinology,Endocrinology, Diabetes and Metabolism,Medicine (miscellaneous)

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