Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Oct;31(10):2615-2626.
doi: 10.1002/oby.23846. Epub 2023 Sep 3.

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

Ninghao Huang  1 , Wendi Xiao  1 , Jun Lv  1   2 , Canqing Yu  1   2 , Yu Guo  3   4 , Pei Pei  2 , Ling Yang  5   6 , Iona Y Millwood  5   6 , Robin G Walters  5   6 , Yiping Chen  5   6 , Huaidong Du  5   6 , Daniel Avery  6 , Tingting Ou  7 , Junshi Chen  8 , Zhengming Chen  6 , Tao Huang  1   9   10 , Liming Li  1   2 ; China Kadoorie Biobank Collaborative Group*
Affiliations

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

Ninghao Huang et al. Obesity (Silver Spring). 2023 Oct.

Abstract

Objective: Type 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.

Methods: This 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.

Results: An 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.

Conclusions: This 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.

PubMed Disclaimer

References

REFERENCES

    1. Berg DD, Wiviott SD, Scirica BM, et al. A biomarker-based score for risk of hospitalization for heart failure in patients with diabetes. Diabetes Care. 2021;44(11):2573-2581.
    1. Wilkinson L, Yi N, Mehta T, Judd S, Garvey WT. Development and validation of a model for predicting incident type 2 diabetes using quantitative clinical data and a Bayesian logistic model: a nationwide cohort and modeling study. PLoS Med. 2020;17(8):e1003232. doi:10.1371/journal.pmed.1003232
    1. Mullee A, Romaguera D, Pearson-Stuttard J, et al. Association between soft drink consumption and mortality in 10 European countries. JAMA Intern Med. 2019;179(11):1479-1490.
    1. Umpierrez GE, Pasquel FJ. Management of inpatient hyperglycemia and diabetes in older adults. Diabetes Care. 2017;40(4):509-517.
    1. Singer RA, Arnes L, Cui Y, et al. The long noncoding RNA Paupar modulates PAX6 regulatory activities to promote alpha cell development and function. Cell Metab. 2019;30(6):1091-1106.e8.

Publication types

MeSH terms

LinkOut - more resources