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Updated: Sep 18, 2025

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
Published on: May 6, 2022
An Integrative Polygenic and Epigenetic Risk Score for Overweight-related Hypertension in Chinese Population
Yaning Zhang 张亚宁1, Qiwen Zheng 郑启文2,3, Qili Qian 钱其溧4,5
1Henan Academy of Sciences, Zhengzhou 450046, China.
Abstract:
Overweight-related hypertension (OrH), defined by the coexistence of excess body weight and hypertension (HTN), is an increasing health concern elevating cardiovascular disease risks. In this study, we evaluated the prediction performance of polygenic risk scores (PRSs) and methylation risk scores (MRSs) for OrH in 7605 Chinese participants from two cohorts: the Chinese Academy of Sciences (CAS) and the National Survey of Physical Traits (NSPT). In the CAS cohort, which predominantly consists of academics, males showed significantly higher prevalence of obesity, HTN, and OrH, along with worse metabolic syndrome indicators, compared to females. This disparity was less pronounced in the NSPT cohort and in broader Chinese epidemiological studies. Among ten PRS methods, PRS-CSx was the most effective, enhancing prediction accuracy for obesity [area under the curve (AUC) = 0.75], HTN (AUC = 0.74), and OrH (AUC = 0.75), compared to baseline models using only age and sex (AUC = 0.55-0.71). Similarly, least absolute shrinkage and selection operator (LASSO)-based MRS models improved prediction accuracy for obesity (AUC = 0.70), HTN (AUC = 0.73), and OrH (AUC = 0.78). Combining PRS and MRS further boosted prediction accuracy, achieving AUC values of 0.77, 0.76, and 0.80 for obesity, HTN, and OrH, respectively. These models stratified individuals into high (> 0.6) or low (< 0.1) risk categories, covering 59.95% for obesity, 31.75% for HTN, and 43.89% for OrH. Our findings highlight a higher OrH risk among male academics, emphasize the influence of metabolic and lifestyle factors on MRS predictions, and highlight the value of multi-omics approaches in enhancing risk stratification.
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