Yaning Zhang 张亚宁, Qiwen Zheng 郑启文, Qili Qian 钱其溧, Na Yuan 苑娜, Tianzi Liu 刘天资, Xingjian Gao 高行健, Xiu Fan 凡秀, Youkun Bi 毕友坤, Guangju Ji 姬广聚, Peilin Jia 贾佩林, Sijia Wang 汪思佳, Fan Liu 刘凡, Changqing Zeng 曾长青
{"title":"中国人群超重相关高血压的综合多基因和表观遗传风险评分。","authors":"Yaning Zhang 张亚宁, Qiwen Zheng 郑启文, Qili Qian 钱其溧, Na Yuan 苑娜, Tianzi Liu 刘天资, Xingjian Gao 高行健, Xiu Fan 凡秀, Youkun Bi 毕友坤, Guangju Ji 姬广聚, Peilin Jia 贾佩林, Sijia Wang 汪思佳, Fan Liu 刘凡, Changqing Zeng 曾长青","doi":"10.1093/gpbjnl/qzaf048","DOIUrl":null,"url":null,"abstract":"<p><p>Overweight-related hypertension (OrH), defined by the coexistence of excess body weight and hypertension (HTN), is an increasing health concern elevating cardiovascular disease risks. This study evaluated the prediction performance of polygenic risk scores (PRS) and methylation risk scores (MRS) for OrH in 7605 Chinese participants from two cohorts: the Chinese Academy of Sciences (CAS) and the National Survey of Physical Traits (NSPT). In 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 NSPT cohort and in broader Chinese 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 accuracies for obesity (AUC = 0.70), HTN (AUC = 0.73), and OrH (AUC = 0.78). Combining PRS and MRS further boosted prediction accuracy to the AUC of 0.77, 0.76, and 0.80, 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, respectively. 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.</p>","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Integrative Polygenic and Epigenetic Risk Score for Overweight-related Hypertension in Chinese Population.\",\"authors\":\"Yaning Zhang 张亚宁, Qiwen Zheng 郑启文, Qili Qian 钱其溧, Na Yuan 苑娜, Tianzi Liu 刘天资, Xingjian Gao 高行健, Xiu Fan 凡秀, Youkun Bi 毕友坤, Guangju Ji 姬广聚, Peilin Jia 贾佩林, Sijia Wang 汪思佳, Fan Liu 刘凡, Changqing Zeng 曾长青\",\"doi\":\"10.1093/gpbjnl/qzaf048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Overweight-related hypertension (OrH), defined by the coexistence of excess body weight and hypertension (HTN), is an increasing health concern elevating cardiovascular disease risks. This study evaluated the prediction performance of polygenic risk scores (PRS) and methylation risk scores (MRS) for OrH in 7605 Chinese participants from two cohorts: the Chinese Academy of Sciences (CAS) and the National Survey of Physical Traits (NSPT). In 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 NSPT cohort and in broader Chinese 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 accuracies for obesity (AUC = 0.70), HTN (AUC = 0.73), and OrH (AUC = 0.78). Combining PRS and MRS further boosted prediction accuracy to the AUC of 0.77, 0.76, and 0.80, 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, respectively. 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.</p>\",\"PeriodicalId\":94020,\"journal\":{\"name\":\"Genomics, proteomics & bioinformatics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genomics, proteomics & bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/gpbjnl/qzaf048\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genomics, proteomics & bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/gpbjnl/qzaf048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Integrative Polygenic and Epigenetic Risk Score for Overweight-related Hypertension in Chinese Population.
Overweight-related hypertension (OrH), defined by the coexistence of excess body weight and hypertension (HTN), is an increasing health concern elevating cardiovascular disease risks. This study evaluated the prediction performance of polygenic risk scores (PRS) and methylation risk scores (MRS) for OrH in 7605 Chinese participants from two cohorts: the Chinese Academy of Sciences (CAS) and the National Survey of Physical Traits (NSPT). In 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 NSPT cohort and in broader Chinese 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 accuracies for obesity (AUC = 0.70), HTN (AUC = 0.73), and OrH (AUC = 0.78). Combining PRS and MRS further boosted prediction accuracy to the AUC of 0.77, 0.76, and 0.80, 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, respectively. 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.