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。

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
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引用次数: 0

摘要

超重相关性高血压(Overweight-related hypertension, OrH)是由超重和高血压(HTN)共存所定义的,是一个日益增加的健康问题,可增加心血管疾病的风险。本研究评估了多基因风险评分(PRS)和甲基化风险评分(MRS)对来自中国科学院(CAS)和全国体格特征调查(NSPT)两个队列的7605名中国参与者OrH的预测效果。在主要由学者组成的CAS队列中,男性的肥胖、HTN和OrH患病率明显高于女性,代谢综合征指标也较差。这种差异在NSPT队列和更广泛的中国研究中不太明显。在10种PRS方法中,与仅使用年龄和性别的基线模型(AUC = 0.55-0.71)相比,PRS- csx方法最有效,提高了肥胖[曲线下面积(AUC) = 0.75]、HTN (AUC = 0.74)和OrH (AUC = 0.75)的预测精度。同样,基于最小绝对收缩和选择算子(LASSO)的MRS模型提高了肥胖(AUC = 0.70)、HTN (AUC = 0.73)和OrH (AUC = 0.78)的预测精度。结合PRS和MRS进一步提高了预测精度,AUC分别为0.77、0.76和0.80。这些模型将个体分为高(>.6)或低(< 0.1)风险类别,分别覆盖了肥胖的59.95%、HTN的31.75%和OrH的43.89%。我们的研究结果强调了男性学者中较高的OrH风险,强调了代谢和生活方式因素对MRS预测的影响,并强调了多组学方法在加强风险分层方面的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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