在高血压预测中,多基因比临床危险因素的价值
Matti Vuori1, Matti O Ruuskanen2,3, Pekka Jousilahti2
1Department of Internal Medicine, University of Turku and Turku University Hospital, Finland (M.V., L.-F.Y., A.K., T.N.).
Hypertension (Dallas, Tex. : 1979)
|December 11, 2025
概括
将多基因风险评分添加到临床因素中略有改善了高血压预测. 在这项研究中,其他OMIC数据,如代谢量和肠道微生物群,并没有提高风险预测.
科学领域:
- 心血管疾病研究研究
- 遗传学和精准医学 遗传学和精准医学
- 生物标志物发现发现
背景情况:
- 高血压预测从omics数据中受益,但跨多omics的比较缺乏.
- 对于高血压预测的临床风险因素而言,多组学的附加值仍然不清楚.
研究的目的:
- 在高血压预测中比较各种多组数据的预测能力.
- 确定多组学数据是否提供超出临床风险因素的额外预测价值.
主要方法:
- 在2573个非高血压个体中,评估了使用多组学 (多基因风险评分,代谢学,肠道微生物群) 的临床数据.
- 利用交叉验证的机器学习模型来预测事件高血压.
- 使用曲线下的面积 (AUC) 评估模型性能.
主要成果:
- 最好的模型将临床数据与多基因风险评分结合起来,达到最高的AUC (0.735).
- 经过对临床因素进行调整后,多基因风险得分显著增加了高血压的几率 (29%).
- 当添加到临床因素时,新陈代谢和微生物群数据没有改善风险预测.
结论:
- 用多基因风险评分增强的临床模型为发生性高血压提供了最好的预测.
- 多基因风险评分为高血压风险评估提供了与已确定的临床风险因素相比有限的增值值.
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