弥合基因组公平差距与美国印第安人的上下文增强风险分层:坚强的心脏研究
medRxiv : the preprint server for health sciences
|February 23, 2026
概括
将生活方式和临床数据与多基因分数 (PGS) 整合,可以改善心脏代谢疾病风险预测,特别是在代表性不足的美洲印第安人群体中. 这种方法增强了精准医学的公平性.
科学领域:
- 基因组学就是基因组学.
- 心血管流行病学心血管流行病学
- 精准医学是一门精准的医学.
背景情况:
- 多基因分数 (PGS) 对疾病风险分层有价值,但往往缺乏跨不同人群的可移植性.
- 美国印第安人患有高心血管疾病 (CVD) 负担,但在基因组研究中代表性不足,阻碍了公平的精准医学.
- PGS在祖先之间具有有限的可移植性限制了它们的临床实用性,特别是在代表性不足的群体中.
研究的目的:
- 评估将生活方式和临床背景变量与PGS整合是否可以改善欧洲和美洲印第安人队伍的心脏代谢风险预测.
- 评估基因背景相互作用对血压,冠心病 (CHD) 和中风风险预测模型的影响.
主要方法:
- 将仅仅基因模型与包括上下文变量和基因-上下文相互作用的完整模型进行比较.
- 利用了来自424,622名欧洲参与者 (英国生物库) 和3,157名美洲印第安人参与者的数据 (强心研究).
- 评估心脏代谢特征的预测准确性和模型歧视.
主要成果:
- 整合上下文变量显著提高了欧洲和美洲印第安人队伍中心脏代谢特征的预测准确度.
- 结合上下文和遗传相互作用的增强模型显示,与现有的临床模型相比,美洲印第安人中CHD的歧视明显改善.
- 基因背景相互作用在完善风险预测方面发挥了关键作用,特别是在代表性不足的人群中.
结论:
- 建模遗传倾向和可修改因素 (生活方式,临床背景) 之间的相互作用可以减轻从不完美的PGS可转移性预测能力的损失.
- 这种综合方法为为代表性不足的人群开发更公平,更有效的精准医学工具提供了一个有希望的战略.
- 通过整合上下文变量来提高PGS可移植性对于促进心血管疾病预防中的健康公平至关重要.
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