机器学习揭示了环境因素和心脏代谢疾病之间在多基因风险评分中的异质关联
Tatsuhiko Naito1,2,3, Kosuke Inoue4,5, Shinichi Namba6,7,8
1Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan. tnaito0315@gmail.com.
Communications medicine
|September 20, 2024
概括
多基因风险评分 (PRS) 可能并不总是识别出从疾病预防中获益最多的个体. 这项研究使用因果森林来预测个体疾病风险,发现与肥胖和吸烟等环境因素的各种关联.
科学领域:
- 遗传学和流行病学
- 精准医学是一门精准的医学.
- 因果推理因果推理
背景情况:
- 多基因风险评分 (PRS) 对精准医学有希望.
- 目前尚不清楚高PRS个体是否从预防性干预中获益最多.
- 现在从方法上来说,对治疗效果的个体级预测是可行的.
研究的目的:
- 探索PRS和个体疾病风险之间的关系.
- 研究肥胖和吸烟等环境因素如何与不同PRS的疾病风险相关联.
- 评估PRS在识别可能受益于预防策略的个人的实用性.
主要方法:
- 用因果森林建模来分析PRS和疾病风险.
- 为了验证因果森林方法,进行了模拟.
- 这项研究分析了英国生物银行 (UKB) 和日本生物银行 (BBJ) 的数据,分析了与肥胖和吸烟有关的心脏代谢疾病 (CAD,T2D).
主要成果:
- 观察到PRS,环境暴露 (肥胖,吸烟) 和疾病风险之间的异质关联.
- 在UKB中发现肥胖和T2D风险,在BBJ中发现吸烟和CAD风险之间存在强烈的正相关性.
- 然而,大多数关系是弱或负面的,这表明PRS高的群体可能不会从预防中获得最大的收益.
结论:
- 与特定暴露相关的疾病风险的个体级预测对于精准医学至关重要.
- 这些发现强调了PRS在预测环境因素预防的好处方面的复杂性.
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