以人工智能为基础的多态学分析揭示了对心血管疾病个性化预测的补充性态学贡献
Yan Luo1,2, Nan Zhang3, Jiannan Yang4,5
1Department of Data Science, City University of Hong Kong, Hong Kong, China.
Nature communications
|February 2, 2026
概括
CardiOmicScore使用深度学习来从蛋白质和代谢数据中预测心血管疾病 (CVD) 风险. 这种多组学方法增强了早期风险预测,有助于精准医学预防心血管疾病.
科学领域:
- 生物医学研究的研究.
- 计算生物学是一种计算生物学.
- 心血管医学 心血管医学
背景情况:
- 心血管疾病 (CVD) 构成了全球健康的重大负担.
- 基因组学,代谢学和蛋白质学为了解心血管疾病风险提供了补充数据.
- 目前的风险预测模型可以通过多组数据来增强.
研究的目的:
- 开发一个深度学习框架 (CardiOmicScore) 用于心血管疾病风险预测.
- 为了生成常见心血管疾病的蛋白质组 (ProScore) 和代谢组 (MetScore) 风险评分.
- 评估ProScore和MetScore的预测性能,单独和与临床数据相结合.
主要方法:
- 利用英国生物库数据,包括2920种蛋白质和168种代谢物.
- 开发了一个多任务深度学习模型,CardiOmicScore.
- 使用C指数对六种常见的CVD进行评估风险预测.
主要成果:
- ProScore和MetScore显示出强大的独立心血管疾病风险预测能力 (C指数:分别为0.69-0.82和0.64-0.74).
- 将多组分数和临床数据结合起来,在诊断前15年内显著改善了心血管疾病风险预测 (C指数增加:0.005-0.102).
- 确定了潜在的CVD相关蛋白质和代谢物,用于生物标志物开发.
结论:
- 集成到临床实践中的多组学分析可以提高早期个性化的心血管疾病风险评估.
- CardiOmicScore提供了对心血管疾病发病的数据驱动的洞察力.
- 这些发现支持开发新的生物标志物和针对心血管疾病初级预防和精准医学的向疗法.
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