基于全血转录组和人工智能的液体活检用于预测冠状动脉化:试点研究
Rosana Poggio1, Gaston A Rodriguez-Granillo2, Florencia De Lillo1
1MultiplAI Health, 184 Cambridge Science Park Rd, Milton, Cambridge CB4 0GA, United Kingdom.
European heart journal. Digital health
|July 24, 2025
概括
分析全血RNA的人工智能 (AI) 模型可以预测冠状动脉化 (CAC). 将人工智能与临床数据相结合,显著提高了这种心血管疾病标志物的预测准确性.
科学领域:
- 心血管疾病研究研究
- 基因组学和生物信息学
- 人工智能在医学中的应用
背景情况:
- 全血RNA表达反映了对组织信号的全身反应,包括来自血管壁的信号.
- 冠状动脉化 (CAC) 是亚临床动脉样硬化和心血管疾病 (CVD) 风险的关键指标.
- 目前用于CAC评估和CVD风险预测的方法存在局限性.
研究的目的:
- 研究通过人工智能 (AI) 分析的全血转录组在预测冠状动脉化 (CAC) 中的有用性.
- 与传统风险模型相比,将转录组数据与临床变量集成的人工智能模型的预测性能进行比较.
主要方法:
- 196名没有已知的心血管疾病的受试者通过计算机断层扫描进行了CAC评估.
- 全血RNA被分离和测序.
- 人工智能模型使用转录和临床变量进行训练,以预测CAC存在 (阿加斯顿分数>0).
主要成果:
- 结合人工智能模型,将转录组数据与临床因素 (年龄,性别,BMI,吸烟,糖尿病,高胆固醇血症) 整合起来,实现了0.92.9的AUC.
- 这种综合模型的预测性能优于仅转录基因 (AUC 0.79),仅临床 (AUC 0.72) 和心血管疾病风险模型 (AUC 0.68) 的预测性能.
- 组合模型的整体准确率为86%,CAC预测的灵敏度为92%和特异性为80%.
结论:
- 一个整合全血转录组数据与临床风险因素的AI模型显示了预测CAC的前景.
- 这种方法提供了超越传统临床模型的增量预测价值.
- 需要进一步的验证研究来证实这些发现.
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