通过多omics集成,对心力衰竭的深度表型与保存的喷射分数进行了深度表型
Jakob Versnjak1,2, Titus Kuehne1,2,3,4, Pauline Fahjen5
1Institute of Computer-assisted Cardiovascular Medicine, Deutsches Herzzentrum der Charité, Berlin, Germany.
European journal of heart failure
|September 22, 2025
概括
这项研究开发了一种机器学习模型,以在症状出现前几年识别心力衰竭与保留射出分数 (HFpEF) 现型. 通过多omics数据进行早期检测,可以为这种复杂的心血管疾病制定个性化的预防策略.
科学领域:
- 心脏病学 心脏病学
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 保存喷射分数 (HFpEF) 的心力衰竭是最常见的心力衰竭类型,由于其异质性和有限的治疗选择,这给心力衰竭带来了重大挑战.
- 早期识别HFpEF对于实施有效的预防策略至关重要,特别是考虑到其与生活方式相关的并发症的关联.
研究的目的:
- 引入和验证基于机器学习的多学科方法,用于HFpEF的早期检测和表征.
- 整合临床和分子数据,以提高HFpEF的诊断和预后能力.
主要方法:
- 在一个大规模的英国生物库队列 (n=401,917) 上使用表型特征训练了一名受监督的分类人员.
- 模型性能在一个独立的保留集 (n=100,946) 中得到验证,包括具有和没有HFpEF的个体.
- 类似性网络融合 (SNF) 用于根据多omics数据识别不同的HFpEF子组.
主要成果:
- 该分类器在识别HFpEF表型方面取得了很高的准确性,ROC AUC为0.931.
- 该模型检测到那些后来在症状出现前平均6.3年发展HFpEF的人.
- 一个高风险的HFpEF亚组,死亡率高和炎症途径失调,ROC AUC为0.988.8,被确定为高风险HFpEF亚组.
结论:
- 该研究在临床表现前几年,在早期,潜在的可修改阶段成功识别了HFpEF表型.
- 多omics方法为HFpEF复杂性提供了新的见解,并使更精确的风险分层成为可能.
- 早期检测和表征是改善HFpEF预防策略和患者结果的关键.
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