早期慢性阻塞性肺病的多奥米克特征
Bolun Li1, Jiangfeng Liu2, Yinghao Cao2
1State Key Laboratory of Respiratory Health and Multimorbidity, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
这项研究使用多omics分析确定了早期慢性阻塞性肺病 (COPD) 的新生物标志物. 这些标志物改善了早期的COPD诊断,并根据炎症和血管路径揭示了不同的患者亚组.
科学领域:
- 肺部医学 肺部医学
- 生物标志物发现发现
- 多主题研究研究 多主题研究
背景情况:
- 慢性阻塞性肺病 (COPD) 是全球主要的死亡原因,早期诊断对管理至关重要.
- 目前的早期COPD (ECOPD) 诊断依赖于肺功能和吸烟史,这可能不完全反映疾病的进展.
- 黄金指南倡导生物标记者超过临床症状,以改善早期检测.
研究的目的:
- 通过多学科的方法探索ECOPD的生物特征.
- 为了识别与ECOPD相关的血蛋白和代谢物签名.
- 调查多omics数据对于ECOPD分组和肺功能预测的潜力.
主要方法:
- 88名ECOPD患者和88名健康对照患者的血样本的蛋白质组学和代谢学分析.
- 无变逻辑回归和基因组丰富分析以确定重要的蛋白质.
- 机器学习模型和相似性网络融合用于子组分析和预测.
主要成果:
- 蛋白质学确定了与ECOPD相关的248种蛋白质,主要涉及炎症.
- 代谢组学将137种代谢物与ECOPD联系起来.
- 一个多omics方法最好地预测了肺功能 (R2=0.74),而蛋白质组单独诊断了ECOPD (AUC=0.949).
- 确定了两个ECOPD亚组:一个是由炎症免疫反应驱动的,另一个是由血静/血管光滑肌肉标志物驱动的.
结论:
- 多学科整合为区分ECOPD子组提供了一个强大的策略.
- 已识别的蛋白质和代谢物签名对早期ECOPD诊断和风险分层有希望.
- 这项研究促进了对ECOPD异质性和潜在治疗点的理解.
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