整合代谢学,蛋白质学和传统的风险因素来预测睡眠障碍和阐明潜在的生物学途径
Ronghui Zhang1, Jia Luo1, Tong Wang1
1Department of Epidemiology and Health Statistics, the School of Public Health of Qingdao University, Qingdao, Shandong Province, China.
Journal of affective disorders
|July 2, 2025
概括
将代谢学和蛋白质学与传统因素相结合,显著改善了睡眠障碍 (SD) 的预测. 这种方法增强了风险分层,并提供了对SD发展的生物学见解.
科学领域:
- 发现生物标志物的发现.
- 基因组学和蛋白质组学
- 睡眠医学 睡眠医学
背景情况:
- 睡眠障碍 (SD) 普遍且异质,对健康构成重大挑战.
- 传统的SD风险因素的预测准确性有限.
- 需要新的方法来改善SD风险预测和了解潜在的生物学.
研究的目的:
- 调查将新陈代谢和蛋白质组数据与传统风险因素结合起来,提高睡眠障碍 (SD) 预测的实用性.
- 确定与SD风险相关的新代谢物和蛋白质生物标志物.
- 开发和验证SD的综合预测模型.
主要方法:
- 利用了来自26569名参与者的英国生物库数据,进行了代谢和蛋白质组测量.
- 使用Cox比例危险和LASSO-Cox回归来识别生物标志物并构建风险分数 (MetaS,ProS,TraS).
- 开发和评估了SD风险的综合预测模型,包括调解分析.
主要成果:
- 确定了8种代谢物和34种蛋白质作为关键的SD生物标志物.
- 组合模型 (MetaS,ProS,TraS) 的C指数为0.78,显示出更好的预测和风险分层.
- 组合得分组中高风险个体的SD风险增加了4.5倍;代谢和蛋白质分数对风险预测有显著的贡献.
结论:
- 将代谢学和蛋白质学与传统的风险因素相结合,大大提高了睡眠障碍风险预测.
- 这种综合方法为SD的机制提供了有价值的初步生物学见解.
- 开发的风险评分和生物标志物可以帮助个性化风险评估和睡眠障碍的管理.
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