通过整合H-NMR代谢和临床特征,开发一种基于机器学习的双极性障碍复发的预测模型
Yan Ren1, Tingting Zheng2, Ruiyuan Liu3
1Department of Psychiatry, The Fifth Hospital of Shanxi Medical University, Taiyuan, 030001, China; The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, 030001, China; Department of Psychiatry, Shanxi Provincial People's Hospital, Taiyuan, 030000, China.
Journal of affective disorders
|December 10, 2025
概括
通过将临床数据与代谢学相结合,可以更好地预测双相情感障碍 (BD) 复发. 这种方法识别了关键的代谢物和临床因素,为个性化干预提供了对疾病机制的新见解.
科学领域:
- 精神病学是一个精神病学.
- 代谢学 代谢学 代谢学
- 生物标志物发现发现
背景情况:
- 双极性障碍 (BD) 是一种具有高复发率的慢性疾病.
- 目前BD复发的预测模型具有有限的临床实用性.
- 了解BD复发机制对于有效治疗至关重要.
研究的目的:
- 整合代谢和临床数据,以改善BD复发预测.
- 为了确定与BD复发相关的新生物标志物.
- 为了深入了解BD复发的潜在生物机制.
主要方法:
- 质子核磁共振 (1H NMR) 代谢学被应用于89名BD患者的队列.
- 多维临床变量与代谢数据相结合.
- 机器学习技术,包括PLS-DA,OPLS-DA和LASSO-逻辑回归,用于分析和模型构建.
主要成果:
- 确定了6个复发的重要预测因素:疾病持续时间,负面生活事件,睡眠障碍,乙,O-乙糖蛋白和胆酸盐.
- 一个多式预测模型取得了强的性能 (训练AUC=0.897,验证AUC=0.846).
- 代谢分析显示了脂生物合成途径的调节失调,这表明复发患者的膜代谢发生了改变.
结论:
- 结合代谢和临床标志物显著提高了BD复发的预测.
- 这种综合方法为BD病理生理学提供了新的见解.
- 这些发现支持开发针对双相情感障碍的个性化干预策略.
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