炎症在骨质疏松症预测中的关键作用由一个机器学习框架揭示,该框架整合了多源数据
Bo Liu1, Hongli Chang2, Peipei Li2
1Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Frontiers in physiology
|December 26, 2025
概括
这项研究开发了使用炎症标志物来预测骨质疏松症的准确机器学习模型,使得早期检测和有针对性的预防策略成为可能. 这些发现凸显了炎症在骨质疏松病变发生过程中的关键作用.
科学领域:
- 生物医学科学 生物医学科学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 骨质疏松症是一个重要的全球健康问题,诊断延迟.
- 目前的预测模型不足以探索炎症因素的作用.
研究的目的:
- 用机器学习研究炎症标志物对骨质疏松症预测的有用性.
- 为早期骨质疏松症查和预防建立一个高精度模型.
主要方法:
- 来自四个独立队列 (NHANES,CPW-BMI,OP-VC,动物模型) 的综合多源数据.
- 构建了一个具有22个临床特征和炎症标志物的预测系统.
- 采用各种机器学习算法 (例如,RUSBoosted Trees,SVM) 进行分类和回归模型.
- 使用五倍交叉验证和外部数据集验证的模型.
主要成果:
- 机器学习模型显示了骨质疏松症在股骨部部 (AUC=0.9643) 和腰椎 (AUC=0.9685) 的高预测性能.
- 外部验证证实了模型的概括性,在中国队列中预测误差很低 (RMSE=0.681).
- 在骨质疏松症患者中观察到IL-6,TNF-α和IL-1β的血清水平升高.
- 动物模型使用炎症因子达到97.5%的准确性 (AUC=0.9574).
结论:
- 炎症标志物是骨质疏松风险评估的有价值的预测因素.
- 整合炎症标志物的机器学习模型为早期骨质疏松症检测提供了一个有希望的方法.
- 这项研究为有针对性的干预和精确预防骨质疏松症提供了新的见解.
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