可解释的机器学习驱动模型用于预测帕金森病及其预后:使用NHANES 1999-2018数据的肥胖模式关联和模型开发
Jiaxin Fan1,2,3, Shuai Cao4, Hang Peng5
1Department of Geriatric Neurology, Shaanxi Provincial People's Hospital, Youyi West Road No. 256, Xi'an, 710068, China.
Lipids in health and disease
|July 17, 2025
概括
复合肥胖会增加帕金森病 (PD) 的风险,但可能会降低PD患者的死亡率. 机器学习模型显示PD的预测和预后性能中等.
科学领域:
- 神经学 神经学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 帕金森病 (PD) 是一种常见的神经退行性疾病.
- 肥胖对PD风险和结果的影响受到辩论.
研究的目的:
- 调查肥胖模式和PD风险之间的关联.
- 检查PD患者中肥胖模式和全因死亡率之间的联系.
- 开发基于机器学习 (ML) 的PD预测和预后模型.
主要方法:
- 利用了来自51,394名成年人的数据 (NHANES 1999-2018).
- 用BMI和腰围 (WC) 将参与者分为四种肥胖模式.
- 在风险和死亡率分析中使用多变量物流和考克斯回归.
- 开发并验证了使用AUCROC和校准曲线进行PD预测和预后的ML编号.
主要成果:
- 复合肥胖症与PD风险显著增加有关 (OR=1.83,P<0.001).
- 复合肥胖与PD患者全因死亡率降低相关 (HR=0.43,P=0.003).
- ML模型在PD预测 (AUCROC=0.75) 和预后 (AUCROC=0.72) 上取得了中等的表现.
结论:
- 复合肥胖与较高的PD风险相关,但PD患者的死亡率较低.
- 经过验证的ML nomograms显示了强大的PD预测和预后能力.
- 需要进一步的纵向研究来证实这些发现.
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