探索病指数和认知功能障碍之间的潜在关系:使用NHANES数据的机器学习方法
Tianyi Wang1, Shuang Tang2, Ruwen Zheng1
1Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
BMC geriatrics
|August 26, 2025
概括
病指数 (KDI) 是老年人认知能力下降的强有力的预测指标, 管理KDI对于保持认知健康至关重要,特别是在慢性病患者中.
科学领域:
- 老年学
- 肝脏病学
- 神经学
背景情况:
- 在老年人群中,认知障碍越来越令人担忧.
- 功能越来越被认为是影响大脑健康的因素.
- 需要新的生物标志物来有效预测认知衰退.
研究的目的:
- 研究病指数 (KDI) 与老年人的认知功能之间的关联.
- 评估KDI作为认知障碍的预测标志物的潜力.
- 将KDI的预测性能与已知的功能标志物 (eGFR,UACR) 进行比较.
主要方法:
- 对60岁以上成年人的国家健康和营养检查调查 (NHANES) 的数据分析.
- 使用多变量回归,限制立方线和机器学习 (LASSO,XGBoost,随机森林) 来进行预测建模.
- 使用ROC曲线,决策曲线分析,校准曲线和SHAP值评估模型性能.
主要成果:
- 较高的KDI水平与较低的认知分数和认知障碍的风险增加有关.
- 每增加0.1个单位的KDI与认知分数下降0.17个点以及认知障碍的概率增加15%.
- 机器学习模型表明,KDI (AUC> 0.7) 在预测认知障碍方面表现优于eGFR和UACR.
结论:
- 作为预测老年人的认知功能和损伤风险的可靠指标,
- 提高KDI水平可能是认知能力下降的危险因素,这凸显了KDI管理对认知健康的重要性.
- 建议进行进一步的纵向研究和临床干预,以确认KDI的实用性,并制定有针对性的治疗策略.
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