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低碳水化合物饮食评分和慢性阻塞性肺病:对NHANES数据的机器学习分析

Xin Zhang¹, Jipeng Mo¹, Kaiyu Yang¹

¹Department of Emergency Medicine, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, The Third Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Changzhou, China.

Frontiers in nutrition

|January 8, 2025

概括

低碳水化合物饮食 (LCD) 评分可能与美国成年人慢性阻塞性肺病 (COPD) 患病率降低有关. 这表明LCD得分可能有助于COPD预防策略.

科学领域:

营养流行病学营养流行病学
肺部医学肺部医学
生物标志物研究生物标志物研究

背景情况:

低碳水化合物饮食 (LCD) 评分是一个与改善肺功能相关的新生物标志物.
之前的研究探讨了液晶显示屏得分与COPD的联系,但其在美国人口中的意义尚不清楚.

研究的目的:

调查LCD得分与美国人口中慢性阻塞性肺病 (COPD) 患病率之间的关联.

主要方法:

从国家健康和营养检查调查 (NHANES) (2007-2023) 中对16030名参与者的分析.
使用倾向得分匹配 (PSM),加权多变量逻辑回归和受限立方线回归 (RCS).
采用了8个机器学习模型,包括随机森林,以评估预测性能.

主要成果:

在LCD得分和COPD患病率之间观察到显著的反向关系.
较高的液晶显示器分数四分位数与COPD的几率降低有关 (ORs范围从0.61到0.77).
随机森林模型实现了71.6%的AUC,表明了强大的预测能力.

结论:

坚持低碳水化合物饮食可能与美国成年人患上COPD的几率较低有关.
液晶显示屏的得分显示了作为预防COPD的工具的潜力.
需要进一步的前性研究来验证这些发现.

关键词:

尼汉斯 (NHANES) 是一个名人.慢性阻塞性肺病慢性阻塞性肺病跨截面研究是跨截面研究.低碳水化合物饮食得分低碳水化合物饮食得分机器学习是机器学习.

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