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通过先进的机器学习,对中国人口中代谢不健康的身体组成参数的早期预测
Xiujuan Deng1, Lin Qiu1, Xin Sun1
1Department of Clinical Nutrition, The Third Hospital of Changsha, Changsha, China.
Frontiers in endocrinology
|September 15, 2023
概括
代谢综合征是一个日益严重的全球健康问题. 这项研究表明,特定的身体成分指标可以有效地识别高风险的个体,有助于在正常体重和肥胖人口中早期预防.
科学领域:
- 内分泌学 在内分泌学.
- 公共卫生 公共卫生
- 生物统计学 生物统计学
背景情况:
- 代谢综合征 (Mets) 是一个重要的全球健康挑战,增加心脏代谢疾病的风险.
- 了解新陈代谢健康 (MH) 和不健康 (MU) 现型之间的身体组成差异对于管理这种流行病至关重要.
研究的目的:
- 为了比较正常体重和肥胖的中国人群与MH和MU表型的身体组成概况.
- 开发机器学习模型,使用人体成分指数来预测代谢不健康.
主要方法:
- 涉及健康检查参与者的横截面研究.
- 通过生物电阻分析评估的身体组成.
- 用于模型开发和解释的梯度增强树算法 (LightGBM) 和夏普利添加式扩展.
主要成果:
- 在代谢表型 (MHNW,MHO,MUNW,MUO) 中观察到显著的身体组成差异.
- 代谢健康的正常体重个体表现出较低的脂肪质量和内脏脂肪.
- 机器学习模型在区分代谢表型方面实现了高精度 (AUC高达0.968).
- 一个简单的2变量模型 (WHR > 0.92,FFMI > 18.5 kg/m2) 预测了代谢不健康的风险增加.
结论:
- 身体成分分析是早期发现和管理代谢综合征的宝贵工具.
- 开发的预测模型提供了一种实际方法,用于在临床环境中识别有风险的个体.
- 早期干预策略可以通过利用正常体重和肥胖人口的身体组成数据来加强.
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