一项基于人体成分的聚类研究及其与中国普通人口中的代谢表型的关联
Dan Xiang1, Li Yuan1, Weimin Chen2
1Department of Endocrinology, Taikang Xianlin Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Frontiers in nutrition
|November 17, 2025
概括
机器学习对身体组成数据的聚类在肥胖个体中确定了不同的代谢表型. 这种方法揭示了特定的亚组,这些亚组具有更高的代谢性疾病风险,如高血压和糖尿病.
科学领域:
- 代谢健康 代谢健康
- 肥胖研究的研究.
- 发现生物标志物的发现.
背景情况:
- 目前的代谢表型化方法在分类肥胖人口方面存在局限性.
- 了解代谢表型对于识别疾病风险至关重要.
- 肥胖与各种代谢疾病有关,需要改进的分类策略.
研究的目的:
- 用身体组成和机器学习将肥胖人口聚集在一起.
- 提高对脂质代谢及其与代谢疾病相关性的理解.
- 识别不同的代谢表型,以改善风险分层.
主要方法:
- 对接受InBody检查的参与者的回顾性分析.
- 根据BMI和代谢综合征的标准将其分为四种表型 (MHNW,MHO,MUNW,MUO).
- 机器学习集群分析应用于InBody索引和临床数据.
主要成果:
- InBody指数与病史和实验室结果有很强的相关性.
- 聚类确定了男性的2个子组和女性的3个子组,在人体测量和体内测量方面存在显著差异 (p < 0.001).
- 亚组分析显示,男性和女性中高血压,高脂血症和糖尿病的患病率存在显著差异.
结论:
- 基于InBody的聚类有效地将肥胖男性分为2个,女性分为3个不同的子组.
- 特定的InBody集群配置文件与代谢疾病的风险增加有关.
- 这种机器学习方法为代谢表型和疾病风险评估提供了一种新方法.
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