Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features.

Journal: Aging clinical and experimental research
PMID: 40021576

Abstract

OBJECTIVES: Sarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify and validate an explainable prediction model of SO using easily available clinical characteristics.

Authors

  • Rongna Lian
    Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
  • Huiyu Tang
    Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
  • Zecong Chen
    Department of Geriatric, Zigong Affiliated Hospital of Southwest Medical University, Zigong, China.
  • Xiaoyan Chen
  • Shuyue Luo
    Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
  • Wenhua Jiang
    The Usher Institute, University of Edinburgh, Edinburgh, UK.
  • Jiaojiao Jiang
    Rehabilitation Center, West China Hospital, Sichuan University, Chengdu, China. jiangjiaojiao1997@163.com.
  • Ming Yang
    Wuhan Institute for Food and Cosmetic Control, Wuhan 430014, China.

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