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.

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.

Setting and participants

A preliminary cohort of 1,431 participants from three community regions in Ziyang city, China, was used for model development and internal validation. For external validation, we utilized data from 832 residents of multi-center nursing homes.

Measurements

The diagnosis of SO was based on the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) criteria

SUBMITTER: Lian R 

PROVIDER: S-EPMC11870957 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature