Development and validation of machine learning models for predicting low muscle mass in patients with obesity and diabetes.

ABSTRACT:

Background and aims

Low muscle mass (LMM) is a critical complication in patients with obesity and diabetes, exacerbating metabolic and cardiovascular risks. Novel obesity indices, such as the body roundness index (BRI), conicity index, and relative fat mass, have shown promise for assessing body composition. This study aimed to investigate the associations of these indices with LMM and to develop machine learning models for accurate and accessible LMM prediction.

Method

Data from NHANES 2011-2018 (n = 2,176) were analyzed. Obesity was defined by body fat percentage, and LMM was determined using skeletal muscle mass index thresholds adjusted for BMI. Predictive models were developed using logistic regression, random forest, and other algorithms, with feature selection via LAS

SUBMITTER: Ge J 

PROVIDER: S-EPMC12039300 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature