Relative fat mass predicts symptomatic knee osteoarthritis in older adults with metabolic syndrome: a longitudinal cohort study.

Authors

Lei Huang, Yuhui Wang, Yuhao Chen, Weilong Yao, Xianxu Zhang, Shicheng Li, Yong Sun, Changlin Zhou, Bin Qian, Zhiqiang Luo, Pandeng Wei

Published in

Journal of rehabilitation medicine. Volume 58. Pages jrm45354. Sep 04, 2026. Epub Sep 04, 2026.

Abstract

Relative fat mass is a novel anthropometric indicator that outperforms body mass index in assessing obesity. Although obesity is a known risk factor for knee osteoarthritis, the prospective association between relative fat mass and symptomatic knee osteoarthritis in middle-aged and older adults with metabolic syndrome remains unclear.
This prospective cohort study used CHARLS data, including 3,301 middle-aged and older metabolic syndrome patients free of knee osteoarthritis at baseline. Participants were grouped into relative fat mass quartiles. Incident symptomatic knee osteoarthritis was the primary outcome. Multivariable Cox models, restricted cubic spline analyses, subgroup analyses, and receiver operating characteristic curves were used to assess associations and predictive performance.
Knee osteoarthritis incidence increased across relative fat mass quartiles (Q1: 21%; Q4: 37%). In fully adjusted models, those in the highest relative fat mass quartile had a 50% higher knee osteoarthritis risk compared with the lowest quartile (HR = 1.50, 95% CI: 1.18-1.90), with a significant dose-response trend. Restricted cubic spline analysis indicated a non-linear association. The relationship was stronger among rural residents. Relative fat mass demonstrated better predictive ability than body mass index (AUC: 0.587 vs 0.532).
Among Chinese middle-aged and older adults with metabolic syndrome, higher relative fat mass was independently and non-linearly associated with an increased risk of incident symptomatic knee osteoarthritis. Relative fat mass outperformed body mass index as a predictive measure, supporting its use as a simple and effective tool to identify high-risk individuals and guide targeted prevention strategies.

PMID:
42693778
Bibliographic data and abstract were imported from PubMed on 04 Sep 2026.

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