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Diabetes, Cardiovascular Risks, and Lipoproteins · Journal article
Journal of Rehabilitation Medicine · September 4, 2026
Well-designed and adequately powered for the question it asks.
This prospective cohort study of 3,301 Chinese adults with metabolic syndrome found that higher relative fat mass was independently associated with a 50% increased risk of incident symptomatic knee osteoarthritis in the highest versus lowest quartile, with a dose-response relationship. Relative fat mass demonstrated superior predictive ability compared to body mass index (AUC 0.587 vs 0.532), and the association was non-linear and stronger in rural residents.
Prospective cohort study. Middle-aged and older adults with metabolic syndrome who were free of knee osteoarthritis at baseline, enrolled from CHARLS (Chinese longitudinal study).. Intervention: Relative fat mass (exposure; stratified into quartiles). Compared with: Lowest relative fat mass quartile; also compared relative fat mass predictive ability to body mass index. n = 3,301. China (CHARLS data).
Knee osteoarthritis incidence increased across relative fat mass quartiles: Q1 21%, Q4 37% Highest relative fat mass quartile had 50% higher osteoarthritis risk versus lowest quartile (HR = 1.50, 95% CI: 1.18–1.90) Significant dose-response trend across quartiles in fully adjusted models
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Clinicians should consider relative fat mass as a superior anthropometric tool to BMI for identifying metabolic syndrome patients at high risk for incident knee osteoarthritis, particularly in rural populations, to guide targeted prevention interventions.
Well-designed prospective cohort study with adequate sample size, multivariable adjustment, and clinically relevant hard endpoint (incident symptomatic knee osteoarthritis); demonstrates superior predictive performance of relative fat mass over BMI with dose-response relationship.
As stated by the source record.
Quoted from the source exactly as published.
Clinicians should consider relative fat mass as a superior anthropometric tool to BMI for identifying metabolic syndrome patients at high risk for incident knee osteoarthritis, particularly in rural populations, to guide targeted prevention interventions.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.
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