To investigate the correlation between fat-to-muscle ratio (FMR) or other body composition and secondary osteoporosis (OP) in patients with rheumatoid arthritis (RA) and to develop a predictive model using FMR and related clinical factors.

Cross-sectional observational study with machine learning-based risk modelling.

Tertiary hospital in eastern China, secondary care level.

A total of 670 hospitalised RA patients (135 males and 535 females; aged 58.00 (50.00–67.00) years; disease duration 8.00 (2.00–16.00) years) and 126 healthy controls were recruited between October 2019 and October 2022. There were no differences in basic indicators such as gender, age distribution and body mass index between the two groups. RA diagnosis followed American College of Rheumatology (ACR) 1987 or ACR/European League Against Rheumatism 2010 criteria. Exclusion criteria included major organ dysfunction, endocrine disease, infection or long-term hormone or psychotropic drug use.

Primary outcomes included total skeletal muscle mass, fat mass, FMR measured by bioelectrical impedance analysis and bone mineral density measured by dual-energy X-ray absorptiometry. Secondary outcomes included RA disease activity scores (clinical disease activity index (CDAI), simplified disease activity index, disease activity score in 28 joints (DAS28)) and glucocorticoid use. Logistic regression and four additional machine learning algorithms were used to build predictive models for OP.

The RA group (age, 58.00; duration, 8.00; DAS28, 5.03; rheumatoid factor, 104.75; C-reactive protein, 25.65; erythrocyte sedimentation rate (ESR), 59.00) exhibited reduced total skeletal muscle mass (19.49 vs 25.38, p

FMR may serve as a useful clinical indicator of secondary OP in RA patients. A model based on FMR and associated risk factors can predict the possibility of secondary OP.