Ischaemic stroke, the most prevalent stroke subtype, imposes a significant long-term disease burden. However, patients with first-ever stroke exhibit substantial individual variability in poststroke health trajectories, manifesting heterogeneous clinical presentations. We therefore started with the overall health of patients in order to delineate heterogeneous clusters characterised by distinct demographic profiles, clinical features and behavioural determinants and elucidate shared longitudinal trajectories in the temporal development of adverse health outcomes.

We designed a multicentre, cross-sectional and longitudinal study focusing on patients with first-ever ischaemic stroke. We will employ patient self-reported outcomes and objective measurements to comprehensively evaluate patients’ health status from a multidimensional perspective. Following baseline assessments, participants will undergo follow-up evaluations at 1 month, 3 months and 6 months post inclusion. The primary objective is twofold: (1) to identify distinct patient clusters with heterogeneous multidimensional health profiles using the k-prototype clustering algorithm and (2) to characterise synergistic trajectories of core health attributes within the largest cluster through parallel process latent class growth modelling. By combining cross-sectional and longitudinal analyses, this phased study should elucidate static heterogeneity and dynamic recovery patterns following a first-ever ischaemic stroke.

The project conforms to the ethical principles enshrined in the Declaration of Helsinki (2013 amendment) and all local ethical guidelines. The ethics committee at the University of South China approved the study (approval no. 2024 NHHL023). The ethics committee of Gansu Provincial Hospital approved the study (approval no. 2025–023). The ethics committee of the Central Hospital of Shaoyang approved the study (approval no.KY-2025–12). The findings will be published and presented at conferences for widespread dissemination.

Trial registration number: ChiCTR2500098442

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.