Deep learning from retinal images for prediction of biomarker-informed obesity subtypes associated with cardiometabolic risk

Individuals with similar body mass index (BMI) often exhibit different cardiometabolic risk profiles, yet current approaches require multiple circulating biomarkers, limiting scalability. Here, we develop ROSA (Retinal-based Obesity Subtyping Algorithm), a deep learning framework that uses knowledge distillation to transfer multimodal cardiometabolic knowledge, derived from latent profile analysis of circulating biomarkers and retinal foundation model features, into a model requiring only a single fundus image. Applied to 63,693 participants across the UK Biobank (UKB; n = 37,137) and the Beijing Health Management Cohort external validation cohort (BHMC-EV; n = 26,556), ROSA classifies participants into three obesity subtypes identified by the multimodal latent profile analysis: baseline concordant, discordant inflammatory, and discordant hyperglycaemic. In the independent external BHMC-EV cohort, ROSA achieved a macro-averaged AUROC of 0.66 (95% CI: 0.64–0.67) and macro-averaged sensitivity of 0.45; class-specific external sensitivity and PPV were 0.84 and 0.87 for BC, 0.02 and 0.73 for DIS, and 0.51 and 0.34 for DHG, respectively. Performance in UKB-CHV was higher, with a macro-averaged AUROC of 0.80 (0.79–0.82) and sensitivity of 0.59. Over 13 years of follow-up in UKB-CHV, the hyperglycaemic subtype was associated with increased type 2 diabetes risk (HR 4.72, 95% CI: 3.70–6.02), consistent with its defining glycaemic profile, and accelerated progression to first cardiometabolic disease (HR 3.21, 2.53–4.06). Multi-omics analyses spanning the phenome, metabolome, proteome, and genome identified distinct biological associations with each subtype. ROSA demonstrates that retinal imaging can capture part of the cardiometabolic heterogeneity within obesity, while its limited sensitivity indicates that further calibration and validation are needed before clinical deployment.