Self-supervised DXA representations encode multi-system disease risk, biological aging and heritability

TL;DR - LeDXA is a JEPA-based self-supervised vision model trained from scratch on 11,540 unlabeled whole-body DXA scans that turns routine bone-density images into representations predicting multi-system disease risk, biological age, and heritable traits. It matters because it extracts prognostic signal that conventional tabular DXA readouts discard, using far less data and compute than general-purpose foundation models.

• Trained on 11,540 Human Phenotype Project scans and validated on 47,400 external UK Biobank scans, it beat both scanner-derived DXA measures and DINOv3 on cross-cohort prevalent disease/biomarker prediction — with ~150,000× fewer training images and ~40× fewer parameters.
• Over a median 4.3-year UKBB follow-up it improved incident disease prediction, largest gains for hip/knee arthrosis and type 2 diabetes; 66% of incident hip arthrosis cases fell in its highest-risk quartile vs 41% for tabular measures.
• Embeddings predicted chronological age externally (r = 0.88, MAE 2.90 years); the biological-age gap tracked disease burden, carried a 45% higher mortality hazard in the oldest-appearing quartile, and decreased in women after starting hormone-replacement therapy, suggesting modifiability.
• GWAS on the embeddings recovered mostly known body-composition and bone-density loci, and LeDXA representations were more heritable than DINOv3's, supporting biological grounding rather than scanner artifacts.