Unsupervised cardiac MRI phenotyping with 3D diffusion autoencoders reveals novel genetic insights

Biobank-scale imaging provides a unique opportunity to characterise structural and functional cardiac phenotypes and how they relate to disease outcomes. However, deriving specific phenotypes from MRI data requires time-consuming expert annotation, limiting scalability and does not exploit how information dense such image acquisitions are. In this study, we applied a 3D diffusion autoencoder to temporally resolved cardiac Magnetic Resonance Imaging (MRI) data from 71,021 UK Biobank participants to derive latent phenotypes representing the human heart in motion. These phenotypes were reproducible, heritable ( h ² = [4 - 18%]), and significantly associated with cardiometabolic traits and outcomes, including atrial fibrillation ( P = 8.5 × 10 ^{− 29} ) and myocardial infarction ( P = 3.7 × 10 ^{− 12} ). By using latent space manipulation techniques, we directly interpreted and visualised what specific latent phenotypes were capturing in a given MRI. To establish the genetic basis of such traits, we performed a genome-wide association study, identifying 89 significant common variants ( P < 2.3 × 10 ^{− 9} ) across 42 loci, including seven novel loci. Extensive multi-trait colocalisation analyses (PP.H ₄ > 0.8) linked these variants to various cardiac traits and diseases, revealing a shared genetic architecture spanning phenotypic scales. Polygenic Risk Scores (PRS) derived from latent phenotypes demonstrated predictive power for a range of cardiometabolic diseases and high risk individuals had substantially increased cumulative hazard rates across a range of diseases. This study showcases the use of diffusion autoencoding methods as powerful tools for unsupervised phenotyping, genetic discovery and disease risk prediction using cardiac MRI imaging data.