Multi-organ AI endophenotypes chart the heterogeneity of brain, eye and heart pan-disease

Disease heterogeneity and commonality pose critical challenges to precision medicine, as traditional approaches frequently focus on single disease entities and overlook shared mechanisms across conditions. Here, inspired by pan-cancer and multi-organ research, we introduce the concept of ‘pan-disease’ to investigate the heterogeneity and shared etiology in brain, eye and heart diseases. Leveraging individual-level data from 129,340 participants and summary-level data, curated from the MULTI consortium, we applied a weakly supervised deep learning model (Surreal-GAN) to multi-organ imaging, genetic and proteomic data, identifying 11 artificial intelligence (AI)-derived biomarkers, called multi-organ AI endophenotypes, for the brain (Brain 1–6), eye (Eye 1–3) and heart (Heart 1–2). We found Brain 3 to be a risk factor for Alzheimer’s disease progression and mortality, whereas Brain 5 was protective against Alzheimer’s disease progression. In data from an anti-amyloid Alzheimer’s disease drug (solanezumab), heterogeneity in cognitive decline trajectories was observed across treatment groups. At week 240, patients with lower Brain 1–3 expression had slower cognitive decline, whereas patients with higher expression had faster cognitive decline. A multilayer causal pathway pinpointed Brain 1 as a mediational endophenotype linking the FLRT2 protein to migraine, exemplifying new therapeutic targets and pathways. In addition, genes associated with Eye 1 and Eye 3 were enriched in cancer drug-related gene sets with causal links to specific cancer types and proteins. Finally, Heart 1 and Heart 2 had the highest mortality risk and unique medication history profiles, with Heart 1 showing favorable responses to antihypertensive medications and Heart 2 to digoxin treatment. The 11 multi-organ AI endophenotypes provide new AI dimensional representations for precision medicine and highlight the potential of AI-driven patient stratification for disease risk monitoring, clinical trials and drug discovery. Disease heterogeneity complicates precision medicine, which focuses on single conditions and ignores shared mechanisms. Here the authors introduce ‘pan-disease’ analysis using a deep learning model on multi-organ data, identifying 11 AI-derived biomarkers that reveal new therapeutic targets and pathways, enhancing patient stratification for disease risk monitoring and drug discovery.