Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease entities and overlook shared mechanisms across conditions1. Inspired by pan-cancer2 and multi-organ research3, 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, as well as summary-level data from the MULTI consortium, we applied a weakly-supervised deep learning model (Surreal-GAN4,5) to multi-organ imaging, genetic, proteomic, and RNA-seq data, identifying 11 AI-derived biomarkers – called Multi-organ AI Endophenotypes (MAEs) – for the brain (Brain 1–6), eye (Eye 1–3), and heart (Heart 1–2), respectively. We found Brain 3 to be a risk factor for Alzheimer’s disease (AD) progression and mortality, whereas Brain 5 was protective against AD progression. Crucially, in data from an anti-amyloid AD drug (solanezumab6), 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 multi-layer causal pathway pinpointed Brain 1 as a mediational endophenotype7 linking the FLRT2 protein to migraine, exemplifying novel therapeutic targets and pathways. Additionally, 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 MAEs provide novel AI dimensional representations for precision medicine and highlight the potential of AI-driven patient stratification for disease risk monitoring, clinical trials, and drug discovery.

The authors have declared no competing interest.

This research is supported through funding from the NIH-supported MULTI Consortium (W.J.; grant number: RF1AG092412).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The MULTI consortium is approved by the Institutional Review Board at Columbia University (AAAV6751).

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