| Title: | Multi-organ AI endophenotypes chart the heterogeneity of brain, eye and heart pan-disease |
| Journal: | Nature Mental Health |
| Published: | 6 Jan 2026 |
| DOI: | https://doi.org/10.1038/s44220-025-00560-x |
Publication 16869
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Abstract
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.</p>
14 Authors
- Aleix Boquet-Pujadas
- Filippos Anagnostakis
- Zhijian Yang
- Ye Ella Tian
- Michael R. Duggan
- Guray Erus
- Dhivya Srinivasan
- Cassandra M. Joynes
- Wenjia Bai
- Praveen J. Patel
- Keenan A. Walker
- Andrew Zalesky
- Christos Davatzikos
- Junhao Wen
1 Application
| Application ID | Title |
|---|---|
| 647044 | MULTI: modeling human aging and disease via multi-organ and multi-omics biomedical data |
Enabling scientific discoveries that improve human health