{"doi":"10.1101/2025.08.09.25333350","title":"Multi-organ AI Endophenotypes Chart the Heterogeneity of Pan-disease in the Brain, Eye, and Heart","abstract":"Abstract Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease entities and overlook shared mechanisms across conditions 1 . Inspired by pan-cancer 2 and multi-organ research 3 , 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-GAN 4,5 ) 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 (solanezumab 6 ), 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 endophenotype 7 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.","journal":"medRxiv","year":2025,"id":556645,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9571,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1360848,"name":"Filippos Anagnostakis","orcid":"0000-0001-7374-8798","position":1,"is_corresponding":false},{"id":1077100,"name":"Zhijian Yang","orcid":"0000-0002-0680-6596","position":2,"is_corresponding":false},{"id":34919,"name":"Ye Ella Tian","orcid":"0000-0003-3107-5550","position":3,"is_corresponding":false},{"id":34928,"name":"Michael R. Duggan","orcid":"0000-0002-1029-4423","position":4,"is_corresponding":false},{"id":34929,"name":"Guray Erus","orcid":"0000-0001-6633-4861","position":5,"is_corresponding":false},{"id":230032,"name":"Dhivya Srinivasan","orcid":"0000-0001-7886-9572","position":6,"is_corresponding":false},{"id":1209405,"name":"Cassandra M Joynes","orcid":null,"position":7,"is_corresponding":false},{"id":34932,"name":"Wenjia Bai","orcid":"0000-0003-2943-7698","position":8,"is_corresponding":false},{"id":557083,"name":"Praveen J. Patel","orcid":"0000-0001-8682-4067","position":9,"is_corresponding":false},{"id":34922,"name":"Keenan A. Walker","orcid":"0000-0002-5989-9853","position":10,"is_corresponding":false},{"id":34923,"name":"Andrew Zalesky","orcid":"0000-0003-2298-9908","position":11,"is_corresponding":false},{"id":34918,"name":"Christos Davatzikos","orcid":"0000-0002-1025-8561","position":12,"is_corresponding":false},{"id":34917,"name":"Junhao Wen","orcid":"0000-0003-2077-3070","position":13,"is_corresponding":false},{"id":34925,"name":"Aleix Boquet-Pujadas","orcid":"0000-0002-3300-8004","position":0,"is_corresponding":true}],"reference_count":148,"raw_metadata":null,"created_at":"2026-07-19T02:55:13.130091Z","pmid":"40832432","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}