Novel AI endophenotypes reveal shared risk factors and treatment responses in brain, eye, and heart diseases, suggesting precision medicine advancements.
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. Inspired by pancancer and multiorgan research, we introduce the concept of pandisease 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 (SurrealGAN) to multiorgan imaging, genetic, proteomic, and RNA seq data, identifying 11 AI derived biomarkers, called Multiorgan AI Endophenotypes (MAEs), for the brain (Brain 1,2,3,4,56), eye (Eye 1,2,3), and heart (Heart 1,2), respectively. We found Brain 3 to be a risk factor for Alzheimers disease (AD) progression and mortality, whereas Brain 5 was protective against AD progression. Crucially, in data from an antiamyloid AD drug (solanezumab), heterogeneity in cognitive decline trajectories was observed across treatment groups. At week 240, patients with lower brain 1,2,3 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 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.
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Consortium et al. (2025) studied this question.