Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits

Abstract

The eye is a recognized source of biomarkers for cardiovascular and neurodegenerative disease risk. Here we characterize the breadth of these associations and identify biological axes that may mediate them. Using UK Biobank data, we developed a multi-omic analysis pipeline integrating physiological, radiomic, metabolomic and genomic information. We trained retinal adversarial autoencoders to represent optical coherence tomography images and color fundus photographs as 256-dimensional embeddings. Retinal adversarial autoencoder-derived embeddings were associated with a range of cardiovascular and neurodegenerative diseases, including ischemic heart disease, cerebrovascular disease, Parkinson’s disease and dementia. Examining associations across diverse omics datasets, we provide evidence linking ophthalmic imaging features to neurological and cardiovascular anatomy and function, lipid metabolism and gene sets associated with neurodegenerative pathology. Collectively, our findings show that ophthalmic features reflect complex, multisystem biological processes and reinforce the role of the eye as a composite indicator of systemic health.

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Item Type:	Article
Authors/Creators:	Julian, T.H. https://orcid.org/0000-0002-5488-5620 Dou, H. https://orcid.org/0000-0001-8628-5489 Duan, J. Huang, J. Yoo, E. https://orcid.org/0000-0001-8759-0098 Green, D.J. Strange, A. https://orcid.org/0000-0002-2004-150X Alhathli, E. https://orcid.org/0009-0008-2147-9715 Sperrin, M. https://orcid.org/0000-0002-5351-9960 Keane, P.A. https://orcid.org/0000-0002-9239-745X Chew, E.Y. https://orcid.org/0000-0003-0999-9802 Keavney, B. https://orcid.org/0000-0001-9573-0812 Fitzgerald, T.W. https://orcid.org/0000-0002-2370-8496 Cooper-Knock, J. https://orcid.org/0000-0002-0873-8689 Birney, E. https://orcid.org/0000-0001-8314-8497 Frangi, A.F. https://orcid.org/0000-0002-2675-528X Sergouniotis, P.I. https://orcid.org/0000-0003-0986-4123
Copyright, Publisher and Additional Information:	© The Author(s) 2026. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords:	Humans; Multiomics; Cardiovascular Diseases; Deep Learning; Phenotype; Autoencoder; UK Biobank; Tomography, Optical Coherence; Neurodegenerative Diseases; Metabolomics; Radiomics; Fundus Oculi
Dates:	Submitted: 20 August 2025 Accepted: 30 March 2026 Published (online): 16 June 2026 Published: June 2026
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Neuroscience (Sheffield) The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health
Date Deposited:	14 Jul 2026 11:06
Last Modified:	14 Jul 2026 11:06
Status:	Published
Publisher:	Springer Science and Business Media LLC
Refereed:	Yes
Identification Number:	10.1038/s44161-026-00815-5
Related URLs:	PubMed URL Software or Code
Sustainable Development Goals:
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:243092

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Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits

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