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Research Article | Open Access

Decoding the Bone–Eye Axis: Machine Learning for Age-Related Macular Degeneration Risk Prediction

Xuehao Cui1,2,3,†( ), Qiuchen Zhao4,†, Jingwen Hui5, Zheya Han5, Patrick Yu-Wai-Man1,2,3( ), Quanhong Han5( )
Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0XY, UK
John van Geest Centre for Brain Repair, University of Cambridge, Cambridge CB2 0PY, UK
Cambridge Eye Unit, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
Department of Ophthalmology, Yale University, New Haven, CT 06520, USA
Tianjin Eye Hospital, Tianjin Key Lab of Ophthalmology and Visual Science, Nankai University, Tianjin 300020, China

†These authors contributed equally to this work.

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Abstract

Age-related macular degeneration (AMD) is a leading cause of irreversible vision loss, yet systemic determinants of its risk remain incompletely understood. Bone mineral density (BMD), a marker of skeletal and biological aging, may reflect shared pathways linking systemic and retinal degeneration. We investigated the association between BMD and AMD using a multilayered framework integrating epidemiological analyses, Mendelian randomization (MR), proteomic and metabolomic profiling, machine learning, and an experimental low-BMD model. Across 3 cohorts, including UK Biobank, National Health and Nutrition Examination Survey, and a hospital-based Tianjin cohort, lower BMD was consistently associated with higher AMD risk, although AMD ascertainment and BMD measurement differed across cohorts. Two-sample MR analyses provided supportive genetic evidence consistent with a modest potential contribution of higher BMD to lower AMD risk, while sensitivity analyses did not indicate strong evidence of reverse causality. Machine learning models identified BMD as a recurrent predictive contributor alongside age, but its incremental clinical utility requires formal evaluation using models with and without BMD. UK Biobank proteomic and metabolomic analyses revealed overlapping molecular signatures involving extracellular matrix remodeling, lipid transport, amino acid metabolism, and inflammatory pathways. Two-step MR prioritized circulating proteins including granzyme A, collagen type Ⅱ alpha 1 chain, and NEL-like protein 1 as candidate molecular intermediates rather than established mechanistic mediators. In a glucocorticoid-induced low-BMD rat model, retinal alterations relevant to degeneration, including outer retinal thinning, vascular narrowing, and delayed visual–spatial performance, were observed, but these findings should not be interpreted as direct validation of AMD pathology. Overall, these findings support an association between lower BMD and AMD risk and suggest that BMD may act as an accessible marker of systemic aging processes related to retinal vulnerability.

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Cyborg and Bionic Systems
Article number: 0676

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Cite this article:
Cui X, Zhao Q, Hui J, et al. Decoding the Bone–Eye Axis: Machine Learning for Age-Related Macular Degeneration Risk Prediction. Cyborg and Bionic Systems, 2026, 7: 0676. https://doi.org/10.34133/cbsystems.0676

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Received: 16 April 2026
Revised: 03 August 2026
Accepted: 06 August 2026
Published: 14 September 2026
© 2026 Xuehao Cui et al. Exclusive licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works.