Abstract
Accelerated biological aging, as well as cardiovascular, kidney, and metabolic (CKM) diseases, contribute to shortened healthspan. We studied a deep-learning model, retinal BioAge, and multiple indicators of CKM syndrome in participants from UK Biobank and the US-based EyePACS dataset. Retinal BioAge was trained on 77,887 retinal images and then used to analyze separate retinal images from UK Biobank (10,976) and EyePACS (19,856). In both datasets, CKM biomarker profiles were significantly worse for the top vs. bottom quartiles of BioAgeGap (retinal BioAge—chronological age), including measures of blood pressure, kidney function, adiposity, and glycemia. The top BioAgeGap quartile also had a significantly higher prevalence of clinical CKM indicators, including hypertension, kidney disease, and diabetes (UK Biobank) or suboptimally controlled diabetes (EyePACS). Thus, analysis of retinal images for accelerated biological aging may provide opportunistic screening to help identify individuals who could benefit from formal CKM assessment, potentially contributing to earlier detection and management of CKM syndrome.
Data availability
UK Biobank and EyePACS datasets are available for research per the policies of those organizations. UK Biobank—https://www.ukbiobank.ac.uk/about-our-data/. EyePACS—https://www.eyepacs.com/data-analysis. Study-specific data are available from the corresponding author upon reasonable request.
References
Global Cardiovascular Risk Consortium et al. Global effect of modifiable risk factors on cardiovascular disease and mortality. N. Engl. J. Med. 389, 1273–1285 (2023).
Martin, S. S. et al. 2025 Heart disease and stroke statistics: A report of US and global data from the American Heart Association. Circulation. 151 (8), e41–e660 (2025).
Kidney disease statistics for the United States [Internet]. National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/health-statistics/kidney-disease (2025).
Balooch Hasankhani, M., Mirzaei, H. & Karamoozian, A. Global trend analysis of diabetes mellitus incidence, mortality, and mortality-to-incidence ratio from 1990 to 2019. Sci. Rep. 13, 21908 (2023).
Ndumele, C. E. et al. Cardiovascular-kidney-metabolic health: A presidential advisory from the American Heart Association. Circulation 148, 1606–1635 (2023).
Ndumele, C. E. et al. A synopsis of the evidence for the science and clinical management of cardiovascular-kidney-metabolic (CKM) syndrome: A scientific statement from the American Heart Association. Circulation 148, 1636–1664 (2023).
Wagner, S. K. et al. Insights into systemic disease through retinal imaging-based oculomics. Transl. Vis. Sci. Technol. 9, 6 (2020).
Cheung, C. Y., Biousse, V., Keane, P. A., Schiffrin, E. L. & Wong, T. Y. Hypertensive eye disease. Nat. Rev. Dis. Primers. 8, 14 (2022).
Yang, Z., Tan, T-E., Shao, Y., Wong, T. Y. & Li, X. Classification of diabetic retinopathy: Past, present and future. Front. Endocrinol. 13, 1079217 (2022).
Chew, E. Y. Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop. Nat. Rev. Cardiol. 22 (1), 47–63 (2025).
AI and the retina. Finding patterns of systemic disease [Internet]. Am. Acad. Ophthalmol. (2021). https://www.aao.org/eyenet/article/ai-and-retina-finding-patterns-of-systemic-disease
Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156–163 (2023).
Weinreb, R. N. et al. Application of artificial intelligence to deliver healthcare from the eye. JAMA Ophthalmol. 143 (6), 529–535 (2025).
Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158–164 (2018).
Vaghefi, E. et al. Development and validation of a deep-learning model to predict 10-year atherosclerotic cardiovascular disease risk from retinal images using the UK Biobank and EyePACS 10K datasets. Cardiovasc. Digit. Health J. 5, 59–69 (2024).
Yi, J. K. et al. Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores. Eur. Heart J. Digit. Health. 4, 236–244 (2023).
Squirrell, D. et al. Blood pressure predicted from artificial intelligence analysis of retinal images correlates with future cardiovascular events. JACC Adv. 3 (12), 101410 (2024).
Zekavat, S. M. et al. Deep learning of the retina enables phenome- and genome-wide analyses of the microvasculature. Circulation. 145, 134–150 (2022).
Mordi, I. R. et al. Prediction of major adverse cardiovascular events from retinal, clinical, and genomic data in individuals with type 2 diabetes: A population cohort study. Diabetes Care. 45, 710–716 (2022).
An, S., Vaghefi, E., Yang, S., Xie, L. & Squirrell, D. Examination of alternative eGFR definitions on the performance of deep learning models for detection of chronic kidney disease from fundus photographs. PLoS One. 18, e0295073 (2023).
Joo, Y. S. et al. Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors. NPJ Digit. Med. 6, 114 (2023).
Tan, Y., Ma, Y., Rao, S. & Sun, X. Performance of deep learning for detection of chronic kidney disease from retinal fundus photographs: A systematic review and meta-analysis. Eur. J. Ophthalmol. 11206721231199848 (2023).
Abrà moff, M. D., Lavin, P. T., Birch, M., Shah, N. & Folk, J. C. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit. Med. 1, 39 (2018).
Vaghefi, E. et al. THEIATMÂ development, and testing of artificial intelligence-based primary triage of diabeticretinopathy screening images in New Zealand. Diabet. Med. 38, e14386 (2021).
Comfort, A. Test-battery to measure ageing-rate in man. Lancet 2, 1411–1414 (1969).
Fuster, V. Chronological vs biological aging. J. Am. Coll. Cardiol. 83, 1614–1618 (2024).
Putin, E. et al. Deep biomarkers of human aging: Application of deep neural networks to biomarker development. Aging 8, 1021–1033 (2016).
Oh, H. S. H. et al. Organ aging signatures in the plasma proteome track health and disease. Nature 624, 164–172 (2023).
Vaghefi, E., An, S., Corbett, R. & Squirrell, D. Association of retinal image-based, deep learning cardiac BioAge with telomere length and cardiovascular biomarkers. Optom. Vis. Sci. 101 (7), 464–469 (2024).
Nusinovici, S. et al. Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk. Age Ageing. 51, afac065 (2022).
Zhu, Z. et al. Retinal age gap as a predictive biomarker for mortality risk. Br. J. Ophthalmol. 107, 547–554 (2023).
Ahadi, S. et al. Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock. Elife 12, e82364 (2023).
Arnett, D. K. et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: A report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation 140 (11), e596–e646 (2019).
Boffa, R. J., Constanti, M., Floyd, C. N. & Wierzbicki, A. S. Guideline committee. hypertension in adults: summary of updated NICE guidance. BMJ 367, l5310 (2019).
Carey Robert, W. P. K. et al. 2017ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for theprevention, detection, evaluation, and management of high blood pressure in adults. J. Am. Coll. Cardiol. 71, e127–e248 (2018).
Blood Cholesterol. Diagnosis. NHLBI, NIH. https://www.nhlbi.nih.gov/health/blood-cholesterol/diagnosis
Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 105, S117–S314 (2024).
American Diabetes Association Professional Practice Committee. 2. Diagnosis and classification of diabetes: Standards of care in diabetes-2024. Diabetes Care. 47, S20–S42 (2024).
American Diabetes Association Professional Practice Committee. 6. Glycemic Goals and Hypoglycemia: Standards of Care in Diabetes-2024. Diabetes Care. 47, S111–S125 (2024).
Ross, R. et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat. Rev. Endocrinol. 16 (3), 177–189 (2020).
Wilkinson, C. P. et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110, 1677–1682 (2003).
Nusinovici, S. et al. Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study. Lancet Healthy Longev. 5 (10), 100593 (2024).
Zhang, S. et al. Association of retinal age gap and risk of kidney failure: A UK biobank study. Am. J. Kidney Dis. 81, 537–544e1 (2023).
Zeng, X. et al. Associations of metabolically healthy obesity and retinal age gap. Transl. Vis. Sci. Technol. 13 (11), 26 (2024).
Tang, H. et al. Association between biological aging and diabetic retinopathy. Sci. Rep. 14, 10123 (2024).
Alfego, D. et al. Chronic kidney disease testing among at-risk adults in the U.S. remains low: real-world evidence from a national laboratory database. Diabetes Care. 44, 2025–2032 (2021).
Afkarian, M. et al. Kidney disease and increased mortality risk in type 2 diabetes. J. Am. Soc. Nephrol. 24, 302–308 (2013).
Matsuzawa, Y., Funahashi, T. & Nakamura, T. The concept of metabolic syndrome: contribution of visceral fat accumulation and its molecular mechanism. J. Atheroscler. Thromb. 18 (8), 629–639 (2011).
Modjtahedi, B. S. et al. Severity of diabetic retinopathy and the risk of future cerebrovascular disease, cardiovascular disease, and all-cause mortality. Ophthalmology 128, 1169–1179 (2021).
Lee, A. K. et al. The risks of cardiovascular disease and mortality following weight change in adults with diabetes: Results from ADVANCE. J. Clin. Endocrinol. Metab. 105 (1), 152–162 (2020).
Hainer, V. & Aldhoon-Hainerová, I. Obesity paradox does exist. Diabetes Care. 36 (Suppl 2), S276–S281 (2013).
Khan, S. S. et al. Novel prediction equations for absolute risk assessment of total cardiovascular disease incorporating cardiovascular-kidney-metabolic health: A scientific statement from the American Heart Association. Circulation. 148 (24), 1982–2004 (2023).
Acknowledgements
The authors wish to acknowledge the lasting contributions of Dr. David Squirrell, who passed away during the preparation of this manuscript. We dedicate this work to his memory in recognition of his significant role in its conception and development.
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D.S., E.V., and M.V.M. contributed to the design of the work, the analysis and interpretation of data, drafting of the manuscript, and reviewing it for important intellectual content. C.N., S.A., S.M., S.Y., L.X., and A.R. contributed to the acquisition, analysis, and interpretation of data, and reviewing the manuscript for important intellectual content. M.K.D., H.H., and R.N.W. contributed to interpretation of data and reviewing the manuscript for important intellectual content. All authors read and approved the final manuscript.
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DS, CN, SA, SM, SY, LX, AR, EV and MVM report employment by Toku, Inc. MVM reports compensation by Porter Health for consultant services. MKD and HH report employment by Topcon Healthcare. RNW reports compensation by Toku, Inc. for consultant and board of directors services and by Topcon Healthcare for consultant services, as well as research instruments from Topcon, Visionix, Centervue, and Konan. We sought to mitigate the risk of bias by fully documenting our methods so they can be scrutinized and replicated by independent groups. The CKM-specific analysis plan was prespecified before the CKM associations with retinal BioAgeGap were examined. All data extraction and quality control procedures adhered to standard operating procedures established in our prior peer-reviewed retinal BioAge studies.
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Squirrell, D., Nielsen, C., Vaghefi, E. et al. Retinal BioAge is associated with indicators of cardiovascular-kidney-metabolic syndrome in UK and US populations. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41465-8
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DOI: https://doi.org/10.1038/s41598-026-41465-8
