ArticleBMC medicine2022
Retinal age gap as a predictive biomarker of stroke risk.
Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- Retinal imaging for the assessment of stroke risk: a systematic review.Journal of neurology · 2024Pooled it
- Retinal age gap in unilateral retinal diseases with unaffected fellow eye: phakia and vascular pathology impact retinal age prediction.Japanese journal of ophthalmology · 2026Article
- Deep learning-derived retinal age gap and its associations with lifestyle, systemic, and ocular health in a health screening cohort.GeroScience · 2026Article
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Predicting stroke risk using retinal imaging with artificial intelligence: a scoping review of current evidence.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Proteomic clocks combined with deep learning phenotypes track eye aging and diseases.NPJ digital medicine · 2026Article
- Macula- Versus Disc-Centered Fundus Photography: Performance in Age-Prediction and Disease Associations.Investigative ophthalmology & visual science · 2026Article
- Fundus Stretch Index: A Centile-Based Retinal Measure of Myopia.Ophthalmology science · 2026Article
- Retinal biological age correlates with bone mineral density and fracture risk score and predicts incident osteoporosis.PLOS digital health · 2026Article
- Replication of the Association between Retinal Aging Clock Susceptibility Genes and Retinal Age Gap in an Asian Population: The Nagahama Study.Ophthalmology science · 2026Article
- Redefining Stroke Care with Artificial Intelligence: Recent Advances.Current neurology and neuroscience reports · 2026Review
- High-accuracy retinal age prediction via fundus-based multitask learning reveals the effect of systemic disease.Communications medicine · 2026Article
- Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from Patients with Diabetes.Ophthalmology science · 2026Article
- Modifiable risk factors associated with increased retinal age gap in an Australian population.GeroScience · 2026Article
- The Retinal Age Gap as a Marker of Accelerated Aging in the Early Course of Schizophrenia.Schizophrenia bulletin · 2026Article
- OCTA-Derived Retinal Biomarkers and Infarct Topography Improve Etiologic Classification of Recent Single Subcortical Infarction: A Nomogram Model.CNS neuroscience & therapeutics · 2026Article
- Quantitative color fundus photography parameters as potential biomarkers of axial length progression: evidence from a machine learning cohort study.Frontiers in cell and developmental biology · 2026Article
- A Review of Emerging Biomarkers Connecting Diabetes and Ischemic Stroke: Implications for Early Detection and Risk Stratification.Journal of diabetes research · 2026Review
- Predicting myopia risk using a machine learning model based on fundus imageomics.Scientific reports · 2025Article
- A deep learning system for detecting silent brain infarction and predicting stroke risk.Nature biomedical engineering · 2025Article
Corrections and comments
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Authors and funding
17 authors at 6 institutions in 3 countries.
Funding
Abstract
backgroundThe aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke.
methodsA total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction. Retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants. Stroke events were determined by data linkage to hospital records on admissions and diagnoses, and national death registers, whichever occurred earliest. Cox proportional hazards regression models were used to estimate the effect of retinal age gap on risk of stroke. Logistic regression models were used to estimate the predictive value of retinal age and well-established risk factors in 10-year stroke risk.
resultsA total of 35,304 participants without history of stroke at baseline were included. During a median follow-up of 5.83 years, 282 (0.80%) participants had stroke events. In the fully adjusted model, each one-year increase in the retinal age gap was associated with a 4% increase in the risk of stroke (hazard ratio [HR] = 1.04, 95% confidence interval [CI]: 1.00-1.08, P = 0.029). Compared to participants with retinal age gap in the first quintile, participants with retinal age gap in the fifth quintile had significantly higher risks of stroke events (HR = 2.37, 95% CI: 1.37-4.10, P = 0.002). The predictive capability of retinal age alone was comparable to the well-established risk factor-based model (AUC=0.676 vs AUC=0.661, p=0.511).
conclusionsWe found that retinal age gap was significantly associated with incident stroke, implying the potential of retinal age gap as a predictive biomarker of stroke risk.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.