ArticleAge and ageing2022
Retinal age gap as a predictive biomarker of future risk of Parkinson's disease.
Article in Age and ageing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.
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Who cites it
54 citing papers in PubMed, 2 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models.Translational vision science & technology · 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
- Fellow-eye retinal age gap and high injection burden over four years in neovascular age-related macular degeneration.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 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
- Deep learning aging marker from retinal images unveils sex-specific clinical and genetic signatures.Nature communications · 2026Article
- Estimating Accelerated Retinal Decline in Mental Health Disorders Through Normative Modeling.Schizophrenia bulletin · 2026Article
- Deep Learning-Derived Retinal Age Detects Cognitive Impairment.Ophthalmology science · 2026Article
- 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
- Replication of the Association between Retinal Aging Clock Susceptibility Genes and Retinal Age Gap in an Asian Population: The Nagahama Study.Ophthalmology science · 2026Article
- Retinal biological age correlates with bone mineral density and fracture risk score and predicts incident osteoporosis.PLOS digital health · 2026Article
- The retina-body axis: proteomic mechanisms linking oculomics and clinical traits in a female aging cohort.npj aging · 2026Article
- High-accuracy retinal age prediction via fundus-based multitask learning reveals the effect of systemic disease.Communications medicine · 2026Article
- The Retinal Age Gap as a Marker of Accelerated Aging in the Early Course of Schizophrenia.Schizophrenia bulletin · 2026Article
- Systemic and local vascular features in branch retinal vein occlusion: analysis of the retinal age gap and crossing pattern.BMJ open ophthalmology · 2026Observational
- Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.Frontiers in neurology · 2026Article
- Accelerated Biological Aging and Genetic Pleiotropy in Age-Related Eye Diseases: A Population-Based Cohort and Integrative Genetic Analysis.Investigative ophthalmology & visual science · 2025Article
- A framework of biomarkers for visual system aging: a consensus statement by the Aging Biomarker Consortium.Life medicine · 2025Article
Corrections and comments
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Authors and funding
16 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionretinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age-chronological age) and incident Parkinson's disease (PD).
methodsa deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value.
resultsa total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01-1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13-6.22, P = 0.024; HR = 4.86, 95% CI: 1.59-14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821).
conclusionretinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.
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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.