Retinal age gap as a predictive biomarker for mortality risk

AimTo develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk.MethodsA total of 80 169 fundus images taken from 46 969 participants...

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Published in:British journal of ophthalmology Vol. 107; no. 4; pp. 547 - 554
Main Authors: Zhu, Zhuoting, Shi, Danli, Guankai, Peng, Tan, Zachary, Shang, Xianwen, Hu, Wenyi, Liao, Huan, Zhang, Xueli, Huang, Yu, Yu, Honghua, Meng, Wei, Wang, Wei, Ge, Zongyuan, Yang, Xiaohong, He, Mingguang
Format: Journal Article
Language:English
Published: BMA House, Tavistock Square, London, WC1H 9JR BMJ Publishing Group Ltd 01.04.2023
BMJ Publishing Group LTD
Subjects:
Age differences
Aging
Biobanks
Biomarkers
Body mass index
Cancer
Clinical science
Datasets
Deep learning
Diabetes
Fundus Oculi
Humans
Mortality
Retina
Telemedicine
United Kingdom > UK
telemedicine
ISSN:0007-1161, 1468-2079, 1468-2079
Online Access:Get full text
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Summary:AimTo develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk.MethodsA total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality.ResultsThe DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality.ConclusionsOur findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.
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ISSN:0007-1161
1468-2079
1468-2079
DOI:10.1136/bjophthalmol-2021-319807