Retinal Age as a Predictive Biomarker for Mortality Risk
Authors: Zhu Zhuoting , Shi Danli , Peng Guankai , Tan Zachary , Shang Xianwen , Hu Wenyi , Liao Huan , Zhang Xueli , Huang Yu , Yu Honghua , Meng Wei , Wang Wei , Yang Xiaohong , He Mingguang 
Published: December 2020

SummaryBackgroundAgeing varies substantially, thus an accurate quantification of ageing is important. We developed a deep learning (DL) model that predicted age from fundus images (retinal age). We investigated the association between retinal age gap (retinal age-chronological age) and mortality risk in a population-based sample of middle-aged and elderly adults.MethodsThe DL model was trained, validated and tested on 46,834, 15,612 and 8,212 fundus images respectively from participants of the UK Biobank study alive on 28th February 2018. Retinal age gap was calculated for participants in the test (n=8,212) and death (n=1,117) datasets. Cox regression models were used to assess association between retinal age gap and mortality risk. A restricted cubic spline analyses was conducted to investigate possible non-linear association between retinal age gap and mortality risk.FindingsThe DL model achieved a strong correlation of 0·83 (P<0·001) between retinal age and chronological age, and an overall mean absolute error of 3·50 years. Cox regression models showed that each one-year increase in the retinal age gap was associated with a 2% increase in mortality risk (hazard ratio=1·02, 95% confidence interval:1·00-1·04, P=0·021). Restricted cubic spline analyses showed a non-linear relationship between retinal age gap and mortality (Pnon-linear=0·001). Higher retinal age gaps were associated with substantially increased risks of mortality, but only if the gap exceeded 3·71 years.InterpretationOur findings indicate that retinal age gap is a robust biomarker of ageing that is closely related to risk of mortality.FundingNational Health and Medical Research Council Investigator Grant, Science and Technology Program of Guangzhou.Research in contextEvidence before this studyAgeing at an individual level is heterogeneous. An accurate quantification of the biological ageing process is significant for risk stratification and delivery of tailored interventions. To date, cell-, molecular-, and imaging-based biomarkers have been developed, such as epigenetic clock, brain age and facial age. While the invasiveness of cellular and molecular ageing biomarkers, high cost and time-consuming nature of neuroimaging and facial ages, as well as ethical and privacy concerns of facial imaging, have limited their utilities. The retina is considered a window to the whole body, implying that the retina could provide clues for ageing.Added value of this studyWe developed a deep learning (DL) model that can detect footprints of aging in fundus images and predict age with high accuracy for the UK population between 40 and 69 years old. Further, we have been the first to demonstrate that each one-year increase in retinal age gap (retinal age-chronological age) was significantly associated with a 2% increase in mortality risk. Evidence of a non-linear association between retinal age gap and mortality risk was observed. Higher retinal age gaps were associated with substantially increased risks of mortality, but only if the retinal age gap exceeded 3·71 years.Implications of all the available evidenceThis is the first study to link the retinal age gap and mortality risk, implying that retinal age is a clinically significant biomarker of ageing. Our findings show the potential of retinal images as a screening tool for risk stratification and delivery of tailored interventions. Further, the capability to use fundus imaging in predicting ageing may improve the potential health benefits of eye disease screening, beyond the detection of sight-threatening eye diseases.