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Clinical science
Retinal age gap as a predictive biomarker for mortality risk
  1. Zhuoting Zhu1,
  2. Danli Shi2,
  3. Peng Guankai3,
  4. Zachary Tan4,
  5. Xianwen Shang1,
  6. Wenyi Hu4,
  7. Huan Liao5,
  8. Xueli Zhang1,
  9. Yu Huang1,
  10. Honghua Yu1,
  11. Wei Meng3,
  12. Wei Wang2,
  13. Zongyuan Ge6,7,
  14. Xiaohong Yang1,
  15. Mingguang He1,2,4
  1. 1Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
  2. 2State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, People's Republic of China
  3. 3Guangzhou Vision Tech Medical Technology Co., Ltd, GuangZhou, China
  4. 4Centre for Eye Research Australia; Ophthalmology, University of Melbourne, East Melbourne, Victoria, Australia
  5. 5Neural Regeneration Group, Institute of Reconstructive Neurobiology, University of Bonn, Bonn, Germany
  6. 6Monash e-Research Centre, Monash University, Melbourne, Victoria, Australia
  7. 7Monash Medical AI Group, Monash University, Melbourne, Victoria, Australia
  1. Correspondence to Dr Mingguang He, Centre for Eye Research Australia, East Melbourne, Victoria, Australia; mingguang_he{at}yahoo.com; Dr Xiaohong Yang; syyangxh{at}scut.edu.cn; Dr Zongyuan Ge; Zongyuan.ge{at}monash.edu; Dr Honghua Yu; yuhonghua{at}gdph.org.cn

Abstract

Aim To 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.

Methods A 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.

Results The 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.

Conclusions Our 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.

  • telemedicine

Data availability statement

Data are available in a public, open access repository.

http://dx.doi.org/10.1136/bjophthalmol-2021-319807

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    Data availability statement

    Data are available in a public, open access repository.

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    Footnotes

    • Contributors ZZ and DS conceptualised and designed the study with WW, MH and XY. ZZ and DS did the literature search and wrote the first draft of the manuscript. DS, PG, ZG and WM did the deep learning modelling, ZZ, XS and WW did the statistical analysis. HY, ZG, MH and XY had full access to all of the data. MH was the guarantor. All authors commented on the manuscript.

    • Funding The present work was supported by Fundamental Research Funds of the State Key Laboratory of Ophthalmology (no grant number), National Natural Science Foundation of China (82000901, 82101173, 81870663, 82171075), Outstanding Young Talent Trainee Program of Guangdong Provincial People’s Hospital (KJ012019087), Guangdong Provincial People’s Hospital Scientific Research Funds for Leading Medical Talents and Distinguished Young Scholars in Guangdong Province (KJ012019457), Talent Introduction Fund of Guangdong Provincial People’s Hospital (Y012018145), Science and Technology Program of Guangzhou, China (202002020049), Project of Special Research on Cardiovascular Diseases (2020XXG007) and Research Foundation of Medical Science and Technology of Guangdong Province (B2021237). MH receives support from the University of Melbourne at Research Accelerator Program (no grant number) and the CERA Foundation (no grant number). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian State Government. The sponsor or funding organisation had no role in the design or conduct of this research.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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