Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock
- Google Research, United States
- Buck Institute for Research on Aging, United States
- Google Health, United States
- Post Graduate Institute of Medical Education and Research, India
- Zuckerberg San Francisco General Hospital, United States
- University of California, San Francisco, United States
Abstract
Biological age, distinct from an individual's chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Here we trained deep learning models on fundus images from the EyePACS dataset to predict individuals' chronological age. Our retinal aging clocking, 'eyeAge', predicted chronological age more accurately than other aging clocks (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UK Biobank, respectively). Additionally, eyeAge was independent of blood marker-based measures of biological age, maintaining an all-cause mortality hazard ratio of 1.026 even when adjusted for phenotypic age. The individual-specific nature of eyeAge was reinforced via multiple GWAS hits in the UK Biobank cohort. The top GWAS locus was further validated via knockdown of the fly homolog, Alk, which slowed age-related decline in vision in flies. This study demonstrates the potential utility of a retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, opening avenues for quick and actionable evaluation of gero-protective therapeutics.
Data availability
A subset of EyePACS data is freely available online (https://www.kaggle.com/competitions/diabetic-retinopathy-detection/data). To enquire about access to the full EyePACS dataset, researchers should contact Jorge Cuadros (jcuadros@eyepacs.com). Proposals and agreements are assessed internally at EyePACS and may be subject to ethics approvals. The UKB data are available for approved projects (application process detailed at https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access) through the UK Biobank Access Management System (https://www.ukbiobank.ac.uk) . We have deposited the derived data fields and model predictions following UKB policy, which will be available through the UK Biobank Access Management System. Full GWAS summary statistics are available in the Supplementary File. To develop the eyeAge model we used the TensorFlow deep learning framework, available at https://www.tensorflow.org . Code and detailed instructions for both model training and prediction of chronological age from fundus images is open-source and freely available as a minor modification (https://gist.github.com/cmclean/a7e01b916f07955b2693112dcd3edb60) of our previously published repository for fundus model training (https://zenodo.org/record/7154413).
Article and author information
Author details
Funding
NIH (T32AG000266-23)
- Kenneth A Wilson Jr
NIH (R01AG038688)
- Pankaj Kapahi
NIH (AG045835)
- Pankaj Kapahi
Larry L. Hillblom Foundation
- Pankaj Kapahi
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The UK Biobank study was reviewed and approved by the North West Multi-Centre Research Ethics Committee. For the EyePACS study, ethics review and IRB exemption was obtained using Quorum Review IRB (Seattle, WA).
Copyright
© 2023, Ahadi et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock
eLife 12:e82364.
https://doi.org/10.7554/eLife.82364
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