Longitudinal fundus imaging and its genome-wide association analysis provide evidence for a human retinal aging clock

  1. Sara Ahadi  Is a corresponding author
  2. Kenneth A Wilson Jr
  3. Boris Babenko
  4. Cory Y McLean  Is a corresponding author
  5. Drew Bryant
  6. Orion Pritchard
  7. Ajay Kumar
  8. Enrique M Carrera
  9. Ricardo Lamy
  10. Jay M Stewart
  11. Avinash Varadarajan
  12. Marc Berndl
  13. Pankaj Kapahi  Is a corresponding author
  14. Ali Bashir
  1. Google Research, United States
  2. Buck Institute for Research on Aging, United States
  3. Google Health, United States
  4. Post Graduate Institute of Medical Education and Research, India
  5. Zuckerberg San Francisco General Hospital, United States
  6. 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).

The following previously published data sets were used

Article and author information

Author details

  1. Sara Ahadi

    Google Research, Mountain View, United States
    For correspondence
    saraahadi@gmail.com
    Competing interests
    Sara Ahadi, Sara Ahadi is not currently affiliated with Google Research, however work for this manuscript was conducted while affiliated with Google Research. The author has no other competing interests to declare..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7849-2135
  2. Kenneth A Wilson Jr

    Kapahi Lab, Buck Institute for Research on Aging, Novato, United States
    Competing interests
    No competing interests declared.
  3. Boris Babenko

    Google Health, Palo Alto, United States
    Competing interests
    Boris Babenko, Boris Babenko is affiliated with Google Health. The author has no other competing interests to declare..
  4. Cory Y McLean

    Google Health, Cambridge, United States
    For correspondence
    cym@google.com
    Competing interests
    Cory Y McLean, Cory Y McLean is affiliated with Google Health. The author has no other competing interests to declare..
  5. Drew Bryant

    Google Research, Mountain View, United States
    Competing interests
    Drew Bryant, Drew Bryant is affiliated with Google Research. The author has no other competing interests to declare..
  6. Orion Pritchard

    Google Research, Mountain View, United States
    Competing interests
    Orion Pritchard, Orion Pritchard is affiliated with Google Research. The author has no other competing interests to declare..
  7. Ajay Kumar

    Department of Biophysics, Post Graduate Institute of Medical Education and Research, Chandigarh, India
    Competing interests
    No competing interests declared.
  8. Enrique M Carrera

    Kapahi Lab, Buck Institute for Research on Aging, Novato, United States
    Competing interests
    No competing interests declared.
  9. Ricardo Lamy

    Department of Ophthalmology, Zuckerberg San Francisco General Hospital, San Francisco, United States
    Competing interests
    No competing interests declared.
  10. Jay M Stewart

    Department of Ophthalmology, University of California, San Francisco, San Francisco, United States
    Competing interests
    No competing interests declared.
  11. Avinash Varadarajan

    Google Health, Palo Alto, United States
    Competing interests
    Avinash Varadarajan, Avinash Varadarajan is affiliated with Google Health. The author has no other competing interests to declare..
  12. Marc Berndl

    Google Research, Mountain View, United States
    Competing interests
    Marc Berndl, Marc Berndl is affiliated with Google Research. The author has no other competing interests to declare..
  13. Pankaj Kapahi

    Kapahi Lab, Buck Institute for Research on Aging, Novato, United States
    For correspondence
    Pkapahi@buckinstitute.org
    Competing interests
    Pankaj Kapahi, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5629-4947
  14. Ali Bashir

    Google Research, Mountain View, United States
    Competing interests
    Ali Bashir, Ali Bashir is not currently affiliated with Google Research, however work for this manuscript was conducted while affiliated with Google Research. The author has no other competing interests to declare..

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.

Reviewing Editor

  1. Sara Hägg, Karolinska Institutet, Sweden

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).

Version history

  1. Preprint posted: July 27, 2022 (view preprint)
  2. Received: August 2, 2022
  3. Accepted: March 22, 2023
  4. Accepted Manuscript published: March 28, 2023 (version 1)
  5. Version of Record published: April 17, 2023 (version 2)

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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  1. Sara Ahadi
  2. Kenneth A Wilson Jr
  3. Boris Babenko
  4. Cory Y McLean
  5. Drew Bryant
  6. Orion Pritchard
  7. Ajay Kumar
  8. Enrique M Carrera
  9. Ricardo Lamy
  10. Jay M Stewart
  11. Avinash Varadarajan
  12. Marc Berndl
  13. Pankaj Kapahi
  14. Ali Bashir
(2023)
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

Share this article

https://doi.org/10.7554/eLife.82364

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