Longitudinal fundus imaging and its genome-wide association analysis provides evidence for a human retinal aging clock
Abstract
Background
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. Deep learning approaches on imaging datasets of the eye have proven powerful for a variety of quantitative phenotype inference and provide an opportunity to explore organismal aging and tissue health.
Methods
Here we trained deep learning models on fundus images from the EyePacs dataset to predict individuals’ chronological age. These predictions lead to the concept of a retinal aging clock which we then employed for a series of downstream longitudinal analyses. The retinal aging clock was used to assess the predictive power of aging inference, termed eyeAge, on short time-scales using longitudinal fundus imaging data from a subset of patients. Additionally, the model was applied to a separate cohort from the UK Biobank to validate the model and perform a GWAS. The top candidate gene was then tested in a fly model of eye aging.
Findings
EyeAge was able to predict the age with a mean absolute error of 3.26 years, which is much less than other aging clocks. Additionally, eyeAge was highly independent of blood marker-based measures of biological age (e.g. “phenotypic age”), maintaining a hazard ratio of 1.026 even in the presence of phenotypic age. Longitudinal studies showed that the resulting models were able to predict individuals’ aging, in time-scales less than a year with 71% accuracy. Notably, we observed a significant individual-specific component to the prediction. This observation was confirmed with the identification of multiple GWAS hits in the independent UK Biobank cohort. The knockdown of the top hit, ALKAL2, which was previously shown to extend lifespan in flies, also slowed age-related decline in vision in flies.
Interpretation
In conclusion, predicted age from retinal images can be used as a biomarker of biological aging in a given individual independently from phenotypic age. This study demonstrates the utility of retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, potentially opening avenues for quick and actionable evaluation of gero-protective therapeutics.
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. Deep learning approaches on imaging datasets of the eye have proven powerful for a variety of quantitative phenotype inference and provide an opportunity to explore organismal aging and tissue health.
Methods
Here we trained deep learning models on fundus images from the EyePacs dataset to predict individuals’ chronological age. These predictions lead to the concept of a retinal aging clock which we then employed for a series of downstream longitudinal analyses. The retinal aging clock was used to assess the predictive power of aging inference, termed eyeAge, on short time-scales using longitudinal fundus imaging data from a subset of patients. Additionally, the model was applied to a separate cohort from the UK Biobank to validate the model and perform a GWAS. The top candidate gene was then tested in a fly model of eye aging.
Findings
EyeAge was able to predict the age with a mean absolute error of 3.26 years, which is much less than other aging clocks. Additionally, eyeAge was highly independent of blood marker-based measures of biological age (e.g. “phenotypic age”), maintaining a hazard ratio of 1.026 even in the presence of phenotypic age. Longitudinal studies showed that the resulting models were able to predict individuals’ aging, in time-scales less than a year with 71% accuracy. Notably, we observed a significant individual-specific component to the prediction. This observation was confirmed with the identification of multiple GWAS hits in the independent UK Biobank cohort. The knockdown of the top hit, ALKAL2, which was previously shown to extend lifespan in flies, also slowed age-related decline in vision in flies.
Interpretation
In conclusion, predicted age from retinal images can be used as a biomarker of biological aging in a given individual independently from phenotypic age. This study demonstrates the utility of retinal aging clock for studying aging and age-related diseases and quantitatively measuring aging on very short time-scales, potentially opening avenues for quick and actionable evaluation of gero-protective therapeutics.
Research Areas
