Physiological and molecular changes associated with aging increase a person’s chances of getting sick and dying. Researchers can find ways to decrease the prevalence and severity of disease by measuring and estimating biomarkers of aging. To distinguish between a person’s biological age and their chronological age, scientists have devised “aging clocks” that use biomarkers such as blood proteins or DNA methylation to estimate a person’s biological age. These aging clocks can estimate the risk of developing an age-related disease. However, due to the need for a blood sample, other methods of locating equivalent measurements may make aging data more accessible.
Recent research published in “Longitudinal Fundus Imaging and Its Genome-Wide Association Analysis Provide Evidence for a Human Retinal Aging Clock” demonstrates that deep learning models can reliably estimate the biological age of a person from an image of the retina and provide new insights into age prediction. related diseases. The researchers are also making available updated source code for these models based on previously disclosed ML frameworks for processing retinal images.
Age estimation using retinal photographs
Anonymized retinal images from multiple primary care clinics were used to train a model to predict the chronological age of participants in a telemedicine-based blindness prevention program. The performance of the resulting model was measured using a hidden dataset of 50,000 retinal images and the main UKBiobank dataset of around 120,000 images. Given the moniker eyeAge, the model’s projections agree quite well with people’s chronological ages.
A precise aging clock based on retinal images has never been manufactured before.
Comparison between expected and actual age difference
Although eyeAge is highly correlated with chronological age in many samples, there are still certain situations where the model predicts a number significantly younger or older than the chronological age, as seen in the figure above. This may suggest that the model is picking up details in the retinal images that indicate real-world biological consequences relevant to age-related disorders.
Applications
- This facilitates the identification of genes whose activities could be altered by drugs to promote healthy aging and the discovery of indicators of aging and age-related disorders.
- The impact of lifestyle behaviors and treatments such as exercise, food, and medications on biological aging may also be better understood.
- The eyeAge clock could be used in the pharmaceutical industry to measure the efficacy of anti-aging and rejuvenation drugs.
- Researchers can determine whether these therapies successfully slow or reverse the aging process by monitoring changes in the retina over time.
The results also show that the blood biomarker-based aging clock cannot be compared to the retina-based aging clock used by eyeAge. EyeAge could be used for actionable biological and behavioral therapies in contrast to conventional aging clocks, as imaging is less intrusive than blood tests. When combined with other indicators, it provides a comprehensive understanding of an individual’s biological age and allows researchers to examine aging from a new perspective.
Predictive aging clocks have been used to gain more information about a person’s biological age, which differs from a person’s chronological age. However, its accuracy over shorter periods could be much better. In this study, the researchers used fundus photographs from the EyePACS dataset to train deep learning models to estimate people’s ages. Compared to alternative aging clocks, the ‘eyeAge’ retinal aging clock predicted chronological age more accurately (mean absolute error of 2.86 and 3.30 years on quality-filtered data from EyePACS and UK Biobank , respectively).
Controlling for phenotypic age, the hazard ratio for all-cause mortality in eyeAge remained at 1,026, demonstrating its independence from blood marker-based assessments of biological age. Multiple GWAS findings in the UK Biobank sample supported the uniqueness of eyeAge across people. Deletion of Alk in the flies helped the superior GWAS locus by reversing age-related deterioration in the eyesight of the flies. This research illustrates the promise of a retinal aging clock as a tool to investigate aging and age-related disorders and to quantitatively quantify aging on extremely short time scales, paving the way for rapid and actionable evaluation of geroprotective drugs. .
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Dhanshree Shenwai is a Computer Engineer and has good experience in FinTech companies covering Finance, Cards & Payments and Banking domain with strong interest in AI applications. She is enthusiastic about exploring new technologies and advancements in today’s changing world, making everyone’s life easier.