AI Aging Clocks Reveal Health and Lifespan Insights
Researchers from King’s College London’s Institute of Psychiatry, Psychology & Neuroscience (IoPPN) have unveiled groundbreaking advancements in AI-based aging clocks. These tools estimate biological age using blood metabolite data and provide valuable predictions about health and lifespan. By analyzing data from more than 225,000 participants in the UK Biobank aged 40–69, the study highlighted significant correlations between accelerated aging, frailty, chronic illnesses, and mortality risks.
How Metabolomic Aging Clocks Work
Metabolomic age, dubbed “MileAge,” offers a personalized biological age based on blood metabolites—small molecules involved in metabolism. The MileAge delta, the difference between predicted and chronological age, measures the pace of biological aging. A positive delta indicates accelerated aging, while a negative delta suggests decelerated aging.
The study, published in Science Advances, marks a major milestone in developing and comparing machine learning algorithms for biological aging clocks. Among 17 tested algorithms, non-linear methods like Cubist regression performed best at identifying health risks and aging patterns.
Key Findings and Implications
The research uncovered stark health disparities tied to biological age:
- Accelerated Aging: Participants with higher metabolomic ages were frailer, more likely to suffer chronic illnesses, and had shorter telomeres, a marker of cellular aging. They also rated their health worse and faced higher mortality risks.
- Decelerated Aging: While associated with younger biological age, the link to overall health was relatively weak.
Aging clocks could revolutionize preventative healthcare by identifying early signs of health decline. This would allow individuals to adopt lifestyle changes and healthcare professionals to implement timely interventions.
Expert Insights on Biological Age
Dr. Julian Mutz, lead author and King’s Prize Research Fellow, emphasized the modifiability of biological age:
“Unlike chronological age, biological age can potentially change. Aging clocks offer valuable proxies for understanding health risks and guiding personal and public health strategies.”
Professor Cathryn Lewis, a senior author, highlighted the importance of big data in advancing aging research:
“Metabolomic aging clocks hold promise for personalized health assessments. This study validates their role in informing health and lifestyle choices.”
The Path Forward
As interest in biological aging continues to grow, researchers stress the need for further studies to refine these AI tools. Aging clocks powered by advanced algorithms may one day become a standard tool for monitoring health, enabling proactive care and improved quality of life.
