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Researchers say having a biological age higher than your chronological age could raise your risk of death from any cause by 51 percent.
LONDON — Your chronological age tells only half the story. Behind the scenes, your body is running its own biological clock – one that might be ticking faster or slower than your years suggest. Now, in a study involving over 225,000 participants, researchers at King’s College London have found a way to read this hidden timekeeper through the chemical signatures in your blood.
This breakthrough research, conducted at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN), introduces MileAge – a sophisticated biological clock that analyzes small molecules in your blood to determine if your body is aging faster or slower than your chronological age would suggest. These molecular timekeepers could hold the key to predicting future health risks and potentially guiding interventions before problems arise.
The research team analyzed blood samples from an unprecedented 225,212 UK Biobank participants, aged 40 to 69 years. Using nuclear magnetic resonance spectroscopy – think of it as an extremely sophisticated chemical analyzer – they measured 168 different metabolites in each person’s blood. Metabolites are small molecules produced when your body breaks down food, drugs, and its own tissues, providing a snapshot of your current biological state.
What makes this study, published in Science Advances, particularly unique is its comprehensive approach to artificial intelligence. The researchers employed 17 different machine learning algorithms, essentially teaching computers to recognize patterns in how these metabolite levels change with age. The best-performing model could predict a person’s age within about 5.3 years on average. More importantly, when someone’s predicted biological age (or metabolic age) differed significantly from their actual age, it often signaled important health implications.
“Metabolomic aging clocks have the potential to provide insights into who might be at greater risk of developing health problems later in life,” says lead author Dr. Julian Mutz, King’s Prize Research Fellow at the IoPPN, in a statement. “Unlike chronological age, which cannot be changed, our biological age is potentially modifiable.”
The findings were striking: people whose metabolic age was older than their chronological age tended to be frailer, had shorter telomeres (protective caps on our DNA that naturally shrink as we age), were more likely to suffer from chronic illnesses, and rated their own health worse than others. Most critically, these individuals had a 51% higher risk of death from any cause during the study period.
Interestingly, the research revealed an asymmetric relationship between biological age and health outcomes. While having an “older” metabolic age strongly predicted poor health outcomes, having a “younger” metabolic age was only weakly associated with better health. This suggests that the relationship between biological age and health is more complex than previously thought.
“There is substantial interest in developing aging clocks that accurately assess our biological age,” says senior author Cathryn Lewis, Professor of Genetic Epidemiology & Statistics at the National Institute for Health and Care Research Maudsley Biomedical Research Centre. “Powerful big data analytics can play a critical role in advancing these tools. This study is an important milestone in establishing the potential of biological aging clocks and their ability to inform health choices.”
Looking ahead, MileAge could potentially be used in healthcare settings to identify people at higher risk of age-related diseases before symptoms appear. It might also help evaluate the effectiveness of interventions meant to slow aging or improve health.
Just as a watch tells us the time of day, MileAge might soon tell us the time of our lives – not in years, but in biological wear and tear. And unlike the relentless forward march of chronological time, this new understanding of aging suggests something both profound and hopeful: that while we can’t add years to our life, we might be able to add life to our years.
Paper Summary
Methodology
The researchers used blood samples from UK Biobank participants, analyzing them with nuclear magnetic resonance spectroscopy to measure 168 metabolites. They then employed various machine learning algorithms to create predictive models, training them on 90% of the data and testing on the remaining 10%. The models learned to associate patterns in metabolite levels with chronological age, creating a system that could predict someone’s biological age based on their metabolic profile.
Results
The study found that metabolite-predicted age could vary significantly from chronological age, with some people showing metabolic profiles suggesting they were up to 19 years younger or 16 years older than their actual age. Those with “older” metabolic profiles had worse health outcomes across multiple measures, including a 51% higher mortality risk. The researchers’ best model could predict age within about 5.3 years on average.
Limitations
The study primarily included middle-aged and older adults from the UK, potentially limiting its generalizability to other age groups and populations. The researchers also note that they couldn’t determine whether changes in metabolic age could be reversed through interventions, as this would require longitudinal studies. The technology used (NMR spectroscopy) focuses mainly on lipid-related metabolites, potentially missing other important biological markers.
Discussion and Takeaways
This research represents a significant advance in our ability to measure biological aging, providing a potentially powerful tool for health risk assessment and preventive medicine. The strong associations between metabolic age and various health outcomes suggest that this measure captures meaningful biological information beyond chronological age. The study’s large scale and comprehensive approach provide robust evidence for the utility of metabolite-based aging clocks in health assessment.
Funding and Disclosures
The research was funded by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. One of the researchers disclosed membership on a scientific advisory board and receiving speaker fees from certain companies, but these relationships were not deemed to affect the study’s outcomes.
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