Researchers Use AI to Predict Lifespan and Health Based on Blood Data

A recent study showed that a metabolomic clock developed using a specific machine learning algorithm, called Cubist rule-based regression, was most strongly associated with most health and ageing markers. They also found that algorithms which can model non-linear relationships between metabolites and age generally performed best at capturing biological signal informative of health and lifespan. The study was published in Science Advances

Researchers conducted a comprehensive study to evaluate artificial intelligence based ageing clocks, which predict health and lifespan using data from blood.

The researchers trained and tested 17 machine learning algorithms using data on markers in the blood from over 225,000 UK Biobank participants, aged 40 to 69 years when they were recruited.

A person's metabolomic age, their "MileAge," is a measure of how old their body seems to be on the inside based on markers in the blood called metabolites. The difference between a person's metabolite-predicted age and their chronological age, termed MileAge delta, indicates whether their biological ageing is accelerated or decelerated.

Individuals with accelerated ageing were, on average, frailer, more likely to have a chronic illness, rated their health worse, and had a higher mortality risk. They also had shorter telomeres, which are a marker of cellular ageing and linked with age-related diseases such as atherosclerosis. However, decelerated biological ageing was only weakly linked with good health.

Ageing clocks could help spot early signs of declining health, enabling preventative strategies and interventions before disease onset. They may also allow people to proactively track their health, make better lifestyle choices, and take steps to stay healthy for longer.

Reference: https://www.kcl.ac.uk/news/researchers-ai-ageing-clocks-predict-health-lifespan

A recent study showed that a metabolomic clock developed using a specific machine learning algorithm, called Cubist rule-based regression, was most strongly associated with most health and ageing markers. They also found that algorithms which can model non-linear relationships between metabolites and age generally performed best at capturing biological signal informative of health and lifespan. The study was published in Science Advances

Researchers conducted a comprehensive study to evaluate artificial intelligence based ageing clocks, which predict health and lifespan using data from blood.

The researchers trained and tested 17 machine learning algorithms using data on markers in the blood from over 225,000 UK Biobank participants, aged 40 to 69 years when they were recruited.

A person's metabolomic age, their "MileAge," is a measure of how old their body seems to be on the inside based on markers in the blood called metabolites. The difference between a person's metabolite-predicted age and their chronological age, termed MileAge delta, indicates whether their biological ageing is accelerated or decelerated.

Individuals with accelerated ageing were, on average, frailer, more likely to have a chronic illness, rated their health worse, and had a higher mortality risk. They also had shorter telomeres, which are a marker of cellular ageing and linked with age-related diseases such as atherosclerosis. However, decelerated biological ageing was only weakly linked with good health.

Ageing clocks could help spot early signs of declining health, enabling preventative strategies and interventions before disease onset. They may also allow people to proactively track their health, make better lifestyle choices, and take steps to stay healthy for longer.

Reference: https://www.kcl.ac.uk/news/researchers-ai-ageing-clocks-predict-health-lifespan