AI Model Trained on UK Biobank Predicts Early Disease Onset

Unlike traditional models that predict risk from the point of a medical check-up, this AI model estimates disease risk from birth. This means healthcare providers can identify individuals who are ageing faster and intervene proactively to delay the onset of illness.

A team of researchers at the University of Westminster’s Research Centre for Optimal Health (ReCOH) has developed an AI-driven model capable of predicting the early onset of 38 age-related diseases, using extensive health data from the UK Biobank.

The breakthrough study, published in GeroScience on 27 June 2025, marks a significant advancement in the use of artificial intelligence for preventive healthcare.

Health records from over 60,000 UK Biobank participants were analysed, including blood tests, MRI scans, body measurements, and medical histories. Using this data, researchers trained a neural network to estimate the risk of individuals developing specific diseases earlier than average, sometimes even before symptoms appear.

Dr Mica Ji, who led the study, highlighted the importance of early diagnosis: “The biomedical community has long suspected that the age at which someone develops a health condition is as important of a clue to their health trajectory as the binary statement of whether they had or will have a diagnosis.

Our study provides evidence for this hypothesis by showing that early onset risk of a given health condition is generally a strong predictor of early onset of multiple other conditions.”

Unlike traditional models that predict risk from the point of a medical check-up, this AI model estimates disease risk from birth. This means healthcare providers can identify individuals who are ageing faster and intervene proactively to delay the onset of illness.

The researchers examined 47 health conditions and uncovered three major clusters where early development of one disease often signals elevated risk for others. These clusters were cardiometabolic, digestive-neuropsychiatric, and vascular-neuropsychiatric.

Professor Louise Thomas, professor of metabolic imaging at Westminster and contributor to the UK Biobank imaging project, commented on the broader implications:

“Mica’s research marks a significant advancement in our understanding of how and when age-related diseases develop.

By highlighting the critical role of precise imaging in detecting early physiological changes, this work underscores the value of detailed body measurements in predicting disease onset.

The ability to identify individuals at risk earlier and with greater accuracy paves the way for proactive, personalised interventions, ultimately helping to reduce risk and improve long-term health outcomes.”

The research has been made possible through the UK Biobank’s large-scale imaging initiative.

The organization recently announced that over 100,000 participants have undergone full-body scans, enhancing early detection, diagnosis, and the development of personalized treatments across a broad range of conditions.

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