UK Biobank data and AI help predict early onset of diseases

A study from the University of Westminster’s Research Centre for Optimal Health (ReCOH) has developed an AI method to predict the early onset of 38 age-related diseases through analysis of UK Biobank data.

The study, published in GeroScience on 27 June 2025, found that some of the diseases that could be predicted before symptoms appear include rheumatoid arthritis and dementia, with the method allowing doctors to act earlier and reduce pressure on healthcare systems.

Health data from more than 60,000 UK Biobank volunteers was analysed, including blood test results, body measurements, magnetic resonance imaging data and medical history, to build an AI-based risk prediction model that estimates the risk of individuals developing certain diseases earlier than average.

Dr Mica Ji, who led the study at the University of Westminster, said: “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.

“On a practical level, our paper is a showcase of the kind of large-scale multi-disease study that would not be possible without UK Biobank and its MRI imaging effort.

“The scale of UK Biobank data has been crucial to get the volume of data required to train the data-hungry neural network models in the study.”

Unlike traditional risk prediction models that only predict risk from the time of a health check, the new method predicts risk from birth, meaning doctors can identify people who are ageing faster and take steps to delay disease onset.

Researchers used the new model to look at 47 different health conditions to analyse which ones tend to occur together and reveal which factors are most important for predicting the timing of disease onset.

The study uncovered three distinct clusters of diseases – cardiometabolic, digestive-neuropsychiatric and vascular-neuropsychiatric – where developing one disease early often signals a higher risk of others.

Professor Louise Thomas, professor of metabolic imaging at Westminster and close contributor to the UK Biobank imaging project, said: “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.”

Meanwhile, UK Biobank announced that more than 100,000 participants have undergone whole-body scans as part of an imaging project to enable earlier detection, improved diagnosis and more personalised treatment for a range of conditions.