AI and UK Biobank data used to predict early onset of multiple age-related diseases
Key Takeaways:
- Researchers at the University of Westminster have developed an AI model capable of predicting the early onset of 38 age-related diseases by analysing extensive UK Biobank health data.
- The study identified three major clusters of diseases where early onset of one condition often signals increased risk of others.
- This predictive method estimates risk from birth, enabling earlier interventions to slow disease progression and reduce strain on healthcare systems.
New AI approach to predict disease onset
A research team from the University of Westminster’s Research Centre for Optimal Health (ReCOH) has created an artificial intelligence (AI) method capable of predicting the early onset of 38 age-related diseases. The approach relies on analysis of large-scale health data from the UK Biobank and could help clinicians intervene before symptoms appear, improving long-term outcomes and easing pressure on healthcare services.
The findings, published in GeroScience on 27 June 2025, show that conditions such as rheumatoid arthritis and dementia could be detected at a pre-symptomatic stage. This allows for timely preventive measures, potentially delaying the onset of disease.
Large-scale data enables powerful predictions
The research analysed health information from more than 60,000 UK Biobank participants. Data included:
- Blood test results
- Body measurements
- Magnetic resonance imaging (MRI) scans
- Detailed medical histories
These were used to train a neural network-based risk prediction model. Unlike conventional approaches, which predict health risks from the date of a specific health check, this model estimates risk from birth. This means it can identify people who may be ageing more rapidly than average, allowing for earlier, targeted interventions.
Lead author Dr Mica Ji explained the importance of this approach:
“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.”
Identifying disease clusters
The model was applied to 47 different health conditions to examine which tend to occur together and to determine the most important predictors of disease onset. The analysis revealed three distinct clusters:
- Cardiometabolic diseases
- Digestive-neuropsychiatric diseases
- Vascular-neuropsychiatric diseases
The study found that developing one condition within these clusters at an earlier-than-average age often indicated a heightened risk of developing others in the same group.
Imaging’s role in early detection
Professor Louise Thomas, Professor of Metabolic Imaging at the University of Westminster and a close contributor to the UK Biobank imaging project, emphasised the significance of precise body measurements in disease prediction:
“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.”
UK Biobank imaging milestone
In related news, UK Biobank announced that more than 100,000 participants have now undergone whole-body scans as part of its extensive imaging project. The initiative aims to enhance early detection, refine diagnosis, and inform more personalised treatment plans across a wide range of health conditions.
