New Data Tool Aims to Prioritise Access to Weight-Loss Medication
A novel tool designed to identify individuals most at risk of obesity-related diseases could assist in determining who would benefit most from weight-loss medications, according to researchers.
Approximately two-thirds of adults in England are classified as overweight or obese, a trend that has raised concerns among health experts.
Researchers have now developed a tool that provides an accurate and personalised method to identify those at risk of conditions linked to obesity.
They suggest this tool could be instrumental in prioritising patients for interventions such as weight-loss injections, especially since access to these treatments within the NHS is limited and currently based primarily on having a high body mass index (BMI) alongside specific obesity-related health issues.
Prof Nick Wareham, from the Medical Research Council Epidemiology Unit, and a co-author of the study, clarified the purpose of the measure.
“It’s about developing and validating a score that can help with more rational resource allocation. So, can we prescribe therapy to those people who are most likely to need it and most likely to benefit from it – which is what we should do within the NHS,”
he said.
Application of AI and Machine Learning to Large-Scale Data
Published in the journal Nature Medicine, the research team applied a form of artificial intelligence known as interpretable machine learning to data from nearly 200,000 participants in the long-standing UK Biobank project. All participants had a BMI of 27 or higher, categorising them as overweight or obese.
This approach enabled the identification of 20 health, lifestyle, and demographic factors—including age, sex, total cholesterol, and creatinine levels—that could predict the 10-year risk of 18 different obesity-related complications, ranging from gout to stroke.
For each condition, participants were stratified into one of five equally sized risk categories, from low to high risk. The researchers then calculated the proportion of individuals within each category who developed the condition over a decade.
The validity of the tool, named Obscore, was tested using UK Biobank data as well as datasets from two independent health studies.
Implications for Risk Assessment Beyond BMI
The findings demonstrated that individuals with identical age, sex, and BMI can have markedly different risks for various obesity-related conditions. This supports the potential utility of the tool in guiding decisions about who should receive weight-loss interventions.
Moreover, for certain conditions such as type 2 diabetes, the highest risk group included a significant number of individuals who are overweight rather than obese.
“These constitute a population of individuals who may be overlooked if we only look at BMI and not other risk factors,”
said Kamil Demircan, a co-author of the study from Queen Mary University of London.
Validation Using Weight-Loss Drug Trial Data
The researchers also applied a version of the tool to data from participants in a randomised controlled trial of the weight-loss drug tirzepatide. This analysis confirmed that individuals predicted to be at highest risk for obesity-related conditions experienced weight loss comparable to others.
Expert Commentary and Future Considerations
Naveed Sattar, professor of cardiometabolic medicine at the University of Glasgow, who was not involved in the study, noted that many obesity-related conditions are closely interconnected, and for some, well-established and simpler risk scores are already in use. He also pointed out that several metrics used in the study are not routinely collected within the NHS.
“Overall, this work represents a thoughtful attempt to move towards more holistic risk prediction across multiple obesity‑related conditions,”
Sattar said.
“But substantial further development and validation will be required before such an approach can be translated into routine clinical practice.”
