A new tool that can shed light on who is most at risk of obesity-related diseases could help identify people who would benefit most from weight-loss medications, researchers have said.
Recent data suggests about two-thirds of adults in England are overweight or obese – a situation that has caused concern among health experts.
Now researchers have developed a tool that, they say, offers an accurate and personalised approach to identifying those at risk of obesity-related conditions.
They add it could be useful for prioritising who should receive interventions, such as weight-loss jabs, given that access on the NHS is limited and currently based simply on having a high body mass index (BMI) and particular obesity-related health problems.
Prof Nick Wareham, of the University of Cambridge, a co-author of the study, said the measure was not about extending the use of particular therapies.
“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.
Writing in the journal Nature Medicine, the team reports how it applied a type of AI called interpretable machine learning to data from almost 200,000 participants of the long-running UK Biobank project, each of whom had a BMI of 27 or greater, meaning they are overweight or obese.
This ultimately allowed the team to identify 20 health, lifestyle and demographic features – including age, sex, total cholesterol, and creatinine levels – which could predict the 10-year risk of 18 different obesity-related complications, from gout to stroke.
More specifically, for each condition, researchers were able to place participants in one of five equal-sized categories, from low to high risk. And, for each category, the team calculated the proportion of people who had the condition over a 10-year period.
The team tested the validity of the tool, dubbed Obscore, using UK Biobank data, and datasets from two independent health studies.
The researchers say the work showed participants with the same age, sex and BMI can have very different risks for various obesity-related conditions, supporting the idea that the tool could help inform strategies for prioritising who should receive weight-loss interventions.
In addition, for some conditions, including type 2 diabetes, those deemed in the highest risk category included a considerable proportion of people 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.
The team also applied a version of the tool to data from participants in a randomised control trial for the weight-loss drug tirzepatide, confirming that people who would be predicted to be at highest risk for obesity-related conditions would experience a similar weight loss to others.
However, Naveed Sattar, a professor of cardiometabolic medicine at the University of Glasgow, who was not involved in the work, said many of the obesity-related conditions were closely interrelated, and for some, robust and more easily implemented risk scores already existed. In addition, he noted several of the metrics used in the study were not routinely available 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.”
