Overweight individuals, not solely those classified as obese, could soon be considered for weight-loss medication following the development of a new scientific tool. This innovation is designed to predict which people face a high risk of developing serious obesity-related complications.
Scientists behind the breakthrough suggest the tool could complement traditional body mass index (BMI) scores, offering a more accurate and personalised method to identify individuals at greater risk of conditions such as cancer and type 2 diabetes. This enhanced foresight could lead to earlier monitoring, proactive interventions – including weight-loss jabs – and ultimately, improved health outcomes for many.
Academics from Queen Mary University of London and the Berlin Institute of Health collaborated to create this open-access risk-prediction tool. Their aim is to assist the NHS and other health officials in formulating prioritisation strategies for those who would most benefit from weight-loss interventions.
The tool was developed using extensive data from 200,000 middle-aged adults participating in the UK Biobank study. Researchers analysed over 2,000 health factors, ranging from simple measures like age and sex to complex blood biomarkers.
Researchers examined information from people with a BMI score of 27 and over – meaning they are classed as overweight but not obese.
They said they wanted to include these people in the study because some people who are overweight can have excess body fat which can lead to “dramatic metabolic complications”.
Data from these participants was used to work out a model, fine-tune it and validate the results.
Their work, published in the journal Nature Medicine, identified 20 commonly collected health measures which can be used to predict 18 different obesity-related complications.
The tool they developed, dubbed Obscore, was found to perform well at predicting all of these 18 outcomes and was tested against data from other studies.
It puts people into low and high-risk groups.
Researchers said they wanted to examine the BMI scores of those in the highest-risk group.
“We actually observed that a considerable proportion of individuals were living with overweight rather than obesity,” said Dr Kamil Demircan, from Queen Mary University of London.
“These constitute a population of individuals who may be overlooked if we only look at BMI and not other risk factors.”
Co-author Professor Nick Wareham, co-director of the Institute of Metabolic Science at the University of Cambridge, said: “For the first time in my clinical career, we actually have some efficacious therapies for obesity, but we are operating in the context of an NHS which has finite resources, so we need accurate and fair mechanisms for allocating those resources.
“For therapies like semaglutide, the prescription threshold is predominantly based on BMI.
“For tirzepatide, it’s actually different, and that’s based on BMI plus evidence of weight-related health problems.
“And NHS England is starting very conservatively, which I think is appropriate, and is currently saying it will be eligible for those people at highest clinical need.”
He added: “So this is step on a journey that we think we need to move on – about how we evolve decision-making for weight reduction therapies.
“I think what needs to happen next is to take this very helpful score and to incorporate it, as the team have done with evidence from trials to show that people (are) not only at risk, but estimate what their capacity to benefit is.”
Lead author Professor Claudia Langenberg, director of Queen Mary University of London’s Precision Healthcare University Research Institute and head of the computational medicine group at Berlin Institute of Health, said: “With obesity affecting a growing proportion of the global population, preventing its long-term health complications has become a major challenge for healthcare systems.
“Our work shows how… large-scale health data can be used to develop data-driven frameworks that identify individuals at higher risk of developing complications and may help support more risk-based approaches to manage obesity.”
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