A prediction tool for identifying those most at risk of obesity-related conditions and helping to inform the use of weight loss injections has been developed by UK researchers.
Known as the OBSCORE Risk Calculator, the open-access tool could be used alongside body mass index (BMI) to provide a more accurate and personalised way to identify who might benefit from closer monitoring, earlier intervention or intensified treatment, the team said.
It needs further evaluation in clinical trials but could one day help the NHS to prioritise patients for pharmacological intervention, surgery or dietary changes, they added.
The OBSCORE Risk Calculator was developed with data from 200,000 middle-aged adults taking part in the UK Biobank study, with a BMI score of 27 or over.
More than 2,000 health factors ranging from simple measures such as age and sex, lifestyle information and blood biomarkers were analysed, with 20 indicators that most effectively predict the risk of developing obesity-related diseases or complications being identified.
Reported in the journal Nature Medicine, the results showed there were substantial differences in risk profiles for the 18 obesity-related complications tested among individuals within the same BMI category.
And people identified as being at the highest risk were not always those with the highest BMIs, the researchers noted.
A considerable proportion of individuals predicted to be at highest risk were people living with overweight rather than obesity, whose combination of metabolic and clinical factors increased their likelihood of developing complications.
Co-author Professor Nick Wareham, director of the Institute of Metabolic Science at the University of Cambridge, said the score would help with ‘more rational resource allocation’ to those who would benefit most.
‘We now have effective treatments for obesity, but the NHS has finite resources so we need accessible and fair mechanisms for allocating them,’ he said.
Data-driven frameworks for risk-based approaches to obesity
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 the Berlin Institute of Health based at Charité – Universitätsmedizin Berlin, added: ‘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 deeply phenotyped 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.’
Naveed Sattar, professor of cardiometabolic medicine and honorary consultant at the University of Glasgow, pointed out that several variables in the study were not routinely accessible in NHS records currently.
And he noted for conditions like cardiovascular disease and type 2 diabetes there were already well-established risk scores in use.
‘Overall, this study represents a considered step towards more integrated risk prediction across multiple obesity‑related conditions, but considerable further refinement and validation will be needed before such an approach is suitable for routine clinical implementation.
He added: ‘It is also worth noting that effective weight‑loss interventions are increasingly being evaluated across many obesity‑related conditions, and as treatment costs decline, the reliance on such risk scores may diminish over the coming decade.’
A version of this article was originally published by our sister publication Pulse.