Simple clinical features can help personalise type 2 diabetes treatment

The new research, published in the The Lancet Diabetes and Endocrinology, provides evidence that using these characteristics is more simple and effective than a model identified by Swedish researchers last year, which divides adults with diabetes into five subgroups. Instead, the new research shows that using simple clinical features is a better way to guide treatment and identify people at increased risk of complications including kidney disease.

Dr John Dennis, of the University of Exeter Medical School, lead author of the study, said: “It’s recognised that not everyone with type 2 diabetes should be treated the same, yet there is currently no way to tell which tablet is likely to be the best for a particular person.

Our research shows that really simple clinical features such as age at diagnosis, sex, and kidney function provide a very effective and practical way to identify the best tablet for a particular person and to identify people at high risk of developing complications. Crucially, this approach does not mean reclassifying people into discrete subtypes of diabetes. Instead, we were able to use a person’s exact characteristics to provide more precise information to guide treatment.”

The new study, supported by the Medical Research Council, looked at data from more than 8,500 participants in two independent clinical trials. The researchers found that their simple approach provided much better information to doctors than the proposed classification of adult diabetes into five subtypes suggested by Swedish researchers in the same journal in May 2018.

Professor Andrew Hattersley, of the University of Exeter Medical School, who oversaw the research, said: “Managing people with type 2 diabetes is complex, and more evidence-based approaches are urgently required. Our research tested whether simple clinical characteristics are useful to help clinicians manage their patients. We found that using simple measurements available freely in clinic can lead to improved prediction of patient outcomes.”

The full paper is entitled ‘Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared to models based on simple clinical features: an evaluation using clinical trial data’.