Exeter, UK—Research using data-driven cluster analysis to propose five subgroups of type 2 diabetes got a lot of notice when it came out last year.

That model focused on differences in diabetes progression and risk of complications, but a new analysis sought to compare the clinical utility of the subgrouping while proposing an alternative strategy of developing models for each outcome using simple patient characteristics.

The new article was published recently in the same journal as the previous research, The Lancet Diabetes and Endocrinology.

The study team offers evidence that their method is a better way to guide treatment and identify people at increased risk of complications, including kidney disease.

Lead author John Dennis, PhD, of the University of Exeter Medical School, said that although “it’s recognized that not everyone with type 2 diabetes should be treated the same,” there is no foolproof way to determine which treatment is best.

“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,” Dennis noted. “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 UK’s Medical Research Council, looked at data from more than 8,500 participants in two independent clinical trials.

Researchers identified five clusters in the ADOPT trial using the same data-driven cluster analysis as in the previous report. The study team investigated differences between clusters in glycemic and renal progression, contrasting the five-type approach with stratification using simple continuous clinical features. Those included age at diagnosis for glycemic progression and baseline renal function for renal progression.

The team then compared the effectiveness of a strategy of selecting glucose-lowering therapy using clusters with one combining simple clinical features—sex, BMI, age at diagnosis, baseline HbA1c—in the 4,447-member RECORD independent trial cohort.

“We found differences in incidence of chronic kidney disease between clusters; however, estimated glomerular filtration rate at baseline was a better predictor of time to chronic kidney disease,” study authors write. “Clusters differed in glycemic response, with a particular benefit for thiazolidinediones in patients in the severe insulin-resistant diabetes cluster and for sulfonylureas in patients in the mild age-related diabetes cluster. However, simple clinical features outperformed clusters to select therapy for individual patients.”

Researchers add, “This finding suggests that precision medicine in type 2 diabetes is likely to have most clinical utility if it is based on an approach of using specific phenotypic measures to predict specific outcomes, rather than assigning patients to subgroups.”

Lead researcher Andrew Hattersley, DM, of the University of Exeter Medical School, explained, “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.”

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