In an analysis published in The Lancet Diabetes & Endocrinology, researchers have developed a novel diabetes classification system, separating adult-onset diabetes cases into five different types. “More accurately diagnosing diabetes could give us valuable insights into how it will develop over time, allowing us to predict and treat complications before they develop,” says the lead author of the study, Leif Groop, PhD, at the Department of Clinical Sciences, Lund University Diabetes Centre, Sweden, and the Institute for Molecular Medicine Finland.

Groop and colleagues performed a data-driven cluster analysis for 8,980 adults with newly diagnosed diabetes from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables: the presence of glutamate decarboxylase antibodies (GADA), age at diabetes diagnosis, BMI, HbA1c, and homoeostatic model assessment 2 estimates of beta-cell function and insulin resistance. Variables were related to prospective data from patient records on development of complications and prescription of medication. Researchers further replicated the findings in three independent cohorts.

The researchers identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes.

This new sub stratification is thought to eventually help tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes. The researchers also noted that a web-based tool to assign patients to specific clusters, is under development.

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