Researchers identify 5 subgroups of patients with type 2 diabetes

The Swedish study grouped patients based on six key variables, including presence of glutamate decarboxylase antibodies, age at diagnosis, body mass index, HbA1c level, and estimates of beta-cell function and insulin resistance.


Using clinical and genetic characteristics, researchers recently identified five distinct categories of adult patients with type 2 diabetes.

The cluster analysis included 8,980 patients with newly diagnosed diabetes from the Swedish All New Diabetics in Scania cohort (a longitudinal cohort that aims to obtain baseline metabolic phenotypes of all patients with newly diagnosed diabetes in the Scandia region of Sweden). Six key variables were measured at baseline and used to characterize each cluster: glutamate decarboxylase antibodies (GADA), age at diagnosis, body mass index (BMI), HbA1c level, and homoeostatic model assessment 2 estimates of beta-cell function (based on C-peptide levels) and insulin resistance. Results were published online on March 1 by The Lancet Diabetes & Endocrinology.

Researchers identified the following five categories of newly diagnosed adults with type 2 diabetes:

  • Cluster 1: 577 (6.4%) patients; characterized by early-onset disease, relatively low BMI, poor metabolic control, insulin deficiency, and presence of GADA (labeled as severe autoimmune diabetes)
  • Cluster 2: 1,575 (17.5%) patients; GADA-negative, but otherwise similar to cluster 1, with low age at onset, relatively low BMI, low insulin secretion, and poor metabolic control (labeled as severe insulin-deficient diabetes)
  • Cluster 3: 1,373 (15.3%) patients; characterized by insulin resistance and high BMI (labeled as severe insulin-resistant diabetes)
  • Cluster 4: 1,942 (21.6%) patients; characterized by obesity but not by insulin resistance (labeled as mild obesity-related diabetes)
  • Cluster 5: 3,513 (39.1%) patients; older than patients in other clusters but, similar to cluster 4, with only modest metabolic abnormalities (labeled as mild age-related diabetes)

Results were similar when researchers replicated the clusters in three other Scandinavian cohorts. Between clusters, the genetic characteristics and risk of diabetes-related complications differed. For example, patients with severe insulin-resistant diabetes had a substantially increased risk of kidney complications compared to those in clusters 4 and 5, despite receiving similar diabetes treatment. Patients in cluster 2 had the highest prevalence of retinopathy among the clusters.

The study authors noted limitations to their analysis, such as how it was derived primarily from Scandinavian patients, how only two types of autoantibodies were measured, and how data on such known risk factors for complications as blood pressure and blood lipids were unavailable. They noted that a web-based tool to assign patients to specific clusters is under development.

Future studies will need to assess whether a patient's classification changes as he or she ages, according to an accompanying editorial comment. In addition, among the important limitations of the study is the fact that the model is partly based on C-peptide concentration, which is not commonly measured in clinic, the comment noted.

“Nevertheless, the finding that simple parameters assessed at the time of diagnosis could reliably stratify patients with diabetes according to prognosis is compelling and poses the challenge of development of methods to predict outcomes of patients with type 2 diabetes that are more generalisable and comprehensive,” the editorialist wrote.