Physicians know every patient is unique. This maxim has been emphasized with increased emphasis on personalized medicine. Recent research from Sweden suggests that the two-pronged classification of diabetes—as type 1 or 2—may not be the best method for diagnosing and treating the disease.

Published online March 1 in The Lancet: Diabetes & Endocrinology, one study—led by Emma Ahlqvist, PhD, with the Lund University Diabetes Center in Malmo, Sweden—suggests patients be divided into five subgroups by disease progression and complications. Such an approach could improve precision care for those early in treatment.

“[O]ur data suggest that the combined information from a few variables central to the development of diabetes is superior to measurement of only one metabolite, glucose,” Ahlqvist said. “Through combining this information from diagnosis with information in the health-care system, this study provides a first step towards a more precise, clinically useful stratification, representing an important step towards precision medicine in diabetes.”

The team, from Lund University and the Institute for Molecular Medicine in Helsinki, examined data from 14,625 patients in five cohorts between Jan. 2008 and Nov. 2016. Median follow-up was 4.01 years

The researchers identified five “clusters” of patients according to the following characteristics:

• Cluster 1 (6.4 percent of patients): Early-onset, low BMI, poor metabolic control, insulin deficiency and severe autoimmune diabetes (SAID).
• Cluster 2 (17.5 percent): Severe insulin-deficient diabetes (SIDD) and similar to Cluster 1.
• Cluster 3 (15.3 percent): Severe insulin resistant diabetes (SIRD) with insulin resistance and high BMI.
• Cluster 4 (21.6 percent): Obese by not insulin resistant, labeled as mild obesity-related diabetes.
• Cluster 5 (39.1 percent): Mild age-related diabetes, older than patients in other clusters and only modest metabolic derangements.

“A data-driven cluster analysis of six simple variables measured at diagnosis in adult patients with newly diagnosed diabetes identified five replicable clusters of patients with significantly different characteristics and risk of diabetic complications,” the authors wrote. “These included a cluster of very insulin-resistant individuals with significantly higher risk of diabetic kidney disease than the other clusters, a cluster of relatively young insulin-deficient individuals with poor metabolic control (high HbA1c), and a large group of elderly patients with the most benign disease course.”