Diabetes classification algorithm identifies subtype differences in Swedish cohort

Researchers developed a diabetes subtype classification algorithm in 18,985 participants from the All New Diabetics in Scania (ANDIS) cohort to assess limitations in register-recorded diagnoses. The observational study linked regional health care databases, national registers, and cohort assessments, using American Diabetes Association/European Association for the Study of Diabetes guidelines to guide classification. Results were compared with Swedish National Diabetes Register (NDR) diagnoses and assessed using type 1 and type 2 diabetes polygenic risk scores.

The algorithm classified 3.3% of participants as having type 1 diabetes, 92% as type 2 diabetes, 3.6% as latent autoimmune diabetes in adults (LADA), 0.5% as pancreatogenic diabetes, and 0.5% as unspecified diabetes. Among 16,591 participants with NDR diagnoses, 3.9% had a different subtype classification, with substantial overall agreement (κ = 0.71). Genetic analyses supported distinctions between type 1 diabetes, type 2 diabetes, and LADA, and showed a consistent genetic gradient among reclassified participants.

The findings suggest that linked clinical data can identify subtype heterogeneity not captured by register diagnoses, supporting subtype-specific research. However, the available abstract does not report external validation or whether reclassification improved treatment decisions or clinical outcomes. Genetic support for classification should not be interpreted as evidence of clinical benefit.