Pathophysiology-based phenotyping in type 2 diabetes: A clinical classification tool.
Stidsen JV., Henriksen JE., Olsen MH., Thomsen RW., Nielsen JS., Rungby J., Ulrichsen SP., Berencsi K., Kahlert JA., Friborg SG., Brandslund I., Nielsen AA., Christiansen JS., Sørensen HT., Olesen TB., Beck-Nielsen H.
BACKGROUND: Type 2 diabetes may be a more heterogeneous disease than previously thought. Better understanding of pathophysiological subphenotypes could lead to more individualized diabetes treatment. We examined the characteristics of different phenotypes among 5813 Danish patients with new clinically diagnosed type 2 diabetes. METHODS: We first identified all patients with rare subtypes of diabetes, latent autoimmune diabetes of adults (LADA), secondary diabetes, or glucocorticoid-associated diabetes. We then used the homeostatic assessment model to subphenotype all remaining patients into insulinopenic (high insulin sensitivity and low beta cell function), classical (low insulin sensitivity and low beta cell function), or hyperinsulinemic (low insulin sensitivity and high beta cell function) type 2 diabetes. RESULTS: Among 5813 patients diagnosed with incident type 2 diabetes in the community clinical setting, 0.4% had rare subtypes of diabetes, 2.8% had LADA, 0.7% had secondary diabetes, 2.4% had glucocorticoid-associated diabetes, and 93.7% had WHO-defined type 2 diabetes. In the latter group, 9.7% had insulinopenic, 63.1% had classical, and 27.2% had hyperinsulinemic type 2 diabetes. Classical patients were obese (median waist 105 cm), and 20.5% had cardiovascular disease (CVD) at diagnosis, while insulinopenic patients were fairly lean (waist 92 cm) and 17.5% had CVD (P = 0.14 vs classical diabetes). Hyperinsulinemic patients were severely obese (waist 112 cm), and 25.5% had CVD (P < 0.0001 vs classical diabetes). CONCLUSIONS: Patients clinically diagnosed with type 2 diabetes are a heterogeneous group. In the future, targeted treatment based on pathophysiological characteristics rather than the current "one size fits all" approach may improve patient prognosis.