2020/08/25 by Emma Ahlqvist, Rashmi B. Prasad, Leif Groop · 231 citations
Biochemistry, Genetics and Molecular Biology · Medicine · #Bioinformatics #Biology #Diabetes Treatment and Management #Diabetes and associated disorders #Diabetes mellitus #Diabetic retinopathy #Disease #Endocrinology #Insulin resistance #Internal medicine #Macrovascular disease #Medicine #Pancreatic function and diabetes #Type 2 diabetes
paper · pdf · doi:10.2337/dbi20-0001
published in Diabetes 69(10), 2086-2093 (American Diabetes Association)
openalex publication_date 2020/08/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Type 2 diabetes (T2D) is defined by a single metabolite, glucose, but is increasingly recognized as a highly heterogeneous disease, including individuals with varying clinical characteristics, disease progression, drug response, and risk of complications. Identification of subtypes with differing risk profiles and disease etiologies at diagnosis could open up avenues for personalized medicine and allow clinical resources to be focused to the patients who would be most likely to develop diabetic complications, thereby both improving patient health and reducing costs for the health sector. More homogeneous populations also offer increased power in experimental, genetic, and clinical studies. Clinical parameters are easily available and reflect relevant disease pathways, including the effects of both genetic and environmental exposures. We used six clinical parameters (GAD autoantibodies, age at diabetes onset, HbA1c, BMI, and measures of insulin resistance and insulin secretion) to cluster adult-onset diabetes patients into five subtypes. These subtypes have been robustly reproduced in several populations and associated with different risks of complications, comorbidities, genetics, and response to treatment. Importantly, the group with severe insulin-deficient diabetes (SIDD) had increased risk of retinopathy and neuropathy, whereas the severe insulin-resistant diabetes (SIRD) group had the highest risk for diabetic kidney disease (DKD) and fatty liver, emphasizing the importance of insulin resistance for DKD and hepatosteatosis in T2D. In conclusion, we believe that subclassification using these highly relevant parameters could provide a framework for personalized medicine in diabetes.