Clinical utility of novel diabetes subgroups in predicting vascular complications and mortality: up to 25 years of follow-up of the HUNT Study - Bjørn Olav Åsvold; Elin Pettersen Sørgjerd; Arnulf Langhammer; Kare I Birkeland; Paz Lopez-Doriga Ruiz; Tore Julsrud Berg; Eirin Beate Haug; Vera Vik Bjarkø; Sofia Carlsson; Valeriya Lyssenko (BMJ Open Diabetes Research & Care)
Introduction Cluster analysis has previously revealed five reproducible subgroups of diabetes, differing in risks of diabetic complications. We aimed to examine the clusters’ predictive ability for vascular complications as compared with established risk factors in a general adult diabetes population.Research design and methods Participants from the second (HUNT2, 1995–1997) and third (HUNT3, 2006–2008) surveys of the Norwegian population-based Trøndelag Health Study (HUNT Study) with adult-onset diabetes were included (n=1899). To identify diabetes subgroups, we used the same variables (age at diagnosis, body mass index, HbA1c, homeostasis model assessment estimates of beta cell function and insulin resistance, and glutamic acid decarboxylase antibodies) and the same data-driven clustering technique as in previous studies. We used Cox proportional hazards models to investigate associations between clusters and risks of vascular complications and mortality. We estimated the C-index and R2 to compare predictive abilities of the clusters to those of established risk factors as continuous variables. All models included adjustment for age, sex, diabetes duration and time of inclusion.Results We reproduced five subgroups with similar key characteristics as identified in previous studies. During median follow-up of 9–13 years (differing between outcomes), the clusters were associated with different risks of vascular complications and all-cause mortality. However, in prediction models, individual established risk factors were at least as good predictors as cluster assignment for all outcomes. For example, for retinopathy, the C-index for the model including clusters (0.65 (95% CI 0.63 to 0.68)) was similar to that of HbA1c (0.65 (95% CI 0.63 to 0.68)) or fasting C-peptide (0.66 (95% CI 0.63 to 0.68)) alone. For chronic kidney disease, the C-index for clusters (0.74 (95% CI 0.72 to 0.76)) was similar to that of triglyceride/high-density lipoprotein ratio (0.74 (95% CI 0.71 to 0.76)) or fasting C-peptide (0.74 (95% CI 0.72 to 0.76)), and baseline estimated glomerular filtration rate yielded a C-index of 0.76 (95% CI 0.74 to 0.78).Conclusions Cluster assignment did not provide better prediction of vascular complications or all-cause mortality compared with established risk factors.