JCM, Vol. 12, Pages 810: Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables

JCM, Vol. 12, Pages 810: Two Distinct Groups Are Shown to Be at Risk of Diabetes by Means of a Cluster Analysis of Four Variables

Journal of Clinical Medicine doi: 10.3390/jcm12030810

Authors: Ryoma Ito Satoru Mizushiri Yuki Nishiya Shoma Ono Ayumi Tamura Kiho Hamaura Akihide Terada Jutaro Tanabe Miyuki Yanagimachi Kyi Mar Wai Yutaro Kudo Kazushige Ihara Yoshiko Takahashi Makoto Daimon

Recent attempts to classify adult-onset diabetes using only six diabetes-related variables (GAD antibody, age at diagnosis, BMI, HbA1c, and homeostatic model assessment 2 estimates of b-cell function and insulin resistance (HOMA2-B and HOMA2-IR)) showed that diabetes can be classified into five clusters, of which four correspond to type 2 diabetes (T2DM). Here, we classified nondiabetic individuals to identify risk clusters for incident T2DM to facilitate the refinement of prevention strategies. Of the 1167 participants in the population-based Iwaki Health Promotion Project in 2014 (baseline), 868 nondiabetic individuals who attended at least once during 2015–2019 were included in a prospective study. A hierarchical cluster analysis was performed using four variables (BMI, HbA1c, and HOMA2 indices). Of the four clusters identified, cluster 1 (n = 103), labeled as “obese insulin resistant with sufficient compensatory insulin secretion”, and cluster 2 (n = 136), labeled as “low insulin secretion”, were found to be at risk of diabetes during the 5-year follow-up period: the multiple factor-adjusted HRs for clusters 1 and 2 were 14.7 and 53.1, respectively. Further, individuals in clusters 1and 2 could be accurately identified: the area under the ROC curves for clusters 1and 2 were 0.997 and 0.983, respectively. The risk of diabetes could be better assessed on the basis of the cluster that an individual belongs to.