Investigation of the degree of family history of diabetes in different clusters of newly diagnosed type 2 diabetes in Thailand

Abstract

Aim: Type 2 diabetes is a heterogeneous disease with strong genetic components. We showed earlier that newly diagnosed type 2 diabetes in Thai patients could be categorized into four clusters. This study aimed to determine the evidence of hereditary factors in these type 2 diabetes clusters. Methods: A total of 487 subjects who were diagnosed with type 2 diabetes in two years were enrolled in the Siriraj Diabetes Center, Siriraj Hospital Bangkok, Bangkok, Thailand. They were divided into four clusters as previously described. The associations between patients’ characteristics, degree of family history of diabetes (FHD), and type 2 diabetes clusters were tested using multinomial logistic regression. Results: Among four clusters of newly diagnosed type 2 diabetes, there were significant differences in characteristics at baseline, including age at diagnosis, BMI, waist circumference, blood sugar levels, vital signs, triglyceride, HDL, calculated LDL, creatinine and eGFR (all p < .05). A relatively young age at the time of diabetes diagnosis was associated with having second-degree relatives with diabetes (p < .05) in all clusters when using mild age-related diabetes (MARD) cluster with no FHD as a control. Patients in the severe insulin-deficient diabetes (SIDD) cluster had more first-degree relatives with diabetes (odds ratio = 1.85; p = .0354), while patients in the metabolic syndrome diabetes (MSD) cluster (odds ratio = 10.73; p < .001) and the mild obesity-related diabetes (MOD) group (odds ratio = 6.66; p = .002), had more second-degree relatives with diabetes. Conclusions: Genetic factors might have various roles in the pathogenesis of type 2 diabetes, at least in newly diagnosed Thai patients. Our findings supported that genetic heterogeneity contributed to clinical heterogeneity or four different clusters. Further studies are needed in a larger sample size of these patients is needed to identify genetic loci associated with each cluster.