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Scientific Reports, 2019. 9(1), 3329-, DOI: https://doi.org/10.1186/1741-7015-9-103
Identification of novel population clusters with different susceptibilities to type 2 diabetes and their impact on the prediction of diabetes
Cho SB, Kim SC; Chung MG
Type 2 diabetes is one of the subtypes of diabetes. However, previous studies have revealed its heterogeneous features. Here, we hypothesized that there would be heterogeneity in its development, resulting in higher susceptibility in some populations. We performed risk-factor based clustering (RFC), which is a hierarchical clustering of the population with profiles of five known risk factors for type 2 diabetes (age, gender, body mass index, hypertension, and family history of diabetes). The RFC identified six population clusters with significantly different prevalence rates of type 2 diabetes in the discovery data (N = 10,023), ranging from 0.09 to 0.44 (Chi-square test, P < 0.001). The machine learning method identified six clusters in the validation data (N = 215,083), which also showed the heterogeneity of prevalence between the clusters (P < 0.001). In addition to the prevalence of type 2 diabetes, the clusters showed different clinical features including biochemical profiles and prediction performance with the risk factors. SOur results seem to implicate a heterogeneous mechanism in the development of type 2 diabetes. These results will provide new insights for the development of more precise management strategy for type 2 diabetes.
- DOI: https://doi.org/10.1186/1741-7015-9-103
- ISBN or ISSN: 2045-2322
- 본 연구는 질병관리본부 연구개발과제연구비를 지원받아 수행되었습니다.
- This research was supported by a fund by Research of Korea Centers for Disease Control and Prevention.