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Abstract：
BACKGROUND: Novel diabetes phenotypes were proposed by the Europeans through cluster analysis, but Chinese community diabetes populations might exhibit different characteristics. This study aims to explore the clinical characteristics of novel diabetes subgroups under data-driven analysis in Chinese community diabetes populations.
METHODS: We used K-means cluster analysis in 6369 newly diagnosed diabetic patients from eight centers of the REACTION (Risk Evaluation of cAncers in Chinese diabeTic Individuals) study. The cluster analysis was performed based on age, body mass index, glycosylated hemoglobin, homeostatic modeled insulin resistance index, and homeostatic modeled pancreatic β-cell functionality index. The clinical features were evaluated with the analysis of variance (ANOVA) and chi-square test. Logistic regression analysis was done to compare chronic kidney disease and cardiovascular disease risks between subgroups.
RESULTS: Overall, 2063 (32.39%), 658 (10.33%), 1769 (27.78%), and 1879 (29.50%) populations were assigned to severe obesity-related and insulin-resistant diabetes (SOIRD), severe insulin-deficient diabetes (SIDD), mild age-associated diabetes mellitus (MARD), and mild insulin-deficient diabetes (MIDD) subgroups, respectively. Individuals in the MIDD subgroup had a low risk burden equivalent to prediabetes, but with reduced insulin secretion. Individuals in the SOIRD subgroup were obese, had insulin resistance, and a high prevalence of fatty liver, tumors, family history of diabetes, and tumors. Individuals in the SIDD subgroup had severe insulin deficiency, the poorest glycemic control, and the highest prevalence of dyslipidemia and diabetic nephropathy. Individuals in MARD subgroup were the oldest, had moderate metabolic dysregulation and the highest risk of cardiovascular disease.
CONCLUSIONS: The data-driven approach to differentiating the status of new-onset diabetes in the Chinese community was feasible. Patients in different clusters presented different characteristics and risks of complications.
METHODS: We used K-means cluster analysis in 6369 newly diagnosed diabetic patients from eight centers of the REACTION (Risk Evaluation of cAncers in Chinese diabeTic Individuals) study. The cluster analysis was performed based on age, body mass index, glycosylated hemoglobin, homeostatic modeled insulin resistance index, and homeostatic modeled pancreatic β-cell functionality index. The clinical features were evaluated with the analysis of variance (ANOVA) and chi-square test. Logistic regression analysis was done to compare chronic kidney disease and cardiovascular disease risks between subgroups.
RESULTS: Overall, 2063 (32.39%), 658 (10.33%), 1769 (27.78%), and 1879 (29.50%) populations were assigned to severe obesity-related and insulin-resistant diabetes (SOIRD), severe insulin-deficient diabetes (SIDD), mild age-associated diabetes mellitus (MARD), and mild insulin-deficient diabetes (MIDD) subgroups, respectively. Individuals in the MIDD subgroup had a low risk burden equivalent to prediabetes, but with reduced insulin secretion. Individuals in the SOIRD subgroup were obese, had insulin resistance, and a high prevalence of fatty liver, tumors, family history of diabetes, and tumors. Individuals in the SIDD subgroup had severe insulin deficiency, the poorest glycemic control, and the highest prevalence of dyslipidemia and diabetic nephropathy. Individuals in MARD subgroup were the oldest, had moderate metabolic dysregulation and the highest risk of cardiovascular disease.
CONCLUSIONS: The data-driven approach to differentiating the status of new-onset diabetes in the Chinese community was feasible. Patients in different clusters presented different characteristics and risks of complications.
摘要：
背景：欧洲人通过聚类分析提出了新的糖尿病表型，但中国社区糖尿病人群可能表现出不同的特征。本研究旨在通过数据驱动分析探讨中国社区糖尿病人群中新型糖尿病亚组的临床特征。
方法：我们使用K-means聚类分析对来自八个中心的反应（中国糖尿病患者的风险评估）研究的6369例新诊断的糖尿病患者进行了分析。聚类分析是根据年龄，身体质量指数,糖化血红蛋白,稳态模型胰岛素抵抗指数，和稳态模型胰腺β细胞功能指数。采用方差分析(ANOVA)和卡方检验评价临床特征。采用Logistic回归分析比较亚组之间的慢性肾脏病和心血管疾病风险。
结果：总体而言，2063（32.39%），658（10.33%），1769（27.78%），和1879（29.50％）人群被分配到严重的肥胖相关和胰岛素抵抗糖尿病（SOIRD），严重的胰岛素缺乏型糖尿病（SIDD），轻度年龄相关性糖尿病（MARD），和轻度胰岛素缺乏型糖尿病（MIDD）亚组，分别。MIDD亚组的个体具有与糖尿病前期相当的低风险负担，但胰岛素分泌减少。SOIRD亚组的个体肥胖，有胰岛素抵抗，脂肪肝的患病率很高，肿瘤，糖尿病家族史,和肿瘤。SIDD亚组的个体有严重的胰岛素缺乏，血糖控制最差，血脂异常和糖尿病肾病患病率最高。MARD亚组中的个体是最古老的，有中度代谢失调和最高的心血管疾病风险。
结论：区分中国社区新发糖尿病状况的数据驱动方法是可行的。不同集群的患者表现出不同的并发症特征和风险。
方法：我们使用K-means聚类分析对来自八个中心的反应（中国糖尿病患者的风险评估）研究的6369例新诊断的糖尿病患者进行了分析。聚类分析是根据年龄，身体质量指数,糖化血红蛋白,稳态模型胰岛素抵抗指数，和稳态模型胰腺β细胞功能指数。采用方差分析(ANOVA)和卡方检验评价临床特征。采用Logistic回归分析比较亚组之间的慢性肾脏病和心血管疾病风险。
结果：总体而言，2063（32.39%），658（10.33%），1769（27.78%），和1879（29.50％）人群被分配到严重的肥胖相关和胰岛素抵抗糖尿病（SOIRD），严重的胰岛素缺乏型糖尿病（SIDD），轻度年龄相关性糖尿病（MARD），和轻度胰岛素缺乏型糖尿病（MIDD）亚组，分别。MIDD亚组的个体具有与糖尿病前期相当的低风险负担，但胰岛素分泌减少。SOIRD亚组的个体肥胖，有胰岛素抵抗，脂肪肝的患病率很高，肿瘤，糖尿病家族史,和肿瘤。SIDD亚组的个体有严重的胰岛素缺乏，血糖控制最差，血脂异常和糖尿病肾病患病率最高。MARD亚组中的个体是最古老的，有中度代谢失调和最高的心血管疾病风险。
结论：区分中国社区新发糖尿病状况的数据驱动方法是可行的。不同集群的患者表现出不同的并发症特征和风险。
