Clinical characteristics and complication risks in data-driven clusters among Chinese community diabetes populations

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.

Conclusion: 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.

Keywords: Chinese community population; K‐means; cluster analysis; diabetes; diabetic complication.

FIGURE 1

Participant distribution and cluster characteristics. The box plots represent the median and interquartile range (IQR) of the data distribution. *Significant difference between two subgroups in the multiple comparisons of analysis of variance (ANOVA) test (age, BMI, HbA1c) and Kruskal–Wallis test (HOMA‐IR，HOMA‐β). BMI, body mass index; HbA1c, hemoglobin A1c; HOMA‐IR, homoeostatic model assessment estimates of insulin resistance; HOMA‐β, homoeostatic model assessment estimates of β‐cell function; MARD, mild age‐associated diabetes mellitus; MIDD, mild insulin‐deficient diabetes; SIDD, severe insulin‐deficient diabetes; SOIRD, severe obesity‐related and insulin‐resistant diabetes. ns: p ≥ 0.05; *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns, non significance.

FIGURE 2

Box plot of major clinical indicators. The box plots represent the median and interquartile range (IQR) of the data distribution. ALT, alanine transferase; AST, aspartate transferase; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; GGT, glutamine transferase; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; MARD, mild age‐associated diabetes mellitus; MIDD, mild insulin‐deficient diabetes; PBG, posting blood glucose; SBP, systolic blood pressure; SIDD, severe insulin‐deficient diabetes; SOIRD, severe obesity‐related and insulin‐resistant diabetes; TC, total cholesterol; TG, triglyceride; WC, waist circumference; WHR, waist‐to‐hip ratio.