Metabolic phenotype stratification identifies patients at high risk of poor glycemic control in type 2 diabetes mellitus: Insights into hepatic metabolic dysfunction

BACKGROUND: To characterize metabolic dphenotypes based on integrated hepatic and metabolic indicators and evaluate their association with poor glycemic control in patients with Type 2 diabetes mellitus (T2DM). METHODS: A total of 320 patients with T2DM were retrospectively enrolled. Data on liver function, blood lipids, insulin resistance, and indicators related to fatty liver were collected. Unsupervised clustering analysis was performed to construct integrated metabolic risk profiles, and glycemic control levels were compared among different groups. Spearman correlation analysis was used to evaluate associations between hepatic metabolic indicators and glycated hemoglobin (HbA1c). Multivariate logistic regression analysis was conducted to identify independent risk factors for poorly controlled blood glucose. Multicollinearity among independent variables was assessed using variance inflation factors (VIF), with VIF < 5 considered indicative of acceptable collinearity. Predictive performance of the models was assessed using receiver operating characteristic (ROC) curves. RESULTS: Two metabolic risk profiles were identified, characterized by differences in hepatic enzyme levels, lipid metabolism, and insulin resistance. Patients in the high hepatic metabolic burden group (Cluster 2) exhibited significantly higher HbA1c levels and a greater incidence of poor glycemic control. HbA1c was positively correlated with alanine aminotransferase (ALT), aspartate aminotransferase (AST), severity of fatty liver, and homeostasis model assessment of insulin resistance (HOMA-IR), but negatively correlated with high-density lipoprotein cholesterol (HDL-C). Multivariate analysis revealed that longer diabetes duration, elevated AST and TG levels, higher HOMA-IR, and metabolic phenotypes classification (Cluster 2) were independent risk factors for poorly controlled blood glucose. The combined predictive model achieved an area under the ROC curve (AUC) of 0.943. CONCLUSION: Metabolic phenotype stratification based on integrated hepatic and metabolic indicators may facilitate identification of T2DM patients at increased risk of poor glycemic control and provide additional information for metabolic risk assessment.