Metabolic Characteristics of Patients With Early-Onset Type 2 Diabetes Mellitus and a Risk Prediction Model for Microvascular Complications
Abstract
To investigate the metabolic characteristics of patients with early-onset type 2 diabetes mellitus (T2DM) and to develop a risk prediction model for microvascular complications.
Methods
A retrospective study was conducted on 980 T2DM patients admitted for treatment between April 2020 and April 2024. Based on age at diagnosis, the patients were divided into two groups, an early-onset T2DM group (age at diagnosis < 40 years, n = 265) and a late-onset T2DM group (age at diagnosis ≥ 40 years, n = 715). Differences in metabolic indicators between the two groups were compared. Patients in the early-onset group were further divided into a complication subgroup (n = 142) and a non-complication subgroup (n = 123) based on the presence or absence of microvascular complications. Data on baseline characteristics, metabolic parameters, and laboratory indicators were collected and compared between the two groups. Multivariate logistic regression analysis was used to identify independent risk factors for microvascular complications, and a nomogram prediction model was constructed. The model's discriminative performance was assessed using receiver operating characteristic (ROC) curves, and its calibration was evaluated using calibration curves and the Hosmer-Lemeshow test. Decision curve analysis (DCA) was also performed to assess the model's clinical utility.
Results
Compared with the late-onset group, patients in the early-onset group exhibited more pronounced metabolic abnormalities, including higher body mass index (BMI), proportion of family history of diabetes mellitus, glycated hemoglobin (HbA1c) levels, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), triglyceride-glucose index (TyG), and lactate dehydrogenase (LDH) levels (all P < 0.05), along with a shorter disease duration and lower levels of high-density lipoprotein cholesterol (HDL-C) (P < 0.05). According to a multivariate analysis, systolic blood pressure (SBP), total bilirubin (TBIL), HDL-C, LDL-C, TyG, and LDH were identified as independent risk factors for microvascular complications in patients with early-onset T2DM. A predictive model based on these factors was established as the follows, Log(P) = －19.915 + 0.017 × SBP － 0.136 × TBIL － 1.241 × HDL-C + 0.684 × LDL-C + 0.769 × TyG + 0.050 × LDH. The area under the ROC curve (AUC) was 0.864 (95% CI, 0.820-0.907), and the Hosmer-Lemeshow test indicated good model fit (χ2 = 10.286, P = 0.246). The slope of the DCA curve was also close to 1.
Conclusion
The nomogram prediction model based on SBP, TBIL, HDL-C, LDL-C, TyG, and LDH demonstrates good predictive performance for microvascular complications and can provide a reference for clinical risk stratification and individualized intervention.
Keywords: Early-onset type 2 diabetes mellitus, Microvascular complications, Metabolic characteristics, Risk prediction, Nomogram model
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