Women with gestational diabetes mellitus (GDM) face an elevated risk of developing metabolic syndrome (MetS) after delivery. Early identification of high-risk individuals is essential to prevent long-term metabolic and cardiovascular complications, yet predictive tools for postpartum MetS remain scarce. To develop and validate a nomogram-based model for estimating postpartum MetS risk in women with GDM using routine clinical and laboratory data. This retrospective cohort study included 522 GDM patients treated at a tertiary hospital in Weifang, China, from April 2022 to September 2024. Participants were randomly assigned to training (n = 404) and validation (n = 118) cohorts. Candidate predictors identified by univariate logistic regression were further analyzed by multivariate regression to determine independent risk factors. A nomogram was built from these predictors, and model performance was evaluated using receiver operating characteristic curves, area under the curve, calibration plots, and decision curve analysis. Robustness was tested through sensitivity and subgroup analyses. The final model included 5 independent predictors: polycystic ovary syndrome (PCOS), homeostatic model assessment for insulin resistance (HOMA-IR), interleukin-6 (IL-6), high-density lipoprotein cholesterol (HDL-C), and serum uric acid. The model achieved area under the curves of 0.818 (95% CI 0.743–0.892) and 0.957 (95% CI 0.926–0.989) in the training and validation cohorts, respectively. Calibration showed strong agreement between predicted and observed outcomes, with mean absolute errors of 0.026 and 0.082. Decision curve analysis confirmed a high clinical net benefit, and sensitivity and subgroup analyses demonstrated stable performance across clinical strata. This validated nomogram, based on readily available clinical and biochemical indicators, accurately predicts postpartum MetS risk in women with GDM and may facilitate early detection and targeted prevention in high-risk patients.
Wang et al. (Fri,) studied this question.