The predictive value of serum MHR combined with classical metabolic syndrome components in the first trimester for gestational metabolic syndrome: a prospective cohort study in China.

Objective: The objective of the study was to investigate the relationship between inflammatory markers, the serum monocyte-to-high-density lipoprotein cholesterol ratio (MHR) in the first trimester, and gestational metabolic syndrome (GMS), and to identify the risk factors for GMS in early pregnancy and its predictive value. Methods: This prospective cohort study included 1,410 pregnant women at gestational ages of 7-12 weeks. Pregnant women underwent regular prenatal examinations. Basic information and clinical data of pregnant women were collected. Univariate analysis was performed to identify factors associated with GMS. Variables with a p-value of < 0.05 in the univariate analysis were included in the LASSO regression to screen for predictive variables. Multivariate logistic regression was performed to construct the predictive model. A nomogram was constructed based on the predictive variables in the model. The discrimination of the predictive model was evaluated using an ROC curve. Internal validation of the model was performed using the bootstrap method with 1,000 resampling iterations. Results: Univariate analysis revealed that age, a history of adverse pregnancy outcomes (APOs), body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL), white blood cell (WBC) counts, monocyte (MONO) levels, and the MHR in early pregnancy were associated with GMS (p < 0.05). Four predictor variables were selected using LASSO regression: MHR, BMI8w, TG8w level, and TC8w. Three multivariable models were developed using GMS as the outcome. Model 1 incorporated predictors selected by LASSO regression as independent variables. Model 2 utilized traditional MS components (BMI8w, TC8w, TG8w, and FBG8w) as independent variables. Model 3 included the MHR, BMI8w, and TG8w as independent variables. The area under the curves (AUCs) were 0.903 (95% CI: 0.862-0.943), 0.896 (95% CI: 0.857-0.935), and 0.895 (95% CI: 0.853-0.938), respectively. The calibrated C-indices for Models 1-3 were 0.898, 0.891, and 0.892, respectively. DeLong's test results suggested that there were no statistically significant differences in predictive performance among the three models for GMS. Conclusion: This study has confirmed the predictive value of serum MHR combined with classical MS components in the first trimester for identifying GMS, which could lead to better and earlier identification of GMS patients and provide new ideas for early diagnosis and prevention of GMS.