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

Sixu Xin^1,2Linong Ji^1*ZHANG XIAOMEI^2*Yuehan Ma²Xin Zhao²Ning Yuan²Jianbin Sun²Dan Zhao²

• ¹Peking University People's Hospital, Beijing, Beijing Municipality, China
• ²Peking University International Hospital, Beijing, China

Objective To investigate the relationship between inflammatory markers the serum 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 1410 pregnant women at gestational 7-12 weeks. Pregnant women should undergo regular prenatal examinations. There basic information and clinical data of were collected. Univariate analysis was performed to identify factors associated with GMS. Variables with p < 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 on the basis of the predictive variables in the model. The discrimination of the predictive model was evaluated using a 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 APOs, BMI, and SBP, DBP, FBG, TC, TG, LDL, WBC counts, 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 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 the predictive performance among the three models for GMS. Conclusions This study has confirmed the predictive value of the 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, provide new ideas for early diagnosis and prevention of GMS.