Prediction of metabolic syndrome following a first pregnancy.

BACKGROUND: The prevalence of metabolic syndrome is rapidly increasing in the United States. We hypothesized that prediction models using data obtained during pregnancy can accurately predict the future development of metabolic syndrome.

OBJECTIVE: To develop machine-learning models to predict the development of metabolic syndrome using factors ascertained in nulliparous pregnant individuals.

STUDY DESIGN: This was a secondary analysis of a prospective cohort study (Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be Heart Health Study [nuMoM2b HHS]). Data were collected from October 2010 to October 2020, and analyzed from July 2023 to October 2023. Participants had in-person visits 2-7 years after their first delivery. The primary outcome was metabolic syndrome, defined by the National Cholesterol Education Program Adult Treatment Panel III criteria, which was measured within 2-7 years after delivery. A total of 127 variables that were obtained during pregnancy were evaluated. The dataset was randomly split into a training set (70%) and a test set (30%). We developed a random forest model and a lasso regression model using variables obtained during pregnancy. We compared the area under the receiver operating characteristic curves (AUROC) for both models. Using the model with the better AUROC, we developed models that included fewer variables based on SHapley Additive exPlanations values and compared them with the original model. The final model chosen would have fewer variables and non-inferior AUROC.

RESULTS: A total of 4225 individuals met inclusion criteria; the mean (SD) age was 27.0 (5.6) years. Of these, 754 (17.8%) developed metabolic syndrome. The AUROC of the random forest model was 0.878 (95%CI 0.846-0.909), which was higher than that of the lasso model of 0.850 (95%CI 0.811-0.888; P <0.001). Therefore, random forest models using fewer variables were developed. The random forest model with the top 3 variables (high-density lipoprotein, insulin, and high-sensitivity C-reactive protein) was chosen as the final model as it had the AUROC of 0.867 (95%CI 0.839-0.895) which was not inferior to the original model (P=0.08). The AUROC of the final model in the test set was 0.847 (95%CI 0.821-0.873). An online application of the final model was developed (https://kawakita.shinyapps.io/metabolic/).

CONCLUSIONS: We developed a model that can accurately predict the development of metabolic syndrome in 2-7 years after delivery.