Prediction of metabolic syndrome following a first pregnancy - 20/11/24
, Philip Greenland, MD b, Victoria L. Pemberton, RNC, MS, CCRC c, William A. Grobman, MD, MBA d, Robert M. Silver, MD e, C. Noel Bairey Merz, MD f, Rebecca B. McNeil, PhD g, David M. Haas, MD, MS h, Uma M. Reddy, MD, MPH i, Hyagriv Simhan, MD j, George R. Saade, MD aAbstract |
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 |
This study aimed 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 to 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 to 7 years after delivery. A total of 127 variables that were obtained during pregnancy were evaluated. The data set 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 curve for both models. Using the model with the better area under the receiver operating characteristic curve, we developed models that included fewer variables based on SHAP (SHapley Additive exPlanations) values and compared them with the original model. The final model chosen would have fewer variables and noninferior areas under the receiver operating characteristic curve.
Results |
A total of 4225 individuals met the inclusion criteria; the mean (standard deviation) age was 27.0 (5.6) years. Of these, 754 (17.8%) developed metabolic syndrome. The area under the receiver operating characteristic curve of the random forest model was 0.878 (95% confidence interval, 0.846–0.909), which was higher than the 0.850 of the lasso model (95% confidence interval, 0.811–0.888; P<.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 because it had the area under the receiver operating characteristic curve of 0.867 (95% confidence interval, 0.839–0.895), which was not inferior to the original model (P=.08). The area under the receiver operating characteristic curve of the final model in the test set was 0.847 (95% confidence interval, 0.821–0.873). An online application of the final model was developed (metabolic/).
Conclusion |
We developed a model that can accurately predict the development of metabolic syndrome in 2 to 7 years after delivery.
Le texte complet de cet article est disponible en PDF.Video |
(15.14 Mo)
Key words : high-density lipoprotein, high-sensitivity C-reactive protein, insulin, machine learning
Plan
| The authors report no conflict of interest. |
|
| Specimen and data collection for the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) were supported by grant funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (U10 HD063036; U10 HD063072; U10 HD063047; U10 HD063037; U10 HD063041; U10 HD063020; U10 HD063046; U10 HD063048; and U10 HD063053). In addition, support was provided by Clinical and Translational Science Institutes (UL1TR001108 and UL1TR000153). The nuMoM2b Heart Health Study was supported by cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (U10-HL119991; U10-HL119989; U10-HL120034; U10-HL119990; U10-HL120006; U10-HL119992; U10-HL120019; U10-HL119993; U10-HL120018, and U01HL145358), with supplemental support from the Office of Research on Women’s Health and the Office of Disease Prevention. Additional support was provided by the National Center for Advancing Translational Sciences (UL-1-TR000124, UL-1-TR000153, UL-1-TR000439, and UL-1-TR001108), the Barbra Streisand Women’s Cardiovascular Research and Education Program, and the Erika J. Glazer Women’s Heart Research Initiative, Cedars-Sinai Medical Center, Los Angeles. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute, the National Institutes of Health, or the United States Department of Health and Human Services. |
|
| Cite this article as: Kawakita T, Greenland P, Pemberton VL, et al. Prediction of metabolic syndrome following a first pregnancy. Am J Obstet Gynecol 2024;231:649.e1-19. |
Vol 231 - N° 6
P. 649.e1-649.e19 - décembre 2024 Retour au numéro
Article précédent
- Magnesium sulfate and risk of hypoxic-ischemic encephalopathy in a high-risk cohort
- Kathleen C. Minor, Jessica Liu, Maurice L. Druzin, Yasser Y. El-Sayed, Susan R. Hintz, Sonia L. Bonifacio, Stephanie A. Leonard, Henry C. Lee, Jochen Profit, Scarlett D. Karakash
- Preoperative free access to water compared to fasting for planned cesarean under spinal anesthesia: a randomized controlled trial
- Yee Ling Ng, Sabeetha Segaran, Carolyn Chue Wai Yim, Boon Kiong Lim, Mukhri Hamdan, Farah Gan, Peng Chiong Tan
Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.
Déjà abonné à cette revue ?