Prediction of metabolic syndrome following a first pregnancy.

ABSTRACT:

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

SUBMITTER: Kawakita T 

PROVIDER: S-EPMC11424779 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature