The influence of gestational weight gain on fetal growth and neonatal outcomes: Araraquara Cohort Study.

• Grant number: 23/07936-3
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: March 01, 2024
• Status: Discontinued
• Field of knowledge: Health Sciences - Collective Health - Public Health
• Principal Investigator: Patricia Helen de Carvalho Rondó
• Grantee: Audencio Victor
• Host Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 15/03333-6 - The relationship between maternal adiposity and adiposity of the offspring in the fetal, neonatal and infant periods: a prospective population-based study, AP.TEM
• Associated scholarship(s): 24/18309-2 - Structural equation modeling and machine learning for predicting fetal growth based on gestational weight gain: Araraquara Cohort Study., BE.EP.DR

Abstract

Introduction: Pregnancy is a critical period for maternal and fetal health, and appropriate gestational weight gain is essential for fetal growth and development. However, the recommendations from the United States Institute of Medicine (IOM) may not be applicable to Brazilian pregnant women due to differences in population, nutrition, and lifestyle. This study aims to evaluate whether pregnant women with gestational weight gain outside the range recommended by the IOM have adverse fetal and neonatal outcomes compared to those with appropriate weight gain.Methods: This is a prospective population-based cohort study embedded within a larger study called the "Araraquara Cohort," conducted in Araraquara. The sample included women with gestational age d 19 weeks who received prenatal care at the Basic Health Units in the municipality of Araraquara and surrounding areas. Socioeconomic and demographic data, lifestyle habits, obstetric conditions, prenatal and birth conditions, as well as anthropometric and body composition data of the mother, fetus, and neonate were collected, starting in March 2017 with an expected completion in September 2023.Data Analysis: The data will be analyzed using inferential analyses, employing machine learning algorithms for predicting the outcomes of interest. Multiple linear or logistic regression models will be used depending on the outcome being investigated.Expected Results: This study can contribute to a better understanding of the relationship between gestational weight gain and maternal, fetal, and neonatal outcomes in Brazilian pregnant women, as well as the adaptation of IOM recommendations to the local context.Keywords: Gestational weight gain; Cohort study, Fetal growth, Neonatal outcomes, Machine learning.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

VICTOR, AUDENCIO; DOS SANTOS, HELLEN GEREMIAS; SILVA, GABRIEL FERREIRA SANTOS; BARCELLOS FILHO, FABIANO; COBRE, ALEXANDRE DE FATIMA; LUZIA, LIANIA A.; RONDO, PATRICIA H. C.; CHIAVEGATTO FILHO, ALEXANDRE DIAS PORTO. Predictive modeling of gestational weight gain: a machine learning multiclass classification study. BMC PREGNANCY AND CHILDBIRTH, v. 24, n. 1, p. 11-pg., 2024-11-08. (23/07936-3, 15/03333-6)