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
• End date: January 04, 2025
• 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

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(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

VICTOR, AUDENCIO; ALMEIDA, FRANCIELLY; XAVIER, SANCHO PEDRO; RONDO, PATRICIA H. C.. Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study. BMC PREGNANCY AND CHILDBIRTH, v. 25, n. 1, p. 9-pg., 2025-03-19. (15/03333-6, 23/07936-3)

VICTOR, AUDENCIO; NHANCOLOLO, ALEX MONITO; BAH, HOMEGNON ANTONIN FERREOL; XAVIER, SANCHO PEDRO; GOTINE, ANA RAQUEL ERNESTO MANUEL; MOREIRA, VANILDA ALVES; CHARLES, CHARLES M'POCA; CERQUEIRA-SILVA, THIAGO; RONDO, PATRICIA HELEN. National and regional Temporal trends and forecasting of preterm birth in brazil: evidence from National birth data (2014-2023) with projections to 2030. BMC PREGNANCY AND CHILDBIRTH, v. 25, n. 1, p. 13-pg., 2025-12-05. (23/07936-3, 24/18309-2)

VICTOR, AUDENCIO; LEITAO, MARIA PAULA DE CARVALHO; BATISTA, LIVIA PATRICIA RODRIGUES; DA SILVA TELES, LAISLA DE FRANCA; ARGENTATO, PERLA PIZZI; LUZIA, LIANIA A.; ARTES, RINALDO; RONDO, PATRICIA HELEN. Risk factors for mental disorders in pregnant women in two cities from São Paulo, Brazil: A cohort study. PLoS One, v. 20, n. 9, p. 16-pg., 2025-09-04. (18/17824-0, 15/03333-6, 23/07936-3)

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)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

VICTOR, Audencio. Gestational weight gain and its effects on fetal growth, neonatal outcomes, and maternal mental health: Integration of machine learning models and generalized estimating equations in cohort studies in Araraquara and Jundiaí. 2025. Doctoral Thesis - Universidade de São Paulo (USP). Faculdade de Saúde Pública (FSP/CIR) São Paulo.