Standardized Site Differences (SSD) for gestational weight gain (kg) according to country and gestational age group (weeks).

A. underweight (BMI/age +1 SD and ≤ +2SD); D. obesity (BMI/age > +2 SD).</p

Distribution of gestational weight gain according to gestational age in early pregnancy for each pre-pregnancy BMI before and after the imputation procedure.

A. underweight (BMI/age +1 SD and ≤ +2SD); D. obesity (BMI/age > +2 SD).</p

Distribution of the variables of interest for adolescents from nine Latin American countries (n = 6,414 individuals).

Distribution of the variables of interest for adolescents from nine Latin American countries (n = 6,414 individuals).</p

S2 Fig -

Standardized Site Differences (SSD) for gestational weight gain (kg) according to country and gestational age group (weeks) removing datasets with n ≤ 10 in the selected time intervals: A. underweight (BMI/age +1 SD and ≤ +2SD); D. obesity (BMI/age > +2 SD). (TIF)</p

Supplementary tables.

Gestational weight gain is an important indicator for monitoring nutritional status during pregnancy. However, there are no gestational weight gain references created for adolescents or national datasets to enable the construction of such graphs up to date. This manuscript aims to describe the creation of a Latin American dataset to construct gestational weight gain references for adolescents aged 10–19 years old. Gestational weight gain data from studies conducted in nine countries (Argentina, Brazil, Chile, Colombia, Mexico, Panama, Paraguay, Peru, and Uruguay) collected between 2003 and 2021 were harmonized. Data on height, weight, and gestational age in at least two gestational trimesters were included. Pregnant adolescents should be free of diseases that could affect weight, and newborns should weigh between 2,500–4,000 g and be free of congenital malformations. The final dataset included 6,414 individuals after data cleaning. Heterogeneity between the countries was assessed by calculating standardized site differences for GWG and z scores of height-for-age. Several imputation procedures were tested, and approximately 10% of the first-trimester weights were imputed. The prevalence of individuals with underweight (1.5%) and obesity (5.3%) was low, which may lead to problems when modeling the curves for such BMI categories. Maternal height and gestational weight gain did not show significant differences by country, according to the standardized site differences. A harmonized dataset of nine countries with imputed data in the first trimester of pregnancy was prepared to construct Latin American gestational weight gain curves for adolescents.</div

Standardized Site Differences and size (SSD) for height-for-age <i>z</i> scores according to country and age (years).

A. underweight (BMI/age +1 SD and ≤ +2SD); D. obesity (BMI/age > +2 SD).</p

Example two women’s weight gain trajectory with measurements flagged as implausible (outliers).

Example two women’s weight gain trajectory with measurements flagged as implausible (outliers).</p

Flowchart for the constitution of the final dataset.

Notes: Diseases considered: Chronic hypertension or hypertensive disorders during pregnancy, diabetes mellitus or gestational diabetes, tuberculosis, or cardiovascular diseases. Abbreviations: BMI: Body mass index; GA: Gestational age; GWG: Gestational weight gain; HAZ: Height-for-age z score.</p

Maternal and child nutrition programme of investigation within the 100 Million Brazilian Cohort: study protocol

Introduction There is a limited understanding of the early nutrition and pregnancy determinants of short-term and long-term maternal and child health in ethnically diverse and socioeconomically vulnerable populations within low-income and middle-income countries. This investigation programme aims to: (1) describe maternal weight trajectories throughout the life course; (2) describe child weight, height and body mass index (BMI) trajectories; (3) create and validate models to predict childhood obesity at 5 years of age; (4) estimate the effects of prepregnancy BMI, gestational weight gain (GWG) and maternal weight trajectories on adverse maternal and neonatal outcomes and child growth trajectories; (5) estimate the effects of prepregnancy BMI, GWG, maternal weight and interpregnancy BMI changes on maternal and child outcomes in the subsequent pregnancy; and (6) estimate the effects of maternal food consumption and infant feeding practices on child nutritional status and growth trajectories.Methods and analysis Linked data from four different Brazilian databases will be used: the 100 Million Brazilian Cohort, the Live Births Information System, the Mortality Information System and the Food and Nutrition Surveillance System. To analyse trajectories, latent-growth, superimposition by translation and rotation and broken stick models will be used. To create prediction models for childhood obesity, machine learning techniques will be applied. For the association between the selected exposure and outcomes variables, generalised linear models will be considered. Directed acyclic graphs will be constructed to identify potential confounders for each analysis investigating potential causal relationships.Ethics and dissemination This protocol was approved by the Research Ethics Committees of the authors’ institutions. The linkage will be carried out in a secure environment. After the linkage, the data will be de-identified, and pre-authorised researchers will access the data set via a virtual private network connection. Results will be reported in open-access journals and disseminated to policymakers and the broader public