Nutritional Epidemiology Observatory, Josué de Castro Institute of Nutrition, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil.
BMJ Open. 2023 Sep 6;13(9):e073479. doi: 10.1136/bmjopen-2023-073479.
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.
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.
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.
在低收入和中等收入国家的种族多样化和社会经济脆弱人群中，对于短期和长期母婴健康的早期营养和妊娠决定因素，人们的了解有限。本研究计划旨在：（1）描述整个生命过程中女性体重的变化轨迹；（2）描述儿童体重、身高和身体质量指数（BMI）的变化轨迹；（3）创建和验证预测儿童 5 岁时肥胖的模型；（4）估计孕前 BMI、妊娠增重（GWG）和女性体重轨迹对不良母婴结局和儿童生长轨迹的影响；（5）估计孕前 BMI、GWG、女性体重和两次妊娠间 BMI 变化对后续妊娠中母婴结局的影响；（6）估计女性食物消费和婴儿喂养方式对儿童营养状况和生长轨迹的影响。
将使用来自巴西四个不同数据库的链接数据：1 亿巴西人队列研究、活产信息系统、死亡率信息系统和食物及营养监测系统。为了分析轨迹，将使用潜在增长模型、平移和旋转叠加模型以及断裂棒模型。为了创建儿童肥胖预测模型，将应用机器学习技术。对于选定的暴露和结局变量之间的关联，将考虑广义线性模型。为了确定每个分析中的潜在混杂因素，以确定潜在的因果关系，将构建有向无环图。
本方案已获得作者所在机构的伦理委员会批准。链接将在安全的环境中进行。链接完成后，数据将被去识别，经授权的研究人员将通过虚拟专用网络连接访问数据集。研究结果将在开放获取期刊上报告，并传播给政策制定者和更广泛的公众。
