Patti Marisa A, Kelsey Karl T, MacFarlane Amanda J, Papandonatos George D, Lanphear Bruce P, Braun Joseph M
Department of Epidemiology, Brown University, Providence, RI, USA.
Nutrition Research Division, Health Canada, Ottawa, Canada.
J Multimorb Comorb. 2025 Jan 10;15:26335565241312840. doi: 10.1177/26335565241312840. eCollection 2025 Jan-Dec.
Evaluating individual health outcomes does not capture co-morbidities children experience.
We aimed to describe profiles of child neurodevelopment and anthropometry and identify their predictors.
Using data from 501 mother-child pairs (age 3-years) in the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a prospective cohort study, we developed phenotypic profiles by applying latent profile analysis to twelve neurodevelopmental and anthropometric traits. Using multinomial regression, we evaluated odds of phenotypic profiles based on maternal, sociodemographic, and child level characteristics.
For neurodevelopmental outcomes, we identified three profiles characterized by Non-optimal (9%), Typical (49%), and Optimal neurodevelopment (42%). For anthropometric outcomes, we observed three profiles of Low (12%), Average (61%), and Excess Adiposity (27%). When examining joint profiles, few children had both Non-optimal neurodevelopment and Excess Adiposity (2%). Lower household income, lower birthweight, younger gestational age, decreased caregiving environment, greater maternal depressive symptoms, and male sex were associated with increased odds of being in the Non-optimal neurodevelopment profile. Higher pre-pregnancy body mass index was associated with increased odds of being in the Excess Adiposity profile.
Phenotypic profiles of child neurodevelopment and adiposity were associated with maternal, sociodemographic, and child level characteristics. Few children had both non-optimal neurodevelopment and excess adiposity.
评估个体健康结果无法涵盖儿童所经历的合并症。
我们旨在描述儿童神经发育和人体测量学特征,并确定其预测因素。
利用环境化学物质母婴研究(MIREC)中的501对母婴(3岁)数据,这是一项前瞻性队列研究,我们通过对12项神经发育和人体测量特征应用潜在类别分析来制定表型特征。使用多项回归,我们根据母亲、社会人口学和儿童层面的特征评估表型特征的几率。
对于神经发育结果,我们确定了三种特征,分别为非最佳(9%)、典型(49%)和最佳神经发育(42%)。对于人体测量结果,我们观察到三种特征,分别为低(12%)、平均(61%)和过度肥胖(27%)。在检查联合特征时,很少有儿童同时具有非最佳神经发育和过度肥胖(2%)。家庭收入较低、出生体重较低、孕周较小、照护环境较差、母亲抑郁症状较重以及男性与处于非最佳神经发育特征的几率增加有关。孕前体重指数较高与处于过度肥胖特征的几率增加有关。
儿童神经发育和肥胖的表型特征与母亲、社会人口学和儿童层面的特征有关。很少有儿童同时具有非最佳神经发育和过度肥胖。