Prenatal Exposure to Per- and Polyfluoroalkyl Substances and Child Growth Trajectories in the First Two Years

Abstract

Background: Pregnant women are ubiquitously exposed to per- and polyfluoroalkyl substances (PFAS). Prenatal exposure to PFAS has been associated with lower birth weight but also with excess adiposity and higher weight in childhood. These mixed findings warrant investigation of the relationship between PFAS and dynamic offspring growth. Objectives: To investigate the association between prenatal PFAS exposure and early-life growth trajectories during the first 2 y. Methods: Pregnant women ( ) were recruited from 2013 to 2016 from the Shanghai Birth Cohort (SBC) Study, and their children were followed up from birth to 2 y of age. Seven PFAS congeners were quantified in pregnant women’s serum during the first trimester. Our study population was restricted to 1,350 children who had five repeated measurements for at least one anthropometric measure. Four anthropometric measures, including weight, length/height, weight-for-length, and head circumference, were evaluated at birth, 42 d, 6 months, 12 months, and 24 months, and standardized into z -scores using the World Health Organization reference. Trajectories of each measure were classified into five groups using group-based trajectory modeling. Multinomial logistic regression was used to estimate odds ratio (OR) and 95% confidence interval (CI) for trajectory groups according to -transformed PFAS concentrations, and the moderate-stable group was selected as the reference group for all measures. Results: Higher prenatal exposure to PFAS was associated with elevated odds for the low-rising weight-for-age z -score (WAZ) trajectory, and the high-rising length-for-age z -score (LAZ) trajectory. Meanwhile, PFAS levels were associated with decreased odds for the low-rising and high-rising weight-for-length z -score (WLZ) trajectories. In addition, the associations of PFAS with growth trajectory groups differed by sex, where males had greater odds for the low-rising and low-stable WAZ trajectories and for the high-stable and low-rising WLZ trajectories. In contrast, inverse associations were consistently observed with trajectories of the high-stable, low-stable, and low-rising head-circumference-for-age z -score (HCZ) in relation to most individual PFAS congeners. PFAS mixtures analysis further confirmed the above findings. Discussion: Trajectory analysis approach provided insight into the complex associations between PFAS exposure and offspring growth. Future studies are warranted to confirm the present findings with trajectory modeling strategies and understand the clinical significance of these trajectory groups. https://doi.org/10.1289/EHP9875

Prenatal Exposure to Legacy and Alternative Per- and Polyfluoroalkyl Substances and Neuropsychological Development Trajectories over the First 3 Years of Life

“…Among total of 1030 mother−child pairs, we excluded the participants who missed/stopped follow-up (n = 192) or failed to provide a blood sample (n = 17). Multiple pregnancy (more than one fetus) may affect intrauterine or postnatal growth, 22 so those participants (n = 103) were also removed. Finally, 718 mother−child pairs remained in the present study (69.7% response rate).…”

“…We selected potential covariates for statistical analysis in accordance with prior literature. ,, During follow-up visits, trained staff applied structured questionnaires into acquiring relevant information on maternal age (years), pre-pregnancy body mass index (BMI, kg/m 2 ), education (≤high school or >high school), family income (<30,000 Chinese Yuan, CNY/year, 30,000–100,000 CNY/year, or >100,000 CNY/year), smoking during pregnancy (yes or no), alcohol drinking (yes or no), nutrient supplementation during pregnancy (yes or no), and breastfeeding duration (0, ≤6, and >6 months). We calculated pre-pregnancy BMI as body weight (kg) divided by squared body height (m).…”

“…Model selection is the critical procedure for GBTM analysis, which encompasses two tasks: (1) the primary task of the model selection process is determining the optimal number of trajectory groups; (2) the polynomial function links age/time with the growth of health outcome in GBTM, so another important task is to identify which polynomial function (linear, quadratic, or cubic) is the most appropriate one. There are several criteria for model selection, the most important one is the Bayesian information criteria (BIC), and higher BIC indicates the goodness of model fitting . Another important principle is the average posterior probability (AvePP) of each group, which has been recommended to be at least 0.70 .…”

“…21 Moreover, prior studies usually applied one-time point investigation or average pattern for neurodevelopment assessment, which may not be able to reveal the dynamic neurodevelopmental processes. 22 To our knowledge, group-based trajectory modeling is a trajectory analysis approach increasingly applied to characterize and predict the distinct trajectories of health outcome over time, as well as identify the risk factors and critical windows. 23 Until recently, Oh et al 24,25 focused on the dynamic growth trajectories of neurodevelopment in the early life course and performed group-based trajectory analysis to explore the longitudinal links between prenatal PFAS exposure and child cognitive development, whereas they detected mixed findings for PFOA and PFOS in two birth cohort studies, and this evidence was restricted to several legacy PFAS with small sample sizes (n = 140 and 368, respectively).…”

“…One recent meta-analysis of nine European population-based studies showed that early life exposure to PFOS or PFOA was not associated with ADHD during childhood . Moreover, prior studies usually applied one-time point investigation or average pattern for neurodevelopment assessment, which may not be able to reveal the dynamic neurodevelopmental processes . To our knowledge, group-based trajectory modeling is a trajectory analysis approach increasingly applied to characterize and predict the distinct trajectories of health outcome over time, as well as identify the risk factors and critical windows .…”