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Pediatric Obesity Promoted By The Barrage Of Chemicals In The Environment

Pediatric Obesity Promoted By The Barrage Of Chemicals In The Environment

Overview

Single chemical exposures have been implicated in obesity, but the role of chemical mixtures in pediatric obesity remains unclear. This study aimed to assess the associations between chemical mixtures and obesity among Canadian children. Utilizing biomonitoring and health data from the cross-sectional Canadian Health Measures Survey (2007-2019), the study examined children aged 3-11. Pediatric obesity was defined through measured anthropometrics, and the effects of three predefined chemical mixtures were quantified using quantile-based G computation. Sociodemographic and environmental factors were controlled for via a directed acyclic graph. Results are reported as adjusted relative risks (RR) with 95% confidence intervals (95% CI). The study included 9147 children, 24.1% of whom were overweight or obese. Exposure to a mixture of bisphenol A, acrylamide, glycidamide, metals, parabens, and arsenic was associated with a 45% increased risk of overweight or obesity (95% CI 1.09, 1.93), a 109% increased risk of obesity (95% CI 1.27, 3.42), and an 82% increased risk of central obesity (95% CI 1.30, 2.56). These findings underscore the potential impact of early childhood chemical exposures on pediatric obesity and the significant combined effects of multiple chemicals.

Introduction

Pediatric obesity is acknowledged as a worldwide epidemic. In 2016, the World Health Organization (WHO) reported that 340 million children and adolescents aged 5–19 years were overweight or obese. Overweight and obesity during childhood and adolescence are linked to premature death and physical health issues in adulthood, such as type-2 diabetes, cardiovascular disease, and cancer. Therefore, understanding the causes of pediatric obesity is crucial for public health prevention strategies.

 

Increasing evidence suggests that early-life exposure to endocrine-disrupting chemicals (EDCs) may contribute to the rise in pediatric obesity. Children are frequently exposed to these toxicants in air, water, and food, often at higher levels than adults relative to their body weight. EDCs are widespread environmental pollutants found in numerous everyday products, including canned foods, water bottles, plastic containers, baby care products, food packaging, and medical devices. Exposure can occur through skin contact, breastfeeding, or inhalation. These chemicals can impact adipogenesis by disrupting hormonal functions. Considering the exposome, which includes all environmental exposures throughout a lifetime, is essential for understanding the negative health effects of these toxicants.

 

Current research primarily focuses on the impact of single chemicals on pediatric obesity. Evidence indicates that certain chemicals, such as bisphenol A (BPA), parabens, metals, perfluorinated compounds (PFCs), phthalates, polycyclic aromatic hydrocarbons (PAHs), and glycidamide, have obesogenic effects. Biomonitoring studies reveal that children are exposed to multiple chemicals simultaneously. However, few studies have examined the effects of chemical mixtures, and those that do vary in methodology, including statistical approaches, study design, and chemical combinations.

 

Evaluating the combined effects of chemical mixtures could help identify additional risk management strategies for modifiable obesogenic factors, which are crucial for addressing the ongoing childhood obesity epidemic. Notably, only one European study has explored the combined influence of environmental factors, such as air pollution and secondhand smoke, alongside common chemical exposures. This study found associations between these exposures and increased body mass index (BMI) in children.

 

This research aims to investigate the combined effects of various chemical mixtures, while adjusting for sociodemographic and environmental factors, on the prevalence of pediatric obesity. This work supports the federal health portfolio’s public health approach to the Chemical Management Program.

Method

The Canadian Health Measures Survey (CHMS) is a detailed cross-sectional survey that gathers both direct measurements and self-reported health data. It surveys a nationally representative sample of the Canadian population aged 3 to 79 years through mobile examination centers that travel across the country. The survey targets approximately 96%–97% of the Canadian population, excluding residents of the three territories, Indigenous reserves, full-time Canadian Forces members, institutional residents, and some remote regions. A full description of the survey’s design and procedures has been previously published.

 

This study focused on children aged 3–11 years who participated in the CHMS and had available biomonitoring data. The availability of this data varied by chemical and age group, with all chemicals being measured in children aged 3–5 years, while in those aged 6–11 years, some chemicals were measured in the full sample and most in a subsample. The data spanned all six cycles of the CHMS from 2007 to 2019, though Cycle 1 did not include data for children aged 3–5 years. Participants only contributed data to a single cycle, with an overall response rate of 51.1%.

 

Ethics approval for the CHMS was granted by the Health Canada and Public Health Agency of Canada Research Ethics Board. Each cycle used standardized procedures for biosample collection and laboratory analysis. Chemicals varied across cycles, selected by Health Canada’s National Biomonitoring Program based on criteria such as health effects, public concern, exposure evidence, and feasibility of measurement. Chemicals were measured in urine and blood, with urine chemicals adjusted for creatinine. Three mixtures were identified for analysis: (1) polycyclic aromatic hydrocarbons (PAHs), bisphenol A (BPA), metals (cadmium and lead), and arsenic; (2) BPA, metals, acrylamide, glycidamide, parabens, and arsenic; and (3) phthalates, metals, BPA, perfluorinated chemicals (PFCs), and arsenic. Chemicals included in the analysis had a detection limit of 70% or greater in the study population.

 

Objective measures of pediatric obesity were derived from anthropometric measurements collected using standardized protocols. Overweight or obesity (OWO) and central obesity (CO) were determined using WHO’s age- and sex-specific Z-scores for BMI and waist circumference cut-offs for children aged 6–11 years. Children under 6 were not evaluated for CO due to a lack of established measures.

 

Covariates included household education level, household income, diet quality, exposure to environmental tobacco smoke (ETS), physical activity levels, maternal age at birth, and breastfeeding status. Environmental factors were assigned using residential postal codes via the Canadian Urban Environmental Health Research Consortium (CANUE) platform, including ambient fine particulate matter, proximity to greenspace, light at night, and walkability. These factors were chosen based on prior studies linking them to pediatric obesity. A directed acyclic graph was developed to display causal assumptions and identify confounders for multivariable analyses.

Statistical Analysis

The study used weighted percentages to summarize the characteristics of all children, stratified by age groups (3–5 and 6–11 years). Logistic regression was employed to determine the odds of each outcome category (OWO, obesity, and CO) based on these characteristics.

 

To evaluate the relationship between exposure to three chemical mixtures and pediatric obesity metrics, the quantile-based G-computational method via the ‘qgcomp’ package in R was applied. This causal inference method, which generalizes standardization, estimates the expected outcome distribution for specific exposure patterns. Quantile g-computation calculates the joint effect of increasing each exposure by one quantile simultaneously, providing a dose-response parameter for the combined exposure mixture. The model included all mixtures to estimate joint effects, leveraging a flexible extension of weighted quantile sum regression. This method accommodates both negative and positive weights, allowing for non-linear and non-additive effects of exposure mixtures.

 

The study incorporated survey and bootstrap weights in our analysis. Results are presented for models adjusted for a minimally sufficient set of sociodemographic and environmental factors (age, sex, diet, breastfeeding, maternal age, parental education, ETS, household income, walkability, proximity to greenspace, average PM2.5, and LAN).  Relative risks (RR) and 95% confidence intervals (95% CI) for pediatric obesity measures were reported, representing the obesity risk per interquartile range increase of the mixtures.

 

The study evaluated the individual contributions of chemicals within each mixture to the overall pediatric obesity risk and examined the correlations among the chemicals. Using generalized additive models, we assessed the associations between 43 individual chemicals and obesity. Additionally, the chemical mixture models were adjusted for each environmental factor individually, analyzed the variability of each environmental factor within the study population, and estimated the correlations between environmental factors and each chemical.

Result

The study included 9,147 children aged 3–11, predominantly white (70.9%) and from households with post-secondary education (85.4%). About 24.1% were overweight or obese, with obese children more likely to be male (OR 1.5 [1.3,1.9]), from lower-income households, and consuming fewer fruits and vegetables. Descriptive variables were consistent across study cycles, with minor differences in physical activity and income.

 

Chemical mixtures were analyzed for associations with obesity outcomes. Mixture 1 (PAHs, BPA, metals, and arsenic) showed no associations with obesity outcomes, either overall or when stratified by sex. Mixture 2 (BPA, metals, acrylamide, glycidamide, parabens, and arsenic) was linked to higher risks of overweight or obesity (OWO) (RR 1.45), obesity (RR 2.09), and central obesity (CO) (RR 1.82). Females had higher risks of OWO (RR 1.35) and CO (RR 1.53), while males had a higher risk of obesity (RR 1.54). Mixture 3 (phthalates, metals, BPA, PFC, and arsenic) was associated with obesity in unadjusted models but not after adjustment.

 

Secondary results indicated single-exposure models showed associations between PAHs and obesity measures. Notably, acrylamide exposure was negatively associated with obesity and CO before adjustment. The elevated risks associated with mixture 2 were primarily driven by glycidamide, propyl paraben, and cadmium. Environmental factors were not correlated with the chemicals but were associated with obesity outcomes, providing some protective effects for CO.

 

Sensitivity analyses showed that cultural origin, geographic area, and physical activity did not significantly affect the robustness of the models.

Conclusion

Understanding the impact of chemical mixtures on pediatric obesity is crucial, as children are often exposed to multiple chemicals simultaneously rather than individually. This study, involving a national sample of Canadian children, found that exposure to a combination of BPA, parabens, acrylamide, glycidamide, metals, and arsenic was linked to an increased risk of obesity. Other examined mixtures did not show a similar association. The study underscores the importance of considering interactions between chemicals, which can influence their combined effects on health outcomes.

 

Notably, while single-exposure models indicated that total PAHs were linked to obesity, this association disappeared in the context of a mixture, potentially due to the interaction between PAHs and other chemicals like lead. The results suggested a combined effect of mixture 2 on endocrine and metabolic systems, with some sex-specific differences. For mixture 3, certain phthalates appeared to have a negative influence, potentially explaining the results.

 

The study also observed consistent contributions of arsenic, BPA, cadmium, lead, and DMA across different models, with cadmium showing varying relevance depending on the mixture. For example, in mixture 1, cadmium’s relevance was diminished due to the presence of PAHs, highlighting the complex interplay between different chemicals.

 

This research aligns with other studies that have found combined chemical exposures to be more significant than single exposures in relation to pediatric obesity. For instance, a study using NHANES data found a 48% greater odds of obesity with a mixture of nine chemicals, including phenols, parabens, and pesticides. Another study on mother-child pairs indicated a dose-response relationship between maternal exposure to low-level toxic metals and childhood obesity.

 

The biological mechanisms behind these effects include increased adipocyte mass, altered insulin sensitivity, and hormone regulation. Chemicals like cadmium and parabens may promote adipogenesis, while PAHs can inhibit lipolysis, leading to weight gain.

 

Environmental factors also play a role in pediatric obesity. For example, urban environments with high walkability might also have high air pollution, influencing obesity outcomes. The study found that environmental variables like walkability and PM2.5 levels were associated with obesity but were protective against central obesity (CO), indicating the complex nature of these relationships.

 

This study’s strengths include a large, representative sample of Canadian children, objective measurement of anthropometrics and chemical biomarkers, and robust statistical analysis. However, its cross-sectional design limits the ability to infer causality. Future research should focus on longitudinal studies to better understand the temporal relationships between chemical exposures and obesity.

 

In conclusion, this study highlights that a mixture of BPA, metals, acrylamides, parabens, and arsenic is associated with pediatric obesity. These findings suggest that ubiquitous chemical mixtures may contribute to the prevalence of obesity and related disorders in children. Further research is necessary to inform environmental regulations and public health guidelines, emphasizing the need for early prevention efforts.

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