In-utero co-exposure to toxic metals and micronutrients on childhood risk of overweight or obesity: new insight on micronutrients counteracting toxic metals

Associations between paediatric obesity, chemical mixtures and environmental factors, in a national cross-sectional study of Canadian children

BACKGROUND

Whilst single chemical exposures are suspected to be obesogenic, the combined role of chemical mixtures in paediatric obesity is not well understood.

OBJECTIVES

We aimed to evaluate the potential associations between chemical mixtures and obesity in a population-based sample of Canadian children.

METHODS

We ascertained biomonitoring and health data for children aged 3-11 from the cross-sectional Canadian Health Measures Survey from 2007 to 2019. Several chemicals of interest were measured in blood or urine and paediatric obesity was defined based on measured anthropometrics. Using quantile-based G computational analysis, we quantified the effects of three chemical mixtures selected a priori. Models were adjusted for sociodemographic and environmental factors identified through a directed acyclic graph. Results are presented through adjusted relative risks (RR) with 95% confidence intervals (95% CI).

RESULTS

We included 9147 children. Of these, 24.1% were overweight or obese. Exposure to the mixture of bisphenol A, acrylamide, glycidamide, metals, parabens and arsenic increased the risk of childhood overweight or obesity by 45% (95% CI 1.09, 1.93), obesity by 109% (95% CI 1.27, 3.42) and central obesity by 82% (95% CI 1.30, 2.56).

CONCLUSIONS

Our findings support the role of early childhood chemical exposures in paediatric obesity and the potential combined effects of chemicals.

Prenatal exposure to metal mixtures and childhood temporal processing in the PROGRESS Birth Cohort Study: Modification by childhood obesity

Children are frequently exposed to various biological trace metals, some essential for their development, while others can be potent neurotoxicants. Furthermore, the inflammatory and metabolic conditions associated with obesity may interact with and amplify the impact of metal exposure on neurodevelopment. However, few studies have assessed the potential modification effect of body mass index (BMI). As a result, we investigated the role of child BMI phenotype on the relationship between prenatal exposure to metal mixtures and temporal processing. Leveraging the PROGRESS birth cohort in Mexico City, children (N = 563) aged 6-9 years completed a Temporal Response Differentiation (TRD) task where they had to hold a lever down for 10-14 s. Blood and urinary metal (As, Pb, Cd, and Mn) measurements were collected from mothers in the 2nd and 3rd trimesters. Child BMI z-scores were dichotomized to normal (between -2 and +0.99) and high (≥1.00). Covariate-adjusted weighted quantile sum (WQS) regression models were used to estimate and examine the combined effect of metal biomarkers (i.e., blood and urine) on TRD measures. Effect modification by the child's BMI was evaluated using 2-way interaction terms. Children with a high BMI and greater exposure to the metal mixture during prenatal development exhibited significant temporal processing deficits compared to children with a normal BMI. Notably, children with increased exposure to the metal mixture and higher BMI had a decrease in the percent of tasks completed (β = -10.13; 95 % CI: -19.84, -0.42), number of average holds (β = -2.15; 95 % CI: -3.88, -0.41), longer latency (β = 0.78; 95 % CI: 0.13, 1.44), and greater variability in the standard deviation of the total hold time (β = 2.08; 95 % CI: 0.34, 3.82) compared to normal BMI children. These findings implicate that high BMI may amplify the effect of metals on children's temporal processing. Understanding the relationship between metal exposures, temporal processing, and childhood obesity can provide valuable insights for developing targeted environmental interventions.

The association of serum toxic metals and essential elements during early pregnancy with body mass index trajectory of infants during the first years: A prospective study

To the best of our knowledge, prior research has yet to delve into the combined and interactive relationships between maternal exposure to essential elements and toxic metals and infancy's continuous growth and trajectories. This study aims to discern infant growth trajectories in the first year of life and to determine the associations of maternal serum levels of essential elements and toxic metals with growth trajectory. Within a Chinese prospective cohort in 2019 - 2021, 407 mother-infant pairs were included, and the serum levels of five essential elements (zinc, calcium, copper, magnesium and iron) and two toxic metals (cadmium and lead) in early pregnancy were assessed. The growth trajectory of infants was followed until age one year. Raw BMI and height values were transformed to age- and sex-specific BMI and height standard deviation (SD) scores. Latent-class group-based trajectory models and piecewise linear mixed regression were estimated to determine infant growth trajectories and growth velocity, respectively. The individual relationship between maternal metallic element levels and infant growth trajectory was examined using multinomial logistic regression models and linear mixed regression, while joint associations and interactive relationships were explored using Bayesian kernel machine regression (BKMR) following confounder adjustments. Four distinct trajectory patterns based on BMI-z score (low-rapid BMI gain group, normal-stable BMI group, very low-rapid BMI gain group and normal-rapid BMI gain group) and length-for-age (high-stable length group, low-stable length group, normal-rapid length gain group, very low-rapid length gain group) were identified during the first year post-birth, respectively. In single-metal and multiple-metal models, infants born to mothers with higher serum Zn and lower serum Cu levels were associated with a normal-rapid BMI gain trajectory during the first year. Serum Cu exhibited a positive correlation with the rate of BMI change solely in infants aged 6-12 months. Further, the BKMR analysis revealed a statistically significant and negative joint effect of the five essential elements on the likelihood of normal-rapid BMI/length gain trajectory when serum levels of these elements fell below the 70th percentile compared to median levels. In addition, high levels of serum copper and calcium interactively affect the rates of BMI change during 6-12 months old (β: -0.21, 95% CI: -0.44, -0.03, P = 0.04, P-interaction=0.04). In conclusion, maternal trace elements at early pregnancy are linked to infant growth patterns and growth velocity in the first year of life.

Maternal folate and metabolic programming of the offspring: A systematic review of the literature

There is emerging evidence suggesting that folate status during pregnancy may play a role in fetal programming of metabolic disease. Therefore, this systematic review aims to summarize and systematize the current evidence surrounding the relationship between maternal folate status during pregnancy and offspring metabolic programming, focusing on both animal and human studies. PubMed, Web of Science and Scopus databases were searched in order to identify studies conducted on pregnant women or in animals studying the association between maternal folate exposure and at least one metabolic syndrome outcome in offspring after birth (weight, blood pressure, glucose regulation parameters, triglycerides and high-density lipoprotein cholesterol (HDL-C) levels). The quality of included studies was assessed using SYRCLE Risk of Bias Tools for animal studies and NHLBI Study Quality Assessment Tools for observational studies and randomized controlled trials. Among the 10 "good" or "fair" studies that investigated excessive folate exposure during the perigestational period, 7 animal studies and 1 human study reported a positive association with development of metabolic outcomes in offspring. On the other hand, 6 of the 7 "good" or "fair" included human studies compared adequate versus low folate exposure, showing a lack of association (n = 3) or a protective effect (n = 3) regarding offspring's dysmetabolism. In conclusion, there is strong evidence from animal trials suggesting that excessive folate intake in early phases of development programs for metabolic dysfunction. While human evidence regarding excessive maternal folate exposure is currently scarce, human studies suggest that folate adequacy in pregnancy is not detrimental for metabolic function of the offspring.

Association between Heavy Metals, Metalloids and Metabolic Syndrome: New Insights and Approaches

Metabolic syndrome (MetS) is an important public health issue that affects millions of people around the world and is growing to pandemic-like proportions. This syndrome is defined by the World Health Organization (WHO) as a pathologic condition characterized by abdominal obesity, insulin resistance, hypertension, and hyperlipidemia. Moreover, the etiology of MetS is multifactorial, involving many environmental factors, including toxicant exposures. Several studies have associated MetS with heavy metals exposure, which is the focus of this review. Environmental and/or occupational exposure to heavy metals are a major risk, contributing to the development of chronic diseases. Of particular note, toxic metals such as mercury, lead, and cadmium may contribute to the development of MetS by altering oxidative stress, IL-6 signaling, apoptosis, altered lipoprotein metabolism, fluid shear stress and atherosclerosis, and other mechanisms. In this review, we discuss the known and potential roles of heavy metals in MetS etiology as well as potential targeted pathways that are associated with MetS. Furthermore, we describe how new approaches involving proteomic and transcriptome analysis, as well as bioinformatic tools, may help bring about an understanding of the involvement of heavy metals and metalloids in MetS.

Defining longitudinal trajectory of body mass index percentile and predicting childhood obesity: methodologies and findings in the Boston Birth Cohort

Background

Overweight or obesity (OWO) in school-age childhood tends to persist into adulthood. This study aims to address a critical need for early identification of children at high risk of developing OWO by defining and analyzing longitudinal trajectories of body mass index percentile (BMIPCT) during early developmental windows.

Methods

We included 3029 children from the Boston Birth Cohort (BBC) with repeated BMI measurements from birth to age 18 years. We applied locally weighted scatterplot smoothing with a time-limit scheme and predefined rules for imputation of missing data. We then used time-series K-means cluster analysis and latent class growth analysis to define longitudinal trajectories of BMIPCT from infancy up to age 18 years. Then, we investigated early life determinants of the BMI trajectories. Finally, we compared whether using early BMIPCT trajectories performs better than BMIPCT at a given age for predicting future risk of OWO.

Results

After imputation, the percentage of missing data ratio decreased from 36.0% to 10.1%. We identified four BMIPCT longitudinal trajectories: early onset OWO; late onset OWO; normal stable; and low stable. Maternal OWO, smoking, and preterm birth were identified as important determinants of the two OWO trajectories. Our predictive models showed that BMIPCT trajectories in early childhood (birth to age 1 or 2 years) were more predictive of childhood OWO (age 5-10 years) than a single BMIPCT at age 1 or 2 years.

Conclusions

Using longitudinal BMIPCT data from birth to age 18 years, this study identified distinct BMIPCT trajectories, examined early life determinants of these trajectories, and demonstrated their advantages in predicting childhood risk of OWO over BMIPCT at a single time point.

Anti-Obesity and Anti-Inflammatory Synergistic Effects of Green Tea Catechins and Citrus β-Cryptoxanthin Ingestion in Obese Mice

Chronic obesity causes various diseases, leading to an urgent need for its treatment and prevention. Using monosodium-glutamate-induced obesity mice, the present study investigated the synergistic obesity-reducing effects of tea catechins and the antioxidant β-cryptoxanthin present in mandarin oranges. The results show that the obese mice that ingested both tea catechin and β-cryptoxanthin for 4 weeks had a significantly decreased body weight, with no difference in body weight compared with control mice. Moreover, the blood biochemical test results were normal, and the body fat percentage was significantly decreased according to the histopathological analysis. Additionally, the abundance of M1 macrophages, which release pro-inflammatories, was significantly reduced in adipose tissue. Indeed, a significant decrease was detected in M1-macrophage-secreted tumor necrosis factor-alpha levels. Meanwhile, M2 macrophage levels were recovered, and adiponectin, which is released from adipocytes and involved in suppressing metabolic syndrome, was increased. Collectively, these results suggest that the combination of tea catechins and antioxidant foods can alleviate chronic obesity, indicating that a combination of various ingredients in foods might contribute to reducing chronic obesity.

Estimating Causal Effects of Interventions on Early-life Environmental Exposures Using Observational Data

PURPOSE OF REVIEW

We discuss how epidemiologic studies have used observational data to estimate the effects of potential interventions on early-life environmental exposures. We summarize the value of posing questions about interventions, how a group of techniques known as "g-methods" can provide advantages for estimating intervention effects, and how investigators have grappled with the strong assumptions required for causal inference.

RECENT FINDINGS

We identified nine studies that estimated health effects of hypothetical interventions on early-life environmental exposures. Of these, six examined air pollution. Interventions evaluated by these studies included setting exposure levels at a specific value, shifting exposure distributions, and limiting exposure levels to less than a threshold value. Only one study linked exposure contrasts to a specific intervention on an exposure source, however. There is growing interest in estimating intervention effects of early-life environmental exposures, in part because intervention effects are directly related to possible public health actions. Future studies can build on existing work by linking research questions to specific hypothetical interventions that could reduce exposure levels. We discuss how framing questions around interventions can help overcome some of the barriers to causal inference and how advances related to machine learning may strengthen studies by sidestepping the overly restrictive assumptions of parametric regression models. By leveraging advancements in causal inference and exposure science, an intervention framework for environmental epidemiology can guide actionable solutions to improve children's environmental health.