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. 2020 Jun;128(6):67009.
doi: 10.1289/EHP5975. Epub 2020 Jun 24.

Early-Life Environmental Exposures and Childhood Obesity: An Exposome-Wide Approach

Martine Vrijheid  1   2   3 , Serena Fossati  1   2   3 , Léa Maitre  1   2   3 , Sandra Márquez  1   2   3 , Theano Roumeliotaki  4 , Lydiane Agier  5 , Sandra Andrusaityte  6 , Solène Cadiou  5 , Maribel Casas  1   2   3 , Montserrat de Castro  1   2   3 , Audrius Dedele  6 , David Donaire-Gonzalez  1   2   3   7 , Regina Grazuleviciene  6 , Line S Haug  8 , Rosemary McEachan  9 , Helle Margrete Meltzer  8 , Eleni Papadopouplou  8 , Oliver Robinson  1   2   3   10 , Amrit K Sakhi  8 , Valerie Siroux  5 , Jordi Sunyer  1   2   3 , Per E Schwarze  8 , Ibon Tamayo-Uria  1   2   3   11 , Jose Urquiza  1   2   3 , Marina Vafeiadi  4 , Antonia Valentin  1   2   3 , Charline Warembourg  1   2   3 , John Wright  9 , Mark J Nieuwenhuijsen  1   2   3 , Cathrine Thomsen  8 , Xavier Basagaña  1   2   3 , Rémy Slama  5 , Leda Chatzi  12
Affiliations

Early-Life Environmental Exposures and Childhood Obesity: An Exposome-Wide Approach

Martine Vrijheid et al. Environ Health Perspect. 2020 Jun.

Abstract

Background: Chemical and nonchemical environmental exposures are increasingly suspected to influence the development of obesity, especially during early life, but studies mostly consider single exposure groups.

Objectives: Our study aimed to systematically assess the association between a wide array of early-life environmental exposures and childhood obesity, using an exposome-wide approach.

Methods: The HELIX (Human Early Life Exposome) study measured child body mass index (BMI), waist circumference, skinfold thickness, and body fat mass in 1,301 children from six European birth cohorts age 6-11 y. We estimated 77 prenatal exposures and 96 childhood exposures (cross-sectionally), including indoor and outdoor air pollutants, built environment, green spaces, tobacco smoking, and biomarkers of chemical pollutants (persistent organic pollutants, metals, phthalates, phenols, and pesticides). We used an exposure-wide association study (ExWAS) to screen all exposure-outcome associations independently and used the deletion-substitution-addition (DSA) variable selection algorithm to build a final multiexposure model.

Results: The prevalence of overweight and obesity combined was 28.8%. Maternal smoking was the only prenatal exposure variable associated with higher child BMI (z-score increase of 0.28, 95% confidence interval: 0.09, 0.48, for active vs. no smoking). For childhood exposures, the multiexposure model identified particulate and nitrogen dioxide air pollution inside the home, urine cotinine levels indicative of secondhand smoke exposure, and residence in more densely populated areas and in areas with fewer facilities to be associated with increased child BMI. Child blood levels of copper and cesium were associated with higher BMI, and levels of organochlorine pollutants, cobalt, and molybdenum were associated with lower BMI. Similar results were found for the other adiposity outcomes.

Discussion: This first comprehensive and systematic analysis of many suspected environmental obesogens strengthens evidence for an association of smoking, air pollution exposure, and characteristics of the built environment with childhood obesity risk. Cross-sectional biomarker results may suffer from reverse causality bias, whereby obesity status influenced the biomarker concentration. https://doi.org/10.1289/EHP5975.

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Figures

Figure 1A and 1B are graphs titled prenatal exposures and childhood exposures, plotting negative log of 10 (p-value), ranging from 0 to 3 in unit increments and 0 to 9 in increments of 3, respectively, (y-axis) across beta estimate, ranging from negative 1.00 to 0.50 in increments of 0.25 (x-axis).
Figure 1.
Association between prenatal and childhood exposures and zBMI in single-exposure ExWAS model. Volcano plot showing significance (p-value) against beta coefficient. [(A) Prenatal exposome and (B) childhood exposome]. Black dashed horizontal line at p-values of 0.05; red solid horizontal line at TEF of 0.001 (prenatal) and 0.0009 (childhood). Beta estimates for all exposures are shown in Table S17. Note: Beta coefficient for change in zBMI compared with reference category for the categorical variables. For continuous variables, beta estimates are calculated per interquartile range increase in exposure. TEF, threshold for effective number of test (i.e., p-value correction for multiple testing).
Figure 2A and 2B are graphs titled prenatal exposures and childhood exposures, plotting negative log of 10 (p-value), ranging from 0 to 3 in unit increments and 0 to 10 in increments of 5 (y-axis), respectively, across OR, ranging from 0.00 to 1.75 in increments of 0.25 (x-axis).
Figure 2.
Association between prenatal and childhood exposures and overweight and obesity status in single-exposure exposure-wide association study (ExWAS) model. Volcano plot showing significance (p-value) against odds ratio (OR). [(A) prenatal exposome and (B) childhood exposome]. Black dashed horizontal line at p-values of 0.05; red solid horizontal line at TEF of 0.001 (prenatal) and 0.0009 (childhood). Note: OR, Odds-ratio for being overweight or obese in comparison with normal weight. OR for overweight and obesity status in comparison with reference category for the categorical variables. For continuous variables, ORs are calculated per interquartile range increase in exposure. TEF, threshold for effective number of test (i.e., p-value correction for multiple testing).
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