Prenatal exposure to mixtures of phthalates and phenols and body mass index and blood pressure in Spanish preadolescents

Background: Pregnant women are simultaneously exposed to several non-persistent endocrine-disrupting chem-icals, which may influence the risk of childhood obesity and cardiovascular diseases later in life. Previous prospective studies have mostly examined single-chemical effects, with inconsistent findings. We assessed the association between prenatal exposure to phthalates and phenols, individually and as a mixture, and body mass index (BMI) and blood pressure (BP) in preadolescents. Methods: We used data from the Spanish INMA birth cohort study (n = 1,015), where the 1st and 3rd-trimester maternal urinary concentrations of eight phthalate metabolites and six phenols were quantified. At 11 years of age, we calculated BMI z-scores and measured systolic and diastolic BP. We estimated individual chemical effects with linear mixed models and joint effects of the chemical mixture with hierarchical Bayesian kernel machine regression (BKMR). Analyses were stratified by sex and by puberty status. Results: In single-exposure models, benzophenone-3 (BP3) was nonmonotonically associated with higher BMI z -score (e.g. Quartile (Q) 3: beta = 0.23 [95% CI = 0.03, 0.44] vs Q1) and higher diastolic BP (Q2: beta = 1.27 [0.00, 2.53] mmHg vs Q1). Methyl paraben (MEPA) was associated with lower systolic BP (Q4: beta =-1.67 [-3.31,-0.04] mmHg vs Q1). No consistent associations were observed for the other compounds. Results from the BKMR confirmed the single-exposure results and showed similar patterns of associations, with BP3 having the highest importance in the mixture models, especially among preadolescents who reached puberty status. No overall mixture effect was found, except for a tendency of higher BMI z-score and lower systolic BP in girls. Conclusions: Prenatal exposure to UV-filter BP3 may be associated with higher BMI and diastolic BP during preadolescence, but there is little evidence for an overall phthalate and phenol mixture effect.We thank all study participants for their generous collaboration. INMA-Sabadell: This study was funded by grants from Instituto de Salud Carlos III (Red INMA G03/176; CB06/02/0041; PI041436; PI081151 incl. FEDER funds), CIBERESP, Generalitat de CatalunyaCIRIT 1999SGR 00241, Generalitat de Catalunya-AGAUR 2009 SGR 501, Fundacio La marato de TV3 (090430), EU Commission (261357, H2020 No 874583, the ATHLETE project, and No 825712, the OBERON project). Maribel Casas holds a Miguel Servet fellowship (MS16/00128) funded by Instituto de Salud Carlos III and co-funded by European Social Fund "Investing in your future". We acknowledge support from the Spanish Ministry of Science and Innovation and the State Research Agency through the "Centro de Excelencia Severo Ochoa 2019-2023" Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program. INMA-Gipuzkoa: This study was funded by grants from Instituto de Salud Carlos III (FIS-PI13/02187 and FIS-PI18/01142 incl. FEDER funds), CIBERESP, Department of Health of the Basque Government (2015111065), and the Provincial Government of Gipuzkoa (DFG15/221) and annual agreements with the municipalities of the study area (Zumarraga, Urretxu, Legazpi, Azkoitia y Azpeitia y Beasain). INMA-Valencia: This study was funded by Grants from UE (FP7-ENV-2011 cod 282957 and HEALTH.2010.2.4.5-1), Spain: ISCIII (G03/176; FIS-FEDER: PI11/01007, PI11/02591, PI11/02038, PI12/00610, PI13/1944, PI13/2032, PI14/00891, PI14/01687, PI16/1288, and PI17/00663; Miguel Servet-FEDER MS11/00178, MS15/00025, and MSII16/00051), Generalitat Valenciana (AICO/2021/182, and FISABIO: UGP 15-230, UGP-15-244, and UGP-15-249), and Alicia Koplowitz Foundation 2017

Prenatal Exposure to Multiple Endocrine-Disrupting Chemicals and Childhood BMI Trajectories in the INMA Cohort Study

BACKGROUND: Prenatal exposure to endocrine-disrupting chemicals (EDCs) may disrupt normal fetal and postnatal growth. Studies have mainly focused on individual aspects of growth at specific time points using single chemical exposure models. However, humans are exposed to multiple EDCs simultaneously, and growth is a dynamic process. OBJECTIVE: The objective of this study was to evaluate the associations between prenatal exposure to EDCs and children's body mass index (BMI) growth trajectories using single exposure and mixture modeling approaches. METHODS: Using data from the INfancia y Medio Ambiente (INMA) Spanish birth cohort (formula presented ), prenatal exposure to persistent chemicals [hexachlorobenzene (HCB), 4-4'-dichlorodiphenyldichloroethylene (DDE), polychlorinated biphenyls (PCB-138, -150, and -180), 4 perfluoroalkyl substances (PFAS)] and nonpersistent chemicals (8 phthalate metabolites, 7 phenols) was assessed using blood and spot urine concentrations. BMI growth trajectories were calculated from birth to 9 years of age using latent class growth analysis. Multinomial regression was used to assess associations for single exposures, and Bayesian weighted quantile sum (BWQS) regression was used to evaluate the EDC mixture's association with child growth trajectories. RESULTS: In single exposure models exposure to HCB, DDE, PCBs, and perfluorononanoic acid (PFNA) were associated with increased risk of belonging to a trajectory of lower birth size followed by accelerated BMI gain by 19%-32%, compared with a trajectory of average birth size and subsequent slower BMI gain [e.g., relative risk ratio (RRR) per doubling in DDE formula presented (95% CI: 1.05, 1.35); RRR for formula presented (95% CI: 1.05, 1.66)]. HCB and DDE exposure were also associated with higher probability of belonging to a trajectory of higher birth size and accelerated BMI gain. Results from the BWQS regression showed the mixture was positively associated with increased odds of belonging to a BMI trajectory of lower birth size and accelerated BMI gain (odds ratio per 1-quantile increase of the formula presented ; credible interval: 1.03, 2.61), with HCB, DDE, and PCBs contributing the most.DISCUSSION:This study provides evidence that prenatal EDC exposure, particularly persistent EDCs, may lead to BMI trajectories in childhood characterized by accelerated BMI gain. Given that accelerated growth is linked to a higher disease risk in later life, continued research is important. https://doi.org/10.1289/EHP11103.</p

State-of-the-art methods for exposure-health studies: results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P>100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother-child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

International audienceThe exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P &gt; 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother-child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field