Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: a scoping review

Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: a scoping review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: a scoping review Léa Maitre, Paulina Jedynak, Marta Gallego, Laura Ciaran, Karine Audouze, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2401240/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Health effects of endocrine disrupting chemicals (EDCs) are challenging to detect in the general population. Omics technologies become increasingly common to identify early biological changes before the apparition of clinical symptoms, to explore toxic mechanisms and to increase biological plausibility of epidemiological associations. This scoping review systematically summarises the application of omics in epidemiological studies assessing EDCs-associated biological effects to identify potential gaps and priorities for future research. Ninety-eight human studies (2004–2021) were identified through database searches (PubMed, Scopus) and citation chaining and focused on phthalates (34 studies), phenols (19) and PFASs (17), while PAHs (12) and recently-used pesticides (3) were less studied. The sample sizes ranged from 10 to 12,476 (median = 159), involving non-pregnant adults (38), pregnant women (11), children/adolescents (15) or both populations studied together (23). Several studies included occupational workers (10) and/or highly exposed groups (11) focusing on PAHs, PFASs and pesticides, while studies on phenols and phthalates were performed in the general population only. Analysed omics layers included metabolic profiles (30, including 14 targeted analyses), miRNA (13), gene expression (11), DNA methylation (8), microbiome (5) and proteins (3). Twenty-one studies implemented targeted multi-assays focusing on clinical routine blood lipid traits, oxidative stress or hormones. Overall, DNA methylation and gene expression associations with EDCs did not overlap across studies, while some EDC-associated metabolite groups, such as carnitines, nucleotides and amino acids in untargeted metabolomic studies, and oxidative stress markers through targeted studies were consistent across studies. Studies had common limitations such as small sample sizes, cross-sectional designs and single sampling for exposure biomonitoring. In conclusion, there is a growing body of evidence evaluating the early biological responses to exposure to EDCs. This review points to a need for larger longitudinal studies, wider coverage of exposures and biomarkers, replication studies and standardisation of research methods and reporting. Molecular Epidemiology Endocrine disruptors omics environmental epidemiology scoping review Figures Figure 1 Figure 2 Figure 3 Introduction The share of chronic diseases and disorders (obesity, diabetes, fertility and neurobehavioral disorders) attributable to endocrine disruptors (EDCs) costs the European Union each year more than 157 billion euros, or approximately 1.23% of the Union's gross domestic product (Trasande et al., 2015 ). Prenatal exposure to EDCs can impair prenatal growth, thyroid function, glucose metabolism, obesity, puberty and fertility (Parker et al., 2002 ; Predieri et al., 2022 ). There is still inadequate evidence in humans, mainly due to the lack of understanding of potential mechanisms, the inaccuracy of exposure assessment for short half-life compounds, their general ubiquity and insufficient assessment of EDC mixtures and other co-exposures (aka exposome) (Braun, 2017 ; Vrijheid et al., 2016 ). Early biomarkers of effect, before clinical symptoms apparition, are needed to identify early EDC effects in humans, in particular during sensitive windows of exposure, such as pregnancy. Biomarkers of effect are measurable molecular, cellular, biochemical, physiologic, behavioural, structural or other alterations in an organism occurring along the temporal and mechanistic pathways connecting exposure to chemicals and an established or possible health impairment or disease (National Research Council, 2006 ). Effect biomarkers ideally reflect subclinical changes before the onset of disease. Consequently, they range from early biological changes (e.g. enzyme induction responses) to altered structure and function. Effect biomarkers can help in identifying early effects at low doses, establish dose–response relationships, explore mechanisms and increase the biological plausibility of epidemiological associations. In addition, they can improve the risk assessment of specific chemical families as well as exposure to chemical mixtures/cocktails. The use of omics platforms, once reserved for improving clinical diagnosis, patient stratification and personalised medicine, is becoming increasingly common to detect subtle biological changes in non-diseased general population. This is partly due to the feasibility of their application in large populations (N > 1000) in epidemiological settings, thanks to their high-throughput and decrease in cost. Omics efforts have been invested mostly in the identification of genes (genomics), messenger RNA (mRNA) and microRNAs (miRNAs) (transcriptomics), proteins (proteomics) and metabolites (metabolomics). Of particular interest has been the identification of epigenetic markers—mostly DNA methylation—of gene expression (epigenomics) acting as a “cell memory” and more recently the gut microbiota (microbiomics). Different omics have proven useful in detecting early biological perturbations before the apparition of clinical symptoms in longitudinal epidemiological settings, predicting later cardio-vascular and metabolic diseases or neurodegeneration (Liu et al., 2019 ; Westerlund et al., 2021 ; Wingo et al., 2022 ). These platforms have also been used in in vivo and in vitro toxicological studies of EDCs to improve understanding of mechanisms (Montjean et al., 2022 ; Sun et al., 2022 ). In addition, increasing numbers of epidemiological studies have employed or will employ -omics in the framework of large exposome studies, to identify early effect predictors for health risks due to EDC exposure (Maitre et al., 2018 ; Vrijheid et al., 2021 ). However, no studies to date have evaluated the methodology and robustness of findings in these environmental omics studies. It would be highly valuable for future studies to identify in which population (general, diseased or with occupational exposure), study design (longitudinal vs. cross-sectional), type of sample matrix and which type of omics, including multi-omics, might be useful. Only a few reviews have covered the topic of omics application in human populations and chemical exposures, focusing on general early life environmental exposures (e.g. smoking, toxic metals, air pollution (Everson & Marsit, 2018 )), perfluorinated compounds (PFASs) and metabolomics (Guo, 2022 ) or multiple chemicals (S. Kim et al., 2022 ) but none focused on EDCs and multi-omics. The purpose of this scoping review is to provide an assessment of the current research regarding omics and associations with selected groups of EDCs exposure in humans that have not been summarised before, focusing on phthalates, phenols, PFASs, recently-used pesticides and polycyclic aromatic hydrocarbons (PAHs). We evaluated the study characteristics, research methods, omics targets and health outcomes investigated, if any. We also identified knowledge gaps and made recommendations for future research that will examine alterations of the human ‘omes’ associated with EDCs. Methods This scoping review protocol was designed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines (PRISMA-ScR) (Tricco et al., 2018 ). Search Strategy and Selection Criteria References were identified by searches of PubMed and Scopus databases. Bibliography citations of the identified publications were also screened to identify additional articles. The automated search focused on papers published from January 1, 2004 (date of the first identified paper) to December 14, 2020, written in English and involving human participants. However, key publications that shaped the field were also included manually if missed during the automated search until February 2022. To be included in the review, papers needed to measure at least one non-persistent EDC and one type of omics (targeted or untargeted). The full list of search terms can be found in Supplementary methods . The list of chemicals included in this review was revised by the research team and members of the H2020 exposome project ATHLETE (Vrijheid et al., 2021 ). Our choice of exposures was based on their widespread occurrence in the general population and on their relevance for at least one of the following health outcomes: cardio-metabolic, neurodevelopment, respiratory health. Besides that, our choice of the chemical pollutant groups was based on recent or current production, plausibility of frequent exposure in European population and alignment with the chemicals prioritised by the human biomonitoring project, HBM4EU ( https://www.hbm4eu.eu/ ). For EDCs, the chemical groups (e.g. phthalates) and individual compounds (list based on annotated metabolites in the analytical protocols of the ATHLETE project WP2, Supplementary methods ) were searched, including synonyms. MeSH terms were used when available and in case the MeSH term was missing, any of the synonyms found were added manually to the search. For omics, the most common types were searched: DNA methylation, gene expression, miRNA, protein profile (including inflammatory proteins and cytokines), metabolic profile (including lipids) and microbiome, using synonyms listed in Supplementary methods . Each search coming from each category of exposure (e.g. phthalates) and omics (e.g. proteome) was saved independently and then combined (e.g. phthalates + proteome, phthalates + metabolome, etc.). The search in PubMed was limited to "Journal article" and "Humans". The search in Scopus was limited to "Articles'' and excluded keywords related to non-human or in vitro studies, such as "Nonhuman", "Animal", "In vitro study" and "Mouse". The resulting papers were imported to the reference manager Mendeley, for further manual filtering. The final search strategy for Scopus can be found in Supplementary methods . Study selection Papers were excluded if they did not fit into the conceptual framework of the study, for example if they focused on EDCs that naturally occur in food (e.g. naturally occurring phenols such as resveratrol). Papers measuring biological markers in a targeted approach, such as inflammatory cytokines or clinical routine blood lipid traits (including total cholesterol, triglycerides, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol) were considered if they included at least three biomarkers. Although these targeted markers are not considered as omics approaches, they provide useful information on key biological systems involved in EDC health effects (inflammation, oxidative stress, lipid metabolism) that may be profiled in an untargeted manner in future studies. A first screening of the titles and abstracts was done to only keep studies conducted in human subjects. In the next steps, to increase consistency among reviewers, at least two reviewers screened the same 98 publications, discussed the results and amended the screening and data extraction manual before beginning data extraction for this review. Reviewers working in pairs sequentially evaluated the titles, abstracts and then full text of all publications identified by our searches for potentially relevant publications. We resolved disagreements on study selection and data extraction by consensus and discussion with other reviewers if needed. Data charting process A data-charting form was developed to determine which variables to extract. We extracted data on the type and method of EDC exposure and omics assessment, study participant characteristics (e.g. country of origin, cohort name if provided, demographic characteristics), the study design (cross-sectional/ longitudinal, cohort/ case-control/ cases only/ randomised intervention and sample size). Detailed definitions can be found in Table 1 . We drew a word cloud network to visualise the number of articles in which individual EDC parents and metabolites were measured, taking into account if they were assessed together in the same study. The layout was drawn in Cytoscape 3.9.0 with the prefuse force directed layout function. Results Our searches spanning from 2004–2020 across the databases retrieved 1411 total records. These studies were identified after using pre-set literature filters (e.g. excluding non-human studies, see Methods ). Finally, 102 unique studies were selected after title/abstract screening and removal of duplicates and off-topic references such as remaining non-human studies (Fig. 1). Further 13 studies were excluded manually for not fitting in our framework of interest (e.g. naturally occurring phenols). Nine studies missed by our automatic search were included manually, which led to a final of 98 unique studies included in this review. The full list of studies is presented in the Supplementary Table S1 . 1. Exposure to EDCs Most of the studies focused on only one family of EDCs and the most represented groups were phthalates (n = 34), phenols (n = 19) and PFASs (n = 17), while PAHs and pesticides were studied less frequently (n = 12 and 3, respectively, Table 2 and Fig. 2 ). Thirteen studies investigated more than one family of EDCs, among which seven included only phthalates and phenols. The frequency at which individual EDC metabolites were studied and if they were studied together was represented as a word cloud network in Fig. 3 . A complete list of all EDC metabolites and their parent compounds assessed in the 98 studies are presented in Supplementary Table S2 . For phthalates, the most studied metabolites were MnBP, MEHP, MEP, MBzP, MEHHP, MEOHP, MiBP and MECPP (the number of studies including these chemicals ranged from 32 for MECPP to 39 for MnBP). Within the group of phenols, BPA was the most studied compound (n = 26), with other members of this group studied much less frequently (n < 10). Among PFASs, the most commonly investigated were PFOA and PFOS (n = 21) and among PAHs, 1-OH-PY (n = 11). Only three studies focused on currently used pesticides and specifically measured malathion (Seth et al., 2008 ), azinphos-methyl (Stanaway et al., 2017 ) or used the surface of land dedicated to agricultural cereal activities as a proxy for pesticide exposure (Bonvallot et al., 2013 ). A few studies measured HCB (n = 3), although this compound was not included in our list of EDCs of interest, it appeared in our results since it was measured together with other EDCs of interest in multi-exposure studies. Almost all studies used biomonitoring to assess exposure to EDCs (n = 95, exclusively or complemented by other methods such as air sampling or questionnaires). Six studies relied on an experimental design where the source of exposure was modified (Deutschle et al., 2008 ; Estill et al., 2019 ; J. H. Kim et al., 2020 ; Poole et al., 2016 ; Ribado et al., 2017 ; Takahashi et al., 2009 ). Information on exposure groups measured in each study is presented in the Supplementary Table S1 . 2. Study designs and populations Details on the study designs and included populations for the 98 studies are presented in the Supplementary Table S1. The vast majority of the studies were conducted in the USA (n = 28), Europe (n = 18) and China (n = 17). Almost all studies (n = 95) were observational and only three studies on phenols involved a randomised intervention ( Table 2 ). These randomised controlled trials included studies on: BPA exposure through providing canned soymilk and association with miRNA profiles (J. H. Kim et al., 2020 ) and triclosan and triclocarban exposure from household and personal care products associated with gut microbiome diversity (Poole et al., 2016 ; Ribado et al., 2017 ). In the observational studies, the data were collected most often in cohort (n = 60) and case-control studies (n = 26), with a few articles featuring cases only (n = 9) ( Table 2 ). For those that relied on established cohorts, we identified two articles per cohort at most. Those included the C8 Health Project and the C8 Short-Term Follow-up Study, both carried in a community with elevated exposure to PFOA (Frisbee et al., 2010 ; Fitz-Simon et al., 2013 ; Fletcher et al., 2013 ; Galloway et al., 2015 ), the Ewha Birth and Growth Study, EDEN, PREVIENI and PROTECT. Most of the studies assessed EDC exposure in a cross-sectional manner (n = 65) and around 30% longitudinally (n = 30), with three additional studies relying on both cross-sectional and longitudinal design ( Table 3 ). Several studies included occupational workers (n = 10) or highly exposed groups (n = 11) and those focused on PAHs, pesticides and PFASs (Table 3 ). The study populations were diverse, with most studies carried out in adult males and/or females (n = 38), infants, children or adolescents (n = 15) and in pregnant women (n = 11). A significant fraction of studies included more than one population and most often these were pregnant women and infants, children or adolescents (n = 23). Among those, studies that used maternal biospecimens to assess EDC exposure collected up to four spot urine samples, but mostly relied on single spot urine samples. Studies in non-pregnant adults focused mostly on phthalates (n = 12), PAHs (n = 9) and PFASs (n = 9), while those on pregnant women, children or both populations most often included phthalates (n = 5, 5 and 10, respectively) and phenols (n = 2, 2 and 8, respectively). The sample size ranged from 10 to 12,476 (median = 159) ( Supplementary Table S1 ). Many studies assessed the associations between EDC and omics in the context of clinical outcomes in case-control, cases-only or cohort studies where omics data were used as potential mediator/intermediate factors. The investigated health outcomes in adults were related mainly to cardio-metabolic outcomes and fertility in women and men. In pregnancy, studies focused on birth outcomes such as birth weight or gestational age with one study measuring newborns’ genital outcomes (Sathyanarayana et al., 2017 ). In children, body mass index (BMI, overweight) was the main focus. 3. Omic types The most exploited omic method was metabolic profiling (n = 30, Table 4 ), including 14 studies with a targeted approach. Other omics were used less frequently (miRNA, n = 13; gene expression, n = 11; DNA methylation, n = 8; proteins, n = 3), with only five studies focusing on the microbiome. Twenty-one studies used a mixed method approach, combining different targeted assays to measure a small number of metabolites and proteins. Finally, seven studies used more than one omic, most commonly combining investigation of DNA methylation with gene expression (n = 4). More than 50% of the studies (n = 57) used blood to assess omic markers ( Table 4 ), while several studies focused on placenta or cord blood (n = 15). Urine samples were solely used for metabolic profiling (n = 9) and in one study applying mixed methods. Finally, the majority of the studies, including those assessing the microbiome, performed targeted analyses (n = 52) and some used both untargeted and targeted approaches (n = 10) ( Table 4 ). The only three protein profiling studies relied on a targeted approach. Details of the omic type used, results and altered pathways (see last column “Main findings”) in each of the 98 studies are presented in Supplementary Table S1 . 3.1. Metabolic profiling, proteins and mixed methods Metabolic profiling studies of EDCs included 16 untargeted approaches (i.e. metabolomics) and 11 targeted approaches, with three studies using both ( Table 4 ). Untargeted methods were mainly based on liquid-chromatography coupled with a mass spectrometer [i.e. LC-MS, using high-resolution quadrupole-time-of-flight mass analyser (QTOF) or Q-Exactive instruments]. Only three studies applied gas-chromatography (GC)-MS and four studies applied proton Nuclear Magnetic Resonance spectroscopy (NMR). All the untargeted studies reported some significant associations with exposure, including at least four studies with lipid and lipid-related metabolites such as the fatty acid esters or fatty acids and the carnitines/acylcarnitines. Nucleotide metabolism (i.e. purine) and amino acids (e.g. arginine and proline) were also reported pathways in at least four studies. Among the 11 targeted metabolic studies, six focused solely on routine clinical lipid metabolites (i.e. cholesterol and total triglycerides) and an additional five studies combined clinical lipids with glucose and insulin measurements or liver function metabolites using mixed assays ( Supplementary Table S1 , see column “Omics targets”). Overall, these studies found null or inconsistent results in the general population for associations with PFASs exposure, except in highly exposed populations, where associations with higher levels of total and low-density lipoprotein cholesterol were reported (Fitz-Simon et al., 2013 , p. 8; Frisbee et al., 2010 , p. 8) and lower high-density lipoprotein cholesterol in occupational exposure settings (Olsen & Zobel, 2007 ; J. Wang et al., 2012 ). Associations with phthalates were also inconsistent, including in studies with a similar design, i.e. when the exposure was measured prenatally and metabolic outcomes in offspring. Other targeted metabolic profiling or mixed method studies focused on oxidative stress biomarkers, indicators of oxidative damage to lipids (e.g. malondialdehyde, 8-iso-prostaglandin), proteins (e.g. o,o'-dityrosine) or DNA (e.g. 8-hydroxy-2′-deoxyguanosine), often combining different targeted assays for metabolites and proteins. Exposure to PAHs (all studies conducted in highly exposed populations) and phthalates were associated with increased oxidative stress markers in populations of different ages and pregnant/non-pregnant (see Supplementary Table S1 ). Finally, remaining targeted studies focused on hormone disruption, eight on the steroidogenesis pathway (testosterone and/or progesterone metabolites) and/or reproductive hormones (e.g. follicle-stimulating hormone) and three on thyroid hormones. In pregnant women, phthalate metabolites were associated with testosterone, sometimes in opposite directions depending on the metabolites, adding to evidence for phthalates impacting the maternal endocrine system (Cathey et al., 2019 ; Sathyanarayana et al., 2017 ). One longitudinal study further demonstrated that prenatal phthalate exposure was associated with lower testosterone in boys (Muerköster et al., 2020 ). Three other phthalate studies focused on adult males and found potential mediating effects of reproductive hormones on sperm quality (Al-Saleh et al., 2019 ; B. Wang et al., 2020 ) and prostatic enlargement (Chang et al., 2019 ). Five studies focused on inflammatory proteins such as interleukins (Liao et al., 2016 ; Seth et al., 2008 ; Takahashi et al., 2009 ). The two studies focusing on phthalate exposure during pregnancy did not find statistically significant associations with inflammatory proteins (Ferguson et al., 2014 , 2015 ). 3.2. DNA methylation All the DNA methylation studies relied on untargeted analysis with Illumina HumanMethylation 450 BeadChip array, including four in the context of pregnancy exposure. Two of these untargeted DNA methylation studies focused on male foetuses and reported hypermethylation in placenta associated with exposure to several phthalates (Jedynak et al., 2022 ) and triclosan (Jedynak et al., 2021 ) and two others showed DEHP exposure-associated hypomethylation (Solomon et al., 2017 ) or sex-specific associations with DNA marks in cord blood linked to BPA (Miura et al., 2019 ). Two further untargeted studies showed that paternal anti-androgenic phthalate metabolites were positively associated with sperm DNA methylation levels and inversely associated with embryo quality (H. Wu et al., 2017 ) and that PFOS exposure was linked to changes in blood DNA methylation levels but not to clinical parameters related to hormone levels and liver enzymes (van den Dungen et al., 2017 ). Four additional studies combined untargeted DNA methylation analysis with gene expression assessment (multi-omic approach), including three in the context of prenatal exposure. Phthalate mixture was associated with a decrease in placental DNA methylation levels (Grindler et al., 2018 ) while BPA was associated with both hyper- and hypomethylation in foetal liver tissue (Faulk et al., 2015 ) and PFUnDA exposure was associated with placental hypermethylation followed by decreased expression of a tumour suppressor gene that further correlated with shorter offspring birth length (Ouidir et al., 2020 ). Finally, exposure to PAHs in adult smokers was found to be associated with a number of DNA methylation markers, including some that were also correlated with gene expression (Zhu et al., 2016 ). Two targeted studies focused on blood methylation of the BMI and/or obesity associated CpGs in children. One study reported postnatal levels of PFOS, among a few other exposome factors, to be negatively associated with BMI via differential DNA methylation (Cadiou et al., 2020 ). In a complementary agnostic approach, the authors identified additional postnatal exposures including BPA, PFOS and the number of phthalates, to be generally unexpectedly associated with decreased BMI, an effect mediated by DNA methylation. The second study identified age- and sex-specific hypermethylation associated to BPA exposure and followed by an increase in child BMI (Choi et al., 2020 ). The last targeted study found no associations between high exposure to PAHs and blood DNA methylation of selected CpG sites of tumour suppressor gene in individuals undergoing Goeckerman therapy of psoriasis (Borsky et al., 2020 ). Finally, an untargeted analysis in adolescent girls followed by a targeted validation on four candidate genes showed BPA exposure-associated decrease in DNA methylation in saliva (J. H. Kim et al., 2013 ). 3.3. Gene expression Gene expression studies mostly investigated exposure to phthalates and PFASs and relied on targeted analyses. Two targeted studies on blood expression levels of different nuclear receptors found their upregulation associated with BPA exposure and downregulation for PFOA levels in infertile men (La Rocca et al., 2015 ), while higher PFOS levels were linked to higher expression of nuclear receptors in infertile couples (La Rocca et al., 2012 ). Three other targeted studies investigated effects of pregnancy exposure to phthalates on expression of placental genes. Certain phthalate metabolites (especially MnBP and MiBP) and outcomes at birth (large for gestational age and gestational diabetes mellitus) were associated in a sex-specific manner with expression of genes involved in placentally-mediated pathologies and foetal programming (Adibi et al., 2017 ). Phthalates were also associated with differential expression of several lncRNAs regulating gene expression during normal and pathological development, with the most substantial upregulations seen with MCNP. Also MEHP, MEHHP, MECPP and MEOHP were positively correlated with expression of lncRNA in two imprinted genes influencing placental and foetal growth (Machtinger et al., 2018 ). Finally, maternal exposure to phthalates (MnBP in particular) was associated with inflammatory variations in placental tissues in a sex-specific manner (J.-Q. Wang et al., 2020 ). The only study in cord blood demonstrated a BPA-associated increase in expression of different upstream genes related to foetal development in utero (X. Xu et al., 2015 ). Two studies involving participants highly exposed to PFASs and relying on targeted analysis of genes expressed in blood showed that PFOA and PFOS were associated with altered expression of genes involved in cholesterol transport or mobilisation (Fletcher et al., 2013 ) and were associated with lower expression of the osteoarthritis susceptibility candidate genes (Galloway et al., 2015 ). Finally, a study focusing on dust-derived DEHP exposure, gene expression and protein profiling reflecting inflammatory response in nasal mucosa tissue of allergic individuals showed attenuating effects of exposure on human nasal immune response in house dust mite-allergic subjects, concerning both gene expression and cytokines (Deutschle et al., 2008 ). Regarding untargeted gene expression studies, two included occupational exposure to PAHs and gene expression levels in blood (M. K. Kim et al., 2004 ; M.-T. Wu et al., 2011 ), with Kim et al. combining gene expression assessment with protein profiling. Both studies showed exposure-linked differential expression of genes involved in processes such as oxidative stress, apoptosis, chromosome stability/DNA repair, cell cycle control/ tumour suppressor, cell adhesion, development/spermatogenesis, immune function and neuronal cell function. The only study on PFASs and gene expression in cord blood found PFOA and PFOS to be associated with enrichment of several metabolically relevant transcription factors (Remy et al., 2016 ). Finally, a study focused on associations between medication-derived di-n-butyl phthalate exposure and gene expression in sperm showed exposure-associated alterations in expression of numerous RNA elements and suggesting that DBP is capable of altering spermatozoal RNAs and expression of genomic repeats in sperm (Estill et al., 2019 ). 3.4. miRNA Studies evaluating miRNAs most frequently focused on phthalate and/or phenol exposure and were mostly targeted. Interestingly, all studies on phthalates and phenols assessed exposure only in women, including three studies focusing on pregnancy exposure and placental miRNA expression. Maternal ∑phthalates and ∑phenols altered expression levels of miRNAs whose potential mRNA targets were associated with several biological pathways, including the regulation of protein serine/ threonine kinase activity (LaRocca et al., 2016 ). One study showed pregnancy BPA concentrations to be related to the overexpression of certain miRNA and correlated with BPA accumulation in the placenta (De Felice et al., 2015 ) while the other one performed on a highly exposed population did not find BPA exposure-associated changes in the placental miRNAs (Li et al., 2015 ). Two more studies focusing on phthalate and phenol exposure in pregnant women showed correlations between MBzP, MEHP, MnBP and MiBP concentrations and blood expression levels of miRNAs involved in metabolic disease (Martínez-Ibarra et al., 2019 ) and between MnBP, MEPA, triclosan, paraben and dichlorophenol metabolites and expression of placenta-derived extracellular vesicles miRNAs (Zhong et al., 2019 ). In a population of elderly women, increased BPA concentrations were associated with differential blood miRNA expression and high blood pressure (J. H. Kim et al., 2020 ). Another study identified MHBP and MEHHP as associated with expression levels of fibrosis tumour derived miRNA whose mRNA gene targets were associated with multiple fibroid-related processes including angiogenesis, apoptosis and proliferation of connective tissues (Zota et al., 2020 ). Finally, a study on follicle fluid miRNA levels in subjects undergoing IVF treatment found MEHP, MEHHP, MEOHP, MECPP, the sum of metabolites of DEHP, MnBP, MHiNCH and ETPA concentrations to be associated with extracellular vesicles miRNAs expression levels (Martinez et al., 2019 ). All studies on PAHs relied on occupational exposure and associations with blood levels of target miRNAs. Two studies selected miRNA targets based on their association with lung cancer and showed that telomere length in peripheral blood leukocytes was modulated by doses of exposures and miRNA polymorphisms (Duan, Zhang, et al., 2020 ) and that some miRNAs and PAH-exposure had a significant association with the mitochondrial DNA copy number (Duan, Yang, et al., 2020 ). Other studies reported vascular-related miRNA expression levels as associated with 1-OH-PY exposure and cardiovascular diseases events (Ruiz-Vera et al., 2019 ) as well as identified links between 1-OH-NAP, 2-OH-NAP, 2-OH-PHE, 4-OH-PHE, and the sum of monohydroxy-PAHs exposure and expression levels of miRNAs associated with higher micronuclei frequency (Deng et al., 2014 ). The only study on PFASs concerned highly exposed women and applied untargeted miRNA analysis in blood followed by validation of candidate miRNAs and it showed downregulation of expression levels of miRNAs annotated to e.g. cardiovascular function and disease, Alzheimer’s disease, growth of cancer cell lines and cancer (Y. Xu et al., 2020 ). 3.5. Microbiome Four studies were conducted on the gut microbiome (stool samples) in the general population and one on the oral microbiota in farmworkers to investigate agricultural pesticide exposure (Stanaway et al., 2017 ). All studies used untargeted 16s rRNA sequencing, a common technique applied to identify bacteria at the species level. One study found that DEHP exposure (through contact with plastic medical devices during intravenous infusions) in newborns affected the composition and diversity of gut microbiota and enhanced vaccine response (Yang et al., 2019 ). Two studies focused on exposure to triclosan and triclocarban, and their effect on the infant gut microbiome. One found an enrichment of broadly antibiotic-resistant species from the phylum Proteobacteria in both the mothers and infants (Bever et al., 2018 ) and the other study found broad bacterial diversity changes related to breast milk exposure (Ribado et al., 2017 ). Finally, the largest study on associations between EDCs and the microbiome, with 267 mother-child participants, focused on 28 chemical exposures measured in breast milk and found that PFASs were associated with less microbiome diversity and functionality, as measured by faecal short chain fatty acids, essential signalling molecules (Iszatt et al., 2019 ). Discussion This review presents an overview of existing research assessing the effects of EDC exposure on human health on a molecular level using different biomarkers and omic platforms. Although omics platforms have been used in biomedical research for decades now, only recently their affordable price, high technical reproducibility and advances in computational power have allowed applications in epidemiological studies (>100 samples). In this review, we identified almost 100 studies that investigated various molecular effects of exposure to EDCs on DNA methylation, expression of protein coding DNA and non-coding miRNAs, protein, metabolic and lipid profiles, as well as on microbiome diversity assessed in various biological matrices. Although there is a wealth of epidemiological studies on environmental pollutants including EDCs, the assessment of causality is often difficult and omic biomarkers promise a better causal attribution, refining one of Bradford Hill’s guidelines for causality assessment in epidemiology, i.e. biological plausibility. However, issues such as confounding, reverse causation and other uncertainties are still present with omics and are discussed below, followed by recommendations for future omics studies on EDCs. Main findings Overall, DNA methylation and gene expression in various tissues have been associated with numerous EDCs (including BPA, triclosan, low- and high molecular weight phthalate metabolites, PFOA, PFOS, PFUnDA, PAHs), however with no overlap in hits (associated omics features) across studies. Our review identifies some common metabolite groups and pathways affected by EDCs through metabolomics, such as carnitines, nucleotide metabolism (i.e. purine) and amino acids (e.g. arginine and proline) in untargeted studies, and oxidative stress through targeted studies. Although we intended to summarise the common and overlapping omic features or pathways associated with specific EDCs from the articles we reviewed, the comparison of results across different studies is challenging due to the small overlap across studies in omic matrices, omics coverage/techniques, study populations, statistical approaches, and potential confounders used as adjustment factors. In addition, there is considerable heterogeneity in the reporting of results, as highlighted in a previous review focusing on PFASs and metabolomics (Guo, 2022). This was particularly true for non-targeted studies that provided hypothesis driven results interpretation: even studies that published all associations in the supplement, showed only results that were relevant for the health outcome or target tissue of interest, in the main text. In the future, efforts should be made to systematically gather all results in one place, such as has been done for DNA methylation (e.g. http://www.ewascatalog.org/). Another future approach would be to adapt text mining-based tools such as AOP-helpFinder (http://aop-helpfinder.u-paris-sciences.fr/) (Jornod et al., 2022), developed under the H2020 projects HBM4EU and OBERON, to explore the literature more rapidly and in an automatic manner. Currently AOP-helpFinder screens all available abstracts from the PubMed database using artificial intelligence and graph theory to identify and extract known linkages between a chemical and key biological events, and was successfully applied to propose adverse outcome pathways (Benoit et al., 2022; Carvaillo et al., 2019; Jaylet et al., 2022; Kaiser et al., 2020). Quality of evidence and risk of bias Multi-exposure (or exposome): Exposure-omics studies including multiple EDCs remain rare, except for phenols and phthalates, although confounding by other environmental pollutants can potentially influence the reported findings. Indeed, simultaneous exposure to different EDCs that have common sources, such as diet or personal product use, is expected. In addition, biological pathways reported to be associated with EDC exposure could be shared between EDCs of different chemical classes and by other environmental contaminants (e.g. heavy metals), since many of the detoxification pathways are shared and many of the contaminants are sex-steroid hormone receptor disruptors that share similar effects. However, blind adjustment for multiple highly correlated exposure variables is not encouraged because that would lead to other problems such as a decrease in statistical efficiency and even the risk for bias amplification. Instead, we recommend statistical analyses able to identify important components of the mixture, capture interactions and cumulative effects (Maitre, Guimbaud, et al., 2022). Exposure assessment: Importantly, most of the studies included in our review relied on a spot urine sample to assess exposure to non-persistent chemicals (phthalates, phenols, some OP pesticides). This is likely to lead to misclassification of long-term exposure and attenuation of associations (Agier et al., 2020; Casas et al., 2018). These biases, in combination with the small size of the association expected for most exposure-omics associations, small average sample size and large number of statistical tests (for untargeted analyses), may lead to a substantial decrease in statistical power to detect true associations. Moreover, except for a few studies on highly exposed populations, in most cases (especially in more recently established cohorts) the exposure levels to certain EDCs are relatively low, which can contribute to false negative conclusions. Further replication studies should ideally rely on multiple repeated biospecimens followed by appropriate methods of combining the information on exposure, such as 1) pooling of biospecimens to improve exposure assessment and reduce analytical cost as recommended by e.g. (Philippat & Calafat, 2021), 2) correction of regression estimates for associations between exposure~omic outcome using e.g. regression calibration or SIMEX, 3) correction of exposure concentrations using e.g. methods of (Carroll, 2006; Hardin et al., 2003). All these approaches could help to better characterise the effect of exposure to EDCs with high within-subject temporal variability on human health assessed via omic methods. Omics matrix: The most represented matrices to assess omic markers were blood and cord blood/placental tissue. These matrices are common sources of DNA in infant and children’s studies, but their interpretation requires caution. First, environmentally induced epigenetic changes are likely to be tissue-specific and epigenetic markers measured in blood might not be relevant for all phenotypes of interest such as brain development. Nevertheless, some epigenetic changes in regions of hypervariable methylation between individuals but stable at an individual level (between tissues), known as metastable epialleles, may be more broadly identified across tissues (Breton et al., 2017). In contrast, blood proteins and metabolites can be good pre-clinical biomarkers of different target tissues such as in the case of liver dysfunction or even brain pathologies (Varma et al., 2018). Other matrices, such as urine or saliva, are less often used in human studies due to challenges such as the absence of standardised collection protocols and varying absolute quantity of collected material, as well as due to the lack of standardised reference materials and normalisation methods for the quantified analytes. Finally, the degree of DNA methylation tends to differ among cell types within certain tissues, therefore some part of the DNA methylation variability observed across biological samples may be explained by the inter-individual heterogeneity of the studied tissue rather than by the variation across individuals. For some tissues with established reference for cellular composition (e.g. blood) this can be accounted for by adjusting the statistical models with estimated cell type proportions, while for other tissues lacking such reference accurate accounting for tissue heterogeneity is more challenging. Further considerations for metabolomics should be made regarding individual differences that may impact both metabolome and exposure metabolite concentrations. Indeed, urine dilution due to personal characteristics (e.g. fluid intake habits) or blood protein content may affect both the concentrations of endogenous and exogenous metabolites and introduce spurious correlations if measured in the same sample. In recent years, the use of extracellular vesicles present in biofluids, known as exosomes, has been proposed as a source of omic biomarkers, given their role in cellular communication and their activity as mediators of specific signals to target cells or tissues (Colombo et al., 2014). Placenta-derived exosomes have gained special attention since they play a crucial role in the communication between the mother and foetus (Burkova et al., 2021). Study design: Ideally, EDC research would leverage a longitudinal prospective design in which omics are sampled repeatedly before an outcome occurs and after the exposure of interest. However, many studies identified in this review used cross-sectional or case-control designs. Reverse causation is a concern with such designs, where omics markers may affect exposure levels through e.g. modified excretion or metabolism. In addition, the timing of omics measurement is key when assessing response to exposure (short-term versus long-term effects). Indeed, it is likely that pathways associated with disease aetiology and progression involve multiple molecules and that perturbations in these response biomarkers are organised in a temporal sequence (e.g. prenatal epigenetics modification due to maternal EDC exposure might lead to disrupted metabolic profile in children and adverse clinical outcomes during adulthood). Finally, long-term health effects of exposure would ideally be identified by the use of omics in longitudinal population cohorts with biological samples stored over many years. In future studies, longitudinal or repeated measurement of omics are needed to distinguish time-specific or cumulative exposure effects of EDCs. Reproducibility : A major challenge in quantitative omics research revolves around the reproducibility of findings—balancing false-positive findings (large number of variables tested in often too small sample size) with generalizability (i.e. results that are too specific to a given study sample due to systematic bias). Median sample size in the described studies was below 160, with 25% of the studies relying on less than 55 subjects. The potential solution for this issue could be pooling of the cohorts. Recognizing the efficiencies enabled by a consortium model, exposomics consortia have been formed (in Europe see https://www.humanexposome.eu/ and https://hheardatacenter.mssm.edu/ in the USA) providing opportunities for replication or data pooling in studies with environmental exposures. Consortiums gathering omics data, such as the COnsortium of METabolomics (COMETS) that comprises 47 international cohorts that include >136,000 participants with blood metabolomics data or the Pregnancy and Children Epigenetics (PACE) consortium, are also potential sources of data for replication studies. However, such studies providing statistical or biological validation of findings are still rare, since most of the cohorts do not have data on EDC exposures or are not adequately designed for their assessment (single spot sampling) and since harmonisation protocols for data collection and analysis can make such an approach challenging. It is nonetheless of utter importance to replicate findings and increase sample sizes, especially because different omic methods applied in the same population may yield inconsistent results (Perng, 2020). Replication issues due to inter-individual (e.g. BMI) or intra-individual (e.g. time of day of data collection, season and circadian or hormonal cycles) variability should be controlled or accounted for in the study design or statistical analysis (Gallego-Paüls et al., 2021). Biological validation : Validation could be achieved through evidence triangulation by identifying independent biological evidence through systematic toxicological literature search [e.g. by screening databases such as The Comparative Toxicogenomics Database (CTD, http://ctdbase.org/, see example in (Chen et al., 2022)) or genetic data (Avery et al., 2022)] or by applying multi-omics approaches where different assays provide complementary information. Only a few studies used more than one omic platform to investigate the relationship between EDC exposure and omic markers (Borsky et al., 2020; Deutschle et al., 2008; Y. Duan et al., 2017; Faulk et al., 2015; Grindler et al., 2018; M. K. Kim et al., 2004; Ouidir et al., 2020; Poole et al., 2016; van den Dungen et al., 2017; J. Wang et al., 2012; Zhu et al., 2016). Using multi-omic approaches requires challenging integration of the biological information assessed from multiple platforms and may need high computational power and advanced techniques for high-dimensional data processing, e.g. dimensionality reduction or network-based methods. However, successful integration of the multi-omic signal allows creating a more holistic picture of mechanisms mediating associations between EDC exposure and health and cross-validate the findings on different biological levels. Indeed, for DNA methylation, gene and miRNA expression studies, there are gaps between gene activity and protein function. As for miRNA studies, miRNAs can target several mRNAs and may affect protein levels at the translational level rather than mRNA degradation. Thus, it is possible that some of the target predictions would be false positives. Validation through cross-omics is particularly challenging for proteins and metabolomics, the most downstream components of the omics cascade. As such, the proteome and metabolome are the closest omics to phenotype (and thus, perhaps most useful as a biomarker of disease risk or prognosis) but also exhibit the most inter- and intra-individual variability (Gallego-Paüls et al., 2021), thereby making replication and validation more challenging. However, result consistency across layers, if it occurs, reinforces causality, as we demonstrated in our recent multi-omics study of the early life exposome (Maitre, Bustamante, et al., 2022). Research gaps and recommendations Most of the identified omic studies focused on exposure to phthalates and phenols (BPA in particular) while, interestingly, only three investigated exposure to currently used pesticides. Several omic studies have assessed the effect of legacy pesticides [dichlorodiphenyltrichloroethane (DDT) or dichlorodiphenyldichloroethylene (DDE)] that we did not review here (Hu et al., 2020; Lind et al., 2013); however, it would be essential to conduct studies on recently-used pesticides such as OP pesticides and pyrethroids due to their abundant use and potential adverse health effects. Despite the epidemiological and toxicological evidence for neurodevelopmental damage due to prenatal EDC exposure and more recently respiratory damage, only one of the omic studies focused on populations with cognitive, mental or respiratory health problems. Although a few studies measured thyroid hormones, which are essential for normal brain development, none considered them as mediators for an adverse brain development. Future studies might consider relevant omic markers that mediate associations between prenatal EDC exposures and these outcomes. In contrast, many studies focused on lipid metabolism and insulin resistance, offering a more direct link to cardio-metabolic outcomes. In summary, our review points to the following needs for future studies on EDC exposure and omic biomarkers: - Study design: to prioritise longitudinal study designs with appropriate timing between exposure and omic assessment, with larger sample sizes and including several exposures at once providing wide coverage of exposures and biomarkers. - Exposure assessment: to apply methods reducing exposure misclassification by using repeated biospecimen sampling to assess exposure, correcting regression estimates obtained for associations between exposure~omic outcome, correcting exposure concentrations before further statistical analyses. - Omics assessment: to include omics sample replicates collected using standardised protocols and with the use of standardised reference materials and normalisation methods. - Reproducibility: to include biological replicates and perform multi-cohort studies. - Biological validation and triangulation of findings: to use several omic platforms where different assays would provide complementary information and to identify independent biological evidence through systematic toxicological literature search. - Confounding: to collect important information about confounders related to inter- and intra-individual variability. - Standardisation of result reporting: to systematically add results in designated databases to facilitate access to the full results. Finally, this review should be considered with a limitation that is the heterogeneity in definitions of omics, in particular for targeted metabolic and protein studies such as those focusing on hormones. To limit this issue, we included specific search terms in our study, such as “lipids” and “Inflammatory proteins”, which enabled the identification of further relevant articles. In addition, we decided to focus on EDCs, excluding persistent organic pollutants or heavy metals which have been covered in other reviews (Everson & Marsit, 2018; S. Kim et al., 2022). In conclusion, there is a fast growing body of evidence evaluating the early biological responses to exposure to EDCs. This review points to a clear need for larger, longitudinal studies, wider coverage of exposures and biomarkers, replication studies, and standardisation of research methods and results reporting to facilitate comparison. Declarations Funding The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements no. 825712 [OBERON] and no. 874583 [ATHLETE]. Acknowledgements LM is funded by a Juan de la Cierva-Incorporación fellowship (IJC2018-035394-I) awarded by the Spanish Ministerio de Economía, Industria y Competitividad. PJ was supported by the French National Agency for Research (ANR, grant n◦ ANR-15-IDEX-02). ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation through the “Centro de Excelencia Severo Ochoa 2019-2023” Program (CEX2018-000806-S) and support from the Generalitat de Catalunya through the CERCA Program. Author contributions Léa Maitre: Conceptualization, Data Curation, Writing - Review & Editing, Supervision, Funding acquisition, Paulina Jedynak: Data Curation, Writing - Review & Editing, Marta Gallego : Conceptualization, Data Curation, Laura Ciaran : Data Curation, Karine Audouze: Writing - Review & Editing, Funding acquisition, Maribel Casas: Writing - Review & Editing, Funding acquisition, Martine Vrijheid: Investigation, Resources, Data Curation, Writing - Review & Editing, Supervision, Project administration, Funding acquisition. Conflicts of Interest The authors declare they have nothing to disclose. References Adibi, J. J., Buckley, J. P., Lee, M. K., Williams, P. L., Just, A. C., Zhao, Y., Bhat, H. K., & Whyatt, R. M. (2017). Maternal urinary phthalates and sex-specific placental mRNA levels in an urban birth cohort. Environmental Health: A Global Access Science Source , 16 (1), 35. https://doi.org/10.1186/s12940-017-0241-5 Agier, L., Slama, R., & Basagaña, X. (2020). 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Food and Chemical Toxicology : An International Journal Published for the British Industrial Biological Research Association , 132 , 110700. https://doi.org/10.1016/j.fct.2019.110700 Zhong, J., Baccarelli, A. A., Mansur, A., Adir, M., Nahum, R., Hauser, R., Bollati, V., Racowsky, C., & Machtinger, R. (2019). Maternal Phthalate and Personal Care Products Exposure Alters Extracellular Placental miRNA Profile in Twin Pregnancies. Reproductive Sciences (Thousand Oaks, Calif.) , 26 (2), 289–294. https://doi.org/10.1177/1933719118770550 Zhu, X., Li, J., Deng, S., Yu, K., Liu, X., Deng, Q., Sun, H., Zhang, X., He, M., Guo, H., Chen, W., Yuan, J., Zhang, B., Kuang, D., He, X., Bai, Y., Han, X., Liu, B., Li, X., … Wu, T. (2016). Genome-Wide Analysis of DNA Methylation and Cigarette Smoking in a Chinese Population. Environ Health Perspect , 124 (7), 966–973. https://doi.org/10.1289/ehp.1509834 Zota, A. R., Geller, R. J., VanNoy, B. N., Marfori, C. Q., Tabbara, S., Hu, L. Y., Baccarelli, A. A., & Moawad, G. N. (2020). Phthalate exposures and MicroRNA expression in uterine fibroids: The FORGE study. Epigenetics Insights , 13 . https://doi.org/10.1177/2516865720904057 Tables Table 1 Data classifiers for the data extraction of the articles corresponding to the Integration -omics approaches into population-based studies of endocrine disrupting chemicals (EDCs) Definition EDC exposure family (to select at least one from the list) Phthalates Phenols Pesticides (excluding OCs, i.e. DDT, DDE) PAHs PFASs Glycol ether Exposome (including at least one of the key chemical groups) Chemical Full list of annotated chemical compounds measured (see list in Supplementary Table S2 ) Omics type (to select at least one from the list) DNA methylation Gene expression (excluding miRNA) miRNA Protein profile (including cytokines) Metabolic profile (including lipids) Microbiome Multi-omics (at least two omics platforms) Mixed methods (if only few targeted markers measured) Population characteristics 1 General population/occupational/highly exposed group, for example resident near contaminated site (based on the exposure) Population characteristics 2 Adult/pregnant/infants & children & adolescents Population characteristics 3 Gender (all/male only/female only) Study design 1 Cohort/case-control/cases-only Study design 2 Cross-sectional/longitudinal Study design 3 Observational/experimental study Sample size Total number of participants included in the study with exposure and omics measured. If case-control design, the sample size in each group was specified Study/cohort name E.g. The C8 Short-Term Follow-up Study Geographical location The country where the participants were recruited for the study Exposure assessment method Biomonitoring/questionnaire/modelling Exposure assessment period (if birth cohort) If pregnancy, specify the trimester or week of gestation. Specify if repeated sampling, time points was done Omics assessment period (if birth cohort) If pregnancy, specify the trimester or week of gestation. Specify if repeated sampling, time points was done Omics matrix Biological samples used for the omics measurements Omics approach Targeted/untargeted/untargeted and targeted (targeted means candidate approach at the stage of the experiment or statistical analysis); studies including both approaches are classified as untargeted and targeted Omics targets If targeted, specify the molecular targets, e.g. total cholesterol Main findings Summary of omics-EDCs association results Table 2 Summary of the study designs of studies included in the review. Study design Observational studies Randomised intervention Cross- sectional Longitudinal Mixed design Exposure family Number of studies Cohort Case- control Cases-only PAHs 12 3 8 1 0 10 2 0 Pesticides 3 1 2 0 0 2 1 0 PFASs 17 11 2 4 0 14 2 1 Phenols 19 11 4 1 3 12 7 0 Phthalates 34 25 6 3 0 21 12 1 Phenols, Phthalates 7 5 2 0 0 3 4 0 Pesticides, PFASs 1 1 0 0 0 0 1 0 PFASs, Phthalates 1 1 0 0 0 1 0 0 PFASs, Phenols, Phthalates 1 0 1 0 0 1 0 0 Pesticides, PFASs, Phenols, Phthalates 2 2 0 0 0 1 0 1 Organophosphate esters, Phenols, Phthalates 1 0 1 0 0 0 1 0 Total 98 60 26 9 3 65 30 3 Abbreviations: PAHs = polycyclic aromatic hydrocarbons; PFASs = per- and polyfluoroalkyl substances. Table 3 Summary of the population characteristics of studies included in the review. Population characteristics General population Highly exposed group Occupational Adults Infants, Children, Adolescents Pregnant women Pregnant women and foetuses, Infants, Children, Adolescents Other populations Exposure family PAHs 2 4 6 9 1 0 0 2 Pesticides 0 1 2 2 0 1 0 0 PFASs 10 5 2 9 5 0 1 2 Phenols 18 1 0 4 2 2 8 3 Phthalates 34 0 0 12 5 5 10 2 Phenols, Phthalates 7 0 0 1 1 2 3 0 Pesticides, PFASs 1 0 0 0 0 0 0 1 PFASs, Phthalates 1 0 0 0 0 0 0 1 PFASs, Phenols, Phthalates 1 0 0 1 0 0 0 0 Pesticides, PFASs, Phenols, Phthalates 2 0 0 0 1 1 0 0 Organophosphate esters, Phenols, Phthalates 1 0 0 0 0 0 1 0 Total 77 11 10 38 15 11 23 11 Abbreviations: PAHs = polycyclic aromatic hydrocarbons; PFASs = per- and polyfluoroalkyl substances. Table 4 Summary of the biological matrices and approach used for omic assessment. Omic matrix Omic approach Omic Blood Placenta and/or Cord blood Semen and/or Follicular fluid Stool Urine Other matrices Total Targeted Untargeted Untargeted and Targeted DNA methylation 2 4 1 0 0 1 8 2 5 1 Gene expression 5 5 1 0 0 0 11 8 3 0 Metabolic profile 19 0 1 0 9 1 30 11 16 3 Protein profile 2 1 0 0 0 0 3 3 0 0 Microbiome 0 0 0 4 0 1 5 0 5 0 miRNA 8 3 1 0 0 1 13 8 3 2 Mixed methods 18 0 2 0 1 0 21 18 0 3 Multi-omics 3 2 0 0 0 2 7 2 4 1 Total 57 15 6 4 10 6 98 52 36 10 Supplementary Tables Supplementary Tables S1-S2 are not available with this version. Supplementary Files Supplementarymethod.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2401240","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":161894194,"identity":"d75eb56c-fe17-404e-9eb0-d99050300329","order_by":0,"name":"Léa Maitre","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACPgglAcTMBw4wMFgwGDAwN+DVwgbVAtTDlnAApNeAgZEoLSBreAwYiNPC3vyA6UaNRZ15e8/Hgz9+SciZsx9sYPi4pxa3Fp5jBsw5xyQkZM6c3XCYt0/C2LInsYFxxrPjuLVIJAC1sEkAQe6Gw4w9EokbDiQ2MPMcOIZbi/zzD8w5/4A65N88OPgTpOX8QwJaJHgMmHPbQLbwMBzg+QHUcgNsSw0ev+QUHM7tk5CcwZNmcJi3AeiXGQ8bDs4AxREOwM9+fOPjnG91/BLshx9//PHHRs6cP/nggw8H6nBqAQGEgYxtcJHDeLUggT9wFn5bRsEoGAWjYEQBAIkBVjYK72yFAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3682-7117","institution":"ISGlobal","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Léa","middleName":"","lastName":"Maitre","suffix":""},{"id":161896334,"identity":"a2ddd45c-1f33-49ff-81a2-aff7d00de308","order_by":1,"name":"Paulina Jedynak","email":"","orcid":"","institution":"ISGlobal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Paulina","middleName":"","lastName":"Jedynak","suffix":""},{"id":161896336,"identity":"cd7f4f2c-228b-458e-b262-18a1ae46a767","order_by":2,"name":"Marta Gallego","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Gallego","suffix":""},{"id":161896889,"identity":"875dec55-70fc-4fec-9b06-74e189c5c94d","order_by":3,"name":"Laura Ciaran","email":"","orcid":"","institution":"ISGlobal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Ciaran","suffix":""},{"id":161896890,"identity":"fe086a91-4019-4943-8115-ba089ed031a4","order_by":4,"name":"Karine Audouze","email":"","orcid":"","institution":"Université Paris Cité","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Karine","middleName":"","lastName":"Audouze","suffix":""},{"id":161896891,"identity":"e594e451-0838-4beb-a125-80ab147915ef","order_by":5,"name":"Maribel Casas","email":"","orcid":"","institution":"ISGlobal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maribel","middleName":"","lastName":"Casas","suffix":""},{"id":161896335,"identity":"62e1a958-bffd-4f9b-ab95-fd2b57273a8e","order_by":6,"name":"Martine Vrijheid","email":"","orcid":"","institution":"ISGlobal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Martine","middleName":"","lastName":"Vrijheid","suffix":""}],"badges":[],"createdAt":"2022-12-21 12:09:24","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-2401240/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2401240/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":30684110,"identity":"6085944c-8b56-441f-9162-eec816b65e1c","added_by":"auto","created_at":"2022-12-22 19:13:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":119485,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA Flow diagram depicting process of article selection\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eUpdated from (Page et al., 2021).\u003c/p\u003e\n\u003cp\u003eAbbreviations: OP pesticides, Organophosphate pesticides; PAHs, Polycyclic aromatic hydrocarbons; PFASs, Per- and polyfluoroalkyl substances.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e* \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eafter removal of duplicates and off-topic references such as remaining non-human studies\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2401240/v1/52ea99b3ffbb9cfdc98caab2.png"},{"id":30684112,"identity":"aa27cc63-0c04-4726-8413-794d7f072e9a","added_by":"auto","created_at":"2022-12-22 19:13:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105248,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary of endocrine disruptingchemical (EDC) exposures and omic types studied in the 98 articles included in the scoping review.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: PAHs, Polycyclic aromatic hydrocarbons; PFASs, Per- and polyfluoroalkyl substances.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2401240/v1/ddcba26c51888fd24bac4530.png"},{"id":30684113,"identity":"d47b3602-c97d-4582-b13b-ff650f6b9911","added_by":"auto","created_at":"2022-12-22 19:13:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":823815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork visualisation of the endocrine disrupting chemicals (EDCs) measured in 98 EDC-omics studies in the human population\u003c/strong\u003e. The node and label sizes represent the number of articles in which the chemicals were measured, the node colours represent the chemical family and the edge width the number of times the chemicals are measured together in the same study.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2401240/v1/71de1c1a1f446bb16749fd6a.png"},{"id":30684461,"identity":"1824600d-c4f8-448a-97ca-c9863e1097c1","added_by":"auto","created_at":"2022-12-22 19:21:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1326656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2401240/v1/3a0530ec-003f-418c-b1f7-789d113841aa.pdf"},{"id":30684460,"identity":"72550034-278a-4c9a-82fe-8eed17364be1","added_by":"auto","created_at":"2022-12-22 19:21:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23656,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymethod.docx","url":"https://assets-eu.researchsquare.com/files/rs-2401240/v1/873c64038d7a383aced129ff.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIntegrating -omics approaches into population-based studies of endocrine disrupting chemicals: a scoping review\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe share of chronic diseases and disorders (obesity, diabetes, fertility and neurobehavioral disorders) attributable to endocrine disruptors (EDCs) costs the European Union each year more than 157\u0026nbsp;billion euros, or approximately 1.23% of the Union's gross domestic product (Trasande et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Prenatal exposure to EDCs can impair prenatal growth, thyroid function, glucose metabolism, obesity, puberty and fertility (Parker et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Predieri et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There is still inadequate evidence in humans, mainly due to the lack of understanding of potential mechanisms, the inaccuracy of exposure assessment for short half-life compounds, their general ubiquity and insufficient assessment of EDC mixtures and other co-exposures (aka exposome) (Braun, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vrijheid et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEarly biomarkers of effect, before clinical symptoms apparition, are needed to identify early EDC effects in humans, in particular during sensitive windows of exposure, such as pregnancy. Biomarkers of effect are measurable molecular, cellular, biochemical, physiologic, behavioural, structural or other alterations in an organism occurring along the temporal and mechanistic pathways connecting exposure to chemicals and an established or possible health impairment or disease (National Research Council, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Effect biomarkers ideally reflect subclinical changes before the onset of disease. Consequently, they range from early biological changes (e.g. enzyme induction responses) to altered structure and function. Effect biomarkers can help in identifying early effects at low doses, establish dose\u0026ndash;response relationships, explore mechanisms and increase the biological plausibility of epidemiological associations. In addition, they can improve the risk assessment of specific chemical families as well as exposure to chemical mixtures/cocktails.\u003c/p\u003e \u003cp\u003eThe use of omics platforms, once reserved for improving clinical diagnosis, patient stratification and personalised medicine, is becoming increasingly common to detect subtle biological changes in non-diseased general population. This is partly due to the feasibility of their application in large populations (N\u0026thinsp;\u0026gt;\u0026thinsp;1000) in epidemiological settings, thanks to their high-throughput and decrease in cost. Omics efforts have been invested mostly in the identification of genes (genomics), messenger RNA (mRNA) and microRNAs (miRNAs) (transcriptomics), proteins (proteomics) and metabolites (metabolomics). Of particular interest has been the identification of epigenetic markers\u0026mdash;mostly DNA methylation\u0026mdash;of gene expression (epigenomics) acting as a \u0026ldquo;cell memory\u0026rdquo; and more recently the gut microbiota (microbiomics). Different omics have proven useful in detecting early biological perturbations before the apparition of clinical symptoms in longitudinal epidemiological settings, predicting later cardio-vascular and metabolic diseases or neurodegeneration (Liu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Westerlund et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wingo et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These platforms have also been used in \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e toxicological studies of EDCs to improve understanding of mechanisms (Montjean et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, increasing numbers of epidemiological studies have employed or will employ -omics in the framework of large exposome studies, to identify early effect predictors for health risks due to EDC exposure (Maitre et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vrijheid et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, no studies to date have evaluated the methodology and robustness of findings in these environmental omics studies. It would be highly valuable for future studies to identify in which population (general, diseased or with occupational exposure), study design (longitudinal vs. cross-sectional), type of sample matrix and which type of omics, including multi-omics, might be useful. Only a few reviews have covered the topic of omics application in human populations and chemical exposures, focusing on general early life environmental exposures (e.g. smoking, toxic metals, air pollution (Everson \u0026amp; Marsit, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)), perfluorinated compounds (PFASs) and metabolomics (Guo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or multiple chemicals (S. Kim et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) but none focused on EDCs and multi-omics.\u003c/p\u003e \u003cp\u003eThe purpose of this scoping review is to provide an assessment of the current research regarding omics and associations with selected groups of EDCs exposure in humans that have not been summarised before, focusing on phthalates, phenols, PFASs, recently-used pesticides and polycyclic aromatic hydrocarbons (PAHs). We evaluated the study characteristics, research methods, omics targets and health outcomes investigated, if any. We also identified knowledge gaps and made recommendations for future research that will examine alterations of the human \u0026lsquo;omes\u0026rsquo; associated with EDCs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis scoping review protocol was designed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines (PRISMA-ScR) (Tricco et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eSearch Strategy and Selection Criteria\u003c/h2\u003e\n \u003cp\u003eReferences were identified by searches of PubMed and Scopus databases. Bibliography citations of the identified publications were also screened to identify additional articles. The automated search focused on papers published from January 1, 2004 (date of the first identified paper) to December 14, 2020, written in English and involving human participants. However, key publications that shaped the field were also included manually if missed during the automated search until February 2022. To be included in the review, papers needed to measure at least one non-persistent EDC and one type of omics (targeted or untargeted). The full list of search terms can be found in \u003cstrong\u003eSupplementary methods\u003c/strong\u003e. The list of chemicals included in this review was revised by the research team and members of the H2020 exposome project ATHLETE (Vrijheid et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our choice of exposures was based on their widespread occurrence in the general population and on their relevance for at least one of the following health outcomes: cardio-metabolic, neurodevelopment, respiratory health. Besides that, our choice of the chemical pollutant groups was based on recent or current production, plausibility of frequent exposure in European population and alignment with the chemicals prioritised by the human biomonitoring project, HBM4EU (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hbm4eu.eu/\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"Underline\"\u003e).\u003c/span\u003e For EDCs, the chemical groups (e.g. phthalates) and individual compounds (list based on annotated metabolites in the analytical protocols of the ATHLETE project WP2, \u003cstrong\u003eSupplementary methods\u003c/strong\u003e) were searched, including synonyms. MeSH terms were used when available and in case the MeSH term was missing, any of the synonyms found were added manually to the search. For omics, the most common types were searched: DNA methylation, gene expression, miRNA, protein profile (including inflammatory proteins and cytokines), metabolic profile (including lipids) and microbiome, using synonyms listed in \u003cstrong\u003eSupplementary methods\u003c/strong\u003e. Each search coming from each category of exposure (e.g. phthalates) and omics (e.g. proteome) was saved independently and then combined (e.g. phthalates\u0026thinsp;+\u0026thinsp;proteome, phthalates\u0026thinsp;+\u0026thinsp;metabolome, etc.). The search in PubMed was limited to \u0026quot;Journal article\u0026quot; and \u0026quot;Humans\u0026quot;. The search in Scopus was limited to \u0026quot;Articles\u0026apos;\u0026apos; and excluded keywords related to non-human or \u003cem\u003ein vitro\u003c/em\u003e studies, such as \u0026quot;Nonhuman\u0026quot;, \u0026quot;Animal\u0026quot;, \u0026quot;In vitro study\u0026quot; and \u0026quot;Mouse\u0026quot;. The resulting papers were imported to the reference manager Mendeley, for further manual filtering. The final search strategy for Scopus can be found in \u003cstrong\u003eSupplementary methods\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003eStudy selection\u003c/h2\u003e\n \u003cp\u003ePapers were excluded if they did not fit into the conceptual framework of the study, for example if they focused on EDCs that naturally occur in food (e.g. naturally occurring phenols such as resveratrol). Papers measuring biological markers in a targeted approach, such as inflammatory cytokines or clinical routine blood lipid traits (including total cholesterol, triglycerides, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol) were considered if they included at least three biomarkers. Although these targeted markers are not considered as omics approaches, they provide useful information on key biological systems involved in EDC health effects (inflammation, oxidative stress, lipid metabolism) that may be profiled in an untargeted manner in future studies. A first screening of the titles and abstracts was done to only keep studies conducted in human subjects. In the next steps, to increase consistency among reviewers, at least two reviewers screened the same 98 publications, discussed the results and amended the screening and data extraction manual before beginning data extraction for this review. Reviewers working in pairs sequentially evaluated the titles, abstracts and then full text of all publications identified by our searches for potentially relevant publications. We resolved disagreements on study selection and data extraction by consensus and discussion with other reviewers if needed.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003eData charting process\u003c/h2\u003e\n \u003cp\u003eA data-charting form was developed to determine which variables to extract. We extracted data on the type and method of EDC exposure and omics assessment, study participant characteristics (e.g. country of origin, cohort name if provided, demographic characteristics), the study design (cross-sectional/ longitudinal, cohort/ case-control/ cases only/ randomised intervention and sample size). Detailed definitions can be found in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eWe drew a word cloud network to visualise the number of articles in which individual EDC parents and metabolites were measured, taking into account if they were assessed together in the same study. The layout was drawn in Cytoscape 3.9.0 with the prefuse force directed layout function.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOur searches spanning from 2004\u0026ndash;2020 across the databases retrieved 1411 total records. These studies were identified after using pre-set literature filters (e.g. excluding non-human studies, see \u003cstrong\u003eMethods\u003c/strong\u003e). Finally, 102 unique studies were selected after title/abstract screening and removal of duplicates and off-topic references such as remaining non-human studies (Fig. 1). Further 13 studies were excluded manually for not fitting in our framework of interest (e.g. naturally occurring phenols). Nine studies missed by our automatic search were included manually, which led to a final of 98 unique studies included in this review. The full list of studies is presented in the \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1. Exposure to EDCs\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMost of the studies focused on only one family of EDCs and the most represented groups were phthalates (n\u0026thinsp;=\u0026thinsp;34), phenols (n\u0026thinsp;=\u0026thinsp;19) and PFASs (n\u0026thinsp;=\u0026thinsp;17), while PAHs and pesticides were studied less frequently (n\u0026thinsp;=\u0026thinsp;12 and 3, respectively, \u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e and \u003cstrong\u003eFig.\u0026nbsp;2\u003c/strong\u003e). Thirteen studies investigated more than one family of EDCs, among which seven included only phthalates and phenols.\u003c/p\u003e\n\u003cp\u003eThe frequency at which individual EDC metabolites were studied and if they were studied together was represented as a word cloud network in \u003cstrong\u003eFig.\u0026nbsp;3\u003c/strong\u003e. A complete list of all EDC metabolites and their parent compounds assessed in the 98 studies are presented in \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e. For phthalates, the most studied metabolites were MnBP, MEHP, MEP, MBzP, MEHHP, MEOHP, MiBP and MECPP (the number of studies including these chemicals ranged from 32 for MECPP to 39 for MnBP). Within the group of phenols, BPA was the most studied compound (n\u0026thinsp;=\u0026thinsp;26), with other members of this group studied much less frequently (n\u0026thinsp;\u0026lt;\u0026thinsp;10). Among PFASs, the most commonly investigated were PFOA and PFOS (n\u0026thinsp;=\u0026thinsp;21) and among PAHs, 1-OH-PY (n\u0026thinsp;=\u0026thinsp;11). Only three studies focused on currently used pesticides and specifically measured malathion (Seth et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e), azinphos-methyl (Stanaway et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) or used the surface of land dedicated to agricultural cereal activities as a proxy for pesticide exposure (Bonvallot et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). A few studies measured HCB (n\u0026thinsp;=\u0026thinsp;3), although this compound was not included in our list of EDCs of interest, it appeared in our results since it was measured together with other EDCs of interest in multi-exposure studies. Almost all studies used biomonitoring to assess exposure to EDCs (n\u0026thinsp;=\u0026thinsp;95, exclusively or complemented by other methods such as air sampling or questionnaires). Six studies relied on an experimental design where the source of exposure was modified (Deutschle et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Estill et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; J. H. Kim et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Poole et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ribado et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Takahashi et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Information on exposure groups measured in each study is presented in the \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2. Study designs and populations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDetails on the study designs and included populations for the 98 studies are presented in the \u003cstrong\u003eSupplementary Table S1.\u003c/strong\u003e The vast majority of the studies were conducted in the USA (n\u0026thinsp;=\u0026thinsp;28), Europe (n\u0026thinsp;=\u0026thinsp;18) and China (n\u0026thinsp;=\u0026thinsp;17). Almost all studies (n\u0026thinsp;=\u0026thinsp;95) were observational and only three studies on phenols involved a randomised intervention (\u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e). These randomised controlled trials included studies on: BPA exposure through providing canned soymilk and association with miRNA profiles (J. H. Kim et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and triclosan and triclocarban exposure from household and personal care products associated with gut microbiome diversity (Poole et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ribado et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the observational studies, the data were collected most often in cohort (n\u0026thinsp;=\u0026thinsp;60) and case-control studies (n\u0026thinsp;=\u0026thinsp;26), with a few articles featuring cases only (n\u0026thinsp;=\u0026thinsp;9) (\u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e). For those that relied on established cohorts, we identified two articles per cohort at most. Those included the C8 Health Project and the C8 Short-Term Follow-up Study, both carried in a community with elevated exposure to PFOA (Frisbee et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fitz-Simon et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fletcher et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Galloway et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), the Ewha Birth and Growth Study, EDEN, PREVIENI and PROTECT. Most of the studies assessed EDC exposure in a cross-sectional manner (n\u0026thinsp;=\u0026thinsp;65) and around 30% longitudinally (n\u0026thinsp;=\u0026thinsp;30), with three additional studies relying on both cross-sectional and longitudinal design (\u003cstrong\u003eTable\u0026nbsp;3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eSeveral studies included occupational workers (n\u0026thinsp;=\u0026thinsp;10) or highly exposed groups (n\u0026thinsp;=\u0026thinsp;11) and those focused on PAHs, pesticides and PFASs \u003cstrong\u003e(Table\u0026nbsp;3\u003c/strong\u003e). The study populations were diverse, with most studies carried out in adult males and/or females (n\u0026thinsp;=\u0026thinsp;38), infants, children or adolescents (n\u0026thinsp;=\u0026thinsp;15) and in pregnant women (n\u0026thinsp;=\u0026thinsp;11). A significant fraction of studies included more than one population and most often these were pregnant women and infants, children or adolescents (n\u0026thinsp;=\u0026thinsp;23). Among those, studies that used maternal biospecimens to assess EDC exposure collected up to four spot urine samples, but mostly relied on single spot urine samples. Studies in non-pregnant adults focused mostly on phthalates (n\u0026thinsp;=\u0026thinsp;12), PAHs (n\u0026thinsp;=\u0026thinsp;9) and PFASs (n\u0026thinsp;=\u0026thinsp;9), while those on pregnant women, children or both populations most often included phthalates (n\u0026thinsp;=\u0026thinsp;5, 5 and 10, respectively) and phenols (n\u0026thinsp;=\u0026thinsp;2, 2 and 8, respectively). The sample size ranged from 10 to 12,476 (median\u0026thinsp;=\u0026thinsp;159) (\u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eMany studies assessed the associations between EDC and omics in the context of clinical outcomes in case-control, cases-only or cohort studies where omics data were used as potential mediator/intermediate factors. The investigated health outcomes in adults were related mainly to cardio-metabolic outcomes and fertility in women and men. In pregnancy, studies focused on birth outcomes such as birth weight or gestational age with one study measuring newborns\u0026rsquo; genital outcomes (Sathyanarayana et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). In children, body mass index (BMI, overweight) was the main focus.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3. Omic types\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe most exploited omic method was metabolic profiling (n\u0026thinsp;=\u0026thinsp;30, \u003cstrong\u003eTable\u0026nbsp;4\u003c/strong\u003e), including 14 studies with a targeted approach. Other omics were used less frequently (miRNA, n\u0026thinsp;=\u0026thinsp;13; gene expression, n\u0026thinsp;=\u0026thinsp;11; DNA methylation, n\u0026thinsp;=\u0026thinsp;8; proteins, n\u0026thinsp;=\u0026thinsp;3), with only five studies focusing on the microbiome. Twenty-one studies used a mixed method approach, combining different targeted assays to measure a small number of metabolites and proteins. Finally, seven studies used more than one omic, most commonly combining investigation of DNA methylation with gene expression (n\u0026thinsp;=\u0026thinsp;4). More than 50% of the studies (n\u0026thinsp;=\u0026thinsp;57) used blood to assess omic markers (\u003cstrong\u003eTable\u0026nbsp;4\u003c/strong\u003e), while several studies focused on placenta or cord blood (n\u0026thinsp;=\u0026thinsp;15). Urine samples were solely used for metabolic profiling (n\u0026thinsp;=\u0026thinsp;9) and in one study applying mixed methods. Finally, the majority of the studies, including those assessing the microbiome, performed targeted analyses (n\u0026thinsp;=\u0026thinsp;52) and some used both untargeted and targeted approaches (n\u0026thinsp;=\u0026thinsp;10) (\u003cstrong\u003eTable\u0026nbsp;4\u003c/strong\u003e). The only three protein profiling studies relied on a targeted approach. Details of the omic type used, results and altered pathways (see last column \u0026ldquo;Main findings\u0026rdquo;) in each of the 98 studies are presented in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.1. Metabolic profiling, proteins and mixed methods\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMetabolic profiling studies of EDCs included 16 untargeted approaches (i.e. metabolomics) and 11 targeted approaches, with three studies using both (\u003cstrong\u003eTable\u0026nbsp;4\u003c/strong\u003e). Untargeted methods were mainly based on liquid-chromatography coupled with a mass spectrometer [i.e. LC-MS, using high-resolution quadrupole-time-of-flight mass analyser (QTOF) or Q-Exactive instruments]. Only three studies applied gas-chromatography (GC)-MS and four studies applied proton Nuclear Magnetic Resonance spectroscopy (NMR). All the untargeted studies reported some significant associations with exposure, including at least four studies with lipid and lipid-related metabolites such as the fatty acid esters or fatty acids and the carnitines/acylcarnitines. Nucleotide metabolism (i.e. purine) and amino acids (e.g. arginine and proline) were also reported pathways in at least four studies.\u003c/p\u003e\n\u003cp\u003eAmong the 11 targeted metabolic studies, six focused solely on routine clinical lipid metabolites (i.e. cholesterol and total triglycerides) and an additional five studies combined clinical lipids with glucose and insulin measurements or liver function metabolites using mixed assays (\u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e, see column \u0026ldquo;Omics targets\u0026rdquo;). Overall, these studies found null or inconsistent results in the general population for associations with PFASs exposure, except in highly exposed populations, where associations with higher levels of total and low-density lipoprotein cholesterol were reported (Fitz-Simon et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e, p. 8; Frisbee et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e, p. 8) and lower high-density lipoprotein cholesterol in occupational exposure settings (Olsen \u0026amp; Zobel, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; J. Wang et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Associations with phthalates were also inconsistent, including in studies with a similar design, i.e. when the exposure was measured prenatally and metabolic outcomes in offspring.\u003c/p\u003e\n\u003cp\u003eOther targeted metabolic profiling or mixed method studies focused on oxidative stress biomarkers, indicators of oxidative damage to lipids (e.g. malondialdehyde, 8-iso-prostaglandin), proteins (e.g. o,o\u0026apos;-dityrosine) or DNA (e.g. 8-hydroxy-2\u0026prime;-deoxyguanosine), often combining different targeted assays for metabolites and proteins. Exposure to PAHs (all studies conducted in highly exposed populations) and phthalates were associated with increased oxidative stress markers in populations of different ages and pregnant/non-pregnant (see \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e). Finally, remaining targeted studies focused on hormone disruption, eight on the steroidogenesis pathway (testosterone and/or progesterone metabolites) and/or reproductive hormones (e.g. follicle-stimulating hormone) and three on thyroid hormones. In pregnant women, phthalate metabolites were associated with testosterone, sometimes in opposite directions depending on the metabolites, adding to evidence for phthalates impacting the maternal endocrine system (Cathey et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sathyanarayana et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). One longitudinal study further demonstrated that prenatal phthalate exposure was associated with lower testosterone in boys (Muerk\u0026ouml;ster et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Three other phthalate studies focused on adult males and found potential mediating effects of reproductive hormones on sperm quality (Al-Saleh et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; B. Wang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and prostatic enlargement (Chang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFive studies focused on inflammatory proteins such as interleukins (Liao et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Seth et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Takahashi et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The two studies focusing on phthalate exposure during pregnancy did not find statistically significant associations with inflammatory proteins (Ferguson et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2. DNA methylation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll the DNA methylation studies relied on untargeted analysis with Illumina HumanMethylation 450 BeadChip array, including four in the context of pregnancy exposure. Two of these untargeted DNA methylation studies focused on male foetuses and reported hypermethylation in placenta associated with exposure to several phthalates (Jedynak et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) and triclosan (Jedynak et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and two others showed DEHP exposure-associated hypomethylation (Solomon et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) or sex-specific associations with DNA marks in cord blood linked to BPA (Miura et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Two further untargeted studies showed that paternal anti-androgenic phthalate metabolites were positively associated with sperm DNA methylation levels and inversely associated with embryo quality (H. Wu et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) and that PFOS exposure was linked to changes in blood DNA methylation levels but not to clinical parameters related to hormone levels and liver enzymes (van den Dungen et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFour additional studies combined untargeted DNA methylation analysis with gene expression assessment (multi-omic approach), including three in the context of prenatal exposure. Phthalate mixture was associated with a decrease in placental DNA methylation levels (Grindler et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) while BPA was associated with both hyper- and hypomethylation in foetal liver tissue (Faulk et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) and PFUnDA exposure was associated with placental hypermethylation followed by decreased expression of a tumour suppressor gene that further correlated with shorter offspring birth length (Ouidir et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, exposure to PAHs in adult smokers was found to be associated with a number of DNA methylation markers, including some that were also correlated with gene expression (Zhu et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTwo targeted studies focused on blood methylation of the BMI and/or obesity associated CpGs in children. One study reported postnatal levels of PFOS, among a few other exposome factors, to be negatively associated with BMI via differential DNA methylation (Cadiou et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). In a complementary agnostic approach, the authors identified additional postnatal exposures including BPA, PFOS and the number of phthalates, to be generally unexpectedly associated with decreased BMI, an effect mediated by DNA methylation. The second study identified age- and sex-specific hypermethylation associated to BPA exposure and followed by an increase in child BMI (Choi et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The last targeted study found no associations between high exposure to PAHs and blood DNA methylation of selected CpG sites of tumour suppressor gene in individuals undergoing Goeckerman therapy of psoriasis (Borsky et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, an untargeted analysis in adolescent girls followed by a targeted validation on four candidate genes showed BPA exposure-associated decrease in DNA methylation in saliva (J. H. Kim et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3. Gene expression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGene expression studies mostly investigated exposure to phthalates and PFASs and relied on targeted analyses. Two targeted studies on blood expression levels of different nuclear receptors found their upregulation associated with BPA exposure and downregulation for PFOA levels in infertile men (La Rocca et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), while higher PFOS levels were linked to higher expression of nuclear receptors in infertile couples (La Rocca et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Three other targeted studies investigated effects of pregnancy exposure to phthalates on expression of placental genes. Certain phthalate metabolites (especially MnBP and MiBP) and outcomes at birth (large for gestational age and gestational diabetes mellitus) were associated in a sex-specific manner with expression of genes involved in placentally-mediated pathologies and foetal programming (Adibi et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Phthalates were also associated with differential expression of several lncRNAs regulating gene expression during normal and pathological development, with the most substantial upregulations seen with MCNP. Also MEHP, MEHHP, MECPP and MEOHP were positively correlated with expression of lncRNA in two imprinted genes influencing placental and foetal growth (Machtinger et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Finally, maternal exposure to phthalates (MnBP in particular) was associated with inflammatory variations in placental tissues in a sex-specific manner (J.-Q. Wang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The only study in cord blood demonstrated a BPA-associated increase in expression of different upstream genes related to foetal development \u003cem\u003ein utero\u003c/em\u003e (X. Xu et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTwo studies involving participants highly exposed to PFASs and relying on targeted analysis of genes expressed in blood showed that PFOA and PFOS were associated with altered expression of genes involved in cholesterol transport or mobilisation (Fletcher et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) and were associated with lower expression of the osteoarthritis susceptibility candidate genes (Galloway et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Finally, a study focusing on dust-derived DEHP exposure, gene expression and protein profiling reflecting inflammatory response in nasal mucosa tissue of allergic individuals showed attenuating effects of exposure on human nasal immune response in house dust mite-allergic subjects, concerning both gene expression and cytokines (Deutschle et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eRegarding untargeted gene expression studies, two included occupational exposure to PAHs and gene expression levels in blood (M. K. Kim et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; M.-T. Wu et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), with Kim et al. combining gene expression assessment with protein profiling. Both studies showed exposure-linked differential expression of genes involved in processes such as oxidative stress, apoptosis, chromosome stability/DNA repair, cell cycle control/ tumour suppressor, cell adhesion, development/spermatogenesis, immune function and neuronal cell function. The only study on PFASs and gene expression in cord blood found PFOA and PFOS to be associated with enrichment of several metabolically relevant transcription factors (Remy et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Finally, a study focused on associations between medication-derived di-n-butyl phthalate exposure and gene expression in sperm showed exposure-associated alterations in expression of numerous RNA elements and suggesting that DBP is capable of altering spermatozoal RNAs and expression of genomic repeats in sperm (Estill et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4. miRNA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStudies evaluating miRNAs most frequently focused on phthalate and/or phenol exposure and were mostly targeted. Interestingly, all studies on phthalates and phenols assessed exposure only in women, including three studies focusing on pregnancy exposure and placental miRNA expression. Maternal \u0026sum;phthalates and \u0026sum;phenols altered expression levels of miRNAs whose potential mRNA targets were associated with several biological pathways, including the regulation of protein serine/ threonine kinase activity (LaRocca et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). One study showed pregnancy BPA concentrations to be related to the overexpression of certain miRNA and correlated with BPA accumulation in the placenta (De Felice et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) while the other one performed on a highly exposed population did not find BPA exposure-associated changes in the placental miRNAs (Li et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Two more studies focusing on phthalate and phenol exposure in pregnant women showed correlations between MBzP, MEHP, MnBP and MiBP concentrations and blood expression levels of miRNAs involved in metabolic disease (Mart\u0026iacute;nez-Ibarra et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and between MnBP, MEPA, triclosan, paraben and dichlorophenol metabolites and expression of placenta-derived extracellular vesicles miRNAs (Zhong et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In a population of elderly women, increased BPA concentrations were associated with differential blood miRNA expression and high blood pressure (J. H. Kim et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another study identified MHBP and MEHHP as associated with expression levels of fibrosis tumour derived miRNA whose mRNA gene targets were associated with multiple fibroid-related processes including angiogenesis, apoptosis and proliferation of connective tissues (Zota et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Finally, a study on follicle fluid miRNA levels in subjects undergoing IVF treatment found MEHP, MEHHP, MEOHP, MECPP, the sum of metabolites of DEHP, MnBP, MHiNCH and ETPA concentrations to be associated with extracellular vesicles miRNAs expression levels (Martinez et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAll studies on PAHs relied on occupational exposure and associations with blood levels of target miRNAs. Two studies selected miRNA targets based on their association with lung cancer and showed that telomere length in peripheral blood leukocytes was modulated by doses of exposures and miRNA polymorphisms (Duan, Zhang, et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and that some miRNAs and PAH-exposure had a significant association with the mitochondrial DNA copy number (Duan, Yang, et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Other studies reported vascular-related miRNA expression levels as associated with 1-OH-PY exposure and cardiovascular diseases events (Ruiz-Vera et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) as well as identified links between 1-OH-NAP, 2-OH-NAP, 2-OH-PHE, 4-OH-PHE, and the sum of monohydroxy-PAHs exposure and expression levels of miRNAs associated with higher micronuclei frequency (Deng et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe only study on PFASs concerned highly exposed women and applied untargeted miRNA analysis in blood followed by validation of candidate miRNAs and it showed downregulation of expression levels of miRNAs annotated to e.g. cardiovascular function and disease, Alzheimer\u0026rsquo;s disease, growth of cancer cell lines and cancer (Y. Xu et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.5. Microbiome\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFour studies were conducted on the gut microbiome (stool samples) in the general population and one on the oral microbiota in farmworkers to investigate agricultural pesticide exposure (Stanaway et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). All studies used untargeted 16s rRNA sequencing, a common technique applied to identify bacteria at the species level. One study found that DEHP exposure (through contact with plastic medical devices during intravenous infusions) in newborns affected the composition and diversity of gut microbiota and enhanced vaccine response (Yang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Two studies focused on exposure to triclosan and triclocarban, and their effect on the infant gut microbiome. One found an enrichment of broadly antibiotic-resistant species from the phylum Proteobacteria in both the mothers and infants (Bever et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the other study found broad bacterial diversity changes related to breast milk exposure (Ribado et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Finally, the largest study on associations between EDCs and the microbiome, with 267 mother-child participants, focused on 28 chemical exposures measured in breast milk and found that PFASs were associated with less microbiome diversity and functionality, as measured by faecal short chain fatty acids, essential signalling molecules (Iszatt et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis review presents an overview of existing research assessing the effects of EDC exposure on human health on a molecular level using different biomarkers and omic platforms. Although omics platforms have been used in biomedical research for decades now, only recently their affordable price, high technical reproducibility and advances in computational power have allowed applications in epidemiological studies (\u0026gt;100 samples). In this review, we identified almost 100 studies that investigated various molecular effects of exposure to EDCs on DNA methylation, expression of protein coding DNA and non-coding miRNAs, protein, metabolic and lipid profiles, as well as on microbiome diversity assessed in various biological matrices. Although there is a wealth of epidemiological studies on environmental pollutants including EDCs, the assessment of causality is often difficult and omic biomarkers promise a better causal attribution, refining one of Bradford Hill\u0026rsquo;s guidelines for causality assessment in epidemiology, i.e. biological plausibility. However, issues such as confounding, reverse causation and other uncertainties are still present with omics and are discussed below, followed by recommendations for future omics studies on EDCs. \u003c/p\u003e\n\u003ch2\u003eMain findings\u003c/h2\u003e\n\u003cp\u003eOverall, DNA methylation and gene expression in various tissues have been associated with numerous EDCs (including BPA, triclosan, low- and high molecular weight phthalate metabolites, PFOA, PFOS, PFUnDA, PAHs), however with no overlap in hits (associated omics features) across studies. Our review identifies some common metabolite groups and pathways affected by EDCs through metabolomics, such as carnitines, nucleotide metabolism (i.e. purine) and amino acids (e.g. arginine and proline) in untargeted studies, and oxidative stress through targeted studies. Although we intended to summarise the common and overlapping omic features or pathways associated with specific EDCs from the articles we reviewed, the comparison of results across different studies is challenging due to the small overlap across studies in omic matrices, omics coverage/techniques, study populations, statistical approaches, and potential confounders used as adjustment factors. In addition, there is considerable heterogeneity in the reporting of results, as highlighted in a previous review focusing on PFASs and metabolomics (Guo, 2022). This was particularly true for non-targeted studies that provided hypothesis driven results interpretation: even studies that published all associations in the supplement, showed only results that were relevant for the health outcome or target tissue of interest, in the main text. In the future, efforts should be made to systematically gather all results in one place, such as has been done for DNA methylation (e.g. http://www.ewascatalog.org/). Another future approach would be to adapt text mining-based tools such as AOP-helpFinder (http://aop-helpfinder.u-paris-sciences.fr/) (Jornod et al., 2022), developed under the H2020 projects HBM4EU and OBERON, to explore the literature more rapidly and in an automatic manner. Currently AOP-helpFinder screens all available abstracts from the PubMed database using artificial intelligence and graph theory to identify and extract known linkages between a chemical and key biological events, and was successfully applied to propose adverse outcome pathways (Benoit et al., 2022; Carvaillo et al., 2019; Jaylet et al., 2022; Kaiser et al., 2020).\u003c/p\u003e\n\u003ch2\u003eQuality of evidence and risk of bias\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eMulti-exposure (or exposome): \u003c/strong\u003eExposure-omics studies including multiple EDCs remain rare, except for phenols and phthalates, although confounding by other environmental pollutants can potentially influence the reported findings. Indeed, simultaneous exposure to different EDCs that have common sources, such as diet or personal product use, is expected. In addition, biological pathways reported to be associated with EDC exposure could be shared between EDCs of different chemical classes and by other environmental contaminants (e.g. heavy metals), since many of the detoxification pathways are shared and many of the contaminants are sex-steroid hormone receptor disruptors that share similar effects. However, blind adjustment for multiple highly correlated exposure variables is not encouraged because that would lead to other problems such as a decrease in statistical efficiency and even the risk for bias amplification. Instead, we recommend statistical analyses able to identify important components of the mixture, capture interactions and cumulative effects (Maitre, Guimbaud, et al., 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure assessment:\u003c/strong\u003e Importantly, most of the studies included in our review relied on a spot urine sample to assess exposure to non-persistent chemicals (phthalates, phenols, some OP pesticides). This is likely to lead to misclassification of long-term exposure and attenuation of associations (Agier et al., 2020; Casas et al., 2018). These biases, in combination with the small size of the association expected for most exposure-omics associations, small average sample size and large number of statistical tests (for untargeted analyses), may lead to a substantial decrease in statistical power to detect true associations. Moreover, except for a few studies on highly exposed populations, in most cases (especially in more recently established cohorts) the exposure levels to certain EDCs are relatively low, which can contribute to false negative conclusions. Further replication studies should ideally rely on multiple repeated biospecimens followed by appropriate methods of combining the information on exposure, such as 1) pooling of biospecimens to improve exposure assessment and reduce analytical cost as recommended by e.g. (Philippat \u0026amp; Calafat, 2021), 2) correction of regression estimates for associations between exposure~omic outcome using e.g. regression calibration or SIMEX, 3) correction of exposure concentrations using e.g. methods of (Carroll, 2006; Hardin et al., 2003). All these approaches could help to better characterise the effect of exposure to EDCs with high within-subject temporal variability on human health assessed via omic methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOmics matrix: \u003c/strong\u003eThe most represented matrices to assess omic markers were blood and cord blood/placental tissue. These matrices are common sources of DNA in infant and children\u0026rsquo;s studies, but their interpretation requires caution. First, environmentally induced epigenetic changes are likely to be tissue-specific and epigenetic markers measured in blood might not be relevant for all phenotypes of interest such as brain development. Nevertheless, some epigenetic changes in regions of hypervariable methylation between individuals but stable at an individual level (between tissues), known as metastable epialleles, may be more broadly identified across tissues (Breton et al., 2017). In contrast, blood proteins and metabolites can be good pre-clinical biomarkers of different target tissues such as in the case of liver dysfunction or even brain pathologies (Varma et al., 2018). Other matrices, such as urine or saliva, are less often used in human studies due to challenges such as the absence of standardised collection protocols and varying absolute quantity of collected material, as well as due to the lack of standardised reference materials and normalisation methods for the quantified analytes. Finally, the degree of DNA methylation tends to differ among cell types within certain tissues, therefore some part of the DNA methylation variability observed across biological samples may be explained by the inter-individual heterogeneity of the studied tissue rather than by the variation across individuals. For some tissues with established reference for cellular composition (e.g. blood) this can be accounted for by adjusting the statistical models with estimated cell type proportions, while for other tissues lacking such reference accurate accounting for tissue heterogeneity is more challenging.\u003c/p\u003e\n\u003cp\u003eFurther considerations for metabolomics should be made regarding individual differences that may impact both metabolome and exposure metabolite concentrations. Indeed, urine dilution due to personal characteristics (e.g. fluid intake habits) or blood protein content may affect both the concentrations of endogenous and exogenous metabolites and introduce spurious correlations if measured in the same sample. In recent years, the use of extracellular vesicles present in biofluids, known as exosomes, has been proposed as a source of omic biomarkers, given their role in cellular communication and their activity as mediators of specific signals to target cells or tissues (Colombo et al., 2014). Placenta-derived exosomes have gained special attention since they play a crucial role in the communication between the mother and foetus (Burkova et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design:\u003c/strong\u003e Ideally, EDC research would leverage a longitudinal prospective design in which omics are sampled repeatedly before an outcome occurs and after the exposure of interest. However, many studies identified in this review used cross-sectional or case-control designs. Reverse causation is a concern with such designs, where omics markers may affect exposure levels through e.g. modified excretion or metabolism. In addition, the timing of omics measurement is key when assessing response to exposure (short-term versus long-term effects). Indeed, it is likely that pathways associated with disease aetiology and progression involve multiple molecules and that perturbations in these response biomarkers are organised in a temporal sequence (e.g. prenatal epigenetics modification due to maternal EDC exposure might lead to disrupted metabolic profile in children and adverse clinical outcomes during adulthood). Finally, long-term health effects of exposure would ideally be identified by the use of omics in longitudinal population cohorts with biological samples stored over many years. In future studies, longitudinal or repeated measurement of omics are needed to distinguish time-specific or cumulative exposure effects of EDCs. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReproducibility\u003c/strong\u003e: A major challenge in quantitative omics research revolves around the reproducibility of findings\u0026mdash;balancing false-positive findings (large number of variables tested in often too small sample size) with generalizability (i.e. results that are too specific to a given study sample due to systematic bias). Median sample size in the described studies was below 160, with 25% of the studies relying on less than 55 subjects. The potential solution for this issue could be pooling of the cohorts. Recognizing the efficiencies enabled by a consortium model, exposomics consortia have been formed (in Europe see https://www.humanexposome.eu/ and https://hheardatacenter.mssm.edu/ in the USA) providing opportunities for replication or data pooling in studies with environmental exposures. Consortiums gathering omics data, such as the COnsortium of METabolomics (COMETS) that comprises 47 international cohorts that include \u0026gt;136,000 participants with blood metabolomics data or the Pregnancy and Children Epigenetics (PACE) consortium, are also potential sources of data for replication studies. However, such studies providing statistical or biological validation of findings are still rare, since most of the cohorts do not have data on EDC exposures or are not adequately designed for their assessment (single spot sampling) and since harmonisation protocols for data collection and analysis can make such an approach challenging. It is nonetheless of utter importance to replicate findings and increase sample sizes, especially because different omic methods applied in the same population may yield inconsistent results (Perng, 2020). Replication issues due to inter-individual (e.g. BMI) or intra-individual (e.g. time of day of data collection, season and circadian or hormonal cycles) variability should be controlled or accounted for in the study design or statistical analysis (Gallego-Pa\u0026uuml;ls et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiological validation\u003c/strong\u003e: Validation could be achieved through evidence triangulation by identifying independent biological evidence through systematic toxicological literature search [e.g. by screening databases such as The Comparative Toxicogenomics Database (CTD, http://ctdbase.org/, see example in (Chen et al., 2022)) or genetic data (Avery et al., 2022)] or by applying multi-omics approaches where different assays provide complementary information. Only a few studies used more than one omic platform to investigate the relationship between EDC exposure and omic markers (Borsky et al., 2020; Deutschle et al., 2008; Y. Duan et al., 2017; Faulk et al., 2015; Grindler et al., 2018; M. K. Kim et al., 2004; Ouidir et al., 2020; Poole et al., 2016; van den Dungen et al., 2017; J. Wang et al., 2012; Zhu et al., 2016). Using multi-omic approaches requires challenging integration of the biological information assessed from multiple platforms and may need high computational power and advanced techniques for high-dimensional data processing, e.g. dimensionality reduction or network-based methods. However, successful integration of the multi-omic signal allows creating a more holistic picture of mechanisms mediating associations between EDC exposure and health and cross-validate the findings on different biological levels. Indeed, for DNA methylation, gene and miRNA expression studies, there are gaps between gene activity and protein function. As for miRNA studies, miRNAs can target several mRNAs and may affect protein levels at the translational level rather than mRNA degradation. Thus, it is possible that some of the target predictions would be false positives. Validation through cross-omics is particularly challenging for proteins and metabolomics, the most downstream components of the omics cascade. As such, the proteome and metabolome are the closest omics to phenotype (and thus, perhaps most useful as a biomarker of disease risk or prognosis) but also exhibit the most inter- and intra-individual variability (Gallego-Pa\u0026uuml;ls et al., 2021), thereby making replication and validation more challenging. However, result consistency across layers, if it occurs, reinforces causality, as we demonstrated in our recent multi-omics study of the early life exposome (Maitre, Bustamante, et al., 2022).\u003c/p\u003e\n\u003ch2\u003eResearch gaps and recommendations\u003c/h2\u003e\n\u003cp\u003eMost of the identified omic studies focused on exposure to phthalates and phenols (BPA in particular) while, interestingly, only three investigated exposure to currently used pesticides. Several omic studies have assessed the effect of legacy pesticides [dichlorodiphenyltrichloroethane (DDT) or dichlorodiphenyldichloroethylene (DDE)] that we did not review here (Hu et al., 2020; Lind et al., 2013); however, it would be essential to conduct studies on recently-used pesticides such as OP pesticides and pyrethroids due to their abundant use and potential adverse health effects.\u003c/p\u003e\n\u003cp\u003eDespite the epidemiological and toxicological evidence for neurodevelopmental damage due to prenatal EDC exposure and more recently respiratory damage, only one of the omic studies focused on populations with cognitive, mental or respiratory health problems. Although a few studies measured thyroid hormones, which are essential for normal brain development, none considered them as mediators for an adverse brain development. Future studies might consider relevant omic markers that mediate associations between prenatal EDC exposures and these outcomes. In contrast, many studies focused on lipid metabolism and insulin resistance, offering a more direct link to cardio-metabolic outcomes.\u003c/p\u003e\n\u003cp\u003eIn summary, our review points to the following needs for future studies on EDC exposure and omic biomarkers:\u003c/p\u003e\n\u003cp\u003e- Study design: to prioritise longitudinal study designs with appropriate timing between exposure and omic assessment, with larger sample sizes and including several exposures at once providing wide coverage of exposures and biomarkers.\u003c/p\u003e\n\u003cp\u003e- Exposure assessment: to apply methods reducing exposure misclassification by using repeated biospecimen sampling to assess exposure, correcting regression estimates obtained for associations between exposure~omic outcome, correcting exposure concentrations before further statistical analyses.\u003c/p\u003e\n\u003cp\u003e- Omics assessment: to include omics sample replicates collected using standardised protocols and with the use of standardised reference materials and normalisation methods.\u003c/p\u003e\n\u003cp\u003e- Reproducibility: to include biological replicates and perform multi-cohort studies.\u003c/p\u003e\n\u003cp\u003e- Biological validation and triangulation of findings: to use several omic platforms where different assays would provide complementary information and to identify independent biological evidence through systematic toxicological literature search.\u003c/p\u003e\n\u003cp\u003e- Confounding: to collect important information about confounders related to inter- and intra-individual variability.\u003c/p\u003e\n\u003cp\u003e- Standardisation of result reporting: to systematically add results in designated databases to facilitate access to the full results.\u003c/p\u003e\n\u003cp\u003eFinally, this review should be considered with a limitation that is the heterogeneity in definitions of omics, in particular for targeted metabolic and protein studies such as those focusing on hormones. To limit this issue, we included specific search terms in our study, such as \u0026ldquo;lipids\u0026rdquo; and \u0026ldquo;Inflammatory proteins\u0026rdquo;, which enabled the identification of further relevant articles. In addition, we decided to focus on EDCs, excluding persistent organic pollutants or heavy metals which have been covered in other reviews (Everson \u0026amp; Marsit, 2018; S. Kim et al., 2022).\u003c/p\u003e\n\u003cp\u003eIn conclusion, there is a fast growing body of evidence evaluating the early biological responses to exposure to EDCs. This review points to a clear need for larger, longitudinal studies, wider coverage of exposures and biomarkers, replication studies, and standardisation of research methods and results reporting to facilitate comparison.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research leading to these results has received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under grant agreements no. 825712 [OBERON] and no. 874583 [ATHLETE].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLM is funded by a Juan de la Cierva-Incorporaci\u0026oacute;n fellowship (IJC2018-035394-I) awarded by the Spanish Ministerio de Econom\u0026iacute;a, Industria y Competitividad. PJ was supported by the French National Agency for Research (ANR, grant n◦ ANR-15-IDEX-02). ISGlobal acknowledges support from the Spanish Ministry of Science and Innovation through the \u0026ldquo;Centro de Excelencia Severo Ochoa 2019-2023\u0026rdquo; Program (CEX2018-000806-S) and support from the Generalitat de Catalunya through the CERCA Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eL\u0026eacute;a Maitre:\u003c/strong\u003e Conceptualization, Data Curation, Writing - Review \u0026amp; Editing, Supervision, Funding acquisition,\u0026nbsp;\u003cstrong\u003ePaulina Jedynak:\u003c/strong\u003e Data Curation, Writing - Review \u0026amp; Editing, \u003cstrong\u003eMarta Gallego\u003c/strong\u003e: Conceptualization, Data Curation, \u003cstrong\u003eLaura Ciaran\u003c/strong\u003e: Data Curation, \u003cstrong\u003eKarine Audouze:\u003c/strong\u003e Writing - Review \u0026amp; Editing, Funding acquisition, \u003cstrong\u003eMaribel Casas:\u003c/strong\u003e Writing - Review \u0026amp; Editing, Funding acquisition, \u003cstrong\u003eMartine Vrijheid:\u003c/strong\u003e Investigation, Resources, Data Curation, Writing - Review \u0026amp; Editing, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have nothing to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdibi, J. 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Sex-specific difference in placental inflammatory transcriptional biomarkers of maternal phthalate exposure: A prospective cohort study. \u003cem\u003eJournal of Exposure Science \u0026amp; Environmental Epidemiology\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(5), 835\u0026ndash;844. https://doi.org/10.1038/s41370-020-0200-z\u003c/li\u003e\n\u003cli\u003eWesterlund, A. M., Hawe, J. S., Heinig, M., \u0026amp; Schunkert, H. (2021). Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(19). https://doi.org/10.3390/ijms221910291\u003c/li\u003e\n\u003cli\u003eWingo, T. S., Liu, Y., Gerasimov, E. S., Vattathil, S. M., Wynne, M. E., Liu, J., Lori, A., Faundez, V., Bennett, D. A., Seyfried, N. T., Levey, A. I., \u0026amp; Wingo, A. P. (2022). Shared mechanisms across the major psychiatric and neurodegenerative diseases. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 4314. https://doi.org/10.1038/s41467-022-31873-5\u003c/li\u003e\n\u003cli\u003eWu, H., Estill, M. S., Shershebnev, A., Suvorov, A., Krawetz, S. A., Whitcomb, B. W., Dinnie, H., Rahil, T., Sites, C. K., \u0026amp; Pilsner, J. R. (2017). Preconception urinary phthalate concentrations and sperm DNA methylation profiles among men undergoing IVF treatment: A cross-sectional study. \u003cem\u003eHuman Reproduction\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(11), 2159\u0026ndash;2169. https://doi.org/10.1093/humrep/dex283\u003c/li\u003e\n\u003cli\u003eWu, M.-T., Lee, T.-C., Wu, I.-C., Su, H.-J., Huang, J.-L., Peng, C.-Y., Wang, W., Chou, T.-Y., Lin, M.-Y., Lin, W.-Y., Huang, C.-T., Pan, C.-H., \u0026amp; Ho, C.-K. (2011). Whole genome expression in peripheral-blood samples of workers professionally exposed to polycyclic aromatic hydrocarbons. \u003cem\u003eChemical Research in Toxicology\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(10), 1636\u0026ndash;1643. https://doi.org/10.1021/tx200181q\u003c/li\u003e\n\u003cli\u003eXu, X., Chiung, Y. M., Lu, F., Qiu, S., Ji, M., \u0026amp; Huo, X. (2015). Associations of cadmium, bisphenol A and polychlorinated biphenyl co-exposure in utero with placental gene expression and neonatal outcomes. \u003cem\u003eReproductive Toxicology (Elmsford, N.Y.)\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e, 62\u0026ndash;70. https://doi.org/10.1016/j.reprotox.2015.02.004\u003c/li\u003e\n\u003cli\u003eXu, Y., Jurkovic-Mlakar, S., Li, Y., Wahlberg, K., Scott, K., Pineda, D., Lindh, C. H., Jakobsson, K., \u0026amp; Engstr\u0026ouml;m, K. (2020). Association between serum concentrations of perfluoroalkyl substances (PFAS) and expression of serum microRNAs in a cohort highly exposed to PFAS from drinking water. \u003cem\u003eEnvironment International\u003c/em\u003e, \u003cem\u003e136\u003c/em\u003e, 105446. https://doi.org/10.1016/j.envint.2019.105446\u003c/li\u003e\n\u003cli\u003eYang, Y.-J. Y.-C. S. H. Y.-N. Y.-J. Y.-C. S. H., Yang, Y.-J. Y.-C. S. H. Y.-N. Y.-J. Y.-C. S. H., Lin, I.-H., Chen, Y.-Y., Lin, H.-Y., Wu, C.-Y., Su, Y.-T., Yang, Y.-J. Y.-C. S. H. Y.-N. Y.-J. Y.-C. S. H., Yang, S.-N., \u0026amp; Suen, J.-L. (2019). Phthalate Exposure Alters Gut Microbiota Composition and IgM Vaccine Response in Human Newborns. \u003cem\u003eFood and Chemical Toxicology : An International Journal Published for the British Industrial Biological Research Association\u003c/em\u003e, \u003cem\u003e132\u003c/em\u003e, 110700. https://doi.org/10.1016/j.fct.2019.110700\u003c/li\u003e\n\u003cli\u003eZhong, J., Baccarelli, A. A., Mansur, A., Adir, M., Nahum, R., Hauser, R., Bollati, V., Racowsky, C., \u0026amp; Machtinger, R. (2019). Maternal Phthalate and Personal Care Products Exposure Alters Extracellular Placental miRNA Profile in Twin Pregnancies. \u003cem\u003eReproductive Sciences (Thousand Oaks, Calif.)\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(2), 289\u0026ndash;294. https://doi.org/10.1177/1933719118770550\u003c/li\u003e\n\u003cli\u003eZhu, X., Li, J., Deng, S., Yu, K., Liu, X., Deng, Q., Sun, H., Zhang, X., He, M., Guo, H., Chen, W., Yuan, J., Zhang, B., Kuang, D., He, X., Bai, Y., Han, X., Liu, B., Li, X., \u0026hellip; Wu, T. (2016). Genome-Wide Analysis of DNA Methylation and Cigarette Smoking in a Chinese Population. \u003cem\u003eEnviron Health Perspect\u003c/em\u003e, \u003cem\u003e124\u003c/em\u003e(7), 966\u0026ndash;973. https://doi.org/10.1289/ehp.1509834\u003c/li\u003e\n\u003cli\u003eZota, A. R., Geller, R. J., VanNoy, B. N., Marfori, C. Q., Tabbara, S., Hu, L. Y., Baccarelli, A. A., \u0026amp; Moawad, G. N. (2020). Phthalate exposures and MicroRNA expression in uterine fibroids: The FORGE study. \u003cem\u003eEpigenetics Insights\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e. https://doi.org/10.1177/2516865720904057\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eData classifiers for the data extraction of the articles corresponding to the Integration -omics approaches into population-based studies of endocrine disrupting chemicals (EDCs)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDefinition\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEDC exposure family\u003c/p\u003e\n\u003cp\u003e(to select at least one from the list)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhthalates\u003c/p\u003e\n\u003cp\u003ePhenols\u003c/p\u003e\n\u003cp\u003ePesticides (excluding OCs, i.e. DDT, DDE)\u003c/p\u003e\n\u003cp\u003ePAHs\u003c/p\u003e\n\u003cp\u003ePFASs\u003c/p\u003e\n\u003cp\u003eGlycol ether\u003c/p\u003e\n\u003cp\u003eExposome (including at least one of the key chemical groups)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChemical\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFull list of annotated chemical compounds measured (see list in \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOmics type\u003c/p\u003e\n\u003cp\u003e(to select at least one from the list)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDNA methylation\u003c/p\u003e\n\u003cp\u003eGene expression (excluding miRNA)\u003c/p\u003e\n\u003cp\u003emiRNA\u003c/p\u003e\n\u003cp\u003eProtein profile (including cytokines)\u003c/p\u003e\n\u003cp\u003eMetabolic profile (including lipids)\u003c/p\u003e\n\u003cp\u003eMicrobiome\u003c/p\u003e\n\u003cp\u003eMulti-omics (at least two omics platforms)\u003c/p\u003e\n\u003cp\u003eMixed methods (if only few targeted markers measured)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePopulation characteristics 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeneral population/occupational/highly exposed group, for example resident near contaminated site (based on the exposure)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePopulation characteristics 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdult/pregnant/infants \u0026amp; children \u0026amp; adolescents\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePopulation characteristics 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender (all/male only/female only)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudy design 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCohort/case-control/cases-only\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudy design 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCross-sectional/longitudinal\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudy design 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eObservational/experimental study\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal number of participants included in the study with exposure and omics measured. If case-control design, the sample size in each group was specified\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudy/cohort name\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eE.g. The C8 Short-Term Follow-up Study\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeographical location\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThe country where the participants were recruited for the study\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExposure assessment method\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBiomonitoring/questionnaire/modelling\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExposure assessment period (if birth cohort)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIf pregnancy, specify the trimester or week of gestation. Specify if repeated sampling, time points was done\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOmics assessment period (if birth cohort)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIf pregnancy, specify the trimester or week of gestation. Specify if repeated sampling, time points was done\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOmics matrix\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBiological samples used for the omics measurements\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOmics approach\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTargeted/untargeted/untargeted and targeted (targeted means candidate approach at the stage of the experiment or statistical analysis); studies including both approaches are classified as untargeted and targeted\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOmics targets\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIf targeted, specify the molecular targets, e.g. total cholesterol\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMain findings\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSummary of omics-EDCs association results\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSummary of the study designs of studies included in the review.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"8\" align=\"left\"\u003e\n\u003cp\u003eStudy design\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eObservational studies\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRandomised intervention\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCross-\u003c/p\u003e\n\u003cp\u003esectional\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eLongitudinal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMixed design\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposure family\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of studies\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCohort\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCase-\u003c/p\u003e\n\u003cp\u003econtrol\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCases-only\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePAHs\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePesticides\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePFASs\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhenols\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhenols, Phthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePesticides, PFASs\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePFASs, Phthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePFASs, Phenols, Phthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePesticides, PFASs, Phenols, Phthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOrganophosphate esters, Phenols, Phthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003eAbbreviations: PAHs\u0026thinsp;=\u0026thinsp;polycyclic aromatic hydrocarbons; PFASs\u0026thinsp;=\u0026thinsp;per- and polyfluoroalkyl substances.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSummary of the population characteristics of studies included in the review.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003ePopulation characteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGeneral population\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHighly exposed group\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOccupational\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAdults\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInfants, Children, Adolescents\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePregnant women\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePregnant women and foetuses, Infants, Children, Adolescents\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOther populations\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposure family\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePAHs\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePesticides\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePFASs\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhenols\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePhenols, Phthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePesticides, PFASs\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePFASs, Phthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePFASs, Phenols, Phthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePesticides, PFASs, Phenols, Phthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOrganophosphate esters, Phenols, Phthalates\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\" align=\"left\"\u003e\n\u003cp\u003eAbbreviations: PAHs\u0026thinsp;=\u0026thinsp;polycyclic aromatic hydrocarbons; PFASs\u0026thinsp;=\u0026thinsp;per- and polyfluoroalkyl substances.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eSummary of the biological matrices and approach used for omic assessment.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eOmic matrix\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eOmic approach\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOmic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBlood\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePlacenta and/or Cord blood\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSemen and/or Follicular fluid\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStool\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUrine\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOther matrices\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTargeted\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUntargeted\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUntargeted and Targeted\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDNA methylation\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGene expression\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolic profile\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eProtein profile\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMicrobiome\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003emiRNA\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMixed methods\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMulti-omics\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Supplementary Tables","content":"\u003cp\u003eSupplementary Tables S1-S2 are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"ISGlobal","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Endocrine disruptors, omics, environmental epidemiology, scoping review","lastPublishedDoi":"10.21203/rs.3.rs-2401240/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2401240/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHealth effects of endocrine disrupting chemicals (EDCs) are challenging to detect in the general population. Omics technologies become increasingly common to identify early biological changes before the apparition of clinical symptoms, to explore toxic mechanisms and to increase biological plausibility of epidemiological associations.\u003c/p\u003e \u003cp\u003eThis scoping review systematically summarises the application of omics in epidemiological studies assessing EDCs-associated biological effects to identify potential gaps and priorities for future research.\u003c/p\u003e \u003cp\u003eNinety-eight human studies (2004\u0026ndash;2021) were identified through database searches (PubMed, Scopus) and citation chaining and focused on phthalates (34 studies), phenols (19) and PFASs (17), while PAHs (12) and recently-used pesticides (3) were less studied. The sample sizes ranged from 10 to 12,476 (median\u0026thinsp;=\u0026thinsp;159), involving non-pregnant adults (38), pregnant women (11), children/adolescents (15) or both populations studied together (23). Several studies included occupational workers (10) and/or highly exposed groups (11) focusing on PAHs, PFASs and pesticides, while studies on phenols and phthalates were performed in the general population only. Analysed omics layers included metabolic profiles (30, including 14 targeted analyses), miRNA (13), gene expression (11), DNA methylation (8), microbiome (5) and proteins (3). Twenty-one studies implemented targeted multi-assays focusing on clinical routine blood lipid traits, oxidative stress or hormones. Overall, DNA methylation and gene expression associations with EDCs did not overlap across studies, while some EDC-associated metabolite groups, such as carnitines, nucleotides and amino acids in untargeted metabolomic studies, and oxidative stress markers through targeted studies were consistent across studies. Studies had common limitations such as small sample sizes, cross-sectional designs and single sampling for exposure biomonitoring.\u003c/p\u003e \u003cp\u003eIn conclusion, there is a growing body of evidence evaluating the early biological responses to exposure to EDCs. This review points to a need for larger longitudinal studies, wider coverage of exposures and biomarkers, replication studies and standardisation of research methods and reporting.\u003c/p\u003e","manuscriptTitle":"Integrating -omics approaches into population-based studies of endocrine disrupting chemicals: a scoping review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-22 19:13:37","doi":"10.21203/rs.3.rs-2401240/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ebea9472-25fe-4780-9346-946e7f8190ef","owner":[],"postedDate":"December 22nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":17875491,"name":"Molecular Epidemiology"}],"tags":[],"updatedAt":"2022-12-22T19:13:37+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-22 19:13:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2401240","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2401240","identity":"rs-2401240","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}