Gene–environment interactions reported in ASD. Listed are gene-environment interactions pairs associated with ASD as identified by the systematic literature review using PRISMA guidelines.
Abstract
Heritability estimates indicate that genetic susceptibility does not fully explain Autism Spectrum Disorder (ASD) risk variance, and that environmental factors may play a role in this disease. To explore the impact of the environment in ASD etiology, we performed a systematic review of the literature on xenobiotics implicated in the disease, and their interactions with gene variants. We compiled 72 studies reporting associations between ASD and xenobiotic exposure, including air pollutants, persistent and non-persistent organic pollutants, heavy metals, pesticides, pharmaceutical drugs and nutrients. Additionally, 9 studies reported that interactions between some of these chemicals (eg. NO2, particulate matter, manganese, folic acid and vitamin D) and genetic risk factors (eg. variants in the CYP2R1, GSTM1, GSTP1, MET, MTHFR and VDR genes) modulate ASD risk. The chemicals highlighted in this review induce neuropathological mechanisms previously implicated in ASD, including oxidative stress and hypoxia, dysregulation of signaling pathways and endocrine disruption. Exposure to xenobiotics may be harmful during critical windows of neurodevelopment, particularly for individuals with variants in genes involved in xenobiotic metabolization or in widespread signaling pathways. We emphasize the importance of leveraging multilevel data collections and integrative approaches grounded on artificial intelligence to address gene–environment interactions and understand ASD etiology, towards prevention and treatment strategies.
Keywords
- autism spectrum disorder
- xenobiotic exposure
- early-life exposure
- genetic risk factors
- gene-environment interactions
- exposome
1. Introduction
Many neuropsychiatric disorders are thought to have a multifactorial etiology, with interactions between genetic susceptibility and environmental factors likely contributing to their onset and progression [1]. ASD has a particularly complex genetic architecture, with implicated genes accumulating thanks to more accessible and less costly high-throughput genotyping and sequencing technologies. Between 15 to 25% of ASD cases occur in the context of clinically defined monogenic syndromes and chromosomal rearrangements [2], and therefore have a genetic diagnosis. However, most patients still do not have a clearly identified genetic cause. Genome-wide association studies (GWAS), carried out in large cohorts using SNP arrays, did not find consistently associated ASD genes [3], but showed that individuals with ASD carry a significantly higher burden of
Recent ASD heritability estimates vary between 64 and 85% [8, 9], and incomplete concordance rates between monozygotic twins are reported [10, 11]. These observations suggest that ASD, and its hallmark clinical heterogeneity, is not solely determined by genetics, and that environmental factors may contribute to its risk. Due to the extreme vulnerability of the developing brain to environmental stressors [12], the impact of environmental factors in this neurodevelopmental pathology is of particular concern. In this context, the environment comprises all non-genetic factors that can influence the onset or progression of the disease. Generally, environmental factors include xenobiotics,
From conception to death, individuals are to some degree shaped by an ever-changing environment. However, its impact in health and disease through the life course is still mostly unexplored. Given the early onset of ASD, environmental exposure during the prenatal period to the second year of life is of particular relevance, while at later stages it may still modulate disease progression and possibly treatment efficacy [13, 15]. In this review we focus specifically on the role of xenobiotics in ASD, and on the impact of interactions between genetic variants and xenobiotic exposure. Literature reporting xenobiotic exposure in ASD is already extensive. We expect this systematic review may guide and encourage further studies to elucidate the impact of gene–environment interactions in ASD.
2. Methods
We systematically reviewed studies in two categories: (a) studies reporting xenobiotic exposure implicated in ASD; (b) studies reporting interactions between the previously defined xenobiotics and any genetic factor. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standard checklist [16]. Systematic reviews of the literature were performed successively for categories (a) and (b).
2.1 Information sources and search strategy
PubMed and EBSCO were queried from inception to November 2020, for records published in peer-reviewed English-language journals.
For records in category (a) PubMed and EBSCO were interrogated using updated and dropped clinical terms (“autis*”; “asperger” and “pervasive developmental disorder”) in combination with the terms “environment*”, or “xenobiotic”, or “toxin” or with terms for xenobiotics’ names (“antidepressants”; “air pollutants”; “bisphenol A”; “folic acid”; “metal”; “PBDE”; “PCB”; “pesticide”; “PFC”; “phthalate”; “vitamin D”). Regarding category (b) the query was done using the same clinical terms in combination with “gene–environment” term and with terms for xenobiotics names identified in previous search.
2.2 Screening and eligibility criteria
All identified records were imported to the Mendeley reference manager. PRISMA flowcharts for (a) and (b) categories are shown in Figure 1. For record screening, the following exclusion criteria were applied: 1) review articles and letters to editor; 2) articles where the participants’ diagnosis of ASD was not confirmed according to criteria from
After screening, for category (a) eligible articles were included in the final results if they reported statistically significant associations between xenobiotic exposure and ASD risk. Prenatal to early postnatal (
3. Results
3.1 Xenobiotic exposure associated with ASD
Figure 1A shows the flowchart for the identification of relevant publications. After removing duplicates, a total of 4108 unique records were screened using the defined exclusion criteria, resulting in 130 eligible research papers. Application of the inclusion criterion (
The identified xenobiotics were categorized in seven major groups: Air Pollutants, Toxic Heavy Metals, Non-Persistent Organic Pollutants (non-POPs), Persistent Organic Pollutants (POPs), Pesticides, Pharmacological Drugs and Nutritional Factors (Figure 2). POPs include bisphenol A and phthalates, while non-POPs include polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs) and perfluorinated compounds (PFCs). The first five groups comprise ubiquitous toxins present in air, daily use products and the food chain, while exposure to the last two groups occurs through ingestion.
Historical proof-of-concept evidence for a role of xenobiotic exposure in ASD comes from three studies, which reported for the first time a very high prevalence of the disorder among subjects prenatally exposed to teratogens. Specifically, these studies reported an ASD prevalence of 4% among individuals exposed to thalidomide [63], of 8.8% in subjects exposed to valproic acid [59] and of 21.4% in individuals exposed to misoprostol [64] (Table S1).
Evidence supporting an association with ASD is stronger for exposure to air pollutants and pesticides, as all studies examining these toxins report an increased risk for the disorder (Table S1). Usually, these studies gather air quality or pesticide application data for large geographical areas and, by applying geocoding methods, investigate how exposure patterns relate to ASD prevalence. Each of these studies includes, at least, one hundred cases, with larger ones examining exposure in thousands of subjects [18, 22, 23, 29, 30, 52]. Environmental agencies are instrumental for collection of airborne pollutant and pesticide data in large populations from geographically defined areas, enabling valuable geocoding approaches. Because heavy metals can circulate in the air, large population geocoding studies, involving hundreds of subjects, are also applied to assess exposure to these chemicals [31, 32, 34]. Some studies quantifying exposure to heavy metals, as well as those that analyze POPs, non-POPs or vitamin D, need to resort to biological matrices. Because this data is so labor intensive to collect, most evidence comes from small datasets of less than one hundred subjects. For instance, this review identified 4 studies assessing heavy metals in biological matrices like hair, nails and teeth, all carried out in small numbers of subjects [33, 35, 36, 38]. Regarding POPs and non-POPs, evidence for an association with ASD is still limited, as fewer reports addressed these chemicals (Table S1). Two studies provide evidence for an increased risk of ASD from prenatal exposure to PCBs [43, 44] while, in other two, PFCs prenatal exposure was found to decrease ASD risk [45, 46]. Concerning PBDEs, the only study reporting associations with the disorder observed a decreased risk due to exposure to BDE-153 and BDE-100 congeners, but an increased risk, only in girls, due to exposure to BDE-47 [42]. For bisphenol A and phthalates, two small size studies for each chemical report an increased risk of ASD associated with childhood exposure [39, 40, 41]. All studies on antidepressants report an increased risk of ASD (Table S1) and, as these usually resort to medical records to assess exposure, include thousands of subjects. A decreased risk of the pathology due to folic acid supplementation is observed by assessing medical records from large samples [68, 69]. However, two recent small size reports, which measured folic acid levels in maternal serum, show an increased risk of ASD associated with prenatal folic acid intake at very high concentrations [70, 71]. In case–control datasets a decreased risk for the disorder is associated with higher prenatal and childhood blood concentrations of 25-hydroxyvitamin D, the main circulating form of this nutrient, with mean serum concentrations values ranging from 9.9 ng/ml to 28.5 ng/ml in cases and 15.0 ng/ml to 40.1 ng/ml in controls [72, 73, 74, 75, 76, 77, 78, 79, 83, 85, 86, 87, 88]. Most of these studies comprise less than one hundred subjects, however 3 studies examining dried blood spots [84, 86] or medical records [81] were carried out in hundreds or thousands of subjects.
3.2 Gene-environment interactions associated with ASD
Figure 1B shows a flowchart for the identification of relevant publications. The query revealed 392 unique records, of which 15 remained after application of exclusion criteria. Nine research articles reported gene–environment interactions in ASD (Table 1). The environmental component of these interactions included air pollutants (PM10, NO2 and O3), PCBs, manganese and nutritional factors (folic acid and vitamin D), while the genetic component was a specific genotype or, in one study, the overall burden of copy number duplications (Table 1).
Study | Genetic factor | Xenobiotic | Ncases|Ncontrols | Main conclusion |
---|---|---|---|---|
Schmidt et al 2012 [65] | 677 C > T genotype in | Folic acid | 272|154 | Daily prenatal maternal folic acid intake >600 μg was associated with a reduced ASD risk when the mother, the child or both had the low-activity 677 C > T variant. |
Volk et al 2014 [21] | rs1858830 CC genotype in | NO2 and PM10 | 251|156 | Carriers of the CC genotype with higher prenatal exposure to NO2 or to PM10 were at increased risk of ASD when compared to subjects with CG or GG genotypes and lower exposure. |
Rahbar et al 2015 [90]; and Rahbar et al 2018 [91] | Ile/Ile genotype of | Manganese | 100|100 [90]; 163|163 [91] | Among carriers of Ile/Ile |
Schmidt et al 2015 [92] | rs10741657 AA genotype in | Vitamin D | 384|234 | AA genotype associated with a decreased ASD risk when maternal vitamin D intake was <400 IU. |
Coşkun et al 2016 [93] | rs2228570 TT genotype in | Vitamin D | 237|243 | Trend for an association of the TT genotype with elevated circulating 25(OH)D levels in children with ASD. |
Kim et al 2017 [94] | Copy number duplications burden | O3 | 158|147 | Higher burden of CNVs, namely duplications, and O3 exposure increases ASD risk. |
Mandic-Maravic et al 2019 [95] | Any medication | 113|114 | Maternal use of medication during pregnancy associated with high ASD risk in offspring with a | |
Bach et al. 2020 [96] | PCB-153 | 169|169 | Positive association between PCB-153 levels and ASD risk among carriers of |
Most of the genes assessed in these studies are involved in the metabolism of xenobiotics.
4. Discussion
4.1 Exposure to xenobiotics and ASD
4.1.1 Air pollutants, heavy metals, POPs and non-POPs, and pesticides
Five attributes that are transversal to many of the xenobiotics reviewed in this study, including air pollutants, toxic heavy metals, POPs, non-POPs and pesticides, likely account for the increased risk of ASD associated with their exposure: 1) ubiquitous exposure; 2) bioaccumulation potential; 3) neurotoxicity; 4) endocrine-disrupting potential and 5) ability to cross physiological barriers.
Exposure to these toxins is ubiquitous, since they are present in the environment, in everyday household and industrial products, and in food. For airborne toxins, this ubiquity is exacerbated by transboundary flows of pollutants, a phenomenon in which toxins circulate long distances and deposit on land and water bodies far from their sources [98]. POPs exhibit high lipid solubility and low hydrophilicity, and are resistant to environmental degradation through chemical or biological processes, increasing their risk of bioaccumulation in human adipose tissue, the ecosystem and in the food chain [99]. Some air pollutants (
Most of these toxins have well established neurotoxic properties [102]. Many, including bisphenol A, phthalates, pesticides, PAHs, PCBs, PBDEs and lead, are also endocrine-disrupting chemicals (EDCs), defined as any “
Given the awareness regarding the hazardous health effects of exposure to these toxins, restrictive policies or bans on their use are often legislated. These include bans on the agricultural application of harmful pesticides [111], the widespread production of bisphenol A-free baby bottles [112] and regulations on PCBs, PBDEs and PFCs production [113]. However, such legislations are not always fully effective. For instance, despite bans, exposure to POPs is still ubiquitous because of their resistance to degradation [113]. Restrictions on bisphenol A use led to replacement by analogues (bisphenol F and bisphenol S) for which harmful effects are also reported [114], and are therefore regrettable substitutions. The transgenerational effects of these toxins are also important, as they can affect not only the exposed individual, but also subsequent generations, through epigenetic mechanisms [99, 104]. Most of the identified chemicals have been persistently used since the 1950s, leading to a growing environmental burden and accumulation of insults over several generations. Consequently, some authors speculate that these delayed effects may account in part for the steady prevalence increase in ASD reported in the last decades [115].
4.1.2 Pharmacological drugs
The increased prevalence of ASD among subjects prenatally exposed to three pharmaceutical drugs (thalidomide, valproic acid and misoprostol) provided the first strong evidence for the involvement of environmental risk factors in ASD. Thalidomide is an immunomodulatory drug, widely prescribed to alleviate morning sickness in pregnant women during the 50s, while misoprostol is a prostaglandin analogue used as an abortion inductor and valproic acid is prescribed for epilepsy and bipolar disorder. These drugs are teratogens (
We also identified 5 research articles associating maternal antidepressant intake during pregnancy with ASD risk, particularly for Selective Serotonin Reuptake Inhibitors (SSRIs). SSRIs act by increasing the extracellular levels of serotonin and are known to cross the placenta [117] and to be secreted through breast milk at low levels [118]. Increased serotonin levels have repeatedly been found in blood samples from ASD subjects [119]. While individual research studies report associations between prenatal exposure to antidepressants and ASD, a recent meta-analysis [120] underpins the inconsistency of overall findings. Thus, a clinical balance between the risks of untreated maternal depression and unclear neurodevelopmental risks of antidepressant exposure for the offspring is warranted.
4.1.3 Nutritional factors
The most encouraging results for protective factors for ASD come from studies examining disease risk and nutrient sufficiency. Overall, there is significant evidence that folic acid and vitamin D supplementation during pregnancy and childhood are prophylactic for neurodevelopmental disorders.
Folic acid promotes the closure of the neural tube, reducing the risk of early neurodevelopmental problems: periconceptional folic acid intake prevents up to 70% of neural tube defects, with national health agencies recommending that women of childbearing age take 0.4 to 1 mg folic acid daily prior and during gestation [121]. However, while the natural folate is initially metabolized in the gut, folic acid is mainly metabolized in the liver, where the activity of dihydrofolate reductase (DHFR), the enzyme that converts folic acid to its biologically active form tetrahydrofolate, is reduced [122]. Thus, sustained high folic acid supplementation may eventually become noxious due to the accumulation of unmetabolized folic acid [122]. In agreement, two studies have observed a higher risk of ASD when mothers consume extremely high levels of this nutrient during pregnancy [70, 71].
Vitamin D plays a fundamental role in calcium and phosphorus metabolism, and is therefore crucial for various biological processes, among which the maintenance of brain homeostasis. Animal studies have also shown that the vitamin D receptor (VDR) is expressed in the brain since early in development [123]. Despite the growing number of studies reporting insufficiency of vitamin D in children with ASD, ambiguous cut-off levels for vitamin D insufficiency render difficult comparisons between studies [124].
4.2 Gene-environment interactions in ASD
The identification of consistent environmental risk factors for ASD is very relevant in view of the failure of genetics to fully explain the disease etiology and the clinical spectrum. However, integrating the emergent data on environmental risk factors for ASD with established genetic findings has been challenging. In this systematic review we identified 9 studies reporting specific gene–environment interaction pairs in ASD.
Because most of the identified genes cluster in biotransformation processes, their dysregulation may result in a deficient metabolism of xenobiotics, inducing pathological mechanisms that contribute to ASD onset.
While relatively scarce, the identified studies already offer valuable insights supporting the potential for preventive strategies based on environmental predictors for subjects carrying a genetic susceptibility variant. For example, controlling exposure to high levels of NO2 or PM10 of carriers of the
4.3 Strategies to assess early environmental exposure in ASD: the exposome
In 2005, Wild introduced the term “
To assess environmental exposure in ASD, the prospective cohort study MARBLES [129] recruits pregnant women who already have a biological child with the disorder, and are therefore at higher risk of a second child with ASD. The MARBLES study collects longitudinal information from the children, up to 36 months old, including environmental exposure, genetic and clinical data. This design allows the assessment of pre and early post-natal exposure to risk factors that may contribute to ASD risk. Because participants are recruited before or during pregnancy, monitoring of gestation and early childhood offers a chance to accurately measure exposures, allowing for the identification of early biomarkers.
Other studies with similar designs apply spectrometric methods to quantify the levels of toxins or their metabolites in biological matrices, usually through the collection of blood [42, 43, 44, 45], urine [39, 41, 51] or hair [33, 38] samples. However, prospective designs are not always possible, and cross-sectional studies do not allow assessment of past exposures. Retrospective studies are a viable alternative, benefiting from new methods that allow assessment of previous exposure [14]. For instance, vanguard studies are now using naturally shed deciduous teeth [35] to retrospectively quantify exposure to xenobiotics in ASD subjects. During odontogenesis, deciduous teeth store signatures of exposure to chemicals, from the second trimester
Another promising matrix takes advantage of archived dried blood spots collected through population-wide newborn-screenings for metabolic and congenital diseases. Chemicals relevant for ASD have been successfully detected in archived blood spots, including bisphenol A, PFCs, lead, mercury, PBDEs and PCBs [130, 131]. When correctly collected and stored, analytes remain stable in neonatal spots for years.
Other retrospective studies employ geo-referencing methods to collect information regarding exposure to air pollutants, pesticides and some heavy metals [18, 19, 24, 27, 32, 34, 49, 52]. These studies leverage indoor and outdoor air quality data, usage of agricultural pesticides or the location of environmentally-significant sites (
Overall, a comprehensive analysis of the exposome must address a multiplicity of factors that includes not only exposure to chemicals in variable settings and situations, but also medical procedures, events and lifestyle, psychosocial and cultural variables.
4.4 Shift towards integrative strategies that address gene-environment interactions in ASD
All research studies identified in this review that report gene–environment interactions in ASD, published up to November 2020, examined specific xenobiotics (Table 1). Knowledge regarding interactions between genetics and the environment is vast outside of ASD context, and might be the basis to define what specific interactions to analyze. Leveraging from public, manually curated, literature-based resources, such as the
Given the emerging evidence highlighted by this literature review, there is a clear need to shift from studies that separately address the role of genetics and the environment towards multidisciplinary strategies that explore both components as interacting risk factors. Such strategies will inform about the mechanisms through which environmental exposure interacts with genetic background, contributing to ASD onset. Models must further consider ASD phenotypic and genetic heterogeneity. To fully understand the etiology of this very complex disorder, genetic, environmental exposure, epigenetic and clinical data needs to be collected simultaneously for the same group of individuals. It is possible that different gene–gene and gene–environment interactions are associated with distinct clinical subgroups of individuals with ASD and, consequently, phenotypic stratification may also be incorporated into study design. Conceiving such designs is challenging, especially given the large population datasets that are needed to achieve statistical power for the discovery of small-effect variables associated with the disorder [136, 137]. Artificial Intelligence (AI) methods, including data mining and machine learning algorithms, will be crucial to overcome the challenge of integrating substantial amounts of data, allowing the detection of environmental exposure patterns contributing to ASD onset.
4.5 Biological mechanisms underlying gene-environment interactions in ASD
Understanding the biological mechanisms underlying gene–environment interactions that contribute to ASD is fundamental to distinguish between causal and non-causal exposures identified through association studies. While knowledge on this is still limited, given the diversity of risk factors it is likely that multiple mechanisms converge in ASD etiology.
Genetic mutations rendering some individuals more susceptible to certain xenobiotics is the simplest gene–environment interaction mechanism. For instance, a gene functional polymorphism that inhibits the enzymatic degradation of a given toxin may lead to its detrimental accumulation in the organism. Many xenobiotic-responding enzymes, like cytochrome P450 enzymes and GSTs, are expressed in the brain, suggesting the occurrence of metabolic processes that inactivate toxins locally [108].
Epigenetics, a gene expression regulatory process that involves heritable and reversible biochemical modifications of DNA or histones, independent of the DNA sequence, acts at the interface between genes and the environment. These processes include DNA methylation, histone methylation and acetylation events, and post-transcriptional regulation by non-coding RNAs, which are known to be involved in brain development [138]. Environmental factors can modulate genetics through epigenetic mechanisms and xenobiotics implicated ASD are known to alter epigenetic patterns. For instance, valproic acid inhibits histone deacetylases up-regulating the expression of various genes [139]. 5-MethylTHF, a metabolite of folic acid produced by MTHFR enzymatic activity, is a donor of the carbon group used to methylate DNA [140]. Consequently,
Neuropathological mechanisms that putatively lead to ASD, such as oxidative stress, neuro-inflammation, hypoxic damage, abnormal signaling pathways and endocrine disruption, can be induced by exposure to xenobiotics. Reduced brain levels of glutathione, the major endogenous cellular antioxidant responsible for the detoxification of xenobiotics, and other oxidative stress biomarkers have been observed in ASD subjects [145]. Evidence for increased levels of neuro-inflammation biomarkers in ASD, including brain levels of pro-inflammatory cytokines and microglia activation, which may be stimulated by allergens such as pesticides, has been reported [146]. Proxies for fetal and newborn hypoxia, indicating a deprivation of oxygen supply, have been reported in neonates that later develop ASD [26] and may be elicited by early-life events. Xenobiotics also interact directly with intracellular neurotransmitter pathways [108] leading to signaling impairments. For example, acetylcholinesterase, the enzyme that catalyzes the acetylcholine neurotransmitter breakdown, is the primary target of inhibition by organophosphate pesticides [147] Most of the identified xenobiotics are endocrine disruptors and a role for hormonal imbalances in the disorder is plausible, particularly given the male skewness in ASD diagnoses. Atypical steroidogenic activity, namely increased androgen [148] and estrogen [149] levels in the amniotic fluid, has been reported in affected males. Gender-specific effects of environmental toxins [110] and consequent hormonal imbalances may also be implicated in the female protective effect, a hypothesis proposed to explain the ASD male bias.
A novel area of interest in ASD is the role of gut-brain axis, which refers to biochemical signaling connections between the gastrointestinal tract and the central nervous system. Dysbiosis of the gut microbiome likely accounts for a high comorbidity of gastrointestinal symptoms in ASD patients [150]. While the liver is the predominant site of xenobiotic metabolism, the gastrointestinal tract is the first line of defense against ingested compounds, and is rich in both host and microbial enzymes. As the gut microbiota metabolize hundreds of dietary, pharmaceutical and industrial chemicals, dysbiosis could lead to impairments in the gut-brain axis resulting in neurological insults.
5. Conclusion
This review highlights the accumulating evidence for a role of exposure to xenobiotics in ASD risk, and reinforces the need of developing strategies that consider genetics and the environment as interacting components in ASD etiology. This is further supported by the still limited but promising results originating from studies that explore gene–environment interactions.
However, the current knowledge is likely just the tip of the iceberg. Given the enormous progress in high throughput methodologies for analysis of biomolecules (genomics, transcriptomics, proteomics, metabolomics), together with the development of comprehensive surveys on environmental exposure and advances in artificial intelligence methods for the integrative analysis of large amounts of data, the field is ripe for new discoveries. The expectation is that knowledge of the exposome of individuals can be integrated with their genomes to define patterns of interactions that cause their particular configuration of behaviors in the autism spectrum. There are however many challenges ahead, particularly concerning the collection of such extensive information from patients in sufficient numbers for integrative analysis.
Because environmental exposure is amenable to adjustment or avoidance, the most important clinical outcome of better understanding gene–environment interactions in ASD is the potential for mitigating risk by controlling exposure of individuals with a genetic vulnerability. This line of research thus opens novel and important perspectives to future prevention and personalized interventions for ASD.
Acknowledgments
This work was supported by Foundation for Science and Technology (FCT), through funding of the project “Gene-environment interactions in Autism Spectrum Disorder” [Grant PTDC/MED-OUT/28937/2017]. JXS was supported by a BioSys PhD programme fellowship from FCT (Portugal) with reference PD/BD/114386/2016. CR was supported by a grant from FCT (Ref: POCI- 01-0145-FEDER-016428).
Xenobiotic | Study | Ncases/ Ncontrols | Time of exposure | Exposure assessment | Risk |
---|---|---|---|---|---|
NO2 | Becerra et al. 2013 | 7421/ 72253 | Prenatal | Geocoding/air quality | Increased |
Volk et al. 2013 | 279/ 245 | Prenatal to postnatal | Geocoding/air quality | Increased | |
Jung et al. 2013 | 342/ 48731 | Prenatal to childhood | Geocoding/air quality | Increased | |
Volk et al. 2014 | 251/156 | Prenatal | Geocoding/air quality | Increased | |
Raz et al. 2017 | 2098/ 54191 | Postnatal (9 m) | Geocoding/air quality | Increased | |
Ritz et al. 2018 | 15387/ 68139 | Postnatal (9 m) | Geocoding/air quality | Increased | |
O3 | Becerra et al. 2013 | 5839/ 55757 | Prenatal | Geocoding/air quality | Increased |
Jung et al. 2013 | 342/ 48731 | Prenatal to childhood | Geocoding/air quality | Increased | |
Kaufman et al. 2019 | 428/ 6420 | Postnatal | Geocoding/air quality | Increased | |
McGuinn et al. 2020 | 674/855 | Prenatal 3rdtrimester | Geocoding/air quality | Increased | |
PM2.5 | Becerra et al. 2013 | 5839/ 55757 | Prenatal | Geocoding/air quality | Increased |
Volk et al. 2013 | 279/245 | Prenatal to postnatal | Geocoding/air quality | Increased | |
Volk et al. 2014 | 251/156 | Prenatal | Geocoding/air quality | Increased | |
Raz et al. 2015 | 245/ 1522 | Prenatal | Geocoding/air quality | Increased | |
Talbott et al. 2015 | 217/226 | Prenatal | Geocoding/air quality | Increased | |
Chen et al. 2018 | 124/ 1240 | Postnatal to childhood | Geocoding/air quality | Increased | |
Ritz et al. 2018 | 15387/ 68139 | Postnatal (9 m) | Geocoding/air quality | Increased | |
Kaufman et al. 2019 | 428/ 6420 | Prenatal to Postnatal | Geocoding/air quality | Increased | |
Jo et al. 2019 | 2471/ 243949 | Prenatal 1sttrimester | Geocoding/air quality | Increased | |
McGuinn et al. 2020 | 674/855 | Postnatal (1st year) | Geocoding/air quality | Increased | |
PM10 | Volk et al. 2013 | 279/245 | Prenatal to postnatal | Geocoding/air quality | Increased |
Volk et al. 2014 | 251/156 | Prenatal | Geocoding/air quality | Increased | |
Kalkbrenner et al. 2015 | 979/ 14666 | Prenatal 3rdtrimester | Geocoding/air quality | Increased | |
Chen et al. 2018 | 124/ 1240 | Postnatal to childhood | Geocoding/air quality | Increased | |
Ritz et al. 2018 | 15387/ 68139 | Postnatal (9 m) | Geocoding/air quality | Increased | |
SO2 | Jung et al. 2013 | 342/ 48731 | Prenatal to childhood | Geocoding/air quality | Increased |
Ritz et al. 2018 | 15387/ 68139 | Postnatal (9 m) | Geocoding/air quality | Increased | |
PAHs | von Ehrenstein et al. 2014 | 104/ 53181 | Prenatal | Geocoding/air quality | Increased |
Talbott et al. 2015 (2) | 215/ 4856 | Prenatal | Geocoding/air quality | Increased | |
Lead | Priya and Geetha 2011 | 45/50 | Childhood (4-12y) | Hair and nails | Increased |
Roberts et al. 2013 | 325/ 22101 | Perinatal (at birth) | Geocoding/air quality | Increased | |
von Ehrenstein et al. 2014 | 348/ 78373 | Prenatal | Geocoding/air quality | Increased | |
Talbott et al. 2015 (2) | 215/ 4856 | Prenatal | Geocoding/air quality | Increased | |
Arora et al. 2017 | 22/54 | Postnatal (15w) | Deciduous teeth | Increased | |
El-Ansary et al. 2017 | 35/30 | Childhood (3-12y) | Red blood cells | Increased | |
Manganese | Roberts et al. 2013 | 325/ 22101 | Perinatal (at birth) | Geocoding/air quality | Increased |
Arora et al. 2017 | 22/54 | Postnatal (15w) | Deciduous teeth | Decreased | |
Mercury | Windham et al. 2006 | 284/657 | Perinatal (at birth) | Geocoding/air quality | Increased |
Obrenovich et al. 2011 | 26/39 | Childhood (up to 6y) | Hair | Decreased | |
Roberts et al. 2013 | 325/ 22101 | Perinatal (at birth) | Geocoding/air quality | Increased | |
Priya and Geetha 2011 | 45/50 | Childhood (4-12y) | Hair and nailS | Increased | |
El-Ansary et al. 2017 | 35/30 | Childhood (3-12y) | Red blood cells | Increased | |
BPA | Stein et al. 2015 | 46/52 | Childhood (10.1 ± 3.7y) | Urine | Increased |
Kardas et al. 2016 | 48/41 | Childhood (7.5 ± 2.9y) | Serum | Increased | |
Phthalates | Testa et al. 2012 | 48/45 | Childhood (11.0 ± 5y) | Urine | Increased |
Kardas et al. 2016 | 48/41 | Childhood (7.5 ± 2.9y) | Serum | Increased | |
PBDEs | Lyall et al. 2017 (1) | 545/418 | Prenatal 2ndtrimester | Maternal serum | Increased Decreased |
PCBs | Cheslack-Postava et al. 2013 | 75/75 | Prenatal (early pregnancy) | Maternal serum | Increased |
Lyall et al. 2017 (2) | 545/418 | Prenatal (2nd trimester) | Maternal serum | Increased | |
PFCs | Lyall et al. 2018 | 553/443 | Prenatal 2ndtrimester | Maternal serum | Decreased |
Long et al. 2019 | 75/135 | Prenatal | Amniotic fluid | Decreased | |
OC pesticides | Roberts et al. 2007 | 465/ 6975 | Prenatal 1sttrimester | Geocoding/pesticides data | Increased |
Cheslack-Postava et al. 2013 | 75/75 | Prenatal 1sttrimester | Maternal serum | Increased | |
Brown et al. 2018 | 778/778 | Prenatal 1st or 2nd trimesters | Maternal serum | Increased | |
OP pesticides | Shelton et al. 2014 | 486/ 315 | Prenatal | Geocoding/pesticides data | Increased |
Schmidt et al. 2017 | 296/ 220 | Prenatal | Survey | Increased | |
Philippat et al. 2018 | 46/102 | Prenatal | Maternal urine | Increased | |
von Ehrenstein et al. 2019 | 2961/ 35370 | Prenatal to postnatal | Geocoding/pesticides data | Increased | |
Pyrethroids | Shelton et al. 2014 | 486/315 | Prenatal | Geocoding/pesticides data | Increased |
Hicks et al. 2017 | 159/298 | Prenatal | Geocoding/pesticides data | Increased | |
von Ehrenstein et al. 2019 | 2961/ 35370 | Prenatal to postnatal | Geocoding/pesticides data | Increased | |
Glyphosate | von Ehrenstein et al. 2019 | 2961/ 35370 | Prenatal to postnatal | Geocoding/pesticides data | Increased |
Antidepressants | Croen et al. 2011 | 298/ 1507 | Prenatal | Medical records | Increased |
Rai et al. 2013 | 4429/ 43277 | Prenatal | Medical records | Increased | |
Gidaya et al. 2014 | 5215/ 52150 | Prenatal | Medical records | Increased | |
Harrington et al. 2015 | 421/ 464 | Prenatal | interview and medical records | Increased | |
Rai et al. 2017 | 5378/ 249232 | Prenatal | Interview and medical records | Increased | |
Valproic Acid | Moore et al. 2000 | 52 | Prenatal | Survey | Increased |
Bromley et al. 2008 | 10/622 | Prenatal | Interview and medical records | Increased | |
Bromley et al. 2013 | 12/509 | Prenatal | Interview and medical records | Increased | |
Christensen et al. 2013 | 5437/ 630178 | Prenatal | Medical records | Increased | |
Thalidomide | Stromland et al. 1994 | 100 | Prenatal 1sttrimester | Medical records | Increased |
Misoprostol | Bandim et al. 2003 | 23 | Prenatal 1sttrimester | Interview | Increased |
Folic Acid | Schmidt et al. 2012 | 429/ 278 | Prenatal (early pregnancy) | Interview | Decreased |
Surén et al. 2013 | 270/ 84906 | Prenatal (early pregnancy) | Survey | Decreased | |
Al-Farsi et al. 2013 | 40/40 | Childhood (3-5y) | Serum | Decreased | |
Nilsen et al. 2013 | 234/89602 | Prenatal | Medical records | Decreased | |
Levine et al. 2018 | 572/ 44728 | Prenatal | Medical records | Decreased | |
Raghavan et al. 2018 | 86/1171 | Postnatal (2-3d) | Maternal plasma | Increased | |
Egorova et al. 2020 | 100/100 | Prenatal | Maternal serum | Increased | |
Vitamin D | Meguid et al. 2010 | 70/42 | Childhood (5.3 ± 2.8y) | Serum | Decreased |
Tostes et al. 2012 | 24/24 | Childhood (7.4 ± 2.7y) | Serum | Decreased | |
Mostafa and AL-Ayadhi 2012 | 50/30 | Childhood (8.2 ± 2.4y) | Serum | Decreased | |
Neumeyer et al. 2013 | 18/19 | Childhood (10.6 ± 0.4y) | Serum | Decreased | |
Gong et al. 2014 | 48/48 | Childhood (3.7 ± 1.2y) | Serum | Decreased | |
Bener et al. 2014 | 254/254 | Childhood (5.5 ± 1.6y) | Serum | Decreased | |
Kocovska et al. 2014 | 40/40 | Early adulthood (18.9 ± 2.9y) | Serum | Decreased | |
Fernell et al. 2015 | 58/58 | Neonatal | Dried Blood Spots | Decreased | |
Magnusson et al. 2016 | 9882/ 499757 | Prenatal to childhood | Medical records | Decreased | |
Bener et al. 2017 | 308/ 308 | Childhood (5.4 ± 1.7y) | Serum | Decreased | |
El-Ansary et al. 2018 | 28/27 | Childhood (7.0 ± 2.3y) | Plasma | Decreased | |
Guo et al. 2018 | 332/197 | Childhood (4.9 ± 1.5y) | Serum | Decreased | |
Wu et al. 2018 | 310/ 1240 | Neonatal | Dried Blood Spots | Decreased | |
Arastoo et al. 2019 | 31/31 | Childhood | Serum | Decreased | |
Lee et al. 2019 | 1399/ 1607 | Neonatal | Dried blood spots | Decreased | |
Alzghoul et al. 2019 | 83/106 | Childhood | Serum | Decreased | |
Sengenc et al. 2020 | 100/100 | Childhood | Serum | Decreased | |
Petruzzelli et al. 2020 | 54/36 | Childhood | Serum | Decreased |
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