Skip to content
naulon

No retraction, correction or concern found when last checked on Oct 21, 2026.

An Overview of the Main Genetic, Epigenetic and Environmental Factors Involved in Autism Spectrum Disorder Focusing on Synaptic Activity

Elena Masini, Eleonora Loi, Ana Florencia Vega-Benedetti and 4 more

International Journal of Molecular Sciences · 2020 · Review · CC BY

doi:10.3390/ijms21218290

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects social interaction and communication, with restricted interests, activity and behaviors. ASD is highly familial, indicating that genetic background strongly contributes to the development of this condition. However, only a fraction of the total number of genes thought to be associated with the condition have been discovered. Moreover, other factors may play an important role in ASD onset. In fact, it has been shown that parental conditions and in utero and perinatal factors may contribute to ASD etiology. More recently, epigenetic changes, including DNA methylation and micro RNA alterations, have been associated with ASD and proposed as potential biomarkers. This review aims to provide a summary of the literature regarding ASD candidate genes, mainly focusing on synapse formation and functionality and relevant epigenetic and environmental aspects acting in concert to determine ASD onset.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 21, 2026. Paragraph numbers are ours, so an answer can point at one.

1. Introduction

Autism is a complex syndrome characterized by a range of conditions and symptoms that frame it as a spectrum of disorders (autism spectrum disorder, ASD), including relevant physiological and biochemical ones, whose core symptoms includes social deficits and restrictive/repetitive behaviors. In the 1960s, thanks to the studies of Bernard Rimland, it was understood that ASD is a psychiatric disorder which might be grounded on a combination of genetic and environmental factors. To cope with ASD, it is necessary to achieve positive results through multidisciplinary, biomedical and behavioral therapies. Early diagnosis and intensive therapeutic interventions greatly improve the disease outcomes. In this context, the discovery of new diagnostic methods to detect ASD-related genetic alterations and biomarkers becomes fundamental in order to make an early diagnosis of the disorder.

In this review, we provide an overview of the genetic, epigenetic and environmental factors contributing to ASD pathogenesis.

2.1. Clinical Characteristics of ASD

ASD can be considered as a group of early-onset neuroevolutionary disorders which seem to be at the basis of alterations in brain connectivity, with cascading effects on many neuropsychological functions. The The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (The American Psychiatric Association, APA, Philadelphia, PA, USA, 2013) gives the condition of autism the attribute of “spectrum” and uses criteria derived from diagnostic research assessment tools.

As indicated in the DSM-5 (APA, Philadelphia, PA, USA, 2013), individuals with ASD are characterized by persistent deficits in social communication and social interaction across multiple contexts and by restricted, repetitive patterns of behaviour, interests or activities. Deficiency in social communication and social interaction might appear in the form of deficits in social-emotional reciprocity, in nonverbal communicative behaviours used for social interaction and deficit in developing, maintaining and understanding age-appropriate relationships. As reported in the DSM-5, symptoms could be masked during early development and fully manifest only when social demands exceed limited capacities or may be hidden by learned strategies in later life. The impairments should cause clinically significant damage in social, occupational or other important areas of current functioning.

ASD symptoms should not be better explained by a diagnosis of intellectual disability (ID) or global developmental delay. Intellectual disability and ASD might co-occur.

The DSM-5 proposes differentiations based on commorbidity with intellectual impairment, language impairment, another neurodevelopmental, mental, or behavioral disorder, genetic or medical condition or environmental factors. Furthermore, it is possible to differentiate between different levels of severity according to the level of support required to function in daily contexts. According to this description, it is clear that different clinical variants of ASD exist and should be taken into account for diagnosis and intervention (APA, 2013). A distinction is made between a congenital form of ASD, representing a small percentage of cases in which the symptoms occur shortly after birth and in which the genetic fingerprint is prevalent, and a regressive or acquired form, in which the disorder appears after a period of typical development and it is not characterised by typical and constant genetic abnormalities, although several single nucleotide polymorphisms (SNPs) have been associated with the disease. SNPs constitute variations of a single nucleotide in certain DNA traits. SNPs associated with ASD have been identified in genes encoding for proteins involved in different processes, including: cellular detoxification, some neuronal receptors and metabolism of several neurotransmitters and metabolites, in particular those of the metabolic circuits of methylation and transulfuration.

2.2. Epidemiology

Autism has been considered relatively rare for many years, with a prevalence of less than 1 in 1000 children, while today, the estimated rate is 1 in 160, and it seems likely to increase in the coming years (World Health Organization, Geneva, Switzerland, 2019). In the last decade, the study of ASD genetics has proved to be crucial not only to interpret and explain its phenotypic heterogeneity but also to discover new diagnostic procedures and therapies. It is estimated to-date that hundreds of genes are involved in ASD, resulting in a unified spectrum of different phenotypes, including different language and social deficits with various associated sub-phenotypes.

ASD show an unequal distribution based on gender: males have a four times higher risk of developing the disorder than females. A number of hypotheses have been made to interpret this unequal prevalence: in females, a higher dose of genetic “defect” is required than in males, consistent with the hypothesis of the contribution of protective genetic factors in females, because in males, there is only one X chromosome, so when alterations appear, it cannot be compensated by the normality of a second X chromosome. An association between testosterone levels and ASD risk has also been described: males have more frequent inflammatory reactions and use the brain in a more focal way and therefore suffer more from alterations in neuronal development that affect the connection systems between the different areas.

Genetic, environmental and developmental factors play a key role in the onset of autism spectrum disorders, as highlighted from many epidemiological studies. It is unlikely that a single condition or event plays a major role in the causality of ASD; based on research to date, rather, none of the risk factors identified is a necessary and sufficient condition for ASD. Even for syndromic or secondary autism, which refers to autism with a single defined cause, such as fragile X syndrome and tuberous sclerosis, none of these etiologies are specific to autism because each of them encompasses a variable proportion of individuals with and without autism. At present, ASD appears to have a multifactorial etiology to which developmental (in utero and early childhood), environmental and genetic aspects contribute, in as-yet unknown and different ways. Emerging methodologies in genomics and epigenomics research could be the key to elucidate the mysteries underlying the epidemiology of autism spectrum disorder.

3. Genetic and Epigenetic Factors

The involvement of genetic etiology in ASD was first suggested by a study on twins reported in the 1970s. The genetic heritability of a trait can be estimated by comparing the phenotypic concordance between monozygotic twins (MZ), which have 100% genetic similarity, and dizygotic twins (DZ), who have approximately 50% genetic similarity. The greater the difference between the concordance of MZ twins and DZ twins, the higher the genetic heritability and the contribution of genetics to that trait. The genetic fingerprint was confirmed by the high concordance of autism in monozygotic twins (60–90%) compared to dizygotic twins (5–40%).

One of the largest studies to date, involving more than two million children born between 1982 and 2006 in Sweden, concluded that ASD has an inheritability of 45–56%. A study of all twins born in the UK between 1994 and 1996 estimated an inheritability of more than 56% using concordance in MZ (0.77–0.99) compared to DZ (0.22–0.65) twins.

Considering all lines of evidence, the genetic heritability of ASD is estimated to play a fundamental role in ASD onset, together with environmental and epigenetic factors. However, despite most of the studies aimed at understanding the etiological basis of ASD having been focused on the genetic component, it has been possible to associate genetic variants to only a relatively small fraction of ASD patients. The problem, known as the “missing heritability issue”, is common to most complex genetic diseases. Several hypotheses have been put forward to justify the missing heritability, such as the existence of poorly characterised variants, genotype/genotype interactions, incomplete penetrance, epigenetic factors and genotype/environment interactions.

In the 1990s, most of the research consisted of candidate gene studies, focusing on a particular gene that might be involved in ASD. From 2005, technologies such as whole-exome sequencing (WES), but also microarrays, have allowed genome-wide studies, leading to the identification of different variations in copy number (CNVs), DNA segments larger than 1 kilobase present in a variable copy number compared to a reference genome and single nucleotide variations (SNVs) in autistic patients, suggesting a highly heterogeneous genetic architecture. CNVs would contribute at about 15% and SNVs at 7% to the causes of ASD. Only a few of these genetic alterations have such complete penetrance that they are associated with ASD in almost every person who carries that variant. On the contrary, genetic alterations with incomplete penetrance, variable expressivity or both are more frequently observed. However, the cause in most ASDs (>75%) remains elusive. Genome-wide association studies (GWAS) have identified several SNPs associated with ASD. The first GWAS study allowed the identification of six polymorphisms, including some localized in CDH10 and CDH9, genes encoding for cadherins, proteins that are important in cell adhesion, as common genetic variants in ASD. However, the fact that several GWASs failed to identify some relevant loci despite the use of genetic data from more than 1000 families affected by autism suggests that the effect of individual common variants is relatively small.

On the other hand, WES analysis of affected and unaffected individuals has proven to be a powerful approach that offers new opportunities of sporadic cases studies and has the ability to detect mutations and de novo variants with incomplete penetrance. WES has already led to the identification of over 150 new candidate genes for ASD.

In addition to SNPs, evidence is accumulating that CNVs play an important role in human neuropsychiatric diseases. ASD patients have three to five times more de novo CNVs than other family members and unaffected controls.

CNVs can influence gene expression, thus contributing to the pathogenesis of the disease through various mechanisms, including gene dosage, gene interruption, position effects, gene fusion and unmasking of recessive alleles or polymorphisms.

Screening for CNVs has proven to be a method of choice for identifying genes associated with ASD susceptibility. Although CNVs associated with the disease are usually unique and show a low frequency in the population, they are identified in 8–21% of individuals with ASD and are most likely related to a severe clinical picture. In addition, previous studies have indicated that individuals with syndromic ASD and intellectual disability have more pathogenic CNVs than individuals with non-syndromic ASD or ID.

CNVs may also lead to the generation of chimeric genes. Several studies have investigated whether fusion transcripts may lead to an increased ASD susceptibility. Holt and colleagues identified a fusion transcript involving MAPKAPK5 and ACAD10 genes in two ASD probands. However, the fusion transcript was detected at similar rates in both ASD patients and controls and had a premature stop codon, suggesting that it may be degraded by nonsense-mediated decay. Similarly, Pagnamenta et al. identified a DOCK4-IMMP2L fusion transcript, likely to be subjected to nonsense-mediated decay in ASD individuals and their unaffected family members. A study conducted on a multiplex family identified a BST1-CD38 fusion transcript in one ASD proband with asthma, suggesting that it may be related to the more severe phenotype of this patient compared to the other ASD sibling. qRT-PCR analysis showed that the fusion transcript was less expressed compared to the wild-type BST1 transcript in the lymphoblastoid cell line derived from the proband, while the aberrant protein was not detected in a preliminary Western blot analysis. Recently, our research group identified a microdeletion leading to the formation of an ELMOD3-SH2D6 chimeric transcript in a multiplex ASD family. SH2D6 is expressed at extremely low levels in blood cells. On the other hand, the fusion transcript was highly expressed in PMBCs from the two ASD siblings and their unaffected mother carrying the deletion, suggesting that it was not subjected to nonsense-mediated decay. Bioinformatic analysis has shown that the fusion transcript would encode for a chimeric protein with an interrupted domain of ELMOD3 and would not contain the canonical SH2D6 sequence, suggesting an impaired function of the protein. These results suggest a possible contribution of fusion transcripts in the complex ASD phenotype. Therefore, in case of copy number loss, the possible transcript fusion and chimeric protein product should be deeply investigated.

Recent studies have shown that epigenetic factors, including DNA methylation, hystone modifications and microRNAs (miRNAs), could play an important role in predisposition to autism.

We herein provide an overview of the main candidate genes (extensively reviewed in) and epigenetic mechanisms involved in ASD etiology. Figure 1 summarizes some ASD candidate genes and epigenetic factors, belonging to the pathways mainly associated with ASD described in this review.

3.1. Relevant Candidate Genes

Case-control studies on population and animal models have pointed out more than 800 genes associated with autism. The most affected genes in ASD encode for proteins involved in chromatin remodeling and transcriptional regulation, cell proliferation and mostly synaptic architecture and functionality. In this review, we will focus on this last category since our recent studies have also pointed out alterations in these genes fundamental for a proper synaptic function. Table 1 provides a summary of several genes clearly implicated in ASD, included in the SFARI Gene database as high confidence ASD genes (release 26 October 2020, gene.sfari.org) belonging to the other categories. Most of these genes were indicated in the largest exome sequencing study of ASD to date, as well as in the list narrowing down the number of amygdala-expressed genes associated to the social pathophysiology of ASD.

Synaptic Architecture and Functionality

It is not surprising that many candidate ASD genes are involved in synaptic architecture and function, which allows the transmission of information between neurons and between neurons and other cells, such as muscle, sensory and other cells. Many ASD candidate genes are involved in dendritic spine formation. Dendritic spines are small actin-rich protrusions that form the postsynaptic part of most excitatory synapses. Remodeling of actin cytoskeleton is responsible for the changes in the shape and size of dendritic spines and, consequently, to the synaptic functions. Actin regulation mechanisms regulate the formation, maturation and plasticity of dendritic spines and of neuronal processes, such as learning and memory. Abnormalities in the number and shape of dendritic spines have been observed in several neurological disorders, including autism, and contribute to brain dysfunction.

Post-synaptic density proteins (PSD), including cell adhesion molecules, scaffold proteins, receptors and cytoskeleton proteins, are fundamental for synaptic transmission and plasticity. Alterations of these proteins have been associated with many neurological disorders, including ASD.

Cell adhesion molecules

Neurexins (NRXN) and neuroligins (NLGN) are transmembrane synaptic proteins that form the neurexin/neuroligin transsynaptic complex, crucial for synaptic function. NLGNs bind to SHANK3 through PSD-95 and other synaptic proteins.

Loss of function NRX1 variants in ASD individuals have been identified in multiple studies. Studies conducted in animal models knockout (KO) for NLGN and NRXN family members showed that mice develop ASD-like symptoms and have confirmed their role in synaptic function.

CNTNAP2, also known as CASPR2, encodes for a member of the NRXN family that serves as an adhesion protein, primarily between neuronal and glial cells. CNVs encompassing CNTNAP2 and resulting in its decreased expression have been described in subjects with ASD. The suppression of CNTNAP2 in murine models causes autistic behaviors, such as repetitive behaviors and reduced socialization and communication.

Scaffold proteins

SHANK gene family, including SHANK1, SHANK2 and SHANK3, has been suggested as a strong candidate for ASD. SHANK proteins are multi-domain post synaptic density scaffold proteins that connect neurotransmitter receptors, ion channels and other membrane proteins to cytoskeleton actin and signaling proteins. These proteins are important for synapse formation and dendritic spine maturation. Rare deletions of SHANK2 and de novo variants causing loss of protein function have been identified in individuals with ASD. A microdeletion encompassing SHANK3 determines Phelan–McDermid syndrome, characterized by intellectual disability, ASD, severe speech disorders and epilepsy. A meta-analysis of SHANK mutations found low frequency of SHANK1 and SHANK2 deleterious mutations in contrast to the high frequency of loss of function SHANK3 mutations in cases with ASD.

Our studies led to the identification of another promising ASD candidate gene, CAPG. This gene encodes for a member of the gelsolin family of actin-regulatory proteins, important for the remodeling of actin architecture. A microdeletion encompassing the entire CAPG gene has been recently described in three completely independent families in the heterozygous and homozygous state. Importantly, a reduced CAPG expression, both at transcriptional and protein levels, has been detected in the Sardinian family members carrying the deletion, and reduced CAPG mRNA levels have been also observed in an independent cohort of 13 non-Sardinian ASD cases compared to age-matched healthy controls.

Several studies have demonstrated the importance of CAPG for the formation of functional synapses. In fact, experiments conducted on cultured hippocampal neurons have demonstrated that capping proteins are present at the branched actin filament network of dendritic spine heads and they are fundamental for dendritic spine development. In fact, CAPG knock-down led to a decline in spine density and to an increased number of filopodia-like protrusions.

Voltage-gated ion channels

The role of genetic defects of different ion channels in the pathogenesis of ASD is well established. In fact, GWAS, WES and WGS have identified several polymorphisms and rare variants in calcium, sodium and potassium channels in ASD subjects (reviewed in).

Point mutations in CACNA1C gene, which encode for l-type voltage-gated Ca2+ channel Cav1.2, lead to Timothy syndrome (TS), a disorder affecting multiple organs and characterized by an autistic phenotype. l-type channels are mainly expressed in neuronal dendrites and cell bodies and are crucial for the activation of Ca2+-signaling pathways and for neuronal excitability. Defects in CACNA1C prevent the inactivation of the channel and lead to its prolonged opening and consequent increase in Ca2+ flux.

Mutations in other genes encoding for l-type and T-type Ca2+ channels, such as CACNA1D, CACNA1E, CACNA1F and CACNA1H, have been described in ASD. Moreover, mutations of CACNB2, the gene encoding for the regulatory β2 subunit of CACNA1C, have been found in ASD families.

Genetic defects in genes encoding for sodium channels, such as SCN1A, SCN2A, SCN3A, SCN7A and SNC8A, have been also associated with ASD. Voltage-dependent sodium channels are mainly expressed in neurons and glial cells and are fundamental for the initiation and propagation of action potentials. Mutations of SCN1A cause Dravet syndrome, characterized by seizures and frequently manifesting, also, autistic symptoms. A study has shown that Scn1a+/− heterozygous KO mice display stereotypical and anxious behaviors other than seizures.

Several studies have also shown that mutations in genes encoding for K+ channels, including KCNMA1, KCND2, KCNJ10, KCNQ3 and KCNQ5, may play a central role in ASD etiology. It has been shown that KO of Fmr1 in mice results in K+ channel dysregulation and consequent dysregulation of synaptic transmission.

Some evidence has shown that voltage-dependent anion channel (VDAC) genes, a class of postsynaptic density genes highly expressed in several brain regions, could be implicated in ASD. In fact, autoantibodies against VDAC proteins have been detected in autistic individuals, suggesting a possible causal role in ASD pathogenesis. Moreover, the beneficial effects observed in ASD patients treated with coenzyme Q, or other agents influencing the transport of electrons, have been attributed to the control of such molecules on the porin channels. Recently, a study conducted by our research group identified a 2-bp frameshift deletion of VDAC3 in an ASD family.

3.2. Epigenetic Factors

There is increasing evidence supporting the possible role of epigenetic aberrations, including DNA methylation alterations and microRNAs, in ASD etiology.

DNA methylation

Several studies have conducted global methylation analyses in peripheral tissues as well as post-mortem brain tissues from ASD subjects and controls.

Studies conducted on lymphoblastoid cell lines and whole-blood DNA from monozygotic twins discordant for ASD diagnosis and controls identified several differentially methylated regions (DMRs) between discordant MZ twins and between ASD patients and control samples.

Zhu et al. identified 400 DMRs, enriched at promoters of genes involved in neuronal development, between placentas from children later diagnosed with ASD and those from typically developing controls. Methylation levels of two DMRs, mapping on CYP2E1 and IRS2, were respectively associated with genotype within the DMR and prenatal vitamin use.

Conversely, a large epigenome-wide association study, performed on blood-DNA from 796 ASD cases and 858 controls, did not detect any differentially methylated CpG site after correction for multiple testing.

Similarly, Siu et al. did not detect any DNA methylation patterns clearly distinguishing heterogenous ASD cases from controls. However, they identified unique DNA methylation signatures for ASD individuals with 16p11.2 deletions or pathogenic variants of CHD8.

On the other hand, Kimura et al. identified a potential biomarker for adult ASD. The identified CpG site was hypermethylated in whole-blood DNA from ASD patients compared to controls and mapped on the PPP2R2C gene, which resulted down-regulated in ASD subjects.

Alterations in Alu methylation patterns have been observed in ASD cases sub-grouped based on Autism Diagnostic Interview-Revised scores compared with matched controls.

More recently, a genome-wide methylation study was performed on post-mortem tissue samples from different brain regions dissected from ASD subjects and controls. Wide-spread methylation differences, with more pronounced effects in cortical regions compared to cerebellum, were detected between idiopathic ASD cases and controls and in individuals carrying 15q11–13 duplication.

Another global methylation study has recently reported that differentially methylated CpG sites identified in ASD cases compared to controls are enriched in pathways converging on mitochondrial metabolism and protein ubiquitination, suggesting a possible role of DNA methylation and mitochondrial dysfunction in ASD.

Other studies measuring methylation levels of candidate genes using targeted approaches detected hypermethylation of genes including APOE and HTR2A and hypomethylation of genes such as HTR4 in ASD subjects compared to controls.

The functional impact of locus-specific Mecp2 methylation on ASD onset has been recently demonstrated in vitro and in vivo. MECP2 is known to be hypermethylated and down-regulated in ASD subjects. The authors of this study employed the CRISPR-dCas9 methylation editing system to induce methylation of the Mecp2 transcription start site in Neuro-2a cells and in mouse models, resulting in Mecp2 down-regulation and the acquisition of behavioral changes attributable to an ASD phenotype in mice.

miRNA

Several studies have found that miRNA expression profiles are dysregulated in different matrices, including saliva, blood and brain tissues, from ASD individuals.

A study conducted on saliva samples from individuals with and without ASD has identified a panel of four microRNAs differentially expressed between ASD patients and controls. A panel of five salivary miRNAs has shown an accuracy of about 90% in the detection of developmental disorders, including ASD.

Down-regulation of miR-6126 has been detected in peripheral blood samples from adult individuals with ASD. The predicted targets of this miRNA belong to neuronal and oxytocin pathways. Similarly, Ozkul et al. identified a consistent decrease and a slight reduction in six microRNAs (miR-19a-3p, miR-361-5p, miR-3613-3p, miR-150-5p, miR-126-3p and miR-499a-5p) in serum samples from ASD children and their unaffected family members compared to healthy controls. This result was replicated in the blood, hypothalamus and sperm of two ASD mouse models. Another study conducted on serum samples identified a panel of three miRNAs (miR-130-3p, miR-181b-5p and miR-320a) showing an area under the curve >0.85 in distinguishing ASD subjects from controls.

Studies conducted on knockout (KO) mouse models for some ASD candidate genes, including Fmr1, Mecp2 and Ube3A, have observed dysregulation of different miRNAs and evaluated their regulatory role in neuronal context.

4. Environmental Factors

As mentioned above, several studies investigated the possible role of environmental factors in the etiology of ASD. According to recent studies, up to 40–50% of variance in ASD liability could be determined by environmental factors, such as drugs, toxic exposures, parental age, nutrition, fetal environment and many others. However, while for some potential risk factors, there is strong evidence, supported by association studies but also by in vitro and in vivo studies, only weak associations have been described for many others.

Below is reported an overview of the most-studied environmental factors that have been found to potentially contribute to cause ASD (recently reviewed in) (Figure 2).

Parental age is one of the most established environmental ASD risk factors. In fact, much evidence has correlated advanced paternal age (APA) with the development of bipolar disorder, schizophrenia, ADHD and ASD. A meta-analysis of 27 studies on the association between advanced parental age and ASD showed that a 10-year increase in maternal and paternal age is associated with a 20% higher risk of ASD in children. A study has shown that age-related methylation changes observed in sperm could be related to an increased ASD risk in the offspring. APA has been associated with reduced cortical thickness of the right posterior ventral cingulate cortex in ASD offspring. Experiments conducted in mouse models have confirmed that APA is associated with the development of autism-like symptoms in the offspring and with altered cortical morphology in male APA mice. Moreover, behaviors related to ASD have also been observed in the second generation of mice with older grandfathers, suggesting that genetic and epigenetic alterations associated with APA are heritable.

Perinatal risk factors are also among the most-studied ASD risk factors and among the most difficult to determine and predict in advance. Two comprehensive meta-analyses examined 60 obstetric factors and found statistically significant associations between ASD risk and umbilical cord complications, injury or trauma at birth, multiple births, maternal hemorrhage, low birth weight, neonatal anemia, genital malformation, ABO or Rh blood group incompatibility and hyperbilirubinemia. Another study has described an association between increased risk of autism and different factors, including caesarean section delivery, induced labor, management age less than 36 weeks and fetal distress.

Fetal exposure to sex steroids represents a potential risk factor for ASD. In fact, the fetal testosterone theory has been proposed to explain the higher ASD prevalence in males. However, this hypothesis is controversial. In fact, while higher testosterone levels have been reported in ASD women, ASD males display testosterone levels similar to controls. Baron-Cohen and colleagues have supposed that testosterone has effects on brain development during the prenatal masculinization window. The authors detected higher levels of sex steroids and cortisol in the amniotic fluid samples from male autistic patients compared with matched typically developing controls. Recently, the same authors reported an association between fetal estrogen levels, important in synaptogenesis and corticogenesis, and autism risk. Similarly, a study conducted on post-mortem brains detected reduced levels of estrogen receptor beta, aromatase and estrogen coactivators in the frontal gyrus of subjects with ASD compared to controls. Moreover, several SNPs in genes encoding for proteins involved in sex steroid synthesis/transport have been associated with autistic traits.

The health condition of the mother has a major impact on the risk of ASD. It appears that maternal nutrition in pregnancy is of fundamental importance as it determines the nutrients available to support fetal growth. Therefore, diets lacking specific nutrients can have adverse effects on fetal development. It has been shown that even short intervals between pregnancies can be harmful, since the body needs time, up to one year after childbirth, to recover acceptable levels of several essential substances.

Deficiencies of micronutrients, including vitamins and trace elements, have been associated with an increased risk of ASD. For instance, a Swedish study found that maternal vitamin D deficiency is associated with the risk of ASD with ID in the offspring. Unbalanced levels of vitamins have also been detected in ASD children. Moreover, beneficial effects of vitamin supplementation have been observed in ASD patients. Similarly, altered hair and/or blood concentrations of several trace elements, including chromium, magnesium and zinc, have been found in ASD patients compared with controls. Association between maternal deficiency of microelements and ASD risk has also been reported. For example, iron deficiency, common in pregnant women, was associated with a five-fold increased risk of ASD, especially in the presence of other risk factors.

The involvement of altered trace elements concentrations on ASD phenotype is also supported by in vitro and animal studies. The effects on unbalanced metal levels on synapse formation and functionality have been evaluated on hippocampal cultured cells from rat brains, finding that the metal profile of autistic children led to down-regulation of crucial synaptic components, including Shank proteins and NMDA receptor subunits, and reduction of synaptic density. Interestingly, it was observed that zinc supplementation was able to revert the observed alterations. The importance of zinc in ASD is supported by multiple pieces of evidence, including a strong association between low levels of this metal and ASD risk as well as a causative role of zinc deficiency in neuronal defects and development of ASD-related symptoms and “therapeutic” effects of zinc supplementation. In fact, low hair and serum levels of zinc and/or altered Zn/Cu ratio have been detected in children with ASD. The effects of zinc deficiency on ASD have been observed in animal models. It has been shown that prenatal zinc deficiency alters social behavior in mice. In vitro and in vivo studies have shown that zinc deficiency at the synaptic level leads to a decrease in ProSAP/Shank family members, a reduction in synaptic density and the development of ASD-related symptoms in mice. In fact, other studies have shown that Zn2+ ions, highly abundant at PSD level, influence the recruitment of ProSAP1/Shank2 or ProSAP2/Shank3 for a correct formation and maturation of synapses. The mechanism regulating the abundance of Zn at PSD levels has been hypothesized: Zn is released from pre-synaptic terminals and can translocate into post-synaptic neurons through Zn-permeable channels, including NMDA, and voltage-gated Ca2+ and AMPA channels. Notably, a recent study has shown maternal zinc supplementation can prevent ASD-associated deficits in Shank3 KO mouse models. Recently, Shih et al. have elegantly demonstrated the impact of the crosstalk between genetic and environmental factors in the development of ASD. The authors have shown that KO of Cttnbp2, an actin cytoskeleton regulator, leads to a reduction in Zn concentration and expression levels of different synaptic proteins, consequently affecting dendritic spine formation and leading to the development of autism-like behaviors rescued by zinc supplementation. The beneficial effects of zinc supplementation have been also observed in pregnant women, where an increase in zinc intake has been shown to reduce the risk of neural tube defects in the offspring.

The association between maternal obesity and risk of ASD in offspring is controversial. A Swedish study described a relationship between maternal BMI and ASD at population level; however, sibling analysis did not reveal any association between elevated maternal BMI and ASD risk. On the other hand, a meta-analysis has shown a 28% and 36% increased risk of ASD in offspring born from overweight and obese mothers, respectively, even though there were relatively small numbers of ASD cases within the category of maternal underweight. However, it has also been shown that children born from underweight mothers are also at high risk of ASD. Another study has shown that the combination of maternal obesity and maternal diabetes was associated with an increased risk of ASD and ID.

Maternal consumption of substances such as smoke, alcohol and medicines during pregnancy might be a potential risk factor as well. However, the association between smoking and alcohol-use with ASD is rather weak; in fact, two meta-analyses showed no evidence that smoking is a risk factor for ASD. Moreover, cohort studies or case-control studies have examined the risk of ASD due to maternal alcohol consumption, indicating that mild to moderate consumption does not pose any risk.

The safety of medicines during pregnancy is very difficult to establish. In the ASD literature, antidepressant and anticonvulsant drugs have emerged as drugs of potential interest. An example is valproic acid, a drug that has been used to treat epilepsy or as a mood stabilizer in bipolar disorder. Its use during pregnancy leads to congenital malformations, developmental delay and cognitive malfunction. Maternal use of selective serotonin uptake inhibitors has been associated with a 50% increase in ASD risk, although maternal psychiatric condition is a confounding factor.

An association between maternal diseases and ASD risk has been shown. A meta-analysis limited to case-control studies identified a 62% increased ASD risk among diabetic mothers compared to non-diabetic mothers, while a second study found a 74% increase in ASD risk for pregestational diabetes and 43% for gestational diabetes.

It has also been shown that maternal viral and bacterial infections are associated with ASD risk. Two meta-analyses found that maternal autoimmune diseases are associated with an increased risk of ASD in offspring. However, it is not the presence of viruses and bacteria per se to be associated with ASD development, but the immune response they cause, a conclusion supported by research identifying elevated inflammatory markers and antibodies in pregnant women with autistic children. This hypothesis is supported by animal studies where maternal immune activation, induced by different immunogens, has been shown to induce post-natal brain dysfunction observable in a phenotype characteristic of ASD and other neurological disorders.

It is interesting to note that, although from different perspectives, much evidence points out the importance of maternal immune system condition during fetal development. Several studies have also reported an association between family history of autoimmune diseases and ASD. It could be speculated that this link could be supported by maternal levels of zinc. In fact, our recent meta-analysis has shown that Zn concentrations in both serum and plasma levels of patients with autoimmunity are significantly lower compared to controls. It is known that Zn plays an important role in the regulation of the immune system and, according to the studies provided above, it plays also an important role in neuronal tube formation.

Finally, exposure to toxic xenobiotics could represent another potential environmental risk factor. Substances such as brominated flame retardants could cause mitochondrial toxicity through a variety of mechanisms, leading to an altered energy balance in the brain, an important association with autism since mitochondrial dysfunction has been documented in patients with ASD. Heavy metals can have a negative impact on many body functions by inducing neurological and behavioral damage. A meta-analysis of three case-control studies found a 60% increase in the risk of ASD due to exposure to high levels of inorganic mercury. A recent case-control study found that exposure to organophosphates during pregnancy is associated with a 60% increase in the risk of ASD. This category includes non-persistent organic pollutants, including phthalates and bisphenol A, and persistent organic pollutants, including DDT, PCB and PBDE. A research group found that three out of five studies on phthalate exposure showed a significant association between phthalate exposure and ASD risk. Exposure to PCBs and PBDEs appears to alter calcium-related signal pathways, leading to alterations in dendritic growth and consequent abnormalities in neuronal connectivity, a key feature of ASD.

Table 2 summarizes several environmental factors associated with ASD described above.

5. Conclusions

Given the complexity of the etiology of autism and the increasing prevalence of new confirmed cases of ASD worldwide, there is an urgent need to find effective diagnostic methods and study as many risk factors as possible—not only genetic but also epigenetic and environmental ones—without neglecting also genetic/environmental interactions, where risk factors influence each other.

References 171

  1. 1.

    Kana R.K., Uddin L.Q., Ekenet T., Echugani D., Müller R.-A. Brain connectivity in autism. Front. Hum. Neurosci. 2014;8:349.

    Not in our library10.3389/fnhum.2014.00349

  2. 2.

    Narzisi A., Muratori F., Calderoni S., Fabbro F., Urgesi C. Neuropsychological Profile in High Functioning Autism Spectrum Disorders. J. Autism Dev. Disord. 2012;43:1895-1909.

    Not in our library10.1007/s10803-012-1736-0

  3. 3.

    Goin-Kochel R.P., Myers B.J. Congenital Versus Regressive Onset of Autism Spectrum Disorders. Focus Autism Other Dev. Disabil. 2005;20:169-179.

    Not in our library10.1177/10883576050200030501

  4. 4.

    Tamouza R., Fernell E., Eriksson M.A., Anderlid B., Manier C., Mariaselvam C.M., Boukouaci W., Leboyer M., Gillberg C. HLA Polymorphism in Regressive and Non-Regressive Autism: A Preliminary Study. Autism Res. 2019;13:182-186.

    Not in our library10.1002/aur.2217

  5. 5.
  6. 6.

    James S.J., Melnyk S., Jernigan S., Cleves M.A., Halsted C.H., Wong D.H., Cutler P., Bock K., Boris M., Bradstreet J.J., et al. Metabolic endophenotype and related genotypes are associated with oxidative stress in children with autism. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 2006;141:947-956.

    Not in our library10.1002/ajmg.b.30366

  7. 7.

    Iossifov I., O’Roak B.J., Sanders S.J., Ronemus M., Krumm N., Levy D., Stessman H.A., Witherspoon K.T., Vives L., Patterson K.E., et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515:216-221.

    Not in our library10.1038/nature13908

  8. 8.
  9. 9.

    Newschaffer C.J., Croen L.A., Daniels J., Giarelli E., Grether J.K., Levy S.E., Mandell D.S., Miller L.A., Pinto-Martin J., Reaven J., et al. The Epidemiology of Autism Spectrum Disorders. Annu. Rev. Public Health. 2007;28:235-258.

    Not in our library10.1146/annurev.publhealth.28.021406.144007

  10. 10.
  11. 11.

    Benvenuto A., Moavero R., Alessandrelli R., Manzi B., Curatolo P. Syndromic autism: Causes and pathogenetic pathways. World J. Pediatr. 2009;5:169-176.

    Not in our library10.1007/s12519-009-0033-2

  12. 12.

    Hu V.W., Devlin C.A., Debski J.J. ASD Phenotype—Genotype Associations in Concordant and Discordant Monozygotic and Dizygotic Twins Stratified by Severity of Autistic Traits. Int. J. Mol. Sci. 2019;20:3804.

    Not in our library10.3390/ijms20153804

  13. 13.

    Colvert E., Tick B., McEwen F., Stewart C., Curran S.R., Woodhouse E., Gillan N., Hallett V., Lietz S., Garnett T., et al. Heritability of Autism Spectrum Disorder in a UK Population-Based Twin Sample. JAMA Psychiatry. 2015;72:415-423.

    Not in our library10.1001/jamapsychiatry.2014.3028

  14. 14.

    Sandin S., Lichtenstein P., Kuja-Halkola R., Larsson H., Hultman C.M., Reichenberg A. The Familial Risk of Autism. JAMA. 2014;311:1770-1777.

    Not in our library10.1001/jama.2014.4144

  15. 15.

    Eichler E.E., Flint J., Gibson G., Kong A., Leal S.M., Moore J.H., Nadeau J.H. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 2010;11:446-450.

    Not in our library10.1038/nrg2809

  16. 16.

    Génin E. Missing heritability of complex diseases: Case solved? Hum. Genet. 2020;139:103-113.

    Not in our library10.1007/s00439-019-02034-4

  17. 17.
  18. 18.

    Fatemi S.H. The Molecular Basis of Autism. 2015.

    Not in our library

  19. 19.

    Wang K., Zhang H., Ma D., Bucan M., Glessner J.T., Abrahams B.S., Salyakina D., Imielinski M., Bradfield J.P., Sleiman P.M.A., et al. Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature. 2009;459:528-533.

    Not in our library10.1038/nature07999

  20. 20.

    Anney R., Klei L., Pinto D., Almeida J., Bacchelli E., Baird G., Bolshakova N., Bölte S., Bolton P.F., Bourgeron T., et al. Individual common variants exert weak effects on the risk for autism spectrum disorders. Hum. Mol. Genet. 2012;21:4781-4792.

    Not in our library10.1093/hmg/dds301

  21. 21.

    Sener E.F., Canatan H., Ozkul Y. Recent Advances in Autism Spectrum Disorders: Applications of Whole Exome Sequencing Technology. Psychiatry Investig. 2016;13:255-264.

    Not in our library10.4306/pi.2016.13.3.255

  22. 22.

    Pinto D., Pagnamenta A.T., Klei L., Anney R., Merico D., Regan R., Conroy J., Magalhaes T.R., Correia C., Abrahams B.S., et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature. 2010;466:368-372.

    Not in our library10.1038/nature09146

  23. 23.

    Marshall C.R., Noor A., Vincent J.B., Lionel A.C., Feuk L., Skaug J., Shago M., Moessner R., Pinto D., Ren Y., et al. Structural Variation of Chromosomes in Autism Spectrum Disorder. Am. J. Hum. Genet. 2008;82:477-488.

    Not in our library10.1016/j.ajhg.2007.12.009

  24. 24.
  25. 25.

    Devlin B., Scherer S.W. Genetic architecture in autism spectrum disorder. Curr. Opin. Genet. Dev. 2012;22:229-237.

    Not in our library10.1016/j.gde.2012.03.002

  26. 26.

    Napoli E., Russo S., Casula L., Alesi V., Amendola F.A., Angioni A., Novelli A., Valeri G., Menghini D., Vicari S. Array-CGH Analysis in a Cohort of Phenotypically Well-Characterized Individuals with “Essential” Autism Spectrum Disorders. J. Autism Dev. Disord. 2018;48:442-449.

    Not in our library10.1007/s10803-017-3329-4

  27. 27.

    Beaudet A.L. The utility of chromosomal microarray analysis in developmental and behavioral pediatrics. Child Dev. 2013;84:121-132.

    Not in our library10.1111/cdev.12050

  28. 28.

    Jacquemont M.-L., Sanlaville D., Redon R., Raoul O., Cormier-Daire V., Lyonnet S., Amiel J., Le Merrer M., Heron D., De Blois M.-C., et al. Array-based comparative genomic hybridisation identifies high frequency of cryptic chromosomal rearrangements in patients with syndromic autism spectrum disorders. J. Med. Genet. 2006;43:843-849.

    Not in our library10.1136/jmg.2006.043166

  29. 29.

    Holt R., Sykes N.H., Conceição I., Cazier J.-B., Anney R.J., Oliveira G.G., Gallagher L., Vicente A.M., Monaco A.P., Pagnamenta A.T. CNVs leading to fusion transcripts in individuals with autism spectrum disorder. Eur. J. Hum. Genet. 2012;20:1141-1147.

    Not in our library10.1038/ejhg.2012.73

  30. 30.
  31. 31.

    Ceroni F., Sagar A., Simpson N.H., Gawthrope A.J., Newbury D.F., Pinto D., Francis S.M., Tessman D.C., Cook E.H., Monaco A.P., et al. A Deletion Involving CD 38 and BST 1 Results in a Fusion Transcript in a Patient With Autism and Asthma. Autism Res. 2014;7:254-263.

    Not in our library10.1002/aur.1365

  32. 32.

    Loi E., Moi L., Blois S., Bacchelli E., Benedetti A.F.V., Cameli C., Fadda R., Maestrini E., Carta M., Doneddu G., et al. ELMOD3—SH2D6 gene fusion as a possible co-star actor in autism spectrum disorder scenario. J. Cell. Mol. Med. 2019;24:2064-2069.

    Not in our library10.1111/jcmm.14733

  33. 33.

    Yin J., Schaaf C.P. Autism genetics—An overview. Prenat. Diagn. 2016;37:14-30.

    Not in our library10.1002/pd.4942

  34. 34.

    Satterstrom F.K., Kosmicki J.A., Wang J., Breen M.S., De Rubeis S., An J.-Y., Peng M., Collins R., Grove J., Klei L., et al. Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism. Cell. 2020;180:568-584.e23.

    Not in our library10.1016/j.cell.2019.12.036

  35. 35.
  36. 36.

    Hotulainen P., Hoogenraad C. Actin in dendritic spines: Connecting dynamics to function. J. Cell Biol. 2010;189:619-629.

    Not in our library10.1083/jcb.201003008

  37. 37.

    Van Spronsen M., Hoogenraad C.C. Synapse Pathology in Psychiatric and Neurologic Disease. Curr. Neurol. Neurosci. Rep. 2010;10:207-214.

    Not in our library10.1007/s11910-010-0104-8

  38. 38.

    Kaizuka T., Takumi T. Postsynaptic density proteins and their involvement in neurodevelopmental disorders. J. Biochem. 2018;163:447-455.

    Not in our library10.1093/jb/mvy022

  39. 39.

    Südhof T.C. Neuroligins and neurexins link synaptic function to cognitive disease. Nature. 2008;455:903-911.

    Not in our library10.1038/nature07456

  40. 40.

    Kim H.-G., Kishikawa S., Higgins A.W., Seong I.-S., Donovan D.J., Shen Y., Lally E., Weiss L.A., Najm J., Kutsche K., et al. Disruption of Neurexin 1 Associated with Autism Spectrum Disorder. Am. J. Hum. Genet. 2008;82:199-207.

    Not in our library10.1016/j.ajhg.2007.09.011

  41. 41.

    Schaaf C.P., Boone P.M., Sampath S., Williams C., Bader P.I., Mueller J.M., Shchelochkov O.A., Brown C.W., Crawford H.P., Phalen J.A., et al. Phenotypic spectrum and genotype–phenotype correlations of NRXN1 exon deletions. Eur. J. Hum. Genet. 2012;20:1240-1247.

    Not in our library10.1038/ejhg.2012.95

  42. 42.

    Radyushkin K., Hammerschmidt K., Boretius S., Varoqueaux F., El-Kordi A., Ronnenberg A., Winter D., Frahm J., Fischer J., Brose N., et al. Neuroligin-3-deficient mice: Model of a monogenic heritable form of autism with an olfactory deficit. Genes Brain Behav. 2009;8:416-425.

    Not in our library10.1111/j.1601-183x.2009.00487.x

  43. 43.

    Liang J., Xu W., Hsu Y.-T., Yee A.X., Chen L., Südhof T.C. Conditional neuroligin-2 knockout in adult medial prefrontal cortex links chronic changes in synaptic inhibition to cognitive impairments. Mol. Psychiatry. 2015;20:850-859.

    Not in our library10.1038/mp.2015.31

  44. 44.

    Pak C., Danko T., Zhang Y., Aoto J., Anderson G.R., Maxeiner S., Yi F., Wernig M., Südhof T.C. Human Neuropsychiatric Disease Modeling using Conditional Deletion Reveals Synaptic Transmission Defects Caused by Heterozygous Mutations in NRXN1. Cell Stem Cell. 2015;17:316-328.

    Not in our library10.1016/j.stem.2015.07.017

  45. 45.

    Arking D.E., Cutler D.J., Brune C.W., Teslovich T.M., West K., Ikeda M., Rea A., Guy M., Lin S., Cook E.H., et al. A Common Genetic Variant in the Neurexin Superfamily Member CNTNAP2 Increases Familial Risk of Autism. Am. J. Hum. Genet. 2008;82:160-164.

    Not in our library10.1016/j.ajhg.2007.09.015

  46. 46.

    Alarcón M., Abrahams B.S., Stone J.L., Duvall J.A., Perederiy J.V., Bomar J.M., Sebat J., Wigler M., Martin C.L., Ledbetter D.H., et al. Linkage, Association, and Gene-Expression Analyses Identify CNTNAP2 as an Autism-Susceptibility Gene. Am. J. Hum. Genet. 2008;82:150-159.

    Not in our library10.1016/j.ajhg.2007.09.005

  47. 47.

    Nord A.S., Network S.P., Roeb W., Dickel D.E., Walsh T., Kusenda M., O’Connor K.L., Malhotra D., McCarthy S.E., Stray S.M., et al. Reduced transcript expression of genes affected by inherited and de novo CNVs in autism. Eur. J. Hum. Genet. 2011;19:727-731.

    Not in our library10.1038/ejhg.2011.24

  48. 48.

    Peñagarikano O., Abrahams B.S., Herman E.I., Winden K.D., Gdalyahu A., Dong H., Sonnenblick L.I., Gruver R., Almajano J., Bragin A., et al. Absence of CNTNAP2 Leads to Epilepsy, Neuronal Migration Abnormalities, and Core Autism-Related Deficits. Cell. 2011;147:235-246.

    Not in our library10.1016/j.cell.2011.08.040

  49. 49.

    Kreienkamp H.-J. Scaffolding Proteins at the Postsynaptic Density: Shank as the Architectural Framework. Protein-Protein Interactions as New Drug Targets. 2008;186:365-380.

    Not in our library10.1007/978-3-540-72843-6_15

  50. 50.

    Berkel S., Marshall C., Weiss B., Howe J., Roeth R., Moog U., Endris V., Roberts W., Szatmari P., Pinto D., et al. Mutations in the SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental retardation. Nat. Genet. 2010;42:489-491.

    Not in our library10.1038/ng.589

  51. 51.

    Jiang Y.-H., Ehlers M.D. Modeling Autism by SHANK Gene Mutations in Mice. Neuron. 2013;78:8-27.

    Not in our library10.1016/j.neuron.2013.03.016

  52. 52.
  53. 53.

    Pinto D., Delaby E., Merico D., Barbosa M., Merikangas A., Klei L., Thiruvahindrapuram B., Xu X., Ziman R., Wang Z., et al. Convergence of Genes and Cellular Pathways Dysregulated in Autism Spectrum Disorders. Am. J. Hum. Genet. 2014;94:677-694.

    Not in our library10.1016/j.ajhg.2014.03.018

  54. 54.

    Bacchelli E., Loi E., Cameli C., Moi L., Vega-Benedetti A.F., Blois S., Fadda A., Bonora E., Mattu S., Fadda R., et al. Analysis of a Sardinian Multiplex Family with Autism Spectrum Disorder Points to Post-Synaptic Density Gene Variants and Identifies CAPG as a Functionally Relevant Candidate Gene. J. Clin. Med. 2019;8:212.

    Not in our library10.3390/jcm8020212

  55. 55.

    Lahbib S., Leblond C.S., Hamza M., Regnault B., Lemée L., Mathieu A., Jaouadi H., Mkaouar R., Ben Youssef-Turki I., Belhadj A., et al. Homozygous 2p11.2 deletion supports the implication of ELMOD3 in hearing loss and reveals the potential association of CAPG with ASD/ID etiology. J. Appl. Genet. 2018;60:49-56.

    Not in our library10.1007/s13353-018-0472-3

  56. 56.

    Fan Y., Tang X., Vitriol E.A., Chen G., Zheng J.Q. Actin capping protein is required for dendritic spine development and synapse formation. J. Neurosci. 2011;31:10228-11033.

    Not in our library10.1523/jneurosci.0115-11.2011

  57. 57.

    Korobova F., Svitkina T.M. Molecular Architecture of Synaptic Actin Cytoskeleton in Hippocampal Neurons Reveals a Mechanism of Dendritic Spine Morphogenesis. Mol. Biol. Cell. 2010;21:165-176.

    Not in our library10.1091/mbc.e09-07-0596

  58. 58.

    Schmunk G., Gargus J.J. Channelopathy pathogenesis in autism spectrum disorders. Front. Genet. 2013;4:222.

    Not in our library10.3389/fgene.2013.00222

  59. 59.

    Despang P., Salamon S., Breitenkamp A.F., Kuzmenkina E., Herzig S., Matthes J. Autism-associated mutations in the CaVβ2 calcium-channel subunit increase Ba2+-currents and lead to differential modulation by the RGK-protein Gem. Neurobiol. Dis. 2020;136:104721.

    Not in our library10.1016/j.nbd.2019.104721

  60. 60.

    Splawski I., Timothy K.W., Sharpe L.M., Decher N., Kumar P., Bloise R., Napolitano C., Schwartz P.J., Joseph R.M., Condouris K., et al. CaV1.2 Calcium Channel Dysfunction Causes a Multisystem Disorder Including Arrhythmia and Autism. Cell. 2004;119:19-31.

    Not in our library10.1016/j.cell.2004.09.011

  61. 61.

    Catterall W.A., Perez-Reyes E., Snutch T.P., Striessnig J. International Union of Pharmacology. XLVIII. Nomenclature and Structure-Function Relationships of Voltage-Gated Calcium Channels. Pharmacol. Rev. 2005;57:411-425.

    Not in our library10.1124/pr.57.4.5

  62. 62.

    Barrett C.F., Tsien R.W. The Timothy syndrome mutation differentially affects voltage- and calcium-dependent inactivation of CaV1.2 L-type calcium channels. Proc. Natl. Acad. Sci. USA. 2008;105:2157-2162.

    Not in our library10.1073/pnas.0710501105

  63. 63.

    Hemara-Wahanui A., Berjukow S., Hope C.I., Dearden P.K., Wu S.-B., Wilson-Wheeler J., Sharp D.M., Lundon-Treweek P., Clover G.M., Hoda J.-C., et al. A CACNA1F mutation identified in an X-linked retinal disorder shifts the voltage dependence of Cav1.4 channel activation. Proc. Natl. Acad. Sci. USA. 2005;102:7553-7558.

    Not in our library10.1073/pnas.0501907102

  64. 64.
  65. 65.

    Pinggera A., Mackenroth L., Rump A., Schallner J., Beleggia F., Wollnik B., Striessnig J. New gain-of-function mutation shows CACNA1D as recurrently mutated gene in autism spectrum disorders and epilepsy. Hum. Mol. Genet. 2017;26:2923-2932.

    Not in our library10.1093/hmg/ddx175

  66. 66.

    Boczek N.J., Miller E.M., Ye D., Nesterenko V.V., Tester D.J., Antzelevitch C., Czosek R.J., Ackerman M.J., Ware S.M. Novel Timothy syndrome mutation leading to increase in CACNA1C window current. Heart Rhythm. 2015;12:211-219.

    Not in our library10.1016/j.hrthm.2014.09.051

  67. 67.

    Splawski I., Yoo D.S., Stotz S.C., Cherry A., Clapham D.E., Keating M.T. CACNA1HMutations in Autism Spectrum Disorders. J. Biol. Chem. 2006;281:22085-22091.

    Not in our library10.1074/jbc.m603316200

  68. 68.

    Breitenkamp A.F.S., Matthes J., Nass R.D., Sinzig J., Lehmkuhl G., Nürnberg P., Herzig S. Rare Mutations of CACNB2 Found in Autism Spectrum Disease-Affected Families Alter Calcium Channel Function. PLoS ONE. 2014;9:e95579.

    Not in our library10.1371/journal.pone.0095579

  69. 69.

    Morrow E.M., Yoo S.-Y., Flavell S.W., Kim T.-K., Lin Y., Hill R.S., Mukaddes N.M., Balkhy S., Gascon G., Hashmi A., et al. Identifying Autism Loci and Genes by Tracing Recent Shared Ancestry. Science. 2008;321:218-223.

    Not in our library10.1126/science.1157657

  70. 70.

    Veeramah K.R., O’Brien J.E., Meisler M.H., Cheng X., Dib-Hajj S.D., Waxman S.G., Talwar D., Girirajan S., Eichler E.E., Restifo L.L., et al. De Novo Pathogenic SCN8A Mutation Identified by Whole-Genome Sequencing of a Family Quartet Affected by Infantile Epileptic Encephalopathy and SUDEP. Am. J. Hum. Genet. 2012;90:502-510.

    Not in our library10.1016/j.ajhg.2012.01.006

  71. 71.

    Chen C.-P., Lin S.-P., Chern S.-R., Chen Y.-J., Tsai F.-J., Wu P.-C., Wang W. Array-CGH detection of a de novo 2.8 Mb deletion in 2q24.2→q24.3 in a girl with autistic features and developmental delay. Eur. J. Med. Genet. 2010;53:217-220.

    Not in our library10.1016/j.ejmg.2010.03.006

  72. 72.

    Sanders S.J., Murtha M.T., Gupta A.R., Murdoch J.D., Raubeson M.J., Willsey A.J., Ercan-Sencicek A.G., DiLullo N.M., Parikshak N.N., Stein J.L., et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature. 2012;485:237-241.

    Not in our library10.1038/nature10945

  73. 73.

    Weiss L.A., Escayg A., Kearney J.A., Trudeau M., Macdonald B.T., Mori M., Reichert J., Buxbaum J.D., Meisler M.H. Sodium channels SCN1A, SCN2A and SCN3A in familial autism. Mol. Psychiatry. 2003;8:186-194.

    Not in our library10.1038/sj.mp.4001241

  74. 74.

    Han S., Tai C., Westenbroek R.E., Yu F.H., Cheah C.S., Potter G.B., Rubenstein J.L., Scheuer T., De La Iglesia H.O., Catterall W.A. Autistic-like behaviour in Scn1a+/− mice and rescue by enhanced GABA-mediated neurotransmission. Nature. 2012;489:385-390.

    Not in our library10.1038/nature11356

  75. 75.

    Gilling M., Rasmussen H.B., Calloe K., Sequeira A.F., Baretto M., Oliveira G., Almeida J., Lauritsen M.B., Ullmann R., Boonen S.E., et al. Dysfunction of the Heteromeric KV7.3/KV7.5 Potassium Channel is Associated with Autism Spectrum Disorders. Front. Genet. 2013;4:54.

    Not in our library10.3389/fgene.2013.00054

  76. 76.

    Gross C., Yao X., Pong D.L., Jeromin A., Bassell G.J. Fragile X Mental Retardation Protein Regulates Protein Expression and mRNA Translation of the Potassium Channel Kv4.2. J. Neurosci. 2011;31:5693-5698.

    Not in our library10.1523/jneurosci.6661-10.2011

  77. 77.

    Sicca F., Imbrici P., D’Adamo M.C., Moro F., Bonatti F., Brovedani P., Grottesi A., Guerrini R., Masi G., Santorelli F.M., et al. Autism with Seizures and Intellectual Disability: Possible Causative Role of Gain-of-function of the Inwardly-Rectifying K+ Channel Kir4.1. Neurobiol. Dis. 2011;43:239-247.

    Not in our library10.1016/j.nbd.2011.03.016

  78. 78.

    Laumonnier F., Eroger S., Guérin P., Molinari F., M’Rad R., Cahard D., Belhadj A., Halayem M., Persico A.M., Elia M., et al. Association of a Functional Deficit of the BKCa Channel, a Synaptic Regulator of Neuronal Excitability, With Autism and Mental Retardation. Am. J. Psychiatry. 2006;163:1622.

    Not in our library10.1176/ajp.2006.163.9.1622

  79. 79.

    Deng P.Y., Rotman Z., Blundon J.A., Cho Y., Cui J., Cavalli V., Zakharenko S.S., Klyachko V.A. FMRP Regulates Neurotransmitter Release and Synaptic Information Transmission by Modulating Action Potential Duration via BK Channels. Neuron. 2013;77:696-711.

    Not in our library10.1016/j.neuron.2012.12.018

  80. 80.

    Gonzalez–Gronow M., Cuchacovich M., Francos R., Cuchacovich S., Fernandez M.D.P., Blanco A., Bowers E.V., Kaczowka S., Pizzo S.V. Antibodies against the voltage-dependent anion channel (VDAC) and its protective ligand hexokinase-I in children with autism. J. Neuroimmunol. 2010;227:153-161.

    Not in our library10.1016/j.jneuroim.2010.06.001

  81. 81.

    Gvozdjáková A., Löw H., Sun I., Navas P., Crane F.L. Plasma membrane coenzyme Q: Evidence for a role in autism. Biol. Targets Ther. 2014;8:199.

    Not in our library10.2147/btt.s53375

  82. 82.
  83. 83.

    Nguyen A., Rauch T.A., Pfeifer G.P., Hu V.W. Global methylation profiling of lymphoblastoid cell lines reveals epigenetic contributions to autism spectrum disorders and a novel autism candidate gene, RORA, whose protein product is reduced in autistic brain. FASEB J. 2010;24:3036-3051.

    Not in our library10.1096/fj.10-154484

  84. 84.

    Zhu Y., Mordaunt C.E., Yasui D.H., Marathe R., Coulson R.L., Dunaway K., Jianu J.M., Walker C.K., Ozonoff S., Hertz-Picciotto I., et al. Placental DNA methylation levels at CYP2E1 and IRS2 are associated with child outcome in a prospective autism study. Hum. Mol. Genet. 2019;28:2659-2674.

    Not in our library10.1093/hmg/ddz084

  85. 85.
  86. 86.

    Siu M.T., Butcher D.T., Turinsky A.L., Cytrynbaum C., Stavropoulos D.J., Walker S., Caluseriu O., Carter M., Lou Y., Nicolson R., et al. Functional DNA methylation signatures for autism spectrum disorder genomic risk loci: 16p11.2 deletions and CHD8 variants. Clin. Epigenetics. 2019;11:1-19.

    Not in our library10.1186/s13148-019-0684-3

  87. 87.

    Kimura R., Nakata M., Funabiki Y., Suzuki S., Awaya T., Murai T., Hagiwara M. An epigenetic biomarker for adult high-functioning autism spectrum disorder. Sci. Rep. 2019;9:13662-13667.

    Not in our library10.1038/s41598-019-50250-9

  88. 88.

    Saeliw T., Tangsuwansri C., Thongkorn S., Chonchaiya W., Suphapeetiporn K., Mutirangura A., Tencomnao T., Hu V.W., Sarachana T. Integrated genome-wide Alu methylation and transcriptome profiling analyses reveal novel epigenetic regulatory networks associated with autism spectrum disorder. Mol. Autism. 2018;9:27.

    Not in our library10.1186/s13229-018-0213-9

  89. 89.

    Wong C.C.Y., Smith R.G., Hannon E., Ramaswami G., Parikshak N.N., Assary E., Troakes C., Poschmann J., Schalkwyk L.C., Sun W., et al. Genome-wide DNA methylation profiling identifies convergent molecular signatures associated with idiopathic and syndromic autism in post-mortem human brain tissue. Hum. Mol. Genet. 2019;28:2201-2211.

    Not in our library10.1093/hmg/ddz052

  90. 90.

    Stathopoulos S., Gaujoux R., Lindeque Z., Mahony C., Van Der Colff R., Van Der Westhuizen F., O’Ryan C. DNA Methylation Associated with Mitochondrial Dysfunction in a South African Autism Spectrum Disorder Cohort. Autism Res. 2020;13:1079-1093.

    Not in our library10.1002/aur.2310

  91. 91.

    Hu Z., Yang Y., Zhao Y., Yu H., Ying X., Zhou D., Zhong J., Zheng Z., Liu J., Pan R., et al. APOE hypermethylation is associated with autism spectrum disorder in a Chinese population. Exp. Ther. Med. 2018;15:4749-4754.

    Not in our library10.3892/etm.2018.6069

  92. 92.

    Hranilović D., Blažević S., Stefulj J., Zill P. DNA Methylation Analysis of HTR2A Regulatory Region in Leukocytes of Autistic Subjects. Autism Res. 2015;9:204-209.

    Not in our library10.1002/aur.1519

  93. 93.

    Hu Z., Ying X., Huang L., Zhao Y., Zhou D., Liu J., Zhong J., Huang T., Zhang W., Cheng F., et al. Association of human serotonin receptor 4 promoter methylation with autism spectrum disorder. Medicine. 2020;99:e18838.

    Not in our library10.1097/md.0000000000018838

  94. 94.

    Lu Z., Liu Z., Mao W., Wang X., Zheng X., Chen S., Cao B., Huang S., Zhang X., Zhou T., et al. Locus-specific DNA methylation of Mecp2 promoter leads to autism-like phenotypes in mice. Cell Death Dis. 2020;11:85.

    Not in our library10.1038/s41419-020-2290-x

  95. 95.
  96. 96.

    Hicks S.D., Carpenter R.L., Wagner K.E., Pauley R., Barros M., Tierney-Aves C., Barns S., Greene C.D., Middleton F.A. Saliva MicroRNA Differentiates Children with Autism from Peers with Typical and Atypical Development. J. Am. Acad. Child. Adolesc. Psychiatry. 2020;59:296-308.

    Not in our library10.1016/j.jaac.2019.03.017

  97. 97.

    Sehovic E., Spahic L., Smajlovic-Skenderagic L., Pistoljevic N., Dzanko E., Hajdarpasic A. Identification of developmental disorders including autism spectrum disorder using salivary miRNAs in children from Bosnia and Herzegovina. PLoS ONE. 2020;15:e0232351.

    Not in our library10.1371/journal.pone.0232351

  98. 98.

    Hagiwara M., Kimura R., Funabiki Y., Awaya T., Murai T., Hagiwara M. MicroRNA profiling in adults with high-functioning autism spectrum disorder. Mol. Brain. 2019;12:82-84.

    Not in our library10.1186/s13041-019-0508-6

  99. 99.

    Ozkul Y., Taheri S., Bayram K.K., Sener E.F., Mehmetbeyoglu E., Öztop D.B., Aybuga F., Tufan E., Bayram A., Dolu N., et al. A heritable profile of six miRNAs in autistic patients and mouse models. Sci. Rep. 2020;10:1-14.

    Not in our library10.1038/s41598-020-65847-8

  100. 100.
  101. 101.

    Vatsa N., Kumar V., Singh B.K., Kumar S.S., Sharma A., Jana N.R. Down-Regulation of miRNA-708 Promotes Aberrant Calcium Signaling by Targeting Neuronatin in a Mouse Model of Angelman Syndrome. Front. Mol. Neurosci. 2019;12:35.

    Not in our library10.3389/fnmol.2019.00035

  102. 102.

    Urdinguio R.G., Fernández A.F., López-Nieva P., Rossi S., Huertas D., Kulis M., Liu C.-G., Croce C.M., Calin G.A., Esteller M. Disrupted microRNA expression caused by Mecp2 loss in a mouse model of Rett syndrome. Epigenetics. 2010;5:656-663.

    Not in our library10.4161/epi.5.7.13055

  103. 103.

    Edbauer D., Neilson J.R., Foster K.A., Wang C.-F., Seeburg D.P., Batterton M.N., Tada T., Dolan B.M., Sharp P.A., Sheng M. Regulation of Synaptic Structure and Function by FMRP-Associated MicroRNAs miR-125b and miR-132. Neuron. 2010;65:373-384.

    Not in our library10.1016/j.neuron.2010.01.005

  104. 104.

    Gaugler T., Klei L., Sanders S.J., Bodea C.A., Goldberg A.P., Lee A.B., Mahajan M.C., Manaa D., Pawitan Y., Reichert J.G., et al. Most genetic risk for autism resides with common variation. Nat. Genet. 2014;46:881-885.

    Not in our library10.1038/ng.3039

  105. 105.

    Deng W., Zou X., Deng H., Li J., Tang C., Wang X., Guo X. The Relationship Among Genetic Heritability, Environmental Effects, and Autism Spectrum Disorders. J. Child Neurol. 2015;30:1794-1799.

    Not in our library10.1177/0883073815580645

  106. 106.

    Edelson L.R., Saudino K.J. Genetic and environmental influences on autistic-like behaviors in 2-year-old twins. Behav. Genet. 2009;39:255-264.

    Not in our library10.1007/s10519-009-9270-3

  107. 107.
  108. 108.

    Janecka M., Mill J., Basson M.A., Goriely A., Spiers H., Reichenberg A., Schalkwyk L., Fernandes C. Advanced paternal age effects in neurodevelopmental disorders—Review of potential underlying mechanisms. Transl. Psychiatry. 2017;7:e1019.

    Not in our library10.1038/tp.2016.294

  109. 109.

    Wu S., Wu F., Ding Y., Hou J., Bi J., Zhang Z. Advanced parental age and autism risk in children: A systematic review and meta-analysis. Acta Psychiatr. Scand. 2016;135:29-41.

    Not in our library10.1111/acps.12666

  110. 110.

    Atsem S., Reichenbach J., Potabattula R., Dittrich M., Nava C., Depienne C., Böhm L., Rost S., Hahn T., Schorsch M., et al. Paternal age effects on spermFOXK1andKCNA7methylation and transmission into the next generation. Hum. Mol. Genet. 2016;25:4996-5005.

    Not in our library10.1093/hmg/ddw328

  111. 111.

    Kojima M., Yassin W., Owada K., Aoki Y., Kuwabara H., Natsubori T., Iwashiro N., Gonoi W., Takao H., Kasai K., et al. Neuroanatomical Correlates of Advanced Paternal and Maternal Age at Birth in Autism Spectrum Disorder. Cereb. Cortex. 2018;29:2524-2532.

    Not in our library10.1093/cercor/bhy122

  112. 112.

    Foldi C.J., Eyles D., McGrath J.J., Burne T.H.J. Advanced paternal age is associated with alterations in discrete behavioural domains and cortical neuroanatomy of C57BL/6J mice. Eur. J. Neurosci. 2010;31:556-564.

    Not in our library10.1111/j.1460-9568.2010.07074.x

  113. 113.

    Sampino S., Juszczak G.R., Zacchini F., Swiergiel A.H., Modlinski J.A., Loi P., Ptak G. Grand-paternal age and the development of autism-like symptoms in mice progeny. Transl. Psychiatry. 2014;4:e386.

    Not in our library10.1038/tp.2014.27

  114. 114.

    Gardener H., Spiegelman D., Buka S.L. Prenatal risk factors for autism: Comprehensive meta-analysis. Br. J. Psychiatry. 2009;195:7-14.

    Not in our library10.1192/bjp.bp.108.051672

  115. 115.

    Gardener H., Spiegelman D., Buka S.L. Perinatal and Neonatal Risk Factors for Autism: A Comprehensive Meta-analysis. Pediatrics. 2011;128:344-355.

    Not in our library10.1542/peds.2010-1036

  116. 116.

    Wang C., Geng H., Liu W., Zhang G. Prenatal, perinatal, and postnatal factors associated with autism. Medicine. 2017;96:e6696.

    Not in our library10.1097/md.0000000000006696

  117. 117.

    Bejerot S., Eriksson J.M., Bonde S., Carlström K., Humble M.B., Eriksson E. The extreme male brain revisited: Gender coherence in adults with autism spectrum disorder. Br. J. Psychiatry. 2012;201:116-123.

    Not in our library10.1192/bjp.bp.111.097899

  118. 118.
  119. 119.
  120. 120.

    Crider A., Thakkar R., Ahmed A.O., Pillai A. Dysregulation of estrogen receptor beta (ERβ), aromatase (CYP19A1), and ER co-activators in the middle frontal gyrus of autism spectrum disorder subjects. Mol. Autism. 2014;5:46.

    Not in our library10.1186/2040-2392-5-46

  121. 121.

    Chakrabarti B., Dudbridge F., Kent L., Wheelwright S., Hill-Cawthorne G., Allison C., Banerjee-Basu S., Baron-Cohen S. Genes related to sex steroids, neural growth, and social-emotional behavior are associated with autistic traits, empathy, and Asperger syndrome. Autism Res. 2009;2:157-177.

    Not in our library10.1002/aur.80

  122. 122.

    Jackson A.A., Robinson S.M. Dietary guidelines for pregnancy: A review of current evidence. Public Health Nutr. 2001;4:625-630.

    Not in our library10.1079/phn2001146

  123. 123.

    Van Eijsden M., Smits L.J.M., Van Der Wal M.F., Bonsel G.J. Association between short interpregnancy intervals and term birth weight: The role of folate depletion. Am. J. Clin. Nutr. 2008;88:147-153.

    Not in our library10.1093/ajcn/88.1.147

  124. 124.
  125. 125.

    Robea M., Luca A.-C., Ciobica A. Relationship between Vitamin Deficiencies and Co-Occurring Symptoms in Autism Spectrum Disorder. Medicine. 2020;56:245.

    Not in our library10.3390/medicina56050245

  126. 126.

    Saghazadeh A., Ahangari N., Hendi K., Saleh F., Rezaei N. Status of essential elements in autism spectrum disorder: Systematic review and meta-analysis. Rev. Neurosci. 2017;28:783-809.

    Not in our library10.1515/revneuro-2017-0015

  127. 127.

    Schmidt R.J., Tancredi D.J., Krakowiak P., Hansen R.L., Ozonoff S. Maternal Intake of Supplemental Iron and Risk of Autism Spectrum Disorder. Am. J. Epidemiol. 2014;180:890-900.

    Not in our library10.1093/aje/kwu208

  128. 128.

    Hagmeyer S., Mangus K., Boeckers T.M., Grabrucker A.M. Effects of Trace Metal Profiles Characteristic for Autism on Synapses in Cultured Neurons. Neural Plast. 2015;2015:1-16.

    Not in our library10.1155/2015/985083

  129. 129.

    Goyal D.K., Neil J.R., Simmons S.D., Mansab F., Benjamin S., Pitfield V., Boulet S., Miyan J.A. Zinc Deficiency in Autism: A Controlled Study. Insights Biomed. 2019;4:4.

    Not in our library10.36648/2572-5610.4.3.63

  130. 130.

    Yasuda H., Yoshida K., Yasuda Y., Tsutsui T. Infantile zinc deficiency: Association with autism spectrum disorders. Sci. Rep. 2011;1:129.

    Not in our library10.1038/srep00129

  131. 131.

    Li S.-O., Wang J.-L., Bjørklund G., Zhao W.-N., Yin C.-H. Serum copper and zinc levels in individuals with autism spectrum disorders. NeuroReport. 2014;25:1216-1220.

    Not in our library10.1097/wnr.0000000000000251

  132. 132.

    Grabrucker S., Boeckers T.M., Grabrucker A.M. Gender Dependent Evaluation of Autism like Behavior in Mice Exposed to Prenatal Zinc Deficiency. Front. Behav. Neurosci. 2016;10:37.

    Not in our library10.3389/fnbeh.2016.00037

  133. 133.

    Grabrucker S., Jannetti L., Eckert M., Gaub S., Chhabra R., Pfaender S., Mangus K., Reddy P.P., Rankovic V., Schmeisser M.J., et al. Zinc deficiency dysregulates the synaptic ProSAP/Shank scaffold and might contribute to autism spectrum disorders. Brain. 2013;137:137-152.

    Not in our library10.1093/brain/awt303

  134. 134.

    Grabrucker A.M., Knight M.J., Proepper C., Bockmann J., Joubert M., Rowan M., Nienhaus G.U., Garner C.C., Bowie J.U., Kreutz M.R., et al. Concerted action of zinc and ProSAP/Shank in synaptogenesis and synapse maturation. EMBO J. 2011;30:569-581.

    Not in our library10.1038/emboj.2010.336

  135. 135.

    Frederickson C.J., Koh J.-Y., Bush A.I. The neurobiology of zinc in health and disease. Nat. Rev. Neurosci. 2005;6:449-462.

    Not in our library10.1038/nrn1671

  136. 136.
  137. 137.

    Shih P.-Y., Hsieh B.-Y., Lin M.-H., Huang T.-N., Tsai C.-Y., Pong W.-L., Lee S.-P., Hsueh Y.-P. CTTNBP2 Controls Synaptic Expression of Zinc-Related Autism-Associated Proteins and Regulates Synapse Formation and Autism-like Behaviors. Cell Rep. 2020;31:107700.

    Not in our library10.1016/j.celrep.2020.107700

  138. 138.

    Velie E.M., Block G., Shaw G.M., Samuels S.J., Schaffer D.M., Kulldorff M. Maternal supplemental and dietary zinc intake and the occurrence of neural tube defects in California. Am. J. Epidemiol. 1999;150:605-616.

    Not in our library10.1093/oxfordjournals.aje.a010059

  139. 139.

    Gardner R.M., Lee B.K., Magnusson C., Rai D., Frisell T., Karlsson H., Idring S., Dalman C. Maternal body mass index during early pregnancy, gestational weight gain, and risk of autism spectrum disorders: Results from a Swedish total population and discordant sibling study. Int. J. Epidemiol. 2015;44:870-883.

    Not in our library10.1093/ije/dyv081

  140. 140.

    Wang Y., Tang S., Xu S., Weng S., Liu Z. Maternal Body Mass Index and Risk of Autism Spectrum Disorders in Offspring: A Meta-analysis. Sci. Rep. 2016;6:34248.

    Not in our library10.1038/srep34248

  141. 141.

    Getz K.D., Anderka M.T., Werler M.M., Jick S.S. Maternal Pre-pregnancy Body Mass Index and Autism Spectrum Disorder among Offspring: A Population-Based Case-Control Study. Paediatr. Périnat. Epidemiol. 2016;30:479-487.

    Not in our library10.1111/ppe.12306

  142. 142.

    Li M., Fallin M.D., Riley A., Landa R., Walker S.O., Silverstein M., Caruso D., Pearson C., Kiang S., Dahm J.L., et al. The Association of Maternal Obesity and Diabetes with Autism and Other Developmental Disabilities. Pediatrics. 2016;137:e20152206.

    Not in our library10.1542/peds.2015-2206

  143. 143.

    Tang S., Wang Y., Gong X., Wang G.-H. A Meta-Analysis of Maternal Smoking during Pregnancy and Autism Spectrum Disorder Risk in Offspring. Int. J. Environ. Res. Public Health. 2015;12:10418-10431.

    Not in our library10.3390/ijerph120910418

  144. 144.

    Rosen B.N., Lee B.K., Lee N.L., Yang Y., Burstyn I. Maternal Smoking and Autism Spectrum Disorder: A Meta-analysis. J. Autism Dev. Disord. 2014;45:1689-1698.

    Not in our library10.1007/s10803-014-2327-z

  145. 145.

    Visser J.C., Rommelse N., Vink L., Schrieken M., Oosterling I.J., Van Der Gaag R.J., Buitelaar J.K. Narrowly Versus Broadly Defined Autism Spectrum Disorders: Differences in Pre- and Perinatal Risk Factors. J. Autism Dev. Disord. 2012;43:1505-1516.

    Not in our library10.1007/s10803-012-1678-6

  146. 146.

    Perrone-McGovern K., Simon-Dack S., Niccolai L. Prenatal and Perinatal Factors Related to Autism, IQ, and Adaptive Functioning. J. Genet. Psychol. 2015;176:1-10.

    Not in our library10.1080/00221325.2014.987201

  147. 147.

    Eliasen M.H., Tolstrup J.S., Andersen A.-M.N., Gronbaek M., Olsen J., Strandberg-Larsen K., Grønbæk M. Prenatal alcohol exposure and autistic spectrum disorders—A population-based prospective study of 80 552 children and their mothers. Int. J. Epidemiol. 2010;39:1074-1081.

    Not in our library10.1093/ije/dyq056

  148. 148.

    Gallagher C., McCarthy F.P., Ryan R.M., Khashan A.S. Maternal Alcohol Consumption during Pregnancy and the Risk of Autism Spectrum Disorders in Offspring: A Retrospective Analysis of the Millennium Cohort Study. J. Autism Dev. Disord. 2018;48:3773-3782.

    Not in our library10.1007/s10803-018-3626-6

  149. 149.

    Meador K.J. Effects of in Utero Antiepileptic Drug Exposure. Epilepsy Curr. 2008;8:143-147.

    Not in our library10.1111/j.1535-7511.2008.00273.x

  150. 150.
  151. 151.

    Kobayashi T., Matsuyama T., Takeuchi M., Ito S. Autism spectrum disorder and prenatal exposure to selective serotonin reuptake inhibitors: A systematic review and meta-analysis. Reprod. Toxicol. 2016;65:170-178.

    Not in our library10.1016/j.reprotox.2016.07.016

  152. 152.

    Wan H., Zhang C., Li H., Luan S., Liu C. Association of maternal diabetes with autism spectrum disorders in offspring. Medicine. 2018;97:e9438.

    Not in our library10.1097/md.0000000000009438

  153. 153.

    Xu G., Jing J., Bowers K., Liu B., Bao W. Maternal diabetes and the risk of autism spectrum disorders in the offspring: A systematic review and meta-analysis. J. Autism Dev. Disord. 2014;44:766-775.

    Not in our library10.1007/s10803-013-1928-2

  154. 154.

    Jiang H.-Y., Xu L.-L., Shao L., Xia R.-M., Yu Z.-H., Ling Z., Yang F., Deng M., Ruan B. Maternal infection during pregnancy and risk of autism spectrum disorders: A systematic review and meta-analysis. Brain Behav. Immun. 2016;58:165-172.

    Not in our library10.1016/j.bbi.2016.06.005

  155. 155.

    Zerbo O., Qian Y., Yoshida C., Grether J.K., Van De Water J., Croen L.A. Maternal Infection during Pregnancy and Autism Spectrum Disorders. J. Autism Dev. Disord. 2015;45:4015-4025.

    Not in our library10.1007/s10803-013-2016-3

  156. 156.

    Atladóttir H.Ó., Thorsen P., Østergaard L., Schendel D.E., Lemcke S., Abdallah M., Parner E.T. Maternal Infection Requiring Hospitalization During Pregnancy and Autism Spectrum Disorders. J. Autism Dev. Disord. 2010;40:1423-1430.

    Not in our library10.1007/s10803-010-1006-y

  157. 157.

    Chen S.-W., Zhong X.-S., Jiang L.-N., Zheng X.-Y., Xiong Y.-Q., Ma S.-J., Qiu M., Huo S.-T., Ge J., Chen Q. Maternal autoimmune diseases and the risk of autism spectrum disorders in offspring: A systematic review and meta-analysis. Behav. Brain Res. 2016;296:61-69.

    Not in our library10.1016/j.bbr.2015.08.035

  158. 158.

    Wu S., Ding Y., Wu F., Li R., Xie G., Hou J., Mao P. Family history of autoimmune diseases is associated with an increased risk of autism in children: A systematic review and meta-analysis. Neurosci. Biobehav. Rev. 2015;55:322-332.

    Not in our library10.1016/j.neubiorev.2015.05.004

  159. 159.

    Brown A.S., Sourander A., Hinkka-Yli-Salomäki S., McKeague I.W., Sundvall J., Surcel H.-M. Elevated maternal C-reactive protein and autism in a national birth cohort. Mol. Psychiatry. 2013;19:259-264.

    Not in our library10.1038/mp.2012.197

  160. 160.

    Brown A.S., Surcel H.-M., Hinkka-Yli-Salomäki S., Cheslack-Postava K., Bao Y., Sourander A. Maternal thyroid autoantibody and elevated risk of autism in a national birth cohort. Prog. Neuro-Psychopharmacol. Biol. Psychiatry. 2015;57:86-92.

    Not in our library10.1016/j.pnpbp.2014.10.010

  161. 161.

    Meyer U., Feldon J., Fatemi S.H. In-vivo rodent models for the experimental investigation of prenatal immune activation effects in neurodevelopmental brain disorders. Neurosci. Biobehav. Rev. 2009;33:1061-1079.

    Not in our library10.1016/j.neubiorev.2009.05.001

  162. 162.

    Comi A.M., Zimmerman A.W., Frye V.H., Law P.A., Peeden J.N. Familial Clustering of Autoimmune Disorders and Evaluation of Medical Risk Factors in Autism. J. Child Neurol. 1999;14:388-394.

    Not in our library10.1177/088307389901400608

  163. 163.

    Atladóttir H.O., Pedersen M.G., Thorsen P., Mortensen P.B., Deleuran B., Eaton W.W., Parner E.T., Sutton R.M., Niles D., Nysaether J., et al. Association of Family History of Autoimmune Diseases and Autism Spectrum Disorders. Pediatrics. 2009;124:687-694.

    Not in our library10.1542/peds.2008-2445

  164. 164.
  165. 165.

    Napoli E., Hung C., Wong S., Giulivi C. Toxicity of the Flame-Retardant BDE-49 on Brain Mitochondria and Neuronal Progenitor Striatal Cells Enhanced by a PTEN-Deficient Background. Toxicol. Sci. 2013;132:196-210.

    Not in our library10.1093/toxsci/kfs339

  166. 166.

    Rossignol D.A., Frye R.E. Mitochondrial dysfunction in autism spectrum disorders: A systematic review and meta-analysis. Mol. Psychiatry. 2012;17:290-314.

    Not in our library10.1038/mp.2010.136

  167. 167.

    Yoshimasu K., Kiyohara C., Takemura S., Nakai K. A meta-analysis of the evidence on the impact of prenatal and early infancy exposures to mercury on autism and attention deficit/hyperactivity disorder in the childhood. NeuroToxicology. 2014;44:121-131.

    Not in our library10.1016/j.neuro.2014.06.007

  168. 168.
  169. 169.

    Jeddi M.Z., Janani L., Memari A.H., Akhondzadeh S., Yunesian M. The role of phthalate esters in autism development: A systematic review. Environ. Res. 2016;151:493-504.

    Not in our library10.1016/j.envres.2016.08.021

  170. 170.
  171. 171.

Cited by papers we hold 2

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.