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Review

Autism Spectrum Disorder as a Multifactorial Disorder: The Interplay of Genetic Factors and Inflammation

Psychology, Santa Barbara City College, Santa Barbara, CA 93109, USA
Int. J. Mol. Sci. 2025, 26(13), 6483; https://doi.org/10.3390/ijms26136483
Submission received: 30 May 2025 / Revised: 26 June 2025 / Accepted: 2 July 2025 / Published: 5 July 2025
(This article belongs to the Special Issue Latest Review Papers in Molecular Neurobiology 2025)

Abstract

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by difficulty with social communication, behavior, and sensory integration. With its prevalence rising worldwide in recent decades, understanding and mitigating the origins of ASD has become a priority. Though its etiology is multifactorial, the current research highlights two major contributors, genetic susceptibilities and environmental inflammatory exposures, leading to oxidative stress during critical developmental periods. We explore how genetic variations, including those affecting cerebral folate metabolism, and various inflammatory triggers, including exposure to inflammatory agents during both the fetal and post-fetal period, intersect to influence the development of ASD, giving rise to specific symptoms seen in autism.

1. Introduction

Autism spectrum disorder (ASD) is a developmental disorder identified in the American Psychiatric Association’s Diagnostic and Statistical Manual, Fifth Edition (DSM-5), by two categories of symptoms: persistent deficits in social communication and social interaction, and restricted, repetitive patterns of behavior. The DSM-5 further provides guidelines as to severity, listing three levels. Level 1 requires support and may struggle with initiating social interactions. Level 2 requires substantial support and may display limited communication. Level 3 requires very substantial support and shows significant challenges in communication and behavior [1].
The diagnosis of ASD has seen a dramatic rise in the 21st century, increasing approximately five-fold and becoming a public health priority [2]. The Simons Foundation created the autism research initiative (SFARI), which houses the evolving database of the genetics of autism [3,4]. There are striking parallels between ASD and other multifactorial diseases such as cancer. In both conditions, risk is influenced by an interplay of genetic predispositions and environmental factors, particularly triggers of inflammation, which lead to oxidative stress. Just as cancer risk can be increased by inherited mutations and external exposures like toxins or infections, ASD may also arise from a combination of genetic vulnerability and environmental stressors. In both diseases, these factors modulate susceptibility rather than determining certainty of onset, effectively raising the probability of development becoming neurodivergent.
Furthermore, recent findings in both fields suggest an important role of the gut microbiome in modulating immune and neurological outcomes. Nutritional and dietary choices appear to influence disease trajectories in both ASD and cancer. For example, diets that reduce inflammation or support detoxification pathways are known to reduce the risk for cancer [5] and depression [6], and may point a path to better outcomes in ASD with nutritional intervention as well.
Another similarity with cancer is that ASD is presumed to have multiple variants with differing etiologies. To date, over 70 genes have been correlated with ASD, with hundreds more believed to be related to ASD susceptibility [7,8,9]. This wide array of genes may help explain the diversity of expression of ASD, as well as point to a rationale for some of the comorbidities commonly diagnosed with ASD [10].
These insights invite a new perspective on ASD not just as a static diagnosis but as a dynamic condition potentially influenced by modifiable risk factors. This opens the door to discussing strategies for reducing risk, and even the possibility of partial or complete remission under certain therapeutic regimens. Understanding the contributing causes of ASD could lead to more personalized, preventive, and responsive treatments.

2. Genetic Factors in ASD

Many hundreds of genes have been implicated as having a role in autism, with multiple developmental impact categories involved [11,12,13]. Comparisons between the AutDB, AutismKB2.0, and SFARI Gene databases show over 400 genes presumed to be linked to ASD [14]. Previously, categorizing autism as syndromic or non-syndromic was useful to obtain a sense of some genes involved, especially for monogenic disorders [15]. The use of such terminology is considered by some to be limiting now [14]. Here, we focus on non-syndromic autism, take a look at some of the more frequently referenced ASD-related genes, and consider the categories of developmental impact these may have to understand which of these categories may be impacted.
We note the substantial positive work in monogenic syndromic autism, where gene therapies can be developed to target the single genetic cause [16,17]. The benefits of this work cannot be overestimated. For the purposes of this review, we seek to draw from the non-syndromic ASD evidence to begin to obtain a sense of the many factors involved in the majority of autism cases.
The following section will look at factors of oxidative stress to provide a compendium of the inflammatory agents that may influence gene expression during critical developmental periods. We hope to provide a sufficient overview of the genes involved to give context for putative inflammation-induced oxidative stress actions, with the goal of stimulating further research in the synergism of oxidative stress on gene regulation and expression in the development of autism.

2.1. Categorizing ASD Genes

Kereszturi (2024) [18] compiled the most commonly referenced genes listed in three databases listing autism-related genes: SFARI, AutDB, and ClinVar. These were then compared for their functional impact and listed in ten areas of developmental differences, which are summarized in the following table. The categories fall into three developmental areas, as shown in Table 1: synaptic, social, and neuronal.
One challenge here is that not only are some genes involved in multiple aspects of development but there are some that are apparently involved in quite different categories of action. For example, SHANK3 is believed to play a role in morphogenesis, synaptic organization, and social behavior. Given that the SHANK1, SHANK2, and SHANK3 genes produce proteins that are differentially expressed in development and affect synaptic structure and neurotransmission, this is unsurprising [19]. However, the multiplicity of categories makes the matching of specific genes to specific traits difficult at best, and potentially misleading.
The gene UBE3A codes for ubiquitin ligase (and its coactivator). The loss of this gene activity causes Angelman syndrome, with lack of speech, seizures, autistic features, and intellectual disability. The gain of function mutations of UBE3A, as well as duplication, also results in autism behaviors [20]. It is the impact of UBE3A on synaptic function and plasticity that appears to be the causative factor [20]. There are other genes involved in ubiquitination, and recent work indicates that these also create autism-like symptoms in an animal model, as with CUL3 [21], or are directly involved in human autism [22]. These demonstrate the critical role of ubiquitination in neuronal synapses as regards autism.
Other sources have evaluated genes based on pathway participation and functional clustering in ASD [23,24,25]. This allowed us to construct Table 2 to compare the list of genes with the pathways involved.
The genes that have been recognized as ASD-related and act by impairing neural connectivity include those involved in synapse formation and function, namely SHANK3, NRXN1, and NGLG3/4 [26,27,28]. Additionally, the gene SCN2A is tied to an alteration in neuronal activity due to its role in ion channels [29].
The genes involved in cell growth, TSC1 and TSC2, can lead to abnormal brain structures by alterations in the mTOR pathway [30,31]. This is a common signaling cascade with central roles in translation, lipid and nucleotide synthesis, and growth factor signaling [32].
Variations in the genes involved in gene regulation, CHD8 and MECP2, can result in disrupted brain development, while variants of a gene from RNA translation, FMR1, is related to learning and memory issues [33,34,35].
Genes associated with folate transport (Section 2.3) are related to folate delivery to the brain and are a biomarker for cerebral folate deficiency (CFD). Therapeutic intervention can overcome CFD, as described in this section. A potentially related genetic risk is for mitochondrial dysfunction, which may play a role in the autism regression seen in some children as they grow.

2.2. Comorbidities of ASD-Related Genes

Another route is to evaluate the disorders comorbid with ASD and which of the ASD-associated genes are also implicated in the comorbid disorder. In a compelling analysis of SFARI data, Khachadourian et al. (2023) [10] identified many comorbidities found with ASD. Such data may be doubly beneficial. There is the main point of helping to identify the specific genes involved in certain developmental symptoms. But the second benefit may come later in providing a means to begin to characterize the spectrum of autism as specific subtypes, based either on the comorbidities or, hopefully, on a full understanding of the genetic and environmental mechanisms involved in the development of these symptoms.
In addition to comorbidities with learning disorders and social communication disorders, some of the common comorbidities found were with anxiety disorder, ADHD, depression, dystonia (motor disorders), and OCD [10,36]. Table 3 compiles the more frequently referenced SFARI-listed genes that are linked to one of the listed comorbidities [37,38].
The psychiatric cell map initiative [39] is an integrated effort to identify genes that are believed to be involved in multiple disorders and evaluate their relationship [40]. It shows ASD having comorbidities with certain epileptic genes, GABRB3 and SCN2A, as well as intellectual disability genes, SCN2A, SLC6A1, SYNGAP1, and WAC [39]. This work was taken further to make a connected cluster map to the curated pathways of SFARI proteins and their neurological relevance and reveal the overlap of neurodevelopmental disease causal networks (in their Figure 6) [41].
Additionally, there is evidence from brain organoids of a form of idiopathic autism in which some of the genes involved lead to an imbalance in excitatory cortical neurons [42]. Given that it is presumed that there are several autism variants, the associated genes are ones that are correlated with a greater risk of developing ASD. Some have specific pathways that are involved, and others are correlated with ASD but in a non-defined manner. Section 2.3 and Section 2.4 provide a brief look at some of the developmental genes that have traditionally been considered for their role in ASD.

2.3. Cerebral Folate Deficiency

2.3.1. Folate Metabolism and Brain Development

Folate is essential for DNA synthesis, repair, and methylation, all of which are essential processes during early brain development. The transport of folate into the central nervous system (CNS) is tightly regulated by specific transport mechanisms, primarily the folate receptor alpha (FRα) and the reduced folate carrier (RFC).

2.3.2. Genetic Variants Impairing Folate Transport

Mutations in the FOLR1 gene can impair FRα function, leading to CFD. Similarly, polymorphisms such as the SLC19A1 variant rs1051266 can reduce RFC efficiency. Both impair the brain’s ability to maintain adequate folate levels, resulting in developmental impairments and ASD features, such as decreased social communication and decreased emotional recognition [43,44].

2.3.3. Folate Receptor Alpha Autoantibodies and Treatments

Folate receptor autoantibodies (FRAAs), which block or bind to FRα, are much more prevalent in ASD children (about 70% of ASD children, compared with 5–10% in the general population). These autoantibodies suppress folate transport across the blood–brain barrier, contributing to CFD. FRAAs are believed to result from environmental exposure [45], further linking genetic and inflammatory pathways.
Folinic acid supplementation has shown promise in reversing CFD symptoms in children with ASD in multiple clinical trials (based in the USA, France, and India) published between 2018 and 2025 [46,47,48,49]. Dietary modifications, such as avoiding cow’s milk products (which may trigger FRAAs) [45], have also demonstrated potential benefits. These findings support the clinical importance of identifying and treating CFD in ASD patients.

2.4. Mitochondrial Dysfunction

There is growing evidence that mitochondrial dysfunction may contribute to neurodevelopmental regression in children [50,51,52,53,54]. This condition is seen when an ASD child shows strong learning, language, and memory capabilities initially, only to regress later in childhood [55]. Of note, while non-regressed children showed that inflammation and immune dysregulation occurs first, in regressed children, mitochondrial dysfunction was first observed, with an impact on immune activity possibly occurring later [52].

3. Inflammation During Critical Developmental Periods and ASD Risk

3.1. The Developing Brain and Vulnerability to Inflammation

Neurodevelopment is highly sensitive to environmental insults during the prenatal and early postnatal periods. Inflammation during these times can disrupt neuronal migration, synaptogenesis, synaptic pruning, and brain connectivity, all of which are critical to neurotypical brain function [56]. Many causes of inflammation have been documented, some of which are acute, such as viral infection and fever, and others chronic, such as microplastics and malnutrition. When inflammation occurs during critical periods in brain development, neurotypical development can be delayed or arrested, resulting in a child reaching milestones later, aberrantly, or possibly not at all if the inflammation exceeds the duration of the critical period [43,56,57,58]. Summary schematics of the categories of inflammation and their presumed action are outlined in Figure 1 and Figure 2.

3.2. Categories of Inflammation

3.2.1. Viral Infections

Maternal infections such as influenza, rubella, and COVID-19 have been linked to increased ASD risk due to maternal immune activation (MIA). Postnatal viral infections can also contribute to neuroinflammatory states [43,57,59,60,61].
An evaluation of studies in a meta-analysis shows there is an association between maternal infection and subsequent autism diagnosis in the child, with little variation based on when the infection occurred during pregnancy or what type of infection it was [62,63]. A genetic analysis showed that the increased risk for ASD with maternal infections may be correlated with a genetically distinct subtype of autism, a subtype that come from the interaction between genetic susceptibility and the exposure to infections in pregnancy [64]. Thus, since the maternal infections may not be causal in the development of ASD, it is unclear if the prevention of infection may reduce autism incidence [65].

3.2.2. Air Pollution and Maternal Asthma

Environmental pollutants are associated with ASD incidence [66]. Exposure to particulate matter (e.g., PM2.5) during pregnancy has been associated with neuroinflammation [67] and developmental disorders, and particularly an increased risk of development of ASD [56,67,68,69,70,71,72]. Airborne toxins can activate microglia and oxidative stress pathways in the fetal brain [73].
Additional studies have examined air pollution from roadways as being particularly problematic, pointing to a potential link to plasticizers used in the manufacture of tires and tailpipe exhausts, as risk was related to distance from roadways [74,75]. Given that immune dysregulation is a common comorbid feature with ASD, the link to maternal exposure to air pollution may be indicating a mechanism of action in the development of autism. In this regard, multiple studies have found an elevated risk of autism in infants born to asthmatic mothers, and that this risk was related to an increase in inflammatory biomarkers [76,77,78].
Of concern, in a study of air pollution pre-conception, women exposed to particulate matter and nitric oxide in the three months prior to conception had children with greater growth in their body weight in their first two years of life [79]. The authors report that the critical period of exposure is the three months prior to conception.

3.2.3. Maternal Immune Activation

Maternal inflammation during pregnancy is associated with an increased risk of ASD, with maternal immune activation (MIA) being evaluated in animal models, where it leads to behavioral changes resembling ASD [80]. This immune activation raises the levels of cytokines, especially interleukin-6 (IL-6) and interleukin 17a (IL-17a), which are implicated in disrupting fetal brain development, in what may lead to some ASD behaviors. The effect of MIA on the developing fetus may be impacted by the fetus’s genetic makeup, with certain genetic profiles making the fetal brain more impacted by maternal inflammation, implicating a gene–environment interaction in the etiology of ASD [80,81,82,83].

3.2.4. Microplastics

Emerging studies in animal models suggest that prenatal and early postnatal exposure to microplastics, especially bisphenol A and phthalates, may lead to inflammation-driven behavioral changes reminiscent of ASD [84,85,86,87,88]. This action may be through immune and epigenetic mechanisms, or as endocrine disrupters. In this regard, plastic food containers and films were tested and found to contain thousands of chemicals, with many being endocrine disruptors (including an estrogen receptor alpha activator and an androgen receptor inhibitor) and metabolism disruptors [89]. Samples tested that were made with fewer chemicals also had fewer toxic chemicals that entered the food. Additionally, exposure to polystyrene nanoplastics caused depression and anxiety in a mouse model [90], and it decreased oligodendrocyte activity and enhanced hyperactivity and aggression in zebrafish [91], while exposure to polyethylene led to repetitive behaviors and diminished social interaction [92].

3.2.5. Malnutrition

Deficiencies in nutrients with anti-inflammatory properties, such as omega-3 fatty acids, zinc, and vitamins B12, B9, A, D, and K, may increase susceptibility to inflammation and neurodevelopmental disorders [93,94,95,96,97,98]. The vitamin A deficiency may have its effect in an impact on the gut microbiome [99,100]. Other micronutrients appear to play important roles in neurotypical development [101]. There is also evidence that autistic children, who frequently eat the same food each day, are lacking in certain nutrients [102,103], or are receiving an abundance of one macronutrient to the exclusion of others, which may contribute to symptoms [104].

3.2.6. Emotional Stress

Maternal stress during pregnancy can result in elevated cortisol and pro-inflammatory cytokines, potentially disrupting fetal brain development. Chronic stress in the early life of the child may also affect immune and neurological outcomes [57,62,105,106,107,108]. One measure of such stress on the child can be quantified by evaluating adverse childhood experiences (ACEs), as ASD children, who are at increased risk of bullying, parental divorce, income, and food insecurity, have an increased risk of comorbid health disorders [109].

3.2.7. Pharmaceuticals

Certain medications, particularly selective serotonin reuptake inhibitors (SSRIs), have been implicated in altering fetal brain development when used during pregnancy [110]. SSRIs cross the placental barrier and can influence serotonin signaling pathways critical to neurodevelopment. Research suggests that prenatal SSRI exposure may disrupt synaptogenesis and increase neuroinflammation, particularly in genetically susceptible offspring [111]. These effects may contribute to ASD-like phenotypes in animal models and warrant a careful consideration of risk–benefit ratios in clinical practice [110,112,113,114].

3.2.8. Maternal Disorders and Diabetes

A recent study in California found a higher ASD incidence in children born to mothers with asthma or obesity, and a much higher incidence for mothers with both conditions [115]. The authors speculate that this incidence increase may be due to the increased inflammatory conditions during pregnancy, and that the earlier screening of children born to mothers with asthma and obesity during pregnancy may be warranted.
A meta-analysis of over 200 studies, and including over 50 million pregnancies, revealed that children born to mothers with diabetes have an increased risk of neurodevelopmental disorders. This was especially true for mothers with pre-gestational diabetes, and less so for gestational diabetes. There was a 25–30% increased risk for ASD, ADHD, and intellectual disability, with a 15–20% increased risk for communication learning disorders [116].

3.2.9. Synthetic Vitamins

Folic acid supplementation has been used in prenatal vitamins for decades and reduces spina bifida in newborns. Folic acid is used rather than natural folate due to its stability; it cannot be further oxidized. However, excessive or poorly metabolized synthetic folic acid may provoke immune responses, especially in individuals with impaired folate pathways. This imbalance can exacerbate inflammation and contribute to FRAA development, which may lead to CFD. Additionally, the intake of folic acid in excess of a few hundred micrograms per day (the typical level of folic acid that can be converted to usable folate in the gut) has been shown to result in unmetabolized folic acid in the blood, which interferes with the absorption of folate across the blood–brain barrier or across the placenta [117,118,119,120].

3.2.10. Vaccines

While the scientific consensus supports vaccine safety, there is speculation that vaccines trigger inflammatory responses in genetically susceptible individuals, as detailed in a recent comprehensive report [121]. If such inflammation occurs during critical periods, it can elevate the risk of altering brain development. This was a larger concern in previous decades, for example, when the earlier pertussis vaccine (in the 1980s) triggered fever in some children, or when the thiomersal (thimerosal) preservative in the measles vaccine was used (until 2001), which could be inflammatory in some children. The fact that diagnosed autism cases have increased five-fold from 2000 to 2025 [2,122] while inflammation from vaccines has diminished indicates that this is likely not a significant factor in this neurodevelopmental disorder.

3.2.11. Microbiome and Metabolic Disorders

This topic may merit a separate category, as processing by the microbiome seems to be essential for the absorption of key nutrients as well as the inactivation of inflammatory agents, and digestive disorders are frequently comorbid with ASD [122,123,124,125]. In this regard, restoring microbiome function with probiotics may alleviate some ASD symptoms [126], and the use of fecal transfer has shown some effectiveness in treating autism symptoms [127].
Several studies have shown a link between the gut microbiome and neurological disorders [128]. Propionic acid, a microbial byproduct, is known to cause neuroinflammation and the over-proliferation of glia in mice offspring born to mothers exposed to propionic acid throughout pregnancy and weaning [129]. This provides direction as to a mechanism of action by which inflammation may be caused, as well as neural correlates of its action.
A screening of the gut microbiome of autistic children and their non-autistic siblings revealed categories of bacteria and fungi that were at different levels in the ASD individuals [130]. When these bacteria and fungi were fed to mice, there was a concomitant change in their behavioral patterns, indicating that the use of certain probiotics may provide some benefit in autism. A similar study found differences in ASD and non-ASD microbiomes, and observed that regressed ASD children had higher levels of Proteobacteria [131]. This link to ASD regression may provide another avenue to explore.
An additional evaluation of mitochondrial metabolism found changes in the energy pathways in ASD children [132]. When a metabolic analysis was made of cord blood to look at such parameters at birth, as well as in maternal mid-gestational blood, a machine-learning analysis found that cord blood was more predictive of ASD and that this was true for girls and boys [133]. A later study showed that the elevation of a specific cord blood component, acylcarnitine, was predictive of both ASD and ADHD outcomes [134].

3.3. Mechanistic Insights

Inflammation can alter neural circuitry by affecting synaptic pruning, neuronal connectivity, and neurotransmitter systems, and this may occur due to alterations in the neural immune system. Genetic predispositions, such as those affecting folate metabolism, may intensify the brain’s vulnerability to these inflammatory insults. Microglial priming and sustained cytokine exposure during development may create a neuroimmune environment conducive to ASD [60,135,136,137,138,139].

3.4. Therapeutic Interventions

Therapeutic intervention to reduce neuroinflammation typically involves reducing exposure to its causative agents. Given the great diversity of such potential sources, and the rampant exposure in daily living to most of these agents, such action is likely to be futile. Some actions can be clearly taken, such as reducing exposure to the agents that one has control over and seeking medical attention to reduce the risk from illnesses during pregnancy.
Additionally, where mechanisms are posited for inflammatory action, and when the impact of such action can be ameliorated, then the use of specific products may provide benefits. In this regard, with maternal infection, there is a concomitant reduction in N-acetyl cysteine and taurine, associated with neuroinflammation, due to a decrease in microglial cells. Each of these compounds can be supplemented into one’s diet to reduce their impact on the brain’s immune function to deter fetal injury.
There was a recent report in a mouse model of autism that an extract of basil, Ocimum basilicum, reduced damage in the pups that were induced by maternal separation and reduced autism behaviors via the antioxidant and anti-inflammatory properties of the extract [140]. This opens an avenue to considering other proven antioxidants in an ASD model. In this regard, the comparison with cancer may be apt, as there are decades of research identifying the mechanisms of action of antioxidant nutritive extracts on cancer cell cultures [5,141]. Some of these may also prove to have an impact in deferring ASD development.

4. Integrative Perspective: Gene–Environment Interactions in ASD

4.1. Synergistic Effects

Genetic susceptibilities, such as impaired folate transport (described above), may amplify the effects of environmental insults. For example, exposure to synthetic folic acid or infections in a child with FOLR1 or RFC polymorphisms may result in a greater risk of ASD due to insufficient levels of natural folate reaching the brain or the uterus [97,120,139].

4.2. Epigenetic Modifications

Disruptions in folate metabolism may lead to altered DNA methylation and gene expression, affecting neurodevelopmental outcomes. These epigenetic changes may bridge the gap between genetic and environmental risk factors.

4.3. Implications for Prevention and Treatment

Screening for CFD in the fetus by testing for parental FRAAs, nutritional support (including prenatal vitamins with natural folate instead of folic acid), and inflammation-reducing strategies during pregnancy could reduce ASD risk [43].
The use of machine learning systems to successfully identify ASD in children could provide much earlier diagnosis [142,143]. Such machine learning provides hope that a similar system may be devised that can identify future ASD risk during pregnancy.
Personalized approaches considering both genetic background and environmental exposures hold promise for future interventions. Follow-up on care in early childhood may successfully identify developing ASD risks and reduce autism severity by treating CFD and nutritional deficits at earlier ages, as well as by reducing inflammatory stimuli and reducing widespread neural inflammation.
We project that ASD treatment may parallel other multifactorial disorders, such as cancer, with a targeted approach to lessen risk and improve neurotypical outcomes, with a similar goal of extended remission to provide an outcome of raised qualify of life. Given that such treatment has a lower societal and health maintenance cost and has been demonstrated to have no deleterious effects, we propose engaging such a model of treatment now.
Figure 3 provides a review of some of the oxidative stress and genetic factors that may give rise to specific symptoms observed in ASD [43].

5. Conclusions

ASD arises from a complex interplay between genetic and environmental factors. The genetic variants impairing folate metabolism and inflammatory exposures during critical periods each play significant roles in disrupting brain development. Understanding and addressing these risk factors through early detection, targeted interventions, and public health strategies may help reduce the incidence and severity of ASD in future generations.
Identifying the genetic causes and treating their ramifications, such as treating CFD with levo-leucovorin (the prescribable, pharmacological name of L-folinic acid, which is a natural, reduced, form of folate), has been shown in multiple clinical trials to make a substantial difference in resolving the communication difficulties of a young autistic child [46,47,48]. It is time to have this become the standard of practice for all ASD children at the earliest ages.
Addressing inflammation causes can also be accomplished now, focusing on the most readily addressable ones first. In this way, reducing infections in pregnancy, eliminating the malnutrition of mothers and children, and finding ways to alleviate stress may have a lasting impact in reducing the severity of autism. In addition, providing support during inflammation to help retain immune functioning, such as has been reported by using N-acetyl cysteine and taurine to replenish their levels when depleted by inflammation, may be an appropriate next step. It is most important to address the readily resolvable inflammation causes, specifically stress, malnutrition, and health protection from disease, now, and to extend efforts to work as a society to reduce the impact of inflammatory agents such as microplastics and pollution in order to provide the healthiest environment for the neurotypical brain development in all children, thus building a better and more productive future for all.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The author declares no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ASDAutism Spectrum Disorder
CFDCerebral Folate Deficiency
CNSCentral Nervous System
FRαFolate Receptor Alpha
FRAAFolate Receptor Auto Antibody
MIAMaternal Immune Activation
RFCReduced Folate Carrier
SSRISelective Serotonin Reuptake Inhibitor

References

  1. Volkmar, F.R.; Reichow, B. Autism in DSM-5: Progress and challenges. Mol. Autism 2013, 4, 13. [Google Scholar] [CrossRef]
  2. Shaw, K.A. Prevalence and Early Identification of Autism Spectrum Disorder Among Children Aged 4 and 8 Years—Autism and Developmental Disabilities Monitoring Network, 16 Sites, United States, 2022. MMWR Surveill. Summ. 2025, 74, 1–22. [Google Scholar] [CrossRef] [PubMed]
  3. Banerjee-Basu, S.; Packer, A. SFARI Gene: An evolving database for the autism research community. Dis. Models Mech. 2010, 3, 133–135. [Google Scholar] [CrossRef] [PubMed]
  4. Arpi, M.N.T.; Simpson, T.I. SFARI genes and where to find them; modelling Autism Spectrum Disorder specific gene expression dysregulation with RNA-seq data. Sci. Rep. 2022, 12, 10158. [Google Scholar] [CrossRef]
  5. Tang, J.; Oroudjev, E.; Wilson, L.; Ayoub, G. Delphinidin and cyanidin exhibit antiproliferative and apoptotic effects in MCF7 human breast cancer cells. Integr. Cancer Sci. Ther. 2015, 2, 82–86. [Google Scholar] [CrossRef]
  6. Kimball, T.N.; Prapiadou, S.; Tack, R.W.P.; Tan, B.Y.-Q.; Senff, J.R.; Kourkoulis, C.; Singh, S.; Rosand, J.; Anderson, C.D. Association of Leucocyte Telomere Length With Stroke, Dementia, and Late-Life Depression. Neurology 2025, 105, e213794. [Google Scholar] [CrossRef]
  7. Gogate, A.; Kaur, K.; Khalil, R.; Bashtawi, M.; Morris, M.A.; Goodspeed, K.; Evans, P.; Chahrour, M.H. The genetic landscape of autism spectrum disorder in an ancestrally diverse cohort. npj Genom. Med. 2024, 9, 62. [Google Scholar] [CrossRef]
  8. Havdahl, A.; Niarchou, M.; Starnawska, A.; Uddin, M.; van der Merwe, C.; Warrier, V. Genetic contributions to autism spectrum disorder. Psychol. Med. 2021, 51, 2260–2273. [Google Scholar] [CrossRef]
  9. Fu, J.M.; Satterstrom, F.K.; Peng, M.; Brand, H.; Collins, R.L.; Dong, S.; Wamsley, B.; Klei, L.; Wang, L.; Hao, S.P.; et al. Rare coding variation provides insight into the genetic architecture and phenotypic context of autism. Nat. Genet. 2022, 54, 1320–1331. [Google Scholar] [CrossRef]
  10. Khachadourian, V.; Mahjani, B.; Sandin, S.; Kolevzon, A.; Buxbaum, J.D.; Reichenberg, A.; Janecka, M. Comorbidities in autism spectrum disorder and their etiologies. Transl. Psychiatry 2023, 13, 71. [Google Scholar] [CrossRef]
  11. Rolland, T.; Cliquet, F.; Anney, R.J.L.; Moreau, C.; Traut, N.; Mathieu, A.; Huguet, G.; Duan, J.; Warrier, V.; Portalier, S.; et al. Phenotypic effects of genetic variants associated with autism. Nat. Med. 2023, 29, 1671–1680. [Google Scholar] [CrossRef] [PubMed]
  12. Manoli, D.S.; State, M.W. Autism Spectrum Disorder Genetics and the Search for Pathological Mechanisms. Am. J. Psychiatry 2021, 178, 30–38. [Google Scholar] [CrossRef] [PubMed]
  13. Wan, L.; Yang, G.; Yan, Z. Identification of a molecular network regulated by multiple ASD high risk genes. Hum. Mol. Genet. 2024, 33, 1176–1185. [Google Scholar] [CrossRef] [PubMed]
  14. Vorstman, J.A.S.; Scherer, S.W. Contemplating syndromic autism. Genet. Med. 2023, 25, 100919. [Google Scholar] [CrossRef]
  15. Sztainberg, Y.; Zoghbi, H.Y. Lessons learned from studying syndromic autism spectrum disorders. Nat. Neurosci. 2016, 19, 1408–1417. [Google Scholar] [CrossRef]
  16. Wang, N.; Lv, L.; Huang, X.; Shi, M.; Dai, Y.; Wei, Y.; Xu, B.; Fu, C.; Huang, H.; Shi, H.; et al. Gene editing in monogenic autism spectrum disorder: Animal models and gene therapies. Front. Mol. Neurosci. 2022, 15, 1043018. [Google Scholar] [CrossRef]
  17. Weuring, W.; Geerligs, J.; Koeleman, B.P.C. Gene Therapies for Monogenic Autism Spectrum Disorders. Genes 2021, 12, 1667. [Google Scholar] [CrossRef]
  18. Kereszturi, É. Database-assisted screening of autism spectrum disorder related gene set. Mol. Brain 2024, 17, 55. [Google Scholar] [CrossRef]
  19. Monteiro, P.; Feng, G. SHANK proteins: Roles at the synapse and in autism spectrum disorder. Nat. Rev. Neurosci. 2017, 18, 147–157. [Google Scholar] [CrossRef]
  20. Vatsa, N.; Jana, N.R. UBE3A and Its Link with Autism. Front. Mol. Neurosci. 2018, 11, 448. [Google Scholar] [CrossRef]
  21. Tener, S.J.; Lin, Z.; Park, S.J.; Oraedu, K.; Ulgherait, M.; Van Beek, E.; Martínez-Muñiz, A.; Pantalia, M.; Gatto, J.A.; Volpi, J.; et al. Neuronal knockdown of Cullin3 as a Drosophila model of autism spectrum disorder. Sci. Rep. 2024, 14, 1541. [Google Scholar] [CrossRef]
  22. Vinci, M.; Treccarichi, S.; Galati Rando, R.; Musumeci, A.; Todaro, V.; Federico, C.; Saccone, S.; Elia, M.; Calì, F. A de novo ARIH2 gene mutation was detected in a patient with autism spectrum disorders and intellectual disability. Sci. Rep. 2024, 14, 15848. [Google Scholar] [CrossRef]
  23. Wen, Y.; Alshikho, M.J.; Herbert, M.R. Pathway Network Analyses for Autism Reveal Multisystem Involvement, Major Overlaps with Other Diseases and Convergence upon MAPK and Calcium Signaling. PLoS ONE 2016, 11, e0153329. [Google Scholar] [CrossRef]
  24. Parikshak, N.N.; Luo, R.; Zhang, A.; Won, H.; Lowe, J.K.; Chandran, V.; Horvath, S.; Geschwind, D.H. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 2013, 155, 1008–1021. [Google Scholar] [CrossRef]
  25. Guo, Q.; Xia, L.; Guo, R.; Xu, W.; Zhang, Y.; Zhao, C.; Zhang, P.; Bai, T.; Ni, X.; Hao, C.; et al. Behavioural deficits of autism spectrum disorder and associations with different gene clusters: A study with the whole-genome transmission disequilibrium test. BMJ Paediatr. Open 2023, 7, e001930. [Google Scholar] [CrossRef]
  26. Betancur, C.; Buxbaum, J.D. SHANK3 haploinsufficiency: A “common” but underdiagnosed highly penetrant monogenic cause of autism spectrum disorders. Mol. Autism 2013, 4, 17. [Google Scholar] [CrossRef]
  27. Glessner, J.T.; Wang, K.; Cai, G.; Korvatska, O.; Kim, C.E.; Wood, S.; Zhang, H.; Estes, A.; Brune, C.W.; Bradfield, J.P.; et al. Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 2009, 459, 569–573. [Google Scholar] [CrossRef]
  28. Jamain, S.; Quach, H.; Betancur, C.; Råstam, M.; Colineaux, C.; Gillberg, I.C.; Soderstrom, H.; Giros, B.; Leboyer, M.; Gillberg, C.; et al. Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat. Genet. 2003, 34, 27–29. [Google Scholar] [CrossRef]
  29. Sanders, S.J.; Campbell, A.J.; Cottrell, J.R.; Moller, R.S.; Wagner, F.F.; Auldridge, A.L.; Bernier, R.A.; Catterall, W.A.; Chung, W.K.; Empfield, J.R.; et al. Progress in Understanding and Treating SCN2A-Mediated Disorders. Trends Neurosci. 2018, 41, 442–456. [Google Scholar] [CrossRef]
  30. Trifonova, E.A.; Mustafin, Z.S.; Lashin, S.A.; Kochetov, A.V. Abnormal mTOR Activity in Pediatric Autoimmune Neuropsychiatric and MIA-Associated Autism Spectrum Disorders. Int. J. Mol. Sci. 2022, 23, 967. [Google Scholar] [CrossRef]
  31. Curatolo, P.; Moavero, R.; de Vries, P.J. Neurological and neuropsychiatric aspects of tuberous sclerosis complex. Lancet Neurol. 2015, 14, 733–745. [Google Scholar] [CrossRef]
  32. Panwar, V.; Singh, A.; Bhatt, M.; Tonk, R.K.; Azizov, S.; Raza, A.S.; Sengupta, S.; Kumar, D.; Garg, M. Multifaceted role of mTOR (mammalian target of rapamycin) signaling pathway in human health and disease. Sig. Transduct. Target Ther. 2023, 8, 375. [Google Scholar] [CrossRef]
  33. Bernier, R.; Golzio, C.; Xiong, B.; Stessman, H.A.; Coe, B.P.; Penn, O.; Witherspoon, K.; Gerdts, J.; Baker, C.; Vulto-van Silfhout, A.T.; et al. Disruptive CHD8 Mutations Define a Subtype of Autism Early in Development. Cell 2014, 158, 263–276. [Google Scholar] [CrossRef]
  34. Amir, R.E.; Van den Veyver, I.B.; Wan, M.; Tran, C.Q.; Francke, U.; Zoghbi, H.Y. Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2. Nat. Genet. 1999, 23, 185–188. [Google Scholar] [CrossRef]
  35. Bassell, G.J.; Warren, S.T. Fragile X Syndrome: Loss of Local mRNA Regulation Alters Synaptic Development and Function. Neuron 2008, 60, 201–214. [Google Scholar] [CrossRef]
  36. David, M.M.; Enard, D.; Ozturk, A.; Daniels, J.; Jung, J.-Y.; Diaz-Beltran, L.; Wall, D.P. Comorbid Analysis of Genes Associated with Autism Spectrum Disorders Reveals Differential Evolutionary Constraints. PLoS ONE 2016, 11, e0157937. [Google Scholar] [CrossRef]
  37. Diaz-Beltran, L.; Esteban, F.J.; Varma, M.; Ortuzk, A.; David, M.; Wall, D.P. Cross-disorder comparative analysis of comorbid conditions reveals novel autism candidate genes. BMC Genom. 2017, 18, 315. [Google Scholar] [CrossRef]
  38. Chen, S.; Xiong, J.; Chen, B.; Zhang, C.; Deng, X.; He, F.; Yang, L.; Chen, C.; Peng, J.; Yin, F. Autism spectrum disorder and comorbid neurodevelopmental disorders (ASD-NDDs): Clinical and genetic profile of a pediatric cohort. Clin. Chim. Acta 2022, 524, 179–186. [Google Scholar] [CrossRef]
  39. Willsey, A.J.; Morris, M.T.; Wang, S.; Willsey, H.R.; Sun, N.; Teerikorpi, N.; Baum, T.B.; Cagney, G.; Bender, K.J.; Desai, T.A.; et al. The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders. Cell 2018, 174, 505–520. [Google Scholar] [CrossRef]
  40. Vilela, J.; Martiniano, H.; Marques, A.R.; Santos, J.X.; Rasga, C.; Oliveira, G.; Vicente, A.M. Disease similarity network analysis of Autism Spectrum Disorder and comorbid brain disorders. Front. Mol. Neurosci. 2022, 15, 932305. [Google Scholar] [CrossRef]
  41. Iannuccelli, M.; Vitriolo, A.; Licata, L.; Lo Surdo, P.; Contino, S.; Cheroni, C.; Capocefalo, D.; Castagnoli, L.; Testa, G.; Cesareni, G.; et al. Curation of causal interactions mediated by genes associated with autism accelerates the understanding of gene-phenotype relationships underlying neurodevelopmental disorders. Mol. Psychiatry 2024, 29, 186–196. [Google Scholar] [CrossRef]
  42. Jourdon, A.; Wu, F.; Mariani, J.; Capauto, D.; Norton, S.; Tomasini, L.; Amiri, A.; Suvakov, M.; Schreiner, J.D.; Jang, Y.; et al. Modeling idiopathic autism in forebrain organoids reveals an imbalance of excitatory cortical neuron subtypes during early neurogenesis. Nat. Neurosci. 2023, 26, 1505–1515. [Google Scholar] [CrossRef]
  43. Ayoub, G. Neurodevelopment of Autism: Critical Periods, Stress and Nutrition. Cells 2024, 13, 1968. [Google Scholar] [CrossRef]
  44. Qiu, S.; Qiu, Y.; Li, Y.; Cong, X. Genetics of autism spectrum disorder: An umbrella review of systematic reviews and meta-analyses. Transl. Psychiatry 2022, 12, 249. [Google Scholar] [CrossRef]
  45. Ramaekers, V.T.; Sequeira, J.M.; Blau, N.; Quadros, E.V. A milk-free diet downregulates folate receptor autoimmunity in cerebral folate deficiency syndrome. Dev. Med. Child Neurol. 2008, 50, 346–352. [Google Scholar] [CrossRef]
  46. Frye, R.E.; Slattery, J.; Delhey, L.; Furgerson, B.; Strickland, T.; Tippett, M.; Sailey, A.; Wynne, R.; Rose, S.; Melnyk, S.; et al. Folinic acid improves verbal communication in children with autism and language impairment: A randomized double-blind placebo-controlled trial. Mol. Psychiatry 2018, 23, 247–256. [Google Scholar] [CrossRef]
  47. Renard, E.; Leheup, B.; Guéant-Rodriguez, R.-M.; Oussalah, A.; Quadros, E.V.; Guéant, J.-L. Folinic acid improves the score of Autism in the EFFET placebo-controlled randomized trial. Biochimie 2020, 173, 57–61. [Google Scholar] [CrossRef]
  48. Panda, P.K.; Sharawat, I.K.; Saha, S.; Gupta, D.; Palayullakandi, A.; Meena, K. Efficacy of oral folinic acid supplementation in children with autism spectrum disorder: A randomized double-blind, placebo-controlled trial. Eur. J. Pediatr. 2024, 183, 4827–4835. [Google Scholar] [CrossRef]
  49. Soetedjo, F.; Kristijanto, J.A.; Durry, F. Folinic acid and autism spectrum disorder in children: A systematic review and meta-analysis of two double-blind randomized placebo-controlled trials. AcTion Aceh Nutr. J. 2025, 10, 194. [Google Scholar] [CrossRef]
  50. Frye, R.E.; McCarty, P.J.; Werner, B.A.; Rose, S.; Scheck, A.C. Bioenergetic signatures of neurodevelopmental regression. Front. Physiol. 2024, 15, 1306038. [Google Scholar] [CrossRef] [PubMed]
  51. Frye, R.E. Mitochondrial Dysfunction in Autism Spectrum Disorder: Unique Abnormalities and Targeted Treatments. Semin. Pediatr. Neurol. 2020, 35, 100829. [Google Scholar] [CrossRef] [PubMed]
  52. Gevezova, M.; Ivanov, Z.; Pacheva, I.; Timova, E.; Kazakova, M.; Kovacheva, E.; Ivanov, I.; Sarafian, V. Bioenergetic and Inflammatory Alterations in Regressed and Non-Regressed Patients with Autism Spectrum Disorder. Int. J. Mol. Sci. 2024, 25, 8211. [Google Scholar] [CrossRef] [PubMed]
  53. Scott, O.; Shi, D.; Andriashek, D.; Clark, B.; Goez, H.R. Clinical clues for autoimmunity and neuroinflammation in patients with autistic regression. Dev. Med. Child Neurol. 2017, 59, 947–951. [Google Scholar] [CrossRef]
  54. He, Y.; Xie, K.; Yang, K.; Wang, N.; Zhang, L. Unraveling the Interplay Between Metabolism and Neurodevelopment in Health and Disease. CNS Neurosci. Ther. 2025, 31, e70427. [Google Scholar] [CrossRef] [PubMed]
  55. The Mystery of Regressive Autism—Mental Health. Available online: https://www.enotalone.com/article/mental-health/the-mystery-of-regressive-autism-r20878/ (accessed on 21 May 2025).
  56. Yenkoyan, K.; Mkhitaryan, M.; Bjørklund, G. Environmental Risk Factors in Autism Spectrum Disorder: A Narrative Review. Curr. Med. Chem. 2024, 31, 2345–2360. [Google Scholar] [CrossRef]
  57. Usui, N.; Kobayashi, H.; Shimada, S. Neuroinflammation and Oxidative Stress in the Pathogenesis of Autism Spectrum Disorder. Int. J. Mol. Sci. 2023, 24, 5487. [Google Scholar] [CrossRef]
  58. Chen, Y.; Du, X.; Zhang, X.; Li, F.; Yuan, S.; Wang, W.; Zhu, Z.; Wang, M.; Gu, C. Research trends of inflammation in autism spectrum disorders: A bibliometric analysis. Front. Immunol. 2025, 16, 1534660. [Google Scholar] [CrossRef]
  59. Ellul, P.; Maruani, A.; Vantalon, V.; Humeau, E.; Amestoy, A.; Anchordoqui, A.; Atzori, P.; Baleyte, J.-M.; Benmansour, S.; Bonnot, O.; et al. Maternal immune activation during pregnancy is associated with more difficulties in socio-adaptive behaviors in autism spectrum disorder. Sci. Rep. 2023, 13, 17687. [Google Scholar] [CrossRef]
  60. Carter, M.; Casey, S.; O’Keeffe, G.W.; Gibson, L.; Gallagher, L.; Murray, D.M. Maternal Immune Activation and Interleukin 17A in the Pathogenesis of Autistic Spectrum Disorder and Why It Matters in the COVID-19 Era. Front. Psychiatry 2022, 13, 823096. [Google Scholar] [CrossRef]
  61. Yin, H.; Wang, Z.; Liu, J.; Li, Y.; Liu, L.; Huang, P.; Wang, W.; Shan, Z.; Sun, R.; Shen, J.; et al. Dysregulation of immune and metabolism pathways in maternal immune activation induces an increased risk of autism spectrum disorders. Life Sci. 2023, 324, 121734. [Google Scholar] [CrossRef]
  62. Alamoudi, R.A.; Al-Jabri, B.A.; Alsulami, M.A.; Sabbagh, H.J. Prenatal maternal stress and the severity of autism spectrum disorder: A cross-sectional study. Dev. Psychobiol. 2023, 65, e22369. [Google Scholar] [CrossRef]
  63. Tioleco, N.; Silberman, A.E.; Stratigos, K.; Banerjee-Basu, S.; Spann, M.N.; Whitaker, A.H.; Turner, J.B. Prenatal maternal infection and risk for autism in offspring: A meta-analysis. Autism Res. 2021, 14, 1296–1316. [Google Scholar] [CrossRef] [PubMed]
  64. Nudel, R.; Thompson, W.K.; Børglum, A.D.; Hougaard, D.M.; Mortensen, P.B.; Werge, T.; Nordentoft, M.; Benros, M.E. Maternal pregnancy-related infections and autism spectrum disorder—The genetic perspective. Transl. Psychiatry 2022, 12, 334. [Google Scholar] [CrossRef] [PubMed]
  65. Brynge, M.; Sjöqvist, H.; Gardner, R.M.; Lee, B.K.; Dalman, C.; Karlsson, H. Maternal infection during pregnancy and likelihood of autism and intellectual disability in children in Sweden: A negative control and sibling comparison cohort study. Lancet Psychiatry 2022, 9, 782–791. [Google Scholar] [CrossRef]
  66. Duque-Cartagena, T.; Dalla, M.D.B.; Mundstock, E.; Neto, F.K.; Espinoza, S.A.R.; de Moura, S.K.; Zanirati, G.; Padoin, A.V.; Jimenez, J.G.P.; Stein, A.T.; et al. Environmental pollutants as risk factors for autism spectrum disorders: A systematic review and meta-analysis of cohort studies. BMC Public Health 2024, 24, 2388. [Google Scholar] [CrossRef]
  67. Phiri, Y.V.A.; Canty, T.; Nobles, C.; Ring, A.M.; Nie, J.; Mendola, P. Neonatal intensive care admissions and exposure to satellite-derived air pollutants in the United States, 2018. Sci. Rep. 2025, 15, 420. [Google Scholar] [CrossRef]
  68. Bragg, M.G.; Gorski-Steiner, I.; Song, A.; Chavarro, J.E.; Hart, J.E.; Tabb, L.P.; Weisskopf, M.G.; Volk, H.; Lyall, K. Prenatal air pollution and children’s autism traits score: Examination of joint associations with maternal intake of vitamin D, methyl donors, and polyunsaturated fatty acids using mixture methods. Environ. Epidemiol. 2024, 8, e316. [Google Scholar] [CrossRef] [PubMed]
  69. Kang, N.; Sargsyan, S.; Chough, I.; Petrick, L.; Liao, J.; Chen, W.; Pavlovic, N.; Lurmann, F.W.; Martinez, M.P.; McConnell, R.; et al. Dysregulated metabolic pathways associated with air pollution exposure and the risk of autism: Evidence from epidemiological studies. Environ. Pollut. 2024, 361, 124729. [Google Scholar] [CrossRef]
  70. Amnuaylojaroen, T.; Parasin, N.; Saokaew, S. Exploring the association between early-life air pollution exposure and autism spectrum disorders in children: A systematic review and meta-analysis. Reprod. Toxicol. 2024, 125, 108582. [Google Scholar] [CrossRef]
  71. Ritz, B.; Liew, Z.; Yan, Q.; Cuia, X.; Virk, J.; Ketzel, M.; Raaschou-Nielsen, O. Air pollution and autism in Denmark. Environ. Epidemiol. 2018, 2, e028. [Google Scholar] [CrossRef]
  72. Ojha, S.K.; Amal, H. Air pollution: An emerging risk factor for autism spectrum disorder. Brain Med. 2024, 1, 31–34. [Google Scholar] [CrossRef]
  73. Dutheil, F.; Comptour, A.; Morlon, R.; Mermillod, M.; Pereira, B.; Baker, J.S.; Charkhabi, M.; Clinchamps, M.; Bourdel, N. Autism spectrum disorder and air pollution: A systematic review and meta-analysis. Environ. Pollut. 2021, 278, 116856. [Google Scholar] [CrossRef] [PubMed]
  74. Carter, S.A.; Rahman, M.M.; Lin, J.C.; Shu, Y.-H.; Chow, T.; Yu, X.; Martinez, M.P.; Eckel, S.P.; Chen, J.-C.; Chen, Z.; et al. In utero exposure to near-roadway air pollution and autism spectrum disorder in children. Environ. Int. 2022, 158, 106898. [Google Scholar] [CrossRef] [PubMed]
  75. Flanagan, E.; Malmqvist, E.; Rittner, R.; Gustafsson, P.; Källén, K.; Oudin, A. Exposure to local, source-specific ambient air pollution during pregnancy and autism in children: A cohort study from southern Sweden. Sci. Rep. 2023, 13, 3848. [Google Scholar] [CrossRef]
  76. Autism Likelihood in Infants Born to Mothers with Asthma Is Associated with Blood Inflammatory Gene Biomarkers in Pregnancy—ScienceDirect. Available online: https://www.sciencedirect.com/science/article/pii/S2666354624001236 (accessed on 20 May 2025).
  77. Gong, T.; Lundholm, C.; Rejnö, G.; Bölte, S.; Larsson, H.; D’Onofrio, B.M.; Lichtenstein, P.; Almqvist, C. Parental asthma and risk of autism spectrum disorder in offspring: A population and family-based case-control study. Clin. Exp. Allergy 2019, 49, 883–891. [Google Scholar] [CrossRef]
  78. Neural Regeneration Research. Available online: https://journals.lww.com/nrronline/fulltext/2025/04000/evidence_supporting_the_relationship_between.26.aspx (accessed on 20 May 2025).
  79. Liao, J.; Yan, W.; Zhang, Y.; Berhane, K.; Chen, W.; Yang, Z.; Qiu, C.; Ge, Y.; Bai, Z.; Han, B.; et al. Associations of preconception air pollution exposure with growth trajectory in young children: A prospective cohort study. Environ. Res. 2025, 267, 120665. [Google Scholar] [CrossRef]
  80. Gardner, R.M.; Brynge, M.; Sjöqvist, H.; Dalman, C.; Karlsson, H. Maternal Immune Activation and Autism in Offspring: What Is the Evidence for Causation? Biol. Psychiatry 2024, 97, 1127–1138. [Google Scholar] [CrossRef]
  81. Brynge, M. Immune Dysregulation in Early Life and Risk of Autism. Ph.D. Thesis, Karolinska Institutet, Stockholm, Sweden, 2022. [Google Scholar]
  82. McLellan, J.; Kim, D.H.J.; Bruce, M.; Ramirez-Celis, A.; Van de Water, J. Maternal Immune Dysregulation and Autism–Understanding the Role of Cytokines, Chemokines and Autoantibodies. Front. Psychiatry 2022, 13, 834910. [Google Scholar] [CrossRef]
  83. Robinson-Agramonte, M.D.L.A.; Noris García, E.; Fraga Guerra, J.; Vega Hurtado, Y.; Antonucci, N.; Semprún-Hernández, N.; Schultz, S.; Siniscalco, D. Immune Dysregulation in Autism Spectrum Disorder: What Do We Know about It? Int. J. Mol. Sci. 2022, 23, 3033. [Google Scholar] [CrossRef]
  84. Kumar, P.; Kumar, A.; Kumar, D.; Prajapati, K.B.; Mahajan, A.K.; Pant, D.; Yadav, A.; Giri, A.; Manda, S.; Bhandari, S.; et al. Microplastics influencing aquatic environment and human health: A review of source, determination, distribution, removal, degradation, management strategy and future perspective. J. Environ. Manag. 2025, 375, 124249. [Google Scholar] [CrossRef]
  85. Thongkorn, S.; Kanlayaprasit, S.; Kasitipradit, K.; Lertpeerapan, P.; Panjabud, P.; Hu, V.W.; Jindatip, D.; Sarachana, T. Investigation of autism-related transcription factors underlying sex differences in the effects of bisphenol A on transcriptome profiles and synaptogenesis in the offspring hippocampus. Biol. Sex Differ. 2023, 14, 8. [Google Scholar] [CrossRef] [PubMed]
  86. Parenti, M.; Schmidt, R.J.; Ozonoff, S.; Shin, H.-M.; Tancredi, D.J.; Krakowiak, P.; Hertz-Picciotto, I.; Walker, C.K.; Slupsky, C.M. Maternal Serum and Placental Metabolomes in Association with Prenatal Phthalate Exposure and Neurodevelopmental Outcomes in the MARBLES Cohort. Metabolites 2022, 12, 829. [Google Scholar] [CrossRef] [PubMed]
  87. Parenti, M.; Slupsky, C.M. Disrupted Prenatal Metabolism May Explain the Etiology of Suboptimal Neurodevelopment: A Focus on Phthalates and Micronutrients and their Relationship to Autism Spectrum Disorder. Adv. Nutr. 2024, 15, 100279. [Google Scholar] [CrossRef]
  88. Symeonides, C.; Vacy, K.; Thomson, S.; Tanner, S.; Chua, H.K.; Dixit, S.; Mansell, T.; O’Hely, M.; Novakovic, B.; Herbstman, J.B.; et al. Male autism spectrum disorder is linked to brain aromatase disruption by prenatal BPA in multimodal investigations and 10HDA ameliorates the related mouse phenotype. Nat. Commun. 2024, 15, 6367. [Google Scholar] [CrossRef]
  89. Stevens, S.; McPartland, M.; Bartosova, Z.; Skåland, H.S.; Völker, J.; Wagner, M. Plastic Food Packaging from Five Countries Contains Endocrine- and Metabolism-Disrupting Chemicals. Environ. Sci. Technol. 2024, 58, 4859–4871. [Google Scholar] [CrossRef]
  90. Su, Z.; Kong, R.; Huang, C.; Wang, K.; Liu, C.; Gu, X.; Wang, H.-L. Exposure to polystyrene nanoplastics causes anxiety and depressive-like behavior and down-regulates EAAT2 expression in mice. Arch. Toxicol. 2025, 99, 2595–2609. [Google Scholar] [CrossRef] [PubMed]
  91. Lin, J.; Pan, D.; Zhu, Y.; Shen, B.; Sun, Z.; Zheng, Y.; Yin, Y.; Huang, C.; Wu, W.; Song, Y.; et al. Polystyrene nanoplastics chronic exposure cause zebrafish visual neurobehavior toxicity through TGFβ-crystallin axis. J. Hazard. Mater. 2025, 492, 138255. [Google Scholar] [CrossRef]
  92. Zaheer, J.; Kim, H.; Ko, I.O.; Jo, E.-K.; Choi, E.-J.; Lee, H.-J.; Shim, I.; Woo, H.; Choi, J.; Kim, G.-H.; et al. Pre/post-natal exposure to microplastic as a potential risk factor for autism spectrum disorder. Environ. Int. 2022, 161, 107121. [Google Scholar] [CrossRef]
  93. Cortés-Albornoz, M.C.; García-Guáqueta, D.P.; Velez-van-Meerbeke, A.; Talero-Gutiérrez, C. Maternal Nutrition and Neurodevelopment: A Scoping Review. Nutrients 2021, 13, 3530. [Google Scholar] [CrossRef]
  94. Siracusano, M.; Riccioni, A.; Abate, R.; Benvenuto, A.; Curatolo, P.; Mazzone, L. Vitamin D Deficiency and Autism Spectrum Disorder. Curr. Pharm. Des. 2020, 26, 2460–2474. [Google Scholar] [CrossRef]
  95. Zwierz, M.; Suprunowicz, M.; Mrozek, K.; Pietruszkiewicz, J.; Oracz, A.J.; Konarzewska, B.; Waszkiewicz, N. Vitamin B12 and Autism Spectrum Disorder: A Review of Current Evidence. Nutrients 2025, 17, 1220. [Google Scholar] [CrossRef] [PubMed]
  96. Pancheva, R.; Toneva, A.; Bocheva, Y.; Georgieva, M.; Koleva, K.; Yankov, I. Prevalence of vitamin D deficiency in children with cerebral palsy and autism spectrum disorder: A comparative pilot study. Folia Med. 2024, 66, 787–794. [Google Scholar] [CrossRef] [PubMed]
  97. Stefanyshyn, V.; Stetsyuk, R.; Hrebeniuk, O.; Ayoub, G.; Fishchuk, L.; Rossokha, Z.; Gorovenko, N. Analysis of the Association Between the SLC19A1 Genetic Variant (rs1051266) and Autism Spectrum Disorders, Cerebral Folate Deficiency, and Clinical and Laboratory Parameters. J. Mol. Neurosci. 2025, 75, 42. [Google Scholar] [CrossRef]
  98. Gusso, D.; Prauchner, G.R.K.; Rieder, A.S.; Wyse, A.T.S. Biological Pathways Associated with Vitamins in Autism Spectrum Disorder. Neurotox. Res. 2023, 41, 730–740. [Google Scholar] [CrossRef] [PubMed]
  99. Kacimi, F.E.; Ed-Day, S.; Didou, L.; Azzaoui, F.Z.; Ramchoun, M.; Arfaoui, A.; Boulbaroud, S. Narrative Review: The Effect of Vitamin A Deficiency on Gut Microbiota and Their Link with Autism Spectrum Disorder. J. Diet Suppl. 2024, 21, 116–134. [Google Scholar] [CrossRef]
  100. Kacimi, F.E.; Didou, L.; Ed Day, S.; Azzaoui, F.Z.; Ramchoun, M.; Berrougui, H.; Khalki, H.; Boulbaroud, S. Gut microbiota, vitamin A deficiency and autism spectrum disorder: An interconnected trio—A systematic review. Nutr. Neurosci. 2025, 28, 492–502. [Google Scholar] [CrossRef]
  101. Savino, R.; Medoro, A.; Ali, S.; Scapagnini, G.; Maes, M.; Davinelli, S. The Emerging Role of Flavonoids in Autism Spectrum Disorder: A Systematic Review. J. Clin. Med. 2023, 12, 3520. [Google Scholar] [CrossRef]
  102. Schimansky, S.; Jasim, H.; Pope, L.; Hinds, P.; Fernandez, D.; Choleva, P.; Dev Borman, A.; Sharples, P.M.; Smallbone, T.; Atan, D. Nutritional blindness from avoidant-restrictive food intake disorder—Recommendations for the early diagnosis and multidisciplinary management of children at risk from restrictive eating. Arch. Dis. Child 2024, 109, 181–187. [Google Scholar] [CrossRef]
  103. Panchawagh, S.J.; Kumar, P.; Srikumar, S.; Sarkar, M.; Ashok, T.; Gupta, S.; Shaikh, H.; Soumya, K.S.; Poorvikha, S.; Abhishek, K.; et al. Role of Micronutrients in the Management of Autism Spectrum Disorders: A Systematic Review and Meta-Analysis. Indian J. Med. Spec. 2023, 14, 187–196. [Google Scholar] [CrossRef]
  104. Sumathi, T.; Manivasagam, T.; Thenmozhi, A.J. The Role of Gluten in Autism. Adv. Neurobiol. 2020, 24, 469–479. [Google Scholar] [CrossRef]
  105. Woo, T.; King, C.; Ahmed, N.I.; Cordes, M.; Nistala, S.; Will, M.J.; Bloomer, C.; Kibiryeva, N.; Rivera, R.M.; Talebizadeh, Z.; et al. microRNA as a Maternal Marker for Prenatal Stress-Associated ASD, Evidence from a Murine Model. J. Pers. Med. 2023, 13, 1412. [Google Scholar] [CrossRef] [PubMed]
  106. Love, C.; Sominsky, L.; O’Hely, M.; Berk, M.; Vuillermin, P.; Dawson, S.L. Prenatal environmental risk factors for autism spectrum disorder and their potential mechanisms. BMC Med. 2024, 22, 393. [Google Scholar] [CrossRef]
  107. Subashi, E.; Lemaire, V.; Petroni, V.; Pietropaolo, S. The Impact of Mild Chronic Stress and Maternal Experience in the Fmr1 Mouse Model of Fragile X Syndrome. Int. J. Mol. Sci. 2023, 24, 11398. [Google Scholar] [CrossRef] [PubMed]
  108. Ahmavaara, K.; Ayoub, G. Stress and Folate Impact Neurodevelopmental Disorders. J. Health Care Res. 2024, 5, 1–6. [Google Scholar] [CrossRef]
  109. Hoover, D.W.; Kaufman, J. Adverse childhood experiences in children with autism spectrum disorder. Curr. Opin. Psychiatry 2018, 31, 128–132. [Google Scholar] [CrossRef]
  110. Liu, K.; Garcia, A.; Park, J.J.; Toliver, A.A.; Ramos, L.; Aizenman, C.D. Early Developmental Exposure to Fluoxetine and Citalopram Results in Different Neurodevelopmental Outcomes. Neuroscience 2021, 467, 110–121. [Google Scholar] [CrossRef]
  111. Bravo, K.; González-Ortiz, M.; Beltrán-Castillo, S.; Cáceres, D.; Eugenín, J. Development of the Placenta and Brain Are Affected by Selective Serotonin Reuptake Inhibitor Exposure During Critical Periods. In Advances in Maternal-Fetal Biomedicine: Cellular and Molecular Mechanisms of Pregnancy Pathologies; Gonzalez-Ortiz, M., Ed.; Springer International Publishing: Cham, Switzerland, 2023; pp. 179–198. [Google Scholar] [CrossRef]
  112. Arzuaga, A.L.; Teneqexhi, P.; Amodeo, K.; Larson, J.R.; Ragozzino, M.E. Prenatal stress and fluoxetine exposure in BTBR and B6 mice differentially affects autism-like behaviors in adult male and female offspring. Physiol. Behav. 2025, 295, 114891. [Google Scholar] [CrossRef]
  113. Arzuaga, A.L.; Edmison, D.D.; Mroczek, J.; Larson, J.; Ragozzino, M.E. Prenatal stress and fluoxetine exposure in mice differentially affect repetitive behaviors and synaptic plasticity in adult male and female offspring. Behav. Brain Res. 2023, 436, 114114. [Google Scholar] [CrossRef]
  114. Lan, Z.; Tachibana, R.O.; Kanno, K. Chronic exposure of female mice to selective serotonin reuptake inhibitors during lactation induces vocal behavior deficits in pre-weaned offspring. Pharmacol. Biochem. Behav. 2023, 230, 173606. [Google Scholar] [CrossRef]
  115. Croen, L.A.; Ames, J.L.; Qian, Y.; Alexeeff, S.; Ashwood, P.; Gunderson, E.P.; Wu, Y.W.; Boghossian, A.S.; Yolken, R.; Van de Water, J.; et al. Inflammatory Conditions During Pregnancy and Risk of Autism and Other Neurodevelopmental Disorders. Biol. Psychiatry Glob. Open Sci. 2024, 4, 39–50. [Google Scholar] [CrossRef]
  116. Ye, W.; Luo, C.; Zhou, J.; Liang, X.; Wen, J.; Huang, J.; Zeng, Y.; Wu, Y.; Gao, Y.; Liu, Z.; et al. Association between maternal diabetes and neurodevelopmental outcomes in children: A systematic review and meta-analysis of 202 observational studies comprising 56·1 million pregnancies. Lancet Diabetes Endocrinol. 2025, 13, 494–504. [Google Scholar] [CrossRef] [PubMed]
  117. Fardous, A.M.; Heydari, A.R. Uncovering the Hidden Dangers and Molecular Mechanisms of Excess Folate: A Narrative Review. Nutrients 2023, 15, 4699. [Google Scholar] [CrossRef] [PubMed]
  118. Miraglia, N.; Dehay, E. Folate Supplementation in Fertility and Pregnancy: The Advantages of (6S)5-Methyltetrahydrofolate. Altern Ther. Health Med. 2022, 28, 12–17. [Google Scholar] [PubMed]
  119. Xu, X.; Zhang, Z.; Lin, Y.; Xie, H. Risk of Excess Maternal Folic Acid Supplementation in Offspring. Nutrients 2024, 16, 755. [Google Scholar] [CrossRef]
  120. Ledowsky, C.J.; Schloss, J.; Steel, A. Variations in folate prescriptions for patients with the MTHFR genetic polymorphisms: A case series study. Explor. Res. Clin. Soc. Pharm. 2023, 10, 100277. [Google Scholar] [CrossRef]
  121. Maguire, G. Vaccine Induced Autoimmunity May Cause Autism and Neurological Disorders. Arch. Microbiol. Immunol. 2025, 9, 103–132. [Google Scholar] [CrossRef]
  122. Sotelo-Orozco, J.; Schmidt, R.J.; Slupsky, C.M.; Hertz-Picciotto, I. Investigating the Urinary Metabolome in the First Year of Life and Its Association with Later Diagnosis of Autism Spectrum Disorder or Non-Typical Neurodevelopment in the MARBLES Study. Int. J. Mol. Sci. 2023, 24, 9454. [Google Scholar] [CrossRef]
  123. Parenti, M.; Shoff, S.; Sotelo-Orozco, J.; Hertz-Picciotto, I.; Slupsky, C.M. Metabolomics of mothers of children with autism, idiopathic developmental delay, and Down syndrome. Sci. Rep. 2024, 14, 31981. [Google Scholar] [CrossRef]
  124. Parenti, M.; Schmidt, R.J.; Tancredi, D.J.; Hertz-Picciotto, I.; Walker, C.K.; Slupsky, C.M. Neurodevelopment and Metabolism in the Maternal-Placental-Fetal Unit. JAMA Netw. Open 2024, 7, e2413399. [Google Scholar] [CrossRef]
  125. Merchak, A.R.; Bolen, M.L.; Tansey, M.G.; Menees, K.B. Thinking outside the brain: Gut microbiome influence on innate immunity within neurodegenerative disease. Neurotherapeutics 2024, 21, e00476. [Google Scholar] [CrossRef]
  126. Sanctuary, M.R.; Kain, J.N.; Chen, S.Y.; Kalanetra, K.; Lemay, D.G.; Rose, D.R.; Yang, H.T.; Tancredi, D.J.; German, J.B.; Slupsky, C.M.; et al. Pilot study of probiotic/colostrum supplementation on gut function in children with autism and gastrointestinal symptoms. PLoS ONE 2019, 14, e0210064. [Google Scholar] [CrossRef]
  127. Dossaji, Z.; Khattak, A.; Tun, K.M.; Hsu, M.; Batra, K.; Hong, A.S. Efficacy of Fecal Microbiota Transplant on Behavioral and Gastrointestinal Symptoms in Pediatric Autism: A Systematic Review. Microorganisms 2023, 11, 806. [Google Scholar] [CrossRef]
  128. Zang, Y.; Lai, X.; Li, C.; Ding, D.; Wang, Y.; Zhu, Y. The Role of Gut Microbiota in Various Neurological and Psychiatric Disorders-An Evidence Mapping Based on Quantified Evidence. Mediat. Inflamm. 2023, 2023, 5127157. [Google Scholar] [CrossRef]
  129. Lagod, P.P.; Abdelli, L.S.; Naser, S.A. An In Vivo Model of Propionic Acid-Rich Diet-Induced Gliosis and Neuro-Inflammation in Mice (FVB/N-Tg(GFAPGFP)14Mes/J): A Potential Link to Autism Spectrum Disorder. Int. J. Mol. Sci. 2024, 25, 8093. [Google Scholar] [CrossRef] [PubMed]
  130. Retuerto, M.; Al-Shakhshir, H.; Herrada, J.; McCormick, T.S.; Ghannoum, M.A. Analysis of Gut Bacterial and Fungal Microbiota in Children with Autism Spectrum Disorder and Their Non-Autistic Siblings. Nutrients 2024, 16, 3004. [Google Scholar] [CrossRef] [PubMed]
  131. Plaza-Díaz, J.; Gómez-Fernández, A.; Chueca, N.; Torre-Aguilar, M.J.D.L.; Gil, Á.; Perez-Navero, J.L.; Flores-Rojas, K.; Martín-Borreguero, P.; Solis-Urra, P.; Ruiz-Ojeda, F.J.; et al. Autism Spectrum Disorder (ASD) with and without Mental Regression is Associated with Changes in the Fecal Microbiota. Nutrients 2019, 11, 337. [Google Scholar] [CrossRef] [PubMed]
  132. Brister, D.; Rose, S.; Delhey, L.; Tippett, M.; Jin, Y.; Gu, H.; Frye, R.E. Metabolomic Signatures of Autism Spectrum Disorder. J. Pers. Med. 2022, 12, 1727. [Google Scholar] [CrossRef]
  133. Che, X.; Roy, A.; Bresnahan, M.; Mjaaland, S.; Reichborn-Kjennerud, T.; Magnus, P.; Stoltenberg, C.; Shang, Y.; Zhang, K.; Susser, E.; et al. Metabolomic analysis of maternal mid-gestation plasma and cord blood in autism spectrum disorders. Mol. Psychiatry 2023, 28, 2355–2369. [Google Scholar] [CrossRef]
  134. Vacy, K.; Thomson, S.; Moore, A.; Eisner, A.; Tanner, S.; Pham, C.; Saffery, R.; Mansell, T.; Burgner, D.; Collier, F.; et al. Cord blood lipid correlation network profiles are associated with subsequent attention-deficit/hyperactivity disorder and autism spectrum disorder symptoms at 2 years: A prospective birth cohort study. eBioMedicine 2024, 100, 104949. [Google Scholar] [CrossRef]
  135. Nabetani, M.; Mukai, T.; Taguchi, A. Cell Therapies for Autism Spectrum Disorder Based on New Pathophysiology: A Review. Cell Transpl. 2023, 32, 9636897231163217. [Google Scholar] [CrossRef]
  136. Ahrens, A.P.; Hyötyläinen, T.; Petrone, J.R.; Igelström, K.; George, C.D.; Garrett, T.J.; Orešič, M.; Triplett, E.W.; Ludvigsson, J. Infant microbes and metabolites point to childhood neurodevelopmental disorders. Cell 2024, 187, 1853–1873.e15. [Google Scholar] [CrossRef] [PubMed]
  137. Freitas, B.C.; Beltrão-Braga, P.C.B.; Marchetto, M.C. Modeling Inflammation on Neurodevelopmental Disorders Using Pluripotent Stem Cells. Adv. Neurobiol. 2020, 25, 207–218. [Google Scholar] [CrossRef] [PubMed]
  138. Tsukada, T.; Shimada, H.; Sakata-Haga, H.; Iizuka, H.; Hatta, T. Molecular mechanisms underlying the models of neurodevelopmental disorders in maternal immune activation relevant to the placenta. Congenit. Anom. 2019, 59, 81–87. [Google Scholar] [CrossRef] [PubMed]
  139. Frye, R.E.; Rincon, N.; McCarty, P.J.; Brister, D.; Scheck, A.C.; Rossignol, D.A. Biomarkers of mitochondrial dysfunction in autism spectrum disorder: A systematic review and meta-analysis. Neurobiol. Dis. 2024, 197, 106520. [Google Scholar] [CrossRef]
  140. Amini-Khoei, H.; Taei, N.; Dehkordi, H.T.; Lorigooini, Z.; Bijad, E.; Farahzad, A.; Madiseh, M.R. Therapeutic Potential of Ocimum basilicum L. Extract in Alleviating Autistic-Like Behaviors Induced by Maternal Separation Stress in Mice: Role of Neuroinflammation and Oxidative Stress. Phytother. Res. 2025, 39, 64–76. [Google Scholar] [CrossRef]
  141. Nguyen, V.; Tang, J.; Oroudjev, E.; Lee, C.J.; Marasigan, C.; Wilson, L.; Ayoub, G. Cytotoxic Effects of Bilberry Extract on MCF7-GFP-Tubulin Breast Cancer Cells. J. Med. Food 2010, 13, 278–285. [Google Scholar] [CrossRef]
  142. Lai, M.; Lee, J.; Chiu, S.; Charm, J.; So, W.Y.; Yuen, F.P.; Kwok, C.; Tsoi, J.; Lin, Y.; Zee, B. A machine learning approach for retinal images analysis as an objective screening method for children with autism spectrum disorder. eClinicalMedicine 2020, 28, 100588. [Google Scholar] [CrossRef]
  143. Farooq, M.S.; Tehseen, R.; Sabir, M.; Atal, Z. Detection of autism spectrum disorder (ASD) in children and adults using machine learning. Sci. Rep. 2023, 13, 9605. [Google Scholar] [CrossRef]
Figure 1. Schematic of maternal inflammatory categories and their impact on fetal developments that can lead to conditions conducive for the development of autism spectrum disorder. Genetic propensity and agents causing oxidative stress are presumed to be the preconditions for neurodivergent fetal development. A similar schematic is seen in Figure 2 for the child.
Figure 1. Schematic of maternal inflammatory categories and their impact on fetal developments that can lead to conditions conducive for the development of autism spectrum disorder. Genetic propensity and agents causing oxidative stress are presumed to be the preconditions for neurodivergent fetal development. A similar schematic is seen in Figure 2 for the child.
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Figure 2. Schematic of inflammatory categories in childhood and their impact on early childhood developments that can lead to autism spectrum disorder symptoms. Genetic propensity and agents causing oxidative stress are presumed to be the preconditions for neurodivergent development.
Figure 2. Schematic of inflammatory categories in childhood and their impact on early childhood developments that can lead to autism spectrum disorder symptoms. Genetic propensity and agents causing oxidative stress are presumed to be the preconditions for neurodivergent development.
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Figure 3. The development of autism spectrum disorder is multifactorial, with factors related to oxidative stress presumably triggering the various symptoms observed. Adapted from Ayoub (2024) [43].
Figure 3. The development of autism spectrum disorder is multifactorial, with factors related to oxidative stress presumably triggering the various symptoms observed. Adapted from Ayoub (2024) [43].
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Table 1. Commonly referenced genes in SFARI, AutDB, and ClinVar, categorized by area of developmental impact [18].
Table 1. Commonly referenced genes in SFARI, AutDB, and ClinVar, categorized by area of developmental impact [18].
CategoryGenesDevelopmental Impact
SynapticADNP, UBE3A, GABRB3, MECP2, NRXN1, SHANK3, GRIN2BCell Junction Organization
ADNP, UBE3A, GABRB3, MECP2, NRXN1, SHANK3, GRIN2BSynapse Organization
ADNP, STXBP1, GABRB3, MECP2, NRXN1, SHANK3, GRIN2BChemical Synaptic Transmission
ADNP, GABRB3, MECP2, NRXN1, SHANK3Synapse Assembly
Social/
Behavioral
CHD8, MECP2, NRXN1, SHANK3Biological Processes in Intraspecies Interaction
CHD8, MECP2, NRXN1, SHANK3Social Behavior
Neuronal/
Cellular
TRIO, ADNP, UBE3A, STXBP1, AUTS2, MECP2, NRXN1, TCF4, SHANK3Neuron Differentiation
TRIO, ADNP, UBE3A, STXBP1, AUTS2, MECP2, NRXN1, SHANK3Neuron Projection Development
TRIO, ADNP, UBE3A, STXBP1, AUTS2, NRXN1, SHANK3Cell Morphogenesis in Differentiation
TRIO, ADNP, UBE3A, STXBP1, AUTS2, NRXN1, SHANK3Cell Part Morphogenesis
Table 2. Compilation of references linking specific genes with pathways in ASD.
Table 2. Compilation of references linking specific genes with pathways in ASD.
CategoryGenePathways and References
Neuronal/
Cellular
MAPK1MAPK signaling, Calcium signaling [23]
MAPK3MAPK signaling, Calcium signaling [23]
HRASMAPK signaling, Calcium signaling [23]
PRKCBCalcium signaling, MAPK signaling [23]
BRAFMAPK signaling, Calcium signaling [23]
CORO1ANeuron function, Immune response [4]
SynapticSCN2ASynaptic development (M16 module) [24]
SHANK2Synaptic development (M16 module) [24]
NRXN1Synaptic development (M16 module) [24]
GRIN2BSynaptic transmission [4]
Cellular/
Metabolic
CNDP1mTOR pathway (neurodevelopment) [25]
PDE4DmTOR pathway (neurodevelopment) [25]
ULK2mTOR pathway (neurodevelopment) [25]
CHD8Gene expression in development [4]
PTENCellular development [4]
Table 3. SFARI-referenced genes that may be involved in commonly identified ASD comorbidities [10,37,38].
Table 3. SFARI-referenced genes that may be involved in commonly identified ASD comorbidities [10,37,38].
Comorbid ConditionSFARI ASD Gene
ADHDPPP3CB, PRKG1
Anxiety DisorderADCYAP1R1, DLGAP4, NPPB, BRP1, VIPR2
Bipolar DisorderADAM10, ADCY9, ADCYAP1R1, AKT1, DLGAP4, HSPA1L, MEGF10, NDE1, NPPB, BRP1, VIPR2
Depressive DisorderADCY9, AKT1, DGCR8, HSPA1L, VIPR2
EpilepsyADCY9, AKT1, ATN1, KCNH2, MMP2, NDE1, SLC29A2, SMARCA2, VIPR2
OCDADCYAP1R1, DLGAP4, NPPB, NRP1, BIPR2
Panic DisorderADCYAP1R1, DLGAP4, NPPB, NRP1, BIPR2
SchizophreniaADCY9, AKT1, ATN1, DGCR8, DLGAP4, HSPA1L, KCNH2, MEGF10, NDE1, PPP3CB, PRKG1, SMARCA2, VIPR2
Sleep DisordersADAM10, ADCY9, ATN1, DGCR8, DLGAP4, KCNH2, MEGF10, NPPB, NRP1, PPP3CB, PRKG1, SLC29A2, SMARCA2
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Ayoub, G. Autism Spectrum Disorder as a Multifactorial Disorder: The Interplay of Genetic Factors and Inflammation. Int. J. Mol. Sci. 2025, 26, 6483. https://doi.org/10.3390/ijms26136483

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Ayoub G. Autism Spectrum Disorder as a Multifactorial Disorder: The Interplay of Genetic Factors and Inflammation. International Journal of Molecular Sciences. 2025; 26(13):6483. https://doi.org/10.3390/ijms26136483

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Ayoub, George. 2025. "Autism Spectrum Disorder as a Multifactorial Disorder: The Interplay of Genetic Factors and Inflammation" International Journal of Molecular Sciences 26, no. 13: 6483. https://doi.org/10.3390/ijms26136483

APA Style

Ayoub, G. (2025). Autism Spectrum Disorder as a Multifactorial Disorder: The Interplay of Genetic Factors and Inflammation. International Journal of Molecular Sciences, 26(13), 6483. https://doi.org/10.3390/ijms26136483

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