Next Article in Journal
Inflammation and Oxidative Stress Gene Variability in Retinal Detachment Patients with and without Proliferative Vitreoretinopathy
Next Article in Special Issue
Characteristics of Neuroimaging and Behavioural Phenotype in Polish Patients with PIGV-CDG—An Observational Study and Literature Review
Previous Article in Journal
Nucleotide Context Can Modulate Promoter Strength in Genes Transcribed by RNA Polymerase III
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 14
Issue 4
10.3390/genes14040803
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Biochemical, Genetic and Clinical Diagnostic Approaches to Autism-Associated Inherited Metabolic Disorders

by
Udara D. Senarathne
1,2,
Neluwa-Liyanage R. Indika
1,
Aleksandra Jezela-Stanek
3,
Elżbieta Ciara
4,
Richard E. Frye
5,
Cliff Chen
6 and
Karolina M. Stepien
7,8,*
1
Department of Biochemistry, Faculty of Medical Sciences, University of Sri Jayewardenepura, Nugegoda 10250, Sri Lanka
2
Department of Chemical Pathology, Monash Health Pathology, Monash Health, Melbourne, VIC 3168, Australia
3
Department of Genetics and Clinical Immunology, National Institute of Tuberculosis and Lung Diseases, 01-138 Warsaw, Poland
4
Department of Medical Genetics, The Children’s Memorial Health Institute, 04-730 Warsaw, Poland
5
Autism Discovery and Treatment Foundation, Phoenix, AZ 85050, USA
6
Clinical Neuropsychology Department, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford M6 8HD, UK
7
Adult Inherited Metabolic Diseases, Mark Holland Unit, Salford Royal NHS Foundation Trust, Salford M6 8HD, UK
8
Division of Diabetes, Endocrinology and Gastroenterology, University of Manchester, Manchester M13 9PL, UK
*
Author to whom correspondence should be addressed.
Genes 2023, 14(4), 803; https://doi.org/10.3390/genes14040803
Submission received: 20 February 2023 / Revised: 22 March 2023 / Accepted: 22 March 2023 / Published: 27 March 2023
(This article belongs to the Special Issue Genetic Diversity - Recent Advances and Applications in Inherited Metabolic Diseases)
Review Reports Versions Notes

Abstract

:
Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders characterized by impaired social interaction, limited communication skills, and restrictive and repetitive behaviours. The pathophysiology of ASD is multifactorial and includes genetic, epigenetic, and environmental factors, whereas a causal relationship has been described between ASD and inherited metabolic disorders (IMDs). This review describes biochemical, genetic, and clinical approaches to investigating IMDs associated with ASD. The biochemical work-up includes body fluid analysis to confirm general metabolic and/or lysosomal storage diseases, while the advances and applications of genomic testing technology would assist with identifying molecular defects. An IMD is considered likely underlying pathophysiology in ASD patients with suggestive clinical symptoms and multiorgan involvement, of which early recognition and treatment increase their likelihood of achieving optimal care and a better quality of life.

1. Introduction

Autism spectrum disorders (ASD) are defined by the revised version of the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5-TR) as neuro-developmental disorders characterized by persistent deficits in reciprocal social communication and social interaction (Criterion A) and restricted and/or repetitive patterns of behaviour, interests, or activities (Criterion B), present from early childhood (Criterion C) causing clinically significant impairment in social, occupational, or other important areas of current functioning (Criterion D). The symptoms under criteria A and B are not better explained by intellectual disability or global developmental delay (Criterion E). It is known as a “spectrum” disorder because there is wide variability in the severity and pattern of symptoms, progression of the disease, and prognosis. ASD is accompanied by varying degrees of intellectual impairment and is associated with many genetic, neurodevelopmental, mental, and behavioural disorders and environmental factors [1].
The global prevalence of ASD is estimated to be 0.4–1%, with American and European countries having a higher prevalence (1%) compared to Asian (0.4%) countries [2]. The biannual estimates by ASD and the Autism and Developmental Disabilities Monitoring (ADDM) Network report a two-fold increase in ASD prevalence among 8-year-old children in the United States during the past decade, from 2008 (1.1%) to 2018 (2.3%), with boys being four times more affected than girls [3].
The etiological factors and the proposed pathogenic mechanisms in ASD are intricate and involve the interaction of genetic, epigenetic, and environmental elements [4]. The main dysregulations, including intestinal dysbiosis [5], immune dysfunction, metabolic dysfunction [6], and metal dyshomeostasis [7], have been identified using molecular biomarkers, such as gut microbiome-related metabolites, cytokine profile, autoantibodies, metabolites, and vitamin and mineral profiles [8]. These are interconnected; hence, an ASD individual can exhibit several dysfunctions. For instance, a child with gut microbial dysbiosis may exhibit core symptoms of ASD along with immune dysfunction, altered microbial metabolites, and epigenetic changes [9,10]. The gut microbiome may modulate the gut–brain axis through microbiota-derived signalling molecules, immune mediators, gastrointestinal hormones, etc. [10,11].
Although each inherited metabolic disorder (IMD) is rare in isolation, collectively, all IMDs have a combined incidence of 1:800 to 1:2500 [12,13,14]. Contributing to the heterogeneity, a number of IMD, such as 22q11.2 deletion, Angelman syndrome, Cohen syndrome, Noonan syndrome, and fragile X syndrome, are known to associate with autistic symptoms, corroborating the evidence for genetic etiologies of ASD [15,16,17,18,19,20,21]. It should also be noted that the symptoms of ASD can be present in association with many IMDs [16]. The prevalence of IMDs among ASD individuals has been estimated in different studies, ranging from 0.7% to 2.7% [22,23,24]. However, the true prevalence of IMDs among ASD patients has been speculated to be higher [25], corroborating the finding of more than 30% of ASD individuals having some form of metabolic derangement by Spilioti et al. [24]. Furthermore, more than 50% of the IMDs present with neurodevelopmental symptoms, and ASD primarily being a neurodevelopmental disorder, the rational investigation of ASD individuals for probable IMDs is appropriate, especially in communities with a high level of consanguinity [26,27,28].
To date, more than 100 autism-risk genes have been identified with a genetic cause found in 10–20% of the cases of ASD during the investigation process [22,29]. Table 1 elaborates the IMDs associated with ASDs. A whole-exome sequencing (WES) study identified that 52% were nuclear sequence-level variants, 46% were nuclear structural variants, and 2% were mitochondrial variants [30]. Exome sequencing is identified as a first-tier clinical diagnostic test for individuals with neurodevelopmental disorders, including ASD, with a molecular diagnostic yield of 16% (CI: 11–24%) in ASD [31]. Additionally, chromosomal microarray (CMA) has revealed definitively pathogenic copy number variants (CNVs) in 5.4% to 14% of individuals with ASD [32]. Even though ASD-associated biallelic genetic variants are commonly reported in consanguineous families, some were observed in non-consanguineous families. Siblings from consanguineous and/or multiplex families who share identical homozygous biallelic mutation may show variable ASD phenotypes with or without intellectual disability, epilepsy, and other clinical features [33]. Moreover, it is estimated that de novo variants in protein-coding genes contribute to risk in approximately 30% of simplex families. At the same time, de novo variants in non-coding regions of the genome (particularly gene promoters) also contribute to ASD pathogenesis [34]. De novo variants in the non-coding region highlight the current need to perform ASD genetic studies using whole genome sequencing (WGS) instead of traditional exome studies [35].
Large population-based studies on the outcome of metabolic investigations in screening for IMDs among ASD individuals are lacking; hence, accurate prevalence and diagnostic yield estimates are not available. Despite the observed low yield of routine metabolic testing in ASD individuals, the positive impact on ASD management may be significantly high as it paves the path to better understanding the underlying pathophysiology, inheritance pattern, and availability of treatment [32].
With the advancement of tandem mass spectrometry, most developed countries employ expanded newborn screening (NBS) as an important public health strategy, though it is yet to become a priority in developing countries [75,76]. Therefore, children who receive an expanded NBS have the advantage of receiving the diagnosis of an IMD, if present, before they would develop any autistic symptoms, whereas, in developing countries, ASD patients may only undergo targeted/selective screening for IMDs following the establishment of the primary diagnosis of a neurological condition or ASD. Selective screening for IMDs in ASD subjects may be a cost-effective strategy to identify the patients with IMDs that remain undiagnosed in countries where mass NBS for IMDs is unavailable. If the diagnosis of an IMD was established, treatment targeted at specific metabolic abnormalities in children with ASD has potential benefits [24,77]. For instance, identifying mitochondrial dysfunction enables the implementation of targeted therapies such as vitamins, carnitine, Coenzyme Q10, etc. [78,79]. In addition, research on advancing medical treatments, such as hematopoietic stem cell transplantation, is underway, offering hope for IMDs associated with ASD [80,81].
Investigating for IMDs associated with ASD should take a multidisciplinary approach led by a clinician (psychiatrist/neurologist/paediatrician/metabolic physician) with a well-informed team, including a chemical pathologist, clinical biochemist, and clinical geneticist. The primary aim of this endeavour is to understand the underlying pathophysiology, to provide genetic counselling where appropriate, and to determine the potential therapies. The success of investigating for IMDs in ASD patients depends on the understanding of different phenotypes of known syndromes and IMDs that overlap with ASD and defining a tailored biochemical and molecular evaluation plan catering to the needs of the individual patient based on their unique clinical information [82]. The assays to be considered as a screening panel should be based on common IMDs associated with ASD, the local epidemiology of IMDs in a given country, and available resources. In addition, the coverage of the existing NBS program should be considered in determining redundant investigations. This review outlines a step-by-step approach to the investigations of ASD when a diagnosis of an IMD is suspected.

2. Biochemical Investigations

The main biochemical mechanisms proposed in ASD include mitochondrial dysfunction [78,83], oxidative stress [84], impaired methylation capacity [85], and altered amino acid metabolism [86]. Interestingly, ASD patients had these metabolic abnormalities in the brain regions involved in speech and auditory processing, social behaviour, sensory and motor coordination, and memory, the core symptoms of ASD [87]. Corroborating the presence of mitochondrial dysfunction in ASD, a 2020 study demonstrated atypical mitochondrial morphology with mitochondrial electron transport chain abnormalities in the fibroblasts of children with ASD [88]. Furthermore, several mitochondrial functional biomarkers, such as lactate, pyruvate, carnitine, and ubiquinone, are significantly altered in ASD, while some even correlate with severity [78,83]. The impaired methylation capacity is evident by the significantly reduced S-adenosylmethionine/S-adenosylhomocysteine ratio (methylation index) in ASD, while reduced methionine levels and increased homocysteine levels indicate the impaired remethylation of homocysteine to methionine [84]. The reduced availability of the cofactors for the remethylation pathway in the brain may be attributed to reduced blood folate [84], vitamin B12 levels [84], low-activity variants of the genes (MTHFR, DHFR, FOLR1) affecting folate and cobalamin metabolism [58,89], and cerebral folate deficiency due to folate receptor α autoantibodies [90]. Deth et al. proposed a “redox/methylation hypothesis of autism” describing the pathogenesis of oxidative stress, precipitated by environmental factors in genetically vulnerable individuals, which limits the activity of methionine synthase due to its dependency on cobalamin and folate, hence impaired methylation, including dopamine-stimulated phospholipid methylation [91]. Reduced methylation capacity may also mediate epigenetic changes through modulating DNA and histone methylation in a sex-dependent manner [58]. These mechanisms are thought to induce changes in the neurotransmitter systems, such as γ-aminobutyric acid (GABA) and glutamate, serotonin, dopamine, melatonin, and acetylcholine [92], resulting in core symptoms and co-occurring behavioural and neurological symptoms.
Pathway enrichment analysis has demonstrated that microRNAs (miRNAs) differently expressed in ASD are involved in metabolic pathways, such as steroid biosynthesis, fatty acid metabolism, lysine degradation, and biotin metabolism [93]. Correspondingly ASD-associated IMDs can be mapped to disorders of the metabolism of amino acids, carbohydrates, fatty acids, sterol biosynthesis, ketone bodies, creatine, vitamins (B12, folate, and biotin), cofactors, nucleotide, mitochondrial metabolite repair, etc. Table 1 summarizes the reported IMDs in ASD according to the international classification of IMDs [94]. Importantly, these genetic and metabolic disorders are associated with marked cognitive impairment and other clinical features, such as macrocephaly, extrapyramidal signs, motor developmental delay, dysmorphic features, failure to thrive, or hepatosplenomegaly, which are atypical for patients with ASD [17,42].
In the context of complex pathogenesis with multiple co-morbidities and the evidence of metabolic involvement and strong heritable component, although the pathophysiology is not well comprehended, the current disease trajectory is developing towards a better understanding of the metabolic component of ASD in order to advance future interventions to improve the overall quality of life for ASD individuals [95,96,97]. The future developments are mainly focused on improving early and accurate diagnostic algorithms in unravelling the metabolic and other components of ASD. The diagnostic yield of metabolic investigations in patients with isolated ASD and no clinical symptoms appears to be low [22,23].
Before the establishment of NBS, a large proportion of these individuals would have been brought to medical attention only after the development of autistic symptoms. However, with the NBS programs, especially in developed countries, many such individuals are brought to medical attention early, and necessary treatments are instigated; a good example is phenylalanine ketonuria, an IMD characterized by intellectual disability and ASD [52,63,98]. Therefore, the diagnostic approach to ASD should be equipped with a rational consideration of possible IMDs, as some are treatable [54]. A recently suggested approach to such investigation is untargeted metabolomic profiling, as many ASD patients demonstrate a wide range of metabolic abnormalities, from micronutrient deficiencies to severe metabolic derangements [99]. The basis for untargeted metabolic profiling is the emerging evidence of potential novel biomarkers of IMD associated with ASD, as found in many cohort studies conducted worldwide, further opening new avenues of treatments [24]. However, the additional healthcare costs need to be considered while expanding the investigation profile to weigh the medical benefits of further testing, and it may not be cost-effective in non-syndromic ASD [99]. Schiff et al. demonstrated that the prevalence of IMD among non-syndromic ASD individuals is not higher than the general population (<0.5%) by conducting a systematic metabolic work-up, highlighting the importance of a rational approach to metabolomics in ASD [22] (Table 1, Table 2 and Table 3).

3. Genetic Investigations

ASD frequently occurs in some types of IMDs, typically alongside some degree of developmental delay, intellectual disability, or motor impairment. The true prevalence of IMDs among ASD individuals could be higher than what is currently estimated due to the possible missed diagnosis of some cases [25]. Due to the observed evidence of heritability, research on the genetic aspects of ASD was observed to advance as early as the 1960s, which initially focused on structural DNA alterations, whereas the investigations are moving towards gene expression and epigenetics in recent years [114]. Advances and applications of genomic testing technology, together with large-scale projects (DDD study [115], 100,000 genome study [116]), have significantly improved the diagnostic yield of metabolic work-up in ASD individuals.
A gender bias has been observed historically in the incidence of neurodevelopmental disorders, with a four-to-five-fold higher incidence in males, which is more prominent in individuals with a milder degree of cognitive impairment [117,118]. Although this phenomenon would point toward the possibility of an association with X-linked genes, only 10% of the reported male excess can be attributed to X-linked genes. Further, ASD recurrent rates among the siblings of a proband are estimated to be 4% and 7%, for males and females, respectively, with the less frequent gender exhibiting a higher recurrence rate [114,119].
CMA allows the detection of clinically relevant chromosomal abnormalities in up to 10% of ASD individuals [98,116]. Similarly, exome sequencing has contributed to identifying rare variants in a significant number of ASD patients, as there is no clear definition with biochemical testing and imaging studies in all cases of IMDs. In fact, some cases are altogether missed unless genetic testing with high-throughput sequencing is performed, such as WES. WES enables the identification of genetic changes (Table 1), mainly within the coding sequences of all genes in the human genome. The analysis of the sequence of all genes, instead of individual candidate genes, significantly reduces diagnostic time and may also enable the discovery of new pathogenic variants. The significant development of NGS sequencing technology in recent years has dramatically reduced the cost of extensive analysis, making it feasible for its application in routine diagnostics. Normally, WES studies covering the exome regarding protein-coding exons and short adjacent regions (e.g., edges of introns) analyse only 1–2% of the genome (NCBI, NIH, USA). However, 85–89% of known pathogenic SNVs (single nucleotide variants; NCBI, NIH, USA) are located in this small study area [120]. Exome comprises only a small percentage of the genome, whereas the bulk of genes are introns, i.e., regions that do not encode protein sequences. Only 19,969 are identified as protein-coding genes out of the 63,494 genes identified as per the current annotation of the human genome (T2T-CHM13) [121].
Recent studies have also pointed toward the role of epigenetic dysregulation, mechanisms with no impact on the DNA sequence, in ASD pathogenesis [122]. Epigenetics can be defined as the regulation of gene expression by modulating chromatin formation [123], and reported instances of such dysregulations include impaired DNA methylation in ASD individuals [124,125], pathogenic variants in the HIST1H1E gene encoding H1 histone linker protein [126], activating immune responses during pregnancy increasing susceptibility to ASD [127], and alteration of the miRNA expression in ASD patients [128]. The WES identifies alterations in most protein-coding genes. Some centres also detect alterations in the remainder of genome encoding, for example, regulatory RNAs, or non-coding areas of the genome containing known pathogenic alterations. An autistic multigene panel sequencing analysis, including SNV and CNV detection, is recommended to identify pathogenic changes effectively. Considering the high diagnostic yield of WES in children with congenital anomalies, developmental delay, or intellectual disability, the American College of Medical Genetics and Genomics (ACMG) strongly recommends that WES/WGS be considered as a first-tier or second-tier test [129]. Its clinical utility can have a significant effect on long-term patient management and, when considered early in the diagnostic evaluation, may offer more cost-effective avenues of treatment in the long run. Furthermore, some genetic variants could be confirmed with biochemical evidence, such as defective enzyme level assessment or demonstrating the pathognomonic biochemical picture.

4. Clinical and Neuropsychology Assessments

The current recommendation is that further metabolic investigation should always be guided by detailed medical and family history and a physical examination to identify suggestive features, such as consanguinity, multiplex families, dysmorphic features, epilepsy, neurocutaneous manifestations, and existing metabolic derangements [82,98].
In some IMDs, some salient features of ASD may correspond to an underlying pathology, which often includes multiorgan involvement, such as in lysosomal storage disorders. Table 1 presents a list of IMDs associated with ASD. Only a few IMDs would be characterised by isolated ASD as the main clinical presentation, particularly at the time of the diagnosis. Schiff et al. (2011) confirmed in their study that routine metabolic screening would not add to the causative diagnosis in non-syndromic ASD [22]. Therefore, although the relationship between ASD and IMDs is unquestionable, it is likely to be confined to the subset of rare patients with clinical symptoms.
Among the common co-morbidities of ASD, the prevalence of epilepsy is estimated as 5–38% in children with ASD [130]. Seizures affect 35–60% of patients with mitochondrial diseases [131], which confirms a coexisting pathology. Gastrointestinal dysfunction is another frequent co-morbidity of ASD [132], also seen in mitochondrial disease [100]. Sleep disturbances, especially when associated with the onset and maintenance of sleep, are commonly observed in ASD and can also implicate the behaviour during the daytime, including ASD symptoms [133].
A definitive test for ASD is not given by the DSM-5, the diagnostic reference for ASD. Therefore, a wide range of evaluation tools has been developed for the diagnosis of ASD, e.g., parental questionnaires, parental interviews, clinical judgments, and direct interactions. Of these tests, the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised are considered the gold standard for diagnosing ASD [134,135]. The index of daily functionality of an ASD individual should be assessed using adaptative behaviour scales, such as the Vineland Adaptive Behavior Scales (VABS) or Adaptive Behavior Assessment System [136]. However, the nature of the ASD definition, which is primarily based on individual behavioural observation, confounds the diagnosis, especially when an association with an IMD is suspected. Therefore, recent studies have focused on exploring more objective means of understanding the underlying pathogenesis of ASD by investigating specific biomarkers, which would further assist in the diagnosis of any associated IMDs. Thus, a multidisciplinary team of experienced professionals, including clinical psychologists, psychiatrists, paediatricians, and speech and language therapists, is recommended for accurate diagnosis and supportive treatment of ASD [137]. However, access to such services may be limited due to funding shortfalls, especially for adults with undiagnosed ASD, leading to worse functional outcomes. Further, the lack of understanding of the pathophysiology interferes with both diagnostic accuracy and planning for appropriate treatments [8].
Many of the IMDs associated with ASD fall under treatable IMDs, and hence can be managed with targeted (non) pharmacological interventions, reducing morbidity and mortality. For instance, urea cycle disorders can be treated with a protein-defined diet, arginine or citrulline, and nitrogen scavenging agents, whereas cerebral creatine deficiency syndromes can be treated with creatine, glycine, and arginine [138]. A proper clinical assessment is required prior to starting such therapies in order to monitor the response to therapy. The Autism Treatment Evaluation Checklist (ATEC) is a parent-completed assessment tool useful in measuring the effectiveness of various therapeutic interventions [139].

5. Conclusions

Metabolic screening is indicated in the diagnostic work-up of children with ASD, especially if they have multiorgan involvement. ASD individuals with IMDs may also exhibit some associated features, such as seizures, gastrointestinal disturbances, and speech problems, which require rapid and specialist intervention. The early recognition and diagnosis of any associated IMDs would increase their likelihood of achieving optimal care and a better quality of life. Furthermore, genetic and epigenetic breakthroughs of underlying pathologies have further paved new avenues to improve the overall health of ASD individuals. The quest for objective biomarkers in ASD and the underlying pathophysiology is still at its preliminary stage, and there is still a vacuum of knowledge yet to be filled with further clinical research to improve the evidence base for various treatment interventions in ASD with associated IMDs. Risk stratification, based on biomarkers with association to IMDs in ASD individuals, would enable improvement of the overall clinical management of ASD.

Author Contributions

K.M.S., U.D.S., N.-L.R.I., A.J.-S., E.C., R.E.F. and C.C.: conception and design, analysis, and interpretation of data, drafting the manuscript, revising the manuscript critically for important intellectual content. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Not applicable.

Data Availability Statement

No raw data is available for this review.

Acknowledgments

The authors would like to thank the patients and families for their participation in metabolic research.

Conflicts of Interest

The authors declare no conflict of interest.

Guarantor

KMS.

References

  1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; Text Revision; American psychiatric Association: Washington, DC, USA, 2022. [Google Scholar]
  2. Salari, N.; Rasoulpoor, S.; Rasoulpoor, S.; Shohaimi, S.; Jafarpour, S.; Abdoli, N.; Khaledi-Paveh, B.; Mohammadi, M. The global prevalence of autism spectrum disorder: A comprehensive systematic review and meta-analysis. Ital. J. Pediatr. 2022, 48, 112. [Google Scholar] [CrossRef]
  3. Kilmer, M.; Boykin, A.A. Analysis of the 2000 to 2018 autism and developmental disabilities monitoring network surveillance reports: Implications for primary care clinicians. J. Pediatr. Nurs. 2022, 65, 55–68. [Google Scholar] [CrossRef]
  4. Bhandari, R.; Paliwal, J.K.; Kuhad, A. Neuropsychopathology of Autism Spectrum Disorder: Complex Interplay of Genetic, Epigenetic, and Environmental Factors. Adv. Neurobiol. 2020, 24, 97–141. [Google Scholar] [CrossRef]
  5. Srikantha, P.; Mohajeri, M.H. The Possible Role of the Microbiota-Gut-Brain-Axis in Autism Spectrum Disorder. Int. J. Mol. Sci. 2019, 20, 2115. [Google Scholar] [CrossRef] [Green Version]
  6. Cheng, N.; Rho, J.M.; Masino, S.A. Metabolic Dysfunction Underlying Autism Spectrum Disorder and Potential Treatment Approaches. Front. Mol. Neurosci. 2017, 10, 34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Zhang, J.; Li, X.; Shen, L.; Khan, N.U.; Zhang, X.; Chen, L.; Zhao, H.; Luo, P. Trace elements in children with autism spectrum disorder: A meta-analysis based on case-control studies. J. Trace Elem. Med. Biol. 2021, 67, 126782. [Google Scholar] [CrossRef] [PubMed]
  8. Jensen, A.R.; Lane, A.L.; Werner, B.A.; McLees, S.E.; Fletcher, T.S.; Frye, R.E. Modern Biomarkers for Autism Spectrum Disorder: Future Directions. Mol. Diagn. Ther. 2022, 26, 483–495. [Google Scholar] [CrossRef] [PubMed]
  9. Alharthi, A.; Alhazmi, S.; Alburae, N.; Bahieldin, A. The Human Gut Microbiome as a Potential Factor in Autism Spectrum Disorder. Int. J. Mol. Sci. 2022, 23, 1363. [Google Scholar] [CrossRef]
  10. Sorboni, S.G.; Moghaddam, H.S.; Jafarzadeh-Esfehani, R.; Soleimanpour, S. A Comprehensive Review on the Role of the Gut Microbiome in Human Neurological Disorders. Clin. Microbiol. Rev. 2022, 35, e0033820. [Google Scholar] [CrossRef]
  11. Ristori, M.V.; Quagliariello, A.; Reddel, S.; Ianiro, G.; Vicari, S.; Gasbarrini, A.; Putignani, L. Autism, Gastrointestinal Symptoms and Modulation of Gut Microbiota by Nutritional Interventions. Nutrients 2019, 11, 2812. [Google Scholar] [CrossRef] [Green Version]
  12. Applegarth, D.A.; Toone, J.R.; Lowry, R.B. Incidence of inborn errors of metabolism in British Columbia, 1969–1996. Pediatrics 2000, 105, e10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Sanderson, S.; Green, A.; Preece, M.A.; Burton, H. The incidence of inherited metabolic disorders in the West Midlands, UK. Arch. Dis. Child. 2006, 91, 896–899. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Pampols, T. Inherited metabolic rare disease. Adv. Exp. Med. Biol. 2010, 686, 397–431. [Google Scholar] [CrossRef]
  15. Žigman, T.; Petković Ramadža, D.; Šimić, G.; Barić, I. Inborn Errors of Metabolism Associated With Autism Spectrum Disorders: Approaches to Intervention. Front. Neurosci. 2021, 15, 673600. [Google Scholar] [CrossRef]
  16. Simons, A.; Eyskens, F.; Glazemakers, I.; Van West, D. Can psychiatric childhood disorders be due to inborn errors of metabolism? Eur. Child Adolesc. Psychiatry 2017, 26, 143–154. [Google Scholar] [CrossRef] [Green Version]
  17. Frye, R.E.; Rossignol, D.A. Metabolic disorders and abnormalities associated with autism spectrum disorder. J. Pediatr. Biochem. 2012, 2, 181–191. [Google Scholar] [CrossRef]
  18. Caglayan, A.O. Genetic causes of syndromic and non-syndromic autism. Dev. Med. Child. Neurol. 2010, 52, 130–138. [Google Scholar] [CrossRef]
  19. Zafeiriou, D.I.; Ververi, A.; Dafoulis, V.; Kalyva, E.; Vargiami, E. Autism spectrum disorders: The quest for genetic syndromes. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 2013, 162, 327–366. [Google Scholar] [CrossRef]
  20. Fernandez, B.A.; Scherer, S.W. Syndromic autism spectrum disorders: Moving from a clinically defined to a molecularly defined approach. Dialogues Clin. Neurosci. 2017, 19, 353. [Google Scholar] [CrossRef]
  21. Genovese, A.; Butler, M.G. Clinical Assessment, Genetics, and Treatment Approaches in Autism Spectrum Disorder (ASD). Int. J. Mol. Sci. 2020, 21, 4726. [Google Scholar] [CrossRef]
  22. Schiff, M.; Benoist, J.F.; Aïssaoui, S.; Boespflug-Tanguy, O.; Mouren, M.C.; de Baulny, H.O.; Delorme, R. Should metabolic diseases be systematically screened in nonsyndromic autism spectrum disorders? PLoS ONE 2011, 6, e21932. [Google Scholar] [CrossRef]
  23. Campistol, J.; Diez-Juan, M.; Callejon, L.; Fernandez-De Miguel, A.; Casado, M.; Garcia Cazorla, A.; Lozano, R.; Artuch, R. Inborn error metabolic screening in individuals with nonsyndromic autism spectrum disorders. Dev. Med. Child Neurol. 2016, 58, 842–847. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Spilioti, M.; Evangeliou, A.E.; Tramma, D.; Theodoridou, Z.; Metaxas, S.; Michailidi, E.; Bonti, E.; Frysira, H.; Haidopoulou, A.; Asprangathou, D.; et al. Evidence for treatable inborn errors of metabolism in a cohort of 187 Greek patients with autism spectrum disorder (ASD). Front. Hum. Neurosci. 2013, 7, 858. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Ghaziuddin, M.; Al-Owain, M. Autism spectrum disorders and inborn errors of metabolism: An update. Pediatr. Neurol. 2013, 49, 232–236. [Google Scholar] [CrossRef] [PubMed]
  26. Saudubray, J.M.; Garcia-Cazorla, A. An overview of inborn errors of metabolism affecting the brain: From neurodevelopment to neurodegenerative disorders. Dialogues Clin. Neurosci. 2018, 20, 301–325. [Google Scholar] [CrossRef] [Green Version]
  27. Celestino-Soper, P.B.; Violante, S.; Crawford, E.L.; Luo, R.; Lionel, A.C.; Delaby, E.; Cai, G.; Sadikovic, B.; Lee, K.; Lo, C.; et al. A common X-linked inborn error of carnitine biosynthesis may be a risk factor for nondysmorphic autism. Proc. Natl. Acad. Sci. USA 2012, 109, 7974–7981. [Google Scholar] [CrossRef] [Green Version]
  28. Novarino, G.; El-Fishawy, P.; Kayserili, H.; Meguid, N.A.; Scott, E.M.; Schroth, J.; Silhavy, J.L.; Kara, M.; Khalil, R.O.; Ben-Omran, T.; et al. Mutations in BCKD-kinase lead to a potentially treatable form of autism with epilepsy. Science 2012, 338, 394–397. [Google Scholar] [CrossRef] [Green Version]
  29. 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. [Google Scholar] [CrossRef]
  30. Trost, B.; Thiruvahindrapuram, B.; Chan, A.J.S.; Engchuan, W.; Higginbotham, E.J.; Howe, J.L.; Loureiro, L.O.; Reuter, M.S.; Roshandel, D.; Whitney, J.; et al. Genomic architecture of autism from comprehensive whole-genome sequence annotation. Cell 2022, 185, 4409–4427.e18. [Google Scholar] [CrossRef]
  31. Srivastava, S.; Love-Nichols, J.A.; Dies, K.A.; Ledbetter, D.H.; Martin, C.L.; Chung, W.K.; Firth, H.V.; Frazier, T.; Hansen, R.L.; Prock, L.; et al. Meta-analysis and multidisciplinary consensus statement: Exomesequencing is a first-tier clinical diagnostic test for individuals with neurodevelopmental disorders. Genet. Med. 2019, 21, 2413–2421. [Google Scholar] [CrossRef] [Green Version]
  32. Hyman, S.L.; Levy, S.E.; Myers, S.M.; Kuo, D.Z.; Apkon, S.; Davidson, L.F.; Ellerbeck, K.A.; Foster, J.E.A.; Noritz, G.H.; Leppert, M.O.; et al. Identification, Evaluation, and Management of Children With Autism Spectrum Disorder. Pediatrics 2020, 145, e20193447. [Google Scholar] [CrossRef] [Green Version]
  33. Yu, T.W.; Chahrour, M.H.; Coulter, M.E.; Jiralerspong, S.; Okamura-Ikeda, K.; Ataman, B.; Schmitz-Abe, K.; Harmin, D.A.; Adli, M.; Malik, A.N.; et al. Using whole-exome sequencing to identify inherited causes of autism. Neuron 2013, 77, 259–273. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. 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]
  35. Alonso-Gonzalez, A.; Rodriguez-Fontenla, C.; Carracedo, A. De novo Mutations (DNMs) in Autism Spectrum Disorder (ASD): Pathway and Network Analysis. Front. Genet. 2018, 9, 406. [Google Scholar] [CrossRef] [PubMed]
  36. Baieli, S.; Pavone, L.; Meli, C.; Fiumara, A.; Coleman, M. Autism and phenylketonuria. J. Autism Dev. Disord. 2003, 33, 201–204. [Google Scholar] [CrossRef] [PubMed]
  37. Khemir, S.; Halayem, S.; Azzouz, H.; Siala, H.; Ferchichi, M.; Guedria, A.; Bedoui, A.; Abdelhak, S.; Messaoud, T.; Tebib, N.; et al. Autism in Phenylketonuria Patients: From Clinical Presentation to Molecular Defects. J. Child Neurol. 2016, 31, 843–849. [Google Scholar] [CrossRef]
  38. Cakar, N.E.; Yilmazbas, P. Cases of inborn errors of metabolism diagnosed in children with autism. Ideggyogy Sz. 2021, 74, 67–72. [Google Scholar] [CrossRef]
  39. Yoldaş, T.; Gürbüz, B.B.; Akar, H.T.; Özmert, E.N.; Coşkun, T. Autism spectrum disorder in patients with inherited metabolic disorders-a large sample from a tertiary center. Turk. J. Pediatr. 2021, 63, 767–779. [Google Scholar] [CrossRef]
  40. El Fotoh, W.; El Naby, S.A.A.; Abd El Hady, N.M.S. Autism Spectrum Disorders: The Association with Inherited Metabolic Disorders and Some Trace Elements. A Retrospective Study. CNS Neurol. Disord. Drug. Targets 2019, 18, 413–420. [Google Scholar] [CrossRef]
  41. Shi, H.; Wang, J.; Zhao, Z. Analysis of inborn error metabolism in 277 children with autism spectrum disorders from Hainan. Zhonghua Yi Xue Yi Chuan Xue Za Zhi 2019, 36, 870–873. [Google Scholar] [CrossRef]
  42. Kiykim, E.; Zeybek, C.A.; Zubarioglu, T.; Cansever, S.; Yalcinkaya, C.; Soyucen, E.; Aydin, A. Inherited metabolic disorders in Turkish patients with autism spectrum disorders. Autism Res. 2016, 9, 217–223. [Google Scholar] [CrossRef] [PubMed]
  43. Tărlungeanu, D.C.; Deliu, E.; Dotter, C.P.; Kara, M.; Janiesch, P.C.; Scalise, M.; Galluccio, M.; Tesulov, M.; Morelli, E.; Sonmez, F.M.; et al. Impaired Amino Acid Transport at the Blood Brain Barrier Is a Cause of Autism Spectrum Disorder. Cell 2016, 167, 1481–1494.e18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Cascio, L.; Chen, C.F.; Pauly, R.; Srikanth, S.; Jones, K.; Skinner, C.D.; Stevenson, R.E.; Schwartz, C.E.; Boccuto, L. Abnormalities in the genes that encode Large Amino Acid Transporters increase the risk of Autism Spectrum Disorder. Mol. Genet. Genom. Med. 2020, 8, e1036. [Google Scholar] [CrossRef] [Green Version]
  45. Schmidtke, K.; Endres, W.; Roscher, A.; Ibel, H.; Herschkowitz, N.; Bachmann, C.; Plöchl, E.; Hadorn, H.B. Hartnup syndrome, progressive encephalopathy and allo-albuminaemia. Eur. J. Pediatr. 1992, 151, 899–903. [Google Scholar] [CrossRef]
  46. Pillai, N.R.; Yubero, D.; Shayota, B.J.; Oyarzábal, A.; Ghosh, R.; Sun, Q.; Azamian, M.S.; Arjona, C.; Brandi, N.; Palau, F.; et al. Loss of CLTRN function produces a neuropsychiatric disorder and a biochemical phenotype that mimics Hartnup disease. Am. J. Med. Genet. A 2019, 179, 2459–2468. [Google Scholar] [CrossRef] [PubMed]
  47. Nava, C.; Rupp, J.; Boissel, J.P.; Mignot, C.; Rastetter, A.; Amiet, C.; Jacquette, A.; Dupuits, C.; Bouteiller, D.; Keren, B.; et al. Hypomorphic variants of cationic amino acid transporter 3 in males with autism spectrum disorders. Amino Acids 2015, 47, 2647–2658. [Google Scholar] [CrossRef] [Green Version]
  48. Namavar, Y.; Duineveld, D.J.; Both, G.I.A.; Fiksinski, A.M.; Vorstman, J.A.S.; Verhoeven-Duif, N.M.; Zinkstok, J.R. Psychiatric phenotypes associated with hyperprolinemia: A systematic review. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2021, 186, 289–317. [Google Scholar] [CrossRef]
  49. Moravej, H.; Inaloo, S.; Nahid, S.; Mazloumi, S.; Nemati, H.; Moosavian, T.; Nasiri, J.; Ghasemi, F.; Alaei, M.R.; Dalili, S.; et al. Inborn Errors of Metabolism Associated With Autism Among Children: A Multicenter Study from Iran. Indian Pediatr. 2023, 60, 193–196. [Google Scholar]
  50. Kido, J.; Matsumoto, S.; Ito, T.; Hirose, S.; Fukui, K.; Kojima-Ishii, K.; Mushimoto, Y.; Yoshida, S.; Ishige, M.; Sakai, N.; et al. Physical, cognitive, and social status of patients with urea cycle disorders in Japan. Mol. Genet. Metab. Rep. 2021, 27, 100724. [Google Scholar] [CrossRef]
  51. Seminara, J.; Tuchman, M.; Krivitzky, L.; Krischer, J.; Lee, H.S.; Lemons, C.; Baumgartner, M.; Cederbaum, S.; Diaz, G.A.; Feigenbaum, A.; et al. Establishing a consortium for the study of rare diseases: The Urea Cycle Disorders Consortium. Mol. Genet. Metab. 2010, 100, S97–S105. [Google Scholar] [CrossRef] [Green Version]
  52. Witters, P.; Debbold, E.; Crivelly, K.; Vande Kerckhove, K.; Corthouts, K.; Debbold, B.; Andersson, H.; Vannieuwenborg, L.; Geuens, S.; Baumgartner, M.; et al. Autism in patients with propionic acidemia. Mol. Genet. Metab. 2016, 119, 317–321. [Google Scholar] [CrossRef] [PubMed]
  53. de la Bâtie, C.D.; Barbier, V.; Roda, C.; Brassier, A.; Arnoux, J.B.; Valayannopoulos, V.; Guemann, A.S.; Pontoizeau, C.; Gobin, S.; Habarou, F.; et al. Autism spectrum disorders in propionic acidemia patients. J. Inherit. Metab. Dis. 2018, 41, 623–629. [Google Scholar] [CrossRef]
  54. Márquez-Caraveo, M.E.; Ibarra-González, I.; Rodríguez-Valentín, R.; Ramírez-García, M.; Pérez-Barrón, V.; Lazcano-Ponce, E.; Vela-Amieva, M. Brief Report: Delayed Diagnosis of Treatable Inborn Errors of Metabolism in Children with Autism and Other Neurodevelopmental Disorders. J. Autism Dev. Disord. 2021, 51, 2124–2131. [Google Scholar] [CrossRef] [PubMed]
  55. Tylki-Szymanska, A.; Gradowska, W.; Sommer, A.; Heer, A.; Walter, M.; Reinhard, C.; Omran, H.; Sass, J.O.; Jurecka, A. Aminoacylase 1 deficiency associated with autistic behavior. J. Inherit. Metab. Dis. 2010, 33, S211–S214. [Google Scholar] [CrossRef] [PubMed]
  56. Zafeiriou, D.I.; Ververi, A.; Salomons, G.S.; Vargiami, E.; Haas, D.; Papadopoulou, V.; Kontopoulos, E.; Jakobs, C. L-2-Hydroxyglutaric aciduria presenting with severe autistic features. Brain Dev. 2008, 30, 305–307. [Google Scholar] [CrossRef]
  57. Boris, M.; Goldblatt, A.; Galanko, J.; James, S.J. Association of MTHFR gene variants with autism. J. Am. Phys. Surg. 2004, 9, 106–108. [Google Scholar]
  58. Tisato, V.; Silva, J.A.; Longo, G.; Gallo, I.; Singh, A.V.; Milani, D.; Gemmati, D. Genetics and Epigenetics of One-Carbon Metabolism Pathway in Autism Spectrum Disorder: A Sex-Specific Brain Epigenome? Genes 2021, 12, 782. [Google Scholar] [CrossRef]
  59. Huijmans, J.G.M.; Schot, R.; de Klerk, J.B.C.; Williams, M.; de Coo, R.F.M.; Duran, M.; Verheijen, F.W.; van Slegtenhorst, M.; Mancini, G.M.S. Molybdenum cofactor deficiency: Identification of a patient with homozygote mutation in the MOCS3 gene. Am. J. Med. Genet. A 2017, 173, 1601–1606. [Google Scholar] [CrossRef]
  60. Lionel, A.C.; Vaags, A.K.; Sato, D.; Gazzellone, M.J.; Mitchell, E.B.; Chen, H.Y.; Costain, G.; Walker, S.; Egger, G.; Thiruvahindrapuram, B.; et al. Rare exonic deletions implicate the synaptic organizer Gephyrin (GPHN) in risk for autism, schizophrenia and seizures. Hum. Mol. Genet. 2013, 22, 2055–2066. [Google Scholar] [CrossRef] [Green Version]
  61. Nashabat, M.; Maegawa, G.; Nissen, P.H.; Nexo, E.; Al-Shamrani, H.; Al-Owain, M.; Alfadhel, M. Long-term Outcome of 4 Patients With Transcobalamin Deficiency Caused by 2 Novel TCN2 Mutations. J. Pediatr. Hematol. Oncol. 2017, 39, e430–e436. [Google Scholar] [CrossRef]
  62. Thurm, A.; Tierney, E.; Farmer, C.; Albert, P.; Joseph, L.; Swedo, S.; Bianconi, S.; Bukelis, I.; Wheeler, C.; Sarphare, G.; et al. Development, behavior, and biomarker characterization of Smith-Lemli-Opitz syndrome: An update. J. Neurodev. Disord. 2016, 8, 12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Sikora, D.M.; Pettit-Kekel, K.; Penfield, J.; Merkens, L.S.; Steiner, R.D. The near universal presence of autism spectrum disorders in children with Smith-Lemli-Opitz syndrome. Am. J. Med. Genet. A 2006, 140, 1511–1518. [Google Scholar] [CrossRef]
  64. Stelten, B.M.L.; Bonnot, O.; Huidekoper, H.H.; van Spronsen, F.J.; van Hasselt, P.M.; Kluijtmans, L.A.J.; Wevers, R.A.; Verrips, A. Autism spectrum disorder: An early and frequent feature in cerebrotendinous xanthomatosis. J. Inherit. Metab. Dis. 2018, 41, 641–646. [Google Scholar] [CrossRef] [PubMed]
  65. Wolfenden, C.; Wittkowski, A.; Hare, D.J. Symptoms of Autism Spectrum Disorder (ASD) in Individuals with Mucopolysaccharide Disease Type III (Sanfilippo Syndrome): A Systematic Review. J. Autism Dev. Disord. 2017, 47, 3620–3633. [Google Scholar] [CrossRef] [Green Version]
  66. Lee, M.S.; Kim, Y.J.; Kim, E.J.; Lee, M.J. Overlap of autism spectrum disorder and glucose transporter 1 deficiency syndrome associated with a heterozygous deletion at the 1p34.2 region. J. Neurol. Sci. 2015, 356, 212–214. [Google Scholar] [CrossRef] [PubMed]
  67. İnci, A.; Özaslan, A.; Okur, İ.; Biberoğlu, G.; Güney, E.; Ezgü, F.S.; Tümer, L.; İşeri, E. Autism: Screening of inborn errors of metabolism and unexpected results. Autism Res. 2021, 14, 887–896. [Google Scholar] [CrossRef] [PubMed]
  68. Griffiths, K.K.; Levy, R.J. Evidence of Mitochondrial Dysfunction in Autism: Biochemical Links, Genetic-Based Associations, and Non-Energy-Related Mechanisms. Oxid. Med. Cell. Longev. 2017, 2017, 4314025. [Google Scholar] [CrossRef] [Green Version]
  69. Connolly, B.S.; Feigenbaum, A.S.J.; Robinson, B.H.; Dipchand, A.I.; Simon, D.K.; Tarnopolsky, M.A. MELAS syndrome, cardiomyopathy, rhabdomyolysis, and autism associated with the A3260G mitochondrial DNA mutation. Biochem. Biophys. Res. Commun. 2010, 402, 443–447. [Google Scholar] [CrossRef]
  70. Knerr, I.; Gibson, K.M.; Jakobs, C.; Pearl, P.L. Neuropsychiatric morbidity in adolescent and adult succinic semialdehyde dehydrogenase deficiency patients. CNS Spectr. 2008, 13, 598–605. [Google Scholar] [CrossRef] [Green Version]
  71. Jurecka, A.; Zikanova, M.; Kmoch, S.; Tylki-Szymańska, A. Adenylosuccinate lyase deficiency. J. Inherit. Metab. Dis. 2015, 38, 231–242. [Google Scholar] [CrossRef] [Green Version]
  72. Stockler, S.; Schutz, P.W.; Salomons, G.S. Cerebral creatine deficiency syndromes: Clinical aspects, treatment and pathophysiology. Subcell. Biochem. 2007, 46, 149–166. [Google Scholar] [CrossRef] [PubMed]
  73. Comeaux, M.S.; Wang, J.; Wang, G.; Kleppe, S.; Zhang, V.W.; Schmitt, E.S.; Craigen, W.J.; Renaud, D.; Sun, Q.; Wong, L.J. Biochemical, molecular, and clinical diagnoses of patients with cerebral creatine deficiency syndromes. Mol. Genet. Metab. 2013, 109, 260–268. [Google Scholar] [CrossRef] [PubMed]
  74. Póo-Argüelles, P.; Arias, A.; Vilaseca, M.A.; Ribes, A.; Artuch, R.; Sans-Fito, A.; Moreno, A.; Jakobs, C.; Salomons, G. X-Linked creatine transporter deficiency in two patients with severe mental retardation and autism. J. Inherit. Metab. Dis. 2006, 29, 220–223. [Google Scholar] [CrossRef]
  75. Mak, C.M.; Lee, H.C.; Chan, A.Y.; Lam, C.W. Inborn errors of metabolism and expanded newborn screening: Review and update. Crit. Rev. Clin. Lab. Sci. 2013, 50, 142–162. [Google Scholar] [CrossRef]
  76. Therrell, B.L., Jr.; Padilla, C.D. Newborn screening in the developing countries. Curr. Opin. Pediatr. 2018, 30, 734–739. [Google Scholar] [CrossRef]
  77. Delhey, L.M.; Tippett, M.; Rose, S.; Bennuri, S.C.; Slattery, J.C.; Melnyk, S.; James, S.J.; Frye, R.E. Comparison of Treatment for Metabolic Disorders Associated with Autism:Reanalysis of Three Clinical Trials. Front. Neurosci. 2018, 12, 19. [Google Scholar] [CrossRef] [PubMed]
  78. Frye, R.E. Mitochondrial Dysfunction in Autism Spectrum Disorder: Unique Abnormalities and Targeted Treatments. Semin. Pediatr. Neurol. 2020, 35, 100829. [Google Scholar] [CrossRef]
  79. Niyazov, D.M.; Kahler, S.G.; Frye, R.E. Primary Mitochondrial Disease and Secondary Mitochondrial Dysfunction: Importance of Distinction for Diagnosis and Treatment. Mol. Syndromol. 2016, 7, 122–137. [Google Scholar] [CrossRef] [Green Version]
  80. Sun, J.M.; Kurtzberg, J. Cell therapy for diverse central nervous system disorders: Inherited metabolic diseases and autism. Pediatr. Res. 2018, 83, 364–371. [Google Scholar] [CrossRef]
  81. Paprocka, J.; Kaminiów, K.; Kozak, S.; Sztuba, K.; Emich-Widera, E. Stem Cell Therapies for Cerebral Palsy and Autism Spectrum Disorder-A Systematic Review. Brain Sci. 2021, 11, 1606. [Google Scholar] [CrossRef]
  82. Schaefer, G.B.; Mendelsohn, N.J. Clinical genetics evaluation in identifying the etiology of autism spectrum disorders: 2013 guideline revisions. Genet. Med. 2013, 15, 399–407. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Rossignol, D.A.; Frye, R.E. Mitochondrial dysfunction in autism spectrum disorders: A systematic review and meta-analysis. Mol. Psychiatry 2012, 17, 290–314. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Chen, L.; Shi, X.J.; Liu, H.; Mao, X.; Gui, L.N.; Wang, H.; Cheng, Y. Oxidative stress marker aberrations in children with autism spectrum disorder: A systematic review and meta-analysis of 87 studies (N = 9109). Transl. Psychiatry 2021, 11, 15. [Google Scholar] [CrossRef]
  85. Guo, B.Q.; Ding, S.B.; Li, H.B. Blood biomarker levels of methylation capacity in autism spectrum disorder: A systematic review and meta-analysis. Acta Psychiatr. Scand. 2020, 141, 492–509. [Google Scholar] [CrossRef] [PubMed]
  86. Indika, N.R.; Deutz, N.E.P.; Engelen, M.; Peiris, H.; Wijetunge, S.; Perera, R. Sulfur amino acid metabolism and related metabotypes of autism spectrum disorder: A review of biochemical evidence for a hypothesis. Biochimie 2021, 184, 143–157. [Google Scholar] [CrossRef]
  87. Rossignol, D.A.; Frye, R.E. Evidence linking oxidative stress, mitochondrial dysfunction, and inflammation in the brain of individuals with autism. Front. Physiol. 2014, 5, 150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Pecorelli, A.; Ferrara, F.; Messano, N.; Cordone, V.; Schiavone, M.L.; Cervellati, F.; Woodby, B.; Cervellati, C.; Hayek, J.; Valacchi, G. Alterations of mitochondrial bioenergetics, dynamics, and morphology support the theory of oxidative damage involvement in autism spectrum disorder. FASEB J. 2020, 34, 6521–6538. [Google Scholar] [CrossRef]
  89. 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. B Neuropsychiatr. Genet. 2006, 141B, 947–956. [Google Scholar] [CrossRef] [Green Version]
  90. Rossignol, D.A.; Frye, R.E. Cerebral Folate Deficiency, Folate Receptor Alpha Autoantibodies and Leucovorin (Folinic Acid) Treatment in Autism Spectrum Disorders: A Systematic Review and Meta-Analysis. J. Pers. Med. 2021, 11, 1141. [Google Scholar] [CrossRef]
  91. Deth, R.; Muratore, C.; Benzecry, J.; Power-Charnitsky, V.A.; Waly, M. How environmental and genetic factors combine to cause autism: A redox/methylation hypothesis. Neurotoxicology 2008, 29, 190–201. [Google Scholar] [CrossRef]
  92. Marotta, R.; Risoleo, M.C.; Messina, G.; Parisi, L.; Carotenuto, M.; Vetri, L.; Roccella, M. The Neurochemistry of Autism. Brain Sci. 2020, 10, 163. [Google Scholar] [CrossRef] [Green Version]
  93. Huang, Z.X.; Chen, Y.; Guo, H.R.; Chen, G.F. Systematic Review and Bioinformatic Analysis of microRNA Expression in Autism Spectrum Disorder Identifies Pathways Associated With Cancer, Metabolism, Cell Signaling, and Cell Adhesion. Front. Psychiatry 2021, 12, 630876. [Google Scholar] [CrossRef] [PubMed]
  94. Ferreira, C.R.; Rahman, S.; Keller, M.; Zschocke, J. An international classification of inherited metabolic disorders (ICIMD). J. Inherit. Metab. Dis. 2021, 44, 164–177. [Google Scholar] [CrossRef] [PubMed]
  95. Toscano, C.V.A.; Barros, L.; Lima, A.B.; Nunes, T.; Carvalho, H.M.; Gaspar, J.M. Neuroinflammation in autism spectrum disorders: Exercise as a “pharmacological” tool. Neurosci. Biobehav. Rev. 2021, 129, 63–74. [Google Scholar] [CrossRef]
  96. McElhanon, B.O.; McCracken, C.; Karpen, S.; Sharp, W.G. Gastrointestinal symptoms in autism spectrum disorder: A meta-analysis. Pediatrics 2014, 133, 872–883. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Granich, J.; Lin, A.; Hunt, A.; Wray, J.; Dass, A.; Whitehouse, A.J. Obesity and associated factors in youth with an autism spectrum disorder. Autism 2016, 20, 916–926. [Google Scholar] [CrossRef]
  98. Asato, M.R.; Goldstein, A.C.; Schiff, M. Autism and inborn errors of metabolism: How much is enough? Dev. Med. Child. Neurol. 2015, 57, 788–789. [Google Scholar] [CrossRef]
  99. Glinton, K.E.; Elsea, S.H. Untargeted Metabolomics for Autism Spectrum Disorders: Current Status and Future Directions. Front. Psychiatry 2019, 10, 647. [Google Scholar] [CrossRef]
  100. Grunert, S.C.; Stucki, M.; Morscher, R.J.; Suormala, T.; Burer, C.; Burda, P.; Christensen, E.; Ficicioglu, C.; Herwig, J.; Kolker, S.; et al. 3-methylcrotonyl-CoA carboxylase deficiency: Clinical, biochemical, enzymatic and molecular studies in 88 individuals. Orphanet J. Rare Dis. 2012, 7, 31. [Google Scholar] [CrossRef] [Green Version]
  101. Herbert, M.; Goldstein, J.L.; Rehder, C.; Austin, S.; Kishnani, P.S.; Bali, D.S. Phosphorylase Kinase Deficiency. In GeneReviews; Adam, M.P., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Bean, L.J.H., Gripp, K.W., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993; Updated 2018. [Google Scholar]
  102. Wolf, B. Biotinidase Deficiency. In GeneReviews; Adam, M.P., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Bean, L.J.H., Gripp, K.W., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993; Updated 2022. [Google Scholar]
  103. Federico, A.; Gallus, G.N. Cerebrotendinous Xanthomatosis. In GeneReviews; Adam, M.P., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Bean, L.J.H., Gripp, K.W., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 1993. [Google Scholar]
  104. Szymanska, E.; Jezela-Stanek, A.; Bogdanska, A.; Rokicki, D.; Ehmke Vel Emczynska-Seliga, E.; Pajdowska, M.; Ciara, E.; Tylki-Szymanska, A. Long Term Follow-Up of Polish Patients with Isovaleric Aciduria. Clinical and Molecular Delineation of Isovaleric Aciduria. Diagnostics 2020, 10, 738. [Google Scholar] [CrossRef]
  105. Blau, N.; Blaskovics, M.E.; Gibson, K.M. (Eds.) Physician’s Guide to the Laboratory Diagnosis of Metabolic Diseases; Springer: Berlin/Heidelberg, Germany, 2003. [Google Scholar] [CrossRef] [Green Version]
  106. Karimzadeh, P.; Alaee, M.R.; Zarafshan, H. The Association between EEG Abnormality and Behavioral Disorder: Developmental Delay in Phenylketonuria. ISRN Pediatr. 2012, 2012, 976206. [Google Scholar] [CrossRef] [Green Version]
  107. Karimzadeh, P.; Ahmadabadi, F.; Jafari, N.; Shariatmadari, F.; Nemati, H.; Ahadi, A.; Karimi Dardashti, S.; Mirzarahimi, M.; Dastborhan, Z.; Zare Noghabi, J. Study on MRI changes in phenylketonuria in patients referred to mofid hospital/iran. Iran. J. Child. Neurol. 2014, 8, 53–56. [Google Scholar]
  108. Murayama, K.; Kimura, M.; Yamaguchi, S.; Shinka, T.; Kodama, K. Isolated 3-methylcrotonyl-CoA carboxylase deficiency in a 15-year-old girl. Brain Dev. 1997, 19, 303–305. [Google Scholar] [CrossRef] [PubMed]
  109. Wu, J.T.; Wilson, L.W.; Christensen, S. Conversion of a qualitative screening test to a quantitative measurement of urinary cystine and homocystine. Ann. Clin. Lab. Sci. 1992, 22, 18–29. [Google Scholar] [PubMed]
  110. Strauss, K.A.; Puffenberger, E.G.; Carson, V.J. Maple Syrup Urine Disease. In GeneReviews; Adam, M.P., Mirzaa, G.M., Pagon, R.A., Wallace, S.E., Bean, L.J.H., Gripp, K.W., Amemiya, A., Eds.; University of Washington: Seattle, WA, USA, 2006. [Google Scholar]
  111. Cleary, M.; Trefz, F.; Muntau, A.C.; Feillet, F.; van Spronsen, F.J.; Burlina, A.; Belanger-Quintana, A.; Gizewska, M.; Gasteyger, C.; Bettiol, E.; et al. Fluctuations in phenylalanine concentrations in phenylketonuria: A review of possible relationships with outcomes. Mol. Genet. Metab. 2013, 110, 418–423. [Google Scholar] [CrossRef]
  112. Rifai, N. (Ed.) Tietz Textbook of Laboratory Medicine; Elsevier: Amsterdam, The Netherlands, 2022. [Google Scholar]
  113. Xia, T.; Gao, S.; Shu, C.; Wen, Y.; Yun, Y.; Tao, X.; Chen, W.; Zhang, F. Analysis of amino acids in human blood using UHPLC-MS/MS: Potential interferences of storage time and vacutainer tube in pre-analytical procedure. Clin. Biochem. 2016, 49, 1372–1378. [Google Scholar] [CrossRef] [PubMed]
  114. Schaefer, G.B. Clinical Genetic Aspects of ASD Spectrum Disorders. Int. J. Mol. Sci. 2016, 17, 180. [Google Scholar] [CrossRef] [Green Version]
  115. Firth, H.V.; Wright, C.F. The Deciphering Developmental Disorders (DDD) study. Dev. Med. Child. Neurol. 2011, 53, 702–703. [Google Scholar] [CrossRef] [PubMed]
  116. Smedley, D.; Smith, K.R.; Martin, A.; Thomas, E.A.; McDonagh, E.M.; Cipriani, V.; Ellingford, J.M.; Arno, G.; Tucci, A.; Vandrovcova, J.; et al. 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care—Preliminary Report. N. Engl. J. Med. 2021, 385, 1868–1880. [Google Scholar] [CrossRef] [PubMed]
  117. Polyak, A.; Rosenfeld, J.A.; Girirajan, S. An assessment of sex bias in neurodevelopmental disorders. Genome Med. 2015, 7, 94. [Google Scholar] [CrossRef] [Green Version]
  118. Jacquemont, S.; Coe, B.P.; Hersch, M.; Duyzend, M.H.; Krumm, N.; Bergmann, S.; Beckmann, J.S.; Rosenfeld, J.A.; Eichler, E.E. A higher mutational burden in females supports a “female protective model“ in neurodevelopmental disorders. Am. J. Hum. Genet. 2014, 94, 415–425. [Google Scholar] [CrossRef] [Green Version]
  119. Schaefer, G.B.; Mendelsohn, N.J. Genetics evaluation for the etiologic diagnosis of autism spectrum disorders. Genet. Med. 2008, 10, 4–12. [Google Scholar] [CrossRef] [Green Version]
  120. Rabbani, B.; Tekin, M.; Mahdieh, N. The promise of whole-exome sequencing in medical genetics. J. Hum. Genet. 2014, 59, 5–15. [Google Scholar] [CrossRef]
  121. Nurk, S.; Koren, S.; Rhie, A.; Rautiainen, M.; Bzikadze, A.V.; Mikheenko, A.; Vollger, M.R.; Altemose, N.; Uralsky, L.; Gershman, A.; et al. The complete sequence of a human genome. Science 2022, 376, 44–53. [Google Scholar] [CrossRef] [PubMed]
  122. Wisniowiecka-Kowalnik, B.; Nowakowska, B.A. Genetics and epigenetics of autism spectrum disorder-current evidence in the field. J. Appl. Genet. 2019, 60, 37–47. [Google Scholar] [CrossRef] [Green Version]
  123. Schiele, M.A.; Domschke, K. Epigenetics at the crossroads between genes, environment and resilience in anxiety disorders. Genes. Brain Behav. 2018, 17, e12423. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  124. Ladd-Acosta, C.; Hansen, K.D.; Briem, E.; Fallin, M.D.; Kaufmann, W.E.; Feinberg, A.P. Common DNA methylation alterations in multiple brain regions in autism. Mol. Psychiatry 2014, 19, 862–871. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  125. Ellis, S.E.; Gupta, S.; Moes, A.; West, A.B.; Arking, D.E. Exaggerated CpH methylation in the autism-affected brain. Mol. Autism 2017, 8, 6. [Google Scholar] [CrossRef] [Green Version]
  126. Duffney, L.J.; Valdez, P.; Tremblay, M.W.; Cao, X.; Montgomery, S.; McConkie-Rosell, A.; Jiang, Y.H. Epigenetics and autism spectrum disorder: A report of an autism case with mutation in H1 linker histone HIST1H1E and literature review. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2018, 177, 426–433. [Google Scholar] [CrossRef]
  127. Atladottir, H.O.; Henriksen, T.B.; Schendel, D.E.; Parner, E.T. Autism after infection, febrile episodes, and antibiotic use during pregnancy: An exploratory study. Pediatrics 2012, 130, e1447–e1454. [Google Scholar] [CrossRef] [Green Version]
  128. Fregeac, J.; Colleaux, L.; Nguyen, L.S. The emerging roles of MicroRNAs in autism spectrum disorders. Neurosci. Biobehav. Rev. 2016, 71, 729–738. [Google Scholar] [CrossRef] [PubMed]
  129. Manickam, K.; McClain, M.R.; Demmer, L.A.; Biswas, S.; Kearney, H.M.; Malinowski, J.; Massingham, L.J.; Miller, D.; Yu, T.W.; Hisama, F.M. Exome and genome sequencing for pediatric patients with congenital anomalies or intellectual disability: An evidence-based clinical guideline of the American College of Medical Genetics and Genomics (ACMG). Genet. Med. 2021, 23, 2029–2037. [Google Scholar] [CrossRef] [PubMed]
  130. Frye, R.E.; Rose, S.; Slattery, J.; MacFabe, D.F. Gastrointestinal dysfunction in autism spectrum disorder: The role of the mitochondria and the enteric microbiome. Microb. Ecol. Health Dis. 2015, 26, 27458. [Google Scholar] [CrossRef] [PubMed]
  131. Rahman, S. Mitochondrial disease and epilepsy. Dev. Med. Child. Neurol. 2012, 54, 397–406. [Google Scholar] [CrossRef] [Green Version]
  132. Chaidez, V.; Hansen, R.L.; Hertz-Picciotto, I. Gastrointestinal problems in children with autism, developmental delays or typical development. J. Autism Dev. Disord. 2014, 44, 1117–1127. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  133. Tilford, J.M.; Payakachat, N.; Kuhlthau, K.A.; Pyne, J.M.; Kovacs, E.; Bellando, J.; Williams, D.K.; Brouwer, W.B.; Frye, R.E. Treatment for Sleep Problems in Children with Autism and Caregiver Spillover Effects. J. Autism Dev. Disord. 2015, 45, 3613–3623. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  134. Kamp-Becker, I.; Albertowski, K.; Becker, J.; Ghahreman, M.; Langmann, A.; Mingebach, T.; Poustka, L.; Weber, L.; Schmidt, H.; Smidt, J.; et al. Diagnostic accuracy of the ADOS and ADOS-2 in clinical practice. Eur. Child. Adolesc. Psychiatry 2018, 27, 1193–1207. [Google Scholar] [CrossRef]
  135. McCarty, P.; Frye, R.E. Early Detection and Diagnosis of Autism Spectrum Disorder: Why Is It So Difficult? Semin. Pediatr. Neurol. 2020, 35, 100831. [Google Scholar] [CrossRef]
  136. Dupuis, A.; Moon, M.J.; Brian, J.; Georgiades, S.; Levy, T.; Anagnostou, E.; Nicolson, R.; Schachar, R.; Crosbie, J. Concurrent Validity of the ABAS-II Questionnaire with the Vineland II Interview for Adaptive Behavior in a Pediatric ASD Sample: High Correspondence Despite Systematically Lower Scores. J. Autism Dev. Disord. 2021, 51, 1417–1427. [Google Scholar] [CrossRef]
  137. Mukherjee, S.B. Autism Spectrum Disorders—Diagnosis and Management. Indian J. Pediatr. 2017, 84, 307–314. [Google Scholar] [CrossRef]
  138. Hoytema van Konijnenburg, E.M.M.; Wortmann, S.B.; Koelewijn, M.J.; Tseng, L.A.; Houben, R.; Stöckler-Ipsiroglu, S.; Ferreira, C.R.; van Karnebeek, C.D.M. Treatable inherited metabolic disorders causing intellectual disability: 2021 review and digital app. Orphanet J. Rare Dis. 2021, 16, 170. [Google Scholar] [CrossRef] [PubMed]
  139. Mahapatra, S.; Khokhlovich, E.; Martinez, S.; Kannel, B.; Edelson, S.M.; Vyshedskiy, A. Longitudinal Epidemiological Study of Autism Subgroups Using Autism Treatment Evaluation Checklist (ATEC) Score. J. Autism Dev. Disord. 2020, 50, 1497–1508. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Table
Table 1. Inherited metabolic diseases associated with autism spectrum disorder (ASD). All variants were annotated according to the MANE Select transcript. † Disorders with no MIM numbers (AA: amino acid, ADSL: adenylosuccinate lyase, ASLD: argininosuccinate lyase deficiency, CAT-3: cationic amino acid transporter-3, CLTRN: collectrin, CNV: copy number variation, CPS1D: carbamoyl phosphate synthetase 1 deficiency, CTLN1: citrullinemia type I, DHFR: dihydrofolate reductase, GLUT1: glucose transporter 1, MELAS: mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes, MTHFR: 5,10-methylenetetrahydrofolate reductase, OTCD: ornithine transcarbamylase deficiency, SCADD: short-chain acyl-CoA dehydrogenase deficiency, SSADH: succinic semialdehyde dehydrogenase, SNV: single nucleotide variants, PV: pathogenic or likely pathogenic variant [updated on 11 February 2023 in ClinVar Database or Human Gene Mutation Database], UCDs: urea cycle disorders).
Table 1. Inherited metabolic diseases associated with autism spectrum disorder (ASD). All variants were annotated according to the MANE Select transcript. † Disorders with no MIM numbers (AA: amino acid, ADSL: adenylosuccinate lyase, ASLD: argininosuccinate lyase deficiency, CAT-3: cationic amino acid transporter-3, CLTRN: collectrin, CNV: copy number variation, CPS1D: carbamoyl phosphate synthetase 1 deficiency, CTLN1: citrullinemia type I, DHFR: dihydrofolate reductase, GLUT1: glucose transporter 1, MELAS: mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes, MTHFR: 5,10-methylenetetrahydrofolate reductase, OTCD: ornithine transcarbamylase deficiency, SCADD: short-chain acyl-CoA dehydrogenase deficiency, SSADH: succinic semialdehyde dehydrogenase, SNV: single nucleotide variants, PV: pathogenic or likely pathogenic variant [updated on 11 February 2023 in ClinVar Database or Human Gene Mutation Database], UCDs: urea cycle disorders).
ICIMD Category > ICIMD SubcategoryInherited Metabolic
Disease, Phenotype MIM Number [Ref.]		Gene (MIM Number)/Methods of Genetic Testing
Disorders of AA metabolism > phenylalanine and tyrosine metabolismPhenylketonuria/phenylalanine hydroxylase deficiency,
#261600 [24,33,36,37,38,39,40,41]		PAH (*612349);
More than 740 PVs have been described.
Sanger sequencing analysis is performed first (SNVs = 97–99%), followed by gene-targeted deletion/duplication analysis (1–3%). There are seven common PVs: c.1222C>T (6.7%), c.1066-11G>A (5.3%), c.194T>C (4.1%), c.782G>A (3.6%), c.842C>T (2.9%), c.1315+1G>A (2.8%), c.473G>A (2.7%).
Disorders of AA metabolism > metabolism of sulfur-containing AAs and hydrogen sulfideHomocystinuria/cystathionine β-synthase deficiency, #236200 [38,41,42]CBS (*613381)
Approximately 164 PVs have been described.
Sanger sequencing analysis is performed first (SNVs = 95–98%), followed by gene-targeted deletion/duplication analysis (<5%). There are three common PVs: c.833T>C (25% pan-ethnic), c.919G>A (71% in Ireland), c.1006C>T (93% in Qatari).
Disorders of AA
metabolism > branched-chain AA metabolism		Branched-chain α-keto acid dehydrogenase kinase
deficiency, #614923 [28]		BCKDK (*614901)
Only eight PVs have been described (SNVs only). Sequence analysis is performed mainly.
Maple syrup urine disease, #248600 [39]DBT (*248610), BCKDHB (*248611), BCKDHA (*608348)
Approximately 146 in BCKDHB, 123 in BCKDHA, and 108 in DBT of PVs have been described.
Sequence analysis is performed first (SNVs 86–93%), followed by gene-targeted deletion/duplication analysis if only one or no
pathogenic variant is found (7–14%).
Disorders of AA
metabolism > AA transport		Large neutral AA transporter defects † [43,44]SLC3A2 (*158070), SLC7A5 (*600182), SLC7A8 (*604235)
Some variants are associated with ASD
Hartnup disease, #234500 [45]SLC6A19 (*608893)
Only 18 PVs have been described (SNVs only).
Sequence analysis is performed mainly.
CLTRN deficiency † [46]CLTRN (*300631)
A deletion spanning exons 1–3 of CLTRN has been reported
CAT-3 defects † [47]SCL7A3 (*300443)
Some variants are associated with ASD
Lysinuric protein intolerance, #222700 [23]SLC7A7 (*603593)
Over 90 PVs have been described. Sequence analysis is performed first (SNVs = 92–95%), followed by gene-targeted
deletion/duplication analysis
(15–20% in the non-Finnish population).
Disorders of AA
metabolism > ornithine,
proline, and hydroxyproline metabolism		Hyperprolinemia type I, #239500 and type II, #239510 [48]PRODH (*606810)
Only 11 PVs have been (only SNVs).
Mainly sequence analysis is performed.
ALDH4A1 (*606811)
Only seven PVs have been described (SNVs only).
Sequence analysis is performed mainly.
Disorders of AA
metabolism > glycine and serine metabolism		Serine deficiency disorders [23]
Nonketotic hyperglycinemia due to aminomethyltransferase deficiency, #605899 [33]		AMT (*238310), GLDC (*238300) and GCSH (*238330)
Approximately 363 in GLDC, 88 in AMT, and 4 in GCSH of PVs have been described. The commonest PV is GLDC (80%), AMT (20%), and GCSH (rare). Sequence analysis is performed first (SNVs = 80% and >99%, respectively), followed by gene-targeted
deletion/duplication analysis if only one or no pathogenic variant is found (PV = 20% and unknown, respectively).
Disorders of AA
metabolism > UCDs and
inherited hyperammonemias		ASLD, #207900 [23,49,50]
CTLN1, #215700 [23,41,50]
CPS1D, #237300 [24,51]
OTCD, #311250 [23,50],
unspecified UCDs [51]		ASL (*608310)
ASS1 (*603470)
CPS1 (*608307)
OTC (*300461)
Approximately 146 in ASL, 141 in ASS1, 223 in CPS1, and 426 in OTC of PVs have been described. SNVs are found in >90% of ASLD cases, 96% of CTLN1 cases, 80–90% of OTCD cases, and >99% of CPS1D cases.
Disorders of AA
metabolism > organic
acidurias		Propionic acidemia, #606054 [41,52,53]PCCA (*232000), PCCB (*232050)
Approximately 181 in PCCA and 172 in PCCB of PVs have been described.
Sequence analysis is performed first (SNVs = 78% and >97%,
respectively), followed by gene-targeted deletion/duplication
analysis if only one or no pathogenic variant is found
(CNVs = 3–18%).
There are three common PVs in PCCB: c.1218_1231del14ins12 (~30% of disease-causing alleles in individuals of northern European origin), c.1304T>C (associated with a milder form of propionic acidemia, accounts for 25% of mutated alleles in Japanese individuals) and c.1606A>G (homozygous in Amish and Mennonite communities, in individuals who can initially present with cardiomyopathy).
3-methylcrotonyl-coA
carboxylase deficiency, #210200 [41]		MCCC1 (*609010)
Approximately 119 PVs have been described (SNVs > 99%).
Sequence analysis is performed mainly.
Isovaleryl-coA dehydrogenase deficiency, #243500 [41,54]IVD (*607036)
Approximately 119 PVs have been described (SNVs = 98%).
Sequence analysis is performed mainly.
2-methylbutyrylglycinuria, #610006 [49]ACADSB (*600301)
Only 16 PVs have been described (SNVs = 94%).
Sequence analysis is performed mainly.
Glutaric aciduria type 1, #231670 [40]GCDH (*608801)
Approximately 223 PVs have been described.
Sequence analysis is performed mainly (SNVs = 99%).
Disorders of AA
metabolism > other disorders of AA metabolism		Aminoacylase 1 deficiency, #609924 [55]ACY1 (*104620)
Only 11 PVs have been described (SNV only).
Sequence analysis is performed mainly.
Disorders of metabolite
repair/proofreading >
mitochondrial metabolite repair		L-2-hydroxyglutaric aciduria, #236792 [56]L2HGDH (*609584)
Only 30 PVs have been described (SNVs > 99%).
Sequence analysis is performed mainly.
Combined malonic and methylmalonic aciduria, #614265 [49]ACSF3 (*614245)
More than 100 PVs have been described (SNVs only).
Sequence analysis is performed mainly.
Disorders of fatty acid and ketone body metabolism > mitochondrial fatty acid
oxidation		SCADD, #201470 [42,49]ACADS (*606885)
Approximately 70 PVs have been described (SNVs only).
Sanger sequencing analysis is performed first.
There are two common PVs: c.511C>Tandc.625G>A, which result in the SCADD biochemical abnormality when in trans with an
apathogenic variant.
Newborns homozygous for the c.625G>A variant have laboratory test values that overlap with those of affected newborns.
Disorders of fatty acid and ketone body metabolism > carnitine metabolismPrimary carnitine deficiency, #212140 [41]SLC22A5 (*603377)
More than 189 PVs have been described (SNVs > 99). Sequence analysis is performed mainly.
Epsilon-n-trimethyllysine
hydroxylase deficiency, #300872 [27]		TMLHE (*300777)
Only three PVs have been described (SNVs only).
Sequence analysis is performed mainly.
Disorders of fatty acid and ketone bodies > ketone body metabolismMitochondrial acetoacetyl-CoA thiolase deficiency, #203750 [54]ACAT1 (*607809)
More than 150 PVs have been described (SNVs = 96%).
Sequence analysis is performed mainly.
Disorders of vitamin and cofactor metabolism > biotin metabolismBiotinidase deficiency, #253260 [23]
Partial biotinidase deficiency [39,42]		BTD (*609019)
Approximately 145 PVs have been described (SNVs only).
Sequence analysis is performed mainly.
Disorders of vitamin and cofactor metabolism > folate metabolismLow-activity variants of MTHFR [57,58]MTHFR (*607093)
Approximately 117 PVs have been described (SNVs only).
Sequence analysis is performed mainly. Thermolabile low-activity c.677C>T variant is associated with ASD
Folate receptor α deficiency, #613068 [23]FOLR1 (*136430)
Only 21 PVs have been described (SNVs only).
Sequence analysis is performed mainly.
Low-activity variants of DHFR [58]DHFR (*126060)
Only two PVs have been described (SNVs only).
Sequence analysis is performed mainly
Disorders of vitamin and cofactor metabolism >
molybdenum cofactor
metabolism		Molybdenum cofactor
deficiency, #252150 [59,60]		MOCS1 (*603707)
Only 32 PVs have been described (SNVs > 99%).
Sequence analysis is performed mainly
Disorders of vitamin and cofactor metabolism >
cobalamin metabolism		Transcobalamin II deficiency, #275350 [61]TCN2 (*613441)
Only 29 PVs have been described (SNVs > 93%).
Sequence analysis is performed mainly.
Disorders of lipid metabolism > sterol biosynthesisSmith–Lemli–Opitz syndrome, #270400 [62,63]DHCR7 (*602858)
More than 180 PVs have been described (SNVs = 96%).
Sanger sequencing analysis is performed first. There are three common variants: c.964-1G>C (~28% of disease alleles; associated with a severe phenotype), c.452G>A (the most common in Central
Europe; associated with a severe phenotype), c.278C>T (common in individuals of Mediterranean or Cuban ancestry; associated with a milder phenotype).
Disorders of lipid
metabolism > bile acid
metabolism		Cerebrotendinous
xanthomatosis, #213700 [64]		CYP27A1 (*606530)
More than 140 PVs have been described (SNVs > 99%).
Sanger sequencing analysis is performed first. There are three common variants: c.355delC (founder variant in Israeli Druze) and c.1183C>T.
Disorders of complex
molecule > glycosaminoglycan degradation		Sanfilippo syndrome, #252920 [23,38,39,42,65]NAGLU (*609701)
More than 200 PVs have been described (SNVs >90%).
Sequence analysis is performed mainly.
Morquio syndrome A, #253000 [39]GALNS (*612222)
Approximately 209 PVs have been described (SNVs > 94%).
Sequence analysis is performed mainly. One of the most common PVs is c.1156C>T, accounting for 8.9%.
Disorders of carbohydrate metabolism > carbohydrate transmembrane transport and absorptionGLUT1 deficiency, #612126 [23,66]SLC2A1 (*138140)
More than 250 PVs have been described (SNVs = 84%).
Sequence analysis is performed mainly. Several pathogenic hot spots have been detected: c.376C>T, c.377G>A, c.377G>T, c.766_767delAAinsGT, c.997C>T, c.884C>T, c.1402C>T.
Disorders of carbohydrate metabolism > glycogen
metabolism		Glycogen storage disease
type IXa1, #306000 [67]		PHKA2 (*300798)
More than 250 PVs have been described (SNVs = 94%).
Sequence analysis is performed mainly.
mtDNA-related disorders
> disorders of mtDNA-encoded tRNA and rRNA
> disorders of mtDNA-encoded oxidative phosphorylation proteins		MELAS syndrome, #540000 [68,69]Several genes: MTTL1 (*590050), MTTQ (*590030), MTTH (*590040), MTTK (*590060), MTTC (*590020), MTTS1 (*590080), MTND1 (*516000), MTND5 (*516005), MTND6 (*516006), and MTTS2 (*590085)
More than 90 PVs have been described (SNVs only).
Proportion PVs are MTTL1 (>80%), MTND5 (<10%), and other genes (rare). Entire mitochondrial genome sequencing that
includes MTTL1, MTND5, and other mtDNA genes is most
effective in identifying the genetic cause of the condition.
Neurotransmitter disorders >
γ-aminobutyric acid
neurotransmitter disorders		SSADH
deficiency, #271980
[23,24,67,70]		ALDH5A1 (*610045)
Approximately 108 PVs have been described (SNVs = 97%).
Sequence analysis is performed mainly.
Disorders of nucleobase,
nucleotide, and nucleic acid metabolism > purine
metabolism		ADSL deficiency, #103050 [23,71]
Lesch–Nyhan syndrome, #300322 [23,24]		ADSL (*608222)
Approximately 58 PVs have been described (SNVs only).
Sequence analysis is performed mainly.
HPRT1 (*308000)
Approximately 87 PVs have been described. Sequence analysis is performed first (SNVs = 80%), followed by gene-targeted deletion/duplication analysis (CNVs = 20%).
Disorders of nucleobase,
nucleotide, and nucleic acid metabolism > pyrimidine
metabolism		Dihydropyrimidine
dehydrogenase deficiency, #274270 [23,67]		DPYD (*612779)
Approximately 50 PVs have been described (SNVs only).
Sequence analysis is performed mainly.
Disorders of energy substrate metabolism > creatine
metabolism		Cerebral creatine deficiency syndromes, #300352
[23,39,49,67,72,73,74]		SLC6A8 (*300036), GAMT (*601240), GATM (*602360)
Approximately 100 in SLC6A8, 73 in GAMT and 17 in GATM of PVs have been described (SNVs 95–100%). The proportion of PVs is SLC6A8 (64–72%), GAMT (20–33%), and GATM (3–8%).
Sequence analysis is performed mainly.
Table
Table 2. Basic screening [100,101,102,103,104,105,106,107,108,109,110,111,112]. (3-MCCD: 3-methylcrotonyl-CoA carboxylase deficiency, CTX: cerebrotendinous xanthomatosis, GSD IX: glycogen storage disorder IX, MTHFRD: methylenetetrahydrofolate reductase deficiency, MSUD: maple syrup urine disease, NKH: nonketotic hyperglycinemia, PA: propionic acidemia, PKU: phenylketonuria, SCADD: short-chain acyl-CoA dehydrogenase deficiency).
Table 2. Basic screening [100,101,102,103,104,105,106,107,108,109,110,111,112]. (3-MCCD: 3-methylcrotonyl-CoA carboxylase deficiency, CTX: cerebrotendinous xanthomatosis, GSD IX: glycogen storage disorder IX, MTHFRD: methylenetetrahydrofolate reductase deficiency, MSUD: maple syrup urine disease, NKH: nonketotic hyperglycinemia, PA: propionic acidemia, PKU: phenylketonuria, SCADD: short-chain acyl-CoA dehydrogenase deficiency).
Test CategoryInvestigationFindings in Different IEM
BiochemicalPlasma glucose aHypoglycemia in 3-MCCD, GSD IX, PA, primary carnitine deficiency, SCADD
Serum liver transaminasesIncreased in GSD IX, lysinuric protein intolerance, primary carnitine deficiency, SCADD, urea cycle disorders, Wilson’s disease
Serum lactate dehydrogenaseIncreased in lysinuric protein intolerance
Serum uric acidIncreased in Lesch–Nyhan syndrome
Decreased in molybdenum cofactor deficiency, Wilson’s disease
Serum ureaDecreased in urea cycle disorders, lysinuric protein intolerance
Serum cholesterolDecreased in Smith–Lemli–Opitz syndrome, CTX
Increased b in GSD IX, lysinuric protein intolerance
Serum triglycerideIncreased in GSD IX, lysinuric protein intolerance
Acid-base statusMetabolic acidosis with high anion gap in biotinidase deficiency, MSUD, organic acidurias, primary carnitine deficiency, SCADD
Respiratory alkalosis in urea cycle disorders
Serum/urine ketonesIncreased in 3-MCCD, biotinidase deficiency, GSD IX, isovaleryl-CoA
dehydrogenase deficiency, MSUD, PA
HaematologicalWBCNeutropenia in isovaleryl-CoA dehydrogenase deficiency, lysinuric protein intolerance, PA, Wilson’s disease
Vacuolated lymphocytes on peripheral blood smear in Morquio syndrome A, Sanfilippo syndrome
PlateletsThrombocytopenia in 3-MCCD, isovaleryl-CoA dehydrogenase deficiency, lysinuric protein intolerance, PA
RBCAnemia in isovaleryl-CoA dehydrogenase deficiency, lysinuric protein intolerance, PA, Wilson’s disease
Preliminary urine screening testsFerric chloride test cProduce an intense blue-green in the presence of phenylpyruvate in PKU, MSUD
Cyanide-nitroprusside test with silver nitrate c Produce a magenta colour in the presence of homocysteine in homocystinuria
2,4-dinitrophenylhydrazine test dOrange precipitate in MSUD
Sulfite test ePositive in molybdenum cofactor deficiency
Glycosaminoglycan/creatinine
ratio (DMMB assay)		Increased in mucopolysaccharidoses (e.g., Sanfilippo syndrome)
Dried blood spot (DBS) screeningPhenylalanine fIncreased in PKU
Hypermethioninemia gIncreased in homocystinuria
Leucine + isoleucineIncreased (leucine + isoleucine) to alanine and phenylalanine in MSUD
Radiological studiesX-rayOsteoporosis in PA
Dysostosis multiplex in Morquio syndrome A
MRIWhite matter involvement with atrophic cortical changes in PKU, 3-MCCD, CTX, ADSL deficiency
EEG Abnormalities in 3-MCCD, biotinidase deficiency, CTX,
Folate receptor α deficiency, NKH, PKU
Special notes [105,109,110,112]. a Hypoglycaemia may not be present if the patient is not in metabolic decompensation. b Cholesterol can be normal. c False-negative results may be seen in dilute urine specimens. d Test detects α-ketoacids formed from the isoleucine, leucine, and valine. e Due to the poor stability of urinary sulfite, drugs, and bacterial degradation, it is prone to false-negative results. f Assayed by TD-MS/MS. Diurnal variability in phenylalanine concentrations may necessitate repeat screening for PKU. g Hypermethioninemia may not always be present in the neonatal period.
Table
Table 3. Metabolic work-up [100,101,102,103,104,105,106,107,108]. (3-MCCD: 3-methylcrotonyl-CoA carboxylase deficiency, AA: amino acid, AIP: acute intermittent porphyria, CSF: cerebrospinal fluid, CTX: cerebrotendinous xanthomatosis, GSD IX: glycogen storage disorder IX, MTHFRD: methylenetetrahydrofolate reductase deficiency, MSUD: maple syrup urine disease, NKH: nonketotic hyperglycinemia, OCT deficiency: ornithine transcarbamylase deficiency, PA: propionic acidemia, PKU: phenylketonuria, SCADD: short-chain acyl-CoA dehydrogenase deficiency, SSADH: succinic semialdehyde dehydrogenase).
Table 3. Metabolic work-up [100,101,102,103,104,105,106,107,108]. (3-MCCD: 3-methylcrotonyl-CoA carboxylase deficiency, AA: amino acid, AIP: acute intermittent porphyria, CSF: cerebrospinal fluid, CTX: cerebrotendinous xanthomatosis, GSD IX: glycogen storage disorder IX, MTHFRD: methylenetetrahydrofolate reductase deficiency, MSUD: maple syrup urine disease, NKH: nonketotic hyperglycinemia, OCT deficiency: ornithine transcarbamylase deficiency, PA: propionic acidemia, PKU: phenylketonuria, SCADD: short-chain acyl-CoA dehydrogenase deficiency, SSADH: succinic semialdehyde dehydrogenase).
Test CategoryInvestigation FindingIEM
Blood metabolitesPlasma lactate aIncreasedBiotinidase deficiency, GSD IX, PA
Plasma ammonia bIncreased3-MCCD, biotinidase deficiency, PA, urea cycle disorders
Serum uric acidDecreasedMolybdenum cofactor deficiency
Plasma free fatty acid cIncreasedSCADD
Serum sterol analysis
(7-dehydrocholesterol,
8-dehydrocholesterol)		IncreasedSmith–Lemli–Opitz syndrome
Serum copper (Cu)IncreasedWilson’s disease
Serum ceruloplasmin dDecreasedWilson’s disease
Serum transcobalamin DecreasedTranscobalamin II deficiency
Plasma succinylaminoimidazole carboxamide riboside,
succinyladenosine		IncreasedAdenylosuccinate lyase deficiency
Blood vitamin levelsSerum folate NormalFolate receptor α deficiency
Serum cobalamin (Vit B12)NormalTranscobalamin II deficiency
Plasma AA ePhenylalanineIncreasedPKU
MethionineIncreasedHomocystinuria
HomocysteineIncreasedHomocystinuria, MTHFRD, transcobalamin II deficiency
CysteineIncreasedHomocystinuria
DecreasedMolybdenum cofactor deficiency
S-sulfocysteineIncreasedMolybdenum cofactor deficiency
TaurineIncreasedMolybdenum cofactor deficiency
Leucine, isoleucine,
alloisoleucine		IncreasedMSUD
Neutral AA (Alanine, serine, threonine, asparagine,
glutamine, valine, leucine,
isoleucine, phenylalanine,
tyrosine, tryptophan, histidine, citrulline)		DecreasedHartnup disorder, collectrin deficiency
ProlineIncreasedHyperprolinemia
GlycineIncreasedNKH-AD, SSADH
ArginineDecreasedArgininosuccinate lyase deficiency, citrullinemia,
OTC deficiency
CitrullineIncreasedCitrullinemia, argininosuccinate lyase deficiency
DecreasedOCT deficiency
ProlineIncreasedCitrullinemia, OCT deficiency
Isovaleric acidIncreasedIsovaleryl-CoA dehydrogenase deficiency
CSF AA fPhenylalanineIncreasedPKU
GlycineIncreasedNKH-AD, SSADH
HomocysteineIncreasedFolate receptor α deficiency
MTFRD
MethionineDecreasedMTFRD
CSF analytesGlucose, lactateDecreasedGLUT1 deficiency
FolateDecreasedFolate receptor α deficiency
5-methyl THFDecreasedMTHFRD
Succinylaminoimidazole
carboxamide riboside,
succinyladenosine		IncreasedADSL deficiency
Plasma carnitine profile gPropionyl carnitineIncreasedBiotinidase deficiency, PA
3-hydroxyisovaleryl (CS-OH)Increased3-MCCD, Biotinidase deficiency
Carnitine; esterifiedIncreased3-MCCD
Carnitine; total and freeDecreased3-MCCD
ButyrylcarnitineIncreasedSCADD
Free carnitineDecreasedSCADD
Plasma porphyrinsALA, PBG, UPIIIIncreasedAIP
Urinary metabolitesALA, PBGIncreasedAIP
Bile acidsIncreasedCTX
PterinsIncreasedPKU
Xanthine and hypoxanthine,
thiosulfate		IncreasedMolybdenum cofactor deficiency
SulfateDecreasedMolybdenum cofactor deficiency
Urine organic acid profile hPhenylpyruvateIncreasedPKU
Branched-chain α-ketoacidsIncreasedMSUD
ArgininosuccinateIncreasedArgininosuccinate lyase deficiency
Orotic acidIncreasedArgininosuccinate lyase deficiency, citrullinemia,
Lysinuric protein intolerance
3-hydroxypropionate, methyicitrate, propionylglycineIncreasedPropionic acidemia
3-hydroxyisovaleric acid,
3-methylcrotonylglycine 		Increased3-MCCD
Iso-valerylglycine,
3-OH-isovaleric acid		IncreasedIsovaleryl-CoA dehydrogenase deficiency
Glutaric acidIncreasedGlutaric aciduria
Ethylmalonate, methylsuccinate, butyrylglycineIncreasedSCADD
γ-Hydroxybutyric acidIncreasedSSADH
CystathionineIncreasedMTHFRD
Methyimalonic acidIncreasedTranscobalamin II deficiency
Urine AAPhenylalanineIncreasedPKU
HomocysteineIncreasedHomocystinuria
Transcobalamin II deficiency
S-sulfocysteineIncreasedMolybdenum cofactor deficiency
TaurineIncreasedMolybdenum cofactor deficiency
Leucine + isoleucineIncreasedMSUD
Neutral AA
(Alanine, serine, threonine,
asparagine, glutamine, valine, leucine, isoleucine,
phenylalanine, tyrosine,
tryptophan, histidine, and
citrulline)		IncreasedHartnup disorder
Hydroxyproline, glycineIncreasedHyperprolinemia
CitrullineIncreasedArgininosuccinate lyase deficiency
HomocitrullineIncreasedCitrullinemia
OTC deficiency
GlycineIncreasedPropionic acidemia
Urine
mucopolysaccharide excretion profile i		Heparan sulfateIncreasedSanfilippo syndrome
Keratan sulfateIncreasedMorquio syndrome A
Stool amino acidNeutral AA
(Alanine, serine, threonine,
asparagine, glutamine, valine, leucine, isoleucine,
phenylalanine, tyrosine,
tryptophan, histidine, and
citrulline)		IncreasedHartnup disorder
Collectrin deficiency
Enzyme activityPropionyl-CoA carboxylase activity (WBC, fibroblasts)DecreasedPropionic acidemia
Carboxylase activity (WBC) Biotinidase activityDecreasedBiotinidase deficiency
27-hydroxylase (Fibroblasts)DecreasedCTX
Glycogen phosphorylase kinase (Lymphocytes, RBC)DecreasedGSD IX
N-acetylglucosamine 6-sulfatase (Fibroblasts)DecreasedSanfilippo syndrome
N-acetylgalactosamine
6-sulfatase		DecreasedMorquio syndrome A
Sphingomyelinase
(WBC, fibroblasts)		DecreasedNiemann–Pick disease
Special notes [105,112,113]. a Specimen: NaF/oxalate tube, immediately transported to lab on ice. b Specimen: heparin tube, immediately transported to the lab on ice and refrigerated centrifugation to separate plasma. Hyperammonemia may not be present if the patient is not in metabolic decompensation. c Specimen: NaF/oxalate tube, store separated plasma frozen. d Being a positive acute-phase reactant, false elevation can be seen in infection/acute hepatitis. Inadequate dietary copper intake and malabsorption can lead to low levels. e Assayed by HPLC. Best done during an acute episode. Patient preparation for non-acute samples: ideally collected after at least a 3-h fast or immediately before the next feed. Specimen: heparin tube on ice, plasma separated within 1 h of collection, and stored frozen. Interferences: post-prandial samples may show spuriously increased amino acid levels. f Assayed by HPLC. No patient preparation is indicated. Specimen: clear CSF, snap frozen on dry ice, and stored frozen. Interferences: results are uninterpretable from blood-stained samples. Blood-stained specimens can be centrifuged to obtain clear supernatant. g Specimen: heparin tube, stored separated plasma frozen. h Assayed by GC-MS. No patient preparation is indicated. Specimen: mid-stream urine, stored frozen. Urinary excretion of BCKD was noted 48 h after birth in infants on an unrestricted diet. i Assayed by LC-MS/MS. Specimen: early morning mid-stream urine. Dilute urines may yield uninterpretable profiles.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Senarathne, U.D.; Indika, N.-L.R.; Jezela-Stanek, A.; Ciara, E.; Frye, R.E.; Chen, C.; Stepien, K.M. Biochemical, Genetic and Clinical Diagnostic Approaches to Autism-Associated Inherited Metabolic Disorders. Genes 2023, 14, 803. https://doi.org/10.3390/genes14040803

AMA Style

Senarathne UD, Indika N-LR, Jezela-Stanek A, Ciara E, Frye RE, Chen C, Stepien KM. Biochemical, Genetic and Clinical Diagnostic Approaches to Autism-Associated Inherited Metabolic Disorders. Genes. 2023; 14(4):803. https://doi.org/10.3390/genes14040803

Chicago/Turabian Style

Senarathne, Udara D., Neluwa-Liyanage R. Indika, Aleksandra Jezela-Stanek, Elżbieta Ciara, Richard E. Frye, Cliff Chen, and Karolina M. Stepien. 2023. "Biochemical, Genetic and Clinical Diagnostic Approaches to Autism-Associated Inherited Metabolic Disorders" Genes 14, no. 4: 803. https://doi.org/10.3390/genes14040803

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop