Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods

A Review of the Nutritional Approach and the Role of Dietary Components in Children with Autism Spectrum Disorders in Light of the Latest Scientific Research

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects several areas of mental development. The onset of ASD occurs in the first few years of life, usually before the age of 3 years. Proper nutrition is important to ensure that an individual's nutrient and energy requirements are met, and it can also have a moderating effect on the progression of the disorder. A systematic database search was conducted as a narrative review to determine whether nutrition and specific diets can potentially alter gastrointestinal symptoms and neurobehavioral disorders. Databases such as Science Direct, PubMed, Scopus, Web of Science (WoS), and Google Scholar were searched to find studies published between 2000 and September 2023 on the relationship between ASD, dietary approaches, and the role of dietary components. The review may indicate that despite extensive research into dietary interventions, there is a general lack of conclusive scientific data about the effect of therapeutic diets on ASD; therefore, no definitive recommendation can be made for any specific nutritional therapy as a standard treatment for ASD. An individualized dietary approach and the dietician's role in the therapeutic team are very important elements of every therapy. Parents and caregivers should work with nutrition specialists, such as registered dietitians or healthcare providers, to design meal plans for autistic individuals, especially those who would like to implement an elimination diet.

Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.

Role of gastrointestinal health in managing children with autism spectrum disorder

Children with autism spectrum disorders (ASD) or autism are more prone to gastrointestinal (GI) disorders than the general population. These disorders can significantly affect their health, learning, and development due to various factors such as genetics, environment, and behavior. The causes of GI disorders in children with ASD can include gut dysbiosis, immune dysfunction, food sensitivities, digestive enzyme deficiencies, and sensory processing differences. Many studies suggest that numerous children with ASD experience GI problems, and effective management is crucial. Diagnosing autism is typically done through genetic, neurological, functional, and behavioral assessments and observations, while GI tests are not consistently reliable. Some GI tests may increase the risk of developing ASD or exacerbating symptoms. Addressing GI issues in individuals with ASD can improve their overall well-being, leading to better behavior, cognitive function, and educational abilities. Proper management can improve digestion, nutrient absorption, and appetite by relieving physical discomfort and pain. Alleviating GI symptoms can improve sleep patterns, increase energy levels, and contribute to a general sense of well-being, ultimately leading to a better quality of life for the individual and improved family dynamics. The primary goal of GI interventions is to improve nutritional status, reduce symptom severity, promote a balanced mood, and increase patient independence.

Applications of Unsupervised Machine Learning in Autism Spectrum Disorder Research: a Review

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of unsupervised machine learning in ASD research and provide insight into the types of questions being answered with these methods.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Validation strategies for subtypes in psychiatry: A systematic review of research on autism spectrum disorder

Heterogeneity within autism spectrum disorder (ASD) is recognized as a challenge to both biological and psychological research, as well as clinical practice. To reduce unexplained heterogeneity, subtyping techniques are often used to establish more homogeneous subtypes based on metrics of similarity and dissimilarity between people. We review the ASD literature to create a systematic overview of the subtyping procedures and subtype validation techniques that are used in this field. We conducted a systematic review of 156 articles (2001-June 2020) that subtyped participants (range N of studies = 17-20,658), of which some or all had an ASD diagnosis. We found a large diversity in (parametric and non-parametric) methods and (biological, psychological, demographic) variables used to establish subtypes. The majority of studies validated their subtype results using variables that were measured concurrently, but were not included in the subtyping procedure. Other investigations into subtypes' validity were rarer. In order to advance clinical research and the theoretical and clinical usefulness of identified subtypes, we propose a structured approach and present the SUbtyping VAlidation Checklist (SUVAC), a checklist for validating subtyping results.

Inborn errors in the vitamin B6 salvage enzymes associated with neonatal epileptic encephalopathy and other pathologies

Pyridoxal 5'-phosphate (PLP), the active cofactor form of vitamin B6 is required by over 160 PLP-dependent (vitamin B6) enzymes serving diverse biological roles, such as carbohydrates, amino acids, hemes, and neurotransmitters metabolism. Three key enzymes, pyridoxal kinase (PL kinase), pyridoxine 5'-phosphate oxidase (PNPO), and phosphatases metabolize and supply PLP to PLP-dependent enzymes through the salvage pathway. In born errors in the salvage enzymes are known to cause inadequate levels of PLP in the cell, particularly in neuronal cells. The resulting PLP deficiency is known to cause or implicated in several pathologies, most notably seizures. One such disorder, PNPO-dependent neonatal epileptic encephalopathy (NEE) results from natural mutations in PNPO and leads to null or reduced enzymatic activity. NEE does not respond to conventional antiepileptic drugs but may respond to treatment with the B6 vitamers PLP and/or pyridoxine (PN). In born errors that lead to PLP deficiency in cells have also been reported in PL kinase, however, to date none has been associated with epilepsy or seizure. One such pathology is polyneuropathy that responds to PLP therapy. Phosphatase deficiency or hypophosphatasia disorder due to pathogenic mutations in alkaline phosphatase is known to cause seizures that respond to PN therapy. In this article, we review the biochemical features of in born errors pertaining to the salvage enzyme's deficiency that leads to NEE and other pathologies. We also present perspective on vitamin B6 treatment for these disorders, along with attempts to develop zebrafish model to study the NEE syndrome in vivo.

Prospective associations of the infant gut microbiome and microbial function with social behaviors related to autism at age 3 years

The hypothesized link between gut bacteria and autism spectrum disorder (ASD) has been explored through animal models and human studies with microbiome assessment after ASD presentation. We aimed to prospectively characterize the association between the infant/toddler gut microbiome and ASD-related social behaviors at age 3 years. As part of an ongoing birth cohort gut bacterial diversity, structure, taxa, and function at 6 weeks (n = 166), 1 year (n = 158), 2 years (n = 129), and 3 years (n = 140) were quantified with 16S rRNA gene and shotgun metagenomic sequencing (n = 101 six weeks, n = 103 one year). ASD-related social behavior was assessed at age 3 years using Social Responsiveness Scale (SRS-2) T-scores. Covariate-adjusted linear and permutation-based models were implemented. Microbiome structure at 1 year was associated with SRS-2 total T-scores (p = 0.01). Several taxa at 1, 2, and 3 years were associated with SRS-2 performance, including many in the Lachnospiraceae family. Higher relative abundance of Adlercreutzia equolifaciens and Ruminococcus torques at 1 year related to poorer SRS-2 performance. Two functional pathways, L-ornithine and vitamin B6 biosynthesis, were associated with better social skills at 3 years. Our results support potential associations between early-childhood gut microbiome and social behaviors. Future mechanistic studies are warranted to pinpoint sensitive targets for intervention.

Clustering by phenotype and genome-wide association study in autism

Autism spectrum disorder (ASD) has phenotypically and genetically heterogeneous characteristics. A simulation study demonstrated that attempts to categorize patients with a complex disease into more homogeneous subgroups could have more power to elucidate hidden heritability. We conducted cluster analyses using the k-means algorithm with a cluster number of 15 based on phenotypic variables from the Simons Simplex Collection (SSC). As a preliminary study, we conducted a conventional genome-wide association study (GWAS) with a data set of 597 ASD cases and 370 controls. In the second step, we divided cases based on the clustering results and conducted GWAS in each of the subgroups vs controls (cluster-based GWAS). We also conducted cluster-based GWAS on another SSC data set of 712 probands and 354 controls in the replication stage. In the preliminary study, which was conducted in conventional GWAS design, we observed no significant associations. In the second step of cluster-based GWASs, we identified 65 chromosomal loci, which included 30 intragenic loci located in 21 genes and 35 intergenic loci that satisfied the threshold of P < 5.0 × 10^-8. Some of these loci were located within or near previously reported candidate genes for ASD: CDH5, CNTN5, CNTNAP5, DNAH17, DPP10, DSCAM, FOXK1, GABBR2, GRIN2A5, ITPR1, NTM, SDK1, SNCA, and SRRM4. Of these 65 significant chromosomal loci, rs11064685 located within the SRRM4 gene had a significantly different distribution in the cases vs controls in the replication cohort. These findings suggest that clustering may successfully identify subgroups with relatively homogeneous disease etiologies. Further cluster validation and replication studies are warranted in larger cohorts.

Artificial intelligence powered statistical genetics in biobanks

Large-scale, sometimes nationwide, prospective genomic cohorts biobanking rich biological specimens such as blood, urine and tissues, have been established and released their vast amount of data in several countries. These genetic and epidemiological resources are expected to allow investigators to disentangle genetic and environmental components conferring common complex diseases. There are, however, two major challenges to statistical genetics for this goal: small sample size-high dimensionality and multilayered-heterogenous endophenotypes. Rather counterintuitively, biobank data generally have small sample size relative to their data dimensionality consisting of genomic variation, lifestyle questionnaire, and sometimes their interaction. This is a widely acknowledged difficulty in data analysis, so-called "p»n problem" in statistics or "curse of dimensionality" in machine-learning field. On the other hand, we have too many measurements of individual health status, which are endophenotypes, such as health check-up data, images, psychological test scores in addition to metabolomics and proteomics data. These endophenotypes are rich but not so tractable because of their worsen dimensionality, and substantial correlation, sometimes confusing causation among them. We have tried to overcome the problems inherent to biobank data, using statistical machine-learning and deep-learning technologies.

Targeted Biomedical Treatment for Autism Spectrum Disorders

BACKGROUND

A diagnosis of autism spectrum disorders (ASD) represents presentations with impairment in communication and behaviour that vary considerably in their clinical manifestations and etiology as well as in their likely pathophysiology. A growing body of data indicates that the deleterious effect of oxidative stress, mitochondrial dysfunction, immune dysregulation and neuroinflammation, as well as their interconnections are important aspects of the pathophysiology of ASD. Glutathione deficiency decreases the mitochondrial protection against oxidants and tumor necrosis factor (TNF)-α; immune dysregulation and inflammation inhibit mitochondrial function through TNF-α; autoantibodies against the folate receptors underpin cerebral folate deficiency, resulting in disturbed methylation, and mitochondrial dysfunction. Such pathophysiological processes can arise from environmental and epigenetic factors as well as their combined interactions, such as environmental toxicant exposures in individuals with (epi)genetically impaired detoxification. The emerging evidence on biochemical alterations in ASD is forming the basis for treatments aimed to target its biological underpinnings, which is of some importance, given the uncertain and slow effects of the various educational interventions most commonly used.

METHODS

Literature-based review of the biomedical treatment options for ASD that are derived from established pathophysiological processes.

RESULTS

Most proposed biomedical treatments show significant clinical utility only in ASD subgroups, with specified pre-treatment biomarkers that are ameliorated by the specified treatment. For example, folinic acid supplementation has positive effects in ASD patients with identified folate receptor autoantibodies, whilst the clinical utility of methylcobalamine is apparent in ASD patients with impaired methylation capacity. Mitochondrial modulating cofactors should be considered when mitochondrial dysfunction is evident, although further research is required to identify the most appropriate single or combined treatment. Multivitamins/multiminerals formulas, as well as biotin, seem appropriate following the identification of metabolic abnormalities, with doses tapered to individual requirements. A promising area, requiring further investigations, is the utilization of antipurinergic therapies, such as low dose suramin.

CONCLUSION

The assessment and identification of relevant physiological alterations and targeted intervention are more likely to produce positive treatment outcomes. As such, current evidence indicates the utility of an approach based on personalized and evidence-based medicine, rather than treatment targeted to all that may not always be beneficial (primum non nocere).

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

Background

In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging.

Objective

This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies.

Methods

We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process.

Results

Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification.

Conclusions

This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.

Nutritional interventions for autism spectrum disorder

Autism spectrum disorder (ASD) is an increasingly prevalent neurodevelopmental disorder with considerable clinical heterogeneity. With no cure for the disorder, treatments commonly center around speech and behavioral therapies to improve the characteristic social, behavioral, and communicative symptoms of ASD. Gastrointestinal disturbances are commonly encountered comorbidities that are thought to be not only another symptom of ASD but to also play an active role in modulating the expression of social and behavioral symptoms. Therefore, nutritional interventions are used by a majority of those with ASD both with and without clinical supervision to alleviate gastrointestinal and behavioral symptoms. Despite a considerable interest in dietary interventions, no consensus exists regarding optimal nutritional therapy. Thus, patients and physicians are left to choose from a myriad of dietary protocols. This review, summarizes the state of the current clinical and experimental literature on nutritional interventions for ASD, including gluten-free and casein-free, ketogenic, and specific carbohydrate diets, as well as probiotics, polyunsaturated fatty acids, and dietary supplements (vitamins A, C, B6, and B12; magnesium and folate).