Urinary Essential Elements of Young Children with Autism Spectrum Disorder and their Mothers

Association between Autism Spectrum Disorder, Trace Elements, and Intracranial Fluid Spaces

(1) Autism spectrum disorder (ASD) belongs to the group of complex developmental disorders. Novel studies have suggested that genetic and environmental factors equally affect the risk of ASD. Identification of environmental factors involved in the development of ASD is therefore crucial for a better understanding of its etiology. Whether there is a causal link between trace elements, brain magnetic resonance imaging (MRI), and ASD remains a matter of debate and requires further studies. (2) In the prospective part of the study, we included 194 children, including an age-matched control group; in the retrospective study, 28 children with available MRI imaging were included. All children had urine analysis of trace elements performed. In those with available brain MRI, linear indexes for the ventricular volumes were measured and calculated. (3) We found the highest vanadium, rubidium, thallium, and silver levels in children with ASD. These elements also correlated with the estimated ventricular volume based on MRI indexes in children with ASD in the subanalysis. However, the severity of the deficits did not correlate with brain MRI indexes of our elements, except negatively with magnesium. (4) Trace elements have an impact on children with ASD, but further multi-centric studies are needed to explain the pathophysiological mechanisms.

Zinc Status and Autism Spectrum Disorder in Children and Adolescents: A Systematic Review

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, the prevalence of which has increased in children and adolescents over the years. Studies point to deficiency of trace elements as one of the factors involved in the etiology of the disorder, with zinc being one of the main trace elements investigated in individuals with ASD. The aim of this review is to summarize scientific evidence about the relationship between zinc status and ASD in children and adolescents. This review has been registered in the International Prospective Register of Systematic Reviews (registration number CRD42020157907). The methodological guidelines adopted were in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Studies were selected from an active investigation of the PubMed, Scopus, LILACS, and Google databases to search for observational studies. Fifty-two studies from twenty-two countries were included. The sample sizes ranged from 20 to 2635, and the participants ranged from 2 to 18 years old. Nine types of biological matrices were used, with hair, serum, and plasma being the most frequently used in the evaluation of zinc concentrations. Significant differences in zinc concentrations between the ASD and control groups were observed in 23 studies, of which 19 (36%) showed lower zinc concentrations in the ASD group. The classification of studies according to methodological quality resulted in high, moderate, and low quality in 10, 21, and 21 studies, respectively. In general, we did not observe a significant difference between zinc concentrations of children and adolescents with ASD compared to controls; however, studies point to an occurrence of lower concentrations of Zn in individuals with ASD. This review reveals that more prospective studies with greater methodological rigor should be conducted in order to further characterize this relation.

Dysregulated phosphate metabolism in autism spectrum disorder: associations and insights for future research

Studies of autism spectrum disorder (ASD) related to exposure to toxic levels of dietary phosphate are lacking. Phosphate toxicity from dysregulated phosphate metabolism can negatively impact almost every major organ system of the body, including the central nervous system. The present paper used a grounded theory-literature review method to synthesise associations of dysregulated phosphate metabolism with the aetiology of ASD. Cell signalling in autism has been linked to an altered balance between phosphoinositide kinases, which phosphorylate proteins, and the counteracting effect of phosphatases in neuronal membranes. Glial cell overgrowth in the developing ASD brain can lead to disturbances in neuro-circuitry, neuroinflammation and immune responses which are potentially related to excessive inorganic phosphate. The rise in ASD prevalence has been suggested to originate in changes to the gut microbiome from increasing consumption of additives in processed food, including phosphate additives. Ketogenic diets and dietary patterns that eliminate casein also reduce phosphate intake, which may account for many of the suggested benefits of these diets in children with ASD. Dysregulated phosphate metabolism is causatively linked to comorbid conditions associated with ASD such as cancer, tuberous sclerosis, mitochondrial dysfunction, diabetes, epilepsy, obesity, chronic kidney disease, tauopathy, cardiovascular disease and bone mineral disorders. Associations and proposals presented in this paper offer novel insights and directions for future research linking the aetiology of ASD with dysregulated phosphate metabolism and phosphate toxicity from excessive dietary phosphorus intake.

Evaluation of School-Age Children's Intelligence Quotient and Their Chronic Exposure to Trace Elements in Ambient Air

Background Children's exposure to different trace elements in their air, water, and food or even present in paints or toys can affect their intelligence quotient (IQ) score. However, this correlation needs to be analyzed and evaluated in different contexts. This study aimed to investigate the associations between airborne concentrations of lead (Pb), manganese (Mn), cadmium (Cd), chromium (Cr), and arsenic (As) and intellectual function in school-age children in Makkah, Kingdom of Saudi Arabia. Methodology Our cohort study aimed to explore the link between exposure to various trace elements in the surrounding air and the IQ scores of children residing in the vicinity of Makkah. We included 430 children in the study and collected information about demographic and lifestyle factors using a structured questionnaire. We employed a mini volume sampler (MiniVol, AirMetrics, Springfield, OR, USA) to collect 24-hour PM10 samples from five locations in Makkah, representing various residential areas with small-to-medium industrial activities and traffic load. We analyzed the samples for Pb, Mn, Cd, Cr, and As concentrations using inductively coupled plasma-mass spectrometry with Perkin Elmer 7300 (Perkin Elmer, Waltham, MA, USA). The combined impact of heavy metals on continuous outcomes was assessed using the Bayesian kernel machine regression model. Results The mean atmospheric concentrations of Pb, Mn, Cd, Cr, and As in summer were 0.093, 0.006, 0.36, 0.15, and 0.017 µg/m³, respectively, while in winter, they were 0.004, 0.003, 0.12, 0.006, and 0.01 µg/m³, respectively. The findings of our study revealed that children's IQ scores were independently associated with co-exposure to the five metals, namely, Pb, Mn, Cd, Cr, and As. Conclusions This study demonstrates a link between combined exposure to five heavy metals (Pb, Mn, Cd, Cr, and As) and children's IQ scores. Regularly evaluating trace elements in children's biological samples is crucial to comprehend their effects on cognitive growth. To explore the possible future health risks of multimetal exposures and their interaction effects, it is imperative to conduct additional studies that involve repeated biological measurements of metal concentrations.

Analysis of Whole Blood and Urine Trace Elements in Children with Autism Spectrum Disorders and Autistic Behaviors

The relationship between trace elements and neurological development is an emerging research focus. We performed a case-control study to explore (1) the differences of 13 trace elements chromium (Cr), manganese (Mn), cobalt (Co), zinc (Zn), arsenic (As), selenium (Se), molybdenum (Mo), cadmium (Cd), stannum (Sn), stibium (Sb), mercury (Hg), titanium (TI), and plumbum (Pb) concentration in whole blood and urine between autism spectrum disorder (ASD) children and their typical development peers, and (2) the association between the 13 trace elements and core behaviors of ASD. Thirty ASD subjects (cases) and 30 age-sex-matched healthy subjects from Baise City, Guangxi Zhuang Autonomous Region, China, were recruited. Element analysis was carried out by inductively coupled plasma-optical emission spectrometry. Autistic behaviors were assessed using Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS), and Children Neuropsychological and Behavior Scale (CNBS). The whole blood concentrations of Mo (p = 0.004), Cd (0.007), Sn (p = 0.003), and Pb (p = 0.037) were significantly higher in the ASD cases than in the controls. Moreover, Se (0.393), Hg (0.408), and Mn (- 0.373) concentrations were significantly correlated between whole blood and urine levels in ASD case subjects. There were significant correlations between whole blood Sb (0.406), Tl (0.365), Mo (- 0.4237), Mn (- 0.389), Zn (0.476), and Se (0.375) levels and core behaviors of ASD. Although the mechanism of trace element imbalance in ASD is unclear, these data demonstrate that core behaviors of ASD may be affected by certain trace elements. Further studies are recommended for exploring the mechanism of element imbalance and providing corresponding clinical treatment measures.

Towards the Development of a Diagnostic Test for Autism Spectrum Disorder: Big Data Meets Metabolomics

Autism spectrum disorder (ASD) is defined as a neurodevelopmental disorder which results in impairments in social communications and interactions as well as repetitive behaviors. Despite current estimates showing that approximately 2.2% of children are affected in the United States, relatively little about ASD pathophysiology is known in part due to the highly heterogenous presentation of the disorder. Given the limited knowledge into the biological mechanisms governing its etiology, the diagnosis of ASD is performed exclusively based on an individual's behavior assessed by a clinician through psychometric tools. Although there is no readily available biochemical test for ASD diagnosis, multivariate statistical methods show considerable potential for effectively leveraging multiple biochemical measurements for classification and characterization purposes. In this work, markers associated with the folate dependent one-carbon metabolism and transulfuration (FOCM/TS) pathways analyzed via both Fisher Discriminant Analysis and Support Vector Machine showed strong capability to distinguish between ASD and TD cohorts. Furthermore, using Kernel Partial Least Squares regression it was possible to assess some degree of behavioral severity from metabolomic data. While the results presented need to be replicated in independent future studies, they represent a promising avenue for uncovering clinically relevant ASD biomarkers.

Framework for Testing Robustness of Machine Learning-Based Classifiers

There has been a rapid increase in the number of artificial intelligence (AI)/machine learning (ML)-based biomarker diagnostic classifiers in recent years. However, relatively little work has focused on assessing the robustness of these biomarkers, i.e., investigating the uncertainty of the AI/ML models that these biomarkers are based upon. This paper addresses this issue by proposing a framework to evaluate the already-developed classifiers with regard to their robustness by focusing on the variability of the classifiers' performance and changes in the classifiers' parameter values using factor analysis and Monte Carlo simulations. Specifically, this work evaluates (1) the importance of a classifier's input features and (2) the variability of a classifier's output and model parameter values in response to data perturbations. Additionally, it was found that one can estimate a priori how much replacement noise a classifier can tolerate while still meeting accuracy goals. To illustrate the evaluation framework, six different AI/ML-based biomarkers are developed using commonly used techniques (linear discriminant analysis, support vector machines, random forest, partial-least squares discriminant analysis, logistic regression, and multilayer perceptron) for a metabolomics dataset involving 24 measured metabolites taken from 159 study participants. The framework was able to correctly predict which of the classifiers should be less robust than others without recomputing the classifiers itself, and this prediction was then validated in a detailed analysis.

Multivariate Analysis of Metabolomic and Nutritional Profiles among Children with Autism Spectrum Disorder

There have been promising results regarding the capability of statistical and machine-learning techniques to offer insight into unique metabolomic patterns observed in ASD. This work re-examines a comparative study contrasting metabolomic and nutrient measurements of children with ASD (n = 55) against their typically developing (TD) peers (n = 44) through a multivariate statistical lens. Hypothesis testing, receiver characteristic curve assessment, and correlation analysis were consistent with prior work and served to underscore prominent areas where metabolomic and nutritional profiles between the groups diverged. Improved univariate analysis revealed 46 nutritional/metabolic differences that were significantly different between ASD and TD groups, with individual areas under the receiver operator curve (AUROC) scores of 0.6–0.9. Many of the significant measurements had correlations with many others, forming two integrated networks of interrelated metabolic differences in ASD. The TD group had 189 significant correlation pairs between metabolites, vs. only 106 for the ASD group, calling attention to underlying differences in metabolic processes. Furthermore, multivariate techniques identified potential biomarker panels with up to six metabolites that were able to attain a predictive accuracy of up to 98% for discriminating between ASD and TD, following cross-validation. Assessing all optimized multivariate models demonstrated concordance with prior physiological pathways identified in the literature, with some of the most important metabolites for discriminating ASD and TD being sulfate, the transsulfuration pathway, uridine (methylation biomarker), and beta-amino isobutyrate (regulator of carbohydrate and lipid metabolism).

Alterations of urinary perchlorate levels in euthyroid postpubertal children with autism spectrum disorder

BACKGROUND

Perchlorates ClO₄(^-) are known environmental and food contaminants that act as inhibitors of iodine uptake by the thyroid gland; however, information concerning their possible association with the development of autism spectrum disorder (ASD) is still missing. The current study is first presenting the alterations in perchlorate urine levels in euthyroid children with ASD.

OBJECTIVES

To examine urinary perchlorates and iodides in euthyroid children diagnosed with ASD, compared to age-, and BMI-matched neurotypical controls, and to verify the association between these two ions in ASD.

METHODS

Ions were determined in 24 h urine samples determined by ion chromatography-conductivity cell detection (IC-CD) and ion chromatography-pulsed amperometric detection (IC-PAD) techniques, respectively, in a total of 130 postpubertal euthyroid children with normal BMI (the mean age 14.46 years, SD = 1.32; the mean BMI 20.6, SD = 1.37), divided into age- and BMI-matched groups of ASD patients and neurotypical, healthy children (control).

RESULTS

The ASD group presented with significantly higher perchlorate urine levels than the controls (median = 1.05 μg/L, interquartile range(IQR) = 1.5 versus median = 0.09 μg/L, IQR = 0.097, respectively), as well as lower iodide urine levels (median = 100.2 μg/L, IQR = 37 versus median = 156.95 μg/L, IQR = 26.11, respectively). The ASD group presented significantly lower TSH and higher free thyroid hormone (fT4, fT3) levels than the controls. In regression analyses, perchlorate urine levels showed significant positive relationships with normal BMI values and serum TSH, and inverse relationships with serum fT4. Urinary iodide levels showed significant inverse relationships with BMI values. The absence of ASD was associated with decreased odds of perchlorate urine levels (OR = 0.012, 95 % confidence interval [CI] 0.0002-0.76), and increased odds of iodide urine levels (OR = 1.15, 95 %CI 1.05-1.27).

CONCLUSIONS

ASD may have an independent and significant impact on perchlorate as well as iodide levels in urine of euthyroid lean postpubertal children. Perchlorate levels do not appear to be directly associated with iodide levels in euthyroid children.