Urinary essential elements of young children with autism spectrum disorder and their mothers

Qureshi, Fatir; Adams, James B.; Coleman, Devon M.; Quig, David; Hahn, Juergen

doi:10.1016/j.rasd.2020.101518

Cited by 11 publications

(9 citation statements)

References 43 publications

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“…A significant body of work has shown that significant differences in sulfation capacity and sulfur-related metabolites have been commonly observed between ASD and TD cohorts [ 36 , 37 ]. Urinary elemental sulfur concentrations were found to be significantly lower in children with ASD and were a prominent contributor to FDA models for distinguishing between ASD and TD groups [ 55 ]. Sulfate metabolism is closely connected to interactions of the gut microbiome, and the presence of certain organic sulfate compounds has been statistically higher in the feces of children with ASD [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, urine organic acids have been shown to have the capacity to also discern between children with and without ASD [ 54 ]. Fecal metabolites provided the basis of a model that was able to achieve a sensitivity of 94% and a specificity of 95% after cross-validation for a cohort of 18 children with ASD and GI issues vs. 20 TD counterparts [ 55 ].…”

Section: Introductionmentioning

confidence: 99%

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

Qureshi

Adams

Audhya³

et al. 2022

JPM

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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).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Qureshi

Adams

Audhya³

et al. 2022

JPM

Self Cite

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“…This subset of important features should then be compared to the input features to determine if the input features are statistically meaningful in the data. The workflow for performing the factor analysis is shown in Figure 1 [ 16 ]. The approach consists of three main parts: (1) false discovery rate calculation, (2) factor loading clustering, and (3) logistic regression variance calculation.…”

Section: Methodsmentioning

confidence: 99%

“…This is repeated until each sample (or pair of samples in the case of leave-two-out) has been left out once. The false discovery rate for each variable is computed as the number of p -values calculated to be greater than 0.05 divided by the number of all p -values calculated [ 16 ]. As such, the false discovery rate of each feature f is computed as the following:

…”

Section: Methodsmentioning

confidence: 99%

Framework for Testing Robustness of Machine Learning-Based Classifiers

Chuah

Krüger

Wang

et al. 2022

JPM

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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.

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“…Trace elements in blood can reflect homeostasis, whereas urine trace elements can indicate the metabolism of certain components. A minority of existing studies have examined trace element levels in urine of children with ASD and healthy peers, but these studies failed to investigate enough elements, samples, or correlations between blood and urine trace element levels [ 17 – 19 ].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Liu

Gan

et al. 2022

Biol Trace Elem Res

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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.

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Urinary essential elements of young children with autism spectrum disorder and their mothers

Cited by 11 publications

References 43 publications

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

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

Framework for Testing Robustness of Machine Learning-Based Classifiers

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

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