Biomarkers in Autism

Goldani, André Akira Sueno; Downs, Susan R.; Widjaja, Felicia; Lawton, Brittany; Hendren, Robert L.

doi:10.3389/fpsyt.2014.00100

Cited by 167 publications

(121 citation statements)

References 176 publications

(231 reference statements)

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“…Plasma 3-chlortyrosine (3CT), reflects both reactive nitrogen species and myeloperoxidase activity (a proinflammatory enzyme), and appears to escalate in ASD patients primarily with mitochondrial dysfunction [93], thereby indicating a mitochondrial-driven OS and inflammation. Another, also mitochondrial OS marker, is 3-Nitrotyrosine (3NT), which rises in plasma of ASD patients in response to oxidative protein damage and neuronal death, thereby also concurring with the extent of behavioral anomalies and aggression observed with ASD patients [94]. Lately, in ASD patient blood, increases were detected in levels of thioredoxin (TRX), a redoxregulating protein with antioxidant activity, indicating the utility of this protein as an oxidative-stress marker in autism [95].…”

Section: Metabolomics Studies: Pathobiologic Diagnostic and Therapeumentioning

confidence: 91%

Emerging Clues and Altered Metabolic Findings in Autism: Breakthroughs and Prospects from Omics Studies

Mowafy¹

2016

Autism Open Access

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Autism spectrum disorder (ASD) is a pervasive broad-spectrum disorder that involves multifaceted delays in the development of many basic, social, and communication skills. The etiology of ASD is multifactorial and involves interplay among genetic, neurologic, hormonal, metabolic, immune-inflammatory, and environmental factors; which further complicate both the diagnosis and management in affected individuals. This review delineates the underpinnings of clinical challenges posed by ASD, like clinical heterogeneity of patient presentation, unresolved ASD genomic map and deficient drug therapy. Besides, it addresses the emerging pathogenic, etiologic, and diagnostic clues of diverse origins, encompassing genetic-, neurotransmitter-, androgenic-and immunoinflammatory-anomalies in ASD patients, as availed by advances in Omics technologies (like genomics and proteomics). Moreover, as unravelled by metabolomics studies, metabolic flaws that pertain to amino acids, fatty acids, mitochondrial anomalies, and oxidative-stress are highlighted and further correlated with the extent of clinical picture and malbehaviors coherent with ASD patients. Lastly, future directions are underlined to promote and rationalize ASD research so as to help translate its findings into remedy.

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Section: Metabolomics Studies: Pathobiologic Diagnostic and Therapeumentioning

confidence: 91%

Emerging Clues and Altered Metabolic Findings in Autism: Breakthroughs and Prospects from Omics Studies

Mowafy¹

2016

Autism Open Access

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“…(1) With the same number of features, we expect the mRMR feature set to be more representative of the target phenotypes, therefore leading to a better generalization property. (2) Equivalently, we can use a smaller mRMR feature set to effectively cover the same space as a larger conventional feature set [52]. The mRMR method is designed to work with discrete data and since we have continuous values in our dataset, before applying this method we need to discretize the data into multiple bins.…”

Section: Experiments 1: Subsampling By Age Matchingmentioning

confidence: 99%

Functional SPECT neuroimaging using machine learning algorithms distinguishes autism spectrum disorder from healthy subjects

Amen¹,

Sarabi²,

Willeumier³

et al. 2017

J Syst Integr Neurosci

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The diagnosis of Autism Spectrum Disorder (ASD) relies on history and behavioral observation, lacking reliable biomarkers. We performed a retrospective analysis using machine learning algorithms of 928 persons with ASD (mean age: 17 ± 10.8 years; age range 4-67) obtained from a multisite psychiatric database with rest and on-task brain SPECT scans to investigate whether or not these scans distinguish ASD from healthy controls (HC, n=101; mean age: 43 ± 17.2 years; age range 13-84). Using 128 regions of interest extracts (ROIs), we applied multiple machine learning algorithms for binary classification. Due to an unbalanced sample size between ASD and controls, we then sub-sampled the data prior to feature selection and classification. Using a subsampled dataset, least absolute shrinkage and selection operator (LASSO) feature selection with Random Forest method baseline accuracy results of approximately 81% were achieved, based on optimal classifier settings with the top selected features. We applied machine learning algorithms to ASD adults only, the majority of our sample, and selected subjects in both AD and HC groups with age range of 13-67 years and found the results consistent with the combined data. These machine learning results identified potential diagnostic biomarkers differentiating ASD from HC in the regions of the cerebellum and vermis, anterior cingulate gyrus, amygdala, thalamus, frontal, and temporal lobes.

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“…6 Aday gen çalış-malarında ise, özellikle RELN, NRXN1, SHANK3, NLGN3, NLGN4, MeCP2, EN2, CNTNAP2, OXTR, GABRB3, GABRA5, GABRG3 başta olmak üzere yüzlerce gen bozukluğun etiyolojisiyle ilişkilendirilmiştir. 7,8 İnsanlarda 7. kromozomda (7q22) lokalize olan reelin geni (RELN), santral sinir sisteminin gelişi-minde önemli bir rol oynayan ekstraselüler bir makriks proteinini sentezler. 9 Reelin hem gelişim sürecinde, hem de erişkin dönemde nörogenezis sürecini olumlu yönde etkilemektedir.…”

Section: Introductionunclassified