Classification of autism spectrum disorder from blood metabolites: Robustness to the presence of co-occurring conditions / Troy VARGASON in Research in Autism Spectrum Disorders, 77 (September 2020)  

Public ISBD [article]  inResearch in Autism Spectrum Disorders > 77 (September 2020) . - 101644 Titre : Classification of autism spectrum disorder from blood metabolites: Robustness to the presence of co-occurring conditions Type de document : Texte imprimé et/ou numérique Auteurs : Troy VARGASON, Auteur ; Emily ROTH, Auteur ; Genevieve GRIVAS, Auteur ; Jennifer FERINA, Auteur ; Richard E. FRYE, Auteur ; Juergen HAHN, Auteur Article en page(s) : 101644 Langues : Anglais (eng) Mots-clés : Autism spectrum disorder  Co-occurring conditions  Folate-dependent one-carbon metabolism  Transsulfuration  Multivariate analysis  Classification Index. décimale : PER Périodiques Résumé : Background Previous studies have found plasma measurements of metabolites from the folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathways to be useful for differentiating individuals with autism spectrum disorder (ASD) from their typically developing peers. However, ASD is heterogeneous due to wide variation in the presence of co-occurring behavioral and medical conditions, and it is unknown how these conditions influence the ability to identify ASD based on FOCM/TS metabolites. Method This study employs a previously developed multivariate model that makes use of five FOCM/TS measurements (S-adenosylmethionine/S-adenosylhomocysteine, glutamylcysteine, glutathione disulfide, free cystine/free cysteine, and percent oxidized glutathione) to distinguish children with ASD from typically developing children. The model is used here to evaluate an independent cohort of individuals having ASD with diagnosed co-occurring conditions (age range 2–17 years old) and assess classifier performance in the presence/absence of these conditions. The four categories of co-occurring conditions considered were allergic disorders, gastrointestinal disorders, immune/metabolic disorders, and neurological disorders. All data were collected and retrospectively analyzed from previous clinical studies. Results The model was able to identify 124 of 131 participants with ASD (94.7 %) correctly regardless of co-occurring condition status. Model performance was generally not sensitive to the absence or presence of most co-occurring conditions, with the exceptions of ever/never having allergies or gastrointestinal symptoms, or currently (not) having any condition, all of which had minor impacts on model prediction accuracy. Conclusion The results of this exploratory study suggest that a FOCM/TS-based classifier for diagnosing ASD may potentially be robust to variations in co-occurring conditions and potentially applicable across ASD subtypes. Larger, more comprehensive follow-up studies with typically developing and/or developmentally delayed control groups are required to provide a more conclusive assessment of classifier robustness to co-occurring conditions. En ligne : https://doi.org/10.1016/j.rasd.2020.101644 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=432 [article] Classification of autism spectrum disorder from blood metabolites: Robustness to the presence of co-occurring conditions [Texte imprimé et/ou numérique] / Troy VARGASON, Auteur ; Emily ROTH, Auteur ; Genevieve GRIVAS, Auteur ; Jennifer FERINA, Auteur ; Richard E. FRYE, Auteur ; Juergen HAHN, Auteur . - 101644. Langues : Anglais (eng) in Research in Autism Spectrum Disorders > 77 (September 2020) . - 101644 Mots-clés : Autism spectrum disorder  Co-occurring conditions  Folate-dependent one-carbon metabolism  Transsulfuration  Multivariate analysis  Classification Index. décimale : PER Périodiques Résumé : Background Previous studies have found plasma measurements of metabolites from the folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathways to be useful for differentiating individuals with autism spectrum disorder (ASD) from their typically developing peers. However, ASD is heterogeneous due to wide variation in the presence of co-occurring behavioral and medical conditions, and it is unknown how these conditions influence the ability to identify ASD based on FOCM/TS metabolites. Method This study employs a previously developed multivariate model that makes use of five FOCM/TS measurements (S-adenosylmethionine/S-adenosylhomocysteine, glutamylcysteine, glutathione disulfide, free cystine/free cysteine, and percent oxidized glutathione) to distinguish children with ASD from typically developing children. The model is used here to evaluate an independent cohort of individuals having ASD with diagnosed co-occurring conditions (age range 2–17 years old) and assess classifier performance in the presence/absence of these conditions. The four categories of co-occurring conditions considered were allergic disorders, gastrointestinal disorders, immune/metabolic disorders, and neurological disorders. All data were collected and retrospectively analyzed from previous clinical studies. Results The model was able to identify 124 of 131 participants with ASD (94.7 %) correctly regardless of co-occurring condition status. Model performance was generally not sensitive to the absence or presence of most co-occurring conditions, with the exceptions of ever/never having allergies or gastrointestinal symptoms, or currently (not) having any condition, all of which had minor impacts on model prediction accuracy. Conclusion The results of this exploratory study suggest that a FOCM/TS-based classifier for diagnosing ASD may potentially be robust to variations in co-occurring conditions and potentially applicable across ASD subtypes. Larger, more comprehensive follow-up studies with typically developing and/or developmentally delayed control groups are required to provide a more conclusive assessment of classifier robustness to co-occurring conditions. En ligne : https://doi.org/10.1016/j.rasd.2020.101644 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=432

Investigating plasma amino acids for differentiating individuals with autism spectrum disorder and typically developing peers / Troy VARGASON in Research in Autism Spectrum Disorders, 50 (June 2018)  

Public ISBD [article]  inResearch in Autism Spectrum Disorders > 50 (June 2018) . - p.60-72 Titre : Investigating plasma amino acids for differentiating individuals with autism spectrum disorder and typically developing peers Type de document : Texte imprimé et/ou numérique Auteurs : Troy VARGASON, Auteur ; Uwe KRUGER, Auteur ; Deborah L. MCGUINNESS, Auteur ; James B. ADAMS, Auteur ; Elizabeth GEIS, Auteur ; Eva GEHN, Auteur ; Devon COLEMAN, Auteur ; Juergen HAHN, Auteur Année de publication : 2018 Article en page(s) : p.60-72 Langues : Anglais (eng) Mots-clés : Autism spectrum disorder  Plasma amino acids  Fisher discriminant analysis  Classification  Multivariate statistics  Cross-validation Index. décimale : PER Périodiques Résumé : Background Plasma amino acid measurements have been extensively investigated in individuals with autism spectrum disorder (ASD). Results thus far have been inconclusive as studies generally disagree on which amino acids are different in individuals with ASD versus their typically developing (TD) peers, due in part to methodological limitations of several studies. Method This paper investigates plasma amino acids in children and adults with ASD using data from Arizona State University’s Comprehensive Nutritional and Dietary Intervention Study. Measurements from 64 individuals with ASD and 49 TD controls were analyzed using univariate and multivariate statistical techniques. Results Univariate analysis indicated increased median levels of glutamate (+21%, p?=?0.014) and serine (+8%, p?=?0.043), and increased mean levels of hydroxyproline (+17%, p?=?0.018) for the ASD cohort, although these differences were insignificant after correcting for multiple comparisons. A multivariate approach was used to classify study participants into ASD/TD cohorts using Fisher discriminant analysis (FDA) and its nonlinear extension, kernel Fisher discriminant analysis (KFDA). Model fitting with FDA using all available measurements produced Type I and Type II errors of 27.0% and 27.8%, respectively. KFDA was most effective when using hydroxyproline, leucine, and threonine as inputs; however, leave-one-out cross-validation with this nonlinear model only resulted in 70.3% sensitivity and 77.6% specificity. Conclusions The finding of elevated glutamate in ASD is in agreement with several other studies. Overall, however, these results suggest that plasma amino acid measurements are of limited use for purposes of ASD classification, which may explain some of the inconsistencies in results presented in the literature. En ligne : https://doi.org/10.1016/j.rasd.2018.03.004 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=356 [article] Investigating plasma amino acids for differentiating individuals with autism spectrum disorder and typically developing peers [Texte imprimé et/ou numérique] / Troy VARGASON, Auteur ; Uwe KRUGER, Auteur ; Deborah L. MCGUINNESS, Auteur ; James B. ADAMS, Auteur ; Elizabeth GEIS, Auteur ; Eva GEHN, Auteur ; Devon COLEMAN, Auteur ; Juergen HAHN, Auteur . - 2018 . - p.60-72. Langues : Anglais (eng) in Research in Autism Spectrum Disorders > 50 (June 2018) . - p.60-72 Mots-clés : Autism spectrum disorder  Plasma amino acids  Fisher discriminant analysis  Classification  Multivariate statistics  Cross-validation Index. décimale : PER Périodiques Résumé : Background Plasma amino acid measurements have been extensively investigated in individuals with autism spectrum disorder (ASD). Results thus far have been inconclusive as studies generally disagree on which amino acids are different in individuals with ASD versus their typically developing (TD) peers, due in part to methodological limitations of several studies. Method This paper investigates plasma amino acids in children and adults with ASD using data from Arizona State University’s Comprehensive Nutritional and Dietary Intervention Study. Measurements from 64 individuals with ASD and 49 TD controls were analyzed using univariate and multivariate statistical techniques. Results Univariate analysis indicated increased median levels of glutamate (+21%, p?=?0.014) and serine (+8%, p?=?0.043), and increased mean levels of hydroxyproline (+17%, p?=?0.018) for the ASD cohort, although these differences were insignificant after correcting for multiple comparisons. A multivariate approach was used to classify study participants into ASD/TD cohorts using Fisher discriminant analysis (FDA) and its nonlinear extension, kernel Fisher discriminant analysis (KFDA). Model fitting with FDA using all available measurements produced Type I and Type II errors of 27.0% and 27.8%, respectively. KFDA was most effective when using hydroxyproline, leucine, and threonine as inputs; however, leave-one-out cross-validation with this nonlinear model only resulted in 70.3% sensitivity and 77.6% specificity. Conclusions The finding of elevated glutamate in ASD is in agreement with several other studies. Overall, however, these results suggest that plasma amino acid measurements are of limited use for purposes of ASD classification, which may explain some of the inconsistencies in results presented in the literature. En ligne : https://doi.org/10.1016/j.rasd.2018.03.004 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=356

---

1 (1 - 2 / 2)