Summary of project PR001606

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001606. The data can be accessed directly via it's Project DOI: 10.21228/M83T4M This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

Project ID: PR001606
Project DOI:doi: 10.21228/M83T4M
Project Title:Metabolomic analysis of maternal mid-gestation plasma and cord blood
Project Summary:Metabolomic analysis of maternal mid-gestation plasma and cord blood reveals evidence in autism spectrum disorder of inflammation, disruption of membrane integrity, and impaired neurotransmission and neurotoxicity. The discovery of prenatal and neonatal molecular biomarkers has the potential to yield insights into autism spectrum disorder (ASD) and facilitate early diagnosis. We characterized metabolomic profiles in ASD using plasma samples collected in the Norwegian Autism Birth Cohort from mothers at weeks 17-21 gestation (maternal mid-gestation, MMG, n=408) and from children on the day of birth (cord blood, CB, n=418). We analyzed associations using sex-stratified adjusted logistic regression models with Bayesian analyses. Chemical enrichment analyses (ChemRICH) were performed to determine altered chemical clusters. We also employed machine learning algorithms to assess the utility of metabolomics as ASD biomarkers. We identified ASD associations with a variety of chemical compounds including arachidonic acid, glutamate, and glutamine, and metabolite clusters including hydroxy eicospentaenoic acids, phosphatidylcholines, and ceramides in MMG and CB plasma that are consistent with inflammation, disruption of membrane integrity, and impaired neurotransmission and neurotoxicity. Girls with ASD have disruption of ether/non-ether phospholipid balance in the MMG plasma that is similar to that found in other neurodevelopmental disorders. ASD boys in the CB analyses had the highest number of dysregulated chemical clusters. Machine learning classifiers distinguished ASD cases from controls with AUC values ranging from 0.710 to 0.853. Predictive performance was better in CB analyses than in MMG. These findings may provide new insights into the sex-specific differences in ASD and have implications for discovery of biomarkers that may enable early diagnosis and intervention.
Institute:Columbia University
Department:Center for Infection and Immunity
Laboratory:Center for Infection and Immunity
Last Name:Lipkin
First Name:W. Ian
Address:722 W. 168th St., 17th Floor, New York, NY, 10032
Email:wil2001@cumc.columbia.edu
Phone:(212) 342-9033

Summary of all studies in project PR001606

Study IDStudy TitleSpeciesInstituteAnalysis
(* : Contains Untargted data)
Release
Date
VersionSamplesDownload
(* : Contains raw data)
ST002484 Metabolomic analysis of maternal mid-gestation plasma and cord blood: primary metabolism Homo sapiens Columbia University MS 2023-07-02 1 918 Uploaded data (9.3G)*
ST002700 Metabolomic analysis of maternal mid-gestation plasma and cord blood: lipidomics Homo sapiens Columbia University MS 2023-07-02 1 1089 Uploaded data (209.3G)*
ST002711 Metabolomic analysis of maternal mid-gestation plasma and cord blood: biogenic amines Homo sapiens Columbia University MS 2023-07-02 1 1088 Uploaded data (179.7G)*
ST002767 Metabolomic analysis of maternal mid-gestation plasma and cord blood: oxylipins Homo sapiens Columbia University MS 2023-07-22 1 783 Uploaded data (241.4M)*
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