Summary of Study ST002700

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

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST002700
Study TitleMetabolomic analysis of maternal mid-gestation plasma and cord blood: lipidomics
Study SummaryMetabolomic 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
DepartmentCenter for Infection and Immunity
LaboratoryCenter for Infection and Immunity
Last NameLipkin
First NameW. Ian
Address722 W. 168th St., 17th Floor, New York, NY, 10032
Emailwil2001@cumc.columbia.edu
Phone(212) 342-9033
Submit Date2023-02-24
Raw Data AvailableYes
Raw Data File Type(s)d
Analysis Type DetailLC-MS
Release Date2023-07-02
Release Version1
W. Ian Lipkin W. Ian Lipkin
https://dx.doi.org/10.21228/M83T4M
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Sex Diagnosis
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SA269903ABC-19926Female CASE
SA269904ABC-17540Female CASE
SA269905ABC-20903Female CASE
SA269906ABC-21479Female CASE
SA269907ABC-20769Female CASE
SA269908ABC-19438Female CASE
SA269909ABC-18738Female CASE
SA269910ABC-19458Female CASE
SA269911ABC-21537Female CASE
SA269912ABC-11191Female CASE
SA269913ABC-2332Female CASE
SA269914ABC-18778Female CASE
SA269915ABC-21318Female CASE
SA269916ABC-21004Female CASE
SA269917ABC-16796Female CASE
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