Summary of Study ST002711
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.
| Study ID | ST002711 |
| Study Title | Metabolomic analysis of maternal mid-gestation plasma and cord blood: biogenic amines |
| Study 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 |
| wil2001@cumc.columbia.edu | |
| Phone | (212) 342-9033 |
| Submit Date | 2023-05-19 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | d |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-07-02 |
| Release Version | 1 |
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Project:
| 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 |
