summary: A new study has linked certain molecules found in maternal and umbilical cord blood samples to an increased risk of a child developing autism later on. Some of the molecules identified are implicated in inflammation, neurotoxicity, impairment of neurotransmission, and disruption of membrane integrity.

source: Columbia University

In a new study, researchers reveal disruptive levels of molecular compounds in maternal and umbilical cord blood that are associated with subsequent diagnoses of autism spectrum disorder (ASD). Identification of these compounds sheds light on the biological processes that lead to autism spectrum disorder and could open the door to early diagnosis and treatment.

The study was led by researchers at Columbia University’s Mailman School of Public Health and the Norwegian Institute of Public Health. The results appear in the journal Molecular Psychiatry.

The researchers analyzed the levels of 1,208 different chemical compounds in plasma samples collected from 408 mothers at mid-pregnancy (17-21 weeks) and in cord blood drawn from 418 babies at birth through the Norwegian Birth Cohort for Autism (ABC). Compounds were analyzed for whether they related to a clinical diagnosis of autism at ages 3-5. The researchers used metabolite-based chromatography/mass spectrometry assays to measure the levels of the chemical compounds. They used machine learning to assess the predictive value of compounds as biomarkers of autism spectrum disorder.

The researchers found 12 chemical compounds in maternal mid-gestational (MMG) samples from ASD girls, 3 compounds in MMG samples of ASD boys, 8 compounds in cord blood (CB) samples of ASD girls, and 12 compounds in CB samples of ASD boys. . Be associated with autism, including those involving inflammation, disruption of membrane integrity, impairment of neurotransmission and neurotoxicity.

Machine learning analyzes suggested the potential utility of compounds as biomarkers, particularly those found in cord blood, for early identification of children at risk for autism.

The study identifies several differences in biomarker levels between boys and girls, including an imbalance of lipid chemical groups in the mother’s blood related to autism in girls, but not boys. This finding may provide insight into the higher incidence of cognitive impairment in girls compared to boys with autism.

The study builds on research published by the same group of scientists in 2022 that found that autism risk is linked to clusters of molecules associated with inflammation.

says first author Xiaoyu (Jason) Che, PhD, assistant professor of biostatistics in the Center for Infection and Immunity (CII) at Columbia Mailman School of Public Health.

“The Autism Born Cohort (ABC) is nested in the Norwegian Mother, Father and Child Population Study (MoBa) in which more than 114,000 children and their parents are involved. Parents were recruited in early pregnancy between 1999 and 2009.

This shows a child's drawing
Machine learning analyzes suggested the potential utility of compounds as biomarkers, particularly those found in cord blood, for early identification of children at risk for autism. The image is in the public domain

Diagnoses of autism spectrum disorder in children were obtained primarily through association with national registries. The ABC, MoBa, and registry data together are a unique resource for the current study and for future research on the causes of ASD,” says founder of the ABC Study.

Approximately 1 in 44 children in the United States has an autism spectrum disorder. Interventions are most effective when implemented early. However, the average age for diagnosis is 4-5 years.

Thus, in addition to providing insight into the pathogenesis of these disorders, our findings may lead to early diagnostic tests to improve outcomes,” says senior author W. Ian Lipkin, John Snow Professor of Epidemiology and director of the Health Insurance Institute.

Other co-authors include Ian Roy, Kiming Zhang, Mikaelyn Bresnahan, and Ezra Susser at the Columbia Mailman; Siri Magland, Ted Richbourne Kjnerud, and Per Magnus at the Norwegian Institute of Public Health, Oslo; Yimeng Shang, Penn State University; and Oliver Finn, University of California, Davis.

Funding: This study was funded by the National Institutes of Health (Grants NS047537, NS086122), the Jane Botsford Johnson Foundation, the Norwegian Ministry of Health and Care Services, the Norwegian Ministry of Education and Research, and the Research Council of Norway (grants 189457, 190694 and 196452). The authors declare no conflicts of interest.

About the Autism Birth Group (ABC) Study

The Autism Born Cohort (ABC) study was conducted in a large Norwegian cohort of more than 100 000 children who were followed from before birth. ABC is a joint effort between the Norwegian National Institute of Public Health (NIPH) and Columbia Mailman School investigators, and is overseen by a four-person steering committee: Camilla Stoltenberg and Per Magnus in Norway; and Ian Lipkin and Ezra Susser at the Columbia Mailman. It is unique in the scope, depth, and breadth of both the biological and social data on autism.

About this search for autism news

author: Timothy Paul
source: Columbia University
communication: Timothy Ball – Columbia University
picture: The image is in the public domain

Original search: Closed access.
“Metabolic analysis of mid-gestation maternal plasma and umbilical cord blood in autism spectrum disorders” by Xiaoyu (Jason) Che et al. Molecular Psychiatry

a summary

Metabolic analysis of mid-gestational plasma and umbilical cord blood of mothers in autism spectrum disorders

The discovery of prenatal and neonatal molecular biomarkers has the potential to provide insights into autism spectrum disorder (ASD) and facilitate early diagnosis.

We characterized the metabolic profiles in ASD using plasma samples collected in the Norwegian Birth Cohort for Autism from mothers at 17-21 weeks of gestation (mid-maternal gestation, MMG, n= 408) and from children on the day of birth (cord blood, CB, n= 418). We analyzed the associations using logistic regression models adjusted for gender with Bayes analyses. Chemical enrichment analyzes (ChemRICH) were performed to determine the variable chemical groups.

We also used machine learning algorithms to assess the usefulness of metabolites as ASD biomarkers. We identified associations of ASD with a variety of chemical compounds including arachidonic acid, glutamate, and glutamine, metabolite groups including hydroxyexpentanoic acids, phosphatidylcholines, and ceramides in MMG and CB-compatible plasma with inflammation, disruption of membrane integrity, impairment of neurotransmission and neurotoxicity.

Girls with autism spectrum disorder have a disturbance in the ether/non-ether phospholipid balance in plasma MMG that is similar to that seen in other neurodevelopmental disorders. ASD boys in the CB analyzes had the highest number of disorganized chemical groups.

Machine learning classifiers distinguished ASD cases from controls with area under receiver operating characteristic (AUROC) values ​​ranging from 0.710 to 0.853. Predictive performance was better in CB analyzes than in MMG.

These findings may provide new insights into sex-specific differences in ASD and have implications for the discovery of biomarkers that may enable early detection and intervention.

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