Summary: A new study has linked specific molecules found in maternal blood and cord blood samples to a child’s increased risk of later being diagnosed with autism. Some of the identified molecules have been implicated in inflammation, neurotoxicity, impaired neurotransmission, and disruption of membrane integrity.
Source: Columbia University
In a new study, researchers have revealed disrupted levels of molecular compounds in maternal blood and cord blood are associated with a later diagnosis of autism spectrum disorder (ASD). The identification of these compounds sheds light on the biological processes that cause ASD and may open the door to early diagnosis and treatment.
The study was led by researchers at the Columbia University Mailman School of Public Health and the Norwegian Institute of Public Health. The findings appear in the journal Molecular Psychiatry.
The researchers analyzed the levels of 1,208 different chemical compounds in plasma samples collected from 408 mothers in mid-pregnancy (17-21 weeks) and in cord blood taken from 418 children at birth through the Norwegian Autism Birth Cohort (ABC). The compounds were tested to see if they were associated with a clinical diagnosis of autism at ages 3-5. The researchers used chromatography/mass spectrometry-based metabolomics assays to measure the levels of the chemical compounds. They used machine learning to assess the predictive value of the compounds as biomarkers for ASD.
The researchers found 12 chemical compounds in maternal mid-gestation (MMG) samples of 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. that have been linked to autism, including those involving inflammation, disruption of membrane integrity, and impaired neurotransmission and neurotoxicity.
Machine learning analyzes suggested the compounds’ potential utility as biomarkers, particularly those in cord blood, for early identification of children at risk for ASD.
The study identifies several differences in biomarker levels between boys and girls, including an imbalance of chemical lipid clusters in the mother’s blood that are linked to autism in girls, not boys. man The finding may provide insight into the higher frequency of cognitive impairment in girls than boys with ASD.
The study builds on research published by the same group of scientists in 2022 that found ASD risk was associated with clusters of molecules associated with inflammation.
“Our latest findings add to the evidence that chemical compounds can be used as an early biomarker for autism spectrum disorders with rapid advances in machine learning suggesting that such a diagnostic test is feasible,” said by first author Xiaoyu (Jason) Che, PhD, assistant professor of biostatistics at the Center for Infection and Immunity (CII) at the Columbia Mailman School of Public Health.
“The Autism Birth Cohort (ABC) is part of the large population-based Norwegian Mother, Father, and Child Cohort Study (MoBa) in which more than 114,000 offspring and their parents participated. Mothers and fathers were recruited in early pregnancy between 1999 and 2009.
Children’s ASD diagnoses were obtained primarily by linking to national registries. The ABC, MoBa, and registry data together are a unique resource for the current study and for future research into the causes of ASD,” said Camilla Stoltenberg, MD, co-author, director general at the Norwegian Institute of Public Health, and a co-founder of the ABC study.
An estimated 1 in 44 children in the United States has an autism spectrum disorder. Interventions are most effective when implemented early. However, the mean age for diagnosis is age 4-5 years.
So, in addition to providing insights into the pathogenesis of these disorders, our findings may lead to tests for early diagnosis that improve outcomes,” said senior author W. Ian Lipkin, MD, John Snow Professor of Epidemiology and director of CII.
Additional co-authors include Ayan Roy, Keming Zhang, Michaeline Bresnahan, and Ezra Susser at Columbia Mailman; Siri Mjaaland, Ted Reichborn-Kjennerud, and Per Magnus at the Norwegian Institute of Public Health, Oslo; Yimeng Shang, Penn State University; and Oliver Fiehn, 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 competing interests.
About the Autism Birth Cohort (ABC) study.
The Autism Birth Cohort (ABC) study was conducted within a large Norwegian cohort of more than 100,000 children followed from before their birth. ABC is a joint effort of the Norwegian National Institute of Public Health (NIPH) and Columbia Mailman School investigators, 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 for the scope, depth, and breadth of both biological and social data on ASD.
About this autism research news
Author: Timothy Paul
Source: Columbia University
Please contact: Timothy Paul – Columbia University
Image: The image is in the public domain
Original Research: Closed access.
“Metabolomic analysis of maternal mid-gestation plasma and cord blood in autism spectrum disorders” by Xiaoyu (Jason) Che et al. Molecular Psychiatry
Abstract
Metabolomic analysis of maternal mid-gestation plasma and cord blood in autism spectrum disorders
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 a sex-stratified adjusted logistic regression model with Bayesian analyses. Chemical enrichment analyzes (ChemRICH) were performed to identify altered chemical clusters.
We also used machine learning algorithms to assess the utility of metabolomics as ASD biomarkers. We identified associations of ASD with various chemical compounds including arachidonic acid, glutamate, and glutamine, and metabolite clusters including hydroxy eicospentaenoic acid, phosphatidylcholines, and ceramides in MMG and CB plasma consistent with inflammation, disruption of membrane integrity, and impaired neurotransmission and neurotoxicity.
Girls with ASD have a disruption of the ether/non-ether phospholipid balance in MMG plasma similar to that seen in other neurodevelopmental disorders. ASD boys in the CB tests had the highest number of dysregulated chemical clusters.
Machine learning classifiers distinguished ASD cases from controls with area under receiver operating characteristic (AUROC) values ranging from 0.710 to 0.853. The predictive performance is better with CB analysis than with MMG.
These findings may provide new insights into gender-specific differences in ASD and have implications for the discovery of biomarkers that may enable early detection and intervention.
