Summary: A new study has linked specific molecules found in maternal blood and cord blood samples to an increased risk of a child being later diagnosed with autism. Some of the molecules identified are implicated in inflammation, neurotoxicity, impaired neurotransmission, and disruption of membrane integrity.
Source: Columbia University
In a new study, researchers revealed disrupted levels of molecular compounds in maternal blood and cord blood linked to later diagnosis of autism spectrum disorder (ASD). Identification of these compounds sheds light on the biological processes that give rise to ASD and could open the door to early diagnosis and treatment.
The study was led by researchers at Columbia University Mailman School of Public Health and the Norwegian Institute of Public Health. Findings appear in the journal Molecular Psychiatry.
The researchers analyzed levels of 1,208 different chemical compounds in plasma samples collected from 408 mothers at mid-gestation (17-21 weeks) and in cord blood taken from 418 children at birth through the Norwegian Autism Birth Cohort (ABC). The compounds were analyzed to see if they relate to clinical diagnosis of autism at ages 3-5. The researchers used chromatography/mass spectrometry-based metabolomics assays to measure levels of 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 to be linked to autism, including those that involve inflammation, disruption of membrane integrity, and impaired neurotransmission and neurotoxicity.
Machine learning analyses suggested the potential utility of the compounds as biomarkers, especially those in cord blood, for early identification of children at risk for ASD.
The study identifies several differences in levels of biomarkers between boys and girls, including an imbalance of lipid chemical clusters in the maternal blood related to autism in girls, not boys. The finding may provide insight into the higher frequency of cognitive impairment in girls than in boys with ASD.
The study builds on research published by the same group of scientists in 2022 which found ASD risk was linked to groupings of inflammation-related molecules.
“Our latest findings add to evidence that chemical compounds can be harnessed as an early biomarker for autism spectrum disorder with rapid developments in machine learning suggesting such a diagnostic test is feasible,” 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 Birth Cohort (ABC) is nested in the large population-based Norwegian Mother, Father, and Child Cohort Study (MoBa) where more than 114,000 offspring and their parents participate. Mothers and fathers were recruited in early pregnancy between 1999 and 2009.
Children’s ASD diagnoses have been obtained primarily through linkage with national registries. ABC, MoBa, and registry data together are a unique resource for the current study and for future research on causes of ASD,” says Camilla Stoltenberg, MD, co-author, director general at the Norwegian Institute of Public Health, and a co-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 mean age for diagnosis is age 4-5 years.
Thus, in addition to providing insights into the pathogenesis of these disorders, our findings may lead to tests for early diagnosis that improve outcomes,” says 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 who have been followed since 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 Steering Committee of four people: Camilla Stoltenberg and Per Magnus in Norway; and Ian Lipkin and Ezra Susser at 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
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
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 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 area under the receiver operating characteristic (AUROC) 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 detection and intervention.