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Disrupted fetal carbohydrate metabolism in children with autism spectrum disorder
Serena B Gumusoglu1,2, Brandon M Schickling1, Donna A Santillan1,2
1Department of Obstetrics and Gynecology, University of Iowa, Iowa City, USA.
Insights
Fetal metabolomics reveal altered carbohydrate metabolism in infants later diagnosed with autism spectrum disorder (ASD). This finding offers potential early biomarkers for ASD, advancing diagnosis and intervention strategies.
Area of Science:
- Biochemistry
- Neuroscience
- Genetics
Background:
- Limited early-life biomarkers hinder early detection and treatment of autism spectrum disorder (ASD).
- Identifying early-life risk biosignatures is crucial for understanding ASD pathogenesis and developing interventions.
- This study investigates fetal metabolomic alterations in idiopathic ASD.
Purpose of the Study:
- To determine if fetal plasma metabolomics differ between infants who develop idiopathic ASD and neurotypical controls.
- To identify potential early biomarkers for ASD based on fetal metabolic profiles.
- To explore the role of carbohydrate metabolism in the early development of ASD.
Main Methods:
- Analysis of banked cord blood plasma samples from 16 infants with ASD and 36 neurotypical controls using gas chromatography-mass spectrometry (GC-MS).
- Application of Metabolite Set Enrichment Analysis (MSEA) and biomarker prediction algorithms (MetaboAnalyst).
- Utilized targeted biomarker assessment with a Random Forest algorithm for ASD prediction.
Main Results:
- Significant differences in 20 out of 76 detected metabolites between ASD and control groups (p < 0.05).
- MSEA indicated significant alterations in carbohydrate metabolism and glycemic control pathways in infants with ASD.
- A Random Forest model using specific metabolites achieved an AUC of 0.766 for ASD prediction.
Conclusions:
- Fetal plasma metabolomic profiles differ based on later ASD diagnosis, highlighting altered carbohydrate metabolism.
- These findings suggest potential fetal biomarkers for ASD, offering new avenues for early diagnosis.
- Future research will explore maternal metabolomics to elucidate maternal-fetal mechanisms in ASD development.
Background:
Despite the power and promise of early detection and treatment in autism spectrum disorder (ASD), early-life biomarkers are limited. An early-life risk biosignature would advance the field's understanding of ASD pathogenies and targets for early diagnosis and intervention. We therefore sought to add to the growing ASD biomarker literature and evaluate whether fetal metabolomics are altered in idiopathic ASD.
Methods:
Banked cord blood plasma samples (N = 36 control, 16 ASD) were analyzed via gas chromatography and mass spectrometry (GC-MS). Samples were from babies later diagnosed with idiopathic ASD (non-familial, non-syndromic) or matched, neurotypical controls. Metabolite set enrichment analysis (MSEA) and biomarker prediction were performed (MetaboAnalyst).
Results:
We detected 76 metabolites in all samples. Of these, 20 metabolites differed significantly between groups: 10 increased and 10 decreased in ASD samples relative to neurotypical controls (p < 0.05). MSEA revealed significant changes in metabolic pathways related to carbohydrate metabolism and glycemic control. Untargeted principle components analysis of all metabolites did not reveal group differences, while targeted biomarker assessment (using only Fructose 6-phosphate, D-Mannose, and D-Fructose) by a Random Forest algorithm generated an area under the curve (AUC) = 0.766 (95% CI: 0.612-0.896) for ASD prediction.
Conclusions:
Despite a high and increasing prevalence, ASD has no definitive biomarkers or available treatments for its core symptoms. ASD's earliest developmental antecedents remain unclear. We find that fetal plasma metabolomics differ with child ASD status, in particular invoking altered carbohydrate metabolism. While prior clinical and preclinical work has linked carbohydrate metabolism to ASD, no prior fetal studies have reported these disruptions in neonates or fetuses who go on to be diagnosed with ASD. Future work will investigate concordance with maternal metabolomics to determine maternal-fetal mechanisms.
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