ArticleFrontiers in cellular neuroscience2019
Urine Organic Acids as Potential Biomarkers for Autism-Spectrum Disorder in Chinese Children.
Article in Frontiers in cellular neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Biomarkers and pathways in autism spectrum disorder: An individual meta-analysis based on proteomic and metabolomic data.European archives of psychiatry and clinical neuroscience · 2025Pooled it
- Trial
- Elevated microbially-derived metabolites in autism: a possible diagnostic screening test for a distinct ASD phenotype.Molecular psychiatry · 2026Article
- Article
- Immunometabolic Stratification of Autism Spectrum Disorder by CD4Metabolites · 2026Article
- Gut-Derived Metabolic Imbalance in Autism Spectrum Disorder: Toward the Concept of a Metabolic Subtype.Nutrients · 2026Review
- The integration of plasma non-target metabolomics and lipidomics analysis for the discovery of global developmental delay/intellectual disability biomarkers.Frontiers in cellular neuroscience · 2026Article
- Impact of dietary intervention and supplementation on the physiological profile of individuals with down syndrome.Scientific reports · 2025Article
- Elevated Microbially-Derived Metabolites in Autism: A Possible Diagnostic Screening Test for a Distinct ASD Phenotype.Research square · 2025Article
- Systemic Uremic Toxin Burden in Autism Spectrum Disorder: A Stratified Urinary Metabolite Analysis.International journal of molecular sciences · 2025Article
- Urine Metabolomic Profiling and Machine Learning in Autism Spectrum Disorder Diagnosis: Toward Precision Treatment.Metabolites · 2025Article
- Review of Elevated Para-Cresol in Autism and Possible Impact on Symptoms.International journal of molecular sciences · 2025Review
- Integrating 16S rRNA Gene Sequencing and Metabolomics Analysis to Reveal the Mechanism of L-Proline in Preventing Autism-like Behavior in Mice.Nutrients · 2025Article
- Comprehensive analysis of gut microbiota and fecal metabolites in patients with autism spectrum disorder.Frontiers in microbiology · 2025Article
- A Clinical Study of Urine Amino Acids in Children with Autism Spectrum Disorder.Life (Basel, Switzerland) · 2024Article
- Review
- Using urine FTIR spectra to screen autism spectrum disorder.Scientific reports · 2023Article
- Metabolomics: Perspectives on Clinical Employment in Autism Spectrum Disorder.International journal of molecular sciences · 2023Review
- A machine learning approach for understanding the metabolomics response of children with autism spectrum disorder to medical cannabis treatment.Scientific reports · 2023Article
- Dysregulated phosphate metabolism in autism spectrum disorder: associations and insights for future research.Expert reviews in molecular medicine · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that lacks clear biological biomarkers. Existing diagnostic methods focus on behavioral and performance characteristics, which complicates the diagnosis of patients younger than 3 years-old. The purpose of this study is to characterize metabolic features of ASD that could be used to identify potential biomarkers for diagnosis and exploration of ASD etiology. We used gas chromatography-mass spectrometry (GC/MS) to evaluate major metabolic fluctuations in 76 organic acids present in urine from 156 children with ASD and from 64 non-autistic children. Three algorithms, Partial Least Squares-Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), were used to develop models to distinguish ASD from typically developing (TD) children and to detect potential biomarkers. In an independent testing set, full model of XGBoost with all 76 acids achieved an AUR of 0.94, while reduced model with top 20 acids discovered by voting from these three algorithms achieved 0.93 and represent a good collection of potential ASD biomarkers. In summary, urine organic acids detection with GC/MS combined with XGBoost algorithm could represent a novel and accurate strategy for diagnosis of autism and the discovered potential biomarkers could be valuable for future research on the pathogenesis of autism and possible interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.