A study published in Molecular Psychiatry suggests that a panel of urinary microbially derived metabolites could help identify a subgroup of children with autism spectrum disorder (ASD), highlighting a potential role for laboratory-based screening.
Researchers analyzed urine samples from 52 children with ASD and 47 neurotypical children aged 2 to 11 years. Using liquid chromatography-mass spectrometry (LC-MS), they measured a range of metabolites produced by gut bacteria and fungi, including compounds derived from phenylalanine, tryptophan, and yeast metabolism.
The study builds on previous research showing that some children with ASD have elevated levels of microbial metabolites, such as p-cresol sulfate and indoxyl sulfate. The investigators wanted to determine whether a broader metabolite profile could be used as a diagnostic screening tool.
Initial semiquantitative LC-MS testing identified numerous metabolites that differed between the ASD and control groups. Quantitative analysis confirmed significantly higher concentrations of several metabolites in children with ASD, including p-cresol sulfate, p-cresol, phenylacetylglutamine, hydroxybenzoic acid, indoxyl sulfate, and arabinitol.
To assess the diagnostic potential of these findings, the researchers developed the Microbially-Derived Metabolite (MDM) System, which measures how many metabolites are present at concentrations above the range observed in typically developing children. Rather than relying on a single biomarker, the approach evaluates a pattern of metabolite abnormalities.
In the initial analysis, 90 percent of children with ASD had at least one markedly elevated metabolite, producing a reported sensitivity of 90 percent and specificity of 100 percent. In a separate quantitative validation analysis, sensitivity was 78 percent while specificity remained 100 percent.
Several metabolites showed the greatest ability to distinguish between groups, including p-cresol, p-cresol sulfate, indole-3-acryloyl glycine, phenylacetylglutamine, and arabinitol. The authors noted that metabolite elevations varied substantially between individuals, suggesting that different children may have different microbial-metabolic profiles.
The study demonstrates how metabolomics may contribute to the development of objective biomarkers for neurodevelopmental disorders. The proposed test uses urine samples and established LC-MS methods, making it compatible with laboratory platforms already used for metabolic testing. It also reflects growing interest in microbiome-associated biomarkers and the use of multi-analyte signatures rather than single markers.
The authors emphasize that the findings require further validation. The study included fewer than 100 participants, excluded children with known genetic causes of autism, and did not collect information on diet, medication use, or body mass index. Larger independent studies will be needed to determine how well the test performs across broader patient populations and clinical settings.
While the test is not ready for routine clinical use, the findings add to growing evidence that altered microbial metabolism may characterize a subset of children with ASD and could eventually support earlier identification and stratification of patients through laboratory testing.
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