Urine-based autism screening using gut microbial metabolites

A pilot study found that elevated gut microbiome-derived metabolites can identify many children with autism, raising the possibility of a simple urine-based screening tool and a newly proposed ASD subtype associated with microbiome dysfunction.

Study: Elevated Microbial-Derived Metabolites in Autism: A Potential Clinical Screening Test for a Specific ASD Phenotype. Image Credit: Prostock-Studio/Shutterstock.com

A recent study published in molecular psychiatry Investigated whether urinary concentrations of microbially derived metabolites (MDMs) could distinguish children with autism spectrum disorders (ASD) from typically developing children.

ASD: clinical complexity, microbiota associations, and unresolved mechanisms

ASD is a complex neurodevelopmental condition characterized by early challenges in social communication, restricted interests, and repetitive behaviors. The presentation and severity of symptoms varies widely, leading to a spectrum ranging from individuals requiring intensive support to those with relatively mild difficulties who can function independently. ASD results from complex genetic-environmental interactions.

In recent decades, the prevalence of ASD in the United States has increased dramatically, placing increasing pressure on families, health care systems, and support services. Although approximately 10% of cases are associated with an identifiable genetic syndrome, the underlying cause of most cases remains unknown. Given this diversity, researchers are considering identifying biologically distinct ASD subtypes as an important step toward developing targeted therapeutic strategies.

Although early behavioral intervention is most effective within the first two years of life, diagnosis usually occurs much later. This delay highlights the urgent need for non-invasive early screening tools to enable timely intervention and minimize long-term clinical and economic impacts.

A large subgroup of individuals with ASD experience chronic gastrointestinal (GI) symptoms, which often parallel the severity of ASD and emerge within the first three years of life. Consistent evidence demonstrates gut dysbiosis in ASD, with distinct microbial profiles compared to neurotypical individuals. This dysbiosis alters metabolic and immune pathways, including the production of short-chain fatty acids (SCFAs), cytokines, and neurotransmitters, supporting a potential mechanistic link between gut microbiota and neurodevelopment through the gut-brain axis.

Microbial metabolites such as p-cresol and indoxyl sulfate are found at higher levels in people with ASD. These compounds can harm gut health, immune function, and brain signaling, especially when present in large amounts early in life. Therefore, it is necessary to identify specific microbial and metabolic markers that can help differentiate different types of ASD and support earlier diagnosis or more targeted treatment.

Analyzing Urine Metabolites to Screen for ASD

The present study developed a biomedical screening test for ASD by measuring MDM in the urine of children with ASD and typically developing (TD) children. A total of 52 children with ASD and 47 TD children between the ages of 2 and 11 years were recruited from four US sites. ASD diagnosis was confirmed by expert evaluators using the Childhood Autism Rating Scale and the Social Responsiveness Scale-2 (SRS-2; score >68). Urine samples were collected.

Metabolite extraction and liquid chromatography–mass spectrometry (LC–MS) analysis was performed according to standard protocols, and metabolites were annotated and quantified relative to urinary creatinine. An initial untargeted LC–MS approach was used for discovery, followed by a targeted quantitative LC–MS follow-up analysis.

A new multivariate assay, the Microbially-Derived Metabolite System™ (MDM System™), was created to identify children with ASD who have intestinal dysbiosis. Each participant’s MDM concentrations were compared to the TD range; Scores reflect the number of increased MDMs.

Distinct microbial metabolite profiles distinguish ASD from TD children.

Both the TD and ASD groups were age-matched, and the TD group was intentionally balanced by gender. No significant gender differences were found in metabolite analysis. The analysis focused on microbially produced metabolites, classified as phenylalanine-derived, tryptophan-derived, or yeast/other.

Six phenylalanine-derived and eight tryptophan-derived metabolites were significantly increased in ASD, with increases ranging from 29% to 18–82%. Many ASD participants had metabolite levels higher than all TD cases. Arabinitol, a yeast metabolite, was also 51% higher in ASD, while N-formyl methionine was 70% lower. These findings highlight a distinct metabolic profile in ASD.

Most ASD participants had significantly higher levels of tryptophan- or phenylalanine-derived metabolites, or both, than TD children. Elevated arabinitol and reduced N-formyl methionine often occur together in a subgroup of ASD participants. Except for N-formyl methionine, metabolite levels were generally higher in ASD.

The MDM System™ total score, which reflects the number of highly elevated metabolites per participant, averaged 3.3 in ASD and 0 in TD. Using an elevated metabolite threshold, semiquantitative analysis achieved 90% sensitivity and 100% specificity for ASD.

There was no significant correlation between MDM total score and age at ASD. Multivariate models including Fisher Discriminant Analysis (FDA), neural networks, and Naive Bayes consistently achieved high diagnostic accuracy, with area under the receiver operating characteristic curve values ​​up to 0.86.

Univariate analysis identified ten metabolites, mostly phenylalanine- and tryptophan-related, that were significantly elevated in ASD. Some, such as p-cresol, were elevated only in a subset of ASD cases, and the detection limit influenced the results for some compounds.

Metabolites such as p-cresol and indole-3-propionic acid provided strong group separation. While tryptophan- or phenylalanine-related metabolites were increased in a large number of ASD participants, yeast metabolite increases occurred less frequently. Highly correlated metabolites, such as indole propionic acid and beta-carbolines, probably reflect microbial dysbiosis rather than external exposure. Metabolites with many undetectable samples contributed little to the MDM System™ algorithm.

In the targeted quantitative analysis, the MDM System™ had 100% specificity and 78% sensitivity, indicating Reproducibility of the overall method, although performance was lower than the initial semiquantitative analysis. The FDA also showed that the most effective metabolite combinations had area under the curve values ​​above 0.7, and adding more metabolites provided minimal additional benefit.

The findings also led researchers to propose a The hypothetical ASD subtype is called “ASD associated with microbially-derived metabolites” (ASD-MDM). Based on study data, the authors suggest that approximately 80–90% of children with ASD in their cohort may belong to this metabolically distinct subgroup, although the proposed classification requires independent validation before it can be considered an established ASD phenotype.

conclusion

The present study highlights the importance of MDM in a large subgroup of children with ASD. Provides development of MDM System™ A promising proof-of-concept approach for early screening and identification of children at increased likelihood of developing ASD in the future.

However, the findings are based on a relatively small pilot group, and the authors emphasize that the test still needs independent validation in larger groups before it can be considered clinically established.. Continued research, including validation in independent cohorts and exploration of microbiome-based treatments, is necessary to fully realize the potential of these advances in improving outcomes in children with ASD.

The paper also notes that several authors hold patents, patent applications or commercial interests related to ASD diagnostics and the MDM System™, underscoring the importance of independent replication of the findings.

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