Toward a rapid, non-invasive predictor of washed microbiota transplantation efficacy in pediatric autism spectrum disorder: considerations for clinical translation

To the Editor:

We read with great interest the recent article by Zhong et al. (2025), which explored tongue-coating microbiota as a predictive biomarker for washed microbiota transplantation (WMT) efficacy in pediatric autism spectrum disorder (ASD) [1]. Their finding that a tongue-coating model performed comparably to a gut microbiota model represents an important step toward rapid, non-invasive evaluation. This aligns with emerging evidence on the oral–gut microbiota axis as a potential therapeutic and diagnostic avenue in neurodevelopmental conditions [2].

Several points may further strengthen the translational relevance of this work. First, the oral–gut microbial relationship deserves deeper mechanistic exploration, as changes in tongue-coating microbiota may reflect or modulate gut microbial functions influencing neuroimmune pathways in ASD [2]. Second, the limited number of “excellent responders” restricts statistical power; resampling methods (e.g., bootstrap) could provide more robust estimates within current sample constraints [3]. Third, oral hygiene and dietary intake should be controlled as potential confounders using simple validated tools (e.g., OHIP-14) and short dietary records to improve model stability and generalizability [4]. Finally, cost-effective functional assays (e.g., targeted qPCR) and portable sequencing devices (e.g., MinION) may enhance feasibility for pediatric clinical use, enabling faster turnaround and broader implementation [5].

In summary, Zhong et al. have commendably introduced tongue-coating microbiota as a promising predictor of WMT efficacy. Addressing these practical and methodological considerations could facilitate translation into a real-world diagnostic tool for pediatric ASD management.

Author contributions

Xinyu Liu: Conceptualization, Methodology design, Advanced bioinformatics analysis, Critical revision of statistical models, Data curation, and Writing—Original draft preparation. Menglu Shen: Investigation, Clinical data collection, Formal analysis of initial data, and Visualization. Bingxue Liu: Project administration, Resources acquisition, Supervision, and Writing—Reviewing and Editing (as Corresponding Author). All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data availability

Not applicable. All materials are based on publicly available literature.

Declarations

Ethics approval and consent to participate

None.

Consent for publication

Not applicable. This manuscript does not contain any individual person’s data in any form that requires specific consent for publication.

Competing interests

The authors declare that they have no competing interests.