Gut Microbiome and Metabolite Profiles for Pediatric Allergic Rhinitis with Constipation

35 fecal samples from 19 children with comorbid allergic rhinitis and functional constipation (ARFC) and 16 healthy controls, analyzed via shotgun metagenomic sequencing and amino acid-targeted metabolomics. The dataset was created by Hui Wu and last updated on 2026-05-26. It includes results of integrative analysis, correlation networks, and a machine learning model.

Use Cases

• Train machine learning models for disease classification based on integrated microbial and metabolic features.
• Analyze correlations between specific microbial genera (e.g., Bacteroides) and immunomodulatory metabolites (e.g., kynurenine).
• Investigate pathways like mTOR and FoxO signaling in the context of gut dysbiosis.
• Study the relationship between gut species (e.g., Bacteroides thetaiotaomicron) and clinical allergy markers like serum IgE.

Strengths

• Includes 50 differentially abundant metabolites identified through targeted metabolomics.
• Integrates two data modalities: shotgun metagenomic sequencing and metabolomics.
• Machine learning model built from the data achieved an AUC of 0.946 under leave-one-out cross-validation.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 50.2 KB, indicating limited scope.

Provenance

Source

Hui Wu via figshare.

Collection Method

Shotgun metagenomic sequencing and amino acid-targeted metabolomics performed on fecal samples.

Freshness

Last updated 2026-05-26 08:16:47; freshness should be verified.