Gut Microbiome and Metabolite Profiles in Children with Allergic Rhinitis and Constipation

Shotgun metagenomic sequencing and amino acid-targeted metabolomics data from fecal samples of 35 children. The dataset compares 19 children with comorbid allergic rhinitis and functional constipation (ARFC) against 16 age-matched healthy controls. It was created by Hui Wu and last updated on 2026-05-26.

Use Cases

• Train machine learning models for disease classification based on integrated microbial and metabolic features.
• Analyze correlations between specific gut microbial genera (e.g., Bacteroides, Proteus) and serum IgE levels.
• Investigate pathways like mTOR and FoxO signaling based on differential metabolite abundance.
• Build correlation networks to explore microbiota-metabolite-host interactions.

Strengths

• Integrates two data modalities: shotgun metagenomic sequencing and amino acid-targeted metabolomics.
• Includes a machine learning model evaluated with leave-one-out cross-validation achieving an AUC of 0.946.
• Identifies 50 differentially abundant metabolites between patient and control groups.

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 small in scale at 77.5 KB, indicating limited scope.

Provenance

Source

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.