Gut Microbiota and Metabolomic Profiles in Myasthenia Gravis Patients and Healthy Controls

A 2026 study by Yunan Shan profiles fecal samples from 29 myasthenia gravis patients and 10 healthy controls using 16S rRNA sequencing and untargeted metabolomics. The dataset includes results from differential taxa analysis, identification of 567 altered metabolites, and correlation analyses with clinical severity. It was published on figshare under a CC-BY-4.0 license.

Use Cases

• Training machine learning models for disease classification based on the reported 567 altered metabolites.
• Investigating associations between specific depleted microbial taxa (e.g., Faecalibacterium prausnitzii) and metabolite levels like short-chain fatty acids.
• Exploring the biological pathways implicated in MG, such as cholinergic synapse and bile secretion pathways, through integrated omics data.
• Validating the reported diagnostic performance (AUC=1.0) of the Random Forest metabolite model on independent cohorts.

Strengths

• Integrated multi-omics data from 16S rRNA sequencing and UHPLC-Q-TOF MS metabolomics on the same samples.
• Reports specific, quantified findings including 232 discriminative taxa and 567 altered metabolites.
• Includes correlation analyses linking microbial abundance, metabolite levels, and clinical severity scores.

Limitations

• The dataset is small in scale (204.6 KB), suggesting it may contain summary results rather than raw sequencing or spectral data.
• Column-level documentation is absent; field semantics must be inferred from the description after download.
• Sample size is limited to 29 patients and 10 controls, which may affect statistical power and generalizability.

Provenance

Source

Yunan Shan via figshare.

Collection Method

16S rRNA sequencing and UHPLC-Q-TOF MS metabolomics performed on fecal samples.

Freshness

Last updated 2026-04-23 05:35:48.