Gut Microbiota and Metabolomic Profiles in Myasthenia Gravis Patients and Controls

A study from 2026 by Yunan Shan presents integrated microbial and metabolomic profiling of fecal samples from 29 myasthenia gravis patients and 10 healthy controls. The dataset likely contains results from 16S rRNA sequencing and UHPLC-Q-TOF MS metabolomics, identifying 232 discriminative taxa and 567 altered metabolites. It was published on figshare under a CC-BY-4.0 license.

Use Cases

• Training machine learning models for disease classification based on the 567 altered metabolite profiles.
• Investigating associations between specific microbial taxa (e.g., Faecalibacterium prausnitzii) and clinical severity scores mentioned in the description.
• Exploring metabolic pathway disturbances (e.g., bile secretion, sphingolipid signaling) implicated by KEGG analysis.
• Validating the diagnostic performance of the Random Forest metabolite model that achieved an AUC of 1.0.

Strengths

• Integrated multi-omics data from 16S rRNA sequencing and untargeted metabolomics on the same cohort.
• Includes results from a Random Forest model with reported perfect diagnostic performance (AUC = 1.0).
• Analysis identifies a specific number of discriminative features: 232 taxa and 567 metabolites.

Limitations

• The dataset is very small in file size (11.7 KB), suggesting it may contain summary results rather than raw sequencing or spectral data.
• Row count and column-level documentation are unknown, limiting assessment of data structure and granularity.
• The cohort size is limited to 39 total participants (29 patients, 10 controls).

Provenance

Source

figshare, author Yunan Shan.

Collection Method

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

Freshness

Last updated 2026-04-23 05:35:52.