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Gut Microbiome Signatures in Multiple Sclerosis: A Case-Control Study with Machine Learning and Global Data

Margarita V Neklesova1,2, Karine S Sogomonyan2, Ivan A Golovkin2,3

  • 1Institute of Cytology of the Russian Academy of Sciences, 194064 St. Petersburg, Russia.

Biomedicines
|August 28, 2025
PubMed
Summary

Gut microbiome alterations are linked to multiple sclerosis (MS). Machine learning models effectively identified these microbial signatures, showing potential for diagnosing MS based on gut bacteria profiles.

Keywords:
16S rRNA gene sequencingLight Gradient Boosting Machine classifiergut microbiomegut microbiotamachine learningmultiple sclerosis

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Area of Science:

  • Microbiome research
  • Computational biology
  • Neuroimmunology

Background:

  • Gut dysbiosis is associated with multiple sclerosis (MS), but consistent microbial biomarkers are lacking.
  • Machine learning (ML) can integrate microbiome data to identify disease-associated biomarkers and understand MS pathogenesis.
  • Previous studies show inconsistent gut microbial signatures in MS patients.

Purpose of the Study:

  • To identify specific gut microbial signatures associated with MS.
  • To evaluate the diagnostic potential of ML models using microbiome data for MS detection.
  • To integrate findings with existing literature for a comprehensive understanding.

Main Methods:

  • 16S rRNA gene sequencing of fecal samples from 29 MS patients and 27 healthy controls.
  • Differential abundance analysis and integration of data with 29 published studies.
  • Development and evaluation of four ML models for distinguishing MS-associated microbiome profiles.

Main Results:

  • MS patients showed reduced abundance of Eubacteriales, Lachnospirales, Oscillospiraceae, Lachnospiraceae, Parasutterella, and Faecalibacterium.
  • Increased abundance of Lachnospiraceae UCG-008 was observed in MS patients.
  • A Light Gradient Boosting Machine classifier achieved high performance (accuracy: 0.88, AUC-ROC: 0.95) in differentiating MS patients.

Conclusions:

  • This study identified specific gut microbiome dysbiosis in MS patients.
  • ML models demonstrate significant potential for the diagnosis of MS based on microbiome profiles.
  • Further research is warranted to explore the mechanistic role and therapeutic implications of these microbial alterations in MS.