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Machine Learning-based Diagnostic Potential of Bipolar Disorder Using Gut Microbiota Signatures.

Hang Li1,2, Yan-Ting Jin3, Dong-Xin Ye4

  • 1Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China.

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|January 14, 2026
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Summary
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Bipolar disorder (BD) is linked to gut microbiome changes. Machine learning models using these gut bacteria biomarkers show promise for noninvasive BD diagnosis and understanding the microbiota-gut-brain axis.

Keywords:
bioinformaticsbiology computingmicroorganisms

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Area of Science:

  • Microbiome research
  • Psychiatric disorders
  • Computational biology

Background:

  • Bipolar disorder (BD) is a chronic illness with significant cognitive and social impairments.
  • The gut microbiota-gut-brain axis is increasingly implicated in BD pathophysiology.
  • Diagnostic and therapeutic strategies for BD require further development.

Purpose of the Study:

  • Investigate gut microbial alterations in BD patients.
  • Evaluate the diagnostic potential of gut microbiota using machine learning.
  • Identify microbial biomarkers for BD noninvasive diagnosis.

Main Methods:

  • 16S rRNA sequencing to analyze gut microbial composition and diversity.
  • Machine learning algorithms, including random forest (RF), for classification.
  • PICRUSt2 for functional prediction of microbial pathways.
  • Feature selection methods to identify optimal microbial biomarkers.

Main Results:

  • Reduced alpha-diversity and altered beta-diversity observed in BD patients compared to healthy controls (HC).
  • RF classifier achieved high diagnostic performance (AUC=0.9316) using 35 microbial biomarkers.
  • Functional analysis revealed altered pathways in neurodegeneration, lipid metabolism, and heme biosynthesis.
  • Combined compositional and functional features further improved diagnostic accuracy (AUC=0.9499).

Conclusions:

  • Significant compositional and functional gut microbial disturbances are present in BD.
  • Machine learning-based microbiome analysis offers a promising approach for noninvasive BD diagnosis.
  • Identified markers provide insights into the microbiota-gut-brain axis, supporting precision psychiatry and microbiome-targeted interventions.