Machine learning-based meta-analysis reveals gut microbiome alterations associated with Parkinson's disease.

Area of Science:

• Microbiome research
• Neurodegenerative diseases
• Machine learning applications

Background:

• Growing interest in the gut microbiome for Parkinson's disease (PD) diagnosis and treatment.
• Lack of consensus on PD-associated microbiome features and their diagnostic value across studies.
• Need for large-scale, multi-study assessment of the PD gut microbiome.

Purpose of the Study:

• To conduct a large-scale machine learning meta-analysis of PD microbiome studies.
• To assess the generalizability and diagnostic accuracy of PD microbiome signatures.
• To identify PD-associated microbial pathways and their role in disease pathogenesis.

Main Methods:

• Machine learning meta-analysis of 4489 gut microbiome samples from PD patients and controls.
• Development and validation of microbiome-based classification models.
• Shotgun metagenomic analysis to identify microbial pathways associated with PD.

Main Results:

• Microbiome models achieved good accuracy within individual studies (AUC 71.9%) but poor generalizability across studies (AUC 61%).
• Multi-dataset training improved model generalizability (LOSO AUC 68%) and disease specificity.
• Enrichment of microbial pathways for solvent and pesticide biotransformation observed in PD, potentially linking environmental exposures to disease risk.

Conclusions:

• The gut microbiome holds diagnostic potential for Parkinson's disease, but robust models require multi-study data.
• Gut microbial pathways may influence PD pathogenesis and interact with environmental risk factors.
• This comprehensive analysis provides a reference for the diagnostic and functional roles of the PD gut microbiome.