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Analysis of oral microbiome in glaucoma patients using machine learning prediction models
Byung Woo Yoon1, Su-Ho Lim2, Jong Hoon Shin3
1Division of Oncology, Department of Internal Medicine, Seoul Paik Hospital, Seoul, Korea.
Journal of Oral Microbiology
|August 16, 2021
Summary
The oral microbiome may indicate glaucoma severity. Lower levels of Lactococcus bacteria in the mouth are linked to glaucoma, suggesting a role for microbial imbalance in the disease.
Area of Science:
- Microbiology
- Ophthalmology
- Genomics
Background:
- The human microbiome is increasingly recognized as a factor in neurodegenerative diseases.
- The specific role of the oral microbiome in glaucoma pathogenesis remains largely unexplored.
Purpose of the Study:
- To investigate oral microbiome characteristics in glaucoma patients.
- To identify potential oral microbiome biomarkers for glaucoma severity using machine learning.
Main Methods:
- 16S rRNA gene sequencing and operational taxonomic unit analysis for microbiome composition.
- Differential gene expression (DEG) analysis to compare glaucoma patients and controls.
- Machine learning (multinomial logistic regression, association rule mining) for biomarker discovery.
Main Results:
- Significant depletion of *Lactococcus* and enrichment of *Faecalibacterium* observed in glaucoma patients.
- Oral microbiome biomarkers, including *Lactococcus*, achieved 96% accuracy in predicting glaucoma association.
- Identified specific microbial signatures associated with glaucoma severity.
Conclusions:
- Oral microbiome biomarkers demonstrate high accuracy for predicting glaucoma severity.
- Reduced *Lactococcus* abundance in the oral cavity suggests microbial dysbiosis may contribute to glaucoma pathophysiology.
