ABSTRACT

Purpose: The microbiome is considered an environmental factor that contributes to the progression of several neurodegenerative diseases. However, the association between microbiome and glaucoma remains unclear. This study investigated the features of the oral microbiome in patients with glaucoma and analyzed the microbiome biomarker candidates using a machine learning approach to predict the severity of glaucoma.

Methods: The taxonomic composition of the oral microbiome was obtained using 16S rRNA gene sequencing, operational taxonomic unit analysis, and diversity analysis. The differentially expressed gene (DEG) analysis was performed to determine the taxonomic differences between the microbiomes of patients with glaucoma and the control participants. Multinomial logistic regression and association rule mining analysis using machine learning were performed to identify the microbiome biomarker related to glaucoma severity.

Results: DEG analysis of the oral microbiome of patients with glaucoma revealed significant depletion of Lactococcus (P = 3.71e⁻³¹), whereas Faecalibacterium was enriched (P = 9.19e⁻¹⁴). The candidate rules generated from the oral microbiome, including Lactococcus, showed 96% accuracy for association with glaucoma.

Conclusions: Our findings indicate microbiome biomarkers for glaucoma severity with high accuracy. The relatively low oral Lactococcus in the glaucoma population suggests that microbial dysbiosis could play an important role in the pathophysiology of glaucoma.

摘要

目的：微生物组被认为是导致多种神经退行性疾病进展的环境因素。然而，微生物组与青光眼之间的关联仍不清楚。本研究调查了青光眼患者口腔微生物组的特征，并使用机器学习方法分析了候选微生物组生物标志物，以预测青光眼的严重程度。

方法：使用 16S rRNA 基因测序、操作分类单元分析和多样性分析获得口腔微生物组的分类组成。进行差异表达基因 (DEG) 分析以确定青光眼患者与对照组参与者的微生物组之间的分类差异。使用机器学习进行多项逻辑回归和关联规则挖掘分析，以确定与青光眼严重程度相关的微生物组生物标志物。

结果：青光眼患者口腔微生物组的 DEG 分析显示乳球菌显着减少（P =  3.71e ^-31），而粪杆菌属富集（P = 9.19e ^-14）。从口腔微生物组（包括乳球菌）生成的候选规则与青光眼的关联准确度为 96%。

结论：我们的研究结果表明，微生物组生物标志物可以高度准确地反映青光眼严重程度。青光眼人群中相对较低的口腔乳球菌表明微生物失调可能在青光眼的病理生理学中发挥重要作用。