Rizzuto Vincenzo, Settino Marzia, Stroffolini Giacomo, Covello Giuseppe, Vanags Juris, Naccarato Marta, Montanari Roberto, de Lossada Carlos Rocha, Mazzotta Cosimo, Forestiero Agostino, Adornetto Carlo, Rechichi Miguel, Ricca Francesco, Greco Gianluigi, Laganovska Guna, Borroni Davide
Clinic of Ophthalmology, P. Stradins Clinical University Hospital, Riga, Latvia; School of Advanced Studies, Center for Neuroscience, University of Camerino, Camerino, Italy; Latvian American Eye Center (LAAC), Riga, Latvia.
Department of Mathematics and Computer Science, University of Calabria, Rende, Italy; Institute of High Performance Computing and Networks-National Research Council (ICAR-CNR), Rende, Italy.
Comput Biol Med. 2025 May;190:110046. doi: 10.1016/j.compbiomed.2025.110046. Epub 2025 Apr 1.
The ocular surface (OS) microbiome is influenced by various factors and impacts on ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes.
The 16S rRNA gene sequencing was performed on OS samples collected from 135 healthy individuals using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were identified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles.
Nationality, allergy, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis revealed significant differences between Spanish and Italian subjects (p-value < 0.001), with a median Shannon index of 1.05 for Spanish subjects and 0.59 for Italian subjects. Additionally, 8 microbial genera were significantly associated with eyeglass usage. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differential abundance analysis identified several microbial genera associated with these PEL factors. The Support Vector Machine (SVM) model for Nationality achieved an accuracy of 100%, with an AUC-ROC score of 1.0, indicating excellent performance in classifying microbial profiles.
This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.
眼表（OS）微生物群受多种因素影响，并对眼部健康产生影响。了解其组成和动态变化对于开发针对眼部疾病的靶向干预措施至关重要。本研究旨在确定影响眼微生物群组成的宿主变量，包括生理、环境和生活方式（PEL）因素，并建立眼微生物群与健康结果之间的有效关联。
使用eSwab对从135名健康个体收集的OS样本进行16S rRNA基因测序。提取DNA，制备文库，纯化并分析PCR产物。确定PEL混杂因素，并使用包括机器学习在内的各种生物信息学方法的交叉验证策略来识别对微生物谱进行分类的特征。
国籍、过敏、运动习惯和眼镜使用是影响眼微生物群的重要PEL混杂因素。α多样性分析显示西班牙和意大利受试者之间存在显著差异（p值<0.001），西班牙受试者的香农指数中位数为1.05，意大利受试者为0.59。此外，8个微生物属与眼镜使用显著相关。β多样性分析表明，基于国籍、年龄、运动和眼镜使用，微生物群落组成存在显著差异。差异丰度分析确定了与这些PEL因素相关的几个微生物属。国籍的支持向量机（SVM）模型准确率达到100%，AUC-ROC得分为1.0，表明在对微生物谱进行分类方面表现出色。
本研究强调了在研究眼微生物群时考虑PEL因素的重要性。我们的研究结果突出了环境、生活方式和人口统计学因素在塑造OS微生物群方面的复杂相互作用。未来的研究应进一步探索这些相互作用，以开发个性化的眼部健康管理方法。
