Ocular Surface Microbiome: Influences of Physiological, Environmental, and Lifestyle Factors

Abstract

PurposePurpose: The ocular surface (OS) microbiome is influenced by various factors and impacts 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.MethodsThe 16S rRNA gene sequencing was performed on OS samples collected 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.ResultsNationality, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis showed higher microbial richness in Spanish subjects compared to Italian subjects and higher biodiversity in sports practitioners. 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. ML approach confirmed the significance of nationality in classifying microbial profiles.ConclusionThis 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.Key PointsIdentify confounding factors influencing the ocular microbiome composition;Characterize the ocular surface microbiome;Analyse 16S rRNA gene sequencing data from ocular surface samples;Perform Diversity Analysis (i.e.; Alpha-diversity and Beta-diversity) and Difference Abundance Analysis;