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

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This paper studied how physiological, environmental, and lifestyle (PEL) factors shape the composition and dynamics of the ocular surface (OS) microbiome, using OS samples collected with eSwab and analyzed by 16S rRNA gene sequencing. The authors assessed diversity (alpha and beta) and performed differential abundance and machine-learning based cross-validation to identify microbial features associated with participant-level variables, while accounting for PEL confounding factors. They found that nationality, sport practice, and eyeglasses usage significantly influenced the OS microbiome, with Spanish subjects showing higher richness and sports practitioners showing higher biodiversity, and they reported distinct community composition patterns by nationality, age, sport, and eyeglasses usage. The paper does not provide additional explicit limitations beyond describing its bioinformatics and analysis approach, and it does not detail longitudinal or causal inference. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Purpose Purpose: 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. Methods The 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. Results Nationality, 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. Conclusion 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. Key Points Identify 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;
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Abstract

Purpose Purpose: 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.

Methods

The 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.

Results

Nationality, 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.

Conclusion

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. Key Points Identify 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; Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Approval for this study is granted by the institutional review board of Riga Stradins Univer- sity (nr.29/20092016) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors

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