Smoking shows no major impact on the eye microbiome in small study

By Vijay Kumar MalesuReviewed by Susha Cheriyedath, M.Sc.Jul 8 2026

Despite smoking’s known effects on other microbial communities, this study suggests the eye’s surface ecosystem may remain surprisingly stable, while leaving open the possibility of smaller changes that larger studies could detect.

Study: Influence of smoking on the human ocular surface microbiome and tear proteome. Image Credit: komokvm / Shutterstock

In a recent study in the journal Scientific Reports, researchers at the University of Bern, Switzerland, evaluated whether smoking is associated with changes in the ocular surface microbiome and tear proteome by comparing microbial composition, diversity, and functional profiles, as well as tear proteins, between smokers and non-smokers.

Background

Did you know that tobacco smoking can affect microbial communities throughout the body, yet its influence on the eye remains largely unknown?

The ocular surface microbiome is a low-biomass community of bacteria, viruses, fungi, and other eukaryotes that may help support ocular health by modulating local immune responses, maintaining epithelial barrier integrity, and limiting pathogen colonization. When this balance is disrupted, it can be associated with conditions such as dry eye disease, conjunctivitis, and keratitis.

Smoking is known to be an important risk factor for the development of various ocular diseases, but the specific effect of smoking on the ocular surface microbiome has not been systematically explored. 

About the Study

The researchers recruited 41 adults from the Department of Ophthalmology at the University Hospital of Bern, Switzerland, including 17 smokers and 24 non-smokers. Smokers smoked at least six cigarettes daily for a minimum of two years, while non-smokers had no recorded history of tobacco use.

Samples were collected during winter, spring, and summer after written informed consent was obtained. Tear fluid was collected using the Schirmer type I tear test, while pooled conjunctival swabs from both eyes were obtained for microbiome analysis. Positive and negative controls were included to ensure data quality.

Microbial DNA extracted from conjunctival swabs underwent shotgun metagenomic sequencing to identify bacterial, fungal, and viral communities after removal of human sequences and contaminants. Functional microbial analyses were also performed, including gene family and pathway profiling.

Tear samples were analyzed with nano-liquid chromatography-tandem mass spectrometry to determine their proteomic profile. Statistical analyses included alpha and beta diversity assessments, Principal Coordinates Analysis (PCoA), Permutational Multivariate Analysis of Variance (PERMANOVA), differential abundance analysis, Principal Component Analysis (PCA), and differential expression analysis with correction for multiple testing. Post-hoc power analyses were also conducted to estimate the ability to detect meaningful differences between the study groups.

Study Results

A total of 41 conjunctival swab samples were sequenced, including 24 from non-smokers and 17 from smokers. No significant differences were observed between the groups in age or sex.

After decontamination with the microDecon pipeline, Actinobacteria (59.1%), Proteobacteria (25.8%), and Firmicutes (13.9%) made up the main bacterial groups in non-smokers, while Proteobacteria (43.2%), Actinobacteria (35.6%), and Firmicutes (20.7%) were the predominant bacterial phyla among smokers.

In general, Cutibacterium acnes had the highest relative abundance in non-smokers (52.1%) and smokers (30.7%), while Moraxella osloensis accounted for 20.9% and 30.6%, respectively. Limosilactobacillus fermentum and Sphingobium yanoikuyae were also frequently detected.

Among eukaryotes, both Basidiomycota and Ascomycota were the predominant groups. In particular, Basidiomycota constituted 51.0% of eukaryotic presence in non-smokers and around 64.1% among smokers, while Ascomycota accounted for 49.0% and 35.9%, respectively.

Saccharomyces cerevisiae was the most prevalent eukaryotic species, accounting for 44.9% and 27.3%, respectively, while Malassezia globosa was the second most frequently found species, accounting for 25.8% and 27.7%, respectively.

In addition, Cryptococcus neoformans contributed to 18.0% and 23.1% of eukaryotic presence in both groups. Finally, there were some variations within the viral communities. Because phylum-level annotations were unavailable for the viral dataset, the researchers examined viral communities at the order level.

The most commonly occurring viral orders included unclassified viruses, Caudovirales, and Herpesvirales. The results also revealed sequences assigned to Glypta fumiferanae ichnovirus (21.3% in non-smokers; 14.3% in smokers), Ictalurid herpesvirus 1 in both groups (11.1%), and BeAn 58058 virus (8.7% in non-smokers; 15.8% in smokers).

Bacterial diversity analyses indicated no significant differences between the smokers and non-smokers groups. The mean Shannon diversity index for bacteria was 0.89 in non-smokers and 0.81 in smokers (p = 0.5235).

Alpha diversity indices for eukaryotes showed no significant difference between the two study groups (p = 0.369), whereas viral alpha diversity remained similar across groups (p = 0.83). PCoA results demonstrated that the bacterial, eukaryotic, and viral communities did not cluster distinctly. PERMANOVA also showed no significant differences for bacteria (R² = 0.052; p = 0.106), eukaryotes (R² = 0.034; p = 0.488), or viruses (R² = 0.035; p = 0.175).

Differential abundance analysis identified no bacterial, eukaryotic, or viral taxa that differed significantly after multiple-testing correction. However, one bacterial gene-level feature was reported to differ between groups, suggesting that the absence of significant taxonomic differences should not be extended to all functional microbial features.

Post-hoc power analysis showed consistently low observed power across bacterial, fungal, and viral alpha-diversity comparisons. The authors therefore cautioned that the findings should be interpreted as evidence against a large smoking-related effect, rather than proof of biological equivalence between smokers and non-smokers. Additional analyses also indicated that sampling season, DNA extraction kit, and pollen allergy status explained more variation in the bacterial community than smoking behavior.

Proteomic analysis of tears identified 1,066 proteins, of which 1,065 were quantified using iMaxLFQ, with a 0.1% missing value rate before filtering across 40 samples. The median coefficient of variation of the study was found to be 8.7%, while the mean coefficient of variation for the study was 8.0%. PCA results showed no notable separation between samples obtained from smokers and non-smokers (PERMANOVA p = 0.116, R² = 0.035).

Additionally, differential expression analysis showed that none of the proteins remained statistically significant after corrections for multiple comparisons were made. Because the proteomic analysis used conservative thresholds, smaller smoking-associated changes in tear protein abundance could not be excluded.

(A–F) Taxonomic profile of the ocular surface microbiome in non-smokers (bacterial n=22, eukaryotic n=12, viral n=23) and smokers (bacterial n=17, eukaryotic n=12, viral n=17). Bar plots show relative abundance of bacterial (A-B), eukaryotic (C-D), and viral (E-F) taxa at the phylum level (left) and species level (right). Individual bars represent samples; two additional bars indicate group means. Colours indicate different taxa.

Conclusion

The findings showed that smoking was not associated with large or consistent changes in the taxonomic composition, microbial diversity, or tear proteome of the ocular surface in this small cohort. No bacterial, eukaryotic, or viral taxon, nor any tear protein marker, showed a significant difference between the two populations after multiple-testing corrections. The tear proteome remained largely stable, consistent with resilient host-microbe interactions on the ocular surface.

The authors concluded that the ocular surface microbiome appears to maintain ecological stability despite smoking, while emphasizing that the results do not rule out smaller smoking-related effects. Future longitudinal studies with larger sample sizes are needed to better understand environmental and disease-related factors that may influence ocular surface microbial balance. More research is needed to see how daily habits affect ocular surface health.