Clinical Case-control Study of Postoperative Ocular Microbiota Colonization Using Microbial Analysis in Patients Undergoing Blepharoplasty

Abstract

Background: Blepharoplasty is the third most common plastic surgical procedure worldwide. However, its impact on the ocular surface microbiota remains unclear. This study aimed to investigate microbial changes before and after blepharoplasty.

Methods: A clinical case-control study was conducted involving 30 blepharoplasty patients and 23 controls. Ocular surface swabs were collected, and 16S rRNA sequencing was used to identify bacterial species and abundance. Bioinformatics analysis was performed to annotate and visualize microbial composition.

Results: Comparison between groups revealed that patients who underwent blepharoplasty had increased colonization by pathogenic bacteria, whereas controls were primarily colonized by neutral bacteria. Alpha diversity analysis showed a significantly higher bacterial abundance in the surgical group. Beta diversity analysis indicated significant differences in microbial community structure between the 2 groups. Subgroup analysis based on age and sex in the surgical group revealed no significant effects of these factors on microbial composition and abundance.

Conclusions: Blepharoplasty may disrupt the ocular mucosal barrier, altering the ocular microenvironment and promoting colonization by pathogenic bacteria. This microbial imbalance may contribute to postoperative ocular discomfort or dysfunction. Notably, age, sex, and surgery frequency did not influence the microbial profile in blepharoplasty patients.

Fig. 1.

The bar chart shows the differences in microbial abundance between the control group and the surgery group in phylum attributes. Three of the 5 differentially expressed microbial communities, Proteobacteria, Firmicutes, and Bacteroidota (P < 0.0001), showed statistically significant differences.

Fig. 2.

Microbial composition analysis between the surgery group and the control group. A, The bar chart shows the difference in microbial abundance between the control group and the surgery group in terms of genus attributes, with the top 9 significantly different microbial communities included. B, The box plot of sample abundance distribution shows the sample distribution of the top 9 significantly different bacterial communities, identifying the differences in colonization and statistical significance of different bacterial communities among different groups.

Fig. 3.

Microbial diversity analysis between the surgery group and the control group. A, The boxplot shows the α diversity analysis of ocular microbiota between the surgery group and the control group, with statistically significant differences (Chao1 P < 0.01 and Simpson P < 0.001). B, The NMDS distribution map shows the differential results of β diversity in microbiota colonization between surgery group patients and control group participants. C, LEfSe analysis demonstrated the ocular-specific colonization of bacterial genera in the surgery group patients and control group participants. D, The heatmap further processed the diversity results of β diversity analysis and visualized the specific bacterial genera that were colonized. NMDS, nonmetric multidimensional scaling. *P < 0.05; **P < 0.01; ***P < 0.001.

Fig. 4.

Intragroup subdivision of surgery patients—diversity analysis based on number of procedures and age at surgery. A, The box plot shows the α diversity analysis of surgery group patients grouped by the frequency of surgery, and the results are not statistically significant (P > 0.05). B, The NMDS distribution map shows the differences in β diversity (microbiota colonization) among surgical patients grouped by the frequency of surgery. C, The box plot shows the α diversity analysis of surgery group patients grouped by age, and the difference results are not statistically significant (P > 0.05). D, The NMDS distribution map shows the differences in β diversity (microbiota colonization) among surgical patients grouped by age. NMDS, nonmetric multidimensional scaling.