Changes in the Oral Microbiota after Switching from Smoking to Exclusive e-Cigarette Use in an 8-Week...

Cross-sectional data indicate that the oral microbiota differs between people who smoke or use e-cigarettes, suggesting potential as a research tool in product-switching trials, as a biomarker of product use status and/or long-term health effects. However, its sensitivity to short-term product switching remains unclear. We examined a subset of participants in an 8-week product-switching study who were asked to switch from smoking to exclusive e-cigarette use and had oral cells collected (n = 58). Using available biomarker data, we determined that 12 achieved complete switching and 46 engaged in dual use. A subgroup of participants in the same study who continued exclusive smoking (n = 20) was included for reference. Oral microbiota profiles were characterized at baseline and at the end of the study (week 8) using 16S rRNA gene sequencing targeting the V3 to V4 hypervariable regions on an Illumina MiSeq platform. Participants who switched to exclusive e-cigarette use had a significant decrease in alpha diversity (Shannon index) at week 8 compared with baseline smoking (P = 0.02), and their beta diversity at week 8 differed from those who engaged in dual use or continued exclusive smoking (r2 = 0.04; P = 0.014). They also showed increased abundances of proinflammatory genera (Streptococcus, Veillonella, Haemophilus, Fusobacterium), whereas dual users had decreased abundances of several commensal genera, including Lactobacillus, Limosilactobacillus, and Neisseria. Our findings indicate that the oral microbiota may serve as a sensitive tool for detecting and interpreting changes in tobacco-related exposures, and potentially for monitoring product use compliance in clinical studies of product switching.

By analyzing the oral microbiome, individuals' tobacco use status could be assessed, and the impact of interventions, such as switching to e-cigarettes, on reducing tobacco exposure could be evaluated. This information can inform tobacco cessation programs, risk stratification, and personalized prevention strategies for individuals at higher risk for tobacco-related cancer.