METHODS: We conducted a systematic search on PubMed, Web of Science, and Cinhal databases for literature published until 15 December 2023, to identify studies that have evaluated the oral microbiome with culture-independent next-generation techniques comparing the oral microbiome of tobacco users and non-users. The search followed the PECO format. The outcomes included changes in microbial diversity and abundance of microbial taxa. The quality assessment was performed using the Newcastle-Ottawa Scale (NOS) (PROSPERO ID CRD42022340151).

RESULTS: Out of 2,435 articles screened, 36 articles satisfied the eligibility criteria and were selected for full-text review. Despite differences in design, quality, and population characteristics, most studies reported an increase in bacterial diversity and richness in tobacco users. The most notable bacterial taxa enriched in users were Fusobacteria and Actinobacteria at the phylum level and Streptococcus, Prevotella, and Veillonella at the genus level. At the functional level, more similarities could be noted; amino acid metabolism and xenobiotic biodegradation pathways were increased in tobacco users compared to non-users. Most of the studies were of good quality on the NOS scale.

CONCLUSION: Tobacco smoking influences oral microbial community harmony, and it shows a definitive shift towards a proinflammatory milieu. Heterogeneities were detected due to sampling and other methodological differences, emphasizing the need for greater quality research using standardized methods and reporting.

MATERIALS AND METHODS: Unstimulated saliva samples were collected from 60 participants, 30 smokers and 30 non-smokers in Kuala Lumpur and Klang Valley, Malaysia. DNA extraction was performed using the Qiagen DNA mini kit, and the 16S rRNA bacterial gene was amplified and sequenced using the Illumina MiSeq platform. Sequencing reads were processed using DADA2, and the alpha and beta diversity of the bacterial community was assessed. Significantly differentiated taxa were identified using LEfSe analysis, while differentially expressed pathways were identified using MaAsLin2.