Abstract
BACKGROUND: The association between acne and gut microbiota has garnered considerable attention; nevertheless, given the substantial diversity within gut microbiota, the precise cause-and-effect relationship linking specific microbial species to acne remains elusive. To address this gap in knowledge, our study utilized Mendelian randomization analysis to elucidate a potential causal link between gut microbiota composition and acne development while also investigating underlying mechanisms involving microbial factors associated with metabolic disorders. MATERIALS AND METHODS: The independent single nucleotide polymorphisms (SNPs) closely associated with 196 gut microbiota samples (N=18340) were selected as variable tools. The relationship between gut microbiota and acne (N=212438) was analyzed using the Twosample package in R4.3.1, employing various methods including inverse variance weighting (IVW), weighted median, MR-Egger, Simple-mode, and Weighted-mode. To ensure the stability of the estimates, a series of sensitivity analyses were conducted, such as Cochran's Q-test, MR-Egger intercept analysis, leave-one-out analysis, and funnel plots. Additionally, the impact of each instrumental variable was calculated. RESULTS: In the Mendelian randomization analysis, we identified twelve microbial taxa potentially associated with acne: family.Bacteroidaceae, family.Clostridiaceae1, genus.Allisonella, genus.Bacteroides, genus.Butyricimonas, genus.Clostridiumsensustricto1, and genus.Coprococcus3. These seven bacterial groups were found to be potential risk factors for acne. Conversely, family.Lactobacillaceae and genus.Ruminococcustorquesgroup along with genus.CandidatusSoleaferrea, genus.Fusicatenibacter, family.Lactobacillaceae, and genus.Lactobacillus exhibited a protective effect against acne. Furthermore, our investigation revealed that some of these microbial taxa have been implicated in metabolic diseases through previous studies. Importantly though, no causal relationship was observed in the reverse Mendelian randomization analysis.