Abstract
BACKGROUND: The skin microbiota, a complex community of microorganisms residing on the skin, plays a crucial role in maintaining skin health and overall homeostasis. Recent research has suggested that alterations in the composition and function of the skin microbiota may influence the aging process. However, the causal relationships between specific skin microbiota and biological aging remain unclear. Mendelian randomization (MR) analysis provides a powerful tool to explore these causal links by utilizing genetic variants as instrumental variables, thereby minimizing confounding factors and reverse causality that often complicate observational studies. METHODS: We utilized a two-sample MR approach with population-based cross-sectional data from two German cohorts, KORA FF4 (n = 324) and PopGen (n = 273). In total, GWAS summary data from 1656 skin samples and datasets on accelerated biological age were analyzed to investigate the causal relationship between skin microbiota and accelerated biological aging. The primary analysis was performed using the inverse variance weighted (IVW) method with random effects and was further supported by MR-Egger regression, Cochran's Q test, and a range of sensitivity analyses. RESULTS: The MR analysis revealed that for biological age acceleration (BioageAccel), the IVW analysis identified protective effects from certain skin microbiota, including Alphaproteobacteria_Dry (p = 0.046), Asv033_sebaceous (p = 0.043), Burkholderiales_Moist (p = 0.008), and Proteobacteria_Moist (p = 0.042). Similar protective effects were observed for Burkholderiales_Moist (p = 0.045) and Proteobacteria_Moist (p = 0.012) in the weighted median analysis. In contrast, Paracoccus_Moist (p = 0.013) and Proteobacteria_Sebaceous (p = 0.005) were associated with accelerated aging. When using PhenoAge acceleration as the outcome, the IVW analysis linked skin microbiota like Asv005_Dry (p = 0.026), ASV039_Dry (p = 0.003), Betaproteobacteria_Sebaceous (p = 0.038), and Chryseobacterium_Moist (p = 0.013) with accelerated aging. The weighted median analysis supported these findings and also identified protective effects from ASV011_Dry (p = 0.021), ASV023_Dry (p = 0.040), Bacteroidales_Dry (p = 0.022), Enhydrobacter_Moist (p = 0.038), Proteobacteria_Moist (p = 0.002), and Rothia_Moist (p = 0.038). CONCLUSIONS: This two-sample MR study reveals potential causal relationships between skin microbiota and aging. However, to confirm these findings, further randomized controlled trials (RCTs) are necessary.