A Mendelian randomization analysis reveals the role of the skin microbiota in systemic lupus erythematosus.

Background: Systemic lupus erythematosus (SLE) is influenced by a complex array of factors, encompassing genetic, hormonal, and microbial components. This study seeks to investigate the causal relationships between specific skin microbiotas and SLE using a Mendelian randomization (MR) analysis and a Bayesian weighted Mendelian randomization (BWMR) analysis.

Methods: We utilized genome-wide association study (GWAS) data to investigate the role of skin microbiota in SLE. Single nucleotide polymorphisms (SNPs) were employed as instrumental variables (IVs) in 9 Mendelian randomization methods, including the inverse variance weighted (IVW) method, MR-Egger method, weighted median method, weighted mode, simple mode method, contamination mixture (ConMix) method, robust adjusted profile score (RAPS) method, constrained maximum likelihood and model averaging (CML-MA), and debiased inverse-variance weighted (dIVW) method. Additionally, sensitivity analyses such as leave-one-out analysis, Cochran's Q test, and Egger intercept test were conducted to ensure the robustness of the Mendelian randomization results. Finally, we applied Bayesian weighted Mendelian randomization (BWMR) approach to validate our MR findings.

Results: In the MR analysis of the skin microbiota (KORA FF4) and SLE, the IVW method revealed that ASV042 (Acinetobacter (unc.)) _Antecubitalfossa_Moist (OR = 0.964, 95% CI = 0.941-0.988, p = 0.003) showed protective effects against SLE, while three taxa exhibited positive associations with SLE risk: ASV005 (Propionibacterium granulosum) _Retroauricularfold_Sebaceous (OR = 1.051, 95% CI = 1.013-1.091, p = 0.008), the phylum Proteobacteria _Retroauricularfold_Sebaceous (OR = 1.048, 95% CI = 1.004-1.094, p = 0.033), and the class Betaproteobacteria _Retroauricularfold_Sebaceous (OR = 1.049, 95% CI = 1.00 l-1.098, p = 0.044). In the MR analysis of the skin microbiota (PopGen) and SLE, five taxa demonstrated protective effects: ASV004 (Corynebacterium (unc.)) _Forehead_Sebaceous (OR = 0.960, 95% CI = 0.927-0.994, p = 0.023), ASV005 (Propionibacterium granulosum) _Antecubitalfossa_Moist (OR = 0.964, 95% CI = 0.930-0.999, p = 0.042), ASV007 (Anaerococcus (unc.)) _Forehead_Sebaceous (OR = 0.969, 95% CI = 0.942-0.996, p = 0.027), the genus Kocuria _Volarforearm_Dry (OR = 0.964, 95% CI = 0.935-0.995, p = 0.023), and the class Betaproteobacteria _Antecubitalfossa_Moist (OR = 0.952, 95% CI = 0.918-0.988, p = 0.010), while ASV039 (Acinetobacter (unc.)) _Antecubitalfossa_Moist (OR = 1.024, 95% CI = 1.000-1.048, p = 0.049) was found to potentially increase risk in SLE. The findings were further supported by BWMR analysis, adding credibility to this research. Sensitivity analyses confirmed robustness of these findings.

Conclusion: Through genetic approaches, our study has illustrated specific skin microbiotas that may influence SLE, potentially facilitating earlier detection and enhancing the efficacy of treatment alternatives for patients with SLE. Key points • This is the first study to uncover the potential causal relationship between skin microbiota and SLE using MR analysis. • The study has identified a potential causal relationship between 10 skin microbiotas and SLE.  • A series of sensitivity analyses and BWMR were conducted to validate the robustness of our findings.