Exploring the association between skin microbiota and inflammatory skin diseases: a two-sample Mendelian randomization analysis.

Dysbiosis in the skin microbiome is closely associated with various inflammatory skin diseases. However, current research on the causal relationship between the skin microbiome and inflammatory skin diseases lacks comprehensive and detailed investigation. We used a two-sample Mendelian randomization (MR) approach to explore associations between the skin microbiome and seven inflammatory skin diseases, including acne, atopic dermatitis, erysipelas, vitiligo, psoriasis, rosacea, and urticaria. The GWAS summary data for the skin microbiome was derived from 647 participants in two German population-based cohorts, and for the inflammatory skin diseases, they were sourced from the FinnGen consortium. Our primary MR analysis method was the inverse variance weighted (IVW) method, complemented by alternatives like MR-Egger regression, weighted median estimation, and constrained maximum likelihood. Sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, and MR-PRESSO outlier detection, were conducted to validate and stabilize our findings. We identified significant causal relationships between the skin microbiome and seven inflammatory skin diseases: acne, atopic dermatitis, erysipelas, vitiligo, psoriasis, rosacea, and urticaria, with 7, 6, 9, 1, 7, 4, and 7 respective causal relationships for each disease. These relationships comprise 20 protective and 14 risk causal relationships. We applied the false discovery rate correction to these results. Sensitivity analysis revealed no significant pleiotropy or heterogeneity. Our study revealed both beneficial and detrimental causal relationships between diverse skin microbiota and inflammatory skin diseases. Additionally, the ecological niche of the skin microbiome was crucial to its functional impact. This research provided new insights into how skin microbiota impacted skin diseases and the development of therapeutic strategies.

Pan L ，Li C ，Liang Z ，Shi J ... -  《-》

The causal relationship between gut microbiota and inflammatory dermatoses: a Mendelian randomization study.

Mao R ，Yu Q ，Li J 《Frontiers in Immunology》

Investigating causal relationships between the gut microbiota and inflammatory skin diseases: A Mendelian randomization study.

Numerous inflammatory skin diseases are associated with the gut microbiota. Studies of the association between gut microbiota and inflammatory skin diseases have yielded conflicting results owing to confounding factors, and the causal relationship between them remains undetermined. Two-sample Mendelian randomization (MR) was used to examine the association between gut microbiota and four common inflammatory skin diseases: acne, psoriasis, urticaria and atopic dermatitis. The summary statistics of the gut microbiota from the largest available genome-wide association study meta-analysis (n = 13,266) conducted by the MiBioGen consortium along with the summary statistics of the four diseases were obtained from the FinnGen consortium. Causal relationships were assessed using the inverse variance weighted (IVW), weighted median, MR-Egger and maximum likelihood methods, and several sensitivity analyses were performed to ensure the accuracy of the results. Finally, reverse and multivariable MR analyses were performed to verify the robustness of the results. We found causal associations of Bacteroidaceae [odds ratio (OR), 2.25; 95% confidence interval (CI), 1.48-3.42; pivw = 0.0001], Allisonella (OR, 1.42; 95% CI, 1.18-1.70; pivw = 0.0002) and Bacteroides (OR, 2.25; 95% CI, 1.48-3.42; pivw = 0.0001) with acne, the Eubacterium fissicatena group with psoriasis (OR, 1.22; 95% CI, 1.10-1.35; pivw = 0.0002) and Intestinibacter with urticaria (OR, 1.28; 95% CI, 1.13-1.45; pivw = 0.0001). These results were corrected for a false discovery rate. Sensitivity analyses were performed to validate the robustness of the associations and reverse MR confirmed that the results were not influenced by the reverse effect. Our study revealed that some gut microbiota are risk factors for inflammatory skin diseases, providing new information on potential therapeutic targets. Additionally, a possible association with the gut-skin axis was confirmed. Further research is required to elucidate the mechanisms underlying these relationships.

Gu Y ，Zhang W ，Zhao W ，Zeng X ... -  《-》

Gut microbiota's influence on erysipelas: evidence from a two-sample Mendelian randomization analysis.

Previous studies have suggested a link between gut microbiota and skin diseases, including erysipelas, an inflammatory skin condition. Despite this, the precise nature of the relationship between erysipelas and gut microbiota remains unclear and subject to debate. We conducted a Mendelian Randomization (MR) analysis using publicly available summary data from genome-wide association studies (GWAS) to explore the potential causal relationship between gut microbiota and erysipelas. Instrumental variables (IVs) were identified using a comprehensive set of screening methods. We then performed MR analyses primarily using the Inverse Variance Weighted (IVW) method, complemented by alternative approaches such as MR Egger, weighted median, simple mode, and weighted mode. A series of sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test, and a leave-one-out test, were executed to ensure the robustness and validity of our findings. We identified potential associations between erysipelas and various gut microbiota, including Alcaligenaceae (OR 1.23; 95% CI 1.06-1.43; p=0.006), Rikenellaceae (OR 0.77; 95% CI 0.67-0.90; p=0.001), and others. Notably, associations with Actinomyces, Lachnospiraceae NC2004 group, Ruminiclostridium 9, Ruminococcaceae UCG014, Odoribacter, and Actinobacteria were also observed. Sensitivity analyses confirmed the robustness of these associations. Our MR analysis suggests both potentially beneficial and harmful causal relationships between various gut microbiota and the incidence of erysipelas. This study provides new theoretical and empirical insights into the pathogenesis of erysipelas and underscores the potential for innovative preventive and therapeutic approaches.

Bao L ，Wang Z ，Wu L ，Luo Z ，Wang Y ... -  《Frontiers in Cellular and Infection Microbiology》

Skin microbiome and causal relationships in three dermatological diseases: Evidence from Mendelian randomization and Bayesian weighting.

Atopic dermatitis (AD), psoriasis (PSO), rosacea, and other related immune skin diseases are affected by multiple complex factors such as genetic and microbial components. This research investigates the causal relationships between specific skin microbiota and these diseases by using Mendelian randomization (MR), and Bayesian weighted Mendelian randomization (BWMR). We utilized genome-wide association study (GWAS) data to analyze the associations between various skin bacteria and three dermatological diseases. Single nucleotide polymorphisms (SNPs) served as instrumental variables (IVs) in MR methods, including inverse variance weighted (IVW), and MR Egger. BWMR was employed to validate results and address pleiotropy. The IVW analysis identified significant associations between specific skin microbiota and dermatological diseases. ASV006_Dry, ASV076_Dry, and Haemophilus_Dry were significantly positively associated with AD, whereas Kocuria_Dry was negatively associated. In PSO, ASV005_Dry was negatively associated, whereas ASV004_Dry, Rothia_Dry, and Streptococcus_Moist showed positive associations. For rosacea, ASV023_Dry was significantly positively associated, while ASV016_Moist, Finegoldia_Dry, and Rhodobacteraceae_Moist were significantly negatively associated. These results were corroborated by BWMR analysis. Bacterial species such as Finegoldia, Rothia, and Streptococcus play crucial roles in the pathogenesis of AD, PSO, and rosacea. Understanding these microbial interactions can aid in developing targeted treatments and preventive strategies, enhancing patient outcomes and quality of life.

Li X ，Chen S ，Chen S ，Cheng S ，Lan H ，Wu Y ，Qiu G ，Zhang L ... -  《-》