Back

Gut microbiome and lichen sclerosus: a two-sample bi-directional Mendelian randomization study

Hu, J.; Chen, J.; Wang, P.; Xiao, C.; Kelaimu, K.; Gao, X.; Li, X.

2024-07-24 dermatology
10.1101/2024.07.23.24310907 medRxiv
Show abstract

(1) BackgroundRecent studies suggest a potential link between gut microbiomes (GMs) and inflammatory diseases, but the role of GMs in lichen sclerosus (LS) remains unclear. This study aims to investigate the causal relationship between GMs and LS, focusing on key GM taxa. (2) MethodsWe utilized GWAS summary statistics for 211 GM taxa and their association with 2,445 LS patients and 353,088 healthy controls, employing Mendelian randomization (MR). GWAS data for GM taxa came from the MiBioGen consortium, and for LS from the FinnGen consortium. The primary analytical tools included the inverse-variance weighted (IVW) method, weighted MR, simple mode, weighted median, and MR-Egger methods. Sensitivity analyses included leave-one-out analysis, MR-Egger intercept test, MR-PRESSO global test, and Cochranes Q-test. A reverse MR analysis was conducted on bacteria identified in the forward MR study. (3) ResultsWe identified one strong causal relationship: order Burkholderiales [odds ratio (OR) = 0.420, 95% confidence interval (CI): 0.230 - 0.765, p = 0.005], and three nominally significant relationships: phylum Cyanobacteria (OR = 0.585, 95% CI: 0.373 - 0.919, p = 0.020), class Betaproteobacteria (OR = 0.403, 95% CI: 0.189 - 0.857, p = 0.018), and genus Butyrivibrio (OR = 0.678, 95% CI: 0.507 - 0.907, p = 0.009). Moreover, this MR analysis was not impacted by horizontal pleiotropy, according to the MR-Egger intercept test and MR-PRESSO global test (p > 0.05). Remarkably, the reliability of our results was confirmed by leave-one-out analysis. Reverse MR analysis showed no significant causal relationship between LS and GM. (4) ConclusionsThis MR study identifies specific gut flora linked to a lower risk of LS, offering new insights for disease treatment and prevention. Future research should incorporate metagenomics sequencing of extensive microbiome GWAS datasets.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.