The gut microbiome in microscopic polyangiitis with kidney involvement: common and unique alterations, clinical association and values for disease diagnosis and outcome prediction

Background: Microscopic polyangiitis (MPA) is an autoimmune disease characterized by frequent kidney involvement. Imbalance of intestinal flora has been found implicated in multiple immune-mediated disorders. However, the profiling and the role of the gut microbiome in MPA remains unclear. Methods: We performed 16S rRNA amplicon sequencing on fecal samples from 71 MPA patients with kidney involvement (35 at incipient active stage, 36 at remissive stage) and 34 healthy controls (HCs). Microbial diversity and abundance were compared among the three cohorts. The correlation between altered microbes and clinical indices were investigated. Two random forest models based on the profiling of the gut microbiome were constructed for the diagnosis of MPA. Results: Two alpha-diversity indices, including Simpson and Shannon index, were decreased in MPA patients (P<0.001), especially in those with active disease (P=0.001). beta-diversity analysis showed biased microbial composition among the three groups. Genus Actinomyces and Streptococcus were more abundant in both MPA cohorts than those in HCs, while genus Subdoligranulum, Eubacterium hallii, Ruminococcaceae UCG013, Eubacterium ventriosum, Dorea and Butyricicoccus were more abundant in HCs than those in both MPA cohorts. All the 6 genera with decreased abundance belong to short-chain fatty acids (SCFA)-producing taxons. Besides, 1 and 2 operational taxonomic units (OTUs) were enriched in patients with active MPA who needed dialysis at sampling and in patients who progressed to end-stage renal disease during follow up, respectively. Furthermore, the model for diagnosis of MPA incorporated 6 OTU markers and achieved an AUC of 93.45% (95% CI, 88.15-98.74%). Similarly, the model for predicting disease activity incorporated 11 OTU markers and achieved an AUC of 90.71% (95% CI, 82.49-98.94%). Conclusions: Alteration of intestinal flora existed in MPA patients with kidney involvement and was characterized by increased abundance of genus Actinomyces and Streptococcus and decreased abundance of 6 SCFA-producing genera. Gut microbial profiling combined with machining-learning methods showed potentials for diagnosing MPA and predicting disease activity.

Automated summary

Microbial profiling in MPA patients with kidney involvement revealed alterations in gut microbiota, characterized by increased abundance of certain genera and decreased abundance of short-chain fatty acid-producing genera. Machine-learning methods combined with gut microbiome analysis showed potential for diagnosing MPA and predicting disease activity.