Identification of robust associations between admission microbiome profiles and complications of acute pancreatitis

Abstract

Objective Patients with acute pancreatitis show reduced gut microbiome diversity and high abundance of pathogenic bacteria compared with healthy subjects. Admission microbiome profiles are increasingly linked to severity, but methodology and study quality hamper interpretation. Our aim was to investigate whether admission microbiome analysis provides robust and reproducible associations with severity and complications of acute pancreatitis.Methods Patients with acute pancreatitis were prospectively enrolled from 20 Dutch hospitals (2019–2022). Admission saliva and rectal samples from 276 patients underwent 16S rDNA sequencing for microbiome profiling. Subgroups were defined based on a literature search. The microbiota endpoints (alpha- and beta-diversity, and genus abundance) were compared across subgroups and with previous studies. Robustness of the significant associations was classified as ‘moderate’ or ‘high’ in case of statistical significance in, respectively, 2 or ≥3 differential abundance models.Results Rectal alpha diversity (Shannon Index 3.55 vs 3.63, p=0.026) was decreased in necrotising (n=49) versus oedematous pancreatitis (n=218). Microbiota communities of either saliva or rectal samples differed in all the subgroups. In total, 270 (rectal) and 138 (saliva) genera were associated with severity or complications, of which 35 and 3 (Anaeroglobus and Finegoldia in saliva; Lachnospiraceae_FE2018_group in rectal) were classified as, respectively, moderately and highly robust. Fourteen associations were previously reported, of which 10 were in the opposite direction compared with this study.Conclusion Three admission microbiome taxa associated with severity and complications were highly robust, although their biological relevance remains unclear. This study also shows the lack of replicable findings of admission microbiome associations, highlighting the need for longitudinal studies to establish temporal relationships between microbiome changes and disease progression.