Wang Qi-Wen, Zheng Haorui, Yang Yang, Chang Xinyao, Du Zengkan, Hang Zi-Ning, Li Zhao-Shen, Liao Zhuan
Department of Gastroenterology, Shanghai Institute of Pancreatic Diseases, Changhai Hospital, Naval Medical University, Shanghai, China.
Front Immunol. 2025 Feb 28;16:1558983. doi: 10.3389/fimmu.2025.1558983. eCollection 2025.
Recurrent acute pancreatitis (RAP) poses significant clinical challenges, with 32.3% developing to chronic pancreatitis within 5 years. The underlying microbial factors contributing to RAP remain poorly understood. This study aims to identify blood microbial signatures associated with RAP and explore the potential microbial predictors for RAP.
In this prospective cohort, 90 acute pancreatitis patients are classified into non-recurrent acute pancreatitis (NRAP, n=68) and RAP (n=22) groups based on the number of pancreatitis episodes. Microbial composition of blood samples is analyzed using 5-region (5R) 16S rRNA gene sequencing. Key microbial taxa and functional predictions are made. A random forest model is used to assess the predictive value of microbial features for RAP. The impact of on RAP is further evaluated in an experimental mouse model.
Linear discriminant analysis effect size (LEfSe) analysis highlights significant microbial differences, with , and being prominent in RAP. Functional predictions indicate enrichment of metabolic pathways in the RAP group. Random forest analysis identifies key microbial taxa with an AUC value of 0.759 for predicting RAP. Experimental validation shows that exacerbates pancreatic inflammation in mice.
This study identifies distinct clinical and microbial features associated with RAP, emphasizing the role of specific bacterial taxa in pancreatitis recurrence. The findings suggest that microbial profiling could enhance the diagnosis and management of RAP, paving the way for personalized therapeutic approaches.
复发性急性胰腺炎（RAP）带来了重大的临床挑战，32.3%的患者会在5年内发展为慢性胰腺炎。导致RAP的潜在微生物因素仍知之甚少。本研究旨在识别与RAP相关的血液微生物特征，并探索RAP的潜在微生物预测指标。
在这个前瞻性队列中，90例急性胰腺炎患者根据胰腺炎发作次数分为非复发性急性胰腺炎（NRAP，n = 68）和RAP（n = 22）组。使用5区域（5R）16S rRNA基因测序分析血样的微生物组成。进行关键微生物分类群和功能预测。使用随机森林模型评估微生物特征对RAP的预测价值。在实验小鼠模型中进一步评估其对RAP的影响。
线性判别分析效应大小（LEfSe）分析突出了显著的微生物差异，[具体微生物名称1]、[具体微生物名称2]和[具体微生物名称3]在RAP中较为突出。功能预测表明RAP组中代谢途径富集。随机森林分析确定了预测RAP的关键微生物分类群，AUC值为0.759。实验验证表明[具体因素]会加重小鼠的胰腺炎症。
本研究确定了与RAP相关的独特临床和微生物特征，强调了特定细菌分类群在胰腺炎复发中的作用。研究结果表明，微生物谱分析可以加强RAP的诊断和管理，为个性化治疗方法铺平道路。
