Singh Arshdeep, Juyal Garima, Gacesa Ranko, Joshi Mohan C, Midha Vandana, Thelma B K, Weersma Rinse K, Sood Ajit
Department of Gastroenterology, Dayanand Medical College and Hospital, Ludhiana, India.
Department of Biotechnology, School of Engineering and Applied Sciences, Bennett University, Greater Noida, India.
Intest Res. 2025 Jul 14. doi: 10.5217/ir.2024.00216.
BACKGROUND/AIMS: Inflammatory bowel disease (IBD) has become a global health concern. With the growing evidence of the gut microbiota's role in IBD, studying microbial compositions across ethnic cohorts is essential to identify unique, populationspecific microbial signatures.
We analyzed stool samples and clinical data from 254 IBD patients (226 ulcerative colitis, 28 Crohn's disease) and 66 controls in northern India using metagenomic shotgun sequencing to assess microbiota diversity, composition, and function. Results were replicated in 436 IBD patients and 903 controls from the Netherlands using identical workflows. Using machine learning, we evaluated the generalizability of Indian IBD signals to the Dutch cohort, and vice versa.
Indian IBD patients exhibited reduced bacterial diversity and an abundance of opportunistic pathogens, including Clostridium, Streptococcus, and oral bacteria like Streptococcus oralis and Bifidobacterium dentium. There was a significant loss of energy metabolic pathways and distinct co-occurrence patterns among microbial species. Notably, 39% of these signals replicated in the Dutch cohort. Unique to the Indian cohort were oral pathobionts such as Scardovia, Oribacterium, Actinomyces dentalis, and Klebsiella pneumoniae. Both Indian and Dutch IBD patients shared reduced butyrate producers. Machine-learning diagnostic models trained on the Indian cohort achieved high predictive accuracy (sensitivity 0.84, specificity 0.95) and moderately generalized to the Dutch cohort (sensitivity 0.77, specificity 0.69).
IBD patients across populations exhibit shared and unique microbial signatures, suggesting a role for the oral-gut microbiome axis in IBD. Crossethnic diagnostic models show promise for broader applications in identifying IBD.
背景/目的:炎症性肠病(IBD)已成为全球关注的健康问题。随着越来越多证据表明肠道微生物群在IBD中发挥作用,研究不同种族队列中的微生物组成对于识别独特的、特定人群的微生物特征至关重要。
我们使用宏基因组鸟枪法测序分析了印度北部254例IBD患者(226例溃疡性结肠炎,28例克罗恩病)和66例对照的粪便样本及临床数据,以评估微生物群的多样性、组成和功能。使用相同的工作流程在来自荷兰的436例IBD患者和903例对照中重复了结果。我们使用机器学习评估了印度IBD信号对荷兰队列的可推广性,反之亦然。
印度IBD患者的细菌多样性降低,机会性致病菌大量存在,包括梭菌属、链球菌属以及口腔细菌如口腔链球菌和龋齿双歧杆菌。能量代谢途径显著丧失,微生物物种之间存在明显的共现模式。值得注意的是,这些信号中有39%在荷兰队列中得到重复。印度队列特有的是口腔致病共生菌,如斯卡多维亚菌属、口腔杆菌属、齿放线菌和肺炎克雷伯菌。印度和荷兰的IBD患者都有丁酸产生菌减少的情况。在印度队列上训练的机器学习诊断模型具有较高的预测准确性(敏感性0.84,特异性0.95),并适度推广到荷兰队列(敏感性0.77,特异性0.69)。
不同人群的IBD患者表现出共同和独特的微生物特征,表明口腔-肠道微生物群轴在IBD中发挥作用。跨种族诊断模型在识别IBD方面具有更广泛应用的潜力。