<b>Shared and distinctive microbial biomarkers enhance precision diagnostics of inflammatory bowel disease and its subtypes</b>

Inflammatory Bowel Disease (IBD) is a chronic inflammatory gut disease with major subtypes of Crohn's disease (CD) and ulcerative colitis (UC). They significantly differ in pathology but are similar in clinical manifestations, bringing considerable challenges for accurate non-invasive diagnosis. Recent research highlights the gut microbiome as a promising physiological indicator for diverse diseases, prompting us to investigate shared or distinctive microbial species insights into IBD and its subtypes. For this purpose, we initially established a standardized computational workflow to identify gut microbial biomarkers and their patterns for IBD diagnosis and subtype discrimination. In the process, 1,210 metagenomic samples were analyzed from 6 cohorts; among them, 81 shared species were identified (18 enriched, 63 depleted) across both CD and UC, whereas CD harbored 73 distinct species (37 enriched, 36 depleted) and UC had 32 distinct species (19 enriched, 13 depleted). Functional profiling illustrated shared and distinctive enriched MetaCyc pathways across the two subtypes, offering a comprehensive view of subtype-specific metabolic pathways. Finally, a two-stage machine learning framework was built for diagnosis and subtyping of IBD, making IBD identification in the first stage and subtype classification in the second stage. The framework was well-trained and achieved considerable accuracy, even leading to a novel discovery that shared microbes dominate IBD diagnosis, while distinct microbes drive subtype discrimination.

炎症性肠病（Inflammatory Bowel Disease，IBD）是一类慢性肠道炎症性疾病，主要亚型包括克罗恩病（Crohn's Disease，CD）与溃疡性结肠炎（Ulcerative Colitis，UC）。二者病理特征差异显著，但临床表现高度相似，给精准无创诊断带来了极大挑战。近期研究表明肠道菌群可作为多种疾病的潜在生理标志物，这促使我们探究与炎症性肠病及其亚型相关的肠道菌群共有与特有物种特征。基于此，本研究首先构建了一套标准化计算流程，用于筛选可用于炎症性肠病诊断与亚型区分的肠道微生物标志物及其特征模式。研究过程中，我们共分析了来自6个队列的1210份宏基因组样本；结果显示，克罗恩病与溃疡性结肠炎中共存在81种共有物种（其中18种富集、63种耗竭）；此外，克罗恩病特有73种独特物种（37种富集、36种耗竭），溃疡性结肠炎特有32种独特物种（19种富集、13种耗竭）。功能注释分析揭示了两种亚型间共有与特有的富集MetaCyc代谢通路，为亚型特异性代谢通路提供了全面的解析视角。最后，本研究构建了一套用于炎症性肠病诊断与亚型分型的两阶段机器学习框架：第一阶段完成炎症性肠病的识别，第二阶段完成亚型分类。该框架经过充分训练后取得了优异的诊断精度，甚至带来了一项全新发现：共有菌群主导炎症性肠病的整体诊断，而特有菌群则负责亚型的精准区分。

搜集汇总

背景与挑战

背景概述

该数据集基于1,210个宏基因组样本，识别了炎症性肠病（IBD）及其亚型（克罗恩病和溃疡性结肠炎）的共享和独特微生物生物标志物，包括物种丰度变化和代谢途径分析。研究构建了一个两阶段机器学习框架，实现了高精度诊断和亚型区分，发现共享微生物主导IBD诊断，而独特微生物驱动亚型分类。