Department of New Drug Development, Graduate School of New Drug Discovery and Development, Chungnam National University, Daejeon, Korea.
Disease Target Structure Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Korea.
BMC Microbiol. 2023 Nov 11;23(1):336. doi: 10.1186/s12866-023-03084-5.
BACKGROUND: Inflammatory bowel disease (IBD) is a multifactorial chronic inflammatory disease resulting from dysregulation of the mucosal immune response and gut microbiota. Crohn's disease (CD) and ulcerative colitis (UC) are difficult to distinguish, and differential diagnosis is essential for establishing a long-term treatment plan for patients. Furthermore, the abundance of mucosal bacteria is associated with the severity of the disease. This study aimed to differentiate and diagnose these two diseases using the microbiome and identify specific biomarkers associated with disease activity. RESULTS: Differences in the abundance and composition of the microbiome between IBD patients and healthy controls (HC) were observed. Compared to HC, the diversity of the gut microbiome in patients with IBD decreased; the diversity of the gut microbiome in patients with CD was significantly lower. Sixty-eight microbiota members (28 for CD and 40 for UC) associated with these diseases were identified. Additionally, as the disease progressed through different stages, the diversity of the bacteria decreased. The abundances of Alistipes shahii and Pseudodesulfovibrio aespoeensis were negatively correlated with the severity of CD, whereas the abundance of Polynucleobacter wianus was positively correlated. The severity of UC was negatively correlated with the abundance of A. shahii, Porphyromonas asaccharolytica and Akkermansia muciniphilla, while it was positively correlated with the abundance of Pantoea candidatus pantoea carbekii. A regularized logistic regression model was used for the differential diagnosis of the two diseases. The area under the curve (AUC) was used to examine the performance of the model. The model discriminated UC and CD at an AUC of 0.873 (train set), 0.778 (test set), and 0.633 (validation set) and an area under the precision-recall curve (PRAUC) of 0.888 (train set), 0.806 (test set), and 0.474 (validation set). CONCLUSIONS: Based on fecal whole-metagenome shotgun (WMS) sequencing, CD and UC were diagnosed using a machine-learning predictive model. Microbiome biomarkers associated with disease activity (UC and CD) are also proposed.
背景：炎症性肠病（IBD）是一种多因素的慢性炎症性疾病，源于黏膜免疫反应和肠道微生物群的失调。克罗恩病（CD）和溃疡性结肠炎（UC）难以区分，对患者进行鉴别诊断对于建立长期治疗方案至关重要。此外，黏膜细菌的丰度与疾病的严重程度相关。本研究旨在利用微生物组对这两种疾病进行区分和诊断，并确定与疾病活动相关的特定生物标志物。
结果：观察到 IBD 患者和健康对照（HC）之间微生物组的丰度和组成存在差异。与 HC 相比，IBD 患者的肠道微生物组多样性降低；CD 患者的肠道微生物组多样性显著降低。鉴定出与这些疾病相关的 68 种微生物群成员（28 种用于 CD，40 种用于 UC）。此外，随着疾病进展到不同阶段，细菌的多样性减少。Alistipes shahii 和 Pseudodesulfovibrio aespoeensis 的丰度与 CD 的严重程度呈负相关，而 Polynucleobacter wianus 的丰度与 CD 的严重程度呈正相关。UC 的严重程度与 A. shahii、Porphyromonas asaccharolytica 和 Akkermansia muciniphilla 的丰度呈负相关，而与 Pantoea candidatus pantoea carbekii 的丰度呈正相关。使用正则化逻辑回归模型对两种疾病进行鉴别诊断。使用曲线下面积（AUC）评估模型的性能。该模型在 AUC 为 0.873（训练集）、0.778（测试集）和 0.633（验证集）时能够区分 UC 和 CD，在精度-召回曲线下面积（PRAUC）为 0.888（训练集）、0.806（测试集）和 0.474（验证集）时能够区分 UC 和 CD。
结论：基于粪便全基因组鸟枪法（WMS）测序，使用机器学习预测模型对 CD 和 UC 进行诊断。还提出了与疾病活动相关的微生物组生物标志物（UC 和 CD）。
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