Sarrabayrouse G, Elias A, Yáñez F, Mayorga L, Varela E, Bartoli C, Casellas F, Borruel N, Herrera de Guise C, Machiels K, Vermeire S, Manichanh C
Department of Gastroenterology, Vall d'Hebron Research Institute, Barcelona, Spain.
CIBERehd, Instituto de Salud Carlos III, Madrid, Spain.
mSystems. 2021 Mar 23;6(2):e01277-20. doi: 10.1128/mSystems.01277-20.
Microbiome sequence data have been used to characterize Crohn's disease (CD) and ulcerative colitis (UC). Based on these data, we have previously identified microbiomarkers at the genus level to predict CD and CD relapse. However, microbial load was underexplored as a potential biomarker in inflammatory bowel disease (IBD). Here, we sought to study the use of fungal and bacterial loads as biomarkers to detect both CD and UC and CD and UC relapse. We analyzed the fecal fungal and bacterial loads of 294 stool samples obtained from 206 participants using real-time PCR amplification of the ITS2 region and the 16S rRNA gene, respectively. We combined the microbial data with demographic and standard laboratory data to diagnose ileal or ileocolonic CD and UC and predict disease relapse using the random forest algorithm. Fungal and bacterial loads were significantly different between healthy relatives of IBD patients and nonrelated healthy controls, between CD and UC patients in endoscopic remission, and between UC patients in relapse and non-UC individuals. Microbial load data combined with demographic and standard laboratory data improved the performance of the random forest models by 18%, reaching an average area under the receiver operating characteristic curve (AUC) of 0.842 (95% confidence interval [CI], 0.65 to 0.98), for IBD diagnosis and enhanced CD and UC discrimination and CD and UC relapse prediction. Our findings show that fecal fungal and bacterial loads could provide physicians with a noninvasive tool to discriminate disease subtypes or to predict disease flare in the clinical setting. Next-generation sequence data analysis has allowed a better understanding of the pathophysiology of IBD, relating microbiome composition and functions to the disease. Microbiome composition profiling may provide efficient diagnosis and prognosis tools in IBD. However, the bacterial and fungal loads of the fecal microbiota are underexplored as potential biomarkers of IBD. Ulcerative colitis (UC) patients have higher fecal fungal and bacterial loads than patients with ileal or ileocolonic CD. CD patients who relapsed harbor more-unstable fungal and bacterial loads than those of relapsed UC patients. Fecal fungal and bacterial load data improved prediction performance by 18% for IBD diagnosis based solely on clinical data and enhanced CD and UC discrimination and prediction of CD and UC relapse. Combined with existing laboratory biomarkers such as fecal calprotectin and C-reactive protein (CRP), microbial loads may improve the diagnostic accuracy of IBD and of ileal CD and UC disease activity and prediction of UC and ileal CD clinical relapse.
微生物组序列数据已被用于表征克罗恩病(CD)和溃疡性结肠炎(UC)。基于这些数据,我们之前已在属水平鉴定出微生物标志物以预测CD及CD复发。然而,微生物负荷作为炎症性肠病(IBD)的一种潜在生物标志物尚未得到充分研究。在此,我们试图研究将真菌和细菌负荷用作生物标志物来检测CD和UC以及CD和UC复发的情况。我们分别使用ITS2区域和16S rRNA基因的实时PCR扩增,分析了从206名参与者获得的294份粪便样本的粪便真菌和细菌负荷。我们将微生物数据与人口统计学和标准实验室数据相结合,以诊断回肠或回结肠CD和UC,并使用随机森林算法预测疾病复发。IBD患者的健康亲属与无亲缘关系的健康对照之间、内镜缓解期的CD和UC患者之间以及复发的UC患者与非UC个体之间的真菌和细菌负荷存在显著差异。微生物负荷数据与人口统计学和标准实验室数据相结合,使随机森林模型的性能提高了18%,IBD诊断的受试者操作特征曲线(AUC)平均达到0.842(95%置信区间[CI],0.65至0.98),并增强了CD和UC的鉴别以及CD和UC复发预测。我们的研究结果表明,粪便真菌和细菌负荷可为医生提供一种非侵入性工具,用于在临床环境中鉴别疾病亚型或预测疾病发作。新一代序列数据分析有助于更好地理解IBD的病理生理学,将微生物组组成和功能与疾病联系起来。微生物组组成分析可能为IBD提供有效的诊断和预后工具。然而,粪便微生物群的细菌和真菌负荷作为IBD的潜在生物标志物尚未得到充分研究。溃疡性结肠炎(UC)患者的粪便真菌和细菌负荷高于回肠或回结肠CD患者。复发的CD患者比复发的UC患者拥有更不稳定的真菌和细菌负荷。粪便真菌和细菌负荷数据使仅基于临床数据的IBD诊断预测性能提高了18%,并增强了CD和UC的鉴别以及CD和UC复发预测。与粪便钙卫蛋白和C反应蛋白(CRP)等现有实验室生物标志物相结合,微生物负荷可能提高IBD以及回肠CD和UC疾病活动度的诊断准确性,并预测UC和回肠CD的临床复发。