Hansen Simen Hyll, Maseng Maria Gjerstad, Grännö Olle, Vestergaard Marie V, Bang Corinna, Olsen Bjørn C, Lund Charlotte, Olbjørn Christine, Løvlund Emma E, Vikskjold Florin B, Huppertz-Hauss Gert, Perminow Gøri, Yassin Hussain, Valeur Jørgen, Aass Holten Kristina I, Henriksen Magne, Bengtson May-Bente, Ricanek Petr, Opheim Randi, Boyar Raziye, Torp Roald, Frigstad Svein O, Aabrekk Tone Bergene, Detlie Trond Espen, Kristensen Vendel A, Strande Vibeke, Hovde Øistein, Asak Øyvind, Jess Tine, Franke Andre, Halfvarsson Jonas, Høivik Marte L, Hov Johannes R
Norwegian PSC Research Center, Department of Transplantation Medicine, Division of Surgery, Inflammatory Diseases and Transplantation, Oslo University Hospital, Oslo, Norway.
Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
Inflamm Bowel Dis. 2025 Apr 25. doi: 10.1093/ibd/izaf060.
We aimed to determine the diagnostic and prognostic potential of baseline microbiome profiling in inflammatory bowel disease (IBD).
Participants with ulcerative colitis (UC), Crohn's disease (CD), suspected IBD, and non-IBD symptomatic controls were included in the prospective population-based cohort Inflammatory Bowel Disease in South-Eastern Norway III (third iteration) based on suspicion of IBD. The participants donated fecal samples that were analyzed with 16S rRNA sequencing. Disease course severity was evaluated at the 1-year follow-up. A stringent statistical consensus approach for differential abundance analysis with 3 different tools was applied, together with machine learning modeling.
A total of 1404 individuals were included, where n = 1229 samples from adults were used in the main analyses (n = 658 UC, n = 324 CD, n = 36 IBD-U, n = 67 suspected IBD, and n = 144 non-IBD symptomatic controls). Microbiome profiles were compared with biochemical markers in machine learning models to differentiate IBD from non-IBD symptomatic controls (area under the receiver operating curve [AUC] 0.75-0.79). For UC vs controls, integrating microbiome data with biochemical markers like fecal calprotectin mildly improved classification (AUC 0.83 to 0.86, P < .0001). Extensive differences in microbiome composition between UC and CD were identified, which could be quantified as an index of differentially abundant genera. This index was validated across published datasets from 3 continents. The UC-CD index discriminated between ileal and colonic CD (linear regression, P = .008) and between colonic CD and UC (P = .005), suggesting a location-dependent gradient. Microbiome profiles outperformed biochemical markers in predicting a severe disease course in UC (AUC 0.72 vs 0.65, P < .0001), even in those with a mild disease at baseline (AUC 0.66 vs 0.59, P < .0001).
Fecal microbiome profiling at baseline held limited potential to diagnose IBD from non-IBD compared with standard-of-care. However, microbiome shows promise for predicting future disease courses in UC.
我们旨在确定炎症性肠病(IBD)基线微生物组分析的诊断和预后潜力。
基于IBD的怀疑,将溃疡性结肠炎(UC)、克罗恩病(CD)、疑似IBD以及非IBD症状对照的参与者纳入挪威东南部炎症性肠病III(第三次迭代)这一基于人群的前瞻性队列研究。参与者捐赠粪便样本,通过16S rRNA测序进行分析。在1年随访时评估疾病进程严重程度。应用一种严格的统计共识方法,使用3种不同工具进行差异丰度分析,并结合机器学习建模。
总共纳入了1404名个体,其中主要分析使用了来自成人的1229份样本(n = 658例UC、n = 324例CD、n = 36例未定型IBD、n = 67例疑似IBD以及n = 144例非IBD症状对照)。在机器学习模型中将微生物组分析结果与生化标志物进行比较,以区分IBD与非IBD症状对照(受试者操作特征曲线下面积[AUC]为0.75 - 0.79)。对于UC与对照,将微生物组数据与粪便钙卫蛋白等生化标志物相结合,分类略有改善(AUC为0.83至0.86,P <.0001)。确定了UC和CD之间微生物组组成的广泛差异,可将其量化为差异丰富属的指数。该指数在来自三大洲的已发表数据集中得到验证。UC - CD指数可区分回肠和结肠CD(线性回归,P =.008)以及结肠CD和UC(P =.005),表明存在位置依赖性梯度。在预测UC的严重疾病进程方面,微生物组分析结果优于生化标志物(AUC分别为0.72和0.65,P <.0001),即使是基线时疾病较轻的患者(AUC分别为0.66和000.59,P <.0001)。
与标准治疗相比,基线粪便微生物组分析在从非IBD中诊断IBD方面潜力有限。然而,微生物组在预测UC未来疾病进程方面显示出前景。