Simen Hyll Hansen, Maria Gjerstad Maseng, Olle Grännö, Marie V Vestergaard, Corinna Bang, Bjørn C Olsen, Charlotte Lund, Christine Olbjørn, Emma E Løvlund, Florin B Vikskjold, Gert Huppertz-Hauss, Gøri Perminow, Hussain Yassin, Jørgen Valeur, Kristina I Aass Holten, Magne Henriksen, May-Bente Bengtson, Petr Ricanek, Randi Opheim, Raziye Boyar, Roald Torp, Svein O Frigstad, Tone Bergene Aabrekk, Trond Espen Detlie, Vendel A Kristensen, Vibeke Strande, Øistein Hovde, Øyvind Asak, Tine Jess, Andre Franke, Jonas Halfvarsson, Marte L Høivik, Johannes R Hov
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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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>Fecal microbiome profiling at baseline held limited potential to diagnose IBD from non-IBD compared with standard-of-care. 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引用次数: 0
摘要
我们的目的是确定炎症性肠病(IBD)的基线微生物组分析的诊断和预后潜力。方法:溃疡性结肠炎(UC)、克罗恩病(CD)、疑似IBD和非IBD症状对照的参与者被纳入挪威东南部基于怀疑IBD的炎症性肠病前瞻性人群队列III(第三次迭代)。参与者捐献的粪便样本用16S rRNA测序进行了分析。1年随访时评估病程严重程度。采用严格的统计共识方法,采用3种不同的工具进行差异丰度分析,并结合机器学习建模。结果:共纳入1404例个体,其中n = 1229例成人样本用于主要分析(n = 658例UC, n = 324例CD, n = 36例IBD- u, n = 67例疑似IBD, n = 144例非IBD症状对照)。将微生物组谱与机器学习模型中的生化标记物进行比较,以区分IBD和非IBD症状对照(受试者工作曲线下面积[AUC] 0.75-0.79)。对于UC与对照组,将微生物组数据与粪便钙保护蛋白等生化标志物相结合,轻度改善了分类(AUC为0.83至0.86)。结论:与标准治疗相比,粪便微生物组基线分析在诊断IBD和非IBD方面的潜力有限。然而,微生物组显示了预测UC未来疾病进程的希望。
Fecal Microbiome Reflects Disease State and Prognosis in Inflammatory Bowel Disease in an Adult Population-Based Inception Cohort.
Introduction: We aimed to determine the diagnostic and prognostic potential of baseline microbiome profiling in inflammatory bowel disease (IBD).
Methods: 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.
Results: 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).
Conclusions: 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.
期刊介绍:
Inflammatory Bowel Diseases® supports the mission of the Crohn''s & Colitis Foundation by bringing the most impactful and cutting edge clinical topics and research findings related to inflammatory bowel diseases to clinicians and researchers working in IBD and related fields. The Journal is committed to publishing on innovative topics that influence the future of clinical care, treatment, and research.