Cancer Research Center, Sheba Medical Center, Tel-Hashomer, Israel.
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Am J Gastroenterol. 2019 Jul;114(7):1142-1151. doi: 10.14309/ajg.0000000000000136.
Crohn's disease (CD) is a chronic relapsing-remitting gut inflammatory disorder with a heterogeneous unpredictable course. Dysbiosis occurs in CD; however, whether microbial dynamics in quiescent CD are instrumental in increasing the risk of a subsequent flare remains undefined.
We analyzed the long-term dynamics of microbial composition in a prospective observational cohort of patients with quiescent CD (45 cases, 217 samples) over 2 years or until clinical flare occurred, aiming to identify whether changes in the microbiome precede and predict clinical relapse. Machine learning was used to prioritize microbial and clinical factors that discriminate between relapsers and nonrelapsers in the quiescent phase.
Patients with CD in clinical, biomarker, and mucosal remission showed significantly reduced microbial richness and increased dysbiosis index compared with healthy controls. Of the 45 patients with quiescent CD, 12 (27%) flared during follow-up. Samples in quiescent patients preceding flare showed significantly reduced abundance of Christensenellaceae and S24.7, and increased abundance of Gemellaceae compared with those in remission throughout. A composite flare index was associated with a subsequent flare. Notably, higher individualized microbial instability in the quiescent phase was associated with a higher risk of a subsequent flare (hazard ratio 11.32, 95% confidence interval 3-42, P = 0.0035) using two preflare samples. Importantly, machine learning prioritized the flare index and the intrapersonal instability over clinical factors to best discriminate between relapsers and nonrelapsers.
Individualized microbial variations in quiescent CD significantly increase the risk of future exacerbation and may provide a model to guide personalized preemptive therapy intensification.
克罗恩病(CD)是一种慢性复发缓解的肠道炎症性疾病,具有异质且不可预测的病程。CD 患者存在肠道菌群失调;然而,静止期 CD 中微生物动力学是否有助于增加随后发作的风险尚不清楚。
我们对 45 例静止期 CD 患者(45 例,217 个样本)进行了前瞻性观察队列的长期微生物组成分析,时间超过 2 年或直至临床发作,旨在确定微生物组的变化是否先于并预测临床复发。使用机器学习来确定在静止期区分缓解者和非缓解者的微生物和临床因素的优先级。
与健康对照相比,处于临床、生物标志物和黏膜缓解期的 CD 患者的微生物丰富度明显降低,且肠道菌群失调指数增加。在 45 例静止期 CD 患者中,12 例(27%)在随访期间发作。与整个缓解期相比,在发作前的静止期患者样本中,Christensenellaceae 和 S24.7 的丰度明显降低,Gemellaceae 的丰度增加。复合发作指数与随后的发作相关。值得注意的是,静止期个体微生物不稳定性增加与随后的发作风险增加相关(危险比 11.32,95%置信区间 3-42,P = 0.0035),使用两个预发作样本。重要的是,机器学习优先考虑发作指数和个体内不稳定性,而不是临床因素,以最佳地区分缓解者和非缓解者。
静止期 CD 中个体微生物的变化显著增加了未来恶化的风险,并可能为指导个体化预防治疗强化提供模型。