Longitudinal Faecal Calprotectin Profiles Characterise Disease Course Heterogeneity in Crohn's Disease
Longitudinal Faecal Calprotectin Profiles Characterise Disease Course Heterogeneity in Crohn's Disease
复制标题
纵向粪便钙卫蛋白谱表征克罗恩病的病程异质性
DOI:
10.1101/2022.08.16.22278320
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发表时间:
2022
期刊:
影响因子:
--
通讯作者:
Constantine-Cooke N
中科院分区:
文献类型:
--
作者:
Constantine-Cooke N
BackgroundHigh faecal calprotectin is associated with poor outcomes in Crohn’s disease. Monitoring of faecal calprotectin trajectories could characterise disease progression before severe complications occur.AimsWe undertook an unbiased assessment of a retrospective incident Crohn’s disease cohort to assess for inter-individual variability in faecal calprotectin levels over time. We aimed to explore whether latent classes of such profiles are associated with a composite endpoint consisting of surgery, hospitalisation, or Montreal behaviour progression and other clinical information.MethodsLatent class mixed models were used to model faecal calprotectin trajectories within five years of diagnosis. Akaike information criterion, Bayesian information criterion, alluvial plots, and class-specific trajectories were used to decide the optimal number of classes. Log-rank tests of Kaplan-Meier estimators were used to test for associations between class membership and outcomes.ResultsOur study cohort comprised 365 subjects and 2856 faecal calprotectin measurements (median 7 per subject). Four latent classes were found and broadly described as a class with consistently high faecal calprotectin and three classes characterised by downward trends for calprotectin. Class membership was significantly associated with the composite endpoint, and separately, hospitalisation and Montreal disease progression, but not surgery. Early biologic therapy was strongly associated with class membership.ConclusionsOur analysis provides a novel stratification approach for Crohn’s disease patients based on faecal calprotectin trajectories. Characterising this heterogeneity helps to better understand different patterns of disease progression and to identify those with a higher risk of worse outcomes. Ultimately, this information will assist the design of more targeted interventions.