Cure8 news brief
The study suggests a decade-long preclinical phase of IBD detectable by blood metabolites, which could open opportunities for earlier identification of people at risk for Crohn’s disease and for studying early disease mechanisms.
Researchers in IBD biomarkers and pathophysiology, clinicians interested in early detection and prevention, and patients and caregivers following advances in Crohn’s disease risk prediction.
The study used untargeted metabolomic profiling on pre-diagnostic serum from the Danish PREDICT cohort (169 IBD cases and 169 matched controls) and found >1,000 altered metabolites, including many eicosanoids, fatty acids, and glycerophosphocholines.
Machine-learning models trained on these metabolites had an average AUC of 0.78 for predicting future Crohn’s disease in the analyzed samples.
The findings point to a long preclinical phase with metabolic signs of mucosal breakdown and systemic subclinical inflammation, but the authors and the article emphasize that these biomarkers need further validation in larger and more diverse cohorts before they could be used for screening or clinical decision-making.
Results derive from a Danish registry-linked biobank cohort with untargeted metabolomics and machine-learning models; the article and authors note the need for replication in larger, diverse populations before clinical application.
Review the original publication for the complete reporting, methods, and context.
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