ArticleNature communications2025
Plasma proteomic profiles identify biomarkers predicting Crohn's disease up to 16 years before onset.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Plasma proteomics framework predicts metabolic dysfunction-associated steatotic liver disease up to 16 years before onset.Nature aging · 2026Article
- Mesenteric adipose tissue: from protective gatekeeper to driver of inflammatory bowel disease.Immunometabolism (Cobham, Surrey) · 2026Review
- Recent Advances in Artificial Intelligence for Endoscopic and Multimodal Assessment of Inflammatory Bowel Disease: A Review.International journal of general medicine · 2026Review
- The role of mucosal immune dysregulation in the pathogenesis of immunoglobulin A nephropathy.Frontiers in immunology · 2026Review
- Plasma proteomic profiles identify biomarkers predicting Crohn's disease up to 16 years before onset.Nature communications · 2025Article
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Authors and funding
19 authors.
Funding
Abstract
Reliable tools for early identification of Crohn's disease (CD) remain lacking. We analyzed 2736 plasma proteins in 39,634 UK Biobank (UKB) participants and identified 44 associated with incident CD. CD274, CHI3L1, REG1B, ITGAV, PRSS8, ITGA11, GDF15, DEFA1_DEFA1B, and IL6 ranked highest in protein importance ordering. A machine learning model based on these 9 proteins achieved high prediction for CD in a geographically distinct UKB testing cohort (n = 13,262, AUC 0.76), outperforming clinical risk models. It was externally validated in EPIC-Norfolk (n = 2944, AUC 0.73) and exhibited high discriminatory capacity for CD in the cross-sectional Southern China cohort (n = 74, AUC 0.79). In the UKB testing cohort, combining proteins with clinical data improved predictive performance (AUC 0.78) up to 16 years pre-diagnosis. In the same cohort, individuals at high risk stratified by the protein model were 4.23 times more likely to develop CD. Our findings highlight proteomics-based models as a promising approach to predict CD up to 16 years before diagnosis, offering opportunities for early screening and intervention.
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