Preclinical Protein Signatures of Crohn's Disease and Ulcerative Colitis: A Nested Case-Control Study Within Large...

Biomarkers are needed to identify individuals at elevated risk of inflammatory bowel disease. This study aimed to identify protein signatures predictive of inflammatory bowel disease.

Using large population-based cohorts (n ≥180,000), blood samples were obtained from individuals who later in life were diagnosed with inflammatory bowel disease and compared with age and sex-matched controls, free from inflammatory bowel disease during follow-up. A total of 178 proteins were measured on Olink platforms. We used machine-learning methods to identify protein signatures of preclinical disease in the discovery cohort (n = 312). Their performance was validated in an external preclinical cohort (n = 222) and assessed in an inception cohort (n = 144) and a preclinical twin cohort (n = 102).

In the discovery cohort, a signature of 29 proteins differentiated preclinical Crohn's disease (CD) cases from controls, with an area under the curve (AUC) of 0.85. Its performance was confirmed in the preclinical validation (AUC = 0.87) and the inception cohort (AUC = 1.0). In preclinical samples, downregulated (but not upregulated) proteins related to gut barrier integrity and macrophage functionality correlated with time to diagnosis of CD. The preclinical ulcerative colitis signature had a significant, albeit lower, predictive ability in the discovery (AUC = 0.77), validation (AUC = 0.67), and inception cohorts (AUC = 0.95). The preclinical signature for CD demonstrated an AUC of 0.89 when comparing twins with preclinical CD with matched external healthy twins, but its predictive ability was lower (AUC = 0.58; P = .04) when comparing them with their healthy twin siblings, that is, when accounting for genetic and shared environmental factors.

We identified protein signatures for predicting a future diagnosis of CD and ulcerative colitis, validated across independent cohorts. In the context of CD, the signature offers potential for early prediction.