Till Örebro universitet

BACKGROUND AND AIMS: Biomarkers are needed to identify individuals at elevated risk of inflammatory bowel disease (IBD). This study aims to identify protein signatures predictive of IBD.

METHODS: Using large population-based cohorts (n≥180,000), blood samples were obtained from individuals who later in life were diagnosed with IBD and compared to age and sex-matched controls, free from IBD during follow-up. 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).

RESULTS: 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 (UC) 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 to matched external healthy twins, but its predictive ability was lower (AUC=0.58; P=.04) when comparing them with their healthy twin siblings, i.e., when accounting for genetic and shared environmental factors.

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