Olle Grännö, Daniel Bergemalm, Benita Salomon, Carl Mårten Lindqvist, Charlotte R. H. Hedin, Marie Carlson, Katharina Dannenberg, Erik Andersson, Åsa V. Keita, Maria K. Magnusson, Carl Eriksson, Vivekananda Lanka, BIOIBD consortium, Patrik KE Magnusson, Mauro D’Amato, Lena Öhman, Johan D. Söderholm, Johan Hultdin, Robert Kruse, Yang Cao, Dirk Repsilber, Olof Grip, Pontus Karling, Jonas Halfvarson

Affiliates

Olle Grännö[1], Daniel Bergemalm[2], Benita Salomon[3], Carl Mårten Lindqvist[3], Charlotte R. H. Hedin[4-5], Marie Carlson[6], Katharina Dannenberg[2], Erik Andersson[2], Åsa V. Keita[7], Maria K. Magnusson[8], Carl Eriksson[2,9], Vivekananda Lanka[10], BIOIBD consortium, Patrik KE Magnusson[10], Mauro D’Amato[11-13], Lena Öhman[8], Johan D. Söderholm[7,14], Johan Hultdin[15], Robert Kruse[16], Yang Cao[17,18], Dirk Repsilber[3], Olof Grip[19], Pontus Karling[20], Jonas Halfvarson[2] Affiliations: [1], Department of Laboratory Medicine, Clinical Microbiology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden [2], Department of Gastroenterology, Faculty of Medicine and Health, Örebro University, Örebro, Sweden [3], Faculty of Medicine and Health, School of Medical Sciences, Örebro University, Örebro, Sweden [4], Gastroenterology Unit, Department of Gastroenterology, Dermatovenereology and Rheumatology, Karolinska University Hospital, [Stockholm, Sweden [5], Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden [6], Department of Medical Sciences, Gastroenterology Research Group, Uppsala University, Uppsala, Sweden [7], Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden [8], Department of Microbiology and Immunology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden [9], Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden [10], Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden [11], Department of Medicine and Surgery, LUM University, Casamassima, Italy [12], Gastrointestinal Genetics Lab, CIC BioGUNE–BRTA, Derio, Spain [13], Ikerbasque, Basque Foundation for Science, Bilbao, Spain [14], Department of Surgery, Linköping University, Linköping, Sweden [15], Department of Medical Biosciences, Clinical Chemistry, Umeå University, Umeå, Sweden [16], Department of Clinical Research Laboratory, Faculty of Medicine and Health, Örebro University, Örebro, Sweden [17], Clinical Epidemiology and Biostatistics, School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden [18], Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden [19], Department of Gastroenterology, Skåne University Hospital, Malmö, Sweden [20], Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden

Abstract

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 (Figure 1A-C). Its performance was confirmed in the preclinical validation (AUC=0.87) and in patients with newly diagnosed CD in 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 (Figure 2A-C). 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; Figure 1D-F).
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=0.04) 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.