Abstract
BACKGROUND AND AIMS: The pathophysiology of inflammatory bowel disease (IBD) including ulcerative colitis (UC) and Crohn's disease (CD) remains unclear. While IBD is heterogeneous, most molecular-targeted drugs (MTDs) are effective for both UC and CD. The immunological pathoetiology can be considered to overlap regardless of clinical manifestations. Classifying IBD based on its immune profile could contribute to understanding its pathophysiology and predict the efficacy of therapy in individual cases. Machine learning has the advantage of being able to analyze complex data and could provide insights into the subcategorization of IBD using its immune profile. METHODS: The study used 20 cytokines and chemokines in serum samples from 69 patients with active UC (n = 51) or CD (n = 18) who were MTD-naïve before starting induction therapy. Multidimensional immune profiles considering the balance of items were used for machine learning to classify samples. The clinical outcome was the steroid-free clinical remission rate at 6 months in the patients treated with an MTD (n = 59). RESULTS: Levels of 13 cytokines and chemokines were analyzed. The balance of these 13 cytokines and chemokines was categorized into 5 groups. Cytokines and chemokines appeared to be more balanced in CD than in UC. Machine learning classified 69 patients with IBD into 5 clusters regardless of diagnosis. Among the 59 patients who started an MTD, the steroid-free clinical remission rate at 6 months was 68.4%, 52.6%, 50.0%, 37.5%, and 28.6% in each cluster. A significant association trend was observed between clustering and clinical outcome (P = .043). CONCLUSION: This proof-of-concept study indicates that machine learning using the serum immune profile can classify active IBD regardless of the clinical diagnosis.