Miyoshi Jun, Tamura Satoshi, Oguri Noriaki, Saito Daisuke, Nishinarita Yuu, Wada Haruka, Nemoto Nobuki, Matsuura Minoru, Hisamatsu Tadakazu
Department of Gastroenterology and Hepatology, Kyorin University School of Medicine, Tokyo, Japan.
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, Gifu, Japan.
Gastro Hep Adv. 2025 Mar 27;4(7):100667. doi: 10.1016/j.gastha.2025.100667. eCollection 2025.
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.
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).
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 ( = .043).
This proof-of-concept study indicates that machine learning using the serum immune profile can classify active IBD regardless of the clinical diagnosis.
包括溃疡性结肠炎（UC）和克罗恩病（CD）在内的炎症性肠病（IBD）的病理生理学仍不清楚。虽然IBD具有异质性，但大多数分子靶向药物（MTD）对UC和CD均有效。无论临床表现如何，免疫病理病因可被认为是重叠的。基于免疫特征对IBD进行分类有助于理解其病理生理学，并预测个体病例的治疗效果。机器学习具有能够分析复杂数据的优势，并且可以利用IBD的免疫特征为其亚分类提供见解。
本研究使用了来自69例活动性UC患者（n = 51）或CD患者（n = 18）血清样本中的20种细胞因子和趋化因子，这些患者在开始诱导治疗前未使用过MTD。考虑项目平衡的多维免疫特征用于机器学习以对样本进行分类。临床结局是接受MTD治疗的患者（n = 59）在6个月时的无类固醇临床缓解率。
分析了13种细胞因子和趋化因子的水平。这13种细胞因子和趋化因子的平衡被分为5组。细胞因子和趋化因子在CD中似乎比在UC中更平衡。机器学习将69例IBD患者分为5个聚类，与诊断无关。在开始使用MTD的59例患者中，各聚类在6个月时的无类固醇临床缓解率分别为68.4%、52.6%、50.0%、37.5%和28.6%。在聚类与临床结局之间观察到显著的关联趋势（P = .043）。
这项概念验证研究表明，使用血清免疫特征的机器学习可以对活动性IBD进行分类，而与临床诊断无关。
