Identification of markers predicting clinical course in patients with Behcet disease by combination of machine learning and unbiased clustering analysis.

IF 2.4 3区 医学 Q2 OPHTHALMOLOGY
Kinya Tsubota, Yoshihiko Usui, Hiroyuki Shimizu, Hiroshi Goto
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Abstract

Purpose: Behçet's disease (BD) is a multisystem inflammatory disorder with diverse clinical manifestations. Identifying biomarkers predictive of clinical outcomes, such as tumor necrosis factor (TNF) inhibitor initiation and ocular inflammatory attack frequency, is critical for improving management. This study aimed to identify biomarkers predicting the clinical course of BD using peripheral blood test data and unbiased clustering combined with machine learning.

Methods: A retrospective cohort study of 238 BD patients diagnosed at Tokyo Medical University Hospital (2004-2020) was conducted. Unsupervised hierarchical clustering was applied to peripheral blood data, dividing patients into distinct groups. Machine learning techniques were used to explore biomarkers predicting the clinical course.

Results: Cluster analysis identified four groups: Group A (low C-reactive protein), Group B (high angiotensin-converting enzyme), Group C (high anti-streptolysin O), and Group D (low neutrophil count). Group C had a higher rate of TNF inhibitor initiation (47%, p = 0.04), while Group D had fewer ocular inflammation attacks per year (1.4, p = 0.04). Logistic regression analysis identified red blood cell count (p < 0.01) and monocyte percentage (p = 0.02) as predictive biomarkers for TNF inhibitor initiation. Machine learning further confirmed mean corpuscular hemoglobin concentration (MCHC) as a significant predictor of TNF inhibitor initiation. Additionally, multiple regression analysis identified the neutrophil/lymphocyte ratio as a predictor of the number of inflammatory attacks per year (p = 0.02).

Conclusions: Unsupervised clustering of blood test data identified distinct BD clinical phenotypes. Monocyte percentage may predict TNF inhibitor initiation, while neutrophil/lymphocyte ratio may predict ocular inflammation frequency, highlighting pathophysiologic heterogeneity in BD.

结合机器学习和无偏聚类分析识别白塞病患者临床病程的标志物。
目的:behet病(BD)是一种临床表现多样的多系统炎性疾病。确定预测临床结果的生物标志物,如肿瘤坏死因子(TNF)抑制剂的起始和眼部炎症发作频率,对于改善治疗至关重要。本研究旨在通过外周血检测数据和无偏聚类结合机器学习来识别预测BD临床病程的生物标志物。方法:对2004-2020年在东京医科大学医院诊断的238例BD患者进行回顾性队列研究。对外周血数据采用无监督分层聚类,将患者分为不同的组。使用机器学习技术探索预测临床病程的生物标志物。结果:聚类分析确定4组:A组(低C反应蛋白)、B组(高血管紧张素转换酶)、C组(高抗溶血素O)和D组(低中性粒细胞计数)。C组有较高的TNF抑制剂起始率(47%,p = 0.04),而D组每年眼部炎症发作次数较少(1.4次,p = 0.04)。结论:血液检测数据的无监督聚类鉴定出不同的BD临床表型。单核细胞百分比可以预测TNF抑制剂的启动,而中性粒细胞/淋巴细胞比例可以预测眼部炎症的频率,突出了BD的病理生理异质性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.40
自引率
7.40%
发文量
398
审稿时长
3 months
期刊介绍: Graefe''s Archive for Clinical and Experimental Ophthalmology is a distinguished international journal that presents original clinical reports and clini-cally relevant experimental studies. Founded in 1854 by Albrecht von Graefe to serve as a source of useful clinical information and a stimulus for discussion, the journal has published articles by leading ophthalmologists and vision research scientists for more than a century. With peer review by an international Editorial Board and prompt English-language publication, Graefe''s Archive provides rapid dissemination of clinical and clinically related experimental information.
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