부산대학교 도서관

Rheumatoid arthritis causes joint inflammation due to immune abnormalities, resulting in joint pain and swelling. In recent years, there have been considerable advancements in the treatment of this disease. However, only approximately 60% of patients achieve remission. Patients with multifactorial diseases shift between states from day to day. Patients may remain in a good or poor state with few or no transitions, or they may switch between states frequently. The visualization of time-dependent state transitions, based on the evaluation axis of stable/unstable states, may provide useful information for achieving rheumatoid arthritis treatment goals. Energy landscape analysis can be used to quantitatively determine the stability/instability of each state in terms of energy. Time-series clustering is another method used to classify transitions into different groups to identify potential patterns within a time-series dataset. The objective of this study was to utilize energy landscape analysis and time-series clustering to evaluate multidimensional time-series data in terms of multistability. We profiled each patient's state transitions during treatment using energy landscape analysis and time-series clustering. Energy landscape analysis divided state transitions into two patterns: "good stability leading to remission" and "poor stability leading to treatment dead-end." The number of patients whose disease status improved increased markedly until approximately 6 months after treatment initiation and then plateaued after 1 year. Time-series clustering grouped patients into three clusters: "toward good stability," "toward poor stability," and "unstable." Patients in the "unstable" cluster are considered to have clinical courses that are difficult to predict; therefore, these patients should be treated with more care. Early disease detection and treatment initiation are important. The evaluation of state multistability enables us to understand a patient's current state in the context of overall state transitions related to rheumatoid arthritis drug treatment and to predict future state transitions.
Competing Interests: he Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, is supported by Nagahama City, Shiga, Japan; Toyooka City, Hyogo, Japan; Asahi Kasei Pharma Corp.; and AYUMI Pharmaceutical Co. MH received research grants and/or speaker fees from Abbvie, Asahi Kasei, Astellas, Ayumi, Brystol Meyers, Chugai, EA Pharma, Eisai, Daiichi Sankyo, Eli Lilly, Novartis Pharma, and Tanabe Mitsubishi. M.T. received research grants and/or speaker fees from AbbVie GK, Asahi Kasei Pharma Corp., Astellas Pharma Inc., Chugai Pharmaceutical Co., Ltd., Daiichi Sankyo Co., Ltd., Eisai Co., Ltd., Eli Lilly Japan K.K., Janssen Pharmaceutical K.K., Kyowa Kirin Co., Ltd., Pfizer Inc., Taisho Pharmaceutical Co., Ltd., Tanabe Mitsubishi Pharma Corp., Teijin Pharma, Ltd., and UCB Japan Co., Ltd. K.M. received speaking and/or consulting fees from AbbVie GK, Eisai Co., Ltd., Pfizer Inc., Chugai Pharmaceutical Co., Ltd., Mitsubishi Tanabe Pharma Corp., Bristol-Myers Squibb, Daiichi Sankyo Co., Ltd., Janssen Pharmaceutical K.K., and Asahi Kasei Pharma Corp. H.O. has received speaker fees from AbbVie, Asahi Kasei, Astellas Pharma Inc., Eisai Co. Ltd., Janssen Pharmaceutical K.K., Mitsubishi Tanabe Pharma Corporation, and Daiichi Sankyo Co. Ltd. The other authors declare that they have no conflicts of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
(Copyright: © 2024 Yamamoto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)