Energy landscape analysis and time-series clustering analysis of patient state multistability related to rheumatoid arthritis drug treatment: The KURAMA cohort study

• Published in: PloS one Vol. 19; no. 5; p. e0302308
• Main Authors: Yamamoto, Keiichi, Sakaguchi, Masahiko, Onishi, Akira, Yokoyama, Shinichiro, Matsui, Yusuke, Yamamoto, Wataru, Onizawa, Hideo, Fujii, Takayuki, Murata, Koichi, Tanaka, Masao, Hashimoto, Motomu, Matsuda, Shuichi, Morinobu, Akio
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 06-05-2024; Public Library of Science (PLoS)
• Subjects: Abnormalities; Adult; Aged; Antirheumatic agents; Antirheumatic Agents - therapeutic use; Arthralgia; Arthritis; Arthritis, Rheumatoid - drug therapy; Autoimmune diseases; Biology and Life Sciences; Blood tests; Cluster Analysis; Clustering; Cohort analysis; Cohort Studies; Computer and Information Sciences; Data analysis; Deep learning; Disease; Disease detection; Drug therapy; Energy; Environmental aspects; Female; Health services; Humans; Intervention; Kinases; Male; Medical research; Medicine and Health Sciences; Medicine, Experimental; Middle Aged; Patient outcomes; Patients; People and Places; Pharmaceutical industry; Remission; Remission (Medicine); Research and Analysis Methods; Rheumatoid arthritis; Rheumatoid factor; Rheumatology; Stability; Statistics; Time dependence; Time Factors; Time series; Time-series analysis; Japan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0302308

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.