Maho Suzukawa1, Ken Ohta2, Yuma Fukutomi3, Hiroya Hashimoto4, Takeo Endo5, Masahiro Abe6, Yosuke Kamide3, Makoto Yoshida7, Yoshihiro Kikuchi8, Toshiyuki Kita9, Kenji Chibana10, Yasushi Tanimoto11, Kentaro Hyodo12, Shohei Takata13, Toshiya Inui14, Masahide Yasui15, Yoshinori Harada16, Toshio Sato17, Yumi Sakakibara18, Yoshiaki Minakata19, Yoshikazu Inoue20, Shinji Tamaki21, Tsutomu Shinohara22, Kazutaka Takami23, Motofumi Tsubakihara24, Masahide Oki25, Kentaro Wakamatsu26, Masahide Horiba27, Gen Ideura28, Koko Hidaka29, Akiko M Saito30, Nobuyuki Kobayashi31, Masami Taniguchi32. 1. Clinical Research Center, National Hospital Organization Tokyo National Hospital, Tokyo, Japan. Electronic address: fueta-tky@umin.ac.jp. 2. Clinical Research Center, National Hospital Organization Tokyo National Hospital, Tokyo, Japan; Japan Anti-Tuberculosis Association, JATA Fukujuji Hospital, Tokyo, Japan. Electronic address: kenohta3@gmail.com. 3. Clinical Research Center, National Hospital Organization Sagamihara National Hospital, Kanagawa, Japan. 4. Clinical Research Center, National Hospital Organization Nagoya Medical Center, Nagoya, Japan; Core Laboratory, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan. 5. National Hospital Organization Mito Medical Center, Ibaraki, Japan. 6. National Hospital Organization Ehime Medical Center, Ehime, Japan. 7. National Hospital Organization Fukuoka National Hospital, Fukuoka, Japan. 8. National Hospital Organization Morioka Medical Center, Iwate, Japan. 9. National Hospital Organization Kanazawa Medical Center, Ishikawa, Japan. 10. National Hospital Organization Okinawa National Hospital, Okinawa, Japan. 11. National Hospital Organization Minami-Okayama Medical Center, Okayama, Japan. 12. National Hospital Organization Ibarakihigashi National Hospital, Ibaraki, Japan. 13. National Hospital Organization Fukuokahigashi Medical Center, Fukuoka, Japan. 14. National Hospital Organization Disaster Medical Center, Tokyo, Japan. 15. National Hospital Organization Nanao National Hospital, Ishikawa, Japan. 16. Department of Rheumatology & Allergology, National Hospital Organization Osaka Minami Medical Center, Osaka, Japan. 17. National Hospital Organization Okayama Medical Center, Okayama, Japan. 18. Federation of National Public Service Personnel Mutual Aid Associations Hiratsuka Kyosai Hospital, Kanagawa, Japan. 19. National Hospital Organization Wakayama Hospital, Wakayama, Japan. 20. National Hospital Organization Kinki-Chuo Chest Medical Center, Osaka, Japan. 21. National Hospital Organization Nara Medical Center, Nara, Japan. 22. National Hospital Organization Kochi National Hospital, Kochi, Japan. 23. Kanto Central Hospital of the Mutual Aid Association of Public School Teachers, Tokyo, Japan. 24. National Hospital Organization Yokohama Medical Center, Kanagawa, Japan. 25. Department of Respiratory Medicine, National Hospital Organization Nagoya Medical Center, Aichi, Japan. 26. National Hospital Organization Omuta National Hospital, Fukuoka, Japan. 27. Division of Respiratory Medicine, National Hospital Organization Higashisaitama National Hospital, Saitama, Japan. 28. National Hospital Organization Shinshu Ueda Medical Center, Nagano, Japan. 29. National Hospital Organization Kokura Medical Center, Fukuoka, Japan. 30. Clinical Research Center, National Hospital Organization Nagoya Medical Center, Nagoya, Japan. 31. Clinical Research Center, National Hospital Organization Tokyo National Hospital, Tokyo, Japan; Fureai Machida Hospital, Tokyo, Japan. 32. Clinical Research Center, National Hospital Organization Sagamihara National Hospital, Kanagawa, Japan; Shonan Kamakura General Hospital, Kanagawa, Japan.
Abstract
BACKGROUND: Asthma is a heterogeneous disease, and phenotyping can facilitate understanding of disease pathogenesis and direct appropriate asthma treatment. This nationwide cohort study aimed to phenotype asthma patients in Japan and identify potential biomarkers to classify the phenotypes. METHODS: Adult asthma patients (n = 1925) from 27 national hospitals in Japan were enrolled and divided into Global Initiative for Asthma (GINA) steps 4 or 5 (GINA 4, 5) and GINA Steps 1, 2, or 3 (GINA 1-3) for therapy. Clinical data and questionnaires were collected. Biomarker levels among GINA 4, 5 patients were measured. Ward's minimum variance hierarchical clustering method and tree analysis were performed for phenotyping. Analysis of variance, the Kruskal-Wallis, and chi-square tests were used to compare cluster differences. RESULTS: The following five clusters were identified: 1) late-onset, old, less-atopic; 2) late-onset, old, eosinophilic, low FEV1; 3) early-onset, long-duration, atopic, poorly controlled; 4) early-onset, young, female-dominant, atopic; and 5) female-dominant, T1/T2-mixed, most severe. Age of onset, disease duration, blood eosinophils and neutrophils, asthma control questionnaire Sum 6, number of controllers, FEV1, body mass index (BMI), and hypertension were the phenotype-classifying variables determined by tree analysis that assigned 79.5% to the appropriate cluster. Among the cytokines measured, IL-1RA, YKL40/CHI3L1, IP-10/CXCL10, RANTES/CCL5, and TIMP-1 were useful biomarkers for classifying GINA 4, 5 phenotypes. CONCLUSIONS: Five distinct phenotypes were identified for moderate to severe asthma and may be classified using clinical and molecular variables (Registered in UMIN-CTR; UMIN000027776.).
BACKGROUND: Asthma is a heterogeneous disease, and phenotyping can facilitate understanding of disease pathogenesis and direct appropriate asthma treatment. This nationwide cohort study aimed to phenotype asthma patients in Japan and identify potential biomarkers to classify the phenotypes. METHODS: Adult asthma patients (n = 1925) from 27 national hospitals in Japan were enrolled and divided into Global Initiative for Asthma (GINA) steps 4 or 5 (GINA 4, 5) and GINA Steps 1, 2, or 3 (GINA 1-3) for therapy. Clinical data and questionnaires were collected. Biomarker levels among GINA 4, 5 patients were measured. Ward's minimum variance hierarchical clustering method and tree analysis were performed for phenotyping. Analysis of variance, the Kruskal-Wallis, and chi-square tests were used to compare cluster differences. RESULTS: The following five clusters were identified: 1) late-onset, old, less-atopic; 2) late-onset, old, eosinophilic, low FEV1; 3) early-onset, long-duration, atopic, poorly controlled; 4) early-onset, young, female-dominant, atopic; and 5) female-dominant, T1/T2-mixed, most severe. Age of onset, disease duration, blood eosinophils and neutrophils, asthma control questionnaire Sum 6, number of controllers, FEV1, body mass index (BMI), and hypertension were the phenotype-classifying variables determined by tree analysis that assigned 79.5% to the appropriate cluster. Among the cytokines measured, IL-1RA, YKL40/CHI3L1, IP-10/CXCL10, RANTES/CCL5, and TIMP-1 were useful biomarkers for classifying GINA 4, 5 phenotypes. CONCLUSIONS: Five distinct phenotypes were identified for moderate to severe asthma and may be classified using clinical and molecular variables (Registered in UMIN-CTR; UMIN000027776.).