A Prospective Cohort Study to Assess Obstructive Respiratory Disease Phenotypes and Endotypes in Japan: The TRAIT Study Design

BACKGROUND: Asthma, chronic obstructive pulmonary disease (COPD), and asthma–COPD overlap (ACO) are complex and heterogeneous diseases that share clinical characteristics (phenotypes) and molecular mechanisms (endotypes). Whilst physicians make clinical decisions on diagnostic groups, for some such as ACO there is no commonly accepted criteria. An alternative approach is to evaluate phenotypes and endotypes that are considered to respond well to a specific type of treatment (“treatable traits”) rather than diagnostic labels. PURPOSE: The prospective, longitudinal, and observational TRAIT study will evaluate disease characteristics, including both phenotypes and endotypes, in relation to the presentation of obstructive respiratory disease characteristics in patients diagnosed with asthma, COPD, or ACO in Japan, with the aim of further understanding the clinical benefit of a treatable traits-based approach. PATIENTS AND METHODS: A total of 1500 participants will be enrolled into three cohorts according to their treating physician’s diagnosis of asthma, COPD, or ACO at screening. Part 1 of the study will involve cross-sectional phenotyping and endotyping at study enrollment. Part 2 of the study will evaluate the progression of clinical characteristics, biomarker profiles, and treatment over a 3-year follow-up period. The follow-up will involve three annual study visits and three telephone calls scheduled at 6-month intervals. A substudy involving 50 participants from the asthma cohort (in which the ratio will be approximately 1:1 including 25 participants with a smoking history of ≥10 pack-years and 25 participants with no smoking history), 100 participants from the ACO cohort, and 100 participants from the COPD cohort will evaluate disease phenotypes using inspiratory and expiratory computed tomography scans. CONCLUSION: TRAIT will describe clinical characteristics of patients with obstructive respiratory diseases to better understand potential differences and similarities between clinical diagnoses, which will support the improvement of personalized treatment strategies.