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Predicting Treatable Traits for Long-Acting Bronchodilators in Patients with Stable COPD

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Abstract
Rationale: There is currently no measure to predict a treatability to a long-acting beta-2 agonist (LABA) or long-acting muscarinic antagonist (LAMA) in patients with COPD. We aimed to build prediction models for the treatment response to these bronchodilators.
Methods: We performed a prospective crossover study, in which each long-acting bronchodilator was given in a random order to 65 patients with stable COPD for 4 weeks, with a washout period in between. We analyzed fourteen baseline clinical traits, expression profiles of 31,426 gene transcripts, and damaged-gene scores of 6,464 genes acquired from leukocytes. Linear regression analyses were performed to build prediction models after using factor and correlation analyses.
Results: By a prediction model for a LABA, traits found associated with the treatment response were post-bronchodilator FEV1, bronchodilator reversibility (BDR) to salbutamol, expression of 3 genes (CLN8, PCSK5, and SKP2), and damage scores of 4 genes (EPG5, FNBP4, SCN10A, and SPTBN5) (R2 = 0.512, p < 0.001). Traits associated with the treatment response to a LAMA were COPD assessment test score, BDR, expression of 4 genes (C1orf115, KIAA1618, PRKX, and RHOQ) and damage scores of 3 genes (FBN3, FDFT1, and ZBED6) (R2 = 0.575, p < 0.001). The prediction models consisting only of clinical traits appeared too weak to predict the treatment response, with R2 = 0.231 for the LABA model and R2 = 0.121 for the LAMA model.
Conclusion: Adding expressions of genes and damaged-gene scores to clinical traits may improve the predictability of treatment response to long-acting bronchodilators.
Author(s)
강지은
Issued Date
2017
Awarded Date
2018-02
Type
Dissertation
URI
https://oak.ulsan.ac.kr/handle/2021.oak/6288
http://ulsan.dcollection.net/common/orgView/200000002949
Affiliation
울산대학교
Department
일반대학원 의학과
Advisor
오연목
Degree
Master
Publisher
울산대학교 일반대학원 의학과
Language
eng
Rights
울산대학교 논문은 저작권에 의해 보호받습니다.
Appears in Collections:
Medicine > 1. Theses (Master)
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