Novel Trajectories for Identifying Asthma Phenotypes

A Longitudinal Study in Korean Asthma Cohort, COREA

COREA investigators

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background: Unbiased cluster analysis has identified several asthma phenotypes. However, these phenotypes did not consistently predict disease prognosis and reflect temporal variability in airway inflammation. Objective: We aimed to identify longitudinal trajectories in terms of pulmonary function parameters and investigated whether the trajectories are associated with prognosis. Methods: Data were extracted from the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA). Three-year pulmonary function test results were used to apply finite mixture models for group-based trajectory in 486 patients with eligible data set. Results: Two main sets of longitudinal trajectories were identified in terms of FEV1% predicted, and FEV1 variability. In the 4 trajectories determined with FEV1% predicted, the pulmonary function showed a consistent course in 4 stratified levels during 3 years of follow-up, which was associated with unexpected hospital visits and the use of steroid bursts due to exacerbation. The variability in pulmonary function showed 3 different patterns, and we found that higher blood and sputum eosinophil levels were associated with the higher variability in pulmonary function and more exacerbations. Conclusions: Trajectory analysis is a novel method that provides longitudinal asthma phenotypes and aids in prediction of future risk of exacerbation. Further analysis is needed to validate the usefulness of these trajectories in an independent population.

Original languageEnglish
Pages (from-to)1850-1857.e4
JournalJournal of Allergy and Clinical Immunology: In Practice
Volume7
Issue number6
DOIs
StatePublished - 1 Jul 2019

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Longitudinal Studies
Asthma
Phenotype
Lung
Respiratory Function Tests
Korea
Sputum
Eosinophils
Cluster Analysis
Steroids
Inflammation
Population

Keywords

  • Asthma
  • Cluster analysis
  • Phenotypes
  • Trajectory

Cite this

@article{93ec914316334b648108d7476e6092fb,
title = "Novel Trajectories for Identifying Asthma Phenotypes: A Longitudinal Study in Korean Asthma Cohort, COREA",
abstract = "Background: Unbiased cluster analysis has identified several asthma phenotypes. However, these phenotypes did not consistently predict disease prognosis and reflect temporal variability in airway inflammation. Objective: We aimed to identify longitudinal trajectories in terms of pulmonary function parameters and investigated whether the trajectories are associated with prognosis. Methods: Data were extracted from the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA). Three-year pulmonary function test results were used to apply finite mixture models for group-based trajectory in 486 patients with eligible data set. Results: Two main sets of longitudinal trajectories were identified in terms of FEV1{\%} predicted, and FEV1 variability. In the 4 trajectories determined with FEV1{\%} predicted, the pulmonary function showed a consistent course in 4 stratified levels during 3 years of follow-up, which was associated with unexpected hospital visits and the use of steroid bursts due to exacerbation. The variability in pulmonary function showed 3 different patterns, and we found that higher blood and sputum eosinophil levels were associated with the higher variability in pulmonary function and more exacerbations. Conclusions: Trajectory analysis is a novel method that provides longitudinal asthma phenotypes and aids in prediction of future risk of exacerbation. Further analysis is needed to validate the usefulness of these trajectories in an independent population.",
keywords = "Asthma, Cluster analysis, Phenotypes, Trajectory",
author = "{COREA investigators} and Park, {So Young} and Jung, {Hee Won} and Lee, {Jae Moon} and Bomi Shin and Kim, {Hyo Jung} and Kim, {Min Hye} and Song, {Woo Jung} and Kwon, {Hyouk Soo} and Jung, {Jae Woo} and Kim, {Sae Hoon} and Park, {Heung Woo} and Jang, {An Soo} and Chang, {Yoon Seok} and Kim, {Sae Hoon} and Heung-Woo Park and Cho, {Sang Heon} and Yoon-Seok Chang and Sungho Won and Taesung Park and Cho, {Sang Heon} and Changsoo Kim and Kim, {Tae Bum} and Shin, {Yoo Seob} and Moon, {Ji Yong} and Kwon, {Jae Woo} and Kim, {Sang Hoon} and Taehoon Lee and Sujeong Kim and Park, {Chan Sun} and Kim, {Joo Hee} and Choi, {Jeong Hee} and Nam, {Young Hee} and Yoon, {Sun Young} and Jin, {Hyun Jung} and Yang, {Min Suk} and Jaechun Lee and Park, {Hye Kyung} and Hur, {Gyu Young} and Yang, {Min Suk} and Kim, {Sang Ha}",
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Novel Trajectories for Identifying Asthma Phenotypes : A Longitudinal Study in Korean Asthma Cohort, COREA. / COREA investigators.

In: Journal of Allergy and Clinical Immunology: In Practice, Vol. 7, No. 6, 01.07.2019, p. 1850-1857.e4.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Novel Trajectories for Identifying Asthma Phenotypes

T2 - A Longitudinal Study in Korean Asthma Cohort, COREA

AU - COREA investigators

AU - Park, So Young

AU - Jung, Hee Won

AU - Lee, Jae Moon

AU - Shin, Bomi

AU - Kim, Hyo Jung

AU - Kim, Min Hye

AU - Song, Woo Jung

AU - Kwon, Hyouk Soo

AU - Jung, Jae Woo

AU - Kim, Sae Hoon

AU - Park, Heung Woo

AU - Jang, An Soo

AU - Chang, Yoon Seok

AU - Kim, Sae Hoon

AU - Park, Heung-Woo

AU - Cho, Sang Heon

AU - Chang, Yoon-Seok

AU - Won, Sungho

AU - Park, Taesung

AU - Cho, Sang Heon

AU - Kim, Changsoo

AU - Kim, Tae Bum

AU - Shin, Yoo Seob

AU - Moon, Ji Yong

AU - Kwon, Jae Woo

AU - Kim, Sang Hoon

AU - Lee, Taehoon

AU - Kim, Sujeong

AU - Park, Chan Sun

AU - Kim, Joo Hee

AU - Choi, Jeong Hee

AU - Nam, Young Hee

AU - Yoon, Sun Young

AU - Jin, Hyun Jung

AU - Yang, Min Suk

AU - Lee, Jaechun

AU - Park, Hye Kyung

AU - Hur, Gyu Young

AU - Yang, Min Suk

AU - Kim, Sang Ha

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Background: Unbiased cluster analysis has identified several asthma phenotypes. However, these phenotypes did not consistently predict disease prognosis and reflect temporal variability in airway inflammation. Objective: We aimed to identify longitudinal trajectories in terms of pulmonary function parameters and investigated whether the trajectories are associated with prognosis. Methods: Data were extracted from the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA). Three-year pulmonary function test results were used to apply finite mixture models for group-based trajectory in 486 patients with eligible data set. Results: Two main sets of longitudinal trajectories were identified in terms of FEV1% predicted, and FEV1 variability. In the 4 trajectories determined with FEV1% predicted, the pulmonary function showed a consistent course in 4 stratified levels during 3 years of follow-up, which was associated with unexpected hospital visits and the use of steroid bursts due to exacerbation. The variability in pulmonary function showed 3 different patterns, and we found that higher blood and sputum eosinophil levels were associated with the higher variability in pulmonary function and more exacerbations. Conclusions: Trajectory analysis is a novel method that provides longitudinal asthma phenotypes and aids in prediction of future risk of exacerbation. Further analysis is needed to validate the usefulness of these trajectories in an independent population.

AB - Background: Unbiased cluster analysis has identified several asthma phenotypes. However, these phenotypes did not consistently predict disease prognosis and reflect temporal variability in airway inflammation. Objective: We aimed to identify longitudinal trajectories in terms of pulmonary function parameters and investigated whether the trajectories are associated with prognosis. Methods: Data were extracted from the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA). Three-year pulmonary function test results were used to apply finite mixture models for group-based trajectory in 486 patients with eligible data set. Results: Two main sets of longitudinal trajectories were identified in terms of FEV1% predicted, and FEV1 variability. In the 4 trajectories determined with FEV1% predicted, the pulmonary function showed a consistent course in 4 stratified levels during 3 years of follow-up, which was associated with unexpected hospital visits and the use of steroid bursts due to exacerbation. The variability in pulmonary function showed 3 different patterns, and we found that higher blood and sputum eosinophil levels were associated with the higher variability in pulmonary function and more exacerbations. Conclusions: Trajectory analysis is a novel method that provides longitudinal asthma phenotypes and aids in prediction of future risk of exacerbation. Further analysis is needed to validate the usefulness of these trajectories in an independent population.

KW - Asthma

KW - Cluster analysis

KW - Phenotypes

KW - Trajectory

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U2 - 10.1016/j.jaip.2019.02.011

DO - 10.1016/j.jaip.2019.02.011

M3 - Article

VL - 7

SP - 1850-1857.e4

JO - Journal of Allergy and Clinical Immunology: In Practice

JF - Journal of Allergy and Clinical Immunology: In Practice

SN - 2213-2198

IS - 6

ER -