Non-elderly asthma phenotypes and blood eosinophils are associated with clinical outcomes in elderly asthma

The number of patients with elderly asthma (EA) is increasing with global population aging. EA differs from non-elderly asthma (NEA) in clinical characteristics and underlying pathophysiology, but it remains unclear which factors before older age are associated with clinical outcomes in older age. This study investigated NEA factors associated with subsequent EA outcomes and phenotype transitions. This multicenter retrospective observational cohort study enrolled 82 patients with EA (≥ 65 years) and collected their earliest available clinical data obtained during the NEA period (< 65 years). Clinical assessments included pulmonary function tests, blood biomarkers, and patient-reported outcomes. Patients were classified into three EA (EA1-EA3) and three NEA clusters (NEA1-NEA3) using a previously established decision tree model. Logistic regression was used to identify NEA factors associated with clinical remission in EA. In addition, correlations between NEA biomarkers and percent forced expiratory volume in 1 s (%FEV1) in EA were calculated. Higher blood eosinophil counts during the NEA period were associated with a lower likelihood of clinical remission in EA, under both 3-way (odds ratio [OR] 0.871 per 100/µL) and 4-way (OR 0.839 per 100/µL) definitions, adjusted for sex and body mass index. Among preceding phenotypes, patients classified as NEA2 (long disease duration with eosinophilic features) showed poorer EA outcomes, including more frequent oral corticosteroid use and lower rates of clinical remission. Both peripheral blood eosinophil counts and total serum immunoglobulin E (IgE) levels during the NEA period negatively correlated with %FEV1 during the EA period. Higher blood eosinophil counts before older age were linked to poorer asthma outcomes after older age, suggesting that persistent eosinophilic inflammation earlier in adulthood may be associated with unfavorable outcomes later in life.

Authors

Institutions

Publication Details

Journal
BMC Pulmonary Medicine
Published
2026-09-12
DOI
https://doi.org/10.1186/s12890-026-04672-7
Primary Topic
Asthma and respiratory diseases
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Non-elderly asthma phenotypes and blood eosinophils are associated with clinical outcomes in elderly asthma

Yoshikazu Inoue, Toshiyuki Kita, Yoshitaka Oyamada, Nobuharu Ohshima et al.
BMC Pulmonary Medicine
Asthma and respiratory diseases
article

Non-elderly asthma phenotypes and blood eosinophils are associated with clinical outcomes in elderly asthma

Yoshikazu Inoue, Toshiyuki Kita, Yoshitaka Oyamada, Nobuharu Ohshima, Shohei Takata, Takako Nakano, Hiroyuki Tashimo, Takeo Endo, Mari Miki, Kazufumi Takada, Eiji Takeuchi, Kenji Chibana, Hiroya Hashimoto, Yasushi Tanimoto, Yoshiaki Minakata, Sumito Ogawa, Maho Suzukawa, Haruhito Sugiyama, Masahiro Sekimizu, Tsutomu Shinohara, Mitsuhiro Kamimura, Ken Ohta, Masaki Ishii, Kazuyuki Tsujino, Hisanori Machida, Nobuyuki Kobayashi, Isoko Owan, Yusuke Imada, Takanori Matsuki, Akiko M. Saito, Shinji Tamaki
article en

Abstract

The number of patients with elderly asthma (EA) is increasing with global population aging. EA differs from non-elderly asthma (NEA) in clinical characteristics and underlying pathophysiology, but it remains unclear which factors before older age are associated with clinical outcomes in older age. This study investigated NEA factors associated with subsequent EA outcomes and phenotype transitions. This multicenter retrospective observational cohort study enrolled 82 patients with EA (≥ 65 years) and collected their earliest available clinical data obtained during the NEA period (< 65 years). Clinical assessments included pulmonary function tests, blood biomarkers, and patient-reported outcomes. Patients were classified into three EA (EA1-EA3) and three NEA clusters (NEA1-NEA3) using a previously established decision tree model. Logistic regression was used to identify NEA factors associated with clinical remission in EA. In addition, correlations between NEA biomarkers and percent forced expiratory volume in 1 s (%FEV1) in EA were calculated. Higher blood eosinophil counts during the NEA period were associated with a lower likelihood of clinical remission in EA, under both 3-way (odds ratio [OR] 0.871 per 100/µL) and 4-way (OR 0.839 per 100/µL) definitions, adjusted for sex and body mass index. Among preceding phenotypes, patients classified as NEA2 (long disease duration with eosinophilic features) showed poorer EA outcomes, including more frequent oral corticosteroid use and lower rates of clinical remission. Both peripheral blood eosinophil counts and total serum immunoglobulin E (IgE) levels during the NEA period negatively correlated with %FEV1 during the EA period. Higher blood eosinophil counts before older age were linked to poorer asthma outcomes after older age, suggesting that persistent eosinophilic inflammation earlier in adulthood may be associated with unfavorable outcomes later in life.

BMC Pulmonary Medicine
National Archives and Records Administration (US), National Hospital Organization Kochi National Hospital (JP), Kanazawa Medical Center (JP), National Center for Global Health and Medicine (JP), Tokyo Medical Center (JP), National Hospital Organization Mito Medical Center (JP), Hidaka Hospital (JP), Toneyama National Hospital (JP), University of Tokyo Hospital (JP), Nara City Hospital (JP), Naruto, Tokushima Prefecture hospital (JP), Fukuoka Higashi Medical Center (JP), United States Naval Hospital Okinawa (JP), Okayama Medical Center (JP), Nagoya Medical Center (JP), Fukujuji Hospital (JP), National Disaster Medical Center (JP), Tokyo National Hospital (JP), NHO Kinki Chuo Chest Medical Center (JP), Nagoya University (JP), Nara Medical University (JP), International University of Health and Welfare (JP)
Environmental Restoration and Conservation Agency
No poverty
Openalex Percentile: Top 11%
Asthma and respiratory diseases
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.