Functional phenotypes determined by fluctuation-based clustering of lung function measurements in healthy and asthmatic cohort participants

Delgado-Eckert, Edgar; Fuchs, Oliver; Kumar, Nitin; Pekkanen, Juha; Dalphin, Jean‐Charles; Riedler, Josef; Lauener, Roger; Kabesch, Michael; Kupczyk, Maciej; Dahlén, Sven‐Erik; Frey, Urs

doi:10.1136/thoraxjnl-2016-209919

Cited by 17 publications

(20 citation statements)

References 39 publications

(25 reference statements)

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“…However, a 20‐year follow‐up rate of 54% in population‐based studies can be considered as acceptable and we showed that the followed‐up participants did not strongly differ from the nonparticipants in regard to the main clinical characteristics, and in particular across the cluster‐based asthma phenotypes, limiting the risk for selection bias in our study. The applied clustering method relied on characteristics measured at a single point in time, therefore does not account for the short‐term fluctuations of the parameters that might contain information for asthma phenotyping, as recently shown for the lung function parameters . The analysis relies on multiple testing, possibly leading to inflation of the false discovery rate.…”

Section: Discussionmentioning

confidence: 99%

Data‐driven adult asthma phenotypes based on clinical characteristics are associated with asthma outcomes twenty years later

Boudier

Chanoine

Accordini

et al. 2019

Allergy

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Background: Research based on cluster analyses led to the identification of particular phenotypes confirming phenotypic heterogeneity of asthma. The long-term clinical course of asthma phenotypes defined by clustering analysis remains unknown, Abbreviations: ACT, asthma control test; AQLQ, asthma quality of life questionnaire; BHR, bronchial hyper-responsiveness; ECRHS, European Community Respiratory Health Survey; EGEA, epidemiological study of the genetics and environment of asthma, bronchial hyper-responsiveness and atopy; ICS, inhaled corticosteroids; LF, lung function; LTA, latent transition analysis; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity. Methods:The study relied on two cohorts of adults with asthma with 20-year follow-up, ECRHS (European Community Respiratory Health Survey) and EGEA (Epidemiological study on Genetics and Environment of Asthma). Regression models were used to compare asthma characteristics (current asthma, asthma exacerbations, asthma control, quality of life, and FEV 1 ) at follow-up and the course of FEV 1 between seven cluster-based asthma phenotypes identified 20 years earlier. Results:The analysis included 1325 adults with ever asthma. For each asthma characteristic assessed at follow-up, the risk for adverse outcomes differed significantly between the seven asthma clusters identified at baseline. As compared with the mildest asthma phenotype, ORs (95% CI) for asthma exacerbations varied from 0.9 (0.4 to 2.0) to 4.0 (2.0 to 7.8) and the regression estimates (95% CI) for FEV 1 % predicted varied from 0.6 (−3.5 to 4.6) to −9.9 (−14.2 to −5.5) between clusters. Change in FEV 1 over time did not differ significantly across clusters. Conclusion:Our findings show that the long-term risk for poor asthma outcomes differed between comprehensive adult asthma phenotypes identified 20 years earlier, and suggest a strong tracking of asthma activity and impaired lung function over time. K E Y W O R D Sasthma, clustering, follow-up, lung function, phenotypes

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Section: Discussionmentioning

confidence: 99%

Data‐driven adult asthma phenotypes based on clinical characteristics are associated with asthma outcomes twenty years later

Boudier

Chanoine

Accordini

et al. 2019

Allergy

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“…SA cohorts have now been followed up in different locations worldwide, with the aim of improving our understanding of asthma and in particular of severe asthma. The more recent cohorts include the ProAR [3] Cohort from Salvador in Brazil and the European U-BIOPRED Cohort (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) [6], which were preceded by others such as ENFUMOSA [7] (The European Network for Understanding Mechanisms of Severe Asthma), BIOAIR [8], The Severe Asthma Research Programme (SARP) in the United States of America [9], The Severe Asthma Cohort of WESSEX, in the United Kingdom [10], and The Cohort for Reality and Evolution of adult Asthma in Korea [11] (COREA) to name but a few. Bringing them together to determine similarities and differences in symptoms, lung function and inflammatory patterns may enable validation of previous findings from bio-clinical phenotyping of individual cohorts and may also indicate differences that will inform our understanding of risk factors, mechanisms of disease, treatments, environment and ultimately leading to improvements in management.…”

Section: Introductionmentioning

confidence: 99%

Asthma similarities across ProAR (Brazil) and U-BIOPRED (Europe) adult cohorts of contrasting locations, ethnicity and socioeconomic status

Riley¹,

Bansal²,

Biao-Lima³

et al. 2020

Respiratory Medicine

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Background: Asthma prevalence is 339 million globally. 'Severe asthma' (SA) comprises subjects with uncontrolled asthma despite proper management. Objectives: To compare asthma from diverse ethnicities and environments. Methods: A cross-sectional analysis of two adult cohorts, a Brazilian (ProAR) and a European (U-BIOPRED). U-BIOPRED comprised of 311 non-smoking with Severe Asthma (SAn), 110 smokers or ex-smokers with SA (SAs) and 88 mild to moderate asthmatics (MMA) while ProAR included 544 SA and 452 MMA. Although these projects were independent, there were similarities in objectives and methodology, with ProAR adopting operating procedures of U-BIOPRED. Results: Among SA subjects, age, weight, proportion of former smokers and FEV 1 pre-bronchodilator were similar. The proportion of SA with a positive skin prick tests (SPT) to aeroallergens, the scores of sino-nasal symptoms and quality of life were comparable. In addition, blood eosinophil counts (EOS) and the % of subjects with EOS > 300 cells/μl were not different. The Europeans with SA however, were more severe with a greater proportion of continuous oral corticosteroids (OCS), worse symptoms and more frequent exacerbations. FEV 1 /FVC pre-and post-bronchodilator were lower among the Europeans. The MMA cohorts were less comparable in control and treatment, but similar in the proportion of allergic rhinitis, gastroesophageal reflux disease and EOS >3%. Conclusions: ProAR and U-BIOPRED cohorts, with varying severity, ethnicity and environment have similarities, which provide the basis for global external validation of asthma phenotypes. This should stimulate collaboration between asthma consortia with the aim of understanding SA, which will lead to better management.

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“…Furthermore, the FBC method includes a data-driven process for determining the tolerable amount of missing measurements (see supplementary material). This data-driven process has been described in detail elsewhere [9]. Briefly, a highly compliant subset of patients (i.e.…”

Section: Computational and Statistical Methodsmentioning

confidence: 99%

“…In this study we applied a recently developed method of fluctuation-based clustering (FBC) [9] to a prospective observational cohort consisting of children with mild to severe asthma. We hypothesized that applying an observer-independent and data-driven asthma phenotyping methodology solely based on fluctuations of daily PEF might help identify clusters that correspond to clinical phenotypes.…”

Section: Introductionmentioning

confidence: 99%

Fluctuation-based clustering reveals phenotypes of patients with different asthma severity

et al. 2020

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Serial peak expiratory flow (PEF) measurements can identify phenotypes in severe adult asthma, enabling more targeted treatment. The feasibility of this approach in children has not been investigated.Overall, 105 children (67% male, median age 12.4 years) with a range of asthma severities were recruited and followed up over a median of 92 days. PEF was measured twice daily. Fluctuation-based clustering (FBC) was used to identify clusters based on PEF fluctuations. The patients’ clinical characteristics were compared between clusters.Three PEF clusters were identified in 44 children with sufficient measurements. Cluster 1 (27% of patients: n=12) had impaired spirometry (mean forced expiratory volume in 1 s (FEV1) 71% predicted), significantly higher exhaled nitric oxide (≥35 ppb) and uncontrolled asthma (asthma control test (ACT) score <20 of 25).Cluster 2 (45%: n=20) had normal spirometry, the highest proportion of difficult asthma and significantly more patients on a high dose of inhaled corticosteroids (≥800 µg budesonide).Cluster 3 (27%: n=12) had mean FEV1 92% predicted, the highest proportion of patients with no bronchodilator reversibility, a low ICS dose (≤400 µg budesonide), and controlled asthma (ACT scores ≥20 of 25).Three clinically relevant paediatric asthma clusters were identified using FBC analysis on PEF measurements, which could improve telemonitoring diagnostics. The method remains robust even when 80% of measurements were removed. Further research will determine clinical applicability.

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Functional phenotypes determined by fluctuation-based clustering of lung function measurements in healthy and asthmatic cohort participants

Cited by 17 publications

References 39 publications

Data‐driven adult asthma phenotypes based on clinical characteristics are associated with asthma outcomes twenty years later

Data‐driven adult asthma phenotypes based on clinical characteristics are associated with asthma outcomes twenty years later

Asthma similarities across ProAR (Brazil) and U-BIOPRED (Europe) adult cohorts of contrasting locations, ethnicity and socioeconomic status

Fluctuation-based clustering reveals phenotypes of patients with different asthma severity

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