Dataset Information

Blood eosinophil level and lung function trajectories: cross-sectional and longitudinal studies in European cohorts.

ABSTRACT: Elevated blood eosinophils have been associated with lower lung function and are believed to be associated with accelerated lung function decline. Blood eosinophils were measured in four cohorts: <45 years cohort within the Vlagtwedde-Vlaardingen (V&V) study, the Uppsala cohort of the European Community Respiratory Health Survey (ECRHS-Uppsala; <45 years), ≥45 years cohort within the V&V study, and the Rotterdam study (≥45 years). Blood eosinophils at baseline were classified as normal (<300 cells·μL-1) or elevated (≥300 cells·μL-1). Lung function was measured at baseline and follow-up with spirometry: forced expiratory volume in 1 s (FEV1), vital capacity (VC) and their ratio FEV1/VC. The association between blood eosinophils and lung function was tested cross-sectionally using linear regression and longitudinally using a mixed model, both adjusted for age, sex, height, pack-years smoking and smoking status. Stratified analyses were done for asthma. Elevated blood eosinophils were associated with lower FEV1 (regression coefficient -147 mL (95% CI -188 to -105 mL)), VC (-120 mL (-165 to -75 mL)) and FEV1/VC (-1.3% (-1.9% to -0.6%)) at baseline in the two <45 years cohorts, and with lower FEV1 (-70 mL (-112 to -27 mL)) and FEV1/VC (-1.8% (-2.6% to -1.0%)) in the two ≥45 years cohorts. Elevated blood eosinophils were associated with an accelerated decline in FEV1 (-5.5 mL·year-1 (95% CI -10.5 to -0.5 mL·year-1)) and VC (-6.4 mL·year-1 (-11.26 to -1.5 mL·year-1)) compared to normal blood eosinophils in the younger asthmatic subjects in the longitudinal studies. Elevated blood eosinophils are associated with lower lung function in the general population and with an accelerated lung function decline among asthmatic individuals.

SUBMITTER: Mogensen I

PROVIDER: S-EPMC7533380 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Blood eosinophil level and lung function trajectories: cross-sectional and longitudinal studies in European cohorts.

Mogensen Ida I Vonk Judith M JM Wijnant Sara R A SRA Zhou Xingwu X Boezen H Marike HM Brusselle Guy G Lahousse Lies L Janson Christer C Malinovschi Andrei A

ERJ open research 20201005 4

<h4>Background</h4>Elevated blood eosinophils have been associated with lower lung function and are believed to be associated with accelerated lung function decline.<h4>Method</h4>Blood eosinophils were measured in four cohorts: <45 years cohort within the Vlagtwedde-Vlaardingen (V&V) study, the Uppsala cohort of the European Community Respiratory Health Survey (ECRHS-Uppsala; <45 years), ≥45 years cohort within the V&V study, and the Rotterdam study (≥45 years). Blood eosinophils at baseline we ...[more]

PMID: 33043054

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Project description:BackgroundOne of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the compositional structure of microbiome data is particularly critical in longitudinal studies where abundances measured at different times can correspond to different sub-compositions.ResultsWe developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The aim of coda4microbiome is prediction, more specifically, the method is designed to identify a model (microbial signature) containing the minimum number of features with the maximum predictive power. The algorithm relies on the analysis of log-ratios between pairs of components and variable selection is addressed through penalized regression on the "all-pairs log-ratio model", the model containing all possible pairwise log-ratios. For longitudinal data, the algorithm infers dynamic microbial signatures by performing penalized regression over the summary of the log-ratio trajectories (the area under these trajectories). In both, cross-sectional and longitudinal studies, the inferred microbial signature is expressed as the (weighted) balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. The package provides several graphical representations that facilitate the interpretation of the analysis and the identified microbial signatures. We illustrate the new method with data from a Crohn's disease study (cross-sectional data) and on the developing microbiome of infants (longitudinal data).Conclusionscoda4microbiome is a new algorithm for identification of microbial signatures in both, cross-sectional and longitudinal studies. The algorithm is implemented as an R package that is available at CRAN ( https://cran.r-project.org/web/packages/coda4microbiome/ ) and is accompanied with a vignette with a detailed description of the functions. The website of the project contains several tutorials: https://malucalle.github.io/coda4microbiome/.

Dataset Information

Blood eosinophil level and lung function trajectories: cross-sectional and longitudinal studies in European cohorts.

Publications

Blood eosinophil level and lung function trajectories: cross-sectional and longitudinal studies in European cohorts.

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