Relationship Between Airway Metabolites and Structural Damage in Young Children with Cystic Fibrosis Public
Portelli, Alexandria (Spring 2018)
Published
Abstract
Background: Cystic Fibrosis (CF) is a genetic disease affecting over 70,000 people worldwide. Airway disease, the main cause of morbidity and mortality in CF, has been found to begin soon after birth. The Perth-Rotterdam Annotated Grid Morphometric Analysis (PRAGMA-CF) method, used to score chest computed tomography (CT) scans, is a sensitive and reproducible measure of the extent of lung disease in CF children. There is limited knowledge of relationships between PRAGMA-CF scoring of chest CT scans and molecular biomarkers of disease measured by metabolomics of bronchoalveolar lavage fluid (BALF) in CF children.
Objective: Identify significant statistical correlations and relationships between the PRAGMA-CF score of overall structural airway damage (PRAGMA-%Dis, or %Dis) in CF children and specific BALF metabolites.
Methods: Univariate and multivariate biostatistical methods were used to assess a longitudinal dataset from a prospective study of CF children (I-BALL study) to identify BALF metabolites associated with airway damage. Pearson and Spearman correlation coefficients of %Dis and metabolite concentration were calculated for cohorts at ages 1, 3, and 5 years old. Linear mixed models assessed the response of metabolite concentration to covariates including %Dis, age, total BALF protein concentration and % BAL neutrophils.
Results: Significant correlations and linear relationships between the concentration of specific BALF metabolites and %Dis were identified. Trends in the Pearson and Spearman correlations coefficients change in the first 5 years of children born with CF. The linear mixed model including %Dis and total BALF protein concentration was chosen because it had the lowest Akaike information criterion (AIC) overall across most metabolites. %Dis showed significant positive associations with diacyl (aa) and acyl ether (ae) phosphatidylcholines (PCs) aa C30:0, aa C34:1, aa C36:2, ae C34:0 and ae C34:1, and sphingomyelin SM C16:0 when adjusting for total BALF protein concentration.
Conclusions: These results add to our growing understanding of early CF pathogenesis, and how metabolomics can be used to generate clinically-relevant molecular outcomes for disease monitoring at a stage when conventional biomarkers remain at low to undetectable levels. More extensive investigation of age as a covariate is needed on progression of early CF per the %Dis outcome.
Table of Contents
Introduction
Background
Methods
Study Cohort: I-BALL Study
Data Acquisition
Targeted Metabolomics
PRAGMA-CF Scoring of Chest CT Scans
Data Cleaning
Metabolomics Data
PRAGMA-CF Scores
Age
Descriptive Characteristics of Study Cohort
Univariate Correlation Analysis
Pearson Correlation
Spearman Correlation
Linear Mixed Modeling
Assessment of Reproducibility of Metabolite Measurements
Computing Environment
Results
Summary Statistics
Pearson Correlations
Spearman Correlations
Linear Mixed Modeling
Akaike Information Criterion
Linear Mixed Model with PRAGMA and Total Protein
Lin’s Concordance Correlation Coefficient
Discussion
Interpretation of Results
Limitations
Further Research
Conclusions
References
Appendix 1: List of Metabolites Detected by AbsoluteIDQ® p180 Kit
Appendix 2: Histograms Comparing Distributions
Appendix 3: Scatterplots of Significant Metabolites
About this Master's Thesis
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