Date of Award
2024
Document Type
Thesis
Publisher
Edith Cowan University
Degree Name
Doctor of Philosophy
School
School of Science
First Supervisor
Stacey Reinke
Second Supervisor
David Broadhurst
Third Supervisor
Jessica Lasky-Su
Fourth Supervisor
Rachel Kelly
Abstract
Asthma is a heterogeneous lung disease characterized by diverse clinical presentations, pathophysiological mechanisms, and treatment responses. Understanding the varied clinical presentations, known as asthma phenotypes, aids in elucidating the underlying biological processes and improving treatment strategies. These phenotypes encompass measures of airway hyperresponsiveness, atopy, lung function, and inflammation, driven by the interplay of genetic and environmental factors. Investigating and characterizing the metabolomic profiles —reflecting both genetic and environmental influences— associated with distinct asthma phenotypes may provide new insights into disease mechanisms and potential therapeutic targets.
This thesis is structured around three specific aims: (1) identify metabolomic associations within and across standard asthma phenotypes; (2) explore metabolomic associations with lung function trajectory (LTF) patterns in asthmatics; and (3) develop and investigate electronic medical record (EMR)-based metrics of lung health decline using metabolomics in asthmatics.
For Aim 1, this thesis utilizes two pediatric asthmatic cohorts with detailed clinical presentations and untargeted metabolomic data: the Childhood Asthma Management Program (CAMP, n=953) and the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS, n=1155). For Aim 2, the thesis focuses on a subset of the CAMP cohort (n=660) where lung function trajectory (LFT) patterns had been characterized, grouping patients into: normal growth (NG), early decline (ED), reduced growth (RG), and reduced growth with early decline (RG/ED). For Aim 3, this thesis incorporates previously extracted data from two EMR-linked large-scale cohorts from Mass General Brigham (MGB): the Omic Determinants of Longitudinal Lung Function cohort (ODOLLFA, n=511), consisting of asthmatic individuals, and the MGB-Aging Biobank Cohort (MGB-ABC, n=488) from the general population. By utilizing longitudinal lung function data extracted from the EMR, more precise metrics of lung health can be developed for asthmatics. Integrating these metrics with other data, including previously developed biological aging biomarkers (e.g., epigenetic aging clocks) and untargeted metabolomic data, enables a comprehensive analysis.
This thesis leverages data from these four large-scale cohorts to achieve its aims. The findings underscore the importance of asthma phenotype research and highlight the potential of metabolomics to provide clinically relevant insights into the understanding and management of asthma.
DOI
10.25958/vm3b-vf08
Access Note
Access to this thesis is embargoed until 20th December 2026
Recommended Citation
Mendez, K. (2024). Metabolomic profiles of asthma phenotypes: From cross-sectional cohorts to longitudinal trajectories using electronic medical records. Edith Cowan University. https://doi.org/10.25958/vm3b-vf08