Abstract
Background: Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants.Materials and Methods: Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV1]/forced vital capacity [FVC], and FEV1 percent predicted) were assessed. A metabolome-wide association study was performed to find genetic associations with metabolite levels. Significantly associated single-nucleotide polymorphisms were tested for replication with independent metabolomic platforms and independent cohorts. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolite-phenotype interactions.Results: Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Associations with airflow obstruction were enriched for diacylglycerols and branched chain amino acids. Genetic associations were observed with 109 (11%) metabolites, 72 (66%) of which replicated in an independent cohort. For 20 metabolites, more than 20% of variance was explained by genetics. A sparse network of COPD phenotype-driven modules was identified, often containing metabolites missed in previous testing. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, although little module variance was explained by genetics.Conclusion: A dysregulation of systemic metabolism was predominantly found in COPD phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances. However, network analysis, which increased the statistical power to detect associations missed previously in classic regression analyses, revealed that the genetic influence on COPD phenotype-driven metabolomic modules was modest when compared with clinical and environmental factors.
Highlights
Metabolites are low molecular weight ( £ 1500 Daltons) molecules, representing both endogenous and exogenous compounds, which play important roles in signaling, energy expenditure, reproduction, and growth
A dysregulation of systemic metabolism was predominantly found in chronic obstructive pulmonary disease (COPD) phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances
Metabolome data substructure Before reducing data by the exclusion criteria, we first explored the metabolomic profiles of all COPDGene subjects with metabolomes quantified by Metabolon at Phase 2
Summary
Metabolites are low molecular weight ( £ 1500 Daltons) molecules, representing both endogenous and exogenous (environmentally derived) compounds, which play important roles in signaling, energy expenditure, reproduction, and growth. There have been several reports suggesting the presence of characteristic metabolic signatures in the blood of individuals with lung diseases such as chronic obstructive pulmonary disease (COPD)[3,4,5,6,7,8]; these reports have typically included only a small number of subjects or a limited annotation of metabolic features ( < 500 metabolites). Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that integrate genetic variants. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolitephenotype interactions. Results: Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, little module variance was explained by genetics
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
