Plasma Metabolomics and Quantitative Interstitial Abnormalities in Ever-Smokers

Authors

Bina Choi
Raúl San José Estépar
Suneeta Godbole
Jeffrey L Curtis
Jennifer M Wang
Rubén San José Estépar
Ivan O Rosas
Jared R Mayers
Brian D Hobbs
Craig P Hersh
Samuel Y Ash
MeiLan K Han
Russell P Bowler
Kathleen A Stringer
George R Washko
Wassim W Labaki

Publication Date

11-4-2023

Journal

Respiratory Research

DOI

10.1186/s12931-023-02576-2

PMID

37925418

PMCID

PMC10625195

PubMedCentral® Posted Date

11-4-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Smokers, Niacin, Lung, Pulmonary Emphysema, Emphysema, Niacinamide, Pulmonary Disease, Chronic Obstructive, Lung Diseases, Interstitial; Metabolomics; Pulmonary Emphysema; Tomography, X-Ray Computed; Cross-Sectional Studies

Abstract

BACKGROUND: Quantitative interstitial abnormalities (QIA) are an automated computed tomography (CT) finding of early parenchymal lung disease, associated with worse lung function, reduced exercise capacity, increased respiratory symptoms, and death. The metabolomic perturbations associated with QIA are not well known. We sought to identify plasma metabolites associated with QIA in smokers. We also sought to identify shared and differentiating metabolomics features between QIA and emphysema, another smoking-related advanced radiographic abnormality.

METHODS: In 928 former and current smokers in the Genetic Epidemiology of COPD cohort, we measured QIA and emphysema using an automated local density histogram method and generated metabolite profiles from plasma samples using liquid chromatography-mass spectrometry (Metabolon). We assessed the associations between metabolite levels and QIA using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, pack-years, and inhaled corticosteroid use, at a Benjamini-Hochberg False Discovery Rate p-value of ≤ 0.05. Using multinomial regression models adjusted for these covariates, we assessed the associations between metabolite levels and the following CT phenotypes: QIA-predominant, emphysema-predominant, combined-predominant, and neither- predominant. Pathway enrichment analyses were performed using MetaboAnalyst.

RESULTS: We found 85 metabolites significantly associated with QIA, with overrepresentation of the nicotinate and nicotinamide, histidine, starch and sucrose, pyrimidine, phosphatidylcholine, lysophospholipid, and sphingomyelin pathways. These included metabolites involved in inflammation and immune response, extracellular matrix remodeling, surfactant, and muscle cachexia. There were 75 metabolites significantly different between QIA-predominant and emphysema-predominant phenotypes, with overrepresentation of the phosphatidylethanolamine, nicotinate and nicotinamide, aminoacyl-tRNA, arginine, proline, alanine, aspartate, and glutamate pathways.

CONCLUSIONS: Metabolomic correlates may lend insight to the biologic perturbations and pathways that underlie clinically meaningful quantitative CT measurements like QIA in smokers.

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