Plasma metabolomics and quantitative interstitial abnormalities in ever-smokers
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..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
Respiratory research - 24(2023), 1 vom: 04. Nov. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Choi, Bina [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
Cross-Sectional Studies |
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| Anmerkungen: |
© The Author(s) 2023 |
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| doi: |
10.1186/s12931-023-02576-2 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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SPR053634489 |
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| 520 | |a 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. | ||
| 650 | 4 | |a Lung Diseases, Interstitial |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Metabolomics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Pulmonary Emphysema |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Tomography, X-Ray Computed |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Cross-Sectional Studies |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a San José Estépar, Raúl |4 aut | |
| 700 | 1 | |a Godbole, Suneeta |4 aut | |
| 700 | 1 | |a Curtis, Jeffrey L. |4 aut | |
| 700 | 1 | |a Wang, Jennifer M. |4 aut | |
| 700 | 1 | |a San José Estépar, Rubén |4 aut | |
| 700 | 1 | |a Rosas, Ivan O. |4 aut | |
| 700 | 1 | |a Mayers, Jared R. |4 aut | |
| 700 | 1 | |a Hobbs, Brian D. |4 aut | |
| 700 | 1 | |a Hersh, Craig P. |4 aut | |
| 700 | 1 | |a Ash, Samuel Y. |4 aut | |
| 700 | 1 | |a Han, MeiLan K. |4 aut | |
| 700 | 1 | |a Bowler, Russell P. |4 aut | |
| 700 | 1 | |a Stringer, Kathleen A. |4 aut | |
| 700 | 1 | |a Washko, George R. |4 aut | |
| 700 | 1 | |a Labaki, Wassim W. |4 aut | |
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