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A Metabolomic Severity Score for Airflow Obstruction and Emphysema

Authors :
Suneeta Godbole
Wassim W. Labaki
Katherine A. Pratte
Andrew Hill
Matthew Moll
Annette T. Hastie
Stephen P. Peters
Andrew Gregory
Victor E. Ortega
Dawn DeMeo
Michael H. Cho
Surya P. Bhatt
J. Michael Wells
Igor Barjaktarevic
Kathleen A. Stringer
Alejandro Comellas
Wanda O’Neal
Katerina Kechris
Russell P. Bowler
Source :
Metabolites, Vol 12, Iss 5, p 368 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Chronic obstructive pulmonary disease (COPD) is a disease with marked metabolic disturbance. Previous studies have shown the association between single metabolites and lung function for COPD, but whether a combination of metabolites could predict phenotype is unknown. We developed metabolomic severity scores using plasma metabolomics from the Metabolon platform from two US cohorts of ever-smokers: the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS) (n = 648; training/testing cohort; 72% non-Hispanic, white; average age 63 years) and the COPDGene Study (n = 1120; validation cohort; 92% non-Hispanic, white; average age 67 years). Separate adaptive LASSO (adaLASSO) models were used to model forced expiratory volume at one second (FEV1) and MESA-adjusted lung density using 762 metabolites common between studies. Metabolite coefficients selected by the adaLASSO procedure were used to create a metabolomic severity score (metSS) for each outcome. A total of 132 metabolites were selected to create a metSS for FEV1. The metSS-only models explained 64.8% and 31.7% of the variability in FEV1 in the training and validation cohorts, respectively. For MESA-adjusted lung density, 129 metabolites were selected, and metSS-only models explained 59.0% of the variability in the training cohort and 17.4% in the validation cohort. Regression models including both clinical covariates and the metSS explained more variability than either the clinical covariate or metSS-only models (53.4% vs. 46.4% and 31.6%) in the validation dataset. The metabolomic pathways for arginine biosynthesis; aminoacyl-tRNA biosynthesis; and glycine, serine, and threonine pathway were enriched by adaLASSO metabolites for FEV1. This is the first demonstration of a respiratory metabolomic severity score, which shows how a metSS can add explanation of variance to clinical predictors of FEV1 and MESA-adjusted lung density. The advantage of a comprehensive metSS is that it explains more disease than individual metabolites and can account for substantial collinearity among classes of metabolites. Future studies should be performed to determine whether metSSs are similar in younger, and more racially and ethnically diverse populations as well as whether a metabolomic severity score can predict disease development in individuals who do not yet have COPD.

Details

Language :
English
ISSN :
22181989
Volume :
12
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Metabolites
Publication Type :
Academic Journal
Accession number :
edsdoj.b4411f44cc184c52ac3ec4001bc3600f
Document Type :
article
Full Text :
https://doi.org/10.3390/metabo12050368