A Metabolomic Severity Score for Airflow Obstruction and Emphysema
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
Affiliations
Suneeta Godbole
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
Wassim W. Labaki
Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI 48109, USA
Katherine A. Pratte
Division of Medicine, National Jewish Health, Denver, CO 80206, USA
Andrew Hill
Division of Medicine, National Jewish Health, Denver, CO 80206, USA
Matthew Moll
Channing Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
Annette T. Hastie
Section on Pulmonary, Critical Care, Allergy & Immunology, Internal Medicine, Wake Forest School of Medicine, Winston Salem, NC 27157, USA
Stephen P. Peters
Section on Pulmonary, Critical Care, Allergy & Immunology, Internal Medicine, Atrium Health Wake Forest Baptist, Winston Salem, NC 20157, USA
Andrew Gregory
Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
Victor E. Ortega
Division of Respiratory Medicine, Department of Internal Medicine, Center for Individualized Medicine, Mayo Clinic, Scottsdale, AZ 85259, USA
Dawn DeMeo
Channing Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
Michael H. Cho
Channing Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
Surya P. Bhatt
Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
J. Michael Wells
UAB Lung Health Center, Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
Igor Barjaktarevic
Division of Pulmonary and Critical Care, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
Kathleen A. Stringer
Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI 48109, USA
Alejandro Comellas
Division of Pulmonary and Critical Care, University of Iowa, Iowa City, IA 52242, USA
Wanda O’Neal
Marsico Lung Institute, University of North Carolina, Chapel Hill, NC 27599, USA
Katerina Kechris
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
Russell P. Bowler
Division of Medicine, National Jewish Health, Denver, CO 80206, USA
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.
