Discussion
COPD is a complex disease, and patients vary greatly and unpredictably in terms of disease subtype, activity, and progression. Pharmacologic agents that prevent disease progression and improve survival are lacking, in part because specific agents are unlikely to benefit such a heterogeneous group of patients [
4]. An attractive notion is that biomarkers may provide insight into this heterogeneity, thus allowing us to cater clinical trials and ultimately therapies to specific groups of patients and provide better prognostic information. An extensive literature on biomarkers in COPD exists [
1]. However, most studies have examined the association between individual biomarkers and cross-sectional outcomes. In addition, the field has been plagued by lack of validation in replication cohorts and inconsistent biomarker platforms, leading to discrepant reports (Additional file
1: Table S1) [
1]. Here, we present a comprehensive analysis of the role of biomarkers, individually and in combination, in predicting both cross-sectional and longitudinal outcomes using two large, multi-center cohorts with identical platforms. We found that individual biomarkers are more closely associated with most outcomes than clinical covariates alone. Moreover, multiple biomarkers are more highly predictive than individual biomarkers for almost all COPD outcomes. With rare exceptions, the associations, including directionality, between biomarkers and outcomes identified in the discovery cohort were validated in the replication cohort (Fig.
1). Additional file
1: Tables S5 and S6 provide an easily accessible and exhaustive resource for investigators to ascertain the association between these biomarkers and almost any clinically important COPD outcome in these two cohorts. To our knowledge, ours is the first study to demonstrate an association between multiple biomarkers and cross-sectional and longitudinal outcomes in two large, multi-center cohorts.
Overall, our findings build upon prior literature, confirming some associations but improving upon existing knowledge by demonstrating that, in most cases, a distinct combination of biomarkers is associated with outcomes. In both cohorts, each of the five biomarkers studied individually correlated with airflow limitation, consistent with previous literature [
4,
8,
12,
16‐
18]. However, a panel of five biomarkers together was more highly predictive of airflow limitation (FEV
1) than any individual biomarker. Similarly, while previous literature suggested that sRAGE and fibrinogen are individually associated with emphysema, [
5,
16] our analysis revealed that the combination of SP-D, CRP, sRAGE, and fibrinogen was more highly correlated.
Although the relationships between biomarkers and disease subtype and severity are interesting and may provide clues into the molecular pathogenesis of the different subtypes, biomarkers will be most useful clinically if they can predict longitudinal outcomes, such as future exacerbations, decline in FEV
1, progression of emphysema, and mortality. Such risk stratification would allow clinical trials to be catered to the patients most likely to progress as well as provide patients with a more accurate and personalized prognosis. Interestingly, in the COPDGene cohort, no biomarker or combination of biomarkers added significant value to predicting an individual’s future risk of exacerbations over clinical variables including history of prior exacerbations. This is consistent with previous studies, which found that certain biomarkers were associated with exacerbations by univariate analysis but not by multivariate analysis that included clinical predictive variables, particularly prior exacerbation history [
3,
17,
19]. (Although ECLIPSE was used here as a validation cohort, it is interesting to note that biomarkers were predicitve of future exacerbations, and this is likely due to differences in the subjects, such as in race and severity of disease.) One limitation of our study is the lack of blood leukocyte values, which may predict exacerbations [
3,
10,
26]. Still, taken together, our findings and the literature suggest that a history of previous exacerbations is so strongly associated with future exacerbations that biomarkers may not provide substantial additional information.
COPD disease progression is highly variable [
4,
5]. CC16 levels have been previously associated with decline in FEV
1 [
4,
22]. Here, the combination of CC16, fibrinogen, and sRAGE best predicted decline in lung function in the COPDGene cohort, and this combination was validated in ECLIPSE, although the addition of SP-D and CRP further improved the model. Whether pro-SP-B, previously implicated in decline in FEV
1 [
33] would further improve the model should be studied. Progression of emphysema has previously been associated with individual abnormal biomarkers [
5]. We found that progression of emphysema, as measured by decline in CT density, was best modeled by the combination of CC16, sRAGE, and fibrinogen in the COPDGene cohort. This model was validated by the ECLIPSE cohort, although the addition of SP-D and CRP further enhanced the model. Whether IL-6, previously associated with progression of emphysema, [
5] would further improve the model should be examined.
Although previous studies revealed that multiple biomarkers predict mortality, [
9,
11] ours is the first to validate such findings in an independent large, multicenter cohort. However, there were notable discrepancies between the two cohorts. In both cohorts, the combination of SP-D, CRP, and fibrinogen improved the model over covariates and individual biomarkers. However, the best combination in COPDGene, all five biomarkers together, did not reach statistical significance in ECLIPSE. In addition, while fibrinogen or CRP alone were significantly associated with mortality in ECLIPSE (Table
6) and other [
9,
13,
15] cohorts, fibrinogen and CRP were not individually predictive of mortality in COPDGene, although they did improve the model when added to the other four biomarkers. These discrepancies may be due to differences between the cohorts, such as in race and severity of disease. The concordance between the two cohorts may become stronger with ongoing follow-up, as the overall mortality rates (9.4% in COPDGene, 8.5% in ECLIPSE) are low, and 3–5 years is a relatively short duration of follow-up considering the natural history of the disease. Future studies should examine mortality over a longer period of follow-up, the contribution of additional biomarkers such as IL-6 and leukocyte count [
11] to the model, and disease-specific mortality. Of note, we also report the important finding that inclusion of additional clinical variables known to be associated with mortality (e.g., age) [
7] yields a novel clinical model that is more highly predictive of mortality than established models such as the BODE index. In both cohorts, biomarkers strengthen the model, and a combination of biomarkers provides enhanced predictive value over individual biomarkers.
We acknowledge that the amount of variance explained by biomarkers, as determined by correlation coefficients, is relatively low. Longer duration of follow-up and inclusion of additional biomarkers or persistence of abnormal biomarkers [
10] may strengthen the correlations. However, our findings are consistent with previously reported weak correlation coefficients (R
2 < 0.3) or relative risks (<1.5) [
3,
4,
11,
15,
22,
24,
33]. Therefore, the field must acknowledge that statistically significant associations between biomarkers and outcomes that can be observed in large cohorts may be largely inadequate to explain remaining variance after strong clinical covariates are included in the models. This suggests that COPD is an exceedingly heterogeneous and complex disease, the extent of which our understanding remains quite limited. Regardless, the impact of the current study lies in the demonstration that, combinations of biomarkers correlate with COPD outcomes much (
two to ten times more) strongly than individual biomarkers.
Limitations of this study, in addition to those discussed above, include the relatively low number of nonsmokers in the COPDGene cohort and the virtual absence of Gold 1 subjects in the ECLIPSE cohort. Neither cohort was population-based. COPDGene results should be generalizable to non-Hispanic white and African American smokers. ECLIPSE results are generalizable to white COPD patients. Although the current findings are overall generalizable because of the size of the discovery and replication cohorts, extensive clinical phenotyping, and adjustment for multiple relevant covariates, these findings should be validated in a third large cohort. Future studies should elucidate the repeatability of biomarker measurements, although most are stable over time [
26].
Acknowledgements
COPDGene Cores:
Administrative Core: James Crapo, MD (PI), Edwin Silverman, MD, PhD (PI), Barry Make, MD, Elizabeth Regan, MD, PhD, Rochelle Lantz, Lori Stepp, Sandra Melanson
Genetic Analysis Core: Terri Beaty, PhD, Barbara Klanderman, PhD, Nan Laird, PhD, Christoph Lange, PhD, Michael Cho, MD, Stephanie Santorico, PhD, John Hokanson, MPH, PhD, Dawn DeMeo, MD, MPH, Nadia Hansel, MD, MPH, Craig Hersh, MD, MPH, Peter Castaldi, MD, MSc, Merry-Lynn McDonald, PhD, Jin Zhou, MD, PhD, Manuel Mattheissen, MD, PhD, Emily Wan, MD, Megan Hardin, MD, Jacqueline Hetmanski, MS, Margaret Parker, MS, Tanda Murray, MS
Imaging Core: David Lynch, MB, Joyce Schroeder, MD, John Newell, Jr., MD, John Reilly, MD, Harvey Coxson, PhD, Philip Judy, PhD, Eric Hoffman, PhD, George Washko, MD, Raul San Jose Estepar, PhD, James Ross, MSc, Mustafa Al Qaisi, MD, Jordan Zach, Alex Kluiber, Jered Sieren, Tanya Mann, Deanna Richert, Alexander McKenzie, Jaleh Akhavan, Douglas Stinson
PFT QA Core, LDS Hospital, Salt Lake City, UT: Robert Jensen, PhD
Biological Repository, Johns Hopkins University, Baltimore, MD: Homayoon Farzadegan, PhD, Stacey Meyerer, Shivam Chandan, Samantha Bragan
Data Coordinating Center and Biostatistics, National Jewish Health, Denver, CO: Douglas Everett, PhD, Andre Williams, PhD, Carla Wilson, MS, Anna Forssen, MS, Amber Powell, Joe Piccoli
Epidemiology Core, University of Colorado School of Public Health, Denver, CO: John Hokanson, MPH, PhD, Marci Sontag, PhD, Jennifer Black-Shinn, MPH, Gregory Kinney, MPH, PhDc, Sharon Lutz, MPH, PhD.
COPDGene Investigators:
Ann Arbor VA: Jeffrey Curtis, MD, Ella Kazerooni, MD
Baylor College of Medicine, Houston, TX: Nicola Hanania, MD, MS, Philip Alapat, MD, Venkata Bandi, MD, Kalpalatha Guntupalli, MD, Elizabeth Guy, MD, Antara Mallampalli, MD, Charles Trinh, MD, Mustafa Atik, MD, Hasan Al-Azzawi, MD, Marc Willis, DO, Susan Pinero, MD, Linda Fahr, MD, Arun Nachiappan, MD, Collin Bray, MD, L. Alexander Frigini, MD, Carlos Farinas, MD, David Katz, MD, Jose Freytes, MD, Anne Marie Marciel, MD
Brigham and Women’s Hospital, Boston, MA: Dawn DeMeo, MD, MPH, Craig Hersh, MD, MPH, George Washko, MD, Francine Jacobson, MD, MPH, Hiroto Hatabu, MD, PhD, Peter Clarke, MD, Ritu Gill, MD, Andetta Hunsaker, MD, Beatrice Trotman-Dickenson, MBBS, Rachna Madan, MD
Columbia University, New York, NY: R. Graham Barr, MD, DrPH, Byron Thomashow, MD, John Austin, MD, Belinda D’Souza, MD
Duke University Medical Center, Durham, NC: Neil MacIntyre, Jr., MD, Lacey Washington, MD, H Page McAdams, MD
Fallon Clinic, Worcester, MA: Richard Rosiello, MD, Timothy Bresnahan, MD, Joseph Bradley, MD, Sharon Kuong, MD, Steven Meller, MD, Suzanne Roland, MD
Health Partners Research Foundation, Minneapolis, MN: Charlene McEvoy, MD, MPH, Joseph Tashjian, MD
Johns Hopkins University, Baltimore, MD: Robert Wise, MD, Nadia Hansel, MD, MPH, Robert Brown, MD, Gregory Diette, MD, Karen Horton, MD
Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center, Los Angeles, CA: Richard Casaburi, MD, Janos Porszasz, MD, PhD, Hans Fischer, MD, PhD, Matt Budoff, MD, Mehdi Rambod, MD
Michael E. DeBakey VAMC, Houston, TX: Amir Sharafkhaneh, MD, Charles Trinh, MD, Hirani Kamal, MD, Roham Darvishi, MD, Marc Willis, DO, Susan Pinero, MD, Linda Fahr, MD, Arun Nachiappan, MD, Collin Bray, MD, L. Alexander Frigini, MD, Carlos Farinas, MD, David Katz, MD, Jose Freytes, MD, Anne Marie Marciel, MD
Minneapolis VA: Dennis Niewoehner, MD, Quentin Anderson, MD, Kathryn Rice, MD, Audrey Caine, MD
Morehouse School of Medicine, Atlanta, GA: Marilyn Foreman, MD, MS, Gloria Westney, MD, MS, Eugene Berkowitz, MD, PhD
National Jewish Health, Denver, CO: Russell Bowler, MD, PhD, David Lynch, MB, Joyce Schroeder, MD, Valerie Hale, MD, John Armstrong, II, MD, Debra Dyer, MD, Jonathan Chung, MD, Christian Cox, MD
Temple University, Philadelphia, PA: Gerard Criner, MD, Victor Kim, MD, Nathaniel Marchetti, DO, Aditi Satti, MD, A. James Mamary, MD, Robert Steiner, MD, Chandra Dass, MD, Libby Cone, MD
University of Alabama, Birmingham, AL: William Bailey, MD, Mark Dransfield, MD, Michael Wells, MD, Surya Bhatt, MD, Hrudaya Nath, MD, Satinder Singh, MD
University of California, San Diego, CA: Joe Ramsdell, MD, Paul Friedman, MD
University of Iowa, Iowa City, IA: Alejandro Cornellas, MD, John Newell, Jr., MD, Edwin JR van Beek, MD, PhD
University of Michigan, Ann Arbor, MI: Fernando Martinez, MD, MeiLan Han, MD, Ella Kazerooni, MD
University of Minnesota, Minneapolis, MN: Christine Wendt, MD, Tadashi Allen, MD
University of Pittsburgh, Pittsburgh, PA: Frank Sciurba, MD, Joel Weissfeld, MD, MPH, Carl Fuhrman, MD, Jessica Bon, MD, Danielle Hooper, MD
University of Texas Health Science Center at San Antonio, San Antonio, TX: Antonio Anzueto, MD, Sandra Adams, MD, Carlos Orozco, MD, Mario Ruiz, MD, Amy Mumbower, MD, Ariel Kruger, MD, Carlos Restrepo, MD, Michael Lane, MD
ECLIPSE Investigators [
11].