The value of blood cytokines and chemokines in assessing COPD
Bradford et al. Respiratory Research
The value of blood cytokines and chemokines in assessing COPD
Eric Bradford 1 4
Sean Jacobson 1 4
Jason Varasteh 1 4
Alejandro P. Comellas 4 6
Prescott Woodruff 4 5
Wanda O'Neal 4 10
Dawn L. DeMeo 4 7
Xingnan Li 4 9
Victor Kim 3 4
Michael Cho 4 8
Peter J. Castaldi 4 7
Craig Hersh 4 7
Edwin K. Silverman 4 8
James D. Crapo 1 4
Katerina Kechris 2 4
Russell P. Bowler 0 1 4
0 Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine, University of Colorado Denver, University of Colorado Anschutz Medical Campus , Research Building 2, 9th Floor, 12700 E. 19th Ave, Aurora, CO , USA
1 Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, National Jewish Health , 1400 Jackson St., K715, Denver, CO 80206 , USA
2 Department of Biostatistics and Informatics, University of Colorado Denver, Colorado School of Public Health , Mail Stop B119, 13001 E. 17th Place, Aurora, CO 80045 , USA
3 Temple University School of Medicine , Pulmonary and Critical Care
4 SPIROMICS acknowledgements investigators and staff for making this research possible. More information about the study and how to access SPIROMICS data is at
5 UCSF, Division of Pulmonary and Critical Care Medicine and Cardiovascular Research Institute , Box 0130, Rm HSE 1305, 513 Parnassus Ave, San Francisco, CA 94143 , USA
6 University of Iowa, Internal Medicine , 200 Hawkins Dr C331-GH, Iowa City, IA 52242 , USA
7 Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital , Boston, Massachusetts , USA
8 Channing Division of Network Medicine and the Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School , Boston, MA 02115 , USA
9 Department of Medicine, University of Arizona College of Medicine , Tucson, AZ , USA
10 Cystic Fibrosis/Pulmonary Research and Treatment Center, University of North Carolina at Chapel Hill , Chapel Hill, NC , USA
Background: Blood biomarkers are increasingly used to stratify high risk chronic obstructive pulmonary disease (COPD) patients; however, there are fewer studies that have investigated multiple biomarkers and replicated in multiple large well-characterized cohorts of susceptible current and former smokers. Methods: We used two MSD multiplex panels to measure 9 cytokines and chemokines in 2123 subjects from COPDGene and 1117 subjects from SPIROMICS. These biomarkers included: interleukin (IL)-2, IL-6, IL-8, IL-10, tumor necrosis factor (TNF)-α, interferon (IFN)-γ, eotaxin/CCL-11, eotaxin-3/CCL-26, and thymus and activation-regulated chemokine (TARC)/CCL-17. Regression models adjusted for clinical covariates were used to determine which biomarkers were associated with the following COPD phenotypes: airflow obstruction (forced expiratory flow at 1 s (FEV1%) and FEV1/forced vital capacity (FEV1/FVC), chronic bronchitis, COPD exacerbations, and emphysema. Biomarker-genotype associations were assessed by genome-wide association of single nucleotide polymorphisms (SNPs). Results: Eotaxin and IL-6 were strongly associated with airflow obstruction and accounted for 3-5% of the measurement variance on top of clinical variables. IL-6 was associated with progressive airflow obstruction over 5 years and both IL-6 and IL-8 were associated with progressive emphysema over 5 years. None of the biomarkers were consistently associated with chronic bronchitis or COPD exacerbations. We identified one novel SNP (rs9302690 SNP) that was associated with CCL17 plasma measurements. Conclusion: When assessing smoking related pulmonary disease, biomarkers of inflammation such as IL-2, IL-6, IL-8, and eotaxin may add additional modest predictive value on top of clinical variables alone. Trial registration: COPDGene (ClinicalTrials.gov Identifier: NCT02445183). Subpopulations and Intermediate Outcomes Measures in COPD Study (SPIROMICS) (ClinicalTrials.gov Identifier: NCT 01969344).
Background
Chronic obstructive pulmonary disease (COPD) is
typically caused by decades of exposure to smoke, dust or other
inhaled toxins. The lung is the primary portal of exposure
and bears most of the disease burden. Smoking related
lung injury includes airflow obstruction, emphysema,
chronic bronchitis, and lung cancer; however, there is also
substantial evidence that tobacco smoke causes systemic
disease. For instance, tobacco smoking is a major risk
factor for cardiovascular disease as well as extrapulmonary
malignancies such as bladder, stomach and pancreas [
1
].
Despite more than 50 years of knowledge that smoking
causes both lung and systemic disease, the molecular basis
for this is not fully understood. Furthermore, most
smokers do not develop clinical lung disease such as
COPD, emphysema, and chronic bronchitis and there is
marked heterogeneity in disease manifestations in those
who do. Since more than 100 million people in the United
States and nearly 1 billion people worldwide are current
or former smokers, there is a great need to identify
diagnostic and prognostic biomarkers to assess disease risk
and severity as well as to identify potential novel
therapeutic targets.
Two strategies exist for developing biomarkers of
COPD. First, one can obtain lung biosamples such as
exhaled breath, sputum, bronchoalveolar lavage fluid
(BALF), and lung brushes and biopsies. Exhaled breath
is non-invasive, but has poor reproducibility and low
protein content. Sputum requires expertise and time.
BALF and lung brushes and biopsies may provide a
more direct readout of the lung compartment; however,
these sampling techniques are invasive, expensive, and
have more than minimal risk. An alternative strategy for
identifying COPD biomarkers is systemic biosampling,
most commonly by obtaining plasma or serum and less
commonly urine. The primary advantage of this strategy
is ease in obtaining samples, low risk, and high
reproducibility. The disadvantage is that blood may have a
smaller biomarker signal compared to a sample obtained
directly from the lung.
There are several blood biomarkers of varying value in
predicting COPD affection status (case versus control),
severity, and disease progression [
2–5
]. For instance,
fibrinogen and C reactive protein (CRP), both
nonspecific markers of inflammation, tend to correlate with
COPD severity and risk of exacerbations [
6–21
],
although data are conflicting [
22
]. A protein which is
abundantly expressed in the lung epithelium, the soluble
receptor for advanced glycation end-products (sRAGE),
is inversely correlated with emphysema and airflow
obstruction [
23–26
]. Lung specific proteins such as
surfactant protein D (SP-D) and club cell-16 (CC16) are also
attractive COPD biomarkers. SP-D has been associated
with COPD [
14, 27–29
], and emphysema [
25
] and
possibly exacerbation frequency [
16, 29
]. CC16 may
correlate with airflow obstruction [30] and emphysema [
25
].
The major limitation to many of the previous
publications include: small sample size, limited clinical
phenotyping, and lack of reproducibility in an independent
cohort. In this study, we address some of these
limitations by studying 9 blood chemokines and cytokines in
more than 3000 subjects from two well phenotyped
longitudinal cohorts of smokers: COPDGene and
SPIROMICS.
Methods
Study populations
This study includes two independent NIH-funded cohorts:
COPDGene (ClinicalTrials.gov Identifier: NCT02445183)
and Subpopulations and Intermediate Outcomes
Measures in COPD Study (SPIROMICS) (ClinicalTrials.gov
Identifier: NCT 01969344). The institutional review board
at all participating sites approved the study protocols
(Additional file 1: Table S1). Study participants provided
written informed consent.
COPDGene is a multicenter prospective observational
study funded by the NIH which enrolled 10,300 subjects
45–80 years old, with at least a 10 pack-year history of
smoking, and who had not had an exacerbation of
COPD for at least the previous 30 days. The cohort also
includes 108 subjects who never smoked (< 100 lifetime
cigarettes). Subjects were recruited from 2008 to 2011
and were invited to return for a 5-year follow up visit
from 2013 to 2017. Blood was drawn into a vacutainer
EDTA plasma tube, immediately spun, aliquoted, and
frozen. The subset for this current analysis includes the
first 2122 who returned and provided a blood sample
during their 5-year follow up visit. Biomarker
measurements were made using plasma from the 5-year follow
up visit. Additional information on the COPDGene
study and the collection of clinical, radiographic, and
spirometry data has been described previously [
31
].
SPIROMICS is an ongoing multicenter prospective
observational study funded by the NIH [
17
] that enrolled
2982 subjects between November 2011 and January
2015. Subjects were 40–80 years old at the time of
enrollment. Subjects were categorized as non-tobacco
smokers (< 1 pack-year; stratum 1) or smokers (> 20
pack-years; Stratum 2–4). At the baseline visit blood was
drawn into a vacutainer EDTA plasma tube, immediately
spun, aliquoted, and frozen. The subset for this current
analysis was 1026 subjects with baseline blood samples
including all subjects with history of smoking but no
airflow obstruction (N = 551) and a random sample of
those with COPD (N = 566). Additional information on
the SPIROMICS study and the collection of clinical,
radiographic, and spirometry data has been described
previously [
32
].
Clinical phenotype definitions
COPD was defined by post-bronchodilator forced
expiratory volume in the first second (FEV1) to forced vital
capacity (FVC) ratio of < 0.70. Smoker controls were
current or former smokers without evidence of airflow
obstruction (FEV1/FVC ≥ 0.70). Emphysema was defined
by the percent of voxels with Hounsfield Units (HU) <
−950 (%LAA) on inspiratory CT. Emphysema
progression was defined as change in lung density adjusted for
predicted total lung capacity (adj. g/L), but only available
in the COPDGene cohort. Chronic bronchitis (CB) was
defined as the subject reporting chronic cough and
sputum production for at least 3 months per year for two
consecutive years [
33
]. Moderate exacerbations were
defined as those treated with steroids and/or antibiotics;
severe exacerbations were defined as those resulting in
hospitalization. For cross sectional analysis, subjects
where further subcategorized as emphysema (LAA > 5%)
or no emphysema (LAA ≤ 5%).
Biomarker selection and measurement
In a previous COPDGene and SPIROMCIS study we
used a 13-panel luminex-RBM assays to measure 114
candidate plasma and serum biomarkers [
34
].
Twentysix of the biomarkers had more than 50% of the values
below lower limit of detection (LLOD) and were not
analyzed. From this list, we selected plasma biomarkers for
further study on a different a Meso Scale Discovery
(MSD, Rockville, Maryland) platform. Biomarker
selection was based on these criteria: (1) inflammation
chemokine or cytokine with plausible association with
COPD-related phenotypes; (2) below lower limit of
detection from a previous study using a luminex-RBM
pane in COPDGene and SPIROMCIS subjects [
34
]; (3)
had the majority of measurements within the limit of
detection in a pilot project (N = 40) using a MSD V-PLEX
Human Cytokine 30-Plex Kit. The 9 cytokines and
chemokines that met these criteria and were run using two
separate multiplex assays: assay 1 (cytokines)-
interleukin (IL)-2, IL-6, IL-8, IL-10, tumor necrosis factor
(TNF)-α, interferon (IFN)-γ assay 2
(chemokines)eotaxin/CCL-11, eotaxin-3/CCL-26, and thymus and
activation-regulated chemokine (TARC)/CCL-17. To
determine assay coefficients of variation (CVs), first 200
cytokine assays and the first 240 chemokine assays were
performed in duplicate. Assay characteristics of the
MSD assays are shown in Additional file 1: Table S1.
Values below the LLOD were assigned half the LLOD
and values above the upper limit of detection (ULOD)
were assigned the ULOD.
Statistical analysis
Data sets used for analysis from COPDGene included:
the COPDGene Phase 2 5000 data set from September
24, 2016. Data sets used for analysis from SPIROMICS
included: the Core 4 datasets. R (v 3.2.0) was used for
analysis unless otherwise indicated. Differences in
demographic characteristics of study subjects were assessed
using a t test or Chi squared test. Because of
nonnormality, biomarker values were log10 transformed
(Additional file 1: Figure S1) and all statistical analysis
was done with the log10 value of the biomarker.
Statistical models and covariates were selected based on
previous literature [
9, 10, 14, 16, 25, 35, 36
] as indicated in
Additional file 1: Table S2. Akaike Information Criteria
(AIC) was used to determine how well a model fit. The
R2 (adj) reported refers to the percent variation of the
phenotype explained by the biomarkers(s) over clinical
covariates alone. The adjusted R2 (adj) was used for
estimating the percent variation of FEV1% explained by the
biomarkers over clinical covariates alone using the Core
R package. For FEV1/FVC we reported the McFadden
pseudo-R2 [
37
] using the betareg package. For chronic
bronchitis we report the Cragg-Uhler pseudo R2 [
38
]
using the pscl package. For decline in FEV1 and
emphysema progression we report the marginal R2 [
39
] using
the MuMin package. Biomarker(s) were considered to
improve the model if the AIC was lower than clinical
covariates alone and the p-value for the complete model
was less than 0.05. P-values were combined using
Stouffer’s Z-score method. Single nucleotide polymorphism
(SNP)-biomarker associations were assessed in
nonHispanic White subjects with PLINK using genetic
ancestry principal components, sex, age, body mass index
(BMI), smoking pack years and current smoker status as
previously described [
34
]. A cutoff of P < 10−9 was used
to account for multiple biomarker testing. For subgroup
analysis (Additional files 2 and 3), we calculated the P
values for the individual cytokine associations in the
same models that included the covariates described
above. Significant P-values (P < 0.05) for the cytokine ß
estimate in each clinical phenotype regression were
shaded on a heatmap according to the -1og10 scale of
the P – value. Colors were blue for negative associations,
red for positive associations, and grey for insufficient
endpoints.
Results
Demographics characteristics of subjects and associations
with biomarkers
Baseline characteristics of the COPDGene and
SPIROMICS subjects are shown in Table 1. The COPDGene
subjects included in this study were generally similar to
the SPIROMICS subjects, but the SPIROMICS subjects
were slightly younger, had lower BMI, greater smoking
intensity, and included a lower percentage of subjects
with moderate COPD and a higher percentage of
subjects with severe COPD. Most of the cytokines and
chemokines were strongly associated with smoking status
and also showed association with age, race, BMI, and
gender (Additional file 1: Tables S3–S6). For instance,
current smoking was associated with lower IL-2 in both
cohorts, but higher CCL17 (TARC). Because of these
associations, these covariates were included in statistical
models. Cytokines were also associated with multiple
different complete blood cell counts consistently
between cohorts (Additional file 1: Figure S2).
Biomarkers associated with COPD affection status and
airflow obstruction
Four biomarkers (eotaxin, IL-6, IL-8, and IL-10) were
independently associated (P < 0.05) with worse airflow
obstruction (FEV1%) in both cohorts, even after
adjustment for clinical covariates (Table 2). Similar associations
Abbreviations: SGRQ St. George’s Respiratory Questionnaire, HU Hounsfield Unites, BODE Body-mass index, airflow Obstruction, Dyspnea, and Exercise, PRISm
preserved ratio, impaired spirometry, GOLD Global Initiative for Chronic Obstructive Lung Disease
P-values are not applicable to race, ever smoking, and spirometry category because these criteria were used as inclusion criteria in one or both studies
were seen for FEV1/FVC (Additional file 1: Table S7). Both
eotaxin and IL-6 were significantly higher in cases
compared to controls and were higher in severe
COPD compared to mild/moderate COPD (Fig. 1). In
a full regression model with clinical covariates,
plasma IL-6 accounted for an additional 4–5% of
variance of FEV1% and 2–3% variance of FEV1/FVC.
Other biomarkers accounted for less of the variance
in these and other outcomes (Additional file 1: Table S8).
Similar results were seen in subgroup analyses when
subjects were grouped on presence or absence of
airflow obstruction (GOLD 1–4), chronic bronchitis, and
emphysema (Additional file 2); however, dividing the
cohort into 4 or more subgroups substantially
reduced the power of the analyses. When adding
biomarkers to a model that included clinical covariates,
higher IL-6 was also associated with more rapid
progression of airflow obstruction at 5 years in the
COPDGene cohort, but not over a 1 year follow up
in SPIROMICS (Additional file 1: Table S9). When
stratifying the COPDGene subjects by GOLD groups,
higher IL-6 was still associated with more rapid decline,
but the association was no longer significant when clinical
covariates were included in the model (Additional file 1:
Table S10). In the COPDGene cohort, there was a
significant association with 5-year decline in FEV1 and IL-6 in
subjects who did not have COPD or emphysema and
significant association with 5-year decline in FEV1 and IL-8
in subjects who had chronic bronchitis, but no
emphysema (Additional file 3). The amount of additional
variance in progression of FEV1 decline explained by
a IL-6 in addition to clinical covariates was 3%.
Biomarkers associated with emphysema severity and
progression, chronic bronchitis and COPD exacerbations
Although none of the 9 biomarkers had independent
cross sectional associations with emphysema severity at
a single time point (LAA% < −950 HU), higher IL-6 and
IL-8 were associated with progression of CT assessed
emphysema over 5 years (Table 3). The IL-6 association
with emphysema progression was also seen in subgroup
analysis which included only subjects without COPD or
chronic bronchitis and no emphysema at baseline
(Additional file 3).
Biomarkers associated with emphysema severity and
progression, chronic bronchitis and COPD exacerbations
None of the 9 biomarkers were reproducibly associated
with chronic bronchitis. Although there were other
chemokines and cytokines that were associated with
exacerbations in either COPDGene or SPIROMICS
(Fig. 2), none of these associations were significant in
both cohorts.
Relationship between genotype and biomarker level
Because we recently reported that genetic factors can
also influence many different biomarker measurements
[
34
], we assessed associations between genetic variants
and biomarker measurements using protein quantitative
trait locus (pQTL) analysis (Additional file 1: Figure S3).
The rs9302690 SNP in CCL17 was the only genetic locus
COPDGene
SPIROMICS
500
200
)
l
/m100
g
p
(
in50
x
a
t
o
E
20
10
200.0
100.0
50.0
significantly associated with a biomarker measurement
in both cohorts (P < 10−11 in COPDGene and P < 10−10
in SPIROMICS). The minor allele was (A) was
associated with higher levels of CCL17 (Additional file 1:
Figure S4) and occurs in intron 3 of CCL17. This is the
first report of this SNP being association with CCL17.
Discussion
Although tobacco smoke is inhaled though the lung,
there is substantial evidence that tobacco exposure has
systemic manifestations and is associated with
extrapulmonary disease [
2–5
]. While the mechanisms of
tobacco smoke-induced systemic injury are not fully
understood, inflammation is thought to play a key role.
This study is one of largest multiplex investigations of
cytokines and chemokine biomarkers to date and is one
of the few that includes two large, independent, well
phenotyped cohorts of current and former smokers.
Although we found that most of the cytokines and
chemokines were associated with some COPD phenotypes,
only eotaxin and IL-6 were strongly and consistently
associated with airflow flow limitation in both cohorts,
even after adjustment for important clinical covariates.
None of the nine biomarkers were associated with
chronic bronchitis. None of the 9 biomarkers were
consistently associated with COPD exacerbations, which is
similar to what has been recently reported for COPD
exacerbations in a more extensive study of other blood
biomarkers, albeit with few subjects [40].
Eotaxin-1 (CCL11) is a potent eosinophil
chemoattractant that is normally associated with asthma [
41
],
but is known to play a role in other mucosal diseases
such as inflammatory bowel disease (see review [
42
]).
Eotaxin and eotaxin receptor (CCR3) positive cells are
higher in acute exacerbations of chronic bronchitis as
well as asthma [
43
]; however, the published associations
between plasma eotaxin and COPD in non-exacerbating
subjects are contradictory, possibly because most include
only a small number of subjects. For instance, in 50
FORTE study participants (34 stable and 16 rapid
decliners) and 11 controls, plasma eotaxin-1 was lower in
FEV1/FVC
FEV1%
Emphysema Severity
Chronic Bronchitis
Mod/Sev. Exacerbations
Severe Exacerbations
rapid decliners compared to stable COPD patients, but
eotaxin was also significantly lower in stable COPD
subjects compared to normal controls (p < 0.03) [
44
]. In a
different study of 21 COPD subjects and 9 controls,
eotaxin was higher in COPD patients compared to
controls [
45
]. In our study, which included more than 3000
subjects, eotaxin was higher in COPD subjects in both
cohorts compared to control subjects with no COPD
and a comparable smoking history. Eotaxin was higher
in subjects with chronic bronchitis and was positively
associated with neutrophils and negatively associated with
eosinophil counts. These findings suggest that eotaxin is
associated with a neutrophilic/inflammatory COPD, but
does not appear to be independently associated
exacerbations or higher eosinophils, as might be expected with
asthma exacerbations.
Another strong association was between IL-6 and
COPD affection status, airflow limitation and
emphysema progression. IL-6 is a 26 kDa, 184 amino acid
multifunctional glycoprotein and pro-inflammatory
cytokine that is produced in a variety of stromal and
immune cells and which is associated with a large number
of pulmonary and extra-pulmonary inflammatory
diseases (see reviews [
46, 47
]). In this study, which is
appreciably larger than previously published studies, we found
that IL-6 was associated with both case-control status,
COPD severity, rate of decline in spirometry, and
independently associated with emphysema progression as
assessed by CT scans; however, it was not independently
associated with exacerbations. The case-control
associations are consistent with several large population studies.
For example, in the Health, Aging, and Body
Composition study which included 3075 subjects [48], the
Framingham Heart Study which included 2553 subjects
[
49
], the Rotterdam Study which included 572 older
subjects [
50
], plasma IL-6 was higher in those with COPD
compared to those without. This is consistent with a
recent meta-analysis of IL-6 and COPD, which included
1891 COPD subjects and 4946 controls from 33 studies
[
51
]. This meta-analysis also reported a non-statistically
significant trend toward the mild-moderate COPD
subjects having lower plasma IL-6 compared to severe
COPD subjects; however, IL-6 was not associated with
disease severity in 1793 subjects from in the ECLIPSE,
which primarily included COPD subjects [
14
]. IL-6 was
also not associated with decline in the ECLIPSE cohort.
Since IL-6 was strongly associated with neutrophils in
both cohorts, this would suggest that IL-6 may drive the
inflammatory phenotype which promotes progressive
airflow limitation. While our analysis showed a
statistically significant independent association with decline in
lung function, adding IL-6 to the model with clinical
covariates (e.g. low FEV1%) added only about 4–5% to the
explanation of variance. This is consistent with the
concept that subjects with low lung function have an
inflammatory phenotype and are predisposed to more
rapid decline in lung function, and that adding
biomarkers to these prediction models will add a small, but
additional benefit to predicting decline on top of clinical
covariates.
The COPDGene study is one of the largest current
and former smoker cohorts with long term CT follow up
and this study is one of the first to report IL-6 as an
independent biomarker of emphysema progression. A
pathologic role for IL-6 is supported by several
observations. First, IL-6 binds to IL-6 receptor and signals
through at gp130 subunit; it transduces inflammatory
gene transcription through JAK-STAT pathways. Second,
genetic blockade of the IL-6 receptor subunit gp130
blocks cigarette smoke induced emphysema [
52
]. Third,
IL-6 is associated with cardiovascular disease in COPD
patients [
53
] and recent literature supports a vascular
etiology of emphysema [
54
]. Although IL-6 specific
treatments (e.g. tocilizumab) have been developed, but
not yet tried as a treatment for COPD, one case report
describes worsening of emphysema during treatment for
rheumatoid arthritis [
55
]. Thus, anti-IL-6 treatment in
COPD should be done with caution.
In additional to eotaxin and IL-6, IL-2, IL-8, and IL-10
were also found to be elevated in COPD patients,
although they accounted for only a small amount of the
variance in airflow obstruction compared to IL-6 and
eotaxin. For several of these cytokines, there are only
smaller studies previously published. In a study of 10
COPD patients and 10 controls, ex vivo IL-2 release
from stimulated T-cells was higher in COPD patients
compared to smoking controls [
56
]. In the 50 FORTE
study participants discussed above, IL-2 was higher in
COPD patients, but was lower in rapid decliners
compared to stable COPD patients [
44
]. Similarly, in small
studies IL-8 has been reported to be elevated in COPD
patients in smaller studies with less than 100 subjects
[
57, 58
]. This is the first large study to show that IL-8 is
independently associated with progression of
emphysema by CT scan and additional studies in independent
longitudinal COPD cohorts should consider measuring
IL-8. Similarly, we find that IL-10 is associated with
worse COPD; however, there are only a few published
studies, which may be underpowered to confirm or
refute these observations. For example in a study of 94
COPD patients and 45 controls, IL-10 was no different
between COPD patients and controls, but lower than in
healthy non-smokers [59]. Since IL-10 was not
associated with progression of COPD or emphysema, it is
unclear whether it may be a useful predictive marker.
Although CCL17 is more expressed in airway cells
from COPD patients and plays a role in Th2
inflammation [
60
], we found no association with any COPD
phenotypes. However, our study is the first report of the
rs9302690 SNP being a pQTL for CCL17, with the
minor allele being associated with higher plasma levels
of CCL17. This finding may be relevant to other clinical
investigators because CCL17 is expressed in many
tissues and has been associated with atopic dermatitis [
61
]
and Hodgkin’s Lymphoma [
62
]. In GTex analysis, the
rs9302690 SNP is also a gene expression QTL (eQTL)
(GTEx V6p) with the minor allele being associated with
higher CCL mRNA in esophagus and testes and lower
expression in adrenal and pituitary tissue. Thus, both
CCL17 gene and protein expression should be adjusted
for the rs9302690 genotype.
While this study was unique in that it featured two
large well-characterized cohorts, confirmed strong
associations of IL-6 and eotaxin, identified new pQTL SNPs,
and identified potentially new biomarkers of COPD and
emphysema progression, there were some important
limitations. Most importantly, biomarkers were assessed
at only a single time point and thus one cannot
determine whether the biomarkers temporally fluctuate with
disease activity. We also only studied 9 biologically
plausible biomarkers, but there are new platforms which
will permit the simultaneous measurements of hundreds
or thousands of proteins, even if these platforms may
not be designed to assay low abundant proteins such as
interleukins. Also, although subgrouping into
phenotypes showed that some cytokines such as IL-6 were
associated with severity and progression of airflow
obstruction and emphysema even in subjects without
COPD or emphysema at baseline, other subgroup
analyses were limited by the loss of power that occurred
when subgroup sizes dropped below 500 subjects. This
might suggest that biomarkers might be useful markers
of disease progression in current and former smokers
who do not yet manifest COPD or emphysema. Finally,
other limitations of this study include the relatively low
number of nonsmokers and only limited progression
data in one of the cohorts (SPIROMICS).
Conclusion
In summary, we show that selected cytokines such as
eotaxin and IL-6 explain a moderate amount of the
clinical COPD phenotypic variance (3–5%) when added to
models with clinical covariates. Eotaxin, IL-6, and IL-8
may also have some value independent of clinical
variables in predicting progression, although this should be
demonstrated in other long term longitudinal cohorts
besides COPDGene. We remain optimistic that some of
these biomarkers may be useful for clinical trials, in which
biomarkers might define inclusion criteria in order to limit
trials to a subgroup of patients, e.g., those more likely to
progress and therefore more likely to benefit from a given
intervention. This has the potential to lead to the
identification of a therapies from which a specific group of
patients may benefit. In addition, biomarker combinations
may serve as surrogate endpoints if they are prospectively
demonstrated to correlate with clinically relevant
outcomes. For these reasons, consideration should be given
to development of panels of multiple biomarkers for
COPD observational and interventional studies.
Additional files
Additional file 1: Table S1. Range of Cytokines and Chemokines for
MSD assay. Table S2. Regression Models and Covariates for each Phenotype.
Table S3. Biomarkers associated with age. Table S4. Biomarkers associated
with female gender. Table S5. Biomarkers associated with BMI. Table S6.
Biomarkers associated with current smoking. Table S7. Biomarkers associated
with FEV1/FVC. Table S8. Amount of variance explained by biomarker and
clinical covariate alone and in combination. Table S9. Biomarkers associated
with decline in FEV1 (ml/yr) for all subjects. Table S10. Biomarkers associated
with decline in FEV1 (ml/yr) by COPD or no COPD in COPDGene subjects.
Figure S1. Subtyping of subjects based on airflow obstruction (FEV1/FVC)
and emphysema severity (LAA% < −950 HU). The vertical line represents the
cutoff for COPD (post-bronchodilator FEV1/FVC < 0.7). The horizontal
line represents the cutoff for emphysema (LAA > 5%). Subjects with
chronic bronchitis are shown by red cross and those without a blue
circle. The upper panel shows those included in analysis and the lower
panels show the whole cohort for COPDGene (left) and SPIROMICS
(right). Figure S2. Histograms of chemokines and cytokines in COPDGene
and SPIROMICS (log10 transformed). Units are pg/ml. Figure S3. Pearson
correlations between cytokines/chemokines and cell counts in COPDGene
and SPIROMICS. Red squares are positive correlation coefficients and blue
negative correlation coefficients with P < 0.05. Cell counts were obtained by
automated complete blood cell counts. The shading of each cell represents
the correlation coefficient as indicated in the legend. Figure S4. Combined
Manhattan plots for all 9 biomarker-genotype associations in non-Hispanic
White subjects from the COPDGene cohort \. The redline represents
genome wide significance level adjusted for multiple testing (P < 10−9).
Results for all 9 biomarkers are superimposed on the graph. Only one SNP
was significantly associated with a biomarker (rs9302690 in CCL17; P = 10−11).
(DOCX 11977 kb)
Additional file 2: Cross Sectional Associations by Subgroup Heat Map.
(PDF 156 kb)
Additional file 3: Associations with Disease Progression by Subgroup
Heatmap. (PDF 7 kb)
Data Coordinating Center and Biostatistics, National Jewish Health, Denver, CO:
Douglas Everett, PhD; Jim Crooks, PhD; Camille Moore, PhD; Matt Strand,
PhD; Carla G. Wilson, MS.
Epidemiology Core, University of Colorado Anschutz Medical Campus, Aurora,
CO: John E. Hokanson, MPH, PhD; John Hughes, PhD; Gregory Kinney, MPH,
PhD; Sharon M. Lutz, PhD; Katherine Pratte, MSPH; Kendra A. Young, PhD.
COPDGene® Investigators – Clinical Centers
Ann Arbor VA: Jeffrey L. Curtis, MD; Carlos H. Martinez, MD, MPH; Perry G.
Pernicano, MD.
Baylor College of Medicine, Houston, TX: Nicola Hanania, MD, MS; Philip Alapat,
MD; Mustafa Atik, MD; Venkata Bandi, MD; Aladin Boriek, PhD; Kalpatha
Guntupalli, MD; Elizabeth Guy, MD; Arun Nachiappan, MD; Amit Parulekar, MD.
Brigham and Women’s Hospital, Boston, MA: Dawn L. DeMeo, MD, MPH; Craig
Hersh, MD, MPH; Francine L. Jacobson, MD, MPH; George Washko, MD.
Columbia University, New York, NY: R. Graham Barr, MD, DrPH; John Austin,
MD; Belinda D’Souza, MD; Gregory D.N. Pearson, MD; Anna Rozenshtein, MD,
MPH, FACR; Byron Thomashow, MD.
Duke University Medical Center, Durham, NC: Neil MacIntyre, Jr., MD; H. Page
McAdams, MD; Lacey Washington, MD.
HealthPartners Research Institute, Minneapolis, MN: Charlene McEvoy, MD,
MPH; Joseph Tashjian, MD.
Johns Hopkins University, Baltimore, MD: Robert Wise, MD; Robert Brown, MD;
Nadia N. Hansel, MD, MPH; Karen Horton, MD; Allison Lambert, MD, MHS;
Nirupama Putcha, MD, MHS.
Los Angeles Biomedical Research Institute at Harbor UCLA Medical Center,
Torrance, CA: Richard Casaburi, PhD, MD; Alessandra Adami, PhD; Matthew
Budoff, MD; Hans Fischer, MD; Janos Porszasz, MD, PhD; Harry Rossiter, PhD;
William Stringer, MD.
Michael E. DeBakey VAMC, Houston, TX: Amir Sharafkhaneh, MD, PhD; Charlie
Lan, DO.
Minneapolis VA: Christine Wendt, MD; Brian Bell, MD.
Morehouse School of Medicine, Atlanta, GA: Marilyn G. Foreman, MD, MS;
Eugene Berkowitz, MD, PhD; Gloria Westney, MD, MS.
National Jewish Health, Denver, CO: Russell Bowler, MD, PhD; David A. Lynch, MB.
Reliant Medical Group, Worcester, MA: Richard Rosiello, MD; David Pace, MD.
Temple University, Philadelphia, PA: Gerard Criner, MD; David Ciccolella, MD;
Francis Cordova, MD; Chandra Dass, MD; Gilbert D’Alonzo, DO; Parag Desai,
MD; Michael Jacobs, PharmD; Steven Kelsen, MD, PhD; Victor Kim, MD; A.
James Mamary, MD; Nathaniel Marchetti, DO; Aditi Satti, MD; Kartik Shenoy,
MD; Robert M. Steiner, MD; Alex Swift, MD; Irene Swift, MD; Maria Elena
Vega-Sanchez, MD.
University of Alabama, Birmingham, AL: Mark Dransfield, MD; William Bailey,
MD; Surya Bhatt, MD; Anand Iyer, MD; Hrudaya Nath, MD; J. Michael Wells, MD.
University of California, San Diego, CA: Joe Ramsdell, MD; Paul Friedman, MD;
Xavier Soler, MD, PhD; Andrew Yen, MD.
University of Iowa, Iowa City, IA: Alejandro P. Comellas, MD; John Newell, Jr.,
MD; Brad Thompson, MD.
University of Michigan, Ann Arbor, MI: MeiLan K. Han, MD, MS; Ella Kazerooni,
MD; Carlos H. Martinez, MD, MPH.
University of Minnesota, Minneapolis, MN: Joanne Billings, MD; Abbie Begnaud,
MD; Tadashi Allen, MD.
University of Pittsburgh, Pittsburgh, PA: Frank Sciurba, MD; Jessica Bon, MD;
Divay Chandra, MD, MSc; Carl Fuhrman, MD; Joel Weissfeld, MD, MPH.
University of Texas Health Science Center at San Antonio, San Antonio, TX:
Antonio Anzueto, MD; Sandra Adams, MD; Diego Maselli-Caceres, MD; Mario
E. Ruiz, MD.
Elizabeth C Oelsner, MD, MPH; Wanda K O’Neal, PhD; Robert Paine, III, MD;
Nirupama Putcha, MD, MHS; Stephen I. Rennard, MD; Donald P Tashkin, MD;
Mary Beth Scholand, MD; J Michael Wells, MD; Robert A Wise, MD; and Prescott
G Woodruff, MD, MPH. The project officers from the Lung Division of the
National Heart, Lung, and Blood Institute were Lisa Postow, PhD, and Thomas
Croxton, PhD, MD.
Funding
This research was supported by Award Number R01 HL129937 from the
National Heart, Lung, and Blood Institute.
COPDGene was supported by Award Number R01 HL089897 and Award
Number R01 HL089856 from the National Heart, Lung, and Blood Institute.
The content is solely the responsibility of the authors and does not
necessarily represent the official views of the National Heart, Lung, and
Blood Institute or the National Institutes of Health. The COPDGene® project is
also supported by the COPD Foundation through contributions made to an
Industry Advisory Board comprised of AstraZeneca, Boehringer Ingelheim,
GlaxoSmithKline, Novartis, Pfizer, Siemens and Sunovion.
SPIROMICS was supported by contracts from the NIH/NHLBI (HHSN2682009
00013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C,
HHSN268200900017C, HHSN268200900018C HHSN268200900019C, HHSN2
68200900020C), which were supplemented by contributions made through
the Foundation for the NIH from AstraZeneca; Bellerophon Pharmaceuticals;
Boehringer-Ingelheim Pharmaceuticals, Inc.; Chiesi Farmaceutici SpA; Forest
Research Institute, Inc.; GSK; Grifols Therapeutics, Inc.; Ikaria, Inc.; Nycomed
GmbH; Takeda Pharmaceutical Company; Novartis Pharmaceuticals
Corporation; Regeneron Pharmaceuticals, Inc.; and Sanofi.
Authors’ contributions
Study design: RPB, KK Supplied samples: see acknowledgements. Generated
data: EB, JV Analyzed data: SJ, and RPB Manuscript writing: EB, RPB Manuscript
editing: {EB, SJ, JV, APC, PW, WO, DLD, XL, VK, MC, PJC, CH, EKS, JDC, KK, RPB}.
RPB had full access to all the data in the study, interpreted the data and
prepared the manuscript independently, and had final responsibility for the
decision to submit for publication. All authors read and approved the final
manuscript.
Ethics approval and consent to participate
The study protocols were approved by the local ethics committees listed
below. Informed written consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Medicine, 785 Parkinson Pavilion, 3401 North Broad Street, Philadelphia, PA
19140, USA. 11Tufts Medical Center, ICRHPS, 800 Washington St, Box 63,
Boston, MA 02111, USA.
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