The study was performed at Fraunhofer ITEM in Hannover. Fraunhofer ITEM has received a grant from Nycomed GmbH for performing the study. The samples obtained from the subjects in this study were partly analysed at Fraunhofer ITEM, partly by Nycomed GmbH. The data analysis was performed by Dr P Ernst, Genedata AG, Dr S Roepcke, Dr G Lauer from Nycomed as well as by Dr O Holz and JM Hohlfeld from Fraunhofer ITEM. N.Krug, JM. Hohlfeld and O. Holz are employed at Fraunhofer ITEM and in addition participate in the Group of “Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), which is a member of the German Center for Lung Research. All authors of this manuscript declare not to have a competing interest with respect to the results of this study. Neither the cooperation and involvement in performing this study by Fraunhofer ITEM, Nycomed GmbH and Genedata AG, nor the participation in the German Center of Lung Research alters the adherence to all the PLOS ONE policies on sharing data and materials. Nycomed, Genedata and the Fraunhofer ITEM (this includes those authors of Fraunhofer ITEM) who additionally participate in the German center of lung research) are fully aware of and agree to the publishing policies of PlOS ONE.
Conceived and designed the experiments: JMH NK G. Lauer S. Roepcke MM ME. Performed the experiments: MM S. Rittinghausen NK JMH. Analyzed the data: OH S. Roepcke MM S. Rittinghausen PE G. Lauer. Contributed reagents/materials/analysis tools: PE. Wrote the paper: OH S. Roepcke G. Lauer JMH G. Lahu.
Chronic Obstructive Pulmonary Disease (COPD) is a chronic inflammatory disease, primarily affecting the airways. Stable biomarkers characterizing the inflammatory phenotype of the disease, relevant for disease activity and suited to predict disease progression are needed to monitor the efficacy and safety of drug interventions. We therefore analyzed a large panel of markers in bronchoalveolar lavage, bronchial biopsies, serum and induced sputum of 23 healthy smokers and 24 smoking COPD patients (GOLD II) matched for age and gender. Sample collection was performed twice within a period of 6 weeks. Assays for over 100 different markers were validated for the respective matrices prior to analysis. In our study, we found 51 markers with a sufficient repeatability (intraclass correlation coefficient >0.6), most of these in serum. Differences between groups were observed for markers from all compartments, which extends (von-Willebrand-factor) and confirms (e.g. C-reactive-protein, interleukin-6) previous findings. No correlations between lung and serum markers were observed, including A1AT. Airway inflammation defined by sputum neutrophils showed only a moderate repeatability. This could be improved, when a combination of neutrophils and four sputum fluid phase markers was used to define the inflammatory phenotype.In summary, our study provides comprehensive information on the repeatability and interrelationship of pulmonary and systemic COPD-related markers. These results are relevant for ongoing large clinical trials and future COPD research. While serum markers can discriminate between smokers with and without COPD, they do not seem to sufficiently reflect the disease-associated inflammatory processes within the airways.
COPD is characterized by chronic airway inflammation, dyspnea, reduced exercise tolerance, cough, increased mucus production, and can lead to emphysema
These trials and our efforts in this study reflect the need for biomarkers that enable researchers and physicians to adequately measure airway inflammation in COPD and to perform a more precise diagnosis of disease states in clinical practice, which could lead to earlier recognition of exacerbations and more tailored interventions. Inflammatory biomarkers could serve as early signals for efficacy or adverse reactions during investigational interventions and would advance pharmacological and clinical research.
Increased numbers and altered activities of pulmonary inflammatory cells as well as enhanced elastolysis are a common feature of COPD. Factors like neutrophil elastase (NE) or matrix metalloproteases (MMP) in bronchoalveolar lavage (BAL) or sputum, are considered as markers for degradation and repair processes
In our study, we therefore assessed the repeatability of a broad panel of markers from serum, sputum, BAL and bronchial biopsies, by collecting samples twice within 6 weeks. Prior to this study all assays for the analysis of biomarkers were extensively validated with samples from the respective matrices. In contrast to other studies, we focused on disease-related differences and aimed to avoid a bias due to active smoking by comparing age and gender matched active smokers with and without COPD (GOLD II). In addition, we compared markers not only between groups but also between the different sampling sites, especially to investigate to what extent serum markers relate to inflammatory markers within the airways.
Healthy Smokers (N = 23) | COPD Smokers (N = 24) | |
female/male | 6/17 | 6/18 |
age [years] | 54 (42, 65) | 54 (46, 68) |
height [cm] | 176.4±11.3 | 174.7±7.4 |
weight [kg] | 79.6±14.0 | 77.4±12.1 |
BMI [kg/m2] | 25.4±2.5 | 25.3±3.4 |
Pack-years | 39±23.2 | 49.2±12.6 |
Cig. per day | 20.7±8.9 | 25.5±6.9 |
Cotinine (ng/mL): Scr. | 1262±722 | 1561±968 |
V1 | 1378±722 | 1772±1107 |
V3 | 1451±823 | 1779±1012 |
FEV1 [L] | 3.8±0.8 | 2.0±0.3 |
FEV1 % pred. | 112.5±14.1 | 60.5±6.8 |
FVC [L] | 5.1±1.0 | 4.2±0.9 |
FEV1/FVC [%] | 75.4±5.0 | 48.7±7.4 |
pO2 [mm Hg] | 82.9±9.7 | 73.3±5.3 |
WPeak [W] | 154.8±35.1 | 110.8±3.4 |
Values are presented as mean ± SD, except for age where we report median (Min, Max);
: p<0.05,
: p<0.01,
: p<0.001 (Scr. = Screening).
Before measurement of study samples, all immunoassays were validated for blood, sputum and BAL fluid using pooled samples from at least 6 independent donors. In total, 107 different assays were tested. The samples were spiked and tested for accuracy, recovery and linearity. The optimal dilution of samples for all analytes and all compartments are listed in the online supplement (
Correlation between samples collected in 2 visits within a time period of up to 6 weeks. The figure shows selected cellular biomarkers (A–D) and pro-inflammatory cytokines (E–H) from serum, BAL and ISP and examples for proteases (J, K), a glycoprotein and a growth-factor (I, L). The line of identity is displayed in all individual graphs. Data is displayed on log scales. The range of concentrations for each selected marker can be found in
ANALYTE | ALL | COPD smokers | Healthy smokers | |||||
ICC | r | ICC | r | SD | ICC | r | SD | |
|
||||||||
CD14 Mono | 0.77 | 0.70 | 0.68 | 0.68 | 2.5 | 0.80 | 0.81 | 2.1 |
Calprotectin/TP | 0.73 | 0.75 | 0.46 | 0.54 | 1.7 | 0.83 | 0.83 | 2.3 |
HSA | 0.69 | 0.72 | 0.50 | 0.51 | 1.5 | 0.73 | 0.80 | 1.6 |
CD16 NG | 0.67 | 0.66 | 0.67 | 0.68 | 3.5 | 0.69 | 0.68 | 3.2 |
TCC | 0.65 | 0.65 | 0.61 | 0.60 | 2.4 | 0.71 | 0.72 | 2.1 |
IL-8/TP | 0.65 | 0.64 | 0.58 | 0.57 | 2.3 | 0.63 | 0.63 | 2.3 |
MMP-9/TP | 0.65 | 0.65 | 0.43 | 0.41 | 3.3 | 0.81 | 0.81 | 4.0 |
total-protein | 0.64 | 0.66 | 0.60 | 0.65 | 1.6 | 0.69 | 0.68 | 1.6 |
NELA/TP | 0.62 | 0.62 | 0.43 | 0.43 | 2.8 | 0.82 | 0.84 | 2.9 |
a1-Antitrypsin | 0.61 | 0.65 | 0.52 | 0.62 | 1.9 | 0.63 | 0.63 | 1.9 |
MPO/TP | 0.39 | 0.41 | 0.04 | 0.04 | 2.5 |
|
0.76 | 2.8 |
|
||||||||
MMP 7 | 0.76 | 0.75 | 0.73 | 0.72 | 2.7 | 0.82 | 0.82 | 4.1 |
EP (%) | 0.76 | 0.76 | 0.82 | 0.82 | 3.3 | 0.64 | 0.62 | 3.2 |
IL-6 | 0.69 | 0.71 | 0.52 | 0.54 | 2.5 | 0.89 | 0.89 | 3.4 |
HSA | 0.61 | 0.60 | 0.57 | 0.56 | 2.2 | 0.58 | 0.61 | 2.2 |
NG (%) | 0.59 | 0.55 | 0.33 | 0.31 | 1.8 |
|
0.85 | 2.1 |
a1-Antitrypsin | 0.56 | 0.55 | 0.36 | 0.35 | 1.6 |
|
0.70 | 1.7 |
AM | 0.53 | 0.52 |
|
0.66 | 2.2 | 0.39 | 0.38 | 2.5 |
TIMP-1 | 0.52 | 0.51 | 0.33 | 0.37 | 1.6 |
|
0.68 | 2.4 |
MMP-1 | 0.37 | 0.40 | 0.24 | 0.27 | 2.6 |
|
0.63 | 2.7 |
MMP-9/TP | 0.24 | 0.32 |
|
0.72 | 2.5 | −0.03 | 0.06 | 4.0 |
Intraclass correlation coefficients (ICC) were derived from one-way ANOVA tables as the ratio of variance among subjects to total variance based on 2 measurements over a 6 week period (for log transformed data only) r: Pearson correlation coefficient. Data is sorted by matrix and decreasing ICC as derived from all subjects. Some markers are listed due to ICC>0.6 in the subgroups (in bold). Mean SD (Standard Deviation) values were derived from log-transformed data of the 2 visits, transformed again and listed for the 2 subgroups. This way these values are factors. To derive the SD value, that together with the median values of a marker (listed in
ANALYTE | ALL | COPD smokers | Healthy smokers | |||||
ICC | r | ICC | r | SD | ICC | r | SD | |
|
||||||||
Leptin | 0.97 | 0.97 | 0.97 | 0.97 | 2.9 | 0.96 | 0.96 | 3.8 |
VEGF | 0.95 | 0.95 | 0.91 | 0.91 | 1.8 | 0.98 | 0.98 | 2.2 |
CREATININE | 0.94 | 0.94 | 0.96 | 0.96 | 1.3 | 0.90 | 0.90 | 1.2 |
IL-1beta | 0.93 | 0.94 | 0.92 | 0.95 | 2.8 | 0.92 | 0.92 | 2.8 |
IGFBP-2 | 0.90 | 0.90 | 0.88 | 0.88 | 1.8 | 0.92 | 0.92 | 1.7 |
MIP-1alpha | 0.88 | 0.89 | 0.92 | 0.91 | 1.7 | 0.72 | 0.72 | 1.3 |
IL-2 | 0.88 | 0.89 | 0.89 | 0.91 | 3.3 | 0.85 | 0.86 | 3.0 |
TNF-alpha | 0.88 | 0.88 | 0.92 | 0.92 | 2.1 | 0.70 | 0.72 | 1.5 |
IL-6 | 0.88 | 0.90 | 0.93 | 0.93 | 2.6 | 0.77 | 0.82 | 2.6 |
MIP-1beta | 0.85 | 0.87 | 0.86 | 0.86 | 1.6 | 0.82 | 0.88 | 1.4 |
IL-15 | 0.85 | 0.85 | 0.87 | 0.88 | 2.0 | 0.72 | 0.73 | 1.5 |
IFN-alpha | 0.84 | 0.85 | 0.91 | 0.92 | 1.7 | 0.58 | 0.56 | 1.4 |
IL-12p40/p70 | 0.83 | 0.86 | 0.85 | 0.89 | 1.5 | 0.76 | 0.78 | 1.2 |
MMP-1 | 0.83 | 0.83 | 0.82 | 0.81 | 2.2 | 0.84 | 0.84 | 2.1 |
IL-7 | 0.82 | 0.82 | 0.80 | 0.79 | 1.6 | 0.79 | 0.80 | 1.5 |
IFN-gamma | 0.82 | 0.83 | 0.86 | 0.87 | 1.9 | 0.70 | 0.73 | 1.6 |
IGF-II | 0.80 | 0.82 | 0.82 | 0.86 | 1.3 | 0.76 | 0.76 | 1.2 |
IGF-I | 0.77 | 0.76 | 0.79 | 0.80 | 1.2 | 0.74 | 0.73 | 1.2 |
CRP | 0.76 | 0.76 | 0.77 | 0.79 | 2.0 | 0.67 | 0.65 | 2.7 |
Serotonin | 0.75 | 0.79 | 0.74 | 0.84 | 1.3 | 0.77 | 0.78 | 1.3 |
PDGF-AA | 0.72 | 0.82 | 0.65 | 0.82 | 1.4 | 0.79 | 0.83 | 1.4 |
IL-8 | 0.72 | 0.81 | 0.78 | 0.80 | 2.0 | 0.59 | 0.86 | 1.6 |
Calprotectin | 0.72 | 0.72 | 0.72 | 0.71 | 1.9 | 0.72 | 0.72 | 1.9 |
NELA | 0.72 | 0.72 | 0.76 | 0.76 | 2.0 | 0.65 | 0.65 | 1.8 |
IGFBP-1 | 0.71 | 0.74 | 0.71 | 0.74 | 2.5 | 0.72 | 0.73 | 2.2 |
Eotaxin | 0.70 | 0.69 | 0.68 | 0.68 | 1.5 | 0.73 | 0.80 | 1.4 |
HGF | 0.69 | 0.71 | 0.83 | 0.87 | 1.6 | 0.47 | 0.46 | 1.6 |
MIG | 0.69 | 0.74 | 0.68 | 0.72 | 1.6 | 0.67 | 0.72 | 1.5 |
IL-2R | 0.64 | 0.74 | 0.66 | 0.78 | 1.4 | 0.61 | 0.72 | 1.4 |
LBP | 0.63 | 0.70 | 0.77 | 0.85 | 1.3 | 0.47 | 0.52 | 1.3 |
TGF-beta | 0.62 | 0.67 | 0.69 | 0.77 | 1.4 | 0.57 | 0.63 | 1.4 |
PDGF-AB/BB | 0.62 | 0.70 | 0.67 | 0.74 | 1.6 | 0.50 | 0.61 | 1.3 |
HSA | 0.60 | 0.59 | 0.74 | 0.73 | 1.1 | 0.32 | 0.31 | 1.1 |
MMP-9 | 0.49 | 0.50 | 0.24 | 0.24 | 1.4 |
|
0.74 | 1.5 |
|
||||||||
CREATININE | 0.77 | 0.51 | 0.79 | 0.47 | 2.4 | 0.53 | 0.57 | 1.6 |
Intraclass (ICC) and Pearsons (r) correlation coefficients for markers in serum and urine (see Legend
Analyte | Sample matrix | M | Unit | First visit | Second visit | LME-ANOVA | ||
healthy smokers | COPD smokers | healthy smokers | COPD smokers | p-value | ||||
TCC | BAL | 106/mL | 0.2 (0.2–0.4) | 0.2 (0.1–0.3) | 0.2 (0.1–0.3) | 0.2 (0.1–0.3) | m: 0.008. f:0.0418 | |
CD14+ MONO | BAL | F | % TC | 1.5 (1.2–2.5) | 1.1 (0.6–1.6) | 1.8 (1.3–2.5) | 0.9 (0.5–1.0) | m: 0.0001. f:0.61 |
CD14+ MONO | BAL | F | 103/mL | 2.7 (1.6–7.4) | 2.4 (0.4–5.8) | 3.9 (2.6–6.0) | 1.5 (0.5–3.8) | m: 0.00045. f:0.11 |
a1-Antitrypsin | BAL | E | ng/ml | 795 (531–1022) | 512 (328–724) | 650 (358–1074) | 345 (275–480) | m: 0.004. f:0.95 |
EGF-R | BAL | E | pg/ml | 67.3 (53.4–90.3) | 56.7 (35.4–72.1) | 82.6 (61.6–100.0) | 55.5 (40.2–87.4) | m: 0.001.f:0.55 |
HSA | BAL | E | µg/ml | 16.8 (12.5–23.9) | 11.7 (7.9–12.9) | 17.8 (13.0–22.4) | 10.5 (9.2–15.2) | m: 1.12e-05. f:0.44 |
TIMP-1 | BAL | E | ng/ml | 2.4 (1.8–3.3) | 3.2 (2.2–4.7) | 2.7 (1.8–3.3) | 4.8 (2.4–8.5) | 0.016 |
a1-Antitrypsin | BAL/TP | E | pg/µg | 9.9 (8.1–11.2) | 7.7 (5.7–10.6) | 8.5 (6.6–11.7) | 6.1 (4.4–7.5) | 0.004 |
Calprotectin | BAL/TP | E | ng/µg | 0.7 (0.4–1.4) | 1.2 (0.8–1.5) | 0.7 (0.4–1.1) | 0.9 (0.7–1.1) | m: 0.016. f:0.59 |
EGF-R | BAL/TP | E | pg/µg | 1.0 (0.8–1.2) | 0.9 (0.6–1.0) | 1.0 (0.8–1.2) | 0.8 (0.7–1.0) | 0.016 |
HSA | BAL/TP | E | ng/µg | 245 (226–268) | 185 (169–208) | 253 (184–283) | 183 (140–215) | 1.62E-05 |
IL-8 | BAL/TP | Lu | pg/µg | 0.3 (0.2–0.4) | 0.4 (0.3–0.8) | 0.2 (0.2–0.4) | 0.5 (0.3–0.7) | 0.025 |
TIMP-1 | BAL/TP | E | ng/µg | 0.0 (0.0–0.0) | 0.1 (0.0–0.1) | 0.0 (0.0–0.0) | 0.1 (0.0–0.1) | 0.000 |
ANISOCYTOSIS | blood | H | % | 44.2 (42.4–46.8) | 46.6 (44.9–47.7) | 45.9 (44.1–46.8) | 46.8 (45.7–47.7) | 0.014 |
CREATINE KIN. | blood | Ch | U/L | 125.0 (96.5–173.0) | 89.5 (67.8–126.3) | 124.0 (91.0–179.0) | 83.0 (65.0–95.0) | 0.007 |
MCV | blood | H | FL | 89.9 (88.2–90.9) | 94.0 (91.2–95.2) | 90.0 (88.6–91.5) | 93.5 (91.0–96.7) | 0.008 |
a1-Antitrypsin | serum | E | µg/ml | 1.39 (1.31–1.49) | 1.47 (1.33–1.70) | 1.90 (1.27–2.12) | 2.27 (1.57–2.43) | m: 0.014. f:0.57 |
CRP | serum | Lu | ng/ml | 301 (146–474) | 540 (368–1018) | 232 (110–569) | 823 (373–1047) | 0.000 |
HGF | serum | Lu | pg/ml | 317 (244–391) | 419 (300–568) | 311 (217–407) | 414 (322–501) | 0.022 |
IL-6 | serum | Lu | pg/ml | 6.9 (4.0–12.0) | 12.7 (7.9–23.3) | 5.0 (2.0–10.1) | 15.8 (9.0–30.0) | 0.002 |
LTB4 | serum | E | µg/ml | 1.23 (1.10–1.35) | 1.26 (1.06–1.58) | 1.21 (1.12–1.36) | 1.40 (1.29–1.66) | m: 0.0051. f:0.72 |
vWF | serum | E | mU/ml | 1586 (1248–2077) | 2089 (1838–2296) | 1523 (984–1778) | 1860 (1562–2301) | 0.003 |
a1-Antitrypsin | ISP | E | ng/ml | 992 (630–1173) | 568 (363–716) | 625 (453–1014) | 540 (432–693) | 0.008 |
HSA | ISP | E | µg/ml | 34.1 (26.9–44.5) | 13.1 (8.9–25.4) | 27.6 (17.6–40.2) | 17.7 (7.6–23.4) | m: 0.0016. f:0.57 |
MMP 3 | ISP | Lu | pg/ml | 28.0 (15.7–42.7) | 22.4 (11.8–33.2) | 40.6 (21.6–56.5) | 22.9 (14.5–42.7) | m: 0.009. f:0.29 |
a1-Antitrypsin | ISP/TP | E | ng/µg | 2.4 (2.1–3.1) | 1.6 (1.3–2.1) | 2.1 (1.6–2.6) | 1.5 (1.3–1.9) | m: 0.0006. f:0.728 |
HSA | ISP/TP | E | ng/µg | 77.3 (66.0–99.8) | 50.1 (32.9–70.2) | 77.0 (56.8–89.4) | 54.7 (27.0–65.5) | m: 0.001. f:0.50 |
CREATININE | urine | EP | mg/dl | 160 (125–206) | 140 (96–230) | 189 (130–272) | 128 (48–190) | m: 0.42. f:0.002 |
Data presented as median (IQR). LME-ANOVA p-value: COPD smokers vs. healthy smokers. M = Method of analysis, TP = normalized to total protein, BAL = bronchoalveolar lavage, ISP = induced sputum, F = Flow cytometry, E = ELISA, Lu = Luminex, H = Hematology, Ch = blood chemistry, EP = Laboratory Eipper Besenthal, Tübingen, Germany.
A difference in total cell numbers and monocytes in BAL was observed in male subjects only. COPD patients had lower BAL concentrations of A1AT, EGF-R, and HSA, but elevated levels of TIMP1, as well as of IL-8 and Calprotectin, two markers associated with neutrophilic airway inflammation. These differences were also seen without normalization to total protein.
Lower creatine kinase concentrations were measured in blood of COPD smokers, while their serum levels of inflammatory mediators, including CRP and IL-6, were higher compared to healthy smokers. In serum of COPD smokers, there were increased levels of von-Willebrandt-factor (vWF), a glycoprotein that is involved in arterial thrombus formation. However there was no significantly negative relationship to partial thromboplastin time (PTT) (r = −0.2), which was clearly visible in healthy smokers (r = −0.75, p<0.0001,
In line with BAL, but in contrast to serum, we detected higher A1AT concentrations in induced sputum of healthy smokers. In these subjects, we also found increased numbers of monocytes, as well as higher concentrations of HSA and MMP3.
First, we tested the relationship between those analyte levels that were assessed in more than one matrix to determine the extent to which the concentration of a specific marker in a more easily accessible sample like serum or sputum agrees with its concentration in a matrix that can only be obtained invasively. This was done for each of the two visits separately and, for those markers with sufficient repeatability (see
Next, we used an exploratory factor analysis to search for overall relationships between markers in order to test whether any analytes in easily accessible serum samples relates to markers in BAL, biopsies or sputum. A factor analysis was used to structure our large dataset and to reduce it to 3 groups of highly correlated variables (factors). Analysis of complete cellular and biochemical parameters of all sample matrices (log mean values of the two visits) showed that markers associated with neutrophilic inflammation in sputum and BAL (e.g. MMP9, Elastase, Calprotectin, MMP9/TIMP1 ratio, IL8, BAL neutrophils) were highly correlated and formed the major factor. Pro-inflammatory cytokines in serum, such as IL-6, IL-1β, IFNα, IL-15, MIG, MIP-1α, and TNFα grouped within the second factor, while the more abundant markers in sputum and BAL, such as total protein, HSA and A1AT were combined in factor 3. None of the 3 factors included both serum and sputum or BAL markers, indicating that no significant correlations between these markers exist. This analysis was limited to 29 cases as we had to deal with all missing cases in all compartments. We also performed the same analysis for serum, BAL and ISP separately and included only values from visit 1, which reduced the number of missing cases. The resulting factors were formed by comparable groups of markers within each matrix.
Although a detailed confounder analysis showed that differences in acute smoking and smoking history did not significantly influence our results, we found that mean urine cotinine levels correlated with the same factor as serum MMP9, hematocrit and hemoglobin levels when smoking behavior was included into the above mentioned analysis.
Finally, we assessed whether it would be possible to define the degree and the different aspects of airway inflammation by multiple lung markers and if such a combined phenotype would be related to a single serum marker. As a large number of different combinations are possible, we focused our analysis on markers related to neutrophilic airway inflammation. Various combinations of BAL markers, including among others calprotectin, IL8, MMP9, and NELA, did not yield a combined score that showed a better repeatability than the respective single markers or a better correlation to a serum marker. Defining an inflammatory phenotype based on the combination of repeatable sputum fluid phase markers (A1AT, IL6, MMP7, HSA and sputum neutrophils) showed a good reproducibility between visits (r = 0.70, p<0.001,
Comparison between visits for the scores of the inflammatory phenotype, which were derived from a combination of repeatable sputum fluid phase markers (A1AT, IL6, MMP7, HSA and sputum neutrophils). This combined score shows a better correlation between visits (r = 0.70, p<0.001) as compared to sputum neutrophils alone (see
In this study, alpha-1-antitrypsin (A1AT) was analyzed in serum, sputum and in BAL fluid and is the only marker, for which serum concentrations are already clinically used to estimate concentration within the lung and to guide treatment in patients with a known A1AT deficiency. Data comparing A1AT levels between the different compartments are scarce; therefore we used our dataset to test these relationships.
For all subjects, the median (interquartile range, IQR) A1AT concentration was 1.69 (0.53) g/L in serum, 505 (596) µg/L in BAL and 568 (475) µg/L in ISP. A1AT showed a moderate to good reproducibility within each matrix (serum: r = 0.55, p<0.001; BAL: r = 0.72, p<0.001; ISP: r = 0.72, p<0.001, derived from untransformed data, ICC of serum A1AT<0.6, therefore not listed in
The majority of adverse events (AEs) in this study were mild (COPD: 29.2%, healthy smokers: 34.8%) or moderate (COPD: 33.3%, healthy smokers: 17.4%) and were related to a study procedure. Overall 17 of 24 COPD smokers and 12 of 23 healthy smokers reported AEs of which cough was the most frequently used term to describe the symptoms. Two subjects experienced serious adverse events, which were not related to study procedures (1 gastrointestinal bleeding, 1 laryngeal leukoplakia), which led to hospitalization and discontinuing of the study. Overall, the conduct of the study was safe and well tolerated.
We screened a large panel of analytes to find informative and robust biomarkers, which can be used to investigate treatment effects of novel anti-inflammatory compounds for COPD. Our approach was comprehensive with respect to the number of markers, which were studied in all relevant compartments, but very focused by including only age, size, and gender matched smokers with and without moderate COPD. With more than 20 subjects in each group, our study was sufficiently sized and able not only to confirm previous results but also to reveal some currently unknown differences between groups. The collection of samples twice within a period of 4–6 weeks allowed us to assess the repeatability of markers and to create a comprehensive dataset, which was used for further exploration of the interrelationship between markers and systemic and pulmonary compartments. Our analysis revealed that only few and weak correlations between lung and serum markers exist, which does not support the hypothesis that a simple “spill over” of mediators from the lung is responsible for the systemic inflammation observed in COPD. Based on our findings, it is also unlikely that the analysis of serum markers alone will be sufficient to reflect ongoing inflammatory processes within the lung. We describe a lung function independent inflammatory phenotype with an improved repeatability compared to sputum neutrophils alone, which could assist in the understanding of how airway inflammation effects disease progression. Overall, the results of this study, including the comprehensive assay validation data, will help to select markers for clinical and COPD cohort trials.
This study was designed to identify and to test COPD disease specific biomarkers, therefore subjects were carefully matched with respect to smoking history and acute smoking. Although COPD patients reported to smoke slightly more, we did not observe significant differences with respect to urine cotinine. For some analytes, significant differences were found in male subjects only, indicating that gender effects might exist. With only 6 female subjects per group, however, we were not able to reveal gender differences, as. these were recently reported for plasma IL-6, IL-16 and VEGF
Our data confirmed COPD-related increases in the levels of several well-studied serum markers, including CRP, IL-6
A novel finding was the upregulation of Calprotectin in BAL fluid. It is considered to be a reliable faecal marker for inflammatory bowel disease. In line with our observation, increased levels of the subunits of calprotectin, S100A8 and S100A9, were found in sputum supernatants of COPD patients
Inflammation is characterized by an increased movement of leucocytes from the microcirculation into the extra-vascular tissue. Cigarette smoke can trigger leukocyte migration and activation
The recovery of BAL fluid was significantly lower in COPD patients compared to controls (median COPD: 38%, median controls: 73%) which is a well known phenomenon
We showed a good reproducibility for a large panel of markers in serum, ISP and BAL when we compared samples collected twice within a period of six weeks, indicating that the marker itself is stable within a subject over this period and that the analysis can be reliably performed. Aaron and coworkers assessed the reproducibility in serum and sputum and even collected 3 samples within the same time period
We detected only weak relationships between central (sputum) and peripheral (BAL) airways, which is compatible with other studies
Next, we used a factor analysis to structure our data and to test whether any serum marker would be related to a marker detected in the lung. While the markers basically grouped as expected, indicating the validity of our measurements, we did not find serum markers that correlated significantly with any lung marker.
Without any direct relationships between individual serum and lung markers, we choose a third approach and combined different lung markers to develop a score that characterizes the inflammatory phenotype and looked for relationships to systemic markers. This concept links to an observation by Hurst and coworkers who showed that there appears to be a COPD patient phenotype that is more susceptible to exacerbations with stable exacerbation rates that were related to the white blood cell count
It could be argued, that limiting disease states to GOLD II, which potentially reduced the variability between patients, was responsible for the lack of correlation between lung and serum markers. In the ECLIPSE study no correlation was found between serum IL-6, IL-8, CRP and SP-D and sputum neutrophils
Novel biomarkers with the ability to predict disease progression will help to test the effectiveness of novel drugs, however, only if the authorities accept that pharmacologically induced changes of these markers are clinically meaningful and expected to predict a difference in symptoms and function in the long term. Our study, with a rather small number of subjects but with well balanced and matched groups of smokers with and without COPD, could contribute to this quest as we assessed a broad panel of potential biomarkers and provided information about their feasibility and repeatability. The tables in the online supplement provide detailed information about sample dilution factors for a large panel of analytes and all relevant matrices. A list of repeatable markers is provided in
Finally, there is a large body of evidence for a role of systemic inflammation in COPD. Serum calprotectin which is related to neutrophilic inflammation as well as serum vWF, an indicator for early structural and functional changes of the pulmonary vasculature could be interesting markers for further exploration.
Twenty-four subjects with moderate COPD (GOLD II) and 23 age- and gender-matched healthy controls were enrolled into this study. All were current smokers (smoking history≥ten pack-years) free of exacerbations or acute infections within four weeks prior to screening and without chronic inflammatory diseases other than COPD. Among other inclusion criteria, a BMI >18 and ≤30 kg/m2 and a post-bronchodilator increase in FEV1 ≤15% was required. Subjects or patients with any evidence for a disease that would affect the safety especially during bronchoscopy, with a history of pneumonia within the last 6 month or of asthma were excluded. The study was conducted in accordance with Good Clinical Practice and the Declaration of Helsinki. Subjects gave their written informed consent. The study was approved by the Ethical Committee of Hannover Medical School.
During screening (maximal 3 weeks prior to visit 1), the subject's demographics and medical history was obtained (
Blood refers to the sample that was used for hematology and blood chemistry. Serum refers to the sample that was used for biomarker analysis. d = day.
The collection of bronchoalveolar lavage fluid (BAL) and bronchial biopsies was performed as described
Sputum was induced as previously described
Blood (90 ml) was collected in S-Monovettes® (Sarstedt, Nuembrecht, Germany), allowed to stand for 30 min, and then centrifuged (15 min, 1600 g). Serum was aliquoted and kept frozen at −80°C until analysis.
The analysis was performed by immunoassays (Luminex or ELISA) using commercially available kits (
A routine blood chemistry panel (25 parameters) was assessed and 4 parameters were analyzed in urine using mass spectrometry (for details please refer to
For immunocytochemical detection formalin-fixed biopsies were embedded into paraffin and 3-µm serial sections were cut and mounted on glass slides. The following primary antibodies were used: anti-CD 4 (Novocastra Laboratories Ltd., Newcastle upon Tyne, United Kingdom), anti-CD 8 (Novocastra), anti-CD68 (DakoCytomation, Glostrup, Denmark), and anti-neutrophil elastase (DakoCytomation). Antigen retrieval was performed by protease (Sigma, St. Louis, USA, P-5147) for CD68, CD4 and CD8 in a citrate-buffered solution. Slides were incubated with the primary antibody for 1 h. As secondary antibodies a biotin-SP-conjugated AffiniPure goat-anti-mouse IgG, Fc, subclass1 (Jackson Immunoresearch, USA), or a biotin-SP-conjugated AffiniPure goat-anti-mouse IgG, Fc, subclass 2b (Jackson Immunoresearch) were applied for 30 minutes. Immunostaining was done using alkaline phosphatase streptavidin-biotin (Vector Laboratories Inc, USA) and Fast Red (Fast Red substrate pack, BioGenex, USA). The slides were counterstained with Mayer's hematoxylin (Merck KGaA, Darmstadt, Germany).
Image analysis was performed using a digital camera (ColorView III Soft Imaging System, Olympus, Hamburg, Germany) connected to an automated transmission light microscope (AX70, Olympus) and the image analysis system AnalySIS Five® (Soft Imaging System GmbH, Münster, Germany). Ten images were evaluated of each slide (40-fold magnification). For marker quantification, the analyzed tissue areas were calculated by the software and all cells with positive red labeling were counted interactively on a monitor.
Prior to the statistical analysis, we corrected the original measurements for plate effects and averaged the duplicated measurements. Based on exploratory data analysis, we standardized the original measurements in BAL and sputum to the total protein content, which accounts for the overall consistency of the sample. The marker concentrations measured in urine were standardized to urine creatinine. In order to identify biomarkers that differed between groups, we conducted an analysis of variance (ANOVA) based on a linear mixed effects model (LME). Differences between groups were reported if the p-values of the parametric as well as of an additional non-parametric analysis were less than 0.05. We conducted a confounder analysis (ANCOVA) for all significant biomarker candidates by extending our original LME models with each confounding factor separately. The following confounders were considered: age, BMI, weight, cigarettes/day, pack-years, urine cotinine, BAL % recovery, FEV1 %pred., FEV1/FVC.
Interrelationships between parameters were investigated by computing the Pearson correlation coefficient. Data displayed in
Factor analysis was performed using Statistica 9.0 (Statsoft, Tulsa, USA) on both log transformed and standardized datasets. The number of factors (principal components) to be extracted was limited to 3 (Factor rotation: Varimax standard).
To obtain the inflammatory phenotype, we first transformed each selected marker and assigned a score of 1 (lowest values, first quartile of the distribution) to 4 (highest values, fourth quartile). Then we computed the mean value of the resulting scores for different combinations of markers separately for each visit.
The intra-class correlation coefficients (ICC) were derived from one-way ANOVA tables as the ratio of variance among subjects to total variance based on 2 measurements over a 6 week period (
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The authors would like to thank all volunteer subjects and patients for their participation, and the staff of the Clinical Airway Research Unit for conduction the study. The authors thank Alma Steinbach, Anita Fritz, Astrid Grunwald, Christina Goetze, Gisela Schuessler, Marion Eisenhauer and Jan-Thomas Raffael of Nycomed for excellent technical assistance and contributions to the study logistics. We are also grateful to Dr. Jürgen Volz and Klaus Hägele for their support regarding the desmosin analysis. The authors would also like to acknowledge Karin Serwatzki for excellent technical support in the analysis of bronchial biopsies. Finally, we would like to thank Linda Knirsch for helping to improve the language.