Document Detail

Persistent systemic inflammation is associated with poor clinical outcomes in COPD: a novel phenotype.
Jump to Full Text
MedLine Citation:
PMID:  22624038     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: Because chronic obstructive pulmonary disease (COPD) is a heterogeneous condition, the identification of specific clinical phenotypes is key to developing more effective therapies. To explore if the persistence of systemic inflammation is associated with poor clinical outcomes in COPD we assessed patients recruited to the well-characterized ECLIPSE cohort (NCT00292552).
METHODS AND FINDINGS: Six inflammatory biomarkers in peripheral blood (white blood cells (WBC) count and CRP, IL-6, IL-8, fibrinogen and TNF-α levels) were quantified in 1,755 COPD patients, 297 smokers with normal spirometry and 202 non-smoker controls that were followed-up for three years. We found that, at baseline, 30% of COPD patients did not show evidence of systemic inflammation whereas 16% had persistent systemic inflammation. Even though pulmonary abnormalities were similar in these two groups, persistently inflamed patients during follow-up had significantly increased all-cause mortality (13% vs. 2%, p<0.001) and exacerbation frequency (1.5 (1.5) vs. 0.9 (1.1) per year, p<0.001) compared to non-inflamed ones. As a descriptive study our results show associations but do not prove causality. Besides this, the inflammatory response is complex and we studied only a limited panel of biomarkers, albeit they are those investigated by the majority of previous studies and are often and easily measured in clinical practice.
CONCLUSIONS: Overall, these results identify a novel systemic inflammatory COPD phenotype that may be the target of specific research and treatment.
Authors:
Alvar Agustí; Lisa D Edwards; Stephen I Rennard; William MacNee; Ruth Tal-Singer; Bruce E Miller; Jørgen Vestbo; David A Lomas; Peter M A Calverley; Emiel Wouters; Courtney Crim; Julie C Yates; Edwin K Silverman; Harvey O Coxson; Per Bakke; Ruth J Mayer; Bartolome Celli;
Related Documents :
1006188 - Kidney function during hydropenia nad water diuresis in patients with idiopathic recurr...
635458 - Evidence for excessive absorption of oxalate by the colon in enteric hyperoxaluria.
22220458 - Corneal thickness in pseudoexfoliative glaucoma.
2931448 - Craniofacial and mucopolysaccharide abnormalities in kniest dysplasia.
21898138 - Patterns of dietary and herbal supplement use by multiple sclerosis patients.
24045088 - Glycine receptor and myelin oligodendrocyte glycoprotein antibodies in turkish patients...
Publication Detail:
Type:  Clinical Trial; Comparative Study; Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-05-18
Journal Detail:
Title:  PloS one     Volume:  7     ISSN:  1932-6203     ISO Abbreviation:  PLoS ONE     Publication Date:  2012  
Date Detail:
Created Date:  2012-05-24     Completed Date:  2012-09-19     Revised Date:  2014-02-20    
Medline Journal Info:
Nlm Unique ID:  101285081     Medline TA:  PLoS One     Country:  United States    
Other Details:
Languages:  eng     Pagination:  e37483     Citation Subset:  IM    
Data Bank Information
Bank Name/Acc. No.:
ClinicalTrials.gov/NCT00292552
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Biological Markers / blood*
C-Reactive Protein / analysis
Cohort Studies
Cross-Sectional Studies
Fibrinogen / analysis
Humans
Interleukin-6 / blood
Interleukin-8 / blood
Leukocyte Count
Phenotype*
Pulmonary Disease, Chronic Obstructive / complications*,  pathology*
Questionnaires
Smoking
Spirometry
Systemic Inflammatory Response Syndrome / complications*
Tumor Necrosis Factor-alpha / blood
Grant Support
ID/Acronym/Agency:
G0901697//Medical Research Council; G0901786//Medical Research Council; UL1 TR000124/TR/NCATS NIH HHS
Chemical
Reg. No./Substance:
0/Biological Markers; 0/Interleukin-6; 0/Interleukin-8; 0/Tumor Necrosis Factor-alpha; 9001-32-5/Fibrinogen; 9007-41-4/C-Reactive Protein
Investigator
Investigator/Affiliation:
Y Ivanov / ; K Kostov / ; J Bourbeau / ; M Fitzgerald / ; P Hernández / ; K Killian / ; R Levy / ; F Maltais / ; D O'Donnell / ; J Krepelka / ; J Vestbo / ; E Wouters / ; D Quinn / ; P Bakke / ; M Kosnik / ; A Agusti / ; Jaume Sauleda / ; Y Feschenko / ; V Gavrisyuk / ; L Yashina / ; W MacNee / ; D Singh / ; J Wedzicha / ; A Anzueto / ; S Braman / ; R Casaburi / ; B Celli / ; G Giessel / ; M Gotfried / ; G Greenwald / ; N Hanania / ; D Mahler / ; B Make / ; S Rennard / ; C Rochester / ; P Scanlon / ; D Schuller / ; F Sciurba / ; A Sharafkhaneh / ; T Siler / ; E Silverman / ; A Wanner / ; R Wise / ; R ZuWallack / ; H Coxson / ; L Edwards / ; R Tal-Singer / ; D Lomas / ; W MacNee / ; E Silverman / ; C Crim / ; J Vestbo / ; J Yates / ; A Agusti / ; P Calverley / ; B Celli / ; C Crim / ; B Miller / ; W MacNee / ; S Rennard / ; R Tal-Singer / ; E Wouters / ; J Yates /
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Full Text
Journal Information
Journal ID (nlm-ta): PLoS One
Journal ID (iso-abbrev): PLoS ONE
Journal ID (publisher-id): plos
Journal ID (pmc): plosone
ISSN: 1932-6203
Publisher: Public Library of Science, San Francisco, USA
Article Information
Download PDF
Agustí et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Received Day: 17 Month: 2 Year: 2012
Accepted Day: 24 Month: 4 Year: 2012
collection publication date: Year: 2012
Electronic publication date: Day: 18 Month: 5 Year: 2012
Volume: 7 Issue: 5
E-location ID: e37483
ID: 3356313
PubMed Id: 22624038
Publisher Id: PONE-D-12-05051
DOI: 10.1371/journal.pone.0037483

Persistent Systemic Inflammation is Associated with Poor Clinical Outcomes in COPD: A Novel Phenotype Alternate Title:The Systemic Inflammatory COPD Phenotype
Alvar Agustí12*
Lisa D. Edwards3
Stephen I. Rennard4
William MacNee5
Ruth Tal-Singer6
Bruce E. Miller6
Jørgen Vestbo78
David A. Lomas9
Peter M. A. Calverley10
Emiel Wouters11
Courtney Crim3
Julie C. Yates3
Edwin K. Silverman12
Harvey O. Coxson13
Per Bakke14
Ruth J. Mayer3
Bartolome Celli12
for the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) Investigators
Juan P. de Torresedit1 Role: Editor
1Thorax Institute, Hospital Clinic, Institut d’investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona and Centro de investigación en red de enfermedades respiratorias (CIBERES), Barcelona, Spain
2Fundación Investigación Sanitaria Illes Balears (FISIB), Palma de Mallorca, Spain
3GlaxoSmithKline, Research Triangle Park, North Carolina, United States of America
4University of Nebraska Medical Center, Omaha, Nebraska, United States of America
5University of Edinburgh, Edinburgh, UK
6GlaxoSmithKline, King of Prussia, Pennsylvania, United States of America
7Respiratory Section, Hvidovre Hospital/University of Copenhagen, Denmark
8Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
9University of Cambridge, Cambridge, UK
10University of Liverpool, Liverpool, UK
11University of Maastricht, Maastricht, The Netherlands
12Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
13University of British Columbia, Vancouver, Canada
14University of Bergen, Bergen, Norway
Clinica Universidad de Navarra, Spain
Correspondence: * E-mail: alvar.agusti@clinic.ub.es
Contributed by footnote: Conceived and designed the experiments: AA LDE SIR WM RT-S BEM JV DAL PMAC EW CC JCY EKS HOC PB RJM BC. Performed the experiments: AA SIR WM JV DAL PMAC EW EKS HOC PB BC. Analyzed the data: AA LDE SIR WM RT-S BEM JV DAL PMAC EW CC JCY EKS HOC PB RJM BC. Wrote the paper: AA LDE SIR WM RT-S BEM JV DAL PMAC EW CC JCY EKS HOC PB RJM BC.

Introduction

Non Communicable Diseases (NCDs), including cardiovascular diseases, chronic respiratory diseases, cancer and diabetes, are the major global health problem of the century [1]. They are the world leading cause of disease burden and mortality, are increasing in prevalence even in low- and middle-income countries, the costs incurred by uncontrolled NCDs are substantial, and they are an under-appreciated cause of poverty and hinder economic development [2]. Chronic obstructive pulmonary disease (COPD) is the major respiratory NCD [2], [3]. It affects around 10% of the adult population [4], and it is predicted that it will be the third cause of death and disability in the world by the year 2020 [5].

Persistent, low-level, systemic inflammation is thought to play a significant pathogenic role in many NCDs including COPD [6]. Elevated circulating levels of white blood cells (WBC), C-reactive protein (CRP), interleukins 6 (IL-6) and 8 (IL-8), fibrinogen and tumor necrosis factor alpha (TNFα) have been reported in patients with COPD [7][9]. However, most previous studies were small and cross-sectional, showed large variability between patients, did not consider the effects of potential confounders, such as smoking status and treatment with anti-inflammatory agents and, importantly, did not investigate their relationship with relevant clinical outcomes of the disease.

The inflammatory response is a complex network of many different cells and molecules [10], [11]. Addressing this complexity is a key challenge for a better understanding and treatment of NCDs in general [2], and COPD in particular [12], [13]. The emerging field of network medicine provides a platform to explore the complexity of apparently distinct phenotypes of a disease [14].

Because COPD is a complex disease with pulmonary and extra-pulmonary manifestations [15], the identification and prospective validation of specific clinical phenotypes is key for the development of novel and more effective therapies [16]. We hypothesized that the persistence of systemic inflammation in COPD constitutes a novel COPD phenotype [16] because it does not occur in all COPD patients but, when persistently present, it is associated with worse clinical outcomes. To test this hypothesis, we determined in 1755 COPD patients, 297 smokers and 202 non-smoker controls included in the ECLIPSE study [17]: (1) the prevalence, temporal stability and network pattern (inflammome [18]) of the six inflammatory biomarkers most often studied in COPD (WBC count, CRP, IL-6, IL-8, fibrinogen and TNFα) [8], [9]; and, (2) their relationship with clinical characteristics and relevant outcomes at 3 years follow-up. Our results support that the presence of persistent systemic inflammation constitutes a novel COPD phenotype.


Methods
Study Design and Ethics

The design and methods of the ECLIPSE study (Clinicaltrials.gov identifier NCT00292552; GSK study code SCO104960) have been published previously [17]. Briefly, ECLIPSE is an observational, longitudinal study in which, after the baseline visit, participants are evaluated at 3 months, 6 months and then every 6 months for 3 years. ECLIPSE complies with the Declaration of Helsinki and Good Clinical Practice Guidelines, and has been approved by the ethics committees/institutional review boards of the participating centers (listed in Information S1). All participants provided written informed consent.

Population

We recruited into the ECLIPSE study 2164 patients with COPD, 337 smoking and 245 non-smoking controls [15]. COPD patients were male/female subjects aged 40–75 yrs., with a baseline post-bronchodilator Forced Expiratory Volume in 1 sec. (FEV1) <80% of the reference value, an FEV1/Forced Vital Capacity (FVC) ratio ≤0.7 and a current or former smoking history of ≥10 pack-yrs., who did not report a COPD exacerbation within the 4 weeks that preceded enrollment [17]. Controls were healthy male/female subjects aged 40–75 yrs. with normal spirometry; smoker controls were current or ex-smokers with a smoking history ≥10 pack-yrs. whereas nonsmoking controls had a smoking history of <1 pack-yrs. In the current analysis we included only those subjects with complete data for the six biomarkers analyzed (1755 COPD patients (81% of the COPD patients recruited into ECLIPSE), 297 smokers with normal lung function (88%) and 202 non-smokers (83%)).

Measurements

The methodology used in the ECLIPSE study has been published at length elsewhere [15], [17]. Briefly, validated questionnaires were used to record clinical data and nutritional status was assessed as the body mass index (BMI) and fat-free mass index (FFMI), the latter measured by bioelectrical impedance [15], [17]. Exacerbations in the year prior to the study and during follow up were recorded as reported elsewhere [19]. Spirometry and the 6 minute walking distance (6MWD) were performed according to international guidelines [20], [21]. The European Community for Coal and Steel Spirometric reference values were used [22]. The BODE index was calculated as previously described [23]. Low-dose computed tomography (CT) scan of the chest (GE Healthcare or Siemens Healthcare) [15], [17] was obtained; the percentage of lung CT voxels <−950 Hounsfield Units was used to quantify level of emphysema (Pulmonary Workstation 2.0. VIDA Diagnostics, Iowa City, IA, USA) [24].

Of particular interest for the current study are the biomarker measurements. To this end, peripheral venous blood was collected into Vacutainer tubes, in the morning, after fasting overnight, at baseline and at the one year follow-up visit. Circulating WBC count was measured in a central clinical laboratory. Serum was prepared by centrifugation of whole blood at 1500 g for 10 to15 minutes and plasma (EDTA as the anticoagulant) was obtained by centrifugation at 2000 g for 10 to 15 minutes. Samples were stored at –80° until analyzed centrally. IL-6, IL-8 and TNF-α serum concentrations were determined by validated immunoassays (SearchLight Array Technology, Thermo Fisher Scientific, Rockford, IL, USA), whereas CRP (Roche Diagnostics, Mannheim, Germany) and fibrinogen (K-ASSAY fibrinogen test, Kamiya Biomedical Co., Seattle, WA, USA) levels were measured using immunoturbidometric assays validated for use with EDTA plasma. The lower limit of quantification (LLQ) for IL-6, IL-8, TNF-α, CRP and fibrinogen were 0.4 pg/mL, 0.8 pg/mL, 4.7 pg/mL, 0.02 µg/mL, and 5.4 mg/dL, respectively. Biomarker concentrations were below the LLQ in some individuals. To avoid a downward bias of the population data, a nominal level of half of the LLQ value was used in the analysis in individuals with values below the LLQ [25].

Statistical Analysis

Results are shown as mean (SD), median values [interquartile range [IQR]], frequency distribution (quartiles) or proportions, as appropriate. Because none of the continuous variables were normally distributed, Kruskal-Wallis tests were used to analyze the statistical significance of differences between groups. Differences in categorical variables were assessed using Cochran-Mantel-Haenszel tests. Logistic regression was used to investigate factors contributing to persistent systemic inflammation in patients with COPD. P-values less than 0.05 (two sided) were considered significant.


Results
Demographics and Clinical Data

Table 1 presents the main demographic and clinical characteristics of all participants at recruitment. On average, COPD patients had moderate to severe airflow limitation and, as expected, complained of more symptoms, exacerbations and cardiovascular disease than controls. Non-smokers and smokers without COPD had normal spirometry and were slightly younger than the COPD patients. There were a higher proportion of females among controls.

Cross-sectional Analysis of Systemic Inflammation at Recruitment

Figure 1 shows a box plot of the six inflammatory biomarkers measured at recruitment in the three groups of subjects studied, and Table 1 shows their median [IQR] values. Despite large variability within each group (note the logarithmic scale on Figure 1) and relatively small absolute differences between groups (Table 1), on average the WBC count and levels of CRP, IL-6 and fibrinogen were significantly higher in COPD patients than in smokers with normal lung function and nonsmokers, whereas IL-8 and TNFα values were higher in smokers without COPD (Figure 1, Table 1). CRP, IL-6 and fibrinogen were not influenced by active smoking, and WBC counts were only slightly higher in current smokers compared with former smokers and non-smokers (Table S1). In patients with COPD, the WBC count and the serum levels of CRP, IL-6 and fibrinogen, but not those of IL-8 and TNFα, tended to increase with the severity of airflow limitation (Table S2).In absolute terms, differences in the levels of systemic biomarkers between GOLD stages were small and often not consistent between stages (Table S2).

To determine the prevalence of elevated inflammatory biomarkers, values >95th percentile of healthy non-smokers were considered abnormal [26], [27] (Table S3). Seventy seven percent of non-smokers, 42% of smokers and, importantly, 30% of COPD patients did not have any abnormal biomarker, so defined. Figure S1 shows that the percentage of individuals with abnormal biomarker values was significantly shifted towards the right (more inflammation) in smokers (vs. nonsmokers), and more so in patients with COPD (vs. smokers and nonsmokers).

Figure 2 presents a network layout of the systemic inflammatory pattern in the three groups of participants. Each node of the network represents one biomarker, its size being proportional to the percentage of abnormal values (exact figure shown inside) in each group. Nodes are linked if 1% or more of subjects share abnormal values for the particular biomarkers, and the width of the link represents the size of this percentage. In non-smokers, nodes are, by definition, small but, interestingly, links are rare and thin, indicating the virtual absence of an inflammome (Figure 2). In smokers with normal spirometry, some nodes (WBC, IL-8 and TNFα) are larger (p<0.001) than in nonsmokers whereas others (CRP, IL-6 and fibrinogen) have a similar size (p = ns), and a network (inflammome) is now clearly visible, with many thick linking lines (Figure 2). In patients with COPD, the network is further developed (more and thicker links) with some nodes (WBC (p<0.03), CRP (p<0.001), IL-6(p<0.001) and fibrinogen (p<0.001)) increasing, and others (IL-8 (p<0.02) and TNFα (p<0.001)) decreasing in size as compared with smokers with normal lung function (Figure 2). This pattern was maintained when current smokers with normal spirometry were compared with former smokers with COPD (Figure S2). Because IL-8 and TNFα appear to be primarily markers of smoking and not of COPD (Table S1, and Figures 2 and S2), we excluded them from further analysis.

Longitudinal Stability of Systemic Inflammation

Figure 3 shows the proportion of COPD patients with zero, one and two (or more) biomarkers (WBC, CRP, IL-6 and fibrinogen) in their upper quartile distribution determined at baseline and one year later (Table S4). At recruitment (left bars), 28% of the COPD patients had two or more biomarkers in the upper quartile, and this was still the case for 56% of these individuals one year later (right-top bars). Overall, subjects with 2 or more biomarkers in the upper quartile both at baseline and after one year represent 16% of the population of patients studied (Figure 3).In contrast, 43% of COPD patients did not have any biomarker in the upper quartile of their distribution and this remained true for 70% of these subjects one year later (right-bottom bars). These subjects correspond to 30% of the total population studied. Their proportion decreased with the GOLD stage of airflow limitation whereas that of persistently inflamed patients increased slightly (Figure S3).The systemic inflammome determined at baseline was stable for the four biomarkers analyzed at one year follow-up in each group of participants (Figure S4).

Relationship between Systemic Inflammation, Disease Characteristics and Clinical Outcomes

Table 2 compares the baseline demographics, clinical, functional and imaging characteristics of the patients with (2+ elevated biomarker levels) and without (none) persistent (at baseline and 1 year later) systemic inflammation. Age and gender were similar in both groups, but patients with persistent systemic inflammation were more obese, had slightly more cumulative exposure to smoking and were more likely to be current smokers, were more symptomatic, had worse health status, reported a higher prevalence of COPD exacerbations and cardiovascular disease and a higher proportion used inhaled steroids, but not statins. Airflow limitation was slightly worse in these patients, as were their exercise tolerance and BODE index, but neither the prevalence of chronic bronchitis, nor the degree of airflow limitation reversibility or the extent of CT- emphysema were different between the two groups (Table 2). Table 3 presents the results of the logistic regression analysis for persistent systemic inflammation in COPD. Age, BMI (but not FFMI, suggesting a role for adipose tissue), current smoking, health status and airflow limitation were associated with increased risk of persistent, systemic inflammation. Interestingly, gender, cumulative smoking exposure, presence of chronic bronchitis, prior exacerbation rate, use of ICS, history of cardiovascular disease, statin use, exercise tolerance and the presence of emphysema were not associated with the presence of persistent systemic inflammation in COPD (Table 3).

During the three year follow up, both all-cause mortality (13% vs. 2%, p<0.001) and the annual rate of COPD exacerbations (adjusted for prior exacerbation rate (1.5 (1.5) vs. 0.9 (1.1), p<0.001) [19]) were significantly higher in individuals with persistent systemic inflammation compared with those without it. By contrast, neither the rate of FEV1 decline (−33±46 vs. −33±43 ml/yr., p = 0.905), weight loss (−1.3 (6.7) vs. −0.7 (5.5) Kg, p = 0.504) or the occurrence of new cardiovascular events (7% vs. 9%, p = 0.500) were significantly different between these two groups.


Discussion

This study provides three relevant and novel observations. First, it characterizes the systemic inflammatory network pattern (inflammome) in patients with COPD and distinguishes it from that of smokers with normal lung function and non-smokers. Secondly, it shows that systemic inflammation is not a constant feature in all COPD patients, since about a third of those studied here did not have any abnormal biomarker at baseline and about the same proportion remained ‘non-inflamed’ after one year of follow up. Finally, it identifies a subgroup of COPD patients with persistently elevated inflammatory biomarker levels that, despite relatively similar lung function impairment, had significantly increased all-cause mortality and exacerbation frequency. These inflamed patients may therefore constitute a novel distinct phenotype within the larger group of patients with COPD and could be the target of novel therapeutic strategies.

Several studies have previously reported elevated levels of circulating WBC, CRP, IL-6, IL-8, fibrinogen and TNFα in patients with clinically stable COPD [8], [28][37]. Yet, they were limited because of the relatively small numbers of patients studied, the large variability of values observed, the fact that measurements were mostly made on a single occasion, potential confounders such as smoking status and treatment with anti-inflammatory drugs were not considered and, importantly, the longitudinal relationship with relevant clinical outcomes of the disease could not be established because of their cross-sectional design. Our study overcomes these limitations and provides, therefore, novel information on the true prevalence of systemic inflammation in COPD and its importance in the progression of disease.

The inflammatory response is a complex network of multiple cell types and mediators [10], [11] which the emerging field of network medicine is only beginning to decipher [14], [38]. We used this approach [2], [12], [13]to identify relationships between systemic inflammatory biomarkers (the inflammome) [18] among smokers with and without COPD. We recognize that our results are incomplete but they showed that, at variance with current understanding [7][9], systemic inflammation is not a constant feature of COPD and that, when present for at least 1 year, it is associated with worse COPD outcomes at 3 years follow-up. Age, gender and smoking exposure were similar between non-inflamed and inflamed patients but the latter were more obese, dyspneic, had lower health related quality of life, more frequent exacerbations, worse exercise tolerance, a higher BODE index and reported more cardiovascular disease, despite similar use of statins (Table 2). Interestingly, although airflow limitation was slightly worse in patients with persistent inflammation, most pulmonary characteristics of COPD, such as the prevalence of chronic bronchitis, the degree of emphysema, the bronchodilator response and the rate of FEV1 decline during follow-up, were similar in both groups (Table 2). Logistic regression analysis identified age, BMI, current smoking, health status and airflow limitation as risk factors for persistent inflammation whereas gender, cumulative smoking exposure, presence of chronic bronchitis, prior exacerbation rate, use of ICS, history of cardiovascular disease, statin use, exercise tolerance and the presence of emphysema were excluded (Table 3). Taken together, these observations suggest that systemic inflammation in COPD need not parallel the severity of the lung disease and raises questions about its pulmonary origin (the “spill-over” hypothesis) [9]. In contrast, the fact that persistently inflamed patients were more obese supports a potential systemic origin of inflammation [39], although other potential mechanisms, such as the presence of airway bacterial colonization [40] and/or sleep apnea syndrome overlap [41] cannot be excluded because they were not investigated in ECLIPSE. The origin of systemic inflammation in COPD remains to be determined. However, our findings are consistent with those of Garcia-Aymerich et al, who using a different methodological approach (cluster analysis) also identified a “systemic” COPD subtype characterized by more systemic inflammation and a higher proportion of obesity in 342 COPD patients followed during 4 years [42].

An important observation of our study is that all-cause mortality (13% vs. 2%) and the annual rate of moderate/severe COPD exacerbations (1.5 vs. 0.9 per year) during the 3 year follow-up were higher (p<0.001) in the persistently inflamed patients, compared with non-inflamed patients. These observations are clinically relevant because the severity of airflow limitation has been used so far as the most important criteria to guide therapy in COPD [43], whereas our study shows that patients with similar levels of airflow limitation may have different outcomes depending on the presence or absence of persistent systemic inflammation. Indeed, a persistent elevation of systemic inflammatory biomarkers can occur even in patients with moderate airflow limitation (Figure S3). In this context, it is worth noting that among the 220 patients identified in this study with persistent systemic inflammation (Table 2), 89 (40%) were frequent exacerbators according to the definition of Hurst et al[19], an additional 61 (28%) had a single exacerbation, and the remaining 70 (32%) reported no exacerbations during the first year of follow up, suggesting that the frequent exacerbator phenotype [19] and the persistently inflamed phenotype described here are not necessarily identifying the same individuals. Finally, given the limited efficacy of inhaled corticosteroids in reducing systemic inflammation in COPD [44], patients with persistent systemic inflammation may require a different therapeutic approach for the optimal management of their disease that will have to be explored in future studies.

Our study has several strengths and limitations. To date, it provides the largest longitudinal investigation of systemic inflammatory biomarkers in a group of stable, well characterized COPD patients and compares their results to those of smoking and non-smoking controls [17]. This latter aspect proved important for the proper interpretation of the findings reported here, since the large biomarker variability observed required the establishment of upper normal values. Likewise, given the significant effect of smoking identified, any accurate interpretation of abnormal levels of inflammatory markers in COPD must take it into account. The fact that patients were followed prospectively for 3 years is another strength of our study because it not only allowed the assessment of the temporal stability of the biomarker levels but, importantly, the investigation of their relationship with clinically relevant outcomes, and thus the identification of a distinct subgroup of COPD patients with worse clinical outcomes associated with the persistence of systemic inflammation. Our study also has some potential limitations. First, this is a descriptive study, so our results only show associations and do not prove causality. Besides, since this is an exploratory analysis, we opted to identify as many possible differences for further investigation by not adjusting for multiple comparisons. Hence, our analyses and conclusions will need to be replicated either prospectively in a study powered for these hypotheses or in other cohorts that contain similar data. Second, the biology of the inflammatory response is complex and we studied only a limited panel of biomarkers. However, the biomarkers we chose correspond to those investigated by the majority of previous studies [8], [28][33] and are often and easily measured in clinical practice. Yet, we did not study markers of tissue repair, and it is likely that the balance between inflammation and repair is important for the pathobiology of COPD [45]. Third, patients were recruited into ECLIPSE mostly from hospital clinics and were treated according to their local physician. These considerations need to be taken into account when comparing results with untreated patients or patients managed in primary care since no patients with mild airflow limitation (GOLD grade 1) were included in the study. Finally, mortality data refers to all-cause mortality since cause-specific mortality was not recorded in the study.

In conclusion, this study begins to describe the systemic inflammatory network pattern (inflammome) associated with COPD and how it differs from that of smokers with normal lung function. It also identifies a sub-group of COPD patients with persistently increased biomarkers levels that is associated with a higher incidence of exacerbations and worse survival despite similar lung impairment, suggesting that this constitutes a novel COPD phenotype [16]. Future clinical trials will have to determine the best therapeutic strategy for these patients. This may have important therapeutic implications also for other major non-communicable diseases, including cardiovascular and metabolic diseases, also characterized by chronic low-level systemic inflammation [7], [46].


Supporting Information Figure S1

Frequency distribution of the percentage of individuals in each group with none, one or more abnormal biomarker values (>95th percentile of the nonsmoker controls) at baseline. For further explanations, see text.

(TIF)


Click here for additional data file (pone.0037483.s001.png)

Figure S2

Systemic inflammome of non-smokers (n = 202), current smokers (only) with normal lung function (n = 187) and former-smokers (only) with COPD (n = 1115) at baseline. IL-8 and TNFα are very much influenced by current smoking whereas hs-CRP, IL-6 and fibrinogen are COPD-related inflammatory biomarkers. WBC counts are influenced both by smoking and COPD. For further explanations, see text.

(TIF)


Click here for additional data file (pone.0037483.s002.png)

Figure S3

Percentage of COPD patients, by GOLD stage of airflow limitation severity, with none (blue bars) or 2+ biomarkers (red bars) in the upper quartile of the COPD distribution of values both at baseline and after one year follow-up. For further discussion, see text.

(TIF)


Click here for additional data file (pone.0037483.s003.png)

Figure S4

Systemic inflammome of the four biomarkers analyzed at baseline (upper panels) and at one year follow-up (bottom panels) in the same individuals in each group (note the same n value). Differences between groups were maintained after one year follow-up but were basically non-existent within groups, indicating stability of the systemic inflammome in each group. For further explanations, see text.

(TIF)


Click here for additional data file (pone.0037483.s004.png)

Table S1

Median [IQR] of the inflammatory biomarkers determined at baseline in COPD patients and smokers with normal lung function by smoking status.

(DOCX)


Click here for additional data file (pone.0037483.s005.docx)

Table S2

Median [IQR] of the inflammatory biomarkers determined at baseline in COPD patients by GOLD stages of airflow limitation.

(DOCX)


Click here for additional data file (pone.0037483.s006.docx)

Table S3

95th percentile values of the six biomarkers determined in healthy non-smokers at baseline. For further explanations, see text.

(DOCX)


Click here for additional data file (pone.0037483.s007.docx)

Table S4

Summary of 75th percentile value of the four biomarkers determined in COPD patients both at baseline and one year later. For further explanations, see text.

(DOCX)


Click here for additional data file (pone.0037483.s008.docx)

Information S1

Members of the ECLIPSE Steering and Scientific Committees. ECLIPSE Study Investigators and Study Centre Locations.

(DOCX)


Click here for additional data file (pone.0037483.s009.docx)


Notes

Competing Interests: BRC: Received consulting fees from Altana, AstraZeneca, Boehringer-Ingelheim and GlaxoSmithKline; speaking fees from Altana, AstraZeneca, Boehringer-Ingelheim and GlaxoSmithKline; and grant support from Boehringer-Ingelheim and GlaxoSmithKline. NL, JY, RT-S, BEM, CC, RJM and LDE: Full-time employees of GlaxoSmithKline and hold stock or stock options in GlaxoSmithKline. PB: Received lecture fees from AstraZeneca, GlaxoSmithKline and NycoMed; has participated in clinical research studies sponsored by GlaxoSmithKline, Pfizer and Boehringer-Ingelheim; is currently member of the Steering Committee and the Scientific Committee of the ECLIPSE study which is sponsored by GlaxoSmithKline PC: Received fees for serving on advisory boards for GlaxoSmithKline, AstraZeneca, Nycomed, Novartis and Boehringer Ingelheim, for expert testimony for Forest/Nycomed, and has received speaker fees from GlaxoSmithKline and Nycomed; has received travel assistance from GlaxoSmithKline to attend ECLIPSE study meetings and from Boehringer Ingelheim to attend a scientific conference. HC: Received an honorarium for serving on the steering committee for the ECLIPSE project for GlaxoSmithKline; was the co-investigator on two multi-center studies sponsored by GlaxoSmithKline and has received travel expenses to attend meetings related to the project; has three contract service agreements with GlaxoSmithKline to quantify the CT scans in subjects with COPD and a service agreement with Spiration Inc to measure changes in lung volume in subjects with severe emphysema; was the co-investigator (D Sin PI) on a Canadian Institutes of Health – Industry (Wyeth) partnership grant; has received a fee for speaking at a conference and related travel expenses from AstraZeneca (Australia); was the recipient of a GSK Clinical Scientist Award (06/2010-07/2011). DAL: Received grant support, honoraria and consultancy fees from GlaxoSmithKline. WM: Received travel assistance from GlaxoSmithKline to attend ECLIPSE study meetings. SR: Received fees for serving on advisory boards, consulting or honoraria from Almirall, APT Pharma, Aradigm, Argenta, AstraZeneca, Boehringer Ingelheim, Chiesi, Dey, Forest, GlaxoSmitkKlein, HoffmanLaRoche, MedImmune, Mpex, Novartis, Nycomed, Oriel, Otsuka, Pearl, Pfizer, Pharmaxis, Merck and Talecris. ES: Received an honorarium for a talk on COPD genetics, grant support for two studies of COPD genetics, and consulting fees from GlaxoSmithKline; honoraria for talks and consulting fees from AstraZeneca. JV: Received fees for serving on advisory boards for GlaxoSmithKline, AstraZeneca, Nycomed and Boehringer Ingelheim, and has received speaker fees from GlaxoSmithKline, AstraZeneca, Pfizer, Boehringer-Ingelheim, Chiesi, Novartis and Nycomed; has received travel assistance from GlaxoSmithKline to attend ECLIPSE study meetings; his wife has previously worked in pharmaceutical companies, including GSK and AstraZeneca. EW: Serves on an advisory board for Nycomed; has received lecture fees from GlaxoSmithKline, AstraZeneca and Novartis, and has received research grants from GlaxoSmithKline and AstraZeneca. AA: Received travel assistance from GlaxoSmithKline to attend ECLIPSE study meetings and honorarium for speaking at conferences and participating in advisory boards from Almirall, Astra-Zeneca, Boheringer-Ingelheim, Chiesi, Esteve, GSK, Medimmune, Novartis, Nycomed, Pfizer, Roche and Procter & Gamble.

Funding: The study was sponsored by GlaxoSmithKline. A Steering Committee and a Scientific Committee comprised of ten academics and six representatives of the sponsor developed the original study design and concepts, the plan for the current analyses, approved the statistical plan, had full access to the data, and were responsible for decisions regarding publication. The study sponsor did not place any restrictions on statements made in the final paper.

Authors thank all participants for their willingness to contribute to this study and all field-personnel for their commitment and quality of their work.

Principal investigators and centers participating in eclipse (NCT00292552, SC0104960)

Bulgaria: Y Ivanov, Pleven; K Kostov, Sofia. Canada: J Bourbeau, Montreal; M Fitzgerald, Vancouver; P Hernández, Halifax; K Killian, Hamilton; R Levy, Vancouver; F Maltais, Montreal; D O’Donnell, Kingston. Czech Republic: J Krepelka, Praha. Denmark: J Vestbo, Hvidovre. The Netherlands: E Wouters, Horn. New Zealand: D Quinn, Wellington. Norway: P Bakke, Bergen, Slovenia: M Kosnik, Golnik. Spain: A Agusti, Jaume Sauleda, Palma de Mallorca. Ukraine: Y Feschenko, Kiev; V Gavrisyuk, Kiev; L Yashina, W MacNee, Edinburgh; D Singh, Manchester; J Wedzicha, London. USA: A Anzueto, San Antonio, TX; S Braman, Providence. RI; R Casaburi, Torrance CA; B Celli, Boston, MA; G Giessel, Richmond, VA; M Gotfried, Phoenix, AZ; G Greenwald, Rancho Mirage, CA; N Hanania, Houston, TX; D Mahler, Lebanon, NH; B Make, Denver, CO; S Rennard, Omaha, NE; C Rochester, New Haven, CT; P Scanlon, Rochester, MN; D Schuller, Omaha, NE; F Sciurba, Pittsburg, PA; A Sharafkhaneh, Houston, TX; T Siler, St Charles, MO; E Silverman, Boston, MA; A Wanner, Miami, FL; R Wise, Baltimore, MD; R ZuWallack, Hartford, CT.

Steering Committee: H Coxson (Canada), L Edwards (GlaxoSmithKline, USA), R Tal-Singer (Co-chair, GlaxoSmithKline, USA), D Lomas (UK), W MacNee (UK), E Silverman (USA), C Crim (GlaxoSmithKline, USA), J Vestbo (Co-chair, Denmark), J Yates (GlaxoSmithKline, USA).

Scientific Committee: A Agusti (Spain), P Calverley (UK), B Celli (USA), C Crim (GlaxoSmithKline, USA), B Miller(GlaxoSmithKline, US), W MacNee (Chair, UK), S Rennard (USA), R Tal-Singer (GlaxoSmithKline, USA), E Wouters (The Netherlands), J Yates (GlaxoSmithKline, USA).


References
1. Rosenbaum L,Lamas D. Year: 2011Facing a “Slow-Motion Disaster” - The UN Meeting on Noncommunicable Diseases.New England Journal of Medicine
2. Bousquet J,Anto J,Sterk P,Adcock I,Chung K,et al. Year: 2011Systems medicine and integrated care to combat chronic noncommunicable diseases.Genome Medicine34321745417
3. Mannino DM,Buist AS. Year: 2007Global burden of COPD: risk factors, prevalence, and future trends.Lancet37076577317765526
4. Buist AS,McBurnie MA,Vollmer WM,Gillespie S,Burney P,et al. Year: 2007International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study.Lancet37074175017765523
5. Lopez AD,Murray CC. Year: 1998The global burden of disease, 1990–2020.Nat Med4124112439809543
6. De Martinis M,Franceschi C,Monti D,Ginaldi L. Year: 2005Inflamm-ageing and lifelong antigenic load as major determinants of ageing rate and longevity.FEBS Lett5792035203915811314
7. Fabbri LM,Rabe KF. Year: 2007From COPD to chronic systemic inflammatory syndrome?Lancet37079779917765529
8. Gan WQ,Man SF,Senthilselvan A,Sin DD. Year: 2004Association between chronic obstructive pulmonary disease and systemic inflammation: a systematic review and a meta-analysis.Thorax5957458015223864
9. Agusti A. Year: 2007Systemic Effects of Chronic Obstructive Pulmonary Disease: What We Know and What We Don’t Know (but Should).Proceedings of the American Thoracic Society452252517878464
10. Calvano SE,Xiao W,Richards DR,Felciano RM,Baker HV,et al. Year: 2005A network-based analysis of systemic inflammation in humans.Nature 4371032–1037. nature03985 [pii];10.1038/nature03985 [doi]
11. Nathan C. Year: 2002Points of control in inflammation.Nature42084685212490957
12. Agusti A,Vestbo J. Year: 2011Current controversies and future perspectives in chronic obstructive pulmonary disease.Am J Respir Crit Care Med 184507–513. 201103–0405PP [pii];10.1164/rccm.201103–0405PP [doi]
13. Agusti A,Sobradillo P,Celli B. Year: 2011Addressing the Complexity of Chronic Obstructive Pulmonary Disease: From Phenotypes and Biomarkers to Scale-Free Networks, Systems Biology, and P4 Medicine.Am J Respir Crit Care Med1831129113721169466
14. Barabasi AL,Gulbahce N,Loscalzo J. Year: 2011Network medicine: a network-based approach to human disease.Nat Rev Genet12566821164525
15. Agusti A,Calverley P,Celli B,Coxson H,Edwards L,et al. Year: 2010Characterisation of COPD heterogeneity in the ECLIPSE cohort.Respiratory Research1112213620831787
16. Han MK,Agusti A,Calverley PM,Celli BR,Criner G,et al. Year: 2010Chronic Obstructive Pulmonary Disease Phenotypes: The Future of COPD.Am J Respir Crit Care Med18259860420522794
17. Vestbo J,Anderson W,Coxson HO,Crim C,Dawber F,et al. Year: 2008Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE).Eur Respir J3186987318216052
18. American Association of Immunologists. Year: 2008The definition of the inflammome.An AAI recommendation for the NIH “Roadmap for Medical Research” FY 2011. The American Association of Immunologists newsletter7
19. Hurst JR,Vestbo J,Anzueto A,Locantore N,Mullerova H,et al. Year: 2010Susceptibility to Exacerbation in Chronic Obstructive Pulmonary Disease.New England Journal of Medicine3631128113820843247
20. American Thoracic Society Official Statement.Year: 1995Standardization of Spirometry. 1994 Update.Am J Respir Crit Care Med152110711367663792
21. American Thoracic Society Official Statement.Year: 2002ATS Statement: Guidelines for the Six-Minute Walk Test.Am J Respir Crit Care Med16611111712091180
22. Quanjer PH,Tammeling GJ,Cotes JE,Pedersen OF,Peslin R,et al. Year: 1993Lung volumes and forced ventilatory flows. Report Working Party Standardization of Lung Function Tests, European Community for Steel and Coal. Official Statement of the European Respiratory Society.Eur RespirJSuppl 16540
23. Celli BR,Cote CG,Marin JM,Casanova C,Montes de Oca M,et al. Year: 2004The Body-Mass Index, Airflow Obstruction, Dyspnea, and Exercise Capacity Index in Chronic Obstructive Pulmonary Disease.N Engl J Med3501005101214999112
24. Patel BD,Coxson HO,Pillai SG,Agusti AG,Calverley PM,et al. Year: 2008Airway Wall Thickening and Emphysema Show Independent Familial Aggregation in COPD.Am J Respir Crit Care Med17850050518565956
25. Muir K,Gomeni R. Year: 2004Non-compartmental analysis.Bonate PL,Howard DRPharmacokinetics in Drug Development: Clinical Study Design and AnalysisArlington, VAAAPS Press235266
26. Marshall WJ. Year: 2008The interpretation of biochemical data.Bangaert SK,Marshall WJ,Leonard MWClinical Biochemistry: metabolic and clinical aspectsPhiladelphiaChurchill Livingstone Elsevier1727
27. Vasan RS. Year: 2006Biomarkers of cardiovascular disease: molecular basis and practical considerations.Circulation 1132335–2362. 113/19/2335 [pii];10.1161/CIRCULATIONAHA.104.482570 [doi]
28. Wouters EF,Creutzberg EC,Schols AM. Year: 2002Systemic effects in COPD.Chest121127S130S12010840
29. Agusti AG,Noguera A,Sauleda J,Sala E,Pons J,et al. Year: 2003Systemic effects of chronic obstructive pulmonary disease.Eur Respir J2134736012608452
30. Pinto-Plata V,Toso J,Lee K,Bilello J,Mullerova H,et al. Year: 2006Use of Proteomic Patterns of Serum Biomarkers in Patients with Chronic Obstructive Pulmonary Disease: Correlation with Clinical Parameters.Proceedings of the American Thoracic Society346546616921105
31. Walter RE,Wilk JB,Larson MG,Vasan RS,Keaney JF,et al. Year: 2008Systemic inflammation and COPD: the Framingham Heart Study.Chest133192517908709
32. Eagan TML,Ueland T,Wagner PD,Hardie JA,Mollnes TE,et al. Year: 2010Systemic inflammatory markers in COPD: results from the Bergen COPD Cohort Study.Eur Respir J3554054819643942
33. Garcia-Rio F,Miravitlles M,Soriano JB,Munoz L,Duran-Tauleria E,et al. Year: 2010Systemic inflammation in chronic obstructive pulmonary disease: a population-based study.Respir Res 1163. 1465–9921–11–63 [pii];10.1186/1465–9921–11–63 [doi]
34. Kolsum U,Roy K,Starkey C,Borrill Z,Truman N,et al. Year: 2009The repeatability of interleukin-6, tumor necrosis factor-alpha, and C-reactive protein in COPD patients over one year.Int J Chron Obstruct Pulmon Dis414915619436686
35. Dahl M,Vestbo J,Lange P,Bojesen SE,Tybjaerg-Hansen A,et al. Year: 2007C-reactive Protein As a Predictor of Prognosis in Chronic Obstructive Pulmonary Disease.Am J Respir Crit Care Med17525025517053205
36. Dahl M,Vestbo J,Zacho J,Lange P,Tybjaerg-Hansen A,et al. Year: 2011C reactive protein and chronic obstructive pulmonary disease: a Mendelian randomisation approach.Thorax 66197–204. thx.2009.131193 [pii];10.1136/thx.2009.131193 [doi]
37. Dahl M,Tybjaerg-Hansen A,Vestbo J,Lange P,Nordestgaard BG. Year: 2001Elevated plasma fibrinogen associated with reduced pulmonary function and increased risk of chronic obstructive pulmonary disease.Am J Respir Crit Care Med1641008101111587987
38. Auffray C,Adcock IM,Chung KF,Djukanovic R,Pison C,et al. Year: 2010An integrative systems biology approach to understanding pulmonary diseases.Chest 1371410–1416. 137/6/1410 [pii];10.1378/chest.09–1850 [doi]
39. Van Gaal LF,Mertens IL,De Block CE. Year: 2006Mechanisms linking obesity with cardiovascular disease.Nature44487588017167476
40. Sethi S,Mallia P,Johnston SL. Year: 2009New Paradigms in the Pathogenesis of Chronic Obstructive Pulmonary Disease II.Proceedings of the American Thoracic Society653253419741263
41. Marin JM,Soriano JB,Carrizo SJ,Boldova A,Celli BR. Year: 2010Outcomes in Patients with Chronic Obstructive Pulmonary Disease and Obstructive Sleep Apnea.The Overlap Syndrome. Am J Respir Crit Care Med. 200912–1869OC [pii];10.1164/rccm.200912–1869OC [doi]
42. Garcia-Aymerich J,Gomez FP,Benet M,Farrero E,Basagana X,et al. Year: 2011Identification and prospective validation of clinically relevant chronic obstructive pulmonary disease (COPD) subtypes.Thorax 66430–437. thx.2010.154484 [pii];10.1136/thx.2010.154484 [doi]
43. Rabe KF,Hurd S,Anzueto A,Barnes PJ,Buist SA,et al. Year: 2007Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary.Am J Respir Crit Care Med17653255517507545
44. Sin DD,Man SFP,Marciniuk DD,Ford G,FitzGerald M,et al. Year: 2008The Effects of Fluticasone with or without Salmeterol on Systemic Biomarkers of Inflammation in Chronic Obstructive Pulmonary Disease.Am J Respir Crit Care Med1771207121418310480
45. Man SF,Xing L,Connett JE,Anthonisen NR,Wise RA,et al. Year: 2008Circulating Fibronectin to C-reactive Protein Ratio and Mortality: A Biomarker In COPD?Eur Respir J
46. De Martinis M,Franceschi C,Monti D,Ginaldi L. Year: 2006Inflammation markers predicting frailty and mortality in the elderly.Exp Mol Pathol8021922716460728

Figures

[Figure ID: pone-0037483-g001]
doi: 10.1371/journal.pone.0037483.g001.
Figure 1  Box plot (log scale) of the different biomarkers determined at baseline in COPD patients, smokers with normal lung function and nonsmokers.

For further explanations, see text.



[Figure ID: pone-0037483-g002]
doi: 10.1371/journal.pone.0037483.g002.
Figure 2  Network layout of the systemic inflammatory response (inflammome) in non-smokers (n = 202), smokers with normal lung function (n = 297) and COPD patients (n = 1755) at recruitment.

Each node of the network corresponds to one of the six inflammatory biomarkers determined in this study (see color code), and its size is proportional to the prevalence of abnormal values (>95th percentile of non-smokers) of that particular biomarker in that particular group of subjects (precise figure shown inside of each node). Two nodes are linked if more than 1% of subjects in the network share abnormal values of these two biomarkers, its width being proportional to that proportion. For further explanations, see text.



[Figure ID: pone-0037483-g003]
doi: 10.1371/journal.pone.0037483.g003.
Figure 3  Proportion of patients with none, one, or two (or more) biomarkers (WBC count, CRP, IL-6 and fibrinogen) in the upper quartile of the COPD distribution, at baseline (left bars) and after one year follow up (right bars).

For further explanations, see text.



Tables
[TableWrap ID: pone-0037483-t001] doi: 10.1371/journal.pone.0037483.t001.
Table 1  Mean (SD), median [IQR], or proportion of the main characteristics of the three groups of participants at baseline.
P-values
COPD Subjects (N = 1755) Smoker Controls (N = 297) Non-smokerControls (N = 202) Overall COPD Subjects vs Smoker Controls COPD Subjects vs Non-smoker Controls Smoker Controls vs Non-smoker Controls
Demographics
Age (yrs.) 63.5 (7.1) 55.5 (8.8) 53.0 (8.6) <0.001 <0.001 <0.001 0.002
Male (%) 1160 (66%) 162 (55%) 76 (38%) <0.001 <0.001 <0.001 <0.001
Current smoker (%) 640 (36%) 187 (63%) 0 <0.001 <0.001 <0.001 <0.001
Smoking, pack-years 48.9 (27.1) 31.7 (22.1) 0.2 (1.2) <0.001 <0.001 <0.001 <0.001
BMI, kg/m2 26.5 (5.6) 26.7 (4.6) 27.7 (5.5) 0.017 NS 0.006 NS
FFMI, kg/m2 17.2 (2.9) 17.0 (2.6) 17.3 (2.7) NS NS NS NS
Chronic bronchitis (%) 599 (34%) 29 (10%) 3 (1%) <0.001 <0.001 <0.001 <0.001
mMRC Score 1.7 (1.1) 0.2 (0.5) 0.1 (0.4) <0.001 <0.001 <0.001 0.001
SGRQ-C Total Score 49.6 (20.1) 9.4 (11.9) 5.0 (6.7) <0.001 <0.001 <0.001 <0.001
Exacerbation rate (Prior Year) 0.8 (1.2) 0.0 (0.0) 0.0 (0.0)
ICS Use (%) 1253 (71%) 3 (1%) 0 <0.001 <0.001 <0.001 NS
Cardiovascular disease (%) 577 (33%) 45 (15%) 31 (15%) <0.001 <0.001 <0.001 NS
Statin Use (%) 396 (23%) 48 (16%) 25 (12%) <0.001 0.013 <0.001 NS
Physiology and Imaging
FEV1/FVC, % 44.6 (11.4) 79.1 (5.1) 81.4 (5.2) <0.001 <0.001 <0.001 <0.001
FEV1 (L) 1.35 (0.52) 3.31 (0.75) 3.34 (0.79) <0.001 <0.001 <0.001 NS
FEV1% Predicted 48.2 (15.6) 108.8 (12.1) 115.3 (14.2) <0.001 <0.001 <0.001 <0.001
FEV1 reversibility, % 10.9 (13.8) 4.4 (5.9) 2.6 (4.0) <0.001 <0.001 <0.001 <0.001
6MWD, m 371 (121)
BODE Index 3.1 (2.1)
%LAA on CT (<−950HU) 17.6 (12.2) 2.4 (3.1) 3.9 (3.9) <0.001 <0.001 <0.001 <0.001
Inflammatory Biomarkers
White Blood Cells (X106/ml) 7.6 [6.3,9.0] 7.1 [6.1,8.6] 5.8 [5.0,7.0] <0.001 <0.001 <0.001 <0.001
High Sensitivity CRP (mg/l) 3.2 [1.5,7.1] 1.6 [0.8,3.3] 1.3 [0.6,2.7] <0.001 <0.001 <0.001 0.041
IL-6 (pg/ml) 1.5 [0.8,3.1] 0.6 [0.3,1.3] 0.4 [0.2,0.9] <0.001 <0.001 <0.001 <0.001
IL-8 (pg/ml) 6.9 [3.2,13.3] 7.8 [3.8,14.2] 4.3 [2.3,7.2] <0.001 0.013 <0.001 <0.001
Fibrinogen (mg/dl) 448.0 [388.0,517.0] 391.0 [348.0,436.0] 369.0 [326.0,432.0] <0.001 <0.001 <0.001 0.003
TNF-alpha (pg/ml) 2.35 [2.35,7.80] 2.35 [2.35,40.70] 2.35 [2.35,2.35] <0.001 <0.001 <0.001 <0.001

NS: non-significant.


[TableWrap ID: pone-0037483-t002] doi: 10.1371/journal.pone.0037483.t002.
Table 2  Comparison of baseline demographics, clinical, physiological and imaging characteristics of COPD patients with none or two (or more) biomarker levels in the upper quartile of the COPD distribution both at baseline and at one year follow-up.
Number of Biomarkers Elevated at Both Visits
0, N = 431 (30%) 2+, N = 220 (16%) p-value
Demographics and clinical data
Age (yrs.) 63.2 (6.9) 64.5 (6.5) 0.03
Male (%) 276 (64%) 152 (69%) NS
BMI, kg/m∧2 25.6 (4.8) 29.4 (7.3) <0.001
FFMI, kg/m∧2 16.9 (2.6) 18.3 (3.9) <0.001
Smoking, pack-years 44.2 (24.2) 54.7 (31.6) <0.001
Current smoker (%) 133 (31%) 88 (40%) 0.02
Chronic Bronchitis (%) 123 (29%) 78 (35%) NS
mMRC Score 1.3 (0.9) 2.0 (1.1) <0.001
SGRQ-C Total Score 42.3 (19.1) 56.8 (19.8) <0.001
Exacerbation rate (Prior Year) 0.7 (1.2) 1.1 (1.5) <0.001
ICS Use (%) 284 (66%) 174 (79%) <0.001
Cardiovascular disease (%) 113 (26%) 80 (36%) 0.007
Statin Use (%) 96 (22%) 56 (25%) NS
Physiology and Imaging
FEV1 (L) 1.49 (0.53) 1.26 (0.44) <0.001
FEV1% Predicted 52.6 (15.2) 46.0 (14.5) <0.001
FEV1 reversibility, % 11.4 (14.8) 11.7 (13.7) NS
FEV1/FVC, % 46.1 (11.1) 44.5 (10.9) NS
6MWD, m 419 (109) 336 (117) <0.001
BODE Index 2.3 (1.8) 3.8 (2.0) <0.001
%LAA on CT (<−950HU) 17.3 (12.4) 16.8 (10.1) NS

NS: non-significant.


[TableWrap ID: pone-0037483-t003] doi: 10.1371/journal.pone.0037483.t003.
Table 3  Summary of logistic regression for persistent systemic inflammation (defined as in upper quartile at both visits for at least 2 biomarkers).
Odds Ratio (95% CI) p-value AUC for Model
0.76
Demographics and clinical data
Age (yrs.) 1.045 (1.014, 1.077) 0.004
Female vs. Male 0.645 (0.383, 1.085) 0.098
BMI, kg/m2 1.125 (1.063, 1.190) <0.001
Fat free mass index, kg/m2 0.979 (0.866, 1.106) 0.728
Current smoker vs. Former smoker 2.228 (1.471, 3.375) <0.001
Smoking, pack-years 1.004 (0.997, 1.011) 0.217
Chronic bronchitis 0.929 (0.624, 1.384) 0.717
mMRC Dyspnea Score 0.949 (0.756, 1.191) 0.65
SGRQ-C Total Score 1.017 (1.004, 1.030) 0.012
Exacerbation rate (prior year) 1.097 (0.956, 1.260) 0.187
ICS Use 1.354 (0.850, 2.159) 0.202
Cardiovascular disease 0.714 (0.470, 1.085) 0.114
Statin Use 0.899 (0.576, 1.403) 0.638
Physiology and imaging
FEV1% Predicted 0.975 (0.956, 0.995) 0.014
FEV1 Reversibility 0.999 (0.987, 1.011) 0.841
FEV1/FVC (%) 1.000 (0.972, 1.028) 0.977
6MWD (m) 0.998 (0.997, 1.000) 0.13
%LAA 0.987 (0.965, 1.008) 0.219

Statistically significant factors are highlighted in bold. For further explanations, see text.



Article Categories:
  • Research Article
Article Categories:
  • Biology
    • Anatomy and Physiology
      • Immune Physiology
        • Cytokines
    • Immunology
      • Immune System
        • Cytokines
      • Immunity
        • Inflammation
Article Categories:
  • Medicine
    • Clinical Immunology
      • Immunity
        • Inflammation
    • Diagnostic Medicine
      • Pathology
        • General Pathology
          • Biomarkers
    • Epidemiology
      • Biomarker Epidemiology
    • Pulmonology
      • Chronic Obstructive Pulmonary Diseases


Previous Document:  Dietary pseudopurpurin improves bone geometry architecture and metabolism in red-bone Guishan goats.
Next Document:  Analysis of C3 suggests three periods of positive selection events and different evolutionary patter...