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.
Citation: Agustí A, Edwards LD, Rennard SI, MacNee W, Tal-Singer R, Miller BE, et al. (2012) Persistent Systemic Inflammation is Associated with Poor Clinical Outcomes in COPD: A Novel Phenotype. PLoS ONE 7(5): e37483. doi:10.1371/journal.pone.0037483
Editor: Juan P. de Torres, Clinica Universidad de Navarra, Spain
Received: February 17, 2012; Accepted: April 24, 2012; Published: May 18, 2012
Copyright: © 2012 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.
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.
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.
Non Communicable Diseases (NCDs), including cardiovascular diseases, chronic respiratory diseases, cancer and diabetes, are the major global health problem of the century . 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 . Chronic obstructive pulmonary disease (COPD) is the major respiratory NCD , . It affects around 10% of the adult population , and it is predicted that it will be the third cause of death and disability in the world by the year 2020 .
Persistent, low-level, systemic inflammation is thought to play a significant pathogenic role in many NCDs including COPD . 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 –. 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 , . Addressing this complexity is a key challenge for a better understanding and treatment of NCDs in general , and COPD in particular , . The emerging field of network medicine provides a platform to explore the complexity of apparently distinct phenotypes of a disease .
Because COPD is a complex disease with pulmonary and extra-pulmonary manifestations , the identification and prospective validation of specific clinical phenotypes is key for the development of novel and more effective therapies . We hypothesized that the persistence of systemic inflammation in COPD constitutes a novel COPD phenotype  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 : (1) the prevalence, temporal stability and network pattern (inflammome ) of the six inflammatory biomarkers most often studied in COPD (WBC count, CRP, IL-6, IL-8, fibrinogen and TNFα) , ; 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.
Study Design and Ethics
The design and methods of the ECLIPSE study (Clinicaltrials.gov identifier NCT00292552; GSK study code SCO104960) have been published previously . 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.
We recruited into the ECLIPSE study 2164 patients with COPD, 337 smoking and 245 non-smoking controls . 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 . 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%)).
The methodology used in the ECLIPSE study has been published at length elsewhere , . 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 , . Exacerbations in the year prior to the study and during follow up were recorded as reported elsewhere . Spirometry and the 6 minute walking distance (6MWD) were performed according to international guidelines , . The European Community for Coal and Steel Spirometric reference values were used . The BODE index was calculated as previously described . Low-dose computed tomography (CT) scan of the chest (GE Healthcare or Siemens Healthcare) ,  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) .
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 .
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.
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).
For further explanations, see text.
To determine the prevalence of elevated inflammatory biomarkers, values >95th percentile of healthy non-smokers were considered abnormal ,  (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.
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.
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) ) 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.
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 , –. 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 ,  which the emerging field of network medicine is only beginning to decipher , . We used this approach , , to identify relationships between systemic inflammatory biomarkers (the inflammome)  among smokers with and without COPD. We recognize that our results are incomplete but they showed that, at variance with current understanding –, 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) . In contrast, the fact that persistently inflamed patients were more obese supports a potential systemic origin of inflammation , although other potential mechanisms, such as the presence of airway bacterial colonization  and/or sleep apnea syndrome overlap  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 .
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 , 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 , 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  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 , 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 . 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 , – 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 . 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 . 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 , .
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.
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.
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.
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.
Median [IQR] of the inflammatory biomarkers determined at baseline in COPD patients and smokers with normal lung function by smoking status.
Median [IQR] of the inflammatory biomarkers determined at baseline in COPD patients by GOLD stages of airflow limitation.
95th percentile values of the six biomarkers determined in healthy non-smokers at baseline. For further explanations, see text.
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.
Members of the ECLIPSE Steering and Scientific Committees. ECLIPSE Study Investigators and Study Centre Locations.
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).
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.
- 1. Rosenbaum L, Lamas D (2011) Facing 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. (2011) Systems medicine and integrated care to combat chronic noncommunicable diseases. Genome Medicine 3: 43.
- 3. Mannino DM, Buist AS (2007) Global burden of COPD: risk factors, prevalence, and future trends. Lancet 370: 765–773.
- 4. Buist AS, McBurnie MA, Vollmer WM, Gillespie S, Burney P, et al. (2007) International variation in the prevalence of COPD (the BOLD Study): a population-based prevalence study. Lancet 370: 741–750.
- 5. Lopez AD, Murray CC (1998) The global burden of disease, 1990–2020. Nat Med 4: 1241–1243.
- 6. De Martinis M, Franceschi C, Monti D, Ginaldi L (2005) Inflamm-ageing and lifelong antigenic load as major determinants of ageing rate and longevity. FEBS Lett 579: 2035–2039.
- 7. Fabbri LM, Rabe KF (2007) From COPD to chronic systemic inflammatory syndrome? Lancet 370: 797–799.
- 8. Gan WQ, Man SF, Senthilselvan A, Sin DD (2004) Association between chronic obstructive pulmonary disease and systemic inflammation: a systematic review and a meta-analysis. Thorax 59: 574–580.
- 9. Agusti A (2007) Systemic Effects of Chronic Obstructive Pulmonary Disease: What We Know and What We Don’t Know (but Should). Proceedings of the American Thoracic Society 4: 522–525.
- 10. Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, et al. (2005) A network-based analysis of systemic inflammation in humans. Nature 437: 1032–1037. nature03985 [pii];10.1038/nature03985 [doi].
- 11. Nathan C (2002) Points of control in inflammation. Nature 420: 846–852.
- 12. Agusti A, Vestbo J (2011) Current controversies and future perspectives in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 184: 507–513. 201103–0405PP [pii];10.1164/rccm.201103–0405PP [doi].
- 13. Agusti A, Sobradillo P, Celli B (2011) Addressing 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 Med 183: 1129–1137.
- 14. Barabasi AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12: 56–68.
- 15. Agusti A, Calverley P, Celli B, Coxson H, Edwards L, et al. (2010) Characterisation of COPD heterogeneity in the ECLIPSE cohort. Respiratory Research 11: 122–136.
- 16. Han MK, Agusti A, Calverley PM, Celli BR, Criner G, et al. (2010) Chronic Obstructive Pulmonary Disease Phenotypes: The Future of COPD. Am J Respir Crit Care Med 182: 598–604.
- 17. Vestbo J, Anderson W, Coxson HO, Crim C, Dawber F, et al. (2008) Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). Eur Respir J 31: 869–873.
- 18. American Association of Immunologists (2008) The definition of the inflammome. An AAI recommendation for the NIH “Roadmap for Medical Research” FY 2011. The American Association of Immunologists newsletter. 7 p.
- 19. Hurst JR, Vestbo J, Anzueto A, Locantore N, Mullerova H, et al. (2010) Susceptibility to Exacerbation in Chronic Obstructive Pulmonary Disease. New England Journal of Medicine 363: 1128–1138.
- 20. American Thoracic Society Official Statement. (1995) Standardization of Spirometry. 1994 Update. Am J Respir Crit Care Med 152: 1107–1136.
- 21. American Thoracic Society Official Statement. (2002) ATS Statement: Guidelines for the Six-Minute Walk Test. Am J Respir Crit Care Med 166: 111–117.
- 22. Quanjer PH, Tammeling GJ, Cotes JE, Pedersen OF, Peslin R, et al. (1993) Lung 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 Respir J: Suppl 165–40.
- 23. Celli BR, Cote CG, Marin JM, Casanova C, Montes de Oca M, et al. (2004) The Body-Mass Index, Airflow Obstruction, Dyspnea, and Exercise Capacity Index in Chronic Obstructive Pulmonary Disease. N Engl J Med 350: 1005–1012.
- 24. Patel BD, Coxson HO, Pillai SG, Agusti AG, Calverley PM, et al. (2008) Airway Wall Thickening and Emphysema Show Independent Familial Aggregation in COPD. Am J Respir Crit Care Med 178: 500–505.
- 25. Muir K, Gomeni R (2004) Non-compartmental analysis. In: Bonate PL, Howard DR, editors. Pharmacokinetics in Drug Development: Clinical Study Design and Analysis. Arlington, VA: AAPS Press. pp. 235–266.
- 26. Marshall WJ (2008) The interpretation of biochemical data. In: Bangaert SK, Marshall WJ, Leonard MW, editors. Clinical Biochemistry: metabolic and clinical aspects. Philadelphia: Churchill Livingstone Elsevier. pp. 17–27.
- 27. Vasan RS (2006) Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation 113: 2335–2362. 113/19/2335 [pii];10.1161/CIRCULATIONAHA.104.482570 [doi].
- 28. Wouters EF, Creutzberg EC, Schols AM (2002) Systemic effects in COPD. Chest 121: 127S–130S.
- 29. Agusti AG, Noguera A, Sauleda J, Sala E, Pons J, et al. (2003) Systemic effects of chronic obstructive pulmonary disease. Eur Respir J 21: 347–360.
- 30. Pinto-Plata V, Toso J, Lee K, Bilello J, Mullerova H, et al. (2006) Use of Proteomic Patterns of Serum Biomarkers in Patients with Chronic Obstructive Pulmonary Disease: Correlation with Clinical Parameters. Proceedings of the American Thoracic Society 3: 465–466.
- 31. Walter RE, Wilk JB, Larson MG, Vasan RS, Keaney JF , et al. (2008) Systemic inflammation and COPD: the Framingham Heart Study. Chest 133: 19–25.
- 32. Eagan TML, Ueland T, Wagner PD, Hardie JA, Mollnes TE, et al. (2010) Systemic inflammatory markers in COPD: results from the Bergen COPD Cohort Study. Eur Respir J 35: 540–548.
- 33. Garcia-Rio F, Miravitlles M, Soriano JB, Munoz L, Duran-Tauleria E, et al. (2010) Systemic inflammation in chronic obstructive pulmonary disease: a population-based study. Respir Res 11: 63. 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. (2009) The repeatability of interleukin-6, tumor necrosis factor-alpha, and C-reactive protein in COPD patients over one year. Int J Chron Obstruct Pulmon Dis 4: 149–156.
- 35. Dahl M, Vestbo J, Lange P, Bojesen SE, Tybjaerg-Hansen A, et al. (2007) C-reactive Protein As a Predictor of Prognosis in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 175: 250–255.
- 36. Dahl M, Vestbo J, Zacho J, Lange P, Tybjaerg-Hansen A, et al. (2011) C reactive protein and chronic obstructive pulmonary disease: a Mendelian randomisation approach. Thorax 66: 197–204. thx.2009.131193 [pii];10.1136/thx.2009.131193 [doi].
- 37. Dahl M, Tybjaerg-Hansen A, Vestbo J, Lange P, Nordestgaard BG (2001) Elevated plasma fibrinogen associated with reduced pulmonary function and increased risk of chronic obstructive pulmonary disease. Am J Respir Crit Care Med 164: 1008–1011.
- 38. Auffray C, Adcock IM, Chung KF, Djukanovic R, Pison C, et al. (2010) An integrative systems biology approach to understanding pulmonary diseases. Chest 137: 1410–1416. 137/6/1410 [pii];10.1378/chest.09–1850 [doi].
- 39. Van Gaal LF, Mertens IL, De Block CE (2006) Mechanisms linking obesity with cardiovascular disease. Nature 444: 875–880.
- 40. Sethi S, Mallia P, Johnston SL (2009) New Paradigms in the Pathogenesis of Chronic Obstructive Pulmonary Disease II. Proceedings of the American Thoracic Society 6: 532–534.
- 41. Marin JM, Soriano JB, Carrizo SJ, Boldova A, Celli BR (2010) Outcomes 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. (2011) Identification and prospective validation of clinically relevant chronic obstructive pulmonary disease (COPD) subtypes. Thorax 66: 430–437. thx.2010.154484 [pii];10.1136/thx.2010.154484 [doi].
- 43. Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, et al. (2007) Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 176: 532–555.
- 44. Sin DD, Man SFP, Marciniuk DD, Ford G, FitzGerald M, et al. (2008) The Effects of Fluticasone with or without Salmeterol on Systemic Biomarkers of Inflammation in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 177: 1207–1214.
- 45. Man SF, Xing L, Connett JE, Anthonisen NR, Wise RA, et al. (2008) Circulating 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 (2006) Inflammation markers predicting frailty and mortality in the elderly. Exp Mol Pathol 80: 219–227.