Abstract
Various biomarkers have emerged as potential surrogates to represent various subgroups of chronic obstructive pulmonary disease (COPD), which manifest with different phenotypes. However, the biomarkers representing never-smokers with COPD have not yet been well elucidated. The aim of this study was to evaluate the associations of certain serum and radiological biomarkers with the presence of COPD in never-smokers. To explore the associations of serum and radiological biomarkers with the presence of COPD in never-smokers, we conducted a cross-sectional patient cohort study composed of never-smokers from the COPD in Dusty Areas (CODA) cohort, consisting of subjects living in dusty areas near cement plants in South Korea. Of the 131 never-smokers in the cohort, 77 (58.8%) had COPD. There were no significant differences in the number of subjects with high levels of inflammatory biomarkers (>90th percentile of never-smokers without COPD), including white blood cell count, total bilirubin, interleukin (IL)-6, IL-8, and C-reactive protein, or radiologic measurements (including emphysema index and mean wall area percentage) between never-smokers with COPD and those without COPD. However, the number of subjects with high uric acid was significantly higher in never-smokers with COPD than never-smokers without COPD (31.2% (24/77) vs. 11.1% (6/54); p = 0.013). In addition, multivariate analysis revealed that high uric acid was significantly associated with the presence of COPD in never-smokers (adjusted relative risk: 1.63; 95% confidence interval: 1.21, 2.18; p = 0.001). Our study suggests that high serum levels of uric acid might be a potential biomarker for assessing the presence of COPD in never-smokers.
Keywords: Serum and radiologic biomarker, uric acid, chronic obstructive pulmonary disease, never-smoker
Introduction
Chronic obstructive pulmonary disease (COPD) is characterized by persistent and generally progressive airflow limitation, which is associated with an abnormal inflammatory response in the airways and the lungs to inhaled cigarette smoke and other noxious particles.1 The heavy exposure to smoke is related to structural changes in the lung. The degree of emphysema and airway wall thickness can be measured by chest computed tomography (CT) scan and CT measurements have shown a significant correlation of emphysema and airway wall thickness with the degree of airflow obstruction in the context of COPD.2–4
COPD is also associated with low-grade systemic inflammation, which is potentially linked to the increased rate of extrapulmonary complications and comorbidities in COPD patients.5,6 Indeed, numerous observational cohort studies have evaluated various inflammatory mediators, including interleukin (IL)-6, IL-8, fibrinogen, C-reactive protein (CRP), and white blood cell (WBC) count, to show the effect of systemic inflammation and its association with comorbidities and poor outcomes in patients with COPD.5,7,8 Given that oxidative stress caused by environmental toxins, such as cigarette smoking and air pollutants, plays a central role in COPD pathogenesis, levels of uric acid and total bilirubin (TB), which possesses antioxidant properties, were assessed in patients with COPD and were found to have a significant negative association with the incidence of COPD and disease progression.9–11 In particular, these biomarkers have the benefit of being measurable via relatively simple, inexpensive, and readily accessible blood tests.
Since cigarette smoking is the primary cause of COPD, these biomarkers have mainly been evaluated in smokers with and without COPD, using never-smokers as a control group. However, the proportion of never-smokers among patients with COPD is substantial. Moreover, this proportion is increasing and COPD imposes a large public health burden on the never-smoking population.12–15 Consequently, the clinical characterization of COPD in never-smokers has begun to receive considerable attention and a recent study reported that never-smokers with COPD had lower levels of inflammatory biomarkers when compared to smokers with COPD.16 However a comprehensive comparison of levels of serum inflammatory biomarkers in never-smokers with COPD versus those in never-smokers without COPD has not been well described. Furthermore, there was no available study investigating whether never-smokers with COPD exhibit a different presentation with respect to airway wall thickness or emphysema compared with never-smokers without COPD. Thus, here we aimed to investigate whether serum inflammatory biomarkers and radiologic measurements were associated with the presence of COPD in never-smokers.
Methods
Study design
The COPD in Dusty Areas (CODA) study was a longitudinal observational study that was conducted on subjects living in dusty areas near cement plants in the Kangwon and Chungbuk provinces of South Korea.17 The participants were recruited from six administrative districts of Korea where the cement plants were located. The administrative districts have areas of approximately 40–80 km2 and 23,457 residents (participants from two districts are aged over 20 years and four districts are aged over 40 years). The cohort was composed of subjects with airflow limitation and healthy volunteers who agreed to undergo examination.
To explore the associations of serum and radiological biomarkers with COPD in never-smokers, we performed a cross-sectional study of never-smokers at the time of study enrollment who were recruited between October 2012 and November 2014 from the CODA cohort. Among the 400 patients in the CODA cohort, 131 subjects who had no smoking history were included in this study (Figure 1). All patients underwent pre- and post-bronchodilator (post-BD) spirometry to diagnose airflow limitation. Airflow limitation was defined as a post-BD forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) of <70%, and the severity of airflow limitation was defined as follows: mild (FEV1 ≥ 80% of the predicted value), moderate (50% ≤ FEV1 < 80% of the predicted value), severe (30% ≤ FEV1 < 50% of the predicted value), and very severe (FEV1 < 30% of the predicted value).1 This study was approved by the Institutional Review Board of the Kangwon National University Hospital (IRB no. 2012-06-007), and written informed consent was provided by all participants.
Figure 1.
Flow chart of study patients. BD: bronchodilator; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; COPD: chronic obstructive pulmonary disease.
Clinical, laboratory, imaging, and quality of life measurements
The detailed methods adopted in the CODA study have been previously described.17,18 The CODA study investigators collected the data detailed below via a questionnaire that was distributed to the study participants. Dyspnea and health-related quality of life were evaluated using the modified Medical Research Council (mMRC) dyspnea scale and the patient-reported COPD assessment test (CAT), respectively.
Volumetric CT scans were taken at full inspiration and expiration using a first-generation dual source CT system (Somatom Definition, Siemens Healthcare, Forchheim, Germany). The CT parameters used in CT scanner were as follows: 16 × 0.75 mm2 collimation, 100 eff. milliampere-seconds (mAs), 140 peak kilovoltage (kVp), 0.9–1 beam pitch, and 0.6 mm section thickness. CT data were reconstructed using soft kernels. The CT images were obtained without the injection of a contrast medium. Imaging data were stored in the Digital Imaging and Communications in Medicine format, which is the international standard for interconnecting medical imaging devices on standard networks. We measured the emphysema index (EI; the percentage of low attenuation area ≤ 950 Hounsfield units (HU, %LAA−950HU)) and mean wall area percentage ((WA%) of two segmental bronchi) using an in-house software program developed by the Korean obstructive lung disease study group.18–20
Spirometry was performed using an EasyOne Kit (NDD, Zurich, Switzerland) as recommended.21 Serum concentrations of IL-6 and IL-8 were measured using commercially available ELISA kits (Cloud-Clone Corp., Houston, TX, USA) according to the manufacturer’s instructions. The lower limits of quantification for IL-6 and IL-8 were 0.6 and 6.5 pg mL−1, respectively.
Statistical analyses
Categorical variables are presented as a number (percentage), and continuous variables are presented as the mean ± standard deviation or median with interquartile range (IQR), as appropriate. Categorical variables were compared using contingency chi-squared tests, and continuous variables were compared using the Mann–Whitney U test or Student’s t-test, as appropriate. In our analysis of the relative risk of inflammatory biomarkers and EI in never-smokers with COPD versus never-smokers without COPD, we defined high levels of serum inflammatory biomarkers (WBC, TB, uric acid, CRP, IL-6, and IL-8) as >90th percentile of never-smokers without COPD. We categorized EI as 5% or 10%.22,23 A modified Poisson regression model was used to determine the factors associated with the presence of COPD in never-smokers.24 In the model, for each serum biomarker and radiologic measurement, we adjusted for age, sex, and body mass index (BMI), which were generally considered to be different between patients with COPD and those without COPD.25 A two-sided p value of <0.05 was regarded as statistically significant. All statistical analyses were performed using R Statistical Software (version 3.2.5; R Foundation for Statistical Computing, Vienna, Austria) and IBM SPSS Statistics for Windows, version 23.0 (Armonk, NY, USA).
Results
Patients
The baseline characteristics of the 131 subjects in the never-smoker cohort of the CODA study are summarized in Table 1. Of the 131 never-smokers, 77 (58.8%) had COPD. The median age was 73.0 years (IQR: 70.0–77.0 years), and 72.5% (n = 95) of subjects were female. The mean BMI was 24.0 ± 3.1 kg m−2. The common extrapulmonary comorbidities were diabetes mellitus (n = 16; 12.2%), followed by cerebrovascular disease (n = 12; 9.2%), malignancy (n = 6; 4.6%), and chronic liver disease (n = 5; 3.8%). The common pulmonary comorbidities were asthma (n = 30; 22.9%) and bronchiectasis (n = 4; 3.1%). The median CAT score was 18.0 (IQR: 9.0–26.0), and the mMRC dyspnea score was 0 in 25 (19.1%), 1 in 56 (42.8%), 2 in 16 (12.2%), 3 in 26 (19.8%), and 4 in 8 subjects (6.1%). The mean post-BD FEV1/FVC was 66.7% ± 9.4%, and the median post-BD FEV1 (L; % predicted) was 1.7 L (94.0% predicted).
Table 1.
Comparison of baseline characteristics between never-smokers without COPD and those with COPD.a
| Total (n = 131) | Never-smokers without COPD (n = 54; 41.2%) | Never-smokers with COPD (n = 77; 58.8%) | p | |
|---|---|---|---|---|
| Age | 73.0 (70.0, 77.0) | 73.0 (69.0, 77.0) | 74.0 (71.0, 78.0) | 0.314 |
| Female | 95 (72.5) | 38 (70.4) | 57 (74.0) | 0.793 |
| BMI (kg m−2) | 24.0 ± 3.1 | 23.9 ± 3.2 | 24.1 ± 3.1 | 0.682 |
| Extra-pulmonary comorbidities | ||||
| Diabetes mellitus | 16 (12.2) | 7 (13.0) | 9 (11.7) | 1.0 |
| Cerebrovascular disease | 12 (9.2) | 5 (9.3) | 7 (9.1) | 1.0 |
| Malignancy | 6 (4.6) | 3 (5.6) | 3 (3.9) | 0.976 |
| Chronic liver disease | 5 (3.8) | 2 (3.7) | 3 (3.9) | 1.0 |
| Chronic kidney disease | 3 (2.3) | 2 (3.7) | 1 (1.3) | 0.772 |
| Pulmonary comorbidities | ||||
| Asthma | 30 (22.9) | 11 (20.4) | 19 (24.7) | 0.575 |
| Bronchiectasis | 4 (3.1) | 1 (1.9) | 3 (3.9) | 0.782 |
| CAT score | 18.0 (9.0, 26.0) | 18.0 (9.0, 24.0) | 19.0 (8.0, 26.0) | 0.830 |
| mMRC dyspnea scale | 0.536 | |||
| 0 | 25 (19.1) | 13 (24.1) | 12 (15.6) | |
| 1 | 56 (42.8) | 22 (40.7) | 34 (44.1) | |
| 2 | 16 (12.2) | 8 (14.8) | 8 (10.4) | |
| 3 | 26 (19.8) | 9 (16.7) | 17 (22.1) | |
| 4 | 8 (6.1) | 2 (3.7) | 6 (7.8) | |
| Severity of airflow limitations | <0.001 | |||
| Mild | 46 (35.1) | — | 46 (59.7) | |
| Moderate | 30 (22.9) | — | 30 (39.0) | |
| Severe | 1 (0.8) | — | 1 (1.3) | |
| Post-BD spirometry | ||||
| FVC (L) | 2.6 (2.1, 3.1) | 2.7 (2.2, 3.1) | 2.4 (2.1, 3.4) | 0.510 |
| FVC (% predicted) | 103.0 (93.5, 114.0) | 100.5 (93.0, 113.0) | 104.0 (94.0, 115.0) | 0.583 |
| FEV1 (L) | 1.7 (1.4, 2.1) | 2.1 (1.7, 2.3) | 1.5 (1.2, 1.9) | <0.001 |
| FEV1 (% predicted) | 94.0 (80.6, 104.5) | 99.5 (94.0, 115.0) | 86.0 (72.0, 96.0) | <0.001 |
| FEV1/FVC (%) | 66.7 ± 9.4 | 75.5 ± 3.7 | 60.6 ± 6.6 | <0.001 |
COPD: chronic obstructive pulmonary disease; BMI: body mass index; mMRC: modified Medical Research Council; BD: bronchodilator; FVC: forced vital capacity; FEV1: forced expiratory volume in 1 s; CAT: COPD assessment test.
aCategorical variables are presented as number (%) and continuous variables are presented as mean and standard deviation or median and interquartile range, as appropriate.
There were no significant differences in age, sex, BMI, the presence of extra-pulmonary comorbidities (diabetes mellitus, cerebrovascular disease, chronic kidney disease, chronic liver disease, and malignancy), the presence of pulmonary comorbidities (asthma and bronchiectasis), CAT score, mMRC dyspnea scale, or post-BD FVC (L; % predicted) between never-smokers with COPD and never-smokers without COPD. In contrast, the median post-BD FEV1 (2.1 L (99.5% predicted) vs. 1.5 L (86.0% predicted); p < 0.001) and mean post-BD FEV1/FVC (75.5% ± 3.7% vs. 60.6% ± 6.6%; p < 0.001) values were significantly higher in never-smokers without COPD than never-smokers with COPD.
Comparison of biomarkers between never-smokers with or without COPD
As shown in Table 2, there were no significant differences in the median values of any of the measured inflammatory biomarkers, including WBC, TB, uric acid, IL-6, and IL-8, between never-smokers with COPD and those without COPD. In contrast, the median CRP level was significantly higher in never-smokers without COPD than never-smokers with COPD (0.1 mg dL−1 (IQR: 0–0.2 mg dL−1) vs. 0.1 mg dL−1 (IQR: 0–0.1 mg dL−1); p = 0.043). There were no significant differences in the number of subjects with high serum WBC, TB, IL-6, IL-8, or CRP between the two groups. However, the number of subjects with high serum uric acid levels was significantly higher in the never-smokers with COPD than the never-smokers without COPD (31.2% (24/77) vs. 11.1% (6/54); p = 0.013). With respect to the total number of high serum inflammatory biomarkers (<2 vs. ≥2), there was no significant difference between never-smokers with COPD and those without COPD. Moreover, no significant differences were observed for EI or WA% between the two groups.
Table 2.
Comparison of inflammatory biomarkers and radiologic measurements between never-smokers COPD and never-smokers with COPD.a
| Never-smokers without COPD (n = 54; 41.2%) | Never-smokers with COPD (n = 77; 58.8%) | p | |
|---|---|---|---|
| Measured values of inflammatory biomarkers | |||
| WBC (× 109 cells L−1) | 6.5 (5.1, 7.5) | 5.8 (4.8, 7.0) | 0.156 |
| TB (mg dL−1) | 0.7 (0.6, 0.9) | 0.8 (0.6, 1.0) | 0.464 |
| Uric acid (mg dL−1) | 4.7 (3.7, 5.4) | 4.7 (4.2, 5.9) | 0.080 |
| IL-6 (pg mL−1) | 1.7 (0.5, 4.1) | 1.1 (0.4, 2.8) | 0.231 |
| IL-8 (pg mL−1) | 13.1 (6.0, 19.2) | 12.3 (7.9, 18.9) | 0.848 |
| CRP (mg dL−1) | 0.1 (0, 0.2) | 0.1 (0, 0.1) | 0.043 |
| High serum inflammatory biomarkersb | |||
| WBC | 6 (11.1) | 8 (10.4) | 1.0 |
| TB | 5 (9.3) | 10 (13.0) | 0.703 |
| Uric acid | 6 (11.1) | 24 (31.2) | 0.013 |
| IL-6 | 6 (11.1) | 8 (10.4) | 1.0 |
| IL-8 | 6 (11.1) | 7 (9.1) | 0.933 |
| CRP | 6 (11.1) | 10 (13.0) | 0.959 |
| Number of high inflammatory biomarkers | 0.456 | ||
| <2 | 47 (87.0) | 62 (80.5) | |
| ≥2 | 7 (13.0) | 15 (19.5) | |
| Radiologic measurements | |||
| EI (%) | 4.5 (1.3, 7.4) | 3.7 (1.6, 9.9) | 0.751 |
| EI > 5% | 24 (44.4) | 31 (40.3) | 0.766 |
| EI > 10% | 10 (18.5) | 19 (24.7) | 0.534 |
| WA% | 69.1 ± 5.1 | 67.8 ± 5.1 | 0.151 |
COPD: chronic obstructive pulmonary disease; WBC: white blood cell; IL: interleukin; TB: total bilirubin; CRP: C-reactive protein; EI: emphysema index; WA%: mean wall area percentage.
aCategorical variables are presented as number (%) and continuous variables are presented as mean and standard deviation or median and interquartile range, as appropriate.
bDefined as >90th percentile of never smokers without COPD.
Association of high-level serum inflammatory biomarkers and radiologic measurements with the presence of COPD in never-smokers
As shown in Figure 2, high serum uric acid was the only biomarker that was significantly associated with the presence of COPD in never-smokers (adjusted relative risk: 1.63; 95% confidence interval: 1.21 and 2.18; p = 0.001). On the other hand, the other inflammatory biomarkers (high levels of WBC, TB, CRP, IL-6, and IL-8) or radiologic measurements (EI > 5%, EI > 10%, and WA%) showed no significant association with the presence of COPD in never-smokers.
Figure 2.
aRR of inflammatory biomarkers and radiologic measurements for the presence of COPD in never-smokers. Each serum biomarker (high levels of WBC, TB, uric acid, CRP, IL-6, and IL-8) and radiologic measurement (EI > 5%, EI > 10%, and WA%) was adjusted for age, sex, and BMI. aRR: adjusted relative risk; COPD: chronic obstructive pulmonary disease; WBC: white blood cell; TB: total bilirubin; CRP: C-reactive protein; IL: interleukin; BMI: body mass index; EI: emphysema index; WA%: mean wall area percentage; CI: confidence interval.
Discussion
In the present study, we compared serum inflammatory biomarkers and radiologic measurements between never-smokers with COPD and never-smokers without COPD using data from the CODA cohort study. The main finding of our study is that high serum uric acid was significantly associated with the presence of COPD in never-smokers, and that this association persisted even after adjustment for other covariates. However, there were no significant differences with respect to any of the other inflammatory biomarkers (high levels of WBC, TB, CRP, IL-6, and IL-8) or radiologic measurements (EI and WA%) between never-smokers with COPD and those without COPD.
Uric acid is an end-product molecule generated from purine degradation26 that shows a complex response in patients with COPD which appears to be heavily influenced by smoking status. One cross-sectional study of smokers showed that serum uric acid levels were reduced in severe COPD.27 Another large population-based cohort study reported that lower serum uric acid levels were significantly associated with a higher rate of COPD development in current smokers.9 In contrast, a population-based cross-sectional study in Japan observed an inverse association between serum uric acid levels and FEV1 in healthy women, the majority of whom had little smoking history.28 Although one large population-based cohort study showed that there was no statistically significant association between serum uric acid and COPD in non-smokers, an incidence of COPD among non-smokers tends to increase as uric acid levels elevate, especially in the highest quintiles of uric acid.9 These results suggest that above certain level, the risk of COPD might increase with elevated uric acid among non-smoker. One in vivo study using human airway epithelial cells demonstrated that exposure to particulate matter (PM)10, a major component of air pollution, induced lung mucosal uric acid production and secretion, indicating that uric acid increases in the lung in response to PM10 exposure.29 Since outdoor air pollution is another important risk factor of the development of COPD in never-smokers, our study with a highly specific group of patients, that is, those without smoking history, extends the findings of previous studies showing that high serum uric acid levels are specifically associated with the presence of COPD in never-smokers.
With the exception of uric acid, high levels of serum inflammatory biomarkers, including WBC, CRP, IL-6, and IL-8, were not shown to be associated with the presence of COPD in never-smokers. These findings are consistent with previous studies, which showed similar levels of WBC, CRP, and fibrinogen in never-smokers with COPD and never-smokers without COPD.16 In addition, we did not identify any significant differences in radiologic measurements, including EI and WA%, between never-smokers with COPD and those without COPD. This lack of association might explain the distinctive characteristics of COPD in never-smokers that have been previously described (fewer symptoms, milder disease severity, and a lower risk of cardiovascular comorbidities and lung cancer), compared with the characteristics seen in patients with a smoking history.16 Our study also showed that never-smokers with COPD and never-smokers without COPD had similar respiratory symptoms (mMRC score) and CAT scores representing health-related quality of life. As the majority of never-smokers with COPD had mild-to-moderate airflow limitations in our study, more symptomatic never-smokers with severe grade of COPD might show a different pattern of inflammatory biomarker profiles and radiologic measurements, compared with never-smokers without COPD.
In our study, almost 59% of never-smokers had COPD, which is much higher than the prevalence of COPD in never-smokers reported in population-based studies with subjects aged ≥40 years.13,30 Age is often regarded as a risk factor for COPD.1 Considering that the median age was 73 years in our cohort, high prevalence of COPD might be due to a selection bias that people with old age or physical symptoms are more likely to participate in study. Given that asthma is a risk factor for COPD, another explanation could be the higher prevalence of bronchial asthma of 23% in our cohort than those in other studies conducted with Koreans: it was 5.4% in subjects with age >65 years in a longitudinal study31 and 13% of the Korean population with obstructive pattern of pulmonary function test had asthma according to the fourth and fifth Korean National Health and Nutrition Examination Survey.30 Although genuine environmental factor, cement plants, would impact on airway disease resulting in increasing prevalence of COPD among never-smokers, further studies are necessary to evaluate the association.
There are several limitations of the present study. First, this study was performed in dusty areas near cement plants in South Korea. Thus, the patients enrolled in this study may have been subject to selection bias, and our results may not be generalizable to all never-smokers with COPD. However, since we enrolled controls in the same district, the amount of dust exposure should be the same between never-smokers with and without COPD. Thus, we believe that the study population used was appropriate for the purposes of this study. Second, the exact mechanism underlying the bidirectional association between uric acid levels and the presence of COPD according to smoking status has not been well elucidated. Thus, further studies are needed to investigate these issues. Finally, the possibility remains that the number of patients was not sufficient to reach statistical significance regarding the negative associations between inflammatory biomarkers other than uric acid and the presence of COPD in never-smokers. Thus, further studies with larger sample sizes are needed to confirm the lack of associations between these biomarkers and the presence of COPD in never-smokers.
In conclusion, our study showed that high uric acid was independently associated with never-smokers with COPD, which suggests that uric acid might be a useful biomarker related to the presence of COPD in never-smokers.
Footnotes
Author contributions: WJK and HYP contributed to this work equally.
Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by a grant from the Ministry of Environment, Republic of Korea and Samsung Medical Center Foundation for Medical Research (SMX1151371). Hye Yun Park has received lecture fees from AstraZeneca, Novartis and Boehringer-Ingelheim.
References
- 1. GOLD. Global strategy for the diagnosis, management and prevention of chronic obstructive pulmonary disease. (2017 Report). www.goldcopd.org. 2017. (assessed July 1, 2017).
- 2. Nakano Y, Muro S, Sakai H, et al. Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function. Am J Respir Crit Care Med 2000; 162: 1102–1108. [DOI] [PubMed] [Google Scholar]
- 3. Patel BD, Coxson HO, Pillai SG, et al. Airway wall thickening and emphysema show independent familial aggregation in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2008; 178: 500–505. [DOI] [PubMed] [Google Scholar]
- 4. Kim V, Desai P, Newell JD, et al. Airway wall thickness is increased in COPD patients with bronchodilator responsiveness. Respir Res 2014; 15: 84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Gan WQ, Man SF, Senthilselvan A, et al. Association between chronic obstructive pulmonary disease and systemic inflammation: a systematic review and a meta-analysis. Thorax 2004; 59: 574–580. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Barnes PJ, Celli BR. Systemic manifestations and comorbidities of COPD. Eur Respir J 2009; 33: 1165–1185. [DOI] [PubMed] [Google Scholar]
- 7. Thomsen M, Dahl M, Lange P, et al. Inflammatory biomarkers and comorbidities in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2012; 186: 982–988. [DOI] [PubMed] [Google Scholar]
- 8. Agusti A, Edwards LD, Rennard SI, et al. Persistent systemic inflammation is associated with poor clinical outcomes in COPD: a novel phenotype. PLoS One 2012; 7: e37483. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Horsfall LJ, Nazareth I, Petersen I. Serum uric acid and the risk of respiratory disease: a population-based cohort study. Thorax 2014; 69: 1021–1026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Horsfall LJ, Rait G, Walters K, et al. Serum bilirubin and risk of respiratory disease and death. JAMA 2011; 305: 691–697. [DOI] [PubMed] [Google Scholar]
- 11. Apperley S, Park HY, Holmes DT, et al. Serum bilirubin and disease progression in Mild COPD. Chest 2015; 148: 169–175. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Tan WC, Sin DD, Bourbeau J, et al. Characteristics of COPD in never-smokers and ever-smokers in the general population: results from the CanCOLD study. Thorax 2015; 70: 822–829. [DOI] [PubMed] [Google Scholar]
- 13. Lamprecht B, McBurnie MA, Vollmer WM, et al. COPD in never smokers: results from the population-based burden of obstructive lung disease study. Chest 2011; 139: 752–763. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Nguyen Viet N, Yunus F, Nguyen Thi Phuong A, et al. The prevalence and patient characteristics of chronic obstructive pulmonary disease in non-smokers in Vietnam and Indonesia: an observational survey. Respirology 2015; 20: 602–611. [DOI] [PubMed] [Google Scholar]
- 15. Salvi SS, Barnes PJ. Chronic obstructive pulmonary disease in non-smokers. Lancet 2009; 374: 733–743. [DOI] [PubMed] [Google Scholar]
- 16. Thomsen M, Nordestgaard BG, Vestbo J, et al. Characteristics and outcomes of chronic obstructive pulmonary disease in never smokers in Denmark: a prospective population study. Lancet Respir Med 2013; 1: 543–550. [DOI] [PubMed] [Google Scholar]
- 17. Hong Y, Kwon JW, Lee SA, et al. Methodology of an observational cohort study for subjects with chronic obstructive pulmonary disease in dusty areas near cement plants. J Pulm Respir Med 2014; 4: 1000169. [Google Scholar]
- 18. Hong Y, Ji W, An S, et al. Sex differences of COPD phenotypes in nonsmoking patients. Int J Chron Obstruct Pulmon Dis 2016; 11: 1657–1662. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Lee YK, Oh YM, Lee JH, et al. Quantitative assessment of emphysema, air trapping, and airway thickening on computed tomography. Lung 2008; 186: 157–165. [DOI] [PubMed] [Google Scholar]
- 20. Hahm CR, Lim MN, Kim HY, et al. Implications of the pulmonary artery to ascending aortic ratio in patients with relatively mild chronic obstructive pulmonary disease. J Thorac Dis 2016; 8: 1524–1531. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J 2005; 26: 319–338. [DOI] [PubMed] [Google Scholar]
- 22. Hersh CP, Make BJ, Lynch DA, et al. Non-emphysematous chronic obstructive pulmonary disease is associated with diabetes mellitus. BMC Pulm Med 2014; 14: 164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Vestbo J, Edwards LD, Scanlon PD, et al. Changes in forced expiratory volume in 1 second over time in COPD. N Engl J Med 2011; 365: 1184–1192. [DOI] [PubMed] [Google Scholar]
- 24. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol 2004; 159: 702–706. [DOI] [PubMed] [Google Scholar]
- 25. Joo H, Park J, Lee SD, et al. Comorbidities of chronic obstructive pulmonary disease in Koreans: a population-based study. J Korean Med Sci 2012; 27: 901–906. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. So A, Thorens B. Uric acid transport and disease. J Clin Invest 2010; 120: 1791–1799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Nicks ME, O’Brien MM, Bowler RP. Plasma antioxidants are associated with impaired lung function and COPD exacerbations in smokers. COPD 2011; 8: 264–269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Aida Y, Shibata Y, Osaka D, et al. The relationship between serum uric acid and spirometric values in participants in a health check: the Takahata study. Int J Med Sci 2011; 8: 470–478. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Gold MJ, Hiebert PR, Park HY, et al. Mucosal production of uric acid by airway epithelial cells contributes to particulate matter-induced allergic sensitization. Mucosal Immunol 2016; 9: 809–820. [DOI] [PubMed] [Google Scholar]
- 30. Park HJ, Leem AY, Lee SH, et al. Comorbidities in obstructive lung disease in Korea: data from the fourth and fifth Korean National Health and Nutrition Examination Survey. Int J Chron Obstruct Pulmon Dis 2015; 10: 1571–1582. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Song WJ, Kim SH, Lim S, et al. Association between obesity and asthma in the elderly population: potential roles of abdominal subcutaneous adiposity and sarcopenia. Ann Allergy Asthma Immunol 2012; 109: 243–248. [DOI] [PubMed] [Google Scholar]