https://orcid.org/0000-0003-4605-9158
Figures
Abstract
Background
Chronic obstructive pulmonary disease (COPD), the most common chronic respiratory disease worldwide, not only leads to the decline of pulmonary function and quality of life consecutively, but also has become a major economic burden on individuals, families, and society in China. The purpose of this meta-analysis was to explore the risk factors for developing COPD in the Chinese population that resides in China and to provide a theoretical basis for the early prevention of COPD.
Methods
A total of 2457 cross-sectional, case-control, and cohort studies published related to risk factors for COPD in China were searched. Based on the inclusion and exclusion criteria, 20 articles were selected. Stata 11.0 was used for meta-analysis. After merging the data, the pooled effect and 95% confidence intervals (CIs) were calculated to assess the association between risk factors and COPD. Heterogeneity between studies was assessed using I2 and Cochran’s Q tests. Begg’s test was used to assess publication bias.
Results
Exposure to particulate matter less than 2.5 μm in diameter (PM2.5) (pooled effect = 1.73; 95%CI: 1.16~2.58; P <0.01), smoking history (pooled effect = 2.58; 95%CI: 2.00~3.32; P <0.01), passive smoking history (pooled effect = 1.39; 95%CI: 1.03~1.87; P = 0.03), male sex(pooled effect = 1.70; 95%CI: 1.31~2.22; P <0.01), body mass index (BMI) <18.5 kg/m2 (pooled effect = 1.73; 95%CI: 1.32~2.25; P <0.01), exposure to biomass burning emissions (pooled effect = 1.65; 95%CI: 1.32~2.06; P <0.01), childhood respiratory infections (pooled effect = 3.44; 95%CI: 1.33~8.90; P = 0.01), residence (pooled effect = 1.24; 95%CI: 1.09~1.42; P <0.01), and a family history of respiratory diseases (pooled effect = 2.04; 95%CI: 1.53~2.71; P <0.01) were risk factors for COPD in the Chinese population.
Conclusion
Early prevention of COPD could be accomplished by quitting smoking, reducing exposure to air pollutants and biomass burning emissions, maintaining body mass index between 18.5 kg/m2 and 28 kg/m2, protecting children from respiratory infections, adopting active treatments to children with respiratory diseases, and conducting regular screening for those with family history of respiratory diseases.
Citation: Chen H, Liu X, Gao X, Lv Y, Zhou L, Shi J, et al. (2021) Epidemiological evidence relating risk factors to chronic obstructive pulmonary disease in China: A systematic review and meta-analysis. PLoS ONE 16(12): e0261692. https://doi.org/10.1371/journal.pone.0261692
Editor: Surasak Saokaew, University of Phayao, THAILAND
Received: May 18, 2021; Accepted: December 8, 2021; Published: December 28, 2021
Copyright: © 2021 Chen 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.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was supported by grants from the Natural Science Foundation of China (71774116; 71603182), the Shanghai Excellent Young Talents Project in Health System (2018YQ52), the Shanghai Public Health Outstanding Young Personnel Training Program (GWV-10.2-XD07), the National Key R&D Program of China (2018YFC1314700), the Shanghai Pujiang Program (2019PJC072), and the Zhejiang Provincial Natural Science Foundation of China (LQ21H100001).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Data from the World Health Organization show that chronic obstructive pulmonary disease (COPD) has become an important contributor to the global burden of non-communicable diseases [1]. From 1990 to 2017, the prevalence of COPD showed an overall upward trend with a relative increase of 5.9%. In 2017, the global prevalence rate of COPD was approximately 3.92%. COPD is also the most common cause of death in patients with chronic respiratory diseases. Data show that in 2017, an average of 41.9 people died of COPD per 100,000 people, accounting for 5.7% of all deaths. In China, COPD has become the third most common chronic disease, with the prevalence of 4.71% in 2017 [2] and the mortality rate of 0.068% [3]. In addition, there were specific characteristics of the development of COPD in Chinese population compared with other groups due to the impact of climate change, environmental pollution, public health literacy and medical technology. Furthermore, the incidence rate of COPD was estimated to be more severe in the future. However, the prevention and control of COPD in China is far from enough.
The main clinical symptoms of COPD that greatly affect the quality of life are chronic cough, sputum expectoration, and shortness of breath after physical activity [4]. Complications such as osteoporosis [2], a decreased ability to keep balanced [5], cardiovascular diseases [6], dysphagia [7], and depression [8] are common in patients with COPD, which further increase the number of acute exacerbations, hospitalization rate, and mortality of patients with COPD and seriously affect the prognosis and quality of life of patients. In addition, patients with COPD generally have a long course of disease, and the condition continues to deteriorate over time. Because patients with advanced COPD have a decreased ability for self-care in daily life and increased disability, their family caregivers have assumed a huge financial burden [9] and experience mental stress [10].
The occurrence of COPD is not only driven by genetic factors but also by environmental factors and demographic characteristics. In domestic studies [11–13], factors such as exposure to smoke (smoking, air pollution, occupational dust, and chemicals), residential radon, inhaled corticosteroids, a low body mass index (BMI), age, sex, socioeconomic status, lung hypoplasia, asthma, airway hyper-responsiveness, HIV infection, and genetic polymorphisms were associated with the occurrence and development of COPD. A cross-sectional study conducted by Chen [14] in 10 provinces in mainland China found that smoking, environmental air pollution, underweight, chronic coughing in children, a history of parental respiratory diseases, and low education levels were the main risk factors for COPD in the Chinese population. A meta-analysis by Yang [15] pointed out that male sex, smoking, low education level, low BMI (<18.5 kg/m2), family history of respiratory diseases, history of allergies, childhood respiratory infections, repeated respiratory infections, exposure to occupational dust and biomass burning emissions, poor residential ventilation, and living in and around polluted areas may be important risk factors for COPD in mainland China. Foreign studies [16–19] also found that altitude, periodontal pathogens, and the intake of processed and unprocessed red meat were significantly correlated with COPD. Research by Busch [20] showed that genes associated with lung function play a role in a person’s susceptibility to COPD. However, there are still many limitations to the existing studies because the occurrence of COPD is associated with environmental, genetic, and other factors. Most of the current research on COPD in China is still based on cross-sectional, case-control studies and other research types with a weak form of evidence. Prospective studies, especially large population cohorts were less frequently conducted due to the difficulty of implementation; the diagnostic criteria, measurements of exposure, and distribution of sample characteristics in different studies are not all the same. Thus, horizontal comparison is difficult. In contrast, foreign researches focus on various race groups, and the results and conclusions of these studies have limited relevance in the early prevention of COPD in the Chinese population. In addition, existing meta-analyses often include retrospective observational studies alone and lack cohort studies with stronger, more reliable causal links. Further, the research included in the meta-analyses are mostly of a single area; thus, the results of the study are not representative.
This study aimed to conduct a meta-analysis on populations in multiple regions of China and to integrate various studies (including cohort, case-control, and cross-sectional studies) to explore potential risk factors for COPD in Chinese residents. This study also hopes to provide a theoretical basis for the early identification and prevention of high-risk COPD.
Methods
The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement was employed to design and report the study. All studies designed to describe risk factors for COPD were searched.
Search strategy
English and Chinese databases such as Web of Science, PubMed, CNKI and WanFang were searched using MESH terms: “COPD,” “Chronic Obstructive Pulmonary Disease,” “risk factors,” “case-control study,” “cross-sectional study,” and “cohort study.” Literature tracing and manual retrieval were also used to collect relevant literature published from January 1, 2000 to November 1, 2021. Articles associated with risk factors for COPD were initially screened using “COPD” and “risk factors,” and then retrieved from the preliminary screening results using “case-control study,” “cross-sectional study,” and “cohort study.” (S1 Table)
Study selection
Inclusion criteria were as follows. (1) Publicly published case-control, cohort, or cross-sectional studies on the risk factors for COPD. (2) Study population: The objects of all studies refers to Asian population that always live in China. (3) The definition of exposure is similar; for example, BMI <18.5 kg/m2 indicates underweight and ≥28 kg/m2 indicates obesity. (4) The case diagnosis is clear and was confirmed clinically. We defined COPD patients as subjects with FEV1 / FVC less than 70% after using post-bronchodilator, or diagnosed with chronic bronchitis, emphysema or other diseases dominated by airflow restriction by doctors.(5) The research results in the article provide the odds ratio (OR), risk ratio (RR), or at least the basic data for OR/RR calculations. Exclusion criteria were as follows. (1) repeated research. (2) OR/RR and 95% confidence intervals (CIs) were not provided and could not be calculated. (3) No confounders were adjusted. The initial search and selection of literature were completed by two authors (H Chen and X Liu) independently. Literature were screened according to the title and abstract, and those not meeting the inclusion criteria were excluded.
Data extraction
The data were extracted by two independent reviewers (X Gao and YP Lv), and judged by another author when contradictions occur. All selected data were arranged as a standard data, including: (I) the first author; (II) year of publication; (III) the area of the research; (IV) sample size; (v) type of research method; (vi) OR/RR values and 95%CIs for potential risk factors.
The quality of the cross-sectional studies was evaluated according to the Agency for Healthcare Research and Quality Literature Quality Evaluation Scale [21], including 11 standards. The quality of case-control and cohort studies were evaluated according to the Newcastle-Ottawa Scale (NOS) [22], including eight standards. Each standard score of the above literature quality evaluation scale was different. Each standard score of the above literature quality evaluation scale was different. The full score was 10 points; Studies that get ≥8 points were considered to be of high quality, 5–7 points matched the criteria of medium-quality studies and <5 points were considered to be poor-quality studies. Two researchers (L Zhou and JW Shi) independently evaluated the quality of each included study. (S1 File)
Statistical analyses
Statistical analysis was performed using Stata 11.0 (StataCorp, College Station, TX, USA). The results were reported as pooled effect with the corresponding 95%CI, and P <0.05 was considered significant. Cochran’s Q and I2 tests were used to evaluate the heterogeneity of the included studies. I2 which ranges from 0 to 100% denotes the percentage of the variability in effect estimates that is due to heterogeneity rather than sampling error (chance). We used the random-effects model when heterogeneity across the studies was large (I2 >50%, P <0.05) and fixed-effects meta-analysis at small heterogeneity (I2 <50%, P <0.05) [23]. When large heterogeneity was present, sensitivity analysis and subgroup analysis were performed to identify responsible outlier studies. The Begg’s test was used to evaluate the publication bias of the included studies, and P <0.10 was considered statistically significant.
Results
Study selection
A total of 2457 articles were identified using both database and manual searches (Fig 1). After duplicate researches were excluded, we then excluded 1672 articles based on titles and abstracts. comments and articles. After reading the full text of the remaining 335 articles, 315 papers were eliminated due to inaccurate exposure definitions or outcome diagnoses, incomplete data and unadjusted confounding factors. Finally, 20 articles with 995190 participants were selected and included in this study. (Table 1)
Study descriptions
All the included studies were performed in China. Five studies were published in Chinese [25, 28, 31, 32, 34], and the others were published in English. Three studies were conducted as case-control studies, five studies were conducted as cohort studies, and the others were conducted as cross-sectional studies.
Assessment of heterogeneity
Clinical and methodological diversity between studies led to statistical heterogeneity. The results of heterogeneity test were listed in Table 2. (Table 2) Heterogeneity was found among the potential risk factors of exposure to particulate matter less than 2.5 μm in diameter (PM2.5), smoking history, passive smoking history, sex, BMI ≥28 kg/m2, BMI <18.5 kg/m2, exposure to biomass burning emissions,family history of respiratory diseases and childhood respiratory infections. Therefore, we calculated the pooled effect values of these factors using a random-effects model.
Risk factors
Meta-analysis results showed that exposure to PM2.5 (pooled effect = 1.73; 95%CI: 1.16~2.58; P <0.01; I2 = 65.7%), smoking history (pooled effect = 2.58; 95%CI: 2.00~3.32; P <0.01; I2 = 78.5%), passive smoking history (pooled effect = 1.39; 95%CI:1.03~1.87; P = 0.03; I2 = 59.5%), male sex (pooled effect = 1.70; 95%CI: 1.31~2.22;P <0.01; I2 = 87.1%), BMI <18.5 kg/m2 (pooled effect = 1.73; 95%CI: 1.32~2.25; P <0.01; I2 = 93.5%), exposure to biomass burning emissions (pooled effect = 1.65; 95%CI: 1.32~2.06; P <0.01; I2 = 88.0%), childhood respiratory infections (pooled effect = 3.44; 95%CI: 1.33~8.90; P = 0.01; I2 = 96.6%), residence (pooled effect = 1.24; 95%CI: 1.09~1.42; P <0.01; I2 = 0.00%) and family history of respiratory diseases (pooled effect = 2.04; 95%CI: 1.53~2.71; P <0.01; I2 = 88.6%) had a significant impact on the Chinese population’s risk of developing COPD. Drinking history (pooled effect = 0.82; 95%CI: 0.54~1.23; P = 0.37; I2 = 0.00%) and body mass index (BMI) ≥28 kg/m2 (pooled effect = 0.96; 95%CI: 0.76~1.22; P = 0.75; I2 = 75.9%) are not associated with COPD of Chinese population. (Table 2)
Sensitivity analysis
Sensitivity analysis was performed to evaluate the stability and reliability of the results. In our study, there was no significant difference in the pooled effect before and after excluding study with high heterogeneity or low quality, which indicated that the results of sensitivity analysis are reliable. (Fig 2)
(A): BMI <18.5 kg/m2. (B): Exposure to PM2.5. (C): Passive smoking history. (D): Family history of respiratory diseases. (E): Residence. (F): Exposure to biomass burning emissions. (G): Smoking history. (H): Male sex.
Publication bias
The Begg’s test was used to assess potential publication bias. The results of the Begg’s test showed that there was a certain degree of asymmetry in the scatter points corresponding to exposure to biomass burning emissions (Fig 3), therefore the trim and fill analysis [43] was further performed and showed no further studies required. The other risk factors did not have significant publication bias (P>0.10). (S2 Table)
Exposure to biomass burning emissions.
Subgroup analysis
In order to address potential confounding and reduce heterogeneity, we performed several subgroup analyses by the source of research object (hospital, population), research method (case-control study, cross-sectional study, cohort study), geographic region (national, single province) and research duration (<5 years, ≥5 years). Stratifying our analysis resulted in a reduction of heterogeneity, which still exists. (S1 Fig)
Discussion
This meta-analysis showed that exposure to PM2.5, smoking history, passive smoking history,BMI <18.5 kg/m2, exposure to biomass burning emissions, childhood respiratory infections and family history of respiratory diseases were risk factors for COPD in the Chinese population.
Air pollution suspended in moist air is usually called “smoke,” which comprises dust particles of different sizes, non-metal oxides, organic compounds, and heavy metals [44]. Harmful substances in smoke can cause bronchospasms that increase airway resistance. Long-term exposure to smoke can lead to the occurrence of COPD [45]. This is consistent with the discovery of Mark et al. that an increase in exposure to smoke over a lifetime can lead to a significant increase in the risk of COPD [46]. As a risk factor for COPD in the Chinese population, exposure to smoke mainly includes exposure to PM2.5, smoking, passive smoking, and exposure to biomass burning emissions.
Atmospheric particulate matter pollution is an important factor affecting the course of various respiratory and cardiovascular diseases and is associated with a higher risk of cardiopulmonary mortality and morbidity [47]. Increasing evidence shows that PM2.5 is the most harmful air pollutant to human health. Long-term exposure to PM2.5 can induce a decline in lung function, emphysema, and changes to airway inflammation [48]. Animal experiments have shown that PM2.5 promotes lung inflammation and oxidative stress in mice [49]. Further, excessive inflammation and oxidative stress cause or aggravate respiratory diseases. A study in France showed that high exposure to PM2.5 was significantly associated with a decrease in serum cytokine levels. PM2.5 induces the expression of inflammatory cytokines in human bronchial epithelial cells through multiple pathways [50]. This change in cytokine levels can become one of the main causes of COPD by disrupting the balance of the immune response [51].
Smoking is the most important risk factors for COPD. A previous meta-analysis showed that the incidence of COPD among ex-smokers and current smokers was higher than that among never-smokers (RR values were 2.35, 2.89, and 3.51, respectively) [52]. Active or passive inhalation of cigarette smoke by the human body can cause damage to the respiratory mucosa, leading to chronic inflammation of the respiratory tract [53]. Animal experiments have shown that smoking can promote the occurrence and development of COPD through a variety of mechanisms, including hypersecretion of airway mucus, increased inflammatory cells in the airway lumen and lung parenchyma such as neutrophils and macrophages, thickening of the airway wall of lung tissue, and excessive deposition of collagen-based extracellular matrix [54]. Poorly ventilated households that use biomass fuels, including wood, animal manure, and crop residues, for cooking and heating in developing countries [55] and women and children have the highest rate of exposure to biomass burning emissions [56].
Organic and inorganic compounds and insoluble particles produced by burning biomass play an important role in inflammatory reactions, which can adversely affect the lung parenchyma, interstitium, and vasculature, thereby affecting the occurrence and development of COPD. Many people, especially in economically underdeveloped countries, cook and heat through using open fire, fuel, coal and simple stoves to burn biomass such as wood, animal manure and crop waste. It is easy to cause airway obstruction and sustained lung damage if long-term exposure to biomass, which results in an increased risk of COPD. A meta-analysis conducted by a Chinese scholar [57] showed that biomass smoke exposure was a risk factor for COPD among Chinese residents. Meanwhile, another scholar [58] found that the exposure of biomass smoke was positively correlated with the risk of developing COPD.
Notably, our results suggest that male sex may be a potential risk factor for COPD. Some studies suggested men are more likely to develop COPD. This may be associated with higher exposure to tobacco in men and changes in sex hormones in women after menopause [59]. However, some studies have shown that the prevalence of COPD has increased faster in women than it has in men in recent years [60]. This may be due to the narrower inner diameter and higher sensitivity of airways [61], more susceptible to risk factors such as biofuels and air pollution, and weaker immune regulation and stronger inflammatory responses in females than that in males. Sex hormones have complex effects on the production of COPD. Matteis et al. indicated that sex hormones in the menstrual cycle affect bronchial responsiveness and PC20FEV1.0 decrease during the follicular phase of the menstrual cycle in about 30% of women [62]. In addition, Firas et al. found that gender significantly influences the levels of inflammatory cytokines in female patients with COPD, and correlates with different clinical and physiological parameters [59].
Low body weight (BMI <18.5 kg/m2) is a risk factor for COPD. Compared with a BMI in the normal range, a low BMI is associated with a faster decline in the forced expiratory volume in 1s [63]. A study by Rabinovich et al. showed that a decrease in BMI had a negative impact on the clinical outcomes of patients with COPD [64]. A decrease in BMI causes atrophy and a decrease in the strength of respiratory muscles. This leads to a decrease in the vitality of lymphocytes and macrophages and in the production of immunoglobulin and complement, which increases the likelihood of respiratory infections and inflammation [65].
To prevent COPD, it is important to remain vigilant on matters regarding the health of the respiratory system. Lung growth and development is affected by exposure during pregnancy, birth, childhood and adolescence, and any factors affecting lung growth and development during pregnancy and childhood may increase the risk of COPD. A study in the United Kingdom showed that childhood respiratory infections had long-term adverse effects on the lungs, including frustration of the respiratory tract, impaired development of lung parenchyma, and lung growth disorders, which may lead to COPD in adulthood [66]. As a result, we recommend taking interventions to protect children from respiratory infections or adopting active treatments to children with respiratory tract infection for early prevention of COPD. In addition, because the lungs of children aged 0–18 years are immature and still undergoing growth, we believe that more attention should be paid to the pulmonary infection among children aged 0–18 years.
In addition, a large number of studies have shown that the incidence of COPD is not only associated with the aforementioned environment, living habits, and other acquired factors, but is also affected by genetic factors. To date, multiple genomic regions have been found to be associated with the COPD phenotype. McCloskey et al. [67]suggested that genetic determinants may interact with smoke to affect susceptibility to COPD. However, no genetic markers were found in the included studies. In addition, our study showed that family history of respiratory diseases (patients whose parents and / or siblings have one of chronic bronchitis, emphysema, COPD, and bronchial asthma were counted as those with family history) was also a risk factor for COPD. Studies have confirmed that there is family aggregation in COPD, however, it is difficult to distinguish whether the family aggregation of patients is caused by genetic factors or environmental factors. Therefore, more research is needed for analysis.
This meta-analysis had certain limitations. First, the most noteworthy limitation of this study was the existence of a large number of heterogeneity. In the included studies, part of the studies having only a single area included, and some studies having varying areas included. Subgroup analysis results shown that the I2 of each group is less than that of the whole group after stratification according to the geographic region (national, single province), which indicated that region is one of the reasons for high heterogeneity. In addition, almost a quarter of the included studies in this meta-analysis were hospital-based, which may have introduced bias. Second, although many of the included studies involved age as a factor, we did not analyze age as a potential risk factor because of the large inconsistencies in age division. Third, some studies mentioned that asthma and occupational exposure may also be potential risk factors for COPD, although due to the small amount or low quality of relevant literature, no specific analysis was performed in this meta-analysis on these factors. Fourth, we tried to explore the relationship between long-term exposure to PM2.5 and disease, however, the specific year of exposure was not indicated in the included literatures, which was a limitation of the study. Last, the literature included in this study did not explain the age, type of infection (virus / bacteria / fungi) and severity of infection in children with respiratory tract infection. Therefore, it is too vague to state that any respiratory infection during childhood could lead to COPD. Therefore, it is necessary to further study the effects of age, asthma, occupational exposure, long-term exposure to PM2.5 and respiratory tract infections of different types and degrees in childhood on the occurrence of COPD in the Chinese population.
Conclusion
Factors related with smoking exposure, body weight, and respiratory infections were identified as significant risk factors and potential preventive strategies for COPD. For the early prevention of COPD, clinicians and public health experts should advocate smokers to quit smoking and never-smokers not to start smoking. The government authorities should take into serious considerations for the measures to reduce air pollution and biomass burning emissions. Body mass index should be encouraged for everyone to be maintained between 18.5 kg/m2 and 28 kg/m2. Child health providers should take interventions to protect children from respiratory infections or adopt active treatments to children with respiratory infections. A regular screening is of great significance for people with family history of respiratory diseases.
Supporting information
S2 Table. Publication bias associated with potential risk factors for COPD.
https://doi.org/10.1371/journal.pone.0261692.s004
(DOCX)
S1 File. Quality evaluation of included studies.
https://doi.org/10.1371/journal.pone.0261692.s005
(XLSX)
References
- 1. GBD Chronic Respiratory Disease Collaborators. Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020 Jun;8(6):585–596. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284317/ pmid:32526187
- 2. Chen YW, Ramsook AH, Coxson HO, Bon J, Reid DW. Prevalence and Risk Factors for Osteoporosis in Individuals With COPD: A Systematic Review and Meta-analysis. Chest. 2019; 156: 1092–1110. pmid:31352034
- 3. Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen WQ, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019 Sep 28;394(10204):1145–1158. pmid:31248666
- 4. Buist AS. Guidelines for the management of chronic obstructive pulmonary disease. Resp Med. 2002; 96:11–16. pmid:12199486
- 5. Loughran KJ, Atkinson G, Beauchamp MK, Dixon J, Martin D, Rahim S, et al. Balance impairment in individuals with COPD: a systematic review with meta-analysis. Thorax. 2020 Jul;75(7):539–546. pmid:32409612
- 6. Chen W, Thomas J, Sadatsafavi M, FitzGerald JM. Risk of cardiovascular comorbidity in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. Lancet Resp Med. 2015 Aug;3(8):631–639. Available from: https://pubmed.ncbi.nlm.nih.gov/26208998/ pmid:26208998
- 7. Lin TF and Shune S. Chronic Obstructive Pulmonary Disease and Dysphagia: A Synergistic Review. Geriatrics (Basel). 2020 Aug 24;5(3):45. pmid:32847110
- 8. Yao HM, Xiao RS, Cao PL, Wang XL, Zuo W, Zhang W, et al. Risk factors for depression in patients with chronic obstructive pulmonary disease. World J Psychiatry. 2020 Apr 19;10(4):59–70. pmid:32399399
- 9. Wu LJ, Liu LX, Zhao J, Tao HX. Investigation on burden level and influencing factors of family caregivers of patients with chronic obstructive pulmonary disease. Health research. 2020; 40: 48–51.
- 10. Bagnasco A, Rosa F, Dasso N, Aleo G, Catania G, Zanini M, et al. Caring for patients at home after acute exacerbation of Chronic Obstructive Pulmonary Disease: A phenomenological study of family caregivers’ experiences. J Clin Nurs. 2020 Dec 22. pmid:33350526
- 11. Chronic obstructive pulmonary disease group, respiratory branch, Chinese Medical Association. Guidelines for the diagnosis and treatment of chronic obstructive pulmonary disease (revised version in 2013). Chinese Journal of Frontier medicine (electronic version). 2014; 6: 67–80.
- 12. Liao Q and Tao YJ. Epidemiology and risk factors of chronic obstructive pulmonary disease. Chinese Journal of clinicians (Electronic Edition). 2018; 12: 468–471.
- 13. LAN FL, Wang SF, Cao WH, Li LM. New progress in epidemiological study on risk factors of chronic obstructive pulmonary disease. Chinese Journal of disease control. 2014 Jun 18;(6): 998–1002
- 14. Wang C, Xu J, Yang L, Xu YJ, Zhang XY, Bai CX, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet. 2018 Apr 28;391(10131):1706–1717. pmid:29650248
- 15. Yang Y, Mao J, Ye Z, Li J, Zhao H, Liu YT. Risk factors of chronic obstructive pulmonary disease among adults in Chinese mainland: A systematic review and meta-analysis. Respir Med. 2017 Oct;131:158–165. pmid:28947023
- 16. Bourgeois D, Inquimbert C, Ottolenghi L, Carrouel F. Periodontal Pathogens as Risk Factors of Cardiovascular Diseases, Diabetes, Rheumatoid Arthritis, Cancer, and Chronic Obstructive Pulmonary Disease-Is There Cause for Consideration? Microorganisms. 2019 Oct 9;7(10):424. pmid:31600905
- 17. Caballero A, Torres-Duque CA, Jaramillo C, Bolívar F, Sanabria F, Osorio P, et al. Prevalence of COPD in five Colombian cities situated at low, medium, and high altitude (PREPOCOL study). Chest. 2008 Feb;133(2):343–9. pmid:17951621.
- 18. Salari-Moghaddam A, Milajerdi A, Larijani B, Esmaillzadeh A. Processed red meat intake and risk of COPD: A systematic review and dose-response meta-analysis of prospective cohort studies. Clin Nutr. 2019 Jun;38(3):1109–1116. pmid:29909249.
- 19. Kaluza J, Larsson SC, Linden A, Wolk A. Consumption of Unprocessed and Processed Red Meat and the Risk of Chronic Obstructive Pulmonary Disease: A Prospective Cohort Study of Men. Am J Epidemiol. 2016 Dec 1;184(11):829–836. pmid:27789447.
- 20. Busch R, Hobbs BD, Zhou J, Castaldi PJ, McGeachie MJ, Hardin ME, et al. Genetic Association and Risk Scores in a Chronic Obstructive Pulmonary Disease Meta-analysis of 16,707 Subjects. Am J Respir Cell Mol Biol. 2017 Jul;57(1):35–46. pmid:28170284
- 21.
Agency for Healthcare Research and Quality (US). Appendix D. Quality Assessment Forms. Available from: https://www.ncbi.nlm.nih.gov/books/NBK35156/ (2004, accessed 28 February 2021).
- 22.
Wells GA, Shea B, Connell DO, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized studies in meta-analyses. Available from: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (2014, accessed 28 February 2021).
- 23.
Higgins JPT and Green S. Cochrane Handbook for Systematic Reviews of Interventions. Available from: http://handbook.cochrane.org/ (2014, accessed 28 February 2021).
- 24. Yan X, Xu L, Shi B, Wang H, Xu X, Xu G. Epidemiology and risk factors of chronic obstructive pulmonary disease in Suzhou: a population-based cross-sectional study. J Thorac Dis. 2020 Oct;12(10):5347–5356. pmid:33209368
- 25. Tang YM, Li X, Zhang L, Liu XN, Li Q, Pan JJ, et al. Epidemiological investigation and influencing factors of chronic obstructive pulmonary disease in Hubei Province. Chinese Journal of disease control. 2018; 22(7): 721–725.
- 26. Peng Y, Li X, Cai S, Chen Y, Dai W, Liu W, et al. Prevalence and characteristics of COPD among pneumoconiosis patients at an occupational disease prevention institute: a cross-sectional study.BMC Pulm Med. 2018 Jan 29;18(1):22. pmid:29378587
- 27. Zhao C, Wong L, Zhu Q, Yang H. Prevalence and correlates of chronic diseases in an elderly population: A community-based survey in Haikou.PLoS One. 2018 Jun 14;13(6):e0199006. pmid:29902222
- 28. Zhang Y, Wu T, Ma L, Tian Y, Jin Y, Ma F, et al. Investigation of the epidemic characteristics and risk factor for chronic obstructive pulmonary disease among residents from monitoring point in Ningxia. Chinese Journal of Disease Control & Prevention. 2018;22(3):231–4.
- 29. Liu S, Zhou Y, Liu S, Chen X, Zou W, Zhao D, et al. Association between exposure to ambient particulate matter and chronic obstructive pulmonary disease: results from a cross-sectional study in China. Thorax. 2017 Sep;72(9):788–795. pmid:27941160
- 30. Ding Y, Xu J, Yao J, Chen Y, He P, Ouyang Y, et al. The analyses of risk factors for COPD in the Li ethnic group in Hainan, People’s Republic of China. Int J Chron Obstruct Pulmon Dis. 2015 Nov 30;10:2593–600. pmid:26664107
- 31. Yu J, Guo S, Peng B. Prevalence and risk factors of chronic obstructive pulmonary disease in health examination population of Chongqing[J]. Journal of Chongqing Medical University. 2015,40(09): 1264–1268.
- 32. Hou G, Yin Y, Sun L. Prevalence Rate and Risk Factors of Chronic Obstructive Pulmonary Disease in Residents Aged 35 Years or Older in Communities of Shenyang City[J]. Chinese General Practice,2012, 15 (6A): 1831–1833.
- 33. Zhong N, Wang C, Yao W, Chen P, Kang J, Huang S, et al. Prevalence of chronic obstructive pulmonary disease in China: a large, population-based survey. Am J Respir Crit Care Med. 2007 Oct 15;176(8):753–60. pmid:17575095.
- 34. Ran P-X, Wang C, Yao W-Z, et al. The risk factors for chronic obstructive pulmonary disease in females in Chinese rural areas[J]. Zhonghua nei ke za zhi,2006, 45 (12): 974–9. pmid:17326992
- 35. Xu F, Yin X, Zhang M, Shen H, Lu L, Xu Y. Prevalence of physician-diagnosed COPD and its association with smoking among urban and rural residents in regional mainland China. Chest. 2005 Oct;128(4):2818–23. pmid:16236960
- 36. Li J, Zhu L, Wei Y, Lv J, Guo Y, Bian Z, et al. Association between adiposity measures and COPD risk in Chinese adults. Eur Respir J. 2020 Apr 30;55(4):1901899. pmid:31980495
- 37. Li J, Qin C, Lv J, Guo Y, Bian Z, Zhou W, et al. Solid Fuel Use and Incident COPD in Chinese Adults: Findings from the China Kadoorie Biobank. Environ Health Perspect. 2019 May;127(5):57008. pmid:31095433
- 38. Zhou Y, Wang D, Liu S, Lu J, Zheng J, Zhong N, et al. The association between BMI and COPD: the results of two population-based studies in Guangzhou, China. Copd. 2013 Oct;10(5): 567–572. pmid:23844907
- 39. Yin P, Jiang CQ, Cheng KK, Lam TH, Lam KH, Miller MR, et al. Passive smoking exposure and risk of COPD among adults in China: the Guangzhou Biobank Cohort Study. Lancet. 2007 Sep 1;370(9589): 751–757. pmid:17765524
- 40. Huang HC, Lin FC, Wu MF, Nfor ON, Hsu SY, Lung CC, et al. Association between chronic obstructive pulmonary disease and PM2.5 in Taiwanese nonsmokers. Int J Hyg Environ Health. 2019 Jun;222(5):884–888. pmid:30962144.
- 41. Chan TC, Wang HW, Tseng TJ, Chiang PH. Spatial Clustering and Local Risk Factors of Chronic Obstructive Pulmonary Disease (COPD). Int J Environ Res Public Health. 2015 Dec 10;12(12): 15716–15727. pmid:26690457
- 42. Chan-Yeung M, Ho AS, Cheung AH, Liu RW, Yee WK, Sin KM, et al. Determinants of chronic obstructive pulmonary disease in Chinese patients in Hong Kong. Int J Tuberc Lung Dis. 2007 May; 11: 502–507. https://pubmed.ncbi.nlm.nih.gov/17439672/ pmid:17439672
- 43. Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method oftesting and adjusting for publication Bias in Meta-analysis. Biometrics. 2000 Jun;56(2):455–63. pmid:10877304.
- 44. Grzywa-Celińska A, Krusiński A and Milanowski J.’Smoging kills’—Effects of air pollution on human respiratory system. Ann Agric Environ Med. 2020 Mar 17;27(1):1–5. pmid:32208572.
- 45. Tonnesen P. Smoking cessation and COPD. Eur Respir Rev. 2013 Mar 1;22(127):37–43. pmid:23457163.
- 46. Eisner MD, Balmes J, Katz PP, Trupin L, Yelin EH, Blanc PD. Lifetime environmental tobacco smoke exposure and the risk of chronic obstructive pulmonary disease. Environ Health. 2005 May 12;4(1):7. pmid:15890079
- 47. Chi R, Chen C, Li H, Pan L, Zhao B, Deng F, et al. Different health effects of indoor- and outdoor-originated PM(2.5) on cardiopulmonary function in COPD patients and healthy elderly adults. Indoor Air. 2019 Mar;29(2):192–201. pmid:30427075.
- 48. Zieliński M, Gąsior M, Jastrzębski D, Desperak A, Ziora D. Influence of particulate matter air pollution on exacerbation of chronic obstructive pulmonary disease depending on aerodynamic diameter and the time of exposure in the selected population with coexistent cardiovascular diseases. Adv Respir Med. 2018;86(5):227–233. pmid:30378650.
- 49. Zhou T, Zhong Y, Hu Y, Sun C, Wang Y, Wang G. PM(2.5) downregulates miR-194-3p and accelerates apoptosis in cigarette-inflamed bronchial epithelium by targeting death-associated protein kinase 1. Int J Chron Obstruct Pulmon Dis. 2018 Aug 1;13:2339–2349. pmid:30122914
- 50. Zou W, Wang X, Hong W, He F, Hu J, Sheng Q, et al. PM2.5 Induces the Expression of Inflammatory Cytokines via the Wnt5a/Ror2 Pathway in Human Bronchial Epithelial Cells. Int J Chron Obstruct Pulmon Dis. 2020 Oct 23;15:2653–2662. pmid:33122903
- 51. Audi C, Baïz N, Maesano CN, Ramousse O, Reboulleau D, Magnan A, et al. Serum cytokine levels related to exposure to volatile organic compounds and PM(2.5) in dwellings and workplaces in French farmers—a mechanism to explain nonsmoking COPD. Int J Chron Obstruct Pulmon Dis. 2017 May 5;12:1363–1374. pmid:28503065
- 52. Forey BA, Thornton AJ, Lee PN. Systematic review with meta-analysis of the epidemiological evidence relating smoking to COPD, chronic bronchitis and emphysema.BMC Pulm Med. 2011 Jun 14;11:36. pmid:21672193.
- 53. Reddy AT, Lakshmi SP, Banno A, Jadhav SK, Pulikkal Kadamberi I, Kim SC,et al. Cigarette smoke downregulates Nur77 to exacerbate inflammation in chronic obstructive pulmonary disease (COPD). PLoS One. 2020 Feb 21;15(2):e0229256. pmid:32084204
- 54. Strzelak A, Ratajczak A, Adamiec A, Feleszko W. Tobacco Smoke Induces and Alters Immune Responses in the Lung Triggering Inflammation, Allergy, Asthma and Other Lung Diseases: A Mechanistic Review. Int J Environ Res Public Health. 2018 May 21;15(5):1033. pmid:29883409
- 55. Balmes JR. Household air pollution from domestic combustion of solid fuels and health. J Allergy Clin Immunol. 2019 Jun;143(6):1979–1987. pmid:31176380.
- 56. Falfán-Valencia R, Ramírez-Venegas A, Pérez Lara-Albisua JL, Ramírez-Rodriguez SL, Márquez-García JE, Buendía-Roldan I, et al. Smoke exposure from chronic biomass burning induces distinct accumulative systemic inflammatory cytokine alterations compared to tobacco smoking in healthy women. Cytokine. 2020 Jul;131:155089. pmid:32283440.
- 57. An J, Bao H, Fang L. Relationship between biomass smoke exposure and chronic obstructive pulmonary disease among residents in China: a meta-analysis[J]. China Journal of Public Health,2016, 32 (7): 999–1004.
- 58. Hu GP. Study on the relationship between biofuel smoke exposure and chronic obstructive pulmonary disease [D]. Guangzhou Medical College, 2009
- 59. Kamil F, Pinzon I, Foreman MG. Sex and race factors in early-onset COPD. Curr Opin Pulm Med. 2013 Mar;19(2):140–4. pmid:23361195
- 60. Varkey AB. Chronic obstructive pulmonary disease in women: exploring gender differences. Curr Opin Pulm Med. 2004 Mar;10(2):98–103. pmid:15021178.
- 61. Chapman KR, Tashkin DP, Pye DJ. Gender bias in the diagnosis of COPD. Chest. 2001 Jun;119(6):1691–5. pmid:11399692.
- 62. Matteis M, Polverino F, Spaziano G, Roviezzo F, Santoriello C, Sullo N, et al. Effects of sex hormones on bronchial reactivity during the menstrual cycle. BMC Pulm Med. 2014 Jul 1;14:108. pmid:24984749
- 63. Sun Y, Milne S, Jaw JE, Yang CX, Xu F, Li X, et al. BMI is associated with FEV(1) decline in chronic obstructive pulmonary disease: a meta-analysis of clinical trials. Respir Res. 2019 Oct 29;20(1):236. pmid:31665000
- 64. Rabinovich RA, Bastos R, Ardite E, Llinàs L, Orozco-Levi M, Gea J, et al. Mitochondrial dysfunction in COPD patients with low body mass index. Eur Respir J. 2007 Apr;29(4):643–50. pmid:17182653.
- 65. Chandra RK. Cell-mediated immunity in nutritional imbalance. Fed Proc. 1980 Nov;39(13):3088–92. pmid:6775981.
- 66. Shaheen SO, Barker DJ, Shiell AW, Crocker FJ, Wield GA, Holgate ST. The relationship between pneumonia in early childhood and impaired lung function in late adult life. Am J Respir Crit Care Med. 1994 Mar;149(3 Pt 1):616–9. pmid:8118627.
- 67. Mccloskey SC, Patel BD, Hinchliffe SJ, Reid ED, Wareham NJ, Lomas DA. Siblings of patients with severe chronic obstructive pulmonary disease have a significant risk of airflow obstruction. Am J Respir Crit Care Med. 2001 Oct 15;164(8 Pt 1):1419–24. pmid:11704589.