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Research Article: Study Protocol Systematic Review

Association of matrix metalloproteinase-12 polymorphisms with chronic obstructive pulmonary disease risk

A protocol for systematic review and meta analysis

Yang, Hongjing MMa; Zhang, Chuantao MDa; Wu, Jianying MMb; Xiao, Wei MDa; Xie, Xiaohong MBa; Zeng, Zhu MDa; Chen, Keling MBa; Wang, Wujun MMa; An, Xing MBa; Tang, Wenjun MMa; Huang, Qingsong MDa,∗

Author Information
doi: 10.1097/MD.0000000000021543
  • Open


1 Introduction

Chronic obstructive pulmonary disease (COPD) is a common disease with persistent respiratory symptoms and airflow limitation. COPD is characterized by symptoms including progressive dyspnea, cough, and expectoration and can lead to adverse outcomes, such as osteoporosis, heart disease, anxiety, depression, and lung cancer.[1] Although COPD is preventable and treatable, it has 1 of the highest morbidity and mortality rates in the world. About 384 million people worldwide suffer from COPD,[2] of which China accounts for 100 million.[3] In 2015, 3.2 million deaths due to COPD were reported, that is, an 11.6% increase in mortality from 1990.[4,5] Hence, COPD exerts enormous economic burden on the society and is likely to remain as a major health care issue in the coming decades.[6,7]

COPD is a complex multifactorial disease and its development is thought to be largely under genetic control. A considerable number of genes and polymorphisms have been identified as possible candidates for COPD risk.[8–12] The gene encoding matrix metalloproteinase 12 (MMP-12) is 1 such candidate. Overexpression of MMP-12 can lead to pathological extracellular matrix protein degradation and excessive airway remodeling, and it may be an important factor in the development of COPD. Extensive evidence from genetic studies, animal models and human diseases suggests that MMP-12 derived from alveolar macrophages plays an indispensable role in lung destruction of COPD.[13–17] Two functional single nucleotide polymorphisms (SNPs) were described in the MMP-12 gene: the (–82) A/G (rs2276109) and Asn357Ser (A/G) (rs652438) polymorphisms are located in the promoter region and haemopexin domain of the enzyme, respectively, and may influence MMP-12 gene expression and activity.[18,19] A case-control genetic association study identified the 2 functional SNPs were associated with severe and very severe COPD (GOLD stages III and IV).[20,21] However, other studies have found that MMP-12 polymorphism are not associated with COPD risk.[13,22–24] Therefore, the association between MMP12 polymorphism and COPD remains unclear. Although a similar systematic review based on COPD was published in 2014,[25] new studies have occurred since. In light of the small sample sizes and limited statistical capacity of individual studies, we will conduct a systematic review and meta-analysis of the existing literature to provide a more comprehensive and precise estimate of the association between MMP-12 polymorphism and COPD risk.

2 Methods/design

2.1 Study registration

The protocol has been registered in the Open Science Framework (OSF) (registration number: DOI 10.17605/OSF.IO/KNGJC). This systematic review and meta-analysis will be reported in accordance with the preferred reporting items for systematic reviews and meta-analysis protocols 2015.[26] Ethical approval is not required for the study.

2.2 Inclusion criteria

2.2.1 Types of studies

Case-control study related to the susceptibility of MMP-12 polymorphisms to the COPD will be incorporated in our review. And these studies provide the available genotype frequencies for the case and control groups to estimate the odds ratio (OR) and its 95% confidence interval. No restriction will be put on the language, publication date or status of the study.

2.2.2 Types of participants

Participants affected by COPD will be included in the meta-analysis. Control subjects should be defined as healthy subjects without history of COPD. No restrictions will be placed on age, gender, or country.

2.2.3 Outcome

COPD risk comparsions.

2.3 Exclusion criteria

Repeat report, conference abstracts, case reports, review paper, or animal study, or study has insufficient data for genotyping distribution calculation or which MMP-12 demonstrated a departure from Hardy-Weinberg equilibrium (HWE) in controls will be excluded. If duplicated studies reporting overlapping data are identified, the most comprehensive 1 will be included in the meta-analysis.

2.4 Search strategy

Publications will be searched through Pubmed, Web of Science, Embase, Google Scholar and China National Knowledge Infrastructure (CNKI) databases up to July, 2020. A combination of Medical Subject Headings (MeSH) alongside free terms will be used to hunt all the potentially eligible publications without any language restriction. The following terms will be utilized (“SNP” or “mutation” or “genetic polymorphism” or “variation” or “polymorphism” or “single nucleotide polymorphism” or “variant”) and (“chronic obstructive pulmonary disease” or “COPD” or “chronic obstructive airway disease” or “chronic obstructive lung disease” or “chronic airflow obstruction”’) and (“matrix metalloproteinase 12” or “MMP12”). We also will examine the cross-references in the retrieved studies for publications that were missed in the above search.

2.5 Data collection and analysis

2.5.1 Selection of studies

The article screening process will involve reading the title first, followed by the abstract and full text to determine whether the study should be included. Apart of the reviewers in our team will be trained regarding the purpose and process of the review. Two reviewers (Chuantao Zhang and Yang Hongjing) will conduct the selection process independently, with cases of disagreement resolved consulting a third reviewer (Qingsong Huang). A flow chart of study selection is shown in Figure 1.

Figure 1
Figure 1:
Flow chart of study selection. CNKI = China National Knowledge Infrastructure.

2.5.2 Data extraction and quality assessment

Data extracted from all qualified articles include: surname of the first author, year of publication, country where the study was performed, ethnicity of enrolled subjects, sample size, the value of HWE, sex composition, mean age, genotyping methods and frequencies of genotypes. All included studies will be evaluated using the Newcastle–Ottawa Scale (NOS).[27] The NOS values arrange from 0 to 9. The studies will be included if the NOS values ≥ 6. Two reviewers (Chuantao Zhang and Yang Hongjing) will conduct the rating independently and a third reviewer (Qingsong Huang) will be consulted for consensus if disagreement occurred. The methodological quality of data will be evaluated strictly in accordance with the STrengthening the REporting of Genetic Association Studies statement.[28]

2.5.3 Dealing with missing data

We will try to contact the corresponding authors if the information of potential studies is missing, insufficient, or ambiguous. However, the studies will be excluded if the data cannot be obtained by the above method.

2.5.4 Statistical analysis

Statistical analysis for the meta-analysis will be conducted by Stata version 12.0 (Stata Corporation, College Station, TX) and Revman 5.3 (Cochrane Collaboration). Departure from HWE will be evaluated by using Chi-square test to assess goodness of fit in control subjects of each included study. In order to avoid an inflated type I error rate, we will not perform any assumptions about the genetic model of inherence in advance. The ORs and 95% confidence intervals will be used to evaluate the association between the allelic, dominant and recessive models of MMP-12 and risk of COPD. Then the most plausible genetic model of inherence will be determined according to the relationships between the 3 pairwise comparisons. After that the underlying genetic model is confirmed, the counts of genotypes will be collapsed into 2 categories to obtain the merged results. The significance of the pooled OR will be assessed by the Z test, and pZ < .05 will be considered as statistically significant.

2.5.5 Assessment of heterogeneity

Heterogeneity will be quantified with the I2 statistic and P value; a I2 statistic < 50% and a P > .1 indicated low heterogeneity among studies, and a fixed model will be applied to estimate the pooled ORs. Otherwise the random model will be used. Subgroup analysis, meta regression analysis, sensitivity analysis will be undertaken to explore potential sources of heterogeneity across studies when statistical heterogeneity is detected.

2.5.6 Subgroup analysis

We will conduct subgroup analyses of the relationships between MMP-12 genetic polymorphisms and the risk of COPD, according to country, ethnicity, and genotyping method, etc.

2.5.7 Sensitivity analysis

Sensitivity analysis will be conducted by removing studies that do not conform to HWE to check the robustness and reliability of pooled outcome results.

2.5.8 Assessment of publication biases

The funnel plots will be utilized to analyze the potential publication bias if there are more than 10 studies. Egger regression test and Begg rank correlation test will be also used to evaluate the publication bias.

2.5.9 Grading the quality of evidence

The quality of evidence for outcome will be assessed by the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group approach. High, medium, low, or very low quality represent the 4 levels of evaluation.

3 Discussion

COPD is the most common chronic respiratory disease characterized by high morbidity, high disability rate, high mortality and high disease burden. Patients with COPD may need more social and nursing care. The increased social and economic burdens associated with COPD worldwide make their targeting treatment a major public health goal.[29] In general, COPD is a multifactorial disease that can be attributed to an interplay of genetic and environmental factors. Although the specific pathogenesis of COPD is unclear, it is widely accepted that airway tissue remodeling results from disorder of the proteinase-antiproteinase balance and aberrant inflammation in the lung.[30] Among various proteinases, MMP-12 have been shown to play a predominant role in the occurrence and progression of COPD.[31,32] Elevated MMP12 may lead to imbalance of protease-antiprotease and degradation of lung extracellular Matrix, thereby increasing individual's susceptibility to COPD. The genetic polymorphism of MMP-12 gene may induce the abnormal expression of MMP12, and may also be related to the occurrence of COPD.[13] Although the results from a meta-analysis conducted in 2014 suggest that genetic polymorphisms in MMP12 gene may be strongly implicated in the development of COPD,[25] some studies since then have not supported this view.[23–24] the association between MMP12 polymorphism and COPD remains unclear. It is necessary to update previous systematic review about the genetic polymorphism of MMP-12 gene and the risk of COPD MMP-12, which can provide a further reference for the diagnosis and potential therapeutic targets of COPD.

The advantages of this review will be:

  • (1) this review will include more clinical studies than the previous 1, since only 5 research reports were included in the previous study.
  • (2) In order to avoid bias, we will collect all relevant documents as comprehensively as possible.

As to the exploration of heterogeneity, post hoc subgroup analysis should be avoided as much as possible. Publishing this protocol will reduce potential biases associated with data mining, thereby helping to generate reliable evidence.

Author contributions

Conceptualization: Hongjing Yang, Chuantao Zhang.

Investigation: Jianying Wu, Xiaohong Xie, Wei Xiao.

Supervision: Zhu Zeng, Keling Chen.

Writingoriginal draft: Wujun Wang, Xing An.

Writingreview & editing: Qingsong Huang, Wenjun Tang.


[1]. Global strategy for the diagnosis, management and prevention of chronic obstructive pulmonary disease 2020 report[EB/OL]. (2019-11-05) [2020-06-20]. Available at:
[2]. GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018;392:1736–88.
[3]. Wang Chen, Xu Jianying, Yang Lan, 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;391:1706–17.
[4]. Adeloye Davies, Chua Stephen, Lee Chinwei, et al. Global and regional estimates of COPD prevalence: systematic review and meta-analysis. J Glob Health 2015;5:020415.
[5]. GBD 2015 Chronic Respiratory Disease Collaborators. Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Respir Med 2017;5:691–706.
[6]. May Sara M, Li James TC. Burden of chronic obstructive pulmonary disease: healthcare costs and beyond. Allergy Asthma Proc 2015;36:4–10.
[7]. Brożek Grzegorz M, Nowak Marcin, Zejda Jan E, et al. Costs of pharmacotherapy of chronic obstructive pulmonary disease in relation to changing Global Initiative for Chronic Obstructive Lung Disease guidelines (2007, 2011, and 2017 updates). Pol Arch Intern Med 2019;129:308–15.
[8]. Karimi Leila, Lahousse Lies, Ghanbari Mohsen, et al. (ADRB2) β-adrenergic receptor gene polymorphisms and risk of COPD exacerbations: The Rotterdam Study. J Clin Med 2019;8:
[9]. Ingebrigtsen TS, Vestbo Jørgen, Rode Line, et al. β-Adrenergic genotypes and risk of severe exacerbations in COPD: a prospective cohort study. Thorax 2019;74:934–40.
[10]. Ishii Takeo, Angata Takashi, Wan Emily S, et al. Influence of SIGLEC9 polymorphisms on COPD phenotypes including exacerbation frequency. Respirology 2017;22:684–90.
[11]. Lin Chii-Lan, Siu Leung-Kei, Lin Jung-Chung, et al. Mannose-binding lectin gene polymorphism contributes to recurrence of infective exacerbation in patients with COPD. Chest 2011;139:43–51.
[12]. Pillai Sreekumar G, Kong Xiangyang, Edwards Lisa D, et al. Loci identified by genome-wide association studies influence different disease-related phenotypes in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2010;182:1498–505.
[13]. Hunninghake Gary M, Cho Michael H, Tesfaigzi Yohannes, et al. MMP12, lung function, and COPD in high-risk populations. N Engl J Med 2009;361:2599–608.
[14]. Babusyte Agne, Stravinskaite Kristina, Jeroch Jolanta, et al. Patterns of airway inflammation and MMP-12 expression in smokers and ex-smokers with COPD. Respir Res 2007;8:81.
[15]. Demedts IK, Morel-Montero A, Lebecque S, et al. Elevated MMP-12 protein levels in induced sputum from patients with COPD. Thorax 2006;61:196–201.
[16]. Aggarwal Taru, Wadhwa Ridhima, Rohil Vishwajeet, et al. Biomarkers of oxidative stress and protein-protein interaction in chronic obstructive pulmonary disease. Arch Physiol Biochem 2018;124:226–31.
[17]. Molet S, Belleguic C, Lena H, et al. Increase in macrophage elastase (MMP-12) in lungs from patients with chronic obstructive pulmonary disease. Inflamm Res 2005;54:31–6.
[18]. Jormsjö S, Ye S, Moritz J, et al. Allele-specific regulation of matrix metalloproteinase-12 gene activity is associated with coronary artery luminal dimensions in diabetic patients with manifest coronary artery disease. Circ Res 2000;86:998–1003.
[19]. Joos Ladina, He Jian-Qing, Shepherdson Megan B, et al. The role of matrix metalloproteinase polymorphisms in the rate of decline in lung function. Hum Mol Genet 2002;11:569–76.
[20]. Haq Imran, Lowrey Gillian E, Kalsheker Noor, et al. Matrix metalloproteinase-12 (MMP-12) SNP affects MMP activity, lung macrophage infiltration and protects against emphysema in COPD. Thorax 2011;66:970–6.
[21]. Haq Imran, Chappell Sally, Johnson Simon R, et al. Association of MMP-2 polymorphisms with severe and very severe COPD: a case control study of MMPs-1, 9 and 12 in a European population. BMC Med Genet 2010;11:7.
[22]. Lee Shin-Yup, Kim Min-Jung, Kang Hyo-Gyung, et al. Polymorphisms in matrix metalloproteinase-1, -9 and -12 genes and the risk of chronic obstructive pulmonary disease in a Korean population. Respiration 2010;80:133–8.
[23]. Gilowska Iwona, Majorczyk Edyta, Kasper Łukasz, et al. The role of MMP-12 gene polymorphism-82 A-to-G (rs2276109) in immunopathology of COPD in polish patients: a case control study. BMC Med Genet 2019;20:19.
[24]. Bchir Sarra, Ben Nasr Hela, Garrouch Abdelhamid, et al. MMP-3 (-1171 5A/6A; Lys45Glu) variants affect serum levels of matrix metalloproteinase (MMP)-3 and correlate with severity of COPD: a study of MMP-3, MMP-7 and MMP-12 in a Tunisian population. J Gene Med 2018;20: undefined.
[25]. Yu Xiao-Ling, Zhang Jun, Zhao Fei, et al. Relationships of COX2 and MMP12 genetic polymorphisms with chronic obstructive pulmonary disease risk: a meta-analysis. Mol Biol Rep 2014;41:8149–62.
[26]. Shamseer Larissa, Moher David, Clarke Mike, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 2015;350:g7647.
[27]. Stang Andreas. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 2010;25:603–5.
[28]. Little Julian, Higgins Julian PT, Ioannidis John PA, et al. STrengthening the REporting of Genetic Association Studies (STREGA): an extension of the STROBE statement. PLoS Med 2009;6:e22.
[29]. Zhang XE, Zhang CL. Research progress on epidemiology and economic burden of chronic obstructive pulmonary disease. Chin J Prevent Control Chronic Dis 2017;25:472–6.
[30]. Hogg JC, Senior RM. Chronic obstructive pulmonary disease-part 2: pathology and biochemistry of emphysema. Thorax 2002;57:830–4.
[31]. Kabesch Michael, Adcock Ian M. Epigenetics in asthma and COPD. Biochimie 2012;94:2231–41.
[32]. Barnes Peter J. New treatments for COPD. Nat Rev Drug Discov 2002;1:437–46.

chronic obstructive pulmonary disease; meta-analysis; matrix metalloproteinase 12; polymorphism

Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc.