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Lipid-lowering drug targets associated with risk of respiratory disease: a Mendelian randomization study

BMC Pulmonary Medicine volume 25, Article number: 71 (2025) Cite this article

Abstract

Background

Observational studies have identified a possible connection between lipid-lowering medications and respiratory illnesses. However, it remains unclear whether lipid-lowering drugs is causative for respiratory diseases, and we aimed to answer this question.

Methods

We performed Mendelian randomization (MR) analyses by integrating data from genome-wide association studies (GWAS). Three statistical approaches were employed for MR analysis: inverse variance weighting (IVW), MR-Egger, and weighted median. The purpose was to evaluate the causal relationships between 10 drug targets that lower lipid levels and the likelihood of developing 7 respiratory diseases. Additional sensitivity analyses were conducted to ensure the robustness and validity of the results.

Results

After adjusting for multiple testing, our MR analysis identified APOB (odd ratios [OR]: 0.86; 95% confidence interval [CI]: 0.77 to 0.97; PIVW = 0.01) and PCSK9 (OR: 0.84; 95% CI: 0.72 to 0.97; PIVW = 0.02) as significant risk targets for asthma. Additionally, LDLR was found to be a significant risk target for chronic obstructive pulmonary disease (OR: 0.81; 95% CI: 0.67 to 0.98; PIVW = 0.03). The sensitivity analysis validated no proof of heterogeneity or pleiotropy amongst the mentioned results.

Conclusions

Our findings suggest a likely causal relationship between respiratory diseases and lipid-lowering drug targets. Further mechanistic and clinical research is needed to confirm and validate these findings.

Peer Review reports

Background

Respiratory diseases pose a major global health challenge, requiring substantial annual investments in research to address their impact [1]. Especially, chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), asthma, and interstitial lung disease, were the third leading cause of death in 2019, resulting in 4.0 million deaths worldwide [2]. Therefore, it is crucial to clarify the correlation between respiratory disorders and relevant risk factors in order to develop efficient preventative and treatment strategies that can reduce their impact on society.

Serum lipids are essential for maintaining cellular structure, transmitting signals, metabolizing energy, and transporting materials [3]. Accumulating evidence has shown that disordered lipid metabolism is associated with multiple respiratory diseases. For example, Xuan et al. found elevated triglyceride levels in COPD patients compared to healthy individuals [4]. Based on a prospective observational study, Liu et al. reported that dyslipidemia is associated with specific asthma phenotypes and increased asthma exacerbations [5]. Mechanistically, the role of dyslipidemia in respiratory diseases may be related to its impact on lung physiology and immune homeostasis [6]. In light of these findings, further investigation is warranted to explore the relationship between lipid-lowering drugs and respiratory illnesses. There is a huge vary of lipid-lowering drugs, usually categorized as fibrates, statins, cholesterol absorption inhibitors and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors. Lipid-lowering medications were initially created to treat lipid problems and have since been extensively researched for their efficacy in reducing cardiovascular morbidity and death in both primary and secondary preventive scenarios [7]. While the positive impact of lipid-lowering pharmaceuticals in reducing cardiovascular and mortality and morbidity has been established, their prospective effects on respiratory disorders are currently being studied and have not been definitively verified.

Human genetics is increasingly used to assess therapeutic targets in drug development, with genetically supported targets being twice as likely to succeed [8]. Genome-wide association studies (GWAS) provide accessible and cost-effective data for such evaluations. Mendelian randomization (MR) utilizes genetic variation as an instrumental variable for investigating target perturbation [9]. Naturally occurring variants in cholesterol-lowering drug targets can help examine therapeutic effects on disease outcomes, minimizing confounding [10, 11]. MR can also investigate long-term effects on respiratory diseases, mimicking clinical trial outcomes and predicting potential benefits and adverse effects [12, 13].

Therefore, this study employed a two-sample MR approach to investigate the potential effects of lipid-lowering drug targets on the risk of various respiratory diseases.

Materials and methods

Prepositions of MR design

Given that cholesterol-lowering drugs primarily function by reducing levels of LDL-C or triglycerides, we leveraged the associations between specific genetic markers and circulating lipid levels to model the pharmacological effects on the drug-target proteins. The underlying assumptions are that the genetic variations are presently unaffected by any confounding variables and that they only impact respiratory illnesses through specified pathways. The key MR assumptions are illustrated in Fig. 1. The Figure was partly generated using Biovisart (https://biovisart.com.cn).

Fig. 1
figure 1

Flowchart of the study design and MR assumptions. Schematic illustration illustrated Mendelian randomization assumptions. The assumptions included: (I) strong association between genetic v ants and the chosen exposure; (II) no independent effect of genetic variants on the specific outcome; and (III) no association between genetic variants and potential confounders

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GWAS summary datasets

The information on lipid-lowering targets and their corresponding encoding genes used for drug-target MR analysis were sourced from the NCBI Gene Database (https://www.ncbi.nlm.nih.gov/gene/). A total of ten target genes were identified: ATP citrate lyase (ACLY), apolipoprotein B (APOB), cholesteryl ester transfer protein (CETP), 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR), low-density lipoprotein receptor (LDLR), NPC1-like intracellular cholesterol transporter 1 (NPC1L1), PCSK9, peroxisome proliferator-activated receptor alpha (PPARA), apolipoprotein C3 (APOC3) and angiopoietin-like 3 (ANGPTL3).

The outcome data consisted of various GWAS summary data sets for a total of seven respiratory diseases: asthma, COPD, idiopathic pulmonary fibrosis (IPF), pulmonary edema, pulmonary embolism, pulmonary eosinophilia, and tuberculosis. The study utilized publicly available GWAS summary data from European populations [14,15,16,17]. Detailed specifics are provided in Table 1. Ethical approval was unnecessary, given the reliance on publicly accessible summary statistics for the analysis.

Table 1 Data resources of the exposures and outcomes used in this study
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Selection of genetic instruments

Genetic variations located within genes encoding protein targets of lipid-lowering medications (known as cis-variants) were identified using GWAS data from the Global Lipids Genetics Consortium (GLGC) (http://www.lipidgenetics.org/) [18]. Cis-variants are genetic variations that are located on the same chromosome as the specific target gene. Serum LDL-C levels were used as indicators for the effectiveness of therapy aimed at reducing LDL-C levels [19]. Single-nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) from GWAS summary data to serve as genetic instruments in MR analysis. Furthermore, the instrumental variations (IVs) were required to satisfy the following conditions: (1) they exhibited a strong correlation with the exposure, (2) they were not linked to confounding variables, and (3) they affected outcomes only through the exposure. We included SNPs with genome-wide significance (P < 5 × 10⁻⁸). These SNPs were then clumped using a 1,000 kb clumping window and a linkage disequilibrium (LD) threshold (r2 < 0.01), with LD estimates derived from the 1000 Genomes Project based on European samples. Palindromic and ambiguous SNPs were excluded using the GWAS Catalog (https://www.ebi.ac.uk/gwas/).

Only two SNPs may lead to inadequate statistical power, necessitating very large sample sizes to identify a true link between exposure and outcome [20]. The ACLY (1 SNP), CETP (1 SNP), HMGCR (2 SNPs), NPC1L1(2 SNPs) and PPARA (2 SNPs) genes have been excluded from further research since there were not enough identified SNPs that may serve as drug proxies. To enhance the adequacy of statistical power, we computed the F statistic for each instrument utilizing the subsequent formula: F = \({\left(\frac{\text{Beta}}{\textrm{Se}}\right)}^{2}\). The analysis did not reveal any notable instrumental bias, as the qualifying SNPs had F-statistics greater than 10 [21]. The inverse-variance-weighted (IVW) method and Cochran’s Q test were applied to assess the heterogeneity assumption, while the I2 statistic was used to evaluate the no measurement error (NOME) assumption [22]. The classification of lipid-lowering medications and their respective target genes was based on the latest expert consensus and guidelines for lipid-lowering therapies, as detailed in Table 2.

Table 2 Summary of genetically proxied lipid-lowering drug targets
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Estimation of causal association

Before performing the MR analysis between lipid-lowering therapy and respiratory diseases, a positive control MR analysis was conducted to evaluate the established effectiveness of lipid-lowering drugs in treating coronary heart disease (CHD). This step aimed to confirm the validity of the approach and ensure the effectiveness of lipid-lowering drugs in addressing CHD. To further validate that the SNPs we screened for were associated with lipid metabolic pathways, we used coronary artery disease as a positive control and further selected significant results with OR greater than 1 to perform Mendelian randomization analysis with a variety of respiratory diseases. Based on meticulously selecting the IVs, the two-sample MR were performed. The principal analytical tool employed to evaluate the causal relationship between lipid-lowering medication genes and the risk of respiratory illnesses was the inverse-variance weighted multiplicative (IVW) method. The method used here quantifies the causal connection between a 1 standard deviation increase in exposure to genetic outcome predictors. In the GWAS data set, beta estimates were utilized for the assessment of the continuous outcomes and odds ratios (OR) were computed for the binary outcomes. To verify the accuracy of the outcomes obtained using the IVW method, we performed sensitivity testing utilizing MR-Egger regression and weighted median. It is important to understand that the findings of two-sample MR may not always have the same meaning when used to drug-target MR studies. If LDL-C is employed as a proxy in a drug-target MR analysis, then an OR larger than 1 suggests that the exposure under investigation—a lipid-lowering medication acting on a pharmacological target to prevent the rise in LDL-C—is a risk factor for the result. Therefore, the findings ought to be construed inversely, indicating that the true impact of the lipid-lowering medication on respiratory illnesses was the inverse of the initial OR number. OR > 1 suggested that the utilization of lipid-lowering medications could reduce the likelihood of the specified result. It is relevant to mention that if the pharmaceutical being researched is a CETP inhibitor, which raises HDL-C and lowers LDL-C, then there would be no need to convert the OR values given earlier when utilizing HDL-C as an alternative for the CETP gene [23].

To evaluate heterogeneity and horizontal pleiotropy, the Cochran Q test and the MR-IVW intercept test were conducted. Furthermore, the results show that the Q pval from the MR-IVW method were not significant, suggesting the absence of directional horizontal pleiotropy (Supplementary Table 3 and Supplementary Table 5) [24]. A significance level of less than 0.05 was employed to address the issue of multiple testing. This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines [25].

The analyses were performed using R software (version 4.3.3) with the "TwosampleMR" package (version 0.5.6). Forest plots were created using the "forestploter" package (version 0.5.6).

Results

We employed a comprehensive GWAS dataset consisting of seven separate sets of summary data pertaining to respiratory disorders. By selecting lipid-lowering drug targets corresponding to specific base pair position on the respective chromosomes and performing LD, we found ten lipid-lowering drug targets available. However, when there are fewer than two SNPs in the exposure, the reliability of the results decreases. Out of these, 6 variants were selected for APOB, 20 for LDLR, 15 for PCSK9, 4 for ANGPTL3, and 5 for APOC3. It is important to highlight that all of these variants had F values more than 10, as indicated in Supplementary Table 1.

Results of positive control analysis

The findings of the MR analysis examining the effects of different lipid-lowering drugs on CHD are presented in Fig. 2 and Supplementary Table 2. The analysis revealed that genetic variants associated with elevated LDL levels, influenced by the APOB, LDLR, NPC1L1, and PCSK9 genes, were linked to an increased risk of CHD (APOB: OR = 1.380, 95% CI: 1.153–1.652; LDLR: OR = 1.887, 95% CI: 1.603–2.221; NPC1L1: OR = 3.479, 95% CI: 1.380–8.771; PCSK9: OR = 1.792, 95% CI: 1.454–2.208). Additionally, exposure to drugs targeting APOC3 was similarly associated with a heightened risk of CHD (OR = 1.242, 95% CI: 1.115–1.384). However, SNPs linked to elevated TG levels in ANGPTL3, APOC3, and PPARA were not associated with CHD. Sensitivity analyses for the MR analysis of LDL-lowering and TG-lowering drugs with CHD revealed no significant heterogeneity for the targets APOB, HMGCR, LDLR, NPC1L1, PCSK9, ANGPTL3, APOC3, and PPARA. The IVW method and Cochran’s Q test were applied to confirm the heterogeneity assumption, while the I2 statistic was used to assess the no measurement error (NOME) assumption. The results of positive controls (Supplementary Table 3) did not show any statistical indication of bias resulting from horizontal pleiotropy and heterogeneity.

Fig. 2
figure 2

Forest plot illustrates the results of the Mendelian randomized IVW approach from lipid-lowering drug targets to coronary heart disease

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Lipid-lowering drug targets and respiratory diseases

Figure 3 presents the main MR outcomes of lipid-lowering drug targets effects on respiratory diseases. MR analysis identified APOB (OR: 0.86; 95% confidence interval [CI]: 0.77 to 0.97; PIVW = 0.01) and PCSK9 (OR: 0.84; 95% CI: 0.72 to 0.97; PIVW = 0.02) as significant risk targets for asthma. Additionally, LDLR was found to be a significant risk target for COPD (OR: 0.81; 95% CI: 0.67 to 0.98; PIVW = 0.03). No significant associations were seen between any of the other drug-target genes and idiopathic pulmonary fibrosis, pneumonia, pulmonary embolism, eosinophilia, pulmonary oedema and tuberculosis, as shown by all P values being greater than 0.05 (Supplementary Table 4). The investigation did not uncover any significant statistical proof of bias resulting from horizontal pleiotropy and heterogeneity, as shown in Supplementary Table 5.

Fig. 3
figure 3

Forest plot illustrates the results of the Mendelian randomized IVW approach from lipid-lowering drug targets to multiple respiratory diseases

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Discussion

This study provides a comprehensive analysis of the causal relationship between lipid-lowering drug targets and various respiratory diseases. After adjusting for multiple testing, we found that genetic proxies for APOB and PCSK9 target were significantly associated with increased risks of asthma, indicating that APOB and PCSK9 modulation might increase susceptibility to asthma. Additionally, LDLR target was identified as a significant risk target for COPD. These findings suggest that the use of specific lipid-lowering drugs could have adverse impacts on certain respiratory conditions.

MR is a method used in epidemiology to investigate the relationship between genetic variants and exposures. It involves analyzing how these genetic variants are connected with an outcome, such as illness incidence or mortality [26]. Traditional observational study designs often gather information about exposures by questionnaires, biochemical indicator tests, or imaging. However, genetic diversity is inherent from birth and remains constant throughout an individual's life cycle [27]. Thus, the relationships derived from MR are not susceptible to causal inversion or confounding variables. The primary premise is to utilize genetic data as an instrumental variable in order to investigate the causal relationship between an exposure (such as an illness or measurement data) and an outcome [28]. Mendelian randomization has a high level of clinical evidence, and MR has the highest evidence-based rating when randomized controlled trials (RCTs) are not feasible, so the results of Mendelian randomization can be used to respond to the relationship between exposure and outcome [29]. Utilizing the latest data from the Global Lipid Genetics Consortium, it is simple and convincing to study the correlation between the two by selecting the appropriate drug target SNPs as the exposure and selecting a variety of respiratory diseases as the outcome.

Target genes of lipid-lowering drugs against LDL-C (APOB, HMGCR, LDLR and PCSK9) and target genes of lipid-lowering drugs against TG (ANGPTL3 and APOC3) are key regulators of lipid metabolism. Specific mechanisms of action, such as PCSK9 inhibitors prevent the degradation of LDLR, increase the expression of LDLR and ultimately help eliminate circulating LDL-C [30]. MR analysis allows us to bypass the clinical trial step and examine the genetic correlation between circulating lipids and a variety of respiratory diseases directly from a genetic perspective [31]. Our research found that an increased LDL-C level driven by APOB (OR = 0.86; 95% CI: 0.77 to 0.97) and PCSK9 (OR = 0.84; 95% [CI]: 0.72 to 0.97) led to a decreased risk of asthma, therefore the use of corresponding lipid-lowering drugs may increase the risk of asthma. The lipid-lowering effect of APOB-inhibitor and PCSK9-inhibitor may increase the risk of the development of asthma, which was consistent with the previous MR studies [32,33,34,35]. Lipid metabolism is closely associated with various respiratory diseases, as lipids serve as potent signaling molecules that regulate numerous cellular responses. A previous study demonstrated that genes involved in lipid metabolism may contribute to the development of asthma, potentially through altering the immune microenvironment via lipid metabolism-mediated effects on immune cells [36]. Mipomersen is a second-generation antisense oligonucleotide targeting liver messenger RNA (mRNA) of apoB, designed to reduce LDL-C levels [37]. The results of a meta-analysis examining the safety and efficacy of mipomersen point out that although the drug regulates disorders of lipid metabolism by acting on the APOB gene, mipomersen may cause influenza-like symptoms, a result that is in the same direction as the results we have obtained in asthma [38]. Alirocumab, a human monoclonal antibody targeting PCSK9, has been shown to improve cardiovascular outcomes in patients receiving high-intensity statin therapy following an acute coronary syndrome. Results of an RCT examining the efficacy and safety of alirocumab found that allergic adverse reactions were found in the medicated group compared to the placebo group. Asthma is a hypersensitivity disease and the findings of this study support our results [39]. In addition to lipids as ligands, we also tested the association of LDLR with a variety of respiratory diseases. Besides, an increased LDL-C level driven by LDLR (OR = 0.81; 95% CI: 0.67 to 0.98) led to a decreased risk of COPD. Dyslipidemia has been linked to the inflammatory and other pathological processes in COPD, including cholesterol levels and oxidized derivatives such as 25-hydroxycholesterol [40]. In this study, we found that lowering LDL levels may lead to an increased risk of COPD, and we speculate that if long-term statin use excessively lowers LDL levels, it may cause other harm to the human body.

Additionally, we observed not significant results in the remaining respiratory diseases. This further explains that lipid-lowering drugs act in asthma and COPD, on the one hand through lipid metabolism pathways and on the other hand in relation to inflammatory responses [41, 42]. The MR results indicate a consistent direction for most of the results, our findings indicate a potential causal relationship between the lipid-lowering drug targets and the development of asthma and COPD.

To ensure the reliability of the drug-target SNPs, we, chose CHD as the outcome for the positive control analysis and demonstrated that the extracted drug-target SNPs was indeed the intended SNPs, belonging to both a subset of LDL-C and TG and a risk factor for coronary heart disease [43]. It was shown that the extracted drug-target SNPs were truly useful. While MR analysis has shown that certain lipid-lowering drugs can cause respiratory issues or worsen existing respiratory diseases, additional research is necessary to investigate the specific molecular mechanisms involved.

The identification of lipid-lowering drug targets that impact respiratory disease risk highlights the need to carefully manage these risks when repurposing cardiovascular drugs for respiratory conditions. Given their established safety profiles and widespread use, these drugs could be rapidly adapted for respiratory disease management if further validated. Future research should focus on elucidating the mechanisms by which lipid-lowering drugs affect respiratory diseases and exploring the role of lipid metabolism in lung physiology and immune responses to provide deeper insights into the interplay between metabolic and respiratory health [6].

There are other constraints that should be considered when interpreting these data, which require caution. Initially, because there were not enough unique SNPs related to the targets in the Global Lipid Genetics Consortium, we were unable to thoroughly examine all the genes that are targeted by lipid-lowering drugs, such as ACLY. Additionally, a number of genetic proxies were used in our work to represent lipid-lowering medications, indicating that these genetic proxies, rather than the total impacts of specific treatments in practical settings, may be responsible for the reported causal effects. Furthermore, our studies were restricted to combined GWAS data exclusively from European populations. Consequently, the impact of alterations in lipid levels can vary in other groups, thereby constraining the applicability of our results. Furthermore, the sample size of our study may have been insufficient to detect smaller effect sizes, potentially limiting the statistical power of our results. Subsequent investigations should incorporate more extensive and varied datasets in order to validate and enhance these connections.

Conclusions

In conclusion, our study provides evidence that certain lipid-lowering drug targets, particularly APOB, PCSK9 and LDLR, are associated with the risk of developing respiratory diseases or exacerbating pre-existing conditions. These findings highlight the potential for lipid-modifying therapies to benefit respiratory health, opening new avenues for treatment and prevention strategies. Additional investigation is required to validate these connections and explore the underlying mechanisms.

Data availability

Publicly available datasets were analyzed in this study. This data can be found here: All GWAS data used in this study are available in the IEU open GWAS (https://gwas.mrcieu.ac.uk/), GWAS Catalog (https://www.ebi.ac.uk/gwas/), FinnGen study (https://www.finngen.fi/en/access_results) and Global Lipids Genetics Consortium (http://www.lipidgenetics.org/).

Abbreviations

GWAS:

Genome-wide association studies

MR:

Mendelian randomization

SNP:

Single nucleotide polymorphism

IVs:

Instrumental variables

IVW:

Inverse variance weighting

OR:

Odd ratios

CI:

Confidence interval

COPD:

Chronic obstructive pulmonary disease

IPF:

Idiopathic pulmonary fibrosis

RCTs:

Randomized controlled trials

ACLY:

ATP citrate lyase

APOB:

Apolipoprotein B

CETP:

Cholesteryl ester transfer protein

HMGCR:

Hydroxy-3-methylglutaryl-coenzyme A reductase

LDLR:

Low-density lipoprotein receptor

NPC1L1:

NPC1-like intracellular cholesterol transporter 1

PCSK9:

Proprotein convertase subtilisin/kexin type 9

PPARA:

Peroxisome proliferator-activated receptor alpha

APOC3:

Apolipoprotein C3

ANGPTL3:

Angiopoietin-like 3

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Acknowledgements

We want to acknowledge the following consortiums: the Global Lipids Genetics Consortium, CARDIoGRAMplusC4D, the UK Biobank, Finngen study and GWAS catalog, for making their GWAS summary level statistics publicly available.

Funding

This study was supported by Postdoctor Research Fund of West China Hospital, Sichuan University (2023HXBH045 to Xiaohu Hao), and 1.3.5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (ZYJC21002 to Lunxu Liu).

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Author notes

  1. Zhipeng Gong, Dongsheng Wu, Yin Ku and Congyao Zou contributed equally to this work.

Authors and Affiliations

  1. Department of Thoracic Surgery and Institute of Thoracic Oncology, West China Hospital, Sichuan University, Chengdu, China

    Zhipeng Gong, Dongsheng Wu, Yin Ku, Xiaohu Hao & Lunxu Liu

  2. West China School of Pharmacy, Sichuan University, Chengdu, China

    Congyao Zou

  3. West China School of Medicine, Sichuan University, Chengdu, China

    Lin Qiu

Authors
  1. Zhipeng Gong
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  2. Dongsheng Wu
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  3. Yin Ku
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  4. Congyao Zou
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  5. Lin Qiu
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  6. Xiaohu Hao
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  7. Lunxu Liu
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Contributions

Zhipeng Gong and Dongsheng Wu: formal analysis, software, validation, investigation, visualization, writing – original draft. Yin Ku and Congyao Zou: formal analysis, validation, visualization, investigation, writing – original draft. Lin Qiu: software, validation, visualization, writing – original draft. Xiaohu Hao: software, validation, conceptualization, funding acquisition, writing review & editing. Lunxu Liu: conceptualization, funding acquisition, resources, supervision, project administration, writing – original draft, writing review & editing. All authors read and approved the final manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Lunxu Liu.

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The ethical review and approval were not required for this study because all data used in this study are publicly available.

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The authors declare no competing interests.

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Supplementary Information

12890_2025_3527_MOESM1_ESM.xlsx

Supplementary Material 1: Supplementary Table 1. Genetically instrumented lipid-lowering genetics variants of target genes, Supplementary Table 2. Sensitivity analysis of genetically proxied lipid-lowering variants on positive control (CHD), Supplementary Table 3. Results of heterogeneity and pleiotropy tests for the causal effects of lipid-lowering gene targets on CHD, Supplementary Table 4. Sensitivity analysis of genetically proxied lipid-lowering variants on respiratory diseases, Supplementary Table 5. Results of heterogeneity and pleiotropy tests for the causal effects of lipid-lowering gene targets on respiratory diseases.

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Gong, Z., Wu, D., Ku, Y. et al. Lipid-lowering drug targets associated with risk of respiratory disease: a Mendelian randomization study. BMC Pulm Med 25, 71 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12890-025-03527-x

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12890-025-03527-x

Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

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