ABSTRACT
OBJECTIVES This genetics-based study aimed to evaluate the relationships of long-term lipid-lowering agents on lung-related diseases (LRD) outcomes.
METHODS We extracted genetic variations of six drug target genes from a major genome-wide association study of low-density lipoprotein cholesterol (LDL-C) in individuals predominantly of European ancestry to represent the effects of LDL-C-lowering treatment. We conducted drug-targeted Mendelian randomization and utilized a range of outcomes to capture evidence of adverse side effects related to the inhibition of the gene.
RESULTS Our study did not find any significant association between genetically proxied APOB, CETP, HMGCR, NPCIL, PCSK9, and LDLR inhibition (equivalent to a one standard deviation reduction in LDL-C) with the risk of Small Cell Lung Cancer, Non-Small Cell Lung Cancer, idiopathic pulmonary fibrosis, and Pneumonia (P > 0.05). However, long-term inhibition of NPC1L that mimics the use of statin drugs may have contradictory effects on pulmonary edema (OR = 0.508, 95%CI = 0.328–0.786,P = 0.002) and chronic obstructive pulmonary disease (OR = 1.524, 95%CI = 1.099–2.115, P = 0.012). A series of sensitivity analyses and positive control analyses have been conducted to confirm the reliability of the results.
CONCLUSIONS In conclusion, this study reveals inconsistent associations between genetic proxy inhibition of APOB, CETP, HMGCR, NPCIL, PCSK9 and LDLR with LRD in specific populations.
Introduction
Cardiovascular disease continues to be a significant global health issue, contributing to a substantial number of deaths and morbidity annually. It is estimated that approximately 17 million people die from cardiovascular disease each year. This alarming figure highlights the urgent need for effective prevention and management strategies to alleviate the burden of cardiovascular disease on individuals and societies worldwide (Roth et al. Citation2015). Despite the potential benefits of lifestyle modifications in lowering the risk of cardiovascular disease, pharmacological interventions play a vital role in managing cardiovascular health, especially in high-risk patients (Holmes et al. Citation2021; World Health Organization Citation2022). Statin medications are widely recognized and commonly prescribed for the treatment of hypercholesterolemia (Reiner Citation2013; Ray et al. Citation2014). Although statin drugs are widely used in patients with high cholesterol, they may still be underutilized overall.
Despite ongoing debates, the utilization of statins has proliferated as primary and secondary prevention medications for cardiovascular diseases (CAD). This surge in usage can be attributed to statins acting as inhibitors of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase – an enzyme pivotal in cholesterol synthesis. Moreover, statins are deemed as one of the most frequently prescribed lipid-lowering drugs. Statin medications have been found to possess various anti-tumor properties, including reducing cell proliferation and angiogenesis, decreasing invasion, and synergistically inhibiting the progression of lung cancer. The occurrence of various types of cancer is closely associated with inflammation. Recent data suggests that statin medications can inhibit the mammalian target of the rapamycin (mTOR) pathway in mammalian epithelial cells, thereby reducing inflammatory responses (Amin et al. Citation2021; Lashgari et al. Citation2023). Emerging as a target in cholesterol-lowering drug development, Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a crucial role in regulating low-density lipoprotein cholesterol (LDL-C) levels (Mullard Citation2017; Sabatine Citation2019). Given the development of new targets for statin drugs and their significant pleiotropic and anti-inflammatory effects, there has been a growing interest in their potential role beyond lipid regulation. Mounting evidence indicates a correlation between statin therapy and the prognosis of lung-related diseases (LRD), suggesting the potential therapeutic effects of statin drugs in treating these conditions (Mari et al. Citation2019). Observational studies carried out globally have discovered an association between statin therapy and the outcomes of patients with LRD. For instance, a study utilizing the UK General Practice Research Database revealed a significant reduction in the likelihood of pneumonia among diabetic patients using statin medications [odds ratio (OR) = 0.49, 95% confidence interval (CI) = 0.35–0.69] (Van de Garde et al. Citation2006). In a research investigation carried out within the United States, it was discovered that elderly male patients with community-acquired pneumonia who were taking statin medications experienced a significant reduction in the 30-day mortality rate (OR = 0.36, 95%CI = 0.14 −0.92) (Mortensen et al. Citation2008). However, these findings have faced criticism as an analysis of 3,415 patients revealed that the use of statin medications no longer showed an association with improved pneumonia outcomes after adjusting for functional status (Chopra and Flanders Citation2009). Raymakers et al. conducted a retrospective cohort study utilizing a large database from the British Columbia region to investigate the potential impact of statin therapy on mortality rates among patients diagnosed with chronic obstructive pulmonary disease (COPD) (Raymakers et al. Citation2017). The study involved a sample of 7,566 COPD patients who were undergoing statin therapy, and they were compared to a control group of 32,112 COPD patients who were not receiving statin therapy. The primary outcome assessed was the overall mortality rate, while the secondary outcome focused on mortality related to pulmonary conditions. During the designated window for exposure assessment, the researchers calculated the extent of patients’ statin therapy by utilizing the medication possession ratio, which involved determining the number of days the patients had been supplied with statin medication during their total observation period. A threshold value of 0.8 was established as the dividing point distinguishing ‘adherent’ patients from ‘non-adherent’ patients. Through multivariate analysis, it was discovered that the utilization of statin therapy demonstrated a significant reduction in both overall mortality (hazard ratio [HR] = 0.79, 95%CI = 0.68–0.92) and mortality associated with pulmonary conditions (HR = 0.55, 95% CI = 0.32–0.93). Furthermore, ongoing observational studies are being conducted to explore the usage of statin therapy in lung diseases like idiopathic pulmonary fibrosis (IPF) (Richeldi et al. Citation2014; Kang et al. Citation2021). Indeed, the conclusions of these studies are still subject to limitations commonly encountered in traditional epidemiological research, such as confounding factors and off-target effects of medications (Criner et al. Citation2014; Young et al. Citation2014; Doganer et al. Citation2023).
Mendelian randomization (MR) is a methodology used for inferring causality by leveraging genetic variations as instrumental variables (Davey Smith and Hemani Citation2014). Genetic variations are randomly assigned during conception and are generally unaffected by potential confounding factors and reverse causation, which are common limitations in traditional observational studies. As a result, MR helps to minimize confounding biases and provides more reliable estimates of causal effects without the need for potentially detrimental interventions. As the underlying theory advances and its applications continue to grow, drug-targeted MR analysis has emerged as an effective tool. It involves utilizing genetic variations located within or near specific genes in DNA sequences to construct instrumental variables for analysis. These genes encode drug targets, allowing for the simulation of genetic variations in the pharmacological inhibition of drug-gene interactions, reflecting the long-term effects of medication (Schmidt et al. Citation2020; Williams et al. Citation2020; Lyall et al. Citation2021).
Our research aims to comprehensively assess the potential effects of genetic lipid-lowering medications on a range of lung-related diseases (LRD), including small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC), chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), pulmonary edema (PE), and pneumonia. We strive to understand the possible impact of genetic lipid-lowering medications on the risk of developing these specific lung diseases. This study is anticipated to offer novel insights into the influence of genetically regulated lipid-lowering medications on LRD.
Materials and methods
Study design
Our study utilized drug-targeted MR analysis to explore the association between drugs targeting HMGCR (the target of statin medications), Niemann-Pick C1-Like 1 (NPC1L1, the target of ezetimibe), proprotein convertase subtilisin/kexin type 9 (PCSK9, the target of alirocumab and evolocumab), CETP (the target of anacetrapib), APOB (the target of mipomersen), and the risk of lung-related diseases influenced by genetic factors (Liu et al. Citation2021). In addition, given the role of LDLR in lipid metabolism, it was also included in the analysis to fully consider its potential impact. For more detailed information on the study design, please refer to Figure .
Figure 1. Study Overview and Mendelian Randomization Model. HMGCR, 3-Hydroxy-3-Methyl-Glutaryl-Coenzyme A Reductase; NPC1L1, Niemann-Pick C1-Like 1; PCSK9, Proprotein Convertase Subtilisin/Kexin type 9; LDLR, Low Density Lipoprotein Receptor; CETP, Cholesteryl Ester Transfer Protein; APOB, Apolipoprotein B; MR, Mendelian Randomization.
Data source
In our study, we utilized publicly available data obtained from large-scale genome-wide association studies (GWASs). These datasets provided us with the necessary genetic information to investigate the relationship between drug targets and the risk of LRD. All studies have received approval from their respective institutional review boards and adhere to the principles outlined in the Helsinki Declaration. In addition, informed consent has been obtained from each participant.
To obtain instrumental variables for generating lipid-lowering drug targets, these SNPs were selected based on the summary statistics of the global lipid genetics consortium (GLGC) LDLC GWAS (Willer et al. Citation2013; Liu et al. Citation2021) in relevant target regions. The dataset included European ancestry participants from 59 studies, with a total sample size of 188,577 individuals. Imputation was performed using the 1000 Genomes Project reference panel to fill in missing genetic information. For our analysis, circulating lipid levels were measured in individuals who had not recently taken lipid-lowering medications, after an overnight fast of 8 h (Liu et al. Citation2021). This rigorous data collection and processing approach allowed us to obtain reliable instrumental variables for studying the effects of lipid-lowering drug targets. Linear regression was utilized to perform association tests for each SNP, wherein the transformed trait value was considered the dependent variable and the number of alleles for each individual served as the independent variable (Willer et al. Citation2013). To estimate the effects of drug targets, we adopted a linkage disequilibrium (LD) clumping approach. In particular, we identified genetic variations within a range of ±0.1 Mb around each target gene that exhibited statistically significant associations with LDL-C at a genome-wide level of significance (P < 5 × 10−8). These SNPs were then grouped based on LD r2 < 0.2 and a physical distance of 250 kb (Rosoff et al. Citation2022). To increase the variability captured by the instrumental variables, we lowered the LD r2 threshold to 0.40, ensuring the inclusion of at least 3 instrumental variables. Ultimately, in the final analysis, we included 5, 4, 11, 12, 8, and 15 SNPs representing HMGCR, APOB, LDLR, CETP, NPC1L1, and PCSK9, respectively (Table S1).
The data on lung-related diseases is sourced from the FinnGen consortium's R9 version. The website (https://www.finngen.fi/fi) provides detailed information on methods such as data collection, participant cohorts, genetic genotyping, and data analysis. The study included a total of 6 GWAS summaries of lung-related diseases, including SCLC (676 cases and 287,137 controls), NSCLC (4,901 cases and 287,137 controls), COPD (18,266cases and 311,286controls), IPF (2,018 cases and 373,064 controls), PE (9,243 cases and 367,108 controls), Pneumoniae (58,174 cases and 319,103 controls). The analyses conducted in this study exclusively focused on participants of European descent, who were identified as individuals with 80% or higher proportion of European ancestry (Table S2).
Power calculation and F-Statistic
To ensure adequate statistical power and address potential biases from weak instruments, we performed calculations for statistical power and F-statistics. The power estimation was performed using the online tool mRnd (http://cnsgenomics.com/shiny/mRnd) (Brion et al. Citation2013; Freeman et al. Citation2013). Table S1 contains the statistical powers necessary to detect an OR of 0.50 for each unit of standard deviation change in circulating LDL-C levels. These calculations were conducted to determine the required sample size for detecting the desired effect size with sufficient statistical power. To assess the strength of each instrument, the F-statistic was calculated. A commonly used threshold to determine the presence of weak instrument bias is an F-statistic greater than 10. When the F-statistic exceeds this threshold, it suggests that the instrument is strong and less likely to introduce bias in the analysis. This criterion is often employed to ensure the reliability of instrumental variable analysis and mitigate potential issues related to weak instrument bias (Haycock et al. Citation2016). F-statistic was calculated using the following formula: R2(N-2)/(1-R2), where R2 is the proportion of variance in variable explained by each instrument and N is the sample size of the GWAS for the SNP-variable association. R2 was computed using the formula: 2 × EAF × (1 – EAF) × beta2, where EAF represents the effect allele frequency and beta denotes the estimated effect on the variable.
Positive validation of the Mendelian randomization analysis
In our study, we anticipated observing a substantial causal impact of lowering LDL-C levels on the risk of CAD to assess the robustness of the MR method and the instrumental variables used. The CARDIoGRAMplusC4D consortium provided the GWAS data for assessing the risk of CAD (N = 184,305) (Nikpay et al. Citation2015). All participants included in the study belonged to populations of European descent.
Statistical analysis
To ensure the consistency of effective allelic coding, we first harmonized the orientation of exposure and outcome instrumental variables. We searched for appropriate proxy SNPs with a correlation coefficient (r2) of at least 0.8 to compensate for the loss of genetic variation in the outcome. However, if no proxies were identified, those SNPs were excluded from the analysis. For instrumental variables comprising three or more variants, we employed the multiplicative random effect inverse-variance weighted (IVW) method to generate a comprehensive estimate of the causal effect. All OR reported for LRD risk corresponded to one standard deviation change in LDL-C levels. We performed sensitivity analyses using MR-Egger and weighted median methods. To assess heterogeneity and horizontal pleiotropy between the causal effects of individual genetic variants, we employed the MR-Egger intercept test and pleiotropy test. Additionally, we assessed the impact of removing individual SNPs from the instrumental variables on the overall estimates of the causal effect using the leave-one-out method. This iterative process allowed us to assess the stability and robustness of the results by evaluating the influence of each SNP on the overall estimate. All statistical analyses were conducted using the R software (version 4.2.1, www.r-project.org) by using the TwoSampleMR (v. 0.5.5) and MendelianRandomization (v. 0.5.0) packages.
Results
Primary analyses
The provided information in Table S1 includes details of all the genetic variants considered in this study, which were used as proxies for the effects of drug target genes. Analysis of six drug target instruments revealed an F-statistic greater than 10, indicating a low probability of bias in the instrumental variables. However, it should be noted that the level of variance interpretation is around 1%, which might be considered a limitation of drug target MR and may result in relatively low statistical power. Based on our analysis (Figure ), the results did not support the relationship between genetically proxied inhibition of APOB, CETP, HMGCR, NPC1L1, PCSK9, and LDLR (which corresponds to a one standard deviation reduction in LDL-C) and the risk of SCLC, NSCLC, IPF and pneumonia. And the genetically proxied HMGCR, NPCIL, PCSK9, and LDLR inhibition were associated with a higher risk of COPD [for HMGCR, OR = 1.619, 95%CI = 1.301–2.015, P < 0.001; for NPCIL, OR = 1.255, 95%CI = 1.114–1.414, P < 0.001; for PCSK9, OR = 1.524, 95%CI = 1.099–2.115, P = 0.012; for LDLR, OR = 1.154, 95%CI = 1.054–1.264, P = 0.002] was found. Similarly, we observed significant associations in MR analyses between LDLR, NPC1L1 inhibitors, and the risk of PE (for LDLR, OR = 0.748, 95%CI = 0.644–0.869, P < 0.001; for NPC1L1, OR = 0.508, 95%CI = 0.328–0.786, P = 0.002; Figure ).
Figure 2. Association between genetically proxied inhibition of 3-Hydroxy-3-Methylglutaryl Coenzyme A Reductase (HMGCR), Niemann-Pick C1-Like 1 (NPC1L1), Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9), Cholesteryl Ester Transfer Protein (CETP), low-density lipoprotein receptor (LDLR), Apolipoprotein B (APOB) with lung-related diseases (LRD) risk.
Sensitivity analysis
It is noted that the effect estimates obtained from the pleiotropy-robust methods were consistent in our study. The leave-one-out analysis results can be found in Table S3-S8. Additionally, the results of MR-Egger and weighted median analyses are presented in Table S9-S10. In the sensitivity analysis of drug-target MR, we considered the linkage disequilibrium (LD) among SNPs. The heterogeneity and pleiotropy test results are displayed in Table S11. Notably, no significant heterogeneity in causal inference was observed in both the IVW and MR-Egger methods, with all P-values exceeding 0.05. The pleiotropy tests conducted in our study indicated that there was no significant horizontal pleiotropy present, with all P-values exceeding 0.05. Additionally, we observed that the results of genetically proxied inhibition of HMGCR, NPC1L1, PCSK9, and LDLR with COPD risk, as well as NPC1L1 with PE risk, remained consistent irrespective of the exclusion of any SNP.
Positive control analyses
The results, which consider weak linkage disequilibrium among variants, present the impact of genetically predicted lipid-lowering drug targets on the risk of CAD in Table and Table S12. The IVW estimation for all six drug targets revealed a significant association with the risk of CAD (all P < 0.05), no heterogeneity or pleiotropy was found in the positive control analyses, indicating the validity of these instruments (Table and Table S12).
Table 1. Inverse variance weighted estimates for positive control analyses.
Discussions
In our study, we conducted drug-target MR analyses and did not find evidence of adverse side effects related to the inhibition of the gene for SCLC, NSCLC, IPF, and pneumonia. However, we observed a causal association between genetically proxied inhibition of LDLR and NPC1L and a higher risk of PE. Conversely, the use of statins is likely to increase the risk of COPD.
Statins are effective cholesterol-lowering drugs that inhibit the synthesis of cholesterol and are widely used to prevent and treat cardiovascular disease. Cholesterol is a key structure of mammalian cell membranes, and many cholesterol metabolites are involved in cell proliferation, membrane integrity, cell signaling, protein synthesis and cell cycle, etc. Cancer cells often exhibit changes in lipid metabolic pathways, cholesterol content is directly associated with cell function (Szlasa et al. Citation2020). In recent years, more and more trials have shown that statins have anti-tumor effects both in vitro and in vivo. These drugs have anticancer effects in lung cancer, breast cancer and digestive tract tumors, and the potential anticancer performance of statins has attracted much attention (Wang et al. Citation2016). However, we did not find any benefit in terms of lung cancer risk in our study. A meta-analysis incorporating 17 studies involving a total of 98,445 patients might support our findings (Xia et al. Citation2019). These 17 studies comprised 11 cohort studies, 3 case–control studies, and 3 randomized controlled trials (RCTs). In the cohort studies, statins were potentially associated with a decrease in all-cause mortality (HR = 0.77, 95%CI: 0.59–0.99). However, no correlation was found in case–control studies (HR = 0.75, 95%CI: 0.50–1.10). In the RCT study, statins did not affect either mortality or OS. When all studies were included in the analysis, statins did not affect progression-free survival in lung cancer patients. However, statins may have the effect of enhancing the effects of tyrosine kinase inhibitors (HR = 0.86, 95%CI: 0.76–0.98) and chemotherapy (HR = 0.86, 95%CI: 0.81–0.91), affecting OS in patients with non-small cell lung cancer, but without increasing the overall response rate and toxicity. From this, we know that statins may reduce mortality and increase OS in lung cancer patients in low – to medium-quality observational studies, but there are no similar results in RCTs. This is likely to support our research findings that there is no association between genetic inhibition agents and lung cancer, suggesting that statin medications may not have a significant therapeutic effect on lung cancer patients.
IPF is defined as a chronic, progressive, fibrosing interstitial lung disease (ILD) of unknown etiology. In 2008, a systematic review of these cases concluded that ILD could be a side effect of statin therapy (Fernández et al. Citation2008), attributed to their effects on inflammatory processes (Xu et al. Citation2012). Although another cohort study failed to replicate this association (Saad et al. Citation2013), some have suggested discontinuing statin medications for patients who may be at risk of ILD. However, there has been increasing evidence for the anti-inflammatory and anti-fibrotic effects of statins, which is their pleiotropic effect in addition to their cholesterol-lowering effect (Ahsan et al. Citation2020). Subsequently, several research groups have found potential benefits of statin therapy for patients with IPF (Kreuter et al. Citation2017). To date, there is still ongoing debate regarding whether statin medications can induce fibrosis and how they may impact established IPF (Kreuter et al. Citation2018). The latest systematic review, consistent with our conclusion, indicates that there is currently insufficient evidence to conclude the effect of statin therapy on disease-related outcomes in IPF (Kim et al. Citation2021 Nov). Furthermore, there is currently insufficient research evidence on the use of statin drugs for the treatment of PE. PE is defined as the abnormal buildup of fluid outside the blood vessels within the lung tissue. This accumulation of fluid impairs the gas exchange at the alveolar level and can potentially lead to respiratory failure. The underlying causes can be either cardiac-related, where the heart fails to adequately clear blood from the pulmonary circulation, or non-cardiogenic, resulting from lung tissue damage (Malek and Soufi Citation2023). In this study, we observed that inhibition of genetic agents LDLR and NPCL1 contributes to a lower risk of PE. In recent years, the utilization of statin medications has evolved beyond their role in lowering cholesterol levels to encompass the prevention of cardiovascular diseases in nearly all patients aged 50 and above. The efficacy of these drugs can be primarily attributed to their pleiotropic effects, including anti-inflammatory and antioxidant properties, as well as the reduction of vascular dysfunction, beyond the ability to lower lipids (Bergt et al. Citation2017). Pneumonia is the most common cause of Acute Lung Injury (ALI) and Acute Respiratory Distress Syndrome (ARDS). It is an infection of the lower respiratory tract involving the lung parenchyma, commonly caused by respiratory viruses, common gram-negative or gram-positive bacteria, as well as worldwide prevalent mycobacteria (Long et al. Citation2022). The latest meta-analysis results indicate that no association was observed between the use of statin medications and the risk of pneumonia, which supports our conclusion (Darvishi et al. Citation2023). COPD is distinguished by persistent inflammation of the airways, damage to the alveolar-capillary units, and a gradual deterioration in lung function (Yang et al. Citation2022). Dyslipidemia has been linked to the inflammatory and other pathological processes in chronic obstructive pulmonary disease (COPD), including cholesterol levels and oxidized derivatives such as 25-hydroxycholesterol (He et al. Citation2019). In this study, we found that lowering LDL levels may lead to an increased risk of chronic obstructive pulmonary disease (COPD), and we speculate that if long-term statin use excessively lowers LDL levels, it may cause other harm to the human body.
There are several limitations to our study, mainly related to the assumptions and constraints of MR. The first and most important limitation is that MR cannot fully replace long-term RCTs as a means of determining causality. In addition, several large epidemiological studies of statins are old, which may have limited our findings. The drug-targeted MR analysis aims to reflect the lifelong effects of lipid-lowering agents on LDL-C levels and their impact on disease, and therefore it cannot indicate the short-term effects of lipid-modifying drugs. Second, this analysis did not consider interactions between genetic variants associated with drug targets and the risk of LRD, such as gene-environment interactions and gene-gene interactions. Lastly, we want to highlight that our study focused only on individuals of European ancestry. Therefore, we urge caution when applying or generalizing our findings to populations of other ethnic backgrounds.
In summary, we found very limited evidence supporting the causal protective role of genetic agents LDLR and NPCL1 inhibition in PE. However, our results suggest a significant association between the inhibition of genetic agents HMGCR, NPCIL, PCSK9, and LDLR and a higher risk of COPD. These findings contribute valuable insights into the potential mechanisms underlying novel therapies aimed at lowering lipid levels.
Author contributions
The study was conceived and designed by Bin Ni. The data were analyzed by Haifeng Yu and Xiaofei Zhang. The first draft of the manuscript was prepared by Haifeng Yu. The data was collected and interpreted by Haifeng Yu, Xiaofei Zhang, Bian Wen and Shuo Hu. All other authors provided the data and revised the manuscript critically for important intellectual content. The authors read and approved the final version of the manuscript.
Supplemental Tables
Download MS Excel (60 KB)Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
This study utilized publicly available data from the GLGC (Global Lipids Genetics Consortium) at http://csg.sph.umich.edu/willer/public/lipids2013 and from the FinnGen consortium at https://www.finngen.fi/fi. Additional data supporting the findings of this study are available in figshare at https://doi.org/10.6084/m9.figshare.24885393.
References
- Ahsan F, Oliveri F, Goud HK, Mehkari Z, Mohammed L, Javed M, Althwanay A, Rutkofsky IH. 2020 Sep 14. Pleiotropic effects of statins in the light of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. Cureus. 12(9):e10446. doi:10.7759/cureus.10446.
- Amin F, Fathi F, Reiner Ž, Banach M, Sahebkar A. 2021 Mar 18. The role of statins in lung cancer. Arch Med Sci. 18(1):141–152. doi:10.5114/aoms/123225.
- Bergt S, Grub A, Wagner S, Engelke H, Nöldge-Schomburg G, Vollmar B, Roesner JP, Wagner NM. 2017 Apr 12. Pravastatin but not simvastatin improves survival and neurofunctional outcome after cardiac arrest and cardiopulmonary resuscitation. JACC Basic Transl Sci. 2(2):149–159. doi:10.1016/j.jacbts.2017.01.009.
- Brion MJ, Shakhbazov K, Visscher PM. 2013. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 42:1497–1501. doi:10.1093/ije/dyt179.
- Chopra V, Flanders SA. 2009. Does statin use improve pneumonia outcomes? Chest. 136:1381–1388. doi:10.1378/chest.09-0941.
- Criner GJ, Connett JE, Aaron SD, Albert RK, Bailey WC, Casaburi R, et al. 2014. COPD clinical research network; Canadian institutes of health research. Simvastatin for the prevention of exacerbations in moderate-to-severe COPD. N Engl J Med. 370:2201–2210. doi:10.1056/NEJMoa1403086.
- Darvishi M, Amiri MM, Heidari-Soureshjani S, Sherwin CM, Mardani-Nafchi H. 2023 Oct 10. The association between statins intake and risk of post stroke pneumonia: a systematic review and meta-analysis. Curr Rev Clin Exp Pharmacol. doi:10.2174/0127724328258172230926070748.
- Davey Smith G, Hemani G. 2014. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 23:R89–R98. doi:10.1093/hmg/ddu328.
- Doganer YC, Aydogan U, Kaplan U, Gormel S, Rohrer JE, Yuksel UC. 2023 May. Statin adherence in patients with high cardiovascular risk: a cross-sectional study. Postgrad Med. 135(4):361–369. doi:10.1080/00325481.2022.2144030.
- Fernández AB, Karas RH, Alsheikh-Ali AA, Thompson PD. 2008 Oct. Statins and interstitial lung disease: a systematic review of the literature and of food and drug administration adverse event reports. Chest. 134(4):824–830. doi:10.1378/chest.08-0943.
- Freeman G, Cowling BJ, Schooling CM. 2013. Power and sample size calculations for Mendelian randomization studies using one genetic instrument. Int J Epidemiol. 42: 1157–1163. doi:10.1093/ije/dyt110.
- Haycock PC, Burgess S, Wade KH, Bowden J, Relton C, Davey Smith G. 2016. Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am J Clin Nutr. 103:965–978. doi:10.3945/ajcn.115.118216.
- He P, Smith A, Gelissen IC, Ammit AJ. 2019 Dec. The effect of statins and the synthetic LXR agonist T0901317 on expression of ABCA1 transporter protein in human lung epithelial cell lines in vitro. Pharmacol Rep. 71(6):1219–1226. doi:10.1016/j.pharep.2019.08.006.
- Holmes MV, Richardson TG, Ference BA, Davies NM, Davey Smith G. 2021. Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development. Nat Rev Cardiol. 18:435–453. doi:10.1038/s41569-020-00493-1.
- Kang J, Jeong SM, Shin DW, Cho M, Cho JH, Kim J. 2021 Jan. The associations of aspirin, statins, and metformin with lung cancer risk and related mortality: a time-dependent analysis of population-based nationally representative data. J Thorac Oncol. 16(1):76–88. doi:10.1016/j.jtho.2020.08.021.
- Kim JW, Barrett K, Loke Y, Wilson AM. 2021 Nov. The effect of statin therapy on disease-related outcomes in idiopathic pulmonary fibrosis: a systematic review and meta-analysis. Respir Med Res. 80:100792.
- Kreuter M, Bonella F, Maher TM, Costabel U, Spagnolo P, Weycker D, Kirchgaessler KU, Kolb M. 2017 Feb. Effect of statins on disease-related outcomes in patients with idiopathic pulmonary fibrosis. Thorax. 72(2):148–153. doi:10.1136/thoraxjnl-2016-208819.
- Kreuter M, Costabel U, Richeldi L, Cottin V, Wijsenbeek M, Bonella F, Bendstrup E, Maher TM, Wachtlin D, Stowasser S, Kolb M. 2018. Statin therapy and outcomes in trials of nintedanib in idiopathic pulmonary fibrosis. Respiration. 95(5):317–326. doi:10.1159/000486286.
- Lashgari NA, Roudsari NM, Zadeh SST, Momtaz S, Abbasifard M, Reiner Ž, Abdolghaffari AH, Sahebkar A. 2023 Feb. Statins block mammalian target of rapamycin pathway: a possible novel therapeutic strategy for inflammatory, malignant and neurodegenerative diseases. Inflammopharmacology. 31(1):57–75. doi:10.1007/s10787-022-01077-w.
- Liu L, Sheng C, Lyu Z, Dai H, Chen K. 2021 Oct 12. Association between genetically proxied lipid-lowering drug targets and renal cell carcinoma: a Mendelian randomization study. Front Nutr. 8:755834. doi:10.3389/fnut.2021.755834.
- Long ME, Mallampalli RK, Horowitz JC. 2022 May 27. Pathogenesis of pneumonia and acute lung injury. Clin Sci (Lond). 136(10):747–769. doi:10.1042/CS20210879.
- Lyall DM, Ward J, Banach M, et al. 2021. PCSK9 genetic variants and cognitive abilities: a largescale Mendelian randomization study. Arch Med Sci. 17:241–244. doi:10.5114/aoms/127226.
- Malek R, Soufi S. 2023 Jan. Pulmonary edema. 2023 Apr 7. In: Statpearls [Internet]. Treasure Island, FL: StatPearls Publishing; PMID: 32491543.
- Mari PV, Lombardi F, Flore MC, Richeldi L. 2019 Oct 1. Statin therapy and lung disorders. Am J Respir Crit Care Med. 200(7):921–923. doi:10.1164/rccm.201804-0725RR. PMID: 31247147.
- Mortensen EM, Restrepo MI, Anzueto A, Pugh J. 2008. The effect of prior statin use on 30-day mortality for patients hospitalized with pneumonia. Eur Respir J. 31:611–617. doi:10.1183/09031936.00162006.
- Mullard A. 2017. Nine paths to PCSK9 inhibition. Nat Rev Drug Discov. 16:299–301. doi:10.1038/nrd.2017.83.
- Nikpay M, Goel A, Won HH, et al. 2015. A comprehensive 1,000 genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 47(10):1121–1130. doi:10.1038/ng.3396.
- Ray S, Jindal AK, Sengupta S, Sinha S. 2014. Statins: can we advocate them for primary prevention of heart disease? Med J Armed Forces India. 70:270–273. doi:10.1016/j.mjafi.2013.05.008.
- Raymakers AJN, Sadatsafavi M, Sin DD, De Vera MA, Lynd LD. 2017. The impact of statin drug use on all-cause mortality in patients with COPD: a population-based cohort study. Chest. 152:486–493. doi:10.1016/j.chest.2017.02.002.
- Reiner Z. 2013. Statins in the primary prevention of cardiovascular disease. Nat Rev Cardiol. 10:453–464. doi:10.1038/nrcardio.2013.80.
- Richeldi L, du Bois RM, Raghu G, Azuma A, Brown KK, Costabel U, et al. 2014. INPULSIS trial investigators. Efficacy and safety of nintedanib in idiopathic pulmonary fibrosis. N Engl J Med. 370:2071–2082. doi:10.1056/NEJMoa1402584.
- Rosoff DB, Bell AS, Jung J, Wagner J, Mavromatis LA, Lohoff FW. 2022 Aug 16. Mendelian randomization study of PCSK9 and HMG-CoA reductase inhibition and cognitive function. J Am Coll Cardiol. 80(7):653–662. doi:10.1016/j.jacc.2022.05.041.
- Roth GA, Forouzanfar MH, Moran AE, et al. 2015. Demographic and epidemiologic drivers of global cardiovascular mortality. N Engl J Med. 372:1333–1341. doi:10.1056/NEJMoa1406656.
- Saad N, Camus P, Suissa S, Ernst P. 2013 Apr. Statins and the risk of interstitial lung disease: a cohort study. Thorax. 68(4):361–364. doi:10.1136/thoraxjnl-2012-201823.
- Sabatine MS. 2019. PCSK9 inhibitors: clinical evidence and implementation. Nat Rev Cardiol. 16:155–165. doi:10.1038/s41569-018-0107-8.
- Schmidt AF, Finan C, Gordillo-Marañón M, Asselbergs FW, Freitag DF, Patel RS, et al. 2020. Genetic drug target validation using Mendelian randomisation. Nat Commun. 11:3255. doi:10.1038/s41467-020-16969-0.
- Szlasa W, Zendran I, Zalesińska A, Tarek M, Kulbacka J. 2020 Oct. Lipid composition of the cancer cell membrane. J Bioenerg Biomembr. 52(5):321–342. doi:10.1007/s10863-020-09846-4.
- Van de Garde EM, Hak E, Souverein PC, Hoes AW, Van Den Bosch JM, Leufkens HG. 2006. Statin treatment and reduced risk of pneumonia in patients with diabetes. Thorax. 61:957–961. doi:10.1136/thx.2006.062885.
- Wang AG, Aragaki AK, Tang JY, et al. 2016. Statin use and all-cancer survival: prospective results from the Women's Health Initiative. Br J Cancer. 115(1):129–135. doi:10.1038/bjc.2016.149.
- Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, et al. 2013. Discovery and refinement of loci associated with lipid levels. Nat Genet. 45:1274–1283. doi:10.1038/ng.2797.
- Williams DM, Finan C, Schmidt AF, Burgess S, Hingorani AD. 2020. Lipid lowering and Alzheimer disease risk: a Mendelian randomization study. Ann Neurol. 87:30–39. doi:10.1002/ana.25642.
- World Health Organization. 2022. 2021 global progress report on implementation of the WHO Framework Convention on Tobacco Control. accessed 2022 July 6. https://apps.who.int/iris/handle/10665/351735.
- Xia DK, Hu ZG, Tian YF, et al. 2019. Statin use and prognosis of lung cancer: a systematic review and meta-analysis of observational studies and randomized controlled trials. Drug Des Devel Ther. 13:405–422. doi:10.2147/DDDT.S187690.
- Xu JF, Washko GR, Nakahira K, et al. 2012. Hunninghake, COPDGene investigators, statins and pulmonary fibrosis: the potential role of NLRP3 inflammasome activation. Am J Respir Crit Care Med. 185:547–556. doi:10.1164/rccm.201108-1574OC.
- Yang IA, Jenkins CR, Salvi SS. 2022 May. Chronic obstructive pulmonary disease in never-smokers: risk factors, pathogenesis, and implications for prevention and treatment. Lancet Respir Med. 10(5):497–511. doi:10.1016/S2213-2600(21)00506-3.
- Young RP, Hopkins RJ, Agusti A. 2014. Statins as adjunct therapy in COPD: how do we cope after STATCOPE? Thorax. 69:891–894. doi:10.1136/thoraxjnl-2014-205814.