Exploring the association between lipid-modifying drugs and idiopathic pulmonary fibrosis: a drug-target Mendelian randomization study

Exploring the association between lipid-modifying drugs and idiopathic pulmonary fibrosis: a drug-target Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the association between lipid-modifying drugs and idiopathic pulmonary fibrosis: a drug-target Mendelian randomization study Gexiang Cai, Jingjing Liu, Mengsi Cai, Lianyou Shao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4387298/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Aug, 2024 Read the published version in Lipids in Health and Disease → Version 1 posted 10 You are reading this latest preprint version Abstract Background Idiopathic pulmonary fibrosis (IPF) is a respiratory disorder of obscure etiology and limited treatment options, possibly linked to dysregulation in lipid metabolism. While several observational studies suggest that lipid-lowering agents may decrease the risk of IPF, the evidence is inconsistent. The present Mendelian randomization (MR) study aims to determine the association between circulating lipid traits and IPF and to assess the potential influence of lipid-modifying medications for IPF. Methods Summary statistics of 5 lipid traits (high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, apolipoprotein A, and apolipoprotein B) and IPF were sourced from the UK Biobank and FinnGen Project Round 10. The study’s focus on lipid-regulatory genes encompassed PCSK9, NPC1L1, ABCG5, ABCG8, HMGCR, APOB, LDLR, CETP, ANGPTL3, APOC3, LPL, and PPARA. The primary effect estimates were determined using the inverse-variance-weighted method, with additional analyses employing the contamination mixture method, robust adjusted profile score, the weighted median, weighted mode methods, and MR-Egger. Summary-data-based Mendelian randomization (SMR) was used to confirm significant lipid-modifying drug targets, leveraging data on expressed quantitative trait loci in relevant tissues. Sensitivity analyses included assessments of heterogeneity, horizontal pleiotropy, and leave-one-out methods. Results There was no significant effect of blood lipid traits on IPF risk (all P>0.05). Drug-target MR analysis indicated that genetic mimicry for inhibitor of NPC1L1, PCSK9, ABCG5, ABCG8, and APOC3 were associated with increased IPF risks, with odds ratios (ORs) and 95% confidence intervals (CIs) as follows: 2.74 (1.05–7.12, P = 0.039), 1.36 (1.02–1.82, P = 0.037), 1.66 (1.12–2.45, P = 0.011), 1.68 (1.14–2.48, P = 0.009), and 1.42 (1.20–1.67, P = 3.17×10 − 5 ), respectively. The SMR method identified a significant association between PCSK9 gene expression in whole blood and reduced IPF risk (OR = 0.71, 95% CI: 0.50–0.99, P = 0.043), and a trend towards a positive correlation for NPC1L1 in adipose subcutaneous tissue (OR = 0.85, 95% CI: 0.73–1.00, P = 0.051). Sensitivity analyses showed no evidence of bias. Conclusions Serum lipid traits did not significantly affect the risk of idiopathic pulmonary fibrosis. Drug targets MR studies examining 12 lipid-modifying drugs indicated that PCSK9 inhibitors could dramatically increase IPF risk, a mechanism that may differ from their lipid-lowering actions and thus warrants further investigation. Idiopathic pulmonary fibrosis Lipids Drug-target Mendelian randomization PCSK9 Summary-data-based Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Idiopathic pulmonary fibrosis (IPF) is a progressive and chronic disorder of unknown origin, affecting an estimated 3 million individuals globally[ 1 ]. The condition is characterized by a high mortality rate, with a median survival time of approximately 3.8 years following diagnosis[ 2 , 3 ]. The pathophysiology of IPF is highly intricate, encompassing alterations in genetic factors, cellular signaling, apoptosis, autophagy, and additional processes[ 1 ]. This multifaceted nature significantly complicates the development of effective therapeutic strategies for IPF. Currently, nintedanib and pirfenidone are the only medications approved by the Food and Drug Administration for IPF treatment[ 4 , 5 ]. Although these treatments can mitigate symptoms, they do not address the underlying disease, and lung transplantation may become necessary for patients to extend their lifespan[ 6 , 7 ]. Current research indicates that metabolic changes play a pivotal role in the fibrosis process[ 8 ]. As a lipid-rich organ, the lung's lipid metabolism and its regulation are essential for normal lung physiology[ 9 , 10 ]. Transcriptomic analyses of various pulmonary cells, such as alveolar epithelial type II cells, alveolar macrophages, and fibroblasts, consistently reveal disruptions in lipid metabolism during fibrosis[ 11 ]. Studies have demonstrated that diminished expression of genes related to lipid, cholesterol, and steroid metabolism can reduce surfactant production in alveolar epithelial type II cells[ 12 ]. Lipid accumulation in alveolar macrophages is linked to elevated CD36 expression, leading to increased absorption of fatty acids. This imbalance in lipid metabolism can precipitate a fibrotic transformation in macrophages, culminating in augmented extracellular matrix (ECM) synthesis. Concurrently, fibroblasts display diminished activity of PPAR-γ, which can drive their metamorphosis from lipid-producing cells into myofibroblast-like entities[ 3 ]. Consequently, the disruption of lipid metabolism is recognized as a key metabolic shift in the pathogenesis of fibrosis[ 3 , 13 ]. Given the strong connection between lipid metabolism disorders and IPF, there is interest in whether lipid-lowering medications have a protective impact on IPF. A cohort study within the Korean population found that the utilization of statins was linked to lower IPF risk[ 14 ]. Among individuals taking statins, the incidence rate of IPF was 15.6 cases per 100,000 person-years, which was less than the rate of 19.3 cases per 100,000 person-years observed in those not taking[ 14 ]. Another Phase III randomized clinical trial involving 624 IPF participants indicated that statins could decrease the mortality rate and the frequency of hospitalizations due to acute exacerbations[ 15 ]. Nonetheless, there is limited randomized controlled trials (RCTs), and some studies present conflicting results. An exploratory analysis of 1,450 IPF patients participating in a Phase III trial found no correlation between statin use and IPF progression[ 16 ]. Similarly, a review of a health management database, which included 6,665 individuals with possible or likely interstitial lung disease (ILD) and 26,660 controls, failed to find a connection between statin use and ILD development[ 17 ]. Moreover, the impact of novel lipid-lowering agents, such as PCSK9 inhibitors and NPC1L1 inhibitors, on IPF remains to be elucidated. Mendelian randomization (MR), a recognized approach, is frequently utilized to explore the potential links between genetically influenced traits, therapeutic drug targets, and disease outcomes[ 18 , 19 ]. For drug-target mendelian randomization, it employs genetic variants situated near or within proximity to the gene encoding the targeted protein as instrumental variables (IVs) to prognosticate treatment efficacy[ 19 ]. The causal inferences derived from MR are considered less prone to bias and reverse causality[ 20 ]. The evidential value of MR analysis is ranked just below that of RCTs, and it can provide significant insights that may presage the findings of RCTs[ 21 – 23 ]. Therefore, our study utilized the Mendelian randomization method to explore the impact of blood lipid traits on IPF risk and to assess the influence of lipid-regulatory medications on IPF. Methods This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization (STROBE-GE), as detailed in Table S1 [ 24 ]. The data were derived from publicly available summary-level data from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies. Comprehensive details regarding these datasets are delineated in Table S2 . The schematic representation of the study design is depicted in Fig. 1.Outline of the study design. Genetic instrumental variables for lipid traits and lipid-modifying targets The largest publicly accessible GWAS data for 3 circulating lipid traits, including high-density lipoprotein cholesterol (HDL-C, N = 291830), low-density lipoprotein cholesterol (LDL-C, N = 318340), and triglyceride (TG, N = 318674) were obtained from the UK Biobank[ 25 ]. Genetic variants linked to these lipid traits were selected, meeting a linkage disequilibrium (LD) clumping threshold of r 2 < 0.001 and a physical distance threshold of 1,000 kb. We selected 20 prevalent lipid-lowering drugs and innovative therapeutics in accordance with recent guidelines for dyslipidemia management[ 26 , 27 ]. Utilizing the DrugBank database ( https://go.drugbank.com/ ) and pertinent scholarly articles, we undertook gene identification for the pharmacological targets of these medications[ 28 , 29 ]. Comprehensive details regarding each target gene are delineated in Table S3 . Based on their principal pharmacological effects, these genes are categorized into lowering LDL-C (i.e. HMGCR, PCSK9, LDLR, APOB, NPC1L1, ABCG5, ABCG8, CETP) and lowering TG (i.e. LPL, ANGPTL3, APOC3). To simulate the lipid-lowering impact of these genes, we identified single nucleotide polymorphisms (SNPs) located within a region extending ± 100 kilobases (kb) around the gene of interest. And these SNPs should also show a significant correlation with lipid levels on a genome-wide scale (p < 5×10 − 8 )[ 30 ]. To optimize the power of the tool, SNPs were permitted to be in weak linkage disequilibrium of less than 0.30 with one another. To reinforce the robustness of the results, we undertook extra analyses using a novel suite of genetic tools that incorporated both Apolipoprotein A (Apo-A) and Apolipoprotein B (Apo-B). Apo-A serves as a critical transporter of HDL-C. Apo-B plays a crucial role in the formation of LDL-C and TG. Apo-A was utilized to develop tools for measuring CETP and LPL. Apo-B was employed to create genetic tools targeting HMGCR, PCSK9, LDLR, APOB, NPC1L1, ABCG5, ABCG8, LPL, ANGPTL3, and APOC3. The GWAS data for both Apo-B and Apo-A were also sourced from the UK Biobank, comprising 317,412 and 290,198 samples, respectively. eQTL Data We used publicly available eQTL data from the Genotype-Tissue Expression (GTEx-V8) project. The GTEx project encompasses eQTL data across 54 distinct human tissues, with participant numbers ranging from 73 to 670[ 31 ]. Approximately 84.6% of these samples originate from individuals of European descent. Cis-eQTLs refer to genetic variations that have a significant link with the expression of specific genes affected by medications. These cis-eQTLs must meet the significance level of a P-value lower than 5×10 − 8 and adhere to the linkage disequilibrium criterion with an r2 value less than 0.1. Outcome GWAS The GWAS data for IPF were obtained from the FinnGen Release 10 ( https://r10.finngen.fi/ ), which included 2189 individuals with IPF and 407609 control subjects. To affirm the efficacy of the selected genetic markers, supplementary analysis was conducted with coronary heart disease (CHD) as the benchmark outcome, serving as a positive control in our study. Summary statistics for CHD were also sourced from the FinnGen project, with 46959 cases and 365222 control individuals (Table S2 ). Cases of missing data were addressed by exclusion, and all participants were of European ancestry. Statistical analysis Mendelian randomization utilizes SNPs as instruments to explore the connection between exposure and outcome variables. The MR must adhere to three fundamental assumptions: (1) Correlation assumption: The IVs demonstrate a significant connection with exposure. The F-statistic is used to evaluate the hypothesis of correlation by quantifying the magnitude of each genetic variant. A larger F statistic (>10) suggests a little chance of weak instrumental variable bias[ 21 , 22 ]; (2) Independence assumption: The IVs should be unconfounded, meaning they are unrelated to factors that could affect both exposure and outcome, ensuring that the observed associations are uniquely due to the exposure under investigation; (3) Exclusivity assumption: The IVs do not have a direct correlation with the outcome (p>5 × 10 − 5 ), nor any other means apart from exposure to correlate with the outcome[ 32 ]. The principal MR analysis was executed utilizing the inverse variance weighted (IVW) method, which has been shown to have the most pronounced statistical impact. Following the statistical methods similar to previous studies, three additional MR methods (MR-Egger, weighted median, and weighted mode) were also implemented as complementary approaches[ 33 – 36 ]. To improve the strength of the results, the contamination mixture method (ConMix) and robust adjusted profile score (RAPS) were also utilized. Compared with other methods, ConMix had the lowest mean squared error[ 37 ]. MR-RAPS considers special pleiotropicity and can provide reliable inferences for MR analysis utilizing weak instrumental variables[ 38 ]. The results of all estimates are typically presented as odds ratio (OR) along with its 95% confidence interval (CI). In this study, we initially assessed the correlation between genetically predicted blood lipids and IPF risk. Subsequently, we employed drug-targeted mendelian randomization to ascertain whether a relationship exists between genetically proxied lipid-modifying interventions and IPF. For drug targets showing suggestive significance, we carried out a summary-data-based MR (SMR) analysis to explore the correlation between gene expression and IPF, synthesizing data from GWAS and eQTL studies. To reinforce the validity of the MR model's assumptions and support the reliability of findings, we undertook a series of extra sensitivity analyses. Methods included MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) and MR-Egger regression for horizontal pleiotropy[ 34 , 39 ], Cochran’s Q test for heterogeneity [ 40 ]; and leave-one-out MR analyses to evaluate whether a single SNP has an excessive impact on MR analysis[ 41 ]. The online tool mRnd ( http://cnsgenomics.com/shiny/mRnd/ ) was used to calculate statistical power to ensure sufficient statistical power[ 42 ]. In the context of the SMR method, we applied the heterogeneity in dependent instruments (HEIDI) test to appraise the robustness and reliability of the results. A P-value cutoff of less than 0.01 was set to suggest that the observed correlation may be a result of linkage disequilibrium. The statistical procedures outlined above were conducted using the R programming language (version 4.3.0). Findings at a P-value threshold below 0.05 were deemed statistically meaningful. Results Lipid traits and IPF risk In the MR study, 237 SNPs were selected for HDL-C, 225 SNPs for LDL-C, 215 SNPs for TG, 213 SNPs for Apo-A, and 220 SNPs for Apo-B as IVs (Table S4 -8). The IVW method indicated that genetically predicted HDL-C with an OR of 0.978 and 95%CI from 0.849 to 1.127 (P = 0.761), LDL-C with an OR of 0.927 and 95% CI from 0.801 to 1.071 (P = 0.302), TG with an OR of 0.908 and 95% CI from 0.777 to 1.060 (P = 0.221), Apo-A with an OR of 0.993 and 95% CI from 0.860 to 1.147 (P = 0.924), and Apo-B with an OR of 0.990 and 95% CI from 0.856 to 1.145 (P = 0.895) were not related to IPF risk (Fig. 2. Forest plots of the association between blood lipid traits and IPF). Five additional MR methods also yielded consistent results (Table S9). Figure S1 displayed scatter plots showing the correlations between lipid traits and IPF. Each SNP had an F-statistics value above the threshold of 10, as detailed in Table S4 -8. The heterogeneities for HDL-C (P MR−Egger = 0.017, P IVW = 0.017) and Apo-B (P MR−Egger = 0.033, P IVW = 0.025) were detected (Table S10). No directional pleiotropies were found in sensitivity analyses. Lipid-lowering drugs and IPF risk We selected SNPs that predict the lipid-modifying effect of genes responsible for the targets affected by lipid-lowering medications as IVs. A total of 21 SNPs as IVs in HMGCR, 5 SNPs in NPC1L1, 33 SNPs in PCSK9, 30 SNPs in APOB, 22 SNPs in ABCG5, 23 SNPs in ABCG8, 42 SNPs in LDLR, 11 SNPs in CETP, 23 SNPs in ANGPTL3, 31 SNPs in APOC3, and 49 SNPs in LPL were identified (Table S11). The positive control assessment revealed a substantial association between drug target inhibitors and lower CHD risk, indicating the efficacy of genetic tools, except for APOB inhibitors (Figure S2 , Table S12). Scatter plots are shown in Figure S3 . The correlation between genetic metabolites influenced by 12 lipid-modifying drugs and IPF is shown in Fig. 3. Forest plots of the association between genetically proxied lipid-modifying drug and IPF using primary effect. The IVW-MR analysis showed that the reduced LDL-C level by inhibitors or enhancements of NPC1L1, PCSK9, ABCG5, and ABCG8 increased IPF risk (OR = 2.74, 95% CI: 1.05–7.12, P = 0.039; OR = 1.36, 95% CI: 1.02–1.82, P = 0.037; OR = 1.66, 95% CI: 1.12–2.48, P = 0.011; OR = 1.68, 95% CI: 1.14–2.48, P = 0.009). Inhibition of APOC3, similar to the decrease in Apo-B level, was significantly correlated with an increased IPF risk (OR = 1.42, 95% CI: 1.20–1.67, P = 3.17×10 − 5 ). The other five supplementary methods showed a consistent tendency (Table S13). Conversely, the MR analyses did not reveal any causal influence on the risk of IPF from genetic mimicries of HMGCR, LDLR, APOB, LPL, ANGPTL3, CETP, and PPARA inhibitors (all P > 0.05) (Fig. 3.Forest plots of the association between genetically proxied lipid-modifying drug and IPF using primary effect, Table S13). The F statistics of each genetic tool range from 29.94 to 3712.59 (Table S11). Scatter plots of the association of lipid-modifying gene targets with IPF risk were presented in Figure S4 . Refer to Table S14 for details regarding the statistical power associated with the MR analyses conducted in this study. Similar results were also obtained from the genetic mimicry analysis with secondary effects on Apo-B and Apo-A. The decreased Apo-B level mediated by NPC1L1, PCSK9, ABCG5, ABCG8, and APOC3 gene targeted drugs were respectively associated with a higher IPF risk (OR = 3.46, 95% CI: 1.13–10.60, P = 0.030; OR = 1.39, 95% CI: 1.02–1.91, P = 0.038; OR = 1.85, 95% CI: 1.19–2.87, P = 0.006; OR = 1.88, 95% CI: 1.21–2.91, P = 0.005; OR = 2.25, 95% CI: 1.54–3.29, P = 2.82×10 − 5 ) (Fig. 4.Forest plots of the association between genetically proxied lipid-modifying drug and IPF using alternative effect). Other genetic mimicries of drug targets showed no significant association with IPF (Table S15). The F statistics values for each SNP exceeded 30 (Table S16). The MR-Egger intercept examination did not uncover any indications of pleiotropy, which enhances the credibility of causal inferences (Table S17-S18). Leave-one-out analyses showed that the IVW method consistently produced results in line with the overall estimate even after excluding each SNP sequentially (Figure S5 - S6 ). Further analyses were carried out using stricter LD thresholds (r2 < 0.2, r2 < 0.1, r2 < 0.01, and r2 < 0.001) for these genes. These analyses did not significantly change the direction of the beta values, although the statistical power was reduced by excluding multiple SNPs (Table S19). Gene expression and IPF risk Given that the NPC1L1, PCSK9, ABCG5, ABCG8, and APOC3 genes showed an association with IPF, genetic variants linked to these gene expressions in blood and relevant tissues were used as IVs for additional validation. However, the limited sample size of eQTL data prevented us from identifying eligible cis-eQTLs for APOC3 in related tissues. SMR analysis results suggested that a higher expression level of PCSK9 in whole blood was associated with a lower risk of IPF (OR = 0.71, 95% CI: 0.50–0.99, P = 0.043) (Table S20). SMR analysis also found that there was a tendency towards significance in the connection between high expression of the NPC1L1 gene in adipose subcutaneous and lower IPF risk (OR = 0.85, 95% CI: 0.73–1.00, P = 0.051) (Table S20). No significant correlation was detected between the ABCG5 gene expression levels in the spleen (OR = 1.11, 95% CI: 0.99–1.24, P = 0.087), ABCG8 expression in colon transverse (OR = 1.04, 95% CI: 0.86–1.25, P = 0.713) and IPF risk. The HEIDI test results demonstrated that the observed associations were unlikely to be due to linkage disequilibrium (p > 0.01) (Table S20). Discussion To our best knowledge, this study represents the inaugural application of MR analysis to explore the relationship between lipids, lipid-modifying pharmacological interventions, and IPF. Our results offer genetic corroboration for the proposition that PCSK9 inhibitors may elevate the risk of IPF. Notably, no evidence was found to suggest that lipid traits have a causal association with IPF risk, which implies that the mechanism by which PCSK9 inhibitors influence IPF risk may be distinct from its effects on lipid metabolism. Additionally, the study uncovered preliminary evidence hinting at a potential positive association between NPC1L1 inhibition and a higher IPF risk. The discovery of these results illuminates the possible adverse effects linked to the use of lipid-modifying drugs and offers an understanding of potential risk factors that may be investigated for IPF management. Although the current evidence did not support serum lipid traits as causal risk factors for IPF, the result should be interpreted with caution. Firstly, lipids are categorized into four primary groups, including glycerides, fatty acids, non-glycerides, and lipoproteins. The plasma lipid traits predominantly featured in this MR study pertain to the lipoprotein class. However, extant literature suggests that dysregulation of phospholipid and sphingolipid metabolism is a more substantial contributor to IPF pathophysiology[ 9 , 43 , 44 ]. A lipidomics analysis has revealed that alterations in the plasma lipid profile of IPF patients are predominantly within the glycerophospholipid class. Among the 159 glycerolipids examined, 30 exhibited significant disparities between the IPF group and controls[ 44 ]. Moreover, the lung, being a lipid-rich organ, engages in intricate lipid metabolic processes. Compared with blood lipids, alveolar lipid levels may provide a more precise reflection of metabolic disturbances. Despite a paucity of research directly comparing lipidomics profiles between bronchoalveolar lavage fluid and blood samples from IPF individuals, the work of Marissa O 'Callaghan et al. offers some valuable insights. This study observed a marked increase in total lipid content within the lung tissue of IPF individuals relative to controls[ 45 ]. In addition, they also assessed pulmonary fat attenuation volume through chest CT images (CT PFAV ). The median CT PFAV in IPF was greater compared to controls, however, there was no association observed with serum lipids and body mass index[ 45 ]. These findings imply that extracellular lipids within the lung may have a closer relationship with IPF than blood lipid traits. Future MR studies could explore the bidirectional causality between intrapulmonary lipid traits and IPF. The MR study we conducted revealed a notable association between PCSK9 inhibitors and IPF. Contrary to manifesting as a protective effect, this association was characterized as a risk factor, which contradicts findings from previous studies. PCSK9, an enzyme, is essential for regulating cholesterol metabolism and maintaining cardiovascular health. Recent studies have shown that the PCSK9 gene may be implicated in the fibrotic processes of the liver, heart, kidney, and other organs. A study by Stefania Grimaudo revealed that increasing PCSK9 expression in male mice led to faster progression of liver fibrosis[ 46 ]. This research also described a protective role for the PCSK9 loss-of-function mutation against the progression of liver fibrosis. Subsequent research has indicated that anti-PCSK9 treatment may hold the potential to mitigate liver fibrosis by modulating the AMPK/mTOR/ULK1 signaling pathway, thus reducing hypoxia-induced autophagy in hepatocytes[ 47 ]. Another in vitro experiment showed that increasing PCSK9 levels could enhance the transition of cardiac fibroblasts into myofibroblasts, impacting fibrosis post-myocardial infarction[ 48 ]. Similar protective effects were also observed in renal fibrosis. Danyu Wu et al. developed a therapeutic vaccine targeting PCSK9. The results indicated that this vaccine could ameliorate kidney fibrosis by controlling fatty acid β-oxidation[ 49 ]. We postulate that several reasons may account for the inconsistent results observed. Firstly, the pathogenesis of IPF is highly complex, and the role of PCSK9 is also not singular. Previous research has concentrated on the anti-fibrotic effect of PCSK9 inhibitors in different organs through autophagy and oxidative regulation, rather than its impact on lipid levels. The balance between the anti-fibrotic effects of PCSK9 inhibitors through similar pathways or the pro-fibrotic effect through other pathways in lung tissue requires further investigation. Secondly, the expression levels of target genes can vary in different tissues, as evidenced by our SMR analysis of PCSK9 gene expression across different tissues. This variability can influence the therapeutic efficacy of drugs in a tissue-specific manner. Thirdly, drug-target Mendelian randomization analysis primarily models impact of gene inhibitors or blockers on outcomes using SNPs within the gene-specific action range (± 100kb). It provides information on the trends of connections rather than the tangible therapeutic advantages of drugs in practical situations. Numerous factors, such as drug dosage, when the drug is given, how inter-individuals metabolize drugs, and how well drugs attach to their intended targets, need to be further considered. Consequently, further RCTs are essential to confirm these observational findings. Our findings also imply that there is a potential for increased IPF risk with exposure to NPC1L1 inhibitors, although the SMR analysis only suggested a near-positive result. Ezetimibe is known as the primary drug for NPC1L1 inhibitors, which are responsible for facilitating the absorption of dietary cholesterol by NPC1L1 protein [ 27 ]. To date, limited research has examined the influence of ezetimibe on the onset of IPF. Chanho Lee et al. conducted a retrospective study of the medical records across three different hospitals and discovered that individuals with IPF who consistently used ezetimibe had lower all-cause mortality and lung function decline rates[ 50 ]. They also found that ezetimibe could prevent mice from developing bleomycin-induced pulmonary fibrosis by suppressing mTORC1 activity in vitro study. The enhancement of autophagy in mouse lung fibroblasts mediates the anti-fibrotic effect of ezetimibe, rather than through lipid-lowering properties[ 50 ]. There may exist differences in the pathogenesis between drug-induced pulmonary fibrosis and IPF, which could account for the discrepancies between our MR findings and the in vitro results. This MR research also has some limitations. Firstly, despite the numerous sensitivity analyses conducted that have reinforced the reliability of our results, the possibility of horizontal pleiotropy cannot be entirely dismissed. Secondly, the sample size of eQTL data is limited, and there are no eligible eQTLs for NPC1L1, ABCG5, ABCG8, and APOC3 in hepatic and pulmonary tissues, which are the primary organs involved in lipid metabolism. This limitation may result in an underestimation of the role these genes play in the pathogenesis of IPF. Thirdly, the initial GWAS data did not categorize by specific subtypes (such as the extent of LDL-C elevation). Consequently, this study was constrained from performing a stratified analysis, an approach that should be contemplated with the availability of more specific datasets in the future. Lastly, we lack a validation cohort because other GWAS data for IPF may have sample overlap with lipid exposure. It also must be acknowledged that the data presented in our study pertains to individuals of European descent. When extrapolating these results to different ethnic groups, caution should be exercised. Additional research encompassing a diverse array of populations is necessary. Conclusions In summary, this study does not support blood lipid traits (i.e., TG, LDL-C, HDL-C, Apo-A, and Apo-B) as a direct risk factor for IPF and should be interpreted with caution. An increased expression level of the PCSK9 gene was found to correlate with a lower risk of IPF. Conversely, the use of PCSK9 inhibitors was associated with an elevated risk of IPF. Further studies are essential to gain a more comprehensive understanding of the underlying mechanisms and to assess the possible effect of PCSK9 inhibitors in IPF progression through a series of preclinical and clinical trials. Abbreviations IPF: Idiopathic pulmonary fibrosis MR: Mendelian randomization SMR: Summary-data-based MR OR: Odds ratio 95%CI: 95% confidence interval RCTs: Randomized controlled trials ILD: Interstitial lung disease GWAS: Genome-wide association studies eQTL: Expression quantitative trait loci HDL-C: High-density lipoprotein cholesterol LDL-C: Low-density lipoprotein cholesterol TG: Triglyceride Kb: kilobases Apo-A: apolipoprotein A Apo-B: Apolipoprotein B LD: Linkage disequilibrium SNP: Single nucleotide polymorphism GTEx-V8: Genotype-Tissue Expression project CHD: Coronary heart disease IVW: Inverse-variance-weighted IVs: Instrumental variables ConMix: Contamination mixture method RAPS: Robust adjusted profile score MR-PRESSO: MR-Pleiotropy Residual Sum and Outlier HEIDI: Heterogeneity in dependent instruments CT PFAV : Pulmonary fat attenuation volume through chest CT images Declarations Supplementary Information Additional file 1: Table S1. STROBE-MR checklist. Table S2. Phenotype descriptions and distributions. Table S3. Lipid-lowering drug classes, substances, and target genes. Table S4. Genetic variants that were used as instrumental variables for high-density lipoprotein cholesterol. Table S5. Genetic variants that were used as instrumental variables for low-density lipoprotein cholesterol. Table S6. Genetic variants that were used as instrumental variables for triglyceride. Table S7. Genetic variants that were used as instrumental variables for apolipoprotein A. Table S8. Genetic variants that were used as instrumental variables for apolipoprotein B. Table S9. Association of genetically proxied lipid traits with risk of IPF. Table S10. Heterogeneity and pleiotropy tests of instrument effects （lipid traits on IPF). Table S11. Characteristics of lipid-modifying genetics variants in target genes using primary effect. Table S12. Association of genetically proxied lipid-modifying drugs with risk of CHD. Table S13. Association of genetically proxied lipid-modifying drugs with risk of IPF using primary effect. Table S14. Statistical power estimates for drug-target MR analyses. Table S15. Association of genetically proxied lipid-modifying drugs with risk of IPF using alternative effect. Table S16. Characteristics of lipid-modifying genetics variants in target genes using alternative effect. Table S17. Heterogeneity and pleiotropy tests of instrument effects (primary lipid-modifying effect). Table S18. Heterogeneity and pleiotropy tests of instrument effects (alternative lipid-modifying effect). Table S19. Association of genetic mimicry of lipid-modifying drugs with risk of IPF accounting for LD structure. Table S20. Association between gene expression in tissues of identified targets and IPF in the SMR analysis. Additional file 2: Figure S1. Scatter plots of the association between lipid traits and IPF; A. High-density lipoprotein cholesterol on idiopathic pulmonary fibrosis; B. Low-density lipoprotein cholesterol on idiopathic pulmonary fibrosis; C. Triglyceride on idiopathic pulmonary fibrosis; D. Apolipoprotein A on idiopathic pulmonary fibrosis; E. Apolipoprotein B on idiopathic pulmonary fibrosis. Additional file 3: Figure S2. Forest plots of the association between genetically proxied lipid-modifying drug and CHD risk. Additional file 4: Figure S3.Scatter plots of the association between genetically proxied lipid-modifying gene targets and CHD. A. HMGCR on coronary heart disease; B. NPC1L1 on coronary heart disease; C. PCSK9 on coronary heart disease; D. APOC on coronary heart disease; E. ABCG5 on coronary heart disease; F. ABCG8 on coronary heart disease; G. LDLR on coronary heart disease; H. CETP on coronary heart disease; I. ANGPTL3 on coronary heart disease; J. APOC3 on coronary heart disease; K. LPL on coronary heart disease; L. PPARA on coronary heart disease. Additional file 5: Figure S4.Scatter plots of the association between genetically proxied lipid-modifying gene targets and IPF. A. HMGCR on idiopathic pulmonary fibrosis; B. NPC1L1 on idiopathic pulmonary fibrosis; C. PCSK9 on idiopathic pulmonary fibrosis; D. APOC on idiopathic pulmonary fibrosis; E. ABCG5 on idiopathic pulmonary fibrosis; F. ABCG8 on idiopathic pulmonary fibrosis; G. LDLR on idiopathic pulmonary fibrosis; H. CETP on idiopathic pulmonary fibrosis; I. ANGPTL3 on idiopathic pulmonary fibrosis; J. APOC3 on idiopathic pulmonary fibrosis; K. LPL on idiopathic pulmonary fibrosis; L. PPARA on idiopathic pulmonary fibrosis. Additional file 6: Figure S5.Plots of “leave-one-out” analyses for MR analyses of the causal effect of lipid-modifying drugs on IPF using primary effect. A. Genetic mimicries of NPC1L1 inhibitor on idiopathic pulmonary fibrosis; B. Genetic mimicries of PCSK9 inhibitor on idiopathic pulmonary fibrosis; C. Genetic mimicries of ABCG5 enhancement on idiopathic pulmonary fibrosis; D. Genetic mimicries of ABCG8 enhancement on idiopathic pulmonary fibrosis; E. Genetic mimicries of APOC3 blocker on idiopathic pulmonary fibrosis. Additional file 7: Figure S6.Plots of “leave-one-out” analyses for MR analyses of the causal effect of lipid-modifying drugs on IPF using alternative effect. A. Genetic mimicries of NPC1L1 inhibitor on idiopathic pulmonary fibrosis; B. Genetic mimicries of PCSK9 inhibitor on idiopathic pulmonary fibrosis; C. Genetic mimicries of ABCG5 enhancement on idiopathic pulmonary fibrosis; D. Genetic mimicries of ABCG8 enhancement on idiopathic pulmonary fibrosis; E. Genetic mimicries of APOC3 blocker on idiopathic pulmonary fibrosis. Acknowledgements The authors extend sincere thanks to the participants of the UK Biobank, the FinnGen project, the GTEx-V8 project and the other included cohorts for their invaluable contributions. We also appreciate the dedication of the numerous investigators and research personnel who have played a pivotal role in the data acquisition phase of this study. Author contributions SLY designed the study and contributed to the data analysis. CGX and LJJ prepared the first draft of the manuscript. CMS organized the tables and figures. Each author has thoroughly reviewed the content, providing their endorsement to the final version of the manuscript. Funding No funding was received for this study. Availability of data and materials The current investigation employed publicly accessible GWAS summary statistics as the primary data source. Additionally, all the datasets utilized for analysis have been duly incorporated within the article. Ethics approval and consent to participate No participants were directly engaged in the overall progression of our investigation. Solely publicly accessible data served as the foundation of our study. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details Division of Pulmonary Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. References Moss BJ, Ryter SW, Rosas IO. Pathogenic Mechanisms Underlying Idiopathic Pulmonary Fibrosis. Annu Rev Pathol. 2022;17:515–46. Hamanaka RB, Mutlu GM. Metabolic requirements of pulmonary fibrosis: role of fibroblast metabolism. FEBS J. 2021;288:6331–52. Rajesh R, Atallah R, Bärnthaler T. Dysregulation of metabolic pathways in pulmonary fibrosis. Pharmacol Ther. 2023;246:108436. Justet A, Klay D, Porcher R, Cottin V, Ahmad K, Molina Molina M, et al. Safety and efficacy of pirfenidone and nintedanib in patients with idiopathic pulmonary fibrosis and carrying a telomere-related gene mutation. Eur Respir J. 2021;57:2003198. Behr J, Nathan SD, Wuyts WA, Mogulkoc Bishop N, Bouros DE, Antoniou K, et al. Efficacy and safety of sildenafil added to pirfenidone in patients with advanced idiopathic pulmonary fibrosis and risk of pulmonary hypertension: a double-blind, randomised, placebo-controlled, phase 2b trial. Lancet Respir Med. 2021;9:85–95. Lederer DJ, Martinez FJ. Idiopathic Pulmonary Fibrosis. N Engl J Med. 2018;378:1811–23. Saito S, Alkhatib A, Kolls JK, Kondoh Y, Lasky JA. Pharmacotherapy and adjunctive treatment for idiopathic pulmonary fibrosis (IPF). J Thorac Dis. 2019;11:S1740–54. Zhao X, Kwan JYY, Yip K, Liu PP, Liu F-F. Targeting metabolic dysregulation for fibrosis therapy. Nat Rev Drug Discov. 2020;19:57–75. Burgy O, Loriod S, Beltramo G, Bonniaud P. Extracellular Lipids in the Lung and Their Role in Pulmonary Fibrosis. Cells. 2022;11:1209. Tian Y, Duan C, Feng J, Liao J, Yang Y, Sun W. Roles of lipid metabolism and its regulatory mechanism in idiopathic pulmonary fibrosis: A review. Int J Biochem Cell Biol. 2023;155:106361. Xu Y, Mizuno T, Sridharan A, Du Y, Guo M, Tang J, et al. Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic pulmonary fibrosis. JCI Insight. 2016;1:e90558. Reyfman PA, Walter JM, Joshi N, Anekalla KR, McQuattie-Pimentel AC, Chiu S, et al. Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis. Am J Respir Crit Care Med. 2019;199:1517–36. Chen R, Dai J. Lipid metabolism in idiopathic pulmonary fibrosis: From pathogenesis to therapy. J Mol Med (Berl). 2023;101:905–15. Jang HJ, Lee DY, Loloci G, Jeong J, Choi W-I. Association between the use of statins and risk of interstitial lung disease/idiopathic pulmonary fibrosis: time-dependent analysis of population-based nationwide data. Eur Respir J. 2023;62:2300291. Kreuter M, Bonella F, Maher TM, Costabel U, Spagnolo P, Weycker D, et al. Effect of statins on disease-related outcomes in patients with idiopathic pulmonary fibrosis. Thorax. 2017;72:148–53. Kreuter M, Lederer DJ, Cottin V, Kahn N, Ley B, Vancheri C, et al. Concomitant medications and clinical outcomes in idiopathic pulmonary fibrosis. Eur Respir J. 2019;54:1901188. Saad N, Camus P, Suissa S, Ernst P. Statins and the risk of interstitial lung disease: a cohort study. Thorax. 2013;68:361–4. Smith GD. Mendelian Randomization for Strengthening Causal Inference in Observational Studies: Application to Gene × Environment Interactions. Perspect Psychol Sci. 2010;5:527–45. Schmidt AF, Finan C, Gordillo-Marañón M, Asselbergs FW, Freitag DF, Patel RS, et al. Genetic drug target validation using Mendelian randomisation. Nat Commun. 2020;11:3255. Smith GD, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1–22. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. Williams DM, Finan C, Schmidt AF, Burgess S, Hingorani AD. Lipid lowering and Alzheimer disease risk: A mendelian randomization study. Ann Neurol. 2020;87:30–9. Reay WR, Cairns MJ. Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet. 2021;22:658–71. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326:1614–21. Sinnott-Armstrong N, Tanigawa Y, Amar D, Mars N, Benner C, Aguirre M, et al. Genetics of 35 blood and urine biomarkers in the UK Biobank. Nat Genet. 2021;53:185–94. Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41:111–88. Y D, K G, S X, F Z, X M, J H. Regulation of cholesterol homeostasis in health and diseases: from mechanisms to targeted therapeutics. Signal transduction and targeted therapy [Internet]. 2022 [cited 2024 Apr 11];7. Available from: https://pubmed.ncbi.nlm.nih.gov/35918332/ Borén J, Taskinen M-R, Björnson E, Packard CJ. Metabolism of triglyceride-rich lipoproteins in health and dyslipidaemia. Nat Rev Cardiol. 2022;19:577–92. Ridker PM. LDL cholesterol: controversies and future therapeutic directions. Lancet. 2014;384:607–17. Li Z, Zhang B, Liu Q, Tao Z, Ding L, Guo B, et al. Genetic association of lipids and lipid-lowering drug target genes with non-alcoholic fatty liver disease. EBioMedicine. 2023;90:104543. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318–30. Ca E, Av K, S K. Mendelian Randomization. JAMA [Internet]. 2017 [cited 2023 Jul 2];318. Available from: https://pubmed.ncbi.nlm.nih.gov/29164242/ Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40:304–14. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption - PubMed [Internet]. [cited 2023 Jul 8]. Available from: https://pubmed.ncbi.nlm.nih.gov/29040600/ Burgess S, Foley CN, Allara E, Staley JR, Howson JMM. A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat Commun. 2020;11:376. Yu K, Chen X-F, Guo J, Wang S, Huang X-T, Guo Y, et al. Assessment of bidirectional relationships between brain imaging-derived phenotypes and stroke: a Mendelian randomization study. BMC Med. 2023;21:271. Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8. Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27:R195–208. Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology. 2017;28:30–42. Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40:740–52. Suryadevara V, Ramchandran R, Kamp DW, Natarajan V. Lipid Mediators Regulate Pulmonary Fibrosis: Potential Mechanisms and Signaling Pathways. International Journal of Molecular Sciences. 2020;21:4257. Yan F, Wen Z, Wang R, Luo W, Du Y, Wang W, et al. Identification of the lipid biomarkers from plasma in idiopathic pulmonary fibrosis by Lipidomics. BMC Pulm Med. 2017;17:174. O’Callaghan M, Duignan J, Tarling EJ, Waters DK, McStay M, O’Carroll O, et al. Analysis of tissue lipidomics and computed tomography pulmonary fat attenuation volume (CTPFAV ) in idiopathic pulmonary fibrosis. Respirology. 2023;28:1043–52. Grimaudo S, Bartesaghi S, Rametta R, Marra F, Margherita Mancina R, Pihlajamäki J, et al. PCSK9 rs11591147 R46L loss-of-function variant protects against liver damage in individuals with NAFLD. Liver Int. 2021;41:321–32. Ning L, Zou Y, Li S, Cao Y, Xu B, Zhang S, et al. Anti-PCSK9 Treatment Attenuates Liver Fibrosis via Inhibiting Hypoxia-Induced Autophagy in Hepatocytes. Inflammation. 2023;46:2102–19. Bao H, Wang X, Zhou H, Zhou W, Liao F, Wei F, et al. PCSK9 regulates myofibroblast transformation through the JAK2/STAT3 pathway to regulate fibrosis after myocardial infarction. Biochem Pharmacol. 2024;220:115996. Wu D, Zhou Y, Pan Y, Li C, Wang Y, Chen F, et al. Vaccine Against PCSK9 Improved Renal Fibrosis by Regulating Fatty Acid β-Oxidation. J Am Heart Assoc. 2020;9:e014358. Lee C, Kwak SH, Han J, Shin JH, Yoo B, Lee YS, et al. Repositioning of ezetimibe for the treatment of idiopathic pulmonary fibrosis. Eur Respir J. 2024;2300580. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Additionalfile1.xlsx FigureS1.ScatterplotsoftheassociationbetweenlipidtraitsandIPF.png FigureS2.ForestplotsoftheassociationbetweengeneticallyproxiedlipidmodifyingdrugandCHDrisk.png FigureS3.ScatterplotsoftheassociationbetweengeneticallyproxiedlipidmodifyinggenetargetsandCHD.png FigureS4.ScatterplotsoftheassociationbetweengeneticallyproxiedlipidmodifyinggenetargetsandIPF.png FigureS5.PlotsofleaveoneoutanalysesforMRanalysesofthecausaleffectoflipidmodifyingdrugsonIPFusingprimaryeffect.png FigureS6.PlotsofleaveoneoutanalysesforMRanalysesofthecausaleffectoflipidmodifyingdrugsonIPFusingalternativeeffect.png Cite Share Download PDF Status: Published Journal Publication published 01 Aug, 2024 Read the published version in Lipids in Health and Disease → Version 1 posted Editorial decision: Revision requested 21 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 28 May, 2024 Reviews received at journal 21 May, 2024 Reviewers agreed at journal 21 May, 2024 Reviewers invited by journal 10 May, 2024 Editor assigned by journal 08 May, 2024 Submission checks completed at journal 08 May, 2024 First submitted to journal 08 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4387298","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301968022,"identity":"ac0d9118-ebca-465b-9645-973057f65199","order_by":0,"name":"Gexiang Cai","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gexiang","middleName":"","lastName":"Cai","suffix":""},{"id":301968024,"identity":"8f9b526e-373a-46ae-bbf3-9085b66ed640","order_by":1,"name":"Jingjing Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Liu","suffix":""},{"id":301968026,"identity":"df1c28c3-71d8-4315-9c78-c2fa2ab1454f","order_by":2,"name":"Mengsi Cai","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengsi","middleName":"","lastName":"Cai","suffix":""},{"id":301968029,"identity":"5bb665ce-a4e2-4900-965d-577b237ac993","order_by":3,"name":"Lianyou Shao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC+XYQaWAjx9je2PjwAzFaDA6DyII0Y+aew83GEkRpYQaRHw4nts9IbxPgIU4Lj+FjHoPDib0zH7YxSDDYyek2ENAi38xjbMxjkG48c3Zi24MChmRjswOErDnMYybNY2Atu3F2YruBBMOBxG1EamFm3H/zYJsEDwlanBUbZzASqcXgMFux4RyDNGPGnkRgIBsQ4Rf59uaND978AUXl8YcPP1TYyRH2PgOHAbKlBJWDAPsDopSNglEwCkbBCAYAlFBAxQQ/xdYAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lianyou","middleName":"","lastName":"Shao","suffix":""}],"badges":[],"createdAt":"2024-05-08 07:27:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4387298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4387298/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12944-024-02218-6","type":"published","date":"2024-08-01T15:57:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56527411,"identity":"dbc8d271-fdd9-4752-aba1-5ac3fcf0192d","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":173075,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure1.Outlineofthestudydesign.png","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/6ecca7fbcd378a362163b529.png"},{"id":56527407,"identity":"828417bf-258c-4eec-bc02-d98b98413c10","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":145497,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure2.ForestplotsoftheassociationbetweenbloodlipidtraitsandIPF.png","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/3701dbca8d5999cedf272c0e.png"},{"id":56527441,"identity":"15a6ad73-042d-4359-871e-be699d2ef121","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":329552,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure3.ForestplotsoftheassociationbetweengeneticallyproxiedlipidmodifyingdrugandIPFusingprimaryeffect.png","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/4a7081e027bde2cc705d9c07.png"},{"id":56527453,"identity":"18da4da0-b74e-4993-aadd-496f8d030596","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":327606,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"Figure4.ForestplotsoftheassociationbetweengeneticallyproxiedlipidmodifyingdrugandIPFusingalternativeeffect.png","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/782062eca2f5a4cc1a215158.png"},{"id":61793523,"identity":"dcb8c6cc-7911-42cb-bb4f-0bf3a6c90d8b","added_by":"auto","created_at":"2024-08-05 16:13:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1249090,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/ff5c65cf-a898-4996-a7b6-38f0853ceeca.pdf"},{"id":56527406,"identity":"dd67cf9b-8e6d-46c1-8a30-600be1ae7f3b","added_by":"auto","created_at":"2024-05-15 10:59:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30694,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/031a630c52e8e34c4983fa5f.docx"},{"id":56527472,"identity":"863c3dfc-c9b2-4af8-8f14-c879dba3aaae","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":234019,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/c7eec8581ae4e93fda1cfa10.xlsx"},{"id":56527558,"identity":"1162fed3-7e38-4749-992e-867b6763a140","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2051048,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.ScatterplotsoftheassociationbetweenlipidtraitsandIPF.png","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/7cdd0149da9822e971dfa13e.png"},{"id":56527500,"identity":"674f3b98-ff1d-4872-bb12-aeffc7b40994","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":499028,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.ForestplotsoftheassociationbetweengeneticallyproxiedlipidmodifyingdrugandCHDrisk.png","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/e04fb50e9710103107678285.png"},{"id":56527557,"identity":"dab7f8bd-da07-45e8-b23e-09f4b0d1861d","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":6834281,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.ScatterplotsoftheassociationbetweengeneticallyproxiedlipidmodifyinggenetargetsandCHD.png","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/45990fd789499ac2ad7bf311.png"},{"id":56527556,"identity":"42e246ba-7d0e-42d0-8ef3-29e6f01818fe","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7330203,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4.ScatterplotsoftheassociationbetweengeneticallyproxiedlipidmodifyinggenetargetsandIPF.png","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/8dbf75983d19770775b4d340.png"},{"id":56527555,"identity":"bcf6661f-85d2-47d5-a7ce-9fdcf7e96bb6","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1875109,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS5.PlotsofleaveoneoutanalysesforMRanalysesofthecausaleffectoflipidmodifyingdrugsonIPFusingprimaryeffect.png","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/43f1449277869ba294d48c13.png"},{"id":56527554,"identity":"cfb8e5b7-f912-4fc5-bb92-a7bee2b70ad4","added_by":"auto","created_at":"2024-05-15 10:59:13","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":2263286,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS6.PlotsofleaveoneoutanalysesforMRanalysesofthecausaleffectoflipidmodifyingdrugsonIPFusingalternativeeffect.png","url":"https://assets-eu.researchsquare.com/files/rs-4387298/v1/cc07387af3c1d6b9b86d5de4.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the association between lipid-modifying drugs and idiopathic pulmonary fibrosis: a drug-target Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIdiopathic pulmonary fibrosis (IPF) is a progressive and chronic disorder of unknown origin, affecting an estimated 3\u0026nbsp;million individuals globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The condition is characterized by a high mortality rate, with a median survival time of approximately 3.8 years following diagnosis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The pathophysiology of IPF is highly intricate, encompassing alterations in genetic factors, cellular signaling, apoptosis, autophagy, and additional processes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This multifaceted nature significantly complicates the development of effective therapeutic strategies for IPF. Currently, nintedanib and pirfenidone are the only medications approved by the Food and Drug Administration for IPF treatment[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although these treatments can mitigate symptoms, they do not address the underlying disease, and lung transplantation may become necessary for patients to extend their lifespan[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent research indicates that metabolic changes play a pivotal role in the fibrosis process[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As a lipid-rich organ, the lung's lipid metabolism and its regulation are essential for normal lung physiology[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Transcriptomic analyses of various pulmonary cells, such as alveolar epithelial type II cells, alveolar macrophages, and fibroblasts, consistently reveal disruptions in lipid metabolism during fibrosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Studies have demonstrated that diminished expression of genes related to lipid, cholesterol, and steroid metabolism can reduce surfactant production in alveolar epithelial type II cells[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Lipid accumulation in alveolar macrophages is linked to elevated CD36 expression, leading to increased absorption of fatty acids. This imbalance in lipid metabolism can precipitate a fibrotic transformation in macrophages, culminating in augmented extracellular matrix (ECM) synthesis. Concurrently, fibroblasts display diminished activity of PPAR-γ, which can drive their metamorphosis from lipid-producing cells into myofibroblast-like entities[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, the disruption of lipid metabolism is recognized as a key metabolic shift in the pathogenesis of fibrosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the strong connection between lipid metabolism disorders and IPF, there is interest in whether lipid-lowering medications have a protective impact on IPF. A cohort study within the Korean population found that the utilization of statins was linked to lower IPF risk[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Among individuals taking statins, the incidence rate of IPF was 15.6 cases per 100,000 person-years, which was less than the rate of 19.3 cases per 100,000 person-years observed in those not taking[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Another Phase III randomized clinical trial involving 624 IPF participants indicated that statins could decrease the mortality rate and the frequency of hospitalizations due to acute exacerbations[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Nonetheless, there is limited randomized controlled trials (RCTs), and some studies present conflicting results. An exploratory analysis of 1,450 IPF patients participating in a Phase III trial found no correlation between statin use and IPF progression[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, a review of a health management database, which included 6,665 individuals with possible or likely interstitial lung disease (ILD) and 26,660 controls, failed to find a connection between statin use and ILD development[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Moreover, the impact of novel lipid-lowering agents, such as PCSK9 inhibitors and NPC1L1 inhibitors, on IPF remains to be elucidated.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR), a recognized approach, is frequently utilized to explore the potential links between genetically influenced traits, therapeutic drug targets, and disease outcomes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For drug-target mendelian randomization, it employs genetic variants situated near or within proximity to the gene encoding the targeted protein as instrumental variables (IVs) to prognosticate treatment efficacy[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The causal inferences derived from MR are considered less prone to bias and reverse causality[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The evidential value of MR analysis is ranked just below that of RCTs, and it can provide significant insights that may presage the findings of RCTs[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, our study utilized the Mendelian randomization method to explore the impact of blood lipid traits on IPF risk and to assess the influence of lipid-regulatory medications on IPF.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization (STROBE-GE), as detailed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The data were derived from publicly available summary-level data from genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies. Comprehensive details regarding these datasets are delineated in Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. The schematic representation of the study design is depicted in Fig.\u0026nbsp;1.Outline of the study design.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenetic instrumental variables for lipid traits and lipid-modifying targets\u003c/h2\u003e \u003cp\u003eThe largest publicly accessible GWAS data for 3 circulating lipid traits, including high-density lipoprotein cholesterol (HDL-C, N\u0026thinsp;=\u0026thinsp;291830), low-density lipoprotein cholesterol (LDL-C, N\u0026thinsp;=\u0026thinsp;318340), and triglyceride (TG, N\u0026thinsp;=\u0026thinsp;318674) were obtained from the UK Biobank[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Genetic variants linked to these lipid traits were selected, meeting a linkage disequilibrium (LD) clumping threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and a physical distance threshold of 1,000 kb.\u003c/p\u003e \u003cp\u003eWe selected 20 prevalent lipid-lowering drugs and innovative therapeutics in accordance with recent guidelines for dyslipidemia management[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Utilizing the DrugBank database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://go.drugbank.com/\u003c/span\u003e\u003cspan address=\"https://go.drugbank.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and pertinent scholarly articles, we undertook gene identification for the pharmacological targets of these medications[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Comprehensive details regarding each target gene are delineated in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. Based on their principal pharmacological effects, these genes are categorized into lowering LDL-C (i.e. HMGCR, PCSK9, LDLR, APOB, NPC1L1, ABCG5, ABCG8, CETP) and lowering TG (i.e. LPL, ANGPTL3, APOC3).\u003c/p\u003e \u003cp\u003eTo simulate the lipid-lowering impact of these genes, we identified single nucleotide polymorphisms (SNPs) located within a region extending\u0026thinsp;\u0026plusmn;\u0026thinsp;100 kilobases (kb) around the gene of interest. And these SNPs should also show a significant correlation with lipid levels on a genome-wide scale (p\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e)[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. To optimize the power of the tool, SNPs were permitted to be in weak linkage disequilibrium of less than 0.30 with one another.\u003c/p\u003e \u003cp\u003eTo reinforce the robustness of the results, we undertook extra analyses using a novel suite of genetic tools that incorporated both Apolipoprotein A (Apo-A) and Apolipoprotein B (Apo-B). Apo-A serves as a critical transporter of HDL-C. Apo-B plays a crucial role in the formation of LDL-C and TG. Apo-A was utilized to develop tools for measuring CETP and LPL. Apo-B was employed to create genetic tools targeting HMGCR, PCSK9, LDLR, APOB, NPC1L1, ABCG5, ABCG8, LPL, ANGPTL3, and APOC3. The GWAS data for both Apo-B and Apo-A were also sourced from the UK Biobank, comprising 317,412 and 290,198 samples, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eeQTL Data\u003c/h2\u003e \u003cp\u003eWe used publicly available eQTL data from the Genotype-Tissue Expression (GTEx-V8) project. The GTEx project encompasses eQTL data across 54 distinct human tissues, with participant numbers ranging from 73 to 670[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Approximately 84.6% of these samples originate from individuals of European descent. Cis-eQTLs refer to genetic variations that have a significant link with the expression of specific genes affected by medications. These cis-eQTLs must meet the significance level of a P-value lower than 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and adhere to the linkage disequilibrium criterion with an r2 value less than 0.1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome GWAS\u003c/h2\u003e \u003cp\u003eThe GWAS data for IPF were obtained from the FinnGen Release 10 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r10.finngen.fi/\u003c/span\u003e\u003cspan address=\"https://r10.finngen.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which included 2189 individuals with IPF and 407609 control subjects. To affirm the efficacy of the selected genetic markers, supplementary analysis was conducted with coronary heart disease (CHD) as the benchmark outcome, serving as a positive control in our study. Summary statistics for CHD were also sourced from the FinnGen project, with 46959 cases and 365222 control individuals (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Cases of missing data were addressed by exclusion, and all participants were of European ancestry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMendelian randomization utilizes SNPs as instruments to explore the connection between exposure and outcome variables. The MR must adhere to three fundamental assumptions: (1) Correlation assumption: The IVs demonstrate a significant connection with exposure. The F-statistic is used to evaluate the hypothesis of correlation by quantifying the magnitude of each genetic variant. A larger F statistic (\u0026gt;10) suggests a little chance of weak instrumental variable bias[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; (2) Independence assumption: The IVs should be unconfounded, meaning they are unrelated to factors that could affect both exposure and outcome, ensuring that the observed associations are uniquely due to the exposure under investigation; (3) Exclusivity assumption: The IVs do not have a direct correlation with the outcome (p\u0026gt;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), nor any other means apart from exposure to correlate with the outcome[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe principal MR analysis was executed utilizing the inverse variance weighted (IVW) method, which has been shown to have the most pronounced statistical impact. Following the statistical methods similar to previous studies, three additional MR methods (MR-Egger, weighted median, and weighted mode) were also implemented as complementary approaches[\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. To improve the strength of the results, the contamination mixture method (ConMix) and robust adjusted profile score (RAPS) were also utilized. Compared with other methods, ConMix had the lowest mean squared error[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. MR-RAPS considers special pleiotropicity and can provide reliable inferences for MR analysis utilizing weak instrumental variables[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The results of all estimates are typically presented as odds ratio (OR) along with its 95% confidence interval (CI).\u003c/p\u003e \u003cp\u003eIn this study, we initially assessed the correlation between genetically predicted blood lipids and IPF risk. Subsequently, we employed drug-targeted mendelian randomization to ascertain whether a relationship exists between genetically proxied lipid-modifying interventions and IPF. For drug targets showing suggestive significance, we carried out a summary-data-based MR (SMR) analysis to explore the correlation between gene expression and IPF, synthesizing data from GWAS and eQTL studies.\u003c/p\u003e \u003cp\u003eTo reinforce the validity of the MR model's assumptions and support the reliability of findings, we undertook a series of extra sensitivity analyses. Methods included MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) and MR-Egger regression for horizontal pleiotropy[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], Cochran\u0026rsquo;s Q test for heterogeneity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]; and leave-one-out MR analyses to evaluate whether a single SNP has an excessive impact on MR analysis[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The online tool mRnd (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cnsgenomics.com/shiny/mRnd/\u003c/span\u003e\u003cspan address=\"http://cnsgenomics.com/shiny/mRnd/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to calculate statistical power to ensure sufficient statistical power[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In the context of the SMR method, we applied the heterogeneity in dependent instruments (HEIDI) test to appraise the robustness and reliability of the results. A P-value cutoff of less than 0.01 was set to suggest that the observed correlation may be a result of linkage disequilibrium.\u003c/p\u003e \u003cp\u003eThe statistical procedures outlined above were conducted using the R programming language (version 4.3.0). Findings at a P-value threshold below 0.05 were deemed statistically meaningful.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLipid traits and IPF risk\u003c/h2\u003e \u003cp\u003eIn the MR study, 237 SNPs were selected for HDL-C, 225 SNPs for LDL-C, 215 SNPs for TG, 213 SNPs for Apo-A, and 220 SNPs for Apo-B as IVs (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e-8). The IVW method indicated that genetically predicted HDL-C with an OR of 0.978 and 95%CI from 0.849 to 1.127 (P\u0026thinsp;=\u0026thinsp;0.761), LDL-C with an OR of 0.927 and 95% CI from 0.801 to 1.071 (P\u0026thinsp;=\u0026thinsp;0.302), TG with an OR of 0.908 and 95% CI from 0.777 to 1.060 (P\u0026thinsp;=\u0026thinsp;0.221), Apo-A with an OR of 0.993 and 95% CI from 0.860 to 1.147 (P\u0026thinsp;=\u0026thinsp;0.924), and Apo-B with an OR of 0.990 and 95% CI from 0.856 to 1.145 (P\u0026thinsp;=\u0026thinsp;0.895) were not related to IPF risk (Fig.\u0026nbsp;2. Forest plots of the association between blood lipid traits and IPF). Five additional MR methods also yielded consistent results (Table S9). Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e displayed scatter plots showing the correlations between lipid traits and IPF. Each SNP had an F-statistics value above the threshold of 10, as detailed in Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e-8. The heterogeneities for HDL-C (P\u003csub\u003eMR\u0026minus;Egger\u003c/sub\u003e = 0.017, P\u003csub\u003eIVW\u003c/sub\u003e = 0.017) and Apo-B (P\u003csub\u003eMR\u0026minus;Egger\u003c/sub\u003e = 0.033, P\u003csub\u003eIVW\u003c/sub\u003e = 0.025) were detected (Table S10). No directional pleiotropies were found in sensitivity analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLipid-lowering drugs and IPF risk\u003c/h2\u003e \u003cp\u003eWe selected SNPs that predict the lipid-modifying effect of genes responsible for the targets affected by lipid-lowering medications as IVs. A total of 21 SNPs as IVs in HMGCR, 5 SNPs in NPC1L1, 33 SNPs in PCSK9, 30 SNPs in APOB, 22 SNPs in ABCG5, 23 SNPs in ABCG8, 42 SNPs in LDLR, 11 SNPs in CETP, 23 SNPs in ANGPTL3, 31 SNPs in APOC3, and 49 SNPs in LPL were identified (Table S11). The positive control assessment revealed a substantial association between drug target inhibitors and lower CHD risk, indicating the efficacy of genetic tools, except for APOB inhibitors (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Table S12). Scatter plots are shown in Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe correlation between genetic metabolites influenced by 12 lipid-modifying drugs and IPF is shown in Fig.\u0026nbsp;3. Forest plots of the association between genetically proxied lipid-modifying drug and IPF using primary effect. The IVW-MR analysis showed that the reduced LDL-C level by inhibitors or enhancements of NPC1L1, PCSK9, ABCG5, and ABCG8 increased IPF risk (OR\u0026thinsp;=\u0026thinsp;2.74, 95% CI: 1.05\u0026ndash;7.12, P\u0026thinsp;=\u0026thinsp;0.039; OR\u0026thinsp;=\u0026thinsp;1.36, 95% CI: 1.02\u0026ndash;1.82, P\u0026thinsp;=\u0026thinsp;0.037; OR\u0026thinsp;=\u0026thinsp;1.66, 95% CI: 1.12\u0026ndash;2.48, P\u0026thinsp;=\u0026thinsp;0.011; OR\u0026thinsp;=\u0026thinsp;1.68, 95% CI: 1.14\u0026ndash;2.48, P\u0026thinsp;=\u0026thinsp;0.009). Inhibition of APOC3, similar to the decrease in Apo-B level, was significantly correlated with an increased IPF risk (OR\u0026thinsp;=\u0026thinsp;1.42, 95% CI: 1.20\u0026ndash;1.67, P\u0026thinsp;=\u0026thinsp;3.17\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). The other five supplementary methods showed a consistent tendency (Table S13). Conversely, the MR analyses did not reveal any causal influence on the risk of IPF from genetic mimicries of HMGCR, LDLR, APOB, LPL, ANGPTL3, CETP, and PPARA inhibitors (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;3.Forest plots of the association between genetically proxied lipid-modifying drug and IPF using primary effect, Table S13). The F statistics of each genetic tool range from 29.94 to 3712.59 (Table S11). Scatter plots of the association of lipid-modifying gene targets with IPF risk were presented in Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e. Refer to Table S14 for details regarding the statistical power associated with the MR analyses conducted in this study.\u003c/p\u003e \u003cp\u003eSimilar results were also obtained from the genetic mimicry analysis with secondary effects on Apo-B and Apo-A. The decreased Apo-B level mediated by NPC1L1, PCSK9, ABCG5, ABCG8, and APOC3 gene targeted drugs were respectively associated with a higher IPF risk (OR\u0026thinsp;=\u0026thinsp;3.46, 95% CI: 1.13\u0026ndash;10.60, P\u0026thinsp;=\u0026thinsp;0.030; OR\u0026thinsp;=\u0026thinsp;1.39, 95% CI: 1.02\u0026ndash;1.91, P\u0026thinsp;=\u0026thinsp;0.038; OR\u0026thinsp;=\u0026thinsp;1.85, 95% CI: 1.19\u0026ndash;2.87, P\u0026thinsp;=\u0026thinsp;0.006; OR\u0026thinsp;=\u0026thinsp;1.88, 95% CI: 1.21\u0026ndash;2.91, P\u0026thinsp;=\u0026thinsp;0.005; OR\u0026thinsp;=\u0026thinsp;2.25, 95% CI: 1.54\u0026ndash;3.29, P\u0026thinsp;=\u0026thinsp;2.82\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) (Fig.\u0026nbsp;4.Forest plots of the association between genetically proxied lipid-modifying drug and IPF using alternative effect). Other genetic mimicries of drug targets showed no significant association with IPF (Table S15). The F statistics values for each SNP exceeded 30 (Table S16).\u003c/p\u003e \u003cp\u003eThe MR-Egger intercept examination did not uncover any indications of pleiotropy, which enhances the credibility of causal inferences (Table S17-S18). Leave-one-out analyses showed that the IVW method consistently produced results in line with the overall estimate even after excluding each SNP sequentially (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e-\u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). Further analyses were carried out using stricter LD thresholds (r2\u0026thinsp;\u0026lt;\u0026thinsp;0.2, r2\u0026thinsp;\u0026lt;\u0026thinsp;0.1, r2\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for these genes. These analyses did not significantly change the direction of the beta values, although the statistical power was reduced by excluding multiple SNPs (Table S19).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGene expression and IPF risk\u003c/h2\u003e \u003cp\u003eGiven that the NPC1L1, PCSK9, ABCG5, ABCG8, and APOC3 genes showed an association with IPF, genetic variants linked to these gene expressions in blood and relevant tissues were used as IVs for additional validation. However, the limited sample size of eQTL data prevented us from identifying eligible cis-eQTLs for APOC3 in related tissues.\u003c/p\u003e \u003cp\u003eSMR analysis results suggested that a higher expression level of PCSK9 in whole blood was associated with a lower risk of IPF (OR\u0026thinsp;=\u0026thinsp;0.71, 95% CI: 0.50\u0026ndash;0.99, P\u0026thinsp;=\u0026thinsp;0.043) (Table S20). SMR analysis also found that there was a tendency towards significance in the connection between high expression of the NPC1L1 gene in adipose subcutaneous and lower IPF risk (OR\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.73\u0026ndash;1.00, P\u0026thinsp;=\u0026thinsp;0.051) (Table S20). No significant correlation was detected between the ABCG5 gene expression levels in the spleen (OR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 0.99\u0026ndash;1.24, P\u0026thinsp;=\u0026thinsp;0.087), ABCG8 expression in colon transverse (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 0.86\u0026ndash;1.25, P\u0026thinsp;=\u0026thinsp;0.713) and IPF risk. The HEIDI test results demonstrated that the observed associations were unlikely to be due to linkage disequilibrium (p\u0026thinsp;\u0026gt;\u0026thinsp;0.01) (Table S20).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our best knowledge, this study represents the inaugural application of MR analysis to explore the relationship between lipids, lipid-modifying pharmacological interventions, and IPF. Our results offer genetic corroboration for the proposition that PCSK9 inhibitors may elevate the risk of IPF. Notably, no evidence was found to suggest that lipid traits have a causal association with IPF risk, which implies that the mechanism by which PCSK9 inhibitors influence IPF risk may be distinct from its effects on lipid metabolism. Additionally, the study uncovered preliminary evidence hinting at a potential positive association between NPC1L1 inhibition and a higher IPF risk. The discovery of these results illuminates the possible adverse effects linked to the use of lipid-modifying drugs and offers an understanding of potential risk factors that may be investigated for IPF management.\u003c/p\u003e \u003cp\u003eAlthough the current evidence did not support serum lipid traits as causal risk factors for IPF, the result should be interpreted with caution. Firstly, lipids are categorized into four primary groups, including glycerides, fatty acids, non-glycerides, and lipoproteins. The plasma lipid traits predominantly featured in this MR study pertain to the lipoprotein class. However, extant literature suggests that dysregulation of phospholipid and sphingolipid metabolism is a more substantial contributor to IPF pathophysiology[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A lipidomics analysis has revealed that alterations in the plasma lipid profile of IPF patients are predominantly within the glycerophospholipid class. Among the 159 glycerolipids examined, 30 exhibited significant disparities between the IPF group and controls[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Moreover, the lung, being a lipid-rich organ, engages in intricate lipid metabolic processes. Compared with blood lipids, alveolar lipid levels may provide a more precise reflection of metabolic disturbances. Despite a paucity of research directly comparing lipidomics profiles between bronchoalveolar lavage fluid and blood samples from IPF individuals, the work of Marissa O 'Callaghan et al. offers some valuable insights. This study observed a marked increase in total lipid content within the lung tissue of IPF individuals relative to controls[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In addition, they also assessed pulmonary fat attenuation volume through chest CT images (CT\u003csub\u003ePFAV\u003c/sub\u003e). The median CT\u003csub\u003ePFAV\u003c/sub\u003e in IPF was greater compared to controls, however, there was no association observed with serum lipids and body mass index[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These findings imply that extracellular lipids within the lung may have a closer relationship with IPF than blood lipid traits. Future MR studies could explore the bidirectional causality between intrapulmonary lipid traits and IPF.\u003c/p\u003e \u003cp\u003eThe MR study we conducted revealed a notable association between PCSK9 inhibitors and IPF. Contrary to manifesting as a protective effect, this association was characterized as a risk factor, which contradicts findings from previous studies. PCSK9, an enzyme, is essential for regulating cholesterol metabolism and maintaining cardiovascular health. Recent studies have shown that the PCSK9 gene may be implicated in the fibrotic processes of the liver, heart, kidney, and other organs. A study by Stefania Grimaudo revealed that increasing PCSK9 expression in male mice led to faster progression of liver fibrosis[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This research also described a protective role for the PCSK9 loss-of-function mutation against the progression of liver fibrosis. Subsequent research has indicated that anti-PCSK9 treatment may hold the potential to mitigate liver fibrosis by modulating the AMPK/mTOR/ULK1 signaling pathway, thus reducing hypoxia-induced autophagy in hepatocytes[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Another in vitro experiment showed that increasing PCSK9 levels could enhance the transition of cardiac fibroblasts into myofibroblasts, impacting fibrosis post-myocardial infarction[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Similar protective effects were also observed in renal fibrosis. Danyu Wu et al. developed a therapeutic vaccine targeting PCSK9. The results indicated that this vaccine could ameliorate kidney fibrosis by controlling fatty acid β-oxidation[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe postulate that several reasons may account for the inconsistent results observed. Firstly, the pathogenesis of IPF is highly complex, and the role of PCSK9 is also not singular. Previous research has concentrated on the anti-fibrotic effect of PCSK9 inhibitors in different organs through autophagy and oxidative regulation, rather than its impact on lipid levels. The balance between the anti-fibrotic effects of PCSK9 inhibitors through similar pathways or the pro-fibrotic effect through other pathways in lung tissue requires further investigation. Secondly, the expression levels of target genes can vary in different tissues, as evidenced by our SMR analysis of PCSK9 gene expression across different tissues. This variability can influence the therapeutic efficacy of drugs in a tissue-specific manner. Thirdly, drug-target Mendelian randomization analysis primarily models impact of gene inhibitors or blockers on outcomes using SNPs within the gene-specific action range (\u0026plusmn;\u0026thinsp;100kb). It provides information on the trends of connections rather than the tangible therapeutic advantages of drugs in practical situations. Numerous factors, such as drug dosage, when the drug is given, how inter-individuals metabolize drugs, and how well drugs attach to their intended targets, need to be further considered. Consequently, further RCTs are essential to confirm these observational findings.\u003c/p\u003e \u003cp\u003eOur findings also imply that there is a potential for increased IPF risk with exposure to NPC1L1 inhibitors, although the SMR analysis only suggested a near-positive result. Ezetimibe is known as the primary drug for NPC1L1 inhibitors, which are responsible for facilitating the absorption of dietary cholesterol by NPC1L1 protein [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To date, limited research has examined the influence of ezetimibe on the onset of IPF. Chanho Lee et al. conducted a retrospective study of the medical records across three different hospitals and discovered that individuals with IPF who consistently used ezetimibe had lower all-cause mortality and lung function decline rates[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. They also found that ezetimibe could prevent mice from developing bleomycin-induced pulmonary fibrosis by suppressing mTORC1 activity in vitro study. The enhancement of autophagy in mouse lung fibroblasts mediates the anti-fibrotic effect of ezetimibe, rather than through lipid-lowering properties[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. There may exist differences in the pathogenesis between drug-induced pulmonary fibrosis and IPF, which could account for the discrepancies between our MR findings and the in vitro results.\u003c/p\u003e \u003cp\u003eThis MR research also has some limitations. Firstly, despite the numerous sensitivity analyses conducted that have reinforced the reliability of our results, the possibility of horizontal pleiotropy cannot be entirely dismissed. Secondly, the sample size of eQTL data is limited, and there are no eligible eQTLs for NPC1L1, ABCG5, ABCG8, and APOC3 in hepatic and pulmonary tissues, which are the primary organs involved in lipid metabolism. This limitation may result in an underestimation of the role these genes play in the pathogenesis of IPF. Thirdly, the initial GWAS data did not categorize by specific subtypes (such as the extent of LDL-C elevation). Consequently, this study was constrained from performing a stratified analysis, an approach that should be contemplated with the availability of more specific datasets in the future. Lastly, we lack a validation cohort because other GWAS data for IPF may have sample overlap with lipid exposure. It also must be acknowledged that the data presented in our study pertains to individuals of European descent. When extrapolating these results to different ethnic groups, caution should be exercised. Additional research encompassing a diverse array of populations is necessary.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study does not support blood lipid traits (i.e., TG, LDL-C, HDL-C, Apo-A, and Apo-B) as a direct risk factor for IPF and should be interpreted with caution. An increased expression level of the PCSK9 gene was found to correlate with a lower risk of IPF. Conversely, the use of PCSK9 inhibitors was associated with an elevated risk of IPF. Further studies are essential to gain a more comprehensive understanding of the underlying mechanisms and to assess the possible effect of PCSK9 inhibitors in IPF progression through a series of preclinical and clinical trials.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIPF:\u0026nbsp;Idiopathic pulmonary fibrosis\u003c/p\u003e\n\u003cp\u003eMR: Mendelian randomization\u003c/p\u003e\n\u003cp\u003eSMR: Summary-data-based MR\u003c/p\u003e\n\u003cp\u003eOR: Odds ratio\u003c/p\u003e\n\u003cp\u003e95%CI: 95% confidence interval\u003c/p\u003e\n\u003cp\u003eRCTs: Randomized controlled trials\u003c/p\u003e\n\u003cp\u003eILD: Interstitial lung disease\u003c/p\u003e\n\u003cp\u003eGWAS:\u0026nbsp;Genome-wide association studies\u003c/p\u003e\n\u003cp\u003eeQTL: Expression quantitative trait loci\u003c/p\u003e\n\u003cp\u003eHDL-C: High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C: Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eTG: Triglyceride\u003c/p\u003e\n\u003cp\u003eKb: kilobases\u003c/p\u003e\n\u003cp\u003eApo-A: apolipoprotein A\u003c/p\u003e\n\u003cp\u003eApo-B: Apolipoprotein B\u003c/p\u003e\n\u003cp\u003eLD: Linkage disequilibrium\u003c/p\u003e\n\u003cp\u003eSNP: Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003eGTEx-V8: Genotype-Tissue Expression project\u003c/p\u003e\n\u003cp\u003eCHD: Coronary heart disease\u003c/p\u003e\n\u003cp\u003eIVW: Inverse-variance-weighted\u003c/p\u003e\n\u003cp\u003eIVs: Instrumental variables\u003c/p\u003e\n\u003cp\u003eConMix: Contamination mixture method\u003c/p\u003e\n\u003cp\u003eRAPS: Robust adjusted profile score\u003c/p\u003e\n\u003cp\u003eMR-PRESSO: MR-Pleiotropy Residual Sum and Outlier\u003c/p\u003e\n\u003cp\u003eHEIDI: Heterogeneity in dependent instruments\u003c/p\u003e\n\u003cp\u003eCT\u003csub\u003ePFAV\u003c/sub\u003e: Pulmonary fat attenuation volume through chest CT images\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional file 1: \u003cstrong\u003eTable S1.\u003c/strong\u003e STROBE-MR checklist. \u003cstrong\u003eTable S2.\u003c/strong\u003e Phenotype descriptions and distributions. \u003cstrong\u003eTable S3.\u003c/strong\u003e Lipid-lowering drug classes, substances, and target genes. \u003cstrong\u003eTable S4.\u003c/strong\u003e Genetic variants that were used as instrumental variables for high-density \u0026nbsp;lipoprotein cholesterol. \u003cstrong\u003eTable S5.\u003c/strong\u003e Genetic variants that were used as instrumental variables for low-density lipoprotein cholesterol. \u003cstrong\u003eTable S6.\u003c/strong\u003e Genetic variants that were used as instrumental variables for triglyceride. \u003cstrong\u003eTable S7.\u003c/strong\u003e Genetic variants that were used as instrumental variables for apolipoprotein A. \u003cstrong\u003eTable S8.\u0026nbsp;\u003c/strong\u003eGenetic variants that were used as instrumental variables for apolipoprotein B. \u003cstrong\u003eTable S9.\u003c/strong\u003e Association of genetically proxied lipid traits with risk of IPF. \u003cstrong\u003eTable S10.\u003c/strong\u003e Heterogeneity and pleiotropy tests of instrument effects （lipid traits on IPF). \u003cstrong\u003eTable S11.\u003c/strong\u003e Characteristics of lipid-modifying genetics variants in target genes using primary effect. \u003cstrong\u003eTable S12.\u003c/strong\u003e Association of genetically proxied lipid-modifying drugs with risk of CHD. \u003cstrong\u003eTable S13.\u003c/strong\u003e Association of genetically proxied lipid-modifying drugs with risk of IPF using primary effect. \u003cstrong\u003eTable S14.\u003c/strong\u003e Statistical power estimates for drug-target MR analyses. \u003cstrong\u003eTable S15.\u003c/strong\u003e Association of genetically proxied lipid-modifying drugs with risk of IPF using alternative effect. \u003cstrong\u003eTable S16.\u003c/strong\u003e Characteristics of lipid-modifying genetics variants in target genes using alternative effect. \u003cstrong\u003eTable S17.\u003c/strong\u003e Heterogeneity and pleiotropy tests of instrument effects (primary lipid-modifying effect). \u003cstrong\u003eTable S18.\u003c/strong\u003e Heterogeneity and pleiotropy tests of instrument effects (alternative lipid-modifying effect). \u003cstrong\u003eTable S19.\u003c/strong\u003e Association of genetic mimicry of lipid-modifying drugs with risk of IPF accounting for LD structure. \u003cstrong\u003eTable S20.\u003c/strong\u003e Association between gene expression in tissues of identified targets and IPF in the SMR analysis.\u003c/p\u003e\n\u003cp\u003eAdditional file 2: Figure S1. Scatter plots of the association between lipid traits and IPF; \u003cstrong\u003eA.\u003c/strong\u003e High-density lipoprotein cholesterol on idiopathic pulmonary fibrosis; \u003cstrong\u003eB.\u003c/strong\u003e Low-density lipoprotein cholesterol on idiopathic pulmonary fibrosis; \u003cstrong\u003eC.\u003c/strong\u003e Triglyceride on idiopathic pulmonary fibrosis; \u003cstrong\u003eD.\u003c/strong\u003e Apolipoprotein A on idiopathic pulmonary fibrosis; \u003cstrong\u003eE.\u003c/strong\u003e Apolipoprotein B on idiopathic pulmonary fibrosis.\u003c/p\u003e\n\u003cp\u003eAdditional file 3: Figure S2. Forest plots of the association between genetically proxied lipid-modifying drug and CHD risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditional file 4: Figure S3.Scatter plots of the association between genetically proxied lipid-modifying gene targets and CHD. \u003cstrong\u003eA.\u003c/strong\u003e HMGCR on coronary heart disease; \u003cstrong\u003eB.\u003c/strong\u003e NPC1L1 on coronary heart disease; \u003cstrong\u003eC.\u003c/strong\u003e PCSK9 on coronary heart disease; \u003cstrong\u003eD.\u003c/strong\u003e APOC on coronary heart disease; \u003cstrong\u003eE.\u003c/strong\u003e ABCG5 on coronary heart disease; \u003cstrong\u003eF.\u003c/strong\u003e ABCG8 on coronary heart disease; \u003cstrong\u003eG.\u003c/strong\u003e LDLR on coronary heart disease; \u003cstrong\u003eH.\u003c/strong\u003e CETP on coronary heart disease; \u003cstrong\u003eI.\u003c/strong\u003e ANGPTL3 on coronary heart disease; \u003cstrong\u003eJ.\u003c/strong\u003e APOC3 on coronary heart disease; \u003cstrong\u003eK.\u003c/strong\u003e LPL on coronary heart disease; \u003cstrong\u003eL.\u003c/strong\u003e PPARA on coronary heart disease.\u003c/p\u003e\n\u003cp\u003eAdditional file 5: Figure S4.Scatter plots of the association between genetically proxied lipid-modifying gene targets and IPF. \u003cstrong\u003eA.\u003c/strong\u003e HMGCR on idiopathic pulmonary fibrosis; \u003cstrong\u003eB.\u003c/strong\u003e NPC1L1 on idiopathic pulmonary fibrosis; \u003cstrong\u003eC.\u003c/strong\u003e PCSK9 on idiopathic pulmonary fibrosis; \u003cstrong\u003eD.\u003c/strong\u003e APOC on idiopathic pulmonary fibrosis; \u003cstrong\u003eE.\u003c/strong\u003e ABCG5 on idiopathic pulmonary fibrosis; \u003cstrong\u003eF.\u003c/strong\u003e ABCG8 on idiopathic pulmonary fibrosis; \u003cstrong\u003eG.\u003c/strong\u003e LDLR on idiopathic pulmonary fibrosis; \u003cstrong\u003eH.\u003c/strong\u003e CETP on idiopathic pulmonary fibrosis; \u003cstrong\u003eI.\u003c/strong\u003e ANGPTL3 on idiopathic pulmonary fibrosis; \u003cstrong\u003eJ.\u003c/strong\u003e APOC3 on idiopathic pulmonary fibrosis; \u003cstrong\u003eK.\u003c/strong\u003e LPL on idiopathic pulmonary fibrosis; \u003cstrong\u003eL.\u003c/strong\u003e PPARA on idiopathic pulmonary fibrosis.\u003c/p\u003e\n\u003cp\u003eAdditional file 6: Figure S5.Plots of \u0026ldquo;leave-one-out\u0026rdquo; analyses for MR analyses of the causal effect of lipid-modifying drugs on IPF using primary effect. \u003cstrong\u003eA.\u003c/strong\u003e Genetic mimicries of NPC1L1 inhibitor on idiopathic pulmonary fibrosis; \u003cstrong\u003eB.\u003c/strong\u003e Genetic mimicries of PCSK9 inhibitor on idiopathic pulmonary fibrosis; \u003cstrong\u003eC.\u003c/strong\u003e Genetic mimicries of ABCG5 enhancement on idiopathic pulmonary fibrosis; \u003cstrong\u003eD.\u003c/strong\u003e Genetic mimicries of ABCG8 enhancement on idiopathic pulmonary fibrosis; \u003cstrong\u003eE.\u003c/strong\u003e Genetic mimicries of APOC3 blocker on idiopathic pulmonary fibrosis.\u003c/p\u003e\n\u003cp\u003eAdditional file 7: Figure S6.Plots of \u0026ldquo;leave-one-out\u0026rdquo; analyses for MR analyses of the causal effect of lipid-modifying drugs on IPF using alternative effect. \u003cstrong\u003eA.\u003c/strong\u003e Genetic mimicries of NPC1L1 inhibitor on idiopathic pulmonary fibrosis; \u003cstrong\u003eB.\u003c/strong\u003e Genetic mimicries of PCSK9 inhibitor on idiopathic pulmonary fibrosis; \u003cstrong\u003eC.\u003c/strong\u003e Genetic mimicries of ABCG5 enhancement on idiopathic pulmonary fibrosis; \u003cstrong\u003eD.\u003c/strong\u003e Genetic mimicries of ABCG8 enhancement on idiopathic pulmonary fibrosis; \u003cstrong\u003eE.\u003c/strong\u003e Genetic mimicries of APOC3 blocker on idiopathic pulmonary fibrosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend sincere thanks to the participants of the UK Biobank, the FinnGen project, the GTEx-V8 project and the other included cohorts for their invaluable contributions. We also appreciate the dedication of the numerous investigators and research personnel who have played a pivotal role in the data acquisition phase of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSLY designed the study and contributed to the data analysis. CGX and LJJ prepared the first draft of the manuscript. CMS organized the tables and figures. Each author has thoroughly reviewed the content, providing their endorsement to the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current investigation employed publicly accessible GWAS summary statistics as the primary data source. Additionally, all the datasets utilized for analysis have been duly incorporated within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo participants were directly engaged in the overall progression of our investigation. Solely publicly accessible data served as the foundation of our study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDivision of Pulmonary Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMoss BJ, Ryter SW, Rosas IO. Pathogenic Mechanisms Underlying Idiopathic Pulmonary Fibrosis. Annu Rev Pathol. 2022;17:515\u0026ndash;46. \u003c/li\u003e\n\u003cli\u003eHamanaka RB, Mutlu GM. Metabolic requirements of pulmonary fibrosis: role of fibroblast metabolism. FEBS J. 2021;288:6331\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eRajesh R, Atallah R, B\u0026auml;rnthaler T. Dysregulation of metabolic pathways in pulmonary fibrosis. Pharmacol Ther. 2023;246:108436. \u003c/li\u003e\n\u003cli\u003eJustet A, Klay D, Porcher R, Cottin V, Ahmad K, Molina Molina M, et al. Safety and efficacy of pirfenidone and nintedanib in patients with idiopathic pulmonary fibrosis and carrying a telomere-related gene mutation. Eur Respir J. 2021;57:2003198. \u003c/li\u003e\n\u003cli\u003eBehr J, Nathan SD, Wuyts WA, Mogulkoc Bishop N, Bouros DE, Antoniou K, et al. Efficacy and safety of sildenafil added to pirfenidone in patients with advanced idiopathic pulmonary fibrosis and risk of pulmonary hypertension: a double-blind, randomised, placebo-controlled, phase 2b trial. Lancet Respir Med. 2021;9:85\u0026ndash;95. \u003c/li\u003e\n\u003cli\u003eLederer DJ, Martinez FJ. Idiopathic Pulmonary Fibrosis. N Engl J Med. 2018;378:1811\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eSaito S, Alkhatib A, Kolls JK, Kondoh Y, Lasky JA. Pharmacotherapy and adjunctive treatment for idiopathic pulmonary fibrosis (IPF). J Thorac Dis. 2019;11:S1740\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eZhao X, Kwan JYY, Yip K, Liu PP, Liu F-F. Targeting metabolic dysregulation for fibrosis therapy. Nat Rev Drug Discov. 2020;19:57\u0026ndash;75. \u003c/li\u003e\n\u003cli\u003eBurgy O, Loriod S, Beltramo G, Bonniaud P. Extracellular Lipids in the Lung and Their Role in Pulmonary Fibrosis. Cells. 2022;11:1209. \u003c/li\u003e\n\u003cli\u003eTian Y, Duan C, Feng J, Liao J, Yang Y, Sun W. Roles of lipid metabolism and its regulatory mechanism in idiopathic pulmonary fibrosis: A review. Int J Biochem Cell Biol. 2023;155:106361. \u003c/li\u003e\n\u003cli\u003eXu Y, Mizuno T, Sridharan A, Du Y, Guo M, Tang J, et al. Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic pulmonary fibrosis. JCI Insight. 2016;1:e90558. \u003c/li\u003e\n\u003cli\u003eReyfman PA, Walter JM, Joshi N, Anekalla KR, McQuattie-Pimentel AC, Chiu S, et al. Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis. Am J Respir Crit Care Med. 2019;199:1517\u0026ndash;36. \u003c/li\u003e\n\u003cli\u003eChen R, Dai J. Lipid metabolism in idiopathic pulmonary fibrosis: From pathogenesis to therapy. J Mol Med (Berl). 2023;101:905\u0026ndash;15. \u003c/li\u003e\n\u003cli\u003eJang HJ, Lee DY, Loloci G, Jeong J, Choi W-I. Association between the use of statins and risk of interstitial lung disease/idiopathic pulmonary fibrosis: time-dependent analysis of population-based nationwide data. Eur Respir J. 2023;62:2300291. \u003c/li\u003e\n\u003cli\u003eKreuter M, Bonella F, Maher TM, Costabel U, Spagnolo P, Weycker D, et al. Effect of statins on disease-related outcomes in patients with idiopathic pulmonary fibrosis. Thorax. 2017;72:148\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eKreuter M, Lederer DJ, Cottin V, Kahn N, Ley B, Vancheri C, et al. Concomitant medications and clinical outcomes in idiopathic pulmonary fibrosis. Eur Respir J. 2019;54:1901188. \u003c/li\u003e\n\u003cli\u003eSaad N, Camus P, Suissa S, Ernst P. Statins and the risk of interstitial lung disease: a cohort study. Thorax. 2013;68:361\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003eSmith GD. Mendelian Randomization for Strengthening Causal Inference in Observational Studies: Application to Gene \u0026times; Environment Interactions. Perspect Psychol Sci. 2010;5:527\u0026ndash;45. \u003c/li\u003e\n\u003cli\u003eSchmidt AF, Finan C, Gordillo-Mara\u0026ntilde;\u0026oacute;n M, Asselbergs FW, Freitag DF, Patel RS, et al. Genetic drug target validation using Mendelian randomisation. Nat Commun. 2020;11:3255. \u003c/li\u003e\n\u003cli\u003eSmith GD, Ebrahim S. \u0026ldquo;Mendelian randomization\u0026rdquo;: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32:1\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eDavies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. \u003c/li\u003e\n\u003cli\u003eWilliams DM, Finan C, Schmidt AF, Burgess S, Hingorani AD. Lipid lowering and Alzheimer disease risk: A mendelian randomization study. Ann Neurol. 2020;87:30\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eReay WR, Cairns MJ. Advancing the use of genome-wide association studies for drug repurposing. Nat Rev Genet. 2021;22:658\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003eSkrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA. 2021;326:1614\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eSinnott-Armstrong N, Tanigawa Y, Amar D, Mars N, Benner C, Aguirre M, et al. Genetics of 35 blood and urine biomarkers in the UK Biobank. Nat Genet. 2021;53:185\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003eMach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020;41:111\u0026ndash;88. \u003c/li\u003e\n\u003cli\u003eY D, K G, S X, F Z, X M, J H. Regulation of cholesterol homeostasis in health and diseases: from mechanisms to targeted therapeutics. Signal transduction and targeted therapy [Internet]. 2022 [cited 2024 Apr 11];7. Available from: https://pubmed.ncbi.nlm.nih.gov/35918332/\u003c/li\u003e\n\u003cli\u003eBor\u0026eacute;n J, Taskinen M-R, Bj\u0026ouml;rnson E, Packard CJ. Metabolism of triglyceride-rich lipoproteins in health and dyslipidaemia. Nat Rev Cardiol. 2022;19:577\u0026ndash;92. \u003c/li\u003e\n\u003cli\u003eRidker PM. LDL cholesterol: controversies and future therapeutic directions. Lancet. 2014;384:607\u0026ndash;17. \u003c/li\u003e\n\u003cli\u003eLi Z, Zhang B, Liu Q, Tao Z, Ding L, Guo B, et al. Genetic association of lipids and lipid-lowering drug target genes with non-alcoholic fatty liver disease. EBioMedicine. 2023;90:104543. \u003c/li\u003e\n\u003cli\u003eGTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eCa E, Av K, S K. Mendelian Randomization. JAMA [Internet]. 2017 [cited 2023 Jul 2];318. Available from: https://pubmed.ncbi.nlm.nih.gov/29164242/\u003c/li\u003e\n\u003cli\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658\u0026ndash;65. \u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40:304\u0026ndash;14. \u003c/li\u003e\n\u003cli\u003eRobust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption - PubMed [Internet]. [cited 2023 Jul 8]. Available from: https://pubmed.ncbi.nlm.nih.gov/29040600/\u003c/li\u003e\n\u003cli\u003eBurgess S, Foley CN, Allara E, Staley JR, Howson JMM. A robust and efficient method for Mendelian randomization with hundreds of genetic variants. Nat Commun. 2020;11:376. \u003c/li\u003e\n\u003cli\u003eYu K, Chen X-F, Guo J, Wang S, Huang X-T, Guo Y, et al. Assessment of bidirectional relationships between brain imaging-derived phenotypes and stroke: a Mendelian randomization study. BMC Med. 2023;21:271. \u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eHemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27:R195\u0026ndash;208. \u003c/li\u003e\n\u003cli\u003eBurgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology. 2017;28:30\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003ePierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40:740\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eSuryadevara V, Ramchandran R, Kamp DW, Natarajan V. Lipid Mediators Regulate Pulmonary Fibrosis: Potential Mechanisms and Signaling Pathways. International Journal of Molecular Sciences. 2020;21:4257. \u003c/li\u003e\n\u003cli\u003eYan F, Wen Z, Wang R, Luo W, Du Y, Wang W, et al. Identification of the lipid biomarkers from plasma in idiopathic pulmonary fibrosis by Lipidomics. BMC Pulm Med. 2017;17:174. \u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Callaghan M, Duignan J, Tarling EJ, Waters DK, McStay M, O\u0026rsquo;Carroll O, et al. Analysis of tissue lipidomics and computed tomography pulmonary fat attenuation volume (CTPFAV ) in idiopathic pulmonary fibrosis. Respirology. 2023;28:1043\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eGrimaudo S, Bartesaghi S, Rametta R, Marra F, Margherita Mancina R, Pihlajam\u0026auml;ki J, et al. PCSK9 rs11591147 R46L loss-of-function variant protects against liver damage in individuals with NAFLD. Liver Int. 2021;41:321\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eNing L, Zou Y, Li S, Cao Y, Xu B, Zhang S, et al. Anti-PCSK9 Treatment Attenuates Liver Fibrosis via Inhibiting Hypoxia-Induced Autophagy in Hepatocytes. Inflammation. 2023;46:2102\u0026ndash;19. \u003c/li\u003e\n\u003cli\u003eBao H, Wang X, Zhou H, Zhou W, Liao F, Wei F, et al. PCSK9 regulates myofibroblast transformation through the JAK2/STAT3 pathway to regulate fibrosis after myocardial infarction. Biochem Pharmacol. 2024;220:115996. \u003c/li\u003e\n\u003cli\u003eWu D, Zhou Y, Pan Y, Li C, Wang Y, Chen F, et al. Vaccine Against PCSK9 Improved Renal Fibrosis by Regulating Fatty Acid \u0026beta;-Oxidation. J Am Heart Assoc. 2020;9:e014358. \u003c/li\u003e\n\u003cli\u003eLee C, Kwak SH, Han J, Shin JH, Yoo B, Lee YS, et al. Repositioning of ezetimibe for the treatment of idiopathic pulmonary fibrosis. Eur Respir J. 2024;2300580. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Idiopathic pulmonary fibrosis, Lipids, Drug-target Mendelian randomization, PCSK9, Summary-data-based Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4387298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4387298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIdiopathic pulmonary fibrosis (IPF) is a respiratory disorder of obscure etiology and limited treatment options, possibly linked to dysregulation in lipid metabolism. While several observational studies suggest that lipid-lowering agents may decrease the risk of IPF, the evidence is inconsistent. The present Mendelian randomization (MR) study aims to determine the association between circulating lipid traits and IPF and to assess the potential influence of lipid-modifying medications for IPF.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSummary statistics of 5 lipid traits (high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, apolipoprotein A, and apolipoprotein B) and IPF were sourced from the UK Biobank and FinnGen Project Round 10. The study\u0026rsquo;s focus on lipid-regulatory genes encompassed PCSK9, NPC1L1, ABCG5, ABCG8, HMGCR, APOB, LDLR, CETP, ANGPTL3, APOC3, LPL, and PPARA. The primary effect estimates were determined using the inverse-variance-weighted method, with additional analyses employing the contamination mixture method, robust adjusted profile score, the weighted median, weighted mode methods, and MR-Egger. Summary-data-based Mendelian randomization (SMR) was used to confirm significant lipid-modifying drug targets, leveraging data on expressed quantitative trait loci in relevant tissues. Sensitivity analyses included assessments of heterogeneity, horizontal pleiotropy, and leave-one-out methods.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere was no significant effect of blood lipid traits on IPF risk (all P\u0026gt;0.05). Drug-target MR analysis indicated that genetic mimicry for inhibitor of NPC1L1, PCSK9, ABCG5, ABCG8, and APOC3 were associated with increased IPF risks, with odds ratios (ORs) and 95% confidence intervals (CIs) as follows: 2.74 (1.05\u0026ndash;7.12, P\u0026thinsp;=\u0026thinsp;0.039), 1.36 (1.02\u0026ndash;1.82, P\u0026thinsp;=\u0026thinsp;0.037), 1.66 (1.12\u0026ndash;2.45, P\u0026thinsp;=\u0026thinsp;0.011), 1.68 (1.14\u0026ndash;2.48, P\u0026thinsp;=\u0026thinsp;0.009), and 1.42 (1.20\u0026ndash;1.67, P\u0026thinsp;=\u0026thinsp;3.17\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), respectively. The SMR method identified a significant association between PCSK9 gene expression in whole blood and reduced IPF risk (OR\u0026thinsp;=\u0026thinsp;0.71, 95% CI: 0.50\u0026ndash;0.99, P\u0026thinsp;=\u0026thinsp;0.043), and a trend towards a positive correlation for NPC1L1 in adipose subcutaneous tissue (OR\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.73\u0026ndash;1.00, P\u0026thinsp;=\u0026thinsp;0.051). Sensitivity analyses showed no evidence of bias.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSerum lipid traits did not significantly affect the risk of idiopathic pulmonary fibrosis. Drug targets MR studies examining 12 lipid-modifying drugs indicated that PCSK9 inhibitors could dramatically increase IPF risk, a mechanism that may differ from their lipid-lowering actions and thus warrants further investigation.\u003c/p\u003e","manuscriptTitle":"Exploring the association between lipid-modifying drugs and idiopathic pulmonary fibrosis: a drug-target Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 10:59:08","doi":"10.21203/rs.3.rs-4387298/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-21T05:17:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-21T04:53:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217660698568264330089861444278874188324","date":"2024-06-19T15:12:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22723119769165983158550584215504166851","date":"2024-05-28T14:12:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-21T09:58:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278954627592241179111141702639980187947","date":"2024-05-21T06:43:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-10T06:37:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-09T02:36:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-08T22:01:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Lipids in Health and Disease","date":"2024-05-08T07:24:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3807519b-902b-4746-b22d-efe3c023704f","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-05T16:03:13+00:00","versionOfRecord":{"articleIdentity":"rs-4387298","link":"https://doi.org/10.1186/s12944-024-02218-6","journal":{"identity":"lipids-in-health-and-disease","isVorOnly":false,"title":"Lipids in Health and Disease"},"publishedOn":"2024-08-01 15:57:33","publishedOnDateReadable":"August 1st, 2024"},"versionCreatedAt":"2024-05-15 10:59:08","video":"","vorDoi":"10.1186/s12944-024-02218-6","vorDoiUrl":"https://doi.org/10.1186/s12944-024-02218-6","workflowStages":[]},"version":"v1","identity":"rs-4387298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4387298","identity":"rs-4387298","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}