Mendelian Randomization revealed a one-way causal association between increased Isovalerylcarnitine (C5) levels and the risk of idiopathic pulmonary fibrosis

Mendelian Randomization revealed a one-way causal association between increased Isovalerylcarnitine (C5) levels and the risk of idiopathic pulmonary fibrosis | 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 Mendelian Randomization revealed a one-way causal association between increased Isovalerylcarnitine (C5) levels and the risk of idiopathic pulmonary fibrosis Jing He, zhengyue Liao, Hongyu Chen, Jiaojiao Fu, sijing Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4233607/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background There have been multiple observational studies that have established a link between metabolite levels in the body and idiopathic pulmonary fibrosis (IPF), specifically focusing on metabolites derived from fatty acids. However, a complete understanding of the precise molecular and biological factors, as well as the causality between them, remains elusive. Objective The main objective of our study was to evaluate the potential causal relationship between blood metabolites and IPF by using Mendelian randomisation (MR). Methods To achieve this goal, we utilized the most comprehensive genome-wide association study (GWAS) to date, which identified genetic variants associated with blood metabolites (1,091 blood metabolites and 309 metabolite ratios). Summary statistics of IPF were collected from Finngen R8 (1,812 IPF patients and 338,784 controls), Inverse Variance Weighted method (IVW) is used as the main method in determining causality. Results Isovalerylcarnitine (C5) levels (OR = 1.2435, 95%CI: 1.0494–1.4736, PIVW = 0.0119) was found significantly related to higher risk of IPF. There was no significant heterogeneity in our study (IVW method: Pval = 0.132; MR-Egger method: Pval = 0.105) and horizontal pleiotropy (β=-0.027; se = 0.0337; Pval = 0.4310). The sensitivity analysis did not reveal any potential abnormal drivers (0.1 < All < 0.3). Conclusion Two-sample MR Method demonstrated the causal relationship between blood metabolites and IPF, and further studies found that C5 levels, as a potential biological risk factor for IPF, may provide a new target for the treatment of IPF. IPF Fatty Acid metabolism Isovalerylcarnitine levels Two-sample MR Single-nucleotide polymorphisms (SNPs). Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction IPF is a progressive and irreversible primary interstitial lung disease characterized by pulmonary inflammation and fibrotic changes ( 1 ). Despite extensive research, the exact cause of IPF remains unknown. Epidemiological data indicates that the prevalence of IPF in Europe ranges from 3 to 9 cases per 100,000 people ( 2 ). Subsequent data revealed that the incidence of IPF has increased by 4%, with a strong correlation between age and incidence ( 3 ). For terminal patients, lung transplantation is often considered as a management strategy ( 4 ). Recent studies have suggested that small lipid molecules, such as fatty acids, cholesterol, arachidonic acid metabolites, and phospholipids, may play a significant role in the development of IPF ( 5 – 8 ). However, the exact causal relationship between lipid metabolites and IPF is still not fully understood. Several investigations in the field of human metabolomics have established a correlation between lipid metabolites and IPF. However, it is worth mentioning that these associations may conflict with prospective findings in different study cohorts. Serum metabolomics analyses were conducted on two observational cohorts of IPF patients from Germany (n = 122), Spain (n = 21), and healthy controls (n = 16), revealed a down-regulation of lysophosphatidylcholine and phosphatidylcholine in the serum of IPF patients ( 9 ). Interestingly, two other independent studies on IPF populations (Cohort 1: IPF n = 10, healthy population n = 10; Cohort 2: IPF n = 11, and healthy individuals n = 10) reported contrasting outcomes ( 10 ). Additionally, the dysregulation of PGE2 has been implicated in the development of IPF ( 11 ). However, a study by Li et al. found no significant difference in serum PGE2 concentrations between IPF patients and healthy volunteers ( 12 ). Possible factors contributing to the disparate experimental outcomes include confounding variables within the observed population, discrepancies in detection methodologies, and the influence of reverse causality, among others ( 13 ). Therefore, it is crucial to urgently identify biomarkers that demonstrate a strong causal relationship with IPF, instead of solely relying on mutually exclusive observations. MR is based on the use of SNPs that are associated with genetic variation as instrumental variables (IVs) ( 14 , 15 ). MR is advantageous in avoiding confounding factors associated with sampling compared to randomized controlled trials (RCTs) ( 16 ). It is also cost-effective, efficient, and increasingly preferred for determining causality between exposure and disease. Therefore, this study aimed to explore potential risk factors related to the association between fatty acid metabolites and IPF using a two-sample MR framework. Materials and methods Bidirectional multivariable two-sample Mendelian randomized design Multivariate MR is a valuable method for assessing the causal effect of multiple exposures on outcomes ( 17 ). The method relies on three fundamental assumptions: A) the hypothesis of correlation, which states that there must be a correlation between genetic IVs and the exposure; B) the hypothesis of independence, which suggests that these IVs should not be associated with any confounding factors; and C) the hypothesis of exclusion, which asserts that the effects of IVs on the results are solely due to their impact on exposure to a specific focal point, without considering the influence of any other restricting factors. This allows for the deduction of the causal effect of the exposure on the disease ( 18 ). The analysis model of this study is illustrated in Fig. 1 . Data source To investigate the relationship between blood fatty acid metabolites and IPF, we utilized a dataset from the Canadian Longitudinal Study of Aging (CLSA) cohort. The dataset consisted of 1,091 metabolites and 309 metabolite ratios obtained from the blood of 8,299 individuals. The original experiment was conducted with the approval of the Ethics boards from the Jewish General Hospital (protocol number 2021–2762) ( 19 ). The GWAS summary included a total of 1,048,575 SNPs from 1,812 IPF patients and 338,784 controls of European ancestry were as outcome. SNPs, the ethics review board of the Hospital District of Helsinki and Uusima approved the FinnGen study protocol. It is important to note that all the populations analyzed in the GWAS were of European descent. The GWAS data was obtained from the UK Biobank cohort and the FinnGen consortium R8 (Table S1 and S2). Instrumental variable (IV) To meet the requirements of IVs, only SNPs with a Pval < 1×10 − 5 for both exposure and outcome were included in the statistical analysis ( 20 ). Clump, an important feature in PLINK software (version 1.90), was used to cluster, and analyze genetic data ( 21 ). It was employed to remove data linkage imbalances and redundant SNPs, based on a linkage disequilibrium (LD) threshold of R 2 < 0.001 within a 10,000 kb distance. Specifically, the closer the R 2 value is to 0, the higher the degree of complete linkage balance between the two SNPs. Rejecting all other SNPs within 10,000 kb of a given SNP ensures independence. Additionally, any SNP with an F statistic value > 10 was considered a strong variable and included in the analysis. After determining the association between SNPs and IPF, we controlled for confounding factors such as smoking, type 2 diabetes, and gastro-oesophageal reflux ( 22 – 25 ). Mendelian randomization (MR) Bidirectional, two-sample MR studies have provided evidence for a causal relationship between blood metabolites and IPF. We used the R package 'TwoSampleMR' (Version R4.3.2) to identify alleles that are significantly related. To ensure unidirectional causality, we employed five statistical approaches ( 26 ). IVW, the primary method, demonstrated a crucial causal association. An odds ratio (OR) 1 indicates reduced risk. PIVW < 0.05 indicates statistical significance between exposure IV and outcome IV ( 27 ). Additionally, we used four auxiliary discrimination methods that required the OR value to be consistent with the IVW OR. These methods include the MR-Egger method, which utilizes Egger regression to estimate the causal effect ( 28 ). The simple mode method, which provides quick results. The weighted median method, which calculates the median of the data ( 29 ), and the weighted pattern method, which calculates the mode (i.e., the value that occurs most frequently) of the data. All relevant analyses were based on the Ensemble GRCh38 reference for genome coordinates ( 30 ). Multiple sensitivity analysis To address the balanced polytropy of the MR hypothesis, a robustness analysis was conducted ( 31 ). Firstly, the MR-Egger intercept method was employed to assess horizontal pleiotropy resulting from genetic variation influenced by multiple traits. The aim was to mitigate bias in the findings, with a Pval > 0.05 indicating no confounding factors ( 32 ). Secondly, the Cochran's Q test was executed under the IVW method and MR-Egger method to check for heterogeneity across the individual causal effects. A Q-pval > 0.05 indicates no heterogeneity level ( 33 ). Finally, the Leave-one-out method was used to test the sensitivity of the model in each iteration of the dataset, ruling out the possibility of summary results being driven by a single abnormal SNP. The value > 0 for all iterations indicates that the result is reliable ( 34 ). Results Instrument Strength We utilized the most up to date GWAS data, which included information on 1400 blood metabolites. To ensure accuracy, we removed any linkage disequilibrium. For a strong IV, we set a criterion of F-statistic values > 10. All SNPs that met this criterion can be found in the supplementary materials (Table S3 displays the SNPs associated with major metabolite results). Additionally, we identified a total of 31 SNPs as IVs for IPF genetic variables, which is considered sufficient for conducting MR analysis (Table S4 provides further details). Causal relationship between blood metabolites and IPF Through rigorous quality control measures, particularly the IVW method, we identified 17 plasma metabolites that showed significant causal associations with IPF (Fig. 2 ). These metabolites consisted of lipid metabolites (9/17), amino acid metabolites (5/17), alkaloids (2/17), and amine metabolites (1/17). The beta values obtained from different methods for all indicators exhibited good consistency, highlighting the robustness of the observed risk or protection across various test methods (Figure S1 ). Our findings revealed that 6 metabolites were positively correlated with IPF, while 11 metabolites were negatively correlated (Fig. 3 , Table S5). Notably, various metabolites, such as fatty acids, lipid metabolites, lipid peroxides, and phospholipids, are involved in lipid metabolic pathways. This provides clear evidence of the involvement of fatty acid metabolites in IPF, which aligns with the lipid changes primarily observed in patients with IPF ( 9 ) (Table 1 ). Table 1 9 lipid metabolites that are causally associated with IPF Outcome Metabolite name Metabolite Class IPF 9-hydroxystearate fatty acid lsovaleryicarnitine (C5) fatty acyls N-palmitloyl-heptadecasphingosine (d17:1/16.0) sphingolipid Cis-4-decenoate (10:1n6) fatty acid 1-(1-enyl-palmiloyl-GPC (p-16:0) phospholipids Docosadienoate (22 2n6) fatty acid 1-myristoyl-2-arachidonoyl-GPC (14:0120:4) phospholipids 13-HODE + 9-HODE lipid peroxide (2 or 3)-decenoale (10:1n7 or n8) fatty aldehydes Reverse MR analysis of IPF associated with 17 candidate metabolites risk To further explore the relationship between IPF and candidate metabolites, we conducted reverse analysis. In this analysis, IPF was considered as the exposure variable and the 17 blood metabolites as the outcome variables. Using the IVW method, we found that IPF did not significantly contribute to abnormal changes in the 17 metabolites (PIVW > 0.05, Table S6). We also assessed the consistency of OR and beta values obtained from five different statistical methods to determine the direction, intensity, and probability of IPF's influence on the candidate metabolites (Fig. 4 ; Table S6). Through this analysis, we excluded 13 metabolites (Figure S2 ) and identified a non-causal relationship between IPF and 4 candidate blood metabolites (Table 2 ): Isovalerylcarnitine (C5) levels (Fig. 5 A), Homostachydrine levels (Fig. 5 B), Gamma-glutamylalanine levels (Fig. 5 C), and Tyramine 0-sulfate levels (Fig. 5 D). Table 2 Reverse MR Analysis results Exposure Outcome Method PIVW OR(95%CI) IPF Isovalerylcarnitine (C5) levels Inverse Variance Weighted 0.6253 1.0159 (0.9533–1.0826) IPF Homostachydrine levels Inverse Variance Weighted 0.1371 1.0392 (0.9877–1.0934) IPF Gamma-glutamylalanine levels Inverse Variance Weighted 0.2638 0.9700 (0.9195–1.0232) IPF Tyramine 0-sulfate levels Inverse Variance Weighted 0.1101 0.9610 (0.9154–1.0090) MR, Mendelian randomization; IVW method, inverse variance weighting method; OR, odds ratio. Statistical significance was defined as P < 0.05. The sensitivity analysis shows that C5 levels is a good index to predict IPF It has been reported that the accumulation of C5 levels leads to mitochondrial dysfunction and impaired oxidation of fatty acid β in pediatric drug-resistant epilepsy (DRE) ( 35 ), while mitochondrial dysfunction and metabolic reprogramming occur frequently in IPF lung ( 36 ). Therefore, we speculate whether C5 levels is also the originator of mitochondrial dysfunction in IPF patients, which is exactly consistent with our results. Increased C5 levels is a risk factor for IPF. Next, we diligently carried out horizontal pleiotropy analysis, heterogeneity analysis, and sensitivity analysis to effectively address estimation bias caused by pleiotropy of genetic variation in causal analysis utilizing MR. Egger Intercept showed that horizontal pleiotropy does not exist in our analysis process (β=-0.027; se = 0.0337; Pval = 0.4310). The MR-egger and IVW methods suggested that there is no heterogeneity in this experiment (MR-egger Q-pval = 0.4439; IVW Q-pval = 0.4669) (Fig. 6 A; Table S7). Furthermore, the leave-one-out method reveals that causal effects do not exist driven by a single IV (Fig. 6 B; Table S8). In short, comprehensive analysis showed that our results have good predictive value. Discussion Patients with IPF exhibit dysregulation of blood metabolism. However, the precise regulatory mechanism underlying the relationship between IPF occurrence and changes in metabolites remains unclear. In this pioneering study, our objective was to investigate the causal relationship between blood metabolites and IPF using a bidirectional, multivariable Two-sample MR (MR) approach. To achieve this, we utilized the largest publicly available GWAS dataset, comprising 1,400 blood metabolites, to identify robust genetic variants associated with IPF. Through comprehensive genetic analysis of over 340,000 Europeans, we discovered that genetic susceptibility to 4 certain blood metabolites is causally linked to IPF. Specifically, this study revealed that C5 levels is a risk factor for IPF via rigorous bidirectional MR testing and sensitivity analysis. C5 levels plays a role in the metabolic oxidation of branchchain fatty acids in mitochondria ( 37 ). A previous study has suggested that ramipril's positive health effects on SARS-CoV-2 might be attributed to its ability to reduce plasma C5 levels, in other words, an increase in C5 levels s in SARS-CoV-2 could potentially interfere with normal lung function ( 38 ). Elevated concentrations of C5 levels have been observed in individuals with obesity, type 2 diabetes, and plasma ND1 mtDNA ≥ 3200 copies/µL. suggested a potential association between C5 levels and mitochondrial dysfunction ( 39 – 41 ). It has been demonstrated that changes in lipid metabolism can strongly contribute to mitochondrial dysfunction ( 42 ), which is known to be a driving factor in IPF ( 43 , 44 ). Impaired fatty acid β-oxidation can also result from mitochondrial dysfunction ( 45 ), De Perrot et al. found that the key enzymes and metabolites of the mitochondrial β-oxidation pathway in the lungs of IPF patients are altered ( 46 ). However, there is currently no clear study investigating whether the disorder of lipid metabolism triggers IPF or if lipid metabolism is altered after the onset of IPF. Our results provide a potential pathogenic biological factor for IPF. We speculate that the increase in C5 levels in serum may lead to lipid deposition in lung cells and disrupt mitochondrial function, thereby increasing the risk of IPF. However, this hypothesis needs to be further validated through a large prospective cohort study. Statistics on the incidence of IPF vary widely between health systems ( 47 ). This variability may be attributed to the increasing awareness and diagnosis of the disease. Currently, the diagnosis of IPF is invasive, such as collecting BAL fluid and freezing lung biopsies, which may increase the risk of disease progression. The latest ATS/ERS/ALAT/JRS guidelines have updated the diagnostic algorithm for IPF ( 48 ), suggesting the use of serological assisted diagnosis. However, serological tests currently lack specific diagnostic markers and need to rule out a variety of diseases that may cause ILD. Therefore, it is important to conduct reliable and convenient biomarker studies for IPF. Qian et al. identified differentially expressed transcripts (DET) involved in lipid metabolism in IPF lung tissue ( 49 ), while Yin Lyu et al. identified five fatty acid metabolite-related genes (FAMRG) in bronchoalveolar lavage fluid of IPF patients ( 50 ). Additionally, Miriana d'Alessandro found differential lipid metabolites in IPF alveolar lavage fluid, however, these lipid metabolites do not possess diagnostic value in serum for distinguishing IPF from hypersensitivity pneumonitis (fHP) ( 51 ). The strong association between IPF and lipid metabolism drove us to look for potential associations between lipid metabolites and IPF by examining blood metabolites. The evidence provided by MR in our study indicates a causal relationship between C5 levels and IPF, with no reverse causality observed. Therefore, increased levels of C5 levels may increase susceptibility to IPF, suggesting its potential as a candidate biomarker for IPF diagnosis. However, further validation from clinical samples is necessary to determine whether C5 levels can be utilized as a diagnostic indicator of IPF. Limitations of this study: Firstly, due to the short median survival time of 2–3 years after IPF diagnosis and the absence of typical process nodes during the stable period, decline period, and rapid deterioration of the disease ( 52 ), we were unable to obtain typical GWAS data for IPF at each stage. Therefore, we cannot discuss the causal relationship between C5 levels and the IPF process. Secondly, since many serum biomarker levels are continuous variables, further measurements, and statistical analysis of causal dichotogenic data are necessary to determine thresholds corresponding to significant abnormalities. Thirdly, it is important to note that our data population consists solely of individuals of European descent. Although the GWAS of exposure and outcome are derived from different public databases, the two-sample MR analysis is often affected by sample overlap, which may limit the causal relationship derived by MR in terms of race selection ( 53 ). Therefore, it is crucial to explore IPF GWAS data of other populations for a more comprehensive understanding. Conclusion Our study utilized publicly published GWAS data to evaluate the random effect of blood metabolites on IPF using a two-way two-sample MR Approach. Specifically, our bidirectional MR Analysis revealed a unidirectional causal relationship between C5 levels and IPF, indicating that C5 levels serves as a risk factor for IPF. Abbreviations idiopathic pulmonary fibrosis (IPF) Inverse Variance Weighted method (IVW) Isovalerylcarnitine (C5) Single-nucleotide polymorphisms (SNPs) instrumental variables (IVs) randomized controlled trials (RCTs) Canadian Longitudinal Study of Aging (CLSA) linkage disequilibrium (LD) Mendelian randomization (MR) odds ratio (OR) pediatric drug-resistant epilepsy (DRE) differentially expressed transcripts (DET) fatty acid metabolite-related genes (FAMRG) Declarations Ethics approval The original experiments of blood metabolites were conducted with the approval of the Ethics boards from the Jewish General Hospital (protocol number 2021-2762); The ethics review board of the Hospital District of Helsinki and Uusimaa approved the FinnGen study protocol. Consent for publication Not applicable Availability of Data and Materials The data needed to evaluate this work are all included in the manuscript and are available upon reasonable request. Competing interests The authors declare no conflict of interest. Funding This work was supported by the National Natural Science Foundation of China (No. 81872959, 81373920, 30801522), Natural Science Foundation of Sichuan Province (Grant number 2022NSFSC0733). Author Contributions J.H, ZY.L, and SJ. L collected relevant documents, ran the statistical analyses; JJ. F and ZY.L made the tables and figures; J.H wrote the first draft of this paper, SJ.L and YN.H reviewed the manuscript, JL.G submitted the final manuscript. Acknowledgement None References Richeldi L, Collard HR, Jones MG. Idiopathic pulmonary fibrosis. The Lancet. 2017 May;389(10082):1941–52. Hutchinson J, Fogarty A, Hubbard R, McKeever T. Global incidence and mortality of idiopathic pulmonary fibrosis: a systematic review. 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Bandres-Ciga S, Noyce AJ, Hemani G, Nicolas A, Calvo A, Mora G, et al. Shared polygenic risk and causal inferences in amyotrophic lateral sclerosis. Ann Neurol. 2019 Apr;85(4):470–81. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFiguresS1S2.docx Supplementarymaterials.zip SupplementaryTablesS1S8.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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-4233607","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":289557216,"identity":"5e1e51c3-5618-4765-82d8-411d6bb0a5e6","order_by":0,"name":"Jing He","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"He","suffix":""},{"id":289557218,"identity":"1cbdab60-3faa-4d16-a4e3-c8fd14bf8cf0","order_by":1,"name":"zhengyue Liao","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"zhengyue","middleName":"","lastName":"Liao","suffix":""},{"id":289557220,"identity":"0f363c4b-62d6-4814-a3b9-a18b077819e7","order_by":2,"name":"Hongyu Chen","email":"","orcid":"","institution":"Southwest Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hongyu","middleName":"","lastName":"Chen","suffix":""},{"id":289557222,"identity":"044f896e-f933-4b08-8704-b089676a83f0","order_by":3,"name":"Jiaojiao Fu","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiaojiao","middleName":"","lastName":"Fu","suffix":""},{"id":289557224,"identity":"2554c01c-fbb9-4a28-abac-3e013ee20903","order_by":4,"name":"sijing Liu","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"sijing","middleName":"","lastName":"Liu","suffix":""},{"id":289557226,"identity":"aebda734-abd8-4190-aaa2-2e5702f9062a","order_by":5,"name":"Yanan Hua","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Hua","suffix":""},{"id":289557229,"identity":"c2d16dac-6ac2-45ee-8a45-ea2212db29f0","order_by":6,"name":"Jinlin Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYHAC9g8fKmwYGA5AeIwNRGhhY5xxJg2ohZkELcy8LYdJ0CIfkXzsMW/DeXm+G/kHP91gsJHdcID52QN8WgxvpKUbzt1x23DmjWRm6RyGNOMNB9jMDfBqmZFjIPH2zO0EgxvJbMw5DIcTNxzgYZMgqIW37RxMy3/CWuQlcswkedsOwLQcIKzFgOdZsuGMM8mGM888NpbOMUg2nnmYzQy/Le3JBx98qLCT5zue+PBzToWdbN/x5mf4bTmAygViZnzqQbY0EFAwCkbBKBgFo4ABADggTRxYoaSeAAAAAElFTkSuQmCC","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jinlin","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2024-04-08 03:29:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4233607/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4233607/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54996377,"identity":"7fdb8ac6-80ee-4b4e-88f4-bad889535345","added_by":"auto","created_at":"2024-04-19 17:58:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":766603,"visible":true,"origin":"","legend":"\u003cp\u003eUnder the two-sample MR framework, forest plots were generated using 1400 plasma metabolites as the exposure variable and IPF as the outcome variable. The primary method employed was IVW. PIVW \u0026lt; 0.05 indicates a statistically significant causal relationship between metabolites and IPF. The strength of the association between exposure and disease is quantified by the OR and its corresponding 95% confidence interval.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4233607/v1/a63dd636743f00e8ccb8b147.png"},{"id":54994825,"identity":"83bd0c6b-984f-43e6-b5fe-4ef1da57563c","added_by":"auto","created_at":"2024-04-19 17:50:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":152800,"visible":true,"origin":"","legend":"\u003cp\u003eHeat maps were drawn according to OR values of five methods to reflect the degree of protection and risk of exposure to IPF.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4233607/v1/8cf7a47111dc300badf06715.png"},{"id":54996378,"identity":"810ea0b8-1256-4717-a0a8-125fcc7d5971","added_by":"auto","created_at":"2024-04-19 17:58:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":164472,"visible":true,"origin":"","legend":"\u003cp\u003eHeat maps were drawn based on the OR values of the five methods to reflect the strength of the association between IPF and 17 candidate metabolites under different methods.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4233607/v1/eb862f95d40c1b23128cd121.png"},{"id":54994827,"identity":"937754a3-58e1-471b-8178-9c0100270dcd","added_by":"auto","created_at":"2024-04-19 17:50:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":183955,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot based on five tests (beta value in the same direction) in reverse mendelian randomization analysis.\u003cstrong\u003e \u003c/strong\u003eThere was no significant causal relationship between IPF and the four plasma metabolites, including \u003cstrong\u003e(A)\u003c/strong\u003elsovaleryicarnitine C5 levels; \u003cstrong\u003e(B)\u003c/strong\u003e Homostachydrine levels; \u003cstrong\u003e(C)\u003c/strong\u003eGamma-glutamytalanine levels; \u003cstrong\u003e(D) \u003c/strong\u003eTyramine O-sulfate levels.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4233607/v1/b046c57e2ef8cbae1624a0ac.png"},{"id":54996380,"identity":"12a984a9-e152-419a-8344-b4ddaca6795a","added_by":"auto","created_at":"2024-04-19 17:58:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90102,"visible":true,"origin":"","legend":"\u003cp\u003eVisualizes Two-sample MR analysis of IPF and Isovallerylcarnitine (C5) levels level pleiotropy and sensitivity analysis through funnel plots and leave-one-out plots, respectively.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Funnel plot of and (\u003cstrong\u003eB\u003c/strong\u003e) Leave-one-out permutation analysis plot of IPF.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4233607/v1/5b9f3178e6d18e8dbef1fc6f.png"},{"id":54994830,"identity":"ee0de7dc-e907-4bc6-a228-2b9c70b32b56","added_by":"auto","created_at":"2024-04-19 17:50:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":275431,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of the two-sample MR hypothesis. The MR analysis investigates the association between plasma metabolite levels and IPF risk.\u003c/strong\u003e LD denotes the non-random association of alleles at different loci; R\u003csup\u003e2\u003c/sup\u003e represents the squared correlation coefficient; F \u0026gt; 10 indicates a strong IV; The Inverse Variance Weighted method is the primary causal determination method, highlighted in blue.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4233607/v1/5b2e64c0f70ad823666868f4.png"},{"id":55264748,"identity":"d003c37e-8e2c-404a-a026-07610c0525c1","added_by":"auto","created_at":"2024-04-25 01:48:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1859044,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4233607/v1/aabe262c-9a3c-45c4-be8e-5dc06f17d734.pdf"},{"id":54994833,"identity":"fb4a3237-45e8-440b-9da9-6b22f5b3748a","added_by":"auto","created_at":"2024-04-19 17:50:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":729214,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresS1S2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4233607/v1/d3b004be86ac56520a61be4d.docx"},{"id":54994831,"identity":"1dd1bad6-5d88-4de1-82b3-b2a75892ef0d","added_by":"auto","created_at":"2024-04-19 17:50:13","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":827736,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.zip","url":"https://assets-eu.researchsquare.com/files/rs-4233607/v1/f2bcc5be1cff00d414c93f7c.zip"},{"id":54996379,"identity":"4b068ad2-8a2e-4f9d-a13a-5850c31245e1","added_by":"auto","created_at":"2024-04-19 17:58:13","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":150929,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTablesS1S8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4233607/v1/068d2d5aa762a49ba5766120.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mendelian Randomization revealed a one-way causal association between increased Isovalerylcarnitine (C5) levels and the risk of idiopathic pulmonary fibrosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIPF is a progressive and irreversible primary interstitial lung disease characterized by pulmonary inflammation and fibrotic changes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite extensive research, the exact cause of IPF remains unknown. Epidemiological data indicates that the prevalence of IPF in Europe ranges from 3 to 9 cases per 100,000 people (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Subsequent data revealed that the incidence of IPF has increased by 4%, with a strong correlation between age and incidence (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). For terminal patients, lung transplantation is often considered as a management strategy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Recent studies have suggested that small lipid molecules, such as fatty acids, cholesterol, arachidonic acid metabolites, and phospholipids, may play a significant role in the development of IPF (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, the exact causal relationship between lipid metabolites and IPF is still not fully understood.\u003c/p\u003e \u003cp\u003eSeveral investigations in the field of human metabolomics have established a correlation between lipid metabolites and IPF. However, it is worth mentioning that these associations may conflict with prospective findings in different study cohorts. Serum metabolomics analyses were conducted on two observational cohorts of IPF patients from Germany (n\u0026thinsp;=\u0026thinsp;122), Spain (n\u0026thinsp;=\u0026thinsp;21), and healthy controls (n\u0026thinsp;=\u0026thinsp;16), revealed a down-regulation of lysophosphatidylcholine and phosphatidylcholine in the serum of IPF patients (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Interestingly, two other independent studies on IPF populations (Cohort 1: IPF n\u0026thinsp;=\u0026thinsp;10, healthy population n\u0026thinsp;=\u0026thinsp;10; Cohort 2: IPF n\u0026thinsp;=\u0026thinsp;11, and healthy individuals n\u0026thinsp;=\u0026thinsp;10) reported contrasting outcomes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Additionally, the dysregulation of PGE2 has been implicated in the development of IPF (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, a study by Li et al. found no significant difference in serum PGE2 concentrations between IPF patients and healthy volunteers (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Possible factors contributing to the disparate experimental outcomes include confounding variables within the observed population, discrepancies in detection methodologies, and the influence of reverse causality, among others (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Therefore, it is crucial to urgently identify biomarkers that demonstrate a strong causal relationship with IPF, instead of solely relying on mutually exclusive observations.\u003c/p\u003e \u003cp\u003eMR is based on the use of SNPs that are associated with genetic variation as instrumental variables (IVs) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). MR is advantageous in avoiding confounding factors associated with sampling compared to randomized controlled trials (RCTs) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). It is also cost-effective, efficient, and increasingly preferred for determining causality between exposure and disease. Therefore, this study aimed to explore potential risk factors related to the association between fatty acid metabolites and IPF using a two-sample MR framework.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBidirectional multivariable two-sample Mendelian randomized design\u003c/h2\u003e \u003cp\u003eMultivariate MR is a valuable method for assessing the causal effect of multiple exposures on outcomes (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The method relies on three fundamental assumptions: A) the hypothesis of correlation, which states that there must be a correlation between genetic IVs and the exposure; B) the hypothesis of independence, which suggests that these IVs should not be associated with any confounding factors; and C) the hypothesis of exclusion, which asserts that the effects of IVs on the results are solely due to their impact on exposure to a specific focal point, without considering the influence of any other restricting factors. This allows for the deduction of the causal effect of the exposure on the disease (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The analysis model of this study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eTo investigate the relationship between blood fatty acid metabolites and IPF, we utilized a dataset from the Canadian Longitudinal Study of Aging (CLSA) cohort. The dataset consisted of 1,091 metabolites and 309 metabolite ratios obtained from the blood of 8,299 individuals. The original experiment was conducted with the approval of the Ethics boards from the Jewish General Hospital (protocol number 2021\u0026ndash;2762) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The GWAS summary included a total of 1,048,575 SNPs from 1,812 IPF patients and 338,784 controls of European ancestry were as outcome. SNPs, the ethics review board of the Hospital District of Helsinki and Uusima approved the FinnGen study protocol. It is important to note that all the populations analyzed in the GWAS were of European descent. The GWAS data was obtained from the UK Biobank cohort and the FinnGen consortium R8 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variable (IV)\u003c/h2\u003e \u003cp\u003eTo meet the requirements of IVs, only SNPs with a Pval\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e for both exposure and outcome were included in the statistical analysis (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Clump, an important feature in PLINK software (version 1.90), was used to cluster, and analyze genetic data (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). It was employed to remove data linkage imbalances and redundant SNPs, based on a linkage disequilibrium (LD) threshold of R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 within a 10,000 kb distance. Specifically, the closer the R\u003csup\u003e2\u003c/sup\u003e value is to 0, the higher the degree of complete linkage balance between the two SNPs. Rejecting all other SNPs within 10,000 kb of a given SNP ensures independence. Additionally, any SNP with an F statistic value\u0026thinsp;\u0026gt;\u0026thinsp;10 was considered a strong variable and included in the analysis. After determining the association between SNPs and IPF, we controlled for confounding factors such as smoking, type 2 diabetes, and gastro-oesophageal reflux (\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization (MR)\u003c/h2\u003e \u003cp\u003eBidirectional, two-sample MR studies have provided evidence for a causal relationship between blood metabolites and IPF. We used the R package 'TwoSampleMR' (Version R4.3.2) to identify alleles that are significantly related. To ensure unidirectional causality, we employed five statistical approaches (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). IVW, the primary method, demonstrated a crucial causal association. An odds ratio (OR)\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates increased risk, while an OR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates reduced risk. PIVW\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance between exposure IV and outcome IV (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Additionally, we used four auxiliary discrimination methods that required the OR value to be consistent with the IVW OR. These methods include the MR-Egger method, which utilizes Egger regression to estimate the causal effect (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The simple mode method, which provides quick results. The weighted median method, which calculates the median of the data (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), and the weighted pattern method, which calculates the mode (i.e., the value that occurs most frequently) of the data. All relevant analyses were based on the Ensemble GRCh38 reference for genome coordinates (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMultiple sensitivity analysis\u003c/h2\u003e \u003cp\u003eTo address the balanced polytropy of the MR hypothesis, a robustness analysis was conducted (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Firstly, the MR-Egger intercept method was employed to assess horizontal pleiotropy resulting from genetic variation influenced by multiple traits. The aim was to mitigate bias in the findings, with a Pval\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating no confounding factors (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Secondly, the Cochran's Q test was executed under the IVW method and MR-Egger method to check for heterogeneity across the individual causal effects. A Q-pval\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates no heterogeneity level (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Finally, the Leave-one-out method was used to test the sensitivity of the model in each iteration of the dataset, ruling out the possibility of summary results being driven by a single abnormal SNP. The value\u0026thinsp;\u0026gt;\u0026thinsp;0 for all iterations indicates that the result is reliable (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eInstrument Strength\u003c/h2\u003e \u003cp\u003eWe utilized the most up to date GWAS data, which included information on 1400 blood metabolites. To ensure accuracy, we removed any linkage disequilibrium. For a strong IV, we set a criterion of F-statistic values\u0026thinsp;\u0026gt;\u0026thinsp;10. All SNPs that met this criterion can be found in the supplementary materials (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e displays the SNPs associated with major metabolite results). Additionally, we identified a total of 31 SNPs as IVs for IPF genetic variables, which is considered sufficient for conducting MR analysis (Table S4 provides further details).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCausal relationship between blood metabolites and IPF\u003c/h2\u003e \u003cp\u003eThrough rigorous quality control measures, particularly the IVW method, we identified 17 plasma metabolites that showed significant causal associations with IPF (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese metabolites consisted of lipid metabolites (9/17), amino acid metabolites (5/17), alkaloids (2/17), and amine metabolites (1/17). The beta values obtained from different methods for all indicators exhibited good consistency, highlighting the robustness of the observed risk or protection across various test methods (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Our findings revealed that 6 metabolites were positively correlated with IPF, while 11 metabolites were negatively correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNotably, various metabolites, such as fatty acids, lipid metabolites, lipid peroxides, and phospholipids, are involved in lipid metabolic pathways. This provides clear evidence of the involvement of fatty acid metabolites in IPF, which aligns with the lipid changes primarily observed in patients with IPF (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e9 lipid metabolites that are causally associated with IPF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolite name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetabolite Class\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eIPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9-hydroxystearate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efatty acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elsovaleryicarnitine (C5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efatty acyls\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN-palmitloyl-heptadecasphingosine (d17:1/16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esphingolipid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCis-4-decenoate (10:1n6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efatty acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-(1-enyl-palmiloyl-GPC (p-16:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ephospholipids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDocosadienoate (22 2n6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efatty acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-myristoyl-2-arachidonoyl-GPC (14:0120:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ephospholipids\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13-HODE\u0026thinsp;+\u0026thinsp;9-HODE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elipid peroxide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2 or 3)-decenoale (10:1n7 or n8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efatty aldehydes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReverse MR analysis of IPF associated with 17 candidate metabolites risk\u003c/h2\u003e \u003cp\u003eTo further explore the relationship between IPF and candidate metabolites, we conducted reverse analysis. In this analysis, IPF was considered as the exposure variable and the 17 blood metabolites as the outcome variables. Using the IVW method, we found that IPF did not significantly contribute to abnormal changes in the 17 metabolites (PIVW\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table S6). We also assessed the consistency of OR and beta values obtained from five different statistical methods to determine the direction, intensity, and probability of IPF's influence on the candidate metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThrough this analysis, we excluded 13 metabolites (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) and identified a non-causal relationship between IPF and 4 candidate blood metabolites (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): Isovalerylcarnitine (C5) levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), Homostachydrine levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), Gamma-glutamylalanine levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), and Tyramine 0-sulfate levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReverse MR Analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIVW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPF\u003c/p\u003e \u003cp\u003eIsovalerylcarnitine (C5) levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInverse Variance Weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0159 (0.9533\u0026ndash;1.0826)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPF\u003c/p\u003e \u003cp\u003eHomostachydrine levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInverse Variance Weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0392 (0.9877\u0026ndash;1.0934)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPF\u003c/p\u003e \u003cp\u003eGamma-glutamylalanine levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInverse Variance Weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9700 (0.9195\u0026ndash;1.0232)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPF\u003c/p\u003e \u003cp\u003eTyramine 0-sulfate levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInverse Variance Weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9610 (0.9154\u0026ndash;1.0090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMR, Mendelian randomization; IVW method, inverse variance weighting method; OR, odds ratio. Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe sensitivity analysis shows that C5 levels is a good index to predict IPF\u003c/h2\u003e \u003cp\u003eIt has been reported that the accumulation of C5 levels leads to mitochondrial dysfunction and impaired oxidation of fatty acid β in pediatric drug-resistant epilepsy (DRE) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), while mitochondrial dysfunction and metabolic reprogramming occur frequently in IPF lung (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Therefore, we speculate whether C5 levels is also the originator of mitochondrial dysfunction in IPF patients, which is exactly consistent with our results. Increased C5 levels is a risk factor for IPF. Next, we diligently carried out horizontal pleiotropy analysis, heterogeneity analysis, and sensitivity analysis to effectively address estimation bias caused by pleiotropy of genetic variation in causal analysis utilizing MR. Egger Intercept showed that horizontal pleiotropy does not exist in our analysis process (β=-0.027; se\u0026thinsp;=\u0026thinsp;0.0337; Pval\u0026thinsp;=\u0026thinsp;0.4310). The MR-egger and IVW methods suggested that there is no heterogeneity in this experiment (MR-egger Q-pval\u0026thinsp;=\u0026thinsp;0.4439; IVW Q-pval\u0026thinsp;=\u0026thinsp;0.4669) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA; Table S7). Furthermore, the leave-one-out method reveals that causal effects do not exist driven by a single IV (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB; Table S8). In short, comprehensive analysis showed that our results have good predictive value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePatients with IPF exhibit dysregulation of blood metabolism. However, the precise regulatory mechanism underlying the relationship between IPF occurrence and changes in metabolites remains unclear. In this pioneering study, our objective was to investigate the causal relationship between blood metabolites and IPF using a bidirectional, multivariable Two-sample MR (MR) approach. To achieve this, we utilized the largest publicly available GWAS dataset, comprising 1,400 blood metabolites, to identify robust genetic variants associated with IPF. Through comprehensive genetic analysis of over 340,000 Europeans, we discovered that genetic susceptibility to 4 certain blood metabolites is causally linked to IPF. Specifically, this study revealed that C5 levels is a risk factor for IPF via rigorous bidirectional MR testing and sensitivity analysis.\u003c/p\u003e \u003cp\u003eC5 levels plays a role in the metabolic oxidation of branchchain fatty acids in mitochondria (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). A previous study has suggested that ramipril's positive health effects on SARS-CoV-2 might be attributed to its ability to reduce plasma C5 levels, in other words, an increase in C5 levels s in SARS-CoV-2 could potentially interfere with normal lung function (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Elevated concentrations of C5 levels have been observed in individuals with obesity, type 2 diabetes, and plasma ND1 mtDNA\u0026thinsp;\u0026ge;\u0026thinsp;3200 copies/\u0026micro;L. suggested a potential association between C5 levels and mitochondrial dysfunction (\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). It has been demonstrated that changes in lipid metabolism can strongly contribute to mitochondrial dysfunction (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), which is known to be a driving factor in IPF (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Impaired fatty acid β-oxidation can also result from mitochondrial dysfunction (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), De Perrot et al. found that the key enzymes and metabolites of the mitochondrial β-oxidation pathway in the lungs of IPF patients are altered (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). However, there is currently no clear study investigating whether the disorder of lipid metabolism triggers IPF or if lipid metabolism is altered after the onset of IPF. Our results provide a potential pathogenic biological factor for IPF. We speculate that the increase in C5 levels in serum may lead to lipid deposition in lung cells and disrupt mitochondrial function, thereby increasing the risk of IPF. However, this hypothesis needs to be further validated through a large prospective cohort study.\u003c/p\u003e \u003cp\u003eStatistics on the incidence of IPF vary widely between health systems (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). This variability may be attributed to the increasing awareness and diagnosis of the disease. Currently, the diagnosis of IPF is invasive, such as collecting BAL fluid and freezing lung biopsies, which may increase the risk of disease progression. The latest ATS/ERS/ALAT/JRS guidelines have updated the diagnostic algorithm for IPF (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), suggesting the use of serological assisted diagnosis. However, serological tests currently lack specific diagnostic markers and need to rule out a variety of diseases that may cause ILD. Therefore, it is important to conduct reliable and convenient biomarker studies for IPF. Qian et al. identified differentially expressed transcripts (DET) involved in lipid metabolism in IPF lung tissue (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), while Yin Lyu et al. identified five fatty acid metabolite-related genes (FAMRG) in bronchoalveolar lavage fluid of IPF patients (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Additionally, Miriana d'Alessandro found differential lipid metabolites in IPF alveolar lavage fluid, however, these lipid metabolites do not possess diagnostic value in serum for distinguishing IPF from hypersensitivity pneumonitis (fHP) (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). The strong association between IPF and lipid metabolism drove us to look for potential associations between lipid metabolites and IPF by examining blood metabolites. The evidence provided by MR in our study indicates a causal relationship between C5 levels and IPF, with no reverse causality observed. Therefore, increased levels of C5 levels may increase susceptibility to IPF, suggesting its potential as a candidate biomarker for IPF diagnosis. However, further validation from clinical samples is necessary to determine whether C5 levels can be utilized as a diagnostic indicator of IPF.\u003c/p\u003e \u003cp\u003eLimitations of this study: Firstly, due to the short median survival time of 2\u0026ndash;3 years after IPF diagnosis and the absence of typical process nodes during the stable period, decline period, and rapid deterioration of the disease (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), we were unable to obtain typical GWAS data for IPF at each stage. Therefore, we cannot discuss the causal relationship between C5 levels and the IPF process. Secondly, since many serum biomarker levels are continuous variables, further measurements, and statistical analysis of causal dichotogenic data are necessary to determine thresholds corresponding to significant abnormalities. Thirdly, it is important to note that our data population consists solely of individuals of European descent. Although the GWAS of exposure and outcome are derived from different public databases, the two-sample MR analysis is often affected by sample overlap, which may limit the causal relationship derived by MR in terms of race selection (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Therefore, it is crucial to explore IPF GWAS data of other populations for a more comprehensive understanding.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study utilized publicly published GWAS data to evaluate the random effect of blood metabolites on IPF using a two-way two-sample MR Approach. Specifically, our bidirectional MR Analysis revealed a unidirectional causal relationship between C5 levels and IPF, indicating that C5 levels serves as a risk factor for IPF.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eidiopathic pulmonary fibrosis (IPF)\u003c/p\u003e\n\u003cp\u003eInverse Variance Weighted method (IVW)\u003c/p\u003e\n\u003cp\u003eIsovalerylcarnitine (C5)\u003c/p\u003e\n\u003cp\u003eSingle-nucleotide polymorphisms (SNPs)\u003c/p\u003e\n\u003cp\u003einstrumental variables (IVs)\u003c/p\u003e\n\u003cp\u003erandomized controlled trials (RCTs)\u003c/p\u003e\n\u003cp\u003eCanadian Longitudinal Study of Aging (CLSA)\u003c/p\u003e\n\u003cp\u003elinkage disequilibrium (LD)\u003c/p\u003e\n\u003cp\u003eMendelian randomization (MR)\u003c/p\u003e\n\u003cp\u003eodds ratio (OR)\u003c/p\u003e\n\u003cp\u003epediatric drug-resistant epilepsy (DRE)\u003c/p\u003e\n\u003cp\u003edifferentially expressed transcripts (DET)\u003c/p\u003e\n\u003cp\u003efatty acid metabolite-related genes (FAMRG)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original experiments of blood metabolites were conducted with the approval of the Ethics boards from the Jewish General Hospital (protocol number 2021-2762); The ethics review board of the Hospital District of Helsinki and Uusimaa approved the FinnGen study protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data needed to evaluate this work are all included in the manuscript and are available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (No. 81872959, 81373920, 30801522), Natural Science Foundation of Sichuan Province (Grant number 2022NSFSC0733).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.H, ZY.L, and SJ. L collected relevant documents, ran the statistical analyses; JJ. F and ZY.L made the tables and figures; J.H wrote the first draft of this paper, SJ.L and YN.H reviewed the manuscript, JL.G submitted the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRicheldi L, Collard HR, Jones MG. Idiopathic pulmonary fibrosis. The Lancet. 2017 May;389(10082):1941\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eHutchinson J, Fogarty A, Hubbard R, McKeever T. Global incidence and mortality of idiopathic pulmonary fibrosis: a systematic review. Eur Respir J. 2015 Sep;46(3):795\u0026ndash;806. \u003c/li\u003e\n\u003cli\u003eMartinez FJ, Collard HR, Pardo A, Raghu G, Richeldi L, Selman M, et al. Idiopathic pulmonary fibrosis. 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Ann Neurol. 2019 Apr;85(4):470\u0026ndash;81. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"IPF, Fatty Acid metabolism, Isovalerylcarnitine levels, Two-sample MR, Single-nucleotide polymorphisms (SNPs).","lastPublishedDoi":"10.21203/rs.3.rs-4233607/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4233607/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThere have been multiple observational studies that have established a link between metabolite levels in the body and idiopathic pulmonary fibrosis (IPF), specifically focusing on metabolites derived from fatty acids. However, a complete understanding of the precise molecular and biological factors, as well as the causality between them, remains elusive.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe main objective of our study was to evaluate the potential causal relationship between blood metabolites and IPF by using Mendelian randomisation (MR).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo achieve this goal, we utilized the most comprehensive genome-wide association study (GWAS) to date, which identified genetic variants associated with blood metabolites (1,091 blood metabolites and 309 metabolite ratios). Summary statistics of IPF were collected from Finngen R8 (1,812 IPF patients and 338,784 controls), Inverse Variance Weighted method (IVW) is used as the main method in determining causality.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIsovalerylcarnitine (C5) levels (OR\u0026thinsp;=\u0026thinsp;1.2435, 95%CI: 1.0494\u0026ndash;1.4736, PIVW\u0026thinsp;=\u0026thinsp;0.0119) was found significantly related to higher risk of IPF. There was no significant heterogeneity in our study (IVW method: Pval\u0026thinsp;=\u0026thinsp;0.132; MR-Egger method: Pval\u0026thinsp;=\u0026thinsp;0.105) and horizontal pleiotropy (β=-0.027; se\u0026thinsp;=\u0026thinsp;0.0337; Pval\u0026thinsp;=\u0026thinsp;0.4310). The sensitivity analysis did not reveal any potential abnormal drivers (0.1\u0026thinsp;\u0026lt;\u0026thinsp;All \u0026lt;\u0026thinsp;0.3).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTwo-sample MR Method demonstrated the causal relationship between blood metabolites and IPF, and further studies found that C5 levels, as a potential biological risk factor for IPF, may provide a new target for the treatment of IPF.\u003c/p\u003e","manuscriptTitle":"Mendelian Randomization revealed a one-way causal association between increased Isovalerylcarnitine (C5) levels and the risk of idiopathic pulmonary fibrosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 17:50:08","doi":"10.21203/rs.3.rs-4233607/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98738f64-74d5-4a2e-b148-742502df06f0","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-22T05:44:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 17:50:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4233607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4233607","identity":"rs-4233607","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}