Association between human blood metabolome and risk of myocarditis: a Mendelian randomization study

Association between human blood metabolome and risk of myocarditis: a 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 Article Association between human blood metabolome and risk of myocarditis: a Mendelian randomization study Ziyi Wang, Haonan Tian, Jun Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4822817/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Objective Myocarditis is a common disease of the cardiovascular and immune systems, but the relationship between relevant metabolites in the blood and the risk of myocarditis has not been established. To identify biometabolic markers in myocarditis blood, we performed a two-sample MR study. Methods MR preliminary analysis: based mainly on the results of IVW, supplemented by MR-Egger, weighted median, and weighted mode for FDR multiple correction; removal of confounders: screened on the GWAS Catalog website; sensitivity analyses: Cochrane Q-test, Egger regression, MR- PRESSO, scatterplot, funnel plot, forest plot; Genetic and directional analysis: co-localization analysis, steiger test; Replicative and Meta-analysis: meta-analysis by extracting the same ending GWAS from another database. Results MR analysis identified significant correlations after FDR for 5 metabolic biomarkers ( P < 0.05). Four known metabolites: kynurenine, 1-stearoyl-GPE (18:0), Deoxycarnitine, 5-acetylamino-6-formylamino-3-methyluracil with one unknown metabolite: X-25422. Among them, kynurenine (OR = 1.441, 95%CI = 1.089–1.906, P = 0.018) and 1-stearoyl-GPE (18:0) (OR = 1.263, 95%CI = 1.029–1.550, P = 0.029) were risk factors for myocarditis, Deoxycarnitine (OR = 0.813, 95%CI = 0.676–0.979, P = 0.029), 5-acetylamino-6-formylamino-3-methyluracil (OR = 0.864, 95%CI = 0.775–0.962, P = 0.018) and X-25422 (OR = 0.721, 95%CI = 0.587–0.886, P = 0.009) were protective factors against myocarditis. There was no heterogeneity, horizontal pleiotropy, or sensitivity ( P < 0.05), no shared genetic factors between exposure and outcome, and the causality was in the right direction. Meta-analysis results again identified five metabolites causally related to myocarditis ( I 2 < 50%, P < 0.05). Conclusion This study identified a causal relationship between five circulating metabolites and myocarditis, and Kynurenine, 1-stearoyl-GPE (18:0), Deoxycarnitine, X-25422, and 5-acetylamino-6-formylamino-3-methyluracil may be as potential drug targets for myocarditis, providing a theoretical basis for the prevention, diagnosis, and treatment of myocarditis. Biological sciences/Immunology/Immunological disorders Biological sciences/Immunology/Inflammation blood metabolomics metabolic markers myocarditis Mendelian Randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Myocarditis is an inflammatory disease of the heart that may be caused by infection, exposure to toxic substances, or activation of the immune system. It is an underdiagnosed cause of acute heart failure, sudden death, and chronic dilated cardiomyopathy 1 , 2 . Clinically, myocarditis can present as acute heart failure, ventricular arrhythmias, or cardiogenic shock with substantial complications and mortality, making early diagnosis and treatment particularly important. The exploration of biomarkers is often a key tool in drug development, and in addition to the importance of accurate diagnosis and prognosis of myocarditis in patients, biomarkers open up new possibilities for the development of new treatments for myocarditis 3 . Magnani et al. noted that troponin is a common and important biomarker when testing cardiac biomarkers for myocarditis 4 , 5 . Other serum immune biomarkers such as erythrocyte sedimentation rate, complement, cytokines, and anti-cardiac antibodies have not been prospectively validated to accurately screen for biopsy-proven myocarditis 4 . Genomic, myocardial injury, cytoarchitectural, and immune cell-related markers of myocarditis have been identified in recent studies 3 . Known and novel pathways of metabolic dysfunction that were previously clinically relevant in older populations or mechanistically relevant in animal models, such as transcriptional regulation, BDNF, nitric oxide, and renin-angiotensin, were identified in a study of coronary risk development in young adults 6 . Tzoulaki et al, on the other hand, showed that metabolites associated with atherosclerosis display disturbances in lipid and carbohydrate metabolism, branched-chain and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analysis of incident cardiovascular events showed negative correlations with creatine, creatinine, and phenylalanine, and direct correlations with mannose, acetaminophen-glucuronide, lactate, and ApoB 7 . In a systematic evaluation, Ruiz et al. noted that there are a limited number of longitudinal studies assessing associations between comprehensive metabolomic profiles and cardiovascular disease risk and that standardization of metabolomics techniques and statistical methods, replication, and combinations of novel and holistic approaches will advance research in this area 8 . It can be seen that studies of metabolites and cardiovascular-related diseases are more common, but involving a specific cardiovascular disease phenotype is uncommon, and previous studies have focused on common cardiovascular diseases such as atherosclerosis, hypertension, myocardial infarction, and ischemic stroke 8 – 11 , while blood metabolomics as a biomarker has not been reported in the field of myocarditis. Randomized controlled trials (RCTs) are considered the gold standard design for inferring causal relationships. However, RCTs are costly, time-consuming, and often impractical. In comparison to RCTs, Mendelian randomization (MR) studies have several advantages. They are typically faster and less expensive because they can utilize existing large-scale GWAS data. MR studies can provide information on potential causal relationships between modifiable risk factors and rare diseases that require large sample sizes and long-term follow-up to achieve sufficient endpoints in RCTs. Additionally, MR studies can investigate exposures expected to have adverse effects on disease risk, which would be unethical to test in trials. Therefore, MR is a valuable research design that can overcome some limitations and issues faced in traditional observational studies and randomized controlled trials 12 . In this paper, we used a two-sample MR study design to systematically assess the causal relationship between 1400 blood metabolites and the risk of developing myocarditis. MR is used as a standardized causal analysis tool to explore the relationship between human blood metabolites and myocarditis, to reveal the key metabolic markers of myocarditis as potential drug targets, and to provide a theoretical basis for the diagnosis and treatment of myocarditis. Methods Study design Three major assumptions that need to be fulfilled by Mendelian randomization are the assumption of association: there is a strong correlation between Single Nucleotide Polymorphism (SNP) and exposure factors; the assumption of independence: SNPs are independent of confounders; and the assumption of exclusivity: SNPs can only contribute to outcomes through exposure factors and confounding factors; and the exclusivity assumption: single nucleotide polymorphisms can only affect outcome through exposure factors. All Mendelian randomization analyses in this study were performed in R software (R 4.3.1). Genetic information on the blood metabolome and myocarditis was based on two large GWAS databases from European populations. This MR study strictly followed the guidelines of STROBE-MR (Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization) 13 . (Table S1 ) Blood metabolome data sources A summary of single nucleotide polymorphisms (SNPs) associated with the human blood metabolome comes from a study by Chen et al. Plasma metabolic markers from 8,299 European populations from the Canadian Longitudinal Study on Aging (CLSA) cohort were selected, and 1,091 metabolites and 309 metabolite ratios were examined through Genome-wide association studies identified novel gene-metabolite associations to provide potential targets for various traits and diseases. Source of data on myocarditis Genetic information on myocarditis was obtained from the FinnGen database (URL https://www.finngen.fi/en ) 14 . The population was a European population of males and females containing a sample size of 212,306, SNPs of 19,338,525, 1,654 cases, and 210,652 controls. To further strengthen the robustness of the results, we selected another myocarditis GWAS from the IEU database for Meta-analysis, with several Cases of 633 and several Noncases of 427,278, combining the same phenotypes from the two databases for data analysis. Screening of blood metabolomic data We performed a series of manipulations among 1400 blood metabolites to screen for metabolites that met the criteria. The population was a European population of males and females containing a sample size of 8699, and SNPs of 2695. First, association analysis was performed to select SNPs that were strongly associated with exposure factors, with a filtering condition of pvalue < 5e-08. And to remove SNPs in linkage disequilibrium, which refers to the fact that genetic variants with similar genomic locations are more likely to be inherited together, resulting in a higher chance of alleles belonging to more than two genetic loci to co-occur on a single chromosome than randomly, and therefore we need to remove SNPs with this characteristic 15 . The SNPs with a strong correlation with the outcome were filtered by setting the range of the region of linkage disequilibrium, kb, to 10,000 and r 2 to 0.01 16 . Next, weak instrumental variables were removed, which implies that the instrumental variables do not have a strong correlation with the SNPs, i.e., SNPs with an F of less than 10 were removed. The final number of SNPs used for MR analysis was 2586, for a total of 1,211 blood metabolites. MR analysis First, perform a Steiger test to exclude SNPs exhibiting reverse causation. The Steiger test performed obtained test pval and dir direction pval is less than 0.05 and dir is TRUE when it proves that there is no reverse causation, filtering the SNP data with reverse causation. The causal relationship between human blood metabolites and myocarditis was assessed primarily based on inverse-variance weighted (IVW) results. Estimating the causal effect of using genetic variant k on Y as the ratio Y/X, the standard error of the ratio estimate can be approximated using the delta method, where the leading term is σ/X. In the Inverse Variance Weighted (IVW) meta-analysis under a fixed-effects model, the combined ratio estimates from each variant are utilized 15 , 17 . In addition, MR-Egger, weighted median, and weighted mode were used as supplementary analyses. The weighted median and weighted mode have been shown to exhibit superior Type 1 error rates in finite sample settings and complement the recently proposed MR-Egger method. Therefore, integrating these methods for analysis is warranted 18 – 20 . Subsequently, FDR multiple correction was performed, which aimed at controlling the false-positive error rate in multiple comparisons. The four methods were selected for results with mid-beta values in the same direction. Finally after clarifying that the phenotype was the relevant phenotype for the outcome, SNPs with confounding factors were screened and removed using the GWAS Catalog website. Sensitivity analysis Sensitivity analyses include heterogeneity analysis, horizontal pleiotropy analysis, outlier detection, and visualization analysis. Heterogeneity analysis includes heterogeneity Q-test 17 , which requires a pval > 0.05 to represent the absence of heterogeneity (mainly looking at IVW); and horizontal multivalence testing (MR-egger test), which indicates that an instrumental variable is multivalent if it influences the occurrence of an outcome through factors other than the exposure factor. Egger regression is a tool for detecting small study biases in meta-analyses and can be applied to multivalence bias tests, and the slope coefficients of Egger regression provide estimates of causal effects. Under the assumption that the association of each genetic variant with exposure is independent of the variant's pleiotropic effect (rather than through exposure), the Egger test gives a valid test of the null hypothesis of causality and a consistent estimate of the causal effect, even if all the genetic variants are null instrumental variables, requiring a pval > 0.05 to represent no horizontal pleiotropy 18 ; outlier detection (MR-PRESSO), which effectively removes outlier SNPs 21 . Visualization analysis included leave-one-out sensitivity analysis: the effect value of removing this SNP was closer to the effect value of all SNPs, indicating that removing this did not have an excessive effect on the MR analysis, and the results of removing a single SNP were all to the left or to the right of the black dotted line, indicating that removing a single SNP had a small effect on the MR analysis; scatterplot: the X-axis represents the effect of the SNPs on the exposure, and the Y-axis represents the SNP on outcome, dots represent SNPs, crosses represent the range of fluctuation of effect values, horizontal lines represent the range of fluctuation of SNPs on exposure, vertical lines represent the range of fluctuation of SNPs on outcome, and the close proximity of the lines indicates that there is a consistency in the conclusions obtained by the four methods; funnel plots: symmetry indicates that there is no heterogeneity; forest plots: a value of the effect of SNPs greater than 0 indicates a risk factor, and a value of SNPs less than 0 indicates a protective factor indicates a protective factor. With the above methods, we screened blood metabolic markers with the following characteristics: 1) the adjusted p-values after False Discovery Rate (FDR) under the IVW method; 2) consistent directionality among the four analytical methods; 3) confounders of exposure-related phenotypic non-endpoints; 4) absence of heterogeneity, horizontal pleiotropy, and outliers; and 5) little effect of individual SNPs on the results of the MR analyses. Co-localization analysis The purpose of co-localization is to assess whether two input phenotypes share the same causal variant within a given region 22 . Venkateswaran et al. conducted the co-localization analysis using the “coloc package” to determine whether causal variants driving two distinct traits are shared or distinct. Coloc employs a Bayesian framework to generate posterior probabilities for five mutually exclusive hypotheses regarding the sharing of causal variants between the two traits: H0 (neither trait has a causal variant); H1 or H2 (causal variant affects one trait only); H3 (two distinct causal variants, one per trait); and H4 (a single causal variant shared between both traits) 23 . Our co-localization analysis principles align closely with these findings. Therefore, a positive co-localization of two GWAS phenotypes indicates shared genetic factors between them, suggesting that genetic variants may jointly influence the development of both diseases or that shared pathological mechanisms exist between the two phenotypes. These shared genetic variants may participate in common biological pathways contributing to the occurrence of both diseases. Conversely, a negative co-localization result between two GWAS phenotypes suggests the absence of shared genetic factors. In such cases, a positive Mendelian randomization result would indicate that any observed relationship between the traits is entirely due to the effects of exposure on the outcomes. Replicative and Meta-analysis we selected myocarditis GWAS from the FinnGen and IEU databases for Meta-analysis. The heterogeneity of included data was assessed by calculating the I 2 statistic. If I 2 ≤ 50% and P ≥ 0.1, it was considered that there was no significant heterogeneity among studies, and a fixed-effects model was used. If I 2 > 50% and P < 0.1, significant heterogeneity among studies was deemed present, and a random-effects model was employed, with a calculation of the 95% confidence interval (CI). Statistical significance was defined as P < 0.05. Figure 1. Flow chart of data processing. Results Preliminary MR analysis The Steiger test was used to detect reverse causality between exposure and outcome, and the results showed that the individual and overall SNP tests were in the right direction (steiger_dir = TRUE), suggesting that there is no reverse causality between the five blood metabolites and myocarditis (Table 1 ). After correlation analysis and chain disequilibrium analysis, a total SNP number of 2586 was obtained for a total of 1211 metabolites, with no F-value less than 10, i.e., weak instrumental variables. Based on these blood metabolites, we performed IVW analysis (after FDR adjustment) and subsequently screened five metabolic markers that were significantly associated ( P < 0.05), including four known metabolites: kynurenine, 1-stearoyl-GPE (18:0), Deoxycarnitine, 5-acetylamino-6-formylamino-3-methyluracil, and one unknown metabolite: X-25422. Kynurenine (OR = 1.441, 95%CI = 1.089–1.906, P = 0.018) and 1-stearoyl-GPE (18:0) OR = 1.263, 95%CI = 1.029–1.550, P = 0.029) were risk factors for myocarditis, Deoxycarnitine (OR = 0.813, 95%CI = 0.676–0.979, P = 0.029), 5-acetylamino-6-formylamino-3-methyluracil (OR = 0.864, 95%CI = 0.775–0.962, P = 0.018) and X-25422 (OR = 0.721, 95%CI = 0.587–0.886, P = 0.009) were protective factors against myocarditis. Heterogeneity Q-test analysis showed no heterogeneity ( P IVW > 0.05) (Fig. 2). Egger intercept, MR-PRESSO analysis was used to detect horizontal pleiotropy, and the results showed no horizontal pleiotropy (pval > 0.05), and consistency among the four methods (Fig. 3). The results of sensitivity analysis indicated that removing a single SNP did not overly affect the results, i.e., there was no sensitivity ( P < 0.05) (Fig. 4). Table 1 Results of MR analysis. Outcome exposure nSNP IVW heterogeneity pleiotropy Steiger test numbers Pval (FDR) OR OR_ci95 Q pval pval direction pval Myocarditis Kynurenine 5 0.018 1.441 1.089–1.906 4.697 0.320 0.652 TRUE 3.35E-70 Myocarditis 1-stearoyl-GPE (18:0) 7 0.029 1.263 1.029–1.550 5.930 0.431 0.796 TRUE 3.80E-111 Myocarditis Deoxycarnitine 5 0.029 0.813 0.676–0.979 2.446 0.654 0.522 TRUE 3.70E-169 Myocarditis X-25422 6 0.018 0.721 0.587–0.886 0.763 0.979 0.868 TRUE 2.91E-112 Myocarditis 5-acetylamino-6-formylamino-3-methyluracil 4 0.009 0.864 0.775–0.962 2.513 0.473 0.906 TRUE 0 Figure 2. Funnel plot. A: Kynurenine; B: 1-stearoyl-GPE (18:0); C: Deoxycarnitine; D: X-25422; E: 5-acetylamino-6-formylamino-3-methyluracil. The main point to look at is that the straight line of IVW shows symmetry at both ends i.e. there is no heterogeneity exists. Figure 3. Scatter plot. A: Kynurenine; B: 1-stearoyl-GPE (18:0); C: Deoxycarnitine; D: X-25422; E: 5-acetylamino-6-formylamino-3-methyluracil. The results of the four methods show directional consistency. Figure 4. Forest plot. A: Kynurenine; B: 1-stearoyl-GPE (18:0); C: Deoxycarnitine; D: X-25422; E: 5-acetylamino-6-formylamino-3-methyluracil. Removal of individual SNPs did not have a disproportionate effect on the results, i.e., no sensitivity existed. Results of genetic and directional analyses Co-localization analysis Co-localization analyses showed no shared genetic factors between kynurenine (PP.H4 = 12%), 1-stearoyl-GPE (18:0) (PP.H4 = 10%), Deoxycarnitine (PP.H4 = 20%), X-25422 (PP.H4 = 13%), 5-acetylamino-6- formylamino-3-methyluracil (PP.H4 = 8%) and myocarditis phenotypes all did not share genetic factors (Fig. 5). Figure 5. Results of co-localization analysis. Replicative and Meta-analysis results We extracted another GWAS related to myocarditis from the IEU database for Meta-analysis, and we found that the results were not significant due to the difference in sample size, but the directions showed consistency and low heterogeneity ( I 2 < 50%), which further confirmed the causal relationship between the five metabolites and myocarditis. Among them, Kynurenine (OR = 1.383, 95%CI = 1.102–1.738, P = 0.005), 1-stearoyl-GPE(18:0) (OR = 1.231, 95%CI = 1.037–1.460, P = 0.017) were risk factors; Deoxycarnitine (OR = 0.840, 95%CI = 0.721–0.980, P = 0.026) were risk factors; X-25422 (OR = 0.781, 95%CI = 0.627–0.974, P = 0.028), 5-acetylamino-6-formylamino-3-methyluracil (OR = 0.871, 95%CI = 0.793–0.955, P = 0.003) as protective factors (Fig. 6). Figure 6. Meta-analysis results. Discussion To the best of our knowledge, this study is the first to leverage MR to investigate the relationship between five metabolites—Kynurenine, 1-stearoyl-GPE(18:0), Deoxycarnitine, X-25422, and 5-acetylamino-6-formylamino-3-methyluracil. We successfully validated these associations in our cohort. Integration of GWAS with the metabolomic data revealed significant findings. Moreover, the integration of sensitivity analysis, colocalization analysis, and meta-analysis further confirmed these relationships. Our study addresses gaps in understanding the potential causal links between these metabolites and myocarditis, exploring their roles in a genomic context. The five blood metabolites identified in this study belong to different kinds of metabolite levels, mainly including amino acid metabolites, lipid metabolites, nucleotide metabolites, and one belonging to an unidentified metabolite. Kynurenine is produced in many different tissues, especially in the liver by enzymes, tryptophan dioxygenase (TDO), and cells of the immune system and the brain, where indoleamine 2,3-dioxygenase (IDO) catalyzes the conversion of TRP to KYN. The kynurenine pathway (KP) of tryptophan metabolism is an endogenous system with immunosuppressive features involved in the control of inflammation and the induction of long-term immune tolerance in different systemic organs for long-term immune tolerance and is closely linked to inflammatory diseases 24 – 26 . The Kynurenine pathway is usually mediated by IDO1, and KP activation appears to be very important in linking innate and adaptive immune processes. During systemic inflammation, CNS concentrations of KYN also appear to be increased by an IDO-independent mechanism, i.e., by increasing the transport of KYN into the brain 25 , 27 , 28 . Kynurenine has also been implicated in several cardiovascular diseases in several published studies: kynurenine lowered blood pressure in a dose-dependent manner in spontaneously hypertensive rats. Kynurenine mediates coronary vasodilation in an endothelium-independent manner and tryptophan mediates coronary vasodilation in an endothelium-dependent manner 29 . It also reduces pulmonary arterial blood pressure by activating nitric oxide (NO)/cGMP and cAMP pathways in pulmonary arteries. In response to hypoxia, mean pulmonary artery pressure and medial pulmonary artery thickness were significantly increased in IDO mice. Endothelial IDO may serve as a protective mechanism against PAH and pulmonary artery remodeling 30 . In addition, the relationship between kynurenine and myocarditis has been somewhat validated in animal models. Inhibition of indoleamine 2,3-dioxygenase (IDO), which catalyzes the degradation of tryptophan (TRP) to kynurenine (KYN). The kynurenine pathway (KP) ameliorates EMCV-induced myocarditis 31 . In contrast, Kubo et al. 32 showed that the knockdown of kynurenine 3-monooxygenase (KMO) in KP led to an increase in serum levels of KP metabolites, thereby reducing mortality in mice with acute viral myocarditis. Not surprisingly, Kynurenine has been implicated in the fields of inflammation, immunity, and cardiovascular disease, whereas studies targeting myocarditis-related studies exist only in animal models, so our study builds on this foundation by reinforcing the causal relationship between Kynurenine and myocarditis in human blood. In mammals, carnitine is synthesized from the protein trimethyllysine in the liver, brain, and (in humans) kidney. In the remaining tissues, the hydroxylase enzyme responsible for the last step (deoxycarnitine to carnitine) is absent, so these tissues are completely dependent on the uptake of carnitine from the bloodstream 33 . The carnitine-related drug Mildronate (mildronate; 3-(2,2,2-trimethylhydrazine) propionate; THP; MET-88) is a clinically used cardioprotective drug effective in the treatment of common cardiovascular diseases such as myocardial infarction, heart failure, arrhythmia, and atherosclerosis, with a mechanism of action based on the regulation of energy metabolism pathways through the lowering of the action of levocarnitine. And we explored L-carnitine Deoxycarnitine as a protective factor against myocarditis, which is in line with the findings of the above existing studies. The biosynthetic enzymes γ-butyl betaine hydroxylase and carnitine/organic cation transporter protein type 2 (OCTN2) are the main drug targets of midazolam 34 . Deoxycarnitine is a member of the trimethylamine group. Deoxycarnitine is a precursor metabolite of trimethylamine N-oxide (TMAO), and TMAO-related metabolites are associated with the formation and development of atherosclerosis, and elevated levels of TMAO-related metabolites are associated with a high atherosclerotic burden, a poor prognosis for ASCVD, and a high rate of major adverse cardiovascular events (MACE) high risk 35 – 37 . It can be hypothesized that there may be a high correlation between deoxycarnitine and cardiovascular disease, which is consistent with our findings and serves as reasonable evidence for our study. 5-acetylamino-6-formylamino-3-methyluracil is destabilized in the presence of dilute bases and/or methanol, resulting in the production of a deformylated compound that is the major metabolite of caffeine 38 . In two MR studies, causal associations between 5-acetylamino-6-formylamino-3-methyluracil and cardiovascular diseases such as myocardial infarction and ischemic stroke were identified, and both showed a positive correlation with the two diseases, with the risk of myocardial infarction and ischemic stroke increasing as metabolite levels increased 39 , 40 . The risk of myocardial infarction and ischemic stroke increases as metabolite levels increase. In the present study, however, 5-acetylamino-6-formylamino-3-methyluracil was considered a protective factor against myocarditis, i.e., as the level of this metabolite increased, the prevalence of myocarditis decreased, which is exactly the opposite of the previous two studies. Therefore, we had to revisit the association between caffeine intake cardiovascular disease, and myocarditis. Turnbull et al. 41 evaluated the effect of caffeine intake on potential cardiovascular disease outcomes and showed that typical moderate caffeine intake was not associated with an increased risk of overall cardiovascular disease. Another study showed that light to moderate coffee/caffeine intake of 2–3 cups per day was beneficial for metabolic syndrome, including hypertension and diabetes. Coffee consumption reduces the risk of coronary heart disease, heart failure, arrhythmia, stroke, cardiovascular disease, and all-cause mortality 42 , 43 . From a mechanistic perspective, et al. showed that caffeine mechanistically increases hepatic endoplasmic reticulum (ER) Ca 2+ levels, which blocks the transcriptional activation of sterol regulatory element-binding protein 2 (SREBP2), which is responsible for the regulation of PCSK9, thereby increasing the expression of LDLR and the clearance of LDLc 44 . LDLR expression and LDLc clearance are increased. However, higher intakes of coffee, tea, and caffeine may increase the risk of all-cause mortality and CVD death in patients with CVD 45 . In summary, as reflected in the studies available so far, whether caffeine intake is a protective or risk factor for cardiovascular seems to depend on the amount of intake and only mechanisms related to caffeine as a protective factor have been explored so far, perhaps the 5-acetylamino-6-formylamino-3-methyluracil in this study as the main caffeine metabolite would be a new breakthrough. Overall, there is some controversy between caffeine intake and cardiovascular disease, and these points of conflict have similarities to those that exist in this and other studies. 1-stearoyl-GPE(18:0) (1-stearoyl-glycerophosphoethanolamine), where "18:0" indicates the structure of the fatty acid portion. Lebkuchen et al. performed metabolomic and lipidomic analyses of patients suffering from the signs of sleep apnea (OSA) and found glycerophosphoethanolamines to be potential markers of OSA in the early stages of the disease 46 . OSA, on the other hand, is independently associated with higher cardiovascular morbidity and mortality, and along with myocarditis, is one of the presenting symptoms of early onset of cardiovascular disease. For X-25422, an unknown metabolite, there is no literature or information on its specifics, and based on current artificial intelligence methods, it may be possible to identify it by means such as machine learning 47 . Taken together, there exists some research on the association of the screened metabolites with cardiovascular, inflammatory, and immune disorders, while there are fewer studies dealing specifically with myocarditis, and thus our study aptly fills the gap in this area. Given the constraints of observational studies, including small sample sizes and potential issues with reverse causality, the MR findings in our study offer more robust evidence for causal inference. Limitation While this study identified potential causal relationships between five human blood metabolites and myocarditis, specific metabolic pathways and mechanisms have not yet been explored. Future research could further investigate these pathways and mechanisms, introducing randomized controlled trials to validate findings and better uncover effective therapeutic targets for myocarditis. Conclusion This MR study explored the relationship between human blood metabolites and the risk of developing myocarditis, screening for five metabolites that were causally associated with myocarditis. These were risk factors: kynurenine, 1-stearoyl-GPE (18:0); and protective factors: deoxycarnitine, X-25422, and 5-acetylamino-6-formylamino-3-methyluracil. The discovery of these serum metabolites offers opportunities for early screening for myocarditis, prevention, and treatment, as well as the design of future clinical studies, which provide valuable guidance. Declarations Author Contribution ZW: Conceptualization, Investigation, Writing—the original draft. HT: Investigation, Modification. JW: Conceptualization, Investigation, Writing—reviewing and editing. All authors contributed to finally manuscript alterations. Acknowledgements We acknowledge the valuable contributions of our peers to this study. The results are presented with transparency, integrity, and adherence to ethical standards, ensuring absence of fabrication, falsification, or inappropriate data manipulation. Importantly, it is emphasized that the findings of this study do not imply endorsement. Data Availability Data is provided within the manuscript or supplementary information files. References Ammirati, E. et al. Management of Acute Myocarditis and Chronic Inflammatory Cardiomyopathy: An Expert Consensus Document. Circ Heart Fail 13 , e007405, doi:10.1161/circheartfailure.120.007405 (2020). Sagar, S., Liu, P. P. & Cooper, L. T., Jr. Myocarditis. Lancet 379 , 738-747, doi:10.1016/s0140-6736(11)60648-x (2012). Suresh, A., Martens, P. & Tang, W. H. W. Biomarkers for Myocarditis and Inflammatory Cardiomyopathy. Curr Heart Fail Rep 19 , 346-355, doi:10.1007/s11897-022-00569-8 (2022). Magnani, J. W. & Dec, G. W. Myocarditis: current trends in diagnosis and treatment. Circulation 113 , 876-890, doi:10.1161/circulationaha.105.584532 (2006). Lehmann, L. H. et al. Cardiomuscular Biomarkers in the Diagnosis and Prognostication of Immune Checkpoint Inhibitor Myocarditis. Circulation 148 , 473-486, doi:10.1161/circulationaha.123.062405 (2023). Murthy, V. L. et al. Comprehensive Metabolic Phenotyping Refines Cardiovascular Risk in Young Adults. Circulation 142 , 2110-2127, doi:10.1161/circulationaha.120.047689 (2020). Tzoulaki, I. et al. Serum metabolic signatures of coronary and carotid atherosclerosis and subsequent cardiovascular disease. Eur Heart J 40 , 2883-2896, doi:10.1093/eurheartj/ehz235 (2019). Ruiz-Canela, M. et al. Comprehensive Metabolomic Profiling and Incident Cardiovascular Disease: A Systematic Review. J Am Heart Assoc 6 , doi:10.1161/jaha.117.005705 (2017). Ke, C., Zhu, X., Zhang, Y. & Shen, Y. Metabolomic characterization of hypertension and dyslipidemia. Metabolomics 14 , 117, doi:10.1007/s11306-018-1408-y (2018). Chevli, P. A. et al. Plasma metabolomic profiling in subclinical atherosclerosis: the Diabetes Heart Study. Cardiovasc Diabetol 20 , 231, doi:10.1186/s12933-021-01419-y (2021). Floegel, A. et al. Serum metabolites and risk of myocardial infarction and ischemic stroke: a targeted metabolomic approach in two German prospective cohorts. Eur J Epidemiol 33 , 55-66, doi:10.1007/s10654-017-0333-0 (2018). Larsson, S. C., Butterworth, A. S. & Burgess, S. Mendelian randomization for cardiovascular diseases: principles and applications. Eur Heart J 44 , 4913-4924, doi:10.1093/eurheartj/ehad736 (2023). Skrivankova, V. W. et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. Jama 326 , 1614-1621, doi:10.1001/jama.2021.18236 (2021). Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613 , 508-518, doi:10.1038/s41586-022-05473-8 (2023). Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G. & Thompson, S. G. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 30 , 543-552, doi:10.1007/s10654-015-0011-z (2015). Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 7 , doi:10.7554/eLife.34408 (2018). Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37 , 658-665, doi:10.1002/gepi.21758 (2013). Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44 , 512-525, doi:10.1093/ije/dyv080 (2015). Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40 , 304-314, doi:10.1002/gepi.21965 (2016). Hartwig, F. P., Davey Smith, G. & Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 46 , 1985-1998, doi:10.1093/ije/dyx102 (2017). 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 50 , 693-698, doi:10.1038/s41588-018-0099-7 (2018). Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 10 , e1004383, doi:10.1371/journal.pgen.1004383 (2014). Venkateswaran, S. et al. Enhanced Contribution of HLA in Pediatric Onset Ulcerative Colitis. Inflamm Bowel Dis 24 , 829-838, doi:10.1093/ibd/izx084 (2018). Kanai, M. et al. Tryptophan 2,3-dioxygenase is a key modulator of physiological neurogenesis and anxiety-related behavior in mice. Mol Brain 2 , 8, doi:10.1186/1756-6606-2-8 (2009). Savitz, J. The kynurenine pathway: a finger in every pie. Mol Psychiatry 25 , 131-147, doi:10.1038/s41380-019-0414-4 (2020). Krupa, A. & Kowalska, I. The Kynurenine Pathway-New Linkage between Innate and Adaptive Immunity in Autoimmune Endocrinopathies. Int J Mol Sci 22 , doi:10.3390/ijms22189879 (2021). Kita, T., Morrison, P. F., Heyes, M. P. & Markey, S. P. Effects of systemic and central nervous system localized inflammation on the contributions of metabolic precursors to the L-kynurenine and quinolinic acid pools in brain. J Neurochem 82 , 258-268, doi:10.1046/j.1471-4159.2002.00955.x (2002). Yoshida, R., Imanishi, J., Oku, T., Kishida, T. & Hayaishi, O. Induction of pulmonary indoleamine 2,3-dioxygenase by interferon. Proc Natl Acad Sci U S A 78 , 129-132, doi:10.1073/pnas.78.1.129 (1981). Wang, Y. et al. Kynurenine is an endothelium-derived relaxing factor produced during inflammation. Nat Med 16 , 279-285, doi:10.1038/nm.2092 (2010). Nagy, B. M. et al. Importance of kynurenine in pulmonary hypertension. Am J Physiol Lung Cell Mol Physiol 313 , L741-l751, doi:10.1152/ajplung.00517.2016 (2017). Hoshi, M. et al. L-tryptophan-kynurenine pathway metabolites regulate type I IFNs of acute viral myocarditis in mice. J Immunol 188 , 3980-3987, doi:10.4049/jimmunol.1100997 (2012). Kubo, H. et al. Absence of kynurenine 3-monooxygenase reduces mortality of acute viral myocarditis in mice. Immunol Lett 181 , 94-100, doi:10.1016/j.imlet.2016.11.012 (2017). Siliprandi, N., Ciman, M. & Sartorelli, L. Myocardial carnitine transport. Basic Res Cardiol 82 Suppl 1 , 53-62, doi:10.1007/978-3-662-08390-1_7 (1987). Dambrova, M. et al. Pharmacological effects of meldonium: Biochemical mechanisms and biomarkers of cardiometabolic activity. Pharmacol Res 113 , 771-780, doi:10.1016/j.phrs.2016.01.019 (2016). Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472 , 57-63, doi:10.1038/nature09922 (2011). Tang, W. H. et al. Prognostic value of elevated levels of intestinal microbe-generated metabolite trimethylamine-N-oxide in patients with heart failure: refining the gut hypothesis. J Am Coll Cardiol 64 , 1908-1914, doi:10.1016/j.jacc.2014.02.617 (2014). Xiong, X. et al. The associations between TMAO-related metabolites and blood lipids and the potential impact of rosuvastatin therapy. Lipids Health Dis 21 , 60, doi:10.1186/s12944-022-01673-3 (2022). Tang, B. K., Grant, D. M. & Kalow, W. Isolation and identification of 5-acetylamino-6-formylamino-3-methyluracil as a major metabolite of caffeine in man. Drug Metab Dispos 11 , 218-220 (1983). He, M. et al. Causal relationship between human blood metabolites and risk of ischemic stroke: a Mendelian randomization study. Front Genet 15 , 1333454, doi:10.3389/fgene.2024.1333454 (2024). Li, D. H. et al. Plasma metabolites and risk of myocardial infarction: a bidirectional Mendelian randomization study. J Geriatr Cardiol 21 , 219-231, doi:10.26599/1671-5411.2024.02.002 (2024). Turnbull, D., Rodricks, J. V., Mariano, G. F. & Chowdhury, F. Caffeine and cardiovascular health. Regul Toxicol Pharmacol 89 , 165-185, doi:10.1016/j.yrtph.2017.07.025 (2017). Chieng, D. & Kistler, P. M. Coffee and tea on cardiovascular disease (CVD) prevention. Trends Cardiovasc Med 32 , 399-405, doi:10.1016/j.tcm.2021.08.004 (2022). Surma, S., Sahebkar, A. & Banach, M. Coffee or tea: Anti-inflammatory properties in the context of atherosclerotic cardiovascular disease prevention. Pharmacol Res 187 , 106596, doi:10.1016/j.phrs.2022.106596 (2023). Lebeau, P. F. et al. Caffeine blocks SREBP2-induced hepatic PCSK9 expression to enhance LDLR-mediated cholesterol clearance. Nat Commun 13 , 770, doi:10.1038/s41467-022-28240-9 (2022). Zheng, H., Lin, F., Xin, N., Yang, L. & Zhu, P. Association of Coffee, Tea, and Caffeine Consumption With All-Cause Risk and Specific Mortality for Cardiovascular Disease Patients. Front Nutr 9 , 842856, doi:10.3389/fnut.2022.842856 (2022). Lebkuchen, A. et al. Metabolomic and lipidomic profile in men with obstructive sleep apnoea: implications for diagnosis and biomarkers of cardiovascular risk. Sci Rep 8 , 11270, doi:10.1038/s41598-018-29727-6 (2018). Asef, C. K. et al. Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry. Anal Chem 95 , 1047-1056, doi:10.1021/acs.analchem.2c03749 (2023). Additional Declarations No competing interests reported. Supplementary Files TableS1.pdf Cite Share Download PDF Status: Published Journal Publication published 03 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Sep, 2024 Reviews received at journal 11 Sep, 2024 Reviewers agreed at journal 31 Aug, 2024 Reviews received at journal 25 Aug, 2024 Reviewers agreed at journal 25 Aug, 2024 Reviewers invited by journal 14 Aug, 2024 Editor assigned by journal 12 Aug, 2024 Editor invited by journal 08 Aug, 2024 Submission checks completed at journal 08 Aug, 2024 First submitted to journal 29 Jul, 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-4822817","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":347714339,"identity":"4583f89f-3ccf-4ddc-92fc-36737fc7d1b9","order_by":0,"name":"Ziyi Wang","email":"","orcid":"","institution":"Beijing Sport University","correspondingAuthor":false,"prefix":"","firstName":"Ziyi","middleName":"","lastName":"Wang","suffix":""},{"id":347714340,"identity":"ec8630b1-96ff-4710-972f-84dac6a0ec2b","order_by":1,"name":"Haonan Tian","email":"","orcid":"","institution":"Beijing Sport University","correspondingAuthor":false,"prefix":"","firstName":"Haonan","middleName":"","lastName":"Tian","suffix":""},{"id":347714341,"identity":"168107b3-1e28-4d61-8927-f554f08d9e74","order_by":2,"name":"Jun Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACAyjNDETHIMwDxGthSyNNCxDwmBGnxZz/jJnEzx217Aa3e749/NnGIMd3I4HxcwEeLZYzcswke88cZza4c3a7MW8bg7HkjQRm6Rn4HHaDx+wGb9sxZoMbudukGdsYEjfcSGBj5sGn5fwZs5t/wVpynkkCHVZPWMuBHLPbvG01IC1sEkCHJRgQ0mI5I638t2zbAWbJG2nmxjznJAxnnnnYLI1Pizn/4c2Gb9vqkvluJD97+KPMRp7vePLBz/i0QMHhZChDAogZGwhrYGCosyNG1SgYBaNgFIxQAACj5E0FiUosRgAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Sport University","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-07-29 14:36:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4822817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4822817/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-78359-6","type":"published","date":"2024-11-03T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63938730,"identity":"98e2c795-86ff-4620-a8b9-771e69426ae4","added_by":"auto","created_at":"2024-09-04 04:16:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":263312,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of data processing.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4822817/v1/2f6494b6ceaaa94edefbee35.png"},{"id":63938729,"identity":"377ad621-46dd-4cc6-8306-6d8990368eca","added_by":"auto","created_at":"2024-09-04 04:16:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184878,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot. A: Kynurenine; B: 1-stearoyl-GPE (18:0); C: Deoxycarnitine; D: X-25422; E: 5-acetylamino-6-formylamino-3-methyluracil. The main point to look at is that the straight line of IVW shows symmetry at both ends i.e. there is no heterogeneity exists.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4822817/v1/bacd4af6af6daa48cd1d8002.png"},{"id":63939695,"identity":"3ebeda95-d3ac-471e-b035-ab79117502bb","added_by":"auto","created_at":"2024-09-04 04:32:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":635809,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot. A: Kynurenine; B: 1-stearoyl-GPE (18:0); C: Deoxycarnitine; D: X-25422; E: 5-acetylamino-6-formylamino-3-methyluracil. The results of the four methods show directional consistency.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4822817/v1/86a09cf01eb7aaf73bf412df.png"},{"id":63939694,"identity":"130b7009-b00e-4551-a42c-a21f94569037","added_by":"auto","created_at":"2024-09-04 04:32:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":353681,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot. A: Kynurenine; B: 1-stearoyl-GPE (18:0); C: Deoxycarnitine; D: X-25422; E: 5-acetylamino-6-formylamino-3-methyluracil. Removal of individual SNPs did not have a disproportionate effect on the results, i.e., no sensitivity existed.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4822817/v1/0cd9b1dc754ac8e2b5a91126.png"},{"id":63938731,"identity":"bffcb636-94d5-411b-876b-d6f79c5a9b83","added_by":"auto","created_at":"2024-09-04 04:16:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1631508,"visible":true,"origin":"","legend":"\u003cp\u003eResults of co-localization analysis.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4822817/v1/683f86ab9cbd83f80c842124.png"},{"id":63938735,"identity":"5f95fb4c-026f-49aa-a880-23d7ae7a0b18","added_by":"auto","created_at":"2024-09-04 04:16:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":868128,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-analysis results.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4822817/v1/cfc2a60cd6d06f65f2275ce0.png"},{"id":68206498,"identity":"ee57a077-b6f8-4b7b-864a-a3e6445fd5f6","added_by":"auto","created_at":"2024-11-04 16:32:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4415364,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4822817/v1/53dcf828-8be7-4c33-a799-07b70d0a1cf9.pdf"},{"id":63939028,"identity":"fd8668d1-6aeb-4f1a-bea7-0a76f973eaf8","added_by":"auto","created_at":"2024-09-04 04:24:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":105670,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4822817/v1/0b1e747ff38d237e1609ed91.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between human blood metabolome and risk of myocarditis: a Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMyocarditis is an inflammatory disease of the heart that may be caused by infection, exposure to toxic substances, or activation of the immune system. It is an underdiagnosed cause of acute heart failure, sudden death, and chronic dilated cardiomyopathy\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Clinically, myocarditis can present as acute heart failure, ventricular arrhythmias, or cardiogenic shock with substantial complications and mortality, making early diagnosis and treatment particularly important.\u003c/p\u003e \u003cp\u003eThe exploration of biomarkers is often a key tool in drug development, and in addition to the importance of accurate diagnosis and prognosis of myocarditis in patients, biomarkers open up new possibilities for the development of new treatments for myocarditis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Magnani et al. noted that troponin is a common and important biomarker when testing cardiac biomarkers for myocarditis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Other serum immune biomarkers such as erythrocyte sedimentation rate, complement, cytokines, and anti-cardiac antibodies have not been prospectively validated to accurately screen for biopsy-proven myocarditis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGenomic, myocardial injury, cytoarchitectural, and immune cell-related markers of myocarditis have been identified in recent studies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Known and novel pathways of metabolic dysfunction that were previously clinically relevant in older populations or mechanistically relevant in animal models, such as transcriptional regulation, BDNF, nitric oxide, and renin-angiotensin, were identified in a study of coronary risk development in young adults\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Tzoulaki et al, on the other hand, showed that metabolites associated with atherosclerosis display disturbances in lipid and carbohydrate metabolism, branched-chain and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analysis of incident cardiovascular events showed negative correlations with creatine, creatinine, and phenylalanine, and direct correlations with mannose, acetaminophen-glucuronide, lactate, and ApoB\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In a systematic evaluation, Ruiz et al. noted that there are a limited number of longitudinal studies assessing associations between comprehensive metabolomic profiles and cardiovascular disease risk and that standardization of metabolomics techniques and statistical methods, replication, and combinations of novel and holistic approaches will advance research in this area\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It can be seen that studies of metabolites and cardiovascular-related diseases are more common, but involving a specific cardiovascular disease phenotype is uncommon, and previous studies have focused on common cardiovascular diseases such as atherosclerosis, hypertension, myocardial infarction, and ischemic stroke\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, while blood metabolomics as a biomarker has not been reported in the field of myocarditis.\u003c/p\u003e \u003cp\u003eRandomized controlled trials (RCTs) are considered the gold standard design for inferring causal relationships. However, RCTs are costly, time-consuming, and often impractical. In comparison to RCTs, Mendelian randomization (MR) studies have several advantages. They are typically faster and less expensive because they can utilize existing large-scale GWAS data. MR studies can provide information on potential causal relationships between modifiable risk factors and rare diseases that require large sample sizes and long-term follow-up to achieve sufficient endpoints in RCTs. Additionally, MR studies can investigate exposures expected to have adverse effects on disease risk, which would be unethical to test in trials. Therefore, MR is a valuable research design that can overcome some limitations and issues faced in traditional observational studies and randomized controlled trials\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this paper, we used a two-sample MR study design to systematically assess the causal relationship between 1400 blood metabolites and the risk of developing myocarditis. MR is used as a standardized causal analysis tool to explore the relationship between human blood metabolites and myocarditis, to reveal the key metabolic markers of myocarditis as potential drug targets, and to provide a theoretical basis for the diagnosis and treatment of myocarditis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThree major assumptions that need to be fulfilled by Mendelian randomization are the assumption of association: there is a strong correlation between Single Nucleotide Polymorphism (SNP) and exposure factors; the assumption of independence: SNPs are independent of confounders; and the assumption of exclusivity: SNPs can only contribute to outcomes through exposure factors and confounding factors; and the exclusivity assumption: single nucleotide polymorphisms can only affect outcome through exposure factors. All Mendelian randomization analyses in this study were performed in R software (R 4.3.1). Genetic information on the blood metabolome and myocarditis was based on two large GWAS databases from European populations. This MR study strictly followed the guidelines of STROBE-MR (Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBlood metabolome data sources\u003c/h2\u003e \u003cp\u003eA summary of single nucleotide polymorphisms (SNPs) associated with the human blood metabolome comes from a study by Chen et al. Plasma metabolic markers from 8,299 European populations from the Canadian Longitudinal Study on Aging (CLSA) cohort were selected, and 1,091 metabolites and 309 metabolite ratios were examined through Genome-wide association studies identified novel gene-metabolite associations to provide potential targets for various traits and diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSource of data on myocarditis\u003c/h2\u003e \u003cp\u003eGenetic information on myocarditis was obtained from the FinnGen database (URL \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e14\u003c/sup\u003e. The population was a European population of males and females containing a sample size of 212,306, SNPs of 19,338,525, 1,654 cases, and 210,652 controls. To further strengthen the robustness of the results, we selected another myocarditis GWAS from the IEU database for Meta-analysis, with several Cases of 633 and several Noncases of 427,278, combining the same phenotypes from the two databases for data analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eScreening of blood metabolomic data\u003c/h2\u003e \u003cp\u003eWe performed a series of manipulations among 1400 blood metabolites to screen for metabolites that met the criteria. The population was a European population of males and females containing a sample size of 8699, and SNPs of 2695. First, association analysis was performed to select SNPs that were strongly associated with exposure factors, with a filtering condition of \u003cem\u003epvalue\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5e-08. And to remove SNPs in linkage disequilibrium, which refers to the fact that genetic variants with similar genomic locations are more likely to be inherited together, resulting in a higher chance of alleles belonging to more than two genetic loci to co-occur on a single chromosome than randomly, and therefore we need to remove SNPs with this characteristic\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The SNPs with a strong correlation with the outcome were filtered by setting the range of the region of linkage disequilibrium, kb, to 10,000 and r\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e to 0.01\u003csup\u003e16\u003c/sup\u003e. Next, weak instrumental variables were removed, which implies that the instrumental variables do not have a strong correlation with the SNPs, i.e., SNPs with an F of less than 10 were removed. The final number of SNPs used for MR analysis was 2586, for a total of 1,211 blood metabolites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis\u003c/h2\u003e \u003cp\u003eFirst, perform a Steiger test to exclude SNPs exhibiting reverse causation. The Steiger test performed obtained test pval and dir direction pval is less than 0.05 and dir is TRUE when it proves that there is no reverse causation, filtering the SNP data with reverse causation. The causal relationship between human blood metabolites and myocarditis was assessed primarily based on inverse-variance weighted (IVW) results. Estimating the causal effect of using genetic variant k on Y as the ratio Y/X, the standard error of the ratio estimate can be approximated using the delta method, where the leading term is σ/X. In the Inverse Variance Weighted (IVW) meta-analysis under a fixed-effects model, the combined ratio estimates from each variant are utilized\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In addition, MR-Egger, weighted median, and weighted mode were used as supplementary analyses. The weighted median and weighted mode have been shown to exhibit superior Type 1 error rates in finite sample settings and complement the recently proposed MR-Egger method. Therefore, integrating these methods for analysis is warranted\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Subsequently, FDR multiple correction was performed, which aimed at controlling the false-positive error rate in multiple comparisons. The four methods were selected for results with mid-beta values in the same direction. Finally after clarifying that the phenotype was the relevant phenotype for the outcome, SNPs with confounding factors were screened and removed using the GWAS Catalog website.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eSensitivity analyses include heterogeneity analysis, horizontal pleiotropy analysis, outlier detection, and visualization analysis. Heterogeneity analysis includes heterogeneity Q-test\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, which requires a pval\u0026thinsp;\u0026gt;\u0026thinsp;0.05 to represent the absence of heterogeneity (mainly looking at IVW); and horizontal multivalence testing (MR-egger test), which indicates that an instrumental variable is multivalent if it influences the occurrence of an outcome through factors other than the exposure factor. Egger regression is a tool for detecting small study biases in meta-analyses and can be applied to multivalence bias tests, and the slope coefficients of Egger regression provide estimates of causal effects. Under the assumption that the association of each genetic variant with exposure is independent of the variant's pleiotropic effect (rather than through exposure), the Egger test gives a valid test of the null hypothesis of causality and a consistent estimate of the causal effect, even if all the genetic variants are null instrumental variables, requiring a pval\u0026thinsp;\u0026gt;\u0026thinsp;0.05 to represent no horizontal pleiotropy\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e; outlier detection (MR-PRESSO), which effectively removes outlier SNPs\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Visualization analysis included leave-one-out sensitivity analysis: the effect value of removing this SNP was closer to the effect value of all SNPs, indicating that removing this did not have an excessive effect on the MR analysis, and the results of removing a single SNP were all to the left or to the right of the black dotted line, indicating that removing a single SNP had a small effect on the MR analysis; scatterplot: the X-axis represents the effect of the SNPs on the exposure, and the Y-axis represents the SNP on outcome, dots represent SNPs, crosses represent the range of fluctuation of effect values, horizontal lines represent the range of fluctuation of SNPs on exposure, vertical lines represent the range of fluctuation of SNPs on outcome, and the close proximity of the lines indicates that there is a consistency in the conclusions obtained by the four methods; funnel plots: symmetry indicates that there is no heterogeneity; forest plots: a value of the effect of SNPs greater than 0 indicates a risk factor, and a value of SNPs less than 0 indicates a protective factor indicates a protective factor.\u003c/p\u003e \u003cp\u003eWith the above methods, we screened blood metabolic markers with the following characteristics: 1) the adjusted p-values after False Discovery Rate (FDR) under the IVW method; 2) consistent directionality among the four analytical methods; 3) confounders of exposure-related phenotypic non-endpoints; 4) absence of heterogeneity, horizontal pleiotropy, and outliers; and 5) little effect of individual SNPs on the results of the MR analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCo-localization analysis\u003c/h2\u003e \u003cp\u003eThe purpose of co-localization is to assess whether two input phenotypes share the same causal variant within a given region\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Venkateswaran et al. conducted the co-localization analysis using the \u0026ldquo;coloc package\u0026rdquo; to determine whether causal variants driving two distinct traits are shared or distinct. Coloc employs a Bayesian framework to generate posterior probabilities for five mutually exclusive hypotheses regarding the sharing of causal variants between the two traits: H0 (neither trait has a causal variant); H1 or H2 (causal variant affects one trait only); H3 (two distinct causal variants, one per trait); and H4 (a single causal variant shared between both traits)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our co-localization analysis principles align closely with these findings. Therefore, a positive co-localization of two GWAS phenotypes indicates shared genetic factors between them, suggesting that genetic variants may jointly influence the development of both diseases or that shared pathological mechanisms exist between the two phenotypes. These shared genetic variants may participate in common biological pathways contributing to the occurrence of both diseases. Conversely, a negative co-localization result between two GWAS phenotypes suggests the absence of shared genetic factors. In such cases, a positive Mendelian randomization result would indicate that any observed relationship between the traits is entirely due to the effects of exposure on the outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eReplicative and Meta-analysis\u003c/h2\u003e \u003cp\u003ewe selected myocarditis GWAS from the FinnGen and IEU databases for Meta-analysis. The heterogeneity of included data was assessed by calculating the \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e statistic. If \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;50% and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.1, it was considered that there was no significant heterogeneity among studies, and a fixed-effects model was used. If \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50% and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, significant heterogeneity among studies was deemed present, and a random-effects model was employed, with a calculation of the 95% confidence interval (CI). Statistical significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eFigure 1. Flow chart of data processing.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePreliminary MR analysis\u003c/h2\u003e \u003cp\u003eThe Steiger test was used to detect reverse causality between exposure and outcome, and the results showed that the individual and overall SNP tests were in the right direction (steiger_dir\u0026thinsp;=\u0026thinsp;TRUE), suggesting that there is no reverse causality between the five blood metabolites and myocarditis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After correlation analysis and chain disequilibrium analysis, a total SNP number of 2586 was obtained for a total of 1211 metabolites, with no F-value less than 10, i.e., weak instrumental variables. Based on these blood metabolites, we performed IVW analysis (after FDR adjustment) and subsequently screened five metabolic markers that were significantly associated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including four known metabolites: kynurenine, 1-stearoyl-GPE (18:0), Deoxycarnitine, 5-acetylamino-6-formylamino-3-methyluracil, and one unknown metabolite: X-25422. Kynurenine (OR\u0026thinsp;=\u0026thinsp;1.441, 95%CI\u0026thinsp;=\u0026thinsp;1.089\u0026ndash;1.906, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and 1-stearoyl-GPE (18:0) OR\u0026thinsp;=\u0026thinsp;1.263, 95%CI\u0026thinsp;=\u0026thinsp;1.029\u0026ndash;1.550, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) were risk factors for myocarditis, Deoxycarnitine (OR\u0026thinsp;=\u0026thinsp;0.813, 95%CI\u0026thinsp;=\u0026thinsp;0.676\u0026ndash;0.979, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), 5-acetylamino-6-formylamino-3-methyluracil (OR\u0026thinsp;=\u0026thinsp;0.864, 95%CI\u0026thinsp;=\u0026thinsp;0.775\u0026ndash;0.962, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and X-25422 (OR\u0026thinsp;=\u0026thinsp;0.721, 95%CI\u0026thinsp;=\u0026thinsp;0.587\u0026ndash;0.886, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) were protective factors against myocarditis. Heterogeneity Q-test analysis showed no heterogeneity (\u003cem\u003eP\u003c/em\u003eIVW\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;2). Egger intercept, MR-PRESSO analysis was used to detect horizontal pleiotropy, and the results showed no horizontal pleiotropy (pval\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and consistency among the four methods (Fig.\u0026nbsp;3). The results of sensitivity analysis indicated that removing a single SNP did not overly affect the results, i.e., there was no sensitivity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;4).\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\u003eResults of MR analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\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\u003eexposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eheterogeneity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003epleiotropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eSteiger test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enumbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePval (FDR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR_ci95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003edirection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocarditis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKynurenine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.089\u0026ndash;1.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.35E-70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocarditis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-stearoyl-GPE (18:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.029\u0026ndash;1.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.80E-111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocarditis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeoxycarnitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.676\u0026ndash;0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.70E-169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocarditis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX-25422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.587\u0026ndash;0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.91E-112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocarditis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-acetylamino-6-formylamino-3-methyluracil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.775\u0026ndash;0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTRUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\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\u003eFigure 2. Funnel plot. A: Kynurenine; B: 1-stearoyl-GPE (18:0); C: Deoxycarnitine; D: X-25422; E: 5-acetylamino-6-formylamino-3-methyluracil. The main point to look at is that the straight line of IVW shows symmetry at both ends i.e. there is no heterogeneity exists.\u003c/p\u003e \u003cp\u003eFigure 3. Scatter plot. A: Kynurenine; B: 1-stearoyl-GPE (18:0); C: Deoxycarnitine; D: X-25422; E: 5-acetylamino-6-formylamino-3-methyluracil. The results of the four methods show directional consistency.\u003c/p\u003e \u003cp\u003eFigure 4. Forest plot. A: Kynurenine; B: 1-stearoyl-GPE (18:0); C: Deoxycarnitine; D: X-25422; E: 5-acetylamino-6-formylamino-3-methyluracil. Removal of individual SNPs did not have a disproportionate effect on the results, i.e., no sensitivity existed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eResults of genetic and directional analyses\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eCo-localization analysis\u003c/h2\u003e \u003cp\u003eCo-localization analyses showed no shared genetic factors between kynurenine (PP.H4\u0026thinsp;=\u0026thinsp;12%), 1-stearoyl-GPE (18:0) (PP.H4\u0026thinsp;=\u0026thinsp;10%), Deoxycarnitine (PP.H4\u0026thinsp;=\u0026thinsp;20%), X-25422 (PP.H4\u0026thinsp;=\u0026thinsp;13%), 5-acetylamino-6- formylamino-3-methyluracil (PP.H4\u0026thinsp;=\u0026thinsp;8%) and myocarditis phenotypes all did not share genetic factors (Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eFigure 5. Results of co-localization analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eReplicative and Meta-analysis results\u003c/h2\u003e \u003cp\u003eWe extracted another GWAS related to myocarditis from the IEU database for Meta-analysis, and we found that the results were not significant due to the difference in sample size, but the directions showed consistency and low heterogeneity (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;50%), which further confirmed the causal relationship between the five metabolites and myocarditis. Among them, Kynurenine (OR\u0026thinsp;=\u0026thinsp;1.383, 95%CI\u0026thinsp;=\u0026thinsp;1.102\u0026ndash;1.738, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), 1-stearoyl-GPE(18:0) (OR\u0026thinsp;=\u0026thinsp;1.231, 95%CI\u0026thinsp;=\u0026thinsp;1.037\u0026ndash;1.460, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) were risk factors;\u003c/p\u003e \u003cp\u003eDeoxycarnitine (OR\u0026thinsp;=\u0026thinsp;0.840, 95%CI\u0026thinsp;=\u0026thinsp;0.721\u0026ndash;0.980, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) were risk factors; X-25422 (OR\u0026thinsp;=\u0026thinsp;0.781, 95%CI\u0026thinsp;=\u0026thinsp;0.627\u0026ndash;0.974, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), 5-acetylamino-6-formylamino-3-methyluracil (OR\u0026thinsp;=\u0026thinsp;0.871, 95%CI\u0026thinsp;=\u0026thinsp;0.793\u0026ndash;0.955, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) as protective factors (Fig.\u0026nbsp;6).\u003c/p\u003e \u003cp\u003eFigure 6. Meta-analysis results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this study is the first to leverage MR to investigate the relationship between five metabolites\u0026mdash;Kynurenine, 1-stearoyl-GPE(18:0), Deoxycarnitine, X-25422, and 5-acetylamino-6-formylamino-3-methyluracil. We successfully validated these associations in our cohort. Integration of GWAS with the metabolomic data revealed significant findings. Moreover, the integration of sensitivity analysis, colocalization analysis, and meta-analysis further confirmed these relationships. Our study addresses gaps in understanding the potential causal links between these metabolites and myocarditis, exploring their roles in a genomic context. The five blood metabolites identified in this study belong to different kinds of metabolite levels, mainly including amino acid metabolites, lipid metabolites, nucleotide metabolites, and one belonging to an unidentified metabolite.\u003c/p\u003e \u003cp\u003eKynurenine is produced in many different tissues, especially in the liver by enzymes, tryptophan dioxygenase (TDO), and cells of the immune system and the brain, where indoleamine 2,3-dioxygenase (IDO) catalyzes the conversion of TRP to KYN. The kynurenine pathway (KP) of tryptophan metabolism is an endogenous system with immunosuppressive features involved in the control of inflammation and the induction of long-term immune tolerance in different systemic organs for long-term immune tolerance and is closely linked to inflammatory diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The Kynurenine pathway is usually mediated by IDO1, and KP activation appears to be very important in linking innate and adaptive immune processes. During systemic inflammation, CNS concentrations of KYN also appear to be increased by an IDO-independent mechanism, i.e., by increasing the transport of KYN into the brain\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Kynurenine has also been implicated in several cardiovascular diseases in several published studies: kynurenine lowered blood pressure in a dose-dependent manner in spontaneously hypertensive rats. Kynurenine mediates coronary vasodilation in an endothelium-independent manner and tryptophan mediates coronary vasodilation in an endothelium-dependent manner\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. It also reduces pulmonary arterial blood pressure by activating nitric oxide (NO)/cGMP and cAMP pathways in pulmonary arteries. In response to hypoxia, mean pulmonary artery pressure and medial pulmonary artery thickness were significantly increased in IDO mice. Endothelial IDO may serve as a protective mechanism against PAH and pulmonary artery remodeling\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In addition, the relationship between kynurenine and myocarditis has been somewhat validated in animal models. Inhibition of indoleamine 2,3-dioxygenase (IDO), which catalyzes the degradation of tryptophan (TRP) to kynurenine (KYN). The kynurenine pathway (KP) ameliorates EMCV-induced myocarditis\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. In contrast, Kubo et al.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e showed that the knockdown of kynurenine 3-monooxygenase (KMO) in KP led to an increase in serum levels of KP metabolites, thereby reducing mortality in mice with acute viral myocarditis. Not surprisingly, Kynurenine has been implicated in the fields of inflammation, immunity, and cardiovascular disease, whereas studies targeting myocarditis-related studies exist only in animal models, so our study builds on this foundation by reinforcing the causal relationship between Kynurenine and myocarditis in human blood.\u003c/p\u003e \u003cp\u003eIn mammals, carnitine is synthesized from the protein trimethyllysine in the liver, brain, and (in humans) kidney. In the remaining tissues, the hydroxylase enzyme responsible for the last step (deoxycarnitine to carnitine) is absent, so these tissues are completely dependent on the uptake of carnitine from the bloodstream\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The carnitine-related drug Mildronate (mildronate; 3-(2,2,2-trimethylhydrazine) propionate; THP; MET-88) is a clinically used cardioprotective drug effective in the treatment of common cardiovascular diseases such as myocardial infarction, heart failure, arrhythmia, and atherosclerosis, with a mechanism of action based on the regulation of energy metabolism pathways through the lowering of the action of levocarnitine. And we explored L-carnitine Deoxycarnitine as a protective factor against myocarditis, which is in line with the findings of the above existing studies. The biosynthetic enzymes γ-butyl betaine hydroxylase and carnitine/organic cation transporter protein type 2 (OCTN2) are the main drug targets of midazolam\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Deoxycarnitine is a member of the trimethylamine group. Deoxycarnitine is a precursor metabolite of trimethylamine N-oxide (TMAO), and TMAO-related metabolites are associated with the formation and development of atherosclerosis, and elevated levels of TMAO-related metabolites are associated with a high atherosclerotic burden, a poor prognosis for ASCVD, and a high rate of major adverse cardiovascular events (MACE) high risk\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. It can be hypothesized that there may be a high correlation between deoxycarnitine and cardiovascular disease, which is consistent with our findings and serves as reasonable evidence for our study.\u003c/p\u003e \u003cp\u003e5-acetylamino-6-formylamino-3-methyluracil is destabilized in the presence of dilute bases and/or methanol, resulting in the production of a deformylated compound that is the major metabolite of caffeine\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In two MR studies, causal associations between 5-acetylamino-6-formylamino-3-methyluracil and cardiovascular diseases such as myocardial infarction and ischemic stroke were identified, and both showed a positive correlation with the two diseases, with the risk of myocardial infarction and ischemic stroke increasing as metabolite levels increased\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The risk of myocardial infarction and ischemic stroke increases as metabolite levels increase. In the present study, however, 5-acetylamino-6-formylamino-3-methyluracil was considered a protective factor against myocarditis, i.e., as the level of this metabolite increased, the prevalence of myocarditis decreased, which is exactly the opposite of the previous two studies. Therefore, we had to revisit the association between caffeine intake cardiovascular disease, and myocarditis. Turnbull et al.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e evaluated the effect of caffeine intake on potential cardiovascular disease outcomes and showed that typical moderate caffeine intake was not associated with an increased risk of overall cardiovascular disease. Another study showed that light to moderate coffee/caffeine intake of 2\u0026ndash;3 cups per day was beneficial for metabolic syndrome, including hypertension and diabetes. Coffee consumption reduces the risk of coronary heart disease, heart failure, arrhythmia, stroke, cardiovascular disease, and all-cause mortality\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. From a mechanistic perspective, et al. showed that caffeine mechanistically increases hepatic endoplasmic reticulum (ER) Ca\u003csup\u003e2+\u003c/sup\u003e levels, which blocks the transcriptional activation of sterol regulatory element-binding protein 2 (SREBP2), which is responsible for the regulation of PCSK9, thereby increasing the expression of LDLR and the clearance of LDLc\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. LDLR expression and LDLc clearance are increased. However, higher intakes of coffee, tea, and caffeine may increase the risk of all-cause mortality and CVD death in patients with CVD\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In summary, as reflected in the studies available so far, whether caffeine intake is a protective or risk factor for cardiovascular seems to depend on the amount of intake and only mechanisms related to caffeine as a protective factor have been explored so far, perhaps the 5-acetylamino-6-formylamino-3-methyluracil in this study as the main caffeine metabolite would be a new breakthrough. Overall, there is some controversy between caffeine intake and cardiovascular disease, and these points of conflict have similarities to those that exist in this and other studies.\u003c/p\u003e \u003cp\u003e1-stearoyl-GPE(18:0) (1-stearoyl-glycerophosphoethanolamine), where \"18:0\" indicates the structure of the fatty acid portion. Lebkuchen et al. performed metabolomic and lipidomic analyses of patients suffering from the signs of sleep apnea (OSA) and found glycerophosphoethanolamines to be potential markers of OSA in the early stages of the disease\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. OSA, on the other hand, is independently associated with higher cardiovascular morbidity and mortality, and along with myocarditis, is one of the presenting symptoms of early onset of cardiovascular disease. For X-25422, an unknown metabolite, there is no literature or information on its specifics, and based on current artificial intelligence methods, it may be possible to identify it by means such as machine learning\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTaken together, there exists some research on the association of the screened metabolites with cardiovascular, inflammatory, and immune disorders, while there are fewer studies dealing specifically with myocarditis, and thus our study aptly fills the gap in this area. Given the constraints of observational studies, including small sample sizes and potential issues with reverse causality, the MR findings in our study offer more robust evidence for causal inference.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eWhile this study identified potential causal relationships between five human blood metabolites and myocarditis, specific metabolic pathways and mechanisms have not yet been explored. Future research could further investigate these pathways and mechanisms, introducing randomized controlled trials to validate findings and better uncover effective therapeutic targets for myocarditis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis MR study explored the relationship between human blood metabolites and the risk of developing myocarditis, screening for five metabolites that were causally associated with myocarditis. These were risk factors: kynurenine, 1-stearoyl-GPE (18:0); and protective factors: deoxycarnitine, X-25422, and 5-acetylamino-6-formylamino-3-methyluracil. The discovery of these serum metabolites offers opportunities for early screening for myocarditis, prevention, and treatment, as well as the design of future clinical studies, which provide valuable guidance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZW: Conceptualization, Investigation, Writing\u0026mdash;the original draft. HT: Investigation, Modification. JW: Conceptualization, Investigation, Writing\u0026mdash;reviewing and editing. All authors contributed to finally manuscript alterations.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe acknowledge the valuable contributions of our peers to this study. The results are presented with transparency, integrity, and adherence to ethical standards, ensuring absence of fabrication, falsification, or inappropriate data manipulation. Importantly, it is emphasized that the findings of this study do not imply endorsement.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmmirati, E.\u003cem\u003e et al.\u003c/em\u003e Management of Acute Myocarditis and Chronic Inflammatory Cardiomyopathy: An Expert Consensus Document. \u003cem\u003eCirc Heart Fail\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, e007405, doi:10.1161/circheartfailure.120.007405 (2020).\u003c/li\u003e\n\u003cli\u003eSagar, S., Liu, P. P. \u0026amp; Cooper, L. T., Jr. Myocarditis. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e379\u003c/strong\u003e, 738-747, doi:10.1016/s0140-6736(11)60648-x (2012).\u003c/li\u003e\n\u003cli\u003eSuresh, A., Martens, P. \u0026amp; Tang, W. H. W. Biomarkers for Myocarditis and Inflammatory Cardiomyopathy. \u003cem\u003eCurr Heart Fail Rep\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 346-355, doi:10.1007/s11897-022-00569-8 (2022).\u003c/li\u003e\n\u003cli\u003eMagnani, J. W. \u0026amp; Dec, G. W. Myocarditis: current trends in diagnosis and treatment. \u003cem\u003eCirculation\u003c/em\u003e \u003cstrong\u003e113\u003c/strong\u003e, 876-890, doi:10.1161/circulationaha.105.584532 (2006).\u003c/li\u003e\n\u003cli\u003eLehmann, L. H.\u003cem\u003e et al.\u003c/em\u003e Cardiomuscular Biomarkers in the Diagnosis and Prognostication of Immune Checkpoint Inhibitor Myocarditis. \u003cem\u003eCirculation\u003c/em\u003e \u003cstrong\u003e148\u003c/strong\u003e, 473-486, doi:10.1161/circulationaha.123.062405 (2023).\u003c/li\u003e\n\u003cli\u003eMurthy, V. L.\u003cem\u003e et al.\u003c/em\u003e Comprehensive Metabolic Phenotyping Refines Cardiovascular Risk in Young Adults. \u003cem\u003eCirculation\u003c/em\u003e \u003cstrong\u003e142\u003c/strong\u003e, 2110-2127, doi:10.1161/circulationaha.120.047689 (2020).\u003c/li\u003e\n\u003cli\u003eTzoulaki, I.\u003cem\u003e et al.\u003c/em\u003e Serum metabolic signatures of coronary and carotid atherosclerosis and subsequent cardiovascular disease. \u003cem\u003eEur Heart J\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 2883-2896, doi:10.1093/eurheartj/ehz235 (2019).\u003c/li\u003e\n\u003cli\u003eRuiz-Canela, M.\u003cem\u003e et al.\u003c/em\u003e Comprehensive Metabolomic Profiling and Incident Cardiovascular Disease: A Systematic Review. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, doi:10.1161/jaha.117.005705 (2017).\u003c/li\u003e\n\u003cli\u003eKe, C., Zhu, X., Zhang, Y. \u0026amp; Shen, Y. Metabolomic characterization of hypertension and dyslipidemia. \u003cem\u003eMetabolomics\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 117, doi:10.1007/s11306-018-1408-y (2018).\u003c/li\u003e\n\u003cli\u003eChevli, P. A.\u003cem\u003e et al.\u003c/em\u003e Plasma metabolomic profiling in subclinical atherosclerosis: the Diabetes Heart Study. \u003cem\u003eCardiovasc Diabetol\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 231, doi:10.1186/s12933-021-01419-y (2021).\u003c/li\u003e\n\u003cli\u003eFloegel, A.\u003cem\u003e et al.\u003c/em\u003e Serum metabolites and risk of myocardial infarction and ischemic stroke: a targeted metabolomic approach in two German prospective cohorts. \u003cem\u003eEur J Epidemiol\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 55-66, doi:10.1007/s10654-017-0333-0 (2018).\u003c/li\u003e\n\u003cli\u003eLarsson, S. C., Butterworth, A. S. \u0026amp; Burgess, S. Mendelian randomization for cardiovascular diseases: principles and applications. \u003cem\u003eEur Heart J\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 4913-4924, doi:10.1093/eurheartj/ehad736 (2023).\u003c/li\u003e\n\u003cli\u003eSkrivankova, V. W.\u003cem\u003e et al.\u003c/em\u003e Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. \u003cem\u003eJama\u003c/em\u003e \u003cstrong\u003e326\u003c/strong\u003e, 1614-1621, doi:10.1001/jama.2021.18236 (2021).\u003c/li\u003e\n\u003cli\u003eKurki, M. I.\u003cem\u003e et al.\u003c/em\u003e FinnGen provides genetic insights from a well-phenotyped isolated population. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e613\u003c/strong\u003e, 508-518, doi:10.1038/s41586-022-05473-8 (2023).\u003c/li\u003e\n\u003cli\u003eBurgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G. \u0026amp; Thompson, S. G. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. \u003cem\u003eEur J Epidemiol\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 543-552, doi:10.1007/s10654-015-0011-z (2015).\u003c/li\u003e\n\u003cli\u003eHemani, G.\u003cem\u003e et al.\u003c/em\u003e The MR-Base platform supports systematic causal inference across the human phenome. \u003cem\u003eElife\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, doi:10.7554/eLife.34408 (2018).\u003c/li\u003e\n\u003cli\u003eBurgess, S., Butterworth, A. \u0026amp; Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. \u003cem\u003eGenet Epidemiol\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 658-665, doi:10.1002/gepi.21758 (2013).\u003c/li\u003e\n\u003cli\u003eBowden, J., Davey Smith, G. \u0026amp; Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. \u003cem\u003eInt J Epidemiol\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 512-525, doi:10.1093/ije/dyv080 (2015).\u003c/li\u003e\n\u003cli\u003eBowden, J., Davey Smith, G., Haycock, P. C. \u0026amp; Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. \u003cem\u003eGenet Epidemiol\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 304-314, doi:10.1002/gepi.21965 (2016).\u003c/li\u003e\n\u003cli\u003eHartwig, F. P., Davey Smith, G. \u0026amp; Bowden, J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. \u003cem\u003eInt J Epidemiol\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 1985-1998, doi:10.1093/ije/dyx102 (2017).\u003c/li\u003e\n\u003cli\u003eVerbanck, M., Chen, C. Y., Neale, B. \u0026amp; Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. \u003cem\u003eNat Genet\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 693-698, doi:10.1038/s41588-018-0099-7 (2018).\u003c/li\u003e\n\u003cli\u003eGiambartolomei, C.\u003cem\u003e et al.\u003c/em\u003e Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. \u003cem\u003ePLoS Genet\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e1004383, doi:10.1371/journal.pgen.1004383 (2014).\u003c/li\u003e\n\u003cli\u003eVenkateswaran, S.\u003cem\u003e et al.\u003c/em\u003e Enhanced Contribution of HLA in Pediatric Onset Ulcerative Colitis. \u003cem\u003eInflamm Bowel Dis\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 829-838, doi:10.1093/ibd/izx084 (2018).\u003c/li\u003e\n\u003cli\u003eKanai, M.\u003cem\u003e et al.\u003c/em\u003e Tryptophan 2,3-dioxygenase is a key modulator of physiological neurogenesis and anxiety-related behavior in mice. \u003cem\u003eMol Brain\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 8, doi:10.1186/1756-6606-2-8 (2009).\u003c/li\u003e\n\u003cli\u003eSavitz, J. The kynurenine pathway: a finger in every pie. \u003cem\u003eMol Psychiatry\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 131-147, doi:10.1038/s41380-019-0414-4 (2020).\u003c/li\u003e\n\u003cli\u003eKrupa, A. \u0026amp; Kowalska, I. The Kynurenine Pathway-New Linkage between Innate and Adaptive Immunity in Autoimmune Endocrinopathies. \u003cem\u003eInt J Mol Sci\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, doi:10.3390/ijms22189879 (2021).\u003c/li\u003e\n\u003cli\u003eKita, T., Morrison, P. F., Heyes, M. P. \u0026amp; Markey, S. P. Effects of systemic and central nervous system localized inflammation on the contributions of metabolic precursors to the L-kynurenine and quinolinic acid pools in brain. \u003cem\u003eJ Neurochem\u003c/em\u003e \u003cstrong\u003e82\u003c/strong\u003e, 258-268, doi:10.1046/j.1471-4159.2002.00955.x (2002).\u003c/li\u003e\n\u003cli\u003eYoshida, R., Imanishi, J., Oku, T., Kishida, T. \u0026amp; Hayaishi, O. Induction of pulmonary indoleamine 2,3-dioxygenase by interferon. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 129-132, doi:10.1073/pnas.78.1.129 (1981).\u003c/li\u003e\n\u003cli\u003eWang, Y.\u003cem\u003e et al.\u003c/em\u003e Kynurenine is an endothelium-derived relaxing factor produced during inflammation. \u003cem\u003eNat Med\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 279-285, doi:10.1038/nm.2092 (2010).\u003c/li\u003e\n\u003cli\u003eNagy, B. M.\u003cem\u003e et al.\u003c/em\u003e Importance of kynurenine in pulmonary hypertension. \u003cem\u003eAm J Physiol Lung Cell Mol Physiol\u003c/em\u003e \u003cstrong\u003e313\u003c/strong\u003e, L741-l751, doi:10.1152/ajplung.00517.2016 (2017).\u003c/li\u003e\n\u003cli\u003eHoshi, M.\u003cem\u003e et al.\u003c/em\u003e L-tryptophan-kynurenine pathway metabolites regulate type I IFNs of acute viral myocarditis in mice. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cstrong\u003e188\u003c/strong\u003e, 3980-3987, doi:10.4049/jimmunol.1100997 (2012).\u003c/li\u003e\n\u003cli\u003eKubo, H.\u003cem\u003e et al.\u003c/em\u003e Absence of kynurenine 3-monooxygenase reduces mortality of acute viral myocarditis in mice. \u003cem\u003eImmunol Lett\u003c/em\u003e \u003cstrong\u003e181\u003c/strong\u003e, 94-100, doi:10.1016/j.imlet.2016.11.012 (2017).\u003c/li\u003e\n\u003cli\u003eSiliprandi, N., Ciman, M. \u0026amp; Sartorelli, L. Myocardial carnitine transport. \u003cem\u003eBasic Res Cardiol\u003c/em\u003e \u003cstrong\u003e82 Suppl 1\u003c/strong\u003e, 53-62, doi:10.1007/978-3-662-08390-1_7 (1987).\u003c/li\u003e\n\u003cli\u003eDambrova, M.\u003cem\u003e et al.\u003c/em\u003e Pharmacological effects of meldonium: Biochemical mechanisms and biomarkers of cardiometabolic activity. \u003cem\u003ePharmacol Res\u003c/em\u003e \u003cstrong\u003e113\u003c/strong\u003e, 771-780, doi:10.1016/j.phrs.2016.01.019 (2016).\u003c/li\u003e\n\u003cli\u003eWang, Z.\u003cem\u003e et al.\u003c/em\u003e Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e472\u003c/strong\u003e, 57-63, doi:10.1038/nature09922 (2011).\u003c/li\u003e\n\u003cli\u003eTang, W. H.\u003cem\u003e et al.\u003c/em\u003e Prognostic value of elevated levels of intestinal microbe-generated metabolite trimethylamine-N-oxide in patients with heart failure: refining the gut hypothesis. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e \u003cstrong\u003e64\u003c/strong\u003e, 1908-1914, doi:10.1016/j.jacc.2014.02.617 (2014).\u003c/li\u003e\n\u003cli\u003eXiong, X.\u003cem\u003e et al.\u003c/em\u003e The associations between TMAO-related metabolites and blood lipids and the potential impact of rosuvastatin therapy. \u003cem\u003eLipids Health Dis\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 60, doi:10.1186/s12944-022-01673-3 (2022).\u003c/li\u003e\n\u003cli\u003eTang, B. K., Grant, D. M. \u0026amp; Kalow, W. Isolation and identification of 5-acetylamino-6-formylamino-3-methyluracil as a major metabolite of caffeine in man. \u003cem\u003eDrug Metab Dispos\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 218-220 (1983).\u003c/li\u003e\n\u003cli\u003eHe, M.\u003cem\u003e et al.\u003c/em\u003e Causal relationship between human blood metabolites and risk of ischemic stroke: a Mendelian randomization study. \u003cem\u003eFront Genet\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1333454, doi:10.3389/fgene.2024.1333454 (2024).\u003c/li\u003e\n\u003cli\u003eLi, D. H.\u003cem\u003e et al.\u003c/em\u003e Plasma metabolites and risk of myocardial infarction: a bidirectional Mendelian randomization study. \u003cem\u003eJ Geriatr Cardiol\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 219-231, doi:10.26599/1671-5411.2024.02.002 (2024).\u003c/li\u003e\n\u003cli\u003eTurnbull, D., Rodricks, J. V., Mariano, G. F. \u0026amp; Chowdhury, F. Caffeine and cardiovascular health. \u003cem\u003eRegul Toxicol Pharmacol\u003c/em\u003e \u003cstrong\u003e89\u003c/strong\u003e, 165-185, doi:10.1016/j.yrtph.2017.07.025 (2017).\u003c/li\u003e\n\u003cli\u003eChieng, D. \u0026amp; Kistler, P. M. Coffee and tea on cardiovascular disease (CVD) prevention. \u003cem\u003eTrends Cardiovasc Med\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 399-405, doi:10.1016/j.tcm.2021.08.004 (2022).\u003c/li\u003e\n\u003cli\u003eSurma, S., Sahebkar, A. \u0026amp; Banach, M. Coffee or tea: Anti-inflammatory properties in the context of atherosclerotic cardiovascular disease prevention. \u003cem\u003ePharmacol Res\u003c/em\u003e \u003cstrong\u003e187\u003c/strong\u003e, 106596, doi:10.1016/j.phrs.2022.106596 (2023).\u003c/li\u003e\n\u003cli\u003eLebeau, P. F.\u003cem\u003e et al.\u003c/em\u003e Caffeine blocks SREBP2-induced hepatic PCSK9 expression to enhance LDLR-mediated cholesterol clearance. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 770, doi:10.1038/s41467-022-28240-9 (2022).\u003c/li\u003e\n\u003cli\u003eZheng, H., Lin, F., Xin, N., Yang, L. \u0026amp; Zhu, P. Association of Coffee, Tea, and Caffeine Consumption With All-Cause Risk and Specific Mortality for Cardiovascular Disease Patients. \u003cem\u003eFront Nutr\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 842856, doi:10.3389/fnut.2022.842856 (2022).\u003c/li\u003e\n\u003cli\u003eLebkuchen, A.\u003cem\u003e et al.\u003c/em\u003e Metabolomic and lipidomic profile in men with obstructive sleep apnoea: implications for diagnosis and biomarkers of cardiovascular risk. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 11270, doi:10.1038/s41598-018-29727-6 (2018).\u003c/li\u003e\n\u003cli\u003eAsef, C. K.\u003cem\u003e et al.\u003c/em\u003e Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry. \u003cem\u003eAnal Chem\u003c/em\u003e \u003cstrong\u003e95\u003c/strong\u003e, 1047-1056, doi:10.1021/acs.analchem.2c03749 (2023).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"blood metabolomics, metabolic markers, myocarditis, Mendelian Randomization","lastPublishedDoi":"10.21203/rs.3.rs-4822817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4822817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eMyocarditis is a common disease of the cardiovascular and immune systems, but the relationship between relevant metabolites in the blood and the risk of myocarditis has not been established. To identify biometabolic markers in myocarditis blood, we performed a two-sample MR study.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMR preliminary analysis: based mainly on the results of IVW, supplemented by MR-Egger, weighted median, and weighted mode for FDR multiple correction; removal of confounders: screened on the GWAS Catalog website; sensitivity analyses: Cochrane Q-test, Egger regression, MR- PRESSO, scatterplot, funnel plot, forest plot; Genetic and directional analysis: co-localization analysis, steiger test; Replicative and Meta-analysis: meta-analysis by extracting the same ending GWAS from another database.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMR analysis identified significant correlations after FDR for 5 metabolic biomarkers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Four known metabolites: kynurenine, 1-stearoyl-GPE (18:0), Deoxycarnitine, 5-acetylamino-6-formylamino-3-methyluracil with one unknown metabolite: X-25422. Among them, kynurenine (OR\u0026thinsp;=\u0026thinsp;1.441, 95%CI\u0026thinsp;=\u0026thinsp;1.089\u0026ndash;1.906, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and 1-stearoyl-GPE (18:0) (OR\u0026thinsp;=\u0026thinsp;1.263, 95%CI\u0026thinsp;=\u0026thinsp;1.029\u0026ndash;1.550, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029) were risk factors for myocarditis, Deoxycarnitine (OR\u0026thinsp;=\u0026thinsp;0.813, 95%CI\u0026thinsp;=\u0026thinsp;0.676\u0026ndash;0.979, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029), 5-acetylamino-6-formylamino-3-methyluracil (OR\u0026thinsp;=\u0026thinsp;0.864, 95%CI\u0026thinsp;=\u0026thinsp;0.775\u0026ndash;0.962, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and X-25422 (OR\u0026thinsp;=\u0026thinsp;0.721, 95%CI\u0026thinsp;=\u0026thinsp;0.587\u0026ndash;0.886, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) were protective factors against myocarditis. There was no heterogeneity, horizontal pleiotropy, or sensitivity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), no shared genetic factors between exposure and outcome, and the causality was in the right direction. Meta-analysis results again identified five metabolites causally related to myocarditis (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;50%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study identified a causal relationship between five circulating metabolites and myocarditis, and Kynurenine, 1-stearoyl-GPE (18:0), Deoxycarnitine, X-25422, and 5-acetylamino-6-formylamino-3-methyluracil may be as potential drug targets for myocarditis, providing a theoretical basis for the prevention, diagnosis, and treatment of myocarditis.\u003c/p\u003e","manuscriptTitle":"Association between human blood metabolome and risk of myocarditis: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-04 04:16:37","doi":"10.21203/rs.3.rs-4822817/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-16T09:02:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-12T01:43:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315124183356534898702201312070080434628","date":"2024-08-31T13:47:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-25T17:33:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335509066960059455239373619086854785728","date":"2024-08-25T16:30:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-14T22:32:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-12T13:02:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-08T23:32:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-08T16:16:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-29T14:33:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a6a5012a-f30a-47b3-9d8b-ccc3e9379736","owner":[],"postedDate":"September 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36890586,"name":"Biological sciences/Immunology/Immunological disorders"},{"id":36890587,"name":"Biological sciences/Immunology/Inflammation"}],"tags":[],"updatedAt":"2024-11-04T16:22:08+00:00","versionOfRecord":{"articleIdentity":"rs-4822817","link":"https://doi.org/10.1038/s41598-024-78359-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-03 15:57:01","publishedOnDateReadable":"November 3rd, 2024"},"versionCreatedAt":"2024-09-04 04:16:37","video":"","vorDoi":"10.1038/s41598-024-78359-6","vorDoiUrl":"https://doi.org/10.1038/s41598-024-78359-6","workflowStages":[]},"version":"v1","identity":"rs-4822817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4822817","identity":"rs-4822817","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}