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

doi:10.21203/rs.3.rs-4822817/v1

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

https://doi.org/10.21203/rs.3.rs-4822817/v1

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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 (I² < 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

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 myocarditis³. 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 myocarditis⁴.

Genomic, myocardial injury, cytoarchitectural, and immune cell-related markers of myocarditis have been identified in recent studies³. 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 adults⁶. 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 ApoB⁷. 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 area⁸. 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 trials¹².

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.

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 characteristic¹⁵. 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 r² to 0.01¹⁶. 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-test¹⁷, 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 pleiotropy¹⁸; outlier detection (MR-PRESSO), which effectively removes outlier SNPs²¹. 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 region²². 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 I² statistic. If I² ≤ 50% and P ≥ 0.1, it was considered that there was no significant heterogeneity among studies, and a fixed-effects model was used. If I² > 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.

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 (PIVW > 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 (I² < 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.

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 manner²⁹. 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 remodeling³⁰. 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 myocarditis³¹. 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 bloodstream³³. 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 midazolam³⁴. 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 caffeine³⁸. 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) Ca²⁺ 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 LDLc⁴⁴. 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 CVD⁴⁵. 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 disease⁴⁶. 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 learning⁴⁷.

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.

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.

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.

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

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Abstract

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Introduction

Methods

Study design

Blood metabolome data sources

Source of data on myocarditis

Screening of blood metabolomic data

MR analysis

Sensitivity analysis

Co-localization analysis

Replicative and Meta-analysis

Results

Preliminary MR analysis

Results of genetic and directional analyses

Co-localization analysis

Replicative and Meta-analysis results

Discussion

Limitation

Conclusion

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Author Contribution

Acknowledgements

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