Nontargeted Metabolomics Revealed Novel Association Between Serum Metabolites and Incident Acute Coronary Syndrome: A Mendelian Randomization Study

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop a predictive model for occupational As exposure and hypertension using metabolomics and machine learning. A total of 365 male smelting workers from southern regions were selected. Forty workers from high and low urinary arsenic (U-As) exposure groups were chosen for non-targeted metabolomics analysis. Univariate analysis revealed that U-As is a risk factor for blood pressure and hypertension (P < 0.05). Restricted cubic spline (RCS) analysis showed that both systolic and diastolic blood pressure, as well as hypertension risks, increased with U-As, with a threshold at 32 µg/L. Of 1145 metabolites, 383 differentially expressed metabolites (382 upregulated, 1 downregulated) were identified. Least absolute shrinkage and selection operator (LASSO) regression was used to construct a predictive model for occupational hypertension, with N-hexosyl leucine, myristic acid, gamma-glutamylvaline, and pregnanediol disulfate as predictors. The area under the curve (AUC) of the receiver operating characteristic (ROC) for the predictive model was 0.917, indicating strong predictability and accuracy. This model, based on metabolomics and machine learning, provides an effective tool for early identification and intervention for occupational populations at high risk of hypertension due to As exposure.

Recent progress in metabolomic analysis of acute coronary syndrome: a narrative review

Background and Objective

Acute coronary syndrome (ACS) is a common cardiovascular disease in clinical practice. It is caused mainly by vulnerable plaque rupture (PR) or surface plaque erosion (PE) caused by serious thrombotic events, and eventually leads to myocardial blood supply insufficiency or necrosis. The disease has high morbidity and mortality rates. In this study, we review the literature on biomarkers of ACS metabolites and modification of disease by altering related metabolic pathways through drugs, aiming to provide clarity on potential biomarkers of disease identified to date.

Methods

PubMed was used for literature review. From January 1, 2014 to December 3, 2024, English articles on clinical trials, randomized controlled trials of metabolomics studies in ACS were included.

Key Content and Findings

In this review, we discuss the advantages and disadvantages of three techniques currently used for metabolomic analysis. In addition, the recent decade of metabolomic approaches to the discovery of potential diagnostic and prognostic biomarkers for ACS is reviewed. It was found that the metabolites changed in patients with ACS were mostly amino acids, lipids and carbohydrates. Tryptophan and glutamine can be used as potential diagnostic biomarkers. Mannitol and ceramide can be used as prognostic biomarkers. Drugs can improve disease by affecting changes in metabolites in the body.

Conclusions

ACS studies based on metabolomics have demonstrated great potential for identifying disease-related metabolomic features in the discovery of potential biomarkers for diagnosis and prognosis and mechanisms of drug therapy.

Association between genetic prediction of 486 blood metabolites and the risk of idiopathic pulmonary fibrosis: A mendelian randomization study

Metabolic disorders are a significant feature of fibrotic diseases. Nevertheless, the lack of sufficient proof regarding the cause-and-effect association between circulating metabolites and the promotion or prevention of idiopathic pulmonary fibrosis (IPF) persists. To assess the causal association between IPF and genetic proxies of 486 blood metabolites, a dual sample Mendelian randomization (MR) analysis was performed. Therefore, the two-sample MR technique and genome-wide association study data were employed to assess the association between 486 serum metabolites and IPF. To produce the primary outcomes, the inverse variance weighted (IVW) technique was applied, while to assess the stability and dependability of the outcomes, sensitivity analysis using MR-Egger analysis was performed. Additionally, weighted median, Cochran's Q-test, Egger intercept test and the leave-one-out method were used. The results of the present study revealed a total of 21 metabolites in blood circulation that could affect the risk of IPF (PIVW<0.05). Among them, 10 compounds were already known, namely cotinine [odds ratio (OR)=1.206; 95% confidence interval (CI), 1.002-1.452; P=0.047], hypoxanthine (OR=0.225; 95% CI, 0.056-0.899; P=0.034), aspartyl phenylalanine (OR=4.309; 95% CI, 1.084-17.131; P=0.038), acetyl-carnitine (OR=5.767; 95% CI, 1.398-23.789; P=0.015), 2-aminobutyrate (OR=0.155; 95% CI, 0.033-0.713; P=0.016), Docosapentaenoic acid (PubChem ID: 5497182; OR=0.214; 95% CI, 0.055-0.833; P=0.026), octanoyl-carnitine (PubChem ID: 177508; OR=3.398; 95% CI, 1.179-9.794; P=0.023), alpha-hydroxy-isovalerate (PubChem ID: 857803-94-2; OR=0.324; 95% CI, 0.112-0.931; P=0.036), 1,7-dimethylurate (PubChem ID: 91611; OR=0.401; 95% CI, 0.172-0.931; P=0.033) and 1-linoleoyl-glycerophosphocholine (PubChem ID: 657272; OR=6.559; 95% CI, 1.060-40.557; P=0.043). Additionally, the study also identified 11 currently unknown chemical structures. The results of Cochran's Q-test indicated that there was no significant heterogeneity, while MR-Egger's intercept analysis verified the lack of horizontal pleiotropy. The retention of one method for plotting also supported the reliability of the MR analysis. Overall, the results of the current study supported the cause-and-effect association between IPF and 21 blood metabolites, including 10 with already known chemical composition and 11 which are still awaiting determination. These findings could provide novel insights for the further investigation of the mechanism underlying the development of IPF.

Causal role of gut microbiota, serum metabolites, immunophenotypes in myocarditis: a mendelian randomization study

Background

The intricate relationship among gut microbiota, serum metabolites, and immunophenotypes may significantly impact myocarditis. However, direct causal links between these domains and myocarditis are not well understood.

Methods

The study performed Mendelian randomization (MR) analysis using genetic data from public sources. Exposure data included 211 gut microbiota, 486 serum metabolites, and 731 immunophenotypes from Mibiogen, the Metabolomics GWAS server, and GWAS catalog databases. Single nucleotide polymorphisms (SNPs) were selected as instrumental variables based on established criteria. Myocarditis data from GWAS (427,911 participants, 24, 180, 570 SNPs) were used as the outcome variable. MR analysis was conducted using Inverse Variance Weighting (IVW), with Cochran's Q test for heterogeneity and Egger's intercept to assess horizontal pleiotropy.

Results

9 gut microbiota, 10 serum metabolites, and 2 immunophenotypes were negatively associated with myocarditis risk. In contrast, 5 gut microbiota, 12 serum metabolites, and 7 immunophenotypes were positively associated with myocarditis risk (all, P < 0.05). Sensitivity analyses confirmed the stability of these results.

Conclusion

This MR study suggests that gut microbiota, serum metabolites, and immunophenotypes may causally influence myocarditis risk. These findings provide genetic evidence for myocarditis etiology and could inform future precision prevention and treatment strategies.

The relationship between serum 1,5-anhydroglucitol and adverse outcomes in acute coronary syndrome with and without chronic kidney disease patients

Purpose

Individuals with chronic kidney disease (CKD) face an elevated residual risk of cardiovascular events, but the relationship between this residual risk and 1,5-anhydroglucitol (1,5-AG) is uncertain. Our study aimed to examine the effect of 1,5-AG on major adverse cardiovascular events (MACEs) and all-cause mortality in acute coronary syndrome (ACS) individuals.

Methods

1253 ACS participants hospitalized were enrolled at Beijing Hospital between March 2017 and March 2020. All participants were classified into 2 groups based on their eGFR (60 ml/min/1.73 m2). The link between 1,5-AG and adverse outcome was investigated in non-CKD and CKD participants.

Results

CKD patients had reduced concentrations of 1,5-AG than those without CKD. Throughout a median follow-up duration of 43 months, 1,5-AG was an autonomous hazard factor for MACEs and all-cause mortality. 1,5-AG<14 μg/ml participants had greater MACEs and all-cause mortality risk than those with 1,5-AG≥14 μg/ml, regardless of renal function. Furthermore, concomitant reduced concentrations of 1,5-AG and CKD portended a dismal prognosis in ACS patients.

Conclusions

1,5-AG was autonomously linked to MACEs and all-cause mortality in ACS participants with both non-CKD and CKD. Co-presence of reduced concentrations of 1,5-AG and CKD may portend adverse clinical outcomes.

Mendelian randomization analysis reveals causal associations of serum metabolites with sepsis and 28-day mortality

Metabolic disorder has been found to be an important factor in the pathogenesis and progression of sepsis. However, the causation of such an association between serum metabolites and sepsis has not been established. We conducted a two-sample Mendelian randomization (MR) study. A genome-wide association study of 486 human serum metabolites was used as the exposure, whereas sepsis and sepsis mortality within 28 days were set as the outcomes. In MR analysis, 6 serum metabolites were identified to be associated with an increased risk of sepsis, and 6 serum metabolites were found to be related to a reduced risk of sepsis. Furthermore, there were 9 metabolites positively associated with sepsis-related mortality, and 8 metabolites were negatively correlated with sepsis mortality. In addition, "glycolysis/gluconeogenesis" (p = 0.001), and "pyruvate metabolism" (p = 0.042) two metabolic pathways were associated with the incidence of sepsis. This MR study suggested that serum metabolites played significant roles in the pathogenesis of sepsis, which may provide helpful biomarkers for early disease diagnosis, therapeutic interventions, and prognostic assessments for sepsis.

Investigating genetic links between blood metabolites and preeclampsia

BACKGROUND

Observational studies have revealed that metabolic disorders are closely related to the development of preeclampsia (PE). However, there is still a research gap on the causal role of metabolites in promoting or preventing PE. We aimed to systematically explore the causal association between circulating metabolites and PE.

METHODS

Single nucleotide polymorphisms (SNPs) from genome-wide association study (GWAS) of 486 blood metabolites (7,824 participants) were extracted as instrumental variables (P < 1 × 10- 5), GWAS summary statistics for PE were obtained from FinnGen consortium (7,212 cases and 194,266 controls) as outcome, and a two-sample Mendelian randomization (MR) analysis was conducted. Inverse variance weighted (IVW) was set as the primary method, with MR-Egger and weighted median as auxiliary methods; the instrumental variable strength and confounding factors were also assessed. Sensitivity analyses including MR-Egger, Cochran's Q test, MR-PRESSO and leave-one-out analysis were performed to test the robustness of the MR results. For significant associations, repeated MR and meta-analysis were performed by another metabolite GWAS (8,299 participants). Furthermore, significantly associated metabolites were subjected to a metabolic pathway analysis.

RESULTS

The instrumental variables for the metabolites ranged from 3 to 493. Primary analysis revealed a total of 12 known (e.g., phenol sulfate, citrulline, lactate and gamma-glutamylglutamine) and 11 unknown metabolites were associated with PE. Heterogeneity and pleiotropy tests verified the robustness of the MR results. Validation with another metabolite GWAS dataset revealed consistency trends in 6 of the known metabolites with preliminary analysis, particularly the finding that genetic susceptibility to low levels of arachidonate (20:4n6) and citrulline were risk factors for PE. The pathway analysis revealed glycolysis/gluconeogenesis and arginine biosynthesis involved in the pathogenesis of PE.

CONCLUSIONS

This study identifies a causal relationship between some circulating metabolites and PE. Our study presented new perspectives on the pathogenesis of PE by integrating metabolomics with genomics, which opens up avenues for more accurate understanding and management of the disease, providing new potential candidate metabolic molecular markers for the prevention, diagnosis and treatment of PE. Considering the limitations of MR studies, further research is needed to confirm the causality and underlying mechanisms of these findings.