Serum metabolites and risk of myocardial infarction and ischemic stroke: a targeted metabolomic approach in two German prospective cohorts

Associations of outdoor ozone concentration with thyroid function and the mediated role of serum metabolites: A panel study of healthy children

Epidemiological evidence linking air pollution to children's thyroid function is inconsistent, and the role of metabolites remains unknown. We conducted a panel study with 3 repeated visits among 143 children aged 4-12 years. The outdoor levels of ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, and fine particulate matter were estimated 3 consecutive days preceding blood draw. Exposure to ozone was linearly associated with the reduction of free thyroxine (FT4) only at lag 0 day. Bayesian kernel machine regression and weighted quantile sum regression indicated that exposure to air pollution mixture linked to reduced FT4 at lag 0 day, with ozone being the primary contributor. Untargeted metabolomics were measured in 48 children, revealing that 27 serum metabolites were associated with ozone, primarily involving ether lipid and glycerophospholipid metabolism pathways. Casual inference tests showed that eight glycerophospholipid metabolites were identified as mediators of ozone's effect on FT4, seven of which were involved in ether lipid pathway. The integrated analysis identified a cluster of children with reduced FT4, characterized by increased ozone and decreased phosphatidylethanolamine plasmalogen and alkyl-phosphatidylcholine. Our findings suggested that short-term exposure to outdoor ozone in children may disrupt glycerophospholipid levels within the ether lipid metabolic pathway, leading to reduced FT4.

Serum sphingomyelin levels define oxyhemoglobin desaturation-related metabolic threshold in symptomatic obstructive sleep apnea

Hypoxia is a contributing factor for the morbidity and mortality in patients with obstructive sleep apnea (OSA). We aimed at identifying the percentage of sleep time with oxyhemoglobin desaturation below 90% (Tc90%) breakpoint from which the most significant changes occur in systemic metabolome of patients with OSA. In a prospective observational study on patients with polysomnography-confirmed symptomatic OSA, profiles of 186 metabolites including amino acids, biogenic amines, acylcarnitines (AC), lysophosphatidylcholines, phosphatidylcholines (PC) and sphingomyelins (SM) were analyzed with liquid chromatography-mass-spectrometry in peripheral blood, obtained at 3 time points that covered patients' night sleep. Comparisons of rank-transformed data with general linear model for repeated measures after dichotomizing the study group at different Tc90% levels were applied to define the best cut-off, hypoxic metabolic threshold (HMT), based on Cohen's f. Fifty-one subjects were recruited with their median Tc90% of 2.1. The mean Cohen's f over the metabolites was highest (0.165) at a Tc90% of 1.8 representing the HMT. Of the different classes of metabolites, the Cohen's f value at HMT was highest for SM (0.322). Compared to patients with Tc90% < HMT, concentrations of 2 PC, 1 AC and 7 SM were significantly higher in patients with Tc90% ≥HMT. The HMT for patients with OSA described in this report for the first time is located at a Tc90% level of 1.8 with SM levels contributing most to the size of this threshold.

Multi-omics analysis of the protective effects of Platycodon grandiflorum -derived inulin-type fructan against low-concentration PM2.5-induced lung microenvironment changes in rats

In northern China, haze events frequently occur during winter, and PM2.5 is recognized as the most significant particulate matter in haze, posing a major threat to human health. Therefore, we employed a PM2.5 inhalation exposure system to investigate the protective effects of Platycodon grandiflorum inulin-type fructan (PGPI-1-a) on low-concentration PM2.5-induced lung microenvironment changes. Our findings revealed that long-term (4-month) PM2.5 exposure did not cause apparent pathological alterations in rat lungs but induced lung inflammation, which was alleviated by PGPI-1-a intervention. Multi-omics analysis demonstrated that PGPI-1-a restored abnormally expressed lung proteins, improved lung microbiota disorders, and regulated serum metabolite imbalances related to lipid and amino acid metabolism, ameliorating low-concentration PM2.5-induced lung microenvironment changes. These results suggest that Platycodon grandiflorum inulin-type fructan could serve as a potential dietary supplement for mitigating PM2.5-induced lung injury.

Pericardial Fluid Biomarkers as Early Predictors for Postoperative Atrial Fibrillation-A Systematic Review

(1) Background: Postoperative atrial fibrillation (POAF) is one of the most common complications of cardiac surgery, frequently occurring in the first 2-4 days after surgery. With a variable incidence depending on the type of surgery, research in recent years has focused on identifying predisposing factors with the aim of correcting them and thus decreasing the risk of cardiovascular and total morbidity and mortality. The analysis of pericardial fluid allowed the identification of biomarkers (interleukin-6, mitochondrial DNA, myeloperoxidase or natriuretic peptides) whose presence postoperatively was associated with increased risk of POAF. (2) Materials and Methods: We conducted a search on EMBASE and PubMed and identified 75 articles, of which 10 entered the final analysis. (3) Results: Patients who develop POAF accumulate large amounts of interleukin 6, mitochondrial DNA, myeloperoxidase, or secondary atrial natriuretic peptide as a consequence of the associated inflammatory status, atrial remodeling, or disturbance of homeostasis of various ions. There are also observations that their levels in the pericardium correlate with blood levels, but further studies on larger cohorts of patients are needed to provide new evidence in this regard. (4) Conclusions: Early recognition of patients at risk of developing POAF based on easy-to-dose and easy-to-use biochemical biomarkers, whose association with POAF has been demonstrated so far in small cohorts of patients, has both therapeutic and prognostic implications, which justifies further research on large cohorts of patients.

Metabolomics in Atrial Fibrillation: Unlocking Novel Biomarkers and Pathways for Diagnosis, Prognosis, and Personalized Treatment

BACKGROUND/OBJECTIVES

Atrial fibrillation (AF) is the most frequent arrhythmia in the adult population associated with a high rate of severe consequences leading to significant morbidity and mortality worldwide. Therefore, its prompt recognition is of high clinical importance. AF detection often remains challenging due to unspecific symptoms and a lack of reliable biomarkers for its prediction. Herein, novel bioanalytical methodologies, such as metabolomics, offer new opportunities for a better understanding of the underlying pathological mechanisms of cardiovascular diseases, including AF. The metabolome, considered a complete set of small molecules present in the organism, directly reflects the current phenotype of the studied system and is highly sensitive to any changes, including arrhythmia's onset. A growing body of evidence suggests that metabolite profiling has prognostic value in AF prediction, highlighting its potential role not only in early diagnosis but also in guiding therapeutic interventions. By identifying specific metabolites as a disease biomarker or recognising particular metabolomic pathways involved in the AF pathomechanisms, metabolomics could be of great clinical value for further clinical decision-making, risk stratification, and an individual personalised approach. The presented narrative review aims to summarise the current state of knowledge on metabolomics in AF with a special emphasis on its implications for clinical practice and personalised medicine.

Advancements in Mass Spectrometry-Based Targeted Metabolomics and Lipidomics: Implications for Clinical Research

Targeted metabolomics and lipidomics are increasingly utilized in clinical research, providing quantitative and comprehensive assessments of metabolic profiles that underlie physiological and pathological mechanisms. These approaches enable the identification of critical metabolites and metabolic alterations essential for accurate diagnosis and precision treatment. Mass spectrometry, in combination with various separation techniques, offers a highly sensitive and specific platform for implementing targeted metabolomics and lipidomics in clinical settings. Nevertheless, challenges persist in areas such as sample collection, quantification, quality control, and data interpretation. This review summarizes recent advances in targeted metabolomics and lipidomics, emphasizing their applications in clinical research. Advancements, including microsampling, dynamic multiple reaction monitoring, and integration of ion mobility mass spectrometry, are highlighted. Additionally, the review discusses the critical importance of data standardization and harmonization for successful clinical implementation.

Impact of Imperata Cylindrica polysaccharide on liver lipid metabolism disorders caused by hyperuricemia

Elevated uric acid levels are associated with lipid metabolism disorders. The effects of Imperata cylindrica polysaccharide (ICPC-a) were explored using a hyperuricemia mouse model and a uric acid-induced HepG2 hepatocyte model. ICPC-a significantly improved total cholesterol, triglycerides, low-density lipoprotein levels, and hepatic lipid deposition in hyperuricemia mice. The liver/body weight ratio decreased, and markers of liver damage, inflammation, and dyslipidemia improved. Metabolomics analysis suggested that ICPC-a modulates lipid metabolism by influencing the glycerophospholipid pathway and the enzyme LPCAT3. Stable HepG2 cell lines with knocked-down LPCAT3 were constructed, and western blot and RT-PCR were used to assess the impact of its knockdown on lipid metabolism under uric acid stimulation. In cells with reduced LPCAT3 expression, ICPC-a was still able to alleviate uric acid-induced lipid accumulation, though the effect was less pronounced compared to cells with normal LPCAT3 levels. However, the effectiveness was diminished compared to cells where LPCAT3 was not knocked down. These findings indicated that LPCAT3 was an important target through which ICPC-a regulated lipid metabolism disorders induced by hyperuricemia. These discoveries emphasized that ICPC-a, as a prebiotic, could modulate hepatic lipid accumulation and inflammation, contributing to the maintenance of hepatic lipid homeostasis.

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

Myocarditis is a common disease of the cardiovascular and immune systems, but the relationship between relevant blood metabolites and the risk of myocarditis has not been well-established. To identify potential biometabolic markers associated with myocarditis, we conducted a two-sample Mendelian randomization (MR) study. We performed preliminary MR analysis using the inverse variance weighted (IVW) method, supplemented by MR-Egger, weighted median, and weighted mode methods to adjust for false discovery rate (FDR). Confounders were screened using the GWAS Catalog website. Sensitivity analyses included Cochrane Q-test, Egger regression, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), scatterplots, funnel plots, and forest plots. For genetic and directional analysis, we employed co-localization analysis and the Steiger test. MR analysis was performed using the FinnGen database and meta-analysis was performed using the IEU database. MR analysis identified significant correlations for five metabolic biomarkers after FDR correction. These included four known metabolites: kynurenine, 1-stearoyl-GPE (18:0), deoxycarnitine, and 5-acetylamino-6-formylamino-3-methyluracil, as well as one unknown metabolite, X-25,422. Among these, kynurenine (OR = 1.441, 95%CI = 1.089-1.906, p-value = 0.018) and 1-stearoyl-GPE (18:0) (OR = 1.263, 95%CI = 1.029-1.550, p-value = 0.029) were identified as risk factors for myocarditis, while deoxycarnitine (OR = 0.813, 95%CI = 0.676-0.979, p-value = 0.029), 5-acetylamino-6-formylamino-3-methyluracil (OR = 0.864, 95% CI = 0.775-0.962, p-value = 0.018), and X-25,422 (OR = 0.721, 95%CI = 0.587-0.886, p-value = 0.009) were found to be protective factors. No evidence of heterogeneity, horizontal pleiotropy, or sensitivity issues was observed, and no shared genetic factors between exposure and outcome were detected. The causality was in the correct direction. Meta-analysis further confirmed the causal relationship between the five metabolites and myocarditis. This study identifies a causal relationship between five circulating metabolites and myocarditis. Kynurenine, 1-stearoyl-GPE (18:0), deoxycarnitine, X-25,422, and 5-acetylamino-6-formylamino-3-methyluracil may serve as potential drug targets for myocarditis, providing a theoretical basis for the prevention, diagnosis, and treatment of the condition.

Lipidomics identified novel cholesterol-independent predictors for risk of incident coronary heart disease: Mediation of risk from diabetes and aggravation of risk by ambient air pollution

INTRODUCTION

Previous lipidomics studies have identified various lipid predictors for cardiovascular risk, however, with limited predictive increment, sometimes using too many predictor variables at the expense of practical efficiency.

OBJECTIVES

To search for lipid predictors of future coronary heart disease (CHD) with stronger predictive power and efficiency to guide primary intervention.

METHODS

We conducted a prospective nested case-control study involving 1,621 incident CHD cases and 1:1 matched controls. Lipid profiling of 161 lipid species for baseline fasting plasma was performed by liquid chromatography-mass spectrometry.

RESULTS

In search of CHD predictors, seven lipids were selected by elastic-net regression during over 90% of 1000 cross-validation repetitions, and the derived composite lipid score showed an adjusted odds ratio of 3.75 (95% confidence interval: 3.15, 4.46) per standard deviation increase. Addition of the lipid score into traditional risk model increased c-statistic to 0.736 by an increment of 0.077 (0.063, 0.092). From the seven lipids, we found mediation of CHD risk from baseline diabetes through sphingomyelin (SM) 41:1b with a considerable mediation proportion of 36.97% (P < 0.05). We further found that the positive associations of phosphatidylcholine (PC) 36:0a, SM 41:1b, lysophosphatidylcholine (LPC) 18:0 and LPC 20:3 were more pronounced among participants with higher exposure to fine particulate matter or its certain components, also to ozone for LPC 18:0 and LPC 20:3, while the negative association of cholesteryl ester (CE) 18:2 was attenuated with higher black carbon exposure (P < 0.05).

CONCLUSION

We identified seven lipid species with greatest predictive increment so-far achieved for incident CHD, and also found novel biomarkers for CHD risk stratification among individuals with diabetes or heavy air pollution exposure.

Online Direct Infusion Mass Spectrometry of Liquid-Liquid Extraction Phases for Metabolite and Lipid Profiling with the Direct Infusion Probe

Background/Objectives: Profiling of metabolites and lipids in biological samples can provide invaluable insights into life-sustaining chemical processes. The ability to detect both metabolites and lipids in the same sample can enhance these understandings and connect cellular dynamics. However, simultaneous detection of metabolites and lipids is generally hampered by chromatographic systems tailored to one molecular type. This void can be filled by direct infusion mass spectrometry (MS), where all ionizable molecules can be detected simultaneously. However, in direct infusion MS, the high chemical complexity of biological samples can introduce limitations in detectability due to matrix effects causing ionization suppression. Methods: Decreased sample complexity and increased detectability and molecular coverage was provided by combining our direct infusion probe (DIP) with liquid-liquid extraction (LLE) and directly sampling the different phases for direct infusion. Three commonly used LLE methods for separating lipids and metabolites were evaluated. Results: The butanol-methanol (BUME) method was found to be preferred since it provides high molecular coverage and have low solvent toxicity. The established BUME DIP-MS method was used as a fast and sensitive analysis tool to study chemical changes in insulin-secreting cells upon glucose stimulation. By analyzing the metabolome at distinct time points, down to 1-min apart, we found high dynamics of the intracellular metabolome. Conclusions: The rapid workflow with LLE DIP-MS enables higher sensitivity of phase separated metabolites and lipids. The application of BUME DIP-MS provides novel information on the dynamics of the intracellular metabolome of INS-1 during the two phases of insulin release for both metabolite and lipid classes.

Validation of two LCHRMS methods for large-scale untargeted metabolomics of serum samples: Strategy to establish method fitness-for-purpose

Untargeted metabolomics by LCHRMS is a powerful tool to enhance our knowledge of pathophysiological processes. Whereas validation of a bioanalytical method is customary in most analytical chemistry fields, it is rarely performed for untargeted metabolomics. This study aimed to establish and validate an analytical platform for a long-term, clinical metabolomics study. Sample preparation was performed with an automated liquid handler and four analytical methods were developed and evaluated. The validation study spanned three batches with twelve runs using individual serum samples and various quality control samples. Data was acquired with untargeted acquisition and only metabolites identified at level 1 were evaluated. Validation parameters were set to evaluate key performance metrics relevant for the intended application: reproducibility, repeatability, stability, and identification selectivity, emphasizing dataset intrinsic variance. Concordance of semi-quantitative results between methods was evaluated to identify potential bias. Spearman rank correlation coefficients (rs) were calculated from individual serum samples. Of the four methods tested, two were selected for validation. A total of 47 and 55 metabolites (RPLC-ESI+- and HILIC-ESI--HRMS, respectively) met specified validation criteria. Quality assurance involved system suitability testing, sample release, run release, and batch release. The median repeatability and within-run reproducibility as coefficient of variation% for metabolites that passed validation on RPLC-ESI+- and HILIC-ESI--HRMS were 4.5 and 4.6, and 1.5 and 3.8, respectively. Metabolites that passed validation on RPLC-ESI+-HRMS had a median D-ratio of 1.91, and 89 % showed good signal intensity after ten-fold dilution. The corresponding numbers for metabolites with the HILIC-ESI--HRMS method was 1.45 and 45 %, respectively. The rs median ({range}) for metabolites that passed validation on RPLC-ESI+- was 0.93 (N = 9 {0.69-0.98}) and on HILIC-ESI--HRMS was 0.93 (N = 22 {0.55-1.00}). The validated methods proved fit-for-purpose and the laboratory thus demonstrated its capability to produce reliable results for a large-scale, untargeted metabolomics study. This validation not only bolsters the reliability of the assays but also significantly enhances the impact and credibility of the hypotheses generated from the studies. Therefore, this validation study serves as a benchmark in the documentation of untargeted metabolomics, potentially guiding future endeavors in the field.

[Relationship between lipid metabolism molecules in plasma and carotid atheroscle-rotic plaques, traditional cardiovascular risk factors, and dietary factors]

OBJECTIVE

To explore the relationship between lipid metabolism molecules in plasma and carotid atherosclerotic plaques, traditional cardiovascular risk factors and possible dietary related factors.

METHODS

Firstly, among 1 312 community people from those who participated in a 10-year follow-up study of subclinical atherosclerosis cohort in Shijingshan District, Beijing, 85 individuals with 2 or more carotid soft plaques or mixed plaques and 89 healthy individuals without plaques were selected according to the inclusive and the exclusive criteria (< 70 years, not having clinical cardiovascular disease and other diseases, etc.). Secondly, 10 cases and 10 controls were randomly selected in the above 85 and 89 individuals respectively. Carotid plaques were detected using GE Vivid i Ultrasound Machine with 8L detector. Lipid metabolism molecules were detected by high performance liquid chromatography-mass spectrometry. The detection indexes included 113 lipid metabolism molecules. Traditional cardiovascular risk factors were collected by unified standard questionnaires, and dietary related factors were collected by main dietary frequency and weight scale. The difference of lipid metabolism molecules between the case group and the control group was analyzed by Wilcoxin rank test. In the control group, the Spearman correlation method was used to analyze the correlation between statistically significant lipid metabolism molecules and traditional cardiovascular risk factors and dietary factors.

RESULTS

Among the 113 lipid metabolism molecules, 53 lipid metabolism molecules were detected. C24:0 sphingomyelin (SM), C22:0/ C24:0 ceramide molecules, C18:0 phosphoethanolamine (PE) molecules, and C18:0/C18:2 (Cis) phosphatidylcholine (PC) were significantly higher in the carotid atherosclerotic plaque group than in the control group. The correlation analysis showed that C24:0 SM was significantly positively correlated with low density lipoprotein cholesterol (LDL-C, r=0.636, P < 0.05), C18:2 (Cis) PC (DLPC) was significantly positively correlated with systolic pressure (r=0.733, P < 0.05), C18:0 PE was significantly positively correlated with high sensitivity C-response protein (r=0.782, P < 0.01), C22:0, C24:0 ceramide and C18:0 PE were negatively correlated with vegetable intake (r=-0.679, P < 0.05;r=-0.711, P < 0.05;r=-0.808, P < 0.01), C24:0 ceramide was also negatively correlated with beans food intake (r=-0.736, P < 0.05) in the control group.

CONCLUSIONS

The increase of plasma C24:0 SM, C22:0, C24:0 ceramide, C18:0 PE, C18:2 (Cis) PC (DLPC), C18:0 PC (DSPC) may be new risk factors for human atherosclerotic plaques. These molecules may be related to blood lipid, blood pressure or inflammatory level and the intake of vegetables and soy products, but the nature of the association needs to be verified in a larger sample population.

Lipidomic-Based Algorithms Can Enhance Prediction of Obstructive Coronary Artery Disease

Lipidomics emerges as a promising research field with the potential to help in personalized risk stratification and improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). This study aimed to utilize a machine learning approach to provide a lipid panel able to identify patients with obstructive CAD. In this posthoc analysis of the prospective CorLipid trial, we investigated the lipid profiles of 146 patients with suspected CAD, divided into two categories based on the existence of obstructive CAD. In total, 517 lipid species were identified, from which 288 lipid species were finally quantified, including glycerophospholipids, glycerolipids, and sphingolipids. Univariate and multivariate statistical analyses have shown significant discrimination between the serum lipidomes of patients with obstructive CAD. Finally, the XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, a triacylglycerol, galectin-3, glucose, LDL, and LDH) as totally sensitive (100% sensitivity, 62.1% specificity, 100% negative predictive value) for the prediction of obstructive CAD. Our findings shed light on dysregulated lipid metabolism's role in CAD, validating existing evidence and suggesting promise for novel therapies and improved risk stratification.

Circulating Metabolite Profiles and Risk of Coronary Heart Disease Among Racially and Geographically Diverse Populations

BACKGROUND

Metabolomics may reveal novel biomarkers for coronary heart disease (CHD). We aimed to identify circulating metabolites and construct a metabolite risk score (MRS) associated with incident CHD among racially and geographically diverse populations.

METHODS

Untargeted metabolomics was conducted using baseline plasma samples from 900 incident CHD cases and 900 age-/sex-/race-matched controls (300 pairs of Black Americans, White Americans, and Chinese adults, respectively), which detected 927 metabolites with known identities among ≥80% of samples. After quality control, 896 case-control pairs remained and were randomly divided into discovery (70%) and validation (30%) sets within each race. In the discovery set, conditional logistic regression and least absolute shrinkage and selection operator over 100 subsamples were applied to identify metabolites robustly associated with CHD risk and construct the MRS. The MRS-CHD association was evaluated using conditional logistic regression and the C-index. Mediation analysis was performed to examine if MRS mediated associations between conventional risk factors and incident CHD. The results from the validation set were presented as the main findings.

RESULTS

Twenty-four metabolites selected in ≥90% of subsamples comprised the MRS, which was significantly associated with incident CHD (odds ratio per 1 SD, 2.21 [95% CI, 1.62-3.00] after adjusting for sociodemographics, lifestyles, family history, and metabolic health status). MRS could distinguish incident CHD cases from matched controls (C-index, 0.69 [95% CI, 0.63-0.74]) and improve CHD risk prediction when adding to conventional risk factors (C-index, 0.71 [95% CI, 0.65-0.76] versus 0.67 [95% CI, 0.61-0.73]; P<0.001). The odds ratios and C-index were similar across subgroups defined by race, sex, socioeconomic status, lifestyles, metabolic health, family history, and follow-up duration. The MRS mediated large portions (46.0%-74.2%) of the associations for body mass index, smoking, diabetes, hypertension, and dyslipidemia with incident CHD.

CONCLUSIONS

In a diverse study sample, we identified 24 circulating metabolites that, when combined into an MRS, were robustly associated with incident CHD and modestly improved CHD risk prediction beyond conventional risk factors.

Serum metabolism distribution in individuals exposed to dioxins: A case study of residents near the municipal solid waste incinerators in China

Polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) have attracted considerable attention owing to their environmental persistence, bioaccumulation, and high toxicity. This study aimed to investigate changes in serum metabolites following exposure to PCDD/Fs and to reveal a novel pathogenesis of PCDD/Fs. Serum samples were collected from 75 residents living near a municipal solid waste incinerator in China to analyse the relationship between PCDD/Fs and serum metabolic components. The serum level in the low-exposure group [19.07 (13.44-23.89) pg-TEQ/L] was significantly lower than that in the high-exposure group [115.60 (52.28-592.65) pg-TEQ/L]. Non-targeted metabolomic studies based on liquid chromatography-high resolution mass spectrometry have been applied to the metabolomic analysis of serum. Thirty-seven metabolites with significant differences among the different groups were identified as biomarkers. Pathway analysis revealed that high dioxin exposure perturbed various biological processes, including glycerol phospholipid metabolism and the interconversion of pentose and glucuronate. The results of a population health survey showed that the serum dioxin concentration in patients with diabetes was significantly higher than that in the control population. These findings suggest that dioxin exposure is associated with several potential adverse health risks, including inflammation, diabetes, and cardiovascular disease, through metabolic changes.

Exploring the causal association between genetically determined circulating metabolome and hemorrhagic stroke

Background

Hemorrhagic stroke (HS), a leading cause of death and disability worldwide, has not been clarified in terms of the underlying biomolecular mechanisms of its development. Circulating metabolites have been closely associated with HS in recent years. Therefore, we explored the causal association between circulating metabolomes and HS using Mendelian randomization (MR) analysis and identified the molecular mechanisms of effects.

Methods

We assessed the causal relationship between circulating serum metabolites (CSMs) and HS using a bidirectional two-sample MR method supplemented with five ways: weighted median, MR Egger, simple mode, weighted mode, and MR-PRESSO. The Cochran Q-test, MR-Egger intercept test, and MR-PRESSO served for the sensitivity analyses. The Steiger test and reverse MR were used to estimate reverse causality. Metabolic pathway analyses were performed using MetaboAnalyst 5.0, and genetic effects were assessed by linkage disequilibrium score regression. Significant metabolites were further synthesized using meta-analysis, and we used multivariate MR to correct for common confounders.

Results

We finally recognized four metabolites, biliverdin (OR 0.62, 95% CI 0.40-0.96, PMVMR = 0.030), linoleate (18. 2n6) (OR 0.20, 95% CI 0.08-0.54, PMVMR = 0.001),1-eicosadienoylglycerophosphocholine* (OR 2.21, 95% CI 1.02-4.76, PMVMR = 0.044),7-alpha-hydroxy-3 -oxo-4-cholestenoate (7-Hoca) (OR 0.27, 95% CI 0.09-0.77, PMVMR = 0.015) with significant causal relation to HS.

Conclusion

We demonstrated significant causal associations between circulating serum metabolites and hemorrhagic stroke. Monitoring, diagnosis, and treatment of hemorrhagic stroke by serum metabolites might be a valuable approach.

Plasma metabolites and risk of myocardial infarction: a bidirectional Mendelian randomization study

BACKGROUND

Myocardial infarction (MI) is a critical cardiovascular event with multifaceted etiology, involving several genetic and environmental factors. It is essential to understand the function of plasma metabolites in the development of MI and unravel its complex pathogenesis.

METHODS

This study employed a bidirectional Mendelian randomization (MR) approach to investigate the causal relationships between plasma metabolites and MI risk. We used genetic instruments as proxies for plasma metabolites and MI and conducted MR analyses in both directions to assess the impact of metabolites on MI risk and vice versa. In addition, the large-scale genome-wide association studies datasets was used to identify genetic variants associated with plasma metabolite (1400 metabolites) and MI (20,917 individuals with MI and 440,906 individuals without MI) susceptibility. Inverse variance weighted was the primary method for estimating causal effects. MR estimates are expressed as beta coefficients or odds ratio (OR) with 95% CI.

RESULTS

We identified 14 plasma metabolites associated with the occurrence of MI (P < 0.05), among which 8 plasma metabolites [propionylglycine levels (OR = 0.922, 95% CI: 0.881-0.965, P < 0.001), gamma-glutamylglycine levels (OR = 0.903, 95% CI: 0.861-0.948, P < 0.001), hexadecanedioate (C16-DC) levels (OR = 0.941, 95% CI: 0.911-0.973, P < 0.001), pentose acid levels (OR = 0.923, 95% CI: 0.877-0.972, P = 0.002), X-24546 levels (OR = 0.936, 95% CI: 0.902-0.971, P < 0.001), glycine levels (OR = 0.936, 95% CI: 0.909-0.964, P < 0.001), glycine to serine ratio (OR = 0.930, 95% CI: 0.888-0.974, P = 0.002), and mannose to trans-4-hydroxyproline ratio (OR = 0.912, 95% CI: 0.869-0.958, P < 0.001)] were correlated with a decreased risk of MI, whereas the remaining 6 plasma metabolites [1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) levels (OR = 1.051, 95% CI: 1.018-1.084, P = 0.002), behenoyl dihydrosphingomyelin (d18:0/22:0) levels (OR = 1.076, 95% CI: 1.027-1.128, P = 0.002), 1-stearoyl-2-docosahexaenoyl-GPE (18:0/22:6) levels (OR = 1.067, 95% CI: 1.027-1.109, P = 0.001), alpha-ketobutyrate levels (OR = 1.108, 95% CI: 1.041-1.180, P = 0.001), 5-acetylamino-6-formylamino-3-methyluracil levels (OR = 1.047, 95% CI: 1.019-1.076, P < 0.001), and N-acetylputrescine to (N (1) + N (8))-acetylspermidine ratio (OR = 1.045, 95% CI: 1.018-1.073, P < 0.001)] were associated with an increased risk of MI. Furthermore, we also observed that the mentioned relationships were unaffected by horizontal pleiotropy (P > 0.05). On the contrary, MI did not lead to significant alterations in the levels of the aforementioned 14 plasma metabolites (P > 0.05 for each comparison).

CONCLUSIONS

Our bidirectional MR study identified 14 plasma metabolites associated with the occurrence of MI, among which 13 plasma metabolites have not been reported previously. These findings provide valuable insights for the early diagnosis of MI and potential therapeutic targets.

Plasma levels of urea cycle related amino acids in association with risk of ischemic stroke: Findings from a nested case-control study

OBJECTIVES

The role of urea cycle related amino acids in the development of ischemic stroke (IS) remains unclear. The study aimed to evaluate the association of these amino acids with IS.

MATERIALS AND METHODS

We conducted a case-control study nested within a cohort study in Changshu, Eastern China. A total of 321 cases and 321 controls matched by age and gender were finally included. Plasma levels of ornithine, arginine, spermidine, and proline were measured using ultra-high performance liquid chromatography-tandem mass-spectrometry (UHPLC-MS/MS). Odds ratios (ORs) and their 95 % confidence intervals (CIs) were calculated by conditional logistic regression analyses.

RESULTS

Plasma ornithine was inversely associated with risk of IS [crude OR: 0.62 (95 % CI: 0.40-0.97)]. After adjustment for body mass index, smoking, hypertension, family history of stroke, estimated glomerular filtration rate, and total cholesterol, the corresponding ORs for the highest compared to the lowest quartiles was essentially unchanged [adjusted OR: 0.62 (95 % CI: 0.39-0.99)]. The risk association remained significant after repeating the analyses by excluding the first two years of follow-up. Plasma arginine, spermidine, and proline were not associated with the risk of IS.

CONCLUSION

We observed that higher plasma levels of ornithine were associated with a lower risk of incident IS. Our novel findings suggest a protective role of ornithine in the pathogenesis of IS.

A 6-month exercise intervention clinical trial in women: effects of physical activity on multi-omics biomarkers and health during the first wave of COVID-19 in Korea

BACKGROUND

Coronavirus disease 2019 (COVID-19) was first reported in December 2019 and the first case in Korea was confirmed on January 20, 2020. Due to the absence of therapeutic agents and vaccines, the Korean government implemented social distancing on February 29, 2020. This study aimed to examine the effect of physical activity (PA) on health through changes in multi-omics biomarkers with a 6-month of exercise intervention during the first wave of COVID-19 in Korea.

METHODS

Twenty-seven healthy middle-aged women were recruited and 14 subjects completed the exercise intervention. The mean age (± SD) was 46.3 (± 5.33) and the mean BMI (± SD) was 24.9 (± 3.88). A total of three blood and stool samples were collected at enrollment, after period 1, and after period 2 (3-month intervals). The amount of PA was measured with an accelerometer and by questionnaire. Clinical variables were used, including blood pressure, grip strength, flexibility, and blood glucose levels and lipid markers obtained from laboratory tests. The concentration of blood metabolites was measured by targeted metabolomics. Fecal microbiome data were obtained by 16 S rRNA gene amplicon sequencing.

RESULTS

During the second half period (period 2), Coronavirus disease 2019 occurred and spread out in Korea, and PA decreased compared with the first half period (period 1) (185.9 ± 168.73 min/week to 102.5 ± 82.30 min/week; p = 0.0101). Blood pressure, hemoglobin A1c (HbA1c), and low-density lipoprotein cholesterol (LDL-C) decreased in period 1 (p < 0.05) and tended to increase again during period 2 (p < 0.05). Forty metabolites were changed significantly during period 1 (FDR p < 0.05), and we found that 6 of them were correlated with changes in blood pressure, HbA1c, and LDL-C via network analysis.

CONCLUSIONS

Our results may suggest that exercise improves health through changes in biomarkers at multi-omics levels. However, reduced PA due to COVID-19 can adversely affect health, emphasizing the necessity for sustained exercise and support for home-based fitness to maintain health.

TRIAL REGISTRATION

The trial is retrospectively registered on ClinicalTrials.gov (NCT05927675; June 30, 2023).

Serum branched amino acids and the risk of all-cause mortality: a meta-analysis and systematic review

Recently, the serum levels of branched-chain amino acids (BCAAs) have been considered as an indicator to evaluate health status and predict chronic diseases risk. This systematic review and meta-analysis aimed to assess the relationship between Serum BCAAs and the risk of all-cause mortality. We carried out a comprehensive and systematic search in various important databases, including PubMed, Scopus, and Web of Science databases to find the relevant studies published up to October 2022 with no language, design, or time limitation. We extracted the reported hazard ratio (HR) with 95% confidence interval (CI) and odds ratio (OR) with 95%CI in cohorts and case-control studies, respectively, and computed the log HR or OR and its standard error. Then, we used the random-effects model with inverse variance weighting method for the present meta-analysis, to calculate the pooled effect size. Ten observational studies, including nine cohort studies and one case-control study, were included in the present meta-analysis. The number of participants ranges from 53 to 26,711, with an age range of 18-99 years. During 6 months to 24 years of follow-up, 3599 deaths were ascertained. The pooled results indicated that there was no significant association between serum BCAAs (RR: 1.17; 95% CI 0.85-1.60), isoleucine (RR: 1.41; 95%CI 0.92-2.17), leucine (RR: 1.13; 95% CI 0.94-1.36), and valine (RR: 1.02; 95%CI 0.86-1.22) and all-cause mortality. Also, there was significant heterogeneity between studies for serum BCAAs (I2 = 74.1% and P-heterogeneity = 0.021), isoleucine (I2 = 89.4% and P-heterogeneity < 0.001), leucine (I2 = 87.8% and P-heterogeneity < 0.001), and valine (I2 = 86.6% and P-heterogeneity < 0.001). Our results suggested that the serum BCAAs and its components, including isoleucine, leucine, and valine, were not associated with the risk of all-cause mortality.

Longitudinal lipidomic signature of carotid atherosclerosis in American Indians: Findings from the Strong Heart Family Study

BACKGROUND AND AIMS

Dyslipidemia is an independent risk factor for atherosclerosis and atherosclerotic cardiovascular disease (ASCVD). To date, a comprehensive assessment of individual lipid species associated with atherosclerosis is lacking in large-scale epidemiological studies, especially in a longitudinal setting. We investigated the association of circulating lipid species and its longitudinal changes with carotid atherosclerosis.

METHODS

Using liquid chromatograph-mass spectrometry, we repeatedly measured 1542 lipid species in 3687 plasma samples from 1918 unique American Indians attending two visits (mean ∼5 years apart) in the Strong Heart Family Study. Carotid atherosclerotic plaques were assessed by ultrasonography at each visit. We identified lipids associated with prevalence or progression of carotid plaques, adjusting age, sex, BMI, smoking, hypertension, diabetes, and eGFR. Then we examined whether longitudinal changes in lipids were associated with changes in cardiovascular risk factors. Multiple testing was controlled at false discovery rate (FDR) < 0.05.

RESULTS

Higher levels of sphingomyelins, ether-phosphatidylcholines, and triacylglycerols were significantly associated with prevalence or progression of carotid plaques (odds ratios ranged from 1.15 to 1.34). Longitudinal changes in multiple lipid species (e.g., acylcarnitines, phosphatidylcholines, triacylglycerols) were associated with changes in cardiometabolic traits (e.g., BMI, blood pressure, fasting glucose, eGFR). Network analysis identified differential lipid networks associated with plaque progression.

CONCLUSIONS

Baseline and longitudinal changes in multiple lipid species were significantly associated with carotid atherosclerosis and its progression in American Indians. Some plaque-related lipid species were also associated with risk for CVD events.

Serum branch-chained amino acids are increased in type 2 diabetes and associated with atherosclerotic cardiovascular disease

BACKGROUND AND AIM

Circulating biomarkers of metabolic and cardiovascular diseases can help in the early detection and prevention of those diseases. Using proton nuclear magnetic resonance (1H-NMR), we aimed to study the plasma levels of low-molecular-weight metabolites (LMWMs) in a cohort of 307 patients with metabolic diseases to assess their relationships with type-2 diabetes (T2D) and incident atherosclerotic cardiovascular disease (ASCVD).

METHODS

We conducted a cross-sectional and prospective study. We included 307 patients attending the Lipid Unit of our University Hospital for the treatment of the following metabolic disturbances and associated disorders: T2D (73.9%), obesity (58.7%), and hypertension (55.1%). 1H-NMR was used to study the plasma levels of 13 LMWMs. LMWM serum concentrations were evaluated in patients with and without T2D. and the correlations with several parameters and their associations with T2D were analyzed. The association between LMWM levels at baseline and the development of ASCVD in patients with T2D after 10 years of follow-up was also evaluated.

RESULTS

Among the LMWMs measured, the branched-chain amino acids (BCAAs) valine, leucine and isoleucine showed a positive association with several clinical and lipid-related biochemical parameters and inflammatory markers (p < 0.05). Likewise, these three BCAAS were associated with diabetes even after adjusting for covariates (p < 0.05). During the follow-up period of 10 years, 29 of the 185 patients with diabetes at baseline (15.68%) developed ASCVD. After adjusting for clinical covariates, baseline levels of valine and alanine were associated with the development of ASCVD (p < 0.05).

CONCLUSION

Overall, our results indicated that plasma levels of LMWMs measured by 1H-NMR could be potential biomarkers associated with T2D. Moreover, alanine and valine can help in the early detection of the cardiovascular risk associated with this metabolic disease.

Longitudinal lipidomic signatures of all-cause and CVD mortality in American Indians: findings from the Strong Heart Study

Dyslipidemia is an independent and modifiable risk factor for aging and age-related disorders. Routine lipid panel cannot capture all individual lipid species in blood (i.e., blood lipidome). To date, a comprehensive assessment of the blood lipidome associated with mortality is lacking in large-scale community-dwelling individuals, especially in a longitudinal setting. Using liquid chromatograph-mass spectrometry, we repeatedly measured individual lipid species in 3,821 plasma samples collected at two visits (~ 5.5 years apart) from 1,930 unique American Indians in the Strong Heart Family Study. We first identified baseline lipids associated with risks for all-cause mortality and CVD mortality (mean follow-up period: 17.8 years) in American Indians, followed by replication of top hits in European Caucasians in the Malmö Diet and Cancer-Cardiovascular Cohort (n = 3,943, mean follow-up period: 23.7 years). The model adjusted age, sex, BMI, smoking, hypertension, diabetes, and LDL-c at baseline. We then examined the associations between changes in lipid species and risk of mortality. Multiple testing was controlled by false discovery rate (FDR). We found that baseline levels and longitudinal changes of multiple lipid species, e.g., cholesterol esters, glycerophospholipids, sphingomyelins, and triacylglycerols, were significantly associated with risks of all-cause or CVD mortality. Many lipids identified in American Indians could be replicated in European Caucasians. Network analysis identified differential lipid networks associated with risk of mortality. Our findings provide novel insight into the role of dyslipidemia in disease mortality and offer potential biomarkers for early prediction and risk reduction in American Indians and other ethnic groups.

Gut microbiota-associated metabolites and risk of ischemic stroke in REGARDS

Several metabolite markers are independently associated with incident ischemic stroke. However, prior studies have not accounted for intercorrelated metabolite networks. We used exploratory factor analysis (EFA) to determine if metabolite factors were associated with incident ischemic stroke. Metabolites (n = 162) were measured in a case-control cohort nested in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study, which included 1,075 ischemic stroke cases and 968 random cohort participants. Cox models were adjusted for age, gender, race, and age-race interaction (base model) and further adjusted for the Framingham stroke risk factors (fully adjusted model). EFA identified fifteen metabolite factors, each representing a well-defined metabolic pathway. Of these, factor 3, a gut microbiome metabolism factor, was associated with an increased risk of stroke in the base (hazard ratio per one-unit standard deviation, HR = 1.23; 95%CI = 1.15-1.31; P = 1.98 × 10-10) and fully adjusted models (HR = 1.13; 95%CI = 1.06-1.21; P = 4.49 × 10-4). The highest tertile had a 45% increased risk relative to the lowest (HR = 1.45; 95%CI = 1.25-1.70; P = 2.24 × 10-6). Factor 3 was also associated with the Southern diet pattern, a dietary pattern previously linked to increased stroke risk in REGARDS (β = 0.11; 95%CI = 0.03-0.18; P = 8.75 × 10-3). These findings highlight the role of diet and gut microbial metabolism in relation to incident ischemic stroke.

The roles and mechanisms of gut microbiome and metabolome in patients with cerebral infarction

As the most common type of stroke, ischemic stroke, also known as cerebral infarction (CI), with its high mortality and disability rate, has placed a huge burden on social economy and public health. Treatment methods for CI mainly include thrombectomy, thrombolysis, drug therapy, and so on. However, these treatments have certain timeliness and different side effects. In recent years, the gut-brain axis has become a hot topic, and its role in nervous system diseases has been confirmed by increasing evidences. The intestinal microbiota, as an important part of the gut-brain axis, has a non-negligible impact on the progression of CI through mechanisms such as inflammatory response and damage-associated molecular patterns, and changes in the composition of intestinal microbiota can also serve as the basis for predicting CI. At the same time, the diagnosis of CI requires more high-throughput techniques, and the analysis method of metabolomics just fits this demand. This paper reviewed the changes of intestinal microbiota in patients within CI and the effects of the intestinal microbiota on the course of CI, and summarized the therapeutic methods of the intervention with the intestinal microbiota. Furthermore, metabolic changes of CI patients were also discussed to reveal the molecular characteristics of CI and to elucidate the potential pathologic pathway of its interference.

Large-Scale Metabolomics and the Incidence of Cardiovascular Disease

Background The study aimed to show the relationship between a large number of circulating metabolites and subsequent cardiovascular disease (CVD) and subclinical markers of CVD in the general population. Methods and Results In 2278 individuals free from CVD in the EpiHealth study (aged 45-75 years, mean age 61 years, 50% women), 790 annotated nonxenobiotic metabolites were measured by mass spectroscopy (Metabolon). The same metabolites were measured in the PIVUS (Prospective Investigation of Vasculature in Uppsala Seniors) study (n=603, all aged 80 years, 50% women), in which cardiac and carotid artery pathologies were evaluated by ultrasound. During a median follow-up of 8.6 years, 107 individuals experienced a CVD (fatal or nonfatal myocardial infarction, stroke, or heart failure) in EpiHealth. Using a false discovery rate of 0.05 for age- and sex-adjusted analyses and P<0.05 for adjustment for traditional CVD risk factors, 37 metabolites were significantly related to incident CVD. These metabolites belonged to multiple biochemical classes, such as amino acids, lipids, and nucleotides. Top findings were dimethylglycine and N-acetylmethionine. A lasso selection of 5 metabolites improved discrimination when added on top of traditional CVD risk factors (+4.0%, P=0.0054). Thirty-five of the 37 metabolites were related to subclinical markers of CVD evaluated in the PIVUS study. The metabolite 1-carboxyethyltyrosine was associated with left atrial diameter as well as inversely related to both ejection fraction and the echogenicity of the carotid artery. Conclusions Several metabolites were discovered to be associated with future CVD, as well as with subclinical markers of CVD. A selection of metabolites improved discrimination when added on top of CVD risk factors.

Metabolic signatures in pericardial fluid and serum are associated with new-onset atrial fibrillation after isolated coronary artery bypass grafting

Postoperative atrial fibrillation (POAF) is a common complication of coronary artery bypass grafting (CABG) procedures. However, the molecular mechanism of POAF remains poorly understood, hence the absence of effective prevention strategies. Here we used targeted metabolomics on pericardial fluid and serum samples from CABG patients to investigate POAF-associated metabolic alterations and related risk prediction of new-onset AF. Nine differential metabolites in various metabolic pathways were found in both pericardial fluid and serum samples from patients with POAF and without POAF. By using machine learning algorithms and regression models, a 4-metabolite (aceglutamide, ornithine, methionine, and arginine) risk prediction model was constructed and showed accurate performance in predicting POAF in both discovery and validation sets. This work extends the metabolic insights of the cardiac microenvironment and blood in patients with POAF and paves the way for the use of targeted metabolomics for predicting POAF in patients with CABG surgery.

Metabolomics as a potential tool for monitoring patients with aneurysmal subarachnoid hemorrhage

Metabolomics has evolved into a particularly useful tool to study interactions between metabolites and serves as an aid in unraveling the complexity of entire metabolomes. Nonetheless, it is increasingly viewed as a methodology with practical applications in the clinical setting, where identifying and quantifying biomarkers of interest could prove useful for diagnostics. Starting from a concise overview of the most prominent analytical techniques employed in metabolomics, herein we present a review of its application in studies of brain metabolism and cerebrovascular diseases, paying most attention to its uses in researching aneurysmal subarachnoid hemorrhage. Both animal models and human studies are considered, and metabolites identified as potential biomarkers are highlighted.

Momordicine I alleviates isoproterenol-induced cardiomyocyte hypertrophy through suppression of PLA2G6 and DGK-ζ

This study aimed to observe the protective effect of momordicine I, a triterpenoid compound extracted from momordica charantia L., on isoproterenol (ISO)-induced hypertrophy in rat H9c2 cardiomyocytes and investigate its potential mechanism. Treatment with 10 μM ISO induced cardiomyocyte hypertrophy as evidenced by increased cell surface area and protein content as well as pronounced upregulation of fetal genes including atrial natriuretic peptide, β-myosin heavy chain, and α-skeletal actin; however, those responses were markedly attenuated by treatment with 12.5 μg/ml momordicine I. Transcriptome experiment results showed that there were 381 and 447 differentially expressed genes expressed in comparisons of model/control and momordicine I intervention/model, respectively. GO enrichment analysis suggested that the anti-cardiomyocyte hypertrophic effect of momordicine I may be mainly associated with the regulation of metabolic processes. Based on our transcriptome experiment results as well as literature reports, we selected glycerophospholipid metabolizing enzymes group VI phospholipase A₂ (PLA2G6) and diacylglycerol kinase ζ (DGK-ζ) as targets to further explore the potential mechanism through which momordicine I inhibited ISO-induced cardiomyocyte hypertrophy. Our results demonstrated that momordicine I inhibited ISO-induced upregulations of mRNA levels and protein expressions of PLA2G6 and DGK-ζ. Collectively, momordicine I alleviated ISO-induced cardiomyocyte hypertrophy, which may be related to its inhibition of the expression of glycerophospholipid metabolizing enzymes PLA2G6 and DGK-ζ.

Sex as a biological variable in determining the metabolic changes influencing acute ischemic stroke outcomes-Where is the data: A systematic review

Women continue to face a greater lifetime morbidity and mortality from stroke and have been shown to respond differently to stroke treatments compared to men. Since 2016, updated National Institutes of Health (NIH) policies require research studies to consider sex as a biological variable. However, the way in which this policy affects study design, analysis, and reporting is variable, with few studies performing and reporting a subgroup analysis based on biological sex. In acute ischemic stroke, the underlying biological explanation for sex-based differences in patient outcomes and response to treatments remains understudied. We performed a systematic review of preclinical and clinical research studies that explored sex differences in the metabolic response to acute ischemic stroke as it relates to neurological outcomes. Through a literature search in Ovid Medline, Embase, and Web of Science, 1,004 potential references were identified for screening. After abstract and full-text review, we identified only two studies which assessed metabolic response to acute ischemic stroke (within 72 h of last known well) and neurological outcome [Barthel Index, modified Rankin Scale (mRS) or an equivalent in preclinical models] and reported results based on biological sex. One article was a preclinical rat model and the other a clinical cohort study. In both studies, metabolites involved in amino acid metabolism, energy metabolism, fat metabolism, or oxidative stress were identified. We review these results and link to additional articles that use metabolomics to identify metabolites differentially expressed by sex or regulated based on stroke outcomes, but not both. The results of this systematic review should not only help identify targets in need of further investigation to improve the understanding of sex differences in the pathophysiology of acute ischemic stroke, but also highlight the critical need to expand the incorporation of sex as a biological variable in acute stroke research beyond simply including both sexes and reporting the proportion of males/females in each population studied.

Associations of Early Pregnancy Metabolite Profiles with Gestational Blood Pressure Development

Blood pressure development plays a major role in both the etiology and prediction of gestational hypertensive disorders. Metabolomics might serve as a tool to identify underlying metabolic mechanisms in the etiology of hypertension in pregnancy and lead to the identification of novel metabolites useful for the prediction of gestational hypertensive disorders. In a population-based, prospective cohort study among 803 pregnant women, liquid chromatography—mass spectrometry was used to determine serum concentrations of amino-acids, non-esterified fatty acids, phospholipids and carnitines in early pregnancy. Blood pressure was measured in each trimester of pregnancy. Information on gestational hypertensive disorders was obtained from medical records. Higher individual metabolite concentrations of the diacyl-phosphatidylcholines and acyl-lysophosphatidylcholines group were associated with higher systolic blood pressure throughout pregnancy (Federal Discovery Rate (FDR)-adjusted p-values < 0.05). Higher concentrations of one non-esterified fatty acid were associated with higher diastolic blood pressure throughout pregnancy (FDR-adjusted p-value < 0.05). Using penalized regression, we identified 12 individual early-pregnancy amino-acids, non-esterified fatty acids, diacyl-phosphatidylcholines and acyl-carnitines and the glutamine/glutamic acid ratio, that were jointly associated with larger changes in systolic and diastolic blood pressure from first to third trimester. These metabolites did not improve the prediction of gestational hypertensive disorders in addition to clinical markers. In conclusion, altered early pregnancy serum metabolite profiles mainly characterized by changes in non-esterified fatty acids and phospholipids metabolites are associated with higher gestational blood pressure throughout pregnancy within the physiological ranges. These findings are important from an etiological perspective and, after further replication, might improve the early identification of women at increased risk of gestational hypertensive disorders.

Evaluating the association between amino acid and acylcarnitine profiles and different levels of coronary artery disease risk in postmenopausal women using targeted metabolomics technique

OBJECTIVES

Postmenopausal women are at increased risk of developing coronary artery disease (CAD). Metabolomic approaches aim at discovering more helpful biomarkers of CAD to reduce the disease burden in the future. Here, we intend to find potential blood biomarkers, amino acids, and acylcarnitines in postmenopausal women with different severity of CAD by using high-throughput methods.

METHOD

This cross-sectional study was performed on postmenopausal women ( n = 183) who underwent coronary CT scans. Coronary artery calcium scoring (CACS) was assessed to detect plaque burden and degree of coronary artery obstruction. The participants were divided into three groups based on the score as follows (i) "low CACS" ( n = 96); a score of 0 to 10, (ii) "medium CACS" ( n = 35); a score between 11 and 100 and (iii) "high CACS" ( n = 52); a score greater than 100. Metabolites, including amino acids and acylcarnitines, were quantified using a targeted mass spectrometry method in serum samples. The association between metabolites and disease status was evaluated using univariate and multivariate regression analyses with adjustment for confounding factors. Factor analysis was used to deal with multiple comparisons.

RESULTS

Metabolites, including proline, glutamic acid, and phenylalanine, were significantly lower in the high CACS group than the low CACS one. Also, a lower level of lysine and phenylalanine in high CACS compared with medium one was observed. Concerning acylcarnitines, it was found that C4 and C8:1 significantly were higher in women with high CACS. The logistic regression analysis revealed that the circulating levels of these metabolites (except C4) were associated with the presence of coronary artery calcification independently of age, body mass index, and time of menopause. Also, the amino acids were associated independently of medication and diabetes.

CONCLUSIONS

The present study indicated that circulating levels of amino acids and acylcarnitines profile in postmenopausal women are partly associated with the severity of CAD in these participants.

Serum Orotidine: A Novel Biomarker of Increased CVD Risk in Type 2 Diabetes Discovered Through Metabolomics Studies

OBJECTIVE

To identify novel biomarkers of cardiovascular disease (CVD) risk in type 2 diabetes (T2D) via a hypothesis-free global metabolomics study, while taking into account renal function, an important confounder often overlooked in previous metabolomics studies of CVD.

RESEARCH DESIGN AND METHODS

We conducted a global serum metabolomics analysis using the Metabolon platform in a discovery set from the Joslin Kidney Study having a nested case-control design comprising 409 individuals with T2D. Logistic regression was applied to evaluate the association between incident CVD events and each of the 671 metabolites detected by the Metabolon platform, before and after adjustment for renal function and other CVD risk factors. Significant metabolites were followed up with absolute quantification assays in a validation set from the Joslin Heart Study including 599 individuals with T2D with and without clinical evidence of significant coronary heart disease (CHD).

RESULTS

In the discovery set, serum orotidine and 2-piperidinone were significantly associated with increased odds of incident CVD after adjustment for glomerular filtration rate (GFR) (odds ratio [OR] per SD increment 1.94 [95% CI 1.39-2.72], P = 0.0001, and 1.62 [1.26-2.08], P = 0.0001, respectively). Orotidine was also associated with increased odds of CHD in the validation set (OR 1.39 [1.11-1.75]), while 2-piperidinone did not replicate. Furthermore, orotidine, being inversely associated with GFR, mediated 60% of the effects of declining renal function on CVD risk. Addition of orotidine to established clinical predictors improved (P < 0.05) C statistics and discrimination indices for CVD risk (ΔAUC 0.053, rIDI 0.48, NRI 0.42) compared with the clinical predictors alone.

CONCLUSIONS

Through a robust metabolomics approach, with independent validation, we have discovered serum orotidine as a novel biomarker of increased odds of CVD in T2D, independent of renal function. Additionally, orotidine may be a biological mediator of the increased CVD risk associated with poor kidney function and may help improve CVD risk prediction in T2D.

The newborn metabolome: associations with gestational diabetes, sex, gestation, birth mode, and birth weight

BACKGROUND

Pathways towards many adult-onset conditions begin early in life, even in utero. Maternal health in pregnancy influences this process, but little is known how it affects neonatal metabolism. We investigated associations between pregnancy and birth factors and cord blood metabolomic profile in a large, population-derived cohort.

METHODS

Metabolites were measured using nuclear magnetic resonance in maternal (28 weeks gestation) and cord serum from 912 mother-child pairs in the Barwon Infant Study pre-birth cohort. Associations between maternal (metabolites, age, BMI, smoking), pregnancy (pre-eclampsia, gestational diabetes (GDM)), and birth characteristics (delivery mode, gestational age, weight, infant sex) with 72 cord blood metabolites were examined by linear regression.

RESULTS

Delivery mode, sex, gestational age, and birth weight were associated with specific metabolite levels in cord blood, including amino acids, fatty acids, and cholesterols. GDM was associated with higher cord blood levels of acetoacetate and 3-hydroxybutyrate.

CONCLUSIONS

Neonatal factors, particularly delivery mode, were associated with many cord blood metabolite differences, including those implicated in later risk of cardiometabolic disease. Associations between GDM and higher offspring ketone levels at birth are consistent with maternal ketosis in diabetic pregnancies. Further work is needed to determine whether these neonatal metabolome differences associate with later health outcomes.

IMPACT

Variations in blood metabolomic profile have been linked to health status in adults and children, but corresponding data in neonates are scarce. We report evidence that pregnancy complications, mode of delivery, and offspring characteristics, including sex, are independently associated with a range of circulating metabolites at birth, including ketone bodies, amino acids, cholesterols, and inflammatory markers. Independent of birth weight, exposure to gestational diabetes is associated with higher cord blood ketone bodies and citrate. These findings suggest that pregnancy complications, mode of delivery, gestational age, and measures of growth influence metabolic pathways prior to birth, potentially impacting later health and development.

Innovative Approaches to Assess Intermediate Cardiovascular Risk Subjects: A Review From Clinical to Metabolomics Strategies

Current risk stratification strategies for coronary artery disease (CAD) have low predictive value in asymptomatic subjects classified as intermediate cardiovascular risk. This is relevant because not all coronary events occur in individuals with traditional multiple risk factors. Most importantly, the first manifestation of the disease may be either sudden cardiac death or acute coronary syndrome, after rupture and thrombosis of an unstable non-obstructive atherosclerotic plaque, which was previously silent. The inaccurate stratification using the current models may ultimately subject the individual to excessive or insufficient preventive therapies. A breakthrough in the comprehension of the molecular mechanisms governing the atherosclerosis pathology has driven many researches toward the necessity for a better risk stratification. In this Review, we discuss how metabolomics screening integrated with traditional risk assessments becomes a powerful approach to improve non-invasive CAD subclinical diagnostics. In addition, this Review highlights the findings of metabolomics studies performed by two relevant analytical platforms in current use-mass spectrometry (MS) hyphenated to separation techniques and nuclear magnetic resonance spectroscopy (NMR) -and evaluates critically the challenges for further clinical implementation of metabolomics data. We also discuss the modern understanding of the pathophysiology of atherosclerosis and the limitations of traditional analytical methods. Our aim is to show how discriminant metabolites originated from metabolomics approaches may become promising candidate molecules to aid intermediate risk patient stratification for cardiovascular events and how these tools could successfully meet the demands to translate cardiovascular metabolic biomarkers into clinical settings.

Effect of a 1-Year Controlled Lifestyle Intervention on Body Weight and Other Risk Markers (the Healthy Lifestyle Community Programme, Cohort 2)

INTRODUCTION

The prevalence of obesity is high and increasing worldwide. Obesity is generally associated with an increased risk of chronic disease and mortality. The objective of the study was to test the effect of a lifestyle intervention on body weight and other chronic disease risk markers.

METHODS

A non-randomized controlled trial was conducted, including mostly middle-aged and elderly participants recruited from the general population in rural northwest Germany (intervention: n = 114; control: n = 87). The intervention consisted of a 1-year lifestyle programme, focussing on four key areas: a largely plant-based diet (strongest emphasis), physical activity, stress management, and community support. Parameters were assessed at baseline, 10 weeks, 6 months, and 1 year. The control group received no intervention.

RESULTS

Compared to the control, in the intervention group, significantly lower 1-year trajectories were observed for body weight, body mass index (BMI), waist circumference (WC), total cholesterol, calculated LDL cholesterol, non-HDL cholesterol, remnant cholesterol (REM-C), glucose, HbA1c, and resting heart rate (RHR). However, between-group differences at 1 year were small for glucose, HbA1c, and cholesterol (apart from REM-C). No significant between-group differences were found for 1-year trajectories of measured LDL cholesterol, HDL cholesterol, triglycerides, insulin, blood pressure, and pulse pressure.

CONCLUSION

The intervention successfully reduced body weight, BMI, WC, REM-C, and RHR. However, at 1 year, effectiveness of the intervention regarding other risk markers was either very modest or could not be shown.

Towards risk stratification and prediction of disease severity and mortality in COVID-19: Next generation metabolomics for the measurement of host response to COVID-19 infection

This study investigated the association between COVID-19 infection and host metabolic signatures as prognostic markers for disease severity and mortality. We enrolled 82 patients with RT-PCR confirmed COVID-19 infection who were classified as mild, moderate, or severe/critical based upon their WHO clinical severity score and compared their results with 31 healthy volunteers. Data on demographics, comorbidities and clinical/laboratory characteristics were obtained from medical records. Peripheral blood samples were collected at the time of clinical evaluation or admission and tested by quantitative mass spectrometry to characterize metabolic profiles using selected metabolites. The findings in COVID-19 (+) patients reveal changes in the concentrations of glutamate, valeryl-carnitine, and the ratios of Kynurenine/Tryptophan (Kyn/Trp) to Citrulline/Ornithine (Cit/Orn). The observed changes may serve as predictors of disease severity with a (Kyn/Trp)/(Cit/Orn) Receiver Operator Curve (ROC) AUC = 0.95. Additional metabolite measures further characterized those likely to develop severe complications of their disease, suggesting that underlying immune signatures (Kyn/Trp), glutaminolysis (Glutamate), urea cycle abnormalities (Cit/Orn) and alterations in organic acid metabolism (C5) can be applied to identify individuals at the highest risk of morbidity and mortality from COVID-19 infection. We conclude that host metabolic factors, measured by plasma based biochemical signatures, could prove to be important determinants of Covid-19 severity with implications for prognosis, risk stratification and clinical management.

Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants

Background

Metabolic syndrome (MetS) is a cluster of multiple cardiometabolic risk factors that increase the risk of type 2 diabetes and cardiovascular diseases. Identifying novel biomarkers of MetS and their genetic associations could provide insights into the mechanisms of cardiometabolic diseases.

Methods

Potential MetS-associated metabolites were screened and internally validated by untargeted metabolomics analyses among 693 patients with MetS and 705 controls. External validation was conducted using two well-established targeted metabolomic methods among 149 patients with MetS and 253 controls. The genetic associations of metabolites were determined by linear regression using our previous genome-wide SNP data. Causal relationships were assessed using a one-sample Mendelian Randomization (MR) approach.

Findings

Nine metabolites were ultimately found to be associated with MetS or its components. Five metabolites, including LysoPC(14:0), LysoPC(15:0), propionyl carnitine, phenylalanine, and docosapentaenoic acid (DPA) were selected to construct a metabolite risk score (MRS), which was found to have a dose-response relationship with MetS and metabolic abnormalities. Moreover, MRS displayed a good ability to differentiate MetS and metabolic abnormalities. Three SNPs (rs11635491, rs7067822, and rs1952458) were associated with LysoPC(15:0). Two SNPs, rs1952458 and rs11635491 were found to be marginally correlated with several MetS components. MR analyses showed that a higher LysoPC(15:0) level was causally associated with the risk of overweight/obesity, dyslipidaemia, high uric acid, high insulin and high HOMA-IR.

Interpretation

We identified five metabolite biomarkers of MetS and three SNPs associated with LysoPC(15:0). MR analyses revealed that abnormal LysoPC metabolism may be causally linked the metabolic risk.

Funding

This work was supported by grants from the National Key Research and Development Program of China (2017YFC0907004).

Metabolomic Profiles of Men and Women Ischemic Stroke Patients

Background: Stroke is known to affect both men and women; however, incidence and outcomes differ between them. Therefore, the discovery of novel, sex-specific, blood-based biomarkers for acute ischemic stroke (AIS) patients has the potential to enhance the understanding of the etiology of this deadly disease in the content of sex. The objective of this study was to identify serum metabolites associated with male and female AIS patients. Methods: Metabolites were measured with the use of untargeted, reverse-phase ultra-performance liquid chromatography-tandem mass spectrometry quantification from blood specimens collected from AIS patients. Samples were collected from 36 patients comprising each of 18 men and women with matched controls. Metabolic pathway analysis and principal component analysis (PCA) was used to differentiate metabolite profiles for male and female AIS patients from the control, while logistic regression was used to determine differences in metabolites between male and female AIS patients. Results: In female AIS patients, 14 distinct altered metabolic pathways and 49 corresponding metabolites were identified, while 39 metabolites and 5 metabolic pathways were identified in male patients. Metabolites that are predictive of ischemic stroke in female patients were 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4) (AUC = 0.914, 0.765–1.000), 1-(1-enyl-palmitoyl)-2-palmitoyl-GPC (P-16:0/16:0) (AUC = 0.840, 0.656–1.000), and 5,6-dihydrouracil (P-16:0/20:2) (AUC = 0.815, 0.601–1.000). Significant metabolites that were predictive of stroke in male patients were 5alpha-androstan-3alpha,17beta-diol disulfate (AUC = 0.951, 0.857–1.000), alpha-hydroxyisocaproate (AUC = 0.938, 0.832–1.000), threonate (AUC = 0.877, 0.716–1.000), and bilirubin (AUC = 0.817, 0.746–1.000). Conclusions: In the current study, the untargeted serum metabolomics platform identified multiple pathways and metabolites associated with male and female AIS patients. Further research is necessary to characterize how these metabolites are associated with the pathophysiology in male and female AIS patients.

Lipid Metabolite Biomarkers in Cardiovascular Disease: Discovery and Biomechanism Translation from Human Studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phospholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites.

Metabolomic Profile of Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA) is characterized by structural deterioration of the aortic wall, leading to aortic dilation and rupture. The aim was to compare 183 low molecular weight metabolites in AAA patients and aorta-healthy controls and to explore if low molecular weight metabolites are linked to AAA growth. Blood samples were collected from male AAA patients with fast (mean 3.3 mm/year; range 1.3-9.4 mm/year; n = 39) and slow growth (0.2 mm/year; range -2.6-1.1 mm/year; n = 40), and from controls with non-aneurysmal aortas (n = 79). Targeted analysis of 183 metabolites in plasma was performed with AbsoluteIDQ p180 kit. The samples were measured on a QTRAP 4500 coupled to an Agilent 1260 series HPLC. The levels of only four amino acids (histidine, asparagine, leucine, isoleucine) and four phosphatidylcholines (PC.ae.C34.3, PC.aa.C34.2, PC.ae.C38.0, lysoPC.a.C18.2) were found to be significantly lower (p < 0.05) after adjustment for confounders among the AAA patients compared with the controls. There were no differences in the metabolites distinguishing the AAA patients with slow or fast growth from the controls, or distinguishing the patients with slow growth from those with fast growth. The current study describes novel significant alterations in amino acids and phosphatidylcholines metabolism associated with AAA occurrence, but no associations were found with AAA growth rate.

Metabolomics and metabolites in ischemic stroke

Stroke is a major reason for disability and the second highest cause of death in the world. When a patient is admitted to a hospital, it is necessary to identify the type of stroke, and the likelihood for development of a recurrent stroke, vascular dementia, and depression. These factors could be determined using different biomarkers. Metabolomics is a very promising strategy for identification of biomarkers. The advantage of metabolomics, in contrast to other analytical techniques, resides in providing low molecular weight metabolite profiles, rather than individual molecule profiles. Technically, this approach is based on mass spectrometry and nuclear magnetic resonance. Furthermore, variations in metabolite concentrations during brain ischemia could alter the principal neuronal functions. Different markers associated with ischemic stroke in the brain have been identified including those contributing to risk, acute onset, and severity of this pathology. In the brain, experimental studies using the ischemia/reperfusion model (IRI) have shown an impaired energy and amino acid metabolism and confirmed their principal roles. Literature data provide a good basis for identifying markers of ischemic stroke and hemorrhagic stroke and understanding metabolic mechanisms of these diseases. This opens an avenue for the successful use of identified markers along with metabolomics technologies to develop fast and reliable diagnostic tools for ischemic and hemorrhagic stroke.

Differential modulation of polyunsaturated fatty acids in patients with myocardial infarction treated with ticagrelor or clopidogrel

Untargeted metabolomics is used to refine the development of biomarkers for the diagnosis of cardiovascular disease. Myocardial infarction (MI) has major individual and societal consequences for patients, who remain at high risk of secondary events, despite advances in pharmacological therapy. To monitor their differential response to treatment, we performed untargeted plasma metabolomics on 175 patients from the platelet inhibition and patient outcomes (PLATO) trial treated with ticagrelor and clopidogrel, two common P₂Y₁₂ inhibitors. We identified a signature that discriminates patients, which involves polyunsaturated fatty acids (PUFAs) and particularly the omega-3 fatty acids docosahexaenoate and eicosapentaenoate. The known cardiovascular benefits of PUFAs could contribute to the efficacy of ticagrelor. Our work, beyond pointing out the high relevance of untargeted metabolomics in evaluating response to treatment, establishes PUFA metabolism as a pathway of clinical interest in the recovery path from MI.

We detect an extreme metabolomic signature of myocardial infarction (MI) in plasma

Polyunsaturated fatty acids (PUFAs) are upregulated in patients taking ticagrelor

PUFA metabolism is a pathway of clinical interest in the recovery path from MI

Data science methods detect biologically meaningful patterns in metabolite signals

Plasma Metabolites Alert Patients With Chest Pain to Occurrence of Myocardial Infarction

Myocardial infarction (MI) is one of the leading causes of death worldwide, and knowing the early warning signs of MI is lifesaving. To expand our knowledge of MI, we analyzed plasma metabolites in MI and non-MI chest pain cases to identify markers for alerting about MI occurrence based on metabolomics. A total of 230 volunteers were recruited, consisting of 146 chest pain patients admitted with suspected MI (85 MIs and 61 non-MI chest pain cases) and 84 control individuals. Non-MI cardiac chest pain cases include unstable angina (UA), myocarditis, valvular heart diseases, etc. The blood samples of all suspected MI cases were collected not longer than 6 h since the onset of chest pain. Gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry were applied to identify and quantify the plasma metabolites. Multivariate statistical analysis was utilized to analyze the data, and principal component analysis showed MI could be clearly distinguished from non-MI chest pain cases (including UA and other cases) in the scores plot of metabolomic data, better than that based on the data constructed with medical history and clinical biochemical parameters. Pathway analysis highlighted an upregulated methionine metabolism and downregulated arginine biosynthesis in MI cases. Receiver operating characteristic curve (ROC) and adjusted odds ratio (OR) were calculated to evaluate potential markers for the diagnosis and prediction ability of MI (MI vs. non-MI cases). Finally, gene expression profiles from the Gene Expression Omnibus (GEO) database were briefly discussed to study differential metabolites' connection with plasma transcriptomics. Deoxyuridine (dU), homoserine, and methionine scored highly in ROC analysis (AUC > 0.91), sensitivity (>80%), and specificity (>94%), and they were correlated to LDH and AST (p < 0.05). OR values suggested, after adjusting for gender, age, lipid levels, smoking, type II diabetes, and hypertension history, that high levels of dU of positive logOR = 3.01, methionine of logOR = 3.48, and homoserine of logOR = 1.61 and low levels of isopentenyl diphosphate (IDP) of negative logOR = -5.15, uracil of logOR = -2.38, and arginine of logOR = -0.82 were independent risk factors of MI. Our study highlighted that metabolites belonging to pyrimidine, methionine, and arginine metabolism are deeply influenced in MI plasma samples. dU, homoserine, and methionine are potential markers to recognize MI cases from other cardiac chest pain cases after the onset of chest pains. Individuals with high plasma abundance of dU, homoserine, or methionine have increased risk of MI, too.

Narrative review of metabolomics in cardiovascular disease

Cardiovascular diseases are accompanied by disorders in the cardiac metabolism. Furthermore, comorbidities often associated with cardiovascular disease can alter systemic and myocardial metabolism contributing to worsening of cardiac performance and health status. Biomarkers such as natriuretic peptides or troponins already support diagnosis, prognosis and treatment of patients with cardiovascular diseases and are represented in international guidelines. However, as cardiovascular diseases affect various pathophysiological pathways, a single biomarker approach cannot be regarded as ideal to reveal optimal clinical application. Emerging metabolomics technology allows the measurement of hundreds of metabolites in biological fluids or biopsies and thus to characterize each patient by its own metabolic fingerprint, improving our understanding of complex diseases, significantly altering the management of cardiovascular diseases and possibly personalizing medicine. This review outlines current knowledge, perspectives as well as limitations of metabolomics for diagnosis, prognosis and treatment of cardiovascular diseases such as heart failure, atherosclerosis, ischemic and non-ischemic cardiomyopathy. Furthermore, an ongoing research project tackling current inconsistencies as well as clinical applications of metabolomics will be discussed. Taken together, the application of metabolomics will enable us to gain more insights into pathophysiological interactions of metabolites and disease states as well as improving therapies of patients with cardiovascular diseases in the future.

Uric Acid and Gluconic Acid as Predictors of Hyperglycemia and Cytotoxic Injury after Stroke

Hyperglycemia is a feature of worse brain injury after acute ischemic stroke, but the underlying metabolic changes and the link to cytotoxic brain injury are not fully understood. In this observational study, we applied regression and machine learning classification analyses to identify metabolites associated with hyperglycemia and a neuroimaging proxy for cytotoxic brain injury. Metabolomics and lipidomics were carried out using liquid chromatography-tandem mass spectrometry in admission plasma samples from 381 patients presenting with an acute stroke. Glucose was measured by a central clinical laboratory, and a subgroup of patients (n = 201) had apparent diffusion coefficient (ADC) imaging quantified on magnetic resonance imaging (MRI) to estimate cytotoxic injury. Uric acid was the leading metabolite in univariate analysis of both hyperglycemia (OR 19.6, 95% CI 8.6-44.7, P = 1.44 × 10-12) and ADC (OR 5.3, 95% CI 2.2-13.0, P = 2.42 × 10-4). To further prioritize model features and account for non-linear correlation structure, a random forest machine learning algorithm was applied to separately model hyperglycemia and ADC. The statistical techniques used have identified uric acid and gluconic acids as leading candidate markers common to all models (R2 = 68%, P = 2.2 × 10-10 for uric acid; R2 = 15%, P = 8.09 × 10-10 for gluconic acid). Both uric acid and gluconic acid were associated with hyperglycemia and cytotoxic brain injury. Both metabolites are linked to oxidative stress, which highlights two candidate targets for limiting brain injury after stroke.

Metabolic Signature of Arrhythmogenic Cardiomyopathy

Arrhythmogenic cardiomyopathy (ACM) is a genetic-based cardiac disease accompanied by severe ventricular arrhythmias and a progressive substitution of the myocardium with fibro-fatty tissue. ACM is often associated with sudden cardiac death. Due to the reduced penetrance and variable expressivity, the presence of a genetic defect is not conclusive, thus complicating the diagnosis of ACM. Recent studies on human induced pluripotent stem cells-derived cardiomyocytes (hiPSC-CMs) obtained from ACM individuals showed a dysregulated metabolic status, leading to the hypothesis that ACM pathology is characterized by an impairment in the energy metabolism. However, despite efforts having been made for the identification of ACM specific biomarkers, there is still a substantial lack of information regarding the whole metabolomic profile of ACM patients. The aim of the present study was to investigate the metabolic profiles of ACM patients compared to healthy controls (CTRLs). The targeted Biocrates AbsoluteIDQ^® p180 assay was used on plasma samples. Our analysis showed that ACM patients have a different metabolome compared to CTRLs, and that the pathways mainly affected include tryptophan metabolism, arginine and proline metabolism and beta oxidation of fatty acids. Altogether, our data indicated that the plasma metabolomes of arrhythmogenic cardiomyopathy patients show signs of endothelium damage and impaired nitric oxide (NO), fat, and energy metabolism.

Novel diagnostic and therapeutic techniques reveal changed metabolic profiles in recurrent focal segmental glomerulosclerosis

Idiopathic forms of Focal Segmental Glomerulosclerosis (FSGS) are caused by circulating permeability factors, which can lead to early recurrence of FSGS and kidney failure after kidney transplantation. In the past three decades, many research endeavors were undertaken to identify these unknown factors. Even though some potential candidates have been recently discussed in the literature, "the" actual factor remains elusive. Therefore, there is an increased demand in FSGS research for the use of novel technologies that allow us to study FSGS from a yet unexplored angle. Here, we report the successful treatment of recurrent FSGS in a patient after living-related kidney transplantation by removal of circulating factors with CytoSorb apheresis. Interestingly, the classical published circulating factors were all in normal range in this patient but early disease recurrence in the transplant kidney and immediate response to CytoSorb apheresis were still suggestive for pathogenic circulating factors. To proof the functional effects of the patient's serum on podocytes and the glomerular filtration barrier we used a podocyte cell culture model and a proteinuria model in zebrafish to detect pathogenic effects on the podocytes actin cytoskeleton inducing a functional phenotype and podocyte effacement. We then performed Raman spectroscopy in the < 50 kDa serum fraction, on cultured podocytes treated with the FSGS serum and in kidney biopsies of the same patient at the time of transplantation and at the time of disease recurrence. The analysis revealed changes in podocyte metabolome induced by the FSGS serum as well as in focal glomerular and parietal epithelial cell regions in the FSGS biopsy. Several altered Raman spectra were identified in the fractionated serum and metabolome analysis by mass spectrometry detected lipid profiles in the FSGS serum, which were supported by disturbances in the Raman spectra. Our novel innovative analysis reveals changed lipid metabolome profiles associated with idiopathic FSGS that might reflect a new subtype of the disease.

Validation of Candidate Phospholipid Biomarkers of Chronic Kidney Disease in Hyperglycemic Individuals and Their Organ-Specific Exploration in Leptin Receptor-Deficient db/db Mouse

Biological exploration of early biomarkers for chronic kidney disease (CKD) in (pre)diabetic individuals is crucial for personalized management of diabetes. Here, we evaluated two candidate biomarkers of incident CKD (sphingomyelin (SM) C18:1 and phosphatidylcholine diacyl (PC aa) C38:0) concerning kidney function in hyperglycemic participants of the Cooperative Health Research in the Region of Augsburg (KORA) cohort, and in two biofluids and six organs of leptin receptor-deficient (db/db) mice and wild type controls. Higher serum concentrations of SM C18:1 and PC aa C38:0 in hyperglycemic individuals were found to be associated with lower estimated glomerular filtration rate (eGFR) and higher odds of CKD. In db/db mice, both metabolites had a significantly lower concentration in urine and adipose tissue, but higher in the lungs. Additionally, db/db mice had significantly higher SM C18:1 levels in plasma and liver, and PC aa C38:0 in adrenal glands. This cross-sectional human study confirms that SM C18:1 and PC aa C38:0 associate with kidney dysfunction in pre(diabetic) individuals, and the animal study suggests a potential implication of liver, lungs, adrenal glands, and visceral fat in their systemic regulation. Our results support further validation of the two phospholipids as early biomarkers of renal disease in patients with (pre)diabetes.

Machine Learning Approaches Reveal Metabolic Signatures of Incident Chronic Kidney Disease in Individuals With Prediabetes and Type 2 Diabetes

Early and precise identification of individuals with prediabetes and type 2 diabetes (T2D) at risk for progressing to chronic kidney disease (CKD) is essential to prevent complications of diabetes. Here, we identify and evaluate prospective metabolite biomarkers and the best set of predictors of CKD in the longitudinal, population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort by targeted metabolomics and machine learning approaches. Out of 125 targeted metabolites, sphingomyelin C18:1 and phosphatidylcholine diacyl C38:0 were identified as candidate metabolite biomarkers of incident CKD specifically in hyperglycemic individuals followed during 6.5 years. Sets of predictors for incident CKD developed from 125 metabolites and 14 clinical variables showed highly stable performances in all three machine learning approaches and outperformed the currently established clinical algorithm for CKD. The two metabolites in combination with five clinical variables were identified as the best set of predictors, and their predictive performance yielded a mean area value under the receiver operating characteristic curve of 0.857. The inclusion of metabolite variables in the clinical prediction of future CKD may thus improve the risk prediction in people with prediabetes and T2D. The metabolite link with hyperglycemia-related early kidney dysfunction warrants further investigation.