The metabolites urobilin and sphingomyelin (30:1) are associated with incident heart failure in the general population

An altered plasma lipidome-phenome network characterizes heart failure with preserved ejection fraction

AIMS

Heart failure with preserved ejection fraction (HFpEF) is a multifactorial, multisystemic syndrome that involves alterations in lipid metabolism. This study aimed to test whether distinct plasma lipid profiles or lipid entities or both are associated with clinical and functional echocardiographic parameters in HFpEF.

METHODS AND RESULTS

We examined the human plasma lipidome in HFpEF patients (n = 18) with left ventricular ejection fraction ≥50% and N-terminal pro-brain natriuretic peptide (NT-proBNP) >125 pg/mL and control subjects (n = 12) using mass spectrometry-based shotgun lipidomics. The cohort included 8 women and 22 men with average age of 67.8 ± 8.6 SD. The control and disease groups were not significantly different with respect to age, body mass index, systolic and diastolic blood pressure, and waist-to-hip ratio. The disease group experienced more fatigue (P < 0.001), had more often coronary artery disease (P = 0.04), and received more medications (beta-blockers, P < 0.001). The disease group had significantly different levels of HFpEF-relevant parameters, including NT-proBNP (P < 0.001), left ventricular mass index (P = 0.005), left atrial volume index (P = 0.001), and left ventricular filling index (P < 0.001), and lower left ventricular end-diastolic diameter (P = 0.014), with no difference in left ventricular ejection fraction. Significant differences in lipid profiles between HFpEF patients and controls could not be detected, including no significant differences in abundance of circulating lipids binned by carbon chain length or by double bonds, nor at the level of individual lipid species. However, there was a striking correlation between selected lipids with smoking status that was independent of disease status, as well as between specific lipids and hyperlipidaemia [with corresponding significance of either false discovery rate (FDR) <0.1 or FDR < 0.01]. In an exploratory network analysis of correlations, we observed significantly stronger correlations within the HFpEF group between individual lipids from the cholesterol ester and phosphatidylcholine (PC) classes and clinical/echocardiographic parameters such as left atrial volume index, left ventricular end-diastolic diameters, and heart rate (FDR < 0.1). In contrast, the control group showed significantly stronger negative correlations (FDR < 0.1) between individual species from the PC and sphingomyelin classes and left ventricular mass index or systolic blood pressure.

CONCLUSIONS

We did not find significant direct associations between plasma lipidomic parameters and HFpEF and therefore could not conclude that any specific lipids are biomarkers of HFpEF. The validation in larger cohort is needed to confidently conclude the absence of first-order associations.

Plasma sphingolipids in patients with sickle cell disease: Multiple-site vaso-occlusive crises could be associated with lower sphingolipid levels

Although sickle cell disease (SCD) and its manifestations have been associated with various lipid alterations, there are a few studies exploring the impact of sphingolipids in SCD. In this study, we determined plasma ceramide (Cer) and sphingomyelin (CerPCho) species and investigated their association with the crisis in SCD. SCD patients (N = 27) suffering from vaso-occlusive crisis (VOC) or acute chest syndrome (ACS) were involved in this study. Blood samples were drawn at crisis and later at steady state periods. Clinical history, white blood cell count (WBC), C-reactive protein and lactate dehydrogenase (LDH) levels were recorded. 16:0, 18:0, 20:0, 22:0 Cer and 16:0, 18:0, 24:0 CerPCho were measured via LC-MS/MS. All measured Cer and CerPCho levels of SCD patients at crisis and steady-state were found to be similar. Inflammation-related parameters were significantly higher in patients with ACS compared to single-site VOC. Patients with multiple-site VOC were found to have significantly lower sphingolipid levels compared with those with single-site VOC, at crisis (16, 18, 24 CerPCho and 18, 22 Cer) and at steady-state (24:0 CerPCho and 18 Cer). Our results show that sphingolipid levels in SCD patients are similar during crisis and at steady state. However, lower sphingolipid levels appear to be associated with the development of multiple-site VOC. Since the differences were observed at both crisis and steady-state, sphingolipid level could be an underlying factor associated with crisis characteristics in patients with SCD.

Metabolomic Association and Risk Prediction With Heart Failure in Older Adults

BACKGROUND

Older adults have markedly increased risks of heart failure (HF), specifically HF with preserved ejection fraction (HFpEF). Identifying novel biomarkers can help in understanding HF pathogenesis and improve at-risk population identification. This study aimed to identify metabolites associated with incident HF, HFpEF, and HF with reduced ejection fraction and examine risk prediction in older adults.

METHODS

Untargeted metabolomic profiling was performed in Black and White adults from the ARIC study (Atherosclerosis Risk in Communities) visit 5 (n=3719; mean age, 75 years). We applied Cox regressions to identify metabolites associated with incident HF and its subtypes. The metabolite risk score (MRS) was constructed and examined for associations with HF, echocardiographic measures, and HF risk prediction. Independent samples from visit 3 (n=1929; mean age, 58 years) were used for replication.

RESULTS

Sixty metabolites (hazard ratios range, 0.79-1.49; false discovery rate, <0.05) were associated with incident HF after adjusting for clinical risk factors, eGFR, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). Mannonate, a hydroxy acid, was replicated (hazard ratio, 1.36 [95% CI, 1.19-1.56]) with full adjustments. MRS was associated with an 80% increased risk of HF per SD increment, and the highest MRS quartile had 8.7× the risk of developing HFpEF than the lowest quartile. High MRS was also associated with unfavorable values of cardiac structure and function. Adding MRS over clinical risk factors and NT-proBNP improved 5-year HF risk prediction C statistics from 0.817 to 0.850 (∆C, 0.033 [95% CI, 0.017-0.047]). The association between MRS and incident HF was replicated after accounting for clinical risk factors (P<0.05).

CONCLUSIONS

Novel metabolites associated with HF risk were identified, elucidating disease pathways, specifically HFpEF. An MRS was associated with HF risk and improved 5-year risk prediction in older adults, which may assist at at-risk population identification.

BilR is a gut microbial enzyme that reduces bilirubin to urobilinogen

Metabolism of haem by-products such as bilirubin by humans and their gut microbiota is essential to human health, as excess serum bilirubin can cause jaundice and even neurological damage. The bacterial enzymes that reduce bilirubin to urobilinogen, a key step in this pathway, have remained unidentified. Here we used biochemical analyses and comparative genomics to identify BilR as a gut-microbiota-derived bilirubin reductase that reduces bilirubin to urobilinogen. We delineated the BilR sequences from similar reductases through the identification of key residues critical for bilirubin reduction and found that BilR is predominantly encoded by Firmicutes species. Analysis of human gut metagenomes revealed that BilR is nearly ubiquitous in healthy adults, but prevalence is decreased in neonates and individuals with inflammatory bowel disease. This discovery sheds light on the role of the gut microbiome in bilirubin metabolism and highlights the significance of the gut-liver axis in maintaining bilirubin homeostasis.

Blood-Derived Lipid and Metabolite Biomarkers in Cardiovascular Research from Clinical Studies: A Recent Update

The primary prevention, early detection, and treatment of cardiovascular disease (CVD) have been long-standing scientific research goals worldwide. In the past decades, traditional blood lipid profiles have been routinely used in clinical practice to estimate the risk of CVDs such as atherosclerotic cardiovascular disease (ASCVD) and as treatment targets for the primary prevention of adverse cardiac events. These blood lipid panel tests often fail to fully predict all CVD risks and thus need to be improved. A comprehensive analysis of molecular species of lipids and metabolites (defined as lipidomics and metabolomics, respectively) can provide molecular insights into the pathophysiology of the disease and could serve as diagnostic and prognostic indicators of disease. Mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based lipidomics and metabolomics analysis have been increasingly used to study the metabolic changes that occur during CVD pathogenesis. In this review, we provide an overview of various MS-based platforms and approaches that are commonly used in lipidomics and metabolomics workflows. This review summarizes the lipids and metabolites in human plasma/serum that have recently (from 2018 to December 2022) been identified as promising CVD biomarkers. In addition, this review describes the potential pathophysiological mechanisms associated with candidate CVD biomarkers. Future studies focused on these potential biomarkers and pathways will provide mechanistic clues of CVD pathogenesis and thus help with the risk assessment, diagnosis, and treatment of CVD.

Metabolite Alterations in Autoimmune Diseases: A Systematic Review of Metabolomics Studies

Autoimmune diseases, characterized by the immune system's loss of self-tolerance, lack definitive diagnostic tests, necessitating the search for reliable biomarkers. This systematic review aims to identify common metabolite changes across multiple autoimmune diseases. Following PRISMA guidelines, we conducted a systematic literature review by searching MEDLINE, ScienceDirect, Google Scholar, PubMed, and Scopus (Elsevier) using keywords "Metabolomics", "Autoimmune diseases", and "Metabolic changes". Articles published in English up to March 2023 were included without a specific start date filter. Among 257 studies searched, 88 full-text articles met the inclusion criteria. The included articles were categorized based on analyzed biological fluids: 33 on serum, 21 on plasma, 15 on feces, 7 on urine, and 12 on other biological fluids. Each study presented different metabolites with indications of up-regulation or down-regulation when available. The current study's findings suggest that amino acid metabolism may serve as a diagnostic biomarker for autoimmune diseases, particularly in systemic lupus erythematosus (SLE), multiple sclerosis (MS), and Crohn's disease (CD). While other metabolic alterations were reported, it implies that autoimmune disorders trigger multi-metabolite changes rather than singular alterations. These shifts could be consequential outcomes of autoimmune disorders, representing a more complex interplay. Further studies are needed to validate the metabolomics findings associated with autoimmune diseases.

Phospholipids, the Masters in the Shadows during Healing after Acute Myocardial Infarction

Phospholipids are major components of cell membranes with complex structures, high heterogeneity and critical biological functions and have been used since ancient times to treat cardiovascular disease. Their importance and role were shadowed by the difficulty or incomplete available research methodology to study their biological presence and functionality. This review focuses on the current knowledge about the roles of phospholipids in the pathophysiology and therapy of cardiovascular diseases, which have been increasingly recognized. Used in singular formulation or in inclusive combinations with current drugs, phospholipids proved their positive and valuable effects not only in the protection of myocardial tissue, inflammation and fibrosis but also in angiogenesis, coagulation or cardiac regeneration more frequently in animal models as well as in human pathology. Thus, while mainly neglected by the scientific community, phospholipids present negligible side effects and could represent an ideal target for future therapeutic strategies in healing myocardial infarction. Acknowledging and understanding their mechanisms of action could offer a new perspective into novel therapeutic strategies for patients suffering an acute myocardial infarction, reducing the burden and improving the general social and economic outcome.

The metabolic profile of waist to hip ratio-A multi-cohort study

BACKGROUND

The genetic background of general obesity and fat distribution is different, pointing to separate underlying physiology. Here, we searched for metabolites and lipoprotein particles associated with fat distribution, measured as waist/hip ratio adjusted for fat mass (WHRadjfatmass), and general adiposity measured as percentage fat mass.

METHOD

The sex-stratified association of 791 metabolites detected by liquid chromatography-mass spectrometry (LC-MS) and 91 lipoprotein particles measured by nuclear magnetic spectroscopy (NMR) with WHRadjfatmass and fat mass were assessed using three population-based cohorts: EpiHealth (n = 2350) as discovery cohort, with PIVUS (n = 603) and POEM (n = 502) as replication cohorts.

RESULTS

Of the 193 LC-MS-metabolites being associated with WHRadjfatmass in EpiHealth (false discovery rate (FDR) <5%), 52 were replicated in a meta-analysis of PIVUS and POEM. Nine metabolites, including ceramides, sphingomyelins or glycerophosphatidylcholines, were inversely associated with WHRadjfatmass in both sexes. Two of the sphingomyelins (d18:2/24:1, d18:1/24:2 and d18:2/24:2) were not associated with fat mass (p>0.50). Out of 91, 82 lipoprotein particles were associated with WHRadjfatmass in EpiHealth and 42 were replicated. Fourteen of those were associated in both sexes and belonged to very-large or large HDL particles, all being inversely associated with both WHRadjfatmass and fat mass.

CONCLUSION

Two sphingomyelins were inversely linked to body fat distribution in both men and women without being associated with fat mass, while very-large and large HDL particles were inversely associated with both fat distribution and fat mass. If these metabolites represent a link between an impaired fat distribution and cardiometabolic diseases remains to be established.

Bilirubin Levels Are Negatively Correlated with Adiposity in Obese Men and Women, and Its Catabolized Product, Urobilin, Is Positively Associated with Insulin Resistance

Bilirubin levels in obese humans and rodents have been shown to be lower than in their lean counterparts. Some studies have proposed that the glucuronyl UGT1A1 enzyme that clears bilirubin from the blood increases in the liver with obesity. UGT1A1 clearance of bilirubin allows more conjugated bilirubin to enter the intestine, where it is catabolized into urobilin, which can be then absorbed via the hepatic portal vein. We hypothesized that when bilirubin levels are decreased, the urobilin increases in the plasma of obese humans, as compared to lean humans. To test this, we measured plasma levels of bilirubin and urobilin, body mass index (BMI), adiposity, blood glucose and insulin, and HOMA IR in a small cohort of obese and lean men and women. We found that bilirubin levels negatively correlated with BMI and adiposity in obese men and women, as compared to their lean counterparts. Contrarily, urobilin levels were positively associated with adiposity and BMI. Only obese women were found to be insulin resistant based on significantly higher HOMA IR, as compared to lean women. The urobilin levels were positively associated with HOMA IR in both groups, but women had a stronger linear correlation. These studies indicate that plasma urobilin levels are associated with obesity and its comorbidities, such as insulin resistance.

Circulating metabolites and molecular lipid species are associated with future cardiovascular morbidity and mortality in type 1 diabetes

Background

Cardiovascular disease remains the leading cause of mortality in individuals with diabetes and improved understanding of its pathophysiology is needed. We investigated the association of a large panel of metabolites and molecular lipid species with future cardiovascular events in type 1 diabetes.

Methods

The study included 669 individuals with type 1 diabetes. Non-targeted serum metabolomics and lipidomics analyses were performed using mass spectrometry. Data on cardiovascular events (cardiovascular mortality, coronary artery disease, stroke, and peripheral arterial interventions) were obtained from Danish Health registries and analyzed by Cox hazards models. Metabolites and molecular lipid species were analyzed in univariate models adjusted for false discovery rate (FDR). Metabolites and molecular lipid species fulfilling a p_FDR < 0.05 were subsequently analyzed in adjusted models including age, sex, hemoglobin A_1c, mean arterial pressure, smoking, body mass index, low-density lipoprotein cholesterol, estimated glomerular filtration rate, urinary albumin excretion rate and previous cardiovascular disease. Analyses of molecular lipid species were further adjusted for triglycerides and statin use.

Results

Of the included participants, 55% were male and mean age was 55 ± 13 years. Higher 4-hydroxyphenylacetic acid (HR 1.35, CI [1.01–1.80], p = 0.04) and lower threonine (HR 0.81, CI [0.67–0.98] p = 0.03) were associated with development of cardiovascular events (n = 95). In lipidomics analysis, higher levels of three different species, diacyl-phosphatidylcholines (PC)(36:2) (HR 0.82, CI [0.70–0.98], p = 0.02), alkyl-acyl-phosphatidylcholines (PC-O)(34:2) (HR 0.76, CI [0.59–0.98], p = 0.03) and (PC-O)(34:3) (HR 0.75, CI [0.58–0.97], p = 0.03), correlated with lower risk of cardiovascular events, whereas higher sphingomyelin (SM)(34:1) (HR 1.32, CI [1.04–1.68], p = 0.02), was associated with an increased risk.

Conclusions

Circulating metabolites and molecular lipid species were associated with future cardiovascular events in type 1 diabetes. While the causal effect of these biomolecules on the cardiovascular system remains unknown, our findings support that omics-based technologies, although still in an early phase, may have the potential to unravel new pathways and biomarkers in the field of cardiovascular disease in type 1 diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01568-8.

Understanding How Heart Metabolic Derangement Shows Differential Stage Specificity for Heart Failure with Preserved and Reduced Ejection Fraction

Heart failure (HF) is a clinical condition defined by structural and functional abnormalities in the heart that gradually result in reduced cardiac output (HFrEF) and/or increased cardiac pressures at rest and under stress (HFpEF). The presence of asymptomatic individuals hampers HF identification, resulting in delays in recognizing patients until heart dysfunction is manifested, thus increasing the chance of poor prognosis. Given the recent advances in metabolomics, in this review we dissect the main alterations occurring in the metabolic pathways behind the decrease in cardiac function caused by HF. Indeed, relevant preclinical and clinical research has been conducted on the metabolite connections and differences between HFpEF and HFrEF. Despite these promising results, it is crucial to note that, in addition to identifying single markers and reliable threshold levels within the healthy population, the introduction of composite panels would strongly help in the identification of those individuals with an increased HF risk. That said, additional research in the field is required to overcome the current drawbacks and shed light on the pathophysiological changes that lead to HF. Finally, greater collaborative data sharing, as well as standardization of procedures and approaches, would enhance this research field to fulfil its potential.

Metabolic Profiling of Obesity With and Without the Metabolic Syndrome: A Multisample Evaluation

CONTEXT

There is a dispute whether obesity without major metabolic derangements may represent a benign condition or not.

OBJECTIVE

We aimed to compare the plasma metabolome in obese subjects without metabolic syndrome (MetS) with normal-weight subjects without MetS and with obese subjects with MetS.

METHODS

This was a cross-sectional study at 2 academic centers in Sweden. Individuals from 3 population-based samples (EpiHealth, n = 2342, SCAPIS-Uppsala, n = 4985, and SCAPIS-Malmö, n = 3978) were divided into groups according to their body mass index (BMI) and presence/absence of MetS (National Cholesterol Education Program [NCEP]/consensus criteria). In total, 791 annotated endogenous metabolites were measured by ultra-performance liquid chromatography-tandem mass spectrometry.

RESULTS

We observed major differences in metabolite profiles (427 metabolites) between obese (BMI ≥ 30 kg/m2) and normal-weight (BMI < 25 kg/m2) subjects without MetS after adjustment for major lifestyle factors. Pathway enrichment analysis highlighted branch-chained and aromatic amino acid synthesis/metabolism, aminoacyl-tRNA biosynthesis, and sphingolipid metabolism. The same pathways, and similar metabolites, were also highlighted when obese subjects with and without MetS were compared despite adjustment for BMI and waist circumference, or when the metabolites were related to BMI and number of MetS components in a continuous fashion. Similar metabolites and pathways were also related to insulin sensitivity (Matsuda index) in a separate study (POEM, n = 501).

CONCLUSION

Our data suggest a graded derangement of the circulating metabolite profile from lean to obese to MetS, in particular for metabolites involved in amino acid synthesis/metabolism and sphingolipid metabolism. Insulin resistance is a plausible mediator of this gradual metabolic deterioration.

Large-Scale Plasma Protein Profiling of Incident Myocardial Infarction, Ischemic Stroke, and Heart Failure

Background

We recently reported a link between plasma levels of 2 of 84 cardiovascular disease (CVD)–related proteins and the 3 major CVDs, myocardial infarction, ischemic stroke, and heart failure. The present study investigated whether measurement of almost 10 times the number of proteins could lead to discovery of additional risk markers for CVD.

Methods and Results

We measured 742 proteins using the proximity extension assay in 826 male participants of ULSAM (Uppsala Longitudinal Study of Adult Men) who were free from CVD at the age of 70 years. Cox proportional hazards models were adjusted for age only, as well as all traditional risk factors. During a 12.5‐year median follow‐up (maximal, 22.0 years), 283 incident CVDs occurred. Forty‐one proteins were significantly (false discovery rate <0.05) related to the combined end point of incident CVD, with N‐terminal pro–brain natriuretic peptide as the top finding, while 53 proteins were related to incident myocardial infarction. A total of 13 and 16 proteins were significantly related to incident ischemic stroke and heart failure, respectively. Growth differentiation factor 15, 4‐disulfide core domain protein 2, and kidney injury molecule were related to all of the 3 major CVD outcomes. A lasso selection of 11 proteins improved discrimination of incident CVD by 5.0% (P=0.0038).

Conclusions

Large‐scale proteomics seem useful for the discovery of new risk markers for CVD and to improve risk prediction in an elderly population of men. Further studies are needed to replicate the findings in independent samples of both men and women of different ages.

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.

A plasma lipid signature predicts incident coronary artery disease

BACKGROUND

Dyslipidemia is a hallmark of cardiovascular disease but is characterized by crude measurements of triglycerides, HDL- and LDL cholesterol. Lipidomics enables more detailed measurements of plasma lipids, which may help improve risk stratification and understand the pathophysiology of cardiovascular disease.

METHODS

Lipidomics was used to measure 184 lipids in plasma samples from the Malmö Diet and Cancer - Cardiovascular Cohort (N = 3865), taken at baseline examination. During an average follow-up time of 20.3 years, 536 participants developed coronary artery disease (CAD). Least absolute shrinkage and selection operator (LASSO) were applied to Cox proportional hazards models in order to identify plasma lipids that predict CAD.

RESULTS

Eight plasma lipids improved prediction of future CAD on top of traditional cardiovascular risk factors. Principal component analysis of CAD-associated lipids revealed one principal component (PC2) that was associated with risk of future CAD (HR per SD increment =1.46, C·I = 1.35-1.48, P < 0.001). The risk increase for being in the highest quartile of PC2 (HR = 2.33, P < 0.001) was higher than being in the top quartile of systolic blood pressure. Addition of PC2 to traditional risk factors achieved an improvement (2%) in the area under the ROC-curve for CAD events occurring within 10 (P = 0.03), 15 (P = 0.003) and 20 (P = 0.001) years of follow-up respectively.

CONCLUSIONS

A lipid pattern improve CAD prediction above traditional risk factors, highlighting that conventional lipid-measures insufficiently describe dyslipidemia that is present years before CAD. Identifying this hidden dyslipidemia may help motivate lifestyle and pharmacological interventions early enough to reach a substantial reduction in absolute risk.

Urine Metabolites Associated with the Dietary Approaches to Stop Hypertension (DASH) Diet: Results from the DASH-Sodium Trial

SCOPE

Serum metabolomic markers of the Dietary Approaches to Stop Hypertension (DASH) diet are previously reported. In an independent study, the similarity of urine metabolomic markers are investigated.

METHODS AND RESULTS

In the DASH-Sodium trial, participants are randomly assigned to the DASH diet or control diet, and received three sodium interventions (high, intermediate, low) within each randomized diet group in random order for 30 days each. Urine samples are collected at the end of each intervention period and analyzed for 938 metabolites. Two comparisons are conducted: 1) DASH-high sodium (n = 199) versus control-high sodium (n = 193), and 2) DASH-low sodium (n = 196) versus control-high sodium. Significant metabolites identified using multivariable linear regression are compared and the top 10 influential metabolites identified using partial least-squares discriminant analysis to the results from the DASH trial. Nine out of 10 predictive metabolites of the DASH-high sodium and DASH-low sodium diets are identical. Most candidate biomarkers from the DASH trial replicated. N-methylproline, chiro-inositol, stachydrine, and theobromine replicated as influential metabolites of DASH diets.

CONCLUSIONS

Candidate biomarkers of the DASH diet identified in serum replicated in urine. Replicated influential metabolites are likely to be objective biomarkers of the DASH diet.

Metabonomics: A Useful Tool to Reveal Underlying Relationships between Altered Chinese Medicine Syndromes and Ultrafiltration in Treatment of Heart Failure

OBJECTIVE

To reveal the underlying relationships between Chinese medicine (CM) syndromes and ultrafiltration (UF) in the treatment of heart failure based on a metabonomic approach.

METHODS

Seventeen acute decompensated heart failure (ADHF) patients were enrolled, and their CM syndromes before and after UF were collected. In addition, their venous plasma collected before and after UF was used for liquid chromatographmass spectrometer-based metabonomic analysis. Both reversed phase liquid chromatography and hydrophilic interaction liquid chromatography were used to analyze the plasma samples. Partial least-squares to latent structure-discriminant analyses were used for data analysis.

RESULTS

An obvious difference was observed pre- and post-treatment. A total of 17 potential biomarkers associating with alterd syndromes with UF including hypoxanthine, 1-methylhistidine, phytosphingosine, O-decanoyl-R-carnitine, etc. were screened out, showing a significant change after UF. The major adjusted metabolic pathways were purine metabolism, histidine metabolism, leucine and isoleucine metabolism, arginine and proline metabolism, carnitine shuttle, sphingolipid metabolism and phospholipid metabolism.

CONCLUSIONS

Metabonomic approach is a useful tool to identify potential biomarkers of altered syndromes link to UF and could provide a theoretical basis for further research on the therapeutic mechanism of UF combined with CM.

Multicohort Metabolomics Analysis Discloses 9-Decenoylcarnitine to Be Associated With Incident Atrial Fibrillation

Background The molecular mechanisms involved in atrial fibrillation are not well known. We used plasma metabolomics to investigate if we could identify novel biomarkers and pathophysiological pathways of incident atrial fibrillation. Methods and Results We identified 200 endogenous metabolites in plasma/serum by nontargeted ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry in 3 independent population-based samples (TwinGene, n=1935, mean age 68, 43% females; PIVUS [Prospective Investigation of the Vasculature in Uppsala Seniors], n=897, mean age 70, 51% females; and ULSAM [Uppsala Longitudinal Study of Adult Men], n=1118, mean age 71, all males), with available data on incident atrial fibrillation during 10 to 12 years of follow-up. A meta-analysis of ULSAM and PIVUS was used as a discovery sample and TwinGene was used for validation. In PIVUS, we also investigated associations between metabolites of interest and echocardiographic indices of myocardial geometry and function. Genome-wide association studies were performed in all 3 cohorts for metabolites of interest. In the meta-analysis of PIVUS and ULSAM with 430 incident cases, 4 metabolites were associated with incident atrial fibrillation at a false discovery rate <5%. Of those, only 9-decenoylcarnitine was associated with incident atrial fibrillation and replicated in the TwinGene sample (288 cases) following adjustment for traditional risk factors (hazard ratio, 1.24 per unit; 95% CI, 1.06-1.45, P=0.0061). A meta-analysis of all 3 cohorts disclosed another 4 significant metabolites. In PIVUS, 9-decenoylcarnitine was related to left atrium size and left ventricular mass. A Mendelian randomization analysis did not suggest a causal role of 9-decenoylcarnitine in atrial fibrillation. Conclusions A nontargeted metabolomics analysis disclosed 1 novel replicated biomarker for atrial fibrillation, 9-Decenoylcarnitine, but this acetylcarnitine is likely not causally related to atrial fibrillation.

Comprehensive plasma metabolites profiling reveals phosphatidylcholine species as potential predictors for cardiac resynchronization therapy response

Aims

This study aimed to identify the plasma metabolite fingerprint in patients with heart failure and to develop a prediction tool based on differential metabolites for predicting the response to cardiac resynchronization therapy (CRT).

Methods and results

We prospectively recruited 32 healthy individuals and 42 consecutive patients with HF who underwent CRT between January 2018 and January 2019. Peripheral venous blood samples, clinical data, and echocardiographic signatures were collected before CRT implantation. Liquid chromatography‐mass spectrometry was used to perform untargeted metabolites profiling for peripheral plasma under ESI+ and ESI− modes. After 6 month follow‐up, patients were categorized as CRT responders or non‐responders based on the alterations of echocardiographic characteristics. Compared with healthy individuals, patients with HF had distinct metabolomic profiles under both ESI+ and ESI− modes, featuring increased free fatty acids, carnitine, β‐hydroxybutyrate, and dysregulated lipids with heterogeneous alterations such as phosphatidylcholines (PCs) and sphingomyelins. Disparities of baseline metabolomics profile were observed between CRT responders and non‐responders under ESI+ mode but not under ESI− mode. Further metabolites analysis revealed that a group of 20 PCs metabolites under ESI+ mode were major contributors to the distinct profiles between the two groups. We utilized LASSO regression model and identified a panel of four PCs metabolites [including PC (20:0/18:4), PC (20:4/20:0), PC 40:4, and PC (20:4/18:0)] as major predictors for CRT response prediction. Among our whole population (n = 42), receive operating characteristics analysis revealed that the four PCs‐based model could nicely discriminate the CRT responders from non‐responders (area under the curve = 0.906) with a sensitivity of 83.3% and a specificity of 90.0%. Cross‐validation analysis also showed a satisfactory and robust performance of the model with the area under the curve of 0.910 in the training dataset and 0.880 in the testing dataset.

Conclusions

Patients with HF held significantly altered plasma metabolomics profile compared with the healthy individuals. Within the HF group, the non‐responders had a distinct plasma metabolomics profile in contrast to the responders to CRT, which was characterized by increased PCs species. A novel predictive model incorporating four PCs metabolites performed well in identifying CRT non‐responders. These four PCs might severe as potential biomarkers for predicting CRT response. Further validations are needed in multi‐centre studies with larger external cohorts.

Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics

Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10–20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.

Ergothioneine is associated with reduced mortality and decreased risk of cardiovascular disease

Objectives

We recently identified a health conscious food pattern (HCFP) associated with reduced risk of cardiometabolic disease. However, the molecular events linking the healthy food pattern to reduced risk of cardiometabolic disease are unknown. Our aim was to identify plasma metabolites associated with the HCFP and test if such metabolites predict cardiometabolic disease and mortality.

Methods

Using liquid-chromatography mass-spectrometry, 112 plasma metabolites were measured in 3236 participants without cardiovascular disease (CVD) and diabetes mellitus from the population-based Malmö Diet and Cancer study. Metabolites associated with the HCFP were identified using multivariable adjusted linear regressions followed by Bonferroni correction. The healthy dietary biomarkers were subsequently related to risk of cardiometabolic disease and mortality during long-term follow-up with multivariable adjusted Cox proportional hazards models.

Results

During a median follow-up time of 21.4 years, 603 participants developed CVD, 362 developed diabetes mellitus and 843 participants died. Five healthy dietary biomarkers were associated with the HCFP at baseline (p<0.0004) and four predicted at least one of the studied end points (p<0.05). Ergothioneine was the metabolite most strongly connected to the HCFP and was associated with a lower risk of coronary disease (HR per 1 SD increment of ergothioneine, HR=0.85, p=0.01), cardiovascular mortality (HR=0.79, p=0.002) and overall mortality (HR=0.86, p=4e-5).

Conclusions

We identified that higher ergothioneine was an independent marker of lower risk of cardiometabolic disease and mortality, which potentially can be induced by a specific healthy dietary intake.

Fluid-based assays and precision medicine of cardiovascular diseases: the ‘hope’ for Pandora’s box?

Progresses in liquid-based assays may provide novel useful non-invasive indicators of cardiovascular (CV) diseases. By analysing circulating cells or their products in blood, saliva and urine samples, we can investigate molecular changes present at specific time points in each patient allowing sequential monitoring of disease evolution. For example, an increased number of circulating endothelial cells may be a diagnostic biomarker for diabetic nephropathy and heart failure with preserved ejection fraction. The assessment of circulating cell-free DNA (cfDNA) levels may be useful to predict severity of acute myocardial infarction, as well as diagnose heart graft rejection. Remarkably, circulating epigenetic biomarkers, including DNA methylation, histone modifications and non-coding RNAs are key pathogenic determinants of CV diseases representing putative useful biomarkers and drug targets. For example, the unmethylated FAM101A gene may specifically trace cfDNA derived from cardiomyocyte death providing a powerful diagnostic biomarker of apoptosis during ischaemia. Moreover, changes in plasma levels of circulating miR-92 may predict acute coronary syndrome onset in patients with diabetes. Now, network medicine provides a framework to analyse a huge amount of big data by describing a CV disease as a result of a chain of molecular perturbations rather than a single defect (reductionism). We outline advantages and challenges of liquid biopsy with respect to traditional tissue biopsy and summarise the main completed and ongoing clinical trials in CV diseases. Furthermore, we discuss the importance of combining fluid-based assays, big data and network medicine to improve precision medicine and personalised therapy in this field.

The metabolites urobilin and sphingomyelin (30:1) are associated with incident heart failure in the general population

Aims

We aimed to investigate whether metabolomic profiling of blood can lead to novel insights into heart failure pathogenesis or improved risk prediction.

Methods and results

Mass spectrometry‐based metabolomic profiling was performed in plasma or serum samples from three community‐based cohorts without heart failure at baseline (total n = 3924; 341 incident heart failure events; median follow‐up ranging from 4.6 to 13.9 years). Cox proportional hazard models were applied to assess the association of each of the 206 identified metabolites with incident heart failure in the discovery cohorts Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) (n = 920) and Uppsala Longitudinal Study of Adult Men (ULSAM) (n = 1121). Replication was undertaken in the independent cohort TwinGene (n = 1797). We also assessed whether metabolites could improve the prediction of heart failure beyond established risk factors (age, sex, body mass index, low‐density and high‐density lipoprotein cholesterol, triglycerides, lipid medication, diabetes, systolic and diastolic blood pressure, blood pressure medication, glomerular filtration rate, smoking status, and myocardial infarction prior to or during follow‐up). Higher circulating urobilin and lower sphingomyelin (30:1) were associated with incident heart failure in age‐adjusted and sex‐adjusted models in the discovery and replication sample. The hazard ratio for urobilin in the replication cohort was estimated to 1.29 per standard deviation unit, 95% confidence interval (CI 1.03–1.63), and for sphingomyelin (30:1) to 0.72 (95% CI 0.58–0.89). Results remained similar after further adjustment for established heart failure risk factors in meta‐analyses of all three cohorts. Urobilin concentrations were inversely associated with left ventricular ejection fraction at baseline in the PIVUS cohort (β = −0.70, 95% CI −1.03 to −0.38). No major improvement in risk prediction was observed when adding the top 2 metabolites (C‐index 0.787, 95% CI 0.752–0.823) or nine Lasso‐selected metabolites (0.790, 95% CI 0.754–0.826) to a modified Atherosclerosis Risk in Communities heart failure risk score model (0.780, 95% CI 0.745–0.816).

Conclusions

Our metabolomic profiling of three community‐based cohorts study identified associations of circulating levels of the haem breakdown product urobilin, and sphingomyelin (30:1), a cell membrane component involved in signal transduction and apoptosis, with incident heart failure.