The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective

Xenobiotics recovery from plasma using solid phase extraction with C-18 sorbent - The reasons of literature discrepancies

Solid phase extraction (SPE) is one of the most popular methods of preparing plasma samples before determining the xenobiotics they contain. The present paper shows that the recovery degree of xenobiotics from plasma samples using SPE with C18 sorbent strongly depends on their storage time and temperature. While xenobiotics can be isolated and recovered in 100 % from fresh plasma samples under optimal conditions of the SPE procedure, their SPE recovery degree from stored plasma is lower. It diminishes with the time increase and temperature reduction of plasma storage. Moreover, a part of xenobiotic in stored plasma samples is not sorbed on SPE-C18 column at all and leaves it together with the waste during loading the column with the examined sample. According to the NMR data, the main reason of the presence of xenobiotics in the waste of the SPE column during its loading are structural-chemical changes occurring in plasma during its storage, leading to the formation of some complex(es) of hydrophilic character with xenobiotic. Another reason for lower SPE recovery degree of xenobiotic from stored plasma is the formation of plasma sediment, which binds/occludes xenobiotic. The presented results expand the current knowledge on why the SPE recovery degree of xenobiotics from plasma reported in the literature may differ. They are important for proper calibration of the chromatographic system in the analysis of xenobiotics in plasma and for achieving high accuracy of the analytical procedure involving SPE in xenobiotic estimation.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Discrimination of serum samples of prostate cancer and benign prostatic hyperplasia with 1H-NMR metabolomics

Prostate cancer continues to be a prominent health concern for men globally. Current screening techniques, primarily the prostate-specific antigen (PSA) test and digital rectal examination (DRE), possess inherent limitations, with prostate biopsy being the definitive diagnostic procedure. The invasive nature of the biopsy and other drawbacks of current screening tests create the need for non-invasive and more accurate diagnostic methods. This study utilized 1H-NMR (Proton Nuclear Magnetic Resonance) based serum metabolomics to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH). Serum samples from 40 PCa and 41 BPH patients were analysed using 1H-NMR spectroscopy. PepsNMR was utilized for preprocessing the raw NMR data, and the binned spectra were examined for patterns distinguishing PCa and BPH. Principal component analysis (PCA) showed a moderate separation between PCa and BPH, highlighting the distinct metabolic profiles of both conditions. A logistic regression model was then developed, which demonstrated good performance in distinguishing between the two conditions. The results showed significant variance in multiple metabolites between PCa and BPH, such as isovaleric acid, ethylmalonic acid, formate, and glutamic acid. This research underlines the potential of 1H-NMR-based serum metabolomics as a promising tool for improved prostate cancer screening, offering an alternative to the limitations of current screening methods.

CeO2-Based Frustrated Lewis Pairs via Defective Engineering: Formation Theory, Site Characterization, and Small Molecule Activation

Activation of small molecules is considered to be a central concern in the theoretical investigation of environment- and energy-related catalytic conversions. Sub-nanostructured frustrated Lewis pairs (FLPs) have been an emerging research hotspot in recent years due to their advantages in small molecule activation. Although the progress of catalytic applications of FLPs is increasingly reported, the fundamental theories related to the structural formation, site regulation, and catalytic mechanism of FLPs have not yet been fully developed. Given this, it is attempted to demonstrate the underlying theory of FLPs formation, corresponding regulation methods, and its activation mechanism on small molecules using CeO2 as the representative metal oxide. Specifically, this paper presents three fundamental principles for constructing FLPs on CeO2 surfaces, and feasible engineering methods for the regulation of FLPs sites are presented. Furthermore, cases where typical small molecules (e.g., hydrogen, carbon dioxide, methane oxygen, etc.) are activated over FLPs are analyzed. Meanwhile, corresponding future challenges for the development of FLPs-centered theory are presented. The insights presented in this paper may contribute to the theories of FLPs, which can potentially provide inspiration for the development of broader environment- and energy-related catalysis involving small molecule activation.

Incremental Value of a Metabolic Risk Score for Heart Failure Mortality: A Population-Based Study

BACKGROUND

Heart failure is heterogeneous syndrome with persistently high mortality. Nuclear magnetic resonance spectroscopy enables high-throughput metabolomics, suitable for precision phenotyping. We aimed to use targeted metabolomics to derive a metabolic risk score (MRS) that improved mortality risk stratification in heart failure.

METHODS

Nuclear magnetic resonance was used to measure 21 metabolites (lipoprotein subspecies, branched-chain amino acids, alanine, GlycA (glycoprotein acetylation), ketone bodies, glucose, and citrate) in plasma collected from a heart failure community cohort. The MRS was derived using least absolute shrinkage and selection operator penalized Cox regression and temporal validation. The association between the MRS and mortality and whether risk stratification was improved over the Meta-Analysis Global Group in Chronic Heart Failure clinical risk score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels were assessed.

RESULTS

The study included 1382 patients (median age, 78 years, 52% men, 43% reduced ejection fraction) with a 5-year survival rate of 48% (95% CI, 46%-51%). The MRS included 9 metabolites measured. In the validation data set, a 1 standard deviation increase in the MRS was associated with a large increased rate of death (hazard ratio, 2.2 [95% CI, 1.9-2.5]) that remained after adjustment for Meta-Analysis Global Group in Chronic Heart Failure score and NT-proBNP (hazard ratio, 1.6 [95% CI, 1.3-1.9]). These associations did not differ by ejection fraction. The integrated discrimination and net reclassification indices, and Uno's C statistic, indicated that the addition of the MRS improved discrimination over Meta-Analysis Global Group in Chronic Heart Failure and NT-proBNP.

CONCLUSIONS

This MRS developed in a heart failure community cohort was associated with a large excess risk of death and improved risk stratification beyond an established risk score and clinical markers.

Placental energy metabolism: Evidence for a placental-maternal lactate-ketone trade in the human

INTRODUCTION

Glucose from placenta is the predominant energy source for the fetus. Individual placentas exhibit a range of glucose handling from apparent net production to high consumption, presumably reflecting an ability of placenta to secure both own and fetal energy needs. A dependency of placenta on glucose as the main energy source could impede fetal supply. Placenta seems to release lactate to maternal side implying loss of energy. Whether placenta takes up ketones is unclear. Our main hypothesis was that the human placenta can release lactate to the maternal side but take up maternal ketones.

METHODS

An in vivo study of term uncomplicated pregnancies including 56 women delivered by cesarean section. We measured uterine and umbilical blood flow by Doppler ultrasonography, combined with blood sampling from maternal radial artery, uterine vein, umbilical artery and vein. Lactate and ketones were determined by quantitative nuclear magnetic resonance.

RESULTS

Placenta released lactate to the maternal side (median -36.65 μmol/min. Q1, Q3: 78.53, 13.29), p < 0.001), but not to the fetal side. A net uptake of maternal ketones was found (median (Q1, Q3): 59.12 (30.64, 131.46) μmol acetate equivalents/min, p < 0.001) which largely was metabolized by the uteroplacenta. The uptake of ketones was comparable in energy to the loss of lactate.

DISCUSSION

Placenta may release lactate to the maternal side. The energy lost by lactate may be compensated by uptake of maternal ketones. This lactate-ketone trade could benefit both placenta and the fetus by providing lactate for maternal gluconeogenesis and ketones for uteroplacental oxidative energy production.

Searching for Metabolic Markers of Stroke in Human Plasma via NMR Analysis

More than 12 million people around the world suffer a stroke every year, one every 3 s. Stroke has a variety of causes and is often the result of a complex interaction of risk factors related to age, genetics, gender, lifestyle, and some cardiovascular and metabolic diseases. Despite this evidence, it is not possible to prevent the onset of stroke. The use of innovative methods for metabolite analysis has been explored in the last years to detect new stroke biomarkers. We use NMR spectroscopy to identify small molecule variations between different stages of stroke risk. The Framingham Stroke Risk Score was used in people over 63 years of age living in long-term care facilities (LTCF) to calculate the probability of suffering a stroke. Using this parameter, three study groups were formed: low stroke risk (LSR, control), moderate stroke risk (MSR) and high stroke risk (HSR). Univariate statistical analysis showed seven metabolites with increasing plasma levels across different stroke risk groups, from LSR to HSR: isoleucine, asparagine, formate, creatinine, dimethylsulfone and two unidentified molecules, which we termed "unknown-1" and "unknown-3". These metabolic markers can be used for early detection and to detect increasing stages of stroke risk more efficiently.

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.

Gradual Increase in Inflammation-Linked Glycoproteins and a Proatherogenic Lipoprotein Profile in the Early Stages of Psychosis as Characterized by 1H NMR Blood Analysis

Minimally invasive prognostic markers of inflammation and dyslipidemia in individuals with a risk of psychosis, also called "at-risk mental state" (ARMS), or in the first episode of psychosis (FEP) are of utmost clinical importance to prevent cardiovascular disorders. We analyzed the plasma concentration of inflammation-linked glycoproteins (Glycs) and lipoprotein subclasses by proton nuclear magnetic resonance (¹H NMR) in a single acquisition. Study participants were healthy controls (HCs, N = 67) and patients with ARMS (N = 58), FEP (N = 110), or early psychosis diagnosis with ≥2 episodes (critical period (CP), N = 53). Clinical biomarkers such as high-sensitivity C-reactive protein, interleukin 6, fibrinogen, insulin, and lipoproteins were also measured. Although all participants had normal lipoprotein profiles and no inflammation according to conventional biomarkers, a gradual increase in the Glyc ¹H NMR levels was observed from HCs to CP patients; this increase was statistically significant for GlycA (CP vs HC). In parallel, a progressive and significant proatherogenic ¹H NMR lipoprotein profile was also identified across stages of psychosis (ARMS and CP vs HC). These findings highlight the potential of using ¹H NMR Glyc and lipoprotein profiling to identify blood changes in individuals with ARMS or FEP and pave the way for applications using this technology to monitor metabolic and cardiovascular risks in clinical psychiatry.

Blood metabolite profiles linking dietary patterns with health-Toward precision nutrition

Diet is one of the most important exposures that may affect health throughout life span. Investigations on dietary patterns rather than single food components are gaining in popularity because they take the complexity of the whole dietary context into account. Adherence to such dietary patterns can be measured by using metabolomics, which allows measurements of thousands of molecules simultaneously. Derived metabolite signatures of dietary patterns may reflect the consumption of specific groups of foods or their constituents originating from the dietary pattern per se, or the physiological response toward the food-derived metabolites, their interaction with endogenous metabolism, and exogenous factors such as gut microbiota. Here, we review and discuss blood metabolite fingerprints of healthy dietary patterns. The plasma concentration of several food-derived metabolites-such as betaines from whole grains and n - 3 polyunsaturated fatty acids and furan fatty acids from fish-seems to consistently reflect the intake of common foods of several healthy dietary patterns. The metabolites reflecting shared features of different healthy food indices form biomarker panels for which specific, targeted assays could be developed. The specificity of such biomarker panels would need to be validated, and proof-of-concept feeding trials are needed to evaluate to what extent the panels may mediate the effects of dietary patterns on disease risk indicators or if they are merely food intake biomarkers. Metabolites mediating health effects may represent novel targets for precision prevention strategies of clinical relevance to be verified in future studies.

Applications and prospects of cryo-EM in drug discovery

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time- and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy (cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence (AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of medium-resolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

Lipoprotein particle alterations due to androgen therapy in individuals with dyskeratosis congenita

Background

Dyskeratosis congenita (DC) is a telomere biology disorder associated with high rates of bone marrow failure (BMF) and other medical complications. Oral androgens are successfully used to treat BMF in DC but often have significant side effects, including elevation of serum lipids. This study sought to determine the extent to which oral androgen therapy altered lipid and lipoprotein levels.

Methods

Nuclear magnetic resonance (NMR) was used to evaluate serum lipid profiles, and lipoprotein particle number and size in nine androgen-treated individuals with DC, 45 untreated individuals with DC, 72 unaffected relatives of DC patients, and 19 untreated individuals with a different inherited BMF syndrome, Fanconi anaemia (FA).

Findings

Androgen-treated individuals with DC had significantly decreased serum HDL cholesterol, HDL particle number and HDL particle size (p < 0·001, p < 0·001 and p < 0·001, respectively); significantly increased serum LDL cholesterol and LDL particle number (p < 0·001, p < 0·001, respectively), decreased apoA-I and increased apoB (p < 0⋅001, p < 0⋅05 respectively) when compared with untreated individuals with DC. There were no significant lipid profile differences between untreated DC and untreated FA participants; or between untreated DC participants and their unaffected relatives. Branched chain amino acids and lipoprotein insulin resistance were not significantly different with androgen treatment. GlycA, an inflammatory acute phase reactant, was significantly increased with androgen treatment (p < 0⋅001).

Interpretation

Androgen treatment in DC creates an atherogenic lipoprotein profile, raising concern for the potential of elevated cardiovascular disease risk. Clinical guidelines for individuals on androgens for DC-related BMF should include cardiovascular disease monitoring. These findings could be relevant in individuals treated with androgen for other indications.

Funding

Intramural research programs of the Division of Cancer Epidemiology and Genetics of the National Cancer Institute and National Heart, Lung, and Blood Institute.

Biomarker Discovery in Atherosclerotic Diseases Using Quantitative Nuclear Magnetic Resonance Metabolomics

Despite great progress in the management of atherosclerosis (AS), its subsequent cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. This is probably due to insufficient risk detection using routine lipid testing; thus, there is a need for more effective approaches relying on new biomarkers. Quantitative nuclear magnetic resonance (qNMR) metabolomics is able to phenotype holistic metabolic changes, with a unique advantage in regard to quantifying lipid-protein complexes. The rapidly increasing literature has indicated that qNMR-based lipoprotein particle number, particle size, lipid components, and some molecular metabolites can provide deeper insight into atherogenic diseases and could serve as novel promising determinants. Therefore, this article aims to offer an updated review of the qNMR biomarkers of AS and CVD found in epidemiological studies, with a special emphasis on lipoprotein-related parameters. As more researches are performed, we can envision more qNMR metabolite biomarkers being successfully translated into daily clinical practice to enhance the prevention, detection and intervention of atherosclerotic diseases.

Use of nuclear magnetic resonance spectroscopy in diagnosis of inborn errors of metabolism

Nuclear Magnetic Resonance (NMR) spectroscopy has been applied in many fields of science and is increasingly being considered as a tool in the clinical setting. This review examines its application for diagnosis of inborn errors of metabolism (IEMs). IEMs, whether involving deficiency in the synthesis and degradation of metabolites, or in lipoprotein metabolism, affect nearly 3% of the global population. NMR is a preferred method for comprehensive evaluation of complex biofluids such as blood or urine, as it can provide a relatively unbiased overview of all compounds that are present and does not destroy or otherwise chemically alter the sample. While current newborn screening programs take advantage of other more sensitive methods, such as mass spectrometry, NMR has advantages especially for urine analysis with respect to ease of sample preparation and the reproducibility of results. NMR spectroscopy is particularly compatible with analysis of lipoproteins because it provides information about their size and density, not easily attained by other methods, that can help the clinician to better manage patients with dyslipidemia. We believe that NMR holds great potential for expanding clinical diagnosis in the future, in the field of IEMs and beyond.

A Mendelian randomization study of the role of lipoprotein subfractions in coronary artery disease

Recent genetic data can offer important insights into the roles of lipoprotein subfractions and particle sizes in preventing coronary artery disease (CAD), as previous observational studies have often reported conflicting results. We used the LD score regression to estimate the genetic correlation of 77 subfraction traits with traditional lipid profile and identified 27 traits that may represent distinct genetic mechanisms. We then used Mendelian randomization (MR) to estimate the causal effect of these traits on the risk of CAD. In univariable MR, the concentration and content of medium high-density lipoprotein (HDL) particles showed a protective effect against CAD. The effect was not attenuated in multivariable analyses. Multivariable MR analyses also found that small HDL particles and smaller mean HDL particle diameter may have a protective effect. We identified four genetic markers for HDL particle size and CAD. Further investigations are needed to fully understand the role of HDL particle size.

High-Density Lipoprotein Cholesterol and the Risk of Myocardial Infarction, Stroke, and Cause-Specific Mortality: a Nationwide Cohort Study in Korea

Objective

We aimed to investigate the relationship between high-density lipoprotein cholesterol (HDL-C) level and the risk of myocardial infarction (MI), stroke, and cause-specific mortality.

Methods

Using the Korean National Health Insurance Service-National Sample Cohort, we identified 343,687 subjects (men, 176,243; women, 167,444) aged ≥20 years who underwent health examinations between 2009 and 2012. HDL-C levels were categorized based on the concentration with 10 mg/dL intervals, starting from levels <30 mg/dL, with levels ≥90 mg/dL considered the highest. The endpoints of the study were newly-diagnosed MI, stroke, or mortality. We used the Cox proportional hazards model with restricted cubic splines.

Results

During a median follow-up of 6.0 years, the number of cases of death, MI, and stroke were 6,617, 4,064, and 3,435 in men and 3,677, 2,804, and 2,891 in women, respectively. The risk of all-cause mortality, cancer mortality, other mortality, and stroke was the lowest at HDL-C concentrations of 57-76 mg/dL in the spline curves; inverse associations with increased risk were observed at the lower HDL-C levels. In contrast, the lowest risk of cardiovascular mortality and MI was observed at the extreme high end. In men, there was a significant inverse and graded increase in hazard ratios of all outcomes in the lower HDL-C categories compared to the reference group (50-59 mg/dL). In the higher HDL-C categories, no significant increase in outcomes was observed. Women showed similar trends.

Conclusion

The risk of mortality, MI, and stroke was high at low HDL-C levels in the Korean general population. However, extremely high HDL-C levels were not associated with an increased risk of mortality, MI, and stroke.

Metabolic changes in the brain and blood of rats following acoustic trauma, tinnitus and hyperacusis

It has been increasingly recognized that tinnitus is likely to be generated by complex network changes. Acoustic trauma that causes tinnitus induces significant changes in multiple metabolic pathways in the brain. However, it is not clear whether those metabolic changes in the brain could also be reflected in blood samples and whether metabolic changes could discriminate acoustic trauma, hyperacusis and tinnitus. We analyzed brain and serum metabolic changes in rats following acoustic trauma or a sham procedure using metabolomics. Hearing levels were recorded before and after acoustic trauma and behavioral measures to quantify tinnitus and hyperacusis were conducted at 4 weeks following acoustic trauma. Tissues from 11 different brain regions and serum samples were collected at about 3 months following acoustic trauma. Among the acoustic trauma animals, eight exhibited hyperacusis-like behavior and three exhibited tinnitus-like behavior. Using Gas chromatography-mass spectrometry and multivariate statistical analysis, significant metabolic changes were found in acoustic trauma animals in both the brain and serum samples with a number of metabolic pathways significantly perturbated. Furthermore, metabolic changes in the serum were able to differentiate sham from acoustic trauma animals, as well as sham from hyperacusis animals, with high accuracy. Our results suggest that serum metabolic profiling in combination with machine learning analysis may be a promising approach for identifying biomarkers for acoustic trauma, hyperacusis and potentially, tinnitus.

DNA methylation and lipid metabolism: an EWAS of 226 metabolic measures

BACKGROUND

The discovery of robust and trans-ethnically replicated DNA methylation markers of metabolic phenotypes, has hinted at a potential role of epigenetic mechanisms in lipid metabolism. However, DNA methylation and the lipid compositions and lipid concentrations of lipoprotein sizes have been scarcely studied. Here, we present an epigenome-wide association study (EWAS) (N = 5414 total) of mostly lipid-related metabolic measures, including a fine profiling of lipoproteins. As lipoproteins are the main players in the different stages of lipid metabolism, examination of epigenetic markers of detailed lipoprotein features might improve the diagnosis, prognosis, and treatment of metabolic disturbances.

RESULTS

We conducted an EWAS of leukocyte DNA methylation and 226 metabolic measurements determined by nuclear magnetic resonance spectroscopy in the population-based KORA F4 study (N = 1662) and replicated the results in the LOLIPOP, NFBC1966, and YFS cohorts (N = 3752). Follow-up analyses in the discovery cohort included investigations into gene transcripts, metabolic-measure ratios for pathway analysis, and disease endpoints. We identified 161 associations (p value < 4.7 × 10-10), covering 16 CpG sites at 11 loci and 57 metabolic measures. Identified metabolic measures were primarily medium and small lipoproteins, and fatty acids. For apolipoprotein B-containing lipoproteins, the associations mainly involved triglyceride composition and concentrations of cholesterol esters, triglycerides, free cholesterol, and phospholipids. All associations for HDL lipoproteins involved triglyceride measures only. Associated metabolic measure ratios, proxies of enzymatic activity, highlight amino acid, glucose, and lipid pathways as being potentially epigenetically implicated. Five CpG sites in four genes were associated with differential expression of transcripts in blood or adipose tissue. CpG sites in ABCG1 and PHGDH showed associations with metabolic measures, gene transcription, and metabolic measure ratios and were additionally linked to obesity or previous myocardial infarction, extending previously reported observations.

CONCLUSION

Our study provides evidence of a link between DNA methylation and the lipid compositions and lipid concentrations of different lipoprotein size subclasses, thus offering in-depth insights into well-known associations of DNA methylation with total serum lipids. The results support detailed profiling of lipid metabolism to improve the molecular understanding of dyslipidemia and related disease mechanisms.

High-frequency metabolite profiling and the incidence of recurrent cardiac events in patients with post-acute coronary syndrome

Purpose: The aim of this study was to study temporal changes in metabolite profiles in patients with post-acute coronary syndrome (ACS), in particular prior to the development of recurrent ACS (reACS).Methods: BIOMArCS (BIOMarker study to identify the Acute risk of a Coronary Syndrome) is a prospective study including patients admitted for ACS, who underwent high-frequency blood sampling during 1-year follow-up. Within BIOMArCS, we performed a nested case-cohort analysis of 158 patients (28 cases of reACS). We determined 151 metabolites by nuclear magnetic resonance in seven (median) blood samples per patient. Temporal evolution of the metabolites and their relation with reACS was assessed by joint modelling. Results are reported as adjusted (for clinical factors) hazard ratios (aHRs).Results: Median age was 64 (25th-75th percentiles; 56-72) years and 78% were men. After multiple testing correction (p < 0.001), high concentrations of extremely large very low density lipoprotein (VLDL) particles (aHR 1.60/SD increase; 95%CI 1.25-2.08), very large VLDL particles (aHR 1.60/SD increase; 95%CI 1.25-2.08) and large VLDL particles (aHR 1.56/SD increase; 95%CI 1.22-2.05) were significantly associated with reACS. Moreover, these longitudinal particle concentrations showed a steady increase over time prior to reACS. Among the other metabolites, no significant associations were observed.Conclusion: Post-ACS patients with persistent high concentrations of extremely large, very large and large VLDL particles have increased risk of reACS within 1 year.

Plasma metabolite biomarkers predictive of radiation induced cardiotoxicity

PURPOSE

Although advancements in cancer treatments using radiation therapy (RT) have led to improved outcomes, radiation-induced heart disease (RIHD) remains a significant source of morbidity and mortality in survivors of cancers in the chest. Currently, there are no diagnostic tests in clinical use due to a lack of understanding of the natural history and mechanisms of RIHD development. Few studies have examined the utility of using metabolomics to prospectively identify cancer survivors who are at risk of developing cardiotoxicity.

METHODS

We analyzed plasma and left ventricle heart tissue samples collected from a cohort of male Sprague Dawley (SD) rats that were either sham irradiated or received fractionated doses (9 Gy per day × 5 days) of targeted X-ray radiation to the heart. Metabolomic and lipidomic analyses were utilized as a correlative approach for delineation of novel biomarkers associated with radiation-induced cardiac toxicity. Additionally, we used high-resolution mass spectrometry to examine the metabolomic profiles of plasma samples obtained from patients receiving high dose thoracic RT for esophageal cancer.

RESULTS

Metabolic alterations in the rat model and patient plasma profiles, showed commonalities of radiation response that included steroid hormone biosynthesis and vitamin E metabolism. Alterations in patient plasma profiles were used to develop classification algorithms predictive of patients at risk of developing RIHD.

CONCLUSION

Herein, we report the feasibility of developing a metabolomics-based biomarker panel that is associated with adverse outcomes of cardiac function in patients who received RT for the treatment of esophageal cancer.

Magnesium: The overlooked electrolyte in blood cancers?

Magnesium is an important element that has essential roles in the regulation of cell growth, division, and differentiation. Mounting evidence in the literature suggests an association between hypomagnesemia and all-cause mortality. In addition, epidemiologic studies have demonstrated that a diet poor in magnesium increases the risk of developing cancer, highlighting its importance in the field of hematology and oncology. In solid malignancies, hypomagnesemia at diagnosis portends a worse prognosis. However, little is known about prognosis in patients with hypomagnesemia and blood cancers in general; lymphoma more specifically. Hypomagnesemia has been associated with a higher viral load of the Epstein Barr virus, a virus associated with a multitude of hematologic malignancies. The role of magnesium in the immune system has been further elucidated in studies of patients with a rare primary immunodeficiency known as XMEN disease (X-linked immunodeficiency with Magnesium defect, Epstein-Barr virus (EBV) infection, and Neoplasia disease). These patients have a mutation in the MAGT1 gene, which codes for a magnesium transporter. The mutation leads to impaired T cell activation and an increased risk of developing hematologic malignancies. In this review we discuss the relevance of magnesium as an electrolyte, current measurement techniques, and the known data related to cause and prognosis of blood cancers. The goal is to use these data to stimulate additional high-quality and well powered studies to further investigate the role of magnesium in preventing cancer and improving outcomes of patients with malignancy and concomitant magnesium deficiency.

Theoretical and Experimental Approaches Aimed at Drug Design Targeting Neurodegenerative Diseases

In recent years, green chemistry has been strengthening, showing how basic and applied sciences advance globally, protecting the environment and human health. A clear example of this evolution is the synergy that now exists between theoretical and computational methods to design new drugs in the most efficient possible way, using the minimum of reagents and obtaining the maximum yield. The development of compounds with potential therapeutic activity against multiple targets associated with neurodegenerative diseases/disorders (NDD) such as Alzheimer’s disease is a hot topic in medical chemistry, where different scientists from various disciplines collaborate to find safe, active, and effective drugs. NDD are a public health problem, affecting mainly the population over 60 years old. To generate significant progress in the pharmacological treatment of NDD, it is necessary to employ different experimental strategies of green chemistry, medical chemistry, and molecular biology, coupled with computational and theoretical approaches such as molecular simulations and chemoinformatics, all framed in the rational drug design targeting NDD. Here, we review how green chemistry and computational approaches have been used to develop new compounds with the potential application against NDD, as well as the challenges and new directions of the drug development multidisciplinary process.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Immunomodulatory Protein from Nectria haematococca Induces Apoptosis in Lung Cancer Cells via the P53 Pathway

Our previous research has shown that a fungal immunomodulatory protein from Nectria haematococca (FIP-nha) possesses a wide spectrum of anti-tumor activities, and FIP-nha induced A549 apoptosis by negatively regulating the PI3K/Akt signaling pathway based on comparative quantitative proteomics. This study further confirmed that the anti-lung cancer activity of FIP-nha was significantly stronger than that of the reported LZ-8 and FIP-fve. Subsequently, ¹H NMR-based metabolomics was applied to comprehensively investigate the underlying mechanism, and a clear separation of FIP-nha-treated and untreated groups was achieved using pattern recognition analysis. Four potential pathways associated with the anti-tumor effect of FIP-nha on A549 cells were identified, and these were mainly involved in glycolysis, taurine and hypotaurine metabolism, fructose and mannose metabolism, and glycerolipid metabolism. Metabolic pathway analysis demonstrated that FIP-nha could induce A549 cell apoptosis partly by regulating the p53 inhibition pathway, which then disrupted the Warburg effect, as well as through other metabolic pathways. Using RT-PCR analysis, FIP-nha-induced apoptosis was confirmed to occur through upregulation of p53 expression. This work highlights the possible use of FIP-nha as a therapeutic adjuvant for lung cancer treatment.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Metabolomics applied to the discovery of tuberculosis and diabetes mellitus biomarkers

Tuberculosis (TB) and diabetes mellitus Type 2 (DM2) are two diseases as ancient as they are harmful to human health. The outcome for both diseases in part depends on immune and metabolic individual responses. DM2 is increasing yearly, mainly due to environmental, genetic and lifestyle habits. There are multiple evidence that DM2 is one of the most important risk factor of becoming infected with TB or reactivating latent TB. Mass spectrometry-based metabolomics is an important tool for elucidating the metabolites and metabolic pathways that influence the immune responses to M. tuberculosis infection during diabetes. We provide an up-to-date review highlighting the importance and benefit of metabolomics for identifying biomarkers as candidate molecules for diagnosis, disease activity or prognosis.

Association of circulating metabolites with healthy diet and risk of cardiovascular disease: analysis of two cohort studies

Diet may modify metabolomic profiles towards higher or lower cardiovascular disease (CVD) risk. We aimed to identify metabolite profiles associated with high adherence to dietary recommendations - the Alternative Healthy Eating Index (AHEI) - and the extent to which metabolites associated with AHEI also predict incident CVD. Relations between AHEI score and 80 circulating lipids and metabolites, quantified by nuclear magnetic resonance metabolomics, were examined using linear regression models in the Whitehall II study (n = 4824, 55.9 ± 6.1 years, 28.0% women) and were replicated in the Cardiovascular Risk in Young Finns Study (n = 1716, 37.7 ± 5.0 years, 56.3% women). We used Cox models to study associations between metabolites and incident CVD over the 15.8-year follow-up in the Whitehall II study. After adjustment for confounders, higher AHEI score (indicating healthier diet) was associated with higher degree of unsaturation of fatty acids (FA) and higher ratios of polyunsaturated FA, omega-3 and docosahexaenoic acid relative to total FA in both Whitehall II and Young Finns studies. A concordance of associations of metabolites with higher AHEI score and lower CVD risk was observed in Whitehall II. Adherence to healthy diet seems to be associated with specific FA that reduce risk of CVD.

Circulating metabolomic profile can predict dyslipidemia in HIV patients undergoing antiretroviral therapy

BACKGROUND AND AIMS

Dyslipidemia in HIV-infected patients is unique and pathophysiologically associated with host factors, HIV itself and the use of antiretroviral therapy (ART). The use of nuclear magnetic resonance spectroscopy (NMR) provides additional data to conventional lipid measurements concerning the number of lipoprotein subclasses and particle sizes.

METHODS

To investigate the ability of lipoprotein profile, we used a circulating metabolomic approach in a cohort of 103 ART-naive HIV-infected patients, who were initiating non-nucleoside analogue transcriptase inhibitor (NNRTI)-based ART, and we subsequently followed up these patients for 36 months. Univariate and multivariate analyses were performed to evaluate the predictive power of NMR spectroscopy.

RESULTS

VLDL-metabolism (including VLDL lipid concentrations, sizes, and particle numbers), total triglycerides and lactate levels resulted in good classifiers of dyslipidemia (AUC 0.903). Total particles/HDL-P ratio was significantly higher in ART-associated dyslipidemia compared to ART-normolipidemia (p = 0.001). Large VLDL-Ps were positively associated with both LDL-triglycerides (ρ 0.682, p < 0.001) and lactate concentrations (ρ 0.416, p < 0.001), the last one a marker of mitochondrial low oxidative capacity.

CONCLUSIONS

Our data suggest that circulating metabolites have better predictive values for HIV/ART-related dyslipidemia onset than do the biochemical markers associated with conventional lipid measurements. NMR identifies changes in VLDL-P, lactate and LDL-TG as potential clinical markers of baseline HIV-dyslipidemia predisposition. Differences in circulating metabolomics, especially differences in particle size, are indicators of important derangements of mitochondrial function that are linked to ART-related dyslipidemia.

Novel targets and future strategies for acute cardioprotection: Position Paper of the European Society of Cardiology Working Group on Cellular Biology of the Heart

Ischaemic heart disease and the heart failure that often results, remain the leading causes of death and disability in Europe and worldwide. As such, in order to prevent heart failure and improve clinical outcomes in patients presenting with an acute ST-segment elevation myocardial infarction and patients undergoing coronary artery bypass graft surgery, novel therapies are required to protect the heart against the detrimental effects of acute ischaemia/reperfusion injury (IRI). During the last three decades, a wide variety of ischaemic conditioning strategies and pharmacological treatments have been tested in the clinic-however, their translation from experimental to clinical studies for improving patient outcomes has been both challenging and disappointing. Therefore, in this Position Paper of the European Society of Cardiology Working Group on Cellular Biology of the Heart, we critically analyse the current state of ischaemic conditioning in both the experimental and clinical settings, provide recommendations for improving its translation into the clinical setting, and highlight novel therapeutic targets and new treatment strategies for reducing acute myocardial IRI.

Nuclear magnetic resonance-based metabolomics identifies phenylalanine as a novel predictor of incident heart failure hospitalisation: results from PROSPER and FINRISK 1997

Aims

We investigated the association between quantified metabolite, lipid and lipoprotein measures and incident heart failure hospitalisation (HFH) in the elderly, and examined whether circulating metabolic measures improve HFH prediction.

Methods and results

Overall, 80 metabolic measures from the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER) trial were measured by proton nuclear magnetic resonance spectroscopy (n = 5341; 182 HFH events during 2.7‐year follow‐up). We repeated the work in FINRISK 1997 (n = 7330; 133 HFH events during 5‐year follow‐up). In PROSPER, the circulating concentrations of 13 metabolic measures were found to be significantly different in those who were later hospitalised for heart failure after correction for multiple comparisons. These included creatinine, phenylalanine, glycoprotein acetyls, 3‐hydroxybutyrate, and various high‐density lipoprotein measures. In Cox models, two metabolites were associated with risk of HFH after adjustment for clinical risk factors and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP): phenylalanine [hazard ratio (HR) 1.29, 95% confidence interval (CI) 1.10–1.53; P = 0.002] and acetate (HR 0.81, 95% CI 0.68–0.98; P = 0.026). Both were retained in the final model after backward elimination. Compared to a model with established risk factors and NT‐proBNP, this model did not improve the C‐index but did improve the overall continuous net reclassification index (NRI 0.21; 95% CI 0.06–0.35; P = 0.007) due to improvement in classification of non‐cases (NRI 0.14; 95% CI 0.12–0.17; P < 0.001). Phenylalanine was replicated as a predictor of HFH in FINRISK 1997 (HR 1.23, 95% CI 1.03–1.48; P = 0.023).

Conclusion

Our findings identify phenylalanine as a novel predictor of incident HFH, although prediction gains are low. Further mechanistic studies appear warranted.

Comprehensive Metabolomic Profiling and Incident Cardiovascular Disease: A Systematic Review

BACKGROUND

Metabolomics is a promising tool of cardiovascular biomarker discovery. We systematically reviewed the literature on comprehensive metabolomic profiling in association with incident cardiovascular disease (CVD).

METHODS AND RESULTS

We searched MEDLINE and EMBASE from inception to January 2016. Studies were eligible if they pertained to adult humans; followed an agnostic and/or comprehensive approach; used serum or plasma (not urine or other biospecimens); conducted metabolite profiling at baseline in the context of examining prospective disease; and included myocardial infarction, stroke, and/or CVD death in the CVD outcome definition. We identified 12 original articles (9 cohort and 3 nested case-control studies); participant numbers ranged from 67 to 7256. Mass spectrometry was the predominant analytical method. The number and chemical diversity of metabolites were very heterogeneous, ranging from 31 to >10 000 features. Four studies used untargeted profiling. Different types of metabolites were associated with CVD risk: acylcarnitines, dicarboxylacylcarnitines, and several amino acids and lipid classes. Only tiny improvements in CVD prediction beyond traditional risk factors were observed using these metabolites (C index improvement ranged from 0.006 to 0.05).

CONCLUSIONS

There are a limited number of longitudinal studies assessing associations between comprehensive metabolomic profiles and CVD risk. Quantitatively synthesizing the literature is challenging because of the widely varying analytical tools and the diversity of methodological and statistical approaches. Although some results are promising, more research is needed, notably standardization of metabolomic techniques and statistical approaches. Replication and combinations of novel and holistic methodological approaches would move the field toward the realization of its promise.

Brain Metabolic Changes in Rats following Acoustic Trauma

Acoustic trauma is the most common cause of hearing loss and tinnitus in humans. However, the impact of acoustic trauma on system biology is not fully understood. It has been increasingly recognized that tinnitus caused by acoustic trauma is unlikely to be generated by a single pathological source, but rather a complex network of changes involving not only the auditory system but also systems related to memory, emotion and stress. One obvious and significant gap in tinnitus research is a lack of biomarkers that reflect the consequences of this interactive "tinnitus-causing" network. In this study, we made the first attempt to analyse brain metabolic changes in rats following acoustic trauma using metabolomics, as a pilot study prior to directly linking metabolic changes to tinnitus. Metabolites in 12 different brain regions collected from either sham or acoustic trauma animals were profiled using a gas chromatography mass spectrometry (GC/MS)-based metabolomics platform. After deconvolution of mass spectra and identification of the molecules, the metabolomic data were processed using multivariate statistical analysis. Principal component analysis showed that metabolic patterns varied among different brain regions; however, brain regions with similar functions had a similar metabolite composition. Acoustic trauma did not change the metabolite clusters in these regions. When analyzed within each brain region using the orthogonal projection to latent structures discriminant analysis sub-model, 17 molecules showed distinct separation between control and acoustic trauma groups in the auditory cortex, inferior colliculus, superior colliculus, vestibular nucleus complex (VNC), and cerebellum. Further metabolic pathway impact analysis and the enrichment overview with network analysis suggested the primary involvement of amino acid metabolism, including the alanine, aspartate and glutamate metabolic pathways, the arginine and proline metabolic pathways and the purine metabolic pathway. Our results provide the first metabolomics evidence that acoustic trauma can induce changes in multiple metabolic pathways. This pilot study also suggests that the metabolomic approach has the potential to identify acoustic trauma-specific metabolic shifts in future studies where metabolic changes are correlated with the animal's tinnitus status.

1H NMR Metabolomic Profiling of Human and Animal Blood Serum Samples

Nuclear magnetic resonance (NMR) spectroscopy techniques allow the acquisition of a large amount of data and when combined with multivariate statistical analysis, it is possible to process and interpret the obtained NMR data in accordance with the biological problem being investigated. In this chapter, the search for biologically relevant biomarkers is addressed using NMR spectroscopy-based metabolomics, due to their clinical relevance for either diagnosis or monitoring of diseases and disorders.

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future

Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact.

Protection of baicalin against lipopolysaccharide induced liver and kidney injuries based on 1H NMR metabolomic profiling

Severe sepsis and septic shock are common and lethal conditions characterized by a systemic inflammatory response that is activated by invasive infection. In this study, a lipopolysaccharide (LPS) induced sepsis mice model was established to investigate the toxicities of LPS and the therapeutic effect of baicalin. Sera for clinical biochemistry and NMR metabolomic investigation, and liver and kidney tissues for histopathological examination, molecular biology measurement and NMR metabolomic profiling were collected. Multivariate analysis of metabolic profiles of the serum, liver and kidney extracts of mice revealed the occurrence of a severe inflammatory response, oxidative stress, and perturbances in energy and amino acid metabolism in LPS induced sepsis mice, which could be greatly ameliorated by baicalin treatment. This integrated 1H NMR based metabolomics approach gave us a new insight into the pathology of LPS induced sepsis, and helped in understanding the therapeutic effects of baicalin in a holistic view.

Nutritional Genomics and the Mediterranean Diet's Effects on Human Cardiovascular Health

The synergies and cumulative effects among different foods and nutrients are what produce the benefits of a healthy dietary pattern. Diets and dietary patterns are a major environmental factor that we are exposed to several times a day. People can learn how to control this behavior in order to promote healthy living and aging, and to prevent diet-related diseases. To date, the traditional Mediterranean diet has been the only well-studied pattern. Stroke incidence, a number of classical risk factors including lipid profile and glycaemia, emergent risk factors such as the length of telomeres, and emotional eating behavior can be affected by genetic predisposition. Adherence to the Mediterranean diet could exert beneficial effects on these risk factors. Our individual genetic make-up should be taken into account to better prevent these traits and their subsequent consequences in cardiovascular disease development. In the present work, we review the results of nutritional genomics explaining the role of the Mediterranean diet in human cardiovascular disease. A multidisciplinary approach is necessary to extract knowledge from large-scale data.

Analysis of Potential Amino Acid Biomarkers in Brain Tissue and the Effect of Galangin on Cerebral Ischemia

Galangin, a potent scavenger of free radicals, has been used as an herbal medicine for various ailments for centuries in Asia. With complex pathophysiology, ischemic stroke is one of the most frequent causes of death and disability worldwide. We have reported that galangin provides direct protection against ischemic injury as a potential neuroprotective agent and has potential therapeutic effects on the changes of serum amino acids in ischemic stroke; however, the mechanism of the changes of amino acids in the ischemic brain tissue has not yet been clarified. In this paper, we explored brain tissue amino acid biomarkers in the acute phase of cerebral ischemia and the effect of galangin on those potential biomarkers. Finally, we identified that glutamic acid, alanine and aspartic acid showed significant changes (p < 0.05 or p < 0.01) in galangin-treated groups compared with vehicle-treated rats and the four enzymes associated with these three AAs’ metabolic pathways; GLUD1, SLC16A10, SLC1A1 and GPT were identified by multiplex interactions with the three amino acids. By metabolite-protein network analysis and molecular docking, six of 28 proteins were identified and might become potential galangin biomarkers for acute ischemic stroke. The data in our study provides thoughts for exploring the mechanism of disease, discovering new targets for drug candidates and elucidating the related regulatory signal network.

Revealing Potential Biomarkers of Functional Dyspepsia by Combining 1H NMR Metabonomics Techniques and an Integrative Multi-objective Optimization Method

Metabonomics methods have gradually become important auxiliary tools for screening disease biomarkers. However, recognition of metabolites or potential biomarkers closely related to either particular clinical symptoms or prognosis has been difficult. The current study aims to identify potential biomarkers of functional dyspepsia (FD) by a new strategy that combined hydrogen nuclear magnetic resonance (¹H NMR)-based metabonomics techniques and an integrative multi-objective optimization (LPIMO) method. First, clinical symptoms of FD were evaluated using the Nepean Dyspepsia Index (NDI), and plasma metabolic profiles were measured by ¹H NMR. Correlations between the key metabolites and the NDI scores were calculated. Then, LPIMO was developed to identify a multi-biomarker panel by maximizing diagnostic ability and correlation with the NDI score. Finally, a KEGG database search elicited the metabolic pathways in which the potential biomarkers are involved. The results showed that glutamine, alanine, proline, HDL, β-glucose, α-glucose and LDL/VLDL levels were significantly altered in FD patients. Among them, phosphatidycholine (PtdCho) and leucine/isoleucine (Leu/Ile) were positively and negatively correlated with the NDI Symptom Index (NDSI) respectively. Our procedure not only significantly improved the credibility of the biomarkers, but also demonstrated the potential of further explorations and applications to diagnosis and treatment of complex disease.

Chromatography/Mass Spectrometry-Based Biomarkers in the Field of Obstructive Sleep Apnea

Biomarker assessment is based on quantifying several proteins and metabolites. Recent developments in proteomics and metabolomics have enabled detection of these small molecules in biological samples and exploration of the underlying disease mechanisms in obstructive sleep apnea (OSA). This systemic review was performed to identify biomarkers, which were only detected by chromatography and/or mass spectrometry (MS) and to discuss the role of these biomarkers in the field of OSA. We systemically reviewed relevant articles from PubMed and EMBASE referring to proteins and metabolite profiles of biological samples in patients with OSA. The analytical platforms in this review were focused on chromatography and/or MS. In total, 30 studies evaluating biomarkers in patients with OSA using chromatography and/or MS methods were included. Numerous proteins and metabolites, including lipid profiles, adrenergic/dopaminergic biomarkers and derivatives, amino acids, oxidative stress biomarkers, and other micromolecules were identified in patients with OSA. Applying chromatography and/or MS methods to detect biomarkers helps develop an understanding of OSA mechanisms. More proteomic and metabolomic studies are warranted to develop potential diagnostic and clinical monitoring methods for OSA.

Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts

BACKGROUND

High-throughput profiling of circulating metabolites may improve cardiovascular risk prediction over established risk factors.

METHODS AND RESULTS

We applied quantitative nuclear magnetic resonance metabolomics to identify the biomarkers for incident cardiovascular disease during long-term follow-up. Biomarker discovery was conducted in the National Finnish FINRISK study (n=7256; 800 events). Replication and incremental risk prediction was assessed in the Southall and Brent Revisited (SABRE) study (n=2622; 573 events) and British Women's Health and Heart Study (n=3563; 368 events). In targeted analyses of 68 lipids and metabolites, 33 measures were associated with incident cardiovascular events at P<0.0007 after adjusting for age, sex, blood pressure, smoking, diabetes mellitus, and medication. When further adjusting for routine lipids, 4 metabolites were associated with future cardiovascular events in meta-analyses: higher serum phenylalanine (hazard ratio per standard deviation, 1.18; 95% confidence interval, 1.12-1.24; P=4×10(-10)) and monounsaturated fatty acid levels (1.17; 1.11-1.24; P=1×10(-8)) were associated with increased cardiovascular risk, while higher omega-6 fatty acids (0.89; 0.84-0.94; P=6×10(-5)) and docosahexaenoic acid levels (0.90; 0.86-0.95; P=5×10(-5)) were associated with lower risk. A risk score incorporating these 4 biomarkers was derived in FINRISK. Risk prediction estimates were more accurate in the 2 validation cohorts (relative integrated discrimination improvement, 8.8% and 4.3%), albeit discrimination was not enhanced. Risk classification was particularly improved for persons in the 5% to 10% risk range (net reclassification, 27.1% and 15.5%). Biomarker associations were further corroborated with mass spectrometry in FINRISK (n=671) and the Framingham Offspring Study (n=2289).

CONCLUSIONS

Metabolite profiling in large prospective cohorts identified phenylalanine, monounsaturated fatty acids, and polyunsaturated fatty acids as biomarkers for cardiovascular risk. This study substantiates the value of high-throughput metabolomics for biomarker discovery and improved risk assessment.

The emergence of metabolomics as a key discipline in the drug discovery process

Metabolomics is a recent science that could be defined as the comprehensive qualitative and quantitative analysis of all small molecular weight compounds present in a cell, organ (including biofluids) or organism at a specific time point. More and more applications have been found these past years to metabolomics in the pharmaceutical field. Specifically in the drug discovery process, metabolomics open new perspectives, in new targets identification, in toxicological studies and in bioactive natural products discovery. The challenge in metabolomics is to find a technological approach allowing the reproducible identification and quantitation of as much metabolites as possible. In this context, mass spectrometry and NMR are emerging as key and complementary technologies.