Unveiling differences between patients with acute coronary syndrome with and without ST elevation through fingerprinting with CE-MS and HILIC-MS targeted analysis

Plasma Levels of Propionylcarnitine Improved Prediction of Heart Failure and All-Cause Mortality in Patients with Stable Coronary Artery Disease

BACKGROUND

Plasma metabolites could be suitable as predictive biomarkers for cardiovascular pathologies or death, thereby improving the prediction of protein biomarkers. The release of acylcarnitines may be altered after coronary artery disease (CAD) in subjects with recurrent clinical outcomes, and this could be used as a prognosis tool.

METHODS

Patients with stable coronary artery disease (SCAD) who had suffered an acute coronary syndrome 6-9 months before were followed for up to 4.3 years for adverse events. Soluble pro-inflammatory/fibrotic proteins, and a panel of 13 amino acids and 13 acylcarnitines, were evaluated by ELISA and metabolomics analyses as potential predictors of a primary outcome [heart failure (HF) or death].

RESULTS

Among 139 patients (67.0 years old, BMI = 28.6 kg/m2, and 71.2% male), 25 developed the primary outcome after a mean follow-up of 2.2 years. These patients showed increased plasma levels of NT-proBNP (1300 vs. 250 pg/mL; p < 0.001), pro-inflammatory/fibrotic MCP-1 (1.7 vs. 1.4 × 102 pg/mL; p = 0.043), Gal-3 (12.7 vs. 7.9 ng/mL; p < 0.001), and NGAL (2.7 vs. 1.6 × 102 ng/mL; p < 0.001), and lower acetyl- and propionylcarnitines (0.59 vs. 0.99 µM, p = 0.007, and 3.22 vs. 6.49 × 10-2 µM, p < 0.001, respectively). Instead, plasma amino acids were not significantly changed. Through a multivariable logistic regression analysis, a combined model of age, Gal-3, and the NGAL/propionylcarnitine ratio showed the highest prediction for HF or death (AUC = 0.88, sensitivity = 0.8, and specificity = 0.81; p < 0.001).

CONCLUSIONS

Patients with SCAD led to recurrent HF or all-cause death. Interestingly, increased levels of plasma NGAL and Gal-3, and a reduction in propionylcarnitine, could predict the occurrence of these events.

Targeted Analysis of Plasma Polar Metabolites in Postmenopausal Depression

Depression will be the disease with the highest incidence worldwide by 2030. Data indicate that postmenopausal women have a higher incidence of mood disorders, and this high vulnerability seems to be related to hormonal changes and weight gain. Although research evaluating the profile of metabolites in mood disorders is advancing, further research, maintaining consistent methodology, is necessary to reach a consensus. Therefore, the objective of the present study was to carry out an exploratory analysis of the plasma polar metabolites of pre- and postmenopausal women to explore whether the profile is affected by depression. The plasma analysis of 50 polar metabolites was carried out in a total of 67 postmenopausal women, aged between 50 and 65 years, either without depression (n = 25) or with depression symptoms (n = 42), which had spontaneous onset of menopause and were not in use of hormone replacement therapy, insulin, or antidepressants; and in 42 healthy premenopausal women (21 without depression and 21 with depression symptoms), aged between 40 and 50 years and who were not in use of contraceptives, insulin, or antidepressants. Ten metabolites were significantly affected by depression symptoms postmenopause, including adenosine (FDR = 3.778 × 10-14), guanosine (FDR = 3.001 × 10-14), proline (FDR = 1.430 × 10-6), citrulline (FDR = 0.0001), lysine (FDR = 0.0004), and carnitine (FDR = 0.0331), which were down-regulated, and dimethylglycine (FDR = 0.0022), glutathione (FDR = 0.0048), creatine (FDR = 0.0286), and methionine (FDR = 0.0484) that were up-regulated. In premenopausal women with depression, oxidized glutathione (FDR = 0.0137) was down-regulated, and dimethylglycine (FDR = 0.0406) and 4-hydroxyproline (FDR = 0.0433) were up-regulated. The present study provided new data concerning the consequences of depression on plasma polar metabolites before and after the establishment of menopause. The results demonstrated that the postmenopausal condition presented more alterations than the premenopausal period and may indicate future measures to treat the disturbances involved in both menopause and depression.

Identifying subgroups of childhood obesity by using multiplatform metabotyping

Introduction: Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. Methods: This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Results and Discussion: Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios.

Amino acids in inflammatory bowel diseases: Modern diagnostic tools and methodologies

Amino acids are crucial building blocks of living organisms. Together with their derivatives, they participate in many intracellular processes to act as hormones, neuromodulators, and neurotransmitters. For several decades amino acids have been studied for their potential as markers of various diseases, including inflammatory bowel diseases. Subsequent improvements in sample pretreatment, separation, and detection methods have enabled the specific and very sensitive determination of these molecules in multicomponent matrices-biological fluids and tissues. The information obtained from targeted amino acid analysis (biomarker-based analytical strategy) can be further used for early diagnostics, to monitor the course of the disease or compliance of the patients. This review will provide an insight into current knowledge about inflammatory bowel diseases, the role of proteinogenic amino acids in intestinal inflammation and modern analytical techniques used in its diagnosis and disease activity monitoring. Current advances in the analysis of amino acids focused on sample pretreatment, separation strategy, or detection methods are highlighted, and their potential in clinical laboratories is discussed. In addition, the latest clinical data obtained from the metabolomic profiling of patients suffering from inflammatory bowel diseases are summarized with a focus on proteinogenic amino acids.

Defining Acute Coronary Syndrome through Metabolomics

As an emerging platform technology, metabolomics offers new insights into the pathomechanisms associated with complex disease conditions, including cardiovascular diseases. It also facilitates assessing the risk of developing the disease before its clinical manifestation. For this reason, metabolomics is of growing interest for understanding the pathogenesis of acute coronary syndromes (ACS), finding new biomarkers of ACS, and its associated risk management. Metabolomics-based studies in ACS have already demonstrated immense potential for biomarker discovery and mechanistic insights by identifying metabolomic signatures (e.g., branched-chain amino acids, acylcarnitines, lysophosphatidylcholines) associated with disease progression. Herein, we discuss the various metabolomics approaches and the challenges involved in metabolic profiling, focusing on ACS. Special attention has been paid to the clinical studies of metabolomics and lipidomics in ACS, with an emphasis on ischemia/reperfusion injury.

Prognostic biomarkers of Parkinson's disease in the Spanish EPIC cohort: a multiplatform metabolomics approach

The lack of knowledge about the onset and progression of Parkinson's disease (PD) hampers its early diagnosis and treatment. Metabolomics might shed light on the PD imprint seeking a broader view of the biochemical remodeling induced by this disease in an early and pre-symptomatic stage and unveiling potential biomarkers. To achieve this goal, we took advantage of the great potential of the European Prospective Study on Nutrition and Cancer (EPIC) cohort to apply metabolomics searching for early diagnostic PD markers. This cohort consisted of healthy volunteers that were followed for around 15 years until June 2011 to ascertain incident PD. For this untargeted metabolomics-based study, baseline preclinical plasma samples of 39 randomly selected individuals that developed PD (Pre-PD group) and the corresponding control group were analyzed using a multiplatform approach. Data were statistically analyzed and exposed alterations in 33 metabolites levels, including significantly lower levels of free fatty acids (FFAs) in the preclinical samples from PD subjects. These results were then validated by adopting a targeted HPLC-QqQ-MS approach. After integrating all the metabolites affected, our finding revealed alterations in FFAs metabolism, mitochondrial dysfunction, oxidative stress, and gut-brain axis dysregulation long before the development of PD hallmarks. Although the biological purpose of these events is still unknown, the remodeled metabolic pathways highlighted in this work might be considered worthy prognostic biomarkers of early prodromal PD. The findings revealed by this work are of inestimable value since this is the first study conducted with samples collected many years before the disease development.

Plasma Metabolic Signature of Atherosclerosis Progression and Colchicine Treatment in Rabbits

Balloon catheter endothelial denudation in New Zealand white rabbits fed high cholesterol diet is a validated atherosclerosis model. Well-characterized in terms of atherosclerosis induction and progression, the metabolic changes associated with the atherosclerosis progression remain indeterminate. Non-targeted metabolomics permits to develop such elucidation and allows to evaluate the metabolic consequences of colchicine treatment, an anti-inflammatory drug that could revert these changes. 16 rabbits underwent 18 weeks of atherosclerosis induction by diet and aortic denudation. Thereafter animals were randomly assigned to colchicine treatment or placebo for 18 weeks while on diet. Plasma samples were obtained before randomization and at 36 weeks. Multiplatform (GC/MS, CE/MS, RP-HPLC/MS) metabolomics was applied. Plasma fingerprints were pre-processed, and the resulting matrixes analyzed to unveil differentially expressed features. Different chemical annotation strategies were accomplished for those significant features. We found metabolites associated with either atherosclerosis progression, or colchicine treatment, or both. Atherosclerosis was profoundly associated with an increase in circulating bile acids. Most of the changes associated with sterol metabolism could not be reverted by colchicine treatment. However, the variations in lysine, tryptophan and cysteine metabolism among others, have shown new potential mechanisms of action of the drug, also related to atherosclerosis progression, but not previously described.

Investigation of the metabolic difference between ST-elevated myocardial infarction and non-ST-elevated myocardial infarction via LC/Q-TOF/MS/MS

Acute coronary syndrome (ACS) is a clinical condition caused by a disturbance in myocardial blood flow. ACS can be basically divided into two forms: ST elevation myocardial infarction (STEMI) due to complete occlusion of the coronary artery and non-ST elevation myocardial infarction (NSTEMI) due to partial occlusion of the coronary artery. In this study, we aimed to monitor the metabolite profile of STEMI and NSTEMI patients and compare the results via untargeted metabolomics approach. Serum samples were collected from STEMI and NSTEMI patients, and each group consists of 20 participants. Extraction was achieved by acetonitrile, and chromatographic separation was performed by LC/Q-TOF/MS/MS accompanied with dual AJS ESI positive ion mode. METLIN, MATLAB 2017a-PLS Toolbox7.2, and Human Metabolome Database were utilized for bioinformatics evaluation of obtained findings. In our results, 203 m/z ratio was detected and 163 m/z ratio passed the significance criteria (fold analysis > 1.5 and p < 0.05). Twenty-five metabolites including BCAAs, LysoPC species, lactic acid, succinate, malonic acid, maleic acid, butyric acid, carnitine, and betaine were identified. In conclusion, new biomarker candidates were identified to differentiate the diagnosis of STEMI and NSTEMI. Identified metabolites are indicative of alterations in oxidative stress, hypoxia, TCA cycle, and amino acid metabolism.

Circadian and wake-dependent changes in human plasma polar metabolites during prolonged wakefulness: A preliminary analysis

Establishing circadian and wake-dependent changes in the human metabolome are critical for understanding and treating human diseases due to circadian misalignment or extended wake. Here, we assessed endogenous circadian rhythms and wake-dependent changes in plasma metabolites in 13 participants (4 females) studied during 40-hours of wakefulness. Four-hourly plasma samples were analyzed by hydrophilic interaction liquid chromatography (HILIC)-LC-MS for 1,740 metabolite signals. Group-averaged (relative to DLMO) and individual participant metabolite profiles were fitted with a combined cosinor and linear regression model. In group-level analyses, 22% of metabolites were rhythmic and 8% were linear, whereas in individual-level analyses, 14% of profiles were rhythmic and 4% were linear. We observed metabolites that were significant at the group-level but not significant in a single individual, and metabolites that were significant in approximately half of individuals but not group-significant. Of the group-rhythmic and group-linear metabolites, only 7% and 12% were also significantly rhythmic or linear, respectively, in ≥50% of participants. Owing to large inter-individual variation in rhythm timing and the magnitude and direction of linear change, acrophase and slope estimates also differed between group- and individual-level analyses. These preliminary findings have important implications for biomarker development and understanding of sleep and circadian regulation of metabolism.

Monitoring clinical progression with mitochondrial disease biomarkers

Mitochondrial disorders are genetically determined metabolic diseases due to a biochemical deficiency of the respiratory chain. Given that multi-system involvement and disease progression are common features of mitochondrial disorders they carry substantial morbidity and mortality. Despite this, no disease-modifying treatments exist with clear clinical benefits, and the current best management of mitochondrial disease is supportive. Several therapeutic strategies for mitochondrial disorders are now at a mature preclinical stage. Some are making the transition into early-phase patient trials, but the lack of validated biomarkers of disease progression presents a challenge when developing new therapies for patients. This update discusses current biomarkers of mitochondrial disease progression including metabolomics, circulating serum markers, exercise physiology, and both structural and functional imaging. We discuss the advantages and disadvantages of each approach, and consider emerging techniques with a potential role in trials of new therapies.

Metabolomics allows the discrimination of the pathophysiological relevance of hyperinsulinism in obese prepubertal children

BACKGROUND/OBJECTIVES

Insulin resistance (IR) is the cornerstone of the obesity-associated metabolic derangements observed in obese children. Targeted metabolomics was employed to explore the pathophysiological relevance of hyperinsulinemia in childhood obesity in order to identify biomarkers of IR with potential clinical application.

SUBJECTS/METHODS

One hundred prepubertal obese children (50 girls/50 boys, 50% IR and 50% non-IR in each group), underwent an oral glucose tolerance test for usual carbohydrate and lipid metabolism determinations. Fasting serum leptin, total and high molecular weight-adiponectin and high-sensitivity C-reactive protein (CRP) levels were measured and the metabolites showing significant differences between IR and non-IR groups in a previous metabolomics study were quantified. Enrichment of metabolic pathways (quantitative enrichment analysis) and the correlations between lipid and carbohydrate metabolism parameters, adipokines and serum metabolites were investigated, with their discriminatory capacity being evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

Twenty-three metabolite sets were enriched in the serum metabolome of IR obese children (P<0.05, false discovery rate (FDR)<5%). The urea cycle, alanine metabolism and glucose-alanine cycle were the most significantly enriched pathways (P_FDR<0.00005). The high correlation between metabolites related to fatty acid oxidation and amino acids (mainly branched chain and aromatic amino acids) pointed to the possible contribution of mitochondrial dysfunction in IR. The degree of body mass index-standard deviation score (BMI-SDS) excess did not correlate with any of the metabolomic components studied. In the ROC analysis, the combination of leptin and alanine showed a high IR discrimination value in the whole cohort (area under curve, AUC_ALL=0.87), as well as in boys (AUC_M=0.84) and girls (AUC_F=0.91) when considered separately. However, the specific metabolite/adipokine combinations with highest sensitivity were different between the sexes.

CONCLUSIONS

Combined sets of metabolic, adipokine and metabolomic parameters can identify pathophysiological relevant IR in a single fasting sample, suggesting a potential application of metabolomic analysis in clinical practice to better identify children at risk without using invasive protocols.

Capillary electrophoresis mass spectrometry as a tool for untargeted metabolomics

Highly polar and ionic metabolites, such as sugars, most amino acids, organic acids or nucleotides are not retained by conventional reversed-phase LC columns and polar stationary phases and hydrophilic-interaction LC lacks of robustness, which is still limiting their applications for untargeted metabolomics where reproducibility is a must. Biological samples such as blood, urine or even tissues include many hydrophilic compounds secreted from cells, their analysis is essential for biomarker discovery, disease progression or treatment effects. This review focuses on CE coupled to MS as a mature technique for untargeted metabolomics including sample pretreatment, types of matrices, analytical methods, applications and data treatment strategies for polar compound analysis in biological matrices. The main applications and results of CE-MS in untargeted metabolomics are discussed and presented in a tabulated format.

Insulin resistance in prepubertal obese children correlates with sex-dependent early onset metabolomic alterations

Background:

Insulin resistance (IR) is usually the first metabolic alteration diagnosed in obese children and the key risk factor for development of comorbidities. The factors determining whether or not IR develops as a result of excess body mass index (BMI) are still not completely understood.

Objectives:

This study aimed to elucidate the mechanisms underpinning the predisposition toward hyperinsulinemia-related complications in obese children by using a metabolomic strategy that allows a profound interpretation of metabolic profiles potentially affected by IR.

Methods:

Serum from 60 prepubertal obese children (30 girls/30 boys, 50% IR and 50% non-IR in each group, but with similar BMIs) were analyzed by using liquid chromatography–mass spectrometry, gas chromatography–mass spectrometry and capillary electrophoresis–mass spectrometry following an untargeted metabolomics approach. Validation was then performed on a group of 100 additional children with the same characteristics.

Results:

When obese children with and without IR were compared, 47 metabolites out of 818 compounds (P<0.05) obtained after data pre-processing were found to be significantly different. Bile acids exhibit the greatest changes (that is, approximately a 90% increase in IR). The majority of metabolites differing between groups were lysophospholipids (15) and amino acids (17), indicating inflammation and central carbon metabolism as the most altered processes in impaired insulin signaling. Multivariate analysis (OPLS-DA models) showed subtle differences between groups that were magnified when females were analyzed alone.

Conclusions:

Inflammation and central carbon metabolism, together with the contribution of the gut microbiota, are the most altered processes in obese children with impaired insulin signaling in a sex-specific fashion despite their prepubertal status.