Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment

Large-scale prospective serum metabolomic profiling reveals candidate predictive biomarkers for suspected preeclampsia patients

Preeclampsia (PE) is a serious pregnancy complication that contributes to maternal and perinatal morbidity and mortality. Understanding its pathogenesis and revealing predictive biomarkers are essential for guiding treatment decisions. In order to explore the global changes of serum metabolites in PE patients and identify potential predictive biomarkers for suspected PE patients (pregnant women who had already shown PE-related symptoms in the middle to late stages of pregnancy, but were not yet confirmatively diagnosed as PE.), a large-scale serum metabolomic analysis was conducted in this study with a prospective cohort of 328 suspected PE patients in the middle or late pregnancy stages, as well as a retrospective cohort of 30 healthy pregnant women and 30 PE patients. Using liquid chromatography mass spectrometry (LC - MS), serum metabolomic profiling revealed that the development of PE was closely associated with disturbed amino acid metabolism. Moreover, a panel of seven predictive biomarkers including 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, gamma-glutamyl-leucine, 2-hydroxyvaleric acid, LysoPC(16:1(9Z)/0:0), PC(DiMe(13,5)/MonoMe(13,5)), ADP-D-glycero-beta-D-manno-heptose and phenylalanyl-tryptophan were identified for PE development by performing multiple statistical analysis and LASSO regression analysis. The combination of these biomarkers showed promise in the prediction of PE development for suspected PE patients, with an AUC of 0.753 and 0.885 for the discovery and validation cohorts, respectively. These findings highlight the potential of large-scale prospective metabolomic studies combined with machine learning algorithms in identifying key biomarkers for predicting PE development, while retrospective metabolomics studies provide insights into the pathogenesis of PE.

Genetically Predicted Higher Levels of Caffeic Acid Are Protective Against Ulcerative Colitis: A Comprehensive Metabolome Analysis

BACKGROUND

It is crucial to pinpoint the metabolites that cause Crohn's disease (CD) and ulcerative colitis (UC) to comprehend their pathogenesis and identify possible targets for therapy. To achieve this goal, we performed the first metabolome-wide Mendelian randomization (MR) study of Japanese patients with CD and UC.

METHODS

As exposure datasets, genetic instruments with blood-circulating metabolites were obtained from the Tohoku Medical Megabank Organization, which includes 204 metabolites from the genome-wide association study data of 7843 Japanese individuals. As outcome datasets, we enrolled Japanese patients with CD (n = 1803), Japanese patients with UC (n = 1992), and healthy controls (n = 2022). The main analysis utilized the inverse variance-weighted method, while stability of the findings was evaluated through sensitivity analyses.

RESULTS

After single nucleotide polymorphism (SNP) filtering, 169 SNPs for 45 metabolites were available for MR. Genetically predicted elevated circulating trans-glutaconic acid and tryptophan were associated with a lower CD risk (odds ratio [OR], 0.68; P = 5.95 × 10-3; and OR, 0.64; P = 1.90 × 10-2, respectively). Genetically predicted elevated caffeic acid was associated with a lower UC risk (OR, 0.67; P = 4.2 × 10-4), which remained significant after multiple testing correction. We identified a causal link between UC and 3-hydroxybutyrate (OR, 2.21; P = 1.41 × 10-2), trans-glutaconic acid (OR, 0.72; P = 1.77 × 10-2), and 2-hydroxyvaleric acid (OR, 1.31; P = 4.23 × 10-2). There was no evidence of pleiotropy or reverse causal effects for these candidate metabolites.

CONCLUSIONS

In our metabolome-wide MR study, we discovered a notable protective effect of caffeic acid against UC.

Statin treatment reduces leucine turnover, but does not affect endogenous production of beta-hydroxy-beta-methylbutyrate (HMB)

BACKGROUND

Statins, or hydroxy-methyl-glutaryl coenzyme A (HMG-CoA) reductase inhibitors, are one of the most commonly prescribed medications for lowering cholesterol. Myopathic side-effects ranging from pain and soreness to critical rhabdomyolysis are commonly reported and often lead to discontinuation. The pathophysiological mechanism is, in general, ascribed to a downstream reduction of Coenzyme Q10 synthesis. HMG-CoA is a metabolite of leucine and its corresponding keto acid α-ketoisocaproic acid (KIC) and β-hydroxy-β-methylbutyrate (HMB), however, little is known about the changes in the metabolism of leucine and its metabolites in response to statins.

OBJECTIVE

We aimed to investigate if statin treatment has implications on the upstream metabolism of leucine to KIC and HMB, as well as on other branched chain amino acids (BCAA).

DESIGN

12 hyperlipidemic older adults under statin treatment were recruited. The study was conducted as a paired prospective study. Included participants discontinued their statin treatment for 4 weeks before they returned for baseline measurements (before). Statin treatment was then reintroduced, and the participants returned for a second study day 7 days after reintroduction (after statin). On study days, participants were injected with stable isotope pulses for measurement of the whole-body production (WBP) of all BCAA (leucine, isoleucine and valine), along with their respective keto acids and HMB.

RESULTS

We found a reduced leucine WBP (22 %, p = 0.0033), along with a reduction in valine WBP (13 %, p = 0.0224). All other WBP of BCAA and keto acids were unchanged. There were no changes in the WBP of HMB.

CONCLUSIONS

Our study shows that statin inhibition of HMG-CoA reductase has an upstream impact on the turnover of leucine and valine. Whether this impairment in WBP of leucine may contribute to the known pathophysiological side effects of statins on muscle remains to be further investigated.

Effect of statin treatment on metabolites, lipids and prostanoids in patients with Statin Associated Muscle Symptoms (SAMS)

BACKGROUND

Between 5-10% of patients discontinue statin therapy due to statin-associated adverse reactions, primarily statin associated muscle symptoms (SAMS). The absence of a clear clinical phenotype or of biomarkers poses a challenge for diagnosis and management of SAMS. Similarly, our incomplete understanding of the pathogenesis of SAMS hinders the identification of treatments for SAMS. Metabolomics, the profiling of metabolites in biofluids, cells and tissues is an important tool for biomarker discovery and provides important insight into the origins of symptomatology. In order to better understand the pathophysiology of this common disorder and to identify biomarkers, we undertook comprehensive metabolomic and lipidomic profiling of plasma samples from patients with SAMS who were undergoing statin rechallenge as part of their clinical care.

METHODS AND FINDINGS

We report our findings in 67 patients, 28 with SAMS (cases) and 39 statin-tolerant controls. SAMS patients were studied during statin rechallenge and statin tolerant controls were studied while on statin. Plasma samples were analyzed using untargeted LC-MS metabolomics and lipidomics to detect differences between cases and controls. Differences in lipid species in plasma were observed between cases and controls. These included higher levels of linoleic acid containing phospholipids and lower ether lipids and sphingolipids. Reduced levels of acylcarnitines and altered amino acid profile (tryptophan, tyrosine, proline, arginine, and taurine) were observed in cases relative to controls. Pathway analysis identified significant increase of urea cycle metabolites and arginine and proline metabolites among cases along with downregulation of pathways mediating oxidation of branched chain fatty acids, carnitine synthesis, and transfer of acetyl groups into mitochondria.

CONCLUSIONS

The plasma metabolome of patients with SAMS exhibited reduced content of long chain fatty acids and increased levels of linoleic acid (18:2) in phospholipids, altered energy production pathways (β-oxidation, citric acid cycle and urea cycles) as well as reduced levels of carnitine, an essential mediator of mitochondrial energy production. Our findings support the hypothesis that alterations in pro-inflammatory lipids (arachidonic acid pathway) and impaired mitochondrial energy metabolism underlie the muscle symptoms of patients with statin associated muscle symptoms (SAMS).

Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS

Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.

Autophagy-urea cycle pathway is essential for the statin-mediated nitric oxide bioavailability in endothelial cells

Statins induce nitric oxide (NO) bioavailability by activating endothelial nitric oxide synthase via kinase- and calcium-dependent pathways in endothelial cells (ECs). However, their effect on the metabolism of L-arginine, the precursor for NO biosynthesis, and regulatory mechanism have not yet been investigated. In this study, we investigated the role of the autophagy-urea cycle-L-arginine pathway in simvastatin-mediated NO bioavailability in ECs. Griess's assay was used to determine the NO bioavailability. Protein expression was assessed using Western blot analysis. Further, immunocytochemistry was performed to observe autophagosome formation, while conventional assay kits were used to quantify the levels of different intermediate substrates of the urea cycle. In ECs, treatment with simvastatin induced the activation of autophagy flux, as evidenced by the increased levels of microtubule-associated protein 1A/1B-light chain 3 II and autophagolysosome formation and decreased levels of p62. Inhibition of autophagy by ATG7 small interfering RNA (siRNA), chloroquine and bafilomycin A1 abolished simvastatin-induced NO bioavailability, EC proliferation, migration, and tube formation. Additionally, simvastatin increased the intermediate substrates levels of the urea cycle, including glutamate, acetyl-CoA, urea, and L-arginine, all of which were abrogated by chloroquine or bafilomycin A1. Genetic knockdown of argininosuccinate lyase using siRNA abrogated simvastatin-induced increase in NO bioavailability and EC-related functions. Moreover, inhibition of AMP-activated protein kinase (AMPK) and transient receptor potential vanilloid 1 (TRPV1) prevented simvastatin-induced activation of the autophagy-urea cycle pathway and NO production. Our findings suggest that simvastatin activates the autophagy-urea cycle pathway via TRPV1-AMPK signaling, which increases L-arginine bioavailability and ultimately promotes NO production in ECs.

Pharmacometabolomics for the Study of Lipid-Lowering Therapies: Opportunities and Challenges

Lipid-lowering therapies are widely used to prevent the development of atherosclerotic cardiovascular disease (ASCVD) and related mortality worldwide. "Omics" technologies have been successfully applied in recent decades to investigate the mechanisms of action of these drugs, their pleiotropic effects, and their side effects, aiming to identify novel targets for future personalized medicine with an improvement of the efficacy and safety associated with the treatment. Pharmacometabolomics is a branch of metabolomics that is focused on the study of drug effects on metabolic pathways that are implicated in the variation of response to the treatment considering also the influences from a specific disease, environment, and concomitant pharmacological therapies. In this review, we summarized the most significant metabolomic studies on the effects of lipid-lowering therapies, including the most commonly used statins and fibrates to novel drugs or nutraceutical approaches. The integration of pharmacometabolomics data with the information obtained from the other "omics" approaches could help in the comprehension of the biological mechanisms underlying the use of lipid-lowering drugs in view of defining a precision medicine to improve the efficacy and reduce the side effects associated with the treatment.

Metabolite Signature of Simvastatin Treatment Involves Multiple Metabolic Pathways

Statins inhibit the 3-hydroxy-3-methylglutaryl-CoA reductase enzyme and are the most widely used medication for hypercholesterolemia. Previous studies on the metabolite signature of simvastatin treatment have included only a small number of metabolites. We performed a high-throughput liquid chromatography–tandem mass spectroscopy profiling on the effects of simvastatin treatment on 1098 metabolite concentrations in the participants of the METSIM (Metabolic Syndrome In Men) study including 1332 participants with simvastatin treatment and 6200 participants without statin treatment. We found that simvastatin exerts profound pleiotropic effects on different metabolite pathways, affecting not only lipids, but also amino acids, peptides, nucleotides, carbohydrates, co-factors, vitamins, and xenobiotics. We identified 321 metabolites significantly associated with simvastatin treatment, and 313 of these metabolites were novel. Our study is the first comprehensive evaluation of the metabolic signature of simvastatin treatment in a large population-based study.

Serum Predose Metabolic Profiling for Prediction of Rosuvastatin Pharmacokinetic Parameters in Healthy Volunteers

Rosuvastatin is a well-known lipid-lowering agent generally used for hypercholesterolemia treatment and coronary artery disease prevention. There is a substantial inter-individual variability in the absorption of statins usually caused by genetic polymorphisms leading to a variation in the corresponding pharmacokinetic parameters, which may affect drug therapy safety and efficacy. Therefore, the investigation of metabolic markers associated with rosuvastatin inter-individual variability is exceedingly relevant for drug therapy optimization and minimizing side effects. This work describes the application of pharmacometabolomic strategies using liquid chromatography coupled to mass spectrometry to investigate endogenous plasma metabolites capable of predicting pharmacokinetic parameters in predose samples. First, a targeted method for the determination of plasma concentration levels of rosuvastatin was validated and applied to obtain the pharmacokinetic parameters from 40 enrolled individuals; then, predose samples were analyzed using a metabolomic approach to search for associations between endogenous metabolites and the corresponding pharmacokinetic parameters. Data processing using machine learning revealed some candidates including sterols and bile acids, carboxylated metabolites, and lipids, suggesting the approach herein described as promising for personalized drug therapy.

Pharmaco-metabolomics opportunities in drug development and clinical research

Pharmaco-metabolomics uses metabolic phenotypes for the prediction of inter-individual variations in drug response and helps in understanding the mechanisms of drug action. The field has made significant progress over the last 14 years with numerous studies providing clinical evidence for personalised medicine. However, discovered pharmaco-metabolomic biomarkers are not yet translated into clinics due to a lack of large-scale validation. Integration of targeted and untargeted metabolomics workflows into pharmacokinetic analysis and drug development can advance the field from bench to bedside. Also, Indian pharmaceutical research and its bioanalytical infrastructure are in a position to take on these opportunities by addressing challenges such as appropriate training and regulatory compliance.

Chemoselective and Highly Sensitive Quantification of Gut Microbiome and Human Metabolites

The microbiome has a fundamental impact on the human host's physiology through the production of highly reactive compounds that can lead to disease development. One class of such compounds are carbonyl-containing metabolites, which are involved in diverse biochemical processes. Mass spectrometry is the method of choice for analysis of metabolites but carbonyls are analytically challenging. Herein, we have developed a new chemical biology tool using chemoselective modification to overcome analytical limitations. Two isotopic probes allow for the simultaneous and semi-quantitative analysis at the femtomole level as well as qualitative analysis at attomole quantities that allows for detection of more than 200 metabolites in human fecal, urine and plasma samples. This comprehensive mass spectrometric analysis enhances the scope of metabolomics-driven biomarker discovery. We anticipate that our chemical biology tool will be of general use in metabolomics analysis to obtain a better understanding of microbial interactions with the human host and disease development.

Multi-omics profiling: the way towards precision medicine in metabolic diseases

Metabolic diseases including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome (MetS) are alarming health burdens around the world, while therapies for these diseases are far from satisfying as their etiologies are not completely clear yet. T2DM, NAFLD, and MetS are all complex and multifactorial metabolic disorders based on the interactions between genetics and environment. Omics studies such as genetics, transcriptomics, epigenetics, proteomics, and metabolomics are all promising approaches in accurately characterizing these diseases. And the most effective treatments for individuals can be achieved via omics pathways, which is the theme of precision medicine. In this review, we summarized the multi-omics studies of T2DM, NAFLD, and MetS in recent years, provided a theoretical basis for their pathogenesis and the effective prevention and treatment, and highlighted the biomarkers and future strategies for precision medicine.

UPLC-MS-Based Serum Metabolic Profiling Reveals Potential Biomarkers for Predicting Propofol Responsiveness in Females

Although previous studies have shown that certain factors interfere with the sensitivity of propofol, the mechanisms for interindividual variability in response to propofol remain unclear. This study aimed to screen the metabolites to predict patients' sensitivity to propofol and to identify metabolic pathways to explore possible mechanisms associated with propofol resistance. Sera from 40 female patients undergoing elective hysteroscopic surgery in a prospective cohort propofol study were obtained before the administration of propofol. The patients' responsiveness to propofol was differentiated based on propofol effect-site concentration. Serum samples from two sets, a discovery set (n = 24) and an independent validation set (n = 16), were analyzed using ultraperformance liquid chromatography coupled with mass spectrometry based untargeted metabolomics. In the discovery set, 494 differential metabolites were screened out, and then 391 potential candidate biomarkers with the area under receiver operating characteristic curve >0.80 were selected. Pathway analysis showed that the pathway of glycerophospholipid metabolism was the most influential pathway. In the independent validation set, six potential biomarkers enabled the discrimination of poor responders from good and intermediate responders, which might be applied to predict propofol sensitivity. The mass spectrometry data are available via MetaboLights (http://www.ebi.ac.uk/metabolights/login) with the identifier MTBLS2311.

The Novel Interplay between Commensal Gut Bacteria and Metabolites in Diet-Induced Hyperlipidemic Rats Treated with Simvastatin

Hyperlipidemia is one kind of metabolic syndrome for which the treatment commonly includes simvastatin (SV). Individuals vary widely in statin responses, and growing evidence implicates gut microbiome involvement in this variability. However, the associated molecular mechanisms between metabolic improvement and microbiota composition following SV treatment are still not fully understood. In this study, combinatory approaches using ultrahigh-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS/MS)-based metabolomic profiling, PCR-denaturing gradient gel electrophoresis (PCR-DGGE), quantitative PCR (qPCR), and 16S rRNA gene sequencing-based gut microbiota profiling were performed to investigate the interplay of endogenous metabolites and the gut microbiota related to SV treatment. A total of 6 key differential endogenous metabolites were identified that affect the metabolism of amino acids (phenylalanine and tyrosine), unsaturated fatty acids (linoleic acid and 9-hydroxyoctadecadienoic acid (9-HODE)), and the functions of gut microbial metabolism. Moreover, a total of 22 differentially abundant taxa were obtained following SV treatment. Three bacterial taxa were identified to be involved in SV treatment, namely, Bacteroidaceae, Prevotellaceae, and Porphyromonadaceae. These findings suggested that the phenylalanine and tyrosine-associated amino acid metabolism pathways, as well as the linoleic acid and 9-HODE-associated unsaturated fatty acid metabolism pathways, which are involved in gut flora interactions, might be potential therapeutic targets for improvement in SV hypolipidemic efficacy. The mass spectrometric data have been deposited to MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp). Username: MSV000087842_reviewer. Password: hardworkingzsr.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Metabolomics applications in coronary artery disease personalized medicine

Coronary artery disease (CAD), the most common cardiovascular disease (CVD), contributes to significant mortality worldwide. CAD is a multifactorial disease wherein various factors contribute to its pathogenesis often complicating management. Biomarker based personalized medicine may provide a more effective way to individualize therapy in multifactorial diseases in general and CAD specifically. Systems' biology "Omics" biomarkers have been investigated for this purpose. These biomarkers provide a more comprehensive understanding on pathophysiology of the disease process and can help in identifying new therapeutic targets and tailoring therapy to achieve optimum outcome. Metabolomics biomarkers usually reflect genetic and non-genetic factors involved in the phenotype. Metabolomics analysis may provide better understanding of the disease pathogenesis and drug response variation. This will help in guiding therapy, particularly for multifactorial diseases such as CAD. In this chapter, advances in metabolomics analysis and its role in personalized medicine will be reviewed with comprehensive focus on CAD. Assessment of risk, diagnosis, complications, drug response and nutritional therapy will be discussed. Together, this chapter will review the current application of metabolomics in CAD management and highlight areas that warrant further investigation.

MitoPlex: A targeted multiple reaction monitoring assay for quantification of a curated set of mitochondrial proteins

Mitochondria are the major source of cellular energy (ATP), as well as critical mediators of widespread functions such as cellular redox balance, apoptosis, and metabolic flux. The organelles play an especially important role in the maintenance of cardiac homeostasis; their inability to generate ATP following impairment due to ischemic damage has been directly linked to organ failure. Methods to quantify mitochondrial content are limited to low throughput immunoassays, measurement of mitochondrial DNA, or relative quantification by untargeted mass spectrometry. Here, we present a high throughput, reproducible and quantitative mass spectrometry multiple reaction monitoring based assay of 37 proteins critical to central carbon chain metabolism and overall mitochondrial function termed 'MitoPlex'. We coupled this protein multiplex with a parallel analysis of the central carbon chain metabolites (219 metabolite assay) extracted in tandem from the same sample, be it cells or tissue. In tests of its biological applicability in cells and tissues, "MitoPlex plus metabolites" indicated profound effects of HMG-CoA Reductase inhibition (e.g., statin treatment) on mitochondria of i) differentiating C2C12 skeletal myoblasts, as well as a clear opposite trend of statins to promote mitochondrial protein expression and metabolism in heart and liver, while suppressing mitochondrial protein and ii) aspects of metabolism in the skeletal muscle obtained from C57Bl6 mice. Our results not only reveal new insights into the metabolic effect of statins in skeletal muscle, but present a new high throughput, reliable MS-based tool to study mitochondrial dynamics in both cell culture and in vivo models.

Current Concepts in Pharmacometabolomics, Biomarker Discovery, and Precision Medicine

Pharmacometabolomics (PMx) studies use information contained in metabolic profiles (or metabolome) to inform about how a subject will respond to drug treatment. Genome, gut microbiome, sex, nutrition, age, stress, health status, and other factors can impact the metabolic profile of an individual. Some of these factors are known to influence the individual response to pharmaceutical compounds. An individual’s metabolic profile has been referred to as his or her “metabotype.” As such, metabolomic profiles obtained prior to, during, or after drug treatment could provide insights about drug mechanism of action and variation of response to treatment. Furthermore, there are several types of PMx studies that are used to discover and inform patterns associated with varied drug responses (i.e., responders vs. non-responders; slow or fast metabolizers). The PMx efforts could simultaneously provide information related to an individual’s pharmacokinetic response during clinical trials and be used to predict patient response to drugs making pharmacometabolomic clinical research valuable for precision medicine. PMx biomarkers can also be discovered and validated during FDA clinical trials. Using biomarkers during medical development is described in US Law under the 21st Century Cures Act. Information on how to submit biomarkers to the FDA and their context of use is defined herein.

Metabolomic analyses of plasma and liver of mice fed with immature Citrus tumida peel

In this study, we investigated the effects of dietary supplementation of Citrus tumida hort. ex Tanaka on food intake, body and fat tissue weights, and metabolic profiles of plasma and liver in mice. Supplementation with 5% (w/w) of peels of immature C. tumida (PIC) for 4 weeks significantly suppressed body weight gain and decreased adipose tissue weight in epididymal, perirenal, and subcutaneous fats. Metabolome analyses showed that 2-hydroxyvaleric acid levels were reduced in the blood plasma of mice fed with PIC. PIC supplementation significantly elevated dipeptide (Thr-Asp, Ser-Glu, and Ala-Ala), glucuronic acid, and S-methylglutathione levels, and significantly reduced betaine aldehyde levels in the liver. In conclusion, PIC supplementation affects the metabolism of fatty acids, pectin, glutathione, and choline, showing potential beneficial effects for metabolic syndrome and obesity. PIC may be developed as a functional food and used in the treatment of these diseases.

Clinical and lifestyle related factors influencing whole blood metabolite levels - A comparative analysis of three large cohorts

Objective

Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels.

Methods

63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N = 16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses.

Results

Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained >1% of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies.

Conclusions

We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

•

Amino-acids and acylcarnitines analyzed in three studies with >16,000 individuals.

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Develop a generic and adaptable bioinformatics workflow.

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Analysis of the impact of 29 clinical and life-style factors on blood metabolites.

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Analysis of network between factors and metabolites.

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Comparison of results between studies.

Metabolomics and Heart Diseases: From Basic to Clinical Approach

BACKGROUND

The field of metabolomics has been steadily increasing in size for the last 15 years. Advances in analytical and statistical methods have allowed metabolomics to flourish in various areas of medicine. Cardiovascular diseases are some of the main research targets in metabolomics, due to their social and medical relevance, and also to the important role metabolic alterations play in their pathogenesis and evolution. Metabolomics has been applied to the full spectrum of cardiovascular diseases: from patient risk stratification to myocardial infarction and heart failure. However - despite the many proof-ofconcept studies describing the applicability of metabolomics in the diagnosis, prognosis and treatment evaluation in cardiovascular diseases - it is not yet used in routine clinical practice. Recently, large phenome centers have been established in clinical environments, and it is expected that they will provide definitive proof of the applicability of metabolomics in clinical practice. But there is also room for small and medium size centers to work on uncommon pathologies or to resolve specific but relevant clinical questions.

OBJECTIVES

In this review, we will introduce metabolomics, cover the metabolomic work done so far in the area of cardiovascular diseases.

CONCLUSION

The cardiovascular field has been at the forefront of metabolomics application and it should lead the transfer to the clinic in the not so distant future.

Pharmacometabonomics: The Prediction of Drug Effects Using Metabolic Profiling

Metabonomics, also known as metabolomics, is concerned with the study of metabolite profiles in humans, animals, plants and other systems in order to assess their health or other status and their responses to experimental interventions. Metabonomics is thus widely used in disease diagnosis and in understanding responses to therapies such as drug administration. Pharmacometabonomics, also known as pharmacometabolomics, is a related methodology but with a prognostic as opposed to diagnostic thrust. Pharmacometabonomics aims to predict drug effects including efficacy, safety, metabolism and pharmacokinetics, prior to drug administration, via an analysis of pre-dose metabolite profiles. This article will review the development of pharmacometabonomics as a new field of science that has much promise in helping to deliver more effective personalised medicine, a major goal of twenty-first century healthcare.

Beta-3 Adrenoceptor Signaling Pathways in Urothelial and Smooth Muscle Cells in the Presence of Succinate

Succinate, an intermediate metabolite of the Krebs cycle, can alter the metabolomics response to certain drugs and controls an array of molecular responses in the urothelium through activation of its receptor, G-protein coupled receptor 91 (GPR91). Mirabegron, a β3-adrenergic receptor (β3-AR) agonist used to treat overactive bladder syndrome (OAB), increases intracellular cAMP in the detrusor smooth muscle cells (SMC), leading to relaxation. We have previously shown that succinate inhibits forskolin-stimulated cAMP production in urothelium. To determine whether succinate interferes with mirabegron-mediated bladder relaxation, we examined their individual and synergistic effect in urothelial-cell and SMC signaling. We first confirmed β3-AR involvement in the mirabegron response by quantifying receptor abundance by immunoblotting in cultured urothelial cells and SMC and cellular localization by immunohistochemistry in rat bladder tissue. Mirabegron increased cAMP levels in SMC but not in urothelial cells, an increase that was inhibited by succinate, suggesting that it impairs cAMP-mediated bladder relaxation by mirabegron. Succinate and mirabegron increased inducible nitric oxide synthesis and nitric oxide secretion only in urothelial cells, suggesting that its release can indirectly induces SMC relaxation. Succinate exposure decreased the expression of β3-AR protein in whole bladder in vivo and in SMC in vitro, indicating that this metabolite may lead to impaired pharmacodynamics of the bladder. Together, our results demonstrate that increased levels of succinate in settings of metabolic stress (e.g., the metabolic syndrome) may lead to impaired mirabegron and β3-AR interaction, inhibition of cAMP production, and ultimately requiring mirabegron dose adjustment for its treatment of OAB related to these conditions.

A Metabolomics Approach to Pharmacotherapy Personalization

The optimization of drug therapy according to the personal characteristics of patients is a perspective direction in modern medicine. One of the possible ways to achieve such personalization is through the application of "omics" technologies, including current, promising metabolomics methods. This review demonstrates that the analysis of pre-dose metabolite biofluid profiles allows clinicians to predict the effectiveness of a selected drug treatment for a given individual. In the review, it is also shown that the monitoring of post-dose metabolite profiles could allow clinicians to evaluate drug efficiency, the reaction of the host to the treatment, and the outcome of the therapy. A comparative description of pharmacotherapy personalization (pharmacogenomics, pharmacoproteomics, and therapeutic drug monitoring) and personalization based on the analysis of metabolite profiles for biofluids (pharmacometabolomics) is also provided.

Pharmacometabolomics Reveals Irinotecan Mechanism of Action in Cancer Patients

The purpose of this study was to identify early circulating metabolite changes implicated in the mechanism of action of irinotecan, a DNA topoisomerase I inhibitor, in cancer patients. A liquid chromatography-tandem mass spectrometry-based targeted metabolomic platform capable of measuring 254 endogenous metabolites was applied to profile circulating metabolites in plasma samples collected pre- and post-irinotecan treatment from 13 cancer patients. To gain further mechanistic insights, metabolic profiling was also performed for the culture medium of human primary hepatocytes (HepatoCells) and 2 cancer cell lines on exposure to SN-38 (an active metabolite of irinotecan). Intracellular reactive oxygen species (ROS) was detected by dihydroethidium assay. Irinotecan induced a global metabolic change in patient plasma, as represented by elevations of circulating purine/pyrimidine nucleobases, acylcarnitines, and specific amino acid metabolites. The plasma metabolic signature was well replicated in HepatoCells medium on SN-38 exposure, whereas in cancer cell medium SN-38 induced accumulation of pyrimidine/purine nucleosides and nucleobases while having no impact on acylcarnitines and amino acid metabolites. SN-38 induced ROS in HepatoCells, but not in cancer cells. Distinct metabolite signatures of SN-38 exposure in HepatoCells medium and cancer cell medium revealed different mechanisms of drug action on hepatocytes and cancer cells. Elevations in circulating purine/pyrimidine nucleobases may stem from nucleotide degradation following irinotecan-induced DNA double-strand breaks. Accumulations of circulating acylcarnitines and specific amino acid metabolites may reflect, at least in part, irinotecan-induced mitochondrial dysfunction and oxidative stress in the liver. The plasma metabolic signature of irinotecan exposure provides early insights into irinotecan mechanism of action in patients.

Metabolomic Approach to Redox and Nitrosative Reactions in Cardiovascular Diseases

Metabolomics, also referred to as metabonomics, is one of the most recent innovative technologies in medicine. It offers a direct functional read-out of phenotypes by the detection, identification, and quantification of a large number of metabolites within a biological sample such as urine and blood. Metabolites (<1500 Da) represent the output of cellular metabolism, accounting for expression and activity of genes, transcripts, and proteins, and offering unique insights into small molecule regulation, which may uncover new biochemical patterns. Metabolomics research has considerable potential for translating the metabolic fingerprint into personalized therapeutic strategies. Within the field of interest, cardiovascular disease (CVD) is one of the most developed areas. However, CVD remains the leading cause of death worldwide with a marked increase in mortality rates over the past six decades. In this scenario, recent findings indicate the important role of redox and nitrosative (RN) reactions in CVD development and progression. RN reactions are generally involved in the homeostatic modulation of a wide number of cellular and organ functions. Conversely, the imbalance of these reactions may lead to a condition of allostasis that in turn can cause CVD. The aim of this review is to highlight how the use of metabolomics may be useful for the study of RN reactions related to CVD, providing a tool to understand the mechanisms underlying reactions that could lead to impaired ROS or RNS formation.

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

Metabolic profiling by NMR spectroscopy or hyphenated mass spectrometry, known as metabonomics or metabolomics, is an important tool for systems-based approaches in biology and medicine. The experiments are typically done in a diagnostic fashion where changes in metabolite profiles are interpreted as a consequence of an intervention or event; be that a change in diet, the administration of a drug, physical exertion or the onset of a disease. By contrast, pharmacometabonomics takes a prognostic approach to metabolic profiling, in order to predict the effects of drug dosing before it occurs. Differences in pre-dose metabolite profiles between groups of subjects are used to predict post-dose differences in response to drug administration. Thus the paradigm is inverted and pharmacometabonomics is the metabolic equivalent of pharmacogenomics. Although the field is still in its infancy, it is expected that pharmacometabonomics, alongside pharmacogenomics, will assist with the delivery of personalised or precision medicine to patients, which is a critical goal of 21st century healthcare.

Statin and Bisphosphonate Induce Starvation in Fast-Growing Cancer Cell Lines

Statins and bisphosphonates are increasingly recognized as anti-cancer drugs, especially because of their cholesterol-lowering properties. However, these drugs act differently on various types of cancers. Thus, the aim of this study was to compare the effects of statins and bisphosphonates on the metabolism (NADP⁺/NADPH-relation) of highly proliferative tumor cell lines from different origins (PC-3 prostate carcinoma, MDA-MB-231 breast cancer, U-2 OS osteosarcoma) versus cells with a slower proliferation rate like MG-63 osteosarcoma cells. Global gene expression analysis revealed that after 6 days of treatment with pharmacologic doses of the statin simvastatin and of the bisphosphonate ibandronate, simvastatin regulated more than twice as many genes as ibandronate, including many genes associated with cell cycle progression. Upregulation of starvation-markers and a reduction of metabolism and associated NADPH production, an increase in autophagy, and a concomitant downregulation of H3K27 methylation was most significant in the fast-growing cancer cell lines. This study provides possible explanations for clinical observations indicating a higher sensitivity of rapidly proliferating tumors to statins and bisphosphonates.

New frontiers in metabolomics: from measurement to insight

Metabolomics is the newest addition to the "omics" disciplines and has shown rapid growth in its application to human health research because of fundamental advancements in measurement and analysis techniques. Metabolomics has unique and proven advantages in systems biology and biomarker discovery. The next generation of analysis techniques promises even richer and more complete analysis capabilities that will enable earlier clinical diagnosis, drug refinement, and personalized medicine. A review of current advancements in methodologies and statistical analysis that are enhancing and improving the performance of metabolomics is presented along with highlights of some recent successful applications.

Novel Applications of Metabolomics in Personalized Medicine: A Mini-Review

Interindividual variability in drug responses and disease susceptibility is common in the clinic. Currently, personalized medicine is highly valued, the idea being to prescribe the right medicine to the right patient. Metabolomics has been increasingly applied in evaluating the therapeutic outcomes of clinical drugs by correlating the baseline metabolic profiles of patients with their responses, i.e., pharmacometabonomics, as well as prediction of disease susceptibility among population in advance, i.e., patient stratification. The accelerated advance in metabolomics technology pinpoints the huge potential of its application in personalized medicine. In current review, we discussed the novel applications of metabolomics with typical examples in evaluating drug therapy and patient stratification, and underlined the potential of metabolomics in personalized medicine in the future.

Gut Microbiota Modulation Attenuated the Hypolipidemic Effect of Simvastatin in High-Fat/Cholesterol-Diet Fed Mice

The hypolipidemic effect of simvastatin varies greatly among patients. In the current study, we investigated the gut microbial-involved mechanisms underlying the different responses to simvastatin. Male C57BL/6J mice were divided into control (Con), high-fat/cholesterol diet (HFD), antibiotic (AB), simvastatin (SV) and antibiotic_simvastatin (AB_SV) groups, respectively. At the end of the experiment, serum samples were collected for lipids and metabolomic analysis, and liver tissues for histology, gene and protein expression analysis. The results showed that antibiotic treatment not only altered the composition of gut microbiota, but attenuated the hypolipidemic effect of SV. A total of 16 differential metabolites between SV and HFD groups were identified with metabolomics, while most of them showed no statistical differences between AB_SV and HFD groups, and similar changes were also observed in bile acids profile. The expressions of several genes and proteins involved in regulating bile acids synthesis were significantly reversed by SV, but not AB_SV in HFD fed mice. In summary, our current study indicated that the hypolipidemic effect of SV was correlated with the composition of the gut microbiota, and the attenuated hypolipidemic effect of SV by gut microbiota modulation was associated with a suppression of bile acids synthesis from cholesterol.

Pharmacometabolomic signature links simvastatin therapy and insulin resistance

Introduction

Statins, widely prescribed drugs for treatment of cardiovascular disease, inhibit the biosynthesis of low density lipoprotein cholesterol (LDL-C). Despite providing major benefits, sub populations of patients experience adverse effects, including muscle myopathy and development of type II diabetes mellitus (T2DM) that may result in premature discontinuation of treatment. There are no reliable biomarkers for predicting clinical side effects in vulnerable individuals. Pharmacometabolomics provides powerful tools for identifying global biochemical changes induced by statin treatment, providing insights about drug mechanism of action, development of side effects and basis of variation of response.

Objective

To determine whether statin-induced changes in intermediary metabolism correlated with statin-induced hyperglycemia and insulin resistance; to identify pre-drug treatment metabolites predictive of post-drug treatment increased diabetic risk.

Methods

Drug-naïve patients were treated with 40 mg/day simvastatin for 6 weeks in the Cholesterol and Pharmacogenetics (CAP) study; metabolomics by gas chromatography-time-of-flight mass-spectrometry (GC-TOF-MS) was performed on plasma pre and post treatment on 148 of the 944 participants.

Results

Six weeks of simvastatin treatment resulted in 6.9% of patients developing hyperglycemia and 25% developing changes consistent with development of pre-diabetes. Altered beta cell function was observed in 53% of patients following simvastatin therapy and insulin resistance was observed in 54% of patients. We identified initial signature of simvastatin-induced insulin resistance, including ethanolamine, hydroxylamine, hydroxycarbamate and isoleucine which, upon further replication and expansion, could be predictive biomarkers of individual susceptibility to simvastatin-induced new onset pre-type II diabetes mellitus. No patients were clinically diagnosed with T2DM.

Conclusion

Within this short 6 weeks study, some patients became hyperglycemic and/or insulin resistant. Diabetic markers were associated with decarboxylated small aminated metabolites as well as a branched chain amino acid directly linked to glucose metabolism and fatty acid biosynthesis. Pharmacometabolomics provides powerful tools for precision medicine by predicting development of drug adverse effects in sub populations of patients. Metabolic profiling prior to start of drug therapy may empower physicians with critical information when prescribing medication and determining prognosis.

Pharmacometabonomics – the novel way to personalized drug therapy

The review is devoted to pharmacometabonomics - a new branch of science focused on personalization of drug therapy through the comprehensive analysis of metabolites of patient's biological fluids. It considers the history of pharmacometabonomic, positioning to other “-omic” sciences, and system approach, realized by this science, in determination of individual therapeutic dose of the drugs and also a technical implementation of pharmacometabonomic based on direct mass spectrometry of blood plasma metabolites. Special attention is paid to a comparative analysis of pharmacometabonomics and other main approaches to personalized therapy in the clinic, such as pharmacogenetics and therapeutic drug monitoring. Finally, prospects of pharmacometabonomics applications in clinical practice were also discussed.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients' (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

A Peculiar Formula of Essential Amino Acids Prevents Rosuvastatin Myopathy in Mice

AIMS

Myopathy, characterized by mitochondrial oxidative stress, occurs in ∼10% of statin-treated patients, and a major risk exists with potent statins such as rosuvastatin (Rvs). We sought to determine whether a peculiar branched-chain amino acid-enriched mixture (BCAAem), found to improve mitochondrial function and reduce oxidative stress in muscle of middle-aged mice, was able to prevent Rvs myopathy.

RESULTS

Dietary supplementation of BCAAem was able to prevent the structural and functional alterations of muscle induced by Rvs in young mice. Rvs-increased plasma 3-methylhistidine (a marker of muscular protein degradation) was prevented by BCAAem. This was obtained without changes of Rvs ability to reduce cholesterol and triglyceride levels in blood. Rather, BCAAem promotes de novo protein synthesis and reduces proteolysis in cultured myotubes. Morphological alterations of C2C12 cells induced by statin were counteracted by amino acids, as were the Rvs-increased atrogin-1 mRNA and protein levels. Moreover, BCAAem maintained mitochondrial mass and density and citrate synthase activity in skeletal muscle of Rvs-treated mice beside oxygen consumption and ATP levels in C2C12 cells exposed to statin. Notably, BCAAem assisted Rvs to reduce oxidative stress and to increase the anti-reactive oxygen species (ROS) defense system in skeletal muscle. Innovation and Conclusions: The complex interplay between proteostasis and antioxidant properties may underlie the mechanism by which a specific amino acid formula preserves mitochondrial efficiency and muscle health in Rvs-treated mice. Strategies aimed at promoting protein balance and controlling mitochondrial ROS level may be used as therapeutics for the treatment of muscular diseases involving mitochondrial dysfunction, such as statin myopathy. Antioxid. Redox Signal. 25, 595-608.

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.

From Metabonomics to Pharmacometabonomics: The Role of Metabolic Profiling in Personalized Medicine

Variable patient responses to drugs are a key issue for medicine and for drug discovery and development. Personalized medicine, that is the selection of medicines for subgroups of patients so as to maximize drug efficacy and minimize toxicity, is a key goal of twenty-first century healthcare. Currently, most personalized medicine paradigms rely on clinical judgment based on the patient's history, and on the analysis of the patients' genome to predict drug effects i.e., pharmacogenomics. However, variability in patient responses to drugs is dependent upon many environmental factors to which human genomics is essentially blind. A new paradigm for predicting drug responses based on individual pre-dose metabolite profiles has emerged in the past decade: pharmacometabonomics, which is defined as “the prediction of the outcome (for example, efficacy or toxicity) of a drug or xenobiotic intervention in an individual based on a mathematical model of pre-intervention metabolite signatures.” The new pharmacometabonomics paradigm is complementary to pharmacogenomics but has the advantage of being sensitive to environmental as well as genomic factors. This review will chart the discovery and development of pharmacometabonomics, and provide examples of its current utility and possible future developments.

Metabolomics enables precision medicine: "A White Paper, Community Perspective"

INTRODUCTION BACKGROUND TO METABOLOMICS

Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.

OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE

We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.

CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE

Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.

Pharmacometabolomic Assessment of Metformin in Non-diabetic, African Americans

Millions of individuals are diagnosed with type 2 diabetes mellitus (T2D), which increases the risk for a plethora of adverse outcomes including cardiovascular events and kidney disease. Metformin is the most widely prescribed medication for the treatment of T2D; however, its mechanism is not fully understood and individuals vary in their response to this therapy. Here, we use a non-targeted, pharmacometabolomics approach to measure 384 metabolites in 33 non-diabetic, African American subjects dosed with metformin. Three plasma samples were obtained from each subject, one before and two after metformin administration. Validation studies were performed in wildtype mice given metformin. Fifty-four metabolites (including 21 unknowns) were significantly altered upon metformin administration, and 12 metabolites (including six unknowns) were significantly associated with metformin-induced change in glucose (q < 0.2). Of note, indole-3-acetate, a metabolite produced by gut microbes, and 4-hydroxyproline were modulated following metformin exposure in both humans and mice. 2-Hydroxybutanoic acid, a metabolite previously associated with insulin resistance and an early biomarker of T2D, was positively correlated with fasting glucose levels as well as glucose levels following oral glucose tolerance tests after metformin administration. Pathway analysis revealed that metformin administration was associated with changes in a number of metabolites in the urea cycle and in purine metabolic pathways (q < 0.01). Further research is needed to validate the biomarkers of metformin exposure and response identified in this study, and to understand the role of metformin in ammonia detoxification, protein degradation and purine metabolic pathways.

Metabolomics and Personalized Medicine

Current clinical practice strongly relies on the prognosis, diagnosis, and treatment of diseases using methods determined and averaged for the specific diseased cohort/population. Although this approach complies positively with most patients, misdiagnosis, treatment failure, relapse, and adverse drug effects are common occurrences in many individuals, which subsequently hamper the control and eradication of a number of diseases. These incidences can be explained by individual variation in the genome, transcriptome, proteome, and metabolome of a patient. Various "omics" approaches have investigated the influence of these factors on a molecular level, with the intention of developing personalized approaches to disease diagnosis and treatment. Metabolomics, the newest addition to the "omics" domain and the closest to the observed phenotype, reflects changes occurring at all molecular levels, as well as influences resulting from other internal and external factors. By comparing the metabolite profiles of two or more disease phenotypes, metabolomics can be applied to identify biomarkers related to the perturbation being investigated. These biomarkers can, in turn, be used to develop personalized prognostic, diagnostic, and treatment approaches, and can also be applied to the monitoring of disease progression, treatment efficacy, predisposition to drug-related side effects, and potential relapse. In this review, we discuss the contributions that metabolomics has made, and can potentially still make, towards the field of personalized medicine.

A UHPLC-TOF/MS method based metabonomic study of total ginsenosides effects on Alzheimer disease mouse model

A metabonomic method was established to find potential biomarkers and study the metabolism disturbance in Alzheimer disease animal model. Total ginsenosides, as potential agent in neuroprotection and anti-inflammation, was also studied to learn the regulation mechanism to plasma metabolites in model animals. In experiment, amyloid beta 1-42 was occupied to form Alzheimer disease animal model. After drug administration, animals were evaluated by Morris water maze behavior test and sacrificed. Plasma samples were then analyzed using UHPLC-TOF/MS method to determine the endogenous metabolites. Behavior test results revealed that the spatial learning and memory abilities were deficit in model mice, and total ginsenosides could improve cognition abilities in dose-dependent manners. Principal component analysis showed that model and sham were divided into two groups, which means the metabolic network of mice was disturbed after modeling. Accordingly, 19 biomarkers were found and identified. In model group, the levels of proline, valine, tryptophan, LPC (14:0), LPC (15:0), LPC (15:1), LPC (17:0), LPC (18:2), LPC (18:3) and LPC (20:4) were up-regulated, while the levels of acetylcarnitine, palmitoylcarnitine, vaccenylcarnitine, phytosphingosine, N-eicosanoylethanolamine, hexadecenoic acid, docosahexaenoic acid, docosapentaenoic acid and octadecadienoic acid were down-regulated. The levels of these metabolites were recovered in different degrees after total ginsenosides administration. Combining with behavior study results, total ginsenosides could ameliorate both cognition symptoms and metabolic changes in model animals. This metabonomic approach provided a feasible way to understand the endogenous alterations of AD and to study the pharmacodynamic activity of novel agents.

Cardiac metabolomics and autopsy in a patient with early diffuse systemic sclerosis presenting with dyspnea: a case report

Introduction

Diffuse systemic sclerosis is associated with high mortality; however, the pathogenesis of cardiac death in these patients is not clear.

Case presentation

A 56-year-old Caucasian female patient presented with dyspnea and requested to donate her body to science in order to improve understanding of diffuse systemic sclerosis pathogenesis. She had extensive testing for dyspnea including pulmonary function tests, an echocardiogram, cardiac magnetic resonance imaging, and right heart catheterization to characterize her condition. Her case highlights the morbidity seen in this disease, including the presence of extensive skin thickening, digital ulcerations, and scleroderma renal crisis.

Conclusion

In this case report, we present the finding of cardiac tissue metabolomics, which may indicate a problem with vasodilation as a contributor to cardiac death in diffuse systemic sclerosis. The use of autopsy and tissue metabolomics in rare disease may help clarify disease pathogenesis.

Metabolomic Signatures for Drug Response Phenotypes: Pharmacometabolomics Enables Precision Medicine

The scaling up of data in clinical pharmacology and the merger of systems biology and pharmacology has led to the emergence of a new discipline of Quantitative and Systems Pharmacology (QSP). This new research direction might significantly advance the discovery, development, and clinical use of therapeutic drugs. Research communities from computational biology, systems biology, and biological engineering--working collaboratively with pharmacologists, geneticists, biochemists, and analytical chemists--are creating and modeling large data on drug effects that is transforming our understanding of how these drugs work at a network level. In this review, we highlight developments in a new and rapidly growing field--pharmacometabolomics--in which large biochemical data-capturing effects of genome, gut microbiome, and environment exposures is revealing information about metabotypes and treatment outcomes, and creating metabolic signatures as new potential biomarkers. Pharmacometabolomics informs and complements pharmacogenomics and together they provide building blocks for QSP.

Pharmacometabonomics in humans: a new tool for personalized medicine

Pharmacogenomics is now over 50 years old and has had some impact in clinical practice, through its use to select patient subgroups who will enjoy efficacy without side effects when treated with certain drugs. However, pharmacogenomics, has had less impact than initially predicted. One reason for this is that many diseases, and the way in which the patients respond to drug treatments, have both genetic and environmental elements. Pure genomics is almost blind to the environmental elements. A new methodology has emerged, termed pharmacometabonomics that is concerned with the prediction of drug effects through the analysis of predose, biofluid metabolite profiles, which reflect both genetic and environmental influences on human physiology. In this review we will cover what pharmacometabonomics is, how it works, what applications exist and what the future might hold in this exciting new area.