Pharmacometabolomics of Lipid-Lowering Therapies

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		ERICA GIANAZZA	--	6375	2023-02-20 10:21:33	\|
2	Reference format revised.	Lindsay Dong	Meta information modification	6375	2023-02-22 01:44:14	\|

This entry is adapted from the peer-reviewed paper 10.3390/ijms24043291

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 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. 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.

metabolomics lipid-lowering drugs statins atherosclerotic cardiovascular disease

1. Introduction

Atherosclerotic cardiovascular disease (ASCVD) is considered the leading cause of death globally ^[1]. Established lipid-lowering therapies are available, which reduce low-density lipoprotein cholesterol (LDL-C), preventing ASCVD and mortality, but they are still insufficient to bring a halt to the ASCVD epidemic. Strong efforts have been made in recent decades to fully characterize the mechanism of action of these drugs, their pleiotropic effects, and their side effects using both hypotheses-driven and “omics” approaches, such as genomics, proteomics, and metabolomics.

Metabolomics allows the quantitative detection of multiple small molecule metabolites and lipids in biological systems and the investigation of the alterations in metabolic pathways and networks, thus providing information on the mechanisms underlying the beneficial effects and adverse metabolic consequences of a drug ^[2]. The quantitative measurement of the dynamic metabolic responses of a person to pathophysiological stimuli, drug therapy, or other interventions is often defined as metabonomics and involves the characterization of the changes of metabolic products in biological fluids and tissues following disease processes, environmental factors, drugs, and gut microflora ^[3]. The difference between metabonomics and metabolomics is minimal and the terms are often used interchangeably because the analytical procedures are the same.

Considering that the metabolome is both impacted by genetic background and environmental exposure, it provides a more specific description of the phenotype. Indeed, the metabolic profile is strongly influenced by both pathophysiological status and external perturbation such as specific drug treatment ^[4].

This intrinsic characteristic of the metabolome leads to the development of a specific metabolomics branch, pharmacometabolomics, which is now viewed as a complementary technique to genomics, transcriptomics, and proteomics for the therapeutic evaluation of specific drug products. Pharmacometabolomics or pharmacometabonomics contributes to a comprehensive understanding of the drug effects by also considering influences from a particular disease, environmental factors, diet, and concomitant pharmacological treatments ^[5]. This approach can be used to better understand the pharmacokinetic profile of a drug or to evaluate the metabolite levels after a pharmaceutical treatment, thus clarifying the mechanisms underlying the variations in response to therapy. In addition, it can provide potential unique signatures useful to stratify patients based on their metabolic heterogeneity within a specific disease state and to predict individual therapeutic responses ^[6].

Through pharmacometabolomics is possible to improve the efficacy and minimize the side effects associated with a treatment ^[7]. Indeed, in the last few years, the focus on drug therapy has moved further toward a personalized approach. Personalized therapy is very important in medical treatment, and it first of all requires the ability to recognize a different response to a specific drug in individuals ^[8]. In this respect, longitudinal studies using pharmacometabonomics have become a valuable tool to examine individual metabolic responses and, consequently, to direct toward a correct personalized medicine ^[9]^[10]^[11]. Thus, thanks to the recent evolution of the metabolomic field, metabolomic-based platforms are now also employed during the early phases of drug discovery, from target engagement to the elucidation of the mechanisms of action and discovery of markers for therapies monitoring, and have the potential to accelerate drug development ^[12].

The highly dynamic profiles of metabolites and their extreme chemical diversity present a challenge to research; thus, there is no single analytical technique that is able to cover the entire range of metabolites in a complex biological sample providing a comprehensive metabolomic analysis ^[13]. Among the analytical platforms employed in metabolomic analysis, nuclear magnetic resonance (NMR) and mass spectrometry (MS) are the most widely used. Due to higher sensitivity and throughput, MS is often applied both in untargeted and targeted metabolomics in combination with previous separation techniques, such as liquid chromatography (LC), gas chromatography (GC), and capillary electrophoresis (CE), that enhance the analytical capabilities of MS, improving the metabolite coverage of the analysis and facilitating the identification of the numerous compound classes ^[14]. Therefore, it is necessary to combine different separation procedures to cover a wide portion of the metabolome, even if this is time-consuming and data processing is more complicated. One of the main analytical methods used for metabolite analysis is LC-MS, and in particular, in recent years, the application of two-dimensional (2D) LC has grown, allowing higher sensitivity of the analysis, an improvement in compound separation when single-dimension separation is difficult, and, consequently, a significant increase in the compound number measured ^[13]. The metabolomic analysis can be either untargeted or targeted, depending on the research question and several other factors, including the classes, stability, and chemical properties of the metabolites of interest, as well as the appropriate analytical accuracy. Metabolomics can provide an untargeted identification of hundreds to thousands of metabolites simultaneously within a sample, achieving qualitative data as relative intensities of the metabolites associated with a particular pathophysiological status ^[15]. On the other hand, a targeted analysis allows the measurement of specific known compound classes using stable isotope labelled internal standards, thus providing absolute quantitative data. The most commonly used technique for targeted analysis is selected reaction monitoring (SRM) or multiple reaction monitoring (MRM) when multiple proteins are measured during a single MS analysis.

2. Methodological Approaches for Pharmacometabolomics and Pharmacolipidomics

Metabolomics can be applied to multiple biological matrices, such as tissues, cells, and biofluids. Plasma and serum are easily accessible and the most widely used matrices in human metabolomic clinical studies ^[16]^[17], together with urine samples which are commonly used for both human and animal metabolomics studies. However, metabolomics has also found several applications with less used biosamples, such as feces, saliva, culture medium, cells, or tissues ^[18], which can likewise provide interesting information on biological functions in health and disease. Feces, for example, are of increasing interest in metabolomics studies, because they reflect the metabolic association between the host and its intestinal microbiota ^[19]^[20]. Instead, even though tissues are invasive sample types, their analysis is also important, as they describe the metabolic changes that occur as a result of a disease. Tissue composition is often inhomogeneous in composition, thus increasing biological variability which should be always considered during sample collection and treatment. Indeed, pre-analytical handling steps are an important aspect of metabolomics study design, because accurate sample collection, processing, and storage are crucial to preserving sample integrity and quality. For this reason, guidelines and specific standard operating procedures for the pre-analytical handling of samples are required prior to initiating a metabolomic study to increase the metabolite recovery and stability for further metabolic investigation ^[19].

Metabolomics is a technology-driven discipline, like all the other “omics” approaches, strongly based on new developments in analytical techniques, instrumentation, software, and methods for data analysis.

MS and NMR are the most widely used technologies for metabolomics, allowing the qualitative and quantitative analysis of metabolites in biological samples. MS is often coupled to other separation techniques such as GC and LC to better resolve and characterize the metabolome, providing analytical platforms able to separate ions beyond the mass-to-charge ratio (m/z).

Therefore, most of the metabolomics studies have been performed using GC-MS, LC-MS, or NMR, which are briefly described below since details of the technical aspects have been clearly reviewed elsewhere ^[21]^[22]. A typical workflow for metabolomic experiments is reported in Figure 1.

/media/item_content/202302/63f56418a2f98ijms-24-03291-g001.png

Figure 1. Schematic workflow of metabolomic studies. Several steps are involved from experimental design and biological sample preparation to data analysis, processing, and interpretation. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the main analytical technologies used in metabolomics research. GC-MS, gas chromatography-MS; LC-MS, liquid chromatography-MS.

Lipidomics is considered a subfield of metabolomics because it gives information about the global lipid profile of a biological system that can be closely associated with the other metabolic pathways in the understanding of all mechanisms mediating statin effects. Similarly to metabolomics, lipids can be divided into several classes based on their different structural properties, and many analytical strategies have been developed, including targeted, untargeted, and shotgun lipidomics ^[23]^[24]. Lipidomics employs similar analytical techniques to metabolomics, even if MS-based techniques are still the most widely used approach by a direct analysis of the sample or following a lipid extraction and separation procedure ^[25].

Due to the advances in MS, lipidomics has grown in the last decade providing both qualitative and quantitative data on multiple lipid categories whose changes can be involved in many metabolic diseases, such as ASCVDs and diabetes. The study of lipids and their metabolic pathways has significant potential for finding biomarkers and developing innovative therapeutic targets for real-world biological questions ^[25].

2.1. Nuclear Magnetic Resonance (NMR)

NMR is an extensively used analytical platform in metabolomics studies, addressing the specific metabolic changes associated with mechanisms of action or toxic effects of several drugs ^[18].

For most ¹H-NMR-based metabolomics studies, sample preparation requires the addition of a deuterated buffer to the blood or urine samples to adjust the pH and provide the necessary lock signal ^[26]. Indeed, pH adjustment is particularly relevant in urine or saliva, which are particularly sensitive to inter-individual pH changes. This approach offers the advantage of easy sample preparation, the ability to obtain and absolute quantitate metabolites, high reproducibility, and, of note, non-destructiveness. The only disadvantage of this approach is in terms of sensitivity, which is 10 to 100 times less than MS-based techniques. Moreover, it is highly reproducible and, therefore, more suitable for large-scale metabolomics studies than MS-based methods, and can be used to detect and characterize sugars, organic acids, alcohols, polyols, and other highly polar compounds, which can be more difficult to detect with MS-based approaches ^[27].

NMR-based metabolomics and lipidomics have been extensively applied to find specific patterns for the diagnosis and prognosis of different human diseases, such as ASCVDs ^[28]. NMR was recently applied in a cross-sectional study to evaluate plasma metabolomics and lipidomics in atherosclerosis according to the presence of type 1 diabetes or previous preeclampsia ^[29]. Significant differences were reported according to the presence of diabetes or preeclampsia, which is known to have implications for future cardiovascular disease (CVD) events, and it has been shown that circulating levels of phosphatidylcholine, free cholesterol, saturated fatty acids, and w-7 fatty acids were independently associated with preclinical carotid atherosclerosis.

2.2. Gas Chromatography-Mass Spectrometry (GC-MS)

GC-MS is the most standardized method for metabolomic studies, based on more than 50 years of analyses, resulting in an increasing number of complete publicly available libraries under standardized conditions of 70 eV electron ionization energy, such as the NIST 14 Mass Spectral Library collection of the U.S. National Institute of Standards and Technology (NIST) containing mass spectra for 242,477 unique compounds, partially characterized also in terms of retention times, for a better performance of metabolite identification. Despite the notable breadth, sensitivity, and specificity of metabolite detections, GC-MS requires a derivatization step performed under very mild conditions, to decrease boiling points and increase the stability of compounds for GC-MS analysis ^[30].

2.3. Liquid Chromatography-Mass Spectrometry (LC-MS)

Alternative to GC, LC is largely used for metabolomic and lipidomic studies in combination with high-resolution mass spectrometers as well as tandem MS for targeted analysis to improve specificity, taking advantage of different selectivities in LC separation and the high sensitivity in MS detection. Thanks to technological advances, thousands of features can be detected. One of the most-used separation techniques is reversed phase for the analysis of mid- to non-polar metabolites and it is often used for lipid analysis, while small polar metabolites, such as amino acids, carboxylic acids, sugars, etc., can be resolved using hydrophilic interaction liquid chromatography separation ^[13].

However, misidentification can be present due to overlapping compounds with similar molecular weight (<5 ppm), in source degradation products, or the presence of isomeric and isobaric species that cannot be discriminated by MS, except for instruments equipped with ion mobility separation ^[31]. Differently from NMR, untargeted LC-MS and GC-MS provide relative quantities with respect to a reference sample, because peak intensities are not directly proportional to concentration due to differential ionization efficiencies of metabolites in complex mixtures ^[32].

LC-MS is a very sensitive and specific technique for lipidomic analysis and has been extensively applied in heart biology for the discovery of potential biomarkers. Recently, a sequential lipid profiling from acute to chronic heart failure was performed in mouse and human samples by an untargeted LC-linear trap quadrupole orbitrap MS ^[33]. Both tissue and plasma lipidomic profiles were acquired and compared to identify potential lipid markers for heart failure. Multivariate analysis showed distinct cardiac lipidomic patterns between healthy and ischemic patients, including significantly reduced glycerophospholipids in the ischemic heart especially phosphatidylethanolamines that were considered the main class of ischemia biomarkers. Phosphatidylethanolamines levels were instead significantly enhanced in tissues and plasma from risk-free mice in chronic myocardial infarction, thus suggesting a possible physiological cardiac remodeling. In addition, a reduced mitochondrial function associated with several altered lipid levels seemed to be an early marker of acute heart failure. The fold change analysis reported site-specific lipid metabolites and inter-organ lipidomic patterns that were significantly associated with acute and chronic heart failure, thus demonstrating a strong pathological lipid remodeling ^[33].

3. Lipid-Lowering Therapies and Metabolomics

Due to the important role of dyslipidaemia in the occurrence of atherosclerotic CVD, different approaches have been developed to lower LDL-cholesterol to improve CVD outcome. Despite the well-demonstrated benefits of statins, a proportion of patients does not reach the target levels of cholesterol recommended by guidelines, mainly due to low compliance associated with side effects ^[34]^[35]. Nowadays, alternative cholesterol lowering approaches have been developed to obtain better results both in terms of efficacy and tolerability, and they are often used in combination with statin. Fibrates act through the activation of peroxisome proliferator-activated receptor-α (PPAR-α) to modulate lipid and lipoprotein metabolism. One of the most recent classes of lipid-lowering therapy is represented by the PCSK9 inhibitors involved in the control of LDL receptor. The reduction of cholesterol absorption in the intestine can be obtained with ezetimibe interacting with the Niemann-Pick C1-like protein 1 (NPC1L1). Bile acid sequestrants indirectly reduce cholesterol that is needed by the liver to synthesize novel bile acids. Microsomal TG transfer protein (MTP) inhibitor, Lomitapide, prevents very-low-density lipoprotein (VLDL) formation in the liver and chylomicron formation in the intestine, acting on the transfer of triglycerides and phospholipids to Apolipoprotein B (ApoB). The antisense oligonucleotide, mipomersen, reduces the liver translation of the ApoB protein. The novel Bempedoic acid (ETC-1002) acts as an inhibitor of adenosine triphosphate citrate lyase (ACL), involved in the production of precursor of cholesterol and fatty acids ^[36]. Novel approaches have been also developed to reduce angiopoietin-like protein 3 (ANGPTL3) with a blocking antibody or antisense nucleotide, or to reduce mRNA levels of apolipoprotein CIII ^[35]. Although the pharmacological mechanisms of lipid-lowering drugs have been extensively studied, their metabolism-regulating effects and adverse effects have not been fully elucidated, especially after long-term treatments ^[37]. Indeed, as shown in Figure 2, describing the sites and targets of the main lipid-lowering drugs or drug classes, it is evident that, at the moment, only few of them have been studied from a metabolomic point of view in preclinical or clinical studies.

/media/item_content/202302/63f564434b4bfijms-24-03291-g002.png

Figure 2. Sites and targets of lipid-lowering therapies. Diagram of the mechanisms of action of the principal lipid-lowering drugs including those drug classes that have not yet been the object of pharmacometabolomic studies. Drug classes analyzed in metabolomic studies are highlighted by yellow boxes. Statins reduce cholesterol synthesis through 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR) inhibition. Fibrates are peroxisome proliferator-activated receptor alpha (PPARα) activators, able to increase Lipoprotein lipase activity and globally reduce triglycerides (TG) levels. Reduction of proprotein convertase subtilisin/kexin type 9 (PCSK9), responsible for the degradation of low-density lipoprotein receptor (LDLR), can be achieved with monoclonal antibodies or Inclisiran, a siRNA specific for PCSK9 that prevents translation of PCSK9 messenger RNA. Other drug classes without pharmacometabolomic studies are reported in green boxes. Bempedoic acid is a potent inhibitor of adenosine triphosphate (ATP) citrate lyase (ACL), a cellular enzyme responsible for the production of precursors for fatty acid and cholesterol synthesis. Lomitapide is an inhibitor of microsomal triglyceride transfer protein (MTP), an enzyme responsible for the synthesis of very low-density lipoproteins in the liver and chylomicrons in the intestine. Anionic exchange resins are bile acids sequestrants. The depletion of the bile acid pool stimulates the conversion of cholesterol to bile acid, reducing intracellular cholesterol in hepatocytes. Ezetimibe selectively inhibits intestinal cholesterol absorption, inhibiting the cholesterol transport protein Nieman Pick C1-like 1 protein (NPC1L1) in the intestine. ApoB100, apolipoprotein B100; LDL, low-density lipoprotein; LDL-r, LDL receptor; mab, monoclonal antibody; CoA, coenzyme A.

3.1. Statins

Statins are a potent group of lipid-lowering drugs, also known as 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase inhibitors, that can lower the levels of LDL-C in the blood. They are the primary cholesterol-lowering medications that work by reducing the LDL-C production inside the liver by competitively inhibiting the enzyme HMG-CoA reductase, which converts HMG-CoA to mevalonate formed during cholesterol synthesis ^[38]. Consequently, statins have proven to be an effective and efficient therapeutic way in the primary and secondary prevention of coronary heart diseases. In patients with very high LDL-C levels and a high risk of cardiovascular diseases, statins reduce the hardening and narrowing of the arteries and stabilize the plaques on blood vessel walls when atherosclerosis is already present. Moreover, they improve endothelial function, decrease inflammation status and oxidative stress, and prevent thrombogenic response ^[39]. A lifestyle modification through a cholesterol-lowering diet and physical exercise is recommended as a first choice but when the cholesterol levels continue to stay high after healthy lifestyle changes are implemented, statins can be helpful. The intake of statins depends on cholesterol levels and other cardiovascular risk factors that must be carefully considered and examined.

It is essential to study in depth the pharmacological response to statins in patients to understand better the real efficacy and safety of statins for definitive future guidelines.

3.1.1. Statin Response Variability

Statins have a variety of pleiotropic effects which are not yet fully understood. Characterizing the metabolic profiling following statin treatment provides further information about metabolic pathways involved in the pleiotropic effects of statins. Recently, Silva et al. ^[40] performed the first comprehensive evaluation of the metabolic signature of simvastatin treatment in a large population-based study, demonstrating that simvastatin shows several pleiotropic effects in the participants with statistically significant changes in multiple metabolite concentrations, affecting not only lipids, but also amino acids, peptides, nucleotides, carbohydrates, co-factors, vitamins, and xenobiotics. They identified more than 300 “novel” metabolites previously unpublished in association with simvastatin treatment, in particular short-chain acyl-carnitines and amino acids, thus reporting a more complex metabolic signature of simvastatin treatment compared to what was thought ^[40].

In addition, pharmacometabolomics can help to study the interindividual variation in the response to statins ^[41]. It is important to evaluate such variability, which may affect drug efficacy and toxicity, before the drug administration in patients. In the literature, several pharmacometabolomic studies focus on the characterization of the metabolic profiles of patients before treatment to stratify them as “responders” or “non-responders” to a specific therapeutic intervention ^[18].

The identification of predictive pre-treatment metabolic markers could be very useful in clinics to define individual variation, improve LDL-C lowering, and minimize drug toxicity ^[8].

For this reason, a pharmacometabonomic approach was applied to predict metabolic phenotypes and pharmacokinetic parameters of atorvastatin in healthy volunteers to investigate the individual differences in drug response without any prior knowledge of the genomic profile ^[8]. The scholars measured the levels of metabolites in pre-dose baseline plasma samples from 48 healthy volunteers using a GC-MS-based global metabolic profiling and quantified the atorvastatin levels in plasma at various time points after oral administration using an LC-MS/MS system operating in MRM mode. The untargeted metabolite profiling was performed on baseline plasma samples of all participants leading to the identification of several peaks, including amino acids, organic acids, carbohydrates, fatty acids, steroids, and many other compounds. In addition, a high degree of individual variation regarding pharmacokinetic responses was reported measuring the plasma concentration of atorvastatin. At this point, the group of participants was randomly divided into a training set and a prediction set for the subsequent multivariate statistical modeling, which was applied to screen potential markers of individual diversity by correlating endogenous metabolites in pre-dose plasma with pharmacokinetic parameters ^[8]. Using the baseline metabolic profiles of the subjects in the training set, a prediction model of multiple features was created and correctly predicted the pharmacokinetic parameters of the healthy volunteers in the prediction group. Endogenous molecules showed a good correlation with pharmacokinetic parameters, and in particular tryptophan, alanine, arachidonic acid, 2-hydroxybutyric acid, cholesterol, and isoleucine were considered as potential markers for predicting individual differences among the volunteers.

Nowadays, the field of metabolomics includes multiple analytical platforms and bioinformatics tools for mapping pathways implicated in disease and individual variation in response to drugs.

Simvastatin produces a more systemic effect not only focused on cholesterol metabolism and that it contributes to reducing the risk of CVDs, suggesting a critical interaction between metabolome, microbiome, and genome in the interindividual differences in response to statin therapy. The characterization of a pre-treatment metabolic signature as a panel of predictive markers can improve the classification of individuals based on the response to a particular drug, thus excluding individuals who are least likely to derive a clinical benefit. In this way, future clinical trials and pharmacological treatments will be more feasible and relevant, focusing on participant characteristics to achieve a personalizing medicine to treat individuals effectively and safely.

3.1.2. Alterations in Gut Microbiota by Statin Therapy

The application of MS-based lipidomics provides a valid integrative approach to studying pharmacometabolomic changes because it gives information about whole lipid metabolism pathways that can be closely correlated to the other metabolic networks in the understanding of all mechanisms mediating statin effects ^[42]^[43]. In a recent study, the potential molecular mechanisms underlying the association between metabolic improvement and microbiota composition following simvastatin treatment were investigated to explain the gut microbiome involvement in the statin response variability ^[44]. A metabolomic profiling using an ultra-high-performance liquid chromatography (UHPLC) system coupled with a hybrid triple quadrupole TOF (Q-TOF) mass spectrometer was performed to study the interactions of endogenous serum metabolites with the gut microbiota following simvastatin treatment in high-lipid diet-induced hyperlipidemic rats. Differential endogenous metabolites were identified that affected the metabolism of amino acids (phenylalanine and tyrosine), unsaturated fatty acids (linoleic acid and 9-hydroxyoctadecadienoic acid), and the functions of gut microbial metabolism (m-coumaric acid and 3-(2-hydroxyphenyl) propionic acid) ^[44]. These data suggested that simvastatin therapy strongly modulates the serum metabolic profile in hyperlipidemic rats, and, since these metabolic pathways are involved in gut flora interactions, they could be potential therapeutic targets for the improvement of simvastatin hypolipidemic efficacy. Indeed, hyperlipidemia is a metabolic syndrome that is commonly linked to cardiovascular diseases. Phenylalanine is a nutrient precursor for gut microbiota-generated metabolites, which are known to be associated with cardiovascular diseases and adverse cardiovascular events, while tyrosine promotes lipid metabolism, therefore representing a potential biomarker for hyperlipidemia ^[44]. Both phenylalanine and tyrosine showed increased levels following statin treatment. In the same way, levels of both linoleic acid and 9-hydroxyoctadecadienoic acid were significantly increased after simvastatin therapy, which confirms their beneficial effects against cardiovascular risk, including hyperlipidemia and hypertension ^[44]. The concentration of the metabolites of the gut microflora, m-coumaric acid and a derivative of phenylpropionic acid, were also higher in hyperlipidemic rats after statin treatment, showing their antilipogenic and cholesterol-lowering properties ^[44].

3.1.3. Adverse Effects of Statins

Statins are highly effective and safe for most people, but they can cause minor or severe side effects that should never be neglected. Statin-related myotoxicity, for example, can range from mild muscle pain up to rhabdomyolysis, which is a serious and fatal disorder that sometimes occurs in patients following pharmacological treatment ^[45]. Statin-associated rhabdomyolysis risk has been reported as dose-dependent and concentration-dependent ^[46].

Metabolomics provides an accurate signature of all metabolite changes in biological fluids, cells, and tissues that can be a source for biomarker discovery. A metabolomic analysis of skeletal muscle and plasma using LC-MS and GC-MS was performed on a rat model treated with two myotoxicants, cerivastatin and tetramethyl-p-phenylenediamine, to induce a skeletal injury and identify candidate biomarkers for skeletal muscle toxicity ^[47]. They observed in skeletal muscle a significant increase in 2-hydroxyglutarate in cerivastatin-treated rats and hexanoylcarnitine in both types of treated rats. These increases were also measured in plasma samples at different times after dosing, demonstrating the possibility to use plasma 2-hydroxyglutarate and hexanoylcarnitine as valid and easily detectable biomarkers for the early detection of skeletal muscle toxicity in rats, with better sensitivity than the conventional markers creatine kinase and aspartate aminotransferase whose utility in clinics is limited due to their low diagnostic power ^[48]. Moreover, this study confirmed the importance and benefit of metabolomics for biomarker discovery in toxicological studies.

Among the potential statin-related adverse events, there is also an increased incidence of type II diabetes mellitus that can lead to premature discontinuation of treatment. Therefore, it is important to evaluate a correlation between statin-induced metabolic changes and statin-induced hyperglycemia and insulin resistance, to identify pre-drug treatment metabolites predictive of increased diabetic risk ^[49]. In this regard, a pharmacometabolomic study was performed by GC-TOF-MS on plasma pre- and post-treatment with simvastatin for 6 weeks from patients enrolled for the CAP study ^[49] to measure changes in intermediary metabolism and the associated high plasma glucose levels as a potentially adverse response to simvastatin. Some patients developed hyperglycemia and pre-diabetes, as well as a dysfunction of beta cells and insulin resistance in more than 50% of patients following statin therapy. An initial metabolic profile of simvastatin-induced insulin resistance was identified, including ethanolamine, hydroxylamine, hydroxycarbamate, and isoleucine, which can be predictive biomarkers of individuals at risk of developing a statin-induced new onset pre-type II diabetes mellitus ^[49]. In particular, the metabolite ethanolamine was identified as the most likely to predict simvastatin-induced diabetic risk, indicating that decarboxylation and oxidation were significantly associated with statin-induced hyperglycemia and insulin resistance. Pharmacometabolomics allows having a baseline metabolic signature before starting the drug therapy that can be then used to find predictive biomarkers able to stratify patients and to identify subjects who are at higher risk of adverse side effects, enabling personalized selection of the most appropriate medication for each patient and personalized monitoring of their prognosis.

3.1.4. Beneficial Effects of Statins

Although the possible side effects of therapy with statins are unpleasant, it is important not to forget the considerable benefits of taking them for the treatment of several pathologies. Metabonomics is used not only for clinical diagnosis, but also for evaluating the clinical course of a disease, prognosis, and treatment effect of drugs, such as statins ^[50].

Many years ago, Ooga et al. performed ^[51] a metabolic analysis in Watanabe heritable hyperlipidemic (WHHL) rabbits as a model of hypercholesterolemia to obtain a determination of all metabolite concentrations and a characterization of the metabolic imbalance of their pathological condition. Numerous metabolites were measured in plasma and several tissues from WHHL and healthy control rabbits using CE-TOF-MS and LC-TOF-MS systems. Several significant metabolic differences between the healthy and the pathological conditions were observed, and the metabolomic features observed in the pathological rabbit model including the modulation of glutathione and phosphatidylcholine metabolism showing advanced oxidative stress in several tissues, especially in the liver ^[51].

Shifting the attention to human subjects with hypercholesterolemia, comprehensive cross-sectional profiling of lipids and metabolites was performed by Christensen et al. in children with and without familial hypercholesterolemia (FH) aiming to characterize the alterations associated with elevated LDL-C in FH patients ^[52]. Elevated plasma cholesterol is the most important risk factor for atherosclerosis and cholesterol-lowering treatment with statins is required to stop or slow down atherosclerotic development in FH children. Plasma metabolites were measured by high-throughput NMR spectroscopy to compare the differences between statin-treated and non-statin-treated FH children, and healthy children ^[52]. It is important to investigate hypercholesterolemia-associated metabolic aberrations in HF children to better understand the disease, and thereby improve the treatment of hypercholesterolemia in children and, more in general terms, the treatment of atherosclerotic processes. The scholars observed increased levels of atherogenic ApoB-containing lipoproteins and lipid fractions in both statin-treated and non-statin-treated FH children compared to healthy children. In addition, FH children showed alterations in HDL subfractions, and in particular, their small HDL particles were characterized by a higher content of cholesteryl esters, and lower levels of free cholesterol and phospholipids ^[52].

Another common application of metabonomics is the study of the development and progression of diseases following a specific pharmacological treatment.

3.2. PCSK9 Inhibitors

PCSK9 inhibitors are pharmacological agents used to reduce blood LDL-C levels and improve cardiovascular outcomes both in primary and secondary prevention ^[53]. They are human monoclonal antibodies that bind PCSK9 protein with high affinity to lower LDL-C concentrations by blocking the degradation of cholesterol receptors available on the hepatocyte cell surface, which are responsible for removing LDL-C from blood ^[54]. PCSK9 inhibitors seem to show a more effective lipid-lowering profile than statins ^[55], even if the efficacy and safety among PCSK9 inhibitors and statins are still a subject of intensive study.

A Mendelian randomization study by Ference et al. also compared the effects of lower LDL-C levels mediated by variants located in HMG-CoA reductase (HMGCR), the gene encoding the target of statins, or in PCSK9 on the risk of cardiovascular events and the risk of diabetes ^[56]. The results showed that variants in PCSK9 had a nearly identical effect as statin therapy on the risk of cardiovascular diseases and diabetes per unit decrease in plasma LDL-C level. Of note, the clinical benefit of PCSK9 and HMGCR variants increased when presented together.

Based on evidence from the two major clinical trials on PCSK9 inhibitors, the FOURIER ^[57] and the ODYSSEY ^[58] outcome trials that used evolocumab and alirocumab, respectively, as fully humanized monoclonal antibodies against PCSK9, a recent paper by Gallego-Colon et al. underlines that the 2019 European Society of Cardiology/European Atherosclerosis Society guidelines for the management of dyslipidemias establish the use of PCSK9 inhibitors to very high-risk atherosclerotic cardiovascular disease patients who are unresponsive to a maximum tolerated dose of statins and ezetimibe ^[59]. Therefore, the discovery of PCSK9 inhibitors has defined a new era of lipid-lowering therapies for patients with atherosclerotic cardiovascular disease which can change future clinical practice.

An untargeted metabolomics approach was also performed to obtain a global view of metabolic and lipidomic pathways and characterize metabolites and lipids that were modified in plasma from patients with FH who received treatment with PCSK9 inhibitors ^[60]. Familial hypercholesterolemia causes extremely high circulating LDL-C levels, which are due to mutations of different genes involved in LDL-C metabolism, such as PCSK9. After 12 weeks of treatment with evolocumab, the scholars observed a significant reduction of LDL-C levels compared to baseline, together with increments in creatine, indole, and indoleacrylic acid concentrations. Instead, a significant decrease in choline and phosphatidylcholine levels, as well as a reduction in platelet-activating factor 16, were reported. The study highlighted for the first time a reduction in inflammation and platelet activation metabolites in FH patients after therapy with PCSK9 inhibitors ^[60]. Moreover, due to the small sample size, further studies are required to clarify the underlying mechanisms and the impact on cardiovascular events, confirming data in a larger number of participants with targeted analysis.

3.3. Fibrates

Fibrates are activators of peroxisome proliferator-activated receptor alpha (PPARα), used to prevent and treat hyperlipidemia often in combination with statins, thanks to their ability to increase fatty acid β-oxidation, fatty acid transport, and HDL metabolism, leading to a global reduction of triglyceride and cholesterol levels ^[37]^[61].

Patterson et al. identified pantothenic acid and acylcarnitines as specific potential indicators of PPARα activation of fatty acid β-oxidation induced by fibrates using a metabolomic approach ^[62]. They treated healthy volunteers with fenofibrate (200 mg/day) for 14 days and analyzed urinary metabolites at time 0, after 2 days, and after 14 days, by LC-MS, using an ultra-performance liquid chromatography (UPLC) system coupled to a high-resolution mass spectrometer (Q-TOF). They evidenced a dramatic decrease in urinary pantothenic acid (>5 fold) and acylcarnitines (>20 folds), and to confirm that these molecules could be biomarkers of PPARα activation, they treated wild-type and Ppara-null mice with 0.1% fenofibrate for 7 days. Of note, only wild-type mice exhibited a reduction of both urinary pantothenic acid (40 folds) and acylcarnitines (88 folds), suggesting that the effect is strongly associated with the activation of PPARα and transcends species ^[62].

Further, a combined transcriptomic and metabolomic approach has been applied to compare in a mouse model 2 weeks of fenofibrate treatment with respect to fish oil treatment ^[63]. Fish oil is indeed rich in eicosapentaenoic acid and docosahexaenoic acid, fatty acids that act through PPARα activation and suppress the activity of the prolipogenic transcription factor SREBP-1. Fish oil specifically decreased the levels of various phospholipid species, while fenofibrate specifically increased the levels of Krebs cycle intermediates (i.e., fumaric acid, isocitric acid, malic acid, succinic acid and α-ketoglutaric acid) and most amino acids. These data correlate well with the induction of genes involved in the Krebs cycle and in the urea cycle or in the metabolism of amino groups. Comparing both plasma metabolome and hepatic transcriptome, it emerged that despite being similarly potent toward modulating plasma lipids, fish oil caused only modest changes in gene expression likely in comparison to fenofibrate, reflecting the activation of multiple mechanistic pathways with fish oil, typical of nutritional interventions ^[63].

Combination Therapy of Statins and Fibrates
Fibrates are frequently used in combination with statins, working in synergy to reduce plasma lipids, even if this type of treatment is associated with a higher incidence of fatal side effects, such as acute tubular necrosis and rhabdomyolysis. Several hypotheses have been formulated including pharmacokinetic interference, displacement of statins from their binding sites, synergistic action on skeletal muscle, or inhibition of statin glucuronidation by fibrates.

3.4. Nutraceutical and Dietary Habits

In the past decades, an increasing number of studies have suggested that nutraceuticals and dietary habits may be also effective for CVD prevention ^[64]^[65]^[66], with significant effects on reducing CVD risk and population mortality ^[67].

In particular, natural micronutrients and non-nutrient components in these foods, such as polyphenols, have been shown to modulate cholesterol metabolism ^[67]. Sommella et al. focused their attention on Malus pumila Miller cv. Annurca, an apple native to southern Italy, containing high levels of procyanidin B2, a dimeric procyanidin, with favorable biochemical effects against metabolic disorders and atherosclerosis ^[67]. They demonstrated that 800 mg/day of Annurca apple polyphenolic extract (AAE) substantially reduced both LDL-C (37.6%) and increased HDL-C (49.3%), similarly to statin treatment ^[68], and applied an untargeted metabolomic approach to depicting the molecular mechanism activated by this nutraceutical treatment ^[67]. They used deuterium labeling for 72 h coupled with GC-MS and Fourier transform-ion cyclotron resonance (FT-ICR) mass spectrometry to highlight primary metabolic pathways influenced by AAE in in vitro cultured human hepatocytes, HuH7 cells. Their results suggested that AAE acts differently from statins, promoting mitochondrial activity, reprogramming fatty acid metabolism, and inhibiting lipogenesis and cholesterogenesis. AAE diverts acetyl-CoA to the Krebs cycle to produce adenosine triphosphate (ATP) and energy for the cell, instead of becoming HMG-CoA. Glutamine levels are also reduced by AAE suggesting that glutamine can be indeed one of the sources of increased mitochondrial activity. Furthermore, AAE stimulates glycolysis ultimately increasing mitochondrial respiration. Thus, inhibition of lipogenesis and cholesterogenesis could be ascribed to a modulation of the entire metabolic process connected with the use of citrate.

In addition, the beneficial effects of probiotics to improve lipid profiles have been demonstrated in animal models and humans. Ding et al. studied the effects of Lactobacillus plantarum LP3, from traditional fermented yak milk, on the plasma lipid profile, gut microbiota, and cecum metabolome, by LC-MS, in rats treated with a high-fat diet ^[69]. Together with a significant reduction of TC, TG, and LDL-C, they evidenced adjustments in the biosynthesis of fatty acids, steroids, and bile acids, and the metabolism of linoleic acid, linolenic acid, and arachidonic acid were the main metabolic pathways in obese rats. The ability of Lactobacillus plantarum LP3 to reduce the ratio of Firmicutes to Bacteroidetes in obese rats could explain the reduction in metabolites associated with the biosynthesis of fatty acids ^[69].

Dietary plant-derived polyphenols are another class of molecules with protective effects against cardiovascular diseases ^[70]. Zhou et al. used a metabolomic approach based on GC-MS analysis of extracts from liver tissues to evaluate the synergistic protective effects of quercetin and resveratrol in mice that were fed a high-fat diet ^[71]. The integration of metabolomic and transcriptomic results clearly showed the enhancement of glycolysis, fatty acid oxidation, and gluconeogenesis. The metabolites that were reduced due to the high-fat diet resulted in being restored by quercetin or resveratrol treatment, such as 4-aminobutyric acid, ornithine, histidine, and lysine ^[71].

4. Conclusions

It is increasingly evident that metabolomic approaches in pharmacology could be useful not only in the understanding of drug safety, toxicity, and metabolism, but also in the prediction of drug response and in the identification of biological mechanisms, even if some limitations should be acknowledged. Indeed, there is still a lack of standardized protocols for both sample preparation (i.e., collection, storage, and processing) and data acquisition, which are very important for a clinical application of these approaches. Data have been obtained in different compartments, both in clinical settings or animal models, at different times, thus making it difficult to compare them and have a comprehensive view of the metabolomic effects. Another important issue that should be taken into consideration is the influence of the environment (i.e., smoking, food, and physical activity) on the metabolic phenotype, thus requiring a large number of samples to obtain reproducible results, as well as very accurate experimental design. It would be important to also perform longitudinal studies increasing the compliance of patients with the introduction of remote sampling or less invasive collection procedures.

Metabolomics combined with multi-omics strategies and advanced bioinformatics tools could definitely improve the drug repurposing which has gained importance in recent years for identifying novel therapeutic indications for already registered drugs. Since lipid-lowering drugs have pleiotropic effects beyond their known mechanism of action, the discovery of repurposed drugs has implications for precision medicine to treat individual patients providing a decrease in the cost of a new drug development and benefits for the treatment of cardiovascular diseases.

References

Larsen, L.E.; Stoekenbroek, R.M.; Kastelein, J.J.P.; Holleboom, A.G. Moving Targets: Recent Advances in Lipid-Lowering Therapies. Arterioscler. Thromb. Vasc. Biol. 2019, 39, 349–359.
Gowda, G.A.; Zhang, S.; Gu, H.; Asiago, V.; Shanaiah, N.; Raftery, D. Metabolomics-based methods for early disease diagnostics. Expert Rev. Mol. Diagn. 2008, 8, 617–633.
Nicholson, J.K.; Lindon, J.C. Systems biology: Metabonomics. Nature 2008, 455, 1054–1056.
Holmes, E.; Wilson, I.D.; Nicholson, J.K. Metabolic phenotyping in health and disease. Cell 2008, 134, 714–717.
Burt, T.; Nandal, S. Pharmacometabolomics in Early-Phase Clinical Development. Clin. Transl. Sci. 2016, 9, 128–138.
Kim, H.; Yoon, Y. Pharmacometabolomics: Current Applications and Future Perspectives. Transl. Clin. Pharmacol. 2014, 22, 8–10.
Rattray, N.; Daouk, R. Pharmacometabolomics and Precision Medicine Special Issue Editorial. Metabolomics 2017, 13, 59.
Huang, Q.; Aa, J.; Jia, H.; Xin, X.; Tao, C.; Liu, L.; Zou, B.; Song, Q.; Shi, J.; Cao, B.; et al. A Pharmacometabonomic Approach To Predicting Metabolic Phenotypes and Pharmacokinetic Parameters of Atorvastatin in Healthy Volunteers. J. Proteome Res. 2015, 14, 3970–3981.
Clayton, T.A.; Lindon, J.C.; Cloarec, O.; Antti, H.; Charuel, C.; Hanton, G.; Provost, J.P.; Le Net, J.L.; Baker, D.; Walley, R.J.; et al. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 2006, 440, 1073–1077.
Wilson, I.D. Drugs, bugs, and personalized medicine: Pharmacometabonomics enters the ring. Proc. Natl. Acad. Sci. USA 2009, 106, 14187–14188.
Winnike, J.H.; Li, Z.; Wright, F.A.; Macdonald, J.M.; O’Connell, T.M.; Watkins, P.B. Use of pharmaco-metabonomics for early prediction of acetaminophen-induced hepatotoxicity in humans. Clin. Pharmacol. Ther. 2010, 88, 45–51.
Alarcon-Barrera, J.C.; Kostidis, S.; Ondo-Mendez, A.; Giera, M. Recent advances in metabolomics analysis for early drug development. Drug Discov. Today 2022, 27, 1763–1773.
Harrieder, E.M.; Kretschmer, F.; Bocker, S.; Witting, M. Current state-of-the-art of separation methods used in LC-MS based metabolomics and lipidomics. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2022, 1188, 123069.
Macedo, A.N.; Faccio, A.T.; Fukuji, T.S.; Canuto, G.A.B.; Tavares, M.F.M. Analytical Platforms for Mass Spectrometry-Based Metabolomics of Polar and Ionizable Metabolites. Adv. Exp. Med. Biol. 2021, 1336, 215–242.
Banoei, M.M.; Donnelly, S.J.; Mickiewicz, B.; Weljie, A.; Vogel, H.J.; Winston, B.W. Metabolomics in critical care medicine: A new approach to biomarker discovery. Clin. Investig. Med. 2014, 37, E363–E376.
Paglia, G.; Del Greco, F.M.; Sigurdsson, B.B.; Rainer, J.; Volani, C.; Hicks, A.A.; Pramstaller, P.P.; Smarason, S.V. Influence of collection tubes during quantitative targeted metabolomics studies in human blood samples. Clin. Chim. Acta 2018, 486, 320–328.
Volani, C.; Caprioli, G.; Calderisi, G.; Sigurdsson, B.B.; Rainer, J.; Gentilini, I.; Hicks, A.A.; Pramstaller, P.P.; Weiss, G.; Smarason, S.V.; et al. Pre-analytic evaluation of volumetric absorptive microsampling and integration in a mass spectrometry-based metabolomics workflow. Anal. Bioanal. Chem. 2017, 409, 6263–6276.
Gómez-Cebrián, N.; Ferreiro, P.V.; Hueso, F.J.C.; Andrés, J.L.P.; Puchades-Carrasco, L.; Pineda-Lucena, A. Pharmacometabolomics by NMR in Oncology: A Systematic Review. Pharmaceuticals 2021, 14, 1015.
Smith, L.; Villaret-Cazadamont, J.; Claus, S.P.; Canlet, C.; Guillou, H.; Cabaton, N.J.; Ellero-Simatos, S. Important Considerations for Sample Collection in Metabolomics Studies with a Special Focus on Applications to Liver Functions. Metabolites 2020, 10, 104.
Zierer, J.; Jackson, M.A.; Kastenmuller, G.; Mangino, M.; Long, T.; Telenti, A.; Mohney, R.P.; Small, K.S.; Bell, J.T.; Steves, C.J.; et al. The fecal metabolome as a functional readout of the gut microbiome. Nat. Genet. 2018, 50, 790–795.
Schrimpe-Rutledge, A.C.; Codreanu, S.G.; Sherrod, S.D.; McLean, J.A. Untargeted Metabolomics Strategies—Challenges and Emerging Directions. J. Am. Soc. Mass Spectrom. 2016, 27, 1897–1905.
Marshall, D.D.; Powers, R. Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics. Prog. Nucl. Magn. Reson. Spectrosc. 2017, 100, 1–16.
Au, A. Metabolomics and Lipidomics of Ischemic Stroke. Adv. Clin. Chem. 2018, 85, 31–69.
Han, X.; Yang, K.; Gross, R.W. Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses. Mass Spectrom. Rev. 2012, 31, 134–178.
Belhaj, M.R.; Lawler, N.G.; Hoffman, N.J. Metabolomics and Lipidomics: Expanding the Molecular Landscape of Exercise Biology. Metabolites 2021, 11, 151.
Haslauer, K.E.; Hemmler, D.; Schmitt-Kopplin, P.; Heinzmann, S.S. Guidelines for the Use of Deuterium Oxide (D2O) in (1)H NMR Metabolomics. Anal. Chem. 2019, 91, 11063–11069.
Emwas, A.H.; Roy, R.; McKay, R.T.; Tenori, L.; Saccenti, E.; Gowda, G.A.N.; Raftery, D.; Alahmari, F.; Jaremko, L.; Jaremko, M.; et al. NMR Spectroscopy for Metabolomics Research. Metabolites 2019, 9, 123.
Bodi, V.; Marrachelli, V.G.; Husser, O.; Chorro, F.J.; Vina, J.R.; Monleon, D. Metabolomics in the diagnosis of acute myocardial ischemia. J. Cardiovasc. Transl. Res. 2013, 6, 808–815.
Amor, A.J.; Vinagre, I.; Valverde, M.; Urquizu, X.; Meler, E.; Lopez, E.; Alonso, N.; Pane, A.; Gimenez, M.; Codina, L.; et al. Nuclear magnetic resonance-based metabolomic analysis in the assessment of preclinical atherosclerosis in type 1 diabetes and preeclampsia. Diabetes Res. Clin. Pract. 2021, 171, 108548.
Fiehn, O. Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling. Curr. Protoc. Mol. Biol. 2016, 114, 30.4.1–30.4.32.
Paglia, G.; Smith, A.J.; Astarita, G. Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics. Mass Spectrom. Rev. 2021, 41, 722–765.
Alseekh, S.; Aharoni, A.; Brotman, Y.; Contrepois, K.; D’Auria, J.; Ewald, J.; Ewald, J.C.; Fraser, P.D.; Giavalisco, P.; Hall, R.D.; et al. Mass spectrometry-based metabolomics: A guide for annotation, quantification and best reporting practices. Nat. Methods 2021, 18, 747–756.
Gowda, S.G.B.; Gowda, D.; Hou, F.; Chiba, H.; Parcha, V.; Arora, P.; Halade, G.V.; Hui, S.P. Temporal lipid profiling in the progression from acute to chronic heart failure in mice and ischemic human hearts. Atherosclerosis 2022, 363, 30–41.
Ruscica, M.; Banach, M.; Sahebkar, A.; Corsini, A.; Sirtori, C.R. ETC-1002 (Bempedoic acid) for the management of hyperlipidemia: From preclinical studies to phase 3 trials. Expert Opin. Pharmacother. 2019, 20, 791–803.
Mach, F.; Baigent, C.; Catapano, A.L.; Koskinas, K.C.; Casula, M.; Badimon, L.; Chapman, M.J.; De Backer, G.G.; Delgado, V.; Ference, B.A.; et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. Eur. Heart J. 2020, 41, 111–188.
Bilen, O.; Ballantyne, C.M. Bempedoic Acid (ETC-1002): An Investigational Inhibitor of ATP Citrate Lyase. Curr. Atheroscler. Rep. 2016, 18, 61.
Xu, Q.Y.; Liu, Y.H.; Zhang, Q.; Ma, B.; Yang, Z.D.; Liu, L.; Yao, D.; Cui, G.B.; Sun, J.J.; Wu, Z.M. Metabolomic analysis of simvastatin and fenofibrate intervention in high-lipid diet-induced hyperlipidemia rats. Acta Pharmacol. Sin. 2014, 35, 1265–1273.
Muller, A.L.; Freed, D.H. Basic and Clinical Observations of Mevalonate Depletion on the Mevalonate Signaling Pathway. Curr. Mol. Pharmacol. 2017, 10, 6–12.
Liao, J.K.; Laufs, U. Pleiotropic effects of statins. Annu. Rev. Pharmacol. Toxicol. 2005, 45, 89–118.
Silva, L.F.; Ravi, R.; Vangipurapu, J.; Laakso, M. Metabolite Signature of Simvastatin Treatment Involves Multiple Metabolic Pathways. Metabolites 2022, 12, 753.
Krauss, R.M.; Zhu, H.; Kaddurah-Daouk, R. Pharmacometabolomics of statin response. Clin. Pharmacol. Ther. 2013, 94, 562–565.
Trupp, M.; Zhu, H.; Wikoff, W.R.; Baillie, R.A.; Zeng, Z.B.; Karp, P.D.; Fiehn, O.; Krauss, R.M.; Kaddurah-Daouk, R. Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment. PLoS ONE 2012, 7, e38386.
Hu, T.; Zhang, J.L. Mass-spectrometry-based lipidomics. J. Sep. Sci. 2018, 41, 351–372.
Zhang, S.; Yuan, L.; Li, H.; Han, L.; Jing, W.; Wu, X.; Ullah, S.; Liu, R.; Wu, Y.; Xu, J. The Novel Interplay between Commensal Gut Bacteria and Metabolites in Diet-Induced Hyperlipidemic Rats Treated with Simvastatin. J. Proteome Res. 2022, 21, 808–821.
Hussain, K.; Xavier, A. Rosuvastatin-related rhabdomyolysis causing severe proximal paraparesis and acute kidney injury. BMJ Case Rep. 2019, 12, e229244.
Graham, D.J.; Staffa, J.A.; Shatin, D.; Andrade, S.E.; Schech, S.D.; La Grenade, L.; Gurwitz, J.H.; Chan, K.A.; Goodman, M.J.; Platt, R. Incidence of hospitalized rhabdomyolysis in patients treated with lipid-lowering drugs. JAMA 2004, 292, 2585–2590.
Obayashi, H.; Kobayashi, N.; Nezu, Y.; Yamoto, T.; Shirai, M.; Asai, F. Plasma 2-hydroxyglutarate and hexanoylcarnitine levels are potential biomarkers for skeletal muscle toxicity in male Fischer 344 rats. J. Toxicol. Sci. 2017, 42, 385–396.
Laaksonen, R. STOMPing forward: Statins, muscle complaints and CK. Atherosclerosis 2013, 230, 256–257.
Elbadawi-Sidhu, M.; Baillie, R.A.; Zhu, H.; Chen, Y.I.; Goodarzi, M.O.; Rotter, J.I.; Krauss, R.M.; Fiehn, O.; Kaddurah-Daouk, R. Pharmacometabolomic signature links simvastatin therapy and insulin resistance. Metabolomics 2017, 13, 11.
Pallares-Mendez, R.; Aguilar-Salinas, C.A.; Cruz-Bautista, I.; Del Bosque-Plata, L. Metabolomics in diabetes, a review. Ann. Med. 2016, 48, 89–102.
Ooga, T.; Sato, H.; Nagashima, A.; Sasaki, K.; Tomita, M.; Soga, T.; Ohashi, Y. Metabolomic anatomy of an animal model revealing homeostatic imbalances in dyslipidaemia. Mol. Biosyst. 2011, 7, 1217–1223.
Christensen, J.J.; Ulven, S.M.; Retterstol, K.; Narverud, I.; Bogsrud, M.P.; Henriksen, T.; Bollerslev, J.; Halvorsen, B.; Aukrust, P.; Holven, K.B. Comprehensive lipid and metabolite profiling of children with and without familial hypercholesterolemia: A cross-sectional study. Atherosclerosis 2017, 266, 48–57.
Zhao, Z.; Du, S.; Shen, S.; Luo, P.; Ding, S.; Wang, G.; Wang, L. Comparative efficacy and safety of lipid-lowering agents in patients with hypercholesterolemia: A frequentist network meta-analysis. Medicine 2019, 98, e14400.
Artenstein, A.W.; Opal, S.M. Proprotein convertases in health and disease. N. Engl. J. Med. 2011, 365, 2507–2518.
Pecin, I.; Hartgers, M.L.; Hovingh, G.K.; Dent, R.; Reiner, Z. Prevention of cardiovascular disease in patients with familial hypercholesterolaemia: The role of PCSK9 inhibitors. Eur. J. Prev. Cardiol. 2017, 24, 1383–1401.
Ference, B.A.; Robinson, J.G.; Brook, R.D.; Catapano, A.L.; Chapman, M.J.; Neff, D.R.; Voros, S.; Giugliano, R.P.; Smith, G.D.; Fazio, S.; et al. Variation in PCSK9 and HMGCR and Risk of Cardiovascular Disease and Diabetes. N. Engl. J. Med. 2016, 375, 2144–2153.
Sabatine, M.S.; Giugliano, R.P.; Keech, A.C.; Honarpour, N.; Wiviott, S.D.; Murphy, S.A.; Kuder, J.F.; Wang, H.; Liu, T.; Wasserman, S.M.; et al. Evolocumab and Clinical Outcomes in Patients with Cardiovascular Disease. N. Engl. J. Med. 2017, 376, 1713–1722.
Schwartz, G.G.; Steg, P.G.; Szarek, M.; Bhatt, D.L.; Bittner, V.A.; Diaz, R.; Edelberg, J.M.; Goodman, S.G.; Hanotin, C.; Harrington, R.A.; et al. Alirocumab and Cardiovascular Outcomes after Acute Coronary Syndrome. N. Engl. J. Med. 2018, 379, 2097–2107.
Gallego-Colon, E.; Daum, A.; Yosefy, C. Statins and PCSK9 inhibitors: A new lipid-lowering therapy. Eur. J. Pharmacol. 2020, 878, 173114.
Di Minno, A.; Orsini, R.C.; Chiesa, M.; Cavalca, V.; Calcaterra, I.; Tripaldella, M.; Anesi, A.; Fiorelli, S.; Eligini, S.; Colombo, G.I.; et al. Treatment with PCSK9 Inhibitors in Patients with Familial Hypercholesterolemia Lowers Plasma Levels of Platelet-Activating Factor and Its Precursors: A Combined Metabolomic and Lipidomic Approach. Biomedicines 2021, 9, 1073.
Ohta, T.; Masutomi, N.; Tsutsui, N.; Sakairi, T.; Mitchell, M.; Milburn, M.V.; Ryals, J.A.; Beebe, K.D.; Guo, L. Untargeted metabolomic profiling as an evaluative tool of fenofibrate-induced toxicology in Fischer 344 male rats. Toxicol. Pathol. 2009, 37, 521–535.
Patterson, A.D.; Slanar, O.; Krausz, K.W.; Li, F.; Hofer, C.C.; Perlik, F.; Gonzalez, F.J.; Idle, J.R. Human urinary metabolomic profile of PPARalpha induced fatty acid beta-oxidation. J. Proteome Res. 2009, 8, 4293–4300.
Lu, Y.; Boekschoten, M.V.; Wopereis, S.; Muller, M.; Kersten, S. Comparative transcriptomic and metabolomic analysis of fenofibrate and fish oil treatments in mice. Physiol. Genom. 2011, 43, 1307–1318.
Hertog, M.G.; Feskens, E.J.; Hollman, P.C.; Katan, M.B.; Kromhout, D. Dietary antioxidant flavonoids and risk of coronary heart disease: The Zutphen Elderly Study. Lancet 1993, 342, 1007–1011.
Mink, P.J.; Scrafford, C.G.; Barraj, L.M.; Harnack, L.; Hong, C.P.; Nettleton, J.A.; Jacobs, D.R., Jr. Flavonoid intake and cardiovascular disease mortality: A prospective study in postmenopausal women. Am. J. Clin. Nutr. 2007, 85, 895–909.
Grosso, G. Effects of Polyphenol-Rich Foods on Human Health. Nutrients 2018, 10, 1089.
Sommella, E.; Badolati, N.; Riccio, G.; Salviati, E.; Bottone, S.; Dentice, M.; Campiglia, P.; Tenore, G.C.; Stornaiuolo, M.; Novellino, E. A Boost in Mitochondrial Activity Underpins the Cholesterol-Lowering Effect of Annurca Apple Polyphenols on Hepatic Cells. Nutrients 2019, 11, 163.
Tenore, G.C.; Caruso, D.; Buonomo, G.; D’Avino, M.; Campiglia, P.; Marinelli, L.; Novellino, E. A Healthy Balance of Plasma Cholesterol by a Novel Annurca Apple-Based Nutraceutical Formulation: Results of a Randomized Trial. J. Med. Food 2017, 20, 288–300.
Ding, Z.; Hani, A.; Li, W.; Gao, L.; Ke, W.; Guo, X. Influence of a cholesterol-lowering strain Lactobacillus plantarum LP3 isolated from traditional fermented yak milk on gut bacterial microbiota and metabolome of rats fed with a high-fat diet. Food Funct. 2020, 11, 8342–8353.
Hughes, D.A. Plant polyphenols: Modifiers of immune function and risk of cardiovascular disease. Nutrition 2005, 21, 422–423.
Zhou, M.; Wang, S.; Zhao, A.; Wang, K.; Fan, Z.; Yang, H.; Liao, W.; Bao, S.; Zhao, L.; Zhang, Y.; et al. Transcriptomic and metabonomic profiling reveal synergistic effects of quercetin and resveratrol supplementation in high fat diet fed mice. J. Proteome Res. 2012, 11, 4961–4971.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Cardiac & Cardiovascular Systems

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Erica Gianazza

Maura Brioschi

Ada Iezzi

Giuseppe Paglia

Cristina Banfi

View Times: 241

Update Date: 22 Feb 2023

Table of Contents

Video Upload Options

Confirm