Metabonomic characterization of early atherosclerosis in hamsters with induced cholesterol

Large-scale, comprehensive plasma metabolomic analyses reveal potential biomarkers for the diagnosis of early-stage coronary atherosclerosis

BACKGROUND

Coronary atherosclerosis (CAS) is a prevalent and chronic life-threatening disease. However, the detection of CAS at an early stage is difficult because of the lack of effective noninvasive diagnostic methods. The present study aimed to characterize the plasma metabolome of early-stage CAS patients to discover metabolomic biomarkers, develop a novel metabolite-based model for accurate noninvasive diagnosis of early-stage CAS, and explore the underlying metabolic mechanisms involved.

METHODS

A total of 100 patients with early-stage CAS and 120 age- and sex-matched control subjects were recruited from the Chinese Han population and further randomly divided into training (n = 120) and test sets (n = 100). The metabolomic profiles of the plasma samples were analyzed by an integrated untargeted liquid chromatography-mass spectrometry approach, including two separation modes and two ionization modes. Univariate and multivariate statistical analyses were employed to identify potential biomarkers and construct an early-stage CAS diagnostic model.

RESULTS

The integrated analytical method established herein improved metabolite coverage compared with single chromatographic separation and MS ionization mode. A total of 80 metabolites were identified as potential biomarkers of early-stage CAS, and these metabolites were mainly involved in glycerophospholipid, fatty acid, sphingolipid, and amino acid metabolism. An effective diagnostic model for early-stage CAS was established, incorporating 11 metabolites and achieving areas under the receiver operating characteristic curve (AUCs) of 0.984 and 0.908 in the training and test sets, respectively.

CONCLUSIONS

Our study not only successfully developed an effective noninvasive diagnostic model for identifying early-stage CAS but also provided novel insights into the pathogenesis of CAS.

Preliminary study of metabonomic changes during the progression of atherosclerosis in miniature pigs

BACKGROUND

To explore potential biomarkers for early diagnosis of atherosclerosis (AS) and provide basic data for further research on AS, the characteristics of serum metabolomics during the progression of AS in mini-pigs were observed dynamically.

METHODS

An AS model in Bama miniature pigs was established by a high-cholesterol and high-fat diet. Fasting serum samples were collected monthly for metabolomics and serum lipid detection. At the end of the treatment period, pathological analysis of the abdominal aorta and coronary artery was performed to evaluate the lesions of AS, thereby distinguishing the susceptibility of mini-pigs to AS. The metabolomics was detected using a high-resolution untargeted metabolomic approach. Statistical analysis was used to identify metabolites associated with AS susceptibility.

RESULTS

Based on pathological analysis, mini-pigs were divided into two groups: a susceptible group (n = 3) and a non-susceptible group (n = 6). A total of 1318 metabolites were identified, with significant shifting of metabolic profiles over time in both groups. Dynamic monitoring analysis highlighted 57 metabolites that exhibited an obvious trend of differential changes between two groups with the advance of time. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis indicated significant disorders in cholesterol metabolism, primary bile acid metabolism, histidine metabolism, as well as taurine and hypotaurine metabolism.

CONCLUSIONS

During the progression of AS in mini-pigs induced by high-cholesterol/high-fat diet, the alterations in serum metabolic profile exhibited a time-dependent pattern, accompanied by notable disturbances in lipid metabolism, cholesterol metabolism, and amino acid metabolism. These metabolites may become potential biomarkers for early diagnosis of AS.

Metabolomic fingerprint of coronary blood in STEMI patients depends on the ischemic time and inflammatory state

In this study we investigated whether the metabolomic analysis could identify a specific fingerprint of coronary blood collected during primary PCI in STEMI patients. Fifteen samples was subjected to metabolomic analysis. Subsequently, the study population was divided into two groups according to the peripheral blood neutrophil-to-lymphocyte ratio (NLR), a marker of the systemic inflammatory response. Regression analysis was then applied separately to the two NLR groups. A partial least square (PLS) regression identified the most significant involved metabolites and the PLS-class analysis revealed a significant correlation between the metabolic profile and the total ischemic time only in patients with an NLR > 5.77.

Large-scale Metabolomic Analysis Reveals Potential Biomarkers for Early Stage Coronary Atherosclerosis

Coronary atherosclerosis (CAS) is the pathogenesis of coronary heart disease, which is a prevalent and chronic life-threatening disease. Initially, this disease is not always detected until a patient presents with seriously vascular occlusion. Therefore, new biomarkers for appropriate and timely diagnosis of early CAS is needed for screening to initiate therapy on time. In this study, we used an untargeted metabolomics approach to identify potential biomarkers that could enable highly sensitive and specific CAS detection. Score plots from partial least-squares discriminant analysis clearly separated early-stage CAS patients from controls. Meanwhile, the levels of 24 metabolites increased greatly and those of 18 metabolites decreased markedly in early CAS patients compared with the controls, which suggested significant metabolic dysfunction in phospholipid, sphingolipid, and fatty acid metabolism in the patients. Furthermore, binary logistic regression showed that nine metabolites could be used as a combinatorial biomarker to distinguish early-stage CAS patients from controls. The panel of nine metabolites was then tested with an independent cohort of samples, which also yielded satisfactory diagnostic accuracy (AUC = 0.890). In conclusion, our findings provide insight into the pathological mechanism of early-stage CAS and also supply a combinatorial biomarker to aid clinical diagnosis of early-stage CAS.

Metabolomics study of the therapeutic mechanism of Schisandra Chinensis lignans in diet-induced hyperlipidemia mice

Background

Schisandra, a globally distributed plant, has been widely applied for the treatment of diseases such as hyperlipidemia, fatty liver and obesity in China. In the present work, a rapid resolution liquid chromatography coupled with quadruple-time-of-flight mass spectrometry (RRLC-Q-TOF-MS)-based metabolomics was conducted to investigate the intervention effect of Schisandra chinensis lignans (SCL) on hyperlipidemia mice induced by high-fat diet (HFD).

Methods

Hyperlipidemia mice were orally administered with SCL (100 mg/kg) once a day for 4 weeks. Serum biochemistry assay of triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c) and high-density lipoprotein cholesterol (HDL-c) was conducted to confirm the treatment of SCL on lipid regulation. Metabolomics analysis on serum samples was carried out, and principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were carried out for the pattern recognition and characteristic metabolites identification. The relative levels of critical regulatory factors of liver lipid metabolism, sterol regulatory element-binding proteins (SREBPs) and its related gene expressions were measured by quantitative real-time polymerase chain reaction (RT-PCR) for investigating the underlying mechanism.

Results

Oral administration of SCL significantly decreased the serum levels of TC, TG and LDL-c and increased the serum level of HDL-c in the hyperlipidemia mice, and no effect of SCL on blood lipid levels was observed in control mice. Serum samples were scattered in the PCA scores plots in response to the control, HFD and SCL group. Totally, thirteen biomarkers were identified and nine of them were recovered to the normal levels after SCL treatment. Based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, the anti-hyperlipidemia mechanisms of SCL may be involved in the following metabolic pathways: tricarboxylic acid (TCA) cycle, synthesis of ketone body and cholesterol, choline metabolism and fatty acid metabolism. Meanwhile, SCL significantly inhibited the mRNA expression level of hepatic lipogenesis genes such as SREBP-1c, fatty acid synthase (FAS) and acetyl-CoA carboxylase (ACC), and decreased the mRNA expression of liver X receptor α (LXRα). Moreover, SCL also significantly decreased the expression level of SREBP-2 and 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) in the liver of hyperlipidemia mice.

Conclusion

Anti-hyperlipidemia effect of SCL was confirmed by both serum biochemistry and metabolomics analysis. The mechanism may be related to the down-regulation of LXRα/SREBP-1c/FAS/ACC and SREBP2/HMGCR signaling pathways.

Multilevel systems biology modeling characterized the atheroprotective efficiencies of modified dairy fats in a hamster model

We assessed the atheroprotective efficiency of modified dairy fats in hyperlipidemic hamsters. A systems biology approach was implemented to reveal and quantify the dietary fat-related components of the disease. Three modified dairy fats (40% energy) were prepared from regular butter by mixing with a plant oil mixture, by removing cholesterol alone, or by removing cholesterol in combination with reducing saturated fatty acids. A plant oil mixture and a regular butter were used as control diets. The atherosclerosis severity (aortic cholesteryl-ester level) was higher in the regular butter-fed hamsters than in the other four groups (P < 0.05). Eighty-seven of the 1,666 variables measured from multiplatform analysis were found to be strongly associated with the disease. When aggregated into 10 biological clusters combined into a multivariate predictive equation, these 87 variables explained 81% of the disease variability. The biological cluster "regulation of lipid transport and metabolism" appeared central to atherogenic development relative to diets. The "vitamin E metabolism" cluster was the main driver of atheroprotection with the best performing transformed dairy fat. Under conditions that promote atherosclerosis, the impact of dairy fats on atherogenesis could be greatly ameliorated by technological modifications. Our modeling approach allowed for identifying and quantifying the contribution of complex factors to atherogenic development in each dietary setup.

Metabolomics in dyslipidemia

Hyperlipidemia is an important public health problem with increased incidence and prevalence worldwide. Current clinical biomarkers, triglyceride, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol lack the necessary specificity and sensitivity and only increase significantly after serious dyslipidemia. Therefore, sensitive biomarkers are needed for hyperlipidemia. Hyperlipidemia-specific biomarkers would improve clinical diagnosis and therapeutic treatment at early disease stages. The aim of metabolomics is to identify untargeted and global small-molecule metabolite profiles from cells, biofluids, and tissues. This method offers the potential for a holistic approach to improve disease diagnoses and our understanding of underlying pathologic mechanisms. This review summarizes analytical techniques, data collection and analysis for metabolomics, and metabolomics in hyperlipidemia animal models and clinical studies. Mechanisms of hypolipemia and antilipemic drug therapy are also discussed. Metabolomics provides a new opportunity to gain insight into metabolic profiling and pathophysiologic mechanisms of hyperlipidemia.

UPLC-Q-TOF/MS based metabolomic profiling of serum and urine of hyperlipidemic rats induced by high fat diet

Hyperlipidemia is considered to be a high lipid level in blood, can induce metabolic disorders and dysfunctions of the body, and results in some severe complications. Therefore, hunting for some metabolite markers and clarifying the metabolic pathways in vivo will be an important strategy in the treatment and prevention of hyperlipidemia. In this study, a rat model of hyperlipidemia was constructed according to histopathological data and biochemical parameters, and the metabolites of serum and urine were analyzed by UPLC-Q-TOF/MS. Combining pattern recognition and statistical analysis, 19 candidate biomarkers were screened and identified. These changed metabolites indicated that during the development and progression of hyperlipidemia, energy metabolism, lipid metabolism, amino acid metabolism and nucleotide metabolism were mainly disturbed, which are reported to be closely related to diabetes, cardiovascular diseases, etc. This study demonstrated that a UPLC-Q-TOF/MS based metabolomic approach is useful to profile the alternation of endogenous metabolites of hyperlipidemia.

Metabonomics study of the therapeutic mechanism of Gynostemma pentaphyllum and atorvastatin for hyperlipidemia in rats

Gynostemma pentaphyllum (GP) is widely used for the treatment of diseases such as hyperlipidemia, fatty liver and obesity in China, and atorvastatin is broadly used as an anti-hyperlipidemia drug. This research focuses on the plasma and liver metabolites in the following four groups of rats: control, a hyperlipidemia model, a hyperlipidemia model treated with GP and a hyperlipidemia model treated with atorvastatin. Using ¹H-NMR-based metabonomics, we elucidated the therapeutic mechanisms of GP and atorvastatin. Orthogonal Partial Least Squares-Discriminant analysis (OPLS-DA) plotting of the metabolic state and analysis of potential biomarkers in the plasma and liver correlated well with the results of biochemical assays. GP can effectively affect lipid metabolism, and it exerts its anti-hyperlipidemia effect by elevating the level of phosphatidylcholine and decreasing the level of trimethylamine N-oxide (TMAO). In contrast, atorvastatin affects hyperlipidemia mainly during lipid metabolism and protein metabolism in vivo.

A (1)H NMR-Based Metabonomic Investigation of Time-Related Metabolic Trajectories of the Plasma, Urine and Liver Extracts of Hyperlipidemic Hamsters

The hamster has been previously found to be a suitable model to study the changes associated with diet-induced hyperlipidemia in humans. Traditionally, studies of hyperlipidemia utilize serum- or plasma-based biochemical assays and histopathological evaluation. However, unbiased metabonomic technologies have the potential to identify novel biomarkers of disease. Thus, to obtain a better understanding of the progression of hyperlipidemia and discover potential biomarkers, we have used a proton nuclear magnetic resonance spectroscopy (¹H-NMR)-based metabonomics approach to study the metabolic changes occurring in the plasma, urine and liver extracts of hamsters fed a high-fat/high-cholesterol diet. Samples were collected at different time points during the progression of hyperlipidemia, and individual proton NMR spectra were visually and statistically assessed using two multivariate analyses (MVA): principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Using the commercial software package Chenomx NMR suite, 40 endogenous metabolites in the plasma, 80 in the urine and 60 in the water-soluble fraction of liver extracts were quantified. NMR analysis of all samples showed a time-dependent transition from a physiological to a pathophysiological state during the progression of hyperlipidemia. Analysis of the identified biomarkers of hyperlipidemia suggests that significant perturbations of lipid and amino acid metabolism, as well as inflammation, oxidative stress and changes in gut microbiota metabolites, occurred following cholesterol overloading. The results of this study substantially broaden the metabonomic coverage of hyperlipidemia, enhance our understanding of the mechanism of hyperlipidemia and demonstrate the effectiveness of the NMR-based metabonomics approach to study a complex disease.

Lipidomic and metabolomic analyses reveal potential plasma biomarkers of early atheromatous plaque formation in hamsters

AIMS

Atherosclerosis is the main pathological process contributing to cardiovascular disease, with diet being the most important factor involved. Although the lipidome of atheromatous plaque has been studied previously, the use of comparative lipidomics and metabolomics in plasma in early atherogenesis could lead to the discovery of plasma biomarkers that allow not only disease prediction but also measurement of disease progression.

METHODS AND RESULTS

High-throughput techniques, such as liquid chromatography/mass spectrometry, allowed us to compare the circulating and aortic lipidome and plasma metabolome in order to look for new molecular targets involved in atherogenesis. To achieve this objective, we chose the hamster (Mesocricetus auratus) as the best small animal model for diet-induced early atherosclerosis, because its lipoprotein metabolism is similar to that of humans. The results revealed the existence of several, previously unreported, changes in lipid and amino-acid metabolism, the peroxisome proliferator-activated receptor γ pathway, and oxidative and endoplasmic reticulum stress, also involving cell senescence. Furthermore, as a proof of concept in the modelling of dietary influences in atherogenesis, we have measured the effect of a potential anti-atherogenic polyphenol extract on the reported pathways. Our results support a previously unknown role for taurocholic acid as a potential plasma biomarker of early atheromatous plaque formation.

CONCLUSION

The use of comparative liquid chromatography/mass spectrometry-based lipidomics and metabolomics allows the discovery of novel pathways in atherogenesis, as well as new potential plasma biomarkers, which could allow us to predict disease in its early stages and measure its progression.

Analytical metabolomics: nutritional opportunities for personalized health

Nutrition is the cornerstone of health; survival depends on acquiring essential nutrients, and dietary components can both prevent and promote disease. Metabolomics, the study of all small molecule metabolic products in a system, has been shown to provide a detailed snapshot of the body's processes at any particular point in time, opening up the possibility of monitoring health and disease, prevention and treatment. Metabolomics has the potential to fundamentally change clinical chemistry and, by extension, the fields of nutrition, toxicology and medicine. Technological advances, combined with new knowledge of the human genome and gut microbiome, have made and will continue to make possible earlier, more accurate, less invasive diagnoses, all while enhancing our understanding of the root causes of disease and leading to a generation of dietary recommendations that enable optimal health. This article reviews the recent contributions of metabolomics to the fields of nutrition, toxicology and medicine. It is expected that these fields will eventually blend together through development of new technologies in metabolomics and genomics into a new area of clinical chemistry: personalized medicine.

Lipidomics: a tool for studies of atherosclerosis

Lipids, abundant constituents of both the vascular plaque and lipoproteins, play a pivotal role in atherosclerosis. Mass spectrometry-based analysis of lipids, called lipidomics, presents a number of opportunities not only for understanding the cellular processes in health and disease but also in enabling personalized medicine. Lipidomics in its most advanced form is able to quantify hundreds of different molecular lipid species with various structural and functional roles. Unraveling this complexity will improve our understanding of diseases such as atherosclerosis at a level of detail not attainable with classical analytical methods. Improved patient selection, biomarkers for gauging treatment efficacy and safety, and translational models will be facilitated by the lipidomic deliverables. Importantly, lipid-based biomarkers and targets should lead the way as we progress toward more specialized therapeutics.