Plasma metabolomics reveals biomarkers of the atherosclerosis

Chen, Xi; Liu, Lian; Palacios, Gustavo; Gao, Jie; Zhang, Ning; Li, Guang; Lu, Haitao; Song, Ting; Zhang, Yingzhi; Lv, Haitao

doi:10.1002/jssc.201000395

Cited by 67 publications

(36 citation statements)

References 32 publications

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“…The most common LCFAs occurring in western diets are myristic acid (14:0), PA (16:0) and SA (18:0). Previous plasma metabolomic studies have confirmed PA as a strong contributing factor to the development of atherosclerosis [75]. Several recent in vivo and in vitro studies have revealed the mechanisms by which PA contributes the pathogenesis of CVDs.…”

Section: Ffas and Their Role In Diseasesmentioning

confidence: 99%

Role of free fatty acids in endothelial dysfunction

et al. 2017

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Plasma free fatty acids levels are increased in subjects with obesity and type 2 diabetes, playing detrimental roles in the pathogenesis of atherosclerosis and cardiovascular diseases. Increasing evidence showing that dysfunction of the vascular endothelium, the inner lining of the blood vessels, is the key player in the pathogenesis of atherosclerosis. In this review, we aimed to summarize the roles and the underlying mechanisms using the evidence collected from clinical and experimental studies about free fatty acid-mediated endothelial dysfunction. Because of the multifaceted roles of plasma free fatty acids in mediating endothelial dysfunction, elevated free fatty acid level is now considered as an important link in the onset of endothelial dysfunction due to metabolic syndromes such as diabetes and obesity. Free fatty acid-mediated endothelial dysfunction involves several mechanisms including impaired insulin signaling and nitric oxide production, oxidative stress, inflammation and the activation of the renin-angiotensin system and apoptosis in the endothelial cells. Therefore, targeting the signaling pathways involved in free fatty acid-induced endothelial dysfunction could serve as a preventive approach to protect against the occurrence of endothelial dysfunction and the subsequent complications such as atherosclerosis.

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Section: Ffas and Their Role In Diseasesmentioning

confidence: 99%

Role of free fatty acids in endothelial dysfunction

et al. 2017

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“…Many diseases—including prevalent human diseases such as diabetes [1], coronary artery disease [2], heart failure [3], and cancer [4]—are either caused by or result in metabolic dysregulation. Consequently, metabolite concentrations quantified from human samples report both constitutive diseases processes such as atherosclerosis [5] and acute disease events such as myocardial infarction [6] and cerebral infarction [7]. While metabolic phenotyping is well suited to inform clinical phenotype prediction, the success of this approach depends on the discriminative power of the statistical classification techniques employed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics

2017

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Statistical classification is a critical component of utilizing metabolomics data for examining the molecular determinants of phenotypes. Despite this, a comprehensive and rigorous evaluation of the accuracy of classification techniques for phenotype discrimination given metabolomics data has not been conducted. We conducted such an evaluation using both simulated and real metabolomics datasets, comparing Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA, Random Forests, Support Vector Machines (SVM), Artificial Neural Network, k-Nearest Neighbors (k-NN), and Naïve Bayes classification techniques for discrimination. We evaluated the techniques on simulated data generated to mimic global untargeted metabolomics data by incorporating realistic block-wise correlation and partial correlation structures for mimicking the correlations and metabolite clustering generated by biological processes. Over the simulation studies, covariance structures, means, and effect sizes were stochastically varied to provide consistent estimates of classifier performance over a wide range of possible scenarios. The effects of the presence of non-normal error distributions, the introduction of biological and technical outliers, unbalanced phenotype allocation, missing values due to abundances below a limit of detection, and the effect of prior-significance filtering (dimension reduction) were evaluated via simulation. In each simulation, classifier parameters, such as the number of hidden nodes in a Neural Network, were optimized by cross-validation to minimize the probability of detecting spurious results due to poorly tuned classifiers. Classifier performance was then evaluated using real metabolomics datasets of varying sample medium, sample size, and experimental design. We report that in the most realistic simulation studies that incorporated non-normal error distributions, unbalanced phenotype allocation, outliers, missing values, and dimension reduction, classifier performance (least to greatest error) was ranked as follows: SVM, Random Forest, Naïve Bayes, sPLS-DA, Neural Networks, PLS-DA and k-NN classifiers. When non-normal error distributions were introduced, the performance of PLS-DA and k-NN classifiers deteriorated further relative to the remaining techniques. Over the real datasets, a trend of better performance of SVM and Random Forest classifier performance was observed.

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“…Protein and metabolite biomarkers of coronary artery disease [51], heart failure [52,53], and atherosclerosis [54,55] have been identified that distinguish control and diseased individuals. In one recent study, plasma protein biomarkers were associated not only with future cardiovascular events, but also with future mortality from cardiovascular disease within an 18-year follow-up time [56].…”

Section: Proteomics and Metabolomics As Ageing Biomarkersmentioning

confidence: 99%

Proteomics and metabolomics in ageing research: from biomarkers to systems biology

Hoffman

Lyu

Pletcher

et al. 2017

Essays in Biochemistry

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Age is the single greatest risk factor for a wide range of diseases, and as the mean age of human populations grows steadily older, the impact of this risk factor grows as well. Laboratory studies on the basic biology of ageing have shed light on numerous genetic pathways that have strong effects on lifespan. However, we still do not know the degree to which the pathways that affect ageing in the lab also influence variation in rates of ageing and age-related disease in human populations. Similarly, despite considerable effort, we have yet to identify reliable and reproducible ‘biomarkers’, which are predictors of one’s biological as opposed to chronological age. One challenge lies in the enormous mechanistic distance between genotype and downstream ageing phenotypes. Here, we consider the power of studying ‘endophenotypes’ in the context of ageing. Endophenotypes are the various molecular domains that exist at intermediate levels of organization between the genotype and phenotype. We focus our attention specifically on proteins and metabolites. Proteomic and metabolomic profiling has the potential to help identify the underlying causal mechanisms that link genotype to phenotype. We present a brief review of proteomics and metabolomics in ageing research with a focus on the potential of a systems biology and network-centric perspective in geroscience. While network analyses to study ageing utilizing proteomics and metabolomics are in their infancy, they may be the powerful model needed to discover underlying biological processes that influence natural variation in ageing, age-related disease, and longevity.

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Plasma metabolomics reveals biomarkers of the atherosclerosis

Cited by 67 publications

References 32 publications

Role of free fatty acids in endothelial dysfunction

Role of free fatty acids in endothelial dysfunction

Evaluation of Classifier Performance for Multiclass Phenotype Discrimination in Untargeted Metabolomics

Proteomics and metabolomics in ageing research: from biomarkers to systems biology

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