Dataset Information

Cardiovascular Metabolomics.

ABSTRACT: Disturbances in cardiac metabolism underlie most cardiovascular diseases. Metabolomics, one of the newer omics technologies, has emerged as a powerful tool for defining changes in both global and cardiac-specific metabolism that occur across a spectrum of cardiovascular disease states. Findings from metabolomics studies have contributed to better understanding of the metabolic changes that occur in heart failure and ischemic heart disease and have identified new cardiovascular disease biomarkers. As technologies advance, the metabolomics field continues to evolve rapidly. In this review, we will discuss the current state of metabolomics technologies, including consideration of various metabolomics platforms and elements of study design; the emerging utility of stable isotopes for metabolic flux studies; and the use of metabolomics to better understand specific cardiovascular diseases, with an emphasis on recent advances in the field.

SUBMITTER: McGarrah RW

PROVIDER: S-EPMC6029726 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Cardiovascular Metabolomics.

McGarrah Robert W RW Crown Scott B SB Zhang Guo-Fang GF Shah Svati H SH Newgard Christopher B CB

Circulation research 20180401 9

Disturbances in cardiac metabolism underlie most cardiovascular diseases. Metabolomics, one of the newer omics technologies, has emerged as a powerful tool for defining changes in both global and cardiac-specific metabolism that occur across a spectrum of cardiovascular disease states. Findings from metabolomics studies have contributed to better understanding of the metabolic changes that occur in heart failure and ischemic heart disease and have identified new cardiovascular disease biomarkers ...[more]

PMID: 29700070

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Project description:BackgroundWalnut consumption is associated with lower risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). However, it is unknown whether plasma metabolites related to walnut consumption are also associated with lower risk of cardiometabolic diseases.ObjectivesThe study aimed to identify plasma metabolites associated with walnut consumption and evaluate the prospective associations between the identified profile and risk of T2D and CVD.MethodsThe discovery population included 1833 participants at high cardiovascular risk from the PREvención con DIeta MEDiterránea (PREDIMED) study with available metabolomics data at baseline. The study population included 57% women (baseline mean BMI (in kg/m2): 29.9; mean age: 67 y). A total of 1522 participants also had available metabolomics data at year 1 and were used as the internal validation population. Plasma metabolomics analyses were performed using LC-MS. Cross-sectional associations between 385 known metabolites and walnut consumption were assessed using elastic net continuous regression analysis. A 10-cross-validation (CV) procedure was used, and Pearson correlation coefficients were assessed between metabolite weighted models and self-reported walnut consumption in each pair of training-validation data sets within the discovery population. We further estimated the prospective associations between the identified metabolite profile and incident T2D and CVD using multivariable Cox regression models.ResultsA total of 19 metabolites were significantly associated with walnut consumption, including lipids, purines, acylcarnitines, and amino acids. Ten-CV Pearson correlation coefficients between self-reported walnut consumption and the plasma metabolite profile were 0.16 (95% CI: 0.11, 0.20) in the discovery population and 0.15 (95% CI: 0.10, 0.20) in the validation population. The metabolite profile was inversely associated with T2D incidence (HR per 1 SD: 0.83; 95% CI: 0.71, 0.97; P = 0.02). For CVD incidence, the HR per 1-SD was 0.71 (95% CI: 0.60, 0.85; P < 0.001).ConclusionsA metabolite profile including 19 metabolites was associated with walnut consumption and with a lower risk of incident T2D and CVD in a Mediterranean population at high cardiovascular risk.

Project description:The interplay between pathogens and hosts has been studied for decades using targeted approaches such as the analysis of mutants and host immunological responses. Although much has been learned from such studies, they focus on individual pathways and fail to reveal the global effects of infection on the host. To alleviate this issue, high-throughput methods such as transcriptomics and proteomics have been used to study host-pathogen interactions. Recently, metabolomics was established as a new method to study changes in the biochemical composition of host tissues. We report a metabolomics study of Salmonella enterica serovar Typhimurium infection. We used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Direct Infusion to reveal that dozens of host metabolic pathways are affected by Salmonella in a murine infection model. In particular, multiple host hormone pathways are disrupted. Our results identify unappreciated effects of infection on host metabolism and shed light on mechanisms used by Salmonella to cause disease, and by the host to counter infection. Female C57BL/6 mice were infected with Salmonella enterica serovar Typhimurium SL1344 cells by oral gavage. Feces and livers were collected and metabolites extracted using acetonitrile. For experiments with feces, samples were collected from 4 mice before and after infection. For liver experiments, 11 uninfected and 11 infected mice were used. Samples were combined into 3 groups of 3-4 mice each, resulting in the analysis of 3 group samples of uninfected and 3 of infected mice. Extracts were infused into a 12-T Apex-Qe hybrid quadrupole-FT-ICR mass spectrometer equipped with an Apollo II electrospray ionization source, a quadrupole mass filter and a hexapole collision cell. Raw mass spectrometry data were processed as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140 [PMID 19081807]). To identify differences in metabolite composition between uninfected and infected samples, we filtered the list of masses for metabolites which were present on one set of samples but not the other. Additionally, we calculated the ratios between averaged intensities of metabolites from uninfected and infected mice. To assign possible metabolite identities, monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org). Masses were searched against the Mus musculus database within a mass error of 3 ppm. Data were analyzed by unpaired t tests with 95% confidence intervals.

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Dataset Information

Cardiovascular Metabolomics.

Publications

Cardiovascular Metabolomics.

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