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Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease.

ABSTRACT: The genetic architecture underlying the heritability of cardiovascular disease is incompletely understood. Metabolomics is an emerging technology platform that has shown early success in identifying biomarkers and mechanisms of common chronic diseases. Integration of metabolomics, genetics, and other omics platforms in a systems biology approach holds potential for elucidating novel genetic markers and mechanisms for cardiovascular disease. We review important studies that have used metabolomic profiling in cardiometabolic diseases, approaches for integrating metabolomics with genetics and other molecular profiling platforms, and key studies showing the potential for such studies in deciphering cardiovascular disease genetics, biomarkers, and mechanisms.

SUBMITTER: Shah SH

PROVIDER: S-EPMC4408557 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease.

Shah Svati H SH Newgard Christopher B CB

Circulation. Cardiovascular genetics 20150401 2

The genetic architecture underlying the heritability of cardiovascular disease is incompletely understood. Metabolomics is an emerging technology platform that has shown early success in identifying biomarkers and mechanisms of common chronic diseases. Integration of metabolomics, genetics, and other omics platforms in a systems biology approach holds potential for elucidating novel genetic markers and mechanisms for cardiovascular disease. We review important studies that have used metabolomic ...[more]

PMID: 25901039

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Project description:BackgroundThe diagnosis of asthma in children is challenging and relies on a combination of clinical factors and biomarkers including methacholine challenge, lung function, bronchodilator responsiveness, and presence of airway inflammation. No single test is diagnostic. We sought to identify a pattern of inflammatory biomarkers that was unique to asthma using a targeted metabolomics approach combined with data science methods.MethodsWe conducted a nested case-control study of 100 children living in a peri-urban community in Lima, Peru. We defined cases as children with current asthma, and controls as children with no prior history of asthma and normal lung function. We further categorized enrollment following a factorial design to enroll equal numbers of children as either overweight or not. We obtained a fasting venous blood sample to characterize a comprehensive panel of targeted markers using a metabolomics approach based on high performance liquid chromatography-mass spectrometry.ResultsA statistical comparison of targeted metabolites between children with asthma (n = 50) and healthy controls (n = 49) revealed distinct patterns in relative concentrations of several metabolites: children with asthma had approximately 40-50% lower relative concentrations of ascorbic acid, 2-isopropylmalic acid, shikimate-3-phosphate, and 6-phospho-d-gluconate when compared to children without asthma, and 70% lower relative concentrations of reduced glutathione (all p < 0.001 after Bonferroni correction). Moreover, a combination of 2-isopropylmalic acid and betaine strongly discriminated between children with asthma (2-isopropylmalic acid ≤ 13 077 normalized counts/second) and controls (2-isopropylmalic acid > 13 077 normalized counts/second and betaine ≤ 16 47 121 normalized counts/second).ConclusionsBy using a metabolomics approach applied to serum, we were able to discriminate between children with and without asthma by revealing different metabolic patterns. These results suggest that serum metabolomics may represent a diagnostic tool for asthma and may be helpful for distinguishing asthma phenotypes.

Dataset Information

Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease.

Publications

Integrated metabolomics and genomics: systems approaches to biomarkers and mechanisms of cardiovascular disease.

Similar Datasets

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