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International Journal of Applied Research
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ISSN Print: 2394-7500, ISSN Online: 2394-5869, CODEN: IJARPF

IMPACT FACTOR (RJIF): 8.4

Vol. 4, Issue 12, Part D (2018)

Based on bioinformatics, a metabolomic analyses for atherosclerosis, diabetes, and obesity

Based on bioinformatics, a metabolomic analyses for atherosclerosis, diabetes, and obesity

Author(s)
Dr. Amitabha Kar, Dr. Partha Majumder and Debraj Nandi
Abstract
Insulin resistance associated with type 2 diabetes mellitus (T2DM), obesity, and atherosclerosis is a global health problem. A portfolio of abnormalities of metabolic and vascular homeostasis accompanies T2DM and obesity, which are believed to conspire to lead to accelerated atherosclerosis and premature death. The complexity of metabolic changes in the diseases presents challenges for a full understanding of the molecular pathways contributing to the development of these diseases. The recent advent of new technologies in this area termed “Metabolomics” may aid in comprehensive metabolic analysis of these diseases. Therefore, metabolomics has been extensively applied to the metabolites of T2DM, obesity, and atherosclerosis not only for the assessment of disease development and prognosis, but also for the biomarker discovery of disease diagnosis. Herein, we summarize the recent applications of metabolomics technology and the generated datasets in the metabolic profiling of these diseases, in particular, the applications of these technologies to these diseases at the cellular, animal models, and human disease levels. In addition, we also extensively discuss the mechanisms linking the metabolic profiling in insulin resistance, T2DM, obesity, and atherosclerosis, with a particular emphasis on potential roles of increased production of reactive oxygen species (ROS) and mitochondria dysfunctions.
Pages: 275-280  |  750 Views  62 Downloads
How to cite this article:
Dr. Amitabha Kar, Dr. Partha Majumder, Debraj Nandi. Based on bioinformatics, a metabolomic analyses for atherosclerosis, diabetes, and obesity. Int J Appl Res 2018;4(12):275-280.
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