Using untargeted metabolomics to find biomarkers and therapeutic targets for CVD

Cardiovascular diseases (CVDs) are proven to be the leading cause of deaths throughout the world. If statistics are reviewed, almost four out of five deaths are due to myocardial infarction or stroke. Efforts to prevent CVD have little effect on the decrease of the number of CVD related deaths despite many medical advances. Therefore, the search for new and even better therapies and treatments for the betterment of those who are suffering from CVD is still in progress. The field of metabolomics has offered a good solution for these diseases. Metabolomic biomarkers help clinicians to identify the risk of CVD and take preventive measures before the diseases can surface. Early diagnosis of CVD is a good sign for a patient's recovery and also for their health. Therefore, there is a need to establish reliable, sensitive and non-invasive biomarkers which can serve as therapeutic targets for prevention and treatment of CVD.

In this study, analytical techniques are discussed along with the workflow that is used in untargeted metabolomics. Case studies that highlight the use of untargeted metabolomics in CVD research are also identified. Five of the case studies show approaches to identify untargeted metabolomics and apply this information in clinical situations. Analysis was conducted for the prediction of cardiovascular disease risk, myocardial ischemia, transient ischemic attack, incident coronary heart disease, and myocardial infarction risk. The use of the untargeted metabolomics for risk assessment is still relatively new and there is still a need for future advancements in metabolomics technologies.

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