Acute Vascular Events: Cellular and Molecular Mechanisms
| Autor: | Varun HK Rao, Rasika T Shankar, Gundu H. R. Rao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | International Journal of Biomedicine, Vol 13, Iss 3, Pp 9-16 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2158-0510 2158-0529 |
| DOI: | 10.21103/Article13(3)_RA1 |
| Popis: | Cardiovascular diseases (CVDs) are the leading cause of death worldwide. An estimated 17.9 million individuals died from CVDs in 2019, representing 32% of all global deaths. Of these deaths, 85% were due to heart attack and stroke. Cardiometabolic risks, such as hypertension, excess weight, obesity, type 2 diabetes, and vascular diseases, contribute significantly to the progression of coronary artery disease. Known sequelae of events that lead to these cardiometabolic diseases include oxidative stress, inflammation, development of dysfunction of vascular adipose tissue, altered blood pressure and blood lipids, altered glucose metabolism, hardening of the arteries, endothelial dysfunction, development of atherosclerotic plaques, and activation of platelet and coagulation pathways. The Framingham Heart Study Group has developed a Risk Score that estimates the risk of developing heart disease in a 10-year period. This group of experts has developed mathematical functions for predicting clinical coronary disease events. These prediction capabilities are derived by assigning weights to major CVD risk factors such as sex, age, blood pressure, total cholesterol, low-density lipoprotein, high-density lipoprotein cholesterol, smoking behavior, and diabetes status. Currently, there is a growing interest in the use of artificial intelligence and machine learning applications. AI-based mimetic pattern-based algorithms seem to be better than the conventional Framingham Risk Score, in predicting clinical events related to CVDs. However, there are limitations to these applications as they do not have access to data on the specific factors that trigger acute vascular events, such as heart attack and stroke. This overview briefly discusses some salient cellular and molecular mechanisms involved in precipitating thrombotic conditions. Further improvements in emerging technologies will provide greater opportunities for patient selection and treatment options. Several clinical studies have demonstrated that most CVDs can be prevented by addressing behavioral risk factors such as tobacco use, unhealthy diet and obesity, physical activity, and harmful use of alcohol. Early detection and better management of the modifiable risks seem to be the only way to reduce, reverse, or prevent these diseases. |
| Databáze: | Directory of Open Access Journals |
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