In Western world, cardiovascular disease (CVD) is among the leading cause of premature death and a major cause of disability. Over the years, the knowledge of CVD risk factors clustering and their multiplicative interactions to promote CVD risk has led to the development of multivariable risk prediction algorithms to use in primary care settings. The guidelines for CVD prevention recommend that an individual’s risk of CVD is estimated by combining different risk factors into a numeric estimate of risk. Most of these risk prediction algorithms include well-known CVD risk factors such as: age, sex, hypertension, cholesterol, smoking, family history of CVD and diabetes mellitus. A variety of risk prediction algorithms are available, as charts, tables, computer programmes, and web-based tools.
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However, the general use of risk prediction models had lagged in primary care. One of the main reasons of physician inertia in using risk prediction models is that current risk scores explain a modest proportion of CVD incidence in the community. In fact, a common misconception is that only 50% of the incidence of CVD is explained by the traditional risk factors, or combination of these into an algorithm, and therefore, novel markers of preclinical disease are needed to refine contemporary risk prediction algorithms. It is estimated that nearly 15% to 20% of myocardial infarction patients have none of the traditional risk factors and would be classified as “low risk” by current risk prediction scores. Nonetheless, the value of incorporation of potentially novel risk factors, including high-density lipoprotein cholesterol, C-reactive protein, and multiple biomarkers has generally resulted in only minor improvements discrimination of risk formulas.
“Given the fact that CVD is preventable, and the utility of current risk prediction algorithms cannot be ignored, efforts to improve risk prediction are needed by searching novel biomarkers that can enhance the currently available risk scores.”
Major advances in genetics, including the sequencing of the human genome in 2001 and the publication of the HapMap in 2005 have paved the way for the revolution in our understanding of the genetics of complex diseases, including CVD. Of special note, in the past 5 years, the advent of high-throughput genotyping platforms and substantial improvements in the available sample size and quality control measures in genetic epidemiology studies have led to the discovery of novel genetic associations with myocardial infarction and cardiovascular risk factors such as lipids, blood pressure, diabetes, aerobic fitness and obesity. Information based on genetic components has already made important clinical inroads in areas such as pharmacogenomics for predicting efficacy, and adverse events of common cardiovascular drugs. More recently, there has been much interest in adding genetic variants into risk prediction models associated with CVD. This is a potentially attractive concept given that measurement or identification of genetic variants is now accurate and relatively cheap.
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The use of genetic testing to predict future disease is not without controversy. Notably, last year FDA in USA banned the California based company for marketing its service in the US, on the basis that it had failed to provide adequate information to support the claims it made about the use of their genetic testing results. However, this same company has been approved by UK’s authorities to sell and promote their genetic testing products across United Kingdom.
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Javaid Nauman, researcher at CERG