Prevention is a valued goal in both public health and clinical medicine, but the methods differ. In clinical medicine, the focus is on the individual patient and “personalized” assessment and intervention are highly desirable. Thus, in prevention of coronary heart disease (CHD), while the public health approach recommends dietary and other lifestyle measures that apply across a wide swath of the population, the clinical approach looks to identify the high-risk individuals within the population for targeted interventions. Therefore, clinicians dealing with individual patients wish to have predictive models and treatment algorithms that will enable personalized approaches. Ideally, each patient is assessed for a distinctive and specific risk of disease development or complications of an existing disease. As genomic and molecular research has advanced, the expectation of improved prediction and more specific personalization has become even more highly expected than in previous years.1
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