Year: 2018
Type: article
Abstract: points• Use of risk factors for decision-making in cardiovascular disease has a long history in medicine.• Early attempts to augment traditional risk factors with genetic risk scores were hampered by too little understanding of the genetic basis of complex cardiovascular disease.• Newer studies based on hundreds of thousands of people and millions of genetic variants indicate that genetic risk scores can now outperform traditional risk factors in risk prediction.• We propose the time has come to incorporate genetic risk scores into clinical practice.• Studies should focus on the most appropriate way to do this to maximize benefit for our patients."[However,] epidemiologic information has accumulated which now allows the physician to recognize certain characteristics of increased risk in patients he [sic] sees in his practice.Some of these characteristics have been convincingly demonstrated, others are still under investigation.More precise identification will undoubtedly be possible in the future."-WilliamKannel, Director, Framingham Heart Study [1] In a classic paper [1], Kannel reported the early results of the longitudinal Framingham Heart Study, demonstrating the identification of factors (for which he coined the term "risk factors") that "precede the development of overt coronary heart disease in humans".Later, the Framingham Risk Score was formalized to include age, sex, diabetes, smoking status, total cholesterol, high-density lipoprotein (HDL) cholesterol, and blood pressure [2], providing a framework for cardiovascular disease (CVD) risk assessment to which all others are compared.Intuitively, the burden of these risk factors accumulates over time (e.g., pack years of smoking or years of hypertension), and some newer risk models allow input of risk factor data from multiple time points [3].While repeated measurement of CVD biomarkers such as total cholesterol may improve risk prediction, lifelong exposure to CVD risk factors is better captured by genetic susceptibility [4].Thus, the quest to improve risk prediction for CVD has naturally come to focus on the development of genetic risk scores [5].This has only been possible because of robust, replicable
Source: PLoS Medicine
Authors: Joshua W. Knowles, Euan A. Ashley
Institutions: Stanford Medicine, Stanford University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,460
Awards: Stanford MoTrPAC Bioinformatics Center, Molecular Mechanisms of Insulin Resistance Associated Loci, G5520316217, Clinically Relevant Genome Variation Database, Genome Editing of Human iPSCs to Study Inherited Hypertrophic Cardiomyopathy, Stanford Center for Undiagnosed Diseases, Human Induced Pluripotent Stem Cells for Cardiovascular Disease Modeling, Elucidating Mechanism of Cardiac Fibrosis with Cell Village of Pooled Human iPSCs, Training in Myocardial Biology at Stanford
OpenAlex ID: W2794819865