Large study challenges the theory that light alcohol consumption benefits heart health
Any observed benefit likely results from other lifestyle factors common among light to moderate drinkers, say researchers.
Dr. Krishna Aragam is a cardiologist and cardiovascular geneticist at Massachusetts General Hospital (MGH). He is a core faculty member within the MGH Cardiovascular Genetics Program and the MGH Cardiovascular Disease Prevention Center. Dr. Aragam received his undergraduate degree from Harvard College and his medical doctorate and graduate training in clinical research at the University of Michigan. He completed his residency in Internal Medicine at the Hospital of the University of Pennsylvania, followed by a fellowship in Cardiovascular Medicine and subspecialty training in Clinical Cardiovascular Genetics at MGH. Additionally, he completed post-doctoral training in computational biology and population genetics at the Broad Institute of MIT and Harvard.
As co-director of the MGH Cardiomyopathy and Atherosclerosis Genetics Clinics, Dr. Aragam specializes in the care of patients and their relatives with a family history of cardiovascular disease and/or a confirmed genetic predisposition. His clinical focus includes the management of coronary artery disease, cardiomyopathy, and heart failure.
In addition to his clinical activities, Dr. Aragam leads an NIH-funded research program at MGH and the Broad Institute, investigating the population genetic determinants of cardiovascular diseases and the implementation of genomic data to improve cardiovascular prevention and management.
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Any observed benefit likely results from other lifestyle factors common among light to moderate drinkers, say researchers.
Un equipo descubrió recientemente que la aplicación de la PRS puede identificar a los pacientes de riesgo que actualmente no se identifican mediante las evaluaciones clínicas estándar.
A team led by investigators at Mass General and the Broad Institute of MIT and Harvard recently found that applying “polygenic risk scores” can identify at-risk patients who are not presently identified through standard clinical evaluations.