Harvard Catalyst Profiles

Contact, publication, and social network information about Harvard faculty and fellows.

Login and Edit functionality is currently unavailable.

Genetic Risk Stratification to Identify Individuals for Early Statin Therapy


Biography

Overview
This application addresses broad Challenge Area (04) Clinical Research and specific Challenge Topic, 04-HL- 113: Cost-Effective Trials of CVD Prevention in Persons with Low Short-Term Risk. Lipid-lowering therapy with HMG-CoA reductase inhibitors (statins) for primary prevention is currently only indicated in older individuals with multiple risk factors or profoundly elevated low-density lipoprotein cholesterol. Yet intravascular ultrasound studies have revealed that the majority of healthy individuals in their 30's have early signs of coronary atherosclerosis. This sobering fact raises the possibility that we are delaying too long before initiating lipid-lowering therapy for so-called primary prevention. However, conducting a clinical trial of statin therapy in relatively young, ostensibly healthy individuals would be a daunting task as the necessary trial sample size would be prohibitively large, the absolute risk reduction modest, and thus the net balance between efficacy and safety questionable. Recently, though, we and others have validated a number of common genetic variants (9p21 and others) as being reproducibly associated with the risk for coronary heart disease (CHD), dyslipidemia, and the development of type 2 diabetes, and that these variants can be used to predict risk independent of traditional risk factors. These data suggest that genetic variants can risk stratify individuals beyond what clinical factors can do, and, by extension, suggest that genotyping may allow for identification of individuals in whom therapy may have a marked influence on the development of complications of atherosclerotic heart disease. Moreover, across multiple studies, carriers of specific genetic variants (e.g., APOE 4, KIF6 719Arg, and shorter telomere length) enjoy 37-67 percent relative risk reductions in major cardiovascular events with statin therapy, whereas non-carriers have only 6-34 percent risk relative risk reductions. Similarly, genetic variants have also been identified that are associated with the safety profile of statin therapy (e.g., SLCO1B1 and myopathy). We therefore hypothesize that genetic risk stratification can be used to identify individuals who enjoy greater clinical benefit from statin therapy and individuals who will be at lower risk for adverse reactions. Validation of this personalized medicine approach would pave the way for launching a tailored trial of the benefit of statin therapy in young to middle-aged individuals who currently would not qualify for statin therapy. Thus, the overall goal of this application is to test the ability of a panel of genetic variants to predict benefits and risks with statin therapy. In Aim 1 we will test whether specific panels of genetic variants identify patients who experience a greater clinical benefit with statin therapy using data from over 40,000 subjects enrolled in 5 clinical trials of statin therapy. In Aim 2 we will test whether specific panels of genetic variants identify patients who experience a higher risk of statin-induced adverse effects in the same cohorts. Treatment with a statin is typically not prescribed until an individual has had overt heart disease or is older and has multiple traditional risk factors;however, evidence of coronary artery disease can be found in a majority of individuals in their 30's. We believe that by examining multiple genes, we can identify individuals who will enjoy a greater benefit from statin therapy and individuals who will be at lower risk for adverse reactions. Validation of such information would pave the way for launching an economical, feasible, and safe trial of the clinical benefit of statin therapy in young to middle-aged individuals who currently do not qualify for statin therapy.

RC1HL099634
SABATINE, MARC S

Time
2009-09-30
2012-08-31
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.