Development of a precision medicine tool to predict dysglycemic effects of statin treatment
Statins are among the most widely prescribed and effective medications for heart disease (including heart attack and stroke), but we may not be prescribing these therapies as effectively as we could. For example, statin use has been found to increase the risk of developing first-time diabetes in some patients, but clinicians do not fully understand who is most at risk and why. More optimal prescribing of statins requires better tools to predict who will develop diabetes and improved knowledge on how to modify statin dose or type in a manner that reduces the potential for diabetes in these high-risk patients. The overarching goal of this proposal is to develop improved strategies that better inform clinicians on how to prescribe statin therapy that minimizes the risk of diabetes based on the unique characteristics of a given patient.
To do this, we will follow the electronic health records for hundreds of thousands of patients to track their individual characteristics, what kind of statin therapy they are taking, and whether they may have developed diabetes from treatment (Aim 1). Results of this aim will identify subsets of the population that are more prone to statin-induced diabetes and statin regimens that are more likely to increase diabetes risk.
In parallel, we will be exposing patient-derived cell lines to statin therapy and determining how that exposure affects the cellular machinery as well as observing if these changes would be anticipated to promote diabetes in a living patient (Aim 2). Results of this aim will validate any genetic determinants of statin-induced diabetes identified in Aim 1 and provide fundamental mechanistic insight for this statin side effect.
With expertise in clinical pharmacology (Akinyemi Oni-Orisan) and cellular biology (Marisa Medina) and statin pharmacogenomics (both PIs), the proposed research team is uniquely suited to complete the outlined project.