Millions of Americans (age t65-years) take statins for the treatment of atherosclerotic cardiovascular disease (CVD) associated with obesity and dyslipidemia. As inhibitors of hydroxymethylglutaryl-Co enzyme reductase (HMGCR), statins elicit pleiotropic actions in the liver and skeletal muscle. Mainly due to statin associated muscle symptoms (SAMS), most commonly manifested as statin associated myalgia muscle fatigue, many patients discontinue taking these drugs leaving them at increased risk of cardiovascular disease. Although some genetic and environmental factors predisposing to development of SAMS have been identified our understanding of the pathogenesis of this common disorder remains incomplete. In particular, the lack of specific and sensitive biomarker(s) hinders the identification of patients that may be predisposed to SAMS. As a result of these gaps in our knowledge our approach to treatment of patients with SAMS is largely a `trial and error' enterprise. We posit that comparative metabolomics of SAMS patients and their statin-tolerant counterparts has the potential to reveal biomarker(s) that may identify patients with a predisposition to develop SAMS. Discovery of such a specific and sensitive biomarker(s) will not only lead to more rational statin therapy but also shed light on the biochemical mechanisms underlying development of SAMS in susceptible patients. Surprisingly this powerful tool has been used to illuminate many clinical scenarios including statin efficacy, but has not yet to our knowledge been applied to the study of SAMS. The Overall Aim of this study is to delineate the metabolome of patients with statin associated myalgia in order to: (1) elucidate putative biomarker(s) that will be useful in prospective identification of patients at risk for SAMS prior to initiating statin therapy and (2) translate the knowledge of the metabolic pathway(s) revealed by such biomarker(s) into greater understanding of the pathogenesis of SAMS. To accomplish these aims we will carry out an unbiased metabolomics analysis in the sera of a cohort of 250 patients, consisting of 125 well characterized patients who discontinue statins due to SAMS and 125 age-, race and gender-matched controls. Blood samples from controls and cases will be analyzed using a liquid chromatography-mass spectrometry (LC-MS) platform, dedicated to unbiased metabolomics. A long-term goal our study is to advance the course of personalized medicine as applied to the prevention and treatment of dyslipidemia and CVD leading to improved therapeutic use of statins. Our ?high risk high reward? proposal is designed to discover biomarker(s) that may prospectively identify such patients at risk for SAMS before beginning statin therapy. Knowledge of such a biomarker(s) and its metabolic origins will help design rational mitigating therapies for SAMS. Strengths of our proposal are: (i) a large highly selected patient population referred to our specialty clinic for evaluation of SAMS (ii) the use of stringent criteria to diagnose SAMS and (iii) a multi-faceted investigation of SAMS using clinical lipidology, metabolomics and genomics.
Although millions of older Americans (age ?65-years) take statins for the treatment of atherosclerotic cardiovascular disease, many patients discontinue statin therapy due to statin associated muscle symptoms (SAMS). Since there are no specific and sensitive biomarker(s) to identify patients that may be predisposed to SAMS, current statin therapies are largely a ?trial and error? enterprise. We will compare the metabolomes of SAMS patients and their statin-tolerant counterparts to discover biomarker(s) that may be used to prospectively identify patients at risk of SAMS and tailor their treatment based on that knowledge.