Abstract
Background: Statins, lipid-lowering drugs, are the first-line pharmaceutical therapy against Atherosclerotic Cardiovascular Disease (ASCVD). The emphasis on providing statin treatment as a preventive measure before the first cardiac event burdens low-risk individuals with adverse side effects. Furthermore, inaccurate self-reporting of statin use by patients or their poor adherence to statins complicates the monitoring of long-term benefits and side effects of statins and ASCVD risk assessment. New sensitive, multiplexed and high throughput methods to confirm actual statin use by patients are urgently needed. Methods: We used 690 plasma samples from individuals with known number of diseased vessels, established lipid profiles, and medication records of statin use. The samples were obtained from the CATHeterization GENetics (CATHGEN) sample archive. The levels of 7 statins (atorvastatin, simvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, and fluvastatin) in the plasma samples were determined by a 4-minute-long Liquid Chromatography-Mass Spectrometry Method (LC-MS/MS). Nominal logistic regression algorithms were employed to model coronary artery disease (CAD) outcomes with the incorporation of statin use, phenotypic, and lipid profile characteristics. Results: Our LC-MS/MS analysis has revealed discrepancies between the reported statin medication use and the detected statin levels in plasma. The reported statin use history was confirmed for 212 out of 261 patients. Among 429 specimens without a recorded history of statin use, statin was detected in 198 specimens. Comparison of logistic regression models built with statin use data as a covariate and with stratification by statin users and non-users revealed significant differences in covariate corrected mean lipid profile measures and their association with predicted atherosclerosis outcomes. Conclusion: In summary, high-throughput multiplexed LC-MS/MS analysis is highly applicable to uncover discrepancies between the self-reported and actual use of statins. By combining LC-MS/MS with statistical modeling, we showed that these discrepancies may significantly affect the modeled association between lipid profile measures and ASCVD outcome.
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