Abstract
Background: Larger within-patient variability of lipid levels has been associated with an increased risk of cardiovascular disease (CVD). However, measures of lipid variability are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record (EHR)-based population cohort and assessed associations with incident CVD. Methods: We identified all individuals ≥40 years of age who resided in Olmsted County, MN on 1/1/2006 (index date) without prior CVD. CVD was defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention or stroke. Patients with ≥3 measurements of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and/or triglycerides during the 5 years before the index date were retained in the analyses. Lipid variability was calculated using variability independent of the mean (VIM). Patients were followed through 9/30/2017 for incident CVD (including CVD death). Cox regression was used to investigate the association between quintiles of lipid VIMs and incident CVD. Results: We identified 18,642 individuals (mean age 60; 55% female) who were free of CVD at baseline and VIM calculated for at least one lipid measurement. After adjustment, those in the highest VIM quintiles of total cholesterol had a 25% increased risk of CVD (Q5 vs. Q1 HR: 1.25, 95% CI: 1.08-1.45; Table). We observed similar results for LDL-C (Q5 vs. Q1 HR: 1.20, 95% CI: 1.04-1.39) and HDL-C (Q5 vs. Q1 HR: 1.25, 95% CI: 1.09-1.43). There was no association between triglyceride variability quintiles and CVD risk. Conclusion: In a large EHR-based population cohort, high variability in total cholesterol, HDL-C and LDL-C was associated with an increased risk of CVD, independently of traditional risk factors, suggesting it may be a target for intervention. Lipid variability can be calculated in the EHR environment but more research is needed to determine its clinical utility.
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