BACKGROUND: Lipid profiling is central for coronary artery disease (CAD) risk assessment. Nonadherence or unreported use of lipid-lowering drugs, particularly statins, can significantly complicate the association between lipid profile measures and CAD clinical outcomes. By combining medication history evaluation with statin analysis in plasma, we determined the effects of inaccurately reported statin use on lipid profile measures and their association with CAD risk. METHODS: We compared medication history of statin use with statin concentration measurements, by liquid chromatography-tandem mass spectrometry, in 690 participants undergoing coronary angiography (63 ± 11 years of age). Nominal logistic regression was employed to model CAD diagnosis with statin measurements, phenotypic, and lipid profile characteristics. RESULTS: Medication history of statin use was confirmed by statin assay for 81% of the patients. Surprisingly, statins were detected in 46% of patients without statin use records. Nonreported statin use was disproportionately higher among older participants. Stratifying samples by statin history resulted in underestimated LDL-lipid measures. Apolipoprotein B concentrations had a significant inverse CAD association, which became nonsignificant upon re-stratification using the statin assay data. CONCLUSIONS: Our study uncovered prominent discrepancies between medication records and actual statin use measured by mass spectrometry. We showed that inaccurate statin use assessments may lead to overestimation and underestimation of LDL levels in statin user and nonuser categories, exaggerating the reverse epidemiology association between LDL levels and CAD diagnosis. Combining medication history and quantitative statin assay data can significantly improve the design, analysis, and interpretation of clinical and epidemiological studies.

Designing studies for lipid-metabolism-related biomarker discovery is challenging because of the high prevalence of various statin and fibrate usage for lipid-lowering therapies. When the statin and fibrate use is determined based on self-reports, patient adherence to the prescribed statin dose regimen remains unknown. A potentially more accurate way to verify a patient's medication adherence is by direct analytical measurements. Current analytical methods are prohibitive because of the limited panel of drugs per test and large sample volume requirement that is not available from archived samples. A 4-min-long method was developed for the detection of seven statins and three fibrates using 10 µL of plasma analyzed via reverse-phase liquid chromatography and tandem mass spectrometry. The method was applied to the analysis of 941 archived plasma samples collected from patients before cardiac catheterization. When statin use was self-reported, statins were detected in 78.6% of the samples. In the case of self-reported atorvastatin use, the agreement with detection was 90.2%. However, when no statin use was reported, 42.4% of the samples had detectable levels of statins, with a similar range of concentrations as the samples from the self-reported statin users. The method is highly applicable in population studies designed for biomarker discovery or diet and lifestyle intervention studies, where the accuracy of statin or fibrate use may strongly affect the statistical evaluation of the biomarker data.

Large epidemiological studies often require sample transportation and storage, presenting unique considerations when applying advanced lipidomics techniques. The goal of this study was to acquire lipidomics data on plasma and serum samples stored at potential preanalytical conditions (e.g., thawing, extracting, evaporating), systematically monitoring lipid species for a period of one month. Split aliquots of 10 plasma samples and 10 serum samples from healthy individuals were kept in three temperature-related environments: refrigerator, laboratory benchtop, or heated incubator. Samples were analyzed at six different time points over 28 days using a Bligh & Dyer lipid extraction protocol followed by direct infusion into a lipidomics platform using differential mobility with tandem mass spectrometry. The observed concentration changes over time were evaluated relative to method and inter-individual biological variability. In addition, to evaluate the effect of lipase enzyme levels on concentration changes during storage, we compared corresponding fasting and post-prandial plasma samples collected from 5 individuals. Based on our data, a series of low abundance free fatty acid (FFA), diacylglycerol (DAG), and cholesteryl ester (CE) species were identified as potential analytical markers for degradation. These FFA and DAG species are typically produced by endogenous lipases from numerous triacylglycerols (TAGs), and certain high abundance phosphatidylcholines (PCs). The low concentration CEs, which appeared to increase several fold, were likely mass-isobars from oxidation of other high concentration CEs. Although the concentration changes of the high abundant TAG, PC, and CE precursors remained within method variability, the concentration trends of FFA, DAG, and oxidized CE products should be systematically monitored over time to inform analysts about possible pre-analytical biases due to degradation in the study sample sets.

Aberrations in lipid and lipoprotein metabolic pathways can lead to numerous diseases, including cardiovascular disease, diabetes, neurological disorders, and cancer. The integration of quantitative lipid and lipoprotein profiling of human plasma may provide a powerful approach to inform early disease diagnosis and prevention. In this study, we leveraged data-driven quantitative targeted lipidomics and proteomics to identify specific molecular changes associated with different metabolic risk categories, including hyperlipidemic, hypercholesterolemic, hypertriglyceridemic, hyperglycemic, and normolipidemic conditions. Based on the quantitative characterization of serum samples from 146 individuals, we have determined individual lipid species and proteins that were significantly up- or down-regulated relative to the normolipidemic group. Then, we established protein-lipid topological networks for each metabolic category and linked dysregulated proteins and lipids with defined metabolic pathways. To evaluate the differentiating power of integrated lipidomics and proteomics data, we have built an artificial neural network model that simultaneously and accurately categorized the samples from each metabolic risk category based on the determined lipidomics and proteomics profiles. Together, our findings provide new insights into molecular changes associated with metabolic risk conditions, suggest new condition-specific associations between apolipoproteins and lipids, and may inform new biomarker discovery in lipid metabolism-associated disorders.