Hartman, Stacy

Abstract Funding Acknowledgements Type of funding sources: Private hospital(s). Main funding source(s): Mayo Clinic Department of Laboratory Medicine and Pathology. Background Elevated lipoprotein(a) [Lp(a)] is associated with increased risk for atherosclerotic cardiovascular disease (ASCVD). Treatment of patients with elevated Lp(a) usually includes marked reduction in low density lipoprotein cholesterol (LDL-C). However, there are uncertainties about whether all increases in Lp(a) are equivalent in there risk relationship. Purpose The aim of this investigation was to risk stratify patients with elevated Lp(a) mass [Lp(a)-M] based on Lp(a) cholesterol (Lp(a)-C) measurements. We assessed coronary artery disease (CAD) by coronary angiogram and major adverse cardiac events (MACE) in a cohort of patients with elevated Lp(a) mass (≥30mg/dL) over a 15 year followup. Methods Electrophoretic and immunologic assays were used to measure Lp(a)-C and Lp(a)-M, respectively. Univariate and multivariate logistic regression analysis was employed to test for association between Lp(a) and CAD in a cohort patients with elevated Lp(a)-M (≥30mg/dL). The relationship between Lp(a) and time to major cardiac events was evaluated using cox regression analysis. Receiver operator curve (ROC) analyses were used to identify optimal cutoff values for Lp(a)-M and C for predicting CAD in our model. Results We analyzed 179 patients with elevated Lp(a)-M. Cutoff values of 3.3mg/dL for Lp(a)-C and 42.1mg/dL for Lp(a)-M optimally predicted CAD in an ROC analysis. Of these patients, 84 were found to have Lp(a)-C < 3.3mg/dL (low Lp(a)-C), while 95 patients had Lp(a)-C levels ≥ 3.3mg/dL (high Lp(a)-C). High Lp(a)-C was associated with CAD identified on coronary angiography in univariate (OR = 2.17, 95% CI = 1.18 – 4.00, p = 0.013) and multivariate (OR = 3.36, 95% CI = 1.64 – 6.89, p < 0.001) models controlling for traditional risk factors. In univariate analyses high Lp(a)-C was also associated with MACE (HR = 2.38, 95% CI = 1.19 – 4.75, p = 0.01) while Lp(a)-M ≥ 42.1mg/dL (high Lp(a)-M) was not (HR = 1.68, 95% CI = 0.82 – 3.44, p = 0.15) (Table 1, Figure 1). Lp(a)-C remained significantly correlated with MACE after adjusting for traditional risk factors. When included in a model together, Lp(a)-C maintained its significance while Lp(a)-M failed to predict MACE, indicating that Lp(a)-C is a stronger predictor of MACE than Lp(a)-M. Conclusion This study demonstrates that patients with elevated Lp(a)-M and high Lp(a)-C are approximately 2.5 times more likely to encounter MACE over time when compared to patients with elevated Lp(a)-M and low Lp(a)-C. Our investigation suggests that Lp(a)-C can be used to risk stratify patients with elevated Lp(a)-M and identify those at higher risk.

AbstractBackgroundCeramides are bioactive lipid species that mediate numerous cell-signaling events. Elevated plasma ceramides concentration constitutes a risk factor for several pathologies. Multiple studies have affirmed the plasma concentrations of 4 specific ceramides (Cer16:0, Cer18:0, Cer24:0, and Cer24:1) can predict cardiovascular disease risk. Furthermore, these ceramides can be altered by many lipid-lowering therapies. Understanding the biological variability within an individual, and within a population, will further inform the clinical use of plasma ceramides as a biomarker. In this study, we aimed to define the intra- and interbiological variability of ceramides in a healthy reference population in a weekly and monthly manner.MethodsFasting plasma from 24 healthy adults was collected daily (5 days), weekly (4 weeks), and monthly (7 months). Ceramide concentrations were measured with liquid chromatography–mass spectrometry (LC–MS). For analysis, we used random-effects regression models to estimate variance components.ResultsThe analytical variability was smaller compared to the biological variability overall. The greatest variation reported was between-subject variation for all ceramide species. The critical difference-reference change value (RCV) for within-subject variations monthly were 0.07 mcmol/L (Cer16:0), 0.04 mcmol/L (Cer18:0), 1.09 mcmol/L (Cer24:0), and 0.27 mcmol/L (Cer24:1). The index of individuality (IOI) of ceramides were 0.82 (Cer16:0), 0.96 (Cer18:0), 1.06 (Cer24:0), and 0.89 (Cer24:1). The most consistent ceramide species was Cer18:0 with the lowest within- and between-subject critical differences in weekly and monthly measurements.ConclusionsOverall, this study demonstrates that the variability of ceramide concentrations at different time points is minimal within individuals, allowing a single draw to be sufficient at least in a yearly time frame.

Introduction: Elevated plasma levels of lipoprotein(a) [Lp(a)] are associated with increased risk for coronary artery disease (CAD), but the strength of the association and whether it is independent of other risk factors remains controversial. Differences in prospective studies may be partially explained by variability in methods used to measure Lp(a). Methods: We utilized two different analytical methods (electrophoretic and immunologic) to measure Lp(a) and determine the predictive value of Llp(a) in assessing angiographic coronary artery disease (CAD) and association with major adverse events in 500 patients. Results: In univariate analyses, median Lp(a) cholesterol and Lp(a) mass were significantly associated with angiographic CAD. In a multivariable model, Lp(a) cholesterol remained a significant correlate of CAD (OR 1.61, 95% CI 1.13-2.31, P = 0.009) while Lp(a) mass was not (OR 1.18, 95% CI 0.95-1.47, P = 0.14). Additionally, on multivariable analysis, the presence of a detectable amount of Lp(a) cholesterol (> or =2.5 mg/dL) was more strongly correlated with CAD than HDL cholesterol < 40 mg/dL, and, along withLp(a) cholesterol, was strongly correlated with major adverse events (OR 2.08 95% CI 1.22-3.56, P = 0.007 and OR 1.22, 95% CI 1.05-1.42, P = 0.012, respectively). Conclusions: Lp(a) cholesterol measured electrophoretically is independently correlated with angiographic CAD and presence of major adverse events, and may be used as an alternative or supplement to Lp(a) mass analysis.

Abstract Context: Levels of dehydroepiandrosterone (DHEA) and its sulfate form (DHEAS) are inversely associated with cardiovascular mortality in men but not women. Very little evidence is available on the impact of DHEA administration on lipoprotein profile in women. DHEAS levels are very low/undetectable in hypoadrenal women. Objective: The objective of the study was to determine the impact of DHEA replacement on lipoprotein profile in hypoadrenal women. Design and Setting: A double-blind, randomized, placebo-controlled, cross-over design study was conducted at the Mayo Clinic. Participants: Thirty-three hypoadrenal Caucasian women (mean ± sd; age 50.3 ± 15.2 yr, body mass index 26.6 ± 4.4 kg/m2) took part in the study. Intervention: Study participants were assigned to receive either a placebo or 50 mg/d of DHEA for 3 months each. Lipid levels and lipoprotein profile were analyzed using the Lipo Science Lipoprotein nuclear magnetic resonance system. Main Outcome Measures: Changes in various lipoprotein sizes and levels were measured. Results: The DHEA period had higher plasma DHEAS levels than during placebo (&lt;0.3 ± 0.0 vs. 3.5 ± 1.3 nmol/liter, P &lt; 0.001). DHEA replacement significantly reduced total cholesterol (20.0 vs. −22, P = 0.02) and high-density lipoprotein (HDL) levels (2.0 vs. −6.0, P = 0.006) and tends to reduce triglyceride and total low-density lipoprotein levels. Although, DHEA replacement had no effect on low-density lipoprotein particle size, it significantly reduced larger HDL particles and to modest extent small HDL particles. Conclusions: Our study findings showed that oral DHEA administration in hypoadrenal women results in an unfavorable lipoprotein profile. The results warrant long-term studies to determine the impact of DHEA replacement on cardiovascular risk.

Abstract Background: We evaluated the sensitivity, precision, and concordance of 4 assays designed to detect aspirin responsiveness or resistance. Methods: Twenty-nine healthy laboratory volunteers took 80 mg aspirin for 7 days, and a subset of volunteers took 325 mg aspirin for an additional 7 days. We measured platelet function by light transmission aggregometry with arachidonic acid, PFA-100, and VerifyNow. PFA-100 and VerifyNow assays were performed in duplicate to assess method imprecision. Some volunteers had samples taken within 2–4 h of the final dose of aspirin and again within 20–24 h of the final dose. We measured urinary 11-dehydro-thromboxane B2 at baseline and after 80 or 325 mg aspirin. Results: No volunteers were nonresponsive to aspirin therapy as measured by the PFA-100. One of 29 participants demonstrated lack of response to aspirin as measured by VerifyNow and urinary 11-dehydro-thromboxane B2; 2 of 29 demonstrated lack of response as measured by light transmission aggregometry. Imprecision was &lt;10% for the PFA-100 and VerifyNow. Concordance was high (&gt;90%) between all assays. Neither aspirin dose (80 vs 325 mg) nor timing between final dose of aspirin and blood draw (2–4 vs 20–24 h) affected any of the assays. Conclusions: Light transmission aggregometry, PFA-100, VerifyNow, and urinary 11-dehydro-thromboxane B2 are all sensitive to the effects of aspirin in healthy individuals. Variables such as aspirin dose, timing between final dose of aspirin and blood collection, and imprecision do not affect the ability of the assays to detect aspirin effect on platelet function.

Abstract Background: Mitochondrial respiratory chain complex (RCC) disorders may occur as commonly as 1 in 8500 individuals. Because of the great variability of phenotypic presentations, measurement of individual RCC enzyme activities is a crucial diagnostic process. Current assay methods are time-consuming and labor-intensive and thus constitute a major impediment to clinical practice. A method with a faster turnaround time would therefore be beneficial. Method: We developed an automated spectrophotometric method for measuring the respiratory chain enzyme activities of complex I, complex II + III, and complex IV with the Hitachi 912, an automated spectrophotometer. Mitochondrial citrate synthase was also determined for normalization of the RCC activities. Results: A blinded method comparison with samples from an external testing center yielded a 91% concordance of interpretations. Mean intraassay imprecision (as CV; n = 20) in a single batch analysis of each RCC was 5.9%. Interassay imprecision, evaluated on 2 samples harvested and analyzed 3 times each, gave mean CVs of 10%–18%. Conclusions: With this automated method, a panel of RCC enzyme activities can be determined in &lt;2 h. In addition, an immunoblot assay using monoclonal antibodies against specific subunits of RCC enzyme complexes can be informative in cases of borderline enzyme activity. Our results suggest that in vitro diagnosis of RCC enzyme deficiencies in skin fibroblasts is an effective alternative to invasive muscle biopsy.