Fatica, Erica M.

Background: Uric acid (UA) concentration within carotid plaque and its association with cerebrovascular events have not been detected or quantified. Systemically, serum UA is a marker of inflammation and risk factor for atherosclerosis. However, its association with carotid plaque instability and stroke pathogenesis remains unclear. In patients undergoing carotid endarterectomy, we aimed to determine whether UA is present differentially in symptomatic versus asymptomatic carotid plaques and whether serum UA is associated with cerebrovascular symptoms (stroke, transient ischemic attack, or amaurosis fugax). Methods: Carotid atherosclerotic plaques were collected during carotid endarterectomy. The presence of UA was assessed using Gomori methenamine silver staining as well as anti-UA immunohistochemical staining and its quantity measured using an enzymatic colorimetric assay. Clinical information was obtained through a retrospective review of data. Results: UA was more commonly detected in symptomatic (n=23) compared with asymptomatic (n=9) carotid plaques by Gomori methenamine silver (20 [86.9%] versus 2 [22.2%]; P =0.001) and anti-UA immunohistochemistry (16 [69.5%] versus 1 [11.1%]; P =0.004). UA concentration was higher in symptomatic rather than asymptomatic plaques (25.1 [9.5] versus 17.9 [3.8] µg/g; P =0.021). Before carotid endarterectomy, serum UA levels were higher in symptomatic (n=341) compared with asymptomatic (n=146) patients (5.9 [interquartile range, 4.6–6.9] mg/dL versus 5.2 [interquartile range, 4.6–6.2] mg/dL; P =0.009). Conclusions: The current study supports a potential role of UA as a potential tissue participant and a systemic biomarker in the pathogenesis of carotid atherosclerosis. UA may provide a mechanistic explanation for plaque instability and subsequent ischemic cerebrovascular events.

Abstract Background Measurement of cholesterol within lipoprotein subfractions may aid in cardiovascular disease prediction. Simple, homogenous enzymatic assays for the direct measurement of lipoprotein subfractions have been developed to measure small dense low-density lipoprotein cholesterol (sdLDL-C), high-density lipoprotein-3 cholesterol (HDL3-C), and triglyceride-rich lipoprotein (TRL-C) cholesterol. The objective of this study was to determine biological variability for sdLDL-C, HDL3-C, and TRL-C in a healthy reference population to facilitate interpretation of these analytes. Methods Serum samples were collected from 24 healthy subjects (n = 14 female/10 male) daily for 3 days while non-fasting, and daily for 5 days, weekly for 4 weeks, and monthly for 6 months after overnight fasting. sdLDL-C, HDL3-C, and TRL-C cholesterol were measured by homogenous enzymatic assays. Sources of variability (between-subject, within-subject, and analytical) were calculated using random-effects regression models. Reference change value (RCV) and index of individuality (II) for each time period were determined from the variance components. Results Analytic variability (daily, weekly, and monthly CVA) was <3% for each analyte. Monthly within-subject variability (CVI) was 17.1% for sdLDL-C, 7.4% for HDL3-C, and 25.7% for TRL-C. Most of the monthly variation was attributed to between-subject variation for all 3 analytes. Overall RCVs for monthly measurements were 18.1 mg/dL for sdLDL-C, 6.1 mg/dL for HDL3-C, and 16.0 mg/dL for TRL-C. IIs were <0.6 for sdLDL-C and HDL3-C, and 0.81 for TRL-C. Conclusions sdLDL-C, HDL3-C, and TRL-C showed moderate within-subject variability, but high between-subject variability, in a healthy reference population. Given the high individuality of each analyte, population-based reference intervals may be inadequate to detect clinically significant changes.

Introduction: Uric acid (UA) level within carotid plaque specimens and its association with cerebrovascular events has not been detected or quantified. Systemically, serum uric acid (SUA) is a marker of inflammation and risk factor for atherosclerosis. However, its association with carotid plaque instability and subsequent cerebrovascular events remains unclear. Hypothesis: We hypothesized that UA may play a role in carotid artery atherosclerosis and stroke pathogenesis. In patients undergoing carotid endarterectomy (CEA), we aimed to determine whether UA is present differentially in carotid plaques of symptomatic versus asymptomatic patients and whether SUA is associated with cerebrovascular symptoms before surgery and major adverse cardiovascular events (MACE) in the follow-up to CEA. Methods: We prospectively collected 32 carotid plaques during CEA. We qualitatively assessed the presence of UA by using Gomori methenamine silver (GMS) staining and immunohistochemistry with anti-UA antibodies. We extracted and measured the quantity of UA in carotid plaques by using an enzymatic colorimetric assay. A retrospective analysis of a clinical database of 534 consecutive patients who underwent CEA was performed. We defined as symptomatic those patients with history of stroke, TIA and amaurosis fugax. Results: UA positive staining was detected in higher number of carotid plaques from symptomatic compared to asymptomatic patients on both staining, GMS [20 (86.9%) vs 2 (22.2%); p=0.001] and immunohistochemistry anti-UA [16 (69.5%) vs 1 (11.1%); p=0.004]. Moreover, we found a significantly higher concentration of UA in carotid plaque from symptomatic compared to asymptomatic patients [25.1 (9.5) μg/g vs 17.9 (3.8) μg/g; p=0.021]. SUA level in patients prior to CEA was significantly higher in the symptomatic compared to the asymptomatic group [6.0 (4.8-7.1) mg/dL vs 5.2 (4.4-6.3) mg/dL; p<0.001] and it was significantly higher in patients who developed MACE after CEA versus those that did not [6.0 (4.8-7.1) mg/dL vs 5.4 (4.5-6.6) mg/dL; p=0.021]. Conclusions: UA may provide a mechanistic explanation as systemic biomarker and potential tissue contributor for carotid plaque instability and subsequent cerebrovascular and cardiovascular symptoms.

Abstract The guideline-recommended lipid panel for cardiovascular disease (CVD) risk assessment measures total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, and calculated low-density lipoprotein (LDL) cholesterol. Measured cholesterol in subfractions of HDL and LDL purportedly improve CVD risk prediction. Homogenous enzymatic methods are now available for measurement of the cholesterol within small dense LDL (sLDL), small dense HDL (HDL3), and triglyceride-rich lipoproteins (TRL). For meaningful interpretation of these measurements, an understanding of the potential sources and extent of result variability is needed. The smallest difference between serial measurements within a patient that likely reflects a change in clinical status is called the reference change value (RCV). Biological variability and reference change values (RCV) are well-characterized for basic lipids but there is limited information for sLDL, HDL3 or TRL. The objective of this study was to determine intra- and inter-individual variability for sLDL, HDL3, and TRL in a healthy reference population. Serum samples were collected from 24 healthy subjects (n=14 female/10 male) daily for three days (non-fasting), daily for five days (fasting), weekly for four weeks (fasting), and monthly for 7 months (fasting). sLDL, HDL3, and TRL cholesterol were measured in duplicate by enzymatic colorimetric assays (Denka, Japan) on a Roche Cobas c501. Each source of variability (between subject, within subject, and analytical) was calculated using random-effects regression models to estimate each variance component including the overall variation, standard deviation (SD), coefficient of variation (CV), and proportion of total variance (between-subject, within-subject, and analytical). Using these analytical and biological variances, the reference change value (RCV), index of individuality (IoI), and intraclass correlation coefficient (ICC) were determined. Analytic variability (CVa) from monthly testing was 1.2%, 1.1%, and 1.5% for sLDL, HDL3, and TRL, respectively. Monthly within-subject variability (CVw) was 17.1% for sLDL, 7.4% for HDL3 and 25.7% for TRL. Monthly between-subject variability (CVb) was 32.2%, 13.93%, and 33.4% for sLDL, HDL3, and TRL, respectively. Most of the monthly variation was attributed to between-subject variation for all three tests. Within-subject variation accounted for 37% of TRL variation and 22% for both sLDL and HLD3. Within-subject RCVs for monthly measurements were 16.9mg/dL for sLDL, 5.3mg/dL for HDL3, and 15.1mg/dL for TRL. IoIs for monthly testing were 0.81 for TRL, 0.57 for sLDL, and 0.61 for HDL3. Our data demonstrate that sLDL, HDL3, and TRL show low analytical variability, moderate within-subject variability, but high between-subject variability when measured by homogenous assays in a healthy population. The IoI value (&gt;0.6) for TRL suggests use of a reference interval is appropriate for result interpretation. Conversely, clinical cut-points may be more useful than reference intervals for sLDL and HDL3 which had IoIs ~0.6. These findings may be useful for clinical interpretation, particularly when comparing successive measurements of these analytes.

Abstract BACKGROUND Dyslipoproteinemias can be classified by their distinct lipoprotein patterns, which helps determine atherosclerotic cardiovascular disease (ASCVD) risk and directs lipid management but this has required advanced laboratory testing. OBJECTIVE To develop a new algorithm for classifying lipoprotein disorders that only relies on the standard lipid panel. METHODS Lipid thresholds for defining the different lipoprotein phenotypes were derived for Non-High-Density Lipoprotein-Cholesterol (NonHDL-C) and Triglycerides (TG) to be concordant when possible with the current US Multi-Society guidelines for blood cholesterol management. RESULTS The new classification method categorizes patients into all the classical Fredrickson-like phenotypes except for Type III dysbetalipoproteinemia. In addition, a new hypolipidemic phenotype (Type VI) due to genetic mutations in apoB-metabolism is described. The validity of the new algorithm was confirmed by lipid analysis by NMR (N = 11,365) and by concordance with classification by agarose gel electrophoresis/beta-quantification (N = 5504). Furthermore, based on the Atherosclerosis Risk in Communities (ARIC) cohort (N = 14742), the lipoprotein phenotypes differ in their association with ASCVD (TypeV > IIb > IVb > IIa > IVa > normolipidemic) and can be used prognostically as risk enhancer conditions in the management of patients. CONCLUSIONS We describe a clinically useful lipoprotein phenotyping system that is only dependent upon the standard lipid panel. It, therefore, can be easily implemented for increasing compliance with current guidelines and for improving the care of patients at risk for ASCVD.

Abstract Lipoprotein(a) [Lp(a)] is a pro-atherogenic and pro-thrombotic LDL-like particle recognized as an independent risk factor for cardiovascular disease (CVD) that is resistant to typical lipid-lowering treatments. The cholesterol within Lp(a) (Lp(a)-C) contributes to the reported LDL-cholesterol (LDL-C) concentration by nearly all available methods including beta-quantification, direct homogenous assays, and all estimating equations. Accurate LDL-C measurements are critical for identification of genetic hyperlipidemia conditions such as familial hypercholesterolemia (FH). FH risk estimators such as the Dutch Lipid Clinic Network (DLCN) criteria utilize LDL-C concentration cut-offs and other clinical inputs to assess the likelihood of FH. Therefore, failure to adjust for Lp(a)-C can impact accurate FH classification, appropriate follow-up testing and treatments, and interpretation of cholesterol-lowering treatment efficacy. Lp(a)-C can be estimated from Lp(a) mass as measured by immunoassay using an average cholesterol content per particle. However, Lp(a)-C size and composition varies significantly within individuals resulting in inaccurate Lp(a)-C estimates. In this study, we use direct Lp(a)-C measurements to assess the potential misclassification of FH risk due to the contribution of Lp(a)-C to LDL-C in patient samples submitted for advanced lipoprotein profiling. A total of 28,200 samples submitted for lipoprotein profiling were included. The profiling included lipid testing in a CDC-certified laboratory on Roche cobas 501 (cholesterol and triglycerides by enzymatic method, high-density lipoprotein cholesterol by MgCl2/dextran sulfate precipitation). LDL-C was measured by beta-quantification, and Lp(a)-C by quantitative lipoprotein electrophoresis (SPIFE Vis Cholesterol, Helena Laboratories). The DLCN LDL-C cut-offs (155, 190, 250, and 330mg/dL) were applied to LDL-C results before and after accounting for Lp(a)-C contribution. Lp(a)-C was detected in 3,728 (13.2%) samples. The median (range) concentrations of Lp(a)-C and LDL-C were 11mg/dL (5-108mg/dL) and 121mg/dL (27-678mg/dL), respectively. Overall, subtracting Lp(a)-C would reclassify 6.5% of all samples into a lower LDL-C category within the DLCN algorithm. Within the LDL-C scoring categories, 7.0% (n=222) of subjects with LDL-C 155-189mg/dL, 5.6% (n=66) of subjects with LDL-C 190-249mg/dL, 5.2% (n=10) of subjects with LDL-C 250-329mg/dL, and 3.4% (n=4) of subjects with LDL-C &gt;330mg/dL would be down-classified after adjusting for Lp(a)-C. Limiting to subjects with measurable Lp(a)-C, reclassification to a lower diagnostic threshold occurred in 47.4% of subjects with LDL-C 155-189mg/dL, 37.5% with LDL-C 190-249mg/dL, 41.6% with LDL-C 250-329mg/dL, and 33.3% with LDL-C &gt;330mg/dL after adjustment. Current guidelines recommend screening for elevated Lp(a) in patients with family history of CVD. Our data show that a high percentage of samples evaluated for advanced lipid testing contain measurable Lp(a)-C that could cause mis-classification in FH prediction algorithms. If labeled high probability of FH, these mis-classifications could trigger inappropriate work-up for suspected FH. As clinical follow-up and therapeutic strategies differ between FH and elevated Lp(a), proper distinction between LDL-C and Lp(a)-C is needed to guide appropriate patient management.

Background:The combination of trimethoprim (TMP) and sulfamethoxazole (SMX) is used to treat a number of bacterial infections. TMP/SMX concentrations in serum are conventionally monitored using high-performance liquid chromatography (HPLC) or liquid chromatography tandem mass spectrometry. These methods require laborious manual extraction techniques and relatively long sample analysis times, necessitating the development of a simple, high-throughput method. A simple, high-throughput method to measure TMP/SMX using ultra-fast solid-phase extraction (SPE)–tandem mass spectrometry has been developed.Methods:Calibration standards, quality control materials, and patient samples were precipitated with acetonitrile containing isotopically labeled internal standards. Samples were vortexed, centrifuged for 5 minutes at 2053g, and the resulting supernatant was diluted in aqueous mobile phase and injected onto the C18 SPE cartridge. MS/MS analysis was performed by electrospray ionization in positive ion mode at a rate of <20 seconds per sample. A 5-point linear 1/x calibration curve was used to calculate sample concentrations.Results:The intra-assay precision coefficients of variation were <6% and <7% for SMX and TMP, respectively, and <10% for both interassay precision coefficients of variation. Comparison studies using 50 patient and spiked serum samples showed r2values of 0.9890 and 0.9853 andy-intercept values of −1.918 and −1.357, respectively compared with the HPLC reference method. All data points were <±15% of the mean. Linearity [r2= 0.9952 (SMX) and 0.9954 (TMP)] was established from 12 to 400 mcg/mL with a detection limit of 0.47 mcg/mL, and 1.2–40 mcg/mL with a detection limit of 0.06 mcg/mL, for SMX and TMP, respectively. For either drug, no significant carryover was observed after samples at the upper limit of quantification. No interference was observed from any of the 77 drugs and respective metabolites tested.Conclusions:A high-throughput SPE–tandem mass spectrometry method for TMP/SMX quantification was developed. The <20 seconds analysis time is a significant improvement compared with traditional HPLC and liquid chromatography tandem mass spectrometry methods, without sacrificing analytical performance.