Donato, Leslie J.

INTRODUCTION: The triglyceride (TG) threshold for diagnosis of chylous ascites in patients with portal hypertension remains uncertain. METHODS: Retrospective analysis of lipoprotein electrophoresis was conducted in 286 consecutive ascites samples. RESULTS: Ascitic TG ≥ 81 mg/dL is 95.4% sensitive and 94.6% specific for chylous ascites diagnosed by the presence of significant chylomicron population. DISCUSSION: The cutoff for chylous ascites diagnosis should be TG ≥ 81 mg/dL.

Abstract Cholestatic liver diseases, such as primary sclerosing cholangitis and primary biliary cirrhosis, can lead to serum accumulation of lipoprotein X (LpX). LpX is a multilamellar particle high in cholesterol but lacking structural apolipoproteins A1 or B. The absence of ApoB results in no negative feedback on cholesterol biosynthesis and prevents LpX clearance from the liver. While clinical signs and symptoms typically precede laboratory findings, it is possible that in medically complex patients the identification of LpX could be the first observation of cholestatic liver disease. Traditional laboratory methods are insufficient to identify LpX as it is of similar density to low-density lipoprotein (LDL). LpX contains a high concentration of cholesterol which is erroneously reported as LDL-C by routine clinical methods. As LpX is a rare complication of liver disease, clinicians may presume the elevation is a coincidental familial hypercholesterolemia rather than a sequela of liver disease. Currently, lipoprotein gel electrophoresis is the only laboratory method to identify LpX. In this method only performed in specialty lipid laboratories, LpX is readily identified by its unique reverse electrophoretic mobility relative to other lipoproteins. The objective of this study was to characterize lipid panels from LpX-positive samples and develop a suitable mechanism to identify LpX-containing samples with good clinical validity. From 21,377 clinical electrophoresis results reported between Nov 2011 to Nov 2021, LpX was identified in 157 serum samples. Overall, patients with LpX were younger (median 44y vs. 55y, p<0.0001) with significantly higher total cholesterol (812mg/dL vs 190mg/dL, p<0.0001) and lower high density lipoprotein-cholesterol (HDL-C; 3mg/dL vs 45mg/dL, p<0.0001). Data were randomly split (70/30) into training (n=14,964) and testing (n=6,413) cohorts. Receiver operator characteristic curve analysis identified optimal thresholds of 22.5 mg/dL HDL-C (AUC = 0.94) and 378.5 mg/dL total cholesterol (AUC = 0.91), as well as a nonHDL-C/HDL-C ratio of 9.2 (AUC = 0.995). Applying these cutoffs to the testing cohort achieved a sensitivity/specificity of 98%/81% for HDL-C, 96%/87% for total cholesterol, and 98%/94% for nonHDL-C/HDL-C ratio. A multivariate model combining these three parameters showed a sensitivity/specificity of 97%/85%, respectively. In conclusion, low HDL-C, elevated total cholesterol and a ratio of nonHDL-C/HDL-C >9.2 are associated with the presence of LpX. The ratio of nonHDL-C/HDL-C is the most sensitive and specific predictor of LpX. If confirmed in other cohorts, laboratories could include a reporting comment on lipid panels with a nonHDL-C/HDL-C ratio >9.2 cautioning a high suspicion for the presence of LpX and recommending confirmatory testing. It may also be prudent to caution that LDL-C results may not be accurate. In conclusion, identifying patients with high suspicion of LpX based on abnormal lipid panel results may aide in clinical diagnosis, even when an assay to detect LpX is not readily available.

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: Plasma ceramides (CER) are sphingolipids associated with adverse events and mortality in patients with established coronary artery disease (CAD) and/or risk factors for CAD. The ability of these same specific ratios of CER to predict major adverse cardiovascular events (MACE) in subjects without CAD in the community is less certain. We tested the hypothesis that CER are associated with MACE in a community-based cohort free of CAD at enrollment. Methods: In a prospective community-based cohort we performed passive follow-up using a record linkage system to ascertain the composite outcome of MACE, defined as acute myocardial infarction, coronary revascularization (coronary artery bypass grafting or percutaneous coronary intervention), stroke or death. CER were analyzed as continuous variables, ratios and quintiles adjusting for potential confounders. Results: We included 1221 subjects, 49% males, mean age ± (SD) 61.1±9.6 years. After a median follow-up of 16 years (IQR 1.7), 297 patients had a MACE: Myocardial infarction (105), coronary artery bypass surgery (53), percutaneous coronary intervention (92), stroke (137) and all-cause death (120). Some had more than one event. Ratios of CER were significantly associated with MACE in unadjusted and adjusted models, see Figure 1-A. This association was independent of LDL-C, total cholesterol, Apo-B and traditional CAD risk factors. Those in the highest quintile of (24:1/24:0) CER ratio had more than two-fold risk of MACE, HR 2.32 (95% CI, 1.59-3.46). There was a dose-response association across quintiles of CER ratios and MACE, Figure 1-B.: Conclusions: Elevated CER plasma ratios are associated with MACE in patients with no CAD in the community. The risk conferred by plasma CER is independent of traditional risk factors, including lipids, and has a dose-response.

Background Accurate measurement of the cholesterol within lipoprotein(a) (Lp[a]‐C) and its contribution to low‐density lipoprotein cholesterol (LDL‐C) has important implications for risk assessment, diagnosis, and treatment of atherosclerotic cardiovascular disease, as well as in familial hypercholesterolemia. A method for estimating Lp(a)‐C from particle number using fixed conversion factors has been proposed (Lp[a]‐C from particle number divided by 2.4 for Lp(a) mass, multiplied by 30% for Lp[a]‐C). The accuracy of this method, which theoretically can isolate “Lp(a)‐free LDL‐C,” has not been validated. Methods and Results In 177 875 patients from the VLDbL (Very Large Database of Lipids), we compared estimated Lp(a)‐C and Lp(a)‐free LDL‐C with measured values and quantified absolute and percent error. We compared findings with an analogous data set from the Mayo Clinic Laboratory. Error in estimated Lp(a)‐C and Lp(a)‐free LDL‐C increased with higher Lp(a)‐C values. Median error for estimated Lp(a)‐C <10 mg/dL was −1.9 mg/dL (interquartile range, −4.0 to 0.2); this error increased linearly, overestimating by +30.8 mg/dL (interquartile range, 26.1–36.5) for estimated Lp(a)‐C ≥50 mg/dL. This error relationship persisted after stratification by overall high‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol subtypes. Similar findings were observed in the Mayo cohort. Absolute error for Lp(a)‐free LDL‐C was +2.4 (interquartile range, −0.6 to 5.3) for Lp(a)‐C<10 mg/dL and −31.8 (interquartile range, −37.8 to −26.5) mg/dL for Lp(a)‐C≥50 mg/dL. Conclusions Lp(a)‐C estimations using fixed conversion factors overestimated Lp(a)‐C and subsequently underestimated Lp(a)‐free LDL‐C, especially at clinically relevant Lp(a) values. Application of inaccurate Lp(a)‐C estimations to correct LDL‐C may lead to undertreatment of high‐risk patients.

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.

Abstract Atherosclerotic cardiovascular disease is a result of low-density lipoprotein (LDL) particles becoming trapped in arterial walls and forming plaques which ultimately restrict blood-flow. LDL cholesterol (LDL-C) and apolipoprotein B (apoB) are highly correlated measures of plaque-causing LDL particles. Both have been shown to predict major adverse cardiac events (MACE). ApoB is also carried on remnant lipoproteins (RLP). RLP-cholesterol (RLP-C) is increasingly appreciated as a MACE risk-factor. This study aimed to define discordances between apoB and LDL-C in a large data set from a clinical reference laboratory. We then applied this definition to evaluate which measure predicted the risk of MACE in a patient cohort referred for coronary angiography with &gt;10 years follow-up. LDL-C was measured by beta-quantification and RLP-C was defined as total cholesterol – LDL-C – HDL-C. Apo B discordance relative to LDL-C was determined by linear regression in a discovery cohort (n=17,203) using beta quantification. Discordance was defined by quartiles of the residual-apoB (expected–actual); discordant-low (&lt;25th percentile), concordant (25th to 75th percentile) and discordant-high (&gt;75th percentile). Associations with prevalence and incident of MACE were evaluated by odds-ratio and logistic regression. Risk of MACE was calculated based on the apoB-discordance and reported MACE events by several years follow up in a separate cohort (n=501). In the discovery cohort, age ranged from 18-95 years, 51% were female and mean (±SD) lipid values were: ApoB: 100.4 ± 30.0mg/dl, LDL-C: 121.7 ± 47.9mg/dl, and RLP-C: 17.2 ± 26.9mg/dl. Expected-apoB was described by the formula: (LDL-c X 0.6278 + 24.07, R=0.88). Residual-apoB (discordance) ranged from -1037 to 581.2 with a mean 0.01±18.6, and notably increased with triglyceride concentration (rho=0.65) and with RLP-C (rho=0.64), but was minimally influenced by apoB (rho=0.35) and LDL-C (rho=0.009) (p&lt;0.001 all cases). In the clinical follow-up cohort, age ranged from 26-77 years, 42% were female, 64% were current/former smokers, and 28% were on lipid-lowering therapy. Mean (±SD) lipids were: apoB: 97.8 ± 20.9mg/dl, LDL-C: 124.6 ± 36.6mg/dl, and RLP-C: 34.9 ± 25.6mg/dl. Serum triglycerides among subjects discordant-low apoB, concordant and discordant-high apoB were 148mg/dL, 157mg/dL and 238mg/dL, respectively; similarly for RLP-C. A total of 192 events occurred during a mean of 9 years follow-up. Subjects with discordantly elevated apoB had a significantly higher incidence of MACE compared to those with concordant values (47% vs. 36%, p=0.03). There was no difference in MACE for subjects with discordantly low apoB (35% vs. 36%). These data support previous reports of an association between apoB and LDL-C and the superior performance of apoB when discordantly elevated. Our data expand on previous studies by applying an externally defined threshold for discordant-apoB. Our data indicate that triglycerides, RLP-C are associated with discordances and MACE.

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 Background We evaluated the analytical sensitivity and specificity of 4 rapid antigen diagnostic tests (Ag RDTs) for severe acute respiratory syndrome coronavirus 2, using reverse transcription quantitative PCR (RT-qPCR) as the reference method and further characterizing samples using droplet digital quantitative PCR (ddPCR) and a mass spectrometric antigen test. Methods Three hundred fifty (150 negative and 200 RT-qPCR positive) residual PBS samples were tested for antigen using the BD Veritor lateral flow (LF), ACON LF, ACON fluorescence immunoassay (FIA), and LumiraDx FIA. ddPCR was performed on RT-qPCR–positive samples to quantitate the viral load in copies/mL applied to each Ag RDT. Mass spectrometric antigen testing was performed on PBS samples to obtain a set of RT-qPCR–positive, antigen-positive samples for further analysis. Results All Ag RDTs had nearly 100% specificity compared to RT-qPCR. Overall analytical sensitivity varied from 66.5% to 88.3%. All methods detected antigen in samples with viral load &gt;1 500 000 copies/mL RNA, and detected ≥75% of samples with viral load of 500 000 to 1 500 000 copies/mL. The BD Veritor LF detected only 25% of samples with viral load between 50 000 to 500 000 copies/mL, compared to 75% for the ACON LF device and &gt;80% for LumiraDx and ACON FIA. The ACON FIA detected significantly more samples with viral load &lt;50 000 copies/mL compared to the BD Veritor. Among samples with detectable antigen and viral load &lt;50 000 copies/mL, sensitivity of the Ag RDT varied between 13.0% (BD Veritor) and 78.3% (ACON FIA). Conclusions Ag RDTs differ significantly in analytical sensitivity, particularly at viral load &lt;500 000 copies/mL.