Meeusen, Jeffrey W.

Abstract Background The accurate measurement of blood lipids and lipoproteins is crucial for the clinical management of atherosclerotic disease risk. Despite progress in standardization, there are still significant variations in pre-analytical requirements, methods, nomenclature, and reporting work flows. Content The guidance document aims to improve standardization of clinical lipid testing work flows. It provides recommendations for the components of the lipid panel, fasting requirements, reporting of results, and specific recommendations for non-high-density lipoprotein cholesterol (non-HDL-C), low-density lipoprotein cholesterol (LDL-C), lipoprotein(a) [Lp(a)], apolipoprotein B (apo B), point-of-care lipid testing, and LDL subfraction testing. Summary Lipid panels should always report non-HDL-C and LDL-C calculations if possible. Fasting is not routinely required except in specific cases. Modern equations should be utilized for LDL-C calculation. These equations allow for LDL-C reporting at elevated concentrations of triglycerides and obviate the need for direct measured LDL-C in most cases.

Abstract Objectives The aims of this study were to (1) establish the maximum allowable interference limits for hemolysis, lipemia, and icterus for chemistry analytes tested in body fluid samples and (2) assess the effectiveness of serial dilution to mitigate spectral interferences. Methods Residual body fluids from clinically ordered testing were mixed (<10% by volume) with stock solutions of interferent (spiked) and compared with a control spiked with an equal volume of 0.9% saline. The analytes were measured on the Roche cobas c501 instrument. Difference and percentage difference were calculated and compared with allowable total error limits. A subset of samples were serially diluted with 0.9% saline. Mean (SD) difference and percentage difference were calculated. Results The interference thresholds were lower than the package insert for lactate dehydrogenase, cholesterol, triglycerides, and total protein for hemolysis; amylase, cholesterol, and total protein for icterus; and albumin for lipemia. Only cholesterol and triglyceride results returned to baseline upon dilution of icteric samples. Conclusions Interference thresholds in body fluids were lower than blood for 6 analytes. Diluting interferences that surpass these limits does not produce reliable results that are comparable to the baseline results before spiking in the interferent.

Background Clinical risk scores are used to identify those at high risk of atherosclerotic cardiovascular disease (ASCVD). Despite preventative efforts, residual risk remains for many individuals. Very low‐density lipoprotein cholesterol (VLDL‐C) and lipid discordance could be contributors to the residual risk of ASCVD. Methods and Results Cardiovascular disease–free residents, aged ≥40 years, living in Olmsted County, Minnesota, were identified through the Rochester Epidemiology Project. Low‐density lipoprotein cholesterol (LDL‐C) and VLDL‐C were estimated from clinically ordered lipid panels using the Sampson equation. Participants were categorized into concordant and discordant lipid pairings based on clinical cut points. Rates of incident ASCVD, including percutaneous coronary intervention, coronary artery bypass grafting, stroke, or myocardial infarction, were calculated during follow‐up. The association of LDL‐C and VLDL‐C with ASCVD was assessed using Cox proportional hazards regression. Interaction between LDL‐C and VLDL‐C was assessed. The study population (n=39 098) was primarily White race (94%) and female sex (57%), with a mean age of 54 years. VLDL‐C (per 10‐mg/dL increase) was significantly associated with an increased risk of incident ASCVD (hazard ratio, 1.07 [95% CI, 1.05–1.09]; P <0.001]) after adjustment for traditional risk factors. The interaction between LDL‐C and VLDL‐C was not statistically significant ( P =0.11). Discordant individuals with high VLDL‐C and low LDL‐C experienced the highest rate of incident ASCVD events, 16.9 per 1000 person‐years, during follow‐up. Conclusions VLDL‐C and lipid discordance are associated with a greater risk of ASCVD and can be estimated from clinically ordered lipid panels to improve ASCVD risk assessment.

Abstract Background Estimated glomerular filtration rate (eGFR) has become incorporated into multiple clinical management situations. Historically, equations included a Black race coefficient, which lacked biological plausibility and created potential to exacerbate health disparities. A new equation created in 2021 changed the weighting of age, sex, and creatinine by modeling against a diverse cohort and removing the Black race coefficient. Content A variety of clinical outcomes including kidney disease risk stratification, medication dosing, patient eligibility for clinical trials, and kidney donation are impacted by implementation of the new equation. Nearly 2 years after its initial publication, many studies have reported on observed analytical performance of the 2021 eGFR determined as diagnostic concordance and percentage of estimates within 30% of measured GFR. Additionally, the potential clinical impacts following adoption of the new eGFR among different patient populations has also been reported. Here we review these studies with a focus on assessing the data associated with the transition from 2009 to 2021 Chronic Kidney Disease Epidemiology Collaboration equations. Summary The reported interindividual variation in eGFR performance is significantly larger than any potential benefit derived from race coefficients. Both the 2021 eGFR and the 2009 eGFR analytical performance fall short of the validation cohort performance in most cohorts. However, the 2021 analytical is similar or better than the 2009 eGFR in most cohorts. Implementing the 2021 eGFR will remove a systematic overestimation of kidney function among Black patients.

Abstract Background Low-density lipoprotein cholesterol (LDL-C) is the primary biomarker for assessing risk for atherosclerotic cardiovascular disease (ASCVD). However, multiple large studies have demonstrated greater accuracy using Apolipoprotein B (apoB). One obstacle to its widespread use is the lack of guideline endorsed apoB treatment targets. A recently published solution has proposed using population percentiles to calculate LDL-C using apoB values (PMID: 36366949). The LDL-C concentration correlated to an individual’s ApoB percentile may more appropriately reflect ASCVD risk compared to lipid panel LDL-C. Here we evaluated this approach using a cohort where LDL-C was measured with the gold-standard beta-quantification method (LDL-CBQ). Method Results from clinically ordered apoB, LDL-CBQ, and lipid panel were included (n = 18 852). Samples with triglycerides &gt;1000 mg/dL or LpX were excluded. Total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and apoB were measured using Roche Cobas c501 analyzers. LDL-CBQ was performed using Beckman LE-80K ultracentrifuge and dextran sulfate-Mg/Cl precipitation. ApoB equivalent LDL-C (eqLDL-CapoB) was calculated according to linear regression of LDL-C percentiles against apoB percentiles described by the equation eqLDL-CapoB = 1.45(apoB)-25. Concordance was determined based on clinical thresholds for LDL-CBQ and eqLDL-CapoB. Results Median (IQR) apoB and LDL-CBQ was 98 (79−119) mg/dL and 120 (94−150) mg/dL, respectively. LDL-CBQ and eqLDL-CapoB were concordant in 60% of samples overall. Discordance between eqLDL-CapoB and LDL-CBQ shifted from primarily overestimating to underestimating as LDL-CBQ increased. Discordant eqLDL-CapoB was directly associated with TG and inversely associated with HDL-C. Conclusion The eqLDL-CapoB calculation is a useful tool to present apoB results in a more familiar context of LDL-C-equivalent units. The concordance findings largely confirm previously published results using eqLDL-CapoB. Furthermore, the pattern of HDL-C and TG suggests that a discordantly elevated eqLDL-CapoB is an indicator of a higher risk lipid profile. More investigation is needed to determine if eqLDL-CapoB is appropriate for widespread adoption.

Abstract Background There is growing evidence that low-density lipoprotein-triglyceride (LDL-TG) may be a better biomarker to define atherosclerotic cardiovascular disease (ASCVD) risk than LDL-cholesterol (LDL-C), which is currently used to stratify risk and as a treatment goal. Because LDL-TG typically requires additional advanced lipid testing, we developed an equation for estimating LDL-TG (eLDL-TG) from the results of the standard lipid panel. Methods Results from a general dyslipidemic population (N = 40 202), which were obtained with beta-quantification (BQ) reference method, were randomly divided into a training and validation datasets and used to develop the equation for estimating LDL-TG shown in Fig. 1A. Association of Sampson-NIH-LDL-C (NIH-LDL-C) and eLDL-TG with ASCVD risk markers was done in the National Heart and Nutrition Examination Survey (NHANES) (N = 37 053) and with ASCVD events in the Atherosclerosis Risk in Communities (ARIC) study (N = 14 749). Results The comparison between eLDL-TG and measured LDL-TG was only modest (R2 = 0.601, slope = 0.611), but eLDL-TG (Fig. 1B) showed better risk stratification than NIH-LDL-C (Fig. 1C) by survival curve analysis in ARIC. Moreover, receiver-operating characteristic (ROC) analysis showed that eLDL-TG was better than all other lipid-marker tests for ASCVD events (AUC: eLDL-TG = 0.633, HDL-C = 0.627, sdLDL-C = 0.621, remnant-cholesterol = 0.613, NonHDL-C = 0.613, TG = 0.603, apoB = 0.598, NIH-LDL-C = 0.590, TC = 0.568). In NHANES, eLDL-TG was more strongly associated with hypertriglyceridemia, obesity, diabetes, metabolic syndrome and increased high-sensitivity C-reactive protein than NIH-LDL-C. Inclusion of eLDL-TG in a Cox-Proportional Hazards Model along with other conventional risk factors (age, sex, race, diabetes, systolic blood pressure, smoking, HDL-C, and total cholesterol) did not further improve risk prediction. Conclusions Like LDL-C, LDL-TG can also be estimated from the results of the standard lipid panel. It is a better univariate risk marker than LDL-C and hence can be useful for initial ASCVD risk stratification.