Citations

Abstract Background β-Quantification (BQ) is the reference method for low-density lipoprotein cholesterol (LDL-C) determination. It is not widely available, making it challenging for laboratories to assess the accuracy of LDL-C methods. Our goal was to develop an indirect graphical approach for comparing LDL-C test results to the BQ reference method. Methods BQ results from Mayo Medical Laboratories (n = 39 969) and the National Institutes of Health (n = 17 825) were used to investigate the interrelationships between lipid panel tests. A plot of LDL-C/non high-density lipoprotein cholesterol (nonHDL-C) vs (triglyceride (TG)/nonHDL-C)0.5 (lipid ratio plot) resulted in a negative linear regression line (y = −34.2x + 115). Based on simulation analysis, the minimum sample size for calculating its slope and intercept with a coefficient of variation of about 7.5% was 80. Results The regression lines for the lipid ratio plot of LDL-C calculated by the Sampson–NIH (y = −37.6x + 118) and enhanced Sampson–NIH (y = −33.1x + 116) equations closely overlapped with the BQ method. In contrast, the Martin–Hopkins equation showed a positive bias with an increasing TG/nonHDL-C ratio and exceeded the recommended bias limit of 4% on hypertriglyceridemic samples (y = −25.5x + 107). The Friedewald equation showed an even larger negative bias with hypertriglyceridemia (y = −47.5x + 126). Lipid ratio plots of the Roche direct assay revealed a fixed positive bias of approximately 4% (y = −33.9x + 120), whereas a much larger proportional positive bias was observed with increasing triglycerides for the Beckman direct assay (y = −14.6x + 97.8). Conclusions The lipid ratio plot is a simple graphical approach that can be readily performed by clinical laboratories for investigating the accuracy of LDL-C assays by calculation methods or direct assays.

Abstract Background Cardiovascular guidelines have long recommended low-density lipoprotein-cholesterol (LDL-C) as the primary target for lipid-lowering therapy. Recent guidelines have emphasized the importance of achieving low LDL-C levels; hence, the accurate measurement of low LDL-C is increasingly clinically relevant. Methods Using lipid panel test results from the Mayo Clinic (n = 24 590) and the FOURIER clinical trial of evolocumab (n = 9605), the following modified Sampson equation was developed by least-squares regression to match LDL-C (mg/dL) by the β-quantification reference method, by combining terms into non High Density Lipoprotein Cholesterol (nonHDLC = Total Cholesterol – High Density Lipoprotein Cholesterol) and forcing the coefficient to be one: Results The modified Sampson equation demonstrated significant improvement in its concordance to the reference method compared to other equations (the Lin Concordance Correlation Coefficient 0.992, P < 0.001). By overall kappa analysis, it showed the best agreement to the reference method at the 55 mg/dL cutpoint (1.4 mmol/L, 0.98 [P < 0.001], Sampson–NIH: 0.96, Martin–Hopkins: 0.96, Friedewald: 0.94) and the 70 mg/dL cutpoint (1.8 mmol/L, 0.97 [P < 0.001], Sampson–NIH: 0.94, Martin–Hopkins: 0.95, Friedewald: 0.92). The false classification rate of the modified Sampson equation was also significantly lower compared to the other equations at 55 mg/dL (15%, [P < 0.001], Sampson–NIH: 29%, Martin–Hopkins: 28%, Friedewald: 37%) and 70 mg/dL (18%, [P < 0.001]; Sampson–NIH: 30%, Martin–Hopkins: 28.%, Friedewald: 34%). The new equation increases the percentage of correctly classified patients with low LDL-C by approximately 10% to 20% over the other equations based on its net reclassification index. Conclusions The modified Sampson equation shows improved accuracy compared to other equations for low LDL-C. It more accurately identifies high-risk patients, who are not at their LDL-C goals and could benefit from more intensive lipid-lowering therapy.

Key Points Studies on GFR assessment in people with cancer are needed. In this study, the creatinine–cystatin C equations performed better than other equations, and this was observed in solid and hematological cancers. Our findings give support to the preferential use of creatinine and cystatin C–based equations in people with cancer. Background GFR assessment is important in clinical practice with implications for diagnosis, prognostication, and drug dosing. People with cancer are at risk of imprecision in GFR estimation. This cross-sectional study evaluated the performance of various creatinine and cystatin C–based equations in comparison with measured GFR (mGFR) in people with cancer. Methods We retrieved data for all adult patients who had mGFR by urinary iothalamate clearance between 2011 and 2023 at Mayo Clinic and use of an electronic health record diagnosis code for cancer within 2 years before mGFR. The CKD Epidemiology Collaboration (CKD-EPI), European Kidney Function Consortium, and Cockcroft–Gault equations were computed, along with performance metrics (bias, precision, and root mean square error [RMSE]. Confidence intervals were generated by bootstrapping, and analysis were stratified by solid and hematological cancers. Results From all adults with cancer and mGFR, 1145 had both creatinine and cystatin C available within 7 days of mGFR. Among all equations, the creatinine–cystatin C CKD-EPI equation provided the best performance, with small bias (median, 3.0; 95% confidence interval, 2.3 to 3.8) and higher precision (RMSE, 14.5) compared with creatinine-only or cystatin C–only equations (RMSE varying from 16.6 to 20), and this was also true in solid and hematological cancers. The creatinine–cystatin European Kidney Function Consortium equation had a similar performance to CKD-EPI, Cockcroft–Gault showed worst precision (30% of people with errors above 30%), and cystatin C CKD-EPI equation was the most biased, prone to underestimation of mGFR. Conclusions In our cohort of patients with mGFR and cancer, the CKD-EPI creatinine–cystatin C equation performed best for GFR assessment, and this was true for both solid and hematological cancers. Our findings give support for the preferential use of creatinine and cystatin C–based equations instead of creatinine-only or cystatin C–only equations in people with cancer. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2025_03_25_CJASNMar20.3.3252025.mp3

Background: Urine mast cell mediator levels have found significant clinical utility in supporting the diagnosis of various allergic disorders in the adult population. There are few studies on the range of these mediator levels in the children and its clinical utility. Objectives: We determined the range of the urine mast cell mediator levels in children. Methods: Pediatric allergy and community-based pediatric clinics including children with and without an allergic disorder, respectively, served as the source of the study. Baseline urine mast cell mediator levels were obtained for all children. Demographics, atopic disorders, clinical symptoms and response to medication were documented. Results: Urine 2,3-Dinor-11Beta-Prostaglandin F2 Alpha (BPG) values were significantly higher in children with an underlying allergic disorder when compared to those without. Stratification based on age, sex and allergic disorder was performed in patients with an allergic disorder. Urine Leukotriene E 4 (LTE 4 ) and n-Methylhistamine (NMH) were significantly correlated with age (Spearman’s rho -0.4874 and -0.6902 respectively). With every year of age, the level of the urine NMH decreased by 18 units and urine LTE 4 by 3 units approximately. Urine LTE 4 and NMH levels were significantly higher in males when compared to females. Urine BPG levels were not significantly affected by age or sex. Conclusion: Distribution of urine mast cell mediator levels are slightly different from the values derived from studies in the adult population. Urine mast cell mediators may provide clinical utility in the diagnosis and management of allergic disorders. Further large-scale studies are warranted to validate clinical utility.

Abstract Background Patients at high risk for Atherosclerotic Cardiovascular Disease (ASCVD), who do not achieve adequate lowering of LDL-cholesterol (LDL-C) with maximally tolerated statins or who are statin-intolerant, are often treated with proprotein convertase subtilisin/kexin (type 9) serine protease inhibitor (PCSK9-inh) therapy. The LDL-C reference method, Beta-Quantitation (BQ), is not routinely performed in these patients. Because low values are measured less accurately with current methods, a variety of equations have been developed to calculate LDL-C from the standard lipid panel test results (total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C)). We further modified the Sampson LDL-C (S-LDL-C) equation by a novel approach by combining logistic regression (LR) and linear regression to minimize errors when compared to BQ for low LDL-C. Methods A dataset of BQ results from Mayo Medical Labs was divided randomly into a training set (TG ≤ 1500 mg/dL, TC ≤ 1050 mg/dL, n=19,639) and a validation set (TG ≤ 800 mg/dL, BQ-LDL-C ≤ 130 mg/dL, n=12,910). The training dataset was analyzed by LR, using BQ-LDL-C to classify patients as being above or below the 70 mg/dL cut-point. Dependent variables in the LR model were the S-LDL-C, HDL-C, TC, TG and an interaction term (TG x NonHDL-C). The sum of the exponential terms in the trained LR equation for the dependent variables were then fitted by least squares linear regression to match BQ-LDL-C to develop the modified S-LDL-C equation (mS-LDL-C). We used the validation dataset to compare Friedewald (F-LDL-C), extended Martin (eM-LDL-C), Sampson (S-LDL-C) and modified Sampson (mS-LDL-C) equations by Cohen’s kappa coefficient for agreement with BQ-LDL-C and normalized Matthew’s correlation coefficient (MCC), which considers any imbalances in the number of false high versus false low results. Results The mS-LDL-C equation (mS-LDL-C = (TC/0.9423 - HDL-C/0.9582 - TG/9.397 - ((TGxNonHDL-C)/1305) + TG2/8569) - 5.688), primarily differs from the S-LDL-C equation in the value of the coefficients that include TG as a variable. For S-LDL-C ≤100 mg/dL, mS-LDL-C had the best concordance with BQ-LDL-C in terms of the total percent of patients misclassified as either high or low (mS-LDL-C: 21.4%, S-LDL-C: 24.4%, eM-LDL-C: 25.4% F-LDL-C: 30.8%). mS-LDL-C also had the best kappa score when compared to BQ (mS-LDL-C: 0.833, S-LDL-C: 0.810, eM-LDL-C: 0.789, F-LDL-C: 0.743). MCC was also the highest for mS-LDL-C (mS-LDL-C: 0.916, S-LDL-C: 0.906, eM-LDL-C: 0.894, F-LDL-C: 0.879). Finally, mS-LDL-C also showed the best concordance with BQ-LDL-C when an alternative lower LDL-C therapeutic decision cut-point of 55 mg/dL is used (kappa of 0.766 versus 0.724) but showed an increasingly poorer performance than S-LDL-C for higher LDL-C whenever it exceeded 150 mg/dL. Conclusions A strategy to maximize the overall accuracy of estimated LDL-C could provide an automatic reflex in a laboratory information system to the mS-LDL-C equation whenever S-LDL-C is <100 mg/dL. By doing so, the use of the more accurate mS-LDL-C equation for low LDL-C should improve PCSK9-inh therapeutic decision making over the other LDL-C equations.

Abstract Background Low-density lipoprotein-cholesterol (LDL-C) is the main univariate risk marker recommended for initial atherosclerotic cardiovascular disease (ASCVD) risk stratification. LDL, however, transports other constituents that can serve as pro-atherogenic risk markers. Like LDL-C, the triglyceride content of LDL (LDL-TG) can also be estimated from the lipid panel. Using data from UK Biobank (UKB), we compared LDL-C to LDL-TG for predicting future ASCVD events. Methods A UKB dataset (N=270,436) of primary prevention individuals not on lipid lowering therapy was used for analysis. LDL-C was calculated by the following equations: Friedewald, Extended Martin-Hopkins, Sampson-NIH, and Enhanced Sampson-NIH. LDL-TG was calculated by our newly devised equation (eLDL-TG = TG/38.46 + nonHDL-C/5.75 + (TG/nonHDL-C)/0.103 + (1/HDL-C)/0.0041 - 2.95)), which was derived from a dyslipidemic cohort (N=40,202) analyzed by the beta-quantification reference method. Results Although there was only a modest comparison between eLDL-TG and measured LDL-TG (R2=0.601, slope=0.611), eLDL-TG appeared to be a better risk marker than LDL-C in UKB. When the population was divided into quintiles and analyzed by survival curve analysis, eLDL-TG was better than LDL-C in segregating patients into low- and high-risk groups (Figure). Similar findings on the relative value of LDL-TG over other risk markers were also found when analyzed by ROC analysis (AUC: eLDL-TG>HDL-C>remnant-cholesterol>NonHDL-C>TG>apoB>Sampson-NIH-LDL-C). By Cox-proportional hazard analysis eLDL-TG showed alongside apoB and NonHDL-C the strongest association with future ASCVD events. Discordance analysis showed that when stratified as LDL-TG/LDL-C as High/Low there was an 26% increased risk for ASCVD, and when as High/High showed an 40% increased risk for ASCVD. Conclusions Like LDL-C, LDL-TG levels can also be estimated from the standard lipid panel. If our findings hold in other populations, the superiority of LDL-TG over LDL-C as a risk marker suggest that the reporting of calculated LDL-TG along with LDL-C could be a new strategy for improving ASCVD risk stratification.

Abstract Background Patients with elevated free glycerol can have falsely elevated triglyceride concentrations known as pseudohypertriglyceridemia. Most clinical laboratory triglycerides methods measure the concentration of glycerol released following the enzymatic hydrolysis of triglycerides to quantitate triglycerides. Thus, increased free glycerol can lead to falsely increased triglyceride concentration. Identifying and measuring the interference is necessary to effectively manage patient care interventions. A glycerol-corrected triglyceride assay was discontinued from Roche Diagnostics in 2021. We created a process to determine glycerol-corrected triglycerides by measuring glycerol directly using an assay from Randox Laboratories. Methods Glycerol (Randox Laboratories) and nonglycerol-blanked triglycerides (Roche Diagnostics) were measured in serum using the Roche Cobas c501. Stability (n=10) and expected values (n=206) were determined using serum from healthy volunteers. Linearity (n=5) and precision (5 replicates on 2 runs over 5 days) were determined using residual clinical serum spiked with exogenous glycerol as necessary to span the analytical measuring range. Glycerol corrected triglyceride concentrations were calculated by subtracting glycerol (mmol/L) from triglyceride (mmol/L) and converting to mg/dL (x88.59). Samples previously measured using the Triglyceride/Glycerol Blanked Reagent (n=53, Roche Diagnostics) were used to assess accuracy. (Figure 1) Results Glycerol measures were stable 3 days refrigerated and 30 days frozen. Precision coefficient of variation was 1.0% within run and 7.1% within lab. Expected values were 10 - 109 mcmol/L, and the method was linear between 2 - 3,971 mcmol/L. The average bias between methods was 11.0% with slope of 1.02 and R2>0.97 (Figure 1). Conclusions Glycerol can be accurately measured using the Randox method and the result can be used to generate an accurate glycerol-corrected triglyceride concentration.

Abstract Background Beta-quantification (BQ) is the reference method for LDL-C but is not widely available. It is difficult, therefore, for clinical laboratories to independently assess the accuracy of their methods for either calculating or directly measuring LDL-C. Our goal was to investigate if an interrelationship between the tests in the standard lipid panel could be used to compare the performance of different LDL-C methods against BQ, specifically in samples with hypertriglyceridemia, a major cause of bias for LDL-C testing. Methods BQ results from Mayo Medical Labs (n=39,667) and the NIH (n=18,338) were used to identify a linear relationship by least-squares regression analysis between lipid panel test results and LDL-C to facilitate a statistical comparison between different LDL-C methods. Results It is well established that the number of VLDL particles increases as TG increases, because VLDL is the main carrier of TG. As a consequence, the ratio of LDL-C to non-HDL-C is inversely related to TG, which can be made linear by taking the square root of TG (Figure). Nearly identical regression equations were found for this relationship, using two different BQ datasets. When LDL-C was calculated by either Sampson equation, the LDL-C/non-HDL-C regression lines nearly overlapped with the one for BQ. In contrast, the Martin equation showed a positive bias with increasing TG, whereas the Friedewald equation showed a negative bias, consistent with past reports on their LDL-C biases with hypertriglyceridemia. In the UKBiobank, the Beckman direct LDL-C showed a positive bias for TG, which was confirmed in CAP proficiency test surveys. By simulation analysis, approximately 80 samples with high (>200 mg/dL) and low (<100 mg/dL) TG is statistically sufficient to assess if the LDL-C/non-HDL-C regressions lines of LDL-C methods differ from BQ. Conclusions The LDL-C/non-HDL-C ratio method is a simple way to examine LDL-C methods for bias on hypertriglyceridemic samples.

In the original publication [...]