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.

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.

Background Frailty is common in heart failure (HF) and is associated with death but not routinely captured clinically. Frailty is linked with inflammation and malnutrition, which can be assessed by a novel plasma multimarker score: the metabolic vulnerability index (MVX). We sought to evaluate the associations between frailty and MVX and their prognostic impact. Methods and Results In an HF community cohort (2003–2012), we measured frailty as a proportion of deficits present out of 32 physical limitations and comorbidities, MVX by nuclear magnetic resonance spectroscopy, and collected extensive longitudinal clinical data. Patients were categorized by frailty score (≤0.14, >0.14 and ≤0.27, >0.27) and MVX score (≤50, >50 and ≤60, >60 and ≤70, >70). Cox models estimated associations of frailty and MVX with death, adjusted for Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) score and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide). Uno's C‐statistic measured the incremental value of MVX beyond frailty and clinical factors. Weibull's accelerated failure time regression assessed whether MVX mediated the association between frailty and death. We studied 985 patients (median age, 77; 48% women). Frailty and MVX were weakly correlated (Spearman's ρ=0.21). The highest frailty group experienced an increased rate of death, independent of MVX, MAGGIC score, and NT‐proBNP (hazard ratio, 3.3 [95% CI, 2.5–4.2]). Frailty improved Uno's c‐statistic beyond MAGGIC score and NT‐proBNP (0.69–0.73). MVX only mediated 3.3% and 4.5% of the association between high and medium frailty groups and death, respectively. Conclusions In this HF cohort, frailty and MVX are weakly correlated. Both independently contribute to stratifying the risk of death, suggesting that they capture distinct domains of vulnerability in HF.

BACKGROUND: Heart failure is heterogeneous syndrome with persistently high mortality. Nuclear magnetic resonance spectroscopy enables high-throughput metabolomics, suitable for precision phenotyping. We aimed to use targeted metabolomics to derive a metabolic risk score (MRS) that improved mortality risk stratification in heart failure. METHODS: Nuclear magnetic resonance was used to measure 21 metabolites (lipoprotein subspecies, branched-chain amino acids, alanine, GlycA (glycoprotein acetylation), ketone bodies, glucose, and citrate) in plasma collected from a heart failure community cohort. The MRS was derived using least absolute shrinkage and selection operator penalized Cox regression and temporal validation. The association between the MRS and mortality and whether risk stratification was improved over the Meta-Analysis Global Group in Chronic Heart Failure clinical risk score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels were assessed. RESULTS: The study included 1382 patients (median age, 78 years, 52% men, 43% reduced ejection fraction) with a 5-year survival rate of 48% (95% CI, 46%–51%). The MRS included 9 metabolites measured. In the validation data set, a 1 standard deviation increase in the MRS was associated with a large increased rate of death (hazard ratio, 2.2 [95% CI, 1.9–2.5]) that remained after adjustment for Meta-Analysis Global Group in Chronic Heart Failure score and NT-proBNP (hazard ratio, 1.6 [95% CI, 1.3–1.9]). These associations did not differ by ejection fraction. The integrated discrimination and net reclassification indices, and Uno’s C statistic, indicated that the addition of the MRS improved discrimination over Meta-Analysis Global Group in Chronic Heart Failure and NT-proBNP. CONCLUSIONS: This MRS developed in a heart failure community cohort was associated with a large excess risk of death and improved risk stratification beyond an established risk score and clinical markers.

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.

Introduction: Over 6 million people in the U.S. have heart failure (HF), less than half are expected to survive beyond 5 years. The need to better stratify mortality risk in HF is recognized. Frailty is associated with mortality in HF but not routinely measured clinically. As frailty is linked to inflammation and malnutrition, we hypothesized that the Metabolic Vulnerability Index (MVX) a multimarker score of systemic inflammation (small HDL particles, GlycA) and malnutrition (leucine, valine, isoleucine, citrate), could serve as a biomarker of frailty to predict mortality risk. Methods: Clinical data and plasma were collected from 1,389 patients from a HF community cohort between 2003-2012. We measured frailty using the Rockwood Index as the proportion of deficits present out of 32 physical limitations and comorbidities. MVX was calculated from the nuclear magnetic resonance LipoProfile® test. Patients were categorized by frailty (0-0.15; 0.16-0.27; 0.28-0.78) and MVX (33.4-50, 50-60, 60-70, 70-85.8) cutpoints. Cox models estimated the association of frailty and MVX assignment with mortality, adjusted for Meta-Analysis Global Group in Chronic HF (MAGGIC) score, a validated clinical risk score for HF mortality. Results: Frailty and MVX scores were available in 985 patients (median age 77, IQR: 67-84; 48% women). Higher frailty was associated with higher MVX (p-trend < 0.001). The highest frailty and MVX groups experienced large increases in risk of death, after adjustment for MAGGIC score (HR=3.3, 95% CI=2.6-4.2) and (HR=2.7, 95% CI=2.1-3.5), respectively. When adjusted for one another and MAGGIC score, MVX and frailty associations with death were only minimally attenuated: frailty (HR=3.2, 95% CI=2.5-4.0) and MVX (HR=2.4, 95% CI=1.9-3.2) (Figure 1). Conclusion: In this community cohort of patients with HF, frailty and MVX are positively associated with one another. However, both indicators are independently associated with an increased risk of death and can contribute to risk stratification.

Background: Heart failure (HF) is associated with metabolic alterations, including ketogenesis. However, determinants of ketogenesis and risk of mortality in HF is not defined. Total ketone bodies (KB) include β-hydroxybutyrate, acetoacetate, and acetone and can be measured in plasma by nuclear magnetic resonance (NMR). The aim of this study is to determine the relationship between KB and clinical characteristics in a community HF cohort and to assess the association between KB and all-cause mortality. Methods: A population-based cohort of 1,389 HF patients was prospectively enrolled between 2003 and 2012. Plasma KB was measured by LP4 NMR LipoProfile ® assay/test on the Vantera® NMR analyzer platform. A conditional inference tree method (ctree R Package) was used to determine optimal KB group cut points. Associations between clinical characteristics and KB were measured with Wilcoxon rank sum test and Pearson’s Chi-squared test. Kaplan-Meier method estimated survival. Cox regression analyses were used to estimate associations between KB concentrations and mortality. Results: Among the 1,382 HF patients with KB measurements, the median age was 78 years (IQR 68-84) and 52% were men. Median KB was 180 μM (IQR 134-308). Patients were divided into two groups with lower KB (≤471.5 μM) and higher KB (>471.5 μM). Patients with higher KB (N=210) had lower BMI, higher BNP, and were more likely to be in the New York Heart Association class III-IV; however, these patients were less likely to have hyperlipidemia, coronary disease, or diabetes mellitus (P < 0.05). Age, sex, creatinine, ejection fraction, or Meta-Analysis Global Group in Chronic HF (MAGGIC) score did not differ by KB group. Higher KB was associated with worse survival (figure). After adjustment for the MAGGIC score, higher KB was associated with increased risk of mortality (HR 1.3; 95% CI, 1.08-1.48). Conclusions: In this community HF cohort, higher KB was associated with increased mortality, independent of the MAGGIC score.

New more effective lipid-lowering therapies have made it important to accurately determine Low-density lipoprotein-cholesterol (LDL-C) at both high and low levels. LDL-C was measured by the β-quantification reference method (BQ) (N = 40,346) and compared to Friedewald (F-LDL-C), Martin (M-LDL-C), extended Martin (eM-LDL-C) and Sampson (S-LDL-C) equations by regression analysis, error-grid analysis, and concordance with the BQ method for classification into different LDL-C treatment intervals. For triglycerides (TG) < 175 mg/dL, the four LDL-C equations yielded similarly accurate results, but for TG between 175 and 800 mg/dL, the S-LDL-C equation when compared to the BQ method had a lower mean absolute difference (mg/dL) (MAD = 10.66) than F-LDL-C (MAD = 13.09), M-LDL-C (MAD = 13.16) or eM-LDL-C (MAD = 12.70) equations. By error-grid analysis, the S-LDL-C equation for TG > 400 mg/dL not only had the least analytical errors but also the lowest frequency of clinically relevant errors at the low (<70 mg/dL) and high (>190 mg/dL) LDL-C cut-points (S-LDL-C: 13.5%, F-LDL-C: 23.0%, M-LDL-C: 20.5%) and eM-LDL-C: 20.0%) equations. The S-LDL-C equation also had the best overall concordance to the BQ reference method for classifying patients into different LDL-C treatment intervals. The S-LDL-C equation is both more analytically accurate than alternative equations and results in less clinically relevant errors at high and low LDL-C levels.