Validity of a Novel Method for Estimating Low-Density Lipoprotein Cholesterol Levels in Cardiovascular Disease Patients Treated with Statins

Remnant cholesterol and the risk of aortic valve calcium progression: insights from the MESA study

BACKGROUND

Remnant cholesterol (RC) is implicated in the risk of cardiovascular disease. However, comprehensive population-based studies elucidating its association with aortic valve calcium (AVC) progression are limited, rendering its precise role in AVC ambiguous.

METHODS

From the Multi-Ethnic Study of Atherosclerosis database, we included 5597 individuals (61.8 ± 10.1 years and 47.5% men) without atherosclerotic cardiovascular disease at baseline for analysis. RC was calculated as total cholesterol minus high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C), as estimated by the Martin/Hopkins equation. Using the adjusted Cox regression analyses, we examined the relationships between RC levels and AVC progression. Furthermore, we conducted discordance analyses to evaluate the relative AVC risk in RC versus LDL-C discordant/concordant groups.

RESULTS

During a median follow-up of 2.4 ± 0.9 years, 568 (10.1%) participants exhibited AVC progression. After adjusting for traditional cardiovascular risk factors, the HRs (95% CIs) for AVC progression comparing the second, third, and fourth quartiles of RC levels with the first quartile were 1.195 (0.925-1.545), 1.322 (1.028-1.701) and 1.546 (1.188-2.012), respectively. Notably, the discordant high RC/low LDL-C group demonstrated a significantly elevated risk of AVC progression compared to the concordant low RC/LDL-C group based on their medians (HR, 1.528 [95% CI 1.201-1.943]). This pattern persisted when clinical LDL-C threshold was set at 100 and 130 mg/dL. The association was consistently observed across various sensitivity analyses.

CONCLUSIONS

In atherosclerotic cardiovascular disease-free individuals, elevated RC is identified as a residual risk for AVC progression, independent of traditional cardiovascular risk factors. The causal relationship of RC to AVC and the potential for targeted RC reduction in primary prevention require deeper exploration.

Dataset dependency of low-density lipoprotein-cholesterol estimation by machine learning

OBJECTIVES

We evaluated the applicability of a machine learning-based low-density lipoprotein-cholesterol (LDL-C) estimation method and the influence of the characteristics of the training datasets.

METHODS

Three training datasets were chosen from training datasets: health check-up participants at the Resource Center for Health Science (N = 2664), clinical patients at Gifu University Hospital (N = 7409), and clinical patients at Fujita Health University Hospital (N = 14,842). Nine different machine learning models were constructed through hyperparameter tuning and 10-fold cross-validation. Another test dataset of another 3711 clinical patients at Fujita Health University Hospital was selected as the test set used for comparing and validating the model against the Friedewald formula and the Martin method.

RESULTS

The coefficients of determination of the models trained on the health check-up dataset produced coefficients of determination that were equal to or inferior to those of the Martin method. In contrast, the coefficients of determination of several models trained on clinical patients exceeded those of the Martin method. The means of the differences and the convergences to the direct method were higher for the models trained on the clinical patients' dataset than for those trained on the health check-up participants' dataset. The models trained on the latter dataset tended to overestimate the 2019 ESC/EAS Guideline for LDL-cholesterol classification.

CONCLUSION

Although machine learning models provide valuable method for LDL-C estimates, they should be trained on datasets with matched characteristics. The versatility of machine learning methods is another important consideration.

Evaluation of low-density lipoprotein cholesterol equations by cross-platform assessment of accuracy-based EQA data against SI-traceable reference value

OBJECTIVES

Low-density lipoprotein cholesterol (LDLC) is the primary cholesterol target for the diagnosis and treatment of cardiovascular disease (CVD). Although beta-quantitation (BQ) is the gold standard to determine LDLC levels accurately, many clinical laboratories apply the Friedewald equation to calculate LDLC. As LDLC is an important risk factor for CVD, we evaluated the accuracy of Friedewald and alternative equations (Martin/Hopkins and Sampson) for LDLC.

METHODS

We calculated LDLC based on three equations (Friedewald, Martin/Hopkins and Sampson) using the total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDLC) in commutable serum samples measured by clinical laboratories participating in the Health Sciences Authority (HSA) external quality assessment (EQA) programme over a 5 years period (number of datasets, n=345). LDLC calculated from the equations were comparatively evaluated against the reference values, determined from BQ-isotope dilution mass spectrometry (IDMS) with traceability to the International System of Units (SI).

RESULTS

Among the three equations, Martin/Hopkins equation derived LDLC had the best linearity against direct measured (y=1.141x - 14.403; R2=0.8626) and traceable LDLC (y=1.1692x - 22.137; R2=0.9638). Martin/Hopkins equation (R2=0.9638) had the strongest R2 in association with traceable LDLC compared with the Friedewald (R2=0.9262) and Sampson (R2=0.9447) equation. The discordance with traceable LDLC was the lowest in Martin/Hopkins (median=-0.725%, IQR=6.914%) as compared to Friedewald (median=-4.094%, IQR=10.305%) and Sampson equation (median=-1.389%, IQR=9.972%). Martin/Hopkins was found to result in the lowest number of misclassifications, whereas Friedewald had the most numbers of misclassification. Samples with high TG, low HDLC and high LDLC had no misclassification by Martin/Hopkins equation, but Friedewald equation resulted in ∼50% misclassification in these samples.

CONCLUSIONS

The Martin/Hopkins equation was found to achieve better agreement with the LDLC reference values as compared to Friedewald and Sampson equations, especially in samples with high TG and low HDLC. Martin/Hopkins derived LDLC also enabled a more accurate classification of LDLC levels.

Correlation of extended Martin/Hopkins equation with a direct homogeneous assay in assessing low-density lipoprotein cholesterol in patients with hypertriglyceridemia

BACKGROUND

The Friedewald or Martin/Hopkins equation is widely used to estimate low-density lipoprotein cholesterol (LDL-C) at triglyceride (TG) levels <400 mg/dL. In this study, we aimed to validate the recently developed Sampson and extended Martin/Hopkins equations intended for use in patients with TG levels up to 800 mg/dL by comparing them to a direct homogenous assay.

METHODS

In total, 8676 participants with serum TG levels <800 mg/dL were enrolled in this study. LDL-C was directly measured using Abbott homogeneous assay (DLDL) and estimated using the Friedewald (FLDL), Martin/Hopkins (MLDL), extended Martin/Hopkins (EMLDL), and Sampson equations (SLDL). The overall concordance between the DLDL and LDL-C estimates was calculated. The performance of the four equations was also compared using Bland-Altman plots and mean absolute difference (MAD).

RESULTS

The EMLDL was more accurate than other LDL-C equations particularly for patients with TG≥400 mg/dL (MAD = 10.43; vs. FLDL: MAD = 21.1; vs. SLDL: MAD 11.62). The overall concordance of FLDL, MLDL, EMLDL, and SLDL with DLDL in TG values ranging from 200 to 799 mg/dL were 52.2, 70.5, 71.6, and 65.7%, respectively (p < 0.001), demonstrating the EMLDL as the most optimal estimation method, particularly for high TG levels (≥200 mg/dL).

CONCLUSION

Both the original and extended Martin/Hopkins method are optimal in estimating LDL-C levels in clinical laboratories using the Abbott analyzer in patients with TG levels of 200-399 and 400-799 mg/dL, respectively. Meanwhile, caution is need that considerable underestimation of Friedewald and Sampson equation could lead to undertreatment in hypertriglyceridemia.

Comparison of Three Methods for LDLC Calculation for Cardiovascular Disease Risk Categorisation in Three Distinct Patient Populations

BACKGROUND

Limitations of the Friedewald equation for low-density-lipoprotein cholesterol (F-LDLC) calculation led to the Martin-Hopkins (M-LDLC) and Sampson-National Institutes of Health (S-LDLC) equations. We studied these newer calculations of LDLC for correlation and discordance for stratification into the Canadian Cardiovascular Society (CCS) 2021 Dyslipidemia Guidelines' cardiovascular disease (CVD) risk categories.

METHODS

We performed analyses on lipid profiles from 3 populations: records of a hospital biochemistry laboratory (population 1), lipid clinic patients without select monogenic dyslipidemias (population 2A), and lipid clinic patients with familial hypercholesterolemia (FH; population 2B).

RESULTS

There was very strong correlation among the 3 calculated LDLC. In populations 1 and 2A, M-LDLC and S-LDLC were progressively higher than F-LDLC as triglyceride (TG) levels increased from normal to ∼ 5 mmol/L. In population 2B, M-LDLC was higher than F-LDLC, but S-LDLC was progressively lower than F-LDLC. Using the CCS 2021 guidelines' 4 CVD risk categories, 7.0% (population 2A) to 7.2% (population 1) of cases for M-LDLC vs F-LDLC and 3.9% (population 2A) to 4.4% (population 1) of cases for S-LDLC vs F-LDLC were reclassified to an adjacent CVD risk category, mostly from a lower to a higher risk category.

CONCLUSIONS

Switching from F-LDLC to S-LDLC or M-LDLC can reclassify up to ∼ 4.4% or 7.2% of patients, respectively, to another CCS CVD risk category. The difference between F-LDLC and M-LDLC or S-LDLC is greater with higher TG, and with lower LDLC. We recommend that clinical laboratories switch to reporting results from either M-LDLC or S-LDLC, but S-LDLC should not be used in FH patients, pending further studies.

Remnant Cholesterol and the Risk of Coronary Artery Calcium Progression: Insights From the CARDIA and MESA Study

BACKGROUND:

Remnant cholesterol (RC) contributes to residual risk of atherosclerotic cardiovascular disease, but population-based evidence on the prospective relationship between RC and coronary artery calcium (CAC) progression is rare.

METHODS:

We pooled data obtained from 6544 atherosclerotic cardiovascular disease–free individuals from the CARDIA study (Coronary Artery Risk Development in Young Adults; n=2635) and MESA (Multi-Ethnic Study of Atherosclerosis; n=3909), with a mean±SD age of 47.2±19.8 years; 3019 (46.1%) were men who completed computed tomography of CAC at baseline. RC was measured as total cholesterol minus HDL (high-density lipoprotein) cholesterol minus calculated LDL (low-density lipoprotein) cholesterol (LDL-C) estimated by using the Martin/Hopkins equation. Adjusted Cox models were used to assess the relationships between RC levels and CAC progression. We also performed discordance analyses examining the risk of CAC progression in RC versus LDL-C discordant/concordant groups using median cut points and clinically relevant LDL-C targets.

RESULTS:

During a median follow-up of 8.6 years, 2778 (42.5%) participants had CAC progression. After multivariable adjustment for demographics and cardiovascular risk factors, a 1-mg/dL increase in RC levels was associated with a 1.3% higher risk of CAC progression (hazard ratio, 1.013 [95% CI, 1.008–1.017]). Results were similar when we categorized individuals by RC quartiles. Furthermore, the discordant high RC/low LDL-C group had a significantly higher risk of CAC progression than the concordant low RC/LDL-C group regarding their medians (hazard ratio, 1.195 [95% CI, 1.063–1.343]) or when setting the clinical LDL-C cut points at 100 and 130 but not 70 mg/dL. The association remained robust across a series of sensitivity analyses.

CONCLUSIONS:

Elevated RC levels were associated with an increased risk of CAC progression independent of traditional cardiovascular risk factors, even in individuals with optimal LDL-C levels.

REGISTRATION:

URL: https://www.clinicaltrials.gov ; Unique identifier: NCT00005130 (CARDIA) and NCT00005487 (MESA).

Validation of the Martin method to estimate low-density lipoprotein cholesterol concentrations in Japanese populations and a modified method for laboratory information system application

OBJECTIVES

High concentrations of low-density lipoprotein cholesterol (LDL-C) are a risk factor for cardiovascular disease. We validated the efficacy of the Martin method is useful in the estimation of LDL-C concentrations was validated in Japanese populations and derived a modified Martin method for easy laboratory information system applications.

METHODS

We created 3 subject groups, including 2664 health check-up participants registered with the Resource Center for Health Science, 29,806 clinical patients (A) in the Gifu University Hospital, and 113,716 clinical patients (B) in the Fujita Health University Hospital. Each method to estimate serum LDL-C concentrations (Friedewald formula, Martin method and modified Martin method) was validated by correlation analysis with serum LDL-C concentrations measured using a direct method.

RESULTS

The correlation coefficients with the direct method in terms of the Friedewald formula, Martin method, and modified Martin method were 0.963, 0.972 and 0.970 in the health check-up participants; 0.946, 0.962 and 0.961 in clinical patients A; and 0.961, 0.979 and 0.978 in clinical patients B, respectively. Concordance ratios with using the direct method in the Friedewald formula, Martin method and modified Martin method were 82.8%, 85.5% and 85.3% in the health check-up participants; 76.4%, 80.5% and 80.2% in clinical patients A; and 76.1%, 82.6% and 82.6% in clinical patients B, respectively.

CONCLUSION

Our results show that the Martin and modified Martin methods display good performance in terms of the estimation of LDL-C concentrations among triglyceride concentrations of a wide range, and they may thus be useful for estimating LDL-C concentrations.

Angiopoietin-like proteins in atherosclerosis

Atherosclerosis, as a chronic inflammatory disease within the arterial wall, is a leading cause of morbidity and mortality worldwide due to its role in myocardial infarction, stroke and peripheral artery disease. Additional evidence is emerging that the angiopoietin-like (ANGPTL) family of proteins participate in the pathology of this disease process via endothelial dysfunction, inflammation, dyslipidemia, calcification, foam cell formation and platelet activation. This review summarizes current knowledge on the ANGPTL family of proteins in atherosclerosis related pathological processes. Moreover, the potential value of ANGPTL family proteins as predictive biomarkers in atherosclerosis is discussed. Given the attractive role of ANGPTL3, ANGPTL4, ANGPTL8 in atherosclerotic dyslipidemia via regulation of lipoprotein lipase (LPL), antisense oligonucleotide or/and monoclonal antibody-based inactivation of these proteins represent potential atherosclerotic therapies.

Calculated values of serum LDL-cholesterol (LDL-C) - for better or worse?

BACKGROUND AND AIMS

The use of Friedewald's formula to calculate serum low-density lipoprotein cholesterol (LDL-C) is well-known to have limitations. A modification of it, in 2013, has been proposed to be superior. However, it was not known whether LDL-C values (calculated by the modified formula) meet laboratory performance criteria for their estimation. This study aimed to evaluate this.

METHODS AND RESULTS

LDL-C values were calculated for 129,821 lipid profiles, using both Friedewald's formula and its modified version. Kappa statistics and intra-class correlation coefficient (ICC) were used to determine degree of agreement between directly measured and calculated values for LDL-C. Bias and total percentage error of the values were calculated. LDL-C concentrations calculated by the modified formula showed a greater degree of agreement with directly measured values (kappa = 0.713) than those calculated by Friedewald's formula (kappa = 0.595). Both the formulae produced values with negative biases (-3.47 for the modified formula and -7.62 for Friedewald's formula) and total percentage errors above the recommended limit of 12% (15.57% for the modified formula and 21.77% for Friedewald's formula). ICC showed that values calculated by the modified formula showed a greater degree of agreement with directly measured values, across a range of LDL-C values.

CONCLUSION

Calculated LDL-C values, using the modified formula, showed better agreement with directly measured values, and less bias and percentage total error than those obtained by use of Friedewald's formula. However, the percentage total error with use of the modified formula exceeded the recommended limit for LDL-C.

Estimation of low-density lipoprotein cholesterol by machine learning methods

BACKGROUND

Accurate determination of low-density lipoprotein cholesterol (LDL) is important for coronary heart disease risk assessment and atherosclerosis. Apart from direct determination of LDL values, models (or equations) are used. A more recent approach is the use of machine learning (ML) algorithms.

METHODS

ML algorithms were used for LDL determination (regression) from cholesterol, HDL and triglycerides. The methods used were multivariate Linear Regression (LR), Support Vector Machines (SVM), Extreme Gradient Boosting (XGB) and Deep Neural Networks (DNN), in both larger and smaller data sets. Also, LDL values were classified according to both NCEP III and European Society of Cardiology guidelines.

RESULTS

The performance of regression was assessed by the Standard Error of the Estimate. ML methods performed better than established equations (Friedewald and Martin). The performance all ML methods was comparable for large data sets and was affected by the divergence of the train and test data sets, as measured by the Jensen-Shannon divergence. Classification accuracy was not satisfactory for any model.

CONCLUSIONS

Direct determination of LDL is the most preferred route. When not available, ML methods can be a good substitute. Not only deep neural networks but other, less computationally expensive methods can work as well as deep learning.

Assessment of Low LDL Cholesterol in Patients Treated by PCSK9 Inhibition: Comparison of Martin/Hopkins and Friedewald Estimations

PURPOSE

Recent guidelines recommend further reduction of low-density lipoprotein cholesterol (LDL-C) in high-risk populations. The use of proprotein convertase subtilisin/kexin type-9 inhibitors (PCSK9i) enables many patients to achieve profound reduction in LDL-C. However, in patients with low cholesterol, the commonly used Friedewald equation tends to underestimate LDL-C, which may result in undertreatment. We aimed to compare Friedewald LDL-C estimation with the more novel Martin/Hopkins method in PCSK9i-treated patients achieving low LDL-C.

METHODS

We investigated high-risk patients treated by PCSK9i in whom Friedewald LDL-C levels were < 70 mg/dL and triglycerides ≤ 300 mg/dL. LDL-C was additionally assessed by the Martin/Hopkins method. The compatibility between estimations was evaluated using methods of concordance and reclassification between LDL-C categories (< 25, 25-40, 40-55, 55-70 mg/dL) and according to triglyceride strata.

RESULTS

Mean age was 65 ± 10 years. The correlation coefficient between LDL-C estimations was r = 0.898. Martin/Hopkins reclassified 269 of the 608 patients (44%) to a higher LDL-C category, with 14% of the patients reaching LDL-C > 70 mg/dL. Of the 390 patients achieving Friedewald LDL-C < 55 mg/dL, 113 (29%) were estimated to have LDL-C ≥ 55 mg/dL by the Martin/Hopkins equation. The magnitude of discordance between LDL-C estimates was more pronounced in hypertriglyceridemic patients in whom LDL-C reclassification from < 55 to ≥ 55 mg/dL was observed in 48%.

CONCLUSIONS

In real-world practice of high-risk patients achieving low LDL-C under PCSK9i, Martin/Hopkins algorithm displayed significant proportion of LDL-C upward discordance compared to the Friedewald equation, particularly observed in patients with elevated triglycerides, identifying patients that may need treatment intensification.

Comparison of Novel Equations for Estimating Low-Density Lipoprotein Cholesterol in Patients Undergoing Coronary Angiography

AIM

The importance of precisely quantifying low-density lipoprotein cholesterol (LDL-C) has become more pronounced over the years, with the rise of metabolic syndrome in the population and the reduction in LDL-C treatment goals. This study aims to compare two novel equations indirectly estimating LDL-C and assess their compatibility with Friedewald formula, in a population with high cardiovascular risk.

METHODS

This study is a retrospective analysis of the lipid profiles of 10,006 patients who underwent coronary angiography. LDL-C was calculated using Friedewald, Martin, and Sampson equations, and the compatibility between estimations was compared using methods of concordance and reclassification.

RESULTS

Our findings show that Martin and Sampson equations displayed high rates of upward LDL-C reclassification (10.8% and 7.5%, respectively) when compared with Friedewald equation. In comparison to the Sampson method, Martin also reclassified 3.8 % of patients to a higher LDL-C category. The magnitude of discordance between LDL-C estimates was more pronounced in hypertriglyceridemic patients, and this increased progressively with the reduction in LDL-C. The proportion of patients with LDL-C ＜70 mg/dL reclassified to a higher LDL-C category reached 44% (Sampson vs. Friedewald), 65% (Martin vs. Friedewald), and 37% (Martin vs.Sampson) in those with triglyceride levels between 200 and 399 mg/dL.

CONCLUSIONS

Both Martin and Sampson LDL-C estimates displayed significant proportion of upward discordance with reclassification to higher LDL-C categories compared to Friedewald formula, particularly in patients with elevated triglycerides and low LDL-C, a population in whom more accurate estimation of LDL-C is required. Further studies are warranted to validate the recently developed Sampson equation with comparison to Martin method that tended to more significantly overestimate LDL-C.

Update on low-density lipoprotein cholesterol quantification

PURPOSE OF REVIEW

β-Quantification is considered the reference measurement procedure for low-density lipoprotein cholesterol (LDL-C). However, this technique is time-consuming and thus is inappropriate for routine clinical practice. Therefore, the Friedewald equation or homogeneous assays have been widely utilized. As several pitfalls exist with these two methods, a novel method for estimating LDL-C was developed by Martin et al. RECENT FINDINGS: Martin's method uses a strata-specific median for the triglycerides/very low-density lipoprotein cholesterol (VLDL-C) ratio on the basis of triglycerides and non-HDL-C concentrations. Recent studies show that Martin's method better correlates with β-quantification or homogeneous assays compared with the Friedewald equation, especially with values of triglycerides at least 150 mg/dl and/or LDL-CD less than 70 mg/dl. Such findings have also been demonstrated in other ethnic groups (Japanese and Korean) and disease populations, including diabetes and cardiovascular disease, in which the triglycerides/VLDL-C ratio can be affected.

SUMMARY

For the current therapeutic goal of LDL-C values below 70 mg/dl in high-risk patients, accurate assessment of LDL-C levels at very low levels is required. Martin's method could overcome pitfalls such as underestimation of the Friedewald equation at this level. Further evaluation of the triglycerides/VLDL-C ratio in participants with diverse ethnic backgrounds or clinical conditions would expand the implementation of this novel method.

Validation of the novel Martin method for LDL cholesterol estimation

INTRODUCTION

Accurate determination of low-density lipoprotein cholesterol (LDL) is important for coronary heart disease (CHD) risk assessment among others. A low-cost method used is the Friedewald equation, calculating LDL from total cholesterol after subtracting high-density lipoprotein cholesterol (HDL) and triglycerides divided by 5. A new calculation method has been proposed where the value of 5 is not fixed but depends on the values of the other parameters.

RESULTS

We validated this method in a Greek population sample, by comparing direct LDL, the Friedewald equation and the novel method. Some clinical laboratories use the direct determination when TG > 200 mg/dl (2.26 mmol/lt). We performed segmented linear regression to check if this value makes sense. Bayesian linear regression was performed to compare the direct determination to the Friedewald and novel one.

CONCLUSIONS

We found that TG > 200 mg/dl is a sensible threshold value since it is a saddle point for the standard error of the regression. For Bayesian linear regression, the results were inconclusive. When the LDL values were used for classification of CHD, it turned out that the novel method was better than the Friedewald equation at correctly classifying LDL levels for CHD risk assessment.