How should low-density lipoprotein cholesterol be calculated in 2022?

Calculated LDL-cholesterol: comparability of the extended Martin/Hopkins, Sampson/NIH, Friedewald and four other equations in South African patients

AIMS

The reference method for low-density lipoprotein-cholesterol (LDL-C) is ultracentrifugation. However, this is unsuitable for routine use and therefore direct LDL-C assays and predictive equations are used. In this study, we compared the Friedewald, extended Martin/Hopkins, Sampson/NIH and four other equations to a direct assay.

METHODS

We analysed 44 194 lipid profiles from a mixed South African population. The LDL-C predictive equations were compared with direct LDL-C assay and analysed using non-parametric statistics and error grid analysis.

RESULTS

Both the extended Martin/Hopkins and Sampson/NIH equations displayed the best correlation with direct LDL-C in terms of desirable bias and total allowable error. The direct LDL-C assay classified 13.9% of patients in the low LDL-C (1.0-1.8 mmol/L) category, in comparison to the extended Martin/Hopkins equation (13.4%), the Sampson equation (14.6%) and the Friedewald equation (16.0%). The Sampson/NIH was least biased in the low LDL-C category (<1.8 mmol/L) and produced the least overall clinically relevant errors compared with the extended Martin/Hopkins and Friedewald equations in the low-LDL-C category.

CONCLUSIONS

Our findings suggest only a marginal difference between the extended Martin/Hopkins equation and the Sampson/NIH equation with the use of the Beckman Coulter DxC800 analyser in this population. The results favour the implementation of the Sampson/NIH equation when the Beckman Coulter DxC analyser is used, but the extended Martin/Hopkins may also be safely implemented. Both of these equations performed significantly better than the Friedewald equation. We recommend that patients be monitored using one of these methods and that each laboratory perform its own validation of either equation to ensure continuation and accuracy, and to prevent between-method variation.

Differences between repeated lipid profile measurements in a tertiary hospital over a short time period

BACKGROUND

Measurement of the plasma lipid profile, mainly low-density lipoprotein cholesterol (LDL-C), is widely used in the management of hospitalized patients as part of their cardiometabolic risk assessment. In common practice, LDL-C is calculated indirectly by the Friedewald equation. For many years, fasting of 8-14 h is needed to obtain an accurate lipid profile measurement, although recent guidelines do not necessitate it. The aim of this study was to find patients with two consecutive LDL-C measurements taken over a short time period on the same admission to see if a significant difference exists and to suggest reasons that may explain it. We also aim to define whether the difference between LDL-C calculated by the Friedewald equation is diminished while using the newer Martin/Hopkins, de Cordova or Sampson/NIH equations.

METHODS

This was a retrospective cohort study performed in one medical center in Israel. In a five-year time period, 772 patients with two repeated LDL-C measurements taken on the same admission were found. The median time gap between tests was 2 days. Correlations between laboratory results and LDL-C measurements were determined.

RESULTS

A total of 414 patients (53.6%) had a difference greater than the acceptable total error of 8.9% in LDL-C calculation using the Friedewald equation, with a mean 25.8% difference between the two tests. Newer LDL-C calculations showed less diversity. Non-HDL-C was found as the only variable with a major correlation with LDL-C results in all equations. A weaker correlation was found with HDL-C. Triglycerides showed an even weaker correlation, and glucose differences had no correlation with LDL-C differences.

CONCLUSIONS

Repeated LDL-C measurements can vary widely, even during a short period of hospitalization. In this study, more than half of the patients had a significant difference between their consecutive LDL-C results. This wide difference between two consecutive tests was diminished using newer calculations, yet not well explained. The fasting state likely has no effect on LDL-C levels. The results of this study might emphasize that many factors influence LDL-C calculation, especially in the disease state. Further research is needed, especially in looking for a more accurate LDL-C calculation from existing formulas.

Triglyceride-Glucose Index and Cardiovascular Events in Kidney Transplant Recipients

Introduction

Kidney transplant recipients (KTRs) have an increased risk of cardiovascular (CV) events (CVEs) compared with the general population. The impact of insulin resistance on CV risk after transplantation is not well defined.

Methods

We tested whether triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance, may predict posttransplant CVEs in a cohort of 715 consecutive KTRs all included 1 year after transplant.

Results

Follow-up was 9.1 ± 4.6 years. Mean TyG at inclusion was 4.75 ± 0.29 (median, 4.73 [4.14-5.84]). In multiple regression analysis, having a TyG above the median value was associated with higher body mass index (BMI), low high-density lipoprotein (HDL) cholesterol level, and greater urinary protein excretion. A total of 127 CVEs (17.7%) occurred during the study period. In univariate analysis, TyG was strongly associated with CVE occurrence (hazard ratio [HR] 2.06, 95% CI 1.42-3.50, for each increase of 0.1 in TyG, P < 0.001). The best predictive value was 4.87 (HR 6.32, 95% CI 3.30-12.11, P < 0.001). The risk of CVE gradually increased with higher TyG index (quartile 2, HR 1.71, 95% CI 0.84-5.20, P = 0.139; quartile 3, HR 3.12, 95% CI 1.61-6.02, P < 0.001; quartile 4, HR 7.46, 95% CI 4.03-13.80, P < 0.001, vs. quartile 1). TyG remained associated with CVE in multivariate analysis (HR 2.11, 95% CI 1.22-3.68, for each increase of 0.1 in TyG, P < 0.001).

Conclusion

Insulin resistance, as measured by the TyG index is strongly associated with CVE in KTRs. Improving insulin sensitivity seems to be a major issue to prevent CV morbidity and mortality in this high-risk population.

Measurement of Serum Low Density Lipoprotein Cholesterol and Triglyceride-Rich Remnant Cholesterol as Independent Predictors of Atherosclerotic Cardiovascular Disease: Possibilities and Limitations

The serum low density lipoprotein cholesterol (LDL-C) concentration is the dominant clinical parameter to judge a patient's risk of developing cardiovascular disease (CVD). Recent evidence supports the theory that cholesterol in serum triglyceride-rich lipoproteins (TRLs) contributes significantly to the atherogenic risk, independent of LDL-C. Therefore, combined analysis of both targets and adequate treatment may improve prevention of CVD. The validity of TRL-C calculation is solely dependent on the accuracy of the LDL-C measurement. Direct measurement of serum LDL- C is more accurate than established estimation procedures based upon Friedewald, Martin-Hopkins, or Sampson equations. TRL-C can be easily calculated as total C minus high density lipoprotein C (HDL-C) minus LDL-C. Enhanced serum LDL-C or TRL-C concentrations require different therapeutic approaches to lower the atherogenic lipoprotein C. This review describes the different atherogenic lipoproteins and their possible analytical properties and limitations.

Performance of equations for calculated LDL-C in hypertriglyceridaemia: which one correlates best with directly measured LDL-C?

BACKGROUND

The gold standard for measuring LDL-C is impractical and direct measurements have numerous shortcomings. Older predictive equations are used only with triglycerides (TG's) below 4.52mmol/L. We evaluated the newer equations validated for use in hypertriglyceridaemia by comparison with direct LDL-C.

MATERIALS AND METHODS

Datasets from two platforms (Abbott Architect and Roche Cobas) comprised of a large cohort of 64765 individuals were used to compare the Sampson-National Institutes of Health 2 (S-NIH2) and Extended Martin-Hopkins (E-MH) equations for LDL-C with direct LDL-C (dLDL-C) assays.

RESULTS

With TG's of 4.52-9.04 mmol/L the S-NIH2 equation tended to calculate lower values than measured by dLDL-C and the E-MH equation calculated higher values. Both equations correlated better with the dLDL-C measured on Abbott than Roche with the E-MH equation having more values falling within acceptable concordance levels on both platforms.

CONCLUSION

The E-MH equation correlates better with dLDL-C than the S-NIH2 on both platforms with TG levels up to 9.04mmol/L. With hypertriglyceridaemia, the E-MH equation is less likely than the S-NIH2 equation to underestimate LDL-C when compared to the dLDL-C and will be less likely to underdiagnose patients with LDL-C levels requiring treatment according to current guidelines.

[HDL - Quo vadis]

Many epidemiological studies found low plasma levels of high-density lipoprotein (HDL) cholesterol (HDL-C) associated with an increased risk of atherosclerotic cardiovascular disease (ASCVD). In cell culture and animal models, HDL particles show many anti-atherogenic actions. However, until now, clinical trials did not find any prevention of ASCVD events by drugs elevating HDL-C levels, at least not beyond statins. Also, genetic studies show no associations of HDL-C levels altering variants with cardiovascular risk. Therefore, the causal role and clinical benefit of HDL-C elevation in ASCVD are questioned. However, the interpretation of previous data has important limitations: First, the inverse relationship of HDL-C with the risk of ASCVD is limited to concentrations < 60 mg/dl (< 1.5 mmol/l). Higher concentrations do not reduce the risk of ASCVD events and are even associated with increased mortality. Therefore, neither the higher-the-better strategies of earlier drug developments nor the assumption of linear cause-and-effect relationships in Mendelian randomization trials are justified. Second, most of the drugs tested so far do not act specifically on HDL metabolism. Therefore, the futile endpoint studies question the clinical benefit of the investigated drugs, but not the importance of HDL in ASCVD. Third, the vascular functions of HDL are not exerted by its cholesterol content (i.e. HDL-C), but by a variety of other molecules. Comprehensive knowledge of the structure-function-disease relationships of HDL particles and their molecules is a prerequisite for testing their physiological and pathogenic relevance and possibly for optimizing the diagnosis and treatment of persons with HDL-associated risk of ASCVD, but also for other diseases, such as diabetes, chronic kidney disease, infections, autoimmune and neurodegenerative diseases.

Best practice for LDL-cholesterol: when and how to calculate

The lipid profile is important in the risk assessment for cardiovascular disease. The lipid profile includes total cholesterol, high-density lipoprotein (HDL)-cholesterol, triglycerides (TGs) and low-density lipoprotein (LDL)-cholesterol (LDL-C). LDL-C has traditionally been calculated using the Friedewald equation (invalid with TGs greater than 4.5 mmol/L and is based on the assumption that the ratio of TG to cholesterol in very- low-density lipoprotein (VLDL) is 5 when measured in mg /dL). LDL-C can be quantified with a reference method, beta-quantification involving ultracentrifugation and this is unsuitable for routine use. Direct measurement of LDL-C was expected to provide a solution with high TGs. However, this has some challenges because of a lack of standardisation between the reagents and assays from different manufacturers as well as the additional costs. Furthermore, mild hypertriglyceridaemia also distorts direct LDL-C measurements. With the limitations of the Friedewald equation, alternatives have been derived. Newer equations include the Sampson-National Institutes of Health (NIH) equation 2 and the Martin-Hopkins equation. The Sampson-NIH2 equation was derived using beta-quantification in a population with high TG and multiple least squares regression to calculate VLDL-C, using TGs and non-HDL-C as independent variables. These data were used in a second equation to calculate LDL-C. The Sampson-NIH2 equation can be used with TGs up to 9 mmol/L. The Martin-Hopkins equation uses a 180 cell stratification of TG/non-HDL-C to determine the TG:VLDL-C ratio and can be used with TGs up to 4.5 mmol/L. Recently, an extended Martin-Hopkins equation has become available for TGs up to 9.04 mmol/L.This article discusses the best practice approach to calculating LDL-C based on the available evidence.

Accuracy and Clinical Impact of Estimating Low-Density Lipoprotein-Cholesterol at High and Low Levels by Different Equations

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.