Assessing the Practical Differences in LDL-C Estimates Calculated by Friedewald, Martin/Hopkins, or NIH Equation 2: An Observation Cross-Sectional Study

Objective

Low-density lipoprotein-cholesterol (LDL-C) remains a clinically important cholesterol target in primary prevention of atherosclerotic cardiovascular disease. The present study aimed to assess the practical differences among three equations utilized for the estimation of LDL-C: the Friedewald, the Martin/Hopkins, and the NIH equation 2.

Methods

Blood lipid measurements from 4,556 noninstitutionalized participants, aged 12 to 80, were obtained from the 2017-2020 National Health and Nutrition Examination Survey study. We 1) assessed the differences between three calculated LDL-C estimates, 2) examined the correlations between LDL-C estimates using correlation coefficients and regression, and 3) investigated the degree of agreement in classifying individuals into the LDL-C category using weighted Kappa and percentage of agreement.

Results

The differences in LDL-C estimates between equations varied by sex and triglyceride levels (p<0.001). Overall, the mean of absolute differences between Friedewald and Martin/Hopkins was 3.17 mg/dL (median=2.0, 95% confidence interval [CI] [3.07-3.27]). The mean of absolute differences between Friedewald and NIH Equation 2 was 2.08 mg/dL (median=2.0, 95% CI [2.03-2.14]). Friedewald correlated highly with Martin/Hopkins (r=0.991, rho=0.989) and NIH Equation 2 (r=0.998, rho=0.997). Cohen's weighted Kappa=0.92 between Friedewald and Martin/Hopkins, and 0.95 between Friedewald and NIH equation 2. The percentage of agreement in classifying individuals into the same LDL-C category was 93.0% between Friedewald and Martin/Hopkins, and 95.4% between Friedewald and NIH equation 2.

Conclusion

Understanding the practical differences in LDL-C calculations can be helpful in facilitating decision-making during a paradigm shift.

Accuracy of 23 Equations for Estimating LDL Cholesterol in a Clinical Laboratory Database of 5,051,467 Patients

Background

Alternatives to the Friedewald low-density lipoprotein cholesterol (LDL-C) equation have been proposed.

Objective

To compare the accuracy of available LDL-C equations with ultracentrifugation measurement.

Methods

We used the second harvest of the Very Large Database of Lipids (VLDbL), which is a population-representative convenience sample of adult and pediatric patients (N = 5,051,467) with clinical lipid measurements obtained via the vertical auto profile (VAP) ultracentrifugation method between October 1, 2015 and June 30, 2019. We performed a systematic literature review to identify available LDL-C equations and compared their accuracy according to guideline-based classification. We also compared the equations by their median error versus ultracentrifugation. We evaluated LDL-C equations overall and stratified by age, sex, fasting status, and triglyceride levels, as well as in patients with atherosclerotic cardiovascular disease, hypertension, diabetes, kidney disease, inflammation, and thyroid dysfunction.

Results

Analyzing 23 identified LDL-C equations in 5,051,467 patients (mean±SD age, 56±16 years; 53.3% women), the Martin/Hopkins equation most accurately classified LDL-C to the correct category (89.6%), followed by the Sampson (86.3%), Chen (84.4%), Puavilai (84.1%), Delong (83.3%), and Friedewald (83.2%) equations. The other 17 equations were less accurate than Friedewald, with accuracy as low as 35.1%. The median error of equations ranged from -10.8 to 18.7 mg/dL, and was best optimized using the Martin/Hopkins equation (0.3, IQR-1.6 to 2.4 mg/dL). The Martin/Hopkins equation had the highest accuracy after stratifying by age, sex, fasting status, triglyceride levels, and clinical subgroups. In addition, one in five patients who had Friedewald LDL-C <70 mg/dL, and almost half of the patients with Friedewald LDL-C <70 mg/dL and triglyceride levels 150-399 mg/dL, had LDL-C correctly reclassified to >70 mg/dL by the Martin/Hopkins equation.

Conclusions

Most proposed alternatives to the Friedewald equation worsen LDL-C accuracy, and their use could introduce unintended disparities in clinical care. The Martin/Hopkins equation demonstrated the highest LDL-C accuracy overall and across subgroups.

Comparison of Newly Proposed LDL-Cholesterol Estimation Equations

BACKGROUND

Low-density lipoprotein cholesterol is an important marker highly associated with cardiovascular disease. Since the direct measurement of it is inefficient in terms of cost and time, it is common to estimate through the Friedewald equation developed about 50 years ago. However, various limitations exist since the Friedewald equation was not designed for Koreans. This study proposes a new low-density lipoprotein cholesterol estimation equation for South Koreans using nationally approved statistical data.

METHODS

This study used data from the Korean National Health and Nutrition Examination Survey from 2009 to 2019. The 18,837 subjects were used to develop the equation for estimating low-density lipoprotein cholesterol. The subjects included individuals with low-density lipoprotein cholesterol levels directly measured among those with high-density lipoprotein cholesterol, triglycerides, and total cholesterol measured. We compared twelve equations developed in the previous studies and the newly proposed equation (model 1) developed in this study with the actual low-density lipoprotein cholesterol value in various ways.

RESULTS

The low-density lipoprotein cholesterol value estimated using the estimation formula and the actual low-density lipoprotein cholesterol value were compared using the root mean squared error. When the triglyceride level was less than 400 mg/dL, the root mean squared of the model 1 was 7.96, the lowest compared to other equations, and the model 2 was 7.82. The degree of misclassification was checked according to the NECP ATP III 6 categories. As a result, the misclassification rate of the model 1 was the lowest at 18.9%, and Weighted Kappa was the highest at 0.919 (0.003), which means it significantly reduced the underestimation rate shown in other existing estimation equations. Root mean square error was also compared according to the change in triglycerides level. As the triglycerides level increased, the root mean square error showed an increasing trend in all equations, but it was confirmed that the model 1 was the lowest compared to other equations.

CONCLUSION

The newly proposed low-density lipoprotein cholesterol estimation equation showed significantly improved performance compared to the 12 existing estimation equations. The use of representative samples and external verification is required for more sophisticated estimates in the future.

A machine learning-based approach for low-density lipoprotein cholesterol calculation using age, and lipid parameters

BACKGROUND

Low-density lipoprotein cholesterol (LDL-C) is a critical biomarker for cardiovascular disease. However, no consensus exists on the best method for estimating LDL-C in Chinese laboratories. This study aimed to develop a machine learning (ML) method for LDL-C estimation.

METHODS

An extensive data set of 111,448 samples were randomized into five equal subsets. ML-based equations were developed using age, sex, and lipid parameters based on five-fold cross-validation. The trained ML equations were externally validated in three different data sets. The performance of the ML equations was compared with the Friedewald, Martin/Hopkins, and Sampson equations.

RESULTS

The selected ML equations showed less bias with direct LDL-C than other LDL-C equations in the Chinese population, including those with triglycerides (TG) ≥ 400 mg / dL and LDL-C < 40 mg / dL. The performance of the ML equations was less susceptible to age. External validation showed the generalization of the ML equations.

CONCLUSIONS

This study highlights the potential of integrating sex, age, and lipid parameters into the ML equations to obtain a more robust and reliable LDL-C calculation.

Indirect calculation of LDL using thirteen equations in Pakistani population

BACKGROUND

Owing to the atherogenic properties, low density lipoprotein cholesterol (LDL-C) is the primary target for treatment and diagnosis of cardiovascular diseases (CVDs), hence accurate measurement of LDL-C is critical. Despite the availability of direct measurement assays for LDL-C, it is routinely calculated by Friedewald equation in clinical settings in Pakistan mostly due to financial constraints. However, the validity of this equation is impacted by several factors, therefore several other equations have been developed for the calculation of LDL-C.

MATERIALS AND METHODS

LDL-C of 39,385 individuals measured directly by homogenous assays (dLDL) was compared with LDL-C calculated by thirteen equations (cLDL-C). Stratifications based on different lipids i.e., triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL) were made to check the validity of these equations across all ranges of lipid profile. The correlation and median difference between dLDL and cLDL-C was statistically analyzed.

RESULTS

Overall Teerakanchana equation displayed a strong positive correlation (ρ = 0.967) and least median difference (-8.81) with dLDL, followed by Martin equation (ρ = 0.967). For higher TG ranges (>500 mg/dL), Teerakanchana equation had the least median difference (1.31) and a strong correlation (ρ = 0.800).

CONCLUSION

Our data suggest that Teerakanchana equation may be employed as an alternative to Friedewald equation for Pakistani population.

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

PURPOSE OF REVIEW

The reference method for low-density lipoprotein-cholesterol (LDL-C) quantitation is β-quantification, a technically demanding method that is not convenient for routine use. Indirect calculation methods to estimate LDL-C, including the Friedewald equation, have been used since 1972. This calculation has several recognized limitations, especially inaccurate results for triglycerides (TG) >4.5 mmol/l (>400 mg/dl). In view of this, several other equations were developed across the world in different datasets.The purpose of this review was to analyze the best method to calculate LDL-C in clinical practice by reviewing studies that compared equations with measured LDL-C.

RECENT FINDINGS

We identified 45 studies that compared these formulae. The Martin/Hopkins equation uses an adjustable factor for TG:very low-density lipoprotein-cholesterol ratios, validated in a large dataset and demonstrated to provide more accurate LDL-C calculation, especially when LDL <1.81 mmol/l (<70 mg/dl) and with elevated TG. However, it is not in widespread international use because of the need for further validation and the use of the adjustable factor. The Sampson equation was developed for patients with TG up to 9 mmol/l (800 mg/dl) and was based on β-quantification and performs well on high TG, postprandial and low LDL-C samples similar to direct LDL-C.

SUMMARY

The choice of equation should take into the level of triglycerides. Further validation of different equations is required in different populations.

Comparative Study of Calculated LDL-Cholesterol Levels with the Direct Assay in Patients with Hypothyroidism

Background Hypothyroidism is one among the many factors that predisposes one to coronary artery disease. As low-density lipoprotein-cholesterol (LDL-C) is associated with cardiovascular risk, calculated LDL-C should have good accuracy with minimal bias. Hypothyroidism alters the lipid composition of lipoproteins by the secretion of triglyceride-rich lipoproteins, which affects the calculation of LDL-C. The present study aimed to compare 13 different formulae for the calculation of LDL-C including the newly derived Martin's formula by direct assay in patients of hypothyroidism.

Method In this analytical cross-sectional study, a total of 105 patients with laboratory evidence of hypothyroidism, from January to June 2019, were studied, and blood samples were subjected for lipid profile analysis at central biochemistry laboratory. Calculated LDL-C was assessed by different formulae.

Result We observed that calculated LDL-C by Friedewald's, Cordova's, Anandaraja's, Hattori's, and Chen's formulae has bias less than ± 5 compared with direct LDL-C, with Anandaraja's formula having the lowest bias (2.744) and Cordova's formula having lowest bias percentage (−1.077) among them. According to the Bland–Altman plots, the bias in Friedewald's and Anandraja's were equally distributed below and above the reference line of direct LDL-C.

Conclusion This is the first study comparing different formulae for LDL-C calculation in patients with hypothyroidism. Anandaraja's formula was as equally effective as Friedewald's formula when used as an alternative cost-effective tool to evaluate LDL-C in hypothyroid patients. The recently proposed Martin's formula for calculated LDL-C had a higher bias when compared with Friedewald's and Anandaraja's formulae in patients with hypothyroidism.

Validation of a novel method for determination of low-density lipoprotein cholesterol levels in Indian patients with type 2 diabetes

BACKGROUND AND AIMS

LDL-cholesterol (LDL-C), being the primary predictor of cardiovascular disease in Type 2 diabetes (T2D), is associated with cardiovascular risk stratification and requires to be estimated with better accuracy with minimal bias. Different formulae have been devised to calculate the LDL-C from the measured lipid profile parameters.

METHODS

In this analytical cross-sectional study, a total of 150 patients with T2D were studied, and blood samples were subjected for lipid profile analysis at the Central Biochemistry laboratory. Different formulae assessed calculated LDL-C.

RESULTS

We observed that all formulae, except Ahmadi, underestimated the LDL-C compared to direct assay. A significant difference was observed between all calculated LDL-C and directly measured LDL-C. On linear regression analysis, the newer formula Martin's has a better approximation with direct assay (slope: 0.9708) than Friedewald (slope: 0.9477). Similarly, Martin's formula exhibited lesser bias (-13.56) in calculating LDL-C in patients with T2D compared with Friedewald's formula.

CONCLUSIONS

The study demonstrated that in patients with T2D, all formulae except Ahmadi significantly underestimated the LDL-C when compared with the direct assay. The newer Martin's formula appeared to more precisely calculate LDL-C in T2D when compared with the traditional Friedewald's formula.

Validation of multiple equations for estimating low-density lipoprotein cholesterol levels in Korean adults

Background

Limited data are available for validation of low-density lipoprotein cholesterol (LDL) calculation (LDL_cal) in the adult Korean population. The aim of this study was to develop and validate a new equation for LDL_cal and to compare it with previous such equations in a Korean population.

Methods

A new equation for LDL_cal was developed (LDL_Choi). LDL_Choi and 11 other previously published equations were applied and compared with directly measured LDL concentration (LDL_direct) in a development cohort (population 1), an independent validation cohort in the same laboratory (population 2), and the Korea National Health and Nutrition Examination Survey 2017 cohort (population 3).

Results

Among the 12 equations, the newly-developed equation (LDL_Choi = total cholesterol – 0.87 x high-density lipoprotein cholesterol – 0.13 x triglycerides) had the highest intraclass correlation coefficient (ICC) and the lowest mean systemic difference and median absolute percentage error in populations 1 and 2 but not in population 3. Subgroup analysis showed good agreement between LDL_Choi and LDL_direct (ICC > 0.75) in population 2, whose LDL_direct < 70 mg/dL. For samples with high triglycerides (> 400 mg/dL), equation accuracy varied. Categorization concordance according to the National Cholesterol Education Program Adult Treatment Panel III criteria with the other 11 equations were less than 80%; that of LDL_Choi was 87.6 and 87.4% in populations 1 and 2, respectively.

Conclusions

Accuracy of 12 equations for LDL_cal varied by cohort and subgroup based on LDL_direct and triglycerides. A laboratory-specific equation for LDL_cal and/or LDL_direct may be needed for accurate evaluation of LDL status.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12944-021-01525-6.

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.

A New Equation for Calculation of Low-Density Lipoprotein Cholesterol in Patients With Normolipidemia and/or Hypertriglyceridemia

Importance

Low-density lipoprotein cholesterol (LDL-C), a key cardiovascular disease marker, is often estimated by the Friedewald or Martin equation, but calculating LDL-C is less accurate in patients with a low LDL-C level or hypertriglyceridemia (triglyceride [TG] levels ≥400 mg/dL).

Objective

To design a more accurate LDL-C equation for patients with a low LDL-C level and/or hypertriglyceridemia.

Design, Setting, and Participants

Data on LDL-C levels and other lipid measures from 8656 patients seen at the National Institutes of Health Clinical Center between January 1, 1976, and June 2, 1999, were analyzed by the β-quantification reference method (18 715 LDL-C test results) and were randomly divided into equally sized training and validation data sets. Using TG and non-high-density lipoprotein cholesterol as independent variables, multiple least squares regression was used to develop an equation for very low-density lipoprotein cholesterol, which was then used in a second equation for LDL-C. Equations were tested against the internal validation data set and multiple external data sets of either β-quantification LDL-C results (n = 28 891) or direct LDL-C test results (n = 252 888). Statistical analysis was performed from August 7, 2018, to July 18, 2019.

Main Outcomes and Measures

Concordance between calculated and measured LDL-C levels by β-quantification, as assessed by various measures of test accuracy (correlation coefficient [R2], root mean square error [RMSE], mean absolute difference [MAD]), and percentage of patients misclassified at LDL-C treatment thresholds of 70, 100, and 190 mg/dL.

Results

Compared with β-quantification, the new equation was more accurate than other LDL-C equations (slope, 0.964; RMSE = 15.2 mg/dL; R2 = 0.9648; vs Friedewald equation: slope, 1.056; RMSE = 32 mg/dL; R2 = 0.8808; vs Martin equation: slope, 0.945; RMSE = 25.7 mg/dL; R2 = 0.9022), particularly for patients with hypertriglyceridemia (MAD = 24.9 mg/dL; vs Friedewald equation: MAD = 56.4 mg/dL; vs Martin equation: MAD = 44.8 mg/dL). The new equation calculates the LDL-C level in patients with TG levels up to 800 mg/dL as accurately as the Friedewald equation does for TG levels less than 400 mg/dL and was associated with 35% fewer misclassifications when patients with hypertriglyceridemia (TG levels, 400-800 mg/dL) were categorized into different LDL-C treatment groups.

Conclusions and Relevance

The new equation can be readily implemented by clinical laboratories with no additional costs compared with the standard lipid panel. It will allow for more accurate calculation of LDL-C level in patients with low LDL-C levels and/or hypertriglyceridemia (TG levels, ≤800 mg/dL) and thus should improve the use of LDL-C level in cardiovascular disease risk management.

Comparison of novel LDL cholesterol equations in myocardial infarction patients: Clinical impact on risk re-classification and lipid treatment goals on secondary prevention

BACKGROUND AND AIMS

Numerous low-density lipoprotein (LDL) calculating equations for more accurate estimation have emerged. With the present study, we assessed the clinical impact of implementing novel equations in terms of risk reclassification and LDL treatment goals in myocardial infarction (MI) patients.

METHODS

This was a post-hoc analysis of a prospective acute MI cohort study. We enrolled 805 consecutive patients presenting with acute MI. Patients with high triglyceride levels (>400 mg/dL) were excluded. In the remaining 773 acute MI patients, LDL cholesterol levels were calculated using 12 different equations including the Friedewald equation. Each patient was categorized into a 5-scale risk strata scheme according to baseline LDL cholesterol levels. Moreover, ΔLDL cholesterol (change in LDL cholesterol levels to achieve the <55 mg/dL LDL treatment goal) was calculated for each patient.

RESULTS

Mean levels and distribution of LDL cholesterol were significantly different compared to those derived from the Friedewald equation. Net reclassification improvement (NRI) analysis, as well as heat maps, showed that this re-categorization had no significant impact on prognostic terms (NRI ranged from -6.1% to 5.9% with p values > 0.05 for each comparison). Statistically significant differences were observed in ΔLDL cholesterol levels between each one of the novel equations and the Friedewald equation.

CONCLUSIONS

Novel LDL cholesterol calculating equations are not associated with a clinically significant risk re-classification in MI patients. In addition, use of these novel equations may have an impact on assessing potency of hypolipidemic therapy use in secondary prevention as far as succeeding lipid treatment goals in MI patients.

Measuring LDL-cholesterol: what is the best way to do it?

PURPOSE OF REVIEW

Cholesterol on low-density lipoproteins (LDL-C) is one of the main drivers of atherosclerotic cardiovascular disease (ASCVD) and hence its measurement is critical in the management of patients at risk. Although LDL-C has routinely been either calculated by the Friedewald equation or measured with direct assays, these methods have limitations, particularly for patients with dyslipidaemias, low LDL-C, and hypertriglyceridemia. The focus of this review will be recent advances in the measurement of LDL for ASCVD risk management.

RECENT FINDINGS

We first describe the recent recommendations on how LDL-C is used in ASCVD risk assessment and management. We then review the current approaches to the measurement of LDL-C and recent developments on new more accurate equations for calculating LDL-C. Finally, we present new and emerging LDL assays that may be superior to LDL-C for risk assessment, such as LDL particle number and small dense LDL-C, and several LDL-based lipid tests in early development.

SUMMARY

LDL-C is valuable in ASCVD risk management but recent improvements in its measurement and the development of other LDL-related tests may further improve its value.

Impact of improved low-density lipoprotein cholesterol assessment on guideline classification in the modern treatment era-Results from a racially diverse Brazilian cross-sectional study

BACKGROUND

The Martin/Hopkins low-density lipoprotein cholesterol equation (LDL-C_N) was previously demonstrated as more accurate than Friedewald LDL-C estimation (LDL-C_F) in a North American database not able to take race into account.

OBJECTIVES

We hypothesized that LDL-C_N would be more accurate than LDL-C_F and correlate better with LDL particle number (LDL-P) in a racially diverse Brazilian cohort.

METHODS

We performed a cross-sectional analysis of 4897 participants in the Brazilian Longitudinal Study of Adult Health, assessing LDL-C_F and LDL-C_N accuracy via overlap with ultracentrifugation-based measurement among clinical guideline LDL-C categories as well as mg/dL and percent error differences. We analyzed by triglyceride categories and correlated LDL-C estimation with LDL-P.

RESULTS

LDL-C_N demonstrated improved accuracy at 70 to <100 and <70 mg/dL (P < .001), with large errors ≥20 mg/dL about 9 times more frequent in LDL-C_F at LDL-C <70 mg/dL, mainly due to underestimation. Among individuals with LDL-C <70 mg/dL and triglycerides ≥150 mg/dL, 65% vs 100% of ultracentrifugation-based low-density lipoprotein cholesterol calculation fell within appropriate categories of estimated LDL-C_F and LDL-C_N, respectively (P < .001). Similar results were observed when analyzed for age, sex, and race. Participants at LDL-C <70 and 70 to <100 mg/dL with discordantly elevated LDL-C_N vs LDL-C_F had a 58.5% and 41.5% higher LDL-P than those with concordance (P < .0001), respectively.

CONCLUSIONS

In a diverse Brazilian cohort, LDL-C_N was more accurate than LDL-C_F at low LDL-C and high triglycerides. LDL-C_N may avoid underestimation of LDL-C and better reflect atherogenic lipid burden in low particle size, high particle count states.

Homogeneous Assays for LDL-C and HDL-C are Reliable in Both the Postprandial and Fasting State

AIM

Most epidemiological and clinical studies calculated low-density lipoprotein-cholesterol (LDL-C) by Friedewald's formula which cannot be used in the postprandial samples. Although the homogeneous assays with poor analytical performance were withdrawn from the market, it remained unclear whether the currently available reagents for LDL-C and high-density lipoprotein-cholesterol (HDL-C) are as accurate for postprandial samples as for fasting samples.

METHODS

Fresh blood samples were collected from 59 non-diseased and 109 diseased subjects. Postprandial samples constituted 72.9% and 39.4% of these samples. LDL-C and HDL-C concentrations were measured using the homogeneous assays of four manufacturers (Denka Seiken, Wako, Kyowa Medex, and Sekisui Medical). Simultaneously, LDL-C and HDL-C concentrations were determined using the reference measurement procedures (RMPs) of the Centers for Disease Control and Prevention (CDC). Total errors were calculated using a routine method (TE_com) and via error component analysis (TE_ECA).

RESULTS

All homogeneous assays for LDL-C and HDL-C met the National Cholesterol Education Program (NCEP) requirements in terms of coefficient of variation, and TE_com in both non-diseased and diseased subjects. LDL-C and HDL-C values measured by the homogeneous assays were in good agreement with those measured by the RMPs in both fasting and postprandial samples. The TE_com and TE_ECA values of the postprandial samples were similar to those of fasting samples, although the TE_ECA values were up to 4.4-fold greater than the TE_com values.

CONCLUSIONS

In both non-diseased and diseased subjects, the homogeneous assays for LDL-C and HDL-C of four manufacturers are as accurate for postprandial samples as for fasting samples.

Comparison and Validation of 10 Equations Including a Novel Method for Estimation of LDL-cholesterol in a 168,212 Asian Population

Low-density lipoprotein cholesterol (LDL-C) is frequently estimated using the empirical Friedewald equation. We compared the accuracy of the novel equation named as the 180-cell method (180-c), which estimates LDL-C using a stratification approach, to those of 9 previously suggested formulas, including the Friedewald equation.We compared the accuracy of 10 equations by calculating intraclass correlation coefficient (ICC) and weighted kappa index in relation to direct LDL-C measurement values. Two independent populations used in the validation were the Severance Hospital LDL-C (SHL) registry (n = 164,358) and the Korea National Health and Nutrition Examination Survey (KNHANES) 2009 to 2010 (n = 3,854), each representing the hospital patient population and the general Korean population, respectively.The 180-c and DeLong equations showed the highest ICCs, indicating the best agreement with direct LDL-C measurement. The 180-c and Chen equations showed the highest kappa indices. For the hypertriglyceridemic subpopulation from SHL, the 180-c equation showed the best agreement with direct LDL-C measurement in terms of ICC.We compared the novel 180-c method for LDL-C estimation with 9 previous formulas in a non-US population as the first external validation. The 180-c equation, with Chen equation, appeared to be more accurate than the Friedewald equation. Although the DeLong equation showed better performance in the hypertriglyceridemic subpopulation, the 180-c equation performed appropriately in Asian population.

Methods for Measurement of LDL-Cholesterol: A Critical Assessment of Direct Measurement by Homogeneous Assays versus Calculation

Background: Because LDL-cholesterol (LDL-C) is a modifiable risk for coronary heart disease, its routine measurement is recommended in the evaluation and management of hypercholesterolemia. We critically examine here the new homogeneous assays for direct determination of LDL-C.

Approach: This review relies on published studies and data of the authors using research and routine methods for LDL-C determination. We review experience with methods from their earlier use in lipid research laboratories through the transition to routine clinical testing and the recent development of homogeneous assays. We focus on comparative evaluations and characterizations and the performance of the assays.

Content: Homogeneous assays seem to be able to meet current National Cholesterol Education Program (NCEP) requirements for LDL-C testing for precision (CV &lt;4%) and accuracy (bias &lt;4%), when samples collected from nonfasting individuals are used. In addition, all five currently available assays have been certified by the Cholesterol Reference Methods Laboratory Network. The homogeneous methods also appear to better classify individuals into NCEP cutpoints than the Friedewald calculation. However, the limited evaluations to date raise questions about their reliability and specificity, especially in samples with atypical lipoproteins.

Conclusions: Available evidence supports recommending the homogeneous assays for LDL-C to supplement the Friedewald calculation in those cases where the calculation is known to be unreliable, e.g., triglycerides &gt;4000 mg/L. Before the homogeneous assays can be confidently recommended to replace the calculation in routine practice, more evaluation is needed.

Relation between self-reported weight cycling history, dieting and bio-behavioral health in Japanese adult males

BACKGROUND

Epidemiological findings suggest that weight fluctuations are associated with unfavorable health outcomes compared with stable weight. However, the interrelationship between the weight cycling history and dieting status in a non-clinical male trial on the risk for bio-behavioral health is unclear.

OBJECTIVE

The purpose of this study was to examine the relation between weight cycling history as a result of intentional weight loss and bio-behavioral health in Japanese adult males.

METHOD

A cross-sectional study was performed on a group of 146 Japanese working males (47.5±9.3 yr.). Each subject completed a series of self-reported questionnaires in which information about weight cycling history, current dieting practices, life-styles, and social background were assessed. Results of the physical check up were used to assess biological parameters. Self-reported weight cycling was defined as intentionally losing 10% of one's weight and regaining the lost weight.

RESULTS

Cyclers reported a significantly greater incidence of current dieting and recent weight gain compared with non-cyclers. Taking regular meals, eating breakfast everyday, and not eating snacks between meals every day were significantly less frequent among cyclers compared with non-cyclers after controlling for BMI. The adjusted odds ratio for AST abnormality was 5.46 (95% CI: 1.08-27.67), ALT abnormality was 3.31 (95% CI: 1.24-8.78), and γ-GTP was 3.38 (95% CI: 1.07-10.67) among cyclers, compared with noncyclers.

CONCLUSION

These findings suggest that a history of weight cycling in men, regardless of current weight status, is associated with adverse bio-behavioral health. The risk for several liver enzyme abnormalities associated with weight cycling history was substantial, independent of relative body weight and lifestyle factors.

The relationship between physical fitness and coronary risk factor profiles in Japanese women

The purpose of this study was to investigate the relationship between physical fitness and coronary risk factor profiles in Japanese women. The subjects were 1,483 women (ages 30 to 69) who participated in a practical health promotion program. After medical examination, physical fitness was evaluated by conducting a symptom limited maximal exercise test by ergometer to measure maximum oxygen uptake (peakVO(2)) with an expired gas analyzer. The subjects were classified into 3 groups (high fitness, moderate fitness, and low fitness) according to age and physical fitness level. The results showed that the subjects in higher fitness groups had lower levels in: body mass index (BMI), percentage of body fat, waist-hip ratio, resting blood pressure, and atherogenic index, and higher HDL-cholesterol compared to those in lower fitness group. Even after adjustment for the effects of age and BMI, die subjects in the higher fitness groups had better coronary risk factor profiles. These results suggest that among Japanese women a high level of physical fitness is related to favorable coronary risk factor profiles.

Effect of hormone replacement therapy on the validity of the Friedewald equation in postmenopausal women: the postmenopausal estrogen/progestins interventions (PEPI) trial

The Friedewald equation is often used to estimate low-density lipoprotein cholesterol (LDL-C). Hormone therapy is known to raise triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) and alter lipid contents of lipoproteins. We compared Friedewald estimated LDL-C to measured LDL-C in PEPI participants on placebo or four different hormone treatment groups. At baseline, the 0.2 coefficient for triglyceride (TG) was accurate for all five treatment groups. Among women who took >80% of their pills and whose TG was <4.5 mmol/l (400 mg/dl), LDL-C was underestimated for 69-82% of the participants in the active treatment groups, compared to 50% in the placebo group. After 3 years of therapy, the TG coefficient that offered a better fit of the Friedewald equation in the active treatment groups was 0.39 for the equation in mmol/l (0.17 for the equation in mg/dl). Using this coefficient is clearly warranted for greater accuracy in research studies.

Poor applicability of the Friedewald formula in the assessment of serum LDL cholesterol for clinical purposes

OBJECTIVE

To determine the accuracy of the estimation of serum low-density lipoprotein (LDL) cholesterol concentration by the Friedewald formula.

METHODS

Modifications of the calculation formula are presented on the basis of ultracentrifugal separation of serum high-density lipoprotein and LDL cholesterol in the specimens collected (n = 1215) in a nationwide health survey.

RESULTS

The formulas obtained from different subject groups differed relatively little from each other. The accuracy of the original Friedewald formula was poor; in about 36% of the subjects the error was more than 5% compared with ultracentrifugally obtained results. By applying the currently recommended coronary heart disease (CHD) risk categorizations, high proportions (5%-28%) of the subjects were classified into wrong CHD risk categories when LDL cholesterol was calculated with any of the formulas. At high serum triglyceride levels, misclassifications were especially common.

CONCLUSIONS

We conclude that even the most accurate LDL cholesterol calculation methods should be used with caution while classifying subjects into categories of CHD risk. In hypertriglyceridemic subjects, the calculation formulas probably should not be used at all.

The measurement of lipoprotein subfractions in plasma using a tabletop ultracentrifuge

We adapted the ultracentrifugation method of the Lipid Research Clinics Program for the separation of lipid subfractions (LDL, VLDL and HDL cholesterol) to a tabletop ultracentrifuge (Beckman TL-100). Centrifugation time was reduced from 18 h to 2.5 h and the sample volume from 5 mL to 2 mL plasma. The imprecision of the LDL-cholesterol estimation (coefficient of variation = CV) was 2.9-7.4% and that of HDL-cholesterol measurement was 1.4-3.9%. Imprecision of the VLDL-C measurement was high (CV = 15.6-29.8%). The results correlated with those obtained by the Lipid Research Clinics method (P less than 0.001). Our method could be conveniently adapted by clinical laboratories serving specialist lipid clinics.