Evaluation of different formulas for LDL-C calculation

Evaluation of 13 Formulae for Calculated LDL-C Using Direct Homogenous Assay in a South Indian Population

BACKGROUND

LDL cholesterol (LDL-C) is regarded as a significant therapeutic target and a known risk factor for atherosclerosis. It can be calculated using the results of the other lipid tests or tested directly. Despite its shortcomings, the Friedewald formula is most frequently utilized since it is simple and practical. Until now, several formulae have been proposed for calculating LDL-C; however, their accuracy has not been evaluated across different populations. We sought to evaluate the validity of calculated LDL-C by comparing the findings with values acquired by the direct homogeneous technique, utilizing 13 distinct formulae from the literature.

METHODS

This study was a retrospective observational study conducted for a year at SRIHER, Chennai, Tamil Nadu, India. From the total 25 043 patients who had their serum lipid profile tested, 16 314 participants had their fasting blood sugar and fasting lipid profile measured simultaneously, and they were chosen for the research.

RESULTS

The de Cordova, Chen, Martin/Hopkins (initial), and Teerakanchana equations correlated well with the direct LDL-C assay. When the dataset was stratified according to triglycerides, the Chen and Martin/Hopkins initial equations had the better measurement of agreement compared to other equations. The Martin/Hopkins initial equation outperformed all the other equations when the whole dataset irrespective of the triglyceride population was considered.

CONCLUSIONS

Our study suggests that the Martin/Hopkins initial equation outperformed all the other equations and can be used as an alternative to direct LDL-C measurement in a South Indian population.

The consumption of ultra-processed foods was associated with adiposity, but not with metabolic indicators in a prospective cohort study of Chilean preschool children

BACKGROUND

Increasing consumption of ultra-processed foods (UPF) has been identified as a risk factor for obesity and various diseases, primarily in adults. Nonetheless, research in children is limited, especially regarding longitudinal studies with metabolic outcomes. We aimed to evaluate the longitudinal association between consumption of UPF, adiposity, and metabolic indicators in Chilean preschool children.

METHODS

We conducted a prospective analysis of 962 children enrolled in the Food and Environment Chilean Cohort (FECHIC). Dietary data were collected in 2016 at age 4 years with 24-h recalls. All reported foods and beverages were classified according to the NOVA food classification, and the usual consumption of UPF in calories and grams was estimated using the Multiple Source Method. Adiposity (z-score of body mass index [BMI z-score], waist circumference [WC], and fat mass [in kg and percentage]) and metabolic indicators (fasting glucose, insulin, HOMA-IR, triglycerides, total cholesterol, and cholesterol fractions) were measured in 2018, at the age of 6 years. Linear regression models ((0) crude, (1) adjusted for covariables, and (2) adjusted for covariables plus total caloric intake) were used to evaluate the association between UPF and outcomes. All models included inverse probability weights to account for the loss to the follow-up.

RESULTS

At 4 years, usual consumption of UPF represented 48% of the total calories and 39% of the total food and beverages grams. In models adjusted for covariables plus caloric intake, we found a positive association between UPF and BMI z-score (for 100 kcal and 100 g, respectively: b = 0.24 [95%CI 0.16-0.33]; b = 0.21 [95%CI 0.10-0.31]), WC in cm (b = 0.89 [95%CI 0.41-1.37]; b = 0.86 [95%CI 0.32-1.40]), log-fat mass in kg b = 0.06 [95%CI 0.03-0.09]; b = 0.04 [95%CI 0.01-0.07]), and log-percentage fat mass (b = 0.03 [95%CI 0.01-0.04]; b = 0.02 [95%CI 0.003-0.04]), but no association with metabolic indicators.

CONCLUSIONS

In this sample of Chilean preschoolers, we observed that higher consumption of UPF was associated with adiposity indicators 2 years later, but not with metabolic outcomes. Longer follow-up might help clarify the natural history of UPF consumption and metabolic risks in children.

The impact of consuming different types of high-caloric fat diet on the metabolic status, liver, and aortic integrity in rats

Consumption of high-caloric diets contributes to the alarming number of overweight and obese individuals worldwide, which in turn leads to several diseases and multiple organ dysfunction. Not only has the number of calories taken per day but also the type of fat in the diet has an important impact on health. Accordingly, the purpose of the current study was to examine the impact of different types of high-caloric fat diets on the metabolic status and the integrity of the liver and aorta in albino rats. Adult male albino rats were divided into 6 groups: Control group, long chain-saturated fat group (SFD), long chain-monounsaturated fat (MUFAs) group, long chain-polyunsaturated fat (PUFAs) group, medium-chain fat (MCFAs) group, and short-chain fat (SCFAs) group. Body mass index (BMI), Lee index, and visceral fat amount were reported. Serum levels of insulin, liver transaminases, lipid profile, and different oxidative stress and inflammatory markers were evaluated. Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), and adiponectin/leptin ratio were also calculated. Histopathological examinations of liver and aorta with Masson's trichrome stain, and immune-staining for Nuclear Factor Erythroid-2-Related Factor-2 (Nrf2) were also done. SFD group showed significantly elevated liver transaminases, inflammatory markers, HOMA-IR, dyslipidemia, reduced adiponectin, and deficient anti-oxidative response compared to other groups together with disturbed hepatic and aortic architecture. Other treated groups showed an improvement. PUFAs group showed the highest level of improvement. Not all high-fat diets are hazardous. Diets rich in PUFAs, MUFAs, MCFAs, or SCFAs may protect against the hazards of high caloric diet.

Choosing the right equation for calculating indirect LDL-Cholesterol (LDL-C) in adult Pakistani population: Evaluation of seven equations using big data analytics

Objective

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Low density lipoprotein cholesterol (LDL-C) contributes to the atherogenic process. However, direct LDL-C (d-LDL) has rarely been estimated by the gold standard method because it is cumbersome and expensive. We aim to evaluate calculated low density lipoprotein (LDL-c) by various equations with reference to directly measured LDL-C in the Pakistani adult population as a cost-effective alternative.

Methods

We retrospectively evaluated the validity of seven equations for estimating calculated LDL-C by computing correlation coefficients (r) and Bland Altman plots to assess agreement (mean %) for (d-LDL) and calculated (LDL-c) on all seven equations. Statistical analysis was performed in Stata Statistical Software: Release 17, College Station, TX: StataCorp LLC.

Results

We analyzed 247082 direct assays of lipid profiles of adults aged ≥18 years. The mean LDL-C levels computed on Friedewald, de Cordova, Chen, Hattori, Vujovic, Teerakanchana, Sampson equations were 106.8 ± 31.4, 103.7 ± 25.0, 108.6 ± 28.2, 100.1 ± 29.5, 115.2 ± 31.2, 113.1 ± 28.3 and 110.3 ± 30.6 respectively. Friedewald and Hattori equations correlated strongly with direct LDL-C (r = 0.937) for each followed by Sampson (r = 0.935) and Vujovic (r = 0.931). However, the median bias was least for the Friedwald equation (-1.6) compared to the other equations.

Conclusion

In contrast to the global literature advocating for the use of newer equations, although the conventional and widely utilized Friedewald equation remains the best alternative for calculated LDL-C estimation in adult Pakistani population.

A comparative evaluation of low-density lipoprotein cholesterol estimation: Machine learning algorithms versus various equations

BACKGROUND

Given the critical importance of Low-density lipoprotein cholesterol (LDL-C) levels in determining cardiovascular risk, it is essential to measure LDL-C accurately. Since the Friedewald formula generates incorrect predictions in many circumstances, new equations have been developed to overcome the Friedewald equations' shortcomings. This study aimed to compare estimated LDL-C with directly measured LDL-C (dLDL-C), as well as their performance in predicting LDL-C, utilizing Friedewald, extended Martin-Hopkins, Sampson, de Cordova, and Vujovic formulas and five machine learning (ML) algorithms.

METHODS

A total of 29,504 samples from the ISLAB-2 Core Laboratory were included in the study. All statistical analysis was performed using R version 4.1.2. Statistical Language.

RESULTS

Bayesian-Regularized Neural Network (BRNN) (r = 0.957) and Random Forest (RF) (r = 0.957) algorithms showed a higher correlation with dLDL-C than the other equations in all-testing dataset. All ML algorithms demonstrated less bias than pre-existing LDL-C equations with dLDL-C and outperformed the LDL-C estimation equations in terms of concordance in all-testing dataset.

CONCLUSIONS

The results of our research indicate that when compared to conventional equations, ML algorithms are much more effective in predicting LDL-C. ML algorithms, aided by a vast dataset, could have the capability to predict LDL-C levels even in cases where triglyceride levels are high, unlike the limited usage of Friedewald formula.

A novel equation for the estimation of low-density lipoprotein cholesterol in the Saudi Arabian population: a derivation and validation study

Low-density lipoprotein cholesterol (LDL-C) is typically estimated by the Friedewald equation to guide atherosclerotic cardiovascular disease (ASCVD) management despite its flaws. Martin-Hopkins and Sampson-NIH equations were shown to outperform Friedewald's in various populations. Our aim was to derive a novel equation for accurate LDL-C estimation in Saudi Arabians and to compare it to Friedewald, Martin-Hopkins and Sampson-NIH equations. This is a cross-sectional study on 2245 subjects who were allocated to 2 cohorts; a derivation (1) and a validation cohort (2). Cohort 1 was analyzed in a multiple regression model to derive an equation (equationD) for estimating LDL-C. The agreement between the measured (LDL-CDM) and calculated levels was tested by Bland-Altman analysis, and the biases by absolute error values. Validation of the derived equation was carried out across LDL-C and triglyceride (TG)-stratified groups. The mean LDL-CDM was 3.10 ± 1.07 and 3.09 ± 1.06 mmol/L in cohorts 1 and 2, respectively. The derived equation is: LDL-CD = 0.224 + (TC × 0.919) - (HDL-C × 0.904) - (TG × 0.236) - (age × 0.001) - 0.024. In cohort 2, the mean LDL-C (mmol/L) was estimated as 3.09 ± 1.06 by equationD, 2.85 ± 1.12 by Friedewald, 2.95 ± 1.09 by Martin-Hopkins, and 2.93 ± 1.11 by Sampson-NIH equations; statistically significant differences between direct and calculated LDL-C was observed with the later three equations (P < 0.001). Bland-Altman analysis showed the lowest bias (0.001 mmol/L) with equationD as compared to 0.24, 0.15, and 0.17 mmol/L with Friedewald, Martin-Hopkins, and Sampson-NIH equations, respectively. The absolute errors in all guideline-stratified LDL-C categories was the lowest with equationD, which also showed the best classifier of LDL-C according to guidelines. Moreover, equationD predicted LDL-C levels with the lowest error with TG levels up to 5.63 mmol/L. EquationD topped the other equations in estimating LDL-C in Saudi Arabians as it could permit better estimation when LDL-C is < 2.4 mmol/L, in familial hyperlipidemia, and in hypertriglyceridemia, which improves cardiovascular outcomes in high-risk patients. We recommend further research to validate equationD in a larger dataset and in other populations.

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.

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.

Development of a Modified Friedewald's Formula to Calculate Low-Density Lipoprotein in an Iranian Population

Background

Elevated low-density lipoprotein cholesterol (LDL-C) is a significant risk factor for cardiovascular diseases. LDL-C can be directly measured using various methods, but this requires expensive equipment. Currently, clinical laboratories estimate LDL-C based on Friedewald's formula (FF). We aimed to develop a modified formula based on directly measured LDL-C (D-LDL-C) values in a large population in Southern Iran and compare the results with various other estimation formulas.

Methods

The participants of this cross-sectional study were adults aged >18 years living in Southern Iran. Blood samples from 15,200 individuals were collected, and the measured lipid parameters were randomly divided into training (n=10,184) and validation (n=5,016) datasets. A new formula was developed using a linear regression model, and its accuracy was validated. Pearson's correlation and Cohen's kappa were used to determin the relationship between D-LDL-C and calculated LDL-C (C-LDL-C).

Results

The developed formula for the estimation of LDL-C was 0.857 total cholesterol (TC)-0.915 high-density lipoprotein cholesterol (HDL-C)-0.115 triglycerides (TG). Based on our proposed formula, for TG<150 and TG≥150 mg/dL, there was a significant correlation between mean values of D-LDL-C and C-LDL-C (r=0.985 and r=0.974, respectively). Compared to other formulas, C-LDL-C obtained from the proposed formula had the highest correlation with D-LDL-C. The agreement between D-LDL-C and C-LDL-C for TC<200, 200-239, and ≥240 mg/dL was 80.8%, 63.2%, and 67.4%, respectively, indicating a higher level of agreement than other formulas.

Conclusion

The new formula appears to be more accurate than FF when applied to the population of Southern Iran.

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.

Impact of Long-Term Cyclamate and Saccharin Consumption on Biochemical Parameters in Healthy Individuals and Type 2 Diabetes Mellitus Patients

Background: Previous studies on saccharin and cyclamate were either limited to experimental animals or lacked evaluation of their long-term consumption effects in humans. Objectives: This study evaluated the effect of chronic consumption of saccharin and cyclamate on biochemical parameters in healthy individuals and patients with type 2 diabetes mellitus. Material and Methods: Healthy and diabetic individuals were classified into two groups based on whether they consumed sweeteners or not. The participants were classified according to the amount of sweetener consumed per day and duration of consumption. Serum catalase activity, peroxynitrite, ceruloplasmin, and malondialdehyde concentrations were determined. Glycated hemoglobin, fasting glucose, creatinine, alanine transaminase, and lipid profile were also evaluated. The results suggest that saccharin and cyclamate increased HbA1C (+11.16%), MDA (+52.38%), TG (+16.74%), LDL (+13.39%), and TC/HDL (+13.11%) in healthy volunteers. Diabetic patients consuming sweeteners showed increased FSG (+17.51%), ceruloplasmin (+13.17%), and MDA (+8.92%). Diabetic patients showed a positive correlation between the number of tablets consumed per day with FSG and serum creatinine. A positive correlation was found between the duration of sweetener consumption and FSG as well as TG. Conclusion: Consumption of saccharin and cyclamate affected biochemical parameters related to metabolic functions in a time and dose-dependent manner and appear to increase oxidative stress in healthy and diabetic type 2 patients.

The Impact of Low-Density Lipoprotein Equation Changes on Cholesterol Treatment in Canada

Background

In cardiovascular disease prevention, low-density lipoprotein cholesterol (LDL-C) values guide treatment for lowering cholesterol level. After 50 years of clinical laboratories using the Friedewald LDL-C equation, the Canadian Society of Clinical Chemists recently recommended adoption of the new and more accurate Sampson / U.S. National Institutes of Health (NIH) LDL-C equation. Here, we estimate the anticipated population-level impact of this equation change.

Methods

We compared lipid profiles from the Canadian Health Measures Survey (CHMS) year 2019 to those from the National Health and Nutrition Examination Survey (NHANES) years 2017 to 2020. Then, based on 10,828 participants in the latter, we calculated the impact of changing the LDL-C equation from the Friedewald to the Sampson.

Results

Sampson- and Friedewald-equation LDL-C values are strongly correlated (r = 0.99, P < 0.001), but differences between them increase with both higher triglyceride and lower LDL-C values. We evaluated the impact of these discordances using LDL-C treatment thresholds from the 2021 Canadian Cardiovascular Society lipid guidelines. Among patients who take cholesterol-lowering medications, the Sampson equation reclassifies 3.3% more patients (95% confidence interval 2.2% to 4.9%), or about 123,000 individuals, as meeting the criteria for treatment intensification.

Conclusion

Although changing the LDL-C equation used from the Friedewald to the Sampson affects only a small proportion of the population, an estimated 123,000 Canadians who are taking cholesterol-lowering medications may need to intensify treatment to lower their cholesterol level, due to small absolute changes around guideline threshold values of LDL-C.

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.

The accuracy of four formulas for LDL-C calculation at the fasting and postprandial states

Background

Elevated level of low-density lipoprotein cholesterol (LDL-C) is concerned as one of the main risk factors for cardiovascular disease, in both the fasting and postprandial states. This study aimed to compare the measured LDL-C with LDL-C calculated by the Friedewald, Martin–Hopkins, Vujovic, and Sampson formulas, and establish which formula could provide the most reliable LDL-C results for Chinese subjects, especially at the postprandial state.

Methods

Twenty-six subjects were enrolled in this study. The blood samples were collected from all the subjects before and after taking a daily breakfast. The calculated LDL-C results were compared with LDL-C measured by the vertical auto profile method, at both the fasting and postprandial states. The percentage difference between calculated and measured LDL-C (total error) and the number of results exceeding the total error goal of 12% were established.

Results

The calculated LDL-C_F levels showed no significant difference from LDL-C_VAP levels at the fasting state. The calculated LDL-C_S were significantly higher than LDL-C_VAP at the fasting state (P < 0.05), while the calculated LDL-C_s were very close to LDL-C_VAP levels after a daily meal. At the fasting state, the median total error of calculated LDL-C_F was 0 (quartile: −3.8 to 6.0), followed by LDL-C_S, LDL-C_MH, and LDL-C_V. At the postprandial states, the median total errors of LDL-C_S were the smallest, 1.0 (−7.5, 8.5) and −0.3 (−10.1, 10.9) at 2 and 4 h, respectively. The calculated LDL-C_F levels showed the highest correlation to LDL-C_VAP and accuracy in evaluating fasting LDL-C levels, while the Sampson formula showed the highest accuracy at the postprandial state.

Conclusion

The Friedewald formula was recommended to calculate fasting LDL-C, while the Sampson formula seemed to be a better choice to calculate postprandial LDL-C levels in Chinese subjects.

DPP-4 inhibition mediated antidiabetic potential of phytoconstituents of an aqueous fruit extract of Withania coagulans (Stocks) Dunal: in-silico, in-vitro and in-vivo assessments

The DPP-4 inhibition is an interesting target for the development of antidiabetic agents which promotes the longevity of GPL-1(Glucagon-like peptide 1). The current study was intended to assess DPP-4(Dipeptidyl Peptidase-4) inhibition mediated antidiabetic effect of phytocompounds of an aqueous fruit extract of Withania coagulans (Stocks) Dunal by in-vitro, in-silico and in-vivo approaches. The phytoconstituents screening was executed by LCMS (Liquid Chromatography with tandem mass spectrometry). The in-vitro and in-vivo, DPP-4 assays were performed by using available kits. The in-vitro DPP-4 activity was inhibited up to 68.3% by the test extract. Accordingly, in-silico determinations of molecular docking, molecular dynamics and pharmacokinetics were performed between the target enzyme DPP-4 and leading phytocompounds. The molecular dynamics authenticated the molecular docking data by crucial parameters of cytosolic milieu by the potential energy, RSMD (Root Mean Square Deviation), RSMF (Root Mean Square Fluctuation), system density, NVT (Number of particles at fixed volume, ensemble) and NPT (Number of particles at fixed pressure, ensemble). Accordingly, ADMET predictions assessed the druggability profile. Subsequently, the course of the test extract and the sitagliptin (positive control), instigated significant (p ≤ 0.001) ameliorations in HOMA indices and the equal of antioxidants in nicotinamide-streptozotocin induced type 2 diabetic animal model. Compassionately, the histopathology represented increased pancreatic cellular mass which caused in restoration of histoarchitectures. It has been concluded that phytoconstituents in W. coagulans aqueous fruit extract can regulate DPP-4, resulting in improved glucose homeostasis and enhanced endocrinal pancreatic cellular mass.Communicated by Ramaswamy H. Sarma.

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.

Evaluation of Sampson equation for LDL-C in acute coronary syndrome patients: a Chinese population-based cohort study

OBJECTIVE

Low-density lipoprotein cholesterol (LDL-C) is an important cardiovascular disease marker that is used to estimate the risk of acute coronary syndrome in patients. The Sampson equation is an accurate LDL-C equation, but its application in Chinese patients is unclear.

METHODS

This study enrolled 12,989 consecutive Chinese patients with the acute coronary syndrome (ACS), LDL-C levels were determined by direct standard method and two indirect equations (Friedewald and Sampson). The detection accuracy and consistency of these two equations were compared in patients classified by triglyceride (TG). In addition, the efficiency of the Sampson equation was also evaluated in patients with different comorbidities.

RESULTS

Patients were divided into six groups according to TG level, and indicated that the Sampson formula was more accurate than the Friedewald formula in all TG spectrums (P < 0.001). The Friedewald formula may underestimate the risk in patients with TG > 400 mg/dL, especially in TG > 800 mg/dL group (r: 0.931 vs. 0.948, 0.666 vs. 0.898, respectively). Compared with the Friedewald equation, the Sampson equation showed more advantages in female, age ≥ 65, body index mass (BMI) < 25, non-smoker, and non-diabetes (0.954 vs. 0.937, 0.956 vs. 0.934, 0.951 vs. 0.939, 0.951 vs. 0.936, and 0.947 vs. 0.938, respectively) than those in male, age < 65, BMI ≥ 25, smoker, and diabetes.

CONCLUSIONS

Compared with the Friedewald equation, the Sampson equation is more accurate for LDL-C evaluation in Chinese patients diagnosed with ACS, especially in patients with hypertriglyceridemia even in those with TG > 800 mg/dL. Additionally, the Sampson equation demonstrates greater accuracy even in subgroups of various baseline characteristics and comorbidities.

Ultrasonographic Study of the Effects of Essential Hypertension on the Luminal Diameter and Doppler Velocimetric Indices of the Abdominal Aorta in Adults

OBJECTIVE

To investigate the effects of essential hypertension on the luminal diameter (caliber) and Doppler velocimetric indices of the abdominal aorta (AA) in adult patients with systemic hypertension.

MATERIALS AND METHODS

This was a prospective descriptive comparative study of 254 participants (127 with essential hypertension and 127 age/sex-matched controls). Their anthropometric parameters, fasting blood pressure, lipid profile, fasting blood sugar, and triplex sonography of the suprarenal and infrarenal abdominal aorta (Peak systolic velocity, PSV; End-diastolic velocity, EDV; Resistive Index, RI; and luminal diameter) were evaluated.

RESULTS

The mean age of the male subjects was 64.02 ± 10.02 years, while the mean age of the male controls was 63.14 ± 10.52 years (P > 0.05). The mean age of female subjects was 61.23 ± 10.09 years, while the mean age of the female controls was 61.76 ± 10.26 years (P > 0.05). The age group 60 - 69 years had the highest number of subjects and controls. The mean duration of hypertension in the subjects was 12.5 ± 5.2 years. The suprarenal and infrarenal abdominal aortic diameters (AAD) were higher in males than age-matched female counterparts. AAD increased with age mostly in hypertensive male subjects. PSV (in males) and RI (in both sexes) were elevated in hypertensive subjects compared to controls, while EDV (in both sexes) was significantly lower in subjects than controls. Multivariate linear regression showed that age and diastolic blood pressure were significant independent predictors for both suprarenal and infrarenal AADs.

CONCLUSION

Systemic hypertension causes structural and hemodynamic changes in the abdominal aorta which are detectable on triplex sonography.

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.

A Tale of Two Approaches

OBJECTIVES

To summarize and assess the literature on the performances of methods beyond the Friedewald formula (FF) used in routine practice to determine low-density lipoprotein cholesterol (LDL-C).

METHODS

A literature review was performed by searching the PubMed database. Many peer-reviewed articles were assessed.

RESULTS

The examined methods included direct homogeneous LDL-C assays, the FF, mathematical equations derived from the FF, the Martin-Hopkins equation (MHE), and the Sampson equation. Direct homogeneous assays perform inconsistently across manufacturers and disease status, whereas most FF-derived methods exhibit variable levels of performance across populations. The MHE consistently outperforms the FF but cannot be applied in the setting of severe hypertriglyceridemia. The Sampson equation shows promise against both the FF and MHE, especially in severe hypertriglyceridemia, but data are still limited on its validation in various settings, including disease and therapeutic states.

CONCLUSIONS

There is still no consensus on a universal best method to estimate LDL-C in routine practice. Further studies are needed to assess the performance of the Sampson equation.

Comparison of low-density lipoprotein cholesterol level calculated using the modified Martin/Hopkins estimation or the Friedewald formula with direct homogeneous assay measured low-density lipoprotein cholesterol

INTRODUCTION

Low-density lipoprotein cholesterol (LDL-C) represents the primary lipoprotein target for reducing cardiovascular risk (CV). The aim of our study is to compare the direct and the calculated LDL-C levels in the range below 1.8 mmol/l and 2.6 mmol/l depending on triglycerides, and to evaluate the variation in remnant lipoprotein cholesterol.

MATERIAL AND METHODS

We investigated 14 906 lipid profiles from fasting blood samples of Hungarian individuals with triglycerides < 4.5 mmol/l. Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG) and direct LDL-C were measured by the enzymatic assay. We calculated LDL-C by Friedewald's formula (F-LDL-C) and by using the new Martin/Hopkins estimation (MH-LDL-C).

RESULTS

For F-LDL-C below 1.8 mmol/l, MH-LDL-C was 58% between 1.8 and 2.59 mmol/l when TG was in the range 2.3-4.5 mmol/l. For F-LDL-C below 2.6 mmol/l, the MH-LDL-C concordance was 73% in the same TG range (2.3-4.5 mmol/l. If MH-LDL-C was less than 1.8 mmol/l or between 1.8 and 2.59 mmol/l, the difference between non-HDL-C (TC - HDL-C = AC: atherogenic cholesterol) and (MH)LDL-C was less than 0.8 mmol/l in the TG range below 2.3 mmol/l. The remnant lipoprotein cholesterol values were on average 0.5 mmol/l lower by the Martin/Hopkins estimation compared to the Friedewald's calculation if the TG was above 2.3 mmol/l.

CONCLUSIONS

The Friedewald equation tends to underestimate LDL-C levels in very high and high-risk settings. Our analysis supports the conclusion that in Hungarian patients, LDL-C estimation using the Martin/Hopkins formula, which is validated by the beta-quantification method, yields a more accurate LDL-C value than that calculated by the Friedewald formula.

Machine learning predictive models of LDL-C in the population of eastern India and its comparison with directly measured and calculated LDL-C

BACKGROUND

LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be used to predict outcomes more accurately. The current study evaluated the predictive performance of three machine learning models-random forests, XGBoost, and support vector Rregression (SVR) to predict LDL-C from total cholesterol, triglyceride, and HDL-C in comparison to linear regression model and some existing formulas for LDL-C calculation, in eastern Indian population.

METHODS

The lipid profiles performed in the clinical biochemistry laboratory of AIIMS Bhubaneswar during 2019-2021, a total of 13,391 samples were included in the study. Laboratory results were collected from the laboratory database. 70% of data were classified as train set and used to develop the three machine learning models and linear regression formula. These models were tested in the rest 30% of the data (test set) for validation. Performance of models was evaluated in comparison to best six existing LDL-C calculating formulas.

RESULTS

LDL-C predicted by XGBoost and random forests models showed a strong correlation with directly estimated LDL-C (r = 0.98). Two machine learning models performed superior to the six existing and commonly used LDL-C calculating formulas like Friedewald in the study population. When compared in different triglycerides strata also, these two models outperformed the other methods used.

CONCLUSION

Machine learning models like XGBoost and random forests can be used to predict LDL-C with more accuracy comparing to conventional linear regression LDL-C formulas.

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.

Comparability of 11 different equations for estimating LDL cholesterol on different analysers

OBJECTIVES

Low-density lipoprotein cholesterol (LDL-C) estimation is critical for risk classification, prevention and treatment of atherosclerotic cardiovascular disease (ASCVD). Predictive equations and direct LDL-C are used. We investigated the comparability between the Martin/Hopkins, Sampson, Friedewald and eight other predictive equations on two analysers, to determine whether the equation or analyser influences predicted LDL-C result.

METHODS

In two unpaired datasets, 9,995 lipid profiles were analysed by the Abbott Architect and 4,782 by the Roche Cobas analysers. Non-parametric statistics and Bland Altman plots were used to compare LDL-C.

RESULTS

On the Abbott analyser; the Martin/Hopkins, Sampson and Friedewald LDL-C were comparable (median bias ≤1.8%) over a range of 1-4.9 mmol/L. On the Roche platform, Martin/Hopkins LDL-C was comparable to Friedewald (median bias 0.3%) but not to Sampson LDL-C (median bias 25%). In patients with LDL-C <1.8 mmol/L and triglycerides (TG) ≤1.7 mmol/L, predicted LDL-C using Abbott reagents was similar between Martin/Hopkins, Sampson and Friedewald equations but not comparable using Roche reagents. Abbott reagents classified 10-20% of patients in the 1.0-1.8 mmol/L range (Martin/Hopkins 13.4%; Sampson 14.5%; Friedewald 16%; direct LDL-C 13.2%). Roche reagents classified 11-30% in the 1.0-1.8 mmol/L range (Martin/Hopkins 23%; Sampson 11%; Friedewald 25%; direct LDL-C 17%).

CONCLUSIONS

Performance of predictive equations is influenced by the choice of analyser for total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and TG. Replacement of the Friedewald equation with Martin/Hopkins estimation to improve quality of LDL-C results can be safely implemented across analysers, whereas caution is advised regarding the Sampson equation.

Task-Oriented Circuit Training as an Alternative to Ergometer-Type Aerobic Exercise Training after Stroke

Moderate-intensity aerobic exercise training is an important treatment strategy to enhance functional recovery and decrease cardiometabolic risk factors after stroke. However, stroke related impairments limit access to ergometer-type exercise. The aims of the current study were (1) to evaluate whether our task-oriented circuit training protocol (intermittent functional training; IFT) could be used to sustain moderate-intensity aerobic workloads over a 10-week intervention period, and (2) to investigate its preliminary effects on cardiorespiratory fitness and metabolic profiles compared to constant-load ergometer-type exercise (CET). Forty chronic hemiparetic stroke survivors were randomized to receive 30 sessions of IFT or CET over ten weeks. Similar proportions of participants were randomized to IFT (7/19) and CET (9/18) sustained workloads associated with moderate-intensity aerobic exercise over the study period (p = 0.515). However, CET was associated with more substantial changes in maximal oxygen uptake (MD = 2.79 mL min⁻¹ kg⁻¹ CI: 0.84 to 4.74) compared to IFT (MD = 0.62 mL min⁻¹ kg⁻¹ CI: −0.38 to 1.62). Pre to post changes in C-reactive protein (−0.9 mg/L; p =0.017), short-term glycemia (+14.7 µmol/L; p = 0.026), and resting whole-body carbohydrate oxidation (+24.2 mg min⁻¹; p = 0.046) were observed when considering both groups together. Accordingly, IFT can replicate the aerobic intensities sustained during traditional ergometer-type exercise training. More work is needed to evaluate the dose–response effects of such task-oriented circuit training protocols on secondary prevention targets across the continuum of stroke recovery.

Is Machine Learning-derived Low-Density Lipoprotein Cholesterol estimation more reliable than standard closed form equations? Insights from a laboratory database by comparison with a direct homogeneous assay

BACKGROUND

There is no consensus on the best method to estimate Low Density Lipoprotein-Cholesterol (LDL-C) in routine laboratories.

METHODS

We conducted a retrospective study to compare the performances of a Machine Learning (ML) algorithm using the K-Nearest Neighbors (LDL-KNN) method with that of the Friedewald formula (LDL-F), the Martin-Hopkins equation (LDL-NF), the de Cordova equation (LDL-CO) and the Sampson equation (LDL-SA) against direct homogeneous LDL-C assay (LDL-D) in patients who presented to the Laboratories of Hôtel Dieu de France university hospital in Beirut, Lebanon, from September 2017 to July 2020. Agreements between methods were analyzed using Intraclass Correlation Coefficients (ICC) and the Bland-Altman method of agreement.

RESULTS

31,922 observations from 19,279 subjects were included, with a mean age of 52 ± 18 years and 10,075 (52.3%) females. All methods except LDL-F and LDL-CO exhibited an overall ICC beyond the 0.9 cut-off. LDL-SA, LDL-NF and LDL-KNN were less susceptible to triglyceridemia than LDL-F and LDL-CO, with LDL-KNN resulting in the lesser fraction of points beyond the Bland-Altman limits of agreement.

CONCLUSION

An ML algorithm using LDL-KNN is promising for the estimation of LDL-C as it agrees better with LDL-D than closed form equations, especially in mild and severe hypertriglyceridemia.

Uncertain association between maximal fat oxidation during exercise and cardiometabolic risk factors in healthy sedentary adults

The present work examines the relationships between maximal fat oxidation during a graded exercise test (MFO), the intensity of exercise that elicits MFO (Fat_max), and traditional cardiometabolic risk factors in healthy, sedentary adults. A total of 119 (81 women) young, sedentary adults (22.1 ± 2.2 years old), and 71 (37 women) middle-aged, sedentary adults (53.4 ± 4.9 years old) participated in the current study. Systolic and diastolic blood pressures were determined following standard procedures. Plasma glucose, insulin, total cholesterol, high-density lipoprotein cholesterol and triglycerides were determined in a fasted state and the homeostatic model assessment of insulin resistance index and low-density lipoprotein cholesterol levels subsequently calculated. A sex and age group-specific cardiometabolic risk Z-score was also calculated for each subject based on waist circumference, systolic and diastolic blood pressure, plasma glucose, high-density lipoprotein cholesterol and triglycerides. MFO and Fat_max were determined using a walking graded exercise test using indirect calorimetry. No clear association was seen of MFO and Fat_max with any cardiometabolic risk factor (all P≥0.05), except for a weak, inverse association between Fat_max and the fatty liver index (P=0.027). Similarly, neither MFO nor Fat_max was apparently associated with the cardiometabolic risk Z-score (all P≥0.05). The current findings suggest an uncertain association of MFO and Fat_max during a graded exercise test with the cardiometabolic profile of healthy, sedentary adults.HighlightsThe study of the physiological mechanisms that trigger the onset of metabolic disorders has received considerable attention in recent years, with changes in MFO and Fat_max being highlighted as a potential key factor.This work shows that MFO and Fat_max during a graded exercise test are not associated with the cardiometabolic profile in sedentary, healthy adults.Further studies are needed to elucidate which other physiological disorders are related to cardiometabolic risk.

Evaluation of twelve formulas for LDL-C estimation in a large, blinded, random Italian population

BACKGROUND AND AIMS

Low-density lipoprotein-cholesterol (LDL-C) is the major determinant of cardiovascular disease (CVD) burden. Being the direct assays time consuming, expensive, not fully standardized and not worldwide available, indirect formulas represent the most used laboratory estimation of LDL-C. In this study we analyzed the accuracy of twelve formulas for LDL-C estimation in an Italian population of 114,774 individuals.

METHODS

All lipid samples were analyzed using direct homogeneous assay. The population was divided into various subgroups based on triglycerides and directly dosed LDL-C (D-LDL) levels. Twelve formulas (Friedewald, DeLong, Hata, Hattori, Puavillai, Anandaraja, Ahmadi, Chen, Vujovic, de Cordova, Martin, and Sampson) were compared in terms of their mean absolute deviations and the correlation and concordance of their estimated LDL-C with the respective D-LDL values.

RESULTS

LCL-C measured by Friedewald formula and direct assay differed by more than 9 mg/dL. For D-LDL>115 mg/dl, we observed a concordance rate of only 55% between Friedewald and the respective D-LDL values. For TG<250 mg/dl, the proportion of reclassification between the different formulas and D-LDL was 14.1% with Vujovic, 14.4% Sampson, 15.9% DeLong, 16.5% Puavilai, 19.9% Martin, 21.9% Friedewald, 23.5% Chen, 29% Anandaraja, 31.1% Ahmadi, 31.5% Hata, 33.2% Hattori, and 44.4% with De Cordova formula.

CONCLUSIONS

Our study compared for the first time 12 different LDL-C formulas on a Southern European population of more than 100,000 people. 'Several formulas showed better accuracy compared to Friedewald. Sampson, Martin and Vujovic resulted the most accurate formulas.

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.

Non-HDL-C/TG ratio indicates significant underestimation of calculated low-density lipoprotein cholesterol (LDL-C) better than TG level: a study on the reliability of mathematical formulas used for LDL-C estimation

OBJECTIVES

Low-density lipoprotein cholesterol (LDL-C) is the main laboratory parameter used for the management of cardiovascular disease. The aim of this study was to compare measured LDL-C with LDL-C as calculated by the Friedewald, Martin/Hopkins, Vujovic, and Sampson formulas with regard to triglyceride (TG), LDL-C and non-high-density lipoprotein cholesterol (non-HDL-C)/TG ratio.

METHODS

The 1,209 calculated LDL-C results were compared with LDL-C measured using ultracentrifugation-precipitation (first study) and direct (second study) methods. The Passing-Bablok regression was applied to compare the methods. The percentage difference between calculated and measured LDL-C (total error) and the number of results exceeding the total error goal of 12% were established.

RESULTS

There was good correlation between the measurement and calculation methods (r 0.962-0.985). The median total error ranged from -2.7%/+1.4% (first/second study) for Vujovic formula to -6.7%/-4.3% for Friedewald formula. The numbers of underestimated results exceeding the total error goal of 12% were 67 (Vujovic), 134 (Martin/Hopkins), 157 (Samspon), and 239 (Friedewald). Less than 7% of those results were obtained for samples with TG >4.5 mmol/L. From 57% (Martin/Hopkins) to 81% (Vujovic) of underestimated results were obtained for samples with a non-HDL-C/TG ratio of <2.4.

CONCLUSIONS

The Martin/Hopkins, Vujovic and Sampson formulas appear to be more accurate than the Friedewald formula. To minimize the number of significantly underestimated LDL-C results, we propose the implementation of risk categories according to non-HDL-C/TG ratio and suggest that for samples with a non-HDL-C/TG ratio of <1.2, the LDL-C level should not be calculated but measured independently from TG level.

Comparison of three equations for estimating low-density lipoprotein-cholesterol in the rural northeastern region of Thailand

BACKGROUND

Cardiovascular disease is the most common cause of death worldwide, and the detection of LDL-C contributes to reducing risks. However, the LDL-C is rarely evaluated according to the gold standard method because it is costly and time-consuming. This study aimed to determine the agreement of LDL-C among three equations, namely Friedewald's equation, Puavilai's equation, and Dansethakul's equation.

METHODS

A cross-sectional descriptive study.

RESULTS

Using the data of lipid measurement from a specific group of people in the remote rural area, we found that the Thai equations have more superior agreement with direct measurement than the Friedewald equation (ICC = 0.870, 95% CI = 0.857-0.882) when the agreement of continuous data was used for total analysis. Although the categorical analysis that gave better agreement was from Friedewald equation (K index = 0.730, 95% CI = 0.720-0.751), the findings from this study confirmed the population-specific use of Pauvilai's equation and Dansethakul's equation for determining the LDL-C.

CONCLUSION

Pauvilai's equation showed better agreement with direct measurement for LDL-C. Thus, it could be considered as an alternative for the direct method, particularly in laboratories in rural areas in Thailand.

Correlation between monocyte chemoattractant protein-1/chemokine (C-C motif) ligand 2 and coronary plaque characteristics

IMPACT STATEMENT

Vulnerable plaques are plaques which are susceptible to rupture or thrombosis and trigger a series of adverse events such as coronary disorders. CCL2 is a soluble basic protein belonging to the CC subfamily. Previous studies have been investigated on the correlation between inflammatory factors and clinical events, but there are few studies on the correlation between CCL2 and plaque characteristics. Our study found that the high expression of CCL2 is involved in multiple processes in the genesis and progression of coronary artery disease, and would be a potential clinical prognostic indicator. In addition, high expression of CCL2 may be related to gene pathways such as Nod-like receptor signaling pathway, suggesting that CCL2 is involved in the inflammatory response and immune process of coronary artery disease.

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.

Adapted recreational football small-sided games improve cardiac capacity, body composition and muscular fitness in patients with type 2 diabetes

BACKGROUND

The usefulness of adapted small-sided games (SSGs) in improving cardiac function in subjects with T2DM is still debated. Here we evaluated the effects of 18 weeks indoor muscular activation training (6 weeks; IMA) followed by adapted SSGs football training (12 weeks) on cardiac function, muscular fitness, body composition and adiponectin expression in sedentary T2DM volunteers.

METHODS

Six T2DM patients underwent IMA protocol of 6 weeks, twice a week followed by 12 weeks SSGs (5-a-side, once a week) training. Glucose, lipid profile and serum homocysteine concentration, body composition (BC), bone mineral density (DEXA), were determined at baseline and after 18 weeks (IMA+SSGs). VO<inf>2max</inf> and muscular fitness were recorded at baseline and after IMA (6 weeks) and SSGs (12 weeks), respectively.

RESULTS

No significant differences were found for VO<inf>2max</inf> and muscular fitness after 6weeks of IMA. After 18 weeks (6 weeks IMA + 12 weeks SSGs) of training, significant improvements were found in the following parameters: work capacity, VO<inf>2peak</inf>, Ventilation (VE<inf>peak</inf>), breathing reserve consumption and oxygen uptake efficiency slope (P<0.05); leg fitness (P<0.05), BC (P<0.05), vertebral column T-score (P<0.01) and adiponectin (total and high-molecular-weight; P<0.05). Compared to baseline, a reduction in serum homocysteine occurred after 18 weeks of training (P<0.05).

CONCLUSIONS

We evidenced that weekly adapted SSGs friendly football matches for 12 weeks improve cardiorespiratory capacity and the expression of independent markers associated with cardiovascular risk in T2DM patients, suggesting an overall reduced CVD-risk in these patients. These preliminary data encourage us to test the efficacy of this type of exercise in a larger population.

The effect of synbiotics pomegranate juice on cardiovascular risk factors in PCOS patients: a randomized, triple-blinded, controlled trial

PURPOSE

Polycystic ovarian syndrome (PCOS) is one of the most common metabolic and endocrine disorders. Functional foods like pomegranate and probiotics are those that are considered to have beneficial effects on metabolic diseases beyond their basic nutritional value. So, we aimed to evaluate the effect of synbiotic pomegranate juice (SPJ) on cardiovascular risk factors on PCOS patients.

METHODS

This was a randomized, triple-blinded, 8-week trial. Participants were randomly assigned to receive 300 mL/day of pomegranate juice (PJ), synbiotic beverage (SB), synbiotic pomegranate juice (SPJ), or placebo beverage (PB). Biochemical indices (lipid profile, Total Antioxidant Capacity (TAC), Malondialdehyde (MDA), high sensitive C-Reactive Protein (hs-CRP)) and blood pressure were assessed before and after the intervention.

RESULTS

Participants in the PJ, SB, and SPJ groups experienced improvement in their lipid profile, oxidative stress, inflammation, and blood pressure during the time. Compared to placebo, Total Cholesterol (TC) was lower in the SB group (P < 0.01), LDL-c was lower in the SPJ and SB groups (P < 0.01), and HDL-c was higher in the SPJ and PJ groups (P < 0.01). With regards to oxidative stress and inflammation, when compared with placebo, MDA was lower in the SPJ, SB, and PJ groups (P < 0.001), TAC was increased in the SPJ and PJ groups (P[Formula: see text] 0.001), and hs-CRP was decreased in the PJ group (P = 0.02). Blood pressure (BP) was lower in the SPJ and PJ groups compared to placebo (P < 0.001; P < 0.01, respectively).

CONCLUSIONS

Consuming daily SPJ for 8 weeks improved metabolic, oxidative, inflammatory, and BP outcomes in females with PCOS. This trial was registered in the Iranian Registry of Clinical Trials (IRCT20170207032439N2).

Comparison of calculated versus directly-measured low-density lipoprotein-cholesterol: An evaluation of ten formulas for an HIV-positive population in Sub-Saharan Africa

BACKGROUND:

Low-density lipoprotein cholesterol (LDLC) is a modifiable risk factor for atherosclerotic cardiovascular disease, therefore needs to be assessed and monitored. Direct homogeneous assays and various formulas exist to determine LDLC. We aimed to compare the directly measured LDL (dLDLC) with ten formulas for estimating LDLC.

MATERIALS AND METHODS:

This was a 2-year retrospective study of fasting lipid profile results obtained from HIV-positive patients attending an outpatient clinic at the University of Nigeria Teaching Hospital, Enugu, Nigeria, using homogeneous direct assays. Estimated LDLC was determined using ten formulas. Pearson's correlation, Bland–Altman plots, and linear regression were performed. Statistical significance was P < 0.05.

RESULTS:

Three thousand four hundred and eighty-two lipid results with mean ± standard deviation (SD) dLDLC of 2.1 ± 1.1 mmol/L were included in this study. There was a strong, positive correlation between Friedewald's LDLC and dLDLC n = 3412, r = 0.84, P < 0.001, but linear regression demonstrated a proportional bias P = 0.005. Ahmadi's equation showed the worst correlation n = 3482, r = 0.35, P < 0.001, but when applied to samples with triglyceride (TG) <1.13 mmol/L (100 mg/dl), the correlation showed a strong, positive relationship n = 1395, r = 0.80, P < 0.001, and no proportional bias P = 0.86. Teerankanchana's equation was the only formula that showed no difference between its LDLC and dLDLC (n = 3482, P = 0.056). It also demonstrated strong, positive correlation (n = 3482, r = 0.84, P < 0.001) and had a mean difference ± SD of −0.68 ± 0.63.

CONCLUSION:

Teerankanchana's formula showed good correlation and minimal bias with dLDLC at all TG levels. Moreover, linear regression showed no difference in the two. It seems to be the most suitable formula for estimating LDLC in our HIV-positive population.

Comparison of Formulas for Low-Density Lipoprotein (LDL) Calculation for Predicting the Risk of Metabolic Syndrome

Background:

The correlation between serum cholesterol level and the risk of developing atherosclerosis and metabolic syndrome has been well established in previous studies. Serum low-density lipoprotein (LDL-C) measurement is conducted using different methods which are generally divided into two groups, namely direct and indirect. Using indirect methods or calculations such as the Friedewald or Iranian formula for measuring LDL, particularly in developing countries, is quite common. The present study has stepped in to compare the robustness of the extant formulas in prognosticating and determining the incidence of metabolic syndrome.

Materials and Methods:

In this cross-sectional study, the target population was the community of Fasa cohort study. According to the views of the statistical advisor, 9530 people were included in the study and clinical laboratory examinations were done for each person. Their serum LDL level was measured using the existing formulas. Then, the results of the serum LDL level that was computed with different formulas, were compared with both the status of metabolic syndrome and laboratory tests of individuals.

Results:

The Iranian formula has the highest area under curve, the sensitivity of 0.73, and specificity of 0.77, higher positive and negative predictive values among other formulas. In Friedewald formula, for example, sensitivity and specificity equal 0.28 and 0.80, respectively. After further analysis, two new models proposed for predicting metabolic syndrome. The results revealed that these two models even outperform the Iranian formula.

Conclusion:

The Iranian formula for plasma LDL calculation has higher precision and application for predicting and measuring the metabolic syndrome in the Iranian population due to its considerable features. It is required to develop a new formula for each population and even for each sex, if possible.

Evaluation of eight formulas for LDL-C estimation in Iranian subjects with different metabolic health statuses

Background

Considering the crucial role of low-density lipoprotein-cholesterol (LDL-C) concentration in determining cardiovascular risk, the accuracy of LDL-C estimation is essential. To date, various types of formulae have been introduced, albeit their accuracy has not been assessed in varied populations. In this study, the accuracy of eight formulae for LDL-C estimation was evaluated in an Iranian population.

Methods

A data set of 2752 individuals was included in the study and all samples were analyzed in term of lipid profiles using direct homogeneous assay. The population was divided into various subgroups based on the triglyceride (TG), high-density lipoprotein- cholesterol (HDL-C), total cholesterol (TC), fasting blood sugar (FBS) and age values and estimated LDL-C values by Friedewald, Chen, de Cordova, Vujovic, Anandaraja, Hattori, Ahmadi, and Puavillai equations were compared to the directly measured LDL-C in each subgroup.

Results

Estimated LDL-C values by Puavillai formulae showed an insignificant difference compared to the directly measured LDL-C in subjects with high level of TG. However, for TG range < 3.38 mmol/L and high levels of HDL-C, the difference between the means of estimated LDL-C by Hattori and de Cordova formulas, and directly measured LDL-C was relatively lower than other equations. In addition, estimated LDL-C by Hattori and de Cordova formulae had insignificant differences as compared to the direct LDL-C at some levels of cholesterol, the normal level of FBS and some age ranges.

Conclusions

Therefore, it seems that Hattori and de Cordova formulas can be considered as the best alternatives for LDL-C direct measurement in the Iranian population, especially for healthy subjects.

Developing a Modified Low-Density Lipoprotein (M-LDL-C) Friedewald's Equation as a Substitute for Direct LDL-C Measure in a Ghanaian Population: A Comparative Study

Despite the availability of several homogenous LDL-C assays, calculated Friedewald's LDL-C equation remains the widely used formula in clinical practice. Several novel formulas developed in different populations have been reported to outperform the Friedewald formula. This study validated the existing LDL-C formulas and derived a modified LDL-C formula specific to a Ghanaian population. In this comparative study, we recruited 1518 participants, derived a new modified Friedewald's LDL-C (M-LDL-C) equation, evaluated LDL-C by Friedewald's formula (F-LDL-C), Martin's formula (N-LDL-C), Anandaraja's formula (A-LDL-C), and compared them to direct measurement of LDL-C (D-LDL-C). The mean D-LDL-C (2.47±0.71 mmol/L) was significantly lower compared to F-LDL-C (2.76±1.05 mmol/L), N-LDL-C (2.74±1.04 mmol/L), A-LDL-C (2.99±1.02 mmol/L), and M-LDL-C (2.97±1.08 mmol/L) p < 0.001. There was a significantly positive correlation between D-LDL-C and A-LDL-C (r=0.658, p<0.0001), N-LDL-C (r=0.693, p<0.0001), and M-LDL-C (r=0.693, p<0.0001). M-LDL-c yielded a better diagnostic performance [(area under the curve (AUC)=0.81; sensitivity (SE) (60%) and specificity (SP) (88%)] followed by N-LDL-C [(AUC=0.81; SE (63%) and SP (85%)], F-LDL-C [(AUC=0.80; SE (63%) and SP (84%)], and A-LDL-C (AUC=0.77; SE (68%) and SP (78%)] using D-LDL-C as gold standard. Bland-Altman plots showed a definite agreement between means and differences of D-LDL-C and the calculated formulas with 95% of values lying within ±0.50 SD limits. The modified LDL-C (M-LDL-C) formula derived by this study yielded a better diagnostic accuracy compared to A-LDL-C and F-LDL-C equations and thus could serve as a substitute for D-LDL-C and F-LDL-C equations in the Ghanaian population.

New modified Friedewald formulae for estimating low-density lipoprotein cholesterol according to triglyceride levels: extraction and validation

PURPOSE

The most commonly used method for estimating low-density lipoprotein cholesterol (LDLC) is Friedewald formula (FF). This study aims to extract and validate new modified Friedewald formulae for estimating LDLC according to triglyceride (TG) levels in Iranians.

METHODS

Total cholesterol (TC), high-density lipoprotein cholesterol (HDLC), LDLC, and TG were measured in 5030 fasted subjects. The original FF was used for estimating LDLC. Data were divided into training and validation data sets. For extracting modified Friedewald formulae, linear regression was used with TG as independent and TC, HDLC, and directly measured LDLC (D-LDLC) as dependent variables, respectively. An overall modified formula was extracted for TG concentrations <400 mg/dL. The specific modified formulae were extracted for different TG categories. The performance of the modified formulae was assessed in the validation data set.

RESULTS

The overall derived formula for calculating LDLC was M-LDLC = TC-HDLC-TG/4. The coefficients of TG (specific TG terms) in modified formulae were 2.7, 3.7, 4.6, and 5 for TG <100, 100-200, 200-300, and 300-400 mg/dL, respectively. Compared to the original FF, applying a new modified formula with TG/4 to a validation data set provided a less mean difference (4.03 vs. 10.86 mg/dL), greater kappa coefficient (0.691 vs. 0.505), and better subject classification (80.8 vs. 67.1%). After applying modified formulae with specific TG terms, the difference between M-LDLC and D-LDLC decreased to 1.41 mg/dL.

CONCLUSIONS

Modified formulae were developed and validated for LDLC estimation that compared to the original FF, provided more accurate estimation of LDLC and better classification of subjects. These findings shall be useful for preventive, diagnostic, and therapeutic purposes.

Impact of glucose and lipid markers on the correlation of calculated and enzymatic measured low-density lipoprotein cholesterol in diabetic patients with coronary artery disease

BACKGROUND AND AIMS

Low-density lipoprotein cholesterol (LDL-C) is widely estimated by Friedewald equation (FE) and Enzymatic test (ET), which are affected by several factors. The aim of this study was to observe the impact of diabetic lipid and glucose patterns on the correlation between FE LDL-C (F-LDL) and ET LDL-C (E-LDL) in patients with coronary artery disease (CAD).

METHODS AND RESULTS

A total of 8155 CAD patients were consecutively enrolled and their lipid profiles were measured. The impacts of triglyceride (TG), glycosylated hemoglobin A1c (HbA1c), and high-density lipoprotein cholesterol (HDL-C) on the correlation of F-LDL and E-LDL were examined. The difference value (DV) between F-LDL and E-LDL was compared using ANOVA test. The CAD patients with DM were elder and had higher body mass index, plasma TG compared with those without DM (P < .05 separately). In the whole population, F-LDL was lower than E-LDL but showed a high correlation with E-LDL (r = .970, P = .000). Moreover, as the TG concentrations increased, the DV increased accordingly but the correlation between F-LDL and E-LDL decreased (P < .01). The similar trend was also found in both DM and non-DM patients comparing with different TG groups. However, in patients with DM, there was no significant difference of DV in different HbA1c groups or HDL-C concentrations (P > .05).

CONCLUSION

Although F-LDL might underestimate the value of LDL-C, the correlation between F-LDL and E-LDL was clinically acceptable (r = .97), suggesting the LDL-C values measured by two methods were similarly reliable in CAD patients with or without DM.

Martin's Equation as the Most Suitable Method for Estimation of Low-Density Lipoprotein Cholesterol Levels in Korean Adults

Background

Friedewald equation is the most widely used method for estimating low-density lipoprotein cholesterol (LDL-C) level. However, due to potential over- or underestimation, many studies have used a modified equation. This study aimed to compare estimates by 4 different equations to directly measured LDL-C concentrations in order to propose the most appropriate method for LDL-C estimation in the Korean population.

Methods

We studied data of 4,350 subjects that included total cholesterol, high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and LDL-C concentrations that had been measured at one university hospital in Seoul. We investigated 4 equations: LDL-C by Friedewald's original equation (LDL-C_F) and its 3 modifications. Pearson correlation analysis was performed to compare these estimates to the direct measurement.

Results

Pearson correlation analysis revealed a good correlation among all 4 estimated LDL-C values and the directly measured LDL-C value. The Pearson coefficients were 0.951 for LDL-C_F, 0.917 for LDL-C by Hatta equation (LDL-C_H), 0.968 for LDL-C by Puavilai equation (LDL-C_P), and 0.983 for LDL-C by Martin equation (LDL-C_M). Martin equation (LDL-C_M) resulted in the best approximation (mean difference from the direct measurement, 5.5 mg/dL; mean percentage difference from the direct measurement, 5.1%) and the best agreement with the direct measurement (86.1%). LDL-C_P resulted in the second-best approximation (mean difference, 7.0 mg/dL; mean percentage difference, 6.2%; concordance, 82.5%). LDL-C_M was found to be less influenced by TG and HDL-C levels than by LDL-C_F.

Conclusion

Estimates by Martin equation had the best agreement with direct LDL-C concentrations and both Martin and Puavilai equations were superior to Friedewald equation for estimating LDL-C concentrations in Korean adults.