Calculation of LDL-Cholesterol vs. Direct Homogenous Assay

Fennel seeds extract prevents fructose-induced cardiac dysfunction in a rat model of metabolic syndrome via targeting abdominal obesity, hyperuricemia and NF-κβ inflammatory pathway

BACKGROUND

Metabolic syndrome (MetS) is commonly associated with increased risk of cardiac disease that affects a large number of world populations.

OBJECTIVE

This research attempted to investigate the efficacy of fennel seeds extract (FSE) in preventing development of cardiac dysfunction in rats on fructose enriched diet for 3 months, as a model of MetS.

MATERIALS & METHODS

Thirty adult Wistar male rats (160-170 g) were assigned into 5 groups including control, vehicle, FSE (200 mg/kg BW) and fructose (60%) fed rats with and without FSE. Following the last treatment, blood pressure, ECG and heart rate were measured. Next, blood and cardiac tissues were taken for biochemical and histological investigations.

RESULTS

Feeding fructose exhibited characteristic features of MetS involving, hypertension, abnormal ECG, elevated heart rate, serum glucose, insulin, lipids and insulin resistance, accompanied by abdominal obesity, cardiac hypertrophy and hyperuricemia. Fructose fed rats also showed significant reduction in cardiac antioxidants (GSH, SOD, CAT) with elevation in oxidative stress indices (NADPH oxidase, O2.-, H2O2, MDA, PCO), NF-κβ, pro-inflammatory cytokines (TNF-α, IL-1β, IL-6), adhesion molecules (ICAM-1, VCAM-1) and serum cardiac biomarkers (AST, LDH, CK-MB, cTn-I). Histopathological changes evidenced by destruction of cardiac myofibrils, cytoplasmic vacuolization, and aggregation of inflammatory cells were also detected. Consumption of FSE showed high ability to alleviate fructose-induced hypertension, ECG abnormalities, cardiac hypertrophy, metabolic alterations, oxidative stress, inflammation and histological injury.

CONCLUSION

Findings could suggest FSE as a complementary supplement for preventing MetS and associated cardiac outcomes. However, well controlled clinical studies are still needed.

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.

Serum total carbohydrates, conjugated carbohydrates and total protein glycation index in diabetes mellitus

BACKGROUND

Diabetes mellitus is defined according to fasting blood glucose and clinical signs. But, the markers of glycation have been used recently as a criterion to diagnose and monitor the therapy.

OBJECTIVES

To measure serum total- and conjugated- saccharides and to define the new marker as serum total protein glycation index (sTPGI ) for diabetes.

DESIGN AND METHODS

The study population consisted of 172 subjects who were divided to control and diabetic cases. Serum total and conjugated saccharides were measured and sTPGI was defined to discriminate serum glycosylated and glycated saccharides.

RESULTS

Patients with diabetes compared with the controls had increased levels of serum (free) glucose, HbA1c, serum total carbohydrates, total conjugated carbohydrates and sTPGI. All three indices of serum carbohydrates showed significant positive correlation with serum glucose, HbA1c and diabetes. The equations: sTPGI = 0.12 Glucose (mg/dL) + 12 and sTPGI = 3.5HbA1c (%) + 5, were deduced for the association of sTPGI with serum free glucose and HbA1c. In ROC analysis, both HbA1c (AUC = 0.965, p ≤ 0.001) and sTPGI (AUC = 0.734, p ≤ 0.001) had strong and significant efficiency to discriminate diabetic cases from control subjects.

CONCLUSIONS

The results confirm that sTPGI obtained by indirect assay has high significant efficiency comparable to HbA1c to diagnose diabetes. sTPGI relative to HbA1c indicates the mean level of glycaemia over a shorter period of about one month so it responds more quickly to changes in therapy.

Hypolipemic effects of histamine is due to inhibition of VLDL secretion from the liver: involvement of both H1 and H2-receptors

The research was performed to study the mechanism whereby histamine affects the profile of plasma lipids. Six groups of ten male rats were received two injections with histamine or its H1- and H2-agonists and antagonists. Histamine caused a significant decrease in the concentrations of triglyceride, total cholesterol, and LDLc, while HDLc had no significant change. The rate of VLDL secretion was 263.6 ± 25.8 mg/h dL in control rats and was inhibited by about 68% in histamine injected rats. These changes have been mimicked by either histamine H1- or H2-agonists. The effects of H1- and H2-agonists were abolished in the presence of cetirizine and famotidine respectively. Histamine causes a significant decrease in serum triglyceride, total, and LDL-cholesterol by both H1 and H2-receptors. The decrease in serum lipids is due to the inhibitory effect of histamine or its agonists on VLDL secretion from the liver.

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.

Comparison of Methods to Estimate Low-Density Lipoprotein Cholesterol in Patients With High Triglyceride Levels

IMPORTANCE

Low-density lipoprotein cholesterol (LDL-C) is typically estimated with the Friedewald or Martin/Hopkins equation; however, if triglyceride levels are 400 mg/dL or greater, laboratories reflexively perform direct LDL-C (dLDL-C) measurement. The use of direct chemical LDL-C assays and estimation of LDL-C via the National Institutes of Health Sampson equation are not well validated, and data on the accuracy of LDL-C estimation at higher triglyceride levels are limited.

OBJECTIVE

To compare an extended Martin/Hopkins equation for triglyceride values of 400 to 799 mg/dL with the Friedewald and Sampson equations.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study evaluated consecutive patients at clinical sites across the US with patient lipid distributions representative of the US population in the Very Large Database of Lipids from January 1, 2006, to December 31, 2015, with triglyceride levels of 400 to 799 mg/dL. Data analysis was performed from November 9, 2020, to March 23, 2021.

MAIN OUTCOMES AND MEASURES

Accuracy in LDL-C classification according to guideline-based categories and absolute errors between estimated LDL-C and dLDL-C levels. Patients were randomly assigned 2:1 to derivation and validation data sets. Levels of dLDL-C were measured by vertical spin-density gradient ultracentrifugation. The LDL-C levels were estimated using the Friedewald method, with a fixed ratio of triglycerides to very low-density lipoprotein cholesterol (VLDL-C ratio of 5:1), extended Martin/Hopkins equation with a flexible ratio, and Sampson equation with VLDL-C estimation by multiple least-squares regression.

RESULTS

A total of 111 939 patients (mean [SD] age, 52 [13] years; 65.0% male) with triglyceride levels of 400 to 799 mg/dL were included, representing 2.2% of 5 081 680 patients in the database. Across all individual guideline LDL-C classes (<40, 40-69, 70-99, 100-129, 130-159, 160-189, and ≥190), estimation of LDL-C by the extended Martin/Hopkins equation was most accurate (62.1%) compared with the Friedewald (19.3%) and Sampson (40.4%) equations. In classifying LDL-C levels less than 70 mg/dL across all triglyceride strata, the extended Martin/Hopkins equation was most accurate (67.3%) compared with Friedewald (5.1%) and Sampson (26.4%) equations. In addition, for classifying LDL-C levels less than 40 mg/dL across all triglyceride strata, the extended Martin/Hopkins equation was most accurate (57.2%) compared with the Friedewald (4.3%) and Sampson (14.4%) equations. However, considerable underclassification of LDL-C occurred. The magnitude of error between the Martin/Hopkins equation estimation and dLDL-C was also smaller: at LDL-C levels less than 40 mg/dL, 2.7% of patients had 30 mg/dL or greater differences between dLDL-C and estimated LDL-C using the Martin/Hopkins equation compared with the Friedewald (92.5%) and Sampson (38.7%) equations.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, the extended Martin/Hopkins equation offered greater LDL-C accuracy compared with the Friedewald and Sampson equations in patients with triglyceride levels of 400 to 799 mg/dL. However, regardless of method used, caution is advised with LDL-C estimation in this triglyceride range.

Estimation of low-density lipoprotein cholesterol by machine learning methods

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Effect of Ramadan Fasting on Body Composition, Biochemical Profile, and Antioxidant Status in a Sample of Healthy Individuals

BACKGROUND

Muslims fast during the month of Ramadan by abstinence from food and drink every day from dawn to sunset. Studies have reported contradictory results with respect to the changes in body weight and biochemical parameters. No study has been conducted on the association between fasting and body weight and biochemical parameters in the Indian setting on healthy Muslim subjects.

OBJECTIVES

To assess the effect of fasting during Ramadan on biochemical parameters such as lipid profile, liver function test, renal function test, antioxidant status, random blood sugar, hemoglobin, body composition, and blood pressure in a sample of healthy individuals.

METHODS

In this study, 52 healthy free-living participants (25 males, 27 females, 21-64 years) who met the inclusion and exclusion criteria and completed both follow-ups (before and after Ramadan) were studied. Participants were fasting 12 hours a day for at least 21 days, including menstruating women. It was a free-living study with no dietary restrictions. Anthropometry, lipid profile, liver and renal function tests were measured by standard methods. Body composition was analyzed by bioelectrical impedance.

RESULTS

Significant beneficial changes in albumin, alanine aminotransferase, creatinine, and high-density lipoprotein (HDL) were observed, while total cholesterol, random blood sugar, aspartate aminotransferase, and alkaline phosphatase enzymes remained unchanged after Ramadan. Fasting did bring in some changes in body composition; among both men and women, mean weight loss ranged from 0.81 - 1.4 kg in majority of the subjects, which was due to loss in muscle mass. Moderate changes in intra- and extracellular water content was observed after fasting.

CONCLUSIONS

Significant improvements were observed in HDL levels and liver function tests, which can be attributed to the loss of body weight. Improvement in liver function tests may be related to the changes in cytokines and alteration in sleep patterns. Ramadan-like fasting, along with the nutritional education prior to fasting, may be beneficial and effective in the spiritual and overall well-being.

Clinical Validation of Eleven Formulas for Calculating LDL-C in Iran

Background & Objective:

Concentration of low-density lipoprotein (LDL) is a known risk factor for cardiovascular disease which is routinely measured or calculated as LDL-C in clinical laboratories. In order to decrease the cost, instead of its measuring, it is recommended to calculate it using multiple formulas that have been introduced up to now. The aim of this study was to assess the results of various formulas and comparison of these results with those of measuring method and to clarify the best formula for the Iranian population.

Methods:

Concentrations of total cholesterol (TC), triglyceride (TG), cholesterol of high-density lipoprotein (HDL-C) and LDL-C in serums of 471 overnight fasting individuals were measured and also LDL-Cs of these samples were calculated by eleven different formulas according to their TC, TG, and HDL-C concentrations. Subsequently, results of measured and calculated LDL-C were analyzed statistically by paired t-test, correlation coefficient, and Passing-Bablok regression. In addition, for clinical evaluation, the differences between calculated and measured mean results were calculated and compared with an allowable total error.

Results:

Paired t-test unraveled a significant difference between the results of measured and calculated LDL-C by various formulas. But for some formulas, these differences were not clinically significant. The best clinical and statistical agreement (correlation coefficient) was obtained by the Friedewald equation.

Conclusion:

By using validated methods which have correct calibration and control system for measuring TC, TG, and HDL-C, we can use the Friedewald formula for calculating LDL-C in serum samples with TG up to 400 mg/dL.

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.

The Triglyceride/HDL Ratio and Triglyceride Glucose Index as Predictors of Glycemic Control in Patients with Diabetes Mellitus Type 2

Introduction:

Poor glycemic control, assessed by higher glycated hemoglobin (HbA1c) levels, is associated with greater risk of diabetic complications.

Aim:

The aim of this study was to assess the association of triglyceride - to - HDL cholesterol (TG/HDL-C) ratio and triglyceride glucose (TyG) index with HbA1c and to evaluate their potential role as predictors of glycemic control in patients with diabetes mellitus type 2 (DM2).

Patients and methods:

This cross-sectional study was conducted in Health Center Banovici and included a total of 113 patients with DM2 classified according to their HbA1c values in two groups: DM2 HbA1c <7% - DM2 patients with good glycemic control (n=39) and DM2 HbA1c ≥7% - DM2 patients with poor glycemic control (n=74). Anthropometric, biochemical parameters and blood pressure values were measured, while TG/HDL-C ratio and TyG index were calculated.

Results:

TG/HDL-C ratio and TyG index were significantly higher in DM2 HbA1c≥7% compared to DM2 HbA1c<7% group (p=0.003 and p<0.001; respectively). Both TG/HDL-C ratio and TyG index were positively associated with HbA1c levels (Rho=0.29; p=0.002; Rho=0.37; p<0.001; respectively). In linear regression analysis TG/HDL-C ratio and BMI, and also TyG index and BMI were significantly independently associated with HbA1c even after controlling for age, gender, diabetes duration and smoking. When we stratified patients according to BMI values, independent association between TG/HDL-C ratio and HbA1c remained significant only in normal weight subjects (OR 0.21; 95%CI: 0.05-0.37; β=0.65; p=0.017), while independent association between TyG index and HbA1c remained significant only in overweight and obese subjects (OR 0.063; 95%CI: 0.01- 0.12; β=0.24; p =0.027).

Conclusion:

TG/HDL-C ratio might be a useful predictor of glycemic control in normal weight, and TyG index in overweight and obese patients with DM2.

Microsomal triglyceride transfer protein inhibitor (lomitapide) efficacy in the treatment of patients with homozygous familial hypercholesterolaemia

Aims

The aim of this study was to evaluate the effect of microsomal triglyceride transfer protein inhibitor (lomitapide) in patients with homozygous familial hypercholesterolaemia.

Methods and results

In 12 homozygous familial hypercholesterolaemia patients treated with lipid-lowering drugs ± biweekly lipoprotein apheresis sessions (nine patients), daily lomitapide was added. The lipid profile (total cholesterol, low-density lipoprotein cholesterol, triglycerides, high-density lipoprotein cholesterol) before and after lomitapide treatment was evaluated. The follow-up period with lomitapide treatment was 3–24 months (13.8 ± 7.9). The median baseline low-density lipoprotein cholesterol level was 900 mg/dl (348–1070), after lipid-lowering drugs therapy was 383.5 mg/dl (214–866) and after lipid-lowering drugs + time-averaged level was 288 mg/dl (183.7–716.6). The addition of lomitapide lowered low-density lipoprotein cholesterol levels further by 56.8% compared to lipid-lowering drugs alone (mean reduction 262, 95% confidence interval (105.5–418.7), p = 0.005) and by 54% (mean reduction 182.9, 95% confidence interval (−342 – −23), p = 0.031) comparing to lipid-lowering drugs + lipoprotein apheresis (time-averaged level). The time-averaged level of low-density lipoprotein cholesterol in lipid-lowering drugs + lipoprotein apheresis patients compared with lipid-lowering drugs + lomitapide was 54% in favour of lomitapide ( p = 0.031).

Conclusions

Treatment with lomitapide in homozygous familial hypercholesterolaemia patients has a beneficial effect with a constant decrease of low-density lipoprotein cholesterol by 57% compared with classical lipid-lowering therapy and by 54% compared with classical lipid-lowering therapy and time-averaged level of low-density lipoprotein cholesterol.

Validation of the novel Martin method for LDL cholesterol estimation

INTRODUCTION

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

RESULTS

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

CONCLUSIONS

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

Esterification of HDL-cholesterol is Decreased in Diabetes Mellitus and CAD and Enhanced Following Treatment with Statins

Background:

The main goal of using statins is to reduce the level of plasma cholesterol, meanwhile they have a wide spectrum of actions.

Objectives:

To identify the effect of statins on fractional cholesterol esterification (FCE) as well as the complete profile of lipids and (apo)lipoproteins.

Design and methods:

In an age and sex matched case-control study, 400 subjects who were referred for coronary angiography were divided into two groups according using statins.

Results:

Total cholesterol was decreased significantly following treatment with statins (165.6 ± 38.0 mg/dL vs. 205.3 ± 48.4, p≤0.001). About 90% of the reduction was occurred in nonHDL and 10% in HDL fraction. Reduction of nonHDL cholesterol (125.2 ± 35.2 mg/dL vs. 162.8 ± 45.2, p≤0.001) occurred on both unesterified (52.4 ± 21.5 mg/dL vs. 65.2 ± 25.5, p≤0.001) and esterified cholesterol (74.7 ± 27.3 mg/dL vs. 96.6 ± 34.1, p≤0.001). But the decrease in HDL cholesterol (40.4 ± 10.0 mg/dL vs. 42.3 ± 9.9, p≤0.079) happened exclusively in unesterified fraction (10.9 ± 3.4 vs. 15.2 ± 5.1, p≤0.001) and was counterbalanced with a significant increase in esterified portion (29.5 ± 8.2 mg/dL vs. 27.2 ± 9.5, p≤0.020). The ratio of esterified- per total- cholesterol in HDL was 67.5 ± 8.1% in the control group and was decreased to 58.0 ± 14.9% (p≤0.01) in diabetes and CAD and increased to 73.5 ± 6.9 (p≤0.01) after using statins.

Conclusions:

The results suggest that the percent of esterified cholesterol in HDL fraction is decreased in diabetes and CAD patients and increased by using statins.

The Ratio of Unesterified/esterified Cholesterol is the Major Determinant of Atherogenicity of Lipoprotein Fractions

Background

The hypothesis is proposed that the atherogenicity of lipoporotein fractions is correlated with the content of unesterified cholesterol.

Objectives

To evaluate the role and prognostic values of unesterified and esterified cholesterol in lipoprotein fractions for coronary artery disease (CAD).

Design and methods

The study population consisted of 400 patients who were divided to CAD controls and cases according to the data of coronary angiography. Fractional cholesterol esterification (FCE) as well as the complete profile of lipids and (apo)lipoproteins were determined.

Results

Total cholesterol was increased significantly in CAD patients (196.3 ± 52.3 mg/dL vs. 185.7 ± 48.0, p≤ 0.049) and the increment occurred totally in unesterified portion (77.2 ± 28.4 mg/dL vs. 71.1 ± 24.4, p≤ 0.031). HDL cholesterol showed a significant decrease in CAD group (39.9 ± 9.5 mg/dL vs. 44.6 ± 10.5, p≤ 0.001), but the decrement occurred wholly in the esterified portion (26.2 ± 9.2 mg/dL vs. 31.1 ± 8.1, p≤ 0.001). NonHDL cholesterol was increased significantly in CAD group (156.8 ± 48.3 mg/dL vs. 140.3 ± 43.6, p≤ 0.001), and the changes occurred in both un- and esterified portions. FCE in HDL was diminished significantly in CAD patients (64.8 ± 13.9% vs. 69.3 ± 7.9, p≤ 0.01). In multivariate logistic regression analysis, unesterified cholesterol in NonHDL (UeNonHDLc) and esterified cholesterol in HDL (EsHDLc) excluded total cholesterol and HDLc respectively from the regression equation. In ROC analysis, the ratio of UeNonHDLc/EsHDLc was the strongest predictor for CAD among cholesterol subfractions.

Conclusions

The results confirm that UeNonHDLc is atherogenic and EsHDLc is antiatherogenic and are independent risk factors for CAD.

Evaluation of a new equation for LDL-c estimation and prediction of death by cardiovascular related events in a German population-based study cohort

A simple equation established by Cordova & Cordova (LDL-COR) was developed to provide an improved estimation of LDL-cholesterol in a large Brazilian laboratory database. We evaluated this new equation in a general population cohort in Pomerania, north-eastern Germany (SHIP Study) compared to other existing formulas (Anandaraja, Teerakanchana, Chen, Hattori, Martin, Friedewald and Ahmadi), and its power in the prediction of death by atherosclerosis related events as the primary outcome. Analysis was conducted on a cohort of 4075 individuals considering age, gender, use of lipid lowering therapy and associated co-morbidities such as diabetes, hepatic, kidney and thyroid disease. LDL-COR values had a lower standard deviation compared to the previously published equations: 0.92 versus 1.02, 1.02, 1.03, 1.04, 1.09, 1.10 and 1.74 mmol/L, respectively. All of the factors known to affect the results obtained by the Friedewald's equation (LDL-FW), except fibrate use, were associated with the difference between LDL-COR and LDL-FW (p < .01), with TSH being borderline (p = .06). LDL-COR determined a higher hazard ratio (1.23 versus 1.12, 1.19, 1.21, 1.19, 1.21 and 1.19) for cardiovascular disease related mortality, incident stroke or myocardial infarction compared to the other evaluated formulas, except for Ahmadi's (1.24), and the same adjusted predictive power considering all confounding factors. The proposed simple equation was demonstrated to be suitable for a more precise LDL-c estimation in the studied population. Since LDL-c is a parameter frequently requested by medical laboratories in clinical routine, and will probably remain so, precise methods for its estimation are needed when direct measurement is not available.