Direct Low-density Lipoprotein Cholesterol Research Articles

The effects of eight serum lipid biomarkers on age-related macular degeneration risk: a Mendelian randomization study.

Age-related macular degeneration (AMD) is a leading cause of vision loss. Whereas lipids have been studied extensively to understand their effects on cardiovascular diseases, their relationship with AMD remains unclear. Two-sample Mendelian randomization (MR) analyses were performed to systematically evaluate the causal relationships between eight serum lipid biomarkers, consisting of apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), total cholesterol (CHOL), high-density lipoprotein cholesterol (HDL-C), direct low-density lipoprotein cholesterol (LDL-C), lipoprotein A [Lp(a)], triglycerides (TG) and non-HDL cholesterol (non-HDL-C), and the risk of different AMD stages and subtypes. We derived 64-407 genetic instruments for eight serum lipid biomarkers in 419649 participants of European descent from the UK Biobank cohort. We conducted genome-wide association studies (GWAS) for 12711 advanced AMD cases [8544 choroidal neovascularization (CNV) and 2656 geographic atrophy (GA) specific AMD subtypes] and 5336 intermediate AMD cases with 14590 controls of European descent from the International AMD Genomics Consortium. Higher genetically predicted HDL-C and ApoA1 levels increased the risk of all AMD subtypes. LDL-C, ApoB, CHOL and non-HDL-C levels were associated with decreased risk of intermediate and GA AMD but not with CNV. Genetically predicted TG levels were associated with decreased risk of different AMD subtypes. Sensitivity analyses revealed no evidence for directional pleiotropy effects. In our multivariable MR analyses, adjusting for the effects of correlated lipid biomarkers yielded similar results. These results suggest the role of lipid metabolism in drusen formation and particularly in AMD development at the early and intermediate stages. Mechanistic studies are warranted to investigate the utility of lipid pathways for therapeutic treatment in preventing AMD.

Abstract 14313: Machine Learning for Prediction of Low Density Lipoprotein Cholesterol in Fasting and Non-fasting Patients With and Without HIV Infection

Background: Low-density lipoprotein cholesterol (LDL-C) is commonly estimated from total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) using predefined equations which assume fixed or varying relationships between these parameters and may under- or overestimate LDL-C. Machine learning (ML) algorithms allow prediction of complex non-linear relationships. We, therefore, sought to investigate the utility of ML to predict LDL-C in comparison with two widely-used methods (Friedewald and Hopkins). Methods: We identified 7397 direct LDL-C (4716 HIV, 2060 uninfected controls) measurements in the Women's Interagency HIV Study (WIHS), a prospective study of women with and without HIV undergoing serial assessments. We trained and optimized 5 ML methods (linear regression, random forest, gradient boosting machine, support vector machine, and neural networks) to predict LDL-C using TC, HDL-C, and TG in 80% of the measurements and tested model performance in a holdout test set (20% of the measurements). Results: Overall, the support vector machine model had the best performance characteristics, outperforming Friedewald’s and Hopkins methods with higher R 2 , lower root mean square error and lower mean absolute error. The support vector machine model performance remained superior in participant subgroups with and without HIV and those with non-fasting measurements. Model performance parameters for the test dataset are shown in the Figure. Conclusions: A support vector machine learning model predicts directly measured LDL-C more accurately than Friedewald and Hopkins methods, especially in non-fasting patients with HIV. Further studies are needed to provide external validation.

Relationships between Smoking Status, Cardiovascular Risk Factors, and Lipoproteins in a Large Japanese Population.

Aims: Smoking is a major risk factor for cardiovascular disease (CVD), a leading cause of death and disability. Other CVD risk factors include age, gender, hypertension, diabetes, increased low-density lipoprotein cholesterol (LDL-C) and decreased high-density lipoprotein cholesterol (HDL-C). Our goal was to assess relationships between smoking status and CVD risk factors, with a focus on direct LDL-C, HDL-C, triglycerides (TG) and small dense LDL-C (sdLDL-C). Methods: A total of 34,497 Japanese men and women, mean age 51 years, had their CVD risk factors including fasting serum total cholesterol, TG, HDL-C, sdLDL-C, and direct LDL-C assessed. One-way ANOVA and multiple linear regression analyses were carried to assess the interrelationships of these parameters with smoking. Results: In both men and women, current smokers had significantly ( p ＜0.001) higher median TG (+19.6%, +16.9%) and sdLDL-C levels (+12.7%, +4.2%) levels, and significantly ( p ＜0.001) lower HDL-C levels (-7.3%, -4.3%) than non-smokers. They were also significantly ( p ＜0.05) more likely to have TG values ＞150 mg/dL (+56.8%, +116.3%), sdLDL-C ＞40.1 mg/dL (+28.8%, +44.9%), and HDL-C ＜40 mg/dL (+89.8%, +114.3%). Ex-smokers generally had lipid values that were intermediate between non-smokers and current smokers. Multivariate analysis confirmed the significance of these relationships. Conclusion: Our data indicate that current cigarette smoking is associated with increased TG and sdLDL-C levels, as well as decreased HDL-C levels. Furthermore, smoking effect on lipid profiles remain after cessation. These data provide further justification for smoking cessation.

Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation.

Low-density lipoprotein cholesterol (LDL-C) is a target for cardiovascular prevention. Contemporary equations for LDL-C estimation have limited accuracy in certain scenarios (high triglycerides [TG], very low LDL-C). We derived a novel method for LDL-C estimation from the standard lipid profile using a machine learning (ML) approach utilizing random forests (the Weill Cornell model). We compared its correlation to direct LDL-C with the Friedewald and Martin-Hopkins equations for LDL-C estimation. The study cohort comprised a convenience sample of standard lipid profile measurements (with the directly measured components of total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], and TG) as well as chemical-based direct LDL-C performed on the same day at the New York-Presbyterian Hospital/Weill Cornell Medicine (NYP-WCM). Subsequently, an ML algorithm was used to construct a model for LDL-C estimation. Results are reported on the held-out test set, with correlation coefficients and absolute residuals used to assess model performance. Between 2005 and 2019, there were 17,500 lipid profiles performed on 10,936 unique individuals (4,456 females; 40.8%) aged 1 to 103. Correlation coefficients between estimated and measured LDL-C values were 0.982 for the Weill Cornell model, compared to 0.950 for Friedewald and 0.962 for the Martin-Hopkins method. The Weill Cornell model was consistently better across subgroups stratified by LDL-C and TG values, including TG >500 and LDL-C <70. An ML model was found to have a better correlation with direct LDL-C than either the Friedewald formula or Martin-Hopkins equation, including in the setting of elevated TG and very low LDL-C.

Abstract MP72: Using Non-HDL-C/Triglyceride Ratio To Screen For Direct LDL-C Testing: Improving LDL-C Estimates At A Cost

Introduction: Despite more accurate low-density lipoprotein cholesterol (LDL-C) estimates by the Martin (mLDL-C) method, most laboratories still use the Friedewald (fLDL-C) equation. Low non-high density lipoprotein (non-HDL-C) and high triglycerides (TG) drive inaccuracy in LDL-C estimation. We compared a strategy of identifying errors in fLDL-C using non-HDL-C/TG ratios with subsequent reflex direct LDL-C testing to a strategy of using mLDL-C. Methods: We included 4,939,542 individuals (2/3 derivation, 1/3 validation dataset) with TG &lt;400 mg/dL with lipid profiles directly measured via Vertical Auto Profile from the Very Large Database of Lipids. We compared directly measured LDL-C with estimated fLDL-C and mLDL-C. The direct LDL-C assay has an allowable error of 12% which was used as the threshold for accuracy assessment. LDL-C estimates above and below non-HDL-C/TG cutpoints (range 0-2.0) were evaluated for accuracy from the derivation dataset and the 4 best performing ratios were tested in the validation set. Individuals with non-HDL-C/TG ratios below the cutpoints were assumed to require direct LDL-C measurement. Medicare costs ($17 lipid panel; $12 direct LDL-C) were used to estimate added costs of direct LDL-C measurement. Results: Nearly 8% of fLDL-C results deviated &gt;12% from direct LDL-C compared with only 2.5% of mLDL-C results in the entire population. Non-HDL-C/TG ratios of 0.6-0.9 performed best in the derivation dataset. In the validation dataset, a non-HDL-C/TG ratio of 0.7 had the highest area under the curve for identifying &gt;12% error in fLDL-C estimates (Table). At this ratio, 14.5% of fLDL-C samples would need direct LDL-C measurement at an increase in costs of lipid testing by 10%. Conclusions: Use of non-HDL-C/TG ratio &lt;0.7 as screening for direct LDL-C measurement can improve the accuracy of LDL-C estimates in labs using the Friedewald equation. However, such an approach is significantly costlier than using mLDL-C.

Abstract P181: Atherogenic Lipoproteins and Incident Atherosclerotic Cardiovascular Disease in the Framingham Offspring Study

Introduction: Elevated plasma low-density lipoprotein cholesterol (LDL-C), small-dense LDL-C (sdLDL-C), LDL-triglyceride (LDL-TG), triglycerides (TG), remnant-lipoprotein cholesterol (RLP-C), triglyceride-rich lipoprotein-C (TRL-C), very low-density lipoprotein cholesterol (VLDL-C), and lipoprotein(a) [Lp(a)] levels have been associated with increased atherosclerotic cardiovascular disease (ASCVD) risk. However, these parameters have not been included in risk factors for ASCVD in the pooled cohort equation (PCE). Hypothesis: We assessed the hypothesis that these atherogenic lipoprotein parameters add significant information for ASCVD risk prediction in the Framingham Offspring Study. Methods: We evaluated 3,147 subjects without ASCVD at baseline (mean age 58 years) from participants of Framingham Offspring Study cycle 6, 677 (21.5%) of whom developed inclusive ASCVD over 16 years. Biomarkers of risk were assessed in frozen plasma samples. Total cholesterol, TG, HDL-C, direct LDL-C, sdLDL-C, LDL-TG, Lp(a), RLP-C, and TRL-C were measured by standardized automated analysis. Calculated LDL-C, large buoyant low-density lipoprotein cholesterol (lbLDL-C), VLDL-C, and non-HDL-C values were calculated. Data were analyzed using Cox proportional regression analysis and net reclassification improvement (NRI) analysis to identify parameters significantly associated with the incidence of ASCVD after controlling for standard ASCVD risk factor and applying the PCE model. Results: All specialized lipoprotein parameters were significant ASCVD risk factors on univariate analysis, but only direct LDL-C, sdLDL-C, and Lp(a) were significant on multivariate analysis with standard risk factors in the model. Together these parameters significantly improved the model c statistic (0.716 vs 0.732, P &lt; 0.05) and net risk reclassification (mean NRI 0.104, P &lt; 0.01) for ASCVD risk. Using the ASCVD risk pooled cohort equation, sdLDL-C, TG, LDL-TG, LDL-C, RLP-C, and TRL-C individually added significant information, but no other parameter added significant information with sdLDL-C (hazard ratio 1.30 for 75th vs 25th percentile, P &lt; 0.0001) in the model. Conclusions: In multivariate analysis, sdLDL-C, direct LDL-C, and Lp(a) contributed significantly to ASCVD risk, but only sdLDL-C added significant risk information to the PCE model, indicating that sdLDL-C may be the most atherogenic lipoprotein particle.

Abstract P493: Small Dense Low-density Lipoprotein Cholesterol and Carotid Intimal Medial Thickness Progression

Introduction: Carotid intimal medical thickness (cIMT) is associated with cardiovascular disease (CVD). The association between atherogenic lipoproteins including small dense low-density lipoprotein cholesterol (sdLDL-C) and carotid intimal medial thickness (cIMT) progression has not been fully evaluated in a prospective cohort study. Hypothesis: We assessed the hypothesis that sdLDL-C is the most atherogenic lipoproteins with regard to cIMT progression. Methods: Kyushu and Okinawa Population Study (KOPS) is a community-based, prospective, and observational study in Japan which has been underway since 2004 and a total of 18,762 participants have been enrolled. For this study, we included 2,030 male and female participants (median age 59 years at baseline) who were free of CVD and off cholesterol lowering medication, and had cIMT measured at both baseline and after 5 years follow-up survey. Using plasma samples obtained from those subjects after overnight fast, we measured total cholesterol, direct low-density lipoprotein cholesterol (LDL-C), sdLDL-C, LDL-triglycerides (LDL-TG), high-density lipoprotein cholesterol (HDL-C), HDL2-C, HDL3-C, triglycerides, Lp(a), adiponectin and high sensitivity C reactive protein (hs CRP). Their cIMT levels were measured by B-mode ultrasonography with a 10 MHz probe at far walls of their both right and left common carotid arteries. Results: Median cIMT at baseline was 0.63 mm and median 5 year progression was 0.18 mm. After adjustment for standard CVD risk factor including age, gender, systolic blood pressure, total cholesterol, HDL-C, smoking, diabetes, and hypertension treatment, only LDL-C, sdLDL-C, and the sdLDL-C/LDL-C ratio were associated with cIMT progression. Even in subjects with direct LDL-C &lt; 100 mg/dL, considered to be at low CVD risk, elevated sdLDL-C were associated with cIMT progression ( P for trend = 0.009) in a model with established CVD risk factors, although the sdLDL-C/LDL-C ratio did not. Conclusions: In conclusion, both sdLDL-C and direct LDL-C are significantly associated with cIMT progression. Moreover sdLDL-C has stronger relationship with cIMT progression than does LDL-C; therefore, measurement of sdLDL-C may allow for the formulation of optimal therapy to cIMT progression.

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

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

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

BackgroundConsidering 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.MethodsA 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.ResultsEstimated 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.ConclusionsTherefore, 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.

Validation of the novel Martin method for LDL cholesterol estimation

IntroductionAccurate 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. ResultsWe 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. ConclusionsWe 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.

Evaluation of Martin's equation for LDL-C estimation in type 2 diabetes mellitus Egyptian patients

IntroductionType 2 Diabetes Mellitus has characteristic dyslipidemia. Low-density lipoprotein cholesterol (LDL-C) measurement plays a role in cardiovascular risk assessment and management. Friedewald equation (FE) has several limitations. This study aims to evaluate the effectiveness of Martin equation (ME) in Egyptian patients, especially those with type 2 diabetes. MethodsA cross-sectional study was conducted on 454 diabetic and non-diabetic patients who were referred to the internal medicine outpatient clinic. Lipid profile was assessed by Cobas 8000 Modular Analyzer. ResultsThe LDL-C was estimated by both FE and ME. In diabetic patients, LDL-C estimated by FE was underestimated with a bias of −3.9 ± 5.3 mg/dL (p = .04). But LDL-C estimated by ME was not significantly different compared to directly measured LDL-C. FE underestimate LDL-C with a bias of −4.6 ± 6.4 mg/dL (p = .042) in uncontrolled diabetic patients. A non-significant difference in both uncontrolled patients and controlled ones was detected by ME. FE had lower sensitivity and specificity (80% and 88.9 respectively) compared to the ME (95.9% sensitivity, and 95.6% specificity). ME was not influenced by triglyceride levels (p = .34). ConclusionThe ME improves concordance of calculated LDL-C with a direct LDL-C assay in Egyptian diabetic patients.

Lipoprotein Subfractions and Glucose Homeostasis in Prediabetes and Diabetes in Taiwan.

Aims: Prediabetes and diabetes are associated with increased insulin resistance and decreased insulin production, dyslipidemia, and increased cardiovascular disease (CVD) risk. Our goals were to assess lipoprotein subfractions using novel assays in such subjects.Methods: Fasting normal, prediabetic, and diabetic Taiwanese men and women (n = 2,049) had their serum glucose, glycosylated hemoglobin, insulin, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), HDL3-C, apolipoprotein E-HDL-C, direct low-density lipoprotein cholesterol (LDL-C), small dense LDL-C (sdLDL-C), LDL-TG, and remnant lipoprotein cholesterol (RLP-C) levels measured using novel assays. HDL2-C, LDL-C, and large-buoyant LDL-C (lbLDL-C) were calculated.Results: Prediabetic male and female subjects had significantly higher levels of TG, RLP-C, sdLDL-C, the sdLDL-C/LDL-C ratio, and LDL-TG than normal subjects, and statin treatment abolished this effect in men, but not in women. Diabetic male and female subjects had significantly higher TG and sdLDL-C/LDL-C ratios, and significantly lower levels of HDL-C, HDL2-C, HDL3-C, and apoE HDL-C than normal subjects, as did prediabetic women. Median direct LDL-C levels were > 100 mg/dL in all groups, even in those receiving statin therapy. Calculated LDL-C significantly underestimated direct LDL-C by > 10% in diabetic subjects.Conclusions: Our data indicate that prediabetic subjects were more likely to have significantly elevated RLP-C, sdLDL-C, and LDL-TG, while diabetic subjects were more likely to have significantly decreased HDL-C, HDL2-C, HDL3-C, and apoE HDL-C than normal subjects, and calculated LDL-C significantly underestimated their direct LDL-C. In our view, direct LDL-C and sdLDL-C should be measured and optimized in both diabetic and prediabetic subjects to reduce CVD risk.

A study to find out association between blood group and lipid profile

Background: In transfusion science, ABO blood group system is the most important blood group system. Numerous studies show that certain types of diseases are more common in certain blood groups such as tuberculosis is more common in Type O and Plasmodium vivax infection is more common in Type A and O blood group. Aims and Objectives: As cardiovascular diseases and lipid profile are strongly associated with genetic predisposition it may be possible to differentiate and quantify the possibility of heart problem in certain blood groups compared to others. Materials and Methods: A total of 50 cases in time span of 1 month have been included in this study. Prior Indian Ethics committee approval was taken and information sheet was provided to subjects about this study. On voluntary consent, subjects’ 0.5 ml venous blood was collected aseptically and lipid profile parameters such as serum cholesterol, serum triglycerides, direct high-density lipoprotein-cholesterol (HDL-C), direct low-density lipoprotein-cholesterol (LDL-C), Choles./HDL-C ratio, LDL-C/HDL-C ratio, and Very LDL cholesterol were measured using autoanalyzer instrument. All healthy male and female subjects above 18 years were included in the study. Data analysis was done by MedCalc v18.6 and single factor ANOVA was applied for various lipid profile parameters. Results: Out of 50 cases, the majority are B positive and O positive blood group with 18–14 cases, respectively each. In the present study, mean and standard deviation of LDL (in mg/dl) of A positive blood group is 88.49 ± 20.17. Similarly for B positive blood group, mean and its standard deviation are 104.82 ± 29.85. Furthermore, for B negative its 127.06 ± 25.13 and A negative its mean 84 and for O positive, its mean and standard deviation is 102.39 ± 19.24. Applying single factor ANOVA in MedCalc v18.6 various blood group system gives P value at 0.135. Conclusion: Out of 50 cases, 46 have Rh positive and 4 are Rh negative. Out of 46 Rh positive, 30 were male, and 16 were female. Out of 4 Rh negative, 2 were male and 2 were female. On applying single factor ANOVA for finding an association between blood group types and lipid profile, P value was found to be >0.05. Thus, no association was found between blood group type and lipid profile parameters.

Abstract 467: Lipoproteins and Cardiovacular Disease Risk

Objectives: Elevated serum levels of low density lipoprotein cholesterol (LDL-C), small dense LDL-C (sdLDL-C), remnant lipoprotein cholesterol (RLP-C), and lipoprotein (a) or Lp(a) and low high density lipoprotein cholesterol (HDL-C) have all been associated with increased cardiovascular disease (CVD) risk. Our goal was to assess direct measurements of these lipoproteins as compared to standard risk factors (age, gender, hypertension, hypertension treatment, diabetes, smoking, total cholesterol, and HDL-C) in the Framingham Offspring Study (FOS). Methods: Stored frozen plasma samples (-80 degrees C) obtained after an overnight fast from male and female participants free of CVD at cycle 6 of FOS were used (n=3,147, mean age 58 years, with 677 (21.5%) developing CVD over 16 years. Total cholesterol, HDL-C, direct LDL-C, sdLDL-C, remnant lipoprotein cholesterol (RLP-C), Lp(a), and high sensitivity C reactive protein (hsCRP) were measured by standardized automated analysis. Large buoyant LDL-C (lbLDL-C) (direct LDL-C - sdLDL-C) and calculated LDL-C (total cholesterol-HDL-C - TG/5) were also calculated. Results: For all CVD risk on univariate analysis significant factors with p values in parentheses in order of significance were: age (8.1x10 -41 ), hypertension (3.2x10 -23 ), HDL-C (4.2x10 -16 ), sdLDL-C (4.2x10 -14 ), hypertension treatment (1.5x10 -14 ), gender (1.7x10 -10 ), diabetes (5.1x10 -9 ), direct LDL-C (8.2x10 -9 ), body mass index (9.2x10 -7 ), calculated LDL-C (6.2X10 -6 ), RLP-C (8.0 X 10 -4 ) cholesterol medication (1.8 X 10 -4 ), total cholesterol (0.00081) smoking (0.0024), hsCRP (0.005), and Lp(a) (0.024). On multivariate analysis sdLDL-C, direct LDL-C, hsCRP, and Lp(a) were still significant using the model including all standard risk factors. All four parameters significantly improved the model C statistic and net risk reclassification. Conclusions: Our data indicate that for CVD risk prediction in FOS direct LDL-C is significantly better than calculated LDL-C, that HDL-C, sdLDL-C, direct LDL-C, RLP-C, and Lp(a) are all atherogenic, and that after standard risk factors, sdLDL-C, direct LDL-C, Lp(a), and hsCRP all add significant information.

Treatment target re-classification of subjects comparing estimation of low-density lipoprotein cholesterol by the Friedewald equation and direct measurement of LDL-cholesterol

Aims: To compare low-density lipoprotein cholesterol (LDL-C) values calculated by the Friedewald equation with direct LDL-C in patient samples and assess the possible impact on re-classification of LDL-C target values for primary prevention or high cardiovascular disease (CVD) risk (<2.5 mmol/L) and secondary prevention or very high CVD risk (<1.8 mmol/L). LDL-C is an important CVD risk factor. Over the last decade, there has been a change in laboratory methodology from indirectly calculated LDL-C with the Friedewald equation to direct LDL-C measurements (dLDL-C).Methods: Reported results for plasma triglycerides, total cholesterol, high-density lipoprotein-cholesterol, and dLDL-C from 34,981 samples analyzed in year 2014 were extracted from the laboratory information system, Uppsala University Hospital, Uppsala, Sweden.Results: dLDL-C was approximately 10% lower than the corresponding LDL-C results calculated by the Friedewald equation in both men and women. In subjects with triglyceride concentrations above 4 mmol/L (n = 1250) the same discordant pattern was seen as for the entire study population. Altogether 5469 out of 18,051 men (30.3%) and 4604 out of 16,928 women (27.2%) were down-classified at least one CVD risk category. A very small number of subject was up-classified, in total 37 out of 18,051 men (0.2%) and 28 out of 16,928 women (0.2%).Conclusions: The two LDL-C methods had a high concordance, but the direct LDL-C measurement consistently gave approx. 10% lower values, and this caused one-third of subjects to be re-classified as having a lower cardiovascular disease risk in relation to recommended LDL-C target values and decision limits.

Validation of the Friedewald formula for the estimation of low density lipoprotein cholesterol in a sub-Saharan African population

Introduction : low density lipoprotein cholesterol (LDL-C) levels are used to estimate cardiovascular disease (CVD) risk and to guide prescriptions. To circumvent the challenges of direct LDL-C measurement, guidelines recommend the use of Friedewald formula derived LDL-C levels. Despite reported limitations of this formula, its validity in sub-Saharan Africans has not been adequately investigated. The objective was to assess the validity of the Friedewald formula derived against directly measured LDL-C in adult Cameroonians. Methods : we reviewed the fasting lipid profiles of 2500 patients, performed between March 2012 and January 2016 using enzymatic colorimetric method (reference), at the Douala General Hospital laboratory. The Friedewald formula was used to calculate LDL-C from total cholesterol, high density lipoprotein cholesterol and triglyceride levels. Calculated LDL-C values were compared to the reference values and clinical significance of differences between the two methods was assessed using total error allowable (TEa). Results : the difference between means of Friedewald-calculated and the reference LDL-C values was neither statistically nor clinically significant (3.33±1.51 vs. 3.33 ± 1.25 mmol/l; p = 0.704). The calculated LDL-C correlated positively with the measured LDL-C value (r = 0.749) and both methods showed a good agreement on Bland-Altman plot. Conversely, there was only moderate agreement (kappa=0.478, 95% CI: 0.455-0.502) between the two values in the stratification of cardiovascular risk according to the National Cholesterol Education Programme/Adult Treatment Panel III. Consequently, 40.6% of the participants were misclassified. Conclusion : Friedewald formula is technically accurate but has a modest clinical accuracy which can translate into a substantial misclassification of patients’ cardiovascular risk and subsequent inappropriate therapeutic decisions.

Lipoprotein X in autoimmune liver disease causing interference in routine and specialist biochemical investigations

AbstractPrimary biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC) are chronic, immune-mediated diseases which may be associated with the presence of lipoprotein X (LpX). This is an abnormal lipoprotein resulting in marked elevation of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) concentration. LpX is rich in free cholesterol (FC) and phospholipids (PL) and low in esterified cholesterol (CE). We describe two cases with florid lipid stigmata and presence of LpX. A patient with PBC presented with itching and palmar xanthomata. TC = 55.5 mmol/L, direct LDL-C = 14.9 mmol/L, high-density lipoprotein cholesterol (HDL-C) = 0.5 mmol and triglycerides (TG) = 11.3 mmol/L. Lipoprotein electrophoresis (LPE) showed the presence of LpX. An apolipoprotein E (Apo E) phenotype and genotype reported E2E3 and E3E3 isoforms, respectively. A patient with PSC presented with itchy eruptive xanthomata. TC = 22.8 mmol/L, calculated LDL-C = 21.7 mmol/L, HDL-C = 0.2 mmol/L and TG = 1.6 mmol/L...

Underestimation of Low Density Lipoprotein-Cholesterol With the Friedewald Equation Versus a Direct Homogenous Low Density Lipoprotein-Cholesterol Assay.

Low-density lipoprotein cholesterol (LDL-C) concentrations are the primary therapeutic target in patients with atherosclerotic cardiovascular disease (ASCVD). However, at low LDL-C concentrations, there is a significant underestimation using the Friedewald equation compared with ultracentrifugation. In this pilot study, we compared LDL-C concentrations obtained using the Friedewald equation (LDL-F) vs those concentrations from a direct LDL-C (LDL-D) assay in 152 consecutive specimens from patients with triglyceride levels between 200-399 mg/dL and LDL-F <100 mg/dL. Also, we compared LDL-F and LDL-D results to the novel formula (LDL-N). The LDL-F value was significantly lower than that of LDL-D when LDL concentrations were 70-99 mg/dL (P <.001, 14% negative bias), and this decrease was accentuated in specimens with LDL <70 mg/dL, (P <.001, 27% negative bias). When compared with the LDL-N value, LDL-F and LDL-D values showed a 17% and 2% negative bias for specimens with LDL-C values of 70-99 mg/dL and 36% and 1% negative bias, respectively, at LDL-C <70 mg/dL (P <.001 for comparisons of LDL-F and LDL-N values). To provide accurate LDL-C levels in patients at high risk for ASCVD, if beta quantification by ultracentrifugation is unavailable and if LDL-C is <100 mg/dL and triglycerides are 200-399 mg/dL, laboratories should revert to direct LDL-C measurements or use the novel formula. Although LDL-N is more cost-effective, LDL-D can be run on most platforms, does not require a specimen from a fasting individual, is standardized, and has the advantage of being validated in large trials such as the Heart Protection Study.

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

BackgroundFriedewald 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.MethodsWe 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-CF) and its 3 modifications. Pearson correlation analysis was performed to compare these estimates to the direct measurement.ResultsPearson 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-CF, 0.917 for LDL-C by Hatta equation (LDL-CH), 0.968 for LDL-C by Puavilai equation (LDL-CP), and 0.983 for LDL-C by Martin equation (LDL-CM). Martin equation (LDL-CM) 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-CP resulted in the second-best approximation (mean difference, 7.0 mg/dL; mean percentage difference, 6.2%; concordance, 82.5%). LDL-CM was found to be less influenced by TG and HDL-C levels than by LDL-CF.ConclusionEstimates 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.

Correlation of Friedewald's calculated low-density lipoprotein cholesterol levels with direct low-density lipoprotein cholesterol levels in a tertiary care hospital.

Background:One of the risk factors for the development of coronary heart disease is high low-density lipoprotein (LDL) cholesterol levels. National Cholesterol Education Program ATP III guidelines suggest drug therapy to be considered at LDL-cholesterol levels >130 mg/dl. This makes accurate reporting of LDL cholesterol crucial in the management of Coronary heart disease. Estimation of LDL cholesterol by direct LDL method is accurate, but it is expensive. Hence, We compared Friedewald's calculated LDL values with direct LDL values.Aim:To evaluate the correlation of Friedewalds calculated LDL with direct LDL method.Materials and Methods:We compared LDL cholesterol measured by Friedewald's formula with direct LDL method in 248 samples between the age group of 20–70 years. Paired t-test was used to test the difference in LDL concentration obtained by a direct method and Friedewald's formula. The level of significance was taken as P < 0.05. Pearsons correlation formula was used to test the correlation between direct LDL values with Friedewald's formula.Results:There was no significant difference between the direct LDL values when compared to calculated LDL by Friedewalds formula (P = 0.140). Pearson correlation showed there exists good correlation between direct LDL versus Friedewalds formula (correlation coefficient = 0.98). The correlation between direct LDL versus Friedewalds calculated LDL was best at triglycerides values between 101 and 200 mg/dl.Conclusion:This study indicates calculated LDL by Friedewalds equation can be used instead of direct LDL in patients who cannot afford direct LDL method.