Estimation Of LDL-C Research Articles

Diagnostic Accuracy Study of Direct and Formula Based Method for Estimation of LDL Cholesterol

Introduction: Increased LDL-C is a well-known risk factor for cardiovascular disease. Also, LDL-C is used for risk stratification and in planning appropriate treatment. This requires the accurate measurement of LDL-C. Though β quantification is the reference method for LDL-C estimation it can’t be used routinely. Even though homogenous assays are available, some clinical laboratories use the Friedewald formula, which has its disadvantages. Aim & Objectives: To identify which LDL-C formula best correlates with direct LDL-C among various levels of TG. Study Design: A retrospective diagnostic accuracy study. Materials and Methods: Complete lipid data of 15,094 was obtained from the hospital information system. Based on TG level data, it was grouped into TG <100 mg/dl (N=6022) and TG 100-200 mg/dl (9072). Direct homogenous assay was used to measure LDL-C. LDL-C was calculated using seven formulae (Friedewald, Hattori, de Cordova, Vujovic, Anandaraja, Chen, and Sampson). Results: Correlation between direct and formula-based LDL-C by ICC showed maximum correlation by Vujovic (0.978). Bland Altman plot between Vujovic and the direct method shows a bias of -0.38 and it may overestimate or underestimate by 23 mg/dl. Conclusion: LDL-C calculated using Vujovic showed maximum correlation with direct homogenous assay when TG <200 mg/dl.

Does Fredrickson Friedewald’s formula and direct assay of LDL concentration in serum give varying results? - A pilot retrospective study in a tertiary care hospital

Background and Aims: The LDL-C levels guide the cardiovascular risk assessment under the National Cholesterol Education Program Adult Treatment Panel (NCEP) as well as used for the management of dyslipidemia. Traditionally, LDL-C level is calculated using the Friedewald formula i.e., cLDL, however, recently many of the laboratories have shifted to newer methods of direct LDL-C estimation, i.e., dLDL. With this transition, there is increased demand to compare and establish updated LDL-C level cutoffs for the risk assessments of cardiovascular events. Methods: A retrospective observational study, comprising 216 study participants, was conducted in the Department of Biochemistry, Lady Hardinge Medical College, and associated hospitals, in New Delhi for 12 months. Data was collected from 216 adult patients (> 18 years) whose routine fasting lipid profile was done and documented in our clinical biochemistry lab. LDL-C levels done by direct method and calculated methods were compared using the Intraclass correlation coefficient (ICC). Appropriate statistical tests were used with a p-value <0.05 taken as statistically significant. Results: When compared between dLDL-C and cLDL-C significant difference was seen with mean values as 104 ±38.8 and 85.5 ±35.6 mg/dl respectively. However, a strong positive correlation was seen between dLDL-C and cLDL-C, r=0.9, p<0.01. The ICC showed good reliability for overall comparison between dLDL-C and cLDL-C, however, reliability was found to be poor for group 2. Conclusions: Friedewald and modified Friedewald equations can be used as a substitute for direct homogenous LDL estimation when resources are scarce.

Lipid Profile Abnormalities in ß-Thalassemia Patients with Multiple Blood Transfusions

Objective: To determine lipid profile abnormalities in ß -thalassemia patients with multiple blood transfusions in tertiary care hospital. Study design and setting: This cross-sectional study was conducted in Chemical Pathology section, Pathology department Sheikh Zayad Medical College and Hospital Rahim Yar Khan from 1st July 2022 to 31st October 2022. Methodology: 110 patients aged 1 to 18 years from both genders enrolled in Thalassemia center of Sheikh Zayed Hospital with multiple blood transfusions were included in study using convenient sampling technique. Patients with cardiac disease, diabetes mellitus and family history of lipid disorders were excluded. Blood samples collected from thalassemia patients were subjected to estimation of serum total cholesterol, serum triglycerides, HDL-C, and LDL-C. Data was recorded on predesigned performa. Post stratification t test was applied and p value <0.05 was considered significant. Results: Mean age of the patients was 7.77±3.846 years. Mean serum cholesterol, serum triglyceride, HDL-C and LDLC were 89.15±20.33mg/dl, 211.00±77.78mg/dl, 21.01±6.554mg/dl and 29.79±17.02 respectively. Mean triglyceride and LDL-C level was higher in patients with weekly transfusion than patients undergoing monthly transfusion with statistically significant difference of triglycerides and LDL-C between two groups with p=0.002 and 0.023 respectively. Conclusion: We found that frequent blood transfusion causes lipid profile abnormalities in thalassemia patients. Lipid profile abnormalities should be screened frequently in ß-thalassamia patients with multiple blood transfusions.

LDL-C Estimation Equation Performance Characteristics Highlight Value of Preferentially Using Non-HDL Cholesterol or Directly Measured Apolipoprotein B.

LDL-C Estimation Equation Performance Characteristics Highlight Value of Preferentially Using Non-HDL Cholesterol or Directly Measured Apolipoprotein B.

Direct LDL-C estimation in preschoolers: Practicable first step for FH screening

Individuals with FH develop cardiovascular disease due to lifelong cumulative exposure to elevated LDL-C. Effective screening for FH is not yet established. To evaluate the practicability of a FH screening by measuring directly the LDL-C in preschoolers. LDL-C measurement through capillary blood sampling during the compulsory routine check-ups by the pediatrician in children aged 2 to 6 years including information on family history as dyslipidemia and/or premature cardiovascular disease in first and second grade of pedigrees. 15,009 children (52.2% males, median age 3.9 years [IQR 3.0-5.1]) participated in the study. Positive family history for hyperlipidemia was stated in 40.9% cases, in 12.0% also in at least one 1st degree relative. In the total cohort, median LDL-C was 93 mg/dL [IQR 79-109 mg/dL]. Boys had significantly higher LDL-C levels than girls (p<0.0001), whereas there was no difference regarding their age (p=0.757). Children from families with a positive history for hypercholesterolemia/dyslipidemia had significantly higher LDL-C levels (p<0.001) and were more frequently among those with LDL-C values above 135 mg/dL (3.5 mmol/L, 96th percentile; 53.2% vs. 40.3%, p<0.001) and those with LDL-C levels above 160 mg/dL (4.1 mmol/L, 99th percentile; 45.3% vs. 40.7%, p<0.001) than children without positive family history. Direct measurement of LDL-C levels in children at ages 2-6 years during the compulsory routine check-ups as well as at any voluntary visits to the pediatrician's office is practicable and delivers reliable information, which can be used for a FH screening strategy in the general population.

PREVALENCE OF METABOLIC SYNDROME IN PATIENTS WITH TYPE 2 DIABETES MELLITUS IN NORTH EAST INDIA

Aim: Prevalence of metabolic syndrome in patients with type 2 diabetes mellitus in North East India Introduction: Metabolic syndrome is an increasingly prevalent condition which predisposes the individual to diabetes mellitus (DM) and atherosclerotic cardiovascular disease (ASCVD). The diagnosis of metabolic syndrome is made as per the AHA/NHLBI 2005 criteria. As per this criteria, metabolic syndrome can be diagnosed by the presence of any 3 of the ve factors (elevated waist circumference, elevated triglycerides, reduced HDL-C, elevated blood pressure and elevated fasting glucose). This study is being conducted to study the prevalence of metabolic syndrome among patients with type 2 diabetes mellitus. Materials and Methods: In this prospective cross-sectional study, 100 adult patients of type 2 diabetes mellitus were selected. Physical examination was conducted including BMI, waist circumference and blood pressure. Blood samples were collected after 14 hours fasting for estimation of serum fasting glucose, serum HDL-C, serum LDL-C, serum triglyceride (TG) and serum cholesterol. The prevalence of metabolic syndrome among the study subjects was accordingly calculated. Results: Among the participants, 57 % were males and 43 % were females. The median age was 57 years (range from 34-78 years). 67% of patients with type 2 diabetes mellitus in this study had metabolic syndrome while 33% did not have metabolic syndrome. The prevalence of metabolic syndrome among males and females was 61% and 74% respectively. Conclusion: This study showed that the prevalence of metabolic syndrome is quite high among patients with diabetes mellitus. Increased waist circumference was the most common abnormal parameter among others for the diagnosis of metabolic syndrome. Application of lower cut-off values for waist circumference among Asians may help in increasing the diagnostic yield.

Intuitive Modification of the Friedewald Formula for Calculation of LDL-Cholesterol.

High LDL-cholesterol (LDL-C) is an established risk factor for cardiovascular disease and is considered an important therapeutic target. It can be measured directly or calculated from the results of other lipid tests. The Friedewald formula is the most widely used formula for calculating LDL-C. We modified the Friedewald formula for a more accurate and practical estimation of LDL-C. Datasets, including measured triglyceride, total cholesterol, HDL-cholesterol, and LDL-C concentrations were collected and assigned to derivation and validation sets. The datasets were further divided into five groups based on triglyceride concentrations. In the modified formula, LDL-C was defined as total cholesterol - HDL-cholesterol - (triglyceride/adjustment factor). For each group, the adjustment factor that minimized the difference between measured LDL-C and calculated LDL-C using modified formula was obtained. For validation, measured LDL-C and LDL-C calculated using the modified formula (LDL-CM), Friedewald formula (LDL-CF), Martin-Hopkins formula (LDL-CMa), and Sampson formula (LDL-CS) were compared. In the derivation set, the adjustment factors were 4.7, 5.9, 6.3, and 6.4 for the groups with triglyceride concentrations <100, 101-200, 201-300, and >300 mg/dL, respectively. In the validation set, the coefficient of determination (R2) between measured and calculated LDL-C was higher for LDL-CM than for LDL-CF (R2=0.9330 vs. 0.9206). The agreement according to the National Cholesterol Education Program Adult Treatment Panel III classification of LDL-C was 86.36%, 86.08%, 86.82%, and 86.15% for LDL-CM, LDL-CF, LDL-CMa, and LDL-CS, respectively. We proposed a practical, improved LDL-C calculation formula by applying different factors depending on the triglyceride concentration.

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

BackgroundLow-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. MethodsAn 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. ResultsThe 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. ConclusionsThis 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.

New equation for LDL-C estimation based on the relationships between extrahepatic VLDL-C and total triglycerides

Background and Aims : Friedwald formula routinely used to determine LDL-C blood levels leads to underestimated results if increased triglyceride levels take place. Proposed before alternative equations are either relatively complex for usage or are also limited by triglyceride levels.

Comparison of a Machine Learning Method and Various Equations for Estimating Low-Density Lipoprotein Cholesterol in Korean Populations.

BackgroundLDL-C is the primary target of lipid-lowering therapy and used to classify patients by cardiovascular disease risk. We aimed to develop a deep neural network (DNN) model to estimate LDL-C levels and compare its performance with that of previous LDL-C estimation equations using two large independent datasets of Korean populations.MethodsThe final analysis included participants from two independent population-based cohorts: 129,930 from the Gangnam Severance Health Check-up (GSHC) and 46,470 participants from the Korean Initiatives on Coronary Artery Calcification registry (KOICA). The DNN model was derived from the GSHC dataset and validated in the KOICA dataset. We measured our proposed model's performance according to bias, root mean-square error (RMSE), proportion (P)10–P20, and concordance. P was defined as the percentage of patients whose LDL was within ±10–20% of the measured LDL. We further determined the RMSE scores of each LDL equation according to Pooled cohort equation intervals.ResultsOur DNN method has lower bias and root mean-square error than Friedewald's, Martin's, and NIH equations, showing a high agreement with LDL-C measured by homogenous assay. The DNN method offers more precise LDL estimation in all pooled cohort equation strata.ConclusionThis method may be particularly helpful for managing a patient's cholesterol levels based on their atherosclerotic cardiovascular disease risk.

Estimation of low-density lipoprotein cholesterol levels using machine learning

BackgroundLow-density lipoprotein-cholesterol (LDL-C) is used as a threshold and target for treating dyslipidemia. Although the Friedewald equation is widely used to estimate LDL-C, it has been known to be inaccurate in the case of high triglycerides (TG) or non-fasting states. We aimed to propose a novel method to estimate LDL-C using machine learning. MethodsUsing a large, single-center electronic health record database, we derived a ML algorithm to estimate LDL-C from standard lipid profiles. From 1,029,572 cases with both standard lipid profiles (total cholesterol, high-density lipoprotein-cholesterol, and TG) and direct LDL-C measurements, 823,657 tests were used to derive LDL-C estimation models. Patient characteristics such as sex, age, height, weight, and other laboratory values were additionally used to create separate data sets and algorithms. ResultsMachine learning with gradient boosting (LDL-CX) and neural network (LDL-CN) showed better correlation with directly measured LDL-C, compared with conventional methods (r = 0.9662, 0.9668, 0.9563, 0.9585; for LDL-CX, LDL-CN, Friedewald [LDL-CF], and Martin [LDL-CM] equations, respectively). The overall bias of LDL-CX (−0.27 mg/dL, 95% CI −0.30 to −0.23) and LDL-CN (−0.01 mg/dL, 95% CI -0.04–0.03) were significantly smaller compared with both LDL-CF (−3.80 mg/dL, 95% CI −3.80 to −3.60) or LDL-CM (−2.00 mg/dL, 95% CI −2.00 to −1.94), especially at high TG levels. ConclusionsMachine learning algorithms were superior in estimating LDL-C compared with the conventional Friedewald or the more contemporary Martin equations. Through external validation and modification, machine learning could be incorporated into electronic health records to substitute LDL-C estimation.

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

Background and aimsLow-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. MethodsAll 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. ResultsLCL-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. ConclusionsOur 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.

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.

IntroductionLow-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 methodsWe 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).ResultsFor 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.ConclusionsThe 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.

P5332Magic mirror on the wall, can we reach our LDL-C goal? Comparison of calculated LDL-C and direct measured LDL-C levels in atherogenic dyslipidemia condition

Abstract Background LDL-C represents the primary lipoprotein target for reducing cardiovascular risk. LDL-C can either be calculated or measured directly. Friedewald equation has certain limitations especially with high triglyceride and low LDL-C levels. Although a number of automated direct LDL-C assays are commercially available, non of them is considered to be equivalent to the “gold standard” of direct LDL-C, beta quantitation, a complex and expensive process that is unavailable in routine clinical practice. In atherogenic dyslipidemia condition (ADC) (triglycerides≥2.3 mmol/L and HDL-C&lt;1.0 mmol/L) non-HDL-C and remnant cholesterol are proven additional risk factors. Purpose We compared one of the direct homogeneous assays with the widely used Friedewald's and the new Martin/Hopkins methods of estimation of LDL-C to see the differences in average LDL-C, remnant cholesterol and non-HDL-C levels and in availability of less than 1.8 mmol/L of LDL-C in atherogenic dyslipidemia condition. Methods We investigated 14 906 lipid profiles from fasting blood samples of Hungarian individuals with triglycerides &lt;4.5 mmol/L. Total cholesterol (TC), HDL-C, triglycerides (TG) and direct LDL-C (D-LDL-C) were measured directly by the enzimatic assays. We also estimated calculated LDL-C by the Friedewald's formula (F-LDL-C) and by using the new Martin/Hopkins estimation (MH-LDL-C). We have now prepared first a variant of Martin/Hopkins table in mmol/L, in which the modified adjustable factors of 2.2 are included. We determined also non-HDL-C and remnant cholesterol (RC) as a difference of non-HDL-C and F-LDL-C (F-RC), MH-LDL-C (MH-RC), D-LDL-C (D-RC). Results In the investigated population 19.25% was F-LDL-C, 15.48% MH-LDL-C and 7.92% D-LDL-C below 1.8 mmol/L. ADC occurred at 8.12%. For ADC, when F-LDL-C&lt;1.8 mmol/L (A), mean values for F-LDL-C, MH-LDL-C, D-LDL-C and non-HDL-C were 1.23±0.4; 1.65±0.39; 2.06±0.4 and 2.46±0.5 mmol/L respectively. These mean levels were 1.01±0.36; 1.4±0.3; 1.83±0.3 and 2.15±0.34 mmol/L for MH-LDL-C&lt;1.8 mmol/L (B). For D-LDL-C&lt;1.8 mmol/L (C), mean values were 0.79±0.35; 1.13±0.26; 1.54±0.19 and 1.83±0.25 mmol/L respectively. The average RC values (in mmol/L) for A were F-RC: 1.23±0.36; MH-RC: 0.81±0.18; D-RC: 0.4±0.17, for B 1.14±0.33; 0.74±0.14; 0.32±0.13, and for C 1.04±0.27; 0.70±0.1; 0.29±0.12 respectively. Conclusions The Friedewald equation tends to underestimate and the homogeneous enzimatic direct LDL-C assays to overestimate the LDL-C levels compared to the new, accurate, calculated LDL-C values in atherogenic dyslipidemia condition. Based on the data presented in our investigation we should like to propose that more realistic vasculo-protective lipid status can be attained if we calculate LDL-C using the Martin/Hopkins estimation.

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

The Martin/Hopkins low-density lipoprotein cholesterol equation (LDL-CN) was previously demonstrated as more accurate than Friedewald LDL-C estimation (LDL-CF) in a North American database not able to take race into account. We hypothesized that LDL-CN would be more accurate than LDL-CF and correlate better with LDL particle number (LDL-P) in a racially diverse Brazilian cohort. We performed a cross-sectional analysis of 4897 participants in the Brazilian Longitudinal Study of Adult Health, assessing LDL-CF and LDL-CN accuracy via overlap with ultracentrifugation-based measurement among clinical guideline LDL-C categories as well as mg/dL and percent error differences. We analyzed by triglyceride categories and correlated LDL-C estimation with LDL-P. LDL-CN demonstrated improved accuracy at 70 to <100 and <70mg/dL (P<.001), with large errors ≥20mg/dL about 9 times more frequent in LDL-CF at LDL-C <70mg/dL, mainly due to underestimation. Among individuals with LDL-C <70mg/dL and triglycerides ≥150mg/dL, 65% vs 100% of ultracentrifugation-based low-density lipoprotein cholesterol calculation fell within appropriate categories of estimated LDL-CF and LDL-CN, respectively (P<.001). Similar results were observed when analyzed for age, sex, and race. Participants at LDL-C <70 and 70 to <100mg/dL with discordantly elevated LDL-CN vs LDL-CF had a 58.5% and 41.5% higher LDL-P than those with concordance (P<.0001), respectively. In a diverse Brazilian cohort, LDL-CN was more accurate than LDL-CF at low LDL-C and high triglycerides. LDL-CN may avoid underestimation of LDL-C and better reflect atherogenic lipid burden in low particle size, high particle count states.

Deep neural network for estimating low density lipoprotein cholesterol

BackgroundLDL cholesterol (LDL-C) has been mainly estimated using the Friedewald equation, and other equations have recently been developed to complement the Friedewald equation. The present study aims to employ a deep neural network (DNN) to improve LDL-C estimation. MethodsWe used two independent datasets obtained from the Korean National Health and Nutrition Examination Survey and the Wonju Severance Christian Hospital as training and test datasets, respectively. We used the training dataset to construct the DNN architecture, which takes three input values of total cholesterol, HDL cholesterol, and triglyceride, and estimates LDL-C as the output. The model consists of six hidden layers, and each hidden layer has 30 nodes. The performance of the DNN model constructed by the training dataset was measured using the test dataset. ResultsIn fivefold cross-validation using the training dataset, the DNN model showed the lowest mean and median squared errors compared to the Friedewald equation and Novel method. For the independent test dataset, our DNN model outperformed other existing methods on the basis of mean and median squared errors. ConclusionsThe DNN model provided the most accurate estimation of LDL-C compared to other existing methods including the Friedewald and Novel methods.

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.

Current Understanding in the Measurement of Low Density Lipoprotein Cholesterol: A Comparison of Direct Measurement by Homogenous Assays versus Calculations

Background: To compare the results obtained by direct homogenous assay for LDL-C to those obtained by Friedwalds and Anandrajas formulas with the assumption that the results obtained by direct assay are most accurate. This was a comparative study for the estimation of LDL-C using two different types of calculation formulas and direct estimation of LDL-C by homogenous method. Method: Serum Lipids and lipoproteins were measured in 505 fasting samples. Serum Total cholesterol was measured using CHOD-PAP method first described by Stadman on Siemens Dimensions Clinical Chemistry RXL Analyzer. Triglycerides were measured by Glycerol Phosphate peroxidase - PAP method. Direct LDL-C was measured by a homogenous assay by siemens diagnostics. A- HDL was measured by a homogenous method which uses PEG- cholesterol esterase using kit from siemens diagnostics. This was a comparative study for the estimation of LDL-C using two different types of calculation formulas and direct estimation of LDL-C by homogenous method. Results: A good correlation was found between D-LDL as compared to both F-LDL and A-LDL. Pearsons coefficient of correlation between F-LDL & D-LDL was 0.891 (p

Diabetes Accelerates Age-Related Lipid Profile Disturbances in Cardiovascular Complications

Cardio Vascular Complications (CVC) are predominant problems both in ageing and diabetes. The present study focuses on the effect of human ageing and diabetes, with special reference to serum lipid profile levels during cardiovascular complications in two age groups namely, 45±5 years and 65±5 years independent of obesity hypertension and nephropathy. The serum samples of study and control group includes estimation of serum total cholesterol, serum triglycerides, HDL-C, LDL-C and VLDL-C. Results shows, that the changes in the levels of lipid parameters follow a near similar trend in the two non-diabetic age groups with cardiovascular complications. Except the HDL-C which decline, total cholesterol, LDL-C, VLDL and triglycerides increases.

A Comparative Study of Methods of Estimation of LDL-Cholesterol: Direct vs Friedewald's Formula and Their Impact on the Risk Assessment for Coronary Artery Diseases

Coronary artery disease is one of the leading causes of mortality and morbidity worldwide. Correct estimation of low density lipoprotein (LDL) cholesterol is not only fundamental but also imperative towards the right diagnosis, risk assessment and management of the patients of cardiac diseases. Many laboratories rely on the calculated method of LDL-C estimation. Direct assays are now replacing the calculated methods owing to some of the limitations of the latter. We carried out a comparative study to evaluate the reliability of both the methods and their impact on assessing cardiac risk. Fasting lipid profile (including TG, Total Cholesterol, LDL-C and HDL-C) was measured by direct enzymatic assays in serum samples of 200 patients who visited the Biochemistry Laboratory for this test. LDL-C was also estimated by Friedewald's formula. Results of LDL-C obtained by two methods were compared and statistically analysed. We found a significant positive correlation between the values of LDL-C obtained by two methods i.e. Friedewald's formula and Direct Assay. Friedewald's formula underestimated the actual LDL-C levels especially when the serum TG levels were high and this difference was significant when the TG levels were >400 mg/dL. Thus, some of our patients who were having high cardiac risk (LDL-C >130 mg/dL) were misdiagnosed to be normal. Friedewald's formula can be used for LDL-C estimation when TG levels are in the normal range but results become doubtful especially when the serum TG levels are high. Direct assay has an advantage that it is not influenced by the concordant serum TG concentrations and can be relied upon confidently in determining cardiac risks in the individuals.