Direct LDL-C Research Articles

A-210 Ldl-c to non-hdl-c ratio method: a simple way to determine bias for ldl-c methods on hypertriglyceridemic samples

Abstract Background Beta-quantification (BQ) is the reference method for LDL-C but is not widely available. It is difficult, therefore, for clinical laboratories to independently assess the accuracy of their methods for either calculating or directly measuring LDL-C. Our goal was to investigate if an interrelationship between the tests in the standard lipid panel could be used to compare the performance of different LDL-C methods against BQ, specifically in samples with hypertriglyceridemia, a major cause of bias for LDL-C testing. Methods BQ results from Mayo Medical Labs (n=39,667) and the NIH (n=18,338) were used to identify a linear relationship by least-squares regression analysis between lipid panel test results and LDL-C to facilitate a statistical comparison between different LDL-C methods. Results It is well established that the number of VLDL particles increases as TG increases, because VLDL is the main carrier of TG. As a consequence, the ratio of LDL-C to non-HDL-C is inversely related to TG, which can be made linear by taking the square root of TG (Figure). Nearly identical regression equations were found for this relationship, using two different BQ datasets. When LDL-C was calculated by either Sampson equation, the LDL-C/non-HDL-C regression lines nearly overlapped with the one for BQ. In contrast, the Martin equation showed a positive bias with increasing TG, whereas the Friedewald equation showed a negative bias, consistent with past reports on their LDL-C biases with hypertriglyceridemia. In the UKBiobank, the Beckman direct LDL-C showed a positive bias for TG, which was confirmed in CAP proficiency test surveys. By simulation analysis, approximately 80 samples with high (>200 mg/dL) and low (<100 mg/dL) TG is statistically sufficient to assess if the LDL-C/non-HDL-C regressions lines of LDL-C methods differ from BQ. Conclusions The LDL-C/non-HDL-C ratio method is a simple way to examine LDL-C methods for bias on hypertriglyceridemic samples.

The Evaluation of Lipid-Lowering Treatment in Patients with Acute Coronary Syndrome in a Hungarian Invasive Centre in 2015, 2017, and during the COVID-19 Pandemic-The Comparison of the Achieved LDL-Cholesterol Values Calculated with Friedewald and Martin-Hopkins Methods.

Background/Objectives: Patients with acute coronary syndrome (ACS) represent a vulnerable population. We aimed to investigate serum lipid levels of patients with ACS upon admission and during one year of the COVID-19 pandemic in a rural county hospital, and compared these findings with the data of patients with ACS in 2015 and 2017. The secondary aim of this paper was the comparison of the LDL-C values calculated with the Friedewald and Martin-Hopkins methods. Methods: A retrospective analysis of lipid-lowering data of patients treated with ACS in 2015, 2017 and in a COVID-19 year (1 April 2020-31 March 2021) was performed; the patient's numbers were 454, 513 and 531, respectively. Results: In the COVID-19 period one year after the index event, only 42% of the patients had lipid values available, while these ratios were 54% and 73% in 2017 and in 2015, respectively. Using the Friedewald formula, in the COVID-19 era the median of LDL cholesterol (LDL-F) was 1.64 (1.09-2.30) mmol/L at six months and 1.60 (1.19-2.27) mmol/L at one year, respectively. These values were 1.92 (1.33-2.27) mmol/L and 1.73 (1.36-2.43) mmol/L using the Martin-Hopkins method (LDL-MH). The LDL-F yielded significantly lower values (15% lower at six months, p = 0.044; and 8% lower at one year, p = 0.014). The LDL-F reached the previous target of 1.8 mmol/L during the COVID-19 pandemic 36% at one year vs. 48% in 2017, and 37% in 2015. The recent target LDL-C level of 1.4 mmol/L was achieved in 22% of cases in the COVID-19 pandemic, 16% in 2015 and 19% in 2017. Conclusions: A significantly lower proportion of patients with ACS had available lipid tests during the COVID-19 pandemic. Besides the lower number of available samples, the proportion of achieved 1.4 mmol/L LDL-C target lipids was stable. More rigorous outpatient care in the follow-up period may help to improve the quality of lipid lowering treatments and subsequent secondary cardiovascular prevention. If direct LDL-C determination is not available, we prefer the LDL calculation with the Martin-Hopkins method.

Comparison of Friedewald, Martin/Hopkins, and Sampson formulae with direct LDL measurement in hyperlipidaemic and normolipidaemic adults


 ABSTRACT
 Objective: In our study, we aimed to compare and validate the performance of the Friedewald, Martin/Hopkins, and Sampson formulae with direct LDL-C measurement.
 Material and Methods: The study was a retrospective investigation which by the Department of Medical Biochemistry of the Ankara Training and Research Hospital between 1 January 2021 and 31 December 2022. Our study evaluated the results of 6297 patients aged 18-99 years who underwent cholesterol panel TC, TG, HDL-C and direct LDL-C in our laboratory. Estimated LDL-C calculated according to Friedewald, Martin/Hopkins, and Sampson formulae. 
 Results: All three formulae showed a stronger positive correlation with d-LDL-C (0.905, 0.897, and 0.886, respectively, for all data, p<0.001). In addition, when we compared the total median difference (1st-3rd quartile) of all formulae, it was it was -0.69 (-1.62-0.39) for Friedewald, 0.034 (-0.74 -1.14) for Martin/Hopkins and -0.40 (-1.19-0.55) for Sampson. According to Passing Bablok regression analyses, the intercept was determined as -0.97 (95% CI = -1.01 to -0.93), 0.41 (95%=0.37 to 0.44) and -0.05 (-0.08 to -0. 03) and slopes were calculated as 1.083 (95% CI = 1.07-1.09), 0.88 (0.88 to 0.89) and 0. 90 (95% = 0.89 to 0.90) for Friedewald, Martin/Hopkins and Sampson, respectively.
 Conclusions: Our findings suggest that Martin/Hopkins formula indicated a better performance than Friedewald and Sampson formulae. We figured out utilizing the Martin/Hopkins formula as a good alternative for estimated LDL-C in Turkish adults. 

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.

A Novel LDLC Equation is Superior to the NIH LDLC Equation and the Friedewald Equation.

LDLC equations have varying levels of underestimation for the calculated LDLC. Therefore, underestimating LDLC should be avoided as much as possible. We need to establish LDLC equations that underestimate LDLC as little as possible. We established the equations with a healthy cohort from Shuyang Hospital and validated the equations with an unselected patient cohort from The Second People's Hospital of Lianyungang. We established the novel LDLC equations by using the regression equation. The relationship between two markers was analysed using Pearson's approach. The 95% limits of measuring agreement within ±2 SD for the LDLC equations was performed using Bland‒Altman analysis. ROC curve analysis was used to predict LDLC levels and the accuracy of the LDLC equation for determining the direct LDLC levels at LDLC cut-offs was assessed. We obtained two novel LDLC equations (LDL_nonHDLC equation=-0.899+1.195*nonHDLC-0.00347*nonHDLC2 and LDL_TC(total cholesterol) equation=-2.775+1.29*TC -0.00990* TC 2). The correlation coefficient between the novel LDLC equation and the direct LDLC measurements is not lower than that between the LDL_NIH equation and the direct LDLC measurements. The AUCs of our novel LDLC equations were greater than those of the LDL_NIH equation and the LDL_F equation at the LDLC cut-offs for clinical decision-making. The measuring agreement in the methods of the LDL_nonHDL equation is superior to that of the LDL_NIH equation. LDLC calculated by the novel LDL_nonHDL equation exhibited superiority over the LDL_NIH equation. Combining the LDL_NIH equation and our novel LDLC equation may improve accuracy and avoid undertreatment of high LDLC levels.

Global Standardization of Lipid/Lipoprotein Testing.

As lipid/lipoprotein research elucidated the mechanism of atherosclerosis, lipid/lipoprotein tests were developed for simple and rapid screening, diagnosis, and management of dyslipidemic patients. In 1988, the Centers of Disease Control and Prevention (CDC) in the United States initiated a global standardization program for lipid/lipoprotein testing through the Cholesterol Reference Measurement Laboratory Network (CRMLN), in which the chemical lipid measurements were adopted as reference measurement procedures (RMPs). In 2010, an American group questioned the accuracy of the direct LDL-C and HDL-C assays, causing a great deal of confusion. Our two comparative studies evaluating the direct LDL-C and HDL-C assays have removed reagents with poor analytical performance from the market and demonstrated that the assays are currently accurate enough for clinical use. Because these traditional chemical methods require a high level of technical expertise, RMP are shifting from chemical reaction-based methods to mass spectrometry-based methods. We are now working on the standardization of lipoprotein(a) using the mass spectrometry-based method as an RMP.

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.

Performance of equations for calculated LDL-C in hypertriglyceridaemia: Which one correlates best with directly measured LDL-C?

BackgroundThe gold standard for measuring LDL-C is impractical and direct measurements have numerous shortcomings. Older predictive equations are used only with triglycerides (TG’s) below 4.52 mmol/L. We evaluated the newer equations validated for use in hypertriglyceridaemia by comparison with direct LDL-C. Materials and methodsDatasets from two platforms (Abbott Architect and Roche Cobas) comprised of a large cohort of 64,765 individuals were used to compare the Sampson-National Institutes of Health 2 (S-NIH2) and Extended Martin-Hopkins (E-MH) equations for LDL-C with direct LDL-C (dLDL-C) assays. ResultsWith TG’s of 4.52–9.04 mmol/L the S-NIH2 equation tended to calculate lower values than measured by dLDL-C and the E-MH equation calculated higher values. Both equations correlated better with the dLDL-C measured on Abbott than Roche with the E-MH equation having more values falling within acceptable concordance levels on both platforms. ConclusionThe E-MH equation correlates better with dLDL-C than the S-NIH2 on both platforms with TG levels up to 9.04 mmol/L. With hypertriglyceridaemia, the E-MH equation is less likely than the S-NIH2 equation to underestimate LDL-C when compared to the dLDL-C and will be less likely to underdiagnose patients with LDL-C levels requiring treatment according to current guidelines.

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.

Comparability of calculated LDL-C with directly measured LDL-C in selected paediatric and adult cohorts

Background and aimsThe gold standard for measuring LDL-C is impractical for routine use while direct measurements have numerous shortcomings. This study investigated the correlation of predictive equations with currently used enzymatic assays in populations where these have historically proven unreliable to determine whether equations may be used interchangeably. No reference measure was available for comparison in this study. Materials and methodsWe examined two analytical datasets from different platforms to evaluate the correlation of predictive equations for LDL-C (Friedewald, Sampson-NIH2, Martin-Hopkins, Extended Martin-Hopkins, Hattori and Anandaraja) with direct LDL-C assays in a large paediatric (n = 7598) and an adult cohort with uncontrolled diabetes (n = 57165). Non-parametric statistics were used for comparison. ResultsIn the paediatric cohort, the Sampson-NIH2 equation correlated best with the direct LDL-C assays with the most values falling within desirable bias (35.9–44%) and TEa (68.6–72.9%) and the lowest RMSE (0.5904–0.6138) across platforms, but tended to underestimate LDL-C levels. The Martin-Hopkins equation is less likely to underestimate these values. In diabetes, the Martin-Hopkins equation correlated the best with values falling within acceptable bias (40.2–50.5%) and TEa (75–80.6%). In hypertriglyceridaemia the Extended Martin-Hopkins equation correlates best with the direct LDL-C assays. ConclusionDifferent measurement platforms influence the results of predictive equations and directly measured LDL-C. We propose utilising the Martin-Hopkins equation as an alternative to dLDL-C assays in adults with diabetes and for screening purposes in paediatric populations to avoid underestimating cardiovascular risk.

Comparison of Estimated LDL Cholesterol Equations with Direct Measurement in Patients with Angiographically Confirmed Coronary Artery Disease.

Background and aims: Our goals in the study were to (1) quantify the discordance in LDL-C levels between equations (the Friedewald, Sampson, and Martin/Hopkins equations) and compare them with direct LDL-C (dLDL-C); and (2) explore the proportion of misclassified patients by calculated LDL-C using these three different equations. Methods: A total of 30,349 consecutive patients with angiographically confirmed coronary artery disease (CAD) were prospectively enrolled. Concordance was defined as if the LDL-C was <1.8 mmol/L with each pairwise comparison of LDL-C equations. Estimated LDL-C that fell into the same category as dLDL-C at the following levels: <1.4, 1.4 to 1.7, 1.8 to 2.5, 2.6 to 2.9, and ≥3.0 mmol/L was considered to have been correctly categorized. Results: The concordance was 96.3% (Sampson vs. Martin/Hopkins), 95.0% (Friedewald vs. Sampson), and 91.4% (Friedewald vs. Martin/Hopkins), respectively. This proportion fell to 82.4% in those with hypertriglyceridemia (TG ≥ 1.7 mmol/L). With an accurate classification rate of 73.6%, the Martin/Hopkins equation outperformed the Sampson equation (69.5%) and the Friedewald equation (59.3%) by a wide margin. Conclusions: Comparing it to the validated Martin/Hopkins equation, the Friedewald equation produced the lowest levels of LDL-C, followed by the Sampson equation. In the classification of LDL-C, the Martin/Hopkins equation has also been shown to be more accurate. There is a significant difference between the equations and the direct measurement method, which may lead to overtreatment or undertreatment.

Comparison of various low density lipoprotein cholesterol calculators. Is it time for the Friedewald equation to go?

Abstract Aim Plasma low density lipoprotein cholesterol (LDL-C) is a unit measure of cholesterol mass &amp; an estimate of circulating LDL-C. LDL-C is commonly calculated indirectly by Friedewald equation and not directly with enzymatic method. The Friedewald equation underestimates the LDLC compared to direct LDLC particularly in patients with low LDLC and high Triglycerides (TGL). Recently developed Sampson equation (2020) also estimates LDL-C indirectly but is less dependent on the TGL values. The present study compares directly measured LDL-C with above friedewald and sampsons equations. Methodology A Multicentric study (three health centers) measured LDL-C with direct enzymatic method in 8332 samples. The data was collected using electronic database and computed in Microsoft excel. Retrospective analysis was performed after ethical committee approval and waiver of consent. LDL-C values derived from Friedewald equation &amp; Sampson equation was compared with LDL-C from direct enzymatic method. Subgroup analysis for accuracy was done among various direct LDL-C subgroups such as values less than 50 mg/dl, 50–70 mg/dl, 70–150 mg/dl and more than 150 mg/dl. The entire cohort was also subdivided into triglyceride subgroups &amp;lt;150 mg/dl, 150–450 mg/dl, &amp;gt;450 mg/dl and compared with direct LDL-C values. Results Our study results shows that mean direct LDL-C was 85.7mg/dl, mean calculated LDL-C by Sampson and Friedewald equation were 80mg/dl and 76mg/dl respectively. There was statistical significance in mean difference when direct LDL-C is compared with combined Sampson and Friedewald equation as per Games - Howell multiple comparison study in which mean difference was more with Friedewal's equation than with Sampson equation. The overall concordance upwards between Friedewald's equation versus direct LDL-C and Sampson LDL-C equation versus direct LDL-C was similar (81% and 83% respectively). The overall discordance upwards was more with Sampson LDL-C when compared with direct LDL-C (4%), unlike 1.5% when Friedewald's LDL-C compared with direct LDL-C. The overall discordance downwards was less with Sampson's LDL-C when compared with direct LDL-C (12%) unlike, 16% when Friedewald's LDL-C compared with direct LDL-C. Our study showed that mean LDL-C by Friedewald's equation is less accurate in comparison to the mean direct LDL-C values. But mean LDL-C by Sampson equation had close proximity to mean direct LDL-C values and less magnitude of discordance was noted in patients with low LDL-C &amp; high TGL Conclusions Sampson's equation is better than friedewald's equation in estimation of LDL-C. Sampson's equation is ideal, better, easily calculated and incorporated. We have also developed a simple android application to estimate the LDL-C using both the Sampson and the Friedewald equation. We recommend that sampson's equation can be utilized in third world developing countries in planning low LDL-C targets during secondary prevention. Funding Acknowledgement Type of funding sources: None.

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.

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.

Derivation of a Modified Friedewald's equation for LDL cholesterol estimation in Sub-Himalayan population: A comparative study.

ABSTRACTBackground:Despite the availability of alternative homogenous assays for LDL-C measurement, most of the laboratories still use Friedewald Equation (FE). However, various novel equations have shown better performance than FE specific to a particular population. Besides, no equation has been devised for use in Sub-Himalayan population.Methods:A cross-sectional laboratory data-based study was conducted by recruiting lipid profiles of 1851 samples to validate 10 different equations for calculating LDL and to devise a novel Modified Friedewald Equation (MFE) specific for Sub-Himalayan population.Results:The novel MFE is presented as: LDL-C = -2.421 + (0.752 × TC) - (0.047 × TG) - (0.350 × HDL). A significant difference was observed between direct LDL-C (118.84 ± 40.39 mg/dL) and all other equations except MFE (118.84 ± 37.96 mg/dl, P > 0.999) and Puavilai Equation (117.99 ± 49.05 mg/dL, P = 0.138). Additionally, MFE showed lowest mean percentage bias of 0.14% with 95% limits of agreement within ± 2SD on Bland-Altman analysis. On ROC analysis at cut-offs of clinical decision limits of 100 mg/dl, 130 mg/dl, 160 mg/dl, and 190 mg/dl, MFE outperformed all other equations with highest AUC (0.974, 0.978, 0.982, and 0.995) respectively with specificity >95% at higher levels. MFE also showed highest correlation (r = 0.954, P < 0.001) and least rMSE (13.8) with direct LDL although all the equations showed significant positive correlation with direct LDL (p < 0.001).Conclusion:MFE derived in this study showed a better diagnostic performance as compared to other 10 equations taking Direct LDL-C as gold standard for Sub-Himalayan Population and may be used as a substitute for FE in the study population.

Paramount Definition of Reference Interval for Biochemistry Parameters in Indian Sub-Population: A Healthy Adult Population Based Cross Sectional Study

Background Biological reference intervals BRI of most of the laboratory parameters are significantly influenced by lifestyle and need to be reviewed periodically.Objectives To determine the reference intervals of common clinical-chemistry parameters such as fasting glucose urea creatinine total cholesterol triglycerides HDL- C Direct LDL-C Direct VLDL-C uric acid total protein albumin T. Bilirubin D. Bilirubin ALT AST g - GT ALP for the healthy adult population and to validate the newly established BRI for national applicability. Methodology A cross sectional observational study was conducted including 253 subjects with a mean age of 38.5plusmn8.7 years belonging to four geographical regions such as Kerala Karnataka Andhra Pradesh and Tamil Nadu. The analytes were measured using standard methods.Results Some of the reference intervals of common clinical chemistry parameters in healthy adults of Indian sub-population were lower than the real-world reference intervals however they were within the range. The important parameters such as blood urea creatinine uric acid total cholesterol total bilirubin and g- GT values were significantly lower than the mean reference interval by 35 30 16 9 20 and 57 respectively. However they were within the range while LDL- Cholesterol level was 37 above the mean reference limit. Rest of the parameters were harmonized with the reference interval.Conclusion The BRI were established in this study by constituting apparently healthy South Indian population. It may be a stepping platform and practical role in the determination of BRI of clinical-chemistry analytes in the future for Pan India.nbsp

Performance of Different LDL-C Equations in an Intervention Improving Atherogenic Dyslipidemia in Participants with Type 2 Diabetes

<h3>Lead Author's Financial Disclosures</h3> S.J.A. is an employee of Virta Health and offered stock options from Virta Health. <h3>Study Funding</h3> None. <h3>Background/Synopsis</h3> LDL-C estimated using the Friedewald equation (LDL-CF) is limited in type 2 diabetes (T2D) due to hypertriglyceridemia (HTG). In our recent study of 262 patients with T2D, a low carbohydrate intervention with nutritional ketosis significantly improved atherogenic dyslipidemia (ALP) in participants with T2D, where a majority of the participants transitioned from a predominantly small, dense LDL phenotype (phenotype B) to a large, buoyant LDL phenotype (phenotype A). However, the improvement in ALP was associated with an increase in LDL-CF. <h3>Objective/Purpose</h3> This study assessed differences between LDL-CF versus other recently adopted LDL-C equations to estimate the effect of our intervention on LDL-C, and assess the performance of these equations across a broad range of HTG. <h3>Methods</h3> LDL-C were recalculated using Martin-Hopkins (LDL-CMH) and the new proposed NIH 2 (LDL-CN2) equations. The effect size of the intervention on LDL-C change from baseline to 2 years was assessed using each LDL-C equation with repeated measure ANOVA. Since LDL-CN2 is recommended for use with triglycerides (TG) up to 800mg/dL, we calculated LDL-C with this equation using two cut-offs: TG≤400mg/dL and TG≤800mg/dL. We then assessed the differences between LDL-CMH, LDL-CN2 and LDL-CF at each time point among those with TG≤400mg/dL using a linear mixed effect model (LMM) and covariates that were initially used for the assessment of LDL-CF (Table 1). <h3>Results</h3> There was an increase in LDL-C in all three LDL-C equations, with medium effect sizes in both LDL-CF (ηp2=0.08, +13.5% from 100.9mg/dL) and LDL-CN2 (ηp2 =0.07 for TG≤400mg/dL, +11.6% from 104.8mg/dL or ηp2 =0.06 for TG≤800mg/dL, +10.7% from 104.6mg/dL) and a small effect with LDL-CMH (ηp2=0.04, +9.2% from 106.5mg/dL). At baseline, LDL-CMH estimated significantly greater LDL-C than the other two equations, especially in individuals with HTG and phenotype B (Table 1). At 1 and 2 years when the proportion of individuals with ALP and HTG significantly decreased in the intervention, there were no significant differences in the estimated LDL-C between the equations. <h3>Conclusions</h3> In patients with T2D where HTG is a common lipid abnormality, LDL-CMH may be the most appropriate equation to use in assessing change in LDL-C. For patients with greater than 400mg/dL of TG, LDL-CN2 can be alternatively considered. Future studies should compare these equations with direct LDL-C measurements in patients with T2D.

Randomized Double-Blind Controlled Trial of Freeze-Dried Strawberry Powder Supplementation in Adults with Overweight or Obesity and Elevated Cholesterol

Objective Recommended dietary patterns improve cardiovascular disease (CVD) risk factors such as blood pressure and LDL-C, as well as emerging markers that confer residual risk. Strawberry consumption has been shown to improve CVD risk factors, but further research is needed to better understand these effects using a dose-response model that evaluates a standard serving and a higher (but still achievable) dose. Methods A randomized, placebo-controlled, double-blinded crossover trial was conducted in middle-aged adults with overweight or obesity (n = 40; mean BMI = 29.4 ± 0.2 kg/m2; mean age = 50 ± 1.0 years) and moderately elevated LDL-C (mean LDL-C: 140 ± 3 mg/dL) to investigate the effect of two doses of strawberry supplementation on LDL-C and other CVD risk factors. Study interventions were: 0 g/d (control), 13 g/d (low-dose), and 40 g/d (high-dose) of freeze-dried strawberry powder (4-week supplementation periods separated by a 2-week compliance break). Results There was a significant main effect of treatment for the primary outcome of LDL-C, with a 4.9% reduction following the low-dose strawberry supplement compared to the high-dose (P = 0.01), but not compared to the control. There was also a significant effect on total cholesterol (TC), with a 2.8% and 2.4% reduction following the low-dose compared to the control and high-dose, respectively (P ≤ 0.05 in post-hoc analyses). There was a near significant effect for direct LDL-C (P = 0.07). There were no significant treatment effects for other atherogenic lipoprotein characteristics, indices of vascular function, measures of inflammation, or HDL efflux. Conclusion Low-dose supplementation with freeze-dried strawberry powder, equivalent to ∼1 serving/day of fresh strawberries, improved cholesterol in adults with overweight or obesity, compared to both the high-dose (∼3 servings/day of fresh strawberries) and control, but did not alter other markers of CVD. Supplemental data for this article is available online at

Comparison of Formula-Based Methods with Diverse TGL: VLDL-C Ratio for Calculating LDL-C in a Tertiary Care Hospital.

Background The most popular Friedewald formula (FF) was tailored with a fixed factor of 5 for triglyceride-very low-density lipoprotein cholesterol (TGL:VLDL-C) ratio. Some of the subsequent studies on diverse population demonstrated modified FF with only altered TGL:VLDL-C ratio, comprising either a fixed or an adjustable factor. Hata and Nakajima as well as Puavilai et al proposed fixed factors of 4 and 6, respectively. Recently, Martin et al recommended an adjustable factor derived as N-strata-specific median TGL:VLDL-C ratio based on TGL and non-high-density lipoprotein cholesterol (non-HDL-C). Aim This comparative retrospective study evaluates the efficacy of LDL-C formulae, varying only in TGL-VLDL-C ratio, using direct LDL-C assay as a reference method in a tertiary care hospital. Materials and Methods A total of 1,747 patient records with lipid profile data were procured. Concordance analysis, absolute difference, and post hoc test were employed as analytical tools. The impact of total cholesterol (TChol), TGL, and HDL-C on formulae was also evaluated. Results Overall, Martin equation had relatively the highest concordance, narrowest absolute difference, and minimal influence of TChol, TGL, and HDL-C. On the contrary, the Hata method revealed comparatively the lowest concordance, widest absolute difference, and high influence of TChol, TGL, and HDL-C. The remaining formula-based approaches, that is, FF and Puavilai calculation, executed mostly inconsistent intermittent features between Martin equation and Hata method. Conclusion Relatively dominant and competitive analytical attributes of the Martin equation with an adjustable TGL:VLDL-C factor outweigh the remaining three formulae-based methods with fixed TGL:VLDL-C factor in Indian adults.

Novel method for quantification of lipoprotein(a)-cholesterol: implications for improving accuracy of LDL-C measurements

Current methods for determining “LDL-C” in clinical practice measure the cholesterol content of both LDL and lipoprotein(a) [Lp(a)-C]. We developed a high-throughput, sensitive, and rapid method to quantitate Lp(a)-C and improve the accuracy of LDL-C by subtracting for Lp(a)-C (LDL-Ccorr). Lp(a)-C is determined following isolation of the Lp(a) on magnetic beads linked to monoclonal antibody LPA4 recognizing apolipoprotein(a). This Lp(a)-C assay does not detect cholesterol in plasma samples lacking Lp(a) and is linear up to 747 nM Lp(a). To validate this method clinically over a wide range of Lp(a) (9.0–822.8 nM), Lp(a)-C and LDL-Ccorr were determined in 21 participants receiving an Lp(a)-specific lowering antisense oligonucleotide and in eight participants receiving placebo at baseline, at 13 weeks during peak drug effect, and off drug. In the groups combined, Lp(a)-C ranged from 0.6 to 35.0 mg/dl and correlated with Lp(a) molar concentration (r = 0.76; P < 0.001). However, the percent Lp(a)-C relative to Lp(a) mass varied from 5.8% to 57.3%. Baseline LDL-Ccorr was lower than LDL-C [mean (SD), 102.2 (31.8) vs. 119.2 (32.4) mg/dl; P < 0.001] and did not correlate with Lp(a)-C. It was demonstrated that three commercially available “direct LDL-C” assays also include measures of Lp(a)-C. In conclusion, we have developed a novel and sensitive method to quantitate Lp(a)-C that provides insights into the Lp(a) mass/cholesterol relationship and may be used to more accurately report LDL-C and reassess its role in clinical medicine.