Direct Low-density Lipoprotein Cholesterol Research Articles

Martin’s formula is best to calculate low-density lipoprotein cholesterol

Objectives: Due to the cost-effectiveness, most of the laboratories in India estimate low-density lipoprotein cholesterol (LDL-C) levels with the Friedewald’s formula. There were many shortcomings of the Friedewald’s formula. Recently, Martins have derived a new formula for calculating LDL-C. The present study was undertaken to calculate LDL-C using various formulae (Friedewald’s formula, Anandaraja’s formula, and Martin’s formula) and to compare directly measured LDL-C with calculated LDL-C at various ranges of triglyceride (TG) concentration. Materials and Methods: The present study compared LDL-C measured by Martin’s formula, Friedewald’s formula, and Anandaraja’s formula with directly measured LDL In 280 Outpatient fasting samples between the age group of 18 and 50 years. Depending on the TG values, study samples are divided into four groups. Group 1: <200 mg/dL, Group 2: 200–300 mg/dL, Group 3: 300–400 mg/dL, Group 4: >400 mg/dL. Statistical Analysis: This was performed by the Statistical package for social sciences version 16. Paired t test and pearson correlation were performed to find the significant difference and correlation between direct LDL-C and calculated LDL-C by different formula. Results: Martin’s formula shows highest correlation with r-value of (0.9979) compared to Friedewald’s (0.9857) and Anandaraja’s (0.9683) r-values. The mean difference was least for Martin’s formula (0.31 ± 3.53) compared to other formulae. Among all the groups, the percentage of error was least for Martin’s formula (0.23%). Martin’s LDL-C shows highest concordance (90.90%) compared to Friedewald’s (79.60%) and Anandaraja’s formulae (82.90%). Conclusions: Among all the groups, Martin’s formula shows highest correlation, least percentage of error, highest concordance, and least mean differences. At all TG levels, Martin’s formula is the best formula compared to the Friedewald’s formula and Anandaraja’s formula.

Concordance between low density lipoprotein cholesterol concentration measurement by enzymatic method and calculation by Friedewald formula in cardiovascular risk classification

Accurate estimation of low-density lipoprotein cholesterol (LDL-C) is important for cardiovascular risk assessment and guiding cholesterol-lowering therapy. Due to the high cost of β-quantification (Gold standard) and time-consuming, direct measurement of LDL-C is an alternative method. However, unlike the calculation of LDL-C by Friedewald formula, there is an additional cost in terms of reagents for performing a direct LDL-C test. The current study aimed to compare direct LDL-C concentration determination to data calculated by Friedewald formula. 752 lipid profiles of 710 people with LDL-C estimated by direct LDL assay, in the Biochemistry laboratory of university hospital center of Angré, were included in the study. In the same group, LDL-C was calculated using Friedewald formula. Lin’s concordance correlation coefficient (ccc) and Passing-Bablok regression analysis using, MedCal software, were performed to assess the strength of concordance between the 2 methods, and identify any possible bias. The concordance between the two methods was moderate (ρc = 0.9466). Passing-Bablok regression analysis revealed a systematic bias between the two methods. The total error observed (TEobs) between the two methods was higher than allowable total error recommended by the NCEP-ATPIII when LDL-C values was less than 159 mg/dL (4.112 mmol/L). The Friedewald formula resulted in lower LDL-C concentration values. Despite its cost-effectiveness in the estimation of LDL-C, an underestimation of LDL-C levels could result in inaccurate cardiovascular diseases (CVD) risk assessments and potentially significant future societal costs due to inadequate prevention and treatment of CVD.

A-215 Calculated LDL-cholesterol: Comparability of the extended Martin-Hopkins,Sampson-NIH, Friedewald and four other equations in South African patients

Abstract Background Ultracentrifugation for LDL-C (low-density lipoprotein cholesterol) measurement is the gold standard, but it is unsuitable for routine use. Direct LDL-C assays and predictive equations such as the Friedewald equation are thus used as alternatives. The Friedewald equation is known to be error-prone in hypertriglyceridaemic patients, as well as in LDL-C levels < 1.8 mmol/L (70 mg/dL) which has become increasingly attainable with the use of pro-protein convertase subtilisin/kexin type 9 inhibitors. This study evaluates the comparability of the Friedewald, extended Martin/Hopkins, Sampson/NIH and four other equations to a direct LDL-C assay to establish a suitable alternative to the Friedewald equation. Methods We analysed 44 194 lipid profiles from a mixed South African population. The Friedewald, extended Martin/Hopkins and Sampson/NIH LDL-C and four other predictive equations (Vujovic, Puavilai, Delong, Anandaraja) were compared to the Beckman Coulter direct LDL-C assay. Non-parametric statistics were used to analyse the data. Results The extended Martin/Hopkins equation displayed the best correlation with direct LDL-C, having the most values fall within the desirable bias (59%) and total allowable error (89%) specifications. In patients with TG values between 4.5 mmol (400 mg/dL) and 9.0 mmol/L (800 mg/dL), the extended Martin/Hopkins equation was more likely to overestimate LDL-C levels, showing a positive median bias of 7.3%, while Friedewald (−21.2%) and Sampson/NIH (−9.8%) showed larger negative median biases as compared to the direct LDL-C assay. The direct LDL-C assay classified 13.9% of patients in the low LDL-C (1.0–1.8 mmol/L) (39–70 mg/dL) category, in comparison to the Martin/Hopkins equation (13.4%), the Sampson equation (14.6%) and the Friedewald equation (16.0%). The extended Martin/Hopkins equation performed better than Sampson/NIH equation in the cohort. Conclusion We suggest the extended Martin/Hopkins equation as an alternative to Friedewald’s equation and direct LDL-C assay. Careful consideration is advised as the choice of analyser platform for the lipid profile used in the equations may influence their performance.

Causal effects of serum lipid biomarkers on early age-related macular degeneration using Mendelian randomization

BackgroundAge-related macular degeneration (AMD) is one of the major causes of vision loss. Early AMD needs to be taken seriously, but the causal effects of lipid biomarkers on early AMD remain unclear.MethodsIn this study, two-sample Mendelian randomization (MR) analysis was performed to systematically assess the causal relationships between seven serum lipid biomarkers (apolipoprotein A (ApoA), apolipoprotein B (ApoB), total cholesterol (CHOL), high-density lipoprotein cholesterol (HDL-C), direct low-density lipoprotein cholesterol (LDL-C), lipoprotein A [Lp(a)], and triglycerides (TG)) and risk of early AMD. In total, 14,034 cases and 91,214 controls of European ancestry were included in the analysis (number of SNPs = 11,304,110).ResultsMR estimates revealed that a higher HDL-C level is strongly associated with increased risk of early AMD (OR = 1.25, 95% CI: 1.15–1.35, P = 2.61 × 10−8). In addition, level of ApoA is also positively associated with risk of early AMD (OR = 2.04, 95% CI: 1.50–2.77, P = 6.27 × 10−6). Conversely, higher levels of TG significantly decrease the risk of early AMD (OR = 0.77, 95% CI: 0.71–0.84, P = 5.02 × 10−10). Sensitivity analyses further supported these associations. Moreover, multivariable MR analyses, adjusted for the effects of correlated lipid biomarkers, yielded similar results.ConclusionThis study identifies causal relationships between elevated circulating HDL-C/ApoA levels and increased risk of early AMD, in addition to finding that TG specifically reduces the risk of early AMD. These findings contribute to a better understanding of the role of lipid metabolism in drusen formation, particularly in early AMD development.

Comparison of existing methods of low-density lipoprotein cholesterol estimation in patients with type 2 diabetes mellitus

Introduction: Elevated low-density lipoprotein cholesterol (LDL-C) is an important risk factor for atherosclerotic cardiovascular disease (ASCVD). Direct LDL-C measurement is not widely performed. LDL-C is routinely calculated using the Friedewald equation (FLDL), which is inaccurate at high triglyceride (TG) or low LDL-C levels. We aimed to compare this routine method with other estimation methods in patients with type 2 diabetes mellitus (T2DM), who typically have elevated TG levels and ASCVD risk. Method: We performed a retrospective cohort study on T2DM patients from a multi-institutional diabetes registry in Singapore from 2013 to 2020. LDL-C values estimated by the equations: FLDL, Martin/Hopkins (MLDL) and Sampson (SLDL) were compared using measures of agreement and correlation. Subgroup analysis comparing estimated LDL-C with directly measured LDL-C (DLDL) was conducted in patients from a single institution. Estimated LDL-C was considered discordant if LDL-C was <1.8mmol/L for the index equation and ≥1.8mmol/L for the comparator. Results: A total of 154,877 patients were included in the final analysis, and 11,475 patients in the subgroup analysis. All 3 equations demonstrated strong overall correlation and goodness-of-fit. Discordance was 4.21% for FLDL-SLDL and 6.55% for FLDL-MLDL. In the subgroup analysis, discordance was 21.57% for DLDL-FLDL, 17.31% for DLDL-SLDL and 14.44% for DLDL-MLDL. All discordance rates increased at TG levels >4.5mmol/L. Conclusion: We demonstrated strong correlations between newer methods of LDL-C estimation, FLDL, and DLDL. At higher TG concentrations, no equation performed well. The Martin/Hopkins equation had the least discordance with DLDL, and may minimise misclassification compared with the FLDL and SLDL.

Calculated LDL-cholesterol: comparability of the extended Martin/Hopkins, Sampson/NIH, Friedewald and four other equations in South African patients

AimsThe reference method for low-density lipoprotein-cholesterol (LDL-C) is ultracentrifugation. However, this is unsuitable for routine use and therefore direct LDL-C assays and predictive equations are used. In this study, we...

Comparison of Sampson and extended Martin/Hopkins methods of low-density lipoprotein cholesterol calculations with direct measurement in pediatric patients with hypertriglyceridemia.

The Friedewald equation is the commonly used method of low-density lipoprotein cholesterol (LDL-C) calculation, requiring reflex to direct LDL-C measurement when triglycerides (TG) ≥ 400mg/dL. Recently formulated Sampson and extended Martin/Hopkins methods have been validated with TG up to 800mg/dL and thus have the potential to replace direct LDL-C measurement. Given the growing prevalence of childhood dyslipidemia, the objective of this study was to compare Sampson and extended Martin/Hopkins methods of LDL-C calculation with the direct measurement in a pediatric cohort with 400≤ TG ≤ 799mg/dL. This study retrieved standard lipid panels and corresponding direct LDL-C measurements of 131 patients with 400≤ TG ≤ 799mg/dL from a pediatric population. Following the application of Sampson and extended Martin/Hopkins calculations, calculated values were compared with direct LDL-C measurements using ordinary least squares linear regression analysis and bias plotting. Both Sampson and extended Martin/Hopkins LDL-C calculations exhibited a strong correlation with the direct measurements (Pearson r=0.89) in patients with 400≤ TG ≤ 800mg/dL. Average percentages of bias of 45% and 21% were found between the direct LDL-C measurements and Sampson or extended Martin/Hopkins calculations, respectively. Both Sampson and extended Martin/Hopkins calculations are applicable as clinical alternatives of direct LDL-C measurement in pediatric patients given 400≤ TG ≤ 799mg/dL.

Comparison of low-density lipoprotein cholesterol estimation methods in individuals with insulin resistance: A cross-sectional study

Background and aimsThe Friedewald, Sampson, and Martin equations were developed to estimate low-density lipoprotein cholesterol levels; however, the validation data of these equations with and without insulin resistance are insufficient. Materials and methodsWe collected data on low-density lipoprotein cholesterol and lipid profiles from the Korea National Health and Nutrition Examination Survey. Using the data on insulin requirement, insulin resistance was calculated for 4,351 participants (median age, 48 [36–59] years; 49.9% male) using the homeostatic model assessment for insulin resistance (n = 2,713) and quantitative insulin-sensitivity check index (n = 2,400). ResultsAccording to the mean and median absolute deviation, the Martin equation yielded more accurate estimates than other equations when the triglyceride level was < 400 mg/dL with insulin resistance; the Sampson equation yielded lower estimates when the direct low-density lipoprotein cholesterol level was < 70 mg/dL and triglyceride level was < 400 mg/dL without insulin resistance. However, the three equations yielded similar estimates when the triglyceride level was < 150 mg/dL with and without insulin resistance. ConclusionThe Martin equation yielded more appropriate estimates than the Friedewald and Sampson equations for triglyceride levels < 400 mg/dL with and without insulin resistance. If the triglyceride level was < 150 mg, the Friedewald equation could also be considered.

Comparison of direct low density lipoprotein cholesterol measurement with the Friedewald formula and alternative formulas

&lt;b&gt;Aim:&lt;/b&gt; Our aim was to compare the direct enzymatic measurement with four formulas which are used in determining the value of low density lipoprotein cholesterol (LDL-C) levels.&lt;br /&gt; &lt;b&gt;Material and methods:&lt;/b&gt; A total of 33842 patients’ files were retrospectively reviewed and data was collected. Triglyceride (TG) group 1, 2, 3, 4 and 5 were consisted of TG levels ≤99 mg/dl, 100-199 mg/dl, 200-299 mg/dl, 300-399 mg/dl and ≥ 400 mg/dl, respectively. LDL-Group 1, 2, 3, 4 and 5 were composed of LDL-C≤100 mg/dl, 101-130 mg/dl, 131-160 mg/dl, 160-190 mg/dl and &amp;gt;190 mg/dl, respectively.&lt;br /&gt; &lt;b&gt;Results&lt;/b&gt;: All formulas tended to undervalue LDL-C concentrations compared to direct method (p&amp;lt;0.001 for all). The Chen formula had higher degree of correlation compared to other formulas. Acceptable result of Friedewald formula was 53.77%, Chen formula was 62.72%, Hattori formula was 24.72, and Anandaraja formula was 45.98%. Bland-Altman plot results showed disagreement of four formulas with significant proportional and systematic bias compared to direct method. There was no agreement of calculated LDL-C with direct LDL-C when the data was subgrouped according to TG levels. No agreement between direct LDL-C and calculated LDL-C was found. Correlation analysis showed moderate to high level of correlation for Friedewald, Chen, and Hattori calculations, whereas Anandaraja formula showed low to moderate correlation. The Friedewald and Anandaraja formulas mostly misclassified LDL-Group 3 subjects, whereas the Chen and Hattori formulas mostly misclassified LDL-Group 4 subjects.&lt;br /&gt; &lt;b&gt;Conclusion&lt;/b&gt;: The Chen formula might be an acceptable alternative of the Friedewald formula and other formulas.

Evaluation of equations for calculating LDL-cholesterol in patients with concomitant hypertriglyceridemia and low LDL-cholesterol: is there a preferred equation?

It is critical to reliably estimate Low Density Lipoprotein Cholesterol (LDL-C) in patients with concomitant hypertriglyceridemia and low LDL-C. We retrospectively compared the performances of the Friedewald (LDL-F), Martin-Hopkins (LDL-MH) and Sampson (LDL-SA) equations against a direct homogeneous LDL-C assay (dLDL-C) on observations presenting mild hypertriglyceridemia (triglycerides between 1.69 and 3.9 mmol/L) and low LDL-C (< 2.58 mmol/L). Observations were stratified according to their LDL-C. Agreement of the equations with dLDL-C was assessed using Intraclass Correlation Coefficients (ICC) with an agreement cut-off of 0.9, and analysis of Bland-Altman plots. Independently of the LDL-C stratum evaluated, the three equations failed to meet the 0.9 ICC cut-off, although their agreement with dLDL-C improves as LDL-C increases. Analysis of Bland-Altman plots shows a downwards discordance of LDL-F with dLDL-C, and an upwards discordance of LDL-MH and LDL-SA with direct LDL-C. LDL-MH resulted in the least observations outside the Bland-Altman limits of agreement. While no equation can be deemed satisfactory enough to replace direct assays in patients with low LDL-C and concomitant hypertriglyceridemia, LDL-MH seems to perform better than the other equations in estimating LDL-C in these patients.

Correlation of LDL Cholesterol Calculated by Friedewald’s, Puavilai’s, Vujovic’s, de Cordova’s and Martin’s Formulae with Directly Measured LDL Cholesterol: A Cross-sectional Study

Introduction: Measurement of Low Density Lipoprotein Cholesterol (LDL-C) carries high importance in the management of Cardiovascular Disease (CVD). Direct LDL-C measurement is preferred method but this is expensive and inconvenient for the routine laboratories. To date, various types of formulae have been introduced. However, accurate estimation of LDL-C by formula is a challenge. Aim: To determine that which of these calculated formulae (Friedewald’s, Puavilai’s, Vujovic’s, de Cordova’s and Martin’s formulae) show maximum correlation with directly measured LDL-C at different serum triglyceride levels. Materials and Methods: The present cross-sectional study was conducted in the Department of Biochemistry, KLE Centenary Charitable Hospital and Medical Research Centre, Belgaum, Karnataka, India, from December 2020 to December 2021. A total of 280 outpatient fasting complete lipid profiles of patients, aged between 18-50 years were included in the study. LDL-C measured by Friedewald’s formula, Puavilai’s formula, Vujovic’s formula, de Cordova’s formula and Martin’s formula were compared with directly measured LDL-C. Comparison of calculated LDL-C with directly measured LDL-C was done at following Triglyceride (TG) ranges as group 1: &lt;200 mg/dL, group 2: 200-300 mg/dL, group 3: 300-400 mg/dL and group 4: &gt;400 mg/dL. Data analysis was done using Pearson’s correlation coefficient and two paired t-test. Results: Of total 280 samples, 124 participants were in group 1, 91 participants in group 2, 36 participants in group 3 and 29 participants in group 4, and there were 130 males and 150 females. The mean age in group 1, 2, 3 and 4 was 40.9±8.0 years, 38.8±9.2 years, 39.1±10.0 years and 39.8±8.2 years, respectively. Martin’s formula showed maximum correlation with r-value of 0.9979 compared to Friedewald’s, Puavilai’s formula, Vujovic’s formula and de Cordova’s formula. The mean difference was least for Martin’s formula 0.31±3.53 compared to other formulas. Percentage of error was least for Martin’s formula (0.23%) in total study sample and in all groups. Martin’s LDL-C shows highest concordance (90.90%) compared to Frielwald’s (79.60%), Puavilai’s (86.00%), Vujovic’s (83.88%) and de Cordova’s formula (82.76%). Conclusion: In the present study, Martin’s formula showed highest correlation, least mean difference, highest concordance and low percentage of errors in all the groups compared to Frieldwald’s formula, Puavilai’s formula, Vujovic’s formula and de Cordova’s formula.

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.

Comparison of the Friedewald equation with Martin and Sampson equations for estimating LDL cholesterol in hypertriglyceridemic adults

Low density lipoprotein cholesterol (LDL-C) is traditionally calculated using the Friedewald (LDL-F) equation. New equations by Martin (LDL-M) and Sampson (LDL-S) have improved accuracy relative to LDL-F for samples with high triglycerides (TG) or low LDL-C. However, most labs still rely on LDL-F and few studies have examined the accuracy and impact of contemporary LDL-C equations applied to a retrospective dataset. 934 lipid panels with a concurrent direct enzymatic LDL-C (dLDL-C) result were extracted from the laboratory information system. LDL-F, LDL-M, and LDL-S were calculated and the accuracy of each equation determined in a predominantly hypertriglyceridemic population. The impact of implementing each equation was compared by analyzing the LDL-C treatment group miscategorization rate relative to dLDL-C. The slope for the LDL-F, LDL-M and LDL-S were 0.59, 0.78, and 0.94, relative to dLDL-C. The three equations performed comparably for samples with TG <4.52 mmol/L (<400 mg/dL). The LDL-C treatment group miscategorization rate was 48.6 % for LDL-F, 28.8 % for LDL-M and 37.2 % for LDL-S in specimens with TG ≥4.52 mmol/L (≥400 mg/dL) (n = 817). LDL-S underestimated treatment group category (31.3 %, 95 % CI 17.2–22.4) relative to LDL-M (9.0 %, 4.39–7.41, P < 0.001). 5.9 % of samples were overestimated for treatment group category by LDL-S vs 19.8 % for LDL-M (P = 0.1883). LDL-M and LDL-S demonstrate reduced bias with a dLDL-C method compared to LDL-F in samples with TG ≥4.52 mmol/L (≥400 mg/dL). LDL-M reduces LDL-C treatment group miscategorization rate leading to fewer underestimations of risk overall compared to LDL-S; however, neither may be sufficiently accurate to report LDL-C in patients with TG ≥4.52 mmol/L (≥400 mg/dL).

How should low-density lipoprotein cholesterol be calculated in 2022?

The reference method for low-density lipoprotein-cholesterol (LDL-C) quantitation is β-quantification, a technically demanding method that is not convenient for routine use. Indirect calculation methods to estimate LDL-C, including the Friedewald equation, have been used since 1972. This calculation has several recognized limitations, especially inaccurate results for triglycerides (TG) >4.5 mmol/l (>400 mg/dl). In view of this, several other equations were developed across the world in different datasets.The purpose of this review was to analyze the best method to calculate LDL-C in clinical practice by reviewing studies that compared equations with measured LDL-C. We identified 45 studies that compared these formulae. The Martin/Hopkins equation uses an adjustable factor for TG:very low-density lipoprotein-cholesterol ratios, validated in a large dataset and demonstrated to provide more accurate LDL-C calculation, especially when LDL <1.81 mmol/l (<70 mg/dl) and with elevated TG. However, it is not in widespread international use because of the need for further validation and the use of the adjustable factor. The Sampson equation was developed for patients with TG up to 9 mmol/l (800 mg/dl) and was based on β-quantification and performs well on high TG, postprandial and low LDL-C samples similar to direct LDL-C. The choice of equation should take into the level of triglycerides. Further validation of different equations is required in different populations.

Directly Measured vs. Calculated Low-Density Lipoprotein Cholesterol Does Not Identify Additional Individuals With Coronary Artery Disease and Diabetes at Higher Risk of Adverse Events: Insight From a Large Percutaneous Coronary Intervention Cohort in Asia.

BackgroundThe objective of our study was to assess whether calculated low-density lipoprotein cholesterol (LDL-C) is inferior to direct LDL-C (dLDL-C) in identifying patients at higher risk of all-cause mortality, recurrent acute myocardial infarction (AMI), and major adverse cardiovascular event (MACE).MethodsA total of 9,751 patients with coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI) in the Fuwai PCI registry were included. DLDL-C was measured by the selective solubilization method (Kyowa Medex, Tokyo, Japan). Correct classification was defined as the proportion of estimated LDL-C in the same category as dLDL-C based on dLDL-C levels: less than 1.4, 1.4–1.8, 1.8–2.6, 2.6–3.0, and 3.0 mmol/L or greater.ResultsUnderestimation of LDL-C was found in 9.7% of patients using the Martin/Hopkins equation, compared with 13.9% using the Sampson equation and 24.6% with the Friedewald equation. Cox regression analysis showed compared the correct estimation group, underestimation of LDL-C by the Martin/Hopkins equation did not reduce all-cause mortality (HR 1.26, 95% CI: 0.72–2.20, P = 0.4), recurrent AMI (HR 1.24, 95% CI: 0.69–2.21, P = 0.5), and MACE (HR 1.02, 95% CI: 0.83–1.26, P = 0.9). Similarly, the overestimated group did not exacerbate all-cause mortality (HR 0.9, 95% CI: 0.45–1.77, P = 0.8), recurrent AMI (HR 0.63, 95% CI: 0.28–1.44, P = 0.3), and MACE (HR 1.07, 95% CI: 0.86–1.32, P = 0.6). The results of the diabetes subgroup analysis were similar to those of the whole population.ConclusionCompared with dLDL-C measurement, misclassification by the Martin/Hopkins and Sampson equations was present in approximately 20% of patients. However, directly measured vs. calculated LDL-C did not identify any more individuals in the PCI population with increased risk of all-cause mortality, recurrent AMI, and MACE, even in high-risk patients such as those with diabetes.

Association of Low-Density Lipoprotein-Cholesterol and Its Small, Dense Phenotype with Six-Month Cardiovascular Morbidity.

Globally, cardiovascular diseases (CVDs) are the leading cause of death and disability. Elevated low-density lipoprotein-cholesterol (LDL-C) and more specifically, elevation of its small, dense phenotype (sdLDL-C) has been regarded as the key modifiable risk factors associated with atherogenesis. This study aimed to determine the association of LDL-C and sdLDL-C with the development of CVDs in the next six months to establish their predictive efficacy. A batch of 162 anonymized serum samples sent for analysis of lipid profile parameters, were classified into tests and controls based on the calculated LDL-C values obtained by Fried Ewald formula. Direct LDL-C was also estimated automatically using assay kits. Using the formula provided by Srisawasdi et al., sdLDL-C was then computed for all samples. Six months later, samples were deanonymized, and the lipid profiles were compared with cardiovascular outcomes of these patients, to determine which parameter had the greatest correlation. Four control group patients and three test group patients developed the outcome (any cardiovascular event) during the 6-month follow-up period. Binary logistic regression analysis showed that none of the lipid profile parameters: calculated LDL-C (OR= 0.99; 95% CI= 0.97-1.01; p= 0.826), direct LDL-C (OR= 0.99; 95% CI= 0.97-1.01; p= 0.818) or sdLDL-C (OR= 0.99; 95% CI= 0.93-1.04; p= 0.734), were significantly associated with the occurrence of outcome. The median % sdLDL-C both with respect to direct and calculated LDL-C was slightly higher in patients with the outcome. The levels of LDL-C or its individual phenotypes may not be used singly as indicator of cardiovascular morbidity in the next six months.

Prospective Validation of a Machine Learning Model for Low-Density Lipoprotein Cholesterol Estimation.

We aim to prospectively validate a previously developed machine learning algorithm for low-density lipoprotein cholesterol (LDL-C) estimation. We retrospectively and prospectively evaluated a machine learning algorithm based on k-nearest neighbors (KNN) according to age, sex, healthcare setting, and triglyceridemia against a direct LDL-C assay. The agreement of low-density lipoprotein-k-nearest neighbors (LDL-KNN) with the direct measurement was assessed using intraclass correlation coefficient (ICC). The analysis comprised 31,853 retrospective and 6599 prospective observations, with a mean age of 54.2 ± 17.2 years. LDL-KNN exhibited an ICC greater than 0.9 independently of age, sex, and disease status. LDL-KNN was in satisfactory agreement with direct LDL-C in observations with normal triglyceridemia and mild hypertriglyceridemia but displayed an ICC slightly below 0.9 in severely hypertriglyceridemic patients and lower in very low LDL-C observations. LDL-KNN performs robustly across ages, genders, healthcare settings, and triglyceridemia. Further algorithm development is needed for very low LDL-C observations.

Comparative Study of Calculated LDL-Cholesterol Levels with the Direct Assay in Patients with Hypothyroidism.

Background Hypothyroidism is one among the many factors that predisposes one to coronary artery disease. As low-density lipoprotein-cholesterol (LDL-C) is associated with cardiovascular risk, calculated LDL-C should have good accuracy with minimal bias. Hypothyroidism alters the lipid composition of lipoproteins by the secretion of triglyceride-rich lipoproteins, which affects the calculation of LDL-C. The present study aimed to compare 13 different formulae for the calculation of LDL-C including the newly derived Martin's formula by direct assay in patients of hypothyroidism. Method In this analytical cross-sectional study, a total of 105 patients with laboratory evidence of hypothyroidism, from January to June 2019, were studied, and blood samples were subjected for lipid profile analysis at central biochemistry laboratory. Calculated LDL-C was assessed by different formulae. Result We observed that calculated LDL-C by Friedewald's, Cordova's, Anandaraja's, Hattori's, and Chen's formulae has bias less than ± 5 compared with direct LDL-C, with Anandaraja's formula having the lowest bias (2.744) and Cordova's formula having lowest bias percentage (-1.077) among them. According to the Bland-Altman plots, the bias in Friedewald's and Anandraja's were equally distributed below and above the reference line of direct LDL-C. Conclusion This is the first study comparing different formulae for LDL-C calculation in patients with hypothyroidism. Anandaraja's formula was as equally effective as Friedewald's formula when used as an alternative cost-effective tool to evaluate LDL-C in hypothyroid patients. The recently proposed Martin's formula for calculated LDL-C had a higher bias when compared with Friedewald's and Anandaraja's formulae in patients with hypothyroidism.

Machine Learning Estimation of Low-Density Lipoprotein Cholesterol in Women With and Without HIV.

Low-density lipoprotein cholesterol (LDL-C) is typically estimated from total cholesterol, high-density lipoprotein cholesterol, and triglycerides. The Friedewald, Martin-Hopkins, and National Institutes of Health equations are widely used but may estimate LDL-C inaccurately in certain patient populations, such as those with HIV. We sought to investigate the utility of machine learning for LDL-C estimation in a large cohort of women with and without HIV. We identified 7397 direct LDL-C measurements (5219 from HIV-infected individuals, 2127 from uninfected controls, and 51 from seroconvertors) from 2414 participants (age 39.4 ± 9.3 years) in the Women's Interagency HIV Study and estimated LDL-C using the Friedewald, Martin-Hopkins, and National Institutes of Health equations. We also optimized 5 machine learning methods [linear regression, random forest, gradient boosting, support vector machine (SVM), and neural network] using 80% of the data (training set). We compared the performance of each method using root mean square error, mean absolute error, and coefficient of determination (R2) in the holdout (20%) set. SVM outperformed all 3 existing equations and other machine learning methods, achieving the lowest root mean square error and mean absolute error, and the highest R2 (11.79 and 7.98 mg/dL, 0.87, respectively, compared with those obtained using the Friedewald equation: 12.45 and 9.14 mg/dL, 0.87). SVM performance remained superior in subgroups with and without HIV, with nonfasting measurements, in LDL <70 mg/dL and triglycerides > 400 mg/dL. In this proof-of-concept study, SVM is a robust method that predicts directly measured LDL-C more accurately than clinically used methods in women with and without HIV. Further studies should explore the utility in broader populations.

A Comparison between Eight Formulas for the Estimation of Plasma Low-Density Lipoprotein Cholesterol in the Saudi Arabian Population

CONTEXT: Low-density lipoprotein cholesterol (LDL-C) is the classical target in cardiovascular (CV) disease management and is usually estimated by Friedewald's formula. However, this formula may over- or underestimate LDL-C levels.AIMS: Our aim is to compare eight LDL-C-estimating formulas to the direct LDL-C measurement and validate their use in the Saudi population.SETTINGS AND DESIGN: This was a retrospective study.SUBJECTS AND METHODS: A blood sample of fasting 669 Saudi subjects was tested for lipid profiles in King Abdulaziz University Hospital Laboratory, from which directly measured LDL-C was obtained. LDL-C was then estimated from eight different formulas: Friedewald's, Cordova's, Hata's, Puavilai's, Chen's, Ahmadi's, Hattori's, and Vujovic's, which were all compared to direct LDL-C.STATISTICAL ANALYSIS USED: Mean and standard deviation, paired t-test, and Pearson's correlation were used for statistical analysis.RESULTS: The mean differences between the direct LDL-C and Hattori and Chen's LDL-C were 0.03 and 0.08 mmol/L, respectively; P < 0.001, while the mean difference observed with Hata, Friedewald, Puavilai, and Vujovic's formulas were higher in comparison: 0.15, 0.24, 0.29, and 0.33 mmol/L, respectively, P < 0.001. Ahmadi and Cordova's LDL-C were estimated to be 0.60 and 0.64 mmol/L more than direct LDL-C levels, respectively, which showed the highest discordance with direct LDL-C, P < 0.001. At a triglyceride (TG) level of <4.5 mmol/L, Hattori also had the best agreement with direct LDL-C, with a mean difference of 0.04 mmol/L, and with TG >4.5 mmol/L, their mean difference was 0.21 mmol/L. All estimated LDL-C strongly correlated with direct LDL-C, except for Ahmadi's.CONCLUSIONS: Hattori's LDL-C had the best agreement with the direct LDL-C, and across all TG levels. However, we recommend directly measuring LDL-C in those with high CV risk.