RESUMEN

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.

RESUMEN

Background: The Friedewald formula (FF) was originally designed 50 years ago and has been in use to this day despite better methods for estimating LDL cholesterol (LDL-C). Its success was mainly due to its simplicity. Nowadays most laboratories determine or can determine LDL-C by the direct method. The SCORE2 tables, recommended by the European Society of Cardiology, are based on non-HDL cholesterol (non-HDL-C). To calculate its value, one needs to know the values of total cholesterol (TC) and HDL-C. The presented idea is to use the FF to calculate non-HDL-C based on the values of LDL-C and TG instead of TC and HDL-C. Methods and findings: Based on database of 26,914 laboratory results, covering the complete lipid panel, the error regarding non-HDL-C values calculated in both ways (recommended and proposed) was determined. The average error in the LDL-C value calculated with the FF compared to the LDL-C value measured in the laboratory is 9.77%, while for non-HDL-C the error between the calculated and laboratory-determined value amounts to 8.88%. The proposed transformation of the FF also yields a much lower percentage of error calculations. Both LDL-C and non-HDL-C (calculated) in our material are strongly correlated with LDL-C and non-HDL-C (measured) values of r = 0.965 (p < 0.000) and r = 0.962 (p < 0.000), respectively. Conclusion: Non-HDL-C may be calculated based on the values of LDL-C and TG (without the need to determine the levels of TC and HDL-C). The proposed calculation may greatly reduce the cost of testing, given the price of a complete lipid profile.

RESUMEN

Background Because of 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, Martin and colleagues 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 (D-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 D-LDL-C in 280 outpatient fasting samples between the age groups of 18 and 50 years. Depending on the TG values, study samples were divided into four groups. Group 1: less than 200 mg/dL; Group 2: 200 to 300 mg/dL; Group 3: 300 to 400 mg/dL; and Group 4: more than 400 mg/dL. Results Martin's formula shows highest correlation with r -value of 0.9979 compared with 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 with other formulae. Among all the groups, percentage of error was least for Martin's formula (0.23%). Martin's LDL-C shows highest concordance (90.90%) compared with Friedewald's (79.60%) and Anandaraja's formulae (82.90%). Conclusion 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 with the Friedewald's formula and Anandaraja's formula.

RESUMEN

OBJECTIVES: We evaluated the applicability of a machine learning-based low-density lipoprotein-cholesterol (LDL-C) estimation method and the influence of the characteristics of the training datasets. METHODS: Three training datasets were chosen from training datasets: health check-up participants at the Resource Center for Health Science (N = 2664), clinical patients at Gifu University Hospital (N = 7409), and clinical patients at Fujita Health University Hospital (N = 14,842). Nine different machine learning models were constructed through hyperparameter tuning and 10-fold cross-validation. Another test dataset of another 3711 clinical patients at Fujita Health University Hospital was selected as the test set used for comparing and validating the model against the Friedewald formula and the Martin method. RESULTS: The coefficients of determination of the models trained on the health check-up dataset produced coefficients of determination that were equal to or inferior to those of the Martin method. In contrast, the coefficients of determination of several models trained on clinical patients exceeded those of the Martin method. The means of the differences and the convergences to the direct method were higher for the models trained on the clinical patients' dataset than for those trained on the health check-up participants' dataset. The models trained on the latter dataset tended to overestimate the 2019 ESC/EAS Guideline for LDL-cholesterol classification. CONCLUSION: Although machine learning models provide valuable method for LDL-C estimates, they should be trained on datasets with matched characteristics. The versatility of machine learning methods is another important consideration.

Asunto(s)

RESUMEN

INTRODUCTION: The Martin (MF) and Sampson (SF) formulas have shown greater accuracy for low-density lipoprotein cholesterol (LDL-C) < 70 mg/dL compared to the Friedewald formula (FF); however, some disagreement is maintained. Non-high-density lipoprotein cholesterol (non-HDL-C) and apolipoprotein B (ApoB) are alternatives to assessing cardiovascular risk in patients with very low LDL-C. The objective was to evaluate the accuracy of FF, MF, and SF formulas to estimate LDL-C < 70 mg/dL vs. directly measured LDL-C (LDLd-C) and to compare non-HDL-C and Apo-B levels between the groups of patients with concordant vs. discordant LDL-C. MATERIAL AND METHODS: This was a prospective clinical study with measurements of lipid profile and LDLd-C in 214 patients with triglycerides < 400 mg/dL. For each formula, the estimated LDL-C was compared with the LDLd-C, and the correlation, the median difference, and the discordance rate were evaluated. Non-HDL-C and Apo-B levels were compared between the groups with concordant and discordant LDL-C. RESULTS: The estimated LDL-C was < 70 mg/dL in 130 (60.7%) patients by FF, 109 (50.9%) by MF, and 113 (52.8%) by SF. The strongest correlation was found between LDLd-C and Sampson estimated LDL-C (LDLs-C) (R2 = 0.778), followed by Friedewald-estimated LDL-C (LDLf-C) (R2 = 0.680) and Martin estimated LDL-C (LDLm-C) (R2 = 0.652). Estimated LDL-C < 70 mg/dL was lower than LDLd-C, with the largest median absolute difference (25-75th) of -15 (-19 to -10) with FF. For estimated LDL-C < 70 mg/dL, the discordant rate was 43.8%, 38.1%, and 35.1%, reaching for 62.3%, 50.9%, and 50% when LDL-C < 55 mg/dL by FF, SF, and MF, respectively. Patients in the discordant group presented significantly higher levels of non-HDL-C and ApoB for all 3 formulas (p < 0.001). CONCLUSION: FF was the most inaccurate formula to estimate very low LDL-C. Despite MF and SF showing better results, their frequency in underestimating LDL-C was still considerable. In patients with falsely low estimated LDL-C, apoB and non-HDL-C were significantly higher, reflecting its true high atherogenic burden.

Asunto(s)

RESUMEN

Lipid accumulation disorders are common in psittacine birds and can be associated with changes in plasma lipoproteins, most notably low-density lipoprotein (LDL) and high-density lipoprotein (HDL). However, lipoprotein analysis by standard laboratory analyzers or an indirect method, such as the Friedewald formula, has not been validated in parrots. A research colony of 12 Quaker parrots (Myiopsitta monachus) were used to compare plasma values from the Roche Cobas c501 biochemistry analyzer for total cholesterol, total triglycerides, LDL, and HDL to gel-permeation high-performance liquid chromatography (GP-HPLC). To increase sample size and broaden the analytical range to include dyslipidemic samples, 2 cross-over studies were performed on a 0.3% cholesterol diet and a 20% fat diet. Agreement between methods was assessed by linear mixed models and Bland and Altman plots. The LDL concentrations calculated by the Friedewald formula and alternative formulas, and the effects of triglycerides on the biases, were also evaluated. Forty-five plasma samples were used. The cholesterol diet induced a marked increase in cholesterol and all lipoproteins, whereas the fat diet did not lead to dyslipidemia. Direct and indirect LDL measurements obtained with the clinical analyzer were not in clinical agreement with GP-HPLC, whereas HDL had acceptable agreement for normotriglyceridemic samples. Hypertriglyceridemic plasma samples were found to interfere with lipoprotein measurements. This study found LDL measured by the Roche Cobas c501 biochemistry analyzer and indirect estimations cannot be recommended in the Quaker parrot, and non-HDL cholesterol should be used instead. Lipoprotein panels obtained from hypertriglyceridemic samples should be interpreted with care.

Asunto(s)

RESUMEN

Background: 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. Methods: 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. Results: 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. Conclusions: We proposed a practical, improved LDL-C calculation formula by applying different factors depending on the triglyceride concentration.

Asunto(s)

RESUMEN

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.

RESUMEN

Background: Elevated level of low-density lipoprotein cholesterol (LDL-C) is concerned as one of the main risk factors for cardiovascular disease, in both the fasting and postprandial states. This study aimed to compare the measured LDL-C with LDL-C calculated by the Friedewald, Martin-Hopkins, Vujovic, and Sampson formulas, and establish which formula could provide the most reliable LDL-C results for Chinese subjects, especially at the postprandial state. Methods: Twenty-six subjects were enrolled in this study. The blood samples were collected from all the subjects before and after taking a daily breakfast. The calculated LDL-C results were compared with LDL-C measured by the vertical auto profile method, at both the fasting and postprandial states. The percentage difference between calculated and measured LDL-C (total error) and the number of results exceeding the total error goal of 12% were established. Results: The calculated LDL-CF levels showed no significant difference from LDL-CVAP levels at the fasting state. The calculated LDL-CS were significantly higher than LDL-CVAP at the fasting state (P < 0.05), while the calculated LDL-Cs were very close to LDL-CVAP levels after a daily meal. At the fasting state, the median total error of calculated LDL-CF was 0 (quartile: -3.8 to 6.0), followed by LDL-CS, LDL-CMH, and LDL-CV. At the postprandial states, the median total errors of LDL-CS were the smallest, 1.0 (-7.5, 8.5) and -0.3 (-10.1, 10.9) at 2 and 4 h, respectively. The calculated LDL-CF levels showed the highest correlation to LDL-CVAP and accuracy in evaluating fasting LDL-C levels, while the Sampson formula showed the highest accuracy at the postprandial state. Conclusion: The Friedewald formula was recommended to calculate fasting LDL-C, while the Sampson formula seemed to be a better choice to calculate postprandial LDL-C levels in Chinese subjects.

RESUMEN

Introduction: Low-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 methods: We 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). Results: For 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. Conclusions: The 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.

RESUMEN

BACKGROUND AND AIMS: LDL-cholesterol (LDL-C), being the primary predictor of cardiovascular disease in Type 2 diabetes (T2D), is associated with cardiovascular risk stratification and requires to be estimated with better accuracy with minimal bias. Different formulae have been devised to calculate the LDL-C from the measured lipid profile parameters. METHODS: In this analytical cross-sectional study, a total of 150 patients with T2D were studied, and blood samples were subjected for lipid profile analysis at the Central Biochemistry laboratory. Different formulae assessed calculated LDL-C. RESULTS: We observed that all formulae, except Ahmadi, underestimated the LDL-C compared to direct assay. A significant difference was observed between all calculated LDL-C and directly measured LDL-C. On linear regression analysis, the newer formula Martin's has a better approximation with direct assay (slope: 0.9708) than Friedewald (slope: 0.9477). Similarly, Martin's formula exhibited lesser bias (-13.56) in calculating LDL-C in patients with T2D compared with Friedewald's formula. CONCLUSIONS: The study demonstrated that in patients with T2D, all formulae except Ahmadi significantly underestimated the LDL-C when compared with the direct assay. The newer Martin's formula appeared to more precisely calculate LDL-C in T2D when compared with the traditional Friedewald's formula.

Asunto(s)

RESUMEN

BACKGROUND: LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be used to predict outcomes more accurately. The current study evaluated the predictive performance of three machine learning models-random forests, XGBoost, and support vector Rregression (SVR) to predict LDL-C from total cholesterol, triglyceride, and HDL-C in comparison to linear regression model and some existing formulas for LDL-C calculation, in eastern Indian population. METHODS: The lipid profiles performed in the clinical biochemistry laboratory of AIIMS Bhubaneswar during 2019-2021, a total of 13,391 samples were included in the study. Laboratory results were collected from the laboratory database. 70% of data were classified as train set and used to develop the three machine learning models and linear regression formula. These models were tested in the rest 30% of the data (test set) for validation. Performance of models was evaluated in comparison to best six existing LDL-C calculating formulas. RESULTS: LDL-C predicted by XGBoost and random forests models showed a strong correlation with directly estimated LDL-C (r = 0.98). Two machine learning models performed superior to the six existing and commonly used LDL-C calculating formulas like Friedewald in the study population. When compared in different triglycerides strata also, these two models outperformed the other methods used. CONCLUSION: Machine learning models like XGBoost and random forests can be used to predict LDL-C with more accuracy comparing to conventional linear regression LDL-C formulas.

Asunto(s)

RESUMEN

OBJECTIVES: To summarize and assess the literature on the performances of methods beyond the Friedewald formula (FF) used in routine practice to determine low-density lipoprotein cholesterol (LDL-C). METHODS: A literature review was performed by searching the PubMed database. Many peer-reviewed articles were assessed. RESULTS: The examined methods included direct homogeneous LDL-C assays, the FF, mathematical equations derived from the FF, the Martin-Hopkins equation (MHE), and the Sampson equation. Direct homogeneous assays perform inconsistently across manufacturers and disease status, whereas most FF-derived methods exhibit variable levels of performance across populations. The MHE consistently outperforms the FF but cannot be applied in the setting of severe hypertriglyceridemia. The Sampson equation shows promise against both the FF and MHE, especially in severe hypertriglyceridemia, but data are still limited on its validation in various settings, including disease and therapeutic states. CONCLUSIONS: There is still no consensus on a universal best method to estimate LDL-C in routine practice. Further studies are needed to assess the performance of the Sampson equation.

Asunto(s)

RESUMEN

INTRODUCTION: Two new formulas, the Martin-Hopkins and the Sampson formula, were recently developed to overcome shortcomings of the Friedewald formula for calculating LDL-cholesterol. We aimed to compare the concordance of the two formulas with apolipoprotein B (apoB), a surrogate marker of the number of LDL particles. MATERIALS AND METHODS: In a study of serum lipid data of 1179 patients who consulted the AZ St-Jan Hospital Bruges for cardiovascular risk assessment, the correlation and concordance of the Friedewald, Martin-Hopkins and Sampson formulas with apoB concentration, measured by immunonephelometry, were determined and compared. RESULTS: The Martin-Hopkins formula showed significantly higher correlation coefficient than the Friedewald formula with apoB in the entire dataset and in patients with low LDL-cholesterol < 1.8 mmol/L. Both Martin-Hopkins and Sampson formulas yielded > 70% concordance of LDL-cholesterol with regard to treatment group classification based on population-equivalent thresholds of apoB in hypertriglyceridemic patients (2-4.5 mmol/L), with the highest concordance (75.6%) obtained using Martin-Hopkins formula vs. 60.5% with Friedewald formula. CONCLUSION: The Martin-Hopkins (and, to a lesser extent, Sampson) formula is more closely associated with the number of LDL particles than Friedewald formula. This, in combination with literature evidence of lesser accuracy of the Friedewald formula, is an argument to switch from Friedewald to a modified, improved formula.

Asunto(s)

RESUMEN

INTRODUCTION: Low-density lipoprotein cholesterol (LDL) is essential in managing cardiovascular disease risk. Since 1972, the Friedewald formula has been used to estimate LDL concentration, although with some limitations. In 2013, Martin et al. proposed a similar but more accurate formula for calculating LDL. AIM: To assess the applicability of the new formula, which we have named the Martin-Hopkins formula, in the Portuguese population and compare it with the Friedewald formula using direct LDL. METHODS: Cross-sectional study, including 1689 participants from the e_COR study. We applied the Martin-Hopkins and Friedewald formulas for estimated LDL (LDL-M and LDL-F). The Friedewald formula was not applied in 12 cases due to triglycerides ≥400mg/dL. Direct LDL was measured and the accepted significance level was p<0.05. RESULTS: Of the total subjects, 50.2% were male and had a median age of 51 (34) years. LDL-D was 117.0 (44.0) mg/dL, LDL-M was 114.6 (43.7) mg/dL and LDL-F was 113.8 (43.2) mg/dL. The Spearman coefficient (ρ) between LDL-M/LDL-D was 0.987 and between LDL-F/LDL-D was 0.983, p=0.001. This strong correlation was maintained in the group with diabetes (LDL-M/LDL-D ρ=0.987; LDL-F/LDL-D ρ=0.978, p=0.001) and hypertriglyceridemia (LDL-M/LDL-D ρ=0.983; LDL-F/LDL-D ρ=0.982, p=0.001). In terms of agreement, the highest value of κ=0.90 was obtained for LDL-M when LDL-D <100mg/dL. CONCLUSION: The Martin-Hopkins formula performed well and had good applicability, showing superiority in relation to the Friedewald formula, especially for LDL-D values <100mg/dL, diabetes, and hypertriglyceridemia.

Asunto(s)

RESUMEN

Objectives LDL cholesterol is routinely estimated by the Friedewald formula to guide the treatment of dyslipidemia. However, Friedewald equation has certain limitations, especially with high triglyceride levels. Direct methods are available for LDL estimation but have received relatively little scrutiny in the Indian setting. This study was aimed at comparing the calculative and direct methods of LDL estimation in Indian hyperlipidemic patients. Materials and Methods In this observational study, data from 380 consecutive lipid profiles of patients visiting a tertiary care hospital in Mumbai were analyzed retrospectively. CHOD PAP method was used to estimate total cholesterol. Enzymatic colorimetric method was used to estimate triglycerides. Enzyme selective protection method was used to estimate HDL. Direct LDL was estimated by homogenous enzymatic colorimetric assay and very low-density lipoprotein was calculated, whereas Friedewald's formula was used to derive calculated LDL. Results Total cholesterol values correlated positively with the LDL values measured by both methods. However, a statistically significant difference was noted between the correlation coefficients of both the methods. Triglyceride values correlated weakly with the LDL levels measured by both the methods. A weak negative correlation was observed with LDL by the calculated method, whereas a weak positive correlation existed between TG and LDL by the direct method. The difference between the correlation coefficients was statistically significant. Conclusion Both direct and calculated methods of LDL estimation have their limitations. A robust study with a larger sample size is needed to further investigate whether the differences in the different LDL estimation methods can translate to "clinical relevance" in the Indian setting.

RESUMEN

INTRODUCTION: Low-density lipoprotein cholesterol (LDL) is essential in managing cardiovascular disease risk. Since 1972, the Friedewald formula has been used to estimate LDL concentration, although with some limitations. In 2013, Martin et al. proposed a similar but more accurate formula for calculating LDL. AIM: To assess the applicability of the new formula, which we have named the Martin-Hopkins formula, in the Portuguese population and compare it with the Friedewald formula using direct LDL. MATERIAL AND METHODS: Cross-sectional study, including 1689 participants from the e_COR study. We applied the Martin-Hopkins and Friedewald formulas for estimated LDL (LDL-M and LDL-F). The Friedewald formula was not applied in 12 cases due to triglycerides ≥400mg/dL. Direct LDL was measured and the accepted significance level was p<0.05. RESULTS: Of the total subjects, 50.2% were male and had a median age of 51 (34) years. LDL-D was 117.0 (44.0) mg/dL, LDL-M was 114.6 (43.7) mg/dL and LDL-F was 113.8 (43.2) mg/dL. The Spearman coefficient (ρ) between LDL-M/LDL-D was 0.987 and between LDL-F/LDL-D was 0.983, p=0.001. This strong correlation was maintained in the group with diabetes (LDL-M/LDL-D ρ=0.987; LDL-F/LDL-D ρ=0.978, p=0.001) and hypertriglyceridemia (LDL-M/LDL-D ρ=0.983; LDL-F/LDL-D ρ=0.982, p=0.001). In terms of agreement, the highest value of κ=0.90 was obtained for LDL-M when LDL-D <100 mg/dL. CONCLUSION: The Martin-Hopkins formula performed well and had good applicability, showing superiority in relation to the Friedewald formula, especially for LDL-D values <100 mg/dL, diabetes, and hypertriglyceridemia.

RESUMEN

BACKGROUND AND AIMS: Low-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. METHODS: All 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. RESULTS: LCL-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. CONCLUSIONS: Our 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.

Asunto(s)

RESUMEN

OBJECTIVES: Low-density lipoprotein cholesterol (LDL-C) is the main laboratory parameter used for the management of cardiovascular disease. The aim of this study was to compare measured LDL-C with LDL-C as calculated by the Friedewald, Martin/Hopkins, Vujovic, and Sampson formulas with regard to triglyceride (TG), LDL-C and non-high-density lipoprotein cholesterol (non-HDL-C)/TG ratio. METHODS: The 1,209 calculated LDL-C results were compared with LDL-C measured using ultracentrifugation-precipitation (first study) and direct (second study) methods. The Passing-Bablok regression was applied to compare the methods. The percentage difference between calculated and measured LDL-C (total error) and the number of results exceeding the total error goal of 12% were established. RESULTS: There was good correlation between the measurement and calculation methods (r 0.962-0.985). The median total error ranged from -2.7%/+1.4% (first/second study) for Vujovic formula to -6.7%/-4.3% for Friedewald formula. The numbers of underestimated results exceeding the total error goal of 12% were 67 (Vujovic), 134 (Martin/Hopkins), 157 (Samspon), and 239 (Friedewald). Less than 7% of those results were obtained for samples with TG >4.5 mmol/L. From 57% (Martin/Hopkins) to 81% (Vujovic) of underestimated results were obtained for samples with a non-HDL-C/TG ratio of <2.4. CONCLUSIONS: The Martin/Hopkins, Vujovic and Sampson formulas appear to be more accurate than the Friedewald formula. To minimize the number of significantly underestimated LDL-C results, we propose the implementation of risk categories according to non-HDL-C/TG ratio and suggest that for samples with a non-HDL-C/TG ratio of <1.2, the LDL-C level should not be calculated but measured independently from TG level.

Asunto(s)

RESUMEN

BACKGROUND & OBJECTIVE: Concentration of low-density lipoprotein (LDL) is a known risk factor for cardiovascular disease which is routinely measured or calculated as LDL-C in clinical laboratories. In order to decrease the cost, instead of its measuring, it is recommended to calculate it using multiple formulas that have been introduced up to now. The aim of this study was to assess the results of various formulas and comparison of these results with those of measuring method and to clarify the best formula for the Iranian population. METHODS: Concentrations of total cholesterol (TC), triglyceride (TG), cholesterol of high-density lipoprotein (HDL-C) and LDL-C in serums of 471 overnight fasting individuals were measured and also LDL-Cs of these samples were calculated by eleven different formulas according to their TC, TG, and HDL-C concentrations. Subsequently, results of measured and calculated LDL-C were analyzed statistically by paired t-test, correlation coefficient, and Passing-Bablok regression. In addition, for clinical evaluation, the differences between calculated and measured mean results were calculated and compared with an allowable total error. RESULTS: Paired t-test unraveled a significant difference between the results of measured and calculated LDL-C by various formulas. But for some formulas, these differences were not clinically significant. The best clinical and statistical agreement (correlation coefficient) was obtained by the Friedewald equation. CONCLUSION: By using validated methods which have correct calibration and control system for measuring TC, TG, and HDL-C, we can use the Friedewald formula for calculating LDL-C in serum samples with TG up to 400 mg/dL.