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

A comparative evaluation of low-density lipoprotein cholesterol estimation: Machine learning algorithms versus various equations

BACKGROUND

Given the critical importance of Low-density lipoprotein cholesterol (LDL-C) levels in determining cardiovascular risk, it is essential to measure LDL-C accurately. Since the Friedewald formula generates incorrect predictions in many circumstances, new equations have been developed to overcome the Friedewald equations' shortcomings. This study aimed to compare estimated LDL-C with directly measured LDL-C (dLDL-C), as well as their performance in predicting LDL-C, utilizing Friedewald, extended Martin-Hopkins, Sampson, de Cordova, and Vujovic formulas and five machine learning (ML) algorithms.

METHODS

A total of 29,504 samples from the ISLAB-2 Core Laboratory were included in the study. All statistical analysis was performed using R version 4.1.2. Statistical Language.

RESULTS

Bayesian-Regularized Neural Network (BRNN) (r = 0.957) and Random Forest (RF) (r = 0.957) algorithms showed a higher correlation with dLDL-C than the other equations in all-testing dataset. All ML algorithms demonstrated less bias than pre-existing LDL-C equations with dLDL-C and outperformed the LDL-C estimation equations in terms of concordance in all-testing dataset.

CONCLUSIONS

The results of our research indicate that when compared to conventional equations, ML algorithms are much more effective in predicting LDL-C. ML algorithms, aided by a vast dataset, could have the capability to predict LDL-C levels even in cases where triglyceride levels are high, unlike the limited usage of Friedewald formula.

A novel equation for the estimation of low-density lipoprotein cholesterol in the Saudi Arabian population: a derivation and validation study

Low-density lipoprotein cholesterol (LDL-C) is typically estimated by the Friedewald equation to guide atherosclerotic cardiovascular disease (ASCVD) management despite its flaws. Martin-Hopkins and Sampson-NIH equations were shown to outperform Friedewald's in various populations. Our aim was to derive a novel equation for accurate LDL-C estimation in Saudi Arabians and to compare it to Friedewald, Martin-Hopkins and Sampson-NIH equations. This is a cross-sectional study on 2245 subjects who were allocated to 2 cohorts; a derivation (1) and a validation cohort (2). Cohort 1 was analyzed in a multiple regression model to derive an equation (equationD) for estimating LDL-C. The agreement between the measured (LDL-CDM) and calculated levels was tested by Bland-Altman analysis, and the biases by absolute error values. Validation of the derived equation was carried out across LDL-C and triglyceride (TG)-stratified groups. The mean LDL-CDM was 3.10 ± 1.07 and 3.09 ± 1.06 mmol/L in cohorts 1 and 2, respectively. The derived equation is: LDL-CD = 0.224 + (TC × 0.919) - (HDL-C × 0.904) - (TG × 0.236) - (age × 0.001) - 0.024. In cohort 2, the mean LDL-C (mmol/L) was estimated as 3.09 ± 1.06 by equationD, 2.85 ± 1.12 by Friedewald, 2.95 ± 1.09 by Martin-Hopkins, and 2.93 ± 1.11 by Sampson-NIH equations; statistically significant differences between direct and calculated LDL-C was observed with the later three equations (P < 0.001). Bland-Altman analysis showed the lowest bias (0.001 mmol/L) with equationD as compared to 0.24, 0.15, and 0.17 mmol/L with Friedewald, Martin-Hopkins, and Sampson-NIH equations, respectively. The absolute errors in all guideline-stratified LDL-C categories was the lowest with equationD, which also showed the best classifier of LDL-C according to guidelines. Moreover, equationD predicted LDL-C levels with the lowest error with TG levels up to 5.63 mmol/L. EquationD topped the other equations in estimating LDL-C in Saudi Arabians as it could permit better estimation when LDL-C is < 2.4 mmol/L, in familial hyperlipidemia, and in hypertriglyceridemia, which improves cardiovascular outcomes in high-risk patients. We recommend further research to validate equationD in a larger dataset and in other populations.

Unravelling the determinants of human health in French Polynesia: the MATAEA project

Background

French Polynesia is a French overseas collectivity in the Southeast Pacific, comprising 75 inhabited islands across five archipelagoes. The human settlement of the region corresponds to the last massive migration of humans to empty territories, but its timeline is still debated. Despite their recent population history and geographical isolation, inhabitants of French Polynesia experience health issues similar to those of continental countries. Modern lifestyles and increased longevity have led to a rise in non-communicable diseases (NCDs) such as obesity, diabetes, hypertension, and cardiovascular diseases. Likewise, international trade and people mobility have caused the emergence of communicable diseases (CDs) including mosquito-borne and respiratory diseases. Additionally, chronic pathologies including acute rheumatic fever, liver diseases, and ciguatera, are highly prevalent in French Polynesia. However, data on such diseases are scarce and not representative of the geographic fragmentation of the population.

Objectives

The present project aims to estimate the prevalence of several NCDs and CDs in the population of the five archipelagoes, and identify associated risk factors. Moreover, genetic analyses will contribute to determine the sequence and timings of the peopling history of French Polynesia, and identify causal links between past genetic adaptation to island environments, and present-day susceptibility to certain diseases.

Methods

This cross-sectional survey is based on the random selection of 2,100 adults aged 18-69 years and residing on 18 islands from the five archipelagoes. Each participant answered a questionnaire on a wide range of topics (including demographic characteristics, lifestyle habits and medical history), underwent physical measurements (height, weight, waist circumference, arterial pressure, and skin pigmentation), and provided biological samples (blood, saliva, and stool) for biological, genetic and microbiological analyses.

Conclusion

For the first time in French Polynesia, the present project allows to collect a wide range of data to explore the existence of indicators and/or risk factors for multiple pathologies of public health concern. The results will help health authorities to adapt actions and preventive measures aimed at reducing the incidence of NCDs and CDs. Moreover, the new genomic data generated in this study, combined with anthropological data, will increase our understanding of the peopling history of French Polynesia.

Clinical trial registration

https://clinicaltrials.gov/, identifier: NCT06133400.

Insulin Receptor Substrates Regulation and Clinical Responses Following Vanadium-Enriched Yeast Supplementation in Obese Type 2 Diabetic Patients: a Randomized, Double-Blind, Placebo-Controlled Clinical Trial

Increasing evidence suggests that organic vanadium compounds are bioavailable and safe therapeutic agents with insulin-mimetic and insulin-enhancing features. The objective of the current study was to examine the effect of vanadium-enriched yeast (VEY) supplementation on the gene expression level of insulin receptor substrates and clinical manifestations of obese type 2 diabetic mellitus (T2DM) patients. In this randomized, double-blind, placebo-controlled clinical trial, 44 obese T2DM patients were randomly allocated into either VEY (0.9 mg/day vanadium pentoxide) or placebo group for 12 weeks. The mRNA expression level of protein tyrosine phosphatase 1B (PTP1B), phosphatase and tensin homolog (PTEN), mitogen-activated protein kinase (MAPK), ribosomal protein S6 kinase (S6K), and nuclear factor kappa-light-chain-enhancer of activated B cells (NFƘB) genes in the peripheral blood mononuclear cells, serum levels of metabolic parameters, anthropometric indices, as well as the quality of life, and dietary intake were collected at pre- and post-intervention phases. Analysis of covariance was performed to obtain the corresponding effect size. Results showed that VEY administration significantly decreased anthropometric indices and glycemic parameters and increased insulin sensitivity after adjusting for potential covariates (p < 0.05), in comparison to the placebo group. Additionally, VEY supplementation was significantly effective on MAPK, PTP1B, and NFƘB gene expression level, compared to the placebo group. No significant changes were noticed for dietary intake, quality of life, and lipid profile in the VEY group, compared to the placebo group. Overall, VEY supplementation can be considered as a promising safe adjunct therapy for improving anthropometric indices and glycemic parameters in T2DM patients.

Development of a Modified Friedewald's Formula to Calculate Low-Density Lipoprotein in an Iranian Population

Background

Elevated low-density lipoprotein cholesterol (LDL-C) is a significant risk factor for cardiovascular diseases. LDL-C can be directly measured using various methods, but this requires expensive equipment. Currently, clinical laboratories estimate LDL-C based on Friedewald's formula (FF). We aimed to develop a modified formula based on directly measured LDL-C (D-LDL-C) values in a large population in Southern Iran and compare the results with various other estimation formulas.

Methods

The participants of this cross-sectional study were adults aged >18 years living in Southern Iran. Blood samples from 15,200 individuals were collected, and the measured lipid parameters were randomly divided into training (n=10,184) and validation (n=5,016) datasets. A new formula was developed using a linear regression model, and its accuracy was validated. Pearson's correlation and Cohen's kappa were used to determin the relationship between D-LDL-C and calculated LDL-C (C-LDL-C).

Results

The developed formula for the estimation of LDL-C was 0.857 total cholesterol (TC)-0.915 high-density lipoprotein cholesterol (HDL-C)-0.115 triglycerides (TG). Based on our proposed formula, for TG<150 and TG≥150 mg/dL, there was a significant correlation between mean values of D-LDL-C and C-LDL-C (r=0.985 and r=0.974, respectively). Compared to other formulas, C-LDL-C obtained from the proposed formula had the highest correlation with D-LDL-C. The agreement between D-LDL-C and C-LDL-C for TC<200, 200-239, and ≥240 mg/dL was 80.8%, 63.2%, and 67.4%, respectively, indicating a higher level of agreement than other formulas.

Conclusion

The new formula appears to be more accurate than FF when applied to the population of Southern Iran.

Best practice for LDL-cholesterol: when and how to calculate

The lipid profile is important in the risk assessment for cardiovascular disease. The lipid profile includes total cholesterol, high-density lipoprotein (HDL)-cholesterol, triglycerides (TGs) and low-density lipoprotein (LDL)-cholesterol (LDL-C). LDL-C has traditionally been calculated using the Friedewald equation (invalid with TGs greater than 4.5 mmol/L and is based on the assumption that the ratio of TG to cholesterol in very- low-density lipoprotein (VLDL) is 5 when measured in mg /dL). LDL-C can be quantified with a reference method, beta-quantification involving ultracentrifugation and this is unsuitable for routine use. Direct measurement of LDL-C was expected to provide a solution with high TGs. However, this has some challenges because of a lack of standardisation between the reagents and assays from different manufacturers as well as the additional costs. Furthermore, mild hypertriglyceridaemia also distorts direct LDL-C measurements. With the limitations of the Friedewald equation, alternatives have been derived. Newer equations include the Sampson-National Institutes of Health (NIH) equation 2 and the Martin-Hopkins equation. The Sampson-NIH2 equation was derived using beta-quantification in a population with high TG and multiple least squares regression to calculate VLDL-C, using TGs and non-HDL-C as independent variables. These data were used in a second equation to calculate LDL-C. The Sampson-NIH2 equation can be used with TGs up to 9 mmol/L. The Martin-Hopkins equation uses a 180 cell stratification of TG/non-HDL-C to determine the TG:VLDL-C ratio and can be used with TGs up to 4.5 mmol/L. Recently, an extended Martin-Hopkins equation has become available for TGs up to 9.04 mmol/L.This article discusses the best practice approach to calculating LDL-C based on the available evidence.

Comparison of measured LDL cholesterol with calculated LDL-cholesterol using the Friedewald and Martin-Hopkins formulae in diabetic adults at Charlotte Maxeke Johannesburg Academic Hospital/NHLS Laboratory

BACKGROUND

The National Cholesterol Education Programme Adult Treatment Panel III (NCEP ATP III) and the European Society of Cardiology recommend using low-density lipoprotein cholesterol (LDL-C) as a treatment target for cholesterol lowering therapy. The Friedewald formula underestimates LDL-C in non-fasted and hypertriglyceridemia patients. This study aimed to compare measured LDL-C to calculated LDL-C in diabetic patients using the Friedewald and Martin-Hopkins formulae.

METHODS

The data of 1 247 adult diabetes patients were retrospectively evaluated, and included triglycerides (TG), LDL-C, total cholesterol, and high-density lipoprotein cholesterol that were measured on the Roche Cobas® c702. Passing-Bablok regression analysis was used to determine the degree of agreement between measured LDL-C and calculated LDL-C using both formulae. The Bland-Altman plots were used to assess the bias at medical decision limits based on the 2021 European Society of Cardiology (ESC) guidelines on cardiovascular disease prevention in clinical practice.

RESULTS

Both formulae showed a good linear relationship against measured LDL-C. However, the Martin-Hopkins formula outperformed the Friedewald formula at LDL-C treatment target <1.4mmol/L. The Friedewald formula and the Martin-Hopkins formula had 14.9% and 10.9% mean positive bias, respectively. At TG-C ≥1.7 mmol/L, the Martin-Hopkins formula had a lower mean positive bias of 4.2% (95% CI 3.0-5.5) compared to the Friedewald formula, which had a mean positive bias of 21.8% (95% CI 19.9-23), which was higher than the NCEP ATP III recommended total allowable limit of 12%.

CONCLUSION

The Martin-Hopkins formula performed better than the Friedewald formula at LDL-C of 1.4 mmol/L and showed the least positive bias in patients with hypertriglyceridemia.

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

BACKGROUND

Low-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.

METHODS

An 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.

RESULTS

The 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.

CONCLUSIONS

This 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.

Indirect calculation of LDL using thirteen equations in Pakistani population

BACKGROUND

Owing to the atherogenic properties, low density lipoprotein cholesterol (LDL-C) is the primary target for treatment and diagnosis of cardiovascular diseases (CVDs), hence accurate measurement of LDL-C is critical. Despite the availability of direct measurement assays for LDL-C, it is routinely calculated by Friedewald equation in clinical settings in Pakistan mostly due to financial constraints. However, the validity of this equation is impacted by several factors, therefore several other equations have been developed for the calculation of LDL-C.

MATERIALS AND METHODS

LDL-C of 39,385 individuals measured directly by homogenous assays (dLDL) was compared with LDL-C calculated by thirteen equations (cLDL-C). Stratifications based on different lipids i.e., triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL) were made to check the validity of these equations across all ranges of lipid profile. The correlation and median difference between dLDL and cLDL-C was statistically analyzed.

RESULTS

Overall Teerakanchana equation displayed a strong positive correlation (ρ = 0.967) and least median difference (-8.81) with dLDL, followed by Martin equation (ρ = 0.967). For higher TG ranges (>500 mg/dL), Teerakanchana equation had the least median difference (1.31) and a strong correlation (ρ = 0.800).

CONCLUSION

Our data suggest that Teerakanchana equation may be employed as an alternative to Friedewald equation for Pakistani population.

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

PURPOSE OF REVIEW

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.

RECENT FINDINGS

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.

SUMMARY

The choice of equation should take into the level of triglycerides. Further validation of different equations is required in different populations.

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.

Lipocalin-2 and insulin as new biomarkers of alopecia areata

Lipocalin-2 and visfatin are proinflammatory adipokines involved in the regulation of glucose homeostasis. Their role has been described in numerous inflammatory skin diseases such as atopic dermatitis and psoriasis. Recently, an increased prevalence of metabolic abnormalities has been reported in patients with alopecia areata. The aim of the study is to determine the serum levels of lipocalin-2 and visfatin in patients with alopecia areata in comparison with healthy controls. Moreover, the serum levels of total cholesterol, low-density lipoprotein cholesterol (LDL-cholesterol), high-density lipoprotein cholesterol (HDL-cholesterol), triglycerides, fasting glucose, insulin, c-peptide, and homeostasis model assessment for insulin resistance (HOMA-IR) were evaluated. Fifty-two patients with alopecia areata and 17 control subjects were enrolled in the study. The serum levels of lipocalin-2 [mean ± standard deviation, SD: 224.55 ± 53.58 ng/ml vs. 188.64 ± 44.75, p = 0.01], insulin [median (interquartile range, IQR): 6.85 (4.7–9.8) μIU/ml vs. 4.5 (3.5–6.6), p<0.05], c-peptide [median (IQR): 1.63 (1.23–2.36) ng/ml vs. 1.37 (1.1–1.58), p<0.05)], and HOMA-IR [median (IQR): 1.44 (0.98–2.15) vs. 0.92 (0.79–1.44), p<0.05) were significantly higher in patients with alopecia areata compared to the controls. The serum concentration of insulin and HOMA-IR correlated with the number of hair loss episodes (r = 0.300, p<0.05 and r = 0.322, p<0.05, respectively). Moreover, a positive correlation occurred between insulin, HOMA-IR, c-peptide and BMI (r = 0.436, p <0.05; r = 0.384, p<0.05 and r = 0.450, p<0.05, respectively). In conclusion, lipocalin-2 and insulin may serve as biomarkers for alopecia areata. Further studies are needed to evaluate the role of insulin as a prognostic factor in alopecia areata.

Patients with alopecia areata are at risk of endothelial dysfunction: results of a case-control study

BACKGROUND

Alopecia areata is an autoimmune form of hair loss which may affect any hair-bearing area. It has been suggested that alopecia areata is associated with an increased risk of metabolic and cardiovascular comorbidities.

OBJECTIVES

The aim of the study was to evaluate the early predictors of cardiovascular diseases (endothelial function and arterial stiffness) in patients with alopecia areata without prior cardiovascular disease in comparison with healthy controls.

METHODS

Fifty-two patients with alopecia areata (38 women and 14 men, mean age: 41 [30 - 52]) and 34 healthy controls matched for age, gender and body mass index were enrolled in the study. Endothelial dysfunction expressed as reactive hyperemia index (RHI) and arterial stiffness identified by augmentation index (AI@75) were assessed with the use of the Endo-PAT 2000 device.

RESULTS

Endothelial dysfunction was observed in 22/52 (42%) patients with alopecia areata and in 4/34 (12%) healthy controls (p=0.002). Moreover, mean RHI was lower in patients with alopecia areata in comparison with control subjects (1.90 ± 0.31 vs 2.11 ± 0.45; p=0.03). No significant difference was present in AI@75 between patients with alopecia areata and the controls.

CONCLUSIONS

Patients with alopecia areata show abnormalities in the early predictors of cardiovascular diseases. Regular cardiovascular screening might be appropriate in every patient with alopecia areata.

A Tale of Two Approaches

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.

Machine learning predictive models of LDL-C in the population of eastern India and its comparison with directly measured and calculated LDL-C

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.

Comparability of 11 different equations for estimating LDL cholesterol on different analysers

OBJECTIVES

Low-density lipoprotein cholesterol (LDL-C) estimation is critical for risk classification, prevention and treatment of atherosclerotic cardiovascular disease (ASCVD). Predictive equations and direct LDL-C are used. We investigated the comparability between the Martin/Hopkins, Sampson, Friedewald and eight other predictive equations on two analysers, to determine whether the equation or analyser influences predicted LDL-C result.

METHODS

In two unpaired datasets, 9,995 lipid profiles were analysed by the Abbott Architect and 4,782 by the Roche Cobas analysers. Non-parametric statistics and Bland Altman plots were used to compare LDL-C.

RESULTS

On the Abbott analyser; the Martin/Hopkins, Sampson and Friedewald LDL-C were comparable (median bias ≤1.8%) over a range of 1-4.9 mmol/L. On the Roche platform, Martin/Hopkins LDL-C was comparable to Friedewald (median bias 0.3%) but not to Sampson LDL-C (median bias 25%). In patients with LDL-C <1.8 mmol/L and triglycerides (TG) ≤1.7 mmol/L, predicted LDL-C using Abbott reagents was similar between Martin/Hopkins, Sampson and Friedewald equations but not comparable using Roche reagents. Abbott reagents classified 10-20% of patients in the 1.0-1.8 mmol/L range (Martin/Hopkins 13.4%; Sampson 14.5%; Friedewald 16%; direct LDL-C 13.2%). Roche reagents classified 11-30% in the 1.0-1.8 mmol/L range (Martin/Hopkins 23%; Sampson 11%; Friedewald 25%; direct LDL-C 17%).

CONCLUSIONS

Performance of predictive equations is influenced by the choice of analyser for total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and TG. Replacement of the Friedewald equation with Martin/Hopkins estimation to improve quality of LDL-C results can be safely implemented across analysers, whereas caution is advised regarding the Sampson equation.

Evaluation of a new equation for estimating low-density lipoprotein cholesterol through the comparison with various recommended methods

Introduction

The accurate estimation of low-density lipoprotein cholesterol (LDL) is crucial for management of patients at risk of cardiovascular events due to dyslipidemia. The LDL is typically calculated using the Friedewald equation and/or direct homogeneous assays. However, both methods have their own limitations, so other equations have been proposed, including a new equation developed by Sampson. The aim of this study was to evaluate Sampson equation by comparing with the Friedewald and Martin-Hopkins equations, and with a direct LDL method.

Materials and methods

Results of standard lipid profile (total cholesterol (CHOL), high-density lipoprotein cholesterol (HDL) and triglycerides (TG)) were obtained from two anonymized data sets collected at two laboratories, using assays from different manufacturers (Beckman Coulter and Roche Diagnostics). The second data set also included LDL results from a direct assay (Roche Diagnostics). Passing-Bablok and Bland-Altman analysis for method comparison was performed.

Results

A total of 64,345 and 37,783 results for CHOL, HDL and TG were used, including 3116 results from the direct LDL assay. The Sampson and Friedewald equations provided similar LDL results (difference ≤ 0.06 mmol/L, on average) at TG ≤ 2.0 mmol/L. At TG between 2.0 and 4.5 mmol/L, the Sampson-calculated LDL showed a constant bias (- 0.18 mmol/L) when compared with the Martin-Hopkins equation. Similarly, at TG between 4.5 and 9.0 mmol/L, the Sampson equation showed a negative bias when compared with the direct assay, which was proportional (- 16%) to the LDL concentration.

Conclusions

The Sampson equation may represent a cost-efficient alternative for calculating LDL in clinical laboratories.

Clinical Validation of Eleven Formulas for Calculating LDL-C in Iran

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.