Comparison of Two Homogeneous LDL-Cholesterol Assays Using Fresh Hypertriglyceridemic Serum and Quantitative Ultracentrifugation Fractions

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.

Development of Direct LDL Measurement by LDL Precipitation Combined with Sandwich ELISA Using In-House-Generated mAbs

One of the major risk factors for the progression of cardiovascular diseases is a high blood level of low-density lipoprotein (LDL), so progression of the disease may be prevented by lowering the plasma LDL. Evaluation and monitoring of LDL concentrations are therefore necessary. Friedewald's equation is a calculation method that is currently used routinely to estimate plasma LDL concentrations in hospital laboratories. However, this method cannot be applied to samples with a high concentration of triglyceride (TG). To overcome this limitation, this study aimed to develop direct LDL measurements using in-house generated monoclonal antibodies against human LDL in combination with LDL precipitation using heparin-containing citrate buffer pH 5.04. The method was applied to measure the LDL concentration in 208 randomized samples from Suranaree University of Technology Hospital. The mean values obtained from the developed method and the hospital laboratory were 126.6 ± 43.1 mg/dL and 123.2 ± 42.3 mg/dL, respectively. Linear regression analysis showed a high correlation between these two methods (r = 0.8491, p < 0.0001). High concentrations of TG, total cholesterol, and HDL have no influence on the LDL values obtained by this method. In this study, we offer an alternative technique for the direct measurement of plasma LDL. Further development for more convenient and easy use can now be undertaken.

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

BACKGROUND AND AIMS

The 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 METHODS

We 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).

RESULTS

According 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.

CONCLUSION

The 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.

Validation of the Martin method to estimate low-density lipoprotein cholesterol concentrations in Japanese populations and a modified method for laboratory information system application

OBJECTIVES

High concentrations of low-density lipoprotein cholesterol (LDL-C) are a risk factor for cardiovascular disease. We validated the efficacy of the Martin method is useful in the estimation of LDL-C concentrations was validated in Japanese populations and derived a modified Martin method for easy laboratory information system applications.

METHODS

We created 3 subject groups, including 2664 health check-up participants registered with the Resource Center for Health Science, 29,806 clinical patients (A) in the Gifu University Hospital, and 113,716 clinical patients (B) in the Fujita Health University Hospital. Each method to estimate serum LDL-C concentrations (Friedewald formula, Martin method and modified Martin method) was validated by correlation analysis with serum LDL-C concentrations measured using a direct method.

RESULTS

The correlation coefficients with the direct method in terms of the Friedewald formula, Martin method, and modified Martin method were 0.963, 0.972 and 0.970 in the health check-up participants; 0.946, 0.962 and 0.961 in clinical patients A; and 0.961, 0.979 and 0.978 in clinical patients B, respectively. Concordance ratios with using the direct method in the Friedewald formula, Martin method and modified Martin method were 82.8%, 85.5% and 85.3% in the health check-up participants; 76.4%, 80.5% and 80.2% in clinical patients A; and 76.1%, 82.6% and 82.6% in clinical patients B, respectively.

CONCLUSION

Our results show that the Martin and modified Martin methods display good performance in terms of the estimation of LDL-C concentrations among triglyceride concentrations of a wide range, and they may thus be useful for estimating LDL-C concentrations.

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.