Low-density lipoprotein cholesterol estimation by the Anandaraja's formula--confirmation

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 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.

Developing a Modified Low-Density Lipoprotein (M-LDL-C) Friedewald's Equation as a Substitute for Direct LDL-C Measure in a Ghanaian Population: A Comparative Study

Despite the availability of several homogenous LDL-C assays, calculated Friedewald's LDL-C equation remains the widely used formula in clinical practice. Several novel formulas developed in different populations have been reported to outperform the Friedewald formula. This study validated the existing LDL-C formulas and derived a modified LDL-C formula specific to a Ghanaian population. In this comparative study, we recruited 1518 participants, derived a new modified Friedewald's LDL-C (M-LDL-C) equation, evaluated LDL-C by Friedewald's formula (F-LDL-C), Martin's formula (N-LDL-C), Anandaraja's formula (A-LDL-C), and compared them to direct measurement of LDL-C (D-LDL-C). The mean D-LDL-C (2.47±0.71 mmol/L) was significantly lower compared to F-LDL-C (2.76±1.05 mmol/L), N-LDL-C (2.74±1.04 mmol/L), A-LDL-C (2.99±1.02 mmol/L), and M-LDL-C (2.97±1.08 mmol/L) p < 0.001. There was a significantly positive correlation between D-LDL-C and A-LDL-C (r=0.658, p<0.0001), N-LDL-C (r=0.693, p<0.0001), and M-LDL-C (r=0.693, p<0.0001). M-LDL-c yielded a better diagnostic performance [(area under the curve (AUC)=0.81; sensitivity (SE) (60%) and specificity (SP) (88%)] followed by N-LDL-C [(AUC=0.81; SE (63%) and SP (85%)], F-LDL-C [(AUC=0.80; SE (63%) and SP (84%)], and A-LDL-C (AUC=0.77; SE (68%) and SP (78%)] using D-LDL-C as gold standard. Bland-Altman plots showed a definite agreement between means and differences of D-LDL-C and the calculated formulas with 95% of values lying within ±0.50 SD limits. The modified LDL-C (M-LDL-C) formula derived by this study yielded a better diagnostic accuracy compared to A-LDL-C and F-LDL-C equations and thus could serve as a substitute for D-LDL-C and F-LDL-C equations in the Ghanaian population.

Correlation of Friedewald's calculated low-density lipoprotein cholesterol levels with direct low-density lipoprotein cholesterol levels in a tertiary care hospital

BACKGROUND

One of the risk factors for the development of coronary heart disease is high low-density lipoprotein (LDL) cholesterol levels. National Cholesterol Education Program ATP III guidelines suggest drug therapy to be considered at LDL-cholesterol levels >130 mg/dl. This makes accurate reporting of LDL cholesterol crucial in the management of Coronary heart disease. Estimation of LDL cholesterol by direct LDL method is accurate, but it is expensive. Hence, We compared Friedewald's calculated LDL values with direct LDL values.

AIM

To evaluate the correlation of Friedewalds calculated LDL with direct LDL method.

MATERIALS AND METHODS

We compared LDL cholesterol measured by Friedewald's formula with direct LDL method in 248 samples between the age group of 20-70 years. Paired t-test was used to test the difference in LDL concentration obtained by a direct method and Friedewald's formula. The level of significance was taken as P < 0.05. Pearsons correlation formula was used to test the correlation between direct LDL values with Friedewald's formula.

RESULTS

There was no significant difference between the direct LDL values when compared to calculated LDL by Friedewalds formula (P = 0.140). Pearson correlation showed there exists good correlation between direct LDL versus Friedewalds formula (correlation coefficient = 0.98). The correlation between direct LDL versus Friedewalds calculated LDL was best at triglycerides values between 101 and 200 mg/dl.

CONCLUSION

This study indicates calculated LDL by Friedewalds equation can be used instead of direct LDL in patients who cannot afford direct LDL method.

Comparing calculated LDL-C with directly measured LDL-C in healthy and in dyslipidemic children

BACKGROUND

LDL-C is one of the strongest markers for atherosclerosis and therapeutic decisions in children are based on its levels. Friedewald formula (FF) which is usually used for the calculation of LDL-C (cLDL-C); and Anandaraja's formula (AF) may under- or overestimate actual levels.

OBJECTIVE

To compare cLDL-C with directly measured LDL-C (dLDL-C) as a screening tool and to evaluate dyslipidemic children.

METHODS

The study population consisted of 1005 children, 2-18years, 688 of whom underwent lipid screening in a regular check-up (group A); and 317 were dyslipidemic (LDL-C ≥130mg/dl) (group B). A fasting serum lipid profile was assessed. LDL-C was measured using a homogenous assay and was calculated using FF and AF.

RESULTS

Each method of calculating LDL-C was highly correlated to dLDL-C. Using FF, cLDL-C was lower than dLDL-C in 75.6% (group A) and in 77.3% (group B) of children; the mean difference was significant in dyslipidemic group. Moreover, in group B, 25% of children with boundary high and 12% with high dLDL-C would be misclassified. Using AF, LDL-C was higher than dLDL-C; the mean difference was significant in group A. Based on cLDL-C, 52% of group A with borderline dLDL-C and 27.5% of group B children with boundary high dLDL-C would be considered as dyslipidemic and eligible for medication respectively.

CONCLUSIONS

Comparing two methods of calculated LDL-C with directly measured LDL-C. FF was more accurate as a screening tool while AF was more accurate in the evaluation and follow-up of the dyslipidemic group.

Assessing the Validity of Friedewald's Formula and Anandraja's Formula For Serum LDL-Cholesterol Calculation

INTRODUCTION

An important aspect of the assessment of cardiovascular risk for a dyslipidemic subject is the estimation of serum Low-Density Lipoprotein Cholesterol (LDL-C). There are many homogenous assays currently available for the estimation of serum LDL-C. Most clinical laboratories determine LDL-C (mg/dL) by Friedewald's formula (FF), LDL-C = (TC) - (HDL-C) - (TG/5). Recently Anandaraja and colleagues have derived a new formula for calculating LDL-C, AR-LDL-C = 0.9 TC- (0.9 TG/5)-28.

AIM & OBJECTIVES

The aim of the study was: a) to determine if, and to what extent, LDL-C level was underestimated/overestimated when it was calculated using the formulae compared with direct measurement of LDL-C, and b) to determine which of the calculated formulae show maximum correlation with direct LDL cholesterol method at different TG levels.

SETTING & DESIGN

A cross-sectional study.

MATERIALS AND METHODS

Record analysis was done from the 370 (TG <400mg/dl) lipid profile reports of patients above 18 years. LDL-C estimation was done by homogenous assay and also calculated using the Friedewald's Formula and Anandaraja's Formula.

RESULTS

The mean LDL-C levels were 105.17± 43.4, 102.98 ±42.5, and 98.20 ±43.7 mg/dl for D-LDL-C, F-LDL-C and AR-LDL-C, respectively. A good correlation was found between the calculated LDL-C methods and Direct Low-Density Lipoprotein Cholesterol method (D-LDL-C) assay, that is, F-LDL-C versus D-LDL-C (r = 0.937) and AR-LDL-C versus D-LDL-C (r= 0.918). Bland-Altman plot for FF-LDL-C & AR-LDL-C showed minimal negative bias.

CONCLUSION

FF-LDL-C correlated maximally with D-LDL-C estimation at all levels of triglycerides except at TG < 100mg/dl. At TG < 100mg/dl, Anandaraja's Formula works better. FF-LDL-C can be used in place of D-LDL-C when the direct method cannot be afforded.

A Leap above Friedewald Formula for Calculation of Low-Density Lipoprotein-Cholesterol

Purpose:

The purpose was to compare the different calculated methods of low-density lipoprotein cholesterol (LDL-C) estimation and to determine which of them correlate best with the direct method.

Materials and Methods:

The records of 480 samples for lipid profile were analyzed. Apart from the direct method, LDL-C was calculated by Friedewald low-density lipoprotein cholesterol method (F-LDL-C), modified Friedewald low-density lipoprotein cholesterol method (MF-LDL-C), and Anandaraja low-density lipoprotein cholesterol method (A-LDL-C). Paired t-test and Pearson correlation were evaluated between the different methods. Degree of agreement between the calculated methods and direct method was detected by Bland–Altman graphical plots.

Results:

A strong correlation was found between all calculated LDL-C methods and direct low-density lipoprotein cholesterol method (D-LDL-C) assay, that is, F-LDL-C versus D-LDL-C = 0.94; A-LDL-C versus D-LDL-C = 0.93 and MF-LDL-C versus D-LDL-C = 0.95. No statistically significant difference was found between D-LDL-C and MF-LDL-C. Bland–Altman plot for MF-LDL-C showed minimal negative bias.

Conclusions:

The study pointed out that MF-LDL-C correlated maximally with D-LDL-C estimation at all levels of triglycerides and MF-LDL-C can be used in place of D-LDL-C when the direct method cannot be afforded.

Does LDL-C Estimation Using Anandaraja's Formula Give a Better Agreement with Direct LDL-C Estimation than the Friedewald's Formula?

Estimation of low density lipoprotein cholesterol (LDL-C) is crucial in management of coronary artery disease patients. Though a number of homogenous assays are available for estimation of LDL-C, use of calculated LDL-C by Friedewald's formula (FF) is common in Indian laboratories for logistic reasons. Recently Anandaraja and colleagues have derived a new formula for calculating LDL-C. This formula needs to be evaluated before it is extensively applied in diagnosis. We measured LDL-C by homogenous method (D-LDL-C) in 515 fasting samples. Friedewald's and Anandaraja's formulas were used for calculation of LDL-C (F-LDL-C and A-LDL-C, respectively). The mean LDL-C levels were 123.3 ± 53.2, 112.4 ± 50.2 and 109.2 ± 49.8 mg/dl for D-LDL-C, F-LDL-C and A-LDL-C, respectively. There was a statistically significant difference between the results (P > 0.001) obtained by calculation formulas compared to the measured LDL-C. There was underestimation of LDL-C by 10.8 and 14 mg/dl by Friedewald's and Anandaraja's formulas respectively. The Pearson's correlation between F-LDL-C and D-LDL-C was 0.931 and that between A-LDL-C and D-LDL-C was 0.930. Bland-Altman graphs showed a definite agreement between mean and differences of the calculation formulas and direct LDL-C with 95% of values lying with in ±2 SD limits. The mean percentage difference (calculated as {(Calculated LDL-C)-(D-LDL-C)}/D-LDL-C × 100) for F-LDL-C was maximum (-11.6%) at HDL-C ≥ 60 mg/dl and TG levels of 200-300 mg/dl (-10.4%) compared to D-LDL-C. A-LDL-C results gave highest mean percentage difference at total cholesterol concentrations <100 mg/dl (-37.3%) and HDL-C < 40 mg/dl (-17.1%), respectively. The results of our study showed that FF is better in agreement with D-LDL-C than Anandaraja's formula for estimation of LDL-C by calculation though both lead to its underestimation.

Extreme concentrations of high density lipoprotein cholesterol affect the calculation of low density lipoprotein cholesterol in the Friedewald formula and other proposed formulas

OBJECTIVES

To investigate the effect of extreme levels of high density lipoprotein cholesterol (HDL-C) in the calculation of low density lipoprotein cholesterol (LDL-C) using Friedewald's formula (FF) and other formulas proposed recently.

DESIGN AND METHODS

Lipoprotein profile was performed in 2603 samples with HDL-C ≤ 20 mg/dL and 1953 samples with HDL-C ≥ 100 mg/dL.

RESULTS

Wilcoxon's and Student's t-tests showed significant differences (p<0.001) between calculated LDL-C by different formulas and direct determination in the two groups of HDL-C values. Passing-Bablok regression and Bland-Altman plot showed disagreement for the four formulas studied, except for Vujovic formula in the HLD-C ≥ 100 mg/dL group.

CONCLUSIONS

Our results suggested that none of the formulas under analysis should be used for estimating LDL-C in samples with extreme HDL-C concentrations due to absence of statistical correlation with LDL-C direct measurement.

Evaluation of different formulas for LDL-C calculation

BACKGROUND

Friedewald's formula for the estimation of LDL-C concentration is the most often used formula in clinical practice. A recent formula by Anandaraja and colleagues for LDL-C estimation still needs to be evaluated before it is extensively applied in diagnosis. In the present study we validated existing formulas and derived a more accurate formula to determine LDL-C in a Serbian population.

METHODS

Our study included 2053 patients with TG < or = 4.52 mmol/L. In an initial group of 1010 patients, Friedewald's and Anandaraja's formulas were compared to a direct homogenous method for LDL-C determination. The obtained results allowed us to modify Friedewald's formula and apply it in a second group of patients.

RESULTS

The mean LDL-C concentrations were 3.9 +/- 1.09 mmol/L, 3.63 +/- 1.06 mmol/L and 3.72 +/- 1.04 mmol/L measured by a direct homogenous assay (D-LDL-C), calculated by Friedewald's formula (F-LDL-C) and calculated by Anandaraja's formula (A-LDL-C), respectively in the 1010 patients. The Student's paired t-test showed that D-LDL-C values were significantly higher than F-LDL-C and A-LDL-C values (p < 0.001). The Passing-Bablok regression analysis indicated good correlation between calculated and measured LDL-Cs (r > 0.89). Using lipoprotein values from the initial group we modified Friedewald's formula by replacing the term 2.2 with 3. The new modified formula for LDL-C estimation (S-LDL-C) showed no statistically significant difference compared to D-LDL-C. The absolute bias between these two methods was -0.06 +/- 0.37 mmol/L with a high correlation coefficient (r = 0.96).

CONCLUSIONS

Our modified formula for LDL-C estimation appears to be more accurate than both Friedewald's and Anandaraja's formulas when applied to a Serbian population.