Determination Of Low-density Lipoprotein Cholesterol Research Articles

Effect of lipoprotein (a) on the analytical determination of low-density lipoprotein cholesterol (LDLc) and its influence on pharmacological treatment with atorvastatin

Lipoprotein(a) (Lp(a)) and Low-density lipoprotein cholesterol (LDLc) is an important risk factor for atherosclerotic cardiovascular disease. The objective of this study was to determine the impact of Lp(a) concentration both on the indirect analytical measurement of LDLc and on the efficacy of dyslipidaemia treatment using the atorvastatin statin. Two retrospective studies were conducted, one with 340 patients and another with 107 patients treated with atorvastatin. Lp(a) concentrations were measured by turbidimetry with an assay independent of the size of the apo(a) isoform. LDLc was calculated using the Friedewald equation and the corrected LDLc was calculated using the Dahlén equation. A strong positive correlation was observed between the serum Lp(a) concentration and the LDLc-overestimation percentage (r = 0.960, p < .001). It was also observed that as the Lp(a) concentration rose there was no significant variation in the percentage decrease in corrected LDLc during atorvastatin treatment (r = 0.186, p > .05). The concentration of LDLc obtained by using the Friedewald equation included Lp(a) cholesterol. The lowering of LDLc in patients treated with atorvastatin depended solely on accessible LDL cholesterol and not on Lp(a) cholesterol.

Estimation of low-density lipoprotein cholesterol by machine learning methods

BackgroundAccurate determination of low-density lipoprotein cholesterol (LDL) is important for coronary heart disease risk assessment and atherosclerosis. Apart from direct determination of LDL values, models (or equations) are used. A more recent approach is the use of machine learning (ML) algorithms. MethodsML algorithms were used for LDL determination (regression) from cholesterol, HDL and triglycerides. The methods used were multivariate Linear Regression (LR), Support Vector Machines (SVM), Extreme Gradient Boosting (XGB) and Deep Neural Networks (DNN), in both larger and smaller data sets. Also, LDL values were classified according to both NCEP III and European Society of Cardiology guidelines. ResultsThe performance of regression was assessed by the Standard Error of the Estimate. ML methods performed better than established equations (Friedewald and Martin). The performance all ML methods was comparable for large data sets and was affected by the divergence of the train and test data sets, as measured by the Jensen-Shannon divergence. Classification accuracy was not satisfactory for any model. ConclusionsDirect determination of LDL is the most preferred route. When not available, ML methods can be a good substitute. Not only deep neural networks but other, less computationally expensive methods can work as well as deep learning.

Clinical utility of LPA genetic characterization for primary prevention of atherosclerotic cardiovascular disease

Background and Aims: Genetic determinants of low-density lipoprotein cholesterol (LDL-C) levels can improve risk prediction of atherosclerotic cardiovascular disease (ASCVD) beyond a single measurement. It is unclear whether genetic factors related to lipoprotein(a), a highly heritable ASCVD biomarker, can also provide prognostic information. Here, we sought to determine whether an LPA genetic risk score (GRS) has clinical utility in assessing risk of incident ASCVD.

Modulation of Cardiovascular Risk by Monogenic and Polygenic Determinants of Low-Density Lipoprotein Cholesterol^: Winner

Modulation of Cardiovascular Risk by Monogenic and Polygenic Determinants of Low-Density Lipoprotein Cholesterol^: Winner

Validation of the novel Martin method for LDL cholesterol estimation

IntroductionAccurate determination of low-density lipoprotein cholesterol (LDL) is important for coronary heart disease (CHD) risk assessment among others. A low-cost method used is the Friedewald equation, calculating LDL from total cholesterol after subtracting high-density lipoprotein cholesterol (HDL) and triglycerides divided by 5. A new calculation method has been proposed where the value of 5 is not fixed but depends on the values of the other parameters. ResultsWe validated this method in a Greek population sample, by comparing direct LDL, the Friedewald equation and the novel method. Some clinical laboratories use the direct determination when TG > 200 mg/dl (2.26 mmol/lt). We performed segmented linear regression to check if this value makes sense. Bayesian linear regression was performed to compare the direct determination to the Friedewald and novel one. ConclusionsWe found that TG > 200 mg/dl is a sensible threshold value since it is a saddle point for the standard error of the regression. For Bayesian linear regression, the results were inconclusive. When the LDL values were used for classification of CHD, it turned out that the novel method was better than the Friedewald equation at correctly classifying LDL levels for CHD risk assessment.

Medical and psychosocial factors and unfavourable low-density lipoprotein cholesterol control in coronary patients.

Objective Understanding the determinants of low-density lipoprotein cholesterol (LDL-C) control constitutes the basis of modelling interventions for optimal lipid control and prognosis. We aim to identify medical and psychosocial (study) factors associated with unfavourable LDL-C control in coronary patients. Methods A cross-sectional explorative study used logistic and linear regression analysis to investigate the association between study factors and LDL-C in 1095 patients, hospitalized with myocardial infarction and/or a coronary revascularization procedure. Data were collected from hospital records, a comprehensive self-report questionnaire, clinical examination and blood samples after 2-36 months follow-up. Results Fifty-seven per cent did not reach the LDL-C target of 1.8 mmol/l at follow-up. Low socioeconomic status and psychosocial factors were not associated with failure to reach the LDL-C target. Statin specific side-effects (odds ratio 3.23), low statin adherence (odds ratio 3.07), coronary artery by-pass graft operation as index treatment (odds ratio 1.95), ≥ 1 coronary event prior to the index event (odds ratio 1.81), female gender (odds ratio 1.80), moderate- or low-intensity statin therapy (odds ratio 1.62) and eating fish < 3 times/week (odds ratio 1.56) were statistically significantly associated with failure to reach the LDL-C target, in adjusted analyses. Only side-effects (standardized β 0.180), low statin adherence ( β 0.209) and moderate- or low-intensity statin therapy ( β 0.228) were associated with LDL-C in continuous analyses. Conclusions Statin specific side-effects, low statin adherence and moderate- or low-intensity statin therapy were the major factors associated with unfavourable LDL-C control. Interventions to improve LDL-C should ensure adherence and prescription of sufficiently potent statins, and address side-effects appropriately.

Genetic Variants Influencing Circulating Lipid Levels and Risk of Coronary Artery Disease

Genetic studies might provide new insights into the biological mechanisms underlying lipid metabolism and risk of CAD. We therefore conducted a genome-wide association study to identify novel genetic determinants of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides. We combined genome-wide association data from 8 studies, comprising up to 17 723 participants with information on circulating lipid concentrations. We did independent replication studies in up to 37 774 participants from 8 populations and also in a population of Indian Asian descent. We also assessed the association between single-nucleotide polymorphisms (SNPs) at lipid loci and risk of CAD in up to 9 633 cases and 38 684 controls. We identified 4 novel genetic loci that showed reproducible associations with lipids (probability values, 1.6×10(-8) to 3.1×10(-10)). These include a potentially functional SNP in the SLC39A8 gene for HDL-C, an SNP near the MYLIP/GMPR and PPP1R3B genes for LDL-C, and at the AFF1 gene for triglycerides. SNPs showing strong statistical association with 1 or more lipid traits at the CELSR2, APOB, APOE-C1-C4-C2 cluster, LPL, ZNF259-APOA5-A4-C3-A1 cluster and TRIB1 loci were also associated with CAD risk (probability values, 1.1×10(-3) to 1.2×10(-9)). We have identified 4 novel loci associated with circulating lipids. We also show that in addition to those that are largely associated with LDL-C, genetic loci mainly associated with circulating triglycerides and HDL-C are also associated with risk of CAD. These findings potentially provide new insights into the biological mechanisms underlying lipid metabolism and CAD risk.

Validation of the Friedewald formula for the determination of low-density lipoprotein cholesterol in renal transplant recipients

In large patient populations, it has been established that calculated (c) and measured (m) plasma levels of low-density lipoprotein cholesterol (LDL-C) were comparable, but this issue is not known to be tested in renal transplant recipients (RTRs). Herein we aimed to compare the plasma levels of LDL-C that was calculated by Friedewald formula (FF) and direct measurement in RTRs. Methods: LDL-C was measured by direct method and by FF in 193 fasting venous blood samples obtained from 103 RTRs. Patients had triglyceride (TG) levels <400 mg/dL. Patients were treated with prednisolone, calcineurin inhibitors (CNIs), and/or sirolimus and everolimus. Results: The mean plasma levels of LDL-C for calculated and direct measurement were 100.81 ± 32.79 mg/dL and 107.82 ± 33.23 mg/dL, respectively (p < 0.01). The differences between cLDL-C and mLDL-C were similar according to usage of angiotensin receptor blockers (ARB)/angiotensin-converting enzyme inhibitors (ACEI), CNI, or mammalian target of rapamycin inhibitor (mTOR), tacrolimus or cyclosporine, and serum creatinine levels. mLDL-C and cLDL (FF) were highly correlated (r = 0.977). The mLDL-C level was calculated by following formula: LDL-C = 8.018 + (0.99 × FF cLDL-C) and the mean difference was 0 for last formula. Conclusion: The LDL-C can be calculated by the following formula: LDL-C = 8.018 + (0.99 × FF LDL-C). The coefficient of determination correlation (r) for this regression was 0.977, which indicates that the calculated LDL-C levels can be used in RTRs with TG lower than 400 mg/dL. mLDL-C was significantly higher than cLDL-C. We observed that difference between cLDL-C and mLDL-C levels were not affected by serum creatinine levels and usage of CNIs, sirolimus, everolimus, ACEI, and ARB in RTRs.

Evaluation of a Homogeneous Assay for Measuring LDL-cholesterol in Hyperlipidemic Serum Specimens

Homogeneous assay reagents for the determination of low-density lipoprotein cholesterol (LDL-C) have been available from several manufacturers. However, there has been considerable controversy due to uncertainty regarding their reactivity with intermediate-density lipoprotein (IDL), which is detected at an especially high frequency in patients with type III hyperlipemia. In this study, we examined the reactivity of a homogeneous assay, Cholestest LDL (R) (CT-LDL), with hyperlipemic sera that were classified according to the WHO system. Sera from 6 normolipidemic and 22 hyperlipidemic patients classified according to the WHO system were used for this study. All serum specimens were fractionated by the ultracentrifugation method of Hatch and Lees, and subjected to lipid and protein measurements. The percent bias of values measured by CT-LDL relative to those determined by the ultracentrifugation method was calculated and compared to the lipid/protein ratios of each lipoprotein fraction. Consequently, the coefficient of correlation between the bias and the Triglyceride/Total cholesterol (TG/TC) ratio in the IDL fraction was 0.742. There were also correlations with the TG/TC ratio and the apo-lipoprotein B/Total Cholesterol ratio in the LDL fraction and in the LDL+IDL fraction, respectively. Further testing will be required in order to know more about the clinical condition of hyperlipidemic patients, since CT-LDL may react differently with some beta lipoproteins having a diverse lipid/protein composition compared to those in normolipidemic specimens.

Endogenous androgen levels and cardiovascular risk profile in women across the adult life span

Whether endogenous androgen levels contribute to the cardiovascular disease (CVD) risk profile in women is controversial. The purpose of this study was to investigate systematically the relationships between serum levels of endogenous androgens and sex hormone-binding globulin (SHBG) and biochemical CVD risk profile, taking other known risk factors into account. This community-based cross-sectional study included 587 non-healthcare-seeking-women, aged 18 to 75 years, who were randomly recruited from the community via the electoral roll from April 2002 to August 2003. Participants were euthyroid; had no usage of exogenous steroids; had no history of tubal ligation, hysterectomy, or bilateral oophorectomy; and did not have hyperprolactinemia or polycystic ovarian syndrome. The relationships between total testosterone, SHBG, free testosterone, dehydroepiandrosterone sulfate, and androstenedione and high-sensitivity C-reactive protein (CRP) and lipids were explored using linear regression with natural logarithm (ln) -or square root-transformed data as indicated. Issues of nonlinearity and interaction were addressed by the inclusion of extra regression terms where appropriate. We determined the change in the proportion of variation for each marker of the CVD risk profile explained by the addition of each hormone term to the models, having adjusted for age, body mass index, smoking, alcohol, and exercise. Menopausal status did not influence the statistical models for high-sensitivity CRP and high-density lipoprotein cholesterol, but for both low-density lipoprotein cholesterol and triglycerides, the proportion of variation explained by the models was substantially less in postmenopausal than in premenopausal women. Almost all of the highly statistically significant findings were related to the addition of the SHBG terms to the models. The changes in r2 values were highly statistically significant for the addition of the SHBG terms to the models for ln CRP and ln high-density lipoprotein for both pre- and postmenopausal women (P <= 0.01 and < 0.001, respectively) and for ln triglycerides in postmenopausal (P < 0.001) and premenopausal women (P < 0.01). Endogenous testosterone and the adrenal preandrogens per se are not significant independent determinants of circulating high-sensitivity CRP or lipoprotein lipids. Our analyses provide further support for the independent predictive value of low SHBG levels for CVD risk profile and an independent contribution of the menopausal transition to the determination of low-density lipoprotein cholesterol and triglycerides.

Determination of Bovine Serum Low-density Lipoprotein Cholesterol using the N-geneous Method

The N-geneous method is a recently developed method for determination of low-density lipoprotein cholesterol (LDL-C) in human serum. In the present study, we attempted to adapt this method to bovine serum. The values of LDL-C obtained using the N-geneous method were highly correlated with those from the method using ultracentrifugation and heparin sepharose affinity chromatography (r = 0.934, p < 0.001). The reproducibility of this method was acceptable (intra-assay CV 4.2%, inter-assay CV 7.6%) for clinical use. Using the N-geneous method, serum LDL-C was evaluated in cows around parturition, and in cows with fatty liver induced by fasting. The concentration of LDL-C decreased significantly in cows close to parturition. A reduced concentration of LDL-C was also observed in cows with fatty liver. In both cases, the changes of LDL-C were similar to those of apolipoprotein B (apoB)-100, and the values of LDL-C were highly correlated (r = 0.876, p < 0.001) with those of apoB-100. These results suggest that the concentration of LDL-C reflects the level of apoB-100. The N-geneous method is simple and rapid, and might to be a useful tool to elucidate the clinical significance of LDL-C in bovine serum.

Validation of the Friedewald formula for the determination of low-density lipoprotein cholesterol compared with β-quantification in a large population

Lipoprotein data from 9477 subjects, covering a wide range of total plasma cholesterol levels, were used to examine the validity of the Friedewald formula for estimating plasma concentrations of low-density lipoprotein cholesterol (LDL-C) using high-density lipoprotein cholesterol (HDL-C) and triglyceride (TG) concentrations. Values of LDL-C obtained from the Friedewald formula were compared with values of LDL-C derived from preparative ultracentrifugation used as a reference method. We found that the bias associated with the Friedewald formula was not related to plasma LDL-C levels and was smaller than −4.0% even for plasma LDL-C values <3.0 mmol/l. Moreover, in the subgroup of individuals with plasma TG levels ≤4.5 mmol/l, the Friedewald formula underestimated LDL-C levels with a bias between −3.1% and −1.9% according to TG quartiles. Interestingly, the Friedewald formula showed no significant bias in patients with plasma TG levels between 4.51 and 8.82 mmol/l, suggesting that the calculated LDL-C are reliable and could be clinically useful in patients with plasma TG levels higher than 4.5 mmol/l which is the reference cut-point value used by most clinical laboratories. Finally, multiple regression analyses showed that the very low-density lipoprotein cholesterol (VLDL-C)/TG ratio represented nearly 63% ( P < 0.0001) of the variance of the bias associated with the Friedewald formula. We concluded that the Friedewald formula may be reliable at low LDL-C levels and at TG levels up to 9 mmol/l but may be used with caution when the VLDL-C/TG ratio is high as observed in patients with type III dysbetalipoproteinemia.

Apolipoprotein E genotype is a determinant of low-density lipoprotein cholesterol and of its response to a low-cholesterol diet in Type 1 diabetic patients with elevated urinary albumin excretion.

The effect of the apolipoprotein (apo) E genotype on the lipoprotein response to a 1 year low cholesterol diet (200 mg cholesterol per day) was evaluated in 36 patients with Type 1 diabetes mellitus with albuminuria between 10 and 200 microg min(-1). Apo E genotype was characterized by polymerase chain reaction and restriction isotyping. In 11 IDDM patients with at least one epsilon4 allele (apo E4 group), baseline serum total and low density lipoprotein (LDL) cholesterol were higher (p < 0.05 for both) than in 25 patients without an epsilon4 allele and with at least one epsilon3 allele (apo E3 group). Dietary counselling resulted in a similar decrease in cholesterol intake in both groups, whereas linoleic acid did not change. In the apo E4 group, serum total and LDL cholesterol at follow-up fell (p < 0.01 for both) to levels that were not different from those in the apo E3 group, and the changes in these parameters were greater (p < 0.02) than those in the apo E3 group. We conclude that the apo E4 allele is associated with atherogenic lipoprotein abnormalities in Type 1 DM patients with minor elevations in albuminuria when they use their habitual diet. Apo E4 carrying patients respond better to a low cholesterol diet.

Determination of low-density lipoprotein-cholesterol by the Friedewald formula

Determination of low-density lipoprotein-cholesterol by the Friedewald formula

Five methods for determining low-density lipoprotein cholesterol compared.

We evaluated three precipitation methods for determination of low-density lipoprotein cholesterol in serum and an indirect method involving the Friedewald formula (Clin Chem 18: 499-502, 1972) by comparison with results by ultracentrifugation. The results of all methods for 83 sera, including 59 hyperlipidemic type IIA, IIB, and IV sera agreed very well, at least for concentrations of serum triglycerides below 8 mmol/L. The accuracy of the Friedewald formula was confirmed in 285 other sera, including 66 sera with triglycerides content between 4.52 and 8.0 mmol/L. For type III sera, the precipitation methods produced similar values to those obtained with the Friedewald formula, all being much higher than the ultracentrifugation values. Density-gradient ultracentrifugation showed that the very-low-density lipoprotein remnants in type III sera almost completely coprecipitated with the low-density lipoproteins. The precipitation methods are not only accurate but also very precise (CV less than 5%); they can therefore be used in clinical laboratories to measure atherogenic low-density lipoproteins plus the remnants of very-low-density lipoproteins. However, when serum triglycerides and high-density lipoprotein cholesterol also are determined, the Friedewald formula is a reliable alternative.