Validation of Friedewald, Martin-Hopkins and Sampson low-density lipoprotein cholesterol equations

Evaluation of current indirect methods for measuring LDL-cholesterol

OBJECTIVES

Accurately quantifying low-density lipoprotein cholesterol (LDL-C) is crucial for precise cardiovascular disease risk assessment and treatment decisions. The commonly used Friedewald equation (LDL-CFW) has faced criticism for its tendency to underestimate LDL-C, particularly at high triglycerides (TG) or low LDL-C, potentially leading to undertreatment. Newer equations, such as those by Martin and Hopkins (LDL-CMH) or Sampson (LDL-CSN), have been proposed as alternatives. Our study aimed to assess the validity of LDL-CFW, LDL-CMH, and LDL-CSN compared to ß-quantification (LDL-CUC), the reference method recommended by the Lipid Research Clinics.

METHODS

Using data from three studies comprising 5,738 datasets, LDL-C was determined with the four methods in samples with TG up to 5.65 mmol/L. We calculated median and mean differences, correlations, and used the Passing and Bablok regression for comparisons. Concordance/discordance analyses were conducted.

RESULTS

All equations provided generally accurate LDL-C estimations with slight differences among them. At TG<1.69 mmol/L, no clinically significant divergences were observed. As TG values increased, LDL-CFW offered the most accurate estimation, followed by LDL-CSN, while LDL-CMH exhibited increasingly strong positive bias. LDL-CFW was not inferior to LDL-CSN and LDL-CMH in terms of concordance/discordance.

CONCLUSIONS

LDL-CFW generally provided reliable estimates of LDL-C in most samples, showing non-inferiority to LDL-CSN or LDL-CMH, thereby confirming its legitimacy for routine use. Since current treatment recommendations are based on studies employing LDL-CFW, its replacement by alternatives is not justified.

Machine learning-based prediction of LDL cholesterol: performance evaluation and validation

Objective

This study aimed to validate and optimize a machine learning algorithm for accurately predicting low-density lipoprotein cholesterol (LDL-C) levels, addressing limitations of traditional formulas, particularly in hypertriglyceridemia.

Methods

Various machine learning models-linear regression, K-nearest neighbors (KNN), decision tree, random forest, eXtreme Gradient Boosting (XGB), and multilayer perceptron (MLP) regressor-were compared to conventional formulas (Friedewald, Martin, and Sampson) using lipid profiles from 120,174 subjects (2020-2023). Predictive performance was evaluated using R-squared (R 2), mean squared error (MSE), and Pearson correlation coefficient (PCC) against measured LDL-C values.

Results

Machine learning models outperformed traditional methods, with Random Forest and XGB achieving the highest accuracy (R 2 = 0.94, MSE = 89.25) on the internal dataset. Among the traditional formulas, the Sampson method performed best but showed reduced accuracy in high triglyceride (TG) groups (TG > 300 mg/dL). Machine learning models maintained high predictive power across all TG levels.

Conclusion

Machine learning models offer more accurate LDL-C estimates, especially in high TG contexts where traditional formulas are less reliable. These models could enhance cardiovascular risk assessment by providing more precise LDL-C estimates, potentially leading to more informed treatment decisions and improved patient outcomes.

Analysis of 26 Studies of the Impact of Coconut Oil on Lipid Parameters: Beyond Total and LDL Cholesterol

Coconut oil (CNO) is often characterized as an "artery-clogging fat" because it is a predominantly saturated fat that ostensibly raises total cholesterol (TChol) and LDL cholesterol (LDL-C). Whereas previous analyses assessed CNO based on the relative effects on lipid parameters against other fats and oils, this analysis focuses on the effects of CNO itself. Here, we review the literature on CNO and analyze 984 lipid profile data sets from 26 CNO studies conducted over the past 40 years. This analysis shows considerable heterogeneity among CNO studies regarding participant selection, the amount consumed, and the study duration. The analysis reveals that, overall, CNO consumption gives variable TChol and LDL-C values, but that the HDL-cholesterol (HDL-C) values increase and triglycerides (TG) decrease. This holistic lipid assessment, together with the consideration of lipid ratios, shows that CNO does not pose a health risk for heart disease. Because the predominantly medium-chain fatty acid profile of CNO is significantly different from that of lard and palm oil, studies using these as reference materials do not apply to CNO. This paper concludes that the recommendation to avoid consuming coconut oil due to the risk of heart disease is not justified.

Nanotechnology and Artificial Intelligence in Dyslipidemia Management-Cardiovascular Disease: Advances, Challenges, and Future Perspectives

This narrative review explores emerging technologies in dyslipidemia management, focusing on nanotechnology and artificial intelligence (AI). It examines the current treatment recommendations and contrasts them with the future prospects enabled by these innovations. Nanotechnology shows significant potential in enhancing drug delivery systems, enabling more targeted and efficient lipid-lowering therapies. In parallel, AI offers advancements in diagnostics, cardiovascular risk prediction, and personalized treatment strategies. AI-based decision support systems and machine learning algorithms are particularly promising for analyzing large datasets and delivering evidence-based recommendations. Together, these technologies hold the potential to revolutionize dyslipidemia management, improving outcomes and optimizing patient care. In addition, this review covers key topics such as cardiovascular disease biomarkers and risk factors, providing insights into the current methods for assessing cardiovascular risk. It also discusses the current understanding of dyslipidemia, including pathophysiology and clinical management. Together, these insights and technologies hold the potential to revolutionize dyslipidemia management, improving outcomes and optimizing patient care.

Biochemical exploration of cholestasis: interpretation, traps and interferences

We described the case of a 33-year-old patient who presented to the emergency department with non-febrile jaundice associated with epigastric pain. He suffered from acute non-severe alcoholic hepatitis and cholestasis. Biochemical investigations highlighted a huge elevation of the alpha-1-globulins fraction with an unexpected peak in the alpha-1-globulins area in serum protein electrophoresis, a severe hypercholesterolemia without xanthelasmas nor cholesterolomas. Investigations revealed an abnormal lipoprotein, Lipoprotein X (LpX) that can be responsible for the hypercholesterolemia, but also interferes with biochemical tests like direct low-density lipoprotein cholesterol, albumin, and serum electrolytes assays. LpX is an abnormal lipoprotein, which can be present in patients with liver dysfunction, notably in cholestasis-related conditions where the metabolism of plasma lipoproteins is altered. Cholestasis prevents the normal formation of bile acids, leading to the formation of LpX, which is rich in phospholipids and unesterified cholesterol, but poor in esterified cholesterol, triglycerides and proteins. The accumulation of LpX can lead to severe hypercholesterolemia, but this remains uncommon and data regarding the pathophysiology and incidence of this disease is scarce. The laboratory investigation of patients with suspected Lpx can be challenging, due to the lack of available methods for measurement of LpX. In conclusion, LpX-induced hyperlipidemia must be identified to prevent interference in results for a number of biochemical tests, and additionally to improve patient care.

Assessing Performance of Martins's and Sampson's Formulae for Calculation of LDL-C in Indian Population: A Single Center Retrospective Study

Various formulae had been derived to calculate the LDL-C from other lipid profile parameters to supplant the need for direct estimation. Martin's, Sampson's, and Cordova's formulae are recently derived formulae for calculating LDL-C. However, no study has been undertaken till now to verify the newer formulae viz. Martins's and Sampson's in Indian population. The retrospective cross-sectional study was carried out after obtaining approval from the Institutional Ethics Committee on human subject research. The lipid profile data were collected for a period of 17 months from January 2020 to May 2021. The formulae proposed by Friedewald, Cordova, Anandaraja, Martin, and Sampson were used to assess calculated LDL-C. Intraclass correlations were performed to assess the effectiveness of each formula when compared with direct estimation. In our study, we observed that LDL-C calculated using Martin was observed to be closer to that of direct estimation. The bias observed was lowest for Martin's formulae, followed by Sampson's. Intraclass correlation analysis for absolute agreement demonstrated Cordova, Martin, and Sampson to have an average ICC > 0.9, with Martin, and Sampson having a p value < 0.05. Martin fared superior to other formulae in intraclass correlation in patients with LDL > 70. In patients with TG below 200 mg/dL, Martin, and Sampson had a significant correlation with comparable average ICC. However, in patients with TG > 300 mg/dL, Cordova appears to fare better than all other formulae. Our study demonstrated a distinctly superior performance of Martin's formula over Friedewald's formula in the Indian patient population.

More precise method of low-density lipoprotein cholesterol estimation for tobacco and electronic cigarette smokers: A cross-sectional study

Smoking is associated with elevated low-density lipoprotein cholesterol (LDL-C) levels. However, the accuracies of the Friedewald, Sampson, and Martin LDL-C-estimating equations based on smoking status are unclear. We analyzed the accuracy of LDL-C levels estimated using these three equations based on tobacco and electronic cigarette smoking status. Data on LDL-C and other lipid components were obtained from the Korea National Health and Nutrition Examination Survey from January 2009 to December 2021. Direct LDL-C (dLDL-C) levels and smoking data of 12,325 participants were evaluated. Current smokers had higher triglyceride levels than never smokers. Electronic cigarette smokers had higher triglyceride and dLDL-C levels than never smokers. The Martin equation yielded more accurate mean absolute deviations than the other equations for the group with triglyceride levels <400 mg/dL as well as more accurate median absolute deviation values, except for the group with dLDL-C levels <40 mg/dL. Similar estimates were derived from the equations when the triglyceride levels were <150 mg/dL. However, the Martin equation may lead to the overestimation of LDL-C levels. In conclusion, the Martin equation is suitable for triglyceride levels <400 mg/dL regardless of the electronic cigarette/tobacco smoking status; if the triglyceride level is <150 mg, the Friedewald equation could also be considered, regardless of the electronic cigarette/tobacco smoking status.

Comparison of Friedewald, Martin/Hopkins, and Sampson formulae with direct LDL measurement in hyperlipidaemic and normolipidaemic adults in a Turkish population

Methods

The study was a retrospective investigation by the Department of Medical Biochemistry of the Ankara Training and Research Hospital between January 1, 2021, and December 31, 2022. Our study evaluated the results of 6297 patients aged 18-95 years who underwent cholesterol panel TC, TG, HDL-C, and direct LDL-C in our laboratory. The estimated LDL-C was calculated according to Friedewald, Martin/Hopkins, and Sampson formulae.

Results

All three formulae showed a stronger positive correlation with d-LDL-C (0.905, 0.897, and 0.886, respectively, for all data, p<0.001). In addition, when we compared the total median difference (1st-3rd quartile) of all formulae, it was -0.69 (-1.62 to 0.39) for Friedewald, 0.034 (-0.74 to 1.14) for Martin/Hopkins and -0.40 (-1.19 to 0.55) for Sampson. According to Passing Bablok regression analyses, the intercept was determined as -0.97 (95% CI=-1.01 to -0.93), 0.41 (95%=0.37 to 0.44) and -0.05 (-0.08 to -0. 03) and slopes were calculated as 1.083 (95% CI=1.07-1.09), 0.88 (0.88 to 0.89) and 0. 90 (95%=0.89 to 0.90) for Friedewald, Martin/Hopkins and Sampson, respectively.

Conclusions

Our findings suggest that the Martin/Hopkins formula performed better than the Friedewald and Sampson formulas. We figured out utilizing the Martin/ Hopkins formula as a good alternative for estimated LDLC in Turkish adults.

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 new equation for estimating low-density lipoprotein cholesterol concentration based on machine learning

Low-density lipoprotein cholesterol (LDL-C) is a crucial marker of cardiovascular system damage. In the Chinese population, the estimation of LDL-C concentration by Friedewald, Martin-Hopkins or Sampson equations is not accurate. The aim of this study was to develop a group of new equations for calculating LDL-C concentration using machine learning techniques and to evaluate their efficacy. A total of 182,901 patient samples were collected with standard lipid panel measurements. These samples were collated and randomly divided into a training set and a test set. In the training set, a new equation was constructed using polynomial ridge-regression and compared to the Friedewald, Martin/Hopkins and extended Martin/Hopkins, or Sampson equations in the test set. Subsequently, an additional set of 17,285 patient samples were collected to evaluate the performance of the new equation in clinical practice. The new equation, a ternary cubic equation, was accurate and easy to use, with a goodness-of-fit R2 of 0.9815 and an uncertainty MSE of 37.4250 on the testing set. The difference between the calculated value by the new equation and the measured value of LDL-C was small (0.0424 ± 5.1161 vs Friedewald equation: -13.3647 ± 17.9198, vs Martin/Hopkins and extended Martin/Hopkins equation: -6.4737 ± 8.1036, vs Sampson equation: -8.9252 ± 12.6522, P < .001). It could accurately calculate LDL-C concentration even at high triglyceride and low LDL-C. Furthermore, the new equation could also precisely calculate LDL-C concentration in actual clinical use (R2 = 0.9780, MSE = 24.8482). The new equation developed in this study can accurately calculate LDL-C concentration within the full concentration range of triglyceride and LDL-C, and can serve as a supplement to the direct determination of LDL-C concentration for the prevention, treatment, evaluation, and monitoring of atherosclerotic diseases, compared to the Friedewald, Martin/Hopkins and extended Martin/Hopkins, or Sampson equations.

Evaluation of low-density lipoprotein cholesterol equations by cross-platform assessment of accuracy-based EQA data against SI-traceable reference value

OBJECTIVES

Low-density lipoprotein cholesterol (LDLC) is the primary cholesterol target for the diagnosis and treatment of cardiovascular disease (CVD). Although beta-quantitation (BQ) is the gold standard to determine LDLC levels accurately, many clinical laboratories apply the Friedewald equation to calculate LDLC. As LDLC is an important risk factor for CVD, we evaluated the accuracy of Friedewald and alternative equations (Martin/Hopkins and Sampson) for LDLC.

METHODS

We calculated LDLC based on three equations (Friedewald, Martin/Hopkins and Sampson) using the total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDLC) in commutable serum samples measured by clinical laboratories participating in the Health Sciences Authority (HSA) external quality assessment (EQA) programme over a 5 years period (number of datasets, n=345). LDLC calculated from the equations were comparatively evaluated against the reference values, determined from BQ-isotope dilution mass spectrometry (IDMS) with traceability to the International System of Units (SI).

RESULTS

Among the three equations, Martin/Hopkins equation derived LDLC had the best linearity against direct measured (y=1.141x - 14.403; R2=0.8626) and traceable LDLC (y=1.1692x - 22.137; R2=0.9638). Martin/Hopkins equation (R2=0.9638) had the strongest R2 in association with traceable LDLC compared with the Friedewald (R2=0.9262) and Sampson (R2=0.9447) equation. The discordance with traceable LDLC was the lowest in Martin/Hopkins (median=-0.725%, IQR=6.914%) as compared to Friedewald (median=-4.094%, IQR=10.305%) and Sampson equation (median=-1.389%, IQR=9.972%). Martin/Hopkins was found to result in the lowest number of misclassifications, whereas Friedewald had the most numbers of misclassification. Samples with high TG, low HDLC and high LDLC had no misclassification by Martin/Hopkins equation, but Friedewald equation resulted in ∼50% misclassification in these samples.

CONCLUSIONS

The Martin/Hopkins equation was found to achieve better agreement with the LDLC reference values as compared to Friedewald and Sampson equations, especially in samples with high TG and low HDLC. Martin/Hopkins derived LDLC also enabled a more accurate classification of LDLC levels.

Friedewald formula may be used to calculate non-HDL-C from LDL-C and TG

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.

Assessing the Practical Differences in LDL-C Estimates Calculated by Friedewald, Martin/Hopkins, or NIH Equation 2: An Observation Cross-Sectional Study

Objective

Low-density lipoprotein-cholesterol (LDL-C) remains a clinically important cholesterol target in primary prevention of atherosclerotic cardiovascular disease. The present study aimed to assess the practical differences among three equations utilized for the estimation of LDL-C: the Friedewald, the Martin/Hopkins, and the NIH equation 2.

Methods

Blood lipid measurements from 4,556 noninstitutionalized participants, aged 12 to 80, were obtained from the 2017-2020 National Health and Nutrition Examination Survey study. We 1) assessed the differences between three calculated LDL-C estimates, 2) examined the correlations between LDL-C estimates using correlation coefficients and regression, and 3) investigated the degree of agreement in classifying individuals into the LDL-C category using weighted Kappa and percentage of agreement.

Results

The differences in LDL-C estimates between equations varied by sex and triglyceride levels (p<0.001). Overall, the mean of absolute differences between Friedewald and Martin/Hopkins was 3.17 mg/dL (median=2.0, 95% confidence interval [CI] [3.07-3.27]). The mean of absolute differences between Friedewald and NIH Equation 2 was 2.08 mg/dL (median=2.0, 95% CI [2.03-2.14]). Friedewald correlated highly with Martin/Hopkins (r=0.991, rho=0.989) and NIH Equation 2 (r=0.998, rho=0.997). Cohen's weighted Kappa=0.92 between Friedewald and Martin/Hopkins, and 0.95 between Friedewald and NIH equation 2. The percentage of agreement in classifying individuals into the same LDL-C category was 93.0% between Friedewald and Martin/Hopkins, and 95.4% between Friedewald and NIH equation 2.

Conclusion

Understanding the practical differences in LDL-C calculations can be helpful in facilitating decision-making during a paradigm shift.

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.

Validation of low-density lipoprotein cholesterol equations in pediatric population

Several studies have shown a high prevalence of dyslipidemia in children. Since childhood lipid concentrations continue into adulthood, recognition of lipid abnormalities in the early period is crucial to prevent the development of future coronary heart disease (CHD). Low density lipoprotein cholesterol (LDL-C) is one of the most used parameters in the initiation and follow-up of treatment in patients with dyslipidemia. It is a well known fact that LDL-C lowering therapy reduces the risk of future CHD. Therefore, accurate determination of the LDL-C levels is so important for the management of lipid abnormalities. This study aimed to validate different LDL-C estimating equations in the Turkish population, composed of children and adolescents. A total of 3,908 children below 18 years old at Sivas Cumhuriyet University Hospital (Sivas, Turkey) were included in this study. LDL-C was directly measured by direct homogeneous assays, i.e., Roche, Beckman, Siemens and estimated by Friedewald's, Martin/Hopkins', extended Martin-Hopkins' and Sampson's formulas. The concordances between the estimations obtained by the formulas and the direct measurements were evaluated both overall and separately for the LDL-C, triglycerides (TG) and non-high-density lipoprotein cholesterol (non-HDL-C) sublevels. Linear regression analysis was performed and residual error plots were generated between each estimation and direct measurement method. Coefficient of determination (R ²) and mean absolute deviations were also evaluated. The overall concordance of Friedewald, Sampson, Martin-Hopkins and the extended Martin-Hopkins formula were 64.6%, 69.9%, 69.4%, and 84.3% for the Roche direct assay, 69.8%, 71.6%, 73.6% and 80.4% for the Siemens direct assay, 66.5%, 68.8%, 68.9% and 82.1% for the Beckman direct assay, respectively. The extended Martin-Hopkins formula had the highest concordance coefficient in both overall and all sublevels of LDL-C, non-HDL-C, and TG. When estimating the LDL-C categories, the highest underestimation degrees were obtained with the Friedewald formula. Our analysis, conducted in a large pediatric population, showed that the extended Martin-Hopkins equation gives more reliable results in estimation of LDL-C compared to other equations.

Low-density lipoprotein cholesterol goal attainment in patients with clinical evidence of familial hypercholesterolemia and elevated Lp(a)

BACKGROUND

Although potent lipid-lowering therapies are available, patients commonly fall short of recommended low-density lipoprotein cholesterol (LDL-C) levels. The aim of this study was to examine the relationship between familial hypercholesterolemia (FH) and elevated lipoprotein(a) [Lp(a)] and LDL-C goal attainment, as well as the prevalence and severity of coronary artery disease (CAD). Moreover, we characterized patients failing to meet recommended LDL-C goals.

METHODS

We performed a cross-sectional analysis in a cohort of patients undergoing cardiac catheterization. Clinical FH was determined by the Dutch Clinical Lipid Network Score, and Lp(a) ≥ 50 mg/dL (≈ 107 nmol/L) was considered elevated.

RESULTS

A total of 838 participants were included. Overall, the prevalence of CAD was 72%, and 62% received lipid-lowering treatment. The prevalence of clinical FH (probable and definite FH) was 4%, and 19% had elevated Lp(a) levels. With 35%, LDL-C goal attainment was generally poor. Among the participants with clinical FH, none reached their LDL-C target. Among patients with elevated Lp(a), LDL-C target achievement was only 28%. The prevalence and severity of CAD were higher in participants with clinical FH (86% prevalence) and elevated Lp(a) (80% prevalence).

CONCLUSION

Most participants failed to meet their individual LDL-C goals according to the ESC 2016 and 2019 guidelines. In particular, high-risk patients with clinical FH or elevated Lp(a) rarely met their target for LDL-C. The identification of these patients and more intense treatment approaches are crucial for the improvement of CAD primary and secondary prevention.