Evaluation of equations for calculating LDL-cholesterol in patients with concomitant hypertriglyceridemia and low LDL-cholesterol: is there a preferred equation? / Hypertriglycéridémie et cholestérol-LDL bas : quelle équation estime mieux le cholestérol-LDL ?

It is critical to reliably estimate Low Density Lipoprotein Cholesterol (LDL-C) in patients with concomitant hypertriglyceridemia and low LDL-C. We retrospectively compared the performances of the Friedewald (LDL-F), Martin-Hopkins (LDL-MH) and Sampson (LDL-SA) equations against a direct homogeneous LDL-C assay (dLDL-C) on observations presenting mild hypertriglyceridemia (triglycerides between 1.69 and 3.9 mmol/L) and low LDL-C (< 2.58 mmol/L). Observations were stratified according to their LDL-C. Agreement of the equations with dLDL-C was assessed using Intraclass Correlation Coefficients (ICC) with an agreement cut-off of 0.9, and analysis of Bland-Altman plots. Independently of the LDL-C stratum evaluated, the three equations failed to meet the 0.9 ICC cut-off, although their agreement with dLDL-C improves as LDL-C increases. Analysis of Bland-Altman plots shows a downwards discordance of LDL-F with dLDL-C, and an upwards discordance of LDL-MH and LDL-SA with direct LDL-C. LDL-MH resulted in the least observations outside the Bland-Altman limits of agreement. While no equation can be deemed satisfactory enough to replace direct assays in patients with low LDL-C and concomitant hypertriglyceridemia, LDL-MH seems to perform better than the other equations in estimating LDL-C in these patients.

Lipid Parameters and Proprotein Convertase Subtilisin/Kexin Type 9 in Healthy Lebanese Adults.

Background: High levels of non-HDL cholesterol (non-HDL-C), triglycerides (TG), lipoprotein (a) (Lp(a)), and Proprotein convertase subtilisin/kexin type 9 (PCSK9) as well as low levels of HDL-C are strongly associated with cardiovascular disease (CVD). Our study aims to estimate the prevalence of dyslipidemia and high Lp(a) in the Lebanese population and to study the relationship of these variables with gender, age, body mass index (BMI), and PCSK9. Methods: This cross-sectional study was carried out on a sample of healthy volunteers aged 18 to 65. Blood samples were drawn from volunteers for total cholesterol (TC), HDL-C, TG, PCSK9, and Lp(a) measurements. Non-HDL-C was calculated by subtracting HDL-C from TC. Results: In total, 303 volunteer subjects with an average age of 38.9 years were included in the study. Respectively, 44%, 29.8%, and 44% of men had high non-HDL-C and TG with low HDL-C versus 23.5%, 8%, and 37% in women. Non-HDL-C and TG were significantly higher in men than in women, while the reverse was observed for HDL-C (p < 0.0001 for the three comparisons). Non-HDL-C and TG were significantly correlated with age and BMI (p< 0.0001 for all correlations), while HDL-C was inversely correlated with BMI (p < 0.0001) but not with age. Abnormal Lp(a) levels (≥75 nmol/L) were found in 19.1% of the population, predominantly in women (24.1% versus 13.4% in men, p = 0.004). The median PCSK9 and its interquartile was 300 (254−382) ng/L with no gender difference (p = 0.18). None of the following factors: gender, age, BMI, non-HDL-C, HDL-C, or TG, were independently associated with Lp(a), while PCSK9 was significantly correlated with age, non-HDL-C, and TG in both men and women and inversely correlated with HDL-C in men. Dyslipidemia is very common in the Lebanese population and is associated with age, high BMI, and male sex. Lp(a) is higher in women without any correlation with the lipid profile, whereas PCSK9 is associated with non-HDL-C and TG. Further studies are needed to evaluate the potential role of Lp(a) and PCSK9 in predicting CVD in healthy populations.

Prospective Validation of a Machine Learning Model for Low-Density Lipoprotein Cholesterol Estimation.

OBJECTIVE: We aim to prospectively validate a previously developed machine learning algorithm for low-density lipoprotein cholesterol (LDL-C) estimation. METHODS: We retrospectively and prospectively evaluated a machine learning algorithm based on k-nearest neighbors (KNN) according to age, sex, healthcare setting, and triglyceridemia against a direct LDL-C assay. The agreement of low-density lipoprotein-k-nearest neighbors (LDL-KNN) with the direct measurement was assessed using intraclass correlation coefficient (ICC). RESULTS: The analysis comprised 31,853 retrospective and 6599 prospective observations, with a mean age of 54.2 ±â€…17.2 years. LDL-KNN exhibited an ICC greater than 0.9 independently of age, sex, and disease status. LDL-KNN was in satisfactory agreement with direct LDL-C in observations with normal triglyceridemia and mild hypertriglyceridemia but displayed an ICC slightly below 0.9 in severely hypertriglyceridemic patients and lower in very low LDL-C observations. CONCLUSION: LDL-KNN performs robustly across ages, genders, healthcare settings, and triglyceridemia. Further algorithm development is needed for very low LDL-C observations.

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.

Is Machine Learning-derived Low-Density Lipoprotein Cholesterol estimation more reliable than standard closed form equations? Insights from a laboratory database by comparison with a direct homogeneous assay.

BACKGROUND: There is no consensus on the best method to estimate Low Density Lipoprotein-Cholesterol (LDL-C) in routine laboratories. METHODS: We conducted a retrospective study to compare the performances of a Machine Learning (ML) algorithm using the K-Nearest Neighbors (LDL-KNN) method with that of the Friedewald formula (LDL-F), the Martin-Hopkins equation (LDL-NF), the de Cordova equation (LDL-CO) and the Sampson equation (LDL-SA) against direct homogeneous LDL-C assay (LDL-D) in patients who presented to the Laboratories of Hôtel Dieu de France university hospital in Beirut, Lebanon, from September 2017 to July 2020. Agreements between methods were analyzed using Intraclass Correlation Coefficients (ICC) and the Bland-Altman method of agreement. RESULTS: 31,922 observations from 19,279 subjects were included, with a mean age of 52 ± 18 years and 10,075 (52.3%) females. All methods except LDL-F and LDL-CO exhibited an overall ICC beyond the 0.9 cut-off. LDL-SA, LDL-NF and LDL-KNN were less susceptible to triglyceridemia than LDL-F and LDL-CO, with LDL-KNN resulting in the lesser fraction of points beyond the Bland-Altman limits of agreement. CONCLUSION: An ML algorithm using LDL-KNN is promising for the estimation of LDL-C as it agrees better with LDL-D than closed form equations, especially in mild and severe hypertriglyceridemia.

Do age-adjusted sex-specific cut-off values improve the agreement between high sensitivity cardiac troponins I and T? A retrospective study.

BACKGROUND: Many reports noted a disagreement between High sensitivity cardiac Troponin (hs-cTn) assays on the diagnosis of Acute Coronary Syndrome (ACS). METHODS: We conducted a retrospective study aiming to assess the agreement between hs-cTn T (Roche) and hs-cTn I (Abbott) in patients presenting with a suspected ACS to the emergency department at Hotel-Dieu hospital between September 2017 and October 2019 using overall, sex-specific, and age-adjusted sex-specific cut-off values. This was measured using Cohen's Kappa. We explored whether renal function, circadian rhythm, age and sex influenced the discordance. And we analyzed the trend of agreement between baseline and repeated measurements. RESULTS: 4856 patients who had simultaneous hs-cTn I and T values were retained for the analysis. 53.5% had a hs-cTn T above the overall 99th percentile, compared to 19.9% for hs-cTn I. The numbers were significantly reduced when applying age-adjusted sex-specific 99th percentile. A disagreement was seen in 34% of cases using overall 99th percentile. Using sex-specific cut off values did not impact this discordance; however, age-adjusted sex-specific cut-off values reduced the percentage of discrepancies to 15.8%. The decreased renal function had a negative effect on the agreement while the circadian rhythm had minimal effect. This initial discordance was carried forward into repeated measurements. CONCLUSION: The disagreement between hs-cTn T and I assays could be imputed to the choice of cut-off values. The use of age-adjusted sex specific 99th percentile reduced majorly these discordances. Further studies are needed in order to evaluate their clinical utility in patients presenting with ACS.

Distribution and correlates of non-high-density lipoprotein cholesterol and triglycerides in Lebanese school children.

BACKGROUND: The prevalence of dyslipidelmia in pediatric Middle-Eastern populations is unknown. Our study aims to investigate the distribution and correlates of non-high-density lipoprotein cholesterol (non-HDL-C) and triglycerides among Lebanese school children. METHODS: A total of 969 subjects aged 8-18 years were included in the study (505 boys and 464 girls). Recruitment was done from 10 schools located in the Great Beirut and Mount-Lebanon areas. Non-fasting total cholesterol, triglycerides, and HDL-cholesterol (HDL-C) were measured. Non-HDL-C was calculated. Schools were categorized into 3 socioeconomic statuses (SESs; low, middle, and high). RESULTS: In the overall population, the prevalence of high non-HDL-C (>3.8 mmol/L), very high non-HDL-C (>4.9 mmol/L), and high triglycerides (>1.5 mmol/l) are respectively 9.2%, 1.24%, and 26.6%. There is no significant gender difference for non-HDL-C or triglycerides. Non-HDL-C and triglycerides are inversely correlated with age in girls (P < .0001 for both variables) but not in boys. They are also positively correlated with body mass index (BMI) in boys and girls (P < .0001 for all variables). There is no relationship between schools' socioeconomic process (SES) and non-HDL-C. However, triglycerides are higher in children from lower SES schools. After adjustment for age and body mass index (BMI), testosterone is inversely associated with triglycerides in boys (P < .0001). In a multivariate regression analysis, non-HDL-C is independently associated with age and BMI in girls (P < .0001 for both variables) but only with BMI in boys (P < .0001), whereas triglycerides are independently associated with BMI and schools' SES in both girls and boys. CONCLUSIONS: This study confirms, in our population, the association between obesity and both high non-HDL-C and triglycerides, and between high triglycerides and low SES.