Mănescu Ion Bogdan, Demian Liliana, Dobreanu Minodora
Department of Laboratory Medicine, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania.
Doctoral School, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 38 Gheorghe Marinescu, 540142 Targu Mures, Romania.
J Pers Med. 2024 Sep 20;14(9):1000. doi: 10.3390/jpm14091000.
The most commonly used method for low-density lipoprotein cholesterol (LDL-C) estimation is the Friedewald equation, which has notable limitations. However, more accurate methods have been proposed. This study investigates the advantages and limitations of these methods and identifies the contexts in which each equation is the most or least applicable.
A cohort of 222 individuals underwent a standard lipid profile assessment, including directly measuring their LDL-C (dLDL-C). LDL-C was also estimated using the Friedewald, Martin-Hopkins, and Sampson equations. The differences (%Delta) between the estimated and measured LDL-C were analyzed in relation to dLDL-C, high-density lipoprotein cholesterol (HDL-C), and triglyceride levels.
The %Delta was significantly lower ( < 0.0001) for the Martin-Hopkins (-8.8 ± 9.8) and Sampson (-9.5 ± 9.2) equations compared to Friedewald (-12.2 ± 9.2). All equations increasingly underestimated LDL-C as the dLDL-C levels decreased. The %Delta of the Martin-Hopkins equation showed significant positive correlations with dLDL-C (≤130 mg/dL) and triglycerides and a significant negative correlation with HDL-C. In a subgroup of 30 individuals with extreme %Delta values, patterns of gross underestimation were observed, particularly when low LDL-C, low triglycerides, and high HDL-C coincided.
The Martin-Hopkins equation is a superior method for LDL-C estimation and a valuable tool in precision medicine. However, clinicians and laboratory professionals must be aware of its limitations and recognize patterns that could lead to significant LDL-C underestimation. We propose an algorithm for clinical laboratories to provide personalized LDL-C assessments.
估计低密度脂蛋白胆固醇(LDL-C)最常用的方法是Friedewald方程,该方程有显著局限性。然而,已提出了更准确的方法。本研究调查了这些方法的优缺点,并确定了每个方程最适用或最不适用的情况。
对222名个体进行队列研究,进行标准血脂谱评估,包括直接测量其LDL-C(dLDL-C)。还使用Friedewald、Martin-Hopkins和Sampson方程估计LDL-C。分析估计的LDL-C与测量的LDL-C之间的差异(%Delta)与dLDL-C、高密度脂蛋白胆固醇(HDL-C)和甘油三酯水平的关系。
与Friedewald方程(-12.2±9.2)相比,Martin-Hopkins方程(-8.8±9.8)和Sampson方程(-9.5±9.2)的%Delta显著更低(<0.0001)。随着dLDL-C水平降低,所有方程对LDL-C的低估程度都越来越大。Martin-Hopkins方程的%Delta与dLDL-C(≤130mg/dL)和甘油三酯呈显著正相关,与HDL-C呈显著负相关。在30名%Delta值极高的个体亚组中,观察到明显低估的模式,特别是当低LDL-C、低甘油三酯和高HDL-C同时出现时。
Martin-Hopkins方程是估计LDL-C的一种优越方法,也是精准医学中的一个有价值工具。然而,临床医生和实验室专业人员必须意识到其局限性,并认识到可能导致LDL-C显著低估的模式。我们提出了一种算法,供临床实验室提供个性化的LDL-C评估。