Refining the accuracy of calculated low-density lipoprotein cholesterol: half a century after Friedewald

Low-density lipoprotein cholesterol (LDL-C) is a key therapeutic target for preventing atherosclerotic cardiovascular disease. Numerous randomized controlled trials and meta-analyses have demonstrated an approximately linear relationship between the magnitude of LDL-C reduction and the relative reduction in cardiovascular event risk, with each 10 mg/dL decrease in LDL-C associated with an estimated 5–10% reduction in cardiovascular events (1,2). Recently, treatment strategies aiming to achieve LDL-C levels below 55 or 70 mg/dL have become standard in patients classified as having high cardiovascular risk (3). When these targets are not attained, the addition of potent lipid-lowering therapies, including proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, is often considered (4). Consequently, the accuracy of reported LDL-C values has become increasingly critical for clinical decision-making.

Although β-quantification is the reference method for LDL-C measurement, it is not routinely available in everyday practice owing to practical constraints (5-7). Consequently, calculated LDL-C remains central to clinical reporting, underscoring the importance of interpreting equation-based estimates in context (8,9).

The systematic bias of the Friedewald equation in the low LDL-C and high triglyceride (TG) ranges has been well-recognized (5). In particular, in the low LDL-C range, patients who have not truly achieved their therapeutic targets may be misclassified as having done so, a misinterpretation with potentially important clinical consequences. Beyond the Friedewald equation, numerous formulas for calculating LDL-C have been proposed, including the Martin-Hopkins and Sampson equations, with more than 23 distinct formulas reported to date (7,10). A large-scale analysis of approximately 5 million clinical samples demonstrated marked variability in accuracy among these formulas, showing that many existing equations—including Friedewald’s—exhibit substantial inaccuracy in the low LDL-C and high TG ranges (7).

In this context, the study entitled “A Modified Sampson-NIH Equation with Improved

Accuracy for Estimating Low Levels of Low-Density Lipoprotein-Cholesterol” provides a comprehensive evaluation of the performance of a newly proposed modified Sampson equation in comparison with the Friedewald, Martin-Hopkins, and original Sampson equations (11). Using approximately 34,000 samples comprising routine clinical data from the Mayo Clinic and data from the FOURIER trial population treated with evolocumab (n=9,605), the investigators compared the performance of these four equations against LDL-C values measured via β-quantification. A major strength of this study is the inclusion of the FOURIER cohort, which provides a rich dataset in the low LDL-C range of approximately 30–70 mg/dL; this range has increasing clinical relevance in the era of intensive lipid-lowering therapy (12).

The modified Sampson equation is a physiologically redesigned model based on the original Sampson equation (13). Total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) are combined into non-HDL-C, with the coefficient fixed at one, thereby directly reflecting the cholesterol content carried by atherogenic lipoproteins. The intercept is constrained to zero, ensuring a natural relationship in which an estimated LDL-C of zero corresponds to a true LDL-C of zero. In addition, the three remaining terms—TG, TG × non-HDL-C, and TG²—are optimized using regression analysis with β-quantification LDL-C as the dependent variable. This structure is specifically designed to correct estimation errors under conditions of low LDL-C and elevated TG levels.

The principal value of this study lies in its demonstration that the modified Sampson equation exhibits the smallest bias and error across a wide range of conditions, with particularly favorable performance in the low LDL-C range, and in its detailed evaluation of misclassification around the clinically relevant treatment thresholds of 55 and 70 mg/dL. In the current era, in which intensive lipid-lowering therapies—including PCSK9 inhibitors—enable the achievement of very low LDL-C levels, accurate assessment in the low LDL-C range is clinically important. This study suggests that the modified Sampson equation may be a useful estimation method in this context. Notably, it demonstrated improved accuracy compared with the Martin-Hopkins method in the higher TG range, which may broaden its applicability in clinical practice.

Nevertheless, several factors warrant consideration when generalizing the modified Sampson equation. The inclusion of a substantial number of patients treated with PCSK9 inhibitors, particularly from the FOURIER trial, means that the optimization of the equation may have been influenced by populations with very low LDL-C levels (12). From a practical standpoint, it is plausible that this data composition contributes to the favorable performance observed in the ultra-low LDL-C range, and broader validation across diverse real-world settings is warranted. Therefore, whether this equation demonstrates comparable performance not only in intensively treated, clinically stable patients but also in clinical settings characterized by marked fluctuations in lipid metabolism, such as the early phase following acute coronary syndrome, or under varying dietary conditions, requires further investigation. In addition, careful evaluation is warranted to determine whether this equation is equally applicable in general screening settings, particularly among younger populations (14,15). Moreover, most of the data analyzed in this study are derived from the U.S. healthcare environment; similar performance cannot be assumed in regions with different measurement systems, patient characteristics, and dyslipidemia phenotypes. Across Europe, Asia, and other regions worldwide, distributions of LDL-C and TG, as well as lifestyle factors and comorbidity profiles, may differ substantially from those in the United States. Accordingly, statements regarding the superiority of the modified Sampson equation should be interpreted cautiously, given that its performance is context dependent and may vary across age groups, ethnicities, and lipid phenotypes. However, improved analytical accuracy of LDL-C estimation does not directly translate into improved clinical outcomes unless it is appropriately integrated into guideline-based decision pathways and evaluated in clinical practice. Furthermore, implementing the modified Sampson equation in routine practice requires seamless integration with laboratory information systems and coordinated efforts across laboratories, including validation, standardized reporting, and consideration of operational costs. Given that estimation methods may continue to evolve, standardized and update-friendly frameworks supported by professional society guidance and ongoing quality monitoring may facilitate broader adoption. Therefore, external validation in diverse populations is essential to further define the applicability and limitations of the modified Sampson equation.

From this perspective, organizing the major LDL-C calculation formulas by developmental population helps clarify their respective scopes of applicability.

Table 1 provides a concise summary of these major LDL-C estimation equations (8,10,11,13). As illustrated in this table, each equation reflects the characteristics of the population used for its development, underscoring the importance of interpreting calculated LDL-C values in light of this background. In recent years, attention has shifted not only to the absolute LDL-C values generated by a single equation but also to the discrepancies between equations as a means of capturing patient-specific lipid metabolism and clinical characteristics. In particular, the discrepancies between the Friedewald and Martin-Hopkins equations have been suggested to serve as a useful predictive marker for identifying pathogenic variants of familial hypercholesterolemia (16,17). Whether evaluating the divergence between the modified Sampson equation and conventional formulas offers additional clinical insight remains to be determined. This approach should be considered hypothesis-generating rather than an established clinical application. However, it implies that LDL-C equations may not only be “chosen” for reporting but also compared and interpreted in combination.

The present study aptly illustrates the notion that “the devil is in the details” in the field of calculated LDL-C. Emerging half a century after the introduction of the Friedewald equation, the modified Sampson equation represents an important advance toward a calculation method better aligned with contemporary patient profiles. However, no equation is universally applicable. To interpret these equations appropriately, one must understand the populations on which they were developed and the conditions in which they demonstrate strengths and weaknesses. Importantly, updating LDL-C estimation equations is an analytical issue, and potentially influences patient communication and longitudinal treatment continuity.

In future, discussions will be needed regarding how the modified Sampson equation can be implemented in clinical practice while maintaining continuity with existing LDL-C reporting and how it should be positioned within clinical guidelines. Calculated LDL-C is undergoing the first major update in its 50-year history.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Laboratory and Precision Medicine. The article has undergone external peer review.

Peer Review File: Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-2025-1-80/prf

Funding: This study was supported by JSPS KAKENHI (grant number JP24K11192).

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-2025-1-80/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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