Old tools, new insights, unlocking the full potential of the standard lipid panel treasure box

Introduction

Cardiovascular disease remains the leading cause of morbidity and mortality globally, making the accurate assessment of lipid risk factors a cornerstone of modern medicine. For decades, the clinical community has relied on a standardized approach: the basic lipid panel, traditionally with low-density lipoprotein cholesterol (LDL-C) calculated through the Friedewald equation (1). When complex cases arose—specifically those involving hypertriglyceridemia—laboratories often pivoted to direct homogeneous LDL-C assays, assuming that a direct biochemical measurement must inherently be superior to a calculation.

However, a convergence of recent research (2,3) suggests that this traditional hierarchy is not only outdated but potentially harmful to patient care when compared to modern calculations. The standard lipid panel, when unlocked with modern mathematical tools, is a “treasure box” of data that yields insights far superior to outdated formulas and expensive direct assays. The evidence is now overwhelming: it is time to phase out the Friedewald equation, limit our reliance on direct LDL-C measurement, and embrace a new generation of calculations that offer precision medicine at no additional cost.

There is an urgent need to translate results from the lipid panel into a more nuanced understanding of disease risk. This includes addressing the inaccuracy of LDL-C calculations in specific populations and extracting additional, often overlooked markers such as non-high density lipoprotein cholesterol (non-HDL-C), small dense LDL-C (sdLDL-C), and LDL-triglyceride (LDL-TG). By re-evaluating our approach to the lipid panel, we can unlock a wealth of data.

The evolution of LDL-C measurement

In 1951, Gofman and colleagues revolutionized the study of atherosclerosis by separating and isolating human blood lipids and lipoproteins using ultracentrifugation. Their study demonstrated the need to identify and measure different classes of lipoproteins, as specific classes are associated with atherosclerosis (4). In 1955, Bragdon and his team developed a two-step method to identify several lipoprotein fractions from the same serum sample and established density cutoffs for different lipoprotein classes. They were the first group to identify LDL-C in the beta region and HDL-C in the alpha region (4). This method was further adopted by the National Cholesterol Education Program (NCEP) in 1995 and became the gold standard method for LDL-C measurement; it is often referred to as the beta-quantification method (5). Although this method is highly standardized and high-resolution, it is tedious, expensive, and not readily available in routine laboratory settings. It also cannot distinguish LDL-C from intermediate density lipoprotein cholesterol (IDL-C) and lipoprotein(a) [LP(a)] (6); therefore, it often overestimates the concentration of LDL-C. However, the overestimation is considered clinically beneficial, as both IDL-C and LP(a) are atherogenic and should be considered during treatment decisions (7).

To address the overestimation of LDL-C, Chung and his colleagues developed a single vertical spin density gradient ultracentrifugation method, known as the Vertical Auto Profile (VAP), to quantitatively profile the major plasma lipoproteins in the early to mid-1980s. Plasma lipoproteins are first separated by a short spin in a vertical rotor, and the effluent cholesterol is monitored using an enzymatic cholesterol method. The VAP method successfully measures HDL-C, LDL-C, and very-low-density lipoprotein cholesterol (VLDL-C), and detects IDL-C and other lipoprotein variants (8). The Segrest group further optimized the method by improving the resolution of IDL-C and LP(a) using a non-segmented continuous flow analyzer (VAP-II) (9). Compared with the beta-quantification method, the VAP-II can accurately distinguish and measure LDL-C, IDL-C, and LP(a). However, as an analogue of the beta-quantification method, VAP-II is also cumbersome and not readily available in routine laboratory settings. In addition, VAP is subject to bias when TG is high (10).

Although the heterogeneity in LDL-C particle size is well known, it was not until 1988 that Krauss and colleagues first identified two LDL-C phenotypes using gradient gel electrophoresis (11). Pattern A consists of large, buoyant LDL-C particles, whereas pattern B consists of sdLDL-C particles (11). In addition to measuring concentration, polyacrylamide gradient gel electrophoresis can also determine lipoprotein particle size. Moreover, this method introduces less distortion and better preserves native particle integrity. As a result, it helps to improve risk stratification, particularly in patients with cardiovascular disease (12). However, like beta-quantification and VAP-II, the method is also cumbersome and time-consuming.

In the 2000s, nuclear magnetic resonance (NMR) spectroscopy was introduced as a method for lipoprotein measurement. Rather than directly measuring lipoprotein cholesterol concentrations, this technique quantifies lipoprotein particle numbers by detecting characteristic signals from different lipoprotein classes. Specifically, ¹H-NMR spectroscopy detects signals from the methyl groups of lipoproteins. Because lipoprotein classes differ in particle size, composition, and the number of methyl groups, each lipoprotein particle emits a characteristic composite signal (13). Since these methyl signals are independent of the concentrations of cholesterol esters, TG, phospholipids, and the degree of unsaturation of lipid fatty acyl chains, ¹H-NMR offers advantages for lipoprotein assessment in individuals with insulin resistance, diabetes, and hypertriglyceridemia (14). It is important to note that ¹H-NMR indirectly estimates cholesterol concentrations rather than measuring them directly. The method requires specialized and costly instruments and demands expertise.

In 2008, the Krauss group successfully measured lipoprotein particles using a gas-phase differential electrophoretic macromolecular mobility-based (ion mobility) method. This approach directly measured both lipoprotein particle size and concentration (15). In the early 2010s, gel-permeation high-performance liquid chromatography (HPLC) was used to separate and quantify lipoprotein particles by size, with minimal sample distortion (16). Once established, the method is high-throughput via automation. However, neither ion mobility nor gel-permeation HPLC methods are widely available in clinical laboratory settings due to their high reliance on specialized, expensive instruments and expertise.

Although these advanced methods offer detailed and informative assessments of LDL-C and other lipoproteins, they share the same drawbacks: they are cumbersome, expensive, and entirely unsuitable for the high-throughput demands of modern routine laboratories. The “Friedewald revolution” of 1972 addressed these limitations by utilizing a simple equation that democratized lipid testing, allowing LDL-C to be estimated from total cholesterol, HDL-C, and TG (1). Despite its widespread adoption, the Friedewald equation has notable limitations, most importantly the systematic underestimation of LDL-C in patients with TG levels greater than 400 mg/dL. In response, the diagnostics industry developed automated direct LDL-C assays, which were intended to provide a “safe harbor” for testing non-fasting or hypertriglyceridemic samples by bypassing the calculation errors inherent to the Friedewald method. In 1998, the Sugiuchi group developed the first fully automated homogeneous LDL-C assay that does not require precipitation or centrifugation. In the method, a selective surfactant and salt were used to block non-LDL lipoproteins, allowing the assay to measure cholesterol only in LDL (17,18). Following the development of the first homogeneous LDL-C assay, many homogeneous LDL-C assays have been introduced. These methods typically include detergent-based, polymer-based, or immune-selective methods that eliminate non-LDL lipoproteins prior to enzymatic cholesterol measurement (17,18). Although homogeneous LDL-C assays are fully automated, easy to use, and cost-effective, they are not fully standardized and may lack accuracy, particularly in patients with dyslipidemias (2,19).

The modern calculation era: moving beyond direct assays

We have now entered the Modern Calculation Era (2013–2025) of LDL-C assessment. The Martin–Hopkins equation, as the first breakthrough, introduced an adjustable factor for the TG-to-VLDL-C ratio using VAP as the reference method, significantly improving accuracy over the Friedewald equation (20). The equation was later expanded from the original formula utilizing a 180-cell table to determine the adjustable factor to the extended version utilizing a 420-cell stratification table. This expansion allows the formula to estimate LDL-C in patients with TG levels up to 800 mg/dL (9.04 mmol/L) (21). Large-scale validation across diverse populations—including against the reference beta-quantification method and patients with low LDL-C, hypertriglyceridemia, and on lipid-lowering therapies—demonstrates the superiority of the Martin equation over the traditional Friedewald equation (22,23).

In 2020, Sampson and colleagues developed and validated a new LDL-C calculation equation using a large clinical dataset from the National Institutes of Health Clinical Center (NIH) collected between 1976 and 1999 (the Sampson–NIH equation) (24). The Sampson–NIH equation was compared with reference methods and demonstrated significantly reduced bias compared with the Friedewald equation, particularly in patients with hypertriglyceridemia.

In 2025, the equation was further modified using datasets from the Mayo Clinic and the FOURIER clinical trial of evolocumab, with a focus on accurately estimating LDL-C at very low cutoffs (the modified Sampson equation) (3). The modified Sampson equation showed better alignment with the reference method at lower clinical decision cutoffs—key decision points in the era of intensive lipid-lowering therapies—and improved accuracy compared with other equations for low LDL-C. It accurately identified high-risk patients who could benefit from more intensive lipid-lowering therapy but had not yet achieved their LDL-C goals.

Concurrently, a growing body of evidence supports limiting the use of the Friedewald equation and direct homogeneous LDL-C assays for clinical decision-making in these patient populations. Studies indicate that both the Friedewald equation and direct methods may produce substantial inaccuracies and increase the risk of LDL-C misclassification, particularly in patients with complex lipid profiles. Notably, recent investigations have demonstrated that modern LDL-C calculation methods outperform direct homogeneous assays in patients with hypertriglyceridemia and low LDL-C concentrations (2,25,26).

Professional society guidelines in recent years have taken into consideration these new advances. The 2026 multi-society guideline on managing cholesterol recommends either the Martin or the Sampson equation over the Friedewald equation for reporting LDL-C (27), and limitations in direct LDL-C measurement were pointed out in guidelines of laboratory societies (28,29).

Expanding the “Treasure Box”: power of the lipid panel beyond LDL-C

While a direct LDL-C assay reports a single variable, the standard lipid panel offers a comprehensive view of the patient’s lipid profile. By utilizing the entire panel, laboratories can offer insights that go beyond a single LDL-C number.

The argument for modernizing lipid reporting extends beyond merely correcting LDL-C. The true “power of the panel” lies in its ability to provide a holistic metabolic snapshot that isolates variables often missed by direct measurement. By utilizing the full spectrum of data in a standard panel (total cholesterol, HDL-C, and TG), laboratories can calculate novel biomarkers that serve as powerful risk enhancers.

Estimated sdLDL-C (E-sdLDL-C)

It is well established that small, dense LDL (sdLDL) particles are more atherogenic than their large, buoyant counterparts due to greater arterial wall penetration and susceptibility to oxidation. While sdLDL-C has previously required specialized, expensive testing, new research confirms it can be accurately estimated from the standard lipid panel (30).

In a massive prospective analysis of the UK Biobank cohort (n=271,760), E-sdLDL-C proved to be a superior predictor of atherosclerotic cardiovascular disease (ASCVD) compared to LDL-C. Unlike LDL-C, which exhibits a J-shaped risk curve, E-sdLDL-C demonstrates a linear, dose-response relationship with ASCVD risk (31).

Most compelling is the utility of E-sdLDL-C in discordant cases. In patients with low LDL-C but high E-sdLDL-C, the risk for ASCVD was found to be 31% higher, identifying a vulnerable subpopulation that traditional metrics would overlook. Even after adjusting for apolipoprotein B (ApoB)—often considered the premier risk marker—E-sdLDL-C retained significant predictive value. This metric acts as a potent “risk-enhancer” test that costs the laboratory nothing in additional reagents.

Estimated LDL-TG (eLDL-TG)

Another emerging marker derived from the standard panel is the TG content of LDL particles (LDL-TG). While TG is typically associated with VLDL, substantial amounts can reside on LDL particles, particularly in states of metabolic dysfunction like obesity and diabetes (32). High LDL-TG is a marker of TG-rich lipoprotein remnants and atherosclerotic risk (33).

Wolska et al. developed an equation for estimating LDL-TG (eLDL-TG) and found it to be a stronger predictor of ASCVD events than LDL-C in primary prevention cohorts (34). When used as a risk-enhancer test, eLDL-TG identified approximately 50% more high-risk individuals than current lipid-enhancer rules based on TG or LDL-C alone. Like E-sdLDL-C, this marker allows for better stratification of residual risk without requiring non-standard assays.

Furthermore, because modern calculations utilize total cholesterol and HDL-C, they inherently support the reporting of non-HDL-C. This robust secondary target captures the full burden of all atherogenic ApoB-containing particles (35). When laboratories combine the modified Sampson equation for LDL-C, automated non-HDL-C reporting, and novel markers like E-sdLDL-C and eLDL-TG, they provide a comprehensive risk profile that direct assays simply cannot match.

This approach also supports the generation of automated Estimated ASCVD (eASCVD) risk scores. By integrating the panel’s components with patient age, laboratories can provide a decision aid that shows 90% concordance with standard risk scores. This serves as an automated decision aid for statin therapy, identifying statin-eligible patients with high specificity of 97.5% (36).

Conclusions

The medical community stands at a crossroads. We can continue to rely on the comfortable but flawed tools of the past—the Friedewald equation and direct LDL-C assays—or we can accept the overwhelming evidence that modern mathematics provides a clearer picture of patient risk. Both the extended Martin equation and the Sampson equation outperform Friedewald or direct assays at the clinically critical thresholds needed for modern lipid management, and they were both recommended in the most recent multi-society guideline.

The addition of calculated E-sdLDL-C, eLDL-TG, and risk scores without requiring additional laboratory tests transforms the standard lipid panel from a basic screening tool into a precision medicine instrument. However, these markers are primarily derived for prognostic evaluation. Their clinical utility remains unclear, and due to the lack of integration into interventional trials, they are not yet ready for widespread routine clinical practice.

While calculated LDL-C remains the best pragmatic tool today, the field may eventually move toward ApoB measurements for true particle quantification. Until then, it is time to trust the math. We must stop leaving valuable data on the table and start unlocking the full potential of the “treasure box”, the standard lipid panel.

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-2026-1-0012/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-2026-1-0012/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.

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