From LDL to apolipoprotein B: shifting the lens on cardiovascular risk

This editorial refers to ‘Apolipoprotein B outperforms low density lipoprotein particle number as a marker of cardiovascular risk in the UK Biobank’, by E. Epstein et al., https://doi.org/10.1093/eurjpc/zwaf554 Atherosclerotic cardiovascular disease (ASCVD) remains a major public health challenge worldwide despite major advances in diagnostic strategies and preventive therapies. LDL cholesterol (LDL-C) is a well-established causal factor for ASCVD and a systematic therapeutic target in clinical practice.1 Yet a persistent and clinically important observation challenges its sufficiency: many patients who achieve guideline-recommended LDL-C goals continue to experience major adverse cardiovascular events (MACE). This gap between biology and clinical outcomes highlights a fundamental question: are we measuring the right marker to assess atherogenic burden? LDL-C quantifies the cholesterol content carried within LDL particles (LDL-P) but does not directly reflect the number of circulating atherogenic particles. The risk of ASCVD is driven not only by the cholesterol amount they transport but, more critically, by the total concentration of circulating apolipoprotein B (apoB)-containing particles capable of penetrating the arterial intima. Small, cholesterol-depleted LDL-P —highly prevalent in insulin resistance, diabetes, or hypertriglyceridaemia—carry less cholesterol yet may be more atherogenic due to their enhanced arterial wall penetration.2 Thus, LDL-C does not capture the full atherogenic load. Non-HDL-C partially addresses this gap, incorporating the cholesterol content of all apoB-containing lipoproteins [very low-density lipoproteins, intermediate-density lipoproteins, LDL, lipoprotein(a) (Lp(a))], and its use has been associated with improved ASCVD risk prediction in high-risk populations.3 A large body of genetic, epidemiologic, and clinical trial evidence has consistently shown that apoB is superior to LDL-C and non-HDL-C for ASCVD risk prediction, both in primary and secondary prevention and whether or not patients receive lipid-lowering therapy.4,5 In parallel, the direct quantification of LDL-P—using nuclear magnetic resonance spectroscopy, ion mobility, or ultracentrifugation—has also emerged as a potential tool to refine residual risk.6 Yet whether LDL-P offers added prognostic value beyond apoB has been unclear. In this issue, Epstein et al. provide a timely and rigorous assessment of this question using the UK Biobank, analysing 41 099 participants with 10 years of follow-up.7 Their approach relies on a discordance analysis, a powerful method for identifying which biomarker more accurately reflects true ASCVD risk when two correlated measures diverge. Individuals whose apoB and LDL-P values were concordant had the lowest event rates. The investigators established a regression-based relation between apoB and LDL-P, then quantified for each participant the degree of deviation (‘discordance’) from this expected relationship. Importantly, discordance was assessed as a continuous gradient (±2 to ±30%), a methodological advantage over previous studies relying on arbitrary fixed thresholds (often ±10% or quartiles).8 Even minimal positive discordance of apoB relative to LDL-P (+2%) was associated with a significantly higher risk of MACE (Hazard Ratio 1.1, P < 0.0001). This excess risk increased progressively, reaching +40% for MACE and +150% for coronary events at +30% discordance. In contrast, discordantly high LDL-P did not predict increased risk at any threshold, suggesting that LDL-P offers little incremental value over apoB. The consistency and dose–response nature of these findings strongly reinforce the biological plausibility of apoB as the most accurate and clinically meaningful marker of atherogenic lipoprotein burden. ApoB–LDL-P discordance is not random; it is particularly prevalent in individuals with metabolic syndrome, type 2 diabetes, or elevated triglycerides—conditions characterized by smaller, cholesterol-depleted LDL-P. In such cases, LDL-P may appear elevated without reflecting a proportional increase in atherogenic potential. ApoB, by capturing the total number of atherogenic particles—including triglyceride-rich lipoprotein remnants—more faithfully reflects true ASCVD risk.9 Similarly, in postmenopausal women, metabolic changes lead to denser, more numerous LDL-P. ApoB remains a consistent predictor of global atherogenic risk, though the relationship with coronary events becomes significant only at higher levels of discordance (≥10%). In individuals with high Lp(a) levels (>50 mg/dL), unlike apoB, LDL-P count underestimates risk because Lp(a) particles are not fully accounted for in LDL-P quantification. Given the six-fold higher atherogenicity of Lp(a) compared with LDL-P,10 apoB’s inclusion of Lp(a) particles represents a crucial advantage. Importantly, apoB remained superior even among individuals with low Lp(a). Although prior evidence strongly supports apoB as the causal driver of ASCVD risk,1,2 this study adds valuable confirmation by demonstrating its prognostic superiority in a large cohort with long-term follow-up, using a granular discordance analysis that represents a major methodological strength. Nevertheless, the lack of standardization across LDL-P measurement platforms, together with the retrospective observational design, limits generalizability and precludes causal inference—limitations appropriately acknowledged by the authors. In contrast, apoB assays are inexpensive, standardized, widely available, and analytically robust, reinforcing their suitability for routine clinical practice. Current European and North American prevention guidelines already emphasize apoB as a primary or co-primary therapeutic target, particularly in high-risk or hypertriglyceridaemic patients. The results by Epstein et al. reinforce these recommendations: apoB should be systematically used to guide risk stratification and lipid-lowering decision-making. Importantly, none of the historical lipid-lowering trials were explicitly designed with apoB-based targets as primary endpoints. As new therapies emerge—targeting triglyceride-rich lipoproteins or Lp(a)—apoB may serve as a more relevant biomarker for future, mechanism-driven trials. Promising agents include apoC-III inhibitors, ANGPTL3/4 inhibitors, and Lp(a)-lowering antisense and siRNA therapies, all currently undergoing evaluation for cardiovascular outcomes. These interventions directly modulate the number of atherogenic particles and could position apoB not only as a risk marker but also as a therapeutic endpoint. Despite compelling evidence, apoB is neither systematically measured in routine clinical care nor incorporated into most widely used cardiovascular risk calculators. This gap is no longer justified. Moving forward requires systematic inclusion of apoB in standard lipid panels, clinical education on its interpretation, integration of apoB into risk-prediction tools, and alignment of therapeutic targets with contemporary pathophysiological understanding. In conclusion, the study by Epstein et al. provides a methodologically rigorous and clinically relevant demonstration that apoB outperforms LDL-P and traditional cholesterol-based markers for predicting ASCVD events in a large population cohort. Together with genetic, mechanistic, and clinical trial evidence, these findings strengthen the rationale for adopting apoB as the principal biomarker of atherogenic lipoprotein burden. Its simplicity, robustness, and biological relevance make apoB the most pragmatic tool to guide cardiovascular prevention in modern practice (Graphical Abstract). Bridging the remaining organizational and clinical gaps by integrating apoB into routine evaluation and risk calculators will be essential to fully realize its potential—and ultimately reduce the persistent burden of residual cardiovascular risk. Sterenn Guillerme (Conceptualization [equal]; Writing—original draft [equal]; Writing—review & editing [equal]), Jean-François Perregaux (Conceptualization [supporting]; Visualization [equal]; Writing—original draft [equal]; Writing—review & editing [supporting]), and Antonio Gallo (Conceptualization [lead]; Supervision [lead]; Validation [lead]; Writing—original draft [equal]; Writing—review & editing [lead])