Introduction

Cardiovascular disease remains the leading cause of mortality worldwide. The World Health Organization estimated that 19.8 million people died from cardiovascular diseases in 2022, representing approximately 32% of all global deaths (World Health Organization, 2025). Atherosclerotic cardiovascular disease (ASCVD), including coronary heart disease, ischemic stroke, and peripheral arterial disease, accounts for a substantial portion of this global burden.

For decades, lipid management has centered on low-density lipoprotein cholesterol (LDL-C). This emphasis is well justified. LDL particles play a causal role in atherosclerosis, and randomized clinical trials have consistently demonstrated that lowering LDL-C reduces ASCVD events (Ference et al., 2017; Mach et al., 2020). LDL-C is also widely available, inexpensive, familiar to clinicians, and embedded in major prevention guidelines.

However, LDL-C measures the cholesterol mass carried within LDL particles, not the number of circulating atherogenic particles. In many patients, LDL-C and atherogenic particle burden are concordant. In others, they are not. Discordance is common in patients with metabolic syndrome, diabetes, hypertriglyceridemia, obesity, insulin resistance, and inflammatory or renal disease. In these settings, LDL particles may be cholesterol-depleted but numerous, causing LDL-C to underestimate risk. Conversely, some individuals may have cholesterol-rich particles with relatively fewer circulating atherogenic particles.

Precision lipidology seeks to refine this assessment by moving beyond LDL-C alone. It incorporates ApoB, non-HDL-C, Lp(a), and, in selected cases, advanced lipoprotein testing to more accurately characterize the burden and biology of atherogenic lipoproteins. This approach does not discard LDL-C. Rather, it recognizes that LDL-C is one important measure within a broader framework of cardiovascular risk assessment.

ApoB has emerged as one of the most clinically practical markers in this framework because each major atherogenic lipoprotein particle contains one ApoB molecule. ApoB therefore approximates the total number of circulating atherogenic particles, including LDL, very-low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), remnant particles, and Lp(a) (Soffer et al., 2024). Lp(a), meanwhile, has gained increasing attention because it is largely genetically determined, independently associated with ASCVD and calcific aortic valve disease, and poorly captured by a standard lipid panel (Koschinsky et al., 2024).

This review examines the rise of precision lipidology, focusing on ApoB, Lp(a), particle size, and subclass analysis, advanced measurement technologies, implementation barriers, and emerging therapeutic strategies.

Historical Evolution of Lipid Assessment

The relationship between cholesterol and coronary heart disease was established through decades of epidemiologic and clinical research. The Framingham Heart Study and subsequent risk prediction models helped establish total cholesterol, HDL-C, blood pressure, smoking, diabetes, and age as major determinants of cardiovascular risk (Kannel et al., 1961; Wilson et al., 1998). Later trials demonstrated that statins reduce cardiovascular events by lowering LDL-C, strengthening LDL-C as a central therapeutic target.

Traditional lipid panels include total cholesterol, LDL-C, HDL-C, and triglycerides. LDL-C is often calculated using the Friedewald equation, although newer equations may improve accuracy, particularly at low LDL-C or high triglyceride concentrations. Non-HDL-C, calculated as total cholesterol minus HDL-C, provides a broader estimate of cholesterol carried by ApoB-containing particles and is especially useful when triglycerides are elevated.

Despite the success of LDL-C-based prevention, residual cardiovascular risk persists. Some patients experience ASCVD events despite achieving LDL-C goals, while others with apparently modest LDL-C elevations have high particle burden or inherited risk factors such as elevated Lp(a). These observations led to growing interest in biomarkers that measure atherogenic particle number and inherited lipoprotein risk more directly.

ApoB: Measuring Atherogenic Particle Number

ApoB is the structural apolipoprotein found on the major atherogenic lipoproteins. ApoB-100 is present on VLDL, IDL, LDL, and Lp(a), while ApoB-48 is present on chylomicrons and chylomicron remnants. Because each atherogenic lipoprotein particle carries one ApoB molecule, plasma ApoB concentration reflects the total number of circulating atherogenic particles.

This distinction matters because cholesterol content varies from particle to particle. A person with many small cholesterol-depleted LDL particles may have a deceptively normal LDL-C but a high ApoB. Another person may have fewer cholesterol-rich LDL particles with a higher LDL-C but a lower ApoB. From a biological standpoint, the number of particles entering, being retained within, and interacting with the arterial wall may be more relevant than the cholesterol mass within those particles alone.

Discordance studies have consistently shown that ApoB and LDL particle number can identify risk not fully captured by LDL-C. In the Multi-Ethnic Study of Atherosclerosis, when LDL-C and LDL particle number were discordant, incident cardiovascular disease and carotid intima-media thickness tracked more closely with LDL particle number than LDL-C (Otvos et al., 2011). Similar analyses have shown that ApoB may outperform LDL-C and non-HDL-C for risk prediction, particularly in patients with metabolic risk factors or lipid marker discordance (Sniderman et al., 2019; Soffer et al., 2024).

The 2024 National Lipid Association Expert Clinical Consensus concluded that ApoB is a validated clinical measurement that augments the standard lipid panel and can help guide ASCVD risk assessment and lipid-lowering therapy decisions (Soffer et al., 2024). ApoB is particularly useful in patients with hypertriglyceridemia, diabetes, metabolic syndrome, obesity, very low LDL-C on therapy, suspected discordance, premature ASCVD, or persistent risk despite apparently controlled LDL-C.

Importantly, ApoB should not be framed as a universal replacement for LDL-C. LDL-C remains central to guidelines, trial interpretation, and therapeutic targets. ApoB is best understood as a complementary and often more precise marker of atherogenic particle burden, especially when LDL-C may misrepresent risk.

Lp(a): A Genetically Determined Risk Enhancer

Lp(a) is an LDL-like particle covalently bound to apolipoprotein(a). Its structure gives it several potentially harmful properties: it carries cholesterol, contributes to arterial wall lipid deposition, promotes inflammation, transports oxidized phospholipids, and may influence thrombosis and calcific aortic valve disease.

Lp(a) levels are primarily genetically determined and remain relatively stable throughout life. Lifestyle interventions have minimal effect on Lp(a), and statins do not meaningfully lower it. This makes Lp(a) different from LDL-C, triglycerides, and many other modifiable lipid parameters. Elevated Lp(a) can therefore explain unexpected ASCVD risk in patients with otherwise acceptable standard lipid profiles.

Recent evidence has clarified the magnitude of Lp(a)-associated risk. Björnson and colleagues estimated that Lp(a) is substantially more atherogenic than LDL on a per-particle basis, with an estimated 6.6-fold greater atherogenicity than LDL (Björnson et al., 2024). Although such estimates should be interpreted in context, they reinforce the concept that Lp(a) is not simply another LDL particle. Its unique apolipoprotein(a) component and oxidized phospholipid content likely contribute to its distinctive risk profile.

Guideline and expert recommendations increasingly support broader Lp(a) testing. The National Lipid Association recommends Lp(a) measurement as part of risk assessment, especially in patients with premature ASCVD, family history of premature ASCVD, severe hypercholesterolemia, calcific aortic valve stenosis, or unexplained residual risk (Koschinsky et al., 2024). Many international frameworks support at least once-in-a-lifetime Lp(a) measurement in adults.

At present, there is no approved therapy specifically indicated to reduce Lp(a)-mediated cardiovascular risk. Management, therefore, focuses on identifying elevated Lp(a), intensifying control of all modifiable risk factors, lowering LDL-C or ApoB aggressively when appropriate, and considering PCSK9 inhibitor therapy in selected high-risk patients because monoclonal PCSK9 inhibitors modestly lower Lp(a) while substantially lowering LDL-C.

LDL Particle Size and Subclass Analysis

LDL particles vary in size, density, cholesterol content, and metabolic behavior. Small dense LDL particles are associated with insulin resistance, hypertriglyceridemia, low HDL-C, abdominal obesity, diabetes, and metabolic syndrome. These associations led to interest in LDL particle size as a marker of cardiovascular risk.

However, particle size is closely linked to particle number and metabolic context. Small LDL particles are often perceived as dangerous because they are found in patients with a high number of ApoB-containing particles. When analyses adjust for ApoB or LDL particle number, the independent contribution of particle size is often reduced or eliminated. Recent evidence suggests that the total number of ApoB-containing particles is generally more important than their size (Morze et al., 2025).

This does not mean particle size has no clinical value. Particle size and subclass patterns can provide insight into insulin resistance, remnant metabolism, triglyceride-rich lipoprotein excess, and metabolic phenotype. However, for routine ASCVD risk assessment and treatment targeting, ApoB or non-HDL-C is usually more clinically actionable than detailed LDL subclass distribution.

Thus, precision lipidology should not be reduced to “advanced particle testing.” The most practical clinical evolution is the broader use of ApoB and Lp(a), with particle size and subclass testing reserved for selected cases where results are likely to change interpretation or management.

Technologies Enabling Advanced Lipoprotein Assessment

Nuclear Magnetic Resonance Spectroscopy

Nuclear magnetic resonance spectroscopy enables characterization of lipoprotein particles based on their lipid methyl group signals. NMR-based platforms can estimate concentrations of VLDL, LDL, HDL, and lipoprotein subclasses, as well as particle size measures. These methods are non-destructive and can generate detailed lipoprotein profiles from plasma or serum samples.

NMR testing has been used in epidemiologic studies, metabolic research, and selected clinical contexts. Lipoprotein particle number measured by NMR may improve risk assessment when LDL-C is discordant with particle burden. However, clinical implementation varies by platform, cost, reimbursement, and clinician familiarity.

Advanced approaches such as diffusion-ordered NMR spectroscopy have further refined lipoprotein particle characterization. The Liposcale test, for example, uses diffusion coefficients to estimate lipoprotein particle size and concentration and has shown correlation with apolipoprotein measurements (Mallol et al., 2015).

Ion Mobility Analysis

Ion mobility analysis separates lipoprotein particles based on gas-phase differential electrophoretic mobility. This permits direct quantification of lipoprotein particles across a range of sizes, including HDL, LDL, IDL, and VLDL subclasses (Caulfield et al., 2008). Ion mobility has contributed to research on lipoprotein heterogeneity and metabolic risk patterns.

In clinical research, ion mobility-derived subfractions have been associated with insulin resistance and cardiovascular risk phenotypes (Musunuru et al., 2009). As with NMR, the main barriers to widespread use include standardization, cost, availability, and uncertainty about when subclass results alter management beyond ApoB, non-HDL-C, triglycerides, and Lp(a).

Integrated Lipidomics Platforms

Lipidomics combines high-dimensional lipidomics with metabolic and clinical data. Mass spectrometry-based lipidomics can characterize lipid species in much greater detail than standard lipid panels. When combined with NMR, genomics, proteomics, and clinical phenotyping, lipidomics may help identify novel risk signatures and therapeutic targets.

For now, lipidomics remains primarily a research tool. Its future clinical value will depend on reproducibility, standardization, interpretability, cost-effectiveness, and evidence that lipidomic-guided treatment improves outcomes.

Clinical Implementation of Precision Lipidology

Precision lipidology is most clinically useful when it improves decision-making. ApoB and Lp(a) meet this standard more clearly than many advanced lipid subfraction measures.

ApoB can help determine whether a patient has achieved adequate reduction in atherogenic particle burden. This is particularly relevant when LDL-C is low, but triglycerides remain elevated, when metabolic syndrome is present, or when residual ASCVD risk appears disproportionate to LDL-C. ApoB can also support intensification of therapy in patients whose standard lipid panel appears falsely reassuring.

Lp(a) measurement identifies inherited risk that is otherwise invisible on routine lipid testing. An elevated Lp(a) may justify earlier or more intensive LDL-C/ApoB lowering, family screening, closer attention to lifetime risk, and avoidance of undertreatment in patients whose traditional risk score may underestimate risk.

The 2022 American College of Cardiology Expert Consensus Decision Pathway and the 2019 ESC/EAS dyslipidemia guideline support intensifying lipid-lowering therapy in high-risk patients using statins, ezetimibe, PCSK9 inhibitors, bempedoic acid, inclisiran, and other agents depending on risk category and response (Lloyd-Jones et al., 2022; Mach et al., 2020). ApoB and Lp(a) can help refine these decisions.

Clinical implementation should prioritize:

1. ApoB measurement in patients with diabetes, metabolic syndrome, hypertriglyceridemia, obesity, premature ASCVD, or suspected LDL-C discordance.
2. Lp(a) measurement at least once in adulthood, especially in patients with premature ASCVD, family history of premature ASCVD, severe hypercholesterolemia, calcific aortic valve disease, or unexplained residual risk.
3. More aggressive treatment of modifiable risk factors when ApoB or Lp(a) indicates a higher lifetime risk.
4. Cautious use of advanced particle size testing when it is unlikely to change management.

Therapeutic Implications

Statins, Ezetimibe, and Bempedoic Acid

Statins remain first-line therapy for LDL-C and ApoB reduction. They lower LDL-C by reducing hepatic cholesterol synthesis and increasing LDL receptor expression. Ezetimibe reduces intestinal cholesterol absorption and provides additional LDL-C lowering when added to statins.

Bempedoic acid inhibits ATP citrate lyase upstream of HMG-CoA reductase and can reduce LDL-C in statin-intolerant patients or as add-on therapy. The CLEAR Outcomes trial demonstrated that bempedoic acid reduced major adverse cardiovascular events in statin-intolerant patients, making it a clinically relevant option for selected patients requiring additional LDL-C or ApoB reduction (Nissen et al., 2023).

PCSK9 Inhibitors

PCSK9 monoclonal antibodies substantially lower LDL-C and ApoB by increasing LDL receptor recycling. Evolocumab and alirocumab have demonstrated cardiovascular outcome benefits in large randomized trials.

In FOURIER, evolocumab reduced LDL-C and significantly lowered cardiovascular event risk in patients with established ASCVD (Sabatine et al., 2017). In ODYSSEY OUTCOMES, alirocumab reduced recurrent ischemic events after acute coronary syndrome (Schwartz et al., 2018). These agents also modestly reduce Lp(a), although they are not specifically approved as Lp(a)-targeted therapies.

Inclisiran

Inclisiran is a small interfering RNA therapy that reduces hepatic PCSK9 synthesis. It produces durable LDL-C lowering with twice-yearly maintenance dosing after initial and 3-month doses. ORION-9, ORION-10, and ORION-11 demonstrated substantial LDL-C reductions in patients with familial hypercholesterolemia or ASCVD risk requiring additional LDL-C lowering (Raal et al., 2020; Ray et al., 2020).

However, definitive cardiovascular outcomes data for inclisiran remain pending. Ongoing outcome trials are expected to clarify whether its LDL-C-lowering translates into event reduction comparable to that of PCSK9 monoclonal antibodies.

Lp(a)-Targeted Therapies

Lp(a)-targeted therapy is one of the most important frontiers in precision lipidology. Antisense oligonucleotides and small interfering RNA therapies can markedly lower Lp(a) by reducing hepatic apolipoprotein(a) production.

Pelacarsen, an antisense oligonucleotide, has produced substantial reductions in Lp(a) in phase 2 studies, and the Lp(a)HORIZON outcomes trial is evaluating whether Lp(a) reduction decreases cardiovascular events (Tsimikas et al., 2020). Olpasiran, a small interfering RNA, significantly reduced Lp(a) levels in the OCEAN(a)-DOSE trial among patients with established ASCVD and elevated Lp(a) (O’Donoghue et al., 2022). Lepodisiran, another long-duration small interfering RNA, has shown prolonged Lp(a) reduction in phase 1 and phase 2 studies (Nissen et al., 2023; Nissen et al., 2025).

These agents are not yet approved for clinical outcome reduction. Their ultimate role depends on whether large outcome trials demonstrate that lowering Lp(a) reduces myocardial infarction, stroke, revascularization, cardiovascular death, or aortic valve disease progression.

Barriers to Adoption

Standardization

ApoB measurement is more standardized than many advanced lipid technologies, but further harmonization remains important. Lp(a) measurement is more complex because results may be reported in mass concentration or particle concentration, and assays can be affected by apolipoprotein(a) isoform size. Reporting Lp(a) in nmol/L is generally preferred because it better reflects particle concentration.

Advanced lipoprotein tests, including NMR and ion mobility, face additional standardization challenges. Results may not be interchangeable across platforms, and clinicians may struggle to interpret subclass data in a management-relevant way.

Cost and Reimbursement

ApoB and Lp(a) are relatively inexpensive compared with many advanced tests, but reimbursement remains inconsistent. Lack of coverage can reduce uptake, especially in primary care and underserved settings. Advanced lipoprotein testing is more costly and less widely reimbursed, limiting routine adoption.

Clinician Education

Many clinicians are comfortable with LDL-C but less familiar with ApoB, Lp(a), discordance, or particle-based risk. Education should emphasize practical interpretation rather than excessive complexity. Clinicians need to know when to order ApoB or Lp(a), how to interpret the results, and how those results should affect therapy.

Health Equity

Precision medicine can worsen disparities if access is limited to affluent or highly specialized settings. Lp(a) is especially important from an equity perspective because levels vary across ancestry groups, and under-testing may contribute to under-recognition of inherited risk. Implementation should prioritize affordable, standardized testing and broad access to evidence-based therapy.

Artificial Intelligence and Future Risk Prediction

Artificial intelligence and machine learning may eventually integrate lipid markers, genetics, imaging, clinical history, medication response, and social determinants of health into individualized risk models. These tools could improve the identification of patients whose risk is underestimated by traditional calculators.

However, AI-based lipidology must be developed carefully. Models trained on narrow or nonrepresentative populations may perpetuate bias. Clinical decision support must be transparent, validated, and outcome-driven. AI should enhance, not replace, clinical judgment.

The most promising future model may combine simple, scalable biomarkers such as ApoB and Lp(a) with selective use of imaging, genetics, and advanced lipidomics in patients whose risk remains uncertain.

Discussion

Precision lipidology represents an evolution rather than a rejection of traditional lipid management. LDL-C remains essential, but it is not always sufficient. ApoB provides a more direct measure of atherogenic particle number, while Lp(a) identifies an inherited risk factor that standard lipid panels often miss.

The clinical case for ApoB is strongest in patients with discordance between cholesterol mass and particle number. In insulin resistance, diabetes, metabolic syndrome, obesity, and hypertriglyceridemia, LDL-C may underestimate atherogenic burden. ApoB can reveal that risk and support more intensive therapy.

The clinical case for Lp(a) is also compelling. Lp(a) is genetically determined, stable over the lifespan, associated with ASCVD and aortic valve disease, and not adequately addressed by lifestyle intervention. Identifying elevated Lp(a) allows clinicians to intensify management of modifiable risk factors and consider family screening.

Particle size and subclass analysis remain more nuanced. Small dense LDL is associated with metabolic risk, but much of its risk appears to be mediated by higher ApoB-containing particle number. Therefore, particle size should be interpreted as a metabolic clue rather than a primary treatment target.

The future of precision lipidology will depend on outcome evidence. ApoB and Lp(a) already play strong roles in risk assessment. The next step is to determine how biomarker-guided therapy improves clinical outcomes in diverse populations. The arrival of Lp(a)-lowering agents may transform Lp(a) from a risk marker into a direct therapeutic target, but outcome trials are needed before routine pharmacologic Lp(a)-lowering can be recommended.

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