The HDL-specific phospholipid efflux (HDL-SPE) assay is a novel cell-free measure of HDL function that is inversely associated with coronary artery disease. However, the effect of exercise training on HDL-SPE is unknown. The purpose of this study was to examine the effect of exercise training on HDL-SPE in a large, diverse cohort free of overt disease. Clinical and functional measures of HDL were taken before and after 20 weeks of endurance exercise training in 508 participants from the HERITAGE Family Study. Associations of HDL-SPE with HDL-related traits were examined using Pearson's correlations at baseline and following exercise training (significance: P < 7.4 × 10−4). The effect of exercise training on HDL-SPE was examined using paired t-tests (significance: P < 0.05). Mean (SD) HDL-SPE was 1.40 (0.19) and higher in females compared with males and in White participants compared with Black participants. Baseline HDL-SPE was strongly associated with HDL-C (r = 0.45) and apoA-I (r = 0.43, both P < 6.9 × 10−24) but not with measures of cholesterol efflux. Mean HDL-SPE increased (0.023, P = 0.002) following exercise training, but these increases only occurred in those with the lowest baseline HDL-SPE levels. Change in HDL-SPE was associated with changes in HDL-C (r = 0.27), medium HDL concentration (r = 0.24), and apoA-I and HDL size (r = 0.17, all P < 1.3 ×10−4). HDL-SPE increased following regular exercise, and changes in HDL-SPE were related to changes in HDL size and subclass concentrations. Our findings demonstrate that individuals at higher risk for coronary artery disease may experience the largest benefits from exercise training as related to this novel biomarker of HDL function.

This review describes the recently developed equations for calculating Low-density lipoprotein cholesterol (LDL-C), and equations for estimating small dense LDL-cholesterol (sdLDL-C), and LDL-triglycerides (LDL-TG) for atherosclerotic cardiovascular disease (ASCVD) risk assessment. The new Modified Sampson-NIH equation provides a more accurate estimation of LDL-C across a wide range of TG levels compared to the traditional and still commonly used Friedewald equation. Furthermore, it is more accurate compared to other equations at the low LDL-C cutpoints used for high-risk and very high-risk ASCVD patients and is valuable for deciding the need for additional lipid-lowering therapy. New equations for calculating sdLDL-C and LDL-TG use the same lipid parameters as for calculating LDL-C but offer additional insights into atherogenic lipoprotein burden. High plasma TG and very low LDL-C concentrations necessitate more accurate LDL-C calculations, which can be readily adopted without additional cost to improve ASCVD risk management.

In this Tools of the Trade article, Zubiran and Remaley describe the importance of the modified Sampson–National Institutes of Health equation as a tool to calculate LDL-cholesterol concentrations.

Background:Familial dysbetalipoproteinemia (FDB) is a genetic lipoprotein disorder that can develop in patients homozygous for the APOE2 genotype (ε2/ε2). It is associated with decreased clearance of remnant lipoproteins and increased atherosclerotic cardiovascular disease (ASCVD) risk disproportionate to their level of LDL-C. A goal of this study was to develop a screening test for the ε2/ε2 genotype based on routinely available lipid tests and to determine those at most risk for ASCVD. Methods:After assembly of a primary prevention cohort from the UK Biobank (n= 269,895), gene array and exome data was utilized to classify patients as being ε2/ε2 genotype positive or negative. Lipid profiles and APOB levels were extracted and the number of ASCVD events was tabulated during a 15-year follow-up period. Results:Using a newly developed equation for estimating APOB (eAPOB) with lipid panel test results, the ratio of measured APOB to eAPOB was better than any other individual lipid test or ratio for identifying patients with the ε2/ε2 genotype (AUC: APOB/eAPOB: 0.990 (0.986-0.994), nonHDL-C/APOB: 0.961 (0.952-0.970), APOB: 0.955 (0.949-0.961), VLDL/TG: 0.788 (0.771-0.804)). The majority of ε2/ε2 patients could be identified with the APOB/eAPOB ratio even before they expressed the FDB phenotype with elevated TG and nonHDL-C. The PCE or PREVENT risk equations were the most accurate method for identifying higher risk patients (AUC: PREVENT: 0.690 (0.637-0.742), PCE: 0.697 (0.645-0.749)). Conclusion:The APOB/eAPOB ratio can be used to accurately identify the ε2/ε2 genotype and conventional risk equations are the best method for determining those at risk for ASCVD.

BACKGROUND:Direct measurement of low-density lipoprotein cholesterol (LDL-C) is widely used and recommended by professional society guidelines despite its potential limitations in patients with hypertriglyceridemia and low LDL-C. This study evaluated the performance of 3 direct LDL-C (LDL-CD) assays, 2 modern LDL-C calculation methods [LDL-C Martin (LDL-CM), LDL-C modified Sampson (LDL-CS)] and the conventional Friedewald (LDL-CF) method against the reference method, beta-quantification (LDL-CBQ). METHODS:A total of 181 remnant sera from patients with standard lipid panel orders or from patients with LDL-CBQ orders with triglycerides (TG) ≥ 400 mg/dL (4.5 mmol/L), or with TG ≥ 150 mg/dL (1.69 mmol/L) and LDL-C < 70 mg/dL (1.8 mmol/L) were included. LDL-CD and lipid panel data were gathered from Abbott Alinity, Roche Cobas, and Siemens Atellica platforms. RESULTS:LDL-CD among the 3 platforms showed a median CV of 11.2%. In patients with TG <400 mg/dL, LDL-CM and LDL-CS demonstrated less bias and less misclassification at the clinical decision LDL-C levels than LDL-CF or LDL-CD. In the 400 to 800 mg/dL (9.0 mmol/L) TG group, LDL-CS was superior to LDL-CD or LDL-CM in accuracy. When TG is ≥ 800 mg/dL, LDL-CD (Roche) showed substantial bias from LDL-CBQ while LDL-CS (Roche) showed smaller but significant bias. CONCLUSIONS:In summary, LDL-CD or LDL-CF showed little advantage over the 2 modern LDL-C calculation methods. LDL-CS showed the best overall correlation with LDL-CBQ and therefore is recommended to replace LDL-CF and potentially LDL-CD when making clinical decisions in patients with low LDL-C and hypertriglyceridemia.

BACKGROUND:A key step in primary prevention is the assessment of atherosclerotic cardiovascular disease (ASCVD) risk. Risk enhancer tests are additional tools used to further improve ASCVD risk assessment over conventional risk markers. Our objective was to determine whether estimated small, dense low-density lipoprotein cholesterol (E-sdLDL-C) can improve risk assessment and serve as a new risk enhancer test. METHODS:We used a prospective cohort analysis of participants in the UK Biobank study with a median (interquartile range) follow-up of 10 (6.7-12.3) years. We included 271 760 individuals who were not on lipid-lowering medication at baseline and did not have incident ASCVD. The primary study outcome was the incidence of all-cause ASCVD. RESULTS:E-sdLDL-C was strongly associated with ASCVD events with a hazard ratio (HR) of 1.23 (95% CI, 1.22-1.24). After multivariable adjustment for age, sex, systolic blood pressure, hypertension, type 2 diabetes, and blood pressure medications, E-sdLDL-C and ApoB (apolipoprotein B) remained the most significant lipid risk factors (HR, 1.18 [95% CI, 1.16-1.19] and 1.17 [95% CI, 1.16-1.18] per SD, respectively). After further adjustment for ApoB, the association between low-density lipoprotein cholesterol (LDL-C) with all-cause ASCVD was completely reversed with an HR of 0.84 (95% CI, 0.81-0.86), but E-sdLDL-C continued to have a significant positive association with an HR of 1.11 (95% CI, 1.08-1.13). When E-sdLDL-C was discordantly higher than either LDL-C or ApoB, the risk for ASCVD was higher (LDL-C, 31% higher; ApoB, 17% higher). When elevated E-sdLDL-C is coupled with other risk enhancer tests, there is a greater risk for developing ASCVD. CONCLUSIONS:In a UK Biobank cohort for primary prevention, the risk of all-cause ASCVD was better captured by E-sdLDL-C than LDL-C. It was also more predictive than LDL-C and ApoB when discordant with these 2 measures. E-sdLDL-C, which can be freely and automatically calculated from a standard lipid panel, can potentially improve ASCVD risk assessment without additional laboratory testing.

The relationship between LDL-C and the risk of developing T2D has been a subject of significant research interest. Observational studies and clinical trials have suggested that lower LDL-C levels, particularly when achieved through pharmacological interventions such as statins, are associated with an increased risk of T2D. However, the TG content of low-density lipoprotein (LDL-TG) has also been shown to be predictive of atherosclerotic cardiovascular disease (ASCVD) as LDL-C. However, its association with the development of T2D has not been explored. We have recently developed an equation for estimation LDL-TG which can also be calculated from the results of the standard lipid panel. Methods: Our goal was to predict all-cause ASCVD and the development of T2D in a UK Biobank(n = 271,760). We excluded individuals on lipid-lowering treatment, preexisting ASCVD or T2D. We estimated LDL-TG, through our newly developed equation using the lipid panel, and LDL-C to determine individual risk. We performed Cox proportional hazard regression (HR) with all models adjusted for age, sex, race, systolic blood pressure, smoking and hypertension. Results: A total of 271,760 participants with a median follow up of 10 years (IQR 6.7-12.3) were included for analysis. A total of 57% were female, with a mean age of 56.31 (SD 8) years. Covariable-adjusted HR for ASCVD in a comparison of the top with the bottom quintile for LDL-TG was 1.70 (95%CI,1.63 to1.77) and for LDL-C of 1.40 (95%CI, 1.35-1.47). A total 9,781participants developed T2D during the follow-up. Interestingly, the covariable-adjusted HR for developing T2D comparing the top with bottom quintile was of 4.62 (95CI, 4.25 to 5.04) for LDL-TG, while the HR for LDL-C was 0.93 (95%CI, 0.86-1). We performed a discordance analysis by plotting LDL-TG and LDL-C and grouping them in 4 groups by using the 50th percentile of LDL-TG (44mg/dL) and LDL-C (144mg/dL). Compared to having both LDL-TG and LDL-C below the 50 th percentile, having a discordantly high LDL-TG increased the risk for the developing T2D by 2.91 (95CI, 2.72 to 3.10) while having a discordantly high LDL-C reduced the risk with a HR of 0.81 (95%CI, 0.74 to 0.87). Having both above the 50 th percentile increased the risk for T2D with a HR 1.96 (95%CI, 1.85 to 2.08). Conclusions: Our findings showed that LDL-TG is a stronger predictor of ASCVD than LDL-C. Having higher levels of LDL-TG were associated with a higher risk for developing T2D in a 10 year period.

PURPOSE OF REVIEW:This review examines the critical role of the high-density lipoprotein (HDL)/scavenger receptor class B type 1 (SCARB1) pathway in adrenal glucocorticoid production during stress, emphasizing recent mechanistic evidence and its clinical implications. RECENT FINDINGS:SCARB1 mediates the selective uptake of HDL-derived cholesteryl esters by adrenocortical cells, providing the cholesterol substrate needed for rapid glucocorticoid synthesis under stress. Experimental models show that loss of SCARB1 function abolishes stress-induced glucocorticoid production even when low-density lipoprotein (LDL) is abundant, confirming that LDL cannot substitute for HDL/SCARB1-mediated cholesterol delivery. ACTH rapidly upregulates SCARB1 expression and function, driving microvillar channel formation and enhancing cholesterol flux to mitochondria. Disruption of this pathway impairs the physiologic stress response and increases vulnerability to inflammatory complications. Human genetic data and clinical observations reinforce these findings and highlight the impact of hypoalphalipoproteinemia and SCARB1 defects on adrenal reserve. SUMMARY:The HDL/SCARB1 axis is essential for acute glucocorticoid synthesis and integrates lipid metabolism with endocrine and immune resilience. This shifts the focus from HDL-C from a passive biomarker to HDL to an active endocrine cofactor. Preserving HDL functionality and SCARB1 integrity should guide the design of HDL-targeted interventions, especially in patients at risk for sepsis, systemic inflammation, or adrenal insufficiency.

The removal of excess cholesterol from the body by High-density lipoprotein (HDL) in a process termed reverse cholesterol transport (RCT) has long been proposed to play a critical role in reduction of the lipid burden in arterial wall atherosclerotic lesions. While HDL-cholesterol levels are associated with decreased cardiovascular risk and considered to be “good-cholesterol”, clinical studies using HDL-raising therapies to potentially enhance RCT have consistently produced disappointing results. In this mini review we evaluate the effects of human disease on RCT along with the changes in this process upon various therapeutic interventions. Despite the importance of assay standardization, the major method for monitoring RCT has relied upon the cholesterol efflux capacity (CEC) assay, a highly-difficult and tedious cell culture assay, which is low-throughput and only suitable for research studies. Hence, we also briefly review several new methods to measure RCT both in vitro and in vivo, along with new cell-free alternative RCT assays, which have the potential to be developed into routine automated diagnostic assay. The benefits of HDL may yet be revealed by the use of these new high-throughput RCT assays perhaps as a screening tool for novel RCT boosting agents or as new biomarkers for cardiovascular disease risk.

Introduction: While the ASCVD risk associated to Lipoprotein (a) [Lp(a)] is linear, the 2022 European consensus suggests a one-size fits all pragmatic approach, with Lp(a) cut-offs to rule out (below 75 nmol/L) or rule in (above 125 nmol/L) risk. Since sex-based differences in the association of Lp(a) with ASCVD outcomes have not been well established we aimed to evaluate sex-related differences in Lp(a) concentrations, their association with incident ASCVD, and the implications of applying a pragmatic Lp(a) threshold approach. Methods: We analyzed baseline measurements Lp(a) from participants in the UK Biobank (n = 271,311) who were followed for 15 years. The primary endpoint was major adverse cardiovascular event, which was a composite of myocardial infarction, CHD or stroke. We calculated cox proportional hazard ratios (HR) and 95% confidence-interval (CI) adjusted for age, systolic blood pressure, hypertension, diabetes, HDL-C, Triglycerides, Non-HDL-C and ApoB. We stratified our analysis base by sex, age and Lp(a) quintiles or guideline cut-offs to further explore the risk for ASCVD. Results: A total of 154,507 (57%) were female, with a mean age of 56.31 (+/- 8) years. In the highest quintile of baseline Lp(a) (130 vs 5.2 nmol/L), the risk of ASCVD events was higher in men than in women (adjusted HR: 1.32 vs 1.13, respectively). When further stratified by age (≤50, 51–59, >60), risk estimates in men were consistent across groups: 1.28 (95%CI: 1.12–1.45), 1.38 (95%CI: 1.25–1.53), and 1.31 (95%CI: 1.22–1.40), respectively. In contrast, risk in women was higher in younger groups and declined with age: 1.34 (95%CI: 1.11–1.61), 1.21 (95%CI: 1.06–1.37), and 1.07 (95%CI: 0.99–1.16). When applying Lp(a) cut-offs proposed by guidelines, women in both the gray zone and high Lp(a) groups had similar ASCVD risk: 1.12 (95%CI: 1.05–1.20) and 1.14 (95%CI: 1.07–1.21), respectively. In men, risk rose more steeply: 1.21 (95%CI: 1.15–1.27) and 1.32 (95%CI: 1.25–1.39), as shown in Figure 1. Notably, among women, there was no statistical difference between the gray zone and high Lp(a) groups; however, women <50 years consistently showed higher ASCVD risk than older women. Conclusions: A one-size-fits-all (pragmatic) threshold may not adequately capture high-risk individuals, particularly among younger women. These results support sex- and age-specific approaches to Lp(a)-based risk stratification.

BACKGROUND:Cardiovascular guidelines have long recommended low-density lipoprotein-cholesterol (LDL-C) as the primary target for lipid-lowering therapy. Recent guidelines have emphasized the importance of achieving low LDL-C levels; hence, the accurate measurement of low LDL-C is increasingly clinically relevant. METHODS:Using lipid panel test results from the Mayo Clinic (n = 24 590) and the FOURIER clinical trial of evolocumab (n = 9605), the following modified Sampson equation was developed by least-squares regression to match LDL-C (mg/dL) by the β-quantification reference method, by combining terms into non High Density Lipoprotein Cholesterol (nonHDLC = Total Cholesterol - High Density Lipoprotein Cholesterol) and forcing the coefficient to be one. RESULTS:The modified Sampson equation demonstrated significant improvement in its concordance to the reference method compared to other equations (the Lin Concordance Correlation Coefficient 0.992, P < 0.001). By overall kappa analysis, it showed the best agreement to the reference method at the 55 mg/dL cutpoint (1.4 mmol/L, 0.98 [P < 0.001], Sampson-NIH: 0.96, Martin-Hopkins: 0.96, Friedewald: 0.94) and the 70 mg/dL cutpoint (1.8 mmol/L, 0.97 [P < 0.001], Sampson-NIH: 0.94, Martin-Hopkins: 0.95, Friedewald: 0.92). The false classification rate of the modified Sampson equation was also significantly lower compared to the other equations at 55 mg/dL (15%, [P < 0.001], Sampson-NIH: 29%, Martin-Hopkins: 28%, Friedewald: 37%) and 70 mg/dL (18%, [P < 0.001]; Sampson-NIH: 30%, Martin-Hopkins: 28.%, Friedewald: 34%). The new equation increases the percentage of correctly classified patients with low LDL-C by approximately 10% to 20% over the other equations based on its net reclassification index. CONCLUSIONS:The modified Sampson equation shows improved accuracy compared to other equations for low LDL-C. It more accurately identifies high-risk patients, who are not at their LDL-C goals and could benefit from more intensive lipid-lowering therapy.

Type III (dysbetalipoproteinemia) is a rare lipoprotein disorder that results in an increased risk of coronary heart disease and peripheral vascular disease. It often goes undiagnosed because it is difficult to identify with a standard lipid panel. For definite diagnosis genotype is usually required. Therefore, a rule-in test for genotyping would be useful. We investigate whether a ratio of very low-density lipoproteins (esVLDL-C) over Apolipoprotein B (ApoB) concentrations could be useful as a rule-in test. We also evaluate whether the addition of other easily obtainable biomarkers could improve this prediction. 413,998 patients were assessed from the UKBiobank, 909 patients were classified as Type III defined as having an E2/E2 genotype, determined by exome and genome data, and presenting with mixed dyslipidemia (total cholesterol =200 mg/dL and triglycerides =175 mg/dL). The data was randomly split with 80% reserved for model training and 20% for testing. VLDL-C was calculated using the enhanced Sampson (es) equation that uses apoB as an independent variable. A logistic regression model was made based on esVLDL-C levels over ApoB and assessed for its performance. A ROC curve was produced for the model and the optimal threshold was identified utilizing the Youden-Index. To further enhance the model, additional biomarkers were assessed: age, BMI, Sex, Apo B, esVLDL, non-HDL-C, Type 2 diabetes, HDL-C, total cholesterol, and triglycerides. Utilizing recursive feature elimination (RFE) with a 10-fold cross-validation the optimal features were selected based on balanced accuracy. With the resulting biomarkers, a new model was built utilizing the logistic regression modeling and setting the class weights to balanced accuracy. The balanced accuracy was used so the loss function was adjusted for a class imbalance between the positive and negative cases. The logistic model of esVLDL-C/ApoB had an area under the curve (AUC) of 0.99. The performance characteristics were 97.6% sensitivity, 95.3% specificity, 3.2% PPV, and 99.60% NPV for the identification of those with Type III. Based on findings from the RFE there was less than a 1% performance difference in accuracy between the model using 5 biomarkers and the model using all 11 biomarkers. However, when only 4 biomarkers were used, there was a noticeable drop in accuracy. Therefore, 5 biomarkers—esVLDL-C/ApoB, sex, non-HDL-C, ApoB, and diabetes diagnosis—were selected for training the final model. The resulting model had an AUC of 1.00 with 98.94%/98.90% sensitivity, 99.03%/98.97% specificity, 14.01%/12.82% PPV, and 99.998%/99.998% NPV within the training/testing for detecting Type III. Within we have described two models, one simple model of esVLDL-C/ApoB with 95% accuracy and another model with 99% accuracy which harnesses additional readily obtainable biomarkers and clinical features. Although the PPV of this model is still only 12.82%, mostly due to the rarity of the disease, this PPV is still considerably improved compared to previous models. The new algorithm could help identify individuals who would benefit most from APOE genotyping to confirm a Type III diagnosis, enabling earlier clinical intervention and potentially reducing the cardiovascular disease risk associated with the disorder.

Familial hypercholesterolemia (FH) is a genetic disorder driven in part by mutations in three genes that encode components of the cholesterol pathway: LDLR, APOB, and PCSK9. However, the majority of FH genetics has been performed in individuals of European descent. Here, we leveraged a cohort of 300 patients from the Mexican FH registry to understand how rare, high liability alleles and common variants might contribute to shaping individual risk. Using a combination of whole exome and of short- and long-read whole genome sequencing, we report three key findings. First, we observed that rare pathogenic point mutations and structural variants in all known FH genes, together with variants in APOE, CREB3L3, and PLIN1, contribute to a molecular FH diagnosis in 67% of families, including novel gene-disruptive copy number variants (CNVs) which arose in a native American background. Second, ancestry-adjusted polygenic risk score analysis identified a significant liability for coronary artery disease, hypertension, LDL, HDL, and Type 2 Diabetes. The polygenic signal for LDL was present in patients with rare, pathogenic FH mutations and was more prominent in individuals bereft of a molecular FH diagnosis. Finally, we report both a whole-gene duplication and common, non-coding variants in a novel locus, PDZK1, which contribute to the genetic burden of FH, a finding we replicated in the UK Biobank (UKB). Together, our analyses illustrate the value of genetic studies in non-European populations and reinforce the notion that individual risk to disease can arise from both rare, large effect alleles (alone or in combination across genes) and common variants that increase the mutational burden of a biological system.

BACKGROUND:β-Quantification (BQ) is the reference method for low-density lipoprotein cholesterol (LDL-C) determination. It is not widely available, making it challenging for laboratories to assess the accuracy of LDL-C methods. Our goal was to develop an indirect graphical approach for comparing LDL-C test results to the BQ reference method. METHODS:BQ results from Mayo Medical Laboratories (n = 39 969) and the National Institutes of Health (n = 17 825) were used to investigate the interrelationships between lipid panel tests. A plot of LDL-C/non high-density lipoprotein cholesterol (nonHDL-C) vs (triglyceride (TG)/nonHDL-C)0.5 (lipid ratio plot) resulted in a negative linear regression line (y = -34.2x + 115). Based on simulation analysis, the minimum sample size for calculating its slope and intercept with a coefficient of variation of about 7.5% was 80. RESULTS:The regression lines for the lipid ratio plot of LDL-C calculated by the Sampson-NIH (y = -37.6x + 118) and enhanced Sampson-NIH (y = -33.1x + 116) equations closely overlapped with the BQ method. In contrast, the Martin-Hopkins equation showed a positive bias with an increasing TG/nonHDL-C ratio and exceeded the recommended bias limit of 4% on hypertriglyceridemic samples (y = -25.5x + 107). The Friedewald equation showed an even larger negative bias with hypertriglyceridemia (y = -47.5x + 126). Lipid ratio plots of the Roche direct assay revealed a fixed positive bias of approximately 4% (y = -33.9x + 120), whereas a much larger proportional positive bias was observed with increasing triglycerides for the Beckman direct assay (y = -14.6x + 97.8). CONCLUSIONS:The lipid ratio plot is a simple graphical approach that can be readily performed by clinical laboratories for investigating the accuracy of LDL-C assays by calculation methods or direct assays.

Background: Lipid levels and markers of inflammation are commonly used to predict the risk of future cardiovascular events. However, the combined impact of these risk factors is still not fully explored. More research is needed to determine the long-term predictive value of these biomarkers in healthy individuals, as early-life interventions play a key role in reducing risk. Aim: To evaluate the additive effect of low grade inflammation and lipid profiles as predictors of ASCVD in a primary prevention cohort from UK Biobank. Methods: We analyzed baseline measurements of LDL-C, non-HDL-C, Estimated (E)-sdLDL-C, Estimated (E)-LDL-TG, ApoB, and C-reactive protein (CRP) from participants in the UK Biobank (n = 271,760), who were followed for up to 15 years. Individuals on lipid-lowering treatment or with incomplete follow-up records were excluded. The primary endpoint was major adverse cardiovascular event, which was a composite of myocardial infarction, CHD or stroke. We calculated hazard ratios (HR) and 95% confidence interval (CI) across quintiles of each biomarker, along its combination with all models adjusted for age, sex, race, diabetes, systolic blood pressure, smoking and hypertension medication. Results: The mean age at baseline was 56.3 years. The increasing quintiles of baseline levels of LDL-C, non-HDL-C, E-sdLDL-C, E-LDL-TG, ApoB, and CRP predicted ASCVD risk. Adjusted HR for the primary endpoint in comparison of the top with the bottom quintile were as follows LDL-C 1.39 (1.34-1.44), Non-HDL-C 1.52 (1.47-1.58), E-sdLDL-C 1.59 (1.53-1.65), E-LDL-TG 1.62 (1.58-1.63), ApoB 1.55(1.50-1.61) and CRP 1.58 (1.52-1.64). Each biomarker showed independent contributions to overall risk. Prediction models that incorporated markers of inflammation in addition to lipids were significantly better at predicting risk than models based on lipid levels alone. However, high particle number assessed by ApoB (>130 mg/dL) combined with CRP (>2 mg/L) conferred a higher risk for the primary endpoint (HR 1.69 [1.63-1.76]) than the rest of the components of the lipid panel. Conclusion: A single combined measure of ApoB with CRP was a better predictor of cardiovascular risk in healthy individuals beyond traditional lipid panel measurements in a 15-year follow-up. Earlier recognition of cardiovascular disease risk with these two markers in healthy individuals and proactive preventive efforts can be taken to mitigate risk.