Lipoprotein(a), C-Reactive Protein, and Cardiovascular Risk in Primary and Secondary Prevention Populations

Importance

Elevated lipoprotein(a) (Lp[a]) is a putative causal risk factor for atherosclerotic cardiovascular disease (ASCVD). There are conflicting data as to whether Lp(a) may increase cardiovascular risk only in the presence of concomitant inflammation.

Objective

To investigate whether Lp(a) is associated with cardiovascular risk independent of high-sensitivity C-reactive protein (hs-CRP) in both primary and secondary prevention populations.

Design, Setting, and Participants

This cohort study uses data from 3 distinct cohorts, 1 population-based cohort and 2 randomized clinical trials. Participants included individuals from the UK Biobank (data from 2006-2010) without prevalent ASCVD, participants in the FOURIER (TIMI 59) trial (data from 2013-2017) who had baseline Lp(a) and hs-CRP data, and participants in the SAVOR-TIMI 53 trial (data from 2010-2013) who had prevalent ASCVD and baseline values for Lp(a) and hs-CRP. The data analysis took place from November 2022 to November 2023.

Exposure

Baseline plasma Lp(a), considered either as a continuous variable or dichotomized at 125 nmol/L.

Main Outcomes and Measures

Risk of major adverse cardiovascular events (MACE) (composite of cardiovascular death, myocardial infarction [MI], or ischemic stroke), the individual MACE components, and peripheral artery disease (PAD).

Results

Among 357 220 individuals in the UK Biobank without prevalent ASCVD, 232 699 (65%) had low hs-CRP (<2 mg/L), and 124 521 (35%) had high hs-CRP (≥2 mg/L) values. In a Cox proportional hazard model adjusted for ASCVD risk factors, higher Lp(a) was associated with increased cardiovascular risk regardless of baseline hs-CRP value for MACE (hs-CRP ≥2 mg/L: hazard ratio [HR] per 50-nmol/L higher Lp[a], 1.05; 95% CI, 1.04-1.07; P < .001; for hs-CRP <2 mg/L: HR, 1.05; 95% CI, 1.04-1.07; P < .001; P = .80 for interaction), as well as MI, ischemic stroke, and PAD individually. Among 34 020 individuals in the FOURIER and SAVOR trials with baseline cardiometabolic disease, there were 17 643 (52%) with low and 16 377 (48%) with high baseline hs-CRP values. In Cox proportional hazard models using aggregated data from FOURIER and SAVOR, higher baseline Lp(a) was associated with increased cardiovascular risk regardless of baseline hs-CRP for MACE (hs-CRP ≥2 mg/L: HR per 50-nmol/L higher Lp[a], 1.02; 95% CI, 1.00-1.05; P = .04; hs-CRP <2 mg/L: HR, 1.05; 95% CI, 1.02-1.08; P < .001; P = .16 for interaction), MI, and PAD.

Conclusions and Relevance

In this study, higher levels of Lp(a) were associated with MACE, MI, and PAD in both primary and secondary prevention populations regardless of baseline hs-CRP value.

The relative importance of particle count, type, and size of ApoB-containing lipoproteins in risk of myocardial infarction

Background

An accumulating body of evidence suggests that the number of apolipoprotein B-containing particles (ApoB-P) is more predictive of cardiovascular risk than their lipid content. However, it is unclear if this association is consistent across different lipoprotein types and sizes.

Purpose

We aimed to evaluate if particle type and size are associated with incident myocardial infarction (MI) beyond ApoB-P count. Moreover, we aimed to determine if the risk associated with lipoprotein(a) is additive to that of ApoB-P.

Methods

This prospective cohort study included 96,126 participants without prior history of stroke, coronary or peripheral artery disease or use of lipid-lowering medication from the UK Biobank. Count and size of VLDL, IDL, LDL, and HDL, as well as ApoB level and total ApoB-P count were measured in non-fasting plasma samples by nuclear magnetic resonance platform. Lipoprotein(a) was measured by immunoturbidimetric assay. We explored associations between these lipoprotein markers and incident MI using Cox proportional hazard models adjusted sequentially for clinical covariates, HDL count and size, and ApoB-P.

Results

Over a median follow-up of 12.1 years, 1702 participants had incident MI. In unadjusted models, 1-SD increases in ApoB-P count, ratio of VLDL to (LDL+IDL) particle counts, VLDL size and lipoprotein(a) were associated with a higher risk of MI, while LDL size was associated with a lower risk of MI (Table 1). When adjusting for clinical covariates and lipid parameters, only ApoB-P and lipoprotein(a) remained significantly associated with a higher risk of MI (HR: 1.40 [1.32; 1.48] and 1.20 [1.14; 1.27], respectively). Adjusted restricted cubic splines confirmed findings from linear trend Cox models (Figure 1). ApoB-P count was highly correlated with ApoB level (r=0.99), and replication of analyses replacing one for another revealed no change in results.

Conclusion

The risk of MI is independently associated with the total particle count of all ApoB-P, and not the size or type of these lipoproteins. ApoB level can be used as a very accurate surrogate of ApoB-P count in the clinical setting. Lipoprotein(a) is associated with MI risk independently of total particle count, and therefore, the combination of ApoB and lipoprotein(a) may provide the optimal clinical evaluation of lipid-mediated MI risk.

Funding Acknowledgement

Type of funding sources: None.

Precision Trial Drawer, a Computational Tool to Assist Planning of Genomics-Driven Trials in Oncology

Purpose

Trials that accrue participants on the basis of genetic biomarkers are a powerful means of testing targeted drugs, but they are often complicated by the rarity of the biomarker-positive population. Umbrella trials circumvent this by testing multiple hypotheses to maximize accrual. However, bigger trials have higher chances of conflicting treatment allocations because of the coexistence of multiple actionable alterations; allocation strategies greatly affect the efficiency of enrollment and should be carefully planned on the basis of relative mutation frequencies, leveraging information from large sequencing projects.

Methods

We developed software named Precision Trial Drawer (PTD) to estimate parameters that are useful for designing precision trials, most importantly, the number of patients needed to molecularly screen (NNMS) and the allocation rule that maximizes patient accrual on the basis of mutation frequency, systematically assigning patients with conflicting allocations to the drug associated with the rarer mutation. We used data from The Cancer Genome Atlas to show their potential in a 10-arm imaginary trial of multiple cancers on the basis of genetic alterations suggested by the past Molecular Analysis for Personalised Therapy (MAP) conference. We validated PTD predictions versus real data from the SHIVA (A Randomized Phase II Trial Comparing Therapy Based on Tumor Molecular Profiling Versus Conventional Therapy in Patients With Refractory Cancer) trial.

Results

In the MAP imaginary trial, PTD-optimized allocation reduces number of patients needed to molecularly screen by up to 71.8% (3.5 times) compared with nonoptimal trial designs. In the SHIVA trial, PTD correctly predicted the fraction of patients with actionable alterations (33.51% [95% CI, 29.4% to 37.6%] in imaginary v 32.92% [95% CI, 28.2% to 37.6%] expected) and allocation to specific treatment groups (RAS/MEK, PI3K/mTOR, or both).

Conclusion

PTD correctly predicts crucial parameters for the design of multiarm genetic biomarker-driven trials. PTD is available as a package in the R programming language and as an open-access Web-based app. It represents a useful resource for the community of precision oncology trialists. The Web-based app is available at https://gmelloni.github.io/ptd/shinyapp.html .