Closing the gaps in patient management of dyslipidemia: stepping into cardiovascular precision diagnostics with apolipoprotein profiling

In persons with dyslipidemia, a high residual risk of cardiovascular disease remains despite lipid lowering therapy. Current cardiovascular risk prediction mainly focuses on low-density lipoprotein cholesterol (LDL-c) levels, neglecting other contributing risk factors. Moreover, the efficacy of LDL-c lowering by statins resulting in reduced cardiovascular risk is only partially effective. Secondly, from a metrological viewpoint LDL-c falls short as a reliable measurand. Both direct and calculated LDL-c tests produce inaccurate test results at the low end under aggressive lipid lowering therapy. As LDL-c tests underperform both clinically and metrologically, there is an urging need for molecularly defined biomarkers. Over the years, apolipoproteins have emerged as promising biomarkers in the context of cardiovascular disease as they are the functional workhorses in lipid metabolism. Among these, apolipoprotein B (ApoB), present on all atherogenic lipoprotein particles, has demonstrated to clinically outperform LDL-c. Other apolipoproteins, such as Apo(a) - the characteristic apolipoprotein of the emerging risk factor lipoprotein(a) -, and ApoC-III - an inhibitor of triglyceride-rich lipoprotein clearance -, have attracted attention as well. To support personalized medicine, we need to move to molecularly defined risk markers, like the apolipoproteins. Molecularly defined diagnosis and molecularly targeted therapy require molecularly measured biomarkers. This review provides a summary of the scientific validity and (patho)physiological role of nine serum apolipoproteins, Apo(a), ApoB, ApoC-I, ApoC-II, ApoC-III, ApoE and its phenotypes, ApoA-I, ApoA-II, and ApoA-IV, in lipid metabolism, their association with cardiovascular disease, and their potential as cardiovascular risk markers when measured in a multiplex apolipoprotein panel.

Relating Lipoprotein(a) Concentrations to Cardiovascular Event Risk After Acute Coronary Syndrome: A Comparison of 3 Tests

BACKGROUND

Lipoprotein(a) is a risk factor for cardiovascular events and modifies the benefit of PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitors. Lipoprotein(a) concentration can be measured with immunoassays reporting mass or molar concentration or a reference measurement system using mass spectrometry. Whether the relationships between lipoprotein(a) concentrations and cardiovascular events in a high-risk cohort differ across lipoprotein(a) methods is unknown. We compared the prognostic and predictive value of these types of lipoprotein(a) tests for major adverse cardiovascular events (MACE).

METHODS

The ODYSSEY OUTCOMES trial (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab) compared the PCSK9 inhibitor alirocumab with placebo in patients with recent acute coronary syndrome. We compared risk of a MACE in the placebo group and MACE risk reduction with alirocumab according to baseline lipoprotein(a) concentration measured by Siemens N-latex nephelometric immunoassay (IA-mass; mg/dL), Roche Tina-Quant turbidimetric immunoassay (IA-molar; nmol/L), and a noncommercial mass spectrometry-based test (MS; nmol/L). Lipoprotein(a) values were transformed into percentiles for comparative modeling. Natural cubic splines estimated continuous relationships between baseline lipoprotein(a) and outcomes in each treatment group. Event rates were also determined across baseline lipoprotein(a) quartiles defined by each assay.

RESULTS

Among 11 970 trial participants with results from all 3 tests, baseline median (Q1, Q3) lipoprotein(a) concentrations were 21.8 (6.9, 60.0) mg/dL, 45.0 (13.2, 153.8) nmol/L, and 42.2 (14.3, 143.1) nmol/L for IA-mass, IA-molar, and MS, respectively. The strongest correlation was between IA-molar and MS (r=0.990), with nominally weaker correlations between IA-mass and MS (r=0.967) and IA-mass and IA-molar (r=0.972). Relationships of lipoprotein(a) with MACE risk in the placebo group were nearly identical with each test, with estimated cumulative incidences differing by ≤0.4% across lipoprotein(a) percentiles, and all were incrementally prognostic after accounting for low-density lipoprotein cholesterol levels (all spline P≤0.0003). Predicted alirocumab treatment effects were also nearly identical for each of the 3 tests, with estimated treatment hazard ratios differing by ≤0.07 between tests across percentiles and nominally less relative risk reduction by alirocumab at lower percentiles for all 3 tests. Absolute risk reduction with alirocumab increased with increasing lipoprotein(a) measured by each test, with significant linear trends across quartiles.

CONCLUSIONS

In patients with recent acute coronary syndrome, 3 lipoprotein(a) tests were similarly prognostic for MACE in the placebo group and predictive of MACE reductions with alirocumab at the cohort level.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT01663402.

High residual cardiovascular risk after lipid-lowering: prime time for Predictive, Preventive, Personalized, Participatory, and Psycho-cognitive medicine

As time has come to translate trial results into individualized medical diagnosis and therapy, we analyzed how to minimize residual risk of cardiovascular disease (CVD) by reviewing papers on "residual cardiovascular disease risk". During this review process we found 989 papers that started off with residual CVD risk after initiating statin therapy, continued with papers on residual CVD risk after initiating therapy to increase high-density lipoprotein-cholesterol (HDL-C), followed by papers on residual CVD risk after initiating therapy to decrease triglyceride (TG) levels. Later on, papers dealing with elevated levels of lipoprotein remnants and lipoprotein(a) [Lp(a)] reported new risk factors of residual CVD risk. And as new risk factors are being discovered and new therapies are being tested, residual CVD risk will be reduced further. As we move from CVD risk reduction to improvement of patient management, a paradigm shift from a reductionistic approach towards a holistic approach is required. To that purpose, a personalized treatment dependent on the individual's CVD risk factors including lipid profile abnormalities should be configured, along the line of P5 medicine for each individual patient, i.e., with Predictive, Preventive, Personalized, Participatory, and Psycho-cognitive approaches.

Quantitative protein mass-spectrometry requires a standardized pre-analytical phase

OBJECTIVES

Quantitative protein mass-spectrometry (QPMS) in blood depends on tryptic digestion of proteins and subsequent measurement of representing peptides. Whether serum and plasma can be used interchangeably and whether in-vitro anticoagulants affect the recovery is unknown. In our laboratory serum samples are the preferred matrix for QPMS measurement of multiple apolipoproteins. In this study, we investigated the effect of different matrices on apolipoprotein quantification by mass spectrometry.

METHODS

Blood samples were collected from 44 healthy donors in Beckton Dickinson blood tubes simultaneously for serum (with/without gel) and plasma (heparin, citrate or EDTA). Nine apolipoproteins were quantified according to standard operating procedure using value-assigned native serum calibrators for quantitation. Tryptic digestion kinetics were investigated in the different matrices by following formation of peptides for each apolipoprotein in time, up to 22 h.

RESULTS

In citrate plasma recovery of apolipoproteins showed an overall reduction with a bias of -14.6%. For heparin plasma only -0.3% bias was found compared to serum, whereas for EDTA-plasma reduction was more pronounced (-5.3% bias) and variable with >14% reduction for peptides of apoA-I, A-II and C-III. Digestion kinetics revealed that especially slow forming peptides showed reduced formation in EDTA-plasma.

CONCLUSIONS

Plasma anticoagulants affect QPMS test results. Heparin plasma showed comparable results to serum. Reduced concentrations in citrate plasma can be explained by dilution, whereas reduced recovery in EDTA-plasma is dependent on altered proteolytic digestion efficiency. The results highlight the importance of a standardized pre-analytical phase for accurate QPMS applications in clinical chemistry.