Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention.

AIMS: In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort. METHODS AND RESULTS: Using the proximity extension assay, 368 proteins were measured in a nested case-control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001). CONCLUSION: In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention

Atherogenic Lipoprotein(a) Increases Vascular Glycolysis, Thereby Facilitating Inflammation and Leukocyte Extravasation

Rationale: Patients with elevated levels of lipoprotein(a) [Lp(a)] are hallmarked by increased metabolic activity in the arterial wall on positron emission tomography/computed tomography, indicative of a proinflammatory state. Objective: We hypothesized that Lp(a) induces endothelial cell inflammation by rewiring endothelial metabolism. Methods and Results: We evaluated the impact of Lp(a) on the endothelium and describe that Lp(a), through its oxidized phospholipid content, activates arterial endothelial cells, facilitating increased transendothelial migration of monocytes. Transcriptome analysis of Lp(a)-stimulated human arterial endothelial cells revealed upregulation of inflammatory pathways comprising monocyte adhesion and migration, coinciding with increased 6-phophofructo-2-kinase/fructose-2,6-biphosphatase (PFKFB)-3-mediated glycolysis. ICAM (intercellular adhesion molecule)-1 and PFKFB3 were also found to be upregulated in carotid plaques of patients with elevated levels of Lp(a). Inhibition of PFKFB3 abolished the inflammatory signature with concomitant attenuation of transendothelial migration. Conclusions: Collectively, our findings show that Lp(a) activates the endothelium by enhancing PFKFB3-mediated glycolysis, leading to a proadhesive state, which can be reversed by inhibition of glycolysis. These findings pave the way for therapeutic agents targeting metabolism aimed at reducing inflammation in patients with cardiovascular disease

B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies

B-type natriuretic peptide (BNP) and C-reactive protein (CRP) predict atrial fibrillation (AF) risk. However, their risk stratification abilities in the broad community remain uncertain. We sought to improve risk stratification for AF using biomarker information

Pleiotropy among common genetic loci identified for cardiometabolic disorders and C-reactive protein.

Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes

Improved cardiovascular risk prediction using targeted plasma proteomics in primary prevention.

AIMS: In the era of personalized medicine, it is of utmost importance to be able to identify subjects at the highest cardiovascular (CV) risk. To date, single biomarkers have failed to markedly improve the estimation of CV risk. Using novel technology, simultaneous assessment of large numbers of biomarkers may hold promise to improve prediction. In the present study, we compared a protein-based risk model with a model using traditional risk factors in predicting CV events in the primary prevention setting of the European Prospective Investigation (EPIC)-Norfolk study, followed by validation in the Progressione della Lesione Intimale Carotidea (PLIC) cohort. METHODS AND RESULTS: Using the proximity extension assay, 368 proteins were measured in a nested case-control sample of 822 individuals from the EPIC-Norfolk prospective cohort study and 702 individuals from the PLIC cohort. Using tree-based ensemble and boosting methods, we constructed a protein-based prediction model, an optimized clinical risk model, and a model combining both. In the derivation cohort (EPIC-Norfolk), we defined a panel of 50 proteins, which outperformed the clinical risk model in the prediction of myocardial infarction [area under the curve (AUC) 0.754 vs. 0.730; P < 0.001] during a median follow-up of 20 years. The clinically more relevant prediction of events occurring within 3 years showed an AUC of 0.732 using the clinical risk model and an AUC of 0.803 for the protein model (P < 0.001). The predictive value of the protein panel was confirmed to be superior to the clinical risk model in the validation cohort (AUC 0.705 vs. 0.609; P < 0.001). CONCLUSION: In a primary prevention setting, a proteome-based model outperforms a model comprising clinical risk factors in predicting the risk of CV events. Validation in a large prospective primary prevention cohort is required to address the value for future clinical implementation in CV prevention

Cardiovascular disease risk associated with elevated lipoprotein(a) attenuates at low low-density lipoprotein cholesterol levels in a primary prevention setting.

Aims: Lipoprotein(a) (Lp(a)) elevation is a causal risk factor for cardiovascular disease (CVD). It has however been suggested that elevated Lp(a) causes CVD mainly in individuals with high low-density lipoprotein cholesterol (LDL-C) levels. We hypothesized that the risk associated with high Lp(a) levels would largely be attenuated at low LDL-C levels. Methods and results: In 16 654 individuals from the EPIC-Norfolk prospective population study, and in 9448 individuals from the Copenhagen City Heart Study (CCHS) parallel statistical analyses were performed. Individuals were categorized according to their Lp(a) and LDL-C levels. Cut-offs were set at the 80th cohort percentile for Lp(a). Low-density lipoprotein cholesterol cut-offs were set at 2.5, 3.5, 4.5, and 5.5 mmol/L. Low-density lipoprotein cholesterol levels in the primary analyses were corrected for Lp(a)-derived LDL-C (LDL-Ccorr). Multivariable-adjusted hazard ratios were calculated for each category. The category with LDL-Ccorr <2.5 mmol/L and Lp(a) <80th cohort percentile was used as reference category. In the EPIC-Norfolk and CCHS cohorts, individuals with an Lp(a) ≥80th percentile were at increased CVD risk compared with those with Lp(a) <80th percentile for any LDL-Ccorr levels ≥2.5 mmol/L. In contrast, for LDL-Ccorr <2.5 mmol/L, the risk associated with elevated Lp(a) attenuated. However, there was no interaction between LDL-Ccorr and Lp(a) levels on CVD risk in either cohort. Conclusion: Lipoprotein(a) and LDL-C are independently associated with CVD risk. At LDL-C levels below <2.5 mmol/L, the risk associated with elevated Lp(a) attenuates in a primary prevention setting

Netrin-1 and the Grade of Atherosclerosis Are Inversely Correlated in Humans

OBJECTIVE: Netrin-1 has been shown to play a role in the initiation of atherosclerosis in mice models. However, little is known about the role of Netrin-1 in humans. We set out to study whether Netrin-1 is associated with different stages of atherosclerosis. Approach and Results: Plasma Netrin-1 levels were measured in different patient cohorts: (1) 22 patients with high cardiovascular risk who underwent arterial wall inflammation assessment using positron-emission tomography / computed tomography, (2) 168 patients with a positive family history of premature atherosclerosis in whom coronary artery calcium scores were obtained, and (3) 104 patients with chest pain who underwent coronary computed tomography angiography imaging to evaluate plaque vulnerability and burden. Netrin-1 plasma levels were negatively correlated with arterial wall inflammation (β, -0.01 [95% CI, 0.02 to -0.01] R2, 0.61; P<0.0001), and concentrations of Netrin-1 were significantly lower when atherosclerosis was present compared with individuals without atherosclerosis (28.01 versus 10.51 ng/mL, P<0.001). There was no difference in Netrin-1 plasma concentrations between patients with stable versus unstable plaques (11.17 versus 11.74 ng/mL, P=0.511). However, Netrin-1 plasma levels were negatively correlated to total plaque volume (β, -0.09 [95% CI, -0.11 to -0.08] R2, 0.57, P<0.0001), calcified plaque volumes (β, -0.10 [95% CI, -0.12 to -0.08] R2, 0.53; P<0.0001), and noncalcified plaque volumes (β, -0.08 [95% CI, -0.10 to -0.06] R2, 0.41; P<0.0001). Treatment of inflammatory stimulated endothelial cells with plasma with high Netrin-1 level resulted in reduced endothelial inflammation and consequently, less monocyte adhesion. CONCLUSIONS: Netrin-1 plasma levels are lower in patients with subclinical atherosclerosis and in patients with arterial wall inflammation. Netrin-1 is not associated with plaque vulnerability; however, it is negatively correlated to plaque burden, suggesting that Netrin-1 is involved in some, but not all, stages of atherosclerosis

Cardiovascular disease risk associated with elevated lipoprotein(a) attenuates at low low-density lipoprotein cholesterol levels in a primary prevention setting.

AIMS: Lipoprotein(a) (Lp(a)) elevation is a causal risk factor for cardiovascular disease (CVD). It has however been suggested that elevated Lp(a) causes CVD mainly in individuals with high low-density lipoprotein cholesterol (LDL-C) levels. We hypothesized that the risk associated with high Lp(a) levels would largely be attenuated at low LDL-C levels. METHODS AND RESULTS: In 16 654 individuals from the EPIC-Norfolk prospective population study, and in 9448 individuals from the Copenhagen City Heart Study (CCHS) parallel statistical analyses were performed. Individuals were categorized according to their Lp(a) and LDL-C levels. Cut-offs were set at the 80th cohort percentile for Lp(a). Low-density lipoprotein cholesterol cut-offs were set at 2.5, 3.5, 4.5, and 5.5 mmol/L. Low-density lipoprotein cholesterol levels in the primary analyses were corrected for Lp(a)-derived LDL-C (LDL-Ccorr). Multivariable-adjusted hazard ratios were calculated for each category. The category with LDL-Ccorr <2.5 mmol/L and Lp(a) <80th cohort percentile was used as reference category. In the EPIC-Norfolk and CCHS cohorts, individuals with an Lp(a) ≥80th percentile were at increased CVD risk compared with those with Lp(a) <80th percentile for any LDL-Ccorr levels ≥2.5 mmol/L. In contrast, for LDL-Ccorr <2.5 mmol/L, the risk associated with elevated Lp(a) attenuated. However, there was no interaction between LDL-Ccorr and Lp(a) levels on CVD risk in either cohort. CONCLUSION: Lipoprotein(a) and LDL-C are independently associated with CVD risk. At LDL-C levels below <2.5 mmol/L, the risk associated with elevated Lp(a) attenuates in a primary prevention setting