Monocytes of FH patients have a pro-inflammatory phenotype, which is dampened by LDL-C lowering by PCSK9 mAb therapy. LDL-C lowering was paralleled by reduced intracellular lipid accumulation, suggesting that LDL-C lowering itself is associated with anti-inflammatory effects on circulating monocytes.

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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.

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.

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.

Aims Elevated lipoprotein(a) [Lp(a)] is strongly associated with an increased cardiovascular disease (CVD) risk. We previously reported that pro-inflammatory activation of circulating monocytes is a potential mechanism by which Lp(a) mediates CVD. Since potent Lp(a)-lowering therapies are emerging, it is of interest whether patients with elevated Lp(a) experience beneficial anti-inflammatory effects following large reductions in Lp(a). Methods and results Using transcriptome analysis, we show that circulating monocytes of healthy individuals with elevated Lp(a), as well as CVD patients with increased Lp(a) levels, both have a pro-inflammatory gene expression profile. The effect of Lp(a)-lowering on gene expression and function of monocytes was addressed in two local sub-studies, including 14 CVD patients with elevated Lp(a) who received apolipoprotein(a) [apo(a)] antisense (AKCEA-APO(a)-LRx) (NCT03070782), as well as 18 patients with elevated Lp(a) who received proprotein convertase subtilisin/kexin type 9 antibody (PCSK9ab) treatment (NCT02729025). AKCEA-APO(a)-LRx lowered Lp(a) by 47% and reduced the pro-inflammatory gene expression in monocytes of CVD patients with elevated Lp(a), which coincided with a functional reduction in transendothelial migration capacity of monocytes ex vivo (−17%, P &lt; 0.001). In contrast, PCSK9ab treatment lowered Lp(a) by 16% and did not alter transcriptome nor functional properties of monocytes, despite an additional reduction of 65% in low-density lipoprotein cholesterol (LDL-C). Conclusion Potent Lp(a)-lowering following AKCEA-APO(a)-LRx, but not modest Lp(a)-lowering combined with LDL-C reduction following PCSK9ab treatment, reduced the pro-inflammatory state of circulating monocytes in patients with elevated Lp(a). These ex vivo data support a beneficial effect of large Lp(a) reductions in patients with elevated Lp(a).

Aims Current risk scores do not accurately identify patients at highest risk of recurrent atherosclerotic cardiovascular disease (ASCVD) in need of more intensive therapeutic interventions. Advances in high-throughput plasma proteomics, analysed with machine learning techniques, may offer new opportunities to further improve risk stratification in these patients. Methods and results Targeted plasma proteomics was performed in two secondary prevention cohorts: the Second Manifestations of ARTerial disease (SMART) cohort (n = 870) and the Athero-Express cohort (n = 700). The primary outcome was recurrent ASCVD (acute myocardial infarction, ischaemic stroke, and cardiovascular death). Machine learning techniques with extreme gradient boosting were used to construct a protein model in the derivation cohort (SMART), which was validated in the Athero-Express cohort and compared with a clinical risk model. Pathway analysis was performed to identify specific pathways in high and low C-reactive protein (CRP) patient subsets. The protein model outperformed the clinical model in both the derivation cohort [area under the curve (AUC): 0.810 vs. 0.750; P &lt; 0.001] and validation cohort (AUC: 0.801 vs. 0.765; P &lt; 0.001), provided significant net reclassification improvement (0.173 in validation cohort) and was well calibrated. In contrast to a clear interleukin-6 signal in high CRP patients, neutrophil-signalling-related proteins were associated with recurrent ASCVD in low CRP patients. Conclusion A proteome-based risk model is superior to a clinical risk model in predicting recurrent ASCVD events. Neutrophil-related pathways were found in low CRP patients, implying the presence of a residual inflammatory risk beyond traditional NLRP3 pathways. The observed net reclassification improvement illustrates the potential of proteomics when incorporated in a tailored therapeutic approach in secondary prevention patients. Key question Does targeted plasma proteomics improve cardiovascular risk prediction in secondary prevention patients? Are different pathways contributing to cardiovascular risk in high and low C-reactive protein (CRP) patients? Key finding Cardiovascular risk prediction with targeted plasma proteomics outperformed prediction with clinical risk factors resulting in major net reclassification improvement. Neutrophil-signalling-related proteins were associated with cardiovascular events in low CRP patients. Take-home message Routine implementation of a targeted protein panel in cardiovascular risk prediction holds promise to improve risk stratification in secondary prevention. The involvement of neutrophil-related pathways in low CRP patients indicates residual inflammatory risk beyond NLRP3.

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