Altered metabolism distinguishes high-risk from stable carotid atherosclerotic plaques

Abstract

Aims: Identification and treatment of the rupture prone atherosclerotic plaque remains a challenge for reducing the burden of cardiovascular disease. The interconnection of metabolic and inflammatory processes in rupture prone plaques is poorly understood. Herein, we investigate associations between metabolite profiles, inflammatory mediators and vulnerability in carotid atherosclerotic plaques.

Methods and results: We collected 159 carotid plaques from patients undergoing endarterectomy and measured 165 different metabolites in a targeted metabolomics approach. We identified a metabolite profile in carotid plaques that associated with histologically evaluated vulnerability and inflammatory mediators, as well as presence of symptoms in patients. The distinct metabolite profiles identified in high-risk and stable plaques were in line with different transcription levels of metabolic enzymes in the two groups, suggesting an altered metabolism in high-risk plaques. The altered metabolic signature in high-risk plaques was consistent with a change to increased glycolysis, elevated amino acid utilization and decreased fatty acid oxidation, similar to what is found in activated leucocytes and cancer cells.

Conclusion: These results highlight a possible key role of cellular metabolism to support inflammation and a high-risk phenotype of atherosclerotic plaques. Targeting the metabolism of atherosclerotic plaques with novel metabolic radiotracers or inhibitors might therefore be valid future approaches to identify and treat the high-risk atherosclerotic plaque.

Figure 1

Atherosclerotic plaques can be assigned to either one of two main clusters based on their metabolite profile. Each row represents one plaque and each column one metabolite, where the chain length and number of double-bonds for lipid metabolites increases from left to right. Red arrows depict acylcarnitines C16:0/C18:0/C18:1. n = 51 (cluster 1), n = 78 (cluster 2), n = 30 (cluster 3).

Figure 2

A distinct metabolite profile is associated with symptomatic and vulnerable plaques. (A) Principal component analysis score plot of the first and second principal components with cluster 1 (blue), cluster 2 (red), and cluster 3 (yellow) plaques. (B) Number of asymptomatic and symptomatic (i.e. patients had a preceding stroke, transient ischaemic attack, or amaurosis fugax within the month prior to surgery) plaques assigned to cluster 1 (n = 51) and 2 (n = 78). (C) Principal component analysis score plot with symptomatic (brown) and asymptomatic (teal) plaques. (D) Number of plaques with a high (above median) and low (below median) vulnerability index assigned to cluster 1 (n = 50) and cluster 2 (n = 77). (E) Principal component analysis score plot showing plaques with a high or low vulnerability index in magenta or lime green, respectively. (F) Kaplan–Meier curves of cardiovascular event-free survival for cluster 1 (n = 51) and 2 (n = 78) and number of subjects at risk. Cardiovascular events were defined as incident myocardial infarction, stroke, transient ischaemic attack, amaurosis fugax, and cardiovascular death. Percentages in bar graphs show the proportion within each cluster. Data for cluster 3 can be found in Supplementary material online, Figure S2.

Figure 3

Several pro-inflammatory mediators are elevated in cluster 2 plaques. Levels of the pro-inflammatory cytokines (A) interleukin-6, (B) interleukin-1β, (C) interleukin-18, (D) interleukin-8 and of the chemokines (E) monocyte chemoattractant protein-1 and (F) macrophage inflammatory protein-1β as well as of (G) interferon-γ and (H) tumour necrosis factor-α in homogenates of cluster 1 and 2 plaques. Vertical lines show the median, dots depict single plaques. Significant P-values after controlling for multiple testing are marked with an asterisk (*). n ≥ 45 (cluster 1) and n ≥ 70 (cluster 2). Data for cluster 3 can be found in Supplementary material online, Figure S6. AU, arbitrary units.

Figure 4

mRNA levels of genes involved in glycolysis and the pentose-phosphate pathway are elevated in high-risk plaques. (A) Hierarchical clustering of the mRNA levels of glucose catabolism genes, as well as two transcription factors (TF) regulating glycolysis. Plaques [n = 10 (cluster 1), n = 13 (cluster 2)] branched into two prominent groups are marked to the right with cyan and magenta coloured bars. Each row represents one plaque and numbers to the right indicate the metabolite cluster which the respective plaque belongs to. (B, C) Plaque levels of vascular endothelial growth factor-A and sirtuin 2 [n = 45 (cluster 1), n = 77 (cluster 2)]. Vertical lines show the median, dots depict single plaques. Data for cluster 3 can be found in Supplementary material online, Figure S9.

Take home figure

Schematic overview summarizing the study design and highlighting the differences in the metabolic profiles between high-risk and stable carotid atherosclerotic plaques. Metabolic pathways which could be targeted therapeutically or for diagnostic purposes are highlighted.