Carotid Stenosis

Carotid stenosis causes ischemic stroke in both sexes, but the clinical presentation and plaque characteristics differ. Here we run deep single-cell sequencing of 7,690 human carotid plaque cells from male and female patients. While we found no sex differences in major cell types, we identified a predominance of the osteogenic phenotype in smooth muscle cells, immunomodulating macrophages (MPs) and endothelial cells (ECs) undergoing endothelial-to-mesenchymal transition in females. In males, we found smooth muscle cells with the chondrocytic phenotype, MPs involved in tissue remodeling and ECs with angiogenic activity. Sex-biased subcellular clusters were integrated with tissue-specific gene-regulatory networks (GRNs) from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task study. We identified GRN195 involved in angiogenesis and T cell-mediated cytotoxicity in male ECs, while in females, we found GRN33 and GRN122 related to TREM2-/TREM1+ MPs and endothelial-to-mesenchymal transition. The impact of GRN195 on EC proliferation in males was functionally validated, providing evidence for potential therapy targets for atherosclerosis that are sex specific.

BackgroundNeovascularisation of carotid plaques contributes to their vulnerability. Current imaging methods such as contrast-enhanced ultrasound (CEUS) usually lack the required spatial resolution and quantification capability for precise neovessels identification. We aimed at quantifying plaque vascularisation with ultrasound localization microscopy (ULM) and compared the results to histological analysis.MethodsWe conducted a prospective, monocentric, study involving patients who were undergoing carotid endarterectomy (CEA) for carotid artery stenosis. The day before CEA ultrasound examination coupled with the injection of microbubbles (MB) as a contrast agent (CEUS) to image the MB circulating within and around the carotid plaque was performed. CEUS images analysis classified patients into 2 groups: absence of neovascularisation (group A) or presence of neovascularisation (group B). ULM was performed by localising and tracking individual MB centres to reconstruct the neovessels structure with a resolution of around 60 μm. Plaques were manually segmented on the images to quantify the number of neovessels and various haemodynamic metrics inside the plaques. Histological analysis of the excised carotid plaque specimens classified patients into 2 groups: absence of neovascularisation (group I) or presence of neovascularisation (group II).FindingsAmong the 26 patients included, classification was as follows: group I: n = 8 and group II: n = 18, 18 patients had analysable CEUS images and were classified as follows: group A: n = 10, group B: n = 8. The median (Q1-Q3) number of MB tracked per second inside the plaque was 0.03 (0-0.37) for patients in group I and 0.51 (0-3) for patients in group A versus (vs.) 3.55 (1.26-17.68) for patients in group II and 9.69 (5.83-34.68) for patients in group B (p = 0.00049; p = 0.010 respectively). The length of the MB tracks was 0.02 mm (0-0.16) in group I vs. 0.29 mm (0.22-0.45) in group II (p = 0.0069). The study also showed that flow in the neovessels was greater during systole than during diastole period: 9.38 (1.67-19.17) MB tracked per second vs. 1.35 (0.28-6.56) (p = 0.021).InterpretationULM allows the detection of neovessels within the carotid atherosclerotic plaque. Thus, ULM provides a precise picture of plaque neovascularisation in patients and could be used as a non-invasive imaging technique to assess carotid plaque vulnerability.FundingThe study was sponsored and funded by Assistance Publique-Hôpitaux de Paris (CRC 1806 APHP INNOVATION 2018). Co-funding by ART (Technological Research Accelerator) biomedical ultrasound program of INSERM, France.

BackgroundRisk models to identify patients at high risk of asymptomatic carotid artery stenosis (ACAS) can help in selecting patients for screening, but long-term outcomes in these patients are unknown. We assessed the diagnostic and prognostic value of the previously published Prevalence of ACAS (PACAS) risk model to detect ACAS at baseline and to predict subsequent risk of stroke and cardiovascular disease (CVD) during follow-up.MethodsWe validated the discrimination and calibration of the PACAS risk model to detect severe (≥70% narrowing) ACAS with patients from the Reduction of Atherothrombosis for Continued Health registry. We subsequently calculated the incidence rates of stroke and CVD (fatal and nonfatal stroke or myocardial infarction or vascular death) during follow-up in 4 risk groups (low, medium, high, and very high, corresponding to sum scores of ≤9, 10-13, 14-17, and ≥18, respectively).ResultsAmong 26 384 patients, aged between 45 and 80 years, without prior carotid procedures, 1662 (6.3%) had severe baseline ACAS. During ≈70 000 patient-years of follow-up, 1124 strokes and 2484 CVD events occurred. Discrimination of the PACAS model was 0.67 (95% CI, 0.65-0.68), and calibration showed adequate concordance between predicted and observed risks of severe baseline ACAS after recalibration. Significantly higher incidence rates of stroke (PtrendPtrendConclusionsThe PACAS risk model can reliably identify patients at high risk of severe baseline ACAS. Incidence rates of stroke and CVD during follow-up were significantly higher in patients with high PACAS sum scores. Selective screening of patients with high PACAS sum scores may help to prevent future stroke or CVD.

BACKGROUND: Understanding the pathophysiology of unstable atherosclerosis is imperative to prevent myocardial infarction and stroke. Here, we used multi-omics integration to identify key molecular targets with diagnostic and therapeutic potential. METHODS: Biobank of Karolinska Endarterectomies encompassing patients with symptomatic (S) and asymptomatic (AS) carotid atherosclerosis was the main resource. Plaques, peripheral blood monocytes and plasma sampled locally from around plaque or periphery of n > 700 individuals, were profiled by transcriptomics, proteomics and metabolomics. A supervised machine learning method DIABLO was used for patient data integration. Multi-omics layers were integrated separately across local and peripheral disease sites, and their intersection, with stratification for symptomatology. Identified analytes were investigated using scRNAseq, clinical and outcome data. RESULTS: In peripheral circulation, FABP4, IL6, Bilirubin and Sphingomyelin were the most prominent analytes. F11, ANGPTL3, ICOSLG, ITGB1 and Sphingomyelin were enriched in the local disease site, while FABP4, C1R, IL6, Bilirubin and Sphingomyelin appeared at the intersection. Coagulation, necroptosis, inflammation and cholesterol metabolism were confirmed as key pathways determining symptomatology. Clinical analyses showed an impact of lipid-lowering therapy on ICOSLG expression, anti-hypertensives on plasma FABP4 and BLVRB levels, anti-diabetics on plasma Sphingomyelins, while no medications affected ANGPTL3. Association with future adverse events was shown for plasma Bilirubin, Sphingomyelin, ANGPTL3 and ICOSLG plaque levels. Open-source target analyses suggested genetic involvement of F11, C1S, EGFR, IL6, ANGPTL3 in the disease. CONCLUSIONS: Using an innovative, multi-modal data integration machine learning framework, this study provides confirmatory and novel information on mechanisms behind atherosclerotic instability. The findings raise possibilities for translational prioritizations to aid personalized medicine.