4.5 Article

Carotid Plaque Phenotyping by Correlating Plaque Morphology from Computed Tomography Angiography with Transcriptional Profiling

Journal

Publisher

W B SAUNDERS CO LTD
DOI: 10.1016/j.ejvs.2021.07.011

Keywords

Atherosclerotic plaque; Carotid stenosis; Computer tomography angiography; Gene expression

Funding

  1. Stockholm County [HMT 20180867]
  2. Swedish Heart-Lung Foundation [20180036, 20170584, 20180244, 201602877, 20180247]
  3. Swedish Research Council [2017-01070, 2019-02027]
  4. Karolinska Institutet
  5. National Heart, Lung, and Blood Institute of the National Institutes of Health, USA [HL126224]
  6. Swedish Research Council [2017-01070, 2019-02027] Funding Source: Swedish Research Council

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The study demonstrates that CTA image analysis of carotid plaque morphology reflects prevalent biological processes relevant for assessment of plaque phenotype. The results support the use of CTA image analysis of plaque morphology for risk stratification and management of patients with carotid stenosis.
Objective: Ischaemic strokes can be caused by unstable carotid atherosclerosis, but methods for identification of high risk lesions are lacking. Carotid plaque morphology imaging using software for visualisation of plaque components in computed tomography angiography (CTA) may improve assessment of plaque phenotype and stroke risk, but it is unknown if such analyses also reflect the biological processes related to lesion stability. Here, we investigated how carotid plaque morphology by image analysis of CTA is associated with biological processes assessed by transcriptomic analyses of corresponding carotid endarterectomies (CEAs). Methods: Carotid plaque morphology was assessed in patients undergoing CEA for symptomatic or asymptomatic carotid stenosis consecutively enrolled between 2006 and 2015. Computer based analyses of pre-operative CTA was performed to define calcification, lipid rich necrotic core (LRNC), intraplaque haemorrhage (IPH), matrix (MATX), and plaque burden. Plaque morphology was correlated with molecular profiles obtained from microarrays of corresponding CEAs and models were built to assess the ability of plaque morphology to predict symptomatology. Results: Carotid plaques (n = 93) from symptomatic patients (n = 61) had significantly higher plaque burden and LRNC compared with plaques from asymptomatic patients (n = 32). Lesions selected from the transcriptomic cohort (n = 40) with high LRNC, IPH, MATX, or plaque burden were characterised by molecular signatures coupled with inflammation and extracellular matrix degradation, typically linked with instability. In contrast, highly calcified plaques had a molecular signature signifying stability with enrichment of profibrotic pathways and repressed inflammation. In a cross validated prediction model for symptoms, plaque morphology by CTA alone was superior to the degree of stenosis. Conclusion: The study demonstrates that CTA image analysis for evaluation of carotid plaque morphology, also reflects prevalent biological processes relevant for assessment of plaque phenotype. The results support the use of CTA image analysis of plaque morphology for risk stratification and management of patients with carotid stenosis.

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