Combined non-invasive assessment of endothelial shear stress and molecular imaging of inflammation for the prediction of inflamed plaque in hyperlipidaemic rabbit aortas

Gitsios Gitsioudis, Ioannis S Chatzizisis, Peter Wolf, Anna Missiou, Antonios P. Antoniadis, Dimitrios Mitsouras, Sönke Bartling, Zeynep Arica, Matthias Stuber, Frank J. Rybicki, Max Nunninger, Christian Erbel, Peter Libby, George D. Giannoglou, Hugo A. Katus, Grigorios Korosoglou

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Aims To evaluate the incremental value of low endothelial shear stress (ESS) combined with high-resolution magnetic resonance imaging (MRI)-And computed tomography angiography (CTA)-based imaging for the prediction of inflamed plaque. Methods and results Twelve hereditary hyperlipidaemic rabbits underwent quantitative analysis of plaque in the thoracic aorta with 256-slice CTA and USPIO-enhanced (ultra-small superparamagnetic nanoparticles, P904) 1.5-T MRI at baseline and at 6-month follow-up. Computational fluid dynamics using CTA-based 3D reconstruction of thoracic aortas identified the ESS patterns in the convex and concave curvature subsegments of interest. Subsegments with low baseline ESS exhibited significant increase in wall thickness and plaque inflammation by MRI, in non-calcified plaque burden by CTA, and developed increased plaque size, lipid and inflammatory cell accumulation (high-risk plaque features) at follow-up by histopathology. Multiple regression analysis identified baseline ESS and inflammation by MRI to be independent predictors of plaque progression, while receiver operating curve analysis revealed baseline ESS alone or in combination with inflammation by MRI as the strongest predictor for augmented plaque burden and inflammation (low ESS at baseline: AUC = 0.84, P , 0.001; low ESS and inflammation by molecular MRI at baseline: AUC = 0.89, P , 0.001). Conclusion Low ESS predicts progression of plaque burden and inflammation as assessed by non-invasive USPIO-enhanced MRI. Combined non-invasive assessment of ESS and imaging of inflammation may serve to predict plaque with high-risk features.

Original languageEnglish (US)
Pages (from-to)19-30
Number of pages12
JournalEuropean heart journal cardiovascular Imaging
Volume18
Issue number1
DOIs
StatePublished - Jan 1 2017

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Molecular Imaging
Aorta
Magnetic Resonance Imaging
Rabbits
Inflammation
Thoracic Aorta
Area Under Curve
Hydrodynamics
Nanoparticles
Regression Analysis
Lipids
Computed Tomography Angiography

Keywords

  • Atherosclerosis
  • Computed tomography angiography
  • Endothelial shear stress
  • Hyperlipidaemic rabbits
  • Imaging
  • Magnetic resonance
  • Molecular imaging

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

Cite this

Combined non-invasive assessment of endothelial shear stress and molecular imaging of inflammation for the prediction of inflamed plaque in hyperlipidaemic rabbit aortas. / Gitsioudis, Gitsios; Chatzizisis, Ioannis S; Wolf, Peter; Missiou, Anna; Antoniadis, Antonios P.; Mitsouras, Dimitrios; Bartling, Sönke; Arica, Zeynep; Stuber, Matthias; Rybicki, Frank J.; Nunninger, Max; Erbel, Christian; Libby, Peter; Giannoglou, George D.; Katus, Hugo A.; Korosoglou, Grigorios.

In: European heart journal cardiovascular Imaging, Vol. 18, No. 1, 01.01.2017, p. 19-30.

Research output: Contribution to journalArticle

Gitsioudis, G, Chatzizisis, IS, Wolf, P, Missiou, A, Antoniadis, AP, Mitsouras, D, Bartling, S, Arica, Z, Stuber, M, Rybicki, FJ, Nunninger, M, Erbel, C, Libby, P, Giannoglou, GD, Katus, HA & Korosoglou, G 2017, 'Combined non-invasive assessment of endothelial shear stress and molecular imaging of inflammation for the prediction of inflamed plaque in hyperlipidaemic rabbit aortas', European heart journal cardiovascular Imaging, vol. 18, no. 1, pp. 19-30. https://doi.org/10.1093/ehjci/jew048
Gitsioudis, Gitsios ; Chatzizisis, Ioannis S ; Wolf, Peter ; Missiou, Anna ; Antoniadis, Antonios P. ; Mitsouras, Dimitrios ; Bartling, Sönke ; Arica, Zeynep ; Stuber, Matthias ; Rybicki, Frank J. ; Nunninger, Max ; Erbel, Christian ; Libby, Peter ; Giannoglou, George D. ; Katus, Hugo A. ; Korosoglou, Grigorios. / Combined non-invasive assessment of endothelial shear stress and molecular imaging of inflammation for the prediction of inflamed plaque in hyperlipidaemic rabbit aortas. In: European heart journal cardiovascular Imaging. 2017 ; Vol. 18, No. 1. pp. 19-30.
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abstract = "Aims To evaluate the incremental value of low endothelial shear stress (ESS) combined with high-resolution magnetic resonance imaging (MRI)-And computed tomography angiography (CTA)-based imaging for the prediction of inflamed plaque. Methods and results Twelve hereditary hyperlipidaemic rabbits underwent quantitative analysis of plaque in the thoracic aorta with 256-slice CTA and USPIO-enhanced (ultra-small superparamagnetic nanoparticles, P904) 1.5-T MRI at baseline and at 6-month follow-up. Computational fluid dynamics using CTA-based 3D reconstruction of thoracic aortas identified the ESS patterns in the convex and concave curvature subsegments of interest. Subsegments with low baseline ESS exhibited significant increase in wall thickness and plaque inflammation by MRI, in non-calcified plaque burden by CTA, and developed increased plaque size, lipid and inflammatory cell accumulation (high-risk plaque features) at follow-up by histopathology. Multiple regression analysis identified baseline ESS and inflammation by MRI to be independent predictors of plaque progression, while receiver operating curve analysis revealed baseline ESS alone or in combination with inflammation by MRI as the strongest predictor for augmented plaque burden and inflammation (low ESS at baseline: AUC = 0.84, P , 0.001; low ESS and inflammation by molecular MRI at baseline: AUC = 0.89, P , 0.001). Conclusion Low ESS predicts progression of plaque burden and inflammation as assessed by non-invasive USPIO-enhanced MRI. Combined non-invasive assessment of ESS and imaging of inflammation may serve to predict plaque with high-risk features.",
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T1 - Combined non-invasive assessment of endothelial shear stress and molecular imaging of inflammation for the prediction of inflamed plaque in hyperlipidaemic rabbit aortas

AU - Gitsioudis, Gitsios

AU - Chatzizisis, Ioannis S

AU - Wolf, Peter

AU - Missiou, Anna

AU - Antoniadis, Antonios P.

AU - Mitsouras, Dimitrios

AU - Bartling, Sönke

AU - Arica, Zeynep

AU - Stuber, Matthias

AU - Rybicki, Frank J.

AU - Nunninger, Max

AU - Erbel, Christian

AU - Libby, Peter

AU - Giannoglou, George D.

AU - Katus, Hugo A.

AU - Korosoglou, Grigorios

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Aims To evaluate the incremental value of low endothelial shear stress (ESS) combined with high-resolution magnetic resonance imaging (MRI)-And computed tomography angiography (CTA)-based imaging for the prediction of inflamed plaque. Methods and results Twelve hereditary hyperlipidaemic rabbits underwent quantitative analysis of plaque in the thoracic aorta with 256-slice CTA and USPIO-enhanced (ultra-small superparamagnetic nanoparticles, P904) 1.5-T MRI at baseline and at 6-month follow-up. Computational fluid dynamics using CTA-based 3D reconstruction of thoracic aortas identified the ESS patterns in the convex and concave curvature subsegments of interest. Subsegments with low baseline ESS exhibited significant increase in wall thickness and plaque inflammation by MRI, in non-calcified plaque burden by CTA, and developed increased plaque size, lipid and inflammatory cell accumulation (high-risk plaque features) at follow-up by histopathology. Multiple regression analysis identified baseline ESS and inflammation by MRI to be independent predictors of plaque progression, while receiver operating curve analysis revealed baseline ESS alone or in combination with inflammation by MRI as the strongest predictor for augmented plaque burden and inflammation (low ESS at baseline: AUC = 0.84, P , 0.001; low ESS and inflammation by molecular MRI at baseline: AUC = 0.89, P , 0.001). Conclusion Low ESS predicts progression of plaque burden and inflammation as assessed by non-invasive USPIO-enhanced MRI. Combined non-invasive assessment of ESS and imaging of inflammation may serve to predict plaque with high-risk features.

AB - Aims To evaluate the incremental value of low endothelial shear stress (ESS) combined with high-resolution magnetic resonance imaging (MRI)-And computed tomography angiography (CTA)-based imaging for the prediction of inflamed plaque. Methods and results Twelve hereditary hyperlipidaemic rabbits underwent quantitative analysis of plaque in the thoracic aorta with 256-slice CTA and USPIO-enhanced (ultra-small superparamagnetic nanoparticles, P904) 1.5-T MRI at baseline and at 6-month follow-up. Computational fluid dynamics using CTA-based 3D reconstruction of thoracic aortas identified the ESS patterns in the convex and concave curvature subsegments of interest. Subsegments with low baseline ESS exhibited significant increase in wall thickness and plaque inflammation by MRI, in non-calcified plaque burden by CTA, and developed increased plaque size, lipid and inflammatory cell accumulation (high-risk plaque features) at follow-up by histopathology. Multiple regression analysis identified baseline ESS and inflammation by MRI to be independent predictors of plaque progression, while receiver operating curve analysis revealed baseline ESS alone or in combination with inflammation by MRI as the strongest predictor for augmented plaque burden and inflammation (low ESS at baseline: AUC = 0.84, P , 0.001; low ESS and inflammation by molecular MRI at baseline: AUC = 0.89, P , 0.001). Conclusion Low ESS predicts progression of plaque burden and inflammation as assessed by non-invasive USPIO-enhanced MRI. Combined non-invasive assessment of ESS and imaging of inflammation may serve to predict plaque with high-risk features.

KW - Atherosclerosis

KW - Computed tomography angiography

KW - Endothelial shear stress

KW - Hyperlipidaemic rabbits

KW - Imaging

KW - Magnetic resonance

KW - Molecular imaging

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