BACKGROUND: Coronary angiography remains the most widely used tool for routine screening and diagnosis of cardiac allograft vasculopathy (CAV), a major pathologic process that develops in 50% of cardiac transplant recipients beyond the first year after transplant. Given the invasiveness, expense, discomfort, and risk of complications associated with angiography, a minimally invasive alternative that is sensitive and specific would be highly desirable for monitoring CAV in patients. METHODS: Plasma proteomic analysis using isobaric tags for relative and absolute quantitation-matrix-assisted laser desorption ionization double time-of-flight mass spectrometry was carried out on samples from 40 cardiac transplant patients (10 CAV, 9 non-significant CAV, 21 possible CAV). Presence of CAV was defined as left anterior descending artery diameter stenosis ≥ 40% by digital angiography and quantitatively measured by blinded expert appraisal. Moderated t-test robust-linear models for microarray data were used to identify biomarkers that are significantly differentially expressed between patient samples with CAV and with non-significant CAV. A proteomic panel for diagnosis of CAV was generated using the Elastic Net classification method. RESULTS: We identified an 18-plasma protein biomarker classifier panel that was able to classify and differentiate patients with angiographically significant CAV from those without significant CAV, with an 80% sensitivity and 89% specificity, while providing insight into the possible underlying immune and non-alloimmune contributory mechanisms of CAV. CONCLUSION: Our results support of the potential utility of proteomic biomarker panels as a minimally invasive means to identify patients with significant, angiographically detectable coronary artery stenosis in the cardiac allograft, in the context of post-cardiac transplantation monitoring and screening for CAV. The potential biologic significance of the biomarkers identified may also help improve our understanding of CAV pathophysiology.
BACKGROUND: Coronary angiography remains the most widely used tool for routine screening and diagnosis of cardiac allograft vasculopathy (CAV), a major pathologic process that develops in 50% of cardiac transplant recipients beyond the first year after transplant. Given the invasiveness, expense, discomfort, and risk of complications associated with angiography, a minimally invasive alternative that is sensitive and specific would be highly desirable for monitoring CAV in patients. METHODS: Plasma proteomic analysis using isobaric tags for relative and absolute quantitation-matrix-assisted laser desorption ionization double time-of-flight mass spectrometry was carried out on samples from 40 cardiac transplant patients (10 CAV, 9 non-significant CAV, 21 possible CAV). Presence of CAV was defined as left anterior descending artery diameter stenosis ≥ 40% by digital angiography and quantitatively measured by blinded expert appraisal. Moderated t-test robust-linear models for microarray data were used to identify biomarkers that are significantly differentially expressed between patient samples with CAV and with non-significant CAV. A proteomic panel for diagnosis of CAV was generated using the Elastic Net classification method. RESULTS: We identified an 18-plasma protein biomarker classifier panel that was able to classify and differentiate patients with angiographically significant CAV from those without significant CAV, with an 80% sensitivity and 89% specificity, while providing insight into the possible underlying immune and non-alloimmune contributory mechanisms of CAV. CONCLUSION: Our results support of the potential utility of proteomic biomarker panels as a minimally invasive means to identify patients with significant, angiographically detectable coronary artery stenosis in the cardiac allograft, in the context of post-cardiac transplantation monitoring and screening for CAV. The potential biologic significance of the biomarkers identified may also help improve our understanding of CAV pathophysiology.
Authors: R C Starling; J Stehlik; D A Baran; B Armstrong; J R Stone; D Ikle; Y Morrison; N D Bridges; P Putheti; T B Strom; M Bhasin; I Guleria; A Chandraker; M Sayegh; K P Daly; D M Briscoe; P S Heeger Journal: Am J Transplant Date: 2015-08-10 Impact factor: 8.086
Authors: Anna J Meredith; Darlene L Y Dai; Virginia Chen; Zsuzsanna Hollander; Raymond Ng; Annemarie Kaan; Scott Tebbutt; Krishnan Ramanathan; Anson Cheung; Bruce M McManus Journal: ESC Heart Fail Date: 2015-11-24
Authors: Dongmei Wei; Sander Trenson; Jan M Van Keer; Jesus Melgarejo; Ella Cutsforth; Lutgarde Thijs; Tianlin He; Agnieszka Latosinska; Agnieszka Ciarka; Thomas Vanassche; Lucas Van Aelst; Stefan Janssens; Johan Van Cleemput; Harald Mischak; Jan A Staessen; Peter Verhamme; Zhen-Yu Zhang Journal: ESC Heart Fail Date: 2022-01-10
Authors: Sonia Mirabet; Alvaro García-Osuna; Pablo Garcia de Frutos; Andreu Ferrero-Gregori; Vicens Brossa; Laura Lopez; Ruben Leta; Joan Garcia-Picart; Josep M Padro; José Luis Sánchez-Quesada; Juan Cinca; Jordi Ordonez-Llanos; Eulalia Roig Journal: Dis Markers Date: 2018-08-29 Impact factor: 3.434