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Discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics
INTRODUCTION: Cardiovascular disease (CVD) in patients with diabetes is the leading cause of death. Finding early biomarkers for detecting asymptomatic patients with CVD can improve survival. Recently, plasma proteomics—targeted selected reaction monitoring/multiple reaction monitoring analyses (MRM...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202779/ https://www.ncbi.nlm.nih.gov/pubmed/32327445 http://dx.doi.org/10.1136/bmjdrc-2019-001152 |
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author | Ku, Eu Jeong Cho, Kyung-Cho Lim, Cheong Kang, Jeong Won Oh, Jae Won Choi, Yu Ri Park, Jong-Moon Han, Na-Young Oh, Jong Jin Oh, Tae Jung Jang, Hak Chul Lee, Hookeun Kim, Kwang Pyo Choi, Sung Hee |
author_facet | Ku, Eu Jeong Cho, Kyung-Cho Lim, Cheong Kang, Jeong Won Oh, Jae Won Choi, Yu Ri Park, Jong-Moon Han, Na-Young Oh, Jong Jin Oh, Tae Jung Jang, Hak Chul Lee, Hookeun Kim, Kwang Pyo Choi, Sung Hee |
author_sort | Ku, Eu Jeong |
collection | PubMed |
description | INTRODUCTION: Cardiovascular disease (CVD) in patients with diabetes is the leading cause of death. Finding early biomarkers for detecting asymptomatic patients with CVD can improve survival. Recently, plasma proteomics—targeted selected reaction monitoring/multiple reaction monitoring analyses (MRM)—has emerged as highly specific and sensitive tools compared with classic ELISA methods. The objective was to identify differentially regulated proteins according to the severity of the coronary artery atherosclerosis. RESEARCH DESIGN AND METHODS: A discovery cohort, a verification cohort and a validation cohort consisted of 18, 53, and 228 subjects, respectively. The grade of coronary artery stenosis was defined as a percentage of luminal stenosis of the major coronary arteries. Participants were divided into six groups, depending on the presence of diabetes and the grade of coronary artery stenosis. Two mass spectrometric approaches were employed: (1) conventional shotgun liquid chromatography tandem mass spectrometry for a discovery and (2) quantitative MRM for verification and validation. An analysis of the covariance was used to examine the biomarkers’ predictivity beyond conventional cardiovascular risks. RESULTS: A total of 1349 different proteins were identified from a discovery cohort. We selected 52 proteins based on the tandem mass tag quantitative analysis then summarized as follows: chemokine (C-X-C motif) ligand 7 (CXCL7), apolipoprotein C-II (APOC2), human lipopolysaccharide-binding protein (LBP) and dedicator of cytokinesis 2 (DOCK2) in diabetes; CXCL7, APOC2, LBP, complement 4A (C4A), vitamin D-binding protein (VTDB) and laminin β1 subunit in non-diabetes. Analysis of covariance showed that APOC2, DOCK2, CXCL7 and VTDB were upregulated and C4A was downregulated in patients with diabetes showing severe coronary artery stenosis. LBP and VTDB were downregulated in patients without diabetes, showing severe coronary artery stenosis. CONCLUSION: We identified significant associations between circulating APOC2, C4A, CXCL7, DOCK2, LBP and VTDB levels and the degree of coronary artery stenosis using the MRM technique. |
format | Online Article Text |
id | pubmed-7202779 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-72027792020-05-13 Discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics Ku, Eu Jeong Cho, Kyung-Cho Lim, Cheong Kang, Jeong Won Oh, Jae Won Choi, Yu Ri Park, Jong-Moon Han, Na-Young Oh, Jong Jin Oh, Tae Jung Jang, Hak Chul Lee, Hookeun Kim, Kwang Pyo Choi, Sung Hee BMJ Open Diabetes Res Care Cardiovascular and Metabolic Risk INTRODUCTION: Cardiovascular disease (CVD) in patients with diabetes is the leading cause of death. Finding early biomarkers for detecting asymptomatic patients with CVD can improve survival. Recently, plasma proteomics—targeted selected reaction monitoring/multiple reaction monitoring analyses (MRM)—has emerged as highly specific and sensitive tools compared with classic ELISA methods. The objective was to identify differentially regulated proteins according to the severity of the coronary artery atherosclerosis. RESEARCH DESIGN AND METHODS: A discovery cohort, a verification cohort and a validation cohort consisted of 18, 53, and 228 subjects, respectively. The grade of coronary artery stenosis was defined as a percentage of luminal stenosis of the major coronary arteries. Participants were divided into six groups, depending on the presence of diabetes and the grade of coronary artery stenosis. Two mass spectrometric approaches were employed: (1) conventional shotgun liquid chromatography tandem mass spectrometry for a discovery and (2) quantitative MRM for verification and validation. An analysis of the covariance was used to examine the biomarkers’ predictivity beyond conventional cardiovascular risks. RESULTS: A total of 1349 different proteins were identified from a discovery cohort. We selected 52 proteins based on the tandem mass tag quantitative analysis then summarized as follows: chemokine (C-X-C motif) ligand 7 (CXCL7), apolipoprotein C-II (APOC2), human lipopolysaccharide-binding protein (LBP) and dedicator of cytokinesis 2 (DOCK2) in diabetes; CXCL7, APOC2, LBP, complement 4A (C4A), vitamin D-binding protein (VTDB) and laminin β1 subunit in non-diabetes. Analysis of covariance showed that APOC2, DOCK2, CXCL7 and VTDB were upregulated and C4A was downregulated in patients with diabetes showing severe coronary artery stenosis. LBP and VTDB were downregulated in patients without diabetes, showing severe coronary artery stenosis. CONCLUSION: We identified significant associations between circulating APOC2, C4A, CXCL7, DOCK2, LBP and VTDB levels and the degree of coronary artery stenosis using the MRM technique. BMJ Publishing Group 2020-04-22 /pmc/articles/PMC7202779/ /pubmed/32327445 http://dx.doi.org/10.1136/bmjdrc-2019-001152 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Cardiovascular and Metabolic Risk Ku, Eu Jeong Cho, Kyung-Cho Lim, Cheong Kang, Jeong Won Oh, Jae Won Choi, Yu Ri Park, Jong-Moon Han, Na-Young Oh, Jong Jin Oh, Tae Jung Jang, Hak Chul Lee, Hookeun Kim, Kwang Pyo Choi, Sung Hee Discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics |
title | Discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics |
title_full | Discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics |
title_fullStr | Discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics |
title_full_unstemmed | Discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics |
title_short | Discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics |
title_sort | discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics |
topic | Cardiovascular and Metabolic Risk |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202779/ https://www.ncbi.nlm.nih.gov/pubmed/32327445 http://dx.doi.org/10.1136/bmjdrc-2019-001152 |
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