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Cardiovascular and Metabolic Risk
Discovery of plasma biomarkers for predicting the severity of coronary artery atherosclerosis by quantitative proteomics
  1. Eu Jeong Ku1,2,
  2. Kyung-Cho Cho3,
  3. Cheong Lim4,5,
  4. Jeong Won Kang3,
  5. Jae Won Oh3,
  6. Yu Ri Choi3,
  7. Jong-Moon Park6,
  8. Na-Young Han6,
  9. Jong Jin Oh7,8,
  10. Tae Jung Oh9,10,
  11. Hak Chul Jang9,10,
  12. Hookeun Lee6,
  13. Kwang Pyo Kim3,
  14. Sung Hee Choi9,10
  1. 1Internal Medicine, Chungbuk National University Hospital, Cheongju, South Korea
  2. 2Internal Medicine, Chungbuk National University College of Medicine, Cheongju, South Korea
  3. 3Applied Chemisty, Kyung Hee University College of Applied Sciences, Yongin, South Korea
  4. 4Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam, South Korea
  5. 5Thoracic and Cardiovascular Surgery, Seoul National University College of Medicine, Seoul, South Korea
  6. 6Pharmaceutics, Gachon University College of Pharmacy, Incheon, South Korea
  7. 7Urology, Seoul National University Bundang Hospital, Seongnam, South Korea
  8. 8Urology, Seoul National University College of Medicine, Seoul, South Korea
  9. 9Internal Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea
  10. 10Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea
  1. Correspondence to Professor Sung Hee Choi; shchoimd{at}gmail.com; Professor Kwang Pyo Kim; kimkp{at}khu.ac.kr

Abstract

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.

  • biomarkers
  • coronary artery disease
  • type 2 diabetes
  • proteomic analysis
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/.

https://doi.org/10.1136/bmjdrc-2019-001152

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    Footnotes

    • EJK and K-CC contributed equally.

    • Contributors SHC and KPK conceived and designed the experiments. EJK and K-CC acquired and analyzed the data, and wrote the manuscript. K-CC, JWK, JWO, YRC, J-MP, and N-YH performed the experiments. CL, JJO, TJO, HCJ, HL and SHC contributed materials/analysis tools. All authors contributed to completion of the manuscript. All authors read and approved the final manuscript.

    • Funding This work was supported by the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number HI11V-0005-020013); the Medical Research Center through the National Research Foundation of Korea funded by the Ministry of Science (grant number NRF-2018R1A5A2024425); ICT and future Planning, and Kyung Hee University Medical Center (grant number HI15C-1595-020017); the Korea Science and Engineering grant funded by Korea government (grant number 201824425); and VHS Medical Center Research Grant, Republic of Korea (grant number: VHSMC 19032).

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Ethics approval This study was conducted in accordance with the Declaration of Helsinki and was approved by the ethics committees of SNUBH (SNUBH IRB #B-1203/147-006, #A111218CP02). All subjects provided their written informed consent.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.