TY - JOUR
T1 - Autoantibodies to Oxidatively Modified Peptide
T2 - Potential Clinical Application in Coronary Artery Disease
AU - Tsai, I. Jung
AU - Shen, Wen Chi
AU - Wu, Jia Zhen
AU - Chang, Yu Sheng
AU - Lin, Ching Yu
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/10
Y1 - 2022/10
N2 - Coronary artery disease (CAD) is a global health issue. Lipid peroxidation produces various by-products that associate with CAD, such as 4-hydroxynonenal (HNE) and malondialdehyde (MDA). The autoantibodies against HNE and MDA-modified peptides may be useful in the diagnosis of CAD. This study included 41 healthy controls (HCs) and 159 CAD patients with stenosis rates of <30%, 30–70%, and >70%. The plasma level of autoantibodies against four different unmodified and HNE-modified peptides were measured in this study, including CFAH1211–1230, HPT78–108, IGKC2–19, and THRB328–345. Furthermore, feature ranking, feature selection, and machine learning models have been utilized to exploit the diagnostic performance. Also, we combined autoantibodies against MDA and HNE-modified peptides to improve the models’ performance. The eXtreme Gradient Boosting (XGBoost) model received a sensitivity of 78.6% and a specificity of 90.4%. Our study demonstrated the combination of autoantibodies against oxidative modification may improve the model performance.
AB - Coronary artery disease (CAD) is a global health issue. Lipid peroxidation produces various by-products that associate with CAD, such as 4-hydroxynonenal (HNE) and malondialdehyde (MDA). The autoantibodies against HNE and MDA-modified peptides may be useful in the diagnosis of CAD. This study included 41 healthy controls (HCs) and 159 CAD patients with stenosis rates of <30%, 30–70%, and >70%. The plasma level of autoantibodies against four different unmodified and HNE-modified peptides were measured in this study, including CFAH1211–1230, HPT78–108, IGKC2–19, and THRB328–345. Furthermore, feature ranking, feature selection, and machine learning models have been utilized to exploit the diagnostic performance. Also, we combined autoantibodies against MDA and HNE-modified peptides to improve the models’ performance. The eXtreme Gradient Boosting (XGBoost) model received a sensitivity of 78.6% and a specificity of 90.4%. Our study demonstrated the combination of autoantibodies against oxidative modification may improve the model performance.
KW - 4-hydroxynonenal
KW - machine learning
KW - oxidative stress
KW - plasma
UR - http://www.scopus.com/inward/record.url?scp=85140606516&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140606516&partnerID=8YFLogxK
U2 - 10.3390/diagnostics12102269
DO - 10.3390/diagnostics12102269
M3 - Article
AN - SCOPUS:85140606516
SN - 2075-4418
VL - 12
JO - Diagnostics
JF - Diagnostics
IS - 10
M1 - 2269
ER -