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Article

Identification of Circulating Diagnostic Biomarkers for Coronary Microvascular Disease in Postmenopausal Women Using Machine-Learning Techniques

1
Department of Food Science and Human Nutrition, Division of Nutritional Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
2
Research and Training Hospital, Katip Celebi University, Izmir 35620, Turkey
3
Department of Computer Science, University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA
4
Izmir Biomedicine and Genome Center, Balcova, Izmir 35340, Turkey
5
Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
6
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
7
Centre for Computational Biology, University of Birmingham, Birmingham B15 2T, UK
8
Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK
9
Cancer Center at Illinois, University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA
10
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally.
Academic Editor: Pollen K. Yeung
Metabolites 2021, 11(6), 339; https://doi.org/10.3390/metabo11060339
Received: 8 April 2021 / Revised: 17 May 2021 / Accepted: 20 May 2021 / Published: 25 May 2021
(This article belongs to the Special Issue Biomarkers for Metabolism and Cardiometabolic Diseases)
Coronary microvascular disease (CMD) is a common form of heart disease in postmenopausal women. It is not due to plaque formation but dysfunction of microvessels that feed the heart muscle. The majority of the patients do not receive a proper diagnosis, are discharged prematurely and must go back to the hospital with persistent symptoms. Because of the lack of diagnostic biomarkers, in the current study, we focused on identifying novel circulating biomarkers of CMV that could potentially be used for developing a diagnostic test. We hypothesized that plasma metabolite composition is different for postmenopausal women with no heart disease, CAD, or CMD. A total of 70 postmenopausal women, 26 healthy individuals, 23 individuals with CMD and 21 individuals with CAD were recruited. Their full health screening and tests were completed. Basic cardiac examination, including detailed clinical history, additional disease and prescribed drugs, were noted. Electrocardiograph, transthoracic echocardiography and laboratory analysis were also obtained. Additionally, we performed full metabolite profiling of plasma samples from these individuals using gas chromatography-mass spectrometry (GC–MS) analysis, identified and classified circulating biomarkers using machine learning approaches. Stearic acid and ornithine levels were significantly higher in postmenopausal women with CMD. In contrast, valine levels were higher for women with CAD. Our research identified potential circulating plasma biomarkers of this debilitating heart disease in postmenopausal women, which will have a clinical impact on diagnostic test design in the future. View Full-Text
Keywords: metabolic-circulating biomarker; coronary microvascular dysfunction; postmenopausal women metabolic-circulating biomarker; coronary microvascular dysfunction; postmenopausal women
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MDPI and ACS Style

Arredondo Eve, A.; Tunc, E.; Liu, Y.-J.; Agrawal, S.; Erbak Yilmaz, H.; Emren, S.V.; Akyıldız Akçay, F.; Mainzer, L.; Žurauskienė, J.; Madak Erdogan, Z. Identification of Circulating Diagnostic Biomarkers for Coronary Microvascular Disease in Postmenopausal Women Using Machine-Learning Techniques. Metabolites 2021, 11, 339. https://doi.org/10.3390/metabo11060339

AMA Style

Arredondo Eve A, Tunc E, Liu Y-J, Agrawal S, Erbak Yilmaz H, Emren SV, Akyıldız Akçay F, Mainzer L, Žurauskienė J, Madak Erdogan Z. Identification of Circulating Diagnostic Biomarkers for Coronary Microvascular Disease in Postmenopausal Women Using Machine-Learning Techniques. Metabolites. 2021; 11(6):339. https://doi.org/10.3390/metabo11060339

Chicago/Turabian Style

Arredondo Eve, Alicia, Elif Tunc, Yu-Jeh Liu, Saumya Agrawal, Huriye Erbak Yilmaz, Sadık V. Emren, Filiz Akyıldız Akçay, Luidmila Mainzer, Justina Žurauskienė, and Zeynep Madak Erdogan. 2021. "Identification of Circulating Diagnostic Biomarkers for Coronary Microvascular Disease in Postmenopausal Women Using Machine-Learning Techniques" Metabolites 11, no. 6: 339. https://doi.org/10.3390/metabo11060339

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