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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Metabolomics is a promising technology for the application of translational medicine to cardiovascular risk. Here, we applied a liquid chromatography/tandem mass spectrometry approach to explore the associations between plasma concentrations of amino acids, methylarginines, acylcarnitines, and tryptophan catabolism metabolites and cardiometabolic risk factors in patients diagnosed with arterial hypertension (HTA) (n = 61), coronary artery disease (CAD) (n = 48), and non-cardiovascular disease (CVD) individuals (n = 27). In total, almost all significantly different acylcarnitines, amino acids, methylarginines, and intermediates of the kynurenic and indolic tryptophan conversion pathways presented increased (p < 0.05) in concentration levels during the progression of CVD, indicating an association of inflammation, mitochondrial imbalance, and oxidative stress with early stages of CVD. Additionally, the random forest algorithm was found to have the highest prediction power in multiclass and binary classification patients with CAD, HTA, and non-CVD individuals and globally between CVD and non-CVD individuals (accuracy equal to 0.80 and 0.91, respectively). Thus, the present study provided a complex approach for the risk stratification of patients with CAD, patients with HTA, and non-CVD individuals using targeted metabolomics profiling.

Details

Title
Target Metabolome Profiling-Based Machine Learning as a Diagnostic Approach for Cardiovascular Diseases in Adults
Author
Moskaleva, Natalia E 1   VIAFID ORCID Logo  ; Shestakova, Ksenia M 1 ; Kukharenko, Alexey V 2 ; Markin, Pavel A 1 ; Kozhevnikova, Maria V 3   VIAFID ORCID Logo  ; Korobkova, Ekaterina O 3   VIAFID ORCID Logo  ; Brito, Alex 2 ; Baskhanova, Sabina N 1   VIAFID ORCID Logo  ; Mesonzhnik, Natalia V 1   VIAFID ORCID Logo  ; Belenkov, Yuri N 3 ; Pyatigorskaya, Natalia V 4 ; Tobolkina, Elena 5 ; Rudaz, Serge 5   VIAFID ORCID Logo  ; Appolonova, Svetlana A 6   VIAFID ORCID Logo 

 World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia 
 Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, 119435 Moscow, Russia 
 Hospital Therapy N°1 Department of the N.V. Sklifosovsky Institute of Clinical Medicine, I.M. Sechenov First Moscow Medical University, 119992 Moscow, Russia 
 Department of Industrial Pharmacy, Institute of Vocational Education I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia 
 Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1206 Geneva, Switzerland 
 Laboratory of Pharmacokinetics and Metabolomic Analysis, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow Medical University, 119435 Moscow, Russia; Department of Industrial Pharmacy, Institute of Vocational Education I.M. Sechenov First Moscow State Medical University, 119435 Moscow, Russia 
First page
1185
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
22181989
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2756741707
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.