Abstract

COVID-19 has caused a worldwide pandemic, creating an urgent need for early detection methods. Breath analysis has shown great potential as a non-invasive and rapid means for COVID-19 detection. The objective of this study is to detect patients infected with SARS-CoV-2 and even the possibility to screen between different SARS-CoV-2 variants by analysis of carbonyl compounds in breath. Carbonyl compounds in exhaled breath are metabolites related to inflammation and oxidative stress induced by diseases. This study included a cohort of COVID-19 positive and negative subjects confirmed by reverse transcription polymerase chain reaction between March and December 2021. Carbonyl compounds in exhaled breath were captured using a microfabricated silicon microreactor and analyzed by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A total of 321 subjects were enrolled in this study. Of these, 141 (85 males, 60.3%) (mean ± SD age: 52 ± 15 years) were COVID-19 (55 during the alpha wave and 86 during the delta wave) positive and 180 (90 males, 50%) (mean ± SD age: 45 ± 15 years) were negative. Panels of a total of 34 ketones and aldehydes in all breath samples were identified for detection of COVID-19 positive patients. Logistic regression models indicated high accuracy/sensitivity/specificity for alpha wave (98.4%/96.4%/100%), for delta wave (88.3%/93.0%/84.6%) and for all COVID-19 positive patients (94.7%/90.1%/98.3%). The results indicate that COVID-19 positive patients can be detected by analysis of carbonyl compounds in exhaled breath. The technology for analysis of carbonyl compounds in exhaled breath has great potential for rapid screening and detection of COVID-19 and for other infectious respiratory diseases in future pandemics.

Details

Title
Detection of COVID-19 by quantitative analysis of carbonyl compounds in exhaled breath
Author
Xie, Zhenzhen 1 ; Morris, James D. 1 ; Pan, Jianmin 2 ; Cooke, Elizabeth A. 3 ; Sutaria, Saurin R. 4 ; Balcom, Dawn 5 ; Marimuthu, Subathra 5 ; Parrish, Leslie W. 5 ; Aliesky, Holly 5 ; Huang, Justin J. 6 ; Rai, Shesh N. 2 ; Arnold, Forest W. 5 ; Huang, Jiapeng 3 ; Nantz, Michael H. 4 ; Fu, Xiao-An 1 

 University of Louisville, Department of Chemical Engineering, Louisville, USA (GRID:grid.266623.5) (ISNI:0000 0001 2113 1622) 
 University of Cincinnati College of Medicine, Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, Cincinnati, USA (GRID:grid.24827.3b) (ISNI:0000 0001 2179 9593); University of Cincinnati College of Medicine, The Cancer Data Science Center, Cincinnati, USA (GRID:grid.24827.3b) (ISNI:0000 0001 2179 9593); University of Cincinnati Cancer Center, Biostatistics and Informatics Shared Resource, Cincinnati, USA (GRID:grid.24827.3b) (ISNI:0000 0001 2179 9593) 
 University of Louisville, Department of Anesthesiology and Perioperative Medicine, Louisville, USA (GRID:grid.266623.5) (ISNI:0000 0001 2113 1622) 
 University of Louisville, Department of Chemistry, Louisville, USA (GRID:grid.266623.5) (ISNI:0000 0001 2113 1622) 
 University of Louisville, Division of Infectious Diseases, Department of Medicine, Louisville, USA (GRID:grid.266623.5) (ISNI:0000 0001 2113 1622) 
 DuPont Manual High School, Louisville, USA (GRID:grid.266623.5) 
Pages
14568
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3071635556
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.