| Title: |
Combination of exhaled volatile organic compounds with serum biomarkers predicts respiratory infection severity |
| Authors: |
Esteban, Patricia; Letona Jiménez, Santiago; Domingo, María Pilar; Morte Romea, Elena; Pellejero, Galadriel; Encabo-Berzosa, M. Mar; Ramírez-Labrada, Ariel; Sanz-Pamplona, Rebeca; Pardo, Julián; Paño, José Ramón; Gálvez Buerba, Eva Mª |
| Contributors: |
CSIC - Plataforma Temática Interdisciplinar del CSIC Salud Global (PTI Salud Global); Ministerio de Ciencia, Innovación y Universidades (España); Instituto de Salud Carlos III; European Commission; Gobierno de Aragón; Agencia Estatal de Investigación (España); Esteban, Patricia; Letona Jiménez, Santiago; Domingo, María Pilar; Morte Romea, Elena; Pellejero, Galadriel; Encabo-Berzosa, M. Mar; Ramírez-Labrada, Ariel; Sanz-Pamplona, Rebeca; Pardo, Julián; Paño, José Ramón; Gálvez Buerba, Eva Mª; Gálvez Buerba, Eva Mª eva@icb.csic.es |
| Publisher Information: |
Taylor & Francis |
| Publication Year: |
2025 |
| Collection: |
Digital.CSIC (Consejo Superior de Investigaciones Científicas / Spanish National Research Council) |
| Subject Terms: |
Machine learning; Respiratory infections; Serum biomarkers; Severity prediction; Volatile organic compounds; Ensure healthy lives and promote well-being for all at all ages; respiratory diseases; infection; biomarkers; disease severity; organic volatile compounds |
| Description: |
3 figures, 1 table.-- Under a Creative Commons Attribution-NonCommercial License. ; [Objective] During respiratory infections, host-pathogen interaction alters metabolism, leading to changes in the composition of expired volatile organic compounds (VOCs) and soluble immunomodulators. This study aims to identify VOC and blood biomarker signatures to develop machine learning-based prognostic models capable of distinguishing infections with similar symptoms. ; [Methods] Twenty-one VOCs and fifteen serum biomarkers were quantified in samples from 86 COVID-19 patients, 75 patients with non-COVID-19 respiratory infections, and 72 healthy donors. The populations were categorized into severity subgroups based on their oxygen support requirements. Descriptive and statistical analyses were conducted to assess group differentiation. Additionally, machine learning classifiers were developed to predict disease severity in both COVID-19 and non-COVID-19 patients. ; [Results] VOC and biomarker profiles differed significantly among groups. Random Forest models demonstrated the best performance for severity prediction. The COVID-19 model achieved 93% accuracy, 100% sensitivity, and 89% specificity, identifying IL-6, IL-8, thrombomodulin, and toluene as key severity predictors. In non-COVID-19 patients, the model reached 89% accuracy, 100% sensitivity, and 67% specificity, with CXCL10 and methyl-isobutyl-ketone as key markers. ; [Conclusion] VOCs and serum biomarkers differentiated HD, COVID-19, and non-COVID-19 patients, and enabled the development of high-performance severity prediction models. While promising, these findings require validation in larger independent cohorts. ; This research was supported by PTI Salud Global CSIC,Ministerio de Ciencia e Innovación and Unión Europea – NextGenerationEU; CIBER- Consorcio Centro de Investigación Biomédica en Red- CIBERINFEC[CB21-13-0087] and CIBERES, Instituto de Salud Carlos III, Ministerio deCiencia e Innovación and Unión Europea – NextGenerationEU, FEDER (FondoEuropeo de ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
Esteban, Patricia; Letona Jiménez, Santiago; Domingo, María Pilar; Morte Romea, Elena; Pellejero, Galadriel; Encabo-Berzosa, M. Mar; Ramírez-Labrada, Ariel; Sanz-Pamplona, Rebeca; Pardo, Julián; Paño, José Ramón; Gálvez Buerba, Eva Mª; 2025; Supplementary information for Combination of exhaled volatile organic compounds with serum biomarkers predicts respiratory infection severity [Dataset]; Taylor & Francis; http://dx.doi.org/10.1080/25310429.2025.2477911; http://dx.doi.org/10.1080/25310429.2025.2477911; Sí; Pulmonology 31(1): 2477911 (2025); http://hdl.handle.net/10261/386643 |
| DOI: |
10.1080/25310429.2025.2477911 |
| Availability: |
http://hdl.handle.net/10261/386643; https://doi.org/10.1080/25310429.2025.2477911 |
| Rights: |
open |
| Accession Number: |
edsbas.B91F3261 |
| Database: |
BASE |