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Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate

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Abstract

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC–MS, LC–MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, l-aspartic, l-glutamic, l-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-l-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-l-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

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Data Availability

Study data may be made available by the corresponding author when requested by readers.

Abbreviations

ANN:

Artificial neural network

EPO:

External parameter orthogonalization

GC–MS:

Gas chromatography coupled to mass spectrometry

GLSW:

Generalized least squares weighting

LC–MS:

Liquid chromatography coupled with mass spectrometry

NMR:

Nuclear magnetic resonance

OSC:

Orthogonal signal correction

PLS-DA:

Discriminant analysis by partial least squares

PCA:

Principal component analysis

RF:

Random forest

RMSEC:

Root mean square error of calibration

RMSECV:

Root mean square cross-validation error

RT-PCR:

Reverse transcription polymerase chain reaction

SVM:

Support vector machine

VIP:

Variable importance in projection

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Acknowledgements

The authors express their gratitude to the Brazilian National Council of Technological and Scientific Development (CNPq) and CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education within the Ministry of Education of Brazil) for research funding – Finance Code 001.

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Authors and Affiliations

  1. Universidade Federal do Paraná, Curitiba, Brazil

    Alexandre de Fátima Cobre, Karime Zeraik Abdalla Domingues & Raul Edison Luna Lazo

  2. School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK

    Alexessander Couto Alves

  3. Public Health College, Universidade de São Paulo, São Paulo, Brazil

    Ana Raquel Manuel Gotine

  4. Department of Pharmacy, Universidade Federal do Paraná, Campus III, Av. Pref. Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil

    Luana Mota Ferreira & Roberto Pontarolo

  5. H&TRC - Health & Technology Research Centre, ESTeSL, Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Lisbon, Portugal

    Fernanda Stumpf Tonin

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  1. Alexandre de Fátima Cobre
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  2. Alexessander Couto Alves
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  8. Roberto Pontarolo
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Contributions

AdFC: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – original draft, Visualization. ACA: Formal analysis, Writing – review & editing, Visualization. ARG: Formal analysis, Writing – review & editing, Visualization, Supervision, Visualization. RELL: Conceptualization, Methodology, Validation, Formal analysis, Investigation. KZAD: Formal analysis, Writing – review & editing. LMF: Methodology, formal analysis, Investigation, Writing – review & editing, Visualization. FST: Conceptualization, Writing – review & editing, Supervision. RP: Conceptualization, Writing – review & editing, Visualization, Supervision.

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Correspondence to Roberto Pontarolo.

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de Fátima Cobre, A., Alves, A.C., Gotine, A.R.M. et al. Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. Intern Emerg Med (2024). https://doi.org/10.1007/s11739-024-03547-1

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