Your browser doesn't support javascript.
loading
Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning.
Elgedawy, Gamalat A; Samir, Mohamed; Elabd, Naglaa S; Elsaid, Hala H; Enar, Mohamed; Salem, Radwa H; Montaser, Belal A; AboShabaan, Hind S; Seddik, Randa M; El-Askaeri, Shimaa M; Omar, Marwa M; Helal, Marwa L.
Afiliación
  • Elgedawy GA; Department of Clinical Biochemistry and Molecular Diagnostics, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
  • Samir M; Faculty of Veterinary Medicine, Department of Zoonoses, Zagazig University, Zagazig, Egypt.
  • Elabd NS; Faculty of Medicine, Department of Tropical Medicine, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
  • Elsaid HH; Department of Clinical Biochemistry and Molecular Diagnostics, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
  • Enar M; Al Mahala Elkobra Fever Hospital, Al Mahala Elkobra, Egypt.
  • Salem RH; Department of Clinical Microbiology and Immunology, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
  • Montaser BA; Faculty of Medicine, Department of Clinical Pathology, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
  • AboShabaan HS; Ph.D. of Biochemistry, National Liver Institute Hospital, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
  • Seddik RM; Faculty of Medicine, Department of Tropical Medicine, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
  • El-Askaeri SM; Department of Clinical Microbiology and Immunology, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
  • Omar MM; Faculty of Medicine, Department of Clinical Pathology, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
  • Helal ML; Department of Clinical Biochemistry and Molecular Diagnostics, National Liver Institute, Menoufia University, Shebin El-Kom, Menoufia, Egypt.
PLoS One ; 19(5): e0302977, 2024.
Article en En | MEDLINE | ID: mdl-38814977
ABSTRACT

BACKGROUND:

After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity.

METHODS:

Given this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models.

RESULTS:

The non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness.

CONCLUSIONS:

Metabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.
Asunto(s)
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Índice de Severidad de la Enfermedad / Biomarcadores / Metabolómica / Aprendizaje Automático / COVID-19 Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Egipto Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Índice de Severidad de la Enfermedad / Biomarcadores / Metabolómica / Aprendizaje Automático / COVID-19 Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Egipto Pais de publicación: Estados Unidos