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Immunometabolic signatures predict risk of progression to sepsis in COVID-19.
Herrera-Van Oostdam, Ana Sofía; Castañeda-Delgado, Julio E; Oropeza-Valdez, Juan José; Borrego, Juan Carlos; Monárrez-Espino, Joel; Zheng, Jiamin; Mandal, Rupasri; Zhang, Lun; Soto-Guzmán, Elizabeth; Fernández-Ruiz, Julio César; Ochoa-González, Fátima; Trejo Medinilla, Flor M; López, Jesús Adrián; Wishart, David S; Enciso-Moreno, José A; López-Hernández, Yamilé.
  • Herrera-Van Oostdam AS; Doctorado en Ciencias Biomédicas Básicas, Centro de Investigación en Ciencias de la Salud y Biomedicina, Universidad Autónoma de San Luis Potosí, San Luis Potosí, San Luis Potosí, México.
  • Castañeda-Delgado JE; Cátedras-CONACyT, Consejo Nacional de Ciencia y Tecnología, Ciudad de México, México.
  • Oropeza-Valdez JJ; Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, Zacatecas, Zacatecas, México.
  • Borrego JC; Doctorado en Ciencias Biomédicas Básicas, Centro de Investigación en Ciencias de la Salud y Biomedicina, Universidad Autónoma de San Luis Potosí, San Luis Potosí, San Luis Potosí, México.
  • Monárrez-Espino J; Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, Zacatecas, Zacatecas, México.
  • Zheng J; Departmento de Epidemiología, Hospital General de Zona #1 "Emilio Varela Luján", Instituto Mexicano del Seguro Social, Zacatecas, Zacatecas, México.
  • Mandal R; Christus Muguerza Hospital Chihuahua - University of Monterrey, Chihuahua, Chihuahua, Mexico.
  • Zhang L; The Metabolomics Innovation Center, University of Alberta, Edmonton, Alberta, Canada.
  • Soto-Guzmán E; The Metabolomics Innovation Center, University of Alberta, Edmonton, Alberta, Canada.
  • Fernández-Ruiz JC; The Metabolomics Innovation Center, University of Alberta, Edmonton, Alberta, Canada.
  • Ochoa-González F; Maestría en Ciencias Biomédicas, Universidad Autónoma de Zacatecas, Zacatecas, Zacatecas, México.
  • Trejo Medinilla FM; Doctorado en Ciencias Biomédicas Básicas, Centro de Investigación en Ciencias de la Salud y Biomedicina, Universidad Autónoma de San Luis Potosí, San Luis Potosí, San Luis Potosí, México.
  • López JA; Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, Zacatecas, Zacatecas, México.
  • Wishart DS; Unidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, Zacatecas, Zacatecas, México.
  • Enciso-Moreno JA; Doctorado en Ciencias Básicas, Universidad Autónoma de Zacatecas, Zacatecas, Zacatecas, México.
  • López-Hernández Y; Doctorado en Ciencias Básicas, Universidad Autónoma de Zacatecas, Zacatecas, Zacatecas, México.
PLoS One ; 16(8): e0256784, 2021.
Article in English | MEDLINE | ID: covidwho-1378138
ABSTRACT
Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C182, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986-0.995), with sensitivity of 0.978 (0.963-0.992) and specificity of 0.920 (0.890-0.949). The panel that included C102, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952-0.977), with sensitivity of 0.993(0.984-1.000) and specificity of 0.851 (0.815-0.887). The panel with citric acid, LysoPC a C281, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800-0.858), with sensitivity of 0.738 (0.695-0.781) and specificity of 0.781 (0.735-0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788-0.874), with sensitivity of 0.765 (0.697-0.832) and specificity of 0.817 (0.770-0.865).
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Sepsis / Metabolomics / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study Topics: Long Covid Limits: Adult / Female / Humans / Male / Middle aged Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Sepsis / Metabolomics / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study Topics: Long Covid Limits: Adult / Female / Humans / Male / Middle aged Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2021 Document Type: Article