ArticleCritical care science2024
Identification of distinct phenotypes and improving prognosis using metabolic biomarkers in COVID-19 patients.
Article in Critical care science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Immune dysregulation through longitudinal lymphocyte trajectories and their clinical determinants in hospitalized COVID-19 patients.Intensive care medicine experimental · 2026Article
- Association of systemic inflammation and long-term dysfunction in COVID-19 patients: A prospective cohort.Psychoneuroendocrinology · 2025Article
- COVID-19: Lessons Learned from Molecular and Clinical Research.International journal of molecular sciences · 2025Article
- Common pitfalls in critical care research.Critical care science · 2025Article
- To: Identification of distinct phenotypes and improving prognosis using metabolic biomarkers in COVID-19 patients.Critical care science · 2025Article
- To: Prognostic significance of gastrointestinal dysfunction in critically ill patients with COVID-19.Critical care science · 2025Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo investigate the relationship between the levels of adipokines and other endocrine biomarkers and patient outcomes in hospitalized patients with COVID-19.
methodsIn a prospective study that included 213 subjects with COVID-19 admitted to the intensive care unit, we measured the levels of cortisol, C-peptide, glucagon-like peptide-1, insulin, peptide YY, ghrelin, leptin, and resistin.; their contributions to patient clustering, disease severity, and predicting in-hospital mortality were analyzed.
resultsCortisol, resistin, leptin, insulin, and ghrelin levels significantly differed between severity groups, as defined by the World Health Organization severity scale. Additionally, lower ghrelin and higher cortisol levels were associated with mortality. Adding biomarkers to the clinical predictors of mortality significantly improved accuracy in determining prognosis. Phenotyping of subjects based on plasma biomarker levels yielded two different phenotypes that were associated with disease severity, but not mortality.
conclusionAs a single biomarker, only cortisol was independently associated with mortality; however, metabolic biomarkers could improve mortality prediction when added to clinical parameters. Metabolic biomarker phenotypes were differentially distributed according to COVID-19 severity but were not associated with mortality.
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