Older patients affected by COVID-19: investigating the existence of biological phenotypes.

Publication date: Nov 07, 2024

COVID-19 provides an opportunity to examine biological phenotypes (observable morphological, functional and biological characteristics) in individuals who experience the same acute condition, potentially revealing differences in response to acute external stressors. The aim our study was to investigate biological phenotypes in older patients hospitalized for COVID-19, exploiting a panel of aging biomarkers. Data were gathered from the FRACOVID Project, an observational multicenter study, aimed to evaluate the impact of frailty on health-related outcomes in patients 60 + with COVID-19 in Northern Italy. A hierarchical cluster analysis was run using log-transformed and scaled values of TNF-a, IL-1 beta, IL-6, PAI-1, GDF-15, NT-proBNP, and Cystatin C evaluated at admission. Eighty-one participants (mean age 75. 3 years; 60. 5% male) were evaluated. Frailty was identified in 42% of the sample and 27. 2% were unable to ambulate outdoors. The mean hospital stay was 24. 7 days, with an in-hospital mortality rate of 18. 5%. Three biological phenotypes were found: (1) ‘inflammatory’, with high inflammatory biomarkers; (2) ‘organ dysfunction’, characterized by elevated cystatin C and NT-proBNP, and lower inflammatory markers; and (3) ‘unspecific’, with lower NT-proBNP and GDF-15 levels, and intermediate concentrations of other biomarkers. The ‘organ dysfunction’ phenotype showed the highest mean age and prevalence of frailty, disability, and chronic diseases. The ‘inflammatory’ phenotype showed the highest burden of respiratory and systemic signs and symptoms of infection. Biological phenotypes might be used to identify different clinical and functional phenotypes in individuals affected by COVID-19.

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Concepts Keywords
Aging Aged
Biomarkers Aged, 80 and over
Hospital Biomarkers
Italy Biomarkers
Organ Biomarkers
COVID-19
COVID-19
Elderly
Female
Frailty
Frailty
Humans
Italy
Male
Middle Aged
Phenotype
SARS-CoV-2

Semantics

Type Source Name
disease MESH COVID-19
disease MESH frailty
disease MESH chronic diseases
disease MESH infection
pathway REACTOME Reproduction
disease MESH phenotypic variability
disease MESH premature aging
pathway REACTOME Cellular Senescence
disease IDO susceptibility
disease MESH emergency
drug DRUGBANK Ranitidine
disease IDO blood
drug DRUGBANK Corticorelin
disease MESH functional status
drug DRUGBANK Oxygen
disease MESH cardiac diseases
disease MESH atrial fibrillation
disease MESH stroke
disease MESH chronic kidney disease
disease MESH COPD
disease MESH syndromes
disease MESH malnutrition
disease MESH dementia
drug DRUGBANK Creatinine
disease MESH inflammation
disease MESH hypoxia
disease MESH oxidative stress
disease MESH mitochondrial dysfunction
disease MESH heart failure
drug DRUGBANK Urokinase
disease MESH aortic valve stenosis
disease MESH atherosclerosis
disease MESH sarcopenia
disease MESH cardiovascular diseases
drug DRUGBANK Medical air
drug DRUGBANK L-Phenylalanine
disease MESH Living alone
disease MESH tumor
drug DRUGBANK Tocilizumab
drug DRUGBANK Anakinra
disease MESH tachypnea
disease MESH tachycardia
disease MESH abnormalities
drug DRUGBANK Indoleacetic acid
disease IDO acute infection
disease MESH infectious diseases
disease MESH morbidities
drug DRUGBANK Coenzyme M
disease IDO symptom
disease IDO process
disease IDO algorithm
drug DRUGBANK Spinosad
drug DRUGBANK Serine
pathway REACTOME Immune System
disease MESH necrosis
pathway REACTOME Pyroptosis
disease MESH Death
disease MESH cognitive decline
disease MESH cytokine storm
disease MESH critically ill
drug DRUGBANK Haloperidol
drug DRUGBANK Sarilumab
disease MESH pneumonia
drug DRUGBANK Immune Globulin Human
disease IDO host
drug DRUGBANK Hexocyclium
disease IDO cell

Original Article

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