Browse
Submit Data
Databases
API
Help

Dataset Information

9 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Iron Related Biomarkers Predict Disease Severity in a Cohort of Portuguese Adult Patients during COVID-19 Acute Infection.

ABSTRACT: Large variability in COVID-19 clinical progression urges the need to find the most relevant biomarkers to predict patients' outcomes. We evaluated iron metabolism and immune response in 303 patients admitted to the main hospital of the northern region of Portugal with variable clinical pictures, from September to November 2020. One hundred and twenty-seven tested positive for SARS-CoV-2 and 176 tested negative. Iron-related laboratory parameters and cytokines were determined in blood samples collected soon after admission. Demographic data, comorbidities and clinical outcomes were recorded. Patients were assigned into five groups according to severity. Serum iron and transferrin levels at admission were lower in COVID-19-positive than in COVID-19-negative patients. The levels of interleukin (IL)-6 and monocyte chemoattractant protein 1 (MCP-1) were increased in COVID-19-positive patients. The lowest serum iron and transferrin levels at diagnosis were associated with the worst outcomes. Iron levels negatively correlated with IL-6 and higher levels of this cytokine were associated with a worse prognosis. Serum ferritin levels at diagnosis were higher in COVID-19-positive than in COVID-19-negative patients. Serum iron is the simplest laboratory test to be implemented as a predictor of disease progression in COVID-19-positive patients.

SUBMITTER: Moreira AC

PROVIDER: S-EPMC8703662 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Biomarkers of ageing and frailty may predict COVID-19 severity.

Project description:Coronavirus Disease 2019 (COVID-19) is caused by the novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) - the culprit of an ongoing pandemic responsible for the loss of over 3 million lives worldwide within a year and a half. While the majority of SARS-CoV-2 infected people develop no or mild symptoms, some become severely ill and may die from COVID-19-related complications. In this review, we compile and comment on a number of biomarkers that have been identified and are expected to enhance the detection, protection and treatment of individuals at high risk of developing severe illnesses, as well as enable the monitoring of COVID-19 prognosis and responsiveness to therapeutic interventions. Consistent with the emerging notion that the majority of COVID-19 deaths occur in older and frail individuals, we researched the scientific literature and report the identification of a subset of COVID-19 biomarkers indicative of increased vulnerability to developing severe COVID-19 in older and frail patients. Mechanistically, increased frailty results from reduced disease tolerance, a phenomenon aggravated by ageing and comorbidities. While biomarkers of ageing and frailty may predict COVID-19 severity, biomarkers of disease tolerance may predict resistance to COVID-19 with socio-economic factors such as access to adequate health care remaining as major non-biomolecular influencers of COVID-19 outcomes.

| S-EPMC8611822 | biostudies-literature

Discovery and systematic assessment of early biomarkers that predict progression to severe COVID-19 disease - 2nd cohort

Project description:In order to identify differentially abundant proteins, human plasma samples from COVID-19 patients with either a mild or moderate (MM) or a critical or severe (CS) disease course from acute phase of infection were analyzed on antibody microarrays 998 different proteins by 1,425 antibodies.

2023-03-15 | E-MTAB-12777 | biostudies-arrayexpress

Discovery and systematic assessment of early biomarkers that predict progression to severe COVID-19 disease - 1st cohort

Project description:In order to identify differentially abundant proteins, human plasma samples from COVID-19 patients with either a mild or moderate (MM) or a critical or severe (CS) disease course from the acute phases of infection were analyzed on antibody microarrays targeting 351 different proteins by 517 antibodies.

2023-04-11 | E-MTAB-12779 | biostudies-arrayexpress

Iron Chelator or Iron Supplement Consumption in COVID-19? The Role of Iron with Severity Infection.

Project description:Iron is a trace element that is used to replicate the virus and has a role in the vital functions of the body and the host's innate immune system. The mechanism of iron in COVID-19 severity is still not well understood. The aim of this study was to evaluate the association of the iron with COVID-19 severity. A case-control study was performed on 147 patients with a positive PCR test result and 39 normal individuals admitted to the Persian Gulf Martyrs Hospital in Bushehr, Iran. The iron profiles and related tests were measured along with hematological analytes. Hemoglobin (Hb), Fe, and saturated transferrin decreased in all the groups compared to the controls, but ferritin increased in the patient groups. After adjusting for age and sex, we found that increased ferritin levels augmented the odds ratio (OR) of the disease in the moderate (OR = 2.95, P = 0.007), severe (OR = 6.1, P < 0.001), and critical groups (OR = 8.34, P < 0.001). The decreased levels of Fe reduced the OR of the disease in the mild (OR = 0.96, P < 0.001), moderate (OR = 0.96, P < 0.001), severe (OR = 0.95, P < 0.001), and critical (OR = 0.98, P = 0.001) groups. Fe (AUC = 85.95, cutoff < 75.5 µg/dL, P < 0.001) and ferritin (AUC = 84.45, cutoff > 157.5 ng/dL, P < 0.001) have higher AUC for disease prognosis, but only ferritin (AUC = 74.89, cutoff > 261.5 ng/dL, P < 0.001) has higher AUC for disease severity assays. It could be concluded that the use of iron chelators to reduce iron intake can be considered a therapeutic goal. In addition, measuring Fe and ferritin is beneficial for the diagnosis of the disease and determining its severity.

| S-EPMC8614629 | biostudies-literature

Cohort survey of healthcare workers with COVID-19 infection

Project description: Not available

| S-BSST563 | biostudies-other

Screening for Anemia and Iron Deficiency in the Adult Portuguese Population.

Project description:Anemia and iron deficiency (ID) can impair quality of life and socioeconomic development. We evaluated the prevalence of anemia and ID in the adult Portuguese population in real-life contexts by gender, age, and pregnancy status. We performed a cross-sectional screening in adult individuals in mainland Portugal from 2013 to 2017. Participants completed a survey about demographics and signs or symptoms compatible with anemia, and ID and hemoglobin and ferritin concentrations were determined by point-of-care tests. We estimated and compared prevalence ratios (PR) of anemia and ID using Poisson regression with robust variance and the Wald chi-square test. We collected data from 11,030 individuals (26% men, 64% nonpregnant women, and 10% pregnant women). We found anemia in 51.8% (95% CI 50.1-53.4%) of nonpregnant women in fertile age, 46.6% (95% CI 44.7-48.6%) of nonpregnant women >51 years, 38.2% (95% CI 35.4-41.1%) of pregnant women, and 33.3% (95% CI 31.6-35.1%) of men. The prevalence of ID was 72.9% (95% CI 71.4-74.4%) in nonpregnant women in fertile age, 50.5% (95% CI 48.5-52.4%) in nonpregnant women >51 years, 94.8% (95% CI 93.3-96.0%) in pregnant women, and 28.9% (95% CI 27.3-30.6%) in men. We found significant associations between the prevalence of anemia or ID and nonpregnant women (PR: 1.50, 95% CI 1.42-1.59 or PR: 2.21, 95% CI 2.09-2.35, respectively), manifestation of signs or symptoms (PR: 1.19, 95% CI 1.53-1.23 or PR: 1.22, 95% CI 1.18-1.26), pregnant women (PR: 0.74, 95% CI 0.68-0.80 or PR: 1.30, 95% CI 1.27-1.33), and nonpregnant women ?51 years (PR: 1.11, 95% CI 1.06-1.17 or PR: 1.42, 95% CI 1.36-1.48). In conclusion, anemia and ID represent moderate to severe public health problems, particularly among women in fertile age and in 3rd trimester, of pregnancy emphasizing the need to raise the public and health professionals' awareness of these problems and their prevention, diagnosis, and treatment.

| S-EPMC7411453 | biostudies-literature

Evaluation of Biomarkers of Severity in Patients with COVID-19 Infection.

Project description:ObjectAlthough many Japanese patients infected with coronavirus disease 2019 (COVID-19) only experience mild symptoms, in some cases a patient's condition deteriorates, resulting in a poor outcome. This study examines the behavior of biomarkers in patients with mild to severe COVID-19.MethodsThe disease severity of 152 COVID-19 patients was classified into mild, moderate I, moderate II, and severe, and the behavior of laboratory biomarkers was examined across these four disease stages.ResultsThe median age and male/female ratio increased with severity. The mortality rate was 12.5% in both moderate II and severe stages. Underlying diseases, which were not observed in 45% of mild stage patients, increased with severity. An ROC analysis showed that C-reactive protein (CRP), ferritin, procalcitonin (PCT), hemoglobin (Hb) A1c, albumin, and lactate dehydrogenase (LDH) levels were significantly useful for the differential diagnosis of mild/moderate I stage and moderate II/severe stage. In the severe stage, Hb levels, coagulation time, total protein, and albumin were significantly different on the day of worsening from those observed on the day of admission. The frequency of hemostatic biomarker abnormalities was high in the severe disease stage.ConclusionThe evaluation of severity is valuable, as the mortality rate was high in the moderate II and severe stages. The levels of CRP, ferritin, PCT, albumin, and LDH were useful markers of severity, and hemostatic abnormalities were frequently observed in patients in the severe disease stage.

| S-EPMC8432011 | biostudies-literature

Postoperative biomarkers predict acute kidney injury and poor outcomes after adult cardiac surgery.

Project description:Acute kidney injury (AKI) is a frequent complication of cardiac surgery and increases morbidity and mortality. The identification of reliable biomarkers that allow earlier diagnosis of AKI in the postoperative period may increase the success of therapeutic interventions. Here, we conducted a prospective, multicenter cohort study involving 1219 adults undergoing cardiac surgery to evaluate whether early postoperative measures of urine IL-18, urine neutrophil gelatinase-associated lipocalin (NGAL), or plasma NGAL could identify which patients would develop AKI and other adverse patient outcomes. Urine IL-18 and urine and plasma NGAL levels peaked within 6 hours after surgery. After multivariable adjustment, the highest quintiles of urine IL-18 and plasma NGAL associated with 6.8-fold and 5-fold higher odds of AKI, respectively, compared with the lowest quintiles. Elevated urine IL-18 and urine and plasma NGAL levels associated with longer length of hospital stay, longer intensive care unit stay, and higher risk for dialysis or death. The clinical prediction model for AKI had an area under the receiver-operating characteristic curve (AUC) of 0.69. Urine IL-18 and plasma NGAL significantly improved the AUC to 0.76 and 0.75, respectively. Urine IL-18 and plasma NGAL significantly improved risk prediction over the clinical models alone as measured by net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In conclusion, urine IL-18, urine NGAL, and plasma NGAL associate with subsequent AKI and poor outcomes among adults undergoing cardiac surgery.

| S-EPMC3171945 | biostudies-literature

Using machine learning to predict COVID-19 infection and severity risk among 4,510 aged adults: a UK Biobank cohort study.

Project description:BackgroundMany risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e., hospitalization).MethodsAmong aged adults (69.3 ± 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4,510 participants with 7,539 test cases. We downloaded baseline data from 10-14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases. Permutation-based linear discriminant analysis was used to predict COVID-19 risk and hospitalization risk. Probability and threshold metrics included receiver operating characteristic curves to derive area under the curve (AUC), specificity, sensitivity, and quadratic mean.ResultsThe "best-fit" model for predicting COVID-19 risk achieved excellent discrimination (AUC=0.969, 95% CI=0.934-1.000). Factors included age, immune markers, lipids, and serology titers to common pathogens like human cytomegalovirus. The hospitalization "best-fit" model was more modest (AUC=0.803, 95% CI=0.663-0.943) and included only serology titers.ConclusionsAccurate risk profiles can be created using standard self-report and biomedical data collected in public health and medical settings. It is also worthwhile to further investigate if prior host immunity predicts current host immunity to COVID-19.

| S-EPMC7302228 | biostudies-literature

Using machine learning to predict COVID-19 infection and severity risk among 4510 aged adults: a UK Biobank cohort study.

Project description:Many risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e., hospitalization). Among aged adults (69.3 ± 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4510 participants with 7539 test cases. We downloaded baseline data from 10 to 14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases in a subset of 80 participants with 124 test cases. Permutation-based linear discriminant analysis was used to predict COVID-19 risk and hospitalization risk. Probability and threshold metrics included receiver operating characteristic curves to derive area under the curve (AUC), specificity, sensitivity, and quadratic mean. Model predictions using the full cohort were marginal. The "best-fit" model for predicting COVID-19 risk was found in the subset of participants with antibody titers, which achieved excellent discrimination (AUC 0.969, 95% CI 0.934-1.000). Factors included age, immune markers, lipids, and serology titers to common pathogens like human cytomegalovirus. The hospitalization "best-fit" model was more modest (AUC 0.803, 95% CI 0.663-0.943) and included only serology titers, again in the subset group. Accurate risk profiles can be created using standard self-report and biomedical data collected in public health and medical settings. It is also worthwhile to further investigate if prior host immunity predicts current host immunity to COVID-19.

| S-EPMC9092926 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data