Browse
Submit Data
Databases
API
Help

Dataset Information

6 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Serum cystatin C and CRP are early predictive biomarkers for emergence of hypoxia in COVID-19.

ABSTRACT:

Introduction

In Japan, during the coronavirus disease 2019 (COVID-19) pandemic, patients with non-hypoxia are recommended to recuperate at home or in pre-hospital facilities. However, it was observed that unexpected hypoxia may occur and become severe subsequently in patients whose symptoms were initially expected to improve naturally. The aim of this study is to validate biomarkers that can predict at an early stage the emergence of hypoxia in COVID-19 patients without hypoxia.

Methods

We retrospectively enrolled 193 patients with COVID-19, excluding patients with hypoxia and severe disease from the onset. Participants were classified into two groups according to the emergence of hypoxia during the clinical course, and the laboratory data were compared to identify biomarkers that could predict early the emergence of hypoxia.

Results

The areas under the curve for serum cystatin C (CysC) and C-reactive protein (CRP) levels for the emergence of hypoxia during the clinical course were higher than those for other biomarkers (CysC, 0.84 and CRP, 0.83). Multivariate analysis showed that high serum CysC and CRP levels were associated with the emergence of hypoxia during the clinical course.

Conclusions

Elevated serum CysC and CRP levels were associated with the emergence of hypoxia during the clinical course in COVID-19 patients without hypoxia. These findings may help determine the need for hospitalization in initially non-hypoxic COVID-19 patients.

SUBMITTER: Miyata Y

PROVIDER: S-EPMC9259004 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

The predictive value of serum level of cystatin C for COVID-19 severity.

Project description:To investigate the potential prognostic value of Serum cystatin C (sCys C) in patients with COVID-19 and determine the association of sCys C with severe COVID-19 illness. We performed a retrospective review of medical records of 162 (61.7 ± 13.5 years) patients with COVID-19. We assessed the predictive accuracy of sCys C for COVID-19 severity by the receiver operating characteristic (ROC) curve analysis. The participants were divided into two groups based on the sCys C cut-off value. We evaluated the association between high sCys C level and the development of severe COVID-19 disease, using a COX proportional hazards regression model. The area under the ROC curve was 0.708 (95% CI 0.594-0.822), the cut-off value was 1.245 (mg/L), and the sensitivity and specificity was 79.1% and 60.7%, respectively. A multivariable Cox analysis showed that a higher level of sCys C (adjusted HR 2.78 95% CI 1.25-6.18, p = 0.012) was significantly associated with an increased risk of developing a severe COVID-19 illness. Patients with a higher sCys C level have an increased risk of severe COVID-19 disease. Our findings suggest that early assessing sCys C could help to identify potential severe COVID-19 patients.

| S-EPMC8578213 | biostudies-literature

Lipocalin-2, S100A8/A9, and cystatin C: Potential predictive biomarkers of cardiovascular complications in COVID-19.

Project description:Severe coronavirus (SARS-COV-2) infection often leads to systemic inflammation accompanied by cardiovascular complications including venous thromboembolism (VTE). However, it is largely undefined if inflammatory markers such as lipocalin-2 (LNC2), calprotectin (S100A8/A9), and cystatin C (CST3), previously linked with VTE, play roles in cardiovascular complications and advancement of COVID-19 severity. To investigate the same, hospitalized moderate and severe (presented pneumonia and required intensive care) COVID-19 patients were recruited. The levels of plasma LNC2, S100A8/A9, CST3, myoglobin, and cardiac Troponin I (cTnI) were assessed through enzyme-linked immunosorbent assay (ELISA). The investigation revealed a significantly upregulated level of plasma LNC2 at the moderate stage of SARS-CoV-2 infection. In contrast, the levels of S100A8/A9 and CST3 in moderate patients were comparable to healthy controls; however, a profound induction was observed only in severe COVID-19 patients. The tissue injury marker myoglobin was unchanged in moderate patients; however, a significantly elevated level was observed in the critically ill COVID-19 patients. In contrast, cTnI level was unchanged both in moderate and severe patients. Analysis revealed a positive correlation between the levels of S100A8/A9 and CST3 with myoglobin in COVID-19. In silico analysis predicted interactions of S100A8/A9 with toll-like receptor 4 (TLR-4), MyD88 LY96, and LCN2 with several other inflammatory mediators including MMP2, MMP9, TIMP1, and interleukins (IL-6, IL-17A, and IL-10). In summary, early induction of LCN2 likely plays a role in advancing the COVID-19 severity. A positive correlation of S100A8/A9 and CST3 with myoglobin suggests that these proteins may serve as predictive biomarkers for thromboembolism and tissue injury in COVID-19.

| S-EPMC9379606 | biostudies-literature

Early prediction of COVID-19-associated acute kidney injury: Are serum NGAL and serum Cystatin C levels better than serum creatinine?

Project description:BackgroundCoronavirus disease-2019 (COVID-19) is associated with a high risk of acute kidney injury (AKI), often requiring renal replacement therapy (RRT). Serum Cystatin C (sCysC) and serum Neutrophil Gelatinase-Associated Lipocalin (sNGAL) are emerging biomarkers for kidney injury, and were suggested to be superior to serum creatinine (sCr) in several clinical settings. Moreover, elevated sCysC is associated with disease severity and mortality in COVID-19. We aimed to assess the utility of sCysC and sNGAL for predicting COVID-19-associated AKI, need for RRT, and need for intensive care unit (ICU) admission, when measured at presentation to the emergency department (ED).MethodsPatients presenting to the ED with laboratory-confirmed COVID-19 were included. The primary outcome was development of COVID-19-associated AKI, while the secondary outcomes were need for RRT and ICU admission.ResultsAmong 52 COVID-19 patients, 22 (42.3%) developed AKI with 8/22 (36.4%) requiring RRT. Both sCr and sCysC demonstrated excellent performance for predicting AKI (AUC, 0.86 and 0.87, respectively) and need for RRT (AUC, 0.94 and 0.95, respectively). sNGAL displayed acceptable performance for predicting AKI (AUC, 0.81) and need for RRT (AUC, 0.87).ConclusionsSCr and sCysC measured at ED presentation are both highly accurate predictors of AKI and need for RRT, whereas sNGAL demonstrated adequate diagnostic performance. While sCyC was previously shown to be superior to sCr as a diagnostic biomarker of kidney injury in certain etiologies, our findings demonstrate that sCr is comparable to sCyC in the context of predicting COVID-19-associated AKI. Given the high sensitivity of these biomarkers for predicting the need for RRT, and as sCysC is associated with mortality in COVID-19 patients, we recommend their measurement for enabling risk stratification and early intervention.

| S-EPMC8801397 | biostudies-literature

Serum metabolomics profiling identifies new predictive biomarkers for disease severity in COVID-19 patients

Project description:Over the last three years, numerous groups have reported on different predictive models of disease severity in COVID-19 patients. However, almost all such models, which relied on serum biomarkers, clinical data or a combination of both, were subsequently deemed as cumbersome, inadequate and/or subject to bias. Moreover, although serum metabolomics profiling has shown significant differences among patients with different degrees of disease severity, the use of serum metabolomics profiling to identify prognostic biomarkers has, so far, been neglected. Herein, we sought to develop highly predictive models of disease severity by integrating routine laboratory findings and serum metabolomics profiling which identified several metabolites including K_4_aminophenol, acetaminophen and cytosine as potential biomarkers of disease severity in COVID-19 patients. Two models were subsequently developed and internally validated on the basis of ROC-AUC values. The predictive accuracy of the first model was 0.998 (95% CI: 0.992 to 1.000) with an optimal cut-off risk score of 4 biomarkers from among 8 linearly-related biomarkers (D-dimer, ferritin, neutrophil counts, Hp, sTfR, K_4_aminophenol, acetaminophen and cytosine). The predictive accuracy of the second model was 0.996 (95% CI: 0.989 to 1.000) with an optimal cut-off risk score of 3 biomarkers from among 6 biomarkers (D-dimer, ferritin, neutrophil counts, Hp, sTfR and cytosine). The two models are of high predictive power, need a small number of variables that can be acquired at minimal cost and effort, and can be applied independent of non-empirical clinical data. In conclusion, the metabolomics profiling data and the modeling work stemming from it, as presented here, could further explain the cause of COVID-19 disease prognosis and patient management.

2022-09-21 | ST002301 | MetabolomicsWorkbench

Serum troponin, D-dimer, and CRP level in severe coronavirus (COVID-19) patients.

Project description:BackgroundAbnormal inflammation coagulation biomarker levels of troponin, C-reactive protein (CRP), and D-dimer levels in serum have been demonstrated to be associated and involved in the disease progression of coronavirus disease 2019 (COVID-19).MethodsFirst: the study aimed to investigate the correlation of troponin, CRP, d-dimer, white blood cell (WBC) and polymerase chain reaction-cycle threshold (PCR-Ct) within COVID-19 survivors (143 patients; 79 males, 64 females) and in deceased (30 patients; 12 males, 18 females) group. Also, assessing any differences between both groups in studied parameters. Second: a correlation study of studied parameters' level has been conducted within families (41 patients; 23 males [seven deaths] and 18 females [eight deaths]) that lost more than one member due to the severity of the disease. Also, differences between these family and control group (132 patients; 69 males and 63 females) group in studied parameters have been assessed.ResultsIn the first week of hospitalization, there were significant differences in D-dimer, CRP and troponin level between survived and deceased patient groups. In the second week of the admission, both groups had significant differences in the level of all studied parameters; troponin I, D-dimer, CRP, and WBCs. WBC levels positively correlated to CRP in male survivors (r = 0.75, p < 0.0001), and to troponin in deceased male patients (r = 0.74, p = 0.007). The second week of patient admission was critical in the group of families who lost more than one person, when troponin was correlated positively with D-dimer, CRP, and WBCs.ConclusionTroponin, D-dimer, CRP, and WBCs level were significantly higher in COVID-19 patients who died than in COVID-19 survivors. High troponin and WBC levels, were considerably associated with families that lost more than one member, when compared with the unrelated COVID-19 patient control.

| S-EPMC8926504 | biostudies-literature

Serum stratifin and presepsin as candidate biomarkers for early detection of COVID-19 disease progression.

Project description:The prognosis of patients with severe cases of COVID-19 is poor; thus, biomarkers for earlier prediction of COVID-19 progression are vital. We measured levels of five lung injury-related biomarkers, SP-D, KL-6, presepsin, kallistatin and stratifin, in serum samples collected serially during hospitalization from 31 patients with mild/moderate or severe/critical COVID-19 pneumonia, and their predictive performances were compared. Like the previously reported presepsin, a new biomarker candidate, stratifin, was significantly elevated with the onset of severe or critical symptoms in COVID-19 patients and decreased with symptom improvement. Notably, changes in stratifin and presepsin levels were distinctly earlier than those in SP-D, KL-6 and even SpO2/FiO2 values. Furthermore, serum levels of these biomarkers were significantly higher at the pre-severe stage (before the start of oxygen support) of patients who eventually advanced to severe/critical stages than in the patients who remained at the mild/moderate stage. These results were confirmed in an independent cohort, including 71 mild/moderate and 14 severe/critical patients, for whom the performance of stratifin and presepsin in discriminating between mild/moderate and pre-severe conditions of COVID-19 patients was superior to that of the SpO2/FiO2 ratio. Therefore, we concluded that stratifin and presepsin could be used as prognostic biomarkers for severe COVID-19 progression.

| S-EPMC9188980 | 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

Multiple Early Factors Anticipate Post-Acute COVID-19 Sequelae

Project description:Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk-factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data, and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific autoantibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

2022-02-01 | E-MTAB-10129 | biostudies-arrayexpress

The serum metabolome of COVID-19 patients is distinctive and predictive.

Project description:BackgroundMetabolism is critical for sustaining life, immunity and infection, but its role in COVID-19 is not fully understood.MethodsSeventy-nine COVID-19 patients, 78 healthy controls (HCs) and 30 COVID-19-like patients were recruited in a prospective cohort study. Samples were collected from COVID-19 patients with mild or severe symptoms on admission, patients who progressed from mild to severe symptoms, and patients who were followed from hospital admission to discharge. The metabolome was assayed using gas chromatography-mass spectrometry.ResultsSerum butyric acid, 2-hydroxybutyric acid, l-glutamic acid, l-phenylalanine, l-serine, l-lactic acid, and cholesterol were enriched in COVID-19 and COVID-19-like patients versus HCs. Notably, d-fructose and succinic acid were enriched, and citric acid and 2-palmitoyl-glycerol were depleted in COVID-19 patients compared to COVID-19-like patients and HCs, and these four metabolites were not differentially distributed in non-COVID-19 groups. COVID-19 patients had enriched 4-deoxythreonic acid and depleted 1,5-anhydroglucitol compared to HCs and enriched oxalic acid and depleted phosphoric acid compared to COVID-19-like patients. A combination of d-fructose, citric acid and 2-palmitoyl-glycerol distinguished COVID-19 patients from HCs and COVID-19-like patients, with an area under the curve (AUC) > 0.92 after validation. The combination of 2-hydroxy-3-methylbutyric acid, 3-hydroxybutyric acid, cholesterol, succinic acid, L-ornithine, oleic acid and palmitelaidic acid predicted patients who progressed from mild to severe COVID-19, with an AUC of 0.969. After discharge, nearly one-third of metabolites were recovered in COVID-19 patients.ConclusionsThe serum metabolome of COVID-19 patients is distinctive and has important value in investigating pathogenesis, determining a diagnosis, predicting severe cases, and improving treatment.

| S-EPMC7920809 | 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