Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study.
Ontology highlight
ABSTRACT:
SUBMITTER: Tan L
PROVIDER: S-EPMC7100419 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The causative organism, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits a wide spectrum of clinical manifestations in disease-ridden patients. Differences in the severity of COVID-19 ranges from asymptomatic infections and mild cases to the severe form, leading to acute respiratory distress syndrome (ARDS) and multiorgan failure with poor survival. MiRNAs can regulate various cellular processes, including proliferation, apoptosis, and differentiation, by binding to the 3′UTR of target mRNAs inducing their degradation, thus serving a fundamental role in post-transcriptional repression. Alterations of miRNA levels in the blood have been described in multiple inflammatory and infectious diseases, including SARS-related coronaviruses. We used microarrays to delineate the miRNAs and snoRNAs signature in the peripheral blood of severe COVID-19 cases (n=9), as compared to mild (n=10) and asymptomatic (n=10) patients, and identified differentially expressed transcripts in severe versus asymptomatic, and others in severe versus mild COVID-19 cases. A cohort of 29 male age-matched patients were selected. All patients were previously diagnosed with COVID-19 using TaqPath COVID-19 Combo Kit (Thermo Fisher Scientific, Waltham, Massachusetts), or Cobas SARS-CoV-2 Test (Roche Diagnostics, Rotkreuz, Switzerland), with a CT value < 30. Additional criterion for selection was age between 35 and 75 years. Participants were grouped into severe, mild and asymptomatic. Classifying severe cases was based on requirement of high-flow oxygen support and ICU admission (n=9). Whereas mild patients were identified based on symptoms and positive radiographic findings with pulmonary involvement (n=10). Patients with no clinical presentation were labelled as asymptomatic cases (n=10).
2022-03-01 | E-MTAB-10970 | biostudies-arrayexpress
Project description:Background: There is a compelling need to identify clinical and laboratory predictors of unfavorable clinical course and death in patients with coronavirus disease (COVID-19). A trend towards low lymphocyte count and high neutrophil counts in patients with poor outcomes has been reported by earlier studies. We aim to synthesize existing data evaluating the relationship between clinical outcomes and abnormal neutrophil and lymphocyte counts at admission in COVID-19 patients.Methods: An electronic search was carried out in PubMed, China National Knowledge Infrastructure (CNKI) and Cochrane Central Register of Controlled Trials (CENTRAL) to identify eligible studies reporting frequency data on neutrophilia and lymphopenia at admission in hospitalization in COVID-19 patients. Pooled odds ratios of clinical outcomes for each parameter were calculated using Comprehensive Meta-Analysis.Results: A total of 22 studies (4,969 patients) were included in this meta-analysis. Lymphopenia at admission was found to be significantly associated with increased odd of progression to severe disease (odds ratio [OR], 4.20; 95% confidence interval [95CI%], 3.46-5.09) and death (OR, 3.71; 95%CI, 1.63-8.44). Neutrophilia at admission was also found to be significantly associated with increased odd of progression to severe disease (OR, 7.99; 95%CI, 1.77-36.14) and death (OR, 7.87; 95%CI, 1.75-35.35). Subgroup analysis revealed that COVID-19 patients with severe lymphopenia (<0.5 x10×9/L) had 12-fold increased odds of in-hospital mortality.Conclusion: Admission lymphopenia and neutrophilia are associated with poor outcomes in patients with COVID-19. Regular monitoring and early and even more aggressive intervention shall hence be advisable in patients with low lymphocyte and high neutrophil counts. These variables may be useful in risk stratification models.
| S-EPMC7716963 | biostudies-literature
Project description:The nature of the immune responses associated with COVID-19 pathogenesis and disease severity, as well as the breadth of vaccine coverage and duration of immunity, is still unclear. Given the unpredictability for developing a severe/complicated disease, there is an urgent need in the field for predictive biomarkers of COVID-19. We have analyzed IgG Fc N-glycan traits of 82 SARS-CoV-2+ unvaccinated patients, at diagnosis, by nano-LC-ESI-MS. We determined the impact of IgG Fc glyco-variations in the induction of NK cells activation, further evaluating the association between IgG Fc N-glycans and disease severity/prognosis. We found that SARS-CoV-2+ individuals display, at diagnosis, variations in the glycans composition of circulating IgGs. Importantly, levels of galactose and sialic acid structures on IgGs are able to predict the development of a poor COVID-19 disease. Mechanistically, we demonstrated that a deficiency on galactose structures on IgG Fc in COVID-19 patients appears to induce NK cells activation associated with increased release of IFN-γ and TNF-α, which indicates the presence of pro-inflammatory immunoglobulins and higher immune activation, associated with a poor disease course. This study brings to light a novel blood biomarker based on IgG Fc glycome composition with capacity to stratify patients at diagnosis.
| S-EPMC9087392 | biostudies-literature
Project description:This study evaluates the predictive value of circulating inflammatory markers, especially neopterin, in patients with coronavirus disease 2019 (COVID-19). Within this retrospective analysis of 115 hospitalized COVID-19 patients, elevated neopterin levels upon admission were significantly associated with disease severity, risk for intensive care unit admission, need for mechanical ventilation, and death. Therefore, neopterin is a reliable predictive marker in patients with COVID-19 and may help to improve the clinical management of patients.
| S-EPMC7665702 | biostudies-literature
Project description:<h4><strong>INTRODUCTION: </strong>Since the beginning of the SARS-CoV-2 pandemic in December 2019 multiple metabolomics studies have proposed predictive biomarkers of infection severity and outcome. Whilst some trends have emerged, the findings remain intangible and uninformative when it comes to new patients.</h4><h4><strong>OBJECTIVES: </strong>In this study, we accurately quantitate a subset of compounds in patient serum that were found predictive of severity and outcome.</h4><h4><strong>METHODS: </strong>A targeted LC-MS method was used in 46 control and 95 acute COVID-19 patient samples to quantitate the selected metabolites. These compounds included tryptophan and its degradation products kynurenine and kynurenic acid (reflective of immune response), butyrylcarnitine and its isomer (reflective of energy metabolism) and finally 3’,4’-didehydro-3’-deoxycytidine, a deoxycytidine analogue, (reflective of host viral defence response). We subsequently examine changes in those markers by disease severity and outcome relative to those of control patients’ levels.</h4><h4><strong>RESULTS & CONCLUSION: </strong>Finally, we demonstrate the added value of the kynurenic acid / tryptophan ratio for severity and outcome prediction and highlight the viral detection potential of ddhC.</h4>
2024-04-23 | MTBLS7429 | MetaboLights
Project description:BackgroundThe severity and outcome of COVID-19 cases has been associated with the percentage of circulating lymphocytes (LYM%), levels of C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT), lactic acid (LA), and viral load (ORF1ab Ct). However, the predictive power of each of these indicators in disease classification and prognosis remains largely unclear.MethodsWe retrospectively collected information on the above parameters in 142 patients with COVID-19, stratifying them by survival or disease severity.FindingsCRP, PCT, IL-6, LYM%, and ORF1ab Ct were significantly altered between survivors and non-survivors. LYM%, CRP, and IL-6 were the most sensitive and reliable factors in distinguishing between survivors and non-survivors. These indicators were significantly different between critically ill and severe/moderate patients. Only LYM% levels were significantly different between severe and moderate types. Among all the investigated indicators, LYM% was the most sensitive and reliable in discriminating between critically ill, severe, and moderate types and between survivors and non-survivors.ConclusionsCRP, PCT, IL-6, LYM%, and ORF1ab Ct, but not LA, could predict prognosis and guide classification of COVID-19 patients. LYM% was the most sensitive and reliable predictor for disease typing and prognosis. We recommend that LYM% be further investigated in the management of COVID-19.FundingThis study was supported in part by awards from the National Natural Science Foundation of China, the Foundation and Frontier Research Project of Chongqing, and the Chongqing Youth Top Talent Project.
| S-EPMC7235581 | biostudies-literature
Project description:Patients infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the coronavirus disease 2019 (COVID-19), exhibit a wide spectrum of disease behavior. Since DNA methylation has been implicated in the regulation of viral infections and the immune system, we performed an epigenome- wide association study (EWAS) to identify candidate loci regulated by this epigenetic mark that could be involved in the onset of COVID-19 in patients without comorbidities.
2021-04-15 | GSE168739 | GEO
Project description:The outbreak of SARS-CoV-2 began in December 2019 and rapidly became a pandemic. The present study investigated the significance of lymphopenia on disease severity. A total of 115 patients with confirmed COVID-19 from a tertiary hospital in Changsha, China, were enrolled. Clinical, laboratory, treatment and outcome data were gathered and compared between patients with and without lymphopenia. The median age was 42 years (1-75). Fifty-four patients (47.0%) of the 115 patients had lymphopenia on admission. More patients in the lymphopenia group had hypertension (30.8% vs. 10.0%, P = 0.006) and coronary heart disease (3.6% vs. 0%, P = 0.029) than in the nonlymphopenia group, and more patients with leukopenia (48.1% vs 14.8%, P<0.001) and eosinopenia (92.6% vs 54.1%, P<0.001) were observed. Lymphopenia was also correlated with severity grades of pneumonia (P<0.001) and C-reactive protein (CRP) level (P = 0.0014). Lymphopenia was associated with a prolonged duration of hospitalization (17.0 days vs. 14.0 days, P = 0.002). Lymphocyte recovery appeared the earliest, prior to CRP and chest radiographs, in severe cases, which suggests its predictive value for disease improvement. Our results demonstrated the clinical significance of lymphopenia for predicting the severity of and recovery from COVID-19, which emphasizes the need to dynamically monitor lymphocyte count.
| S-EPMC7673513 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2023-07-19 | GSE236017 | GEO
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