Risk Factors for Mortality of COVID-19 Patient Based on Clinical Course: A Single Center Retrospective Case-Control Study

Shared genetic correlations between kidney diseases and sepsis

Background

Clinical studies have indicated a comorbidity between sepsis and kidney diseases. Individuals with specific mutations that predispose them to kidney conditions are also at an elevated risk for developing sepsis, and vice versa. This suggests a potential shared genetic etiology that has not been fully elucidated.

Methods

Summary statistics data on exposure and outcomes were obtained from genome-wide association meta-analysis studies. We utilized these data to assess genetic correlations, employing a pleiotropy analysis method under the composite null hypothesis to identify pleiotropic loci. After mapping the loci to their corresponding genes, we conducted pathway analysis using Generalized Gene-Set Analysis of GWAS Data (MAGMA). Additionally, we utilized MAGMA gene-test and eQTL information (whole blood tissue) for further determination of gene involvement. Further investigation involved stratified LD score regression, using diverse immune cell data, to study the enrichment of SNP heritability in kidney-related diseases and sepsis. Furthermore, we employed Mendelian Randomization (MR) analysis to investigate the causality between kidney diseases and sepsis.

Results

In our genetic correlation analysis, we identified significant correlations among BUN, creatinine, UACR, serum urate, kidney stones, and sepsis. The PLACO analysis method identified 24 pleiotropic loci, pinpointing a total of 28 nearby genes. MAGMA gene-set enrichment analysis revealed a total of 50 pathways, and tissue-specific analysis indicated significant enrichment of five pairs of pleiotropic results in kidney tissue. MAGMA gene test and eQTL information (whole blood tissue) identified 33 and 76 pleiotropic genes, respectively. Notably, genes PPP2R3A for BUN, VAMP8 for UACR, DOCK7 for creatinine, and HIBADH for kidney stones were identified as shared risk genes by all three methods. In a series of immune cell-type-specific enrichment analyses of pleiotropy, we identified a total of 37 immune cells. However, MR analysis did not reveal any causal relationships among them.

Conclusions

This study lays the groundwork for shared etiological factors between kidney and sepsis. The confirmed pleiotropic loci, shared pathogenic genes, and enriched pathways and immune cells have enhanced our understanding of the multifaceted relationships among these diseases. This provides insights for early disease intervention and effective treatment, paving the way for further research in this field.

The role of SARS-CoV-2-mediated NF-κB activation in COVID-19 patients

The SARS-CoV-2 pandemic, now in its third year, has had a profound impact on public health and economics all over the world. Different populations showed varied susceptibility to this virus and mortality after infection. Clinical and laboratory data revealed that the uncontrolled inflammatory response plays an important role in their poor outcome. Herein, we summarized the role of NF-κB activation during SARS-CoV-2 invasion and replication, particularly the angiotensin-converting enzyme 2 (ACE2)-mediated NF-κB activation. Then we summarized the COVID-19 drugs' impact on NF-κB activation and their problems. A favorable prognosis is linked with timely treatment with NF-κB activation inhibitors, such as TNFα, IL-1β, and IL-6 monoclonal antibodies. However, further clinical researches are still required to clarify the time window, dosage of administration, contraindication, and potential side effects of these drugs, particularly for COVID-19 patients with hypertension, hyperglycemia, diabetes, or other chronic diseases.

Association of Serum Complement C3 Levels with Severity and Mortality in COVID 19

The severe acute respiratory distress syndrome-associated coronavirus-2 infection can activate innate and adaptive immune responses which may lead to harmful tissue damage, both locally and systemically. C3, a member of complement system of serum proteins, is a major component of innate immune and inflammatory responses. This study is aimed to assess serum C3 as a marker of COVID-19 severity and a predictor of disease progression. A total of 150 COVID-19 patients, confirmed by RT-PCR, and 50 healthy controls were recruited. Serum C3 levels were determined by using direct colorimetric method. Median levels of serum C3 in total cases and controls were 157.8 and 165.7 mg/dL respectively. Serum C3 although not significantly decreased, they were lower in cases when compared to controls. Similarly, significant differences were found between the groups, with severe group (140.6 mg/dL) having low levels of serum C3 protein when compared to mild (161.0 mg/dL) and moderate group (167.1 mg/dL). Interestingly, during hospitalization, significant difference between baseline (admission) and follow-up (discharge) was observed only in patients with moderate disease. Based on our results, lower levels of C3, with an increase in IL-6 and d-dimer levels, are associated with higher odds of mortality. Therefore, we would like to emphasize that measuring serum C3 levels along with other inflammatory markers might give an added advantage in early identification of patients who are prone to having a severe disease course and can help in a more effective follow-up of disease progression.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12291-023-01148-x.

A deep learning model for predicting COVID-19 ARDS in critically ill patients

Background

The coronavirus disease 2019 (COVID-19) is an acute infectious pneumonia caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection previously unknown to humans. However, predictive studies of acute respiratory distress syndrome (ARDS) in patients with COVID-19 are limited. In this study, we attempted to establish predictive models to predict ARDS caused by COVID-19 via a thorough analysis of patients' clinical data and CT images.

Method

The data of included patients were retrospectively collected from the intensive care unit in our hospital from April 2022 to June 2022. The primary outcome was the development of ARDS after ICU admission. We first established two individual predictive models based on extreme gradient boosting (XGBoost) and convolutional neural network (CNN), respectively; then, an integrated model was developed by combining the two individual models. The performance of all the predictive models was evaluated using the area under receiver operating characteristic curve (AUC), confusion matrix, and calibration plot.

Results

A total of 103 critically ill COVID-19 patients were included in this research, of which 23 patients (22.3%) developed ARDS after admission; five predictive variables were selected and further used to establish the machine learning models, and the XGBoost model yielded the most accurate predictions with the highest AUC (0.94, 95% CI: 0.91-0.96). The AUC of the CT-based convolutional neural network predictive model and the integrated model was 0.96 (95% CI: 0.93-0.98) and 0.97 (95% CI: 0.95-0.99), respectively.

Conclusion

An integrated deep learning model could be used to predict COVID-19 ARDS in critically ill patients.

Humulus lupus extract rich in xanthohumol improves the clinical course in critically ill COVID-19 patients

BACKGROUND

The systemic inflammatory response following severe COVID-19 is associated with poor outcomes. Several anti-inflammatory medications have been studied in COVID-19 patients. Xanthohumol (Xn), a natural extract from hop cones, possesses strong anti-inflammatory and antioxidative properties. The aim of this study was to analyze the effect of Xn on the inflammatory response and the clinical outcome of COVID-19 patients.

METHODS

Adult patients treated for acute respiratory failure (PaO₂/FiO₂ less than 150) were studied. Patients were randomized into two groups: Xn - patients receiving adjuvant treatment with Xn at a daily dose of 4.5 mg/kg body weight for 7 days, and C - controls. Observations were performed at four time points: immediately after admission to the ICU and on the 3rd, 5th, and 7th days of treatment. The inflammatory response was assessed based on the plasma IL-6 concentration, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP) and D-dimer levels. The mortality rate was determined 28 days after admission to the ICU.

RESULTS

Seventy-two patients were eligible for the study, and 50 were included in the final analysis. The mortality rate was significantly lower and the clinical course was shorter in the Xn group than in the control group (20% vs. 48%, p < 0.05, and 9 ± 3 days vs. 22 ± 8 days, p < 0.001). Treatment with Xn decreased the plasma IL-6 concentration (p < 0.01), D-dimer levels (p < 0.05) and NLR (p < 0.01) more significantly than standard treatment alone.

CONCLUSION

Adjuvant therapy with Xn appears to be a promising anti-inflammatory treatment in COVID-19 patients.

Identification of Parameters Representative of Immune Dysfunction in Patients with Severe and Fatal COVID-19 Infection: a Systematic Review and Meta-analysis

Abnormal immunological indicators associated with disease severity and mortality in patients with COVID-19 have been reported in several observational studies. However, there are marked heterogeneities in patient characteristics and research methodologies in these studies. We aimed to provide an updated synthesis of the association between immune-related indicators and COVID-19 prognosis. We conducted an electronic search of PubMed, Scopus, Ovid, Willey, Web of Science, Cochrane library, and CNKI for studies reporting immunological and/or immune-related parameters, including hematological, inflammatory, coagulation, and biochemical variables, tested on hospital admission of COVID-19 patients with different severities and outcomes. A total of 145 studies were included in the current meta-analysis, with 26 immunological, 11 hematological, 5 inflammatory, 4 coagulation, and 10 biochemical variables reported. Of them, levels of cytokines, including IL-1β, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, IFN-γ, IgA, IgG, and CD4+ T/CD8+ T cell ratio, WBC, neutrophil, platelet, ESR, CRP, ferritin, SAA, D-dimer, FIB, and LDH were significantly increased in severely ill patients or non-survivors. Moreover, non-severely ill patients or survivors presented significantly higher counts of lymphocytes, monocytes, lymphocyte/monocyte ratio, eosinophils, CD3+ T,CD4+T and CD8+T cells, B cells, and NK cells. The currently updated meta-analysis primarily identified a hypercytokinemia profile with the severity and mortality of COVID-19 containing IL-1β, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, and IFN-γ. Impaired innate and adaptive immune responses, reflected by decreased eosinophils, lymphocytes, monocytes, B cells, NK cells, T cells, and their subtype CD4+ and CD8+ T cells, and augmented inflammation, coagulation dysfunction, and nonpulmonary organ injury, were marked features of patients with poor prognosis. Therefore, parameters of immune response dysfunction combined with inflammatory, coagulated, or nonpulmonary organ injury indicators may be more sensitive to predict severe patients and those non-survivors.

Susceptibility of Fat Tissue to SARS-CoV-2 Infection in Female hACE2 Mouse Model

The coronavirus disease (COVID-19) is a highly contagious viral illness caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). COVID-19 has had a catastrophic effect globally causing millions of deaths worldwide and causing long-lasting health complications in COVID-19 survivors. Recent studies including ours have highlighted that adipose tissue can act as a reservoir where SARS-CoV-2 can persist and cause long-term health problems. Here, we evaluated the effect of SARS-CoV-2 infection on adipose tissue physiology and the pathogenesis of fat loss in a murine COVID-19 model using humanized angiotensin-converting enzyme 2 (hACE2) mice. Since epidemiological studies reported a higher mortality rate of COVID-19 in males than in females, we examined hACE2 mice of both sexes and performed a comparative analysis. Our study revealed for the first time that: (a) viral loads in adipose tissue and the lungs differ between males and females in hACE2 mice; (b) an inverse relationship exists between the viral loads in the lungs and adipose tissue, and it differs between males and females; and (c) CoV-2 infection alters immune signaling and cell death signaling differently in SARS-CoV-2 infected male and female mice. Overall, our data suggest that adipose tissue and loss of fat cells could play important roles in determining susceptibility to CoV-2 infection in a sex-dependent manner.

Validation of the T-Lymphocyte Subset Index (TLSI) as a Score to Predict Mortality in Unvaccinated Hospitalized COVID-19 Patients

Lymphopenia has been consistently reported as associated with severe coronavirus disease 2019 (COVID-19). Several studies have described a profound decline in all T-cell subtypes in hospitalized patients with severe and critical COVID-19. The aim of this study was to assess the role of T-lymphocyte subset absolute counts measured at ward admission in predicting 30-day mortality in COVID-19 hospitalized patients, validating a new prognostic score, the T-Lymphocyte Subset Index (TLSI, range 0−2), based on the number of T-cell subset (CD4+ and CD8+) absolute counts that are below prespecified cutoffs. These cutoff values derive from a previously published work of our research group at Policlinico Tor Vergata, Rome, Italy: CD3+CD4+ < 369 cells/μL, CD3+CD8+ < 194 cells/μL. In the present single-center retrospective study, T-cell subsets were assessed on admission to the infectious diseases ward. Statistical analysis was performed using JASP (Version 0.16.2. JASP Team, 2022, Amsterdam, The Netherlands) and Prism8 (version 8.2.1. GraphPad Software, San Diego, CA, USA). Clinical and laboratory parameters of 296 adult patients hospitalized because of COVID-19 were analyzed. The overall mortality rate was 22.3% (66/296). Survivors (S) had a statistically significant lower TLSI score compared to non-survivors (NS) (p < 0.001). Patients with increasing TLSI scores had proportionally higher rates of 30-day mortality (p < 0.0001). In the multivariable logistic analysis, the TLSI was an independent predictor of in-hospital 30-day mortality (OR: 1.893, p = 0.003). Survival analysis showed that patients with a TLSI > 0 had an increased risk of death compared to patients with a TLSI = 0 (hazard ratio: 2.83, p < 0.0001). The TLSI was confirmed as an early and independent predictor of COVID-19 in-hospital 30-day mortality.

D-dimer levels and characteristics of lymphocyte subsets, cytokine profiles in peripheral blood of patients with severe COVID-19: A systematic review and meta-analysis

Purpose

A series of complications caused by severe COVID-19 can significantly affect short-term results. Therefore, early diagnosis is essential for critically COVID-19 patients. we aimed to investigate the correlation among D-dimer levels, lymphocyte subsets, cytokines, and disease severity in COVID-19 patients.

Methods

Systematic review and meta- analysis of PubMed, Scopus, Web of Science, Cochrane Central Register of Controlled Trials, Embase, clinical trials, and China National Knowledge Infrastructure (CNKI) until 1 August 2022. We considered case-control, and cohort studies that compared laboratory parameters between patients with severe or non-serious diseases or between survivors and non-survivors. Pooled data was assessed by use of a random-effects model and used I 2 to test heterogeneity. We assessed the risk of bias using the Newcastle- Ottawa Scale.

Results

Of the 5,561 identified studies, 32 were eligible and included in our analysis (N = 3,337 participants). Random-effect results indicated that patients with COVID-19 in severe group had higher levels for D-dimer (WMD = 1.217 mg/L, 95%CI=[0.788, 1.646], P < 0.001), neutrophil-to-lymphocyte ratio (NLR) (WMD = 6.939, 95%CI = [4.581, 9.297], P < 0.001), IL-2 (WMD = 0.371 pg/ml, 95%CI = [-0.190, 0.932], P = 0.004), IL-4 (WMD = 0.139 pg/ml, 95%CI = [0.060, 0.219], P = 0.717), IL-6 (WMD = 44.251 pg/ml, 95%CI = [27.010, 61.493], P < 0.001), IL-10 (WMD = 3.718 pg/ml, 95%CI = [2.648, 4.788], P < 0.001) as well as lower levels of lymphocytes (WMD = -0.468( × 109/L), 95%CI = [-0.543, -0.394], P < 0.001), T cells (WMD = -446.746(/μL), 95%CI = [-619.607, -273.885], P < 0.001), B cells (WMD = -60.616(/μL), 95%CI = [-96.452, -24.780], P < 0.001), NK cells (WMD = -68.297(/μL), 95%CI = [-90.600, -45.994], P < 0.001), CD3+T cells (WMD = -487.870(/μL), 95%CI = [-627.248, -348.492], P < 0.001), CD4+T cells (WMD = -290.134(/μL), 95%CI = [-370.834, -209.435], P < 0.001), CD8+T cells (WMD = -188.781(/μL), 95%CI = [-227.806, -149.757], P < 0.001).

Conclusions

There is a correlation among higher levels of D-dimer, cytokines, lower levels of lymphocyte subsets, and disease severity in COVID-19 patients. These effective biomarkers may help clinicians to evaluate the severity and prognosis of COVID-19. This study is registered with PROSPERO, number CRD42020196659.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=196659; PROSPERO registration number: CRD42020196659.

Characteristics of lymphocyte subsets and cytokine profiles of patients with COVID-19

Background

Abnormalities of lymphocyte subsets and cytokine profiles have been observed in most patients with coronavirus disease (COVID-19). Here, we explore the role of lymphocyte subsets and cytokines on hospital admission in predicting the severity of COVID-19.

Methods

This study included 214 patients with COVID-19 who were treated at Chongqing University Three Gorges Hospital from January 19, 2020 to April 30, 2020. Any mutants were not detected in the studied patients. Patients were divided into non-intensive care unit (ICU) (mild/moderate) group and ICU (severe/critical) group, according to the severity of the disease. Clinical and laboratory data, including peripheral lymphocyte subsets and cytokines, were analyzed and compared. Logistic regression was used to analyze the predictive factors for ICU admission. Receiver operating characteristic (ROC) curves were drawn to evaluate the predictive value of selected indicators for the severity of COVID-19.

Results

Of the 214 patients enrolled, 161 were non-ICU patients and 53 were ICU patients. Lymphopenia was observed in nearly all of ICU patients (96.2%) and 84.5% of non-ICU patients on hospital admission. The absolute number of lymphocytes, CD3⁺ T cells, CD4⁺ T cells, CD8⁺ T cells, CD19⁺ B cells, and natural killer (NK) cells were lower in ICU group (659.00 × 10⁶/L, 417.00 × 10⁶/L, 261.00 × 10⁶/L, 140.00 × 10⁶/L, 109.00 × 10⁶/L, 102.00 × 10⁶/L, respectively) than in non-ICU group (1063.00 × 10⁹/L, 717.00 × 10⁶/L, 432.00 × 10⁶/L, 271.00 × 10⁶/L, 133.00 × 10⁶/L, 143.00 × 10⁶/L, respectively). Interleukin (IL)-6 was significantly higher in ICU patients than in non-ICU patients (18.08 pg/mL vs. 3.13 pg/mL, P < 0.001). Multivariate logistic regression analysis showed that age (odds ratio: 1.067 [1.034–1.101]), diabetes mellitus (odds ratio: 9.154 [2.710–30.926]), CD3⁺ T cells (odds ratio: 0.996 [0.994–0.997]), and IL-6 (odds ratio: 1.006 [1.000–1.013]) were independent predictors for the development of severe disease. ROC curve analysis showed that the area under the ROC curve (AUC) of CD3⁺ T cells and IL-6 was 0.806 (0.737–0.874) and 0.785 (0.705–0.864), respectively, and the cutoff values were 510.50 × 10⁶/L (sensitivity, 71.7%; specificity, 79.5%) and 6.58 pg/mL (77.4%, 74.5%), respectively. There were no statistical differences among all tested indicators of lymphocyte subsets and cytokines between severe group (n = 38) and critical group (n = 15) on hospital admission or ICU admission, respectively.

Conclusions

The levels of lymphocyte subsets decreased and the level of IL-6 increased significantly in ICU COVID-19 patients compared with non-ICU COVID-19 patients. Therefore, the number of CD3⁺ T cells and the level of IL-6 on hospital admission may serve as predictive factors for identifying patients with wild-type virus infection who will have severe disease.

Codonopsis pilosula polysaccharide attenuates Escherichia coli-induced acute lung injury in mice

Acute lung injury (ALI) is an inflammatory lung disease could be caused by bacterial infection. Lipopolysaccharide (LPS), a prototype pathogen-associated molecular pattern (PAMP) from gram-negative bacteria such as Escherichia coli...

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Prevalence and Outcomes Associated with Hyperuricemia in Hospitalized Patients with COVID-19

INTRODUCTION

Coronavirus 2019 (COVID-19) can increase catabolism and result in hyperuricemia. Uric acid (UA) potentially causes kidney damage by alteration of renal autoregulation, inhibition of endothelial cell proliferation, cell apoptosis, activation of the pro-inflammatory cascade, and crystal deposition. Hyperuricemia in patients with COVID-19 may contribute to acute kidney injury (AKI) and poor outcomes.

METHODS

We included 834 patients with COVID-19 who were >18 years old and hospitalized for >24 h in the Mount Sinai Health System and had at least 1 measurement of serum UA. We examined the association between the first serum UA level and development of acute kidney injury (AKI, defined by KDIGO criteria), major adverse kidney events (MAKE, defined by a composite of all-cause in-hospital mortality or dialysis or 100% increase in serum creatinine from baseline), as well as markers of inflammation and cardiac injury.

RESULTS

Among the 834 patients, the median age was 66 years, 42% were women, and the median first serum UA was 5.9 mg/dL (interquartile range 4.5-8.8). Overall, 60% experienced AKI, 52% experienced MAKE, and 32% died during hospitalization. After adjusting for demographics, comorbidities, and laboratory values, a doubling in serum UA was associated with increased AKI (odds ratio [OR] 2.8, 95% confidence interval [CI] 1.9-4.1), MAKE (OR 2.5, 95% CI 1.7-3.5), and in-hospital mortality (OR 1.7, 95% CI 1.3-2.3). Higher serum UA levels were independently associated with a higher level of procalcitonin (β, 0.6; SE 0.2) and troponin I (β, 1.2; SE 0.2) but were not associated with serum ferritin, C-reactive protein, and interleukin-6.

CONCLUSION

In patients admitted to the hospital for COVID-19, higher serum UA levels were independently associated with AKI, MAKE, and in-hospital mortality in a dose-dependent manner. In addition, hyperuricemia was associated with higher procalcitonin and troponin I levels.