Obesity Parameters as Predictor of Poor Outcomes in Hospitalized Patients with Confirmed Mild-to-Moderate COVID-19.

(1) Background: This study aims to assess visceral fat values, waist circumference (WC), body mass index (BMI), and body fat percentage for their ability to predict poor outcomes during COVID-19 patients' hospitalization; (2) Methods: This study was a prospective cohort of mild-moderate COVID-19 patients hospitalized at Dr. Cipto Mangunkusumo National General Hospital from December 2020 to March 2021. This study includes hospitalized patients over 18 diagnosed with COVID-19 using RT-PCR. Patients who do not have chest radiography, waist circumference, a bioimpedance analyzer (BIA) error, or are unable to stand or mobilize during the examination are excluded from this study. Cox regression was used for multivariate analysis; (3) Results: The study included two hundred sixty-one patients. The median visceral fat value was 10 (equivalent to 100 cm2), the WC was 93.4 cm, the BMI was 26.1 kg/m2, and the body fat percentage was 31.5%. Based on multivariate Cox regression, WC was statistically significant as an independent factor influencing poor outcomes in COVID-19 patients (RR 1.037 [95% CI 1.011-1.064]) along with COVID-19 degree of severity (RR 3.063 [95% CI 1.537-6.104]) and comorbidities (RR 2.123 [95% CI 1.017-4.435]); (4) Conclusions: Waist circumference can influence poor outcomes in confirmed COVID-19 patients during hospitalization.

The gut microbiota as an early predictor of COVID-19 severity.

Several studies reported alterations of the human gut microbiota (GM) during COVID-19. To evaluate the potential role of the GM as an early predictor of COVID-19 at disease onset, we analyzed gut microbial samples of 315 COVID-19 patients that differed in disease severity. We observed significant variations in microbial diversity and composition associated with increasing disease severity, as the reduction of short-chain fatty acid producers such as Faecalibacterium and Ruminococcus, and the growth of pathobionts as Anaerococcus and Campylobacter. Notably, we developed a multi-class machine-learning classifier, specifically a convolutional neural network, which achieved an 81.5% accuracy rate in predicting COVID-19 severity based on GM composition at disease onset. This achievement highlights its potential as a valuable early biomarker during the first week of infection. These findings offer promising insights into the intricate relationship between GM and COVID-19, providing a potential tool for optimizing patient triage and streamlining healthcare during the pandemic.IMPORTANCEEfficient patient triage for COVID-19 is vital to manage healthcare resources effectively. This study underscores the potential of gut microbiota (GM) composition as an early biomarker for COVID-19 severity. By analyzing GM samples from 315 patients, significant correlations between microbial diversity and disease severity were observed. Notably, a convolutional neural network classifier was developed, achieving an 81.5% accuracy in predicting disease severity based on GM composition at disease onset. These findings suggest that GM profiling could enhance early triage processes, offering a novel approach to optimizing patient management during the pandemic.

Effectiveness of Updated 2023-2024 (Monovalent XBB.1.5) COVID-19 Vaccination Against SARS-CoV-2 Omicron XBB and BA.2.86/JN.1 Lineage Hospitalization and a Comparison of Clinical Severity-IVY Network, 26 Hospitals, October 18, 2023-March 9, 2024.

BACKGROUND: Assessing variant-specific COVID-19 vaccine effectiveness (VE) and severity can inform public health risk assessments and decisions about vaccine composition. BA.2.86 and its descendants, including JN.1 (referred to collectively as "JN lineages"), emerged in late 2023 and exhibited substantial divergence from co-circulating XBB lineages. METHODS: We analyzed patients hospitalized with COVID-19-like illness at 26 hospitals in 20 U.S. states admitted October 18, 2023-March 9, 2024. Using a test-negative, case-control design, we estimated effectiveness of an updated 2023-2024 (Monovalent XBB.1.5) COVID-19 vaccine dose against sequence-confirmed XBB and JN lineage hospitalization using logistic regression. Odds of severe outcomes, including intensive care unit (ICU) admission and invasive mechanical ventilation (IMV) or death, were compared for JN versus XBB lineage hospitalizations using logistic regression. RESULTS: 585 case-patients with XBB lineages, 397 case-patients with JN lineages, and 4,580 control-patients were included. VE in the first 7-89 days after receipt of an updated dose was 54.2% (95% CI = 36.1%-67.1%) against XBB lineage hospitalization and 32.7% (95% CI = 1.9%-53.8%) against JN lineage hospitalization. Odds of ICU admission (adjusted odds ratio [aOR] 0.80; 95% CI = 0.46-1.38) and IMV or death (aOR 0.69; 95% CI = 0.34-1.40) were not significantly different among JN compared to XBB lineage hospitalizations. CONCLUSIONS: Updated 2023-2024 COVID-19 vaccination provided protection against both XBB and JN lineage hospitalization, but protection against the latter may be attenuated by immune escape. Clinical severity of JN lineage hospitalizations was not higher relative to XBB.

Rare host variants in ciliary expressed genes contribute to COVID-19 severity in Bulgarian patients.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a pneumonia with extremely heterogeneous clinical presentation, ranging from asymptomatic to severely ill patients. Previous studies have reported links between the presence of host genetic variants and the outcome of the COVID-19 infection. In our study, we used whole exome sequencing in a cohort of 444 SARS-CoV-2 patients, admitted to hospital in the period October-2020-April-2022, to search for associations between rare pathogenic/potentially pathogenic variants and COVID-19 progression. We used gene prioritization-based analysis in genes that have been reported by host genetic studies. Although we did not identify correlation between the presence of rare pathogenic variants and COVID-19 outcome, in critically ill patients we detected known mutations in a number of genes associated with severe disease related to cardiovascular disease, primary ciliary dyskinesia, cystic fibrosis, DNA damage repair response, coagulation, primary immune disorder, hemoglobin subunit ß, and others. Additionally, we report 93 novel pathogenic variants found in severely infected patients who required intubation or died. A network analysis showed main component, consisting of 13 highly interconnected genes related to epithelial cilium. In conclusion, we have detected rare pathogenic host variants that may have influenced the COVID-19 outcome in Bulgarian patients.

The longitudinal characterization of immune responses in COVID-19 patients reveals novel prognostic signatures for disease severity, patients' survival and long COVID.

Introduction: SARS-CoV-2 pandemic still poses a significant burden on global health and economy, especially for symptoms persisting beyond the acute disease. COVID-19 manifests with various degrees of severity and the identification of early biomarkers capable of stratifying patient based on risk of progression could allow tailored treatments. Methods: We longitudinally analyzed 67 patients, classified according to a WHO ordinal scale as having Mild, Moderate, or Severe COVID-19. Peripheral blood samples were prospectively collected at hospital admission and during a 6-month follow-up after discharge. Several subsets and markers of the innate and adaptive immunity were monitored as putative factors associated with COVID-19 symptoms. Results: More than 50 immunological parameters were associated with disease severity. A decision tree including the main clinical, laboratory, and biological variables at admission identified low NK-cell precursors and CD14+CD91+ monocytes, and high CD8+ Effector Memory T cell frequencies as the most robust immunological correlates of COVID-19 severity and reduced survival. Moreover, low regulatory B-cell frequency at one month was associated with the susceptibility to develop long COVID at six months, likely due to their immunomodulatory ability. Discussion: These results highlight the profound perturbation of the immune response during COVID-19. The evaluation of specific innate and adaptive immune-cell subsets allows to distinguish between different acute and persistent COVID-19 symptoms.

Predictive value of hematocrit, serum albumin level difference, and fibrinogen-to-albumin ratio for COVID-19-associated acute respiratory failure.

Background: Acute respiratory failure is the main clinical manifestation and a major cause of death in patients with COVID-19. However, few reports on its prevention and control have been published because of the need for laboratory predictive indicators. This study aimed to evaluate the predictive value of hematocrit level, serum albumin level difference, and fibrinogen-to-albumin ratio for COVID-19-associated acute respiratory failure. Material and methods: A total of 120 patients with COVID-19 from the First Affiliated Hospital of Anhui Medical University were selected between December 2022 and March 2023. Patients were divided into acute respiratory failure and non-acute respiratory failure groups and compared patient-related indicators between them using univariate and multivariate logistic regression analyses. Receiver operating characteristic analysis was performed to determine the discrimination accuracy. Results: In total, 48 and 72 patients were enrolled in the acute respiratory failure and non-acute respiratory failure groups, respectively. The Quick COVID-19 Severity Index scores, fibrinogen-to-albumin ratio, hematocrit and serum albumin level difference, fibrinogen, and hematocrit levels were significantly higher in the acute respiratory failure group than in the non-acute respiratory failure group. A Quick COVID-19 Severity Index >7, fibrinogen-to-albumin ratio >0.265, and hematocrit and serum albumin level difference >12.792 had a 96.14 % positive predictive rate and a 94.06 % negative predictive rate. Conclusion: Both fibrinogen-to-albumin ratio and hematocrit and serum albumin level difference are risk factors for COVID-19-associated acute respiratory failure. The Quick COVID-19 Severity Index score combined with fibrinogen-to-albumin ratio, and hematocrit and serum albumin level difference predict high and low risks with better efficacy and sensitivity than those of the Quick COVID-19 Severity Index score alone; therefore, these parameters can be used collectively as a risk stratification method for assessing patients with COVID-19.

Association between asthma and COVID-19 severity during Omicron epidemic: a retrospective cohort study using real-world data.

BACKGROUND: The available evidence presented inconsistencies and inconclusive findings regarding the associations between co-existing asthma and mortality among COVID-19 patients. The objective of the current study is to investigate the relationship between asthma and severe outcomes after SARS-CoV-2 Omicron infection in an infection-naïve population. METHODS: A retrospective cohort study using propensity score matching was conducted. The COVID-19 patients requiring hospitalisation in Hong Kong from January 1, 2022, to November 13, 2022, an Omicron-predominated period, were identified. Severe clinical outcomes were defined as ICU admission and inpatient death after the first positive PCR results as well as a composite outcome of both. RESULTS: Of the 74,396 hospitalised COVID-19 patients admitted, 1,290 asthma patients and 18,641 non-asthma patients were included in the matched cohort. The rates of death and the composite outcome were 15·3% and 17·2%, respectively, among the non-asthma patients,12·2% and 13·6%, respectively, among the asthma patients, with adjusted hazard ratios equal to 0·775 (95% CI: 0·660-0·909) and 0·770 (95% CI: 0·662-0·895), respectively. The negative association was more apparent in the elderly and female groups. Asthma remained a factor that lowered the risk of disease severity even though the patients were not fully vaccinated with at least two doses. CONCLUSIONS: We used real-world data to demonstrate that asthma was not a risk factor for COVID-19 severity of the infections of Omicron variant, even though the patients were not fully vaccinated.

Machine learning-aided algorithm design for prediction of severity from clinical, demographic, biochemical and immunological parameters: Our COVID-19 experience from the pandemic.

Background: The severity of laboratory and imaging finding was found to be inconsistent with clinical symptoms in COVID-19 patients, thereby increasing casualties. As compared to conventional biomarkers, machine learning algorithms can learn nonlinear and complex interactions and thus improve prediction accuracy. This study aimed at evaluating role of biochemical and immunological parameters-based machine learning algorithms for severity indexing in COVID-19. Methods: Laboratory biochemical results of 5715 COVID-19 patients were mined from electronic records including 509 admitted in COVID-19 ICU. Random Forest Classifier (RFC), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and K-Nearest Neighbours (KNN) classifier models were used. Lasso regression helped in identifying the most influential parameter. A decision tree was made for subdivided data set, based on randomization. Results: Accuracy of SVM was highest with 94.18% and RFC with 94.04%. SVM had highest PPV (1.00), and NBC had highest NPV (0.95). QUEST modelling ignored age, urea and total protein, and only C-reactive protein and lactate dehydrogenase were considered to be a part of decision-tree algorithm. The overall percentage of correct classification was 78.31% in the overall algorithm with a sensitivity of 87.95% and an AUC of 0.747. Conclusion: C-reactive protein and lactate dehydrogenase being routinely performed tests in clinical laboratories in peripheral setups, this algorithm could be an effective predictive tool. SVM and RFC models showed significant accuracy in predicting COVID-19 severity and could be useful for future pandemics.

Increased disease severity during COVID-19 related hospitalization in black non-hispanic, hispanic and medicaid-insured young children.

Background: The COVID-19 pandemic has disproportionately affected marginalized groups in the United States. Although most children have mild or asymptomatic COVID-19, some experience severe disease and long-term complications. However, few studies have examined health disparities in severe COVID-19 outcomes among US children. Objective: To examine disparities in the clinical outcomes of infants and children aged <5 years hospitalized with COVID-19 by race/ethnicity and payer status. Methods: Children aged <5 years hospitalized with an admission diagnosis of COVID-19 (April 2021-February 2023) were selected from the PINC AI™ Healthcare Database. Hospital outcomes included length of stay (LOS), intensive care unit (ICU) admission, oxygen supplementation, invasive mechanical ventilation (IMV), and prolonged duration of each outcome. Multivariable logistic regression models compared hospitalization outcomes by race/ethnicity and payer status. Results: Among 10,190 children (mean age: 0.9 years, 56.5% male, 66.7% Medicaid-insured), race/ethnicity was distributed as follows: White non-Hispanic (35.1%), Hispanic (any or Unknown race; 28.3%), Black non-Hispanic (15.2%), Other race/ethnicity (8.9%) and Unknown (12.5%). Payer status varied by race/ethnicity. White non-Hispanic children had the highest proportion with commercial insurance (42.9%) while other racial/ethnic groups ranged between 13.8% to 26.1%. Black non-Hispanic children had the highest proportion with Medicaid (82.3%) followed by Hispanic children (76.9%). Black non-Hispanic children had higher odds of prolonged outcomes: LOS (adjusted odds ratio [aOR] = 1.20, 95% confidence interval [CI]:1.05-1.38), ICU days (aOR = 1.44, 95% CI: 1.07-1.93), and IMV days (aOR = 1.80, 95% CI: 1.09-2.97) compared to White non-Hispanic children. Similar patterns were observed for Hispanic and children of Other race/ethnicity. Medicaid-insured and children with other insurance had higher odds of prolonged LOS and oxygen days than commercially insured patients. Conclusion: There were disparities in clinical outcomes of COVID-19 by race/ethnicity and insurance type, particularly for prolonged-duration outcomes. Further research is required to fully comprehend the causes and consequences of these disparities and develop strategies to reduce them while ensuring equitable healthcare delivery.

T cell-mediated Immune response and correlates of inflammation and their relationship with COVID-19 clinical severity: not an intuitive guess.

BACKGROUND: Predictors of the outcome of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remain to be fully determined. We evaluated selected viral characteristics and immunological responses that might predict and/or correlate to the clinical outcome of COVID-19. METHODS: For individuals developing divergent clinical outcomes, the magnitude and breadth of T cell-mediated responses were measured within 36 h of symptom onset. Peripheral Blood Mononuclear Cells (PBMCs) were subjected to in vitro stimulation with SARS-CoV-2-based peptides. In addition, SARS-CoV-2 sequences were generated by metagenome, and HLA typing was performed using Luminex technology. FINDINGS: CD4+ T cell activation was negatively correlated with SARS-CoV-2 basal viral load in patients with severe COVID-19 (p = 0·043). The overall cellular immune response, as inferred by the IFN-γ signal, was higher at baseline for patients who progressed to mild disease compared to patients who progressed to severe disease (p = 0·0044). Subjects with milder disease developed higher T cell responses for MHC class I and II-restricted peptides (p = 0·033). INTERPRETATION: Mounting specific cellular immune responses in the first days after symptom onset, as inferred by IFN-γ magnitude in the ELISPOT assay, may efficiently favor a positive outcome. In contrast, progression to severe COVID-19 was accompanied by stronger cellular immune responses, higher CD4 + T cell activation, and a higher number of in silico predicted high-affinity class I HLA alleles.

Predictive value of D-Dimer and thromboplastin time as coagulation indicators for COVID-19 patients.

INTRODUCTION: Coronavirus 2019 symptoms include coagulopathy and thromboembolic risk. Using one parameter to diagnose coagulopathy has little predictive value. OBJECTIVE: This study will examine if D-dimer and APTT testing can predict COVID-19 severity and aid triage and manage patients. METHODS: 214 COVID-19 patients were enrolled and classified into two categories based on their respiratory manifestations; mild (126 cases) and severe (88 cases). Patient data regarding age, gender, D-Dimer level, and APTT level were collected. When both D-Dimer and APTT levels were abnormal, in this study, the patient was considered to have a coagulation disorder. Indicators of coagulation in the COVID-19 patients were collected and compared between the two groups. Chi-square (χ2) tests were used to determine the significant differences between coagulation disorders in the two groups. RESULTS: Our findings showed that patients with coagulopathies were more likely to belong to the severe group. Within the two groups of patients, the rate of coagulation disorders was as follows: mild = 8.8 % within coagulation disorders, 4.8% within the two Groups; severe = 91.2 % within coagulation disorders, 77.8 % within the two Groups. There was a statistically significant relationship between coagulation disorder and severe COVID-19 patients compared to mild patients (p < 0.05). CONCLUSIONS: Coagulation disorders are more likely to occur in severe COVID-19 patients. D-Dimer and APTT tests are significant indicators for predicting COVID-19 severity. Our research found an abnormal pattern of coagulation disorders and COVID-19 severity that should be considered in the COVID-19 treatment protocol.

Microbial Signatures in COVID-19: Distinguishing Mild and Severe Disease via Gut Microbiota.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has significantly impacted global healthcare, underscoring the importance of exploring the virus's effects on infected individuals beyond treatments and vaccines. Notably, recent findings suggest that SARS-CoV-2 can infect the gut, thereby altering the gut microbiota. This study aimed to analyze the gut microbiota composition differences between COVID-19 patients experiencing mild and severe symptoms. We conducted 16S rRNA metagenomic sequencing on fecal samples from 49 mild and 43 severe COVID-19 cases upon hospital admission. Our analysis identified a differential abundance of specific bacterial species associated with the severity of the disease. Severely affected patients showed an association with Enterococcus faecium, Akkermansia muciniphila, and others, while milder cases were linked to Faecalibacterium prausnitzii, Alistipes putredinis, Blautia faecis, and additional species. Furthermore, a network analysis using SPIEC-EASI indicated keystone taxa and highlighted structural differences in bacterial connectivity, with a notable disruption in the severe group. Our study highlights the diverse impacts of SARS-CoV-2 on the gut microbiome among both mild and severe COVID-19 patients, showcasing a spectrum of microbial responses to the virus. Importantly, these findings align, to some extent, with observations from other studies on COVID-19 gut microbiomes, despite variations in methodologies. The findings from this study, based on retrospective data, establish a foundation for future prospective research to confirm the role of the gut microbiome as a predictive biomarker for the severity of COVID-19.

Association between lipid profile and clinical outcomes in COVID-19 patients.

High-density lipoprotein cholesterol (HDL-c) removes cholesterol, an essential component in lipid rafts, and this cholesterol removal can regulate protein attachment to lipid rafts, modulating their functionality in the immune cell response. Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can alter the lipid profile, there is little information on the role of HDL-c and other lipids in prognostic of the coronavirus disease 2019 (COVID-19) in Mexican population. This study aims to evaluate the predictive value of HDL-c and lipid profile on severity and survival of 102 patients infected with SARS-CoV-2 during the COVID-19 first wave. Our findings, derived from univariate and multivariate Cox proportional hazards regression models, highlighted age and hypertension as significant predictors of survival (HR = 1.04, p = 0.012; HR = 2.78, p = 0.027), while gender, diabetes, and obesity showed no significant impact. Triglycerides and HDL-c levels notably influenced mortality, with elevated triglycerides and lower HDL-c associated with higher mortality risk (p = 0.032). This study underscores the importance of lipid profiles alongside traditional risk factors in assessing COVID-19 risk and outcomes. It contributes to the understanding of COVID-19 patient management and emphasizes the need for further investigation into the role of dyslipidemia in influencing COVID-19 prognosis, potentially aiding in refined risk stratification and therapeutic strategies.

Relationship between neutrophil-lymphocyte ratio and platelet-lymphocyte ratio with the severity of COVID-19.

Coronavirus disease 2019 (COVID-19) is an infectious disease that spreads rapidly causing a high case fatality rate in vulnerable populations. Neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) are known to be inflammatory biomarkers in certain infections. The aim of this study was to determine the relationship between NLR and PLR with the severity of COVID-19. A cross-sectional study was conducted at Tabanan Regency General Hospital, Bali, Indonesia, from January 2021 to December 2022. All patients included in the study tested positive for COVID-19 by real-time polymerase chain reaction (RT-PCR), aged 18-50 years with no comorbid. Laboratory examinations were carried out on admission. The patients were categorized into two groups based on the severity: moderate and severe/critical. The Mann-Whitney test was used to determine the association between NLR and PLR with the severity of COVID-19. A total of 104 patients were included in the study, the majority of COVID-19 patients had moderate (77.9%) severity. The average NLR was 5.8 and the PLR was 21.7. There was a significant relationship between NLR (p=0.002) and PLR (p=0.001) with the severity of COVID-19. The defined cut-off values of NLR and PLR were ≥3.8 and ≥106, yielding sensitivities of 95% and 70%, and specificities of 74% and 50%, respectively. This study highlights the promising role of NLR and PLR as predictive biomarkers to assess COVID-19 severity.

Inflammasome-Related Genetic Polymorphisms as Severity Biomarkers of COVID-19.

The most critical forms of coronavirus disease 2019 (COVID-19) are associated with excessive activation of the inflammasome. Despite the COVID-19 impact on public health, we still do not fully understand the mechanisms by which the inflammatory response influences disease prognosis. Accordingly, we aimed to elucidate the role of polymorphisms in the key genes of the formation and signaling of the inflammasome as biomarkers of COVID-19 severity. For this purpose, a large and well-defined cohort of 377 COVID-19 patients with mild (n = 72), moderate (n = 84), severe (n = 100), and critical (n = 121) infections were included. A total of 24 polymorphisms located in inflammasome-related genes (NLRP3, NLRC4, NLRP1, CARD8, CASP1, IL1B, IL18, NFKB1, ATG16L1, and MIF) were genotyped in all of the patients and in the 192 healthy controls (HCs) (who were without COVID-19 at the time of and before the study) by RT-qPCR. Our results showed that patients with mild, moderate, severe, and critical COVID-19 presented similar allelic and genotypic distribution in all the variants studied. No statistically significant differences in the haplotypic distribution of NLRP3, NLRC4, NLRP1, CARD8, CASP1, IL1B, and ATG16L1 were observed between COVID-19 patients, who were stratified by disease severity. Each stratified group of patients presented a similar genetic distribution to the HCs. In conclusion, our results suggest that the inflammasome polymorphisms studied are not associated with the worsening of COVID-19.

Impact of Anti-angiogenic Drugs on Severity of COVID-19 in Patients with Non-Small Cell Lung Cancer.

Introduction: The 2019 coronavirus disease (COVID-19) pandemic has reshaped oncology practice, but the impact of anti-angiogenic drugs on the severity of COVID-19 in patients with non-small cell lung cancer (NSCLC) remains unclear. Patients and Methods: We carried out a retrospective study involving 166 consecutive patients with NSCLC who were positive for COVID-19, aiming to determine the effects of anti-angiogenic drugs on disease severity, as defined by severe/critical symptoms, intensive care unit (ICU) admission/intubation, and mortality outcomes. Risk factors were identified using univariate and multivariate logistic regression models. Results: Of the participants, 73 had been administered anti-angiogenic drugs (termed the anti-angiogenic therapy (AT) group), while 93 had not (non-AT group). Comparative analyses showed no significant disparity in the rates of severe/critical symptoms (21.9% vs 35.5%, P = 0.057), ICU admission/intubation (6.8% vs 7.5%, P = 0.867), or death (11.0% vs 9.7%, P = 0.787) between these two groups. However, elevated risk factors for worse outcomes included age ≥ 60 (odds ratio (OR): 2.52, 95% confidence interval (CI): 1.07-5.92), Eastern Cooperative Oncology Group performance status of 2 or higher (OR: 21.29, 95% CI: 4.98-91.01), chronic obstructive pulmonary disease (OR: 7.25, 95% CI: 1.65-31.81), hypertension (OR: 2.98, 95% CI: 1.20-7.39), and use of immunoglobulin (OR: 5.26, 95% CI: 1.06-26.25). Conclusion: Our data suggests that the use of anti-angiogenic drugs may not exacerbate COVID-19 severity in NSCLC patients, indicating their potential safe application even during the pandemic period.

Body Mass Index as a Predictor of COVID-19 Severity in ICU Patients in Saudi Arabia: A Retrospective Analysis.

Introduction The global coronavirus disease 2019 (COVID-19) pandemic has prompted research into various risk factors, including the role of body mass index (BMI) in disease severity. This study specifically examines the correlation between BMI and the severity of COVID-19 among intensive care unit (ICU) patients in Saudi Arabia, addressing a gap in region-specific data. The study aims to assess the impact of BMI on the severity of COVID-19 in a Saudi Arabian ICU patient cohort, providing insights into how this relationship varies in different demographic contexts. Materials and methods Employing a retrospective cohort design, the study analyzed data from adult ICU patients in Saudi Arabia diagnosed with COVID-19. It focused on variables like BMI at admission, demographic information, and COVID-19 outcomes including severity, recovery, and mortality. Statistical analysis involved regression models, adjusting for age, gender, and comorbidities. Results Unlike global observations, the study found no significant correlation between BMI and COVID-19 severity in the Saudi Arabian context. This suggests that in this specific demographic, other factors may be more critical in determining the severity of the disease. Conclusion Our findings challenge the global consensus on BMI as a key factor in COVID-19 severity, highlighting the importance of regional differences in disease dynamics. They underscore the need for localized healthcare strategies and further research into diverse demographic factors affecting COVID-19. This study contributes to a broader understanding of the pandemic and encourages region-specific approaches in both clinical and public health spheres.

The relation between proteinuria and the severity of COVID-19.

BACKGROUND: The association between proteinuria, which is also an indicator of chronic kidney disease (CKD), and coronavirus disease 2019 (COVID-19) severity is unclear. METHODS: We selected 342 hospitalized patients with COVID-19 diagnosed via polymerase chain reaction testing between February 2020 and October 2022 and who had at least one urinalysis 14-365 days before admission. RESULTS: Proteinuria before admission was associated neither with oxygen administration nor developing pneumonia in multivariate analysis (odds ratio [OR] 1.03; 95% confidence interval (CI) 0.44-2.40, p = 0.95 and OR 1.01; 95% CI 0.47-2.17, p = 0.98, respectively). Proteinuria on admission was associated both with oxygen administration and developing pneumonia in multivariate analysis (OR 3.29; 95% CI 1.37-7.88, p < 0.01 and OR 3.81; 95% CI 1.68-8.62, p < 0.01, respectively). The percentage of patients with proteinuria on admission was significantly higher than those before admission (37.4% vs. 17.8%; p < 0.01). In the subgroup analysis, proteinuria on admission among patients with eGFR ≥ 60 mL/min/1.73 m2 was associated with both oxygen administration and developing pneumonia (OR 4.86; 95% CI 1.22-19.38, p = 0.03, OR 3.65; 95% CI 1.06-12.58, p = 0.04, respectively). In contrast, proteinuria on admission among patients with eGFR < 60 mL/min/1.73 m2 was associated with developing pneumonia (OR 6.45; 95%CI 1.78-23.35, p = 0.01), not with oxygen administration (OR 3.28; 95% CI 0.92-11.72, p = 0.07). CONCLUSIONS: Although underlying proteinuria before admission was not associated with COVID-19 severity, proteinuria on admission was associated with oxygen demand and developing pneumonia.

Identification of Whole-Blood DNA Methylation Signatures and Rules Associated with COVID-19 Severity.

BACKGROUND: Different severities of coronavirus disease 2019 (COVID-19) cause different levels of respiratory symptoms and systemic inflammation. DNA methylation, a heritable epigenetic process, also shows differential changes in different severities of COVID-19. DNA methylation is involved in regulating the activity of various immune cells and influences immune pathways associated with viral infections. It may also be involved in regulating the expression of genes associated with the progression of COVID-19. METHODS: In this study, a sophisticated machine-learning workflow was designed to analyze whole-blood DNA methylation data from COVID-19 patients with different severities versus healthy controls. We aimed to understand the role of DNA methylation in the development of COVID-19. The sample set contained 101 negative controls, 360 mildly infected individuals, and 113 severely infected individuals. Each sample involved 768,067 methylation sites. Three feature-ranking algorithms (least absolute shrinkage and selection operator (LASSO), light gradient-boosting machine (LightGBM), and Monte Carlo feature selection (MCFS)) were used to rank and filter out sites highly correlated with COVID-19. Based on the obtained ranking results, a high-performance classification model was constructed by combining the feature incremental approach with four classification algorithms (decision tree (DT), k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM)). RESULTS: Some essential methylation sites and decision rules were obtained. CONCLUSIONS: The genes (IGSF6, CD38, and TLR2) of some essential methylation sites were confirmed to play important roles in the immune system.

Genetic Predisposition to Elevated Levels of Circulating ADAM17 Is Associated with the Risk of Severe COVID-19.

High levels of ADAM17 activity have emerged as an important mediator in severe COVID-19. This study aims to characterize eventual causal relationships between ADAM17 and COVID-19. Using Mendelian randomization analyses, we examined the causal effects of circulating ADAM17 on COVID-19 outcomes using summary statistics from large, genome-wide association studies of ADAM17 (up to 35,559 individuals) from the Icelandic Cancer Project and deCODE genetics, as well as critically ill COVID-19 patients (cases: 13,769; controls: 1,072,442), hospitalized COVID-19 patients (cases: 32,519; controls: 2,062,805) and reported SARS-CoV-2 infections (cases: 122,616; controls: 2,475,240) from the COVID-19 Host Genetics Initiative. The Mendelian randomization (MR) analyses demonstrated that a 1 standard deviation increase in genetically determined circulating ADAM17 (extracellular domain) was associated with an increased risk of developing critical ill COVID-19 (odds ratio [OR] = 1.26, 95% confidence interval [CI]:1.03-1.55). The multivariable MR analysis suggested a direct causal role of circulating ADAM17 (extracellular domain) in the risk of developing critical COVID-19 (OR = 1.09; 95% CI:1.01-1.17) when accounting for body mass index. No causal effect for the cytoplasmic domain of ADAM17 on COVID-19 was observed. Our results suggest that an increased genetic susceptibility to elevated levels of circulating ADAM17 (extracellular domain) is associated with a higher risk of suffering from severe COVID-19, strengthening the idea that the timely selective inhibition of ADAM17 could be a potential therapeutic target worthy of investigation.