Association of serum uric acid levels with COVID-19 severity

Time Course of Biochemical and Metabolic Parameters During and After COVID-19

BACKGROUND

Long COVID is characterized by the persistence of symptoms among individuals who are infected with the SARS-CoV-2 virus. The enduring impact of these long-term effects on the health and well-being of those affected cannot be denied.

METHODS

470 patients with SARS-CoV-2 were consecutively recruited in this longitudinal study. The participants were entered into moderate, severe, and critical groups. 235 out of 470 participants were female. The levels of fasting blood sugar (FBS), alanine transaminase (SGPT), aspartate aminotransferase (SGOT), alkaline phosphatase (ALP), creatinine (Cr), urea, uric acid (UA), and total protein (TP) were measured during hospitalization and again at one and three months after infection. The levels of Zn and hemoglobin A1c (HbA1c) were also measured only during hospitalization.

RESULTS

COVID-19 severity was associated with high levels of glucose, urea, Cr, ALT, AST, ALP, and HbA1c, and low levels of Zn, UA, and TP. There were significant sex differences for these markers at all three-time points. Glucose, urea, Cr, ALT, AST, and ALP all decreased three months after infection, whereas the levels of UA and TP returned towards normal.

CONCLUSION

COVID-19 infection affects the levels of multiple biochemical factors in a gender-dependent manner. The biochemical changes become more tangible with increasing disease severity, and several of these predict mortality. Levels begin to return to normal after the acute phase of the disease, but in some individuals, at three months, several markers were still not within the normal range. Whether the trajectory of these changes can predict long COVID requires further testing.

The predictive significance of uric acid to high density lipoprotein- cholesterol ratio and uric acid for the severity and mortality of coronavirus disease-19

OBJECTIVE

The non-invasive and inexpensive predictive indicators seem to be essential for the evaluation of coronavirus disease-19 (COVID-19) prognosis. Uric acid to high-density lipoprotein-cholesterol ratio (UHR) have been known as inflammatory and metabolic biomarker in some disorders. This study aimed to evaluate the usefulness of serum uric acid (UA) and UHR values on admission as prognostic indicators for the severity and mortality of COVID-19. Regression models were accomplished to assess the association between UA and UHR with the severity and mortality of COVID-19.

RESULTS

This study was performed with 424 confirmed COVID-19 patients. The mean UA and UHR values of the severe group and deceased group were statistically higher than those mild group and survivor group, respectively (P < 0.05). Compared to the survivor cases, deceased subjects had lower serum concentrations of HDL-c (p < 0.05). Multivariate logistic regression analysis showed that UHR and UA values statistically are correlated with the severity (OR = 1.20 CI:1.07-1.35, OR = 1.19 CI:1.023-1.381 respectively) and mortality (OR = 10.04 CI:1.50-67.30, OR = 10.73 CI:1.47-87.11, respectively) of COVID-19. Compared with a reference range, serum UA levels ≥ 7.3 mg/dl and a UHR value greater than 0.185 increase the risk of critical care of COVID-19 almost 2.5 and 3.5 times, respectively. In summary, our results revealed that UHR index value and serum UA levels are useful biochemical indicators for predicting the severity and mortality of COVID-19.

Exploring the Relationship of SARS-CoV-2 and Uric Acid Levels With a Focus on Gout Patients: A Scoping Review

Rheumatic diseases are a group of conditions including arthritis and various other conditions that can lead to chronic inflammation within the musculoskeletal system, which can have negative effects on soft tissues, bones, muscles, joints, and connective tissue. One form of arthritis is gout, which is an inflammatory condition in which urate acid crystals build up in joints. Gout is associated with joint swelling, pain, redness, and joint mobility issues. Early diagnosis and treatment are essential to prevent joint degradation and other adverse complications. The condition has been shown to increase the incidence of diseases outside the musculoskeletal system, including the renal and cardiovascular systems. Comorbid conditions associated with gout include but are not limited to type 2 diabetes mellitus (T2DM), hypertension, hyperlipidemia, chronic kidney disease, cardiovascular disease, and heart failure. This systematic review aims to provide insight into the relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, uric acid levels, and gout.

Biomarkers of oxidative stress and inflammation in subjects with COVID-19: Characterization and prognosis of the disease

Coronavirus disease (COVID-19) is an acute respiratory disease caused by the new coronavirus (SARS-CoV-2) that has spread throughout the world causing millions of deaths. COVID-19 promotes excessive release of pro-inflammatory cytokines leading to acute lung injury and death. Reactive oxygen species (ROS) and oxidative stress (OS) may also play a role in the pathophysiology of COVID-19. The present study investigated levels of inflammatory cytokines (IL-1β, IL-6, IL-8, IL-10, IL-12) and OS biomarkers (MPO, SOD, CAT, GST enzymes and contents of GSH, TBARS and PC) in patients with SARS-CoV-2 infection, which were correlated with disease severity. Patients with SARS significantly increased IL-1β levels, while IL-6 levels were elevated in both groups of SARS-CoV-2 positive patients. The most severe patients showed increased levels of IL-8 and IL-10, while subjects without SARS showed lower values. MPO activity were higher in both groups of SARS-CoV-2 positive patients, while SOD and CAT activity were decreased in both groups. Compared to controls, GGT was elevated only in the SARS patient group, while GST values were increased in the group of positive patients in SARS-CoV-2 without SARS and were decreased in patients with SARS. GSH and UA contents decreased in SARS-CoV-2 positive subjects, whereas TBARS and PC contents increased in both groups of SARS-CoV-2 positive patients, particularly in the SARS patient group. In addition, several important correlations were found between cytokines and the different OS parameters suggesting some inter-relationship in the complex antioxidant system of the patients. In general, patients with SARS-CoV-2 infection showed higher levels of OS biomarkers, and also elevated contents of IL-6 and IL-10, probably worsening the damage caused by SARS-CoV-2 infection. This damage may contribute to the severity of the disease and its complications, as well as a prognosis for SARS-CoV-2 patients.

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.

Old Age is an Independent Risk Factor for Pneumonia Development in Patients with SARS-CoV-2 Omicron Variant Infection and a History of Inactivated Vaccine Injection

Objective

Analyzing the risk factors for pneumonia development in breakthrough cases with a history of inactivated vaccine injection is important. The present study aimed to investigate the risk factors for pneumonia development during Omicron variant infection.

Design and Methods

The clinical data were retrospectively collected from 187 patients who previously received inactivated vaccine and were infected by the Omicron variant.

Results

Among the 187 patients, 73 had 2 doses of inactivated vaccine injection and the remaining 114 had 3 doses; 19 patients had pneumonia at admission. The univariate logistic analysis showed that age, baseline platelet count, D-dimer level, and CD8⁺ T lymphocyte count were associated with pneumonia development at admission. The multivariate analysis showed that only age was the independent risk factor for pneumonia development (odds ratio = 1.046, 95% confidence interval: 1.003-1.091, P = 0.04). With an optimal cutoff value of 46, 4.4% (4/91) patients in the age <46 years group and 15.63% (15/96) patients in the age ≥46 years group had pneumonia (χ ² = 6.454, P = 0.01). Moreover, age negatively correlated with CD8⁺ T cell count, B cell count, and albumin and uric acid levels (all P < 0.01), while age positively correlated with the glucose level (P < 0.01).

Conclusion

Old age was the only independent risk factor for pneumonia development in patients with Omicron variant infection and a history of inactivated vaccine injection.

SARS-CoV-2 omicron variant clearance delayed in breakthrough cases with elevated fasting blood glucose

Background

Omicron variant (B.1.1.529) is a dominant variant worldwide. However, the risk factors for Omicron variant clearance are yet unknown. The present study aimed to investigate the risk factors for early viral clearance of Omicron variant in patients with a history of inactivated vaccine injection.

Methods

Demographic, clinical, and epidemiological data from 187 patients were collected retrospectively during the Omicron variant wave.

Results

73/187 and 114/187 patients were administered two and three doses of vaccine, respectively. The median duration of SARS-CoV-2 RNA positivity was 9 days, and the difference between patients with two and three vaccine injections was insignificant (P = 0.722). Fever was the most common symptom (125/187), and most patients (98.4%) had a fever for < 7 days. The RNA was undetectable in 65/187 patients on day 7. Univariable logistic analysis showed that baseline glucose, uric acid, lymphocytes count, platelet count, and CD4⁺ T lymphocyte count were associated with SARS-CoV-2 RNA-positivity on day 7. Multivariable analysis showed that glucose ≥ 6.1 mmol/L and CD4⁺T lymphocytes count were independent risk factors for RNA positivity on day 7. 163/187 patients had an undetectable RNA test on day 14, and uric acid was the only independent risk factor for RNA positivity. Moreover, baseline glucose was negatively correlated with uric acid and CD4⁺ and CD8⁺ T cell count, while uric acid was positively correlated with CD4⁺ and CD8⁺ T cell count.

Conclusions

Omicron variant clearance was delayed in breakthrough cases with elevated fasting blood glucose, irrespective of the doses of inactivated vaccine.

Additional Evidence for Commonalities between COVID-19 and Radiation Injury: Novel Insight into COVID-19 Candidate Drugs

COVID-19 is a challenge to biosecurity and public health. The speed of vaccine development lags behind that of virus evolution and mutation. To date, no agent has been demonstrated to be fully effective against COVID-19. Therefore, it remains of great urgency to rapidly develop promising therapeutic and diagnostic candidates. Intriguingly, mounting evidence hints at parallel etiologies between SARS-CoV-2 infection and radiation injury. Herein, from the perspectives of immunogenic pathway activation and metabolic alterations, we provide novel evidence of commonalities between these two pathological conditions based on the most recent findings. Since numerous agents have been developed to prevent or reverse radiation injury in the past 70 years to ensure nuclear safety, we also advocate investigating the promising function of radioprotectors and radiomitigators against COVID-19 in clinical settings.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

Prognostic value of albumin-to-globulin ratio in COVID-19 patients: A systematic review and meta-analysis

Background and aims

The albumin-to-globulin ratio (AGR) has been used to predict severity and mortality in infectious diseases. The aim of this study is to evaluate the prognostic value of the AGR in COVID-19 patients.

Methods

A systematic review and meta-analysis were conducted. We included observational studies assessing the association between the AGR values upon hospital admission and severity or all-cause mortality in COVID-19 patients. In the meta-analyses we used random effect models. Risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). The effect measures were expressed as mean difference (MD) and their 95% confidence intervals (CI). We performed Egger's test and funnel plots to assess the publication bias.

Results

The included studies had a total of 11356 patients corresponding to 31 cohort studies. Severe COVID-19 patients had lower AGR values than non-severe COVID-19 patients (mean difference (MD), −0.27; 95% IC, −0.32 to −0.22; p < 0.001; I² = 88%). Non-survivor patients with COVID-19 had lower AGR values than survivor patients (MD, −0.29; 95% IC, −0.35 to −0.24; p < 0.001; I² = 79%). In the sensitivity analysis, we only included studies with low risk of bias, which decreased the heterogeneity for both outcomes (severity, I² = 20%; mortality, I² = 5%).

Conclusions

Low AGR values upon hospital admission were found in COVID-19 patients with a worse prognosis.

Gout and the risk of COVID-19 diagnosis and death in the UK Biobank: a population-based study

BACKGROUND

There is a paucity of data on outcomes for people with gout and COVID-19. We aimed to assess whether gout is a risk factor for diagnosis of COVID-19 and COVID-19-related death, and to test for sex- and drug-specific differences in risk.

METHODS

We used data from the UK Biobank, which included 15 871 people with gout. We used multivariable-adjusted logistic regression in the following analyses using a case-control study design: to test for an association between gout and COVID-19 diagnosis in the entire UK Biobank cohort (n=459 837); to test for an association between gout and COVID-19-related death in people who were known to have died or survived with COVID-19 (n=15 772); to test for an association between gout and COVID-19-related death in the entire UK Biobank cohort (n=459 837); and to assess risk of COVID-19-related death in a subset of patients from the UK Biobank cohort with prescription data, stratified by prescription of urate-lowering therapy and colchicine (n=341 398). Models 1 and 2 were adjusted for age group, sex, ethnicity, Townsend deprivation index, BMI, and smoking status. Model 2 was also adjusted for diagnosis of 16 other diseases that are established comorbidities of gout or established risk factors for COVID-19-related death.

FINDINGS

Gout was associated with diagnosis of COVID-19 (odds ratio [OR] 1·20, 95% CI 1·11-1·29) but not with risk of COVID-19-related death in the cohort of patients diagnosed with COVID-19 (1·20, 0·96-1·51). In the entire cohort, gout was associated with COVID-19-related death (1·29, 1·06-1·56); women with gout had an increased risk of COVID-19-related death (1·98, 1·34-2·94), whereas men with gout did not (1·16, 0·93-1·45). We found no significant differences in the risk of COVID-19-related death according to prescription of urate-lowering therapy or colchicine. When patients with gout were stratified by vaccination status, the risk of diagnosis with COVID-19 was significant in the non-vaccinated group (1·21, 1·11-1·30) but not the vaccinated group (1·09, 0·65-1·85).

INTERPRETATION

Gout is a risk factor for COVID-19-related death in the UK Biobank cohort, with an increased risk in women with gout, which was driven by risk factors independent of the metabolic comorbidities of gout.

FUNDING

Health Research Council of New Zealand.