Comparing the Value of Cystatin C and Serum Creatinine for Evaluating the Renal Function and Predicting the Prognosis of COVID-19 Patients

Integrated Analysis of Noncoding RNAs (PVT-1 and miR-200c) and Their Correlation with STAT4/IL-6 Axis as Reliable Biomarkers for COVID-19 Severity

Inefficient control of elevated inflammatory mediators in coronavirus disease 2019 (COVID-19) has led to health complications, prompting the exploration of efficient biomarkers for monitoring this condition. We herein sought to investigate the implications of plasmacytoma variant translocation 1 (PVT-1), microRNA-200c (miR-200c), signal transducer and activator of transcription 4 (STAT-4), and interleukin-6 (IL-6), as well as how they correlated with creatinine, C-reactive protein (CRP), and lactate dehydrogenase (LDH) activity to identify biomarkers able to the early prognosis and diagnosis of COVID-19. Our study included a total of 105 infected COVID-19 patients and 35 healthy subjects as controls. Individuals with COVID-19 showed a significant increase in CRP, creatinine, and LDH activity. In addition, COVID-19 patients exhibited significantly higher levels of IL-6. These patients also demonstrated notably elevated expressions of miR-200c and PVT-1. The expression level of STAT4 decreased in the COVID-19 patients, and this decrease was negatively correlated with creatinine and LDH activity. The levels of miR-200c and PVT-1 expressions, and their connections with IL-6 and STAT4 levels, increased significantly with the severity of COVID-19 cases. In addition, receiver operating characteristic analysis showed that PVT-1 and miR-200c could be reliable biomarkers for determining the severity of COVID-19.

Efficient clinical decision-making process via AI-based multimodal data fusion: A COVID-19 case study

COVID-19 is an infectious disease that caused a global pandemic in 2020. In the critical moments of this healthcare emergencies, the medical staff needs to take important decisions in a context of limited resources that must be carefully managed. To this end, the computer-aided diagnosis methods are extremely powerful and help them to better recognize the evidences of high-risk patients. This can be done with the support of relevant information extracted from electronic health records, lab tests and imaging studies. In this work, we present a novel fully-automatic efficient method to help the clinical decision-making process in the context of COVID-19 risk estimation, using multimodal data fusion of clinical features and deep features extracted from chest X-ray images. The risk estimation is studied in two of the most relevant and critical encountered scenarios: the risk of hospitalization and mortality. This study shows which are the most important features for each scenario, the ratio of clinical and imaging features present in the top ranking and the performance of the used machine learning models. The results demonstrate a great performance by the classifiers, estimating the risk of hospitalization with an AUC-ROC of 0.8452 ± 0.0133 and the risk of death with an AUC-ROC of 0.8285 ± 0.0210, only using a subset of the original features, and highlight the significant contribution of imaging features to hospitalization risk assessment, while clinical features become more crucial for mortality risk evaluation. Furthermore, multimodal data fusion can outperform the approaches that use one data source. Despite the model's complexity, it requires fewer features, an advantage in scenarios with limited computational resources. This streamlined, fully-automated method shows promising potential to improve the clinical decision-making process and better manage medical resources, not only in the context of COVID-19, but also in other clinical scenarios.

Association of serum creatinine to cystatin C to waist circumference ratios and hypertension: evidence from China health and retirement longitudinal study

Background

The objective of this study was to explore the association between the ratio of serum creatinine to cystatin C to waist circumference (CCR/WC) and hypertension.

Methods

The study utilized data extracted from the China Health and Retirement Longitudinal Study. In the cross-sectional analysis, logistic regression analyses were employed to examine the association between the CCR/WC ratio and hypertension. By utilizing restricted cubic splines, potential non-linear associations between the CCR/WC ratio and hypertension were explored. In the longitudinal analysis, the association between CCR/WC quartiles (Q1-Q4) and the risk of new-onset hypertension was evaluated by Cox proportional-hazards models.

Results

In total, 7,253 participants were enrolled. The study unveiled an inverse association with hypertension, demonstrating an odds ratio (OR) of 0.29 (95% confidence interval [CI]: 0.23-0.37, P < 0.001). Among males, an OR of 0.38 (95% CI: 0.25-0.58, P < 0.001) was observed, while among females, an OR of 0.41 (95% CI: 0.28-0.60, P < 0.001) was noted. There was an absence of a nonlinear association between the CCR/WC ratio and hypertension. Cox regression analysis unveiled a reduced risk of hypertension in Q3 (Hazard ratios [HR]: 0.69, 95% CI: 0.58-0.82, P < 0.001) and Q4: (HR: 0.70, 95% CI: 0.59-0.83, P < 0.001) in compared to the Q1 of the CCR/WC ratio, and sex-specific analysis yielded consistent results.

Conclusion

This study emphasizes the potential association between an elevated CCR/WC ratio and a reduced risk of hypertension.

NEW PREDICTIVE BIOMARKERS FOR SCREENING COVID-19 PATIENTS WITH RHABDOMYOLYSIS IN COMBINATION WITH CYSTATIN C

ABSTRACT

Purpose: Cystatin C (CysC) has been linked to the prognosis of corona virus disease 2019 (COVID-19). The study aims to investigate a predictor correlated with CysC screening for poor prognosis in COVID-19 patients combined with skeletal muscle (SKM) impairment and rhabdomyolysis (RM). Methods: A single-center retrospective cohort analysis was carried out. Demographic information, clinical data, laboratory test results, and clinical outcome data were gathered and analyzed. Results: According to the inclusion and exclusion criteria, 382 patients were included in this study. The subjects were divided into three groups based on CysC tertiles. Multivariate analysis revealed that SaO 2 (hazard ratio [HR], 0.946; 95% confidence interval [CI], 0.906-0.987; P = 0.011), CysC (HR, 2.124; 95% CI, 1.223-3.689; P = 0.008), aspartate aminotransferase (AST) (HR, 1.009; 95% CI, 1.000-1.018; P = 0.041), and hypersensitive C-reactive protein (HR, 1.005; 95% CI, 1.000-1.010; P = 0.045) were significantly associated with survivals. The area under curve (AUC) in the model characterized by RM incidence was 0.819 (0.698-0.941), as shown by CysC receiver operating characteristic curves. LDH*CysC and AST*CysC had better predictive values than CysC and the best prediction for RM, with an AUC of 0.880 (0.796,0.964) for LDH*CysC ( P < 0.05, vs CysC) and 0.925 (0.878,0.972) for AST*CysC ( P < 0.05, vs CysC). Conclusion: CysC is an essential evaluation indicator for COVID-19 patients' prognosis. AST*CysC and LDH*CysC have superior predictive value to CysC for SKM, RM, and death, and optimal classification for RM.

Prognostic Value of Creatinine Levels at Admission on Disease Progression and Mortality in Patients with COVID-19-An Observational Retrospective Study

INTRODUCTION

Acute kidney disease and chronic kidney disease are considered conditions that can increase the mortality and severity of COVID-19. However, few studies have investigated the impact of creatinine levels on COVID-19 progression in patients without a history of chronic kidney disease. The aim of the study was to assess the impact of creatinine levels at hospital admission on COVID-19 progression and mortality.

METHODS

We performed a multicenter, observational, retrospective study involving seventeen COVID-19 Units in the Campania region in southern Italy. All adult (≥18 years) patients, hospitalized with a diagnosis of SARS-CoV-2 infection confirmed by a positive reverse transcriptase-polymerase chain reaction on a naso-oropharyngeal swab, from 28 February 2020 to 31 May 2021, were enrolled in the CoviCamp cohort.

RESULTS

Evaluating inclusion/exclusion criteria, 1357 patients were included. Considering in-hospital mortality and creatinine value at admission, the best cut-off point to discriminate a death during hospitalization was 1.115 mg/dL. The logistic regression demonstrated that factors independently associated with mortality were age (OR 1.082, CI: 1.054-1.110), Charlson Comorbidity Index (CCI) (OR 1.341, CI: 1.178-1.526), and an abnormal creatinine value at admission, defined as equal to or above 1.12 mg/dL (OR 2.233, CI: 1.373-3.634).

DISCUSSION

In conclusion, our study is in line with previous studies confirming that the creatinine serum level can predict mortality in COVID-19 patients and defining that the best cut-off of the creatinine serum level at admission to predict mortality was 1.12 mg/dL.

Serum cystatin C and inflammatory factors related to COVID-19 consequences

BACKGROUND

Besides impaired respiratory function and immune system, COVID-19 can affect renal function from elevated blood urea nitrogen (BUN) or serum creatinine (sCr) levels to acute kidney injury (AKI) and renal failure. This study aims to investigate the relationship between Cystatin C and other inflammatory factors with the consequences of COVID-19.

METHODS

A total of 125 patients with confirmed Covid-19 pneumonia were recruited in this cross-sectional study from March 2021 to May 2022 at Firoozgar educational hospital in Tehran, Iran. Lymphopenia was an absolute lymphocyte count of less than 1.5 × 109/L. AKI was identified as elevated serum Cr concentration or reduced urine output. Pulmonary consequences were evaluated. Mortality was recorded in the hospital one and three months after discharge. The effect of baseline biochemical and inflammatory factors on odds of death was examined. SPSS, version 26, was used for all analyses. P-vale less than 0.05 was considered significant.

RESULTS

The highest amount of co-morbidities was attributed to COPD (31%; n = 39), dyslipidemia and hypertension (27%; n = 34 for each) and diabetes (25%; n = 31). The mean baseline cystatin C level was 1.42 ± 0.93 mg/L, baseline creatinine was 1.38 ± 0.86 mg/L, and baseline NLR was 6.17 ± 4.50. Baseline cystatin C level had a direct and highly significant linear relationship with baseline creatinine level of patients (P < 0.001; r: 0.926). ). The average score of the severity of lung involvement was 31.42 ± 10.80. There is a direct and highly significant linear relationship between baseline cystatin C level and lung involvement severity score (r = 0.890, P < 0.001). Cystatin C has a higher diagnostic power in predicting the severity of lung involvement (B = 3.88 ± 1.74, p = 0.026). The mean baseline cystatin C level in patients with AKI was 2.41 ± 1.43 mg/L and significantly higher than patients without AKI (P > 0.001). 34.4% (n = 43) of patients expired in the hospital, and the mean baseline cystatin C level of this group of patients was 1.58 ± 0.90 mg/L which was significantly higher than other patients (1.35 ± 0.94 mg/L, P = 0.002).

CONCLUSION

cystatin C and other inflammatory factors such as ferritin, LDH and CRP can help the physician predict the consequences of COVID-19. Timely diagnosis of these factors can help reduce the complications of COVID-19 and better treat this disease. More studies on the consequences of COVID-19 and knowing the related factors will help treat the disease as well as possible.

Cystatin-c May Indicate Subclinical Renal Involvement, While Orosomucoid Is Associated with Fatigue in Patients with Long-COVID Syndrome

Long-COVID syndrome is associated with high healthcare costs, but its pathophysiology is not yet fully understood. Inflammation, renal impairment or disturbance of the NO system emerge as potential pathogenetic factors. We aimed to investigate the relationship between symptoms of long-COVID syndrome and serum levels of cystatin-c (CYSC), orosomucoid (ORM), l-arginine, symmetric dimethylarginine (SDMA) and asymmetric dimethylarginine (ADMA). A total of 114 patients suffering from long-COVID syndrome were included in this observational cohort study. We found that serum CYSC was independently associated with the anti-spike immunoglobulin (S-Ig) serum level (OR: 5.377, 95% CI: 1.822-12.361; p = 0.02), while serum ORM (OR: 9.670 (95% CI: 1.34-9.93; p = 0.025) independently predicted fatigue in patients with long-COVID syndrome, both measured at baseline visit. Additionally, the serum CYSC concentrations measured at the baseline visit showed a positive correlation with the serum SDMA levels. The severity of abdominal and muscle pain indicated by patients at the baseline visit showed a negative correlation with the serum level of L-arginine. In summary, serum CYSC may indicate subclinical renal impairment, while serum ORM is associated with fatigue in long-COVID syndrome. The potential role of l-arginine in alleviating pain requires further studies.

Shrunken Pore Syndrome Is Frequently Occurring in Severe COVID-19

A selective decrease in the renal filtration of larger molecules is attributed to the shrinkage of glomerular pores, a condition termed Shrunken Pore Syndrome (SPS). SPS is associated with poor long-term prognosis. We studied SPS as a risk marker in a cohort of patients with COVID-19 treated in an intensive care unit. SPS was defined as a ratio < 0.7 when the estimated glomerular filtration rate (eGFR), determined by cystatin C, calculated by the Cystatin C Caucasian-Asian-Pediatric-Adult equation (CAPA), was divided by the eGFR determined by creatinine, calculated by the revised Lund−Malmö creatinine equation (LMR). Clinical data were prospectively collected. In total, SPS was present in 86 (24%) of 352 patients with COVID-19 on ICU admission. Patients with SPS had a higher BMI, Simplified Physiology Score (SAPS3), and had diabetes and/or hypertension more frequently than patients without SPS. Ninety-nine patients in the total cohort were women, 50 of whom had SPS. In dexamethasone-naïve patients, C-reactive protein (CRP ), TNF-alpha, and interleukin-6 did not differ between SPS and non-SPS patients. Demographic factors (gender, BMI) and illness severity (SAPS3) were independent predictors of SPS. Age and dexamethasone treatment did not affect the frequency of SPS after adjustments for age, sex, BMI, and acute severity. SPS is frequent in severely ill COVID-19 patients. Female gender was associated with a higher proportion of SPS. Demographic factors and illness severity were independent predictors of SPS.

The role of kidney injury biomarkers in COVID-19

The coronavirus disease-2019 (COVID-19) outbreak has been declared a global pandemic. COVID-19-associated acute kidney injury (COVID-19 AKI) is related to a high mortality rate and serves as an independent risk factor for hospital death in patients with COVID-19. Early diagnosis would allow for earlier intervention and potentially improve patient outcomes. The goal of early identification of AKI has been the primary impetus for AKI biomarker research, and several kidney injury biomarkers have been demonstrated to be beneficial in predicting COVID-19 AKI as well as disease progression in COVID-19. Furthermore, such data provide valuable insights into the molecular mechanisms underlying this complex and unique disease and serve as a molecular phenotyping tool that could be utilized to direct clinical intervention. This review focuses on a number of kidney injury biomarkers, such as CysC, NAGAL, KIM-1, L-FABP, IL-18, suPAR, and [TIMP-2] • [IGFBP7], which have been widely studied in common clinical settings, such as sepsis, cardiac surgery, and contrast-induced AKI. We explore the role of kidney injury biomarkers in COVID-19 and discuss what remains to be learned.

Differential Bias for Creatinine- and Cystatin C- Derived Estimated Glomerular Filtration Rate in Critical COVID-19

COVID-19 is a systemic disease, frequently affecting kidney function. Dexamethasone is standard treatment in severe COVID-19 cases, and is considered to increase plasma levels of cystatin C. However, this has not been studied in COVID-19. Glomerular filtration rate (GFR) is a clinically important indicator of renal function, but often estimated using equations (eGFR) based on filtered metabolites. This study focuses on sources of bias for eGFRs (mL/min) using a creatinine-based equation (eGFRLMR) and a cystatin C-based equation (eGFRCAPA) in intensive-care-treated patients with COVID-19. This study was performed on 351 patients aged 18 years old or above with severe COVID-19 infections, admitted to the intensive care unit (ICU) in Uppsala University Hospital, a tertiary care hospital in Uppsala, Sweden, between 14 March 2020 and 10 March 2021. Dexamethasone treatment (6 mg for up to 10 days) was introduced 22 June 2020 (n = 232). Values are presented as medians (IQR). eGFRCAPA in dexamethasone-treated patients was 69 (37), and 74 (46) in patients not given dexamethasone (p = 0.01). eGFRLMR was not affected by dexamethasone. eGFRLMR in females was 94 (20), and 75 (38) in males (p = 0.00001). Age and maximal CRP correlated negatively to eGFRCAPA and eGFRLMR, whereas both eGFR equations correlated positively to BMI. In ICU patients with COVID-19, dexamethasone treatment was associated with reduced eGFRCAPA. This finding may be explained by corticosteroid-induced increases in plasma cystatin C. This observation is important from a clinical perspective since adequate interpretation of laboratory results is crucial.

A Review of Routine Laboratory Biomarkers for the Detection of Severe COVID-19 Disease

As the COVID-19 pandemic continues, there is an urgent need to identify clinical and laboratory predictors of disease severity and prognosis. Once the coronavirus enters the cell, it triggers additional events via different signaling pathways. Cellular and molecular deregulation evoked by coronavirus infection can manifest as changes in laboratory findings. Understanding the relationship between laboratory biomarkers and COVID-19 outcomes would help in developing a risk-stratified approach to the treatment of patients with this disease. The purpose of this review is to investigate the role of hematological (white blood cell (WBC), lymphocyte, and neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet, and red blood cell (RBC) count), inflammatory (C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and lactate dehydrogenase (LDH)), and biochemical (Albumin, aspartate aminotransferase (AST) and alanine aminotransferase (ALT), blood urea nitrogen (BUN), creatinine, D-dimer, total Cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL)) biomarkers in the pathogenesis of COVID-19 disease and how their levels vary according to disease severity.