TFPI and FXIII negatively and S100A8/A9 and Cystatin C positively correlate with D-dimer in COVID-19

Plasma tissue factor pathway inhibitor levels in coronavirus disease 2019 patients: a systematic review and meta-analysis

Studies have suggested a relationship between tissue factor pathway inhibitor (TFPI) and coronavirus disease 2019 (COVID-19) severity. However, there is inconsistency in the findings of the studies. To enhance comprehension of this relationship, a meta-analysis was conducted. PubMed, Web of Science, and Scopus databases were searched to identify eligible studies. The mean difference was employed as effect measures and the standardized mean difference (SMD) and the 95% confidence interval (CI) were utilized as a summary statistic. Heterogeneity was assessed through the application of the chi-square test and the I2 statistic. The included studies' quality and risk of bias were assessed using the Newcastle-Ottawa assessment scale, adapted for case-control studies. A total of six studies were included with 684 cases and healthy controls (180 healthy controls and 504 COVID-19 patients with different severity, 76 mild, 292 moderate, and 136 severe). The analysis revealed a significant increase in the TFPI level in COVID-19 patients with moderate severity compared with healthy controls (SMD = 0.95 ng/ml, 95% confidence interval (CI) 0.27, 1.63 ng/ml; I2 : 87.2%). The increased TFPI level in mild and moderate COVID-19 was not significant, SMD = 0.68 ng/ml, 95% CI -0.64 to 2.0 ng/ml; I2 92.9% and SMD = 0.62 ng/ml, 95% CI -0.62 to 1.86 ng/ml; I2 91.5%, respectively. In addition, most studies indicate an association of the increased TFPI concentrations with increased markers of inflammation, endothelial damage, and hypercoagulation. Considering the anticoagulant and anti-inflammatory roles of TFPI, its increase seems to be aimed at modulating COVID-19-induced hyper-inflammation and hyper-coagulation state.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42023437353.

Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study

BACKGROUND

Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This study aimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals.

METHODS

In this prospective, multicentre, observational cohort study done in nine centres in six countries (Australia, Indonesia, Singapore, Italy, Czechia, and France) blood samples and RNA sequencing were used to develop a robust model of predicting secondary bacterial infections in the respiratory tract of patients with COVID-19. Eligible participants were older than 18 years, had known or suspected COVID-19, and symptoms of a recent respiratory infection. A control cohort of participants without COVID-19 who were older than 18 years and with no infection symptoms was also recruited from one Australian centre. In the pre-analysis phase, data were filtered to include only individuals with complete blood transcriptomics and patient data (ie, age, sex, location, and WHO severity score at the time of sample collection). The dataset was then divided randomly (4:1) into a training set (80%) and a test set (20%). Gene expression data in the training set and control cohort were used for differential expression analysis. Differentially expressed genes, along with WHO severity score, location, age, and sex, were used for feature selection with least absolute shrinkage and selection operator (LASSO) in the training set. For LASSO analysis, samples were excluded if gene expression data were not obtained at study admission, no longitudinal clinical information was available, a bacterial infection at the time of study admission was present, or a fungal infection in the absence of a bacterial infection was detected. LASSO regression was performed using three subsets of predictor variables: patient data alone, gene expression data alone, or a combination of patient data and gene expression data. The accuracy of the resultant models was tested on data from the test set.

FINDINGS

Between March, 2020, and October, 2021, we recruited 536 SARS-CoV-2-positive individuals and between June, 2013, and January, 2020, we recruited 74 participants into the control cohort. After prefiltering analysis and other exclusions, samples from 158 individuals were analysed in the training set and 47 in the test set. The expression of seven host genes (DAPP1, CST3, FGL2, GCH1, CIITA, UPP1, and RN7SL1) in the blood at the time of study admission was identified by LASSO as predictive of the risk of developing a secondary bacterial infection of the respiratory tract more than 24 h after study admission. Specifically, the expression of these genes in combination with a patient's WHO severity score at the time of study enrolment resulted in an area under the curve of 0·98 (95% CI 0·89-1·00), a true positive rate (sensitivity) of 1·00 (95% CI 1·00-1·00), and a true negative rate (specificity) of 0·94 (95% CI 0·89-1·00) in the test cohort. The combination of patient data and host transcriptomics at hospital admission identified all seven individuals in the training and test sets who developed a bacterial infection of the respiratory tract 5-9 days after hospital admission.

INTERPRETATION

These data raise the possibility that host transcriptomics at the time of clinical presentation, together with machine learning, can forward predict the risk of secondary bacterial infections and allow for the more targeted use of antibiotics in viral infection.

FUNDING

Snow Medical Research Foundation, the National Health and Medical Research Council, the Jack Ma Foundation, the Helmholtz-Association, the A2 Milk Company, National Institute of Allergy and Infectious Disease, and the Fondazione AIRC Associazione Italiana per la Ricerca contro il Cancro.

Circulating H-FABP as a biomarker of frailty in patients with chronic heart failure

Increased vulnerability to physiologic stressors, termed frailty, is a common occurrence in patients with chronic heart failure (CHF). However, the definite biomarkers to assess frailty in CHF patients are not known. Here, we assessed the frailty phenotype and its potential association with heart failure (HF) markers in CHF patients. We categorized controls (n = 59) and CHF patients (n = 80), the participants, into robust, pre-frail, and frail based on the cardiovascular health study (CHS) frailty index. The plasma levels of HF markers, including tumorigenicity 2 (s-ST2), galectin-3, and heart-type fatty acid binding protein (H-FABP), were measured and correlated with frailty phenotype and cardiac function. The levels of plasma s-ST2, galectin-3, and H-FABP were profoundly elevated in CHF patients. Conversely, the frailty index scores were significantly lower in ischemic and non-ischemic CHF patients versus controls. Of the assessed HF markers, only H-FABP was positively correlated (r2 = 0.07, P = 0.02) with the frailty score in CHF patients. Collectively, these observations suggest that circulating H-FABP may serve as a biomarker of frailty in CHF patients.

Status of major hemostatic components in the setting of COVID-19: the effect on endothelium, platelets, coagulation factors, fibrinolytic system, and complement

The coagulation, fibrinolytic, anticoagulation, and complement systems are in delicate balance with the vessel wall endothelium ensuring appropriate hemostasis. Coagulopathy in coronavirus disease 2019 (COVID-19) is not a simple disorder of one hemostatic component but a complicated process affecting most of the hemostasis system. COVID-19 disturbs the balance between the procoagulant systems and the regulatory mechanisms. Here, we investigate the effect of COVID-19 on key hemostatic components, including platelets, endothelial cells, coagulation factors, fibrinolytic system, anticoagulant protein system, and complement system, to improve our understanding of the pathophysiological processes underlying COVID-19 coagulopathy based on evidence.

Plasma Galectin-3 and H-FABP correlate with poor physical performance in patients with congestive heart failure

Heart failure (HF) is often associated with compromised physical capacity in patients. However, it is unclear if established HF markers correlate with the physical performance of patients with congestive HF (CHF). We assessed the left ventricular end-systolic dimension (LVESD) and ejection fraction (LVEF) and, physical performance parameters, including short physical performance battery (SPPB), gait speed (GS), and handgrip strength (HGS) in 80 patients with CHF along with 59 healthy controls. Furthermore, levels of plasma HF markers galectin-3 and heart-specific fatty acid binding protein (H-FABP) were measured in relation to the severity of HF and physical performance. Irrespective of etiology, significantly greater LVESD and lower LVEF were observed in HF patients versus controls. As expected, the levels of HF markers galectin-3 and H-FABP were upregulated in the CHF patients which were accompanied by significantly elevated levels of plasma zonulin and inflammatory marker C-reactive protein (CRP). The SPPB scores, GS, and HGS were significantly lower in the ischemic and non-ischemic HF patients than controls. The level of galectin-3 was inversely correlated with SPPB scores (r2 = 0.089, P = 0.01) and HGS (r2 = 0.078, P = 0.01). Similarly, H-FABP levels were also inversely correlated with SPPB scores (r2 = 0.06, P = 0.03) and HGS (r2 = 0.109, P = 0.004) in the patients with CHF. Taken together, CHF adversely affects physical performance, and galectin-3 and H-FABP may serve as biomarkers of physical disability in patients with CHF. The robust correlations of galectin-3 and H-FABP with the physical performance parameters and CRP in CHF patients suggest that the poor physical performance may partly be caused due to systemic inflammation.

SARS-CoV-2 infection- induced growth factors play differential roles in COVID-19 pathogenesis

Aims

Biologically active molecules cytokines and growth factors (GFs) are critical regulators of tissue injury/repair and emerge as key players in COVID-19 pathophysiology. However, specific disease stage of GFs dysregulation and, whether these GFs have associations with thromboembolism and tissue injury/repair in COVID-19 remain vague.

Main methods

GF profiling in hospitalized moderate (non-ICU) and critically ill (ICU) COVID-19 patients was performed through legendPlex assay.

Key findings

Investigation revealed profound elevation of VEGF, PDGFs, EGF, TGF-α, FGF-basic, and erythropoietin (EPO) in moderate cases and decline or trend of decline with disease advancement. We found strong positive correlations of plasma VEGF, PDGFs, and EPO with endothelial dysfunction markers P-selectin and sCD40L. Interestingly, the HGF and G-CSF were upregulated at the moderate stage and remained elevated at the severe stage of COVID-19. Moreover, strong negative correlations of PDGFs (r² = 0.238, P = 0.006), EPO (r² = 0.18, P = 0.01) and EGF (r² = 0.172, P = 0.02) and positive correlation of angiopoietin-2 (r² = 0.267, P = 0.003) with D-dimer, a marker of thromboembolism, was observed. Further, plasma PDGFs (r² = 0.199, P = 0.01), EPO (r² = 0.115, P = 0.02), and EGF (r² = 0.108, P = 0.07) exhibited negative correlations with tissue injury marker, myoglobin.

Significance

Taken together, unlike cytokines, most of the assessed GFs were upregulated at the moderate stage of COVID-19. The induction of GFs likely occurs due to endothelial dysfunction and may counter the adverse effects of cytokine storms which is reflected by inverse correlations of PDGFs, EPO, and EGF with thromboembolism and tissue injury markers. The findings suggest that the assessed GFs play differential roles in the pathogenesis of COVID-19.