Pentameric C-reactive protein is a better prognostic biomarker and remains elevated for longer than monomeric CRP in hospitalized patients with COVID-19

Higher monomeric C-reactive protein levels are associated with premature coronary artery disease

Introduction

Chronic inflammation is a major risk factor for coronary artery disease (CAD). Currently, the inflammatory cardiovascular risk is assessed via C-reactive protein (CRP) levels measured using a high-sensitivity assay (hsCRP). Monomeric CRP (mCRP) is a locally produced form of CRP that has emerged as a potential biomarker of inflammation.

Aim

This study investigated whether mCRP levels are associated with premature CAD.

Materials and methods

This study comprised 103 participants of both sexes, including 50 patients 56 ± 7 years old with premature CAD and 53 patients 51 ± 10 years old without CAD. CAD was verified using coronary angiography, hsCRP levels were measured using a standard assay, and mCRP levels were measured using fluorescent cytometric beads conjugated with an anti-mCRP antibody.

Results

The levels of hsCRP were 0.99 (0.59; 3.10) mg/L vs. 0.63 (0.35; 1.85) mg/L (p = 0.067), and mCRP 6.84 (4.20; 13.78) µg/L vs. 2.57 (0.32; 5.66) µg/L (p <0.001) in patients with CAD vs. patients without CAD, respectively. There was a weak positive correlation between the mCRP and hsCRP levels (ρ = 0.214; p = 0.030). hsCRP levels were below 2.0 mg/L (i.e., residual inflammatory cardiovascular risk should have been excluded) in 70% of patients with CAD and 79% of patients without CAD (p = 0.365). mCRP levels differed between the groups of patients with hsCRP levels below 2.0 mg/L: 5.14 (4.07; 10.68) µg/L vs. 2.77 (0.53; 5.00) µg/L in patients with or without CAD, respectively (p <0.001). Logistic regression analysis demonstrated that mCRP levels were independently associated with premature CAD. The adjusted odds ratio was 1.18 (95% CI 1.06-1.33, p = 0.004) per each µg/L increase in mCRP levels.

Conclusion

Higher mCRP levels were associated with premature CAD, independent of hsCRP levels and traditional risk factors.

Proteomic Profiling of COVID-19 Patients Sera: Differential Expression with Varying Disease Stage and Potential Biomarkers

Background/Objectives: SARS-CoV-2 is one of the viruses that caused worldwide health issues. This effect is mainly due to the wide range of disease prognoses it can cause. The aim of this study is to determine protein profiles that can be used as potential biomarkers for patients' stratification, as well as potential targets for drug development. Methods: Eighty peripheral blood samples were collected from heathy as well as SARS-CoV-2 patients admitted at a major tertiary care center in Riyadh, Saudi Arabia. A label-free quantitative mass spectrometry-based proteomic analysis was conducted on the extracted sera. Protein-protein interactions and functional annotations of identified proteins were performed using the STRING. Results: In total, two-hundred-eighty-eight proteins were dysregulated among all four categories. Dysregulated proteins were mainly involved in the network map of SARS-CoV-2, immune responses, complement activation, and lipid transport. Compared to healthy subjects, the most common upregulated protein in all three categories were CRP, LGALS3BP, SAA2, as well as others involved in SARS-CoV-2 pathways such as ZAP70 and IGLL1. Notably, we found fifteen proteins that significantly discriminate between healthy/recovered subjects and moderate/under medication patients, among which are the SERPINA7, HSPD1 and TTC41P proteins. These proteins were also significantly downregulated in under medication versus moderate patients. Conclusions: Our results emphasize the possible association of specific proteins with the SARS-CoV-2 pathogenesis and their potential use as disease biomarkers and drug targets. Our study also gave insights about specific proteins that are likely increased upon infection but are likely restored post recovery.

Searching for New Biomarkers to Assess COVID-19 Patients: A Pilot Study

During the initial diagnosis of urgent medical conditions, which include acute infectious diseases, it is important to assess the severity of the patient's clinical state as quickly as possible. Unlike individual biochemical or physiological indicators, derived indices make it possible to better characterize a complex syndrome as a set of symptoms, and therefore quickly take a set of adequate measures. Recently, we reported on novel diagnostic indices containing butyrylcholinesterase (BChE) activity, which is decreased in COVID-19 patients. Also, in these patients, the secretion of von Willebrand factor (vWF) increases, which leads to thrombosis in the microvascular bed. The objective of this study was the determination of the concentration and activity of vWF in patients with COVID-19, and the search for new diagnostic indices. One of the main objectives was to compare the prognostic values of some individual and newly derived indices. Patients with COVID-19 were retrospectively divided into two groups: survivors (n = 77) and deceased (n = 24). According to clinical symptoms and computed tomography (CT) results, the course of disease was predominantly moderate in severity. The first blood sample (first point) was taken upon admission to the hospital, the second sample (second point)-within 4-6 days after admission. Along with the standard spectrum of biochemical indicators, BChE activity (BChEa or BChEb for acetylthiocholin or butyrylthiocholin, respectively), malondialdehyde (MDA), and vWF analysis (its antigen level, AGFW, and its activity, ActWF) were determined and new diagnostic indices were derived. The pooled sensitivity, specificity, and area under the receiver operating curve (AUC), as well as Likelihood ratio (LR) and Odds ratio (OR) were calculated. The level of vWF antigen in the deceased group was 1.5-fold higher than the level in the group of survivors. Indices that include vWF antigen levels are superior to indices using vWF activity. It was found that the index [Urea] × [AGWF] × 1000/(BChEb × [ALB]) had the best discriminatory power to predict COVID-19 mortality (AUC = 0.91 [0.83, 1.00], p < 0.0001; OR = 72.0 [7.5, 689], p = 0.0002). In addition, [Urea] × 1000/(BChEb × [ALB]) was a good predictor of mortality (AUC = 0.95 [0.89, 1.00], p < 0.0001; OR = 31.5 [3.4, 293], p = 0.0024). The index [Urea] × [AGWF] × 1000/(BChEb × [ALB]) was the best predictor of mortality associated with COVID-19 infection, followed by [Urea] × 1000/(BChEb × [ALB]). After validation in a subsequent cohort, these two indices could be recommended for diagnostic laboratories.