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Abstract

Background:

The central role of inflammatory progression in the development of Coronavirus disease 2019 (COVID-19), especially in severe cases, is indisputable. However, the role of some novel inflammatory biomarkers in the prognosis of COVID-19 remains controversial.

Objective:

To assess the effect of some novel inflammatory biomarkers in the occurrence and prognosis of COVID-19.

Methods:

We systematically retrieved the studies related to COVID-19 and the inflammatory biomarkers of interest. The data of each biomarker in different groups were extracted, then were categorized and pooled. The standardized mean difference was chosen as an effect size measure to compare the difference between groups.

Results:

A total of 90 studies with 12,059 participants were included in this study. We found higher levels of endocan, PTX3, suPAR, sRAGE, galectin-3, and monocyte distribution width (MDW) in the COVID-19 positive groups compared to the COVID-19 negative groups. No significant differences for suPAR and galectin-3 were detected between the severe group and mild/moderate group of COVID-19. In addition, the deaths usually had higher levels of PTX3, sCD14-ST, suPAR, and MDW at admission compared to the survivors. Furthermore, patients with higher levels of endocan, galectin-3, sCD14-ST, suPAR, and MDW usually developed poorer comprehensive clinical prognoses.

Conclusions:

In summary, this meta-analysis provides the most up-to-date and comprehensive evidence for the role of the mentioned novel inflammatory biomarkers in the prognosis of COVID-19, especially in evaluating death and other poor prognoses, with most biomarkers showing a better discriminatory ability.

Keywords

COVID-19 inflammatory biomarkers meta-analysis prognosis

Introduction

The outbreak of Coronavirus disease 2019 (COVID-19) in December 2019, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a serious global public health threat. Although the disease is currently mild in most patients, severe cases can cause irreversible damage to the patient’s lung tissue.¹ Due to the rapid spread of COVID-19, timely management and treatment of patients are necessary.

There is increasing evidence that inflammatory processes are involved in the progression of COVID-19,^2,3 and inflammatory biomarkers play important roles in the prognoses of patients with COVID-19.^4,5 Zeng et al. and collaborators first reported the associations of classical inflammatory biomarkers with the severity of COVID-19, including CRP, PCT, ESR, and serum ferritin, considering that the measurement of inflammatory biomarkers could evaluate the severity and prognosis of COVID-19.⁶ Recent studies have found that some novel inflammatory biomarkers play a better role in predicting clinical endpoints of COVID-19 patients. Brunetta et al. found that plasma pentraxin 3 (PTX3) concentration may be an independent, strong prognostic indicator of short-term mortality in COVID-19, better than conventional markers of inflammation. The authors believe this is because PTX3 reflects both macrophage-driven inflammation and vascular involvement.⁷ Huang et al. suggested that soluble urokinase plasminogen activator receptor (suPAR) may be an early predictor of severe respiratory failure,⁸ and the uPA/uPAR system may be used as a therapeutic target to reduce mortality in COVID-19.⁹ There are increasing reports of such novel inflammatory biomarkers, and their measurement at an early stage has the potential to be used as a complementary or alternative approach to assessing disease progression. However, the results across these studies are not entirely consistent and include limited sample sizes. There is a need to review the available studies with greater statistical power to explore the specific associations between COVID-19 progression and some novel inflammatory biomarkers.

Through searching the available studies, we first identified seven inflammatory biomarkers of interest: endocan, PTX3, soluble CD14 subtype (sCD14-ST), suPAR, soluble receptor for advanced glycation end-products (sRAGE), galectin-3, and monocyte distribution width (MDW). By pooling the relevant results of these studies, we primarily investigated the relationship between these biomarkers and COVID-19, including SARS-CoV-2 infection, disease severity, and poor clinical outcomes.

Methods

Search strategy and selection criteria

This study followed the guidelines proposed by the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). For the 2.5 years from 1 December 2019 up to 18 May 2023, we searched PubMed, EMBASE, and the Cochrane Library using MeSH and keyword search terms including ‘COVID-19’, ‘SARS-CoV-2’, ‘Endocan’, ‘PTX3’, ‘sCD14-ST’, ‘suPAR’, ‘sRAGE’, ‘galectin-3’, and ‘MDW’, and each search term was a combination of the search term ‘COVID-19’ or ‘SARS-CoV-2’ and the names of different biomarkers.

Included studies had to meet the following criteria: (a) The study used a cohort, case-control, or cross-sectional design in human beings. (b) The study provided sufficient information on the distribution of each biomarker for both case and control groups. (c) Blood samples were taken from COVID-19 patients at admission to measure the levels of each biomarker. There were no language restrictions in the search for trials. Two reviewers independently screened the title, abstract, and full text of potentially eligible studies, and any disagreements were resolved by discussion or, if necessary, by third-party adjudication.

Quality assessment

The New Castle Ottawa Scale (NOS), with the three items of selection, comparability, and outcome, was used to assess the quality of the selected studies.¹⁰ Each included study was assigned a score of 0–9 based on its quality, and studies with a NOS score of 6 or more were considered as high quality.

Data extraction

Data extraction was performed by two authors and reviewed by a third party. A standardized extraction form was developed to collect the following information from all the eligible studies: title, first author, publication year, demographic characteristics (the distribution of age and gender), the definition of case and control groups, the sample size of each group, and the distribution of biomarker levels in each group [descriptive statistics were presented as the mean with standard deviation (SD) or median (M) with interquartile range (IQR)].

Definition

First, we compared the biomarker levels between the COVID-19 positive and negative groups. The COVID-19 positive group included all patients who had a positive SARS-CoV-2 RT-PCR test, regardless of their disease severity or hospital admission, and the negative group included healthy controls and comparable patients without COVID-19. Based on the severity of COVID-19 at admission recorded in the included studies, we compared the biomarker levels in the severe and non-severe groups, and the severity of COVID-19 was assessed in most studies using World Health Organization’s (WHO) criteria.¹¹ In addition, given that studies on multiple biomarkers recorded the different clinical endpoints, we considered that the endpoints of these biomarkers should be combined according to the clinical endpoints documented in the included studies. As some clinical endpoints were only available in a limited number of studies, we had to combine them to meet the statistical power requirements.

Statistical analysis

As variations in laboratory determination methods were expected, the standardized mean difference (SMD) was chosen as the effect size measure. We noted that some studies only reported the median and interquartile range (IQR), a calibration formula from Wan et al.¹² was used to estimate the mean and SD from the median and IQR. The I² statistic was used to assess the degree of heterogeneity between studies. When I² exceeded 50%, high heterogeneity was considered, a random effects model for pooled analysis was selected, or a fixed-effects model was applied. We plotted the receiver operating characteristic (ROC) curves of each study and pooled the area under curve (AUC) values by the inverse variance method using the random effects model. The AUC was used to assess the discriminatory ability of the biomarker for the endpoint, and the cutoff of an AUC above 0.7 was considered to have a relatively good discriminatory ability. We applied the quality effects (QE) model to control for heterogeneity between studies as part of the sensitivity analysis. The funnel plot was used to evaluate the qualitative risk of publication bias, while Egger’s regression method was used to assess the quantitative risk of publication bias. Meta-analysis was performed with the statistical package ‘meta’ in R (version 4.0.5), and QE model analysis was performed in the Microsoft Excel add-in MetaXL version 5.3 (Microsoft corporation). All statistical tests were two-tailed tests, with a significance threshold of p = 0.05.

Results

A flow chart describing the screening process is presented in Figure 1. A total of 569 records were identified. After eliminating 260 duplicates, 309 records remained. A total of 187 articles were excluded for the titles and abstracts, 122 articles were further evaluated by full text, and 32 records were excluded for following reasons: (1) no complete data available (n = 20); (2) no full text available (n = 2); (3) without eligible objects (n = 7); and (4) duplicate study cohorts (n = 3). Finally, 90 studies, involving a total sample size of 12,059, were included in our meta-analysis. Of them, 11 studies related to endocan, 14 to PTX3, 15 to sCD14-ST, 21 to suPAR, 10 to sRAGE, 11 to galectin-3, and 13 to MDW. The basic characteristics of the studies are presented in Supplemental Table 1. All of the eligible studies were of high quality with a NOS score ⩾6.

Figure 1.

Flow chart of the study.

Endocan

A total of 11 studies reported circulating levels of endocan at admission in case and control groups as means and SDs, or these data could be estimated. Five studies recorded the endocan value in COVID-19 patients and the controls, higher endocan levels were found in COVID-19 patients at admission compared with the control group [SMD:1.40, 95% CI: 0.21–2.58; Supplemental Figure 1(a)]. Six studies have examined the prognosis of patients with COVID-19, with the endpoints of poor prognosis, including death, acute respiratory failure (ARF) worsening, oxygen requirement, transfer to ICU, acute respiratory distress syndrome (ARDS), and thrombophilia. The pooled results reveal that poor prognosis groups have higher endocan levels at admission than the control group [SMD: 0.64, 95% CI: 0.29–0.99; Supplemental Figure 1(b)]. Two studies recorded the difference in endocan value between the severe group and mild/moderate group, the severe group always showed higher levels of endocan in both studies.

PTX3

Fourteen studies related to COVID-19 reported the PTX3 levels on admission. PTX3 levels were compared in COVID-19 patients and controls in four studies. Pooling the means and SDs in three studies (one study did not record the SD of PTX3 distribution) showed COVID-19 group has slightly higher circulating PTX3 levels compared with the controls group [SMD: 0.32, 95% CI: 0.06–0.59; Supplemental Figure 2(a)]. Death and transfer to ICU were the main clinical endpoints of interest and were reported in seven and four studies related to PTX3, respectively. For both endpoints, PTX3 levels were shown to be significantly higher in the COVID-19 groups than in the control groups [Death SMD: 1.17 95% CI: 0.73–1.60; transfer to ICU SMD: 0.87 95% CI: 0.31–1.42; Supplemental Figure 2(b) and (c)].

SCD14-ST

All 15 studies which recorded the sCD14-ST levels reported several clinical endpoints of patients with COVID-19. Death, as a main adverse clinical endpoint, was reported in eight studies, and a significant association between patients’ sCD14-ST levels and death was found [SMD: 1.49, 95% CI: 1.14–1.83; Supplemental Figure 3(a)]. Meanwhile, six studies documented other adverse clinical endpoints, including oxygen therapy, transfer to ICU, development of Acute kidney injury (AKI), and receiving a tracheostomy. We pooled these clinical endpoints as a composite indicator for a combined analysis, and results showed COVID-19 patients who develop a poor prognosis usually have higher sCD14-ST levels at admission [SMD: 1.09, 95% CI: 0.71–1.48; Supplemental Figure 3(b)].

SuPAR

After screening and selection, we obtained 21 studies related to COVID-19 which documented the on-admission suPAR levels. A total of nine studies, with 541 COVID-19 patients and 771 controls, were pooled to analyze the difference in the distribution of suPAR levels at admission between the COVID-19 and control groups. The suPAR levels in the COVID-19 group were higher than in the control group [SMD: 0.97, 95% CI: 0.52–1.42; Supplemental Figure 4(a)]. There was a significant difference in suPAR levels at admission between the severe COVID-19 and mild/moderate groups, however, the study by Nekrasova contributed greatly to the pooled estimate [SMD: 1.69, 95% CI: 0.49–2.89; Without Nekrasova’ study: SMD: 0.64, 95% CI: −0.08 to 1.37; Supplemental Figure 4(b)]. Based on the number of studies available and the endpoints recorded, we divided adverse clinical endpoints into death and others, and the specific clinical endpoints are shown in Supplemental Figures 4(c) and (d). Similar to other biomarkers of interest, suPAR also showed higher levels in the death and other poor prognosis groups (Death SMD: 1.08 95% CI: 0.61–1.55; Other poor prognosis SMD: 1.00 95% CI: 0.89–1.11).

SRAGE

Ten studies reported the sRAGE levels at admission in several clinical outcomes. We only pooled the results of five studies that compared sRAGE levels in COVID-19 patients and controls, and patients with COVID-19 had higher on-admission sRAGE levels (SMD: 1.33, 95% CI: 0.69–1.98; Supplemental Figure 5). Three studies showed the distribution of sRAGE in the non-survivors and the patients receiving mechanical ventilation, both of whom had significantly higher sRAGE levels than their control groups.

Galectin-3

Eleven studies that reported the galectin-3 levels on admission were included in this review. We found that five studies described the distribution of galectin-3 in the COVID-19 group and controls, and the COVID-19 patients tended to have higher levels of galectin-3 [SMD: 1.20, 95% CI: 0.41–2.00; Supplemental Figure 6(a)]. Five studies documented galectin-3 levels in COVID-19 patients with different disease severity, and no significant difference in galectin-3 levels was found between severe patients and mild/moderate patients [SMD: 0.45, 95% CI: −0.19 to 1.10; Supplemental Figure 6(b)]. All the adverse outcomes of COVID-19, including death, transfer to ICU, ARDS, and cardiac dysfunction, were pooled for the merger analysis of the six studies, and COVID-19 patients with poor prognosis usually have higher galectin-3 levels on admission [SMD: 1.45, 95% CI: 0.90–2.00; Supplemental Figure 6(c)].

MDW

Thirteen eligible studies with recorded MDW values were included in this pooled analysis, comprising a total of 3937 subjects. MDW values in the COVID-19 group were higher than in the control group in all six included studies, with the pooled results showing a statistically significant difference [SMD: 1.43, 95% CI: 1.11–1.74; Supplemental Figure 7(a)]. Three studies reported MDW values in COVID-19 patients with a fatal outcome, all of which were higher than in the survivor group [SMD: 0.83, 95% CI: 0.18–1.48; Supplemental Figure 7(b)]. Respiratory failure, oxygen therapy, and hospital days >14 were recorded as adverse outcomes in three studies, and MDW values were significantly higher in patients with any of these adverse outcomes than in controls [SMD: 0.68, 95% CI: 0.28–1.07; Supplemental Figure 7(c)]. In addition, two studies compared MDW levels in the severe and mild COVID-19 groups, with higher MDW levels in the severe group.

ROC curve analysis

ROC curve could assess the predictive value of biomarker levels at different cutoff values. We pooled the AUC for each study according to the different biomarkers and their results. Endocan and PTX3 were not statistically significant in identifying COVID-19 patients, galectin-3 did not significantly discriminate between the severe and mild/moderate groups of COVID-19, and MDW has no apparent discriminatory power for death in COVID-19 patients. The remaining biomarkers showed relatively good discriminatory ability for several outcomes. Figure 2 shows the details of the pooled AUC for each biomarker.

Figure 2.

Pooled results of area under the curve for different biomarkers. (a) Endocan. (b) PTX3. (c) sCD14-ST. (d) sRAGE. (e) suPAR. (f) Galectin-3. (g) MDW

Sensitivity analysis

We performed a QE model analysis of the included studies on COVID-19 infection rates and mortality. By including study quality as a factor, the QE model recalculates the weight of each study at the time of pooling.¹³ In this model, the levels of endocan and PTX3 were no longer significantly different between COVID-19 patients and the controls, but other results were consistent with the main analysis. Forest plots in the QE model are shown in Supplemental Figures 8–17.

Publication bias

Both Egger’s test (when ⩾5 studies were included) and funnel plots were used to assess the publication bias. General symmetry was revealed in all funnel plots, with all p values calculated by Egger’s regression test above 0.05, indicating no significant publication bias among the included studies (Supplemental Figures 18–24).

Discussion

Based on a pooled analysis of the available evidence, higher levels of six biomarkers, including endocan, PTX3, suPAR, sRAGE, galectin-3, and MDW, were found in the COVID-19 groups compared to control groups. However, in the ROC curve analysis, endocan and PTX3 did not show significant identification of COVID-19 patients. In addition, we explored the differences in levels of the two biomarkers, suPAR, and galectin-3, between the COVID-19 severe groups and mild/moderate groups, respectively. There were no statistically significant differences between the two groups in either biomarker. As an important clinical endpoint of interest, death was recorded and analyzed. Forest plots reveal that dead patients usually have higher levels of PTX3, sCD14-ST, suPAR, and MDW at admission. We compared the PTX3 levels in the ICU and non-ICU admission groups, and ICU admission patients had higher PTX3 levels at admission. Furthermore, we found that COVID-19 patients with higher levels of endocan, galectin-3, sCD14-ST, suPAR, and MDW usually developed poorer comprehensive clinical prognoses. Details are documented in Table 1.

Table 1.

Inflammatory biomarker levels in patients with different outcomes.

Markers	No. studies	No. patients	Heterogeneity		Model	SMD (95%)
Markers	No. studies	No. patients	I²(%)	p	Model	SMD (95%)
COVID-19 positive versus COVID-19 negative
Endocan	5	369	95	<0.01	Random effects model	1.40 (0.21–2.58)
Gal-3	4	714	92	<0.01	Random effects model	1.20 (0.41–2.00)
PTX3	3	466	47	0.15	Fixed effect model	0.32 (0.06–0.59)
sRAGE	5	448	87	<0.01	Random effects model	1.33 (0.69–1.98)
suPAR	9	1312	91	<0.01	Random effects model	0.97 (0.52–1.42)
MDW	6	2712	75	<0.01	Random effects model	1.43 (1.11–1.74)
Severe versus Mild/Moderate
Gal-3	3	295	84	<0.01	Random effects model	0.45 (−0.19–1.10)
suPAR	5	947	95	<0.01	Random effects model	1.69 (0.49–2.89)
Death versus Survivors
PTX3	7	803	87	<0.01	Random effects model	1.17 (0.73–1.60)
sCD14-ST	6	736	63	<0.01	Random effects model	1.49 (1.14–1.83)
suPAR	6	976	68	<0.01	Random effects model	1.08 (0.61–1.55)
MDW	3	287	76	<0.01	Random effects model	0.83 (0.18–1.48)
ICU versus non-ICU
PTX3	4	335	64	<0.01	Random effects model	0.87 (0.31–1.42)
Poor prognosis versus control
Endocan	4	302	7	0.36	Random effects model	0.64 (0.29–0.99)
Gal-3	5	719	89	<0.01	Random effects model	1.45 (0.90–2.00)
sCD14-ST	6	771	82	<0.01	Random effects model	1.09 (0.71–1.48)
suPAR	5	1242	39	0.22	Fixed effect model	1.00 (0.89–1.11)
MDW	3	757	74	<0.01	Random effects model	0.68 (0.28–1.07)

Endocan, also known as endothelial cell-specific molecule 1 (ESM-1), is a novel proteoglycan mainly expressed by pulmonary and renal endothelial cells, with a well-described inhibitory role on leukocyte binding to the vascular endothelium.^14,15 Several previous studies have shown that endocan triggers an intense pulmonary inflammatory response with accumulation of pro-inflammatory mediators,^16–19 which could be a novel marker of endothelial dysfunction. And according to more explicit reports, COVID-19 infection and poor prognosis are also partly attributed in part to the destruction of endothelial tissue.¹⁹ Laloglu and Alay constructed a prediction model with a cutoff value of 444.2 pg/mL serum endocan level to distinguish COVID-19 cases from healthy individuals,²⁰ with a positive predictive value of 82% and a negative predictive value of 91%, and the authors suggested that endocan was a useful clinical marker for diagnosis of COVID-19. Medetalibeyoglu et al. developed the logistic regression models for COVID-19 patients’ composite endpoint (mortality and transfer to ICU), and found that only high endocan levels and increase in age were independent predictors,²¹ supporting that endocan is a predictive biomarker for COVID-19.

Similar to C-reactive protein (CRP), PTX3 has been demonstrated to be a useful serological inflammatory biomarker, reflecting inflammation and injury in tissue.^22–24 Previous studies have shown that increased PTX3 levels correlate with disease severity in several infectious and inflammatory conditions, including cardiovascular disease, rheumatoid arthritis, and COVID-19.^25–29 A large proteomic study found PTX3 to be one of the proteins most strongly associated with 28-day ICU mortality.³⁰ Based on a cohort with 96 COVID-19 patients, Assandri et al. indicated that each 1 ng/mL increase in PTX3 concentration was associated with a 65% increase in the risk of ICU admission.³¹ A validation cohort from Denmark also showed that each doubling of PTX3 concentration in COVID-19 patients on admission was associated with a twofold increase in the odds of 30-day mortality.²⁹ Another recent study highlighted that PTX3 is involved in the activation and amplification of the complement system, contributing to microvascular thrombosis and endothelial damage,³² and that high levels of PTX3 in COVID-19 may reflect a failure of the immune system to inhibit factors that fight uncontrolled inflammation.⁷

The soluble CD14 subtype, also known as presepsin, is an established biomarker for sepsis.^33,34 Because of its association with inflammatory cytokine production, it is also used as a diagnostic and prognostic marker for various types of infection.³⁵ A recent study concluded that the circulating level of sCD14-ST may directly reflect the clinical severity of COVID-19,³⁶ which is consistent with the results of our study. In a cohort of 25 ICU COVID-19 patients, Galliera et al. performed a longitudinal assessment of the difference in sCD14-ST levels between the death and recovery groups, and found that patients in the death group consistently had significantly higher levels of sCD14-ST which increased over time.³⁷ Arakawa et al. measured levels of presepsin and SP-D, KL-6, and SpO2/FiO2 in serum samples from 31 patients with severe and mild COVID-19, and found that changes in presepsin levels were seen earlier in the period of infection and performed better than other lung injury-related biomarkers in predicting performance.³⁸ In addition, sCD14-ST can be measured rapidly by laboratory analyzers, allowing a rapid response to clinical questions,³⁹ and may be an ideal biomarker for predicting COVID-19 outcomes.

Among the novel inflammatory biomarkers of interest in our study, the suPAR has been documented by most studies, indicating that suPAR levels have been measured extensively in COVID-19 patients. In previous studies, suPAR was mainly used as a biomarker of kidney injury and inflammation,⁴⁰ and more recently, as a biological indicator to predict disease severity or poor prognosis in COVID-19 patients.^41–43 Alfano et al. explained the association of suPAR with viral pathogenesis mechanisms, including the involvement of suPAR in S protein cleavage, and fibrinolytic balance.^9,44 Rovina et al. noted that suPAR is an early predictor of the development of severe respiratory failure (SRF)⁴⁵ and that higher plasma suPAR levels predict the risk of kidney disease in patients with COVID-19.⁴⁶ In a recent study, Sarif et al. investigated the use of suPAR to stratify clinical outcomes in severe COVID-19 patients with ARDS.⁴⁷ Chalkias et al. used the WHO Clinical Progression Scale (WHO-CPS) to assess the prognosis of COVID-19 patients and found that each doubling of suPAR increased the score by one point, indicating that suPAR levels at admission were significantly correlated with the clinical prognosis of COVID-19.⁴⁸

The soluble receptor for advanced glycation end-products (sRAGE) is a well-characterized marker of lung alveolar epithelial injury, and its importance in ARDS patients has been proven to correlate with prognostic and pathogenic values.^49,50 The sRAGE can directly or indirectly combine with different types of ligands and stimulate RAGE-associated inflammatory pathways.^51–54 An existing study has identified a role for the RAGE signaling pathway in the pathogenesis of COVID-19.⁵⁵ A recent neural network study found that sRAGE levels on admission were higher in IgG-positive patients than in IgG-negative patients, suggesting that RAGE pathway biomarkers could be used to diagnose COVID-19.⁵⁶ Wick et al. found a weak correlation between sRAGE and biomarkers of systemic inflammation (including IL-6, CRP, etc.) in COVID-19 patients, suggesting that elevated sRAGE is probably not confounded by systemic inflammation, further emphasizing its specificity to the pulmonary compartment.⁵⁷ Notably, soluble RAGE has been implicated in the development of diabetes mellitus (DM)-related morbidities.⁵⁸ Dozio et al. investigated sRAGE levels in COVID-19 patients with and without DM, and they suggest that sRAGE is particularly important as a predictor of clinical prognosis in patients with DM and COVID-19.⁵⁹

Galectin-3 also appears to be a potential biomarker for predicting the prognosis of COVID-19 patients. Galectin-3 is a ß-galactoside-binding lectin that effectively activates cytokine production by various innate immune cells,⁶⁰ and the association of inflammatory cytokine release with severe disease and poor prognosis in COVID-19 is evident.⁶¹ Cervantes-Alvarez et al. found a strong association of galectin-3 levels with several inflammatory and thrombotic inflammatory biomarkers, highlighting the predictive function of galectin-3 in the inflammatory response to COVID-19.⁶² Interestingly, we noted that galectin-3 inhibitor has been proposed as a treatment for COVID-19 in several studies, not only because of its role in suppressing the host inflammatory response, but also because it may prevent SARS-CoV-2 virus attachment to host cells.^63–66 The spike protein of the SARS-CoV2 virus bears a striking morphological resemblance to galectin-3 and exhibits similar sugar-binding capabilities, while the S1 subunit of the spike protein mediates the recognition and binding of the virus to cellular receptors,^67,68 so galectin-3 inhibitors have the potential to interfere with virus–host interactions, thus potentially reducing viral load. However, a randomized controlled trial testing galectin-3 inhibitor intervention found no significant difference in the incidence of adverse events between the experimental and control groups in COVID-19 patients,⁶⁹ and the potential of galectin-3 inhibitors as a therapeutic agent for COVID-19 remains to be validated in clinical trials.

MDW, which has been used as a novel biomarker for the detection of sepsis,⁷⁰ has been reported in several recent studies on COVID-19.^71,72 In our pooled results, MDW showed the superior discriminatory ability in identifying SARS-CoV-2 infection. Shahri et al. identified significant differences in the function and morphology of monocytes between COVID-19 patients and healthy individuals,⁷³ supporting the view that MDW can help to identify COVID-19 patients. Riva et al. found that significant correlations between MDW and common inflammatory markers, and the risk of fatal outcomes increased significantly when MDW value was above 26.4.⁷⁴ A recent study conducted by Ligi et al. identified a role for circulating histones in inducing functional changes in monocytes,⁷⁵ suggesting that MDW may be a useful tool for predicting a higher risk of adverse outcomes in COVID-19. Furthermore, some studies have suggested that MDW can be combined with other inflammatory markers to achieve a higher level of diagnostic performance.^76–78 With the advantages of low cost, ease of measurement and strong discriminatory power, MDW may be a potential predictor for early identification of COVID-19 patients and adverse outcomes.

In summary, we identified two biomarkers that showed good discriminatory ability for COVID-19: galectin-3 (AUC: 0.87, 95% CI: 0.71–1.00) and MDW (AUC: 0.83, 95% CI: 0.72–0.74). Of all the biomarkers investigated, sCD14-ST showed the best discrimination of COVID-19-related deaths (AUC: 0.86, 95% CI: 0.71–1.00), which we recommend for the prediction of mortality in COVID-19 patients. None of the biomarkers showed a clear advantage in discriminating patients with a poor clinical prognosis, which may be due to the fact that we merged for different clinical outcomes.

Our study has several limitations. Importantly, there was significant heterogeneity in the results of most studies, and despite our rigorous partitioning of clinical outcomes in patients, this heterogeneity remains unavoidable. A possible explanation is the variation in the laboratory assays used to measure serum biomarker levels, which may amplify the heterogeneity of the pooled results. In addition, the characteristics of the patients in the studies, including potential comorbidities, may have been a confounding factor in our pooling of results. Due to the limited number of available studies, we did not distinguish the other clinical outcomes of COVID-19 patients other than death and transfer to ICU. These factors may place some limitations on the interpretation of our data. We noted that some of the results in the ROC curve analysis and the main analysis were inconsistent, for example, Figure 2(e) shows that suPAR had a better discriminatory ability in patients with severe disease, but the forest plot showed that suPAR levels did not differ significantly between the severe and mild disease groups. In addition, the results for endocan and PTX3 in COVID-19 patients and controls in the sensitivity analysis were not consistent with those of the main analysis. We believe that the limited original research could reduce the stability of the pooled results, and more reliable studies are needed to further support the results of this study.

In conclusion, we summarized several novel inflammatory biomarkers in patients with COVID-19, the levels of all these biomarkers were elevated in COVID-19 patients, and most of them were associated with more severe disease, poor prognosis, and mortality. In clinical practice, physicians could assess the status of COVID-19 patients based on a comprehensive selection of biochemical indicators and provide timely and targeted care.

Supplemental Material

sj-docx-1-tar-10.1177_17534666231199679 – Supplemental material for Novel inflammatory biomarkers in the prognosis of COVID-19

Supplemental material, sj-docx-1-tar-10.1177_17534666231199679 for Novel inflammatory biomarkers in the prognosis of COVID-19 by Kegang Zhan, Luhan Wang, Hao Lin, Xiaoyu Fang, Hong Jia and Xiangyu Ma in Therapeutic Advances in Respiratory Disease

Footnotes

Acknowledgements

None.

Declarations

ORCID iD

Xiangyu Ma

Supplemental material

Supplemental material for this article is available online.

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