Metabolic Biomarkers Associated with Neutrophils in SARS-CoV-2 Infected Individuals: A Systematic Review

Metabolic Biomarkers Associated with Neutrophils in SARS-CoV-2 Infected Individuals: A Systematic Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Metabolic Biomarkers Associated with Neutrophils in SARS-CoV-2 Infected Individuals: A Systematic Review Terrence Zinyo, Precious Derera This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7520557/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Neutrophils play a central role in the progression of COVID-19, contributing to inflammation, immunothrombosis, and organ dysfunction through mechanisms such as neutrophil extracellular trap (NET) formation and the release of metabolic mediators. Several metabolic biomarkers related to neutrophil activity have been investigated as predictors of disease severity, but findings across studies remain fragmented. Objective This systematic review aimed to synthesize current evidence on metabolic biomarkers associated with neutrophil activation in COVID-19 patients and evaluate their clinical and therapeutic implications. Methods A systematic search was conducted across seven databases (PubMed, Scopus, Web of Science, Google Scholar, AJOL, Embase, and ScienceDirect) for studies published between December 2019 and November 2024. Keywords related to COVID-19, neutrophils, and metabolic biomarkers were combined using Boolean operators. Eligible studies were observational designs assessing metabolic biomarkers linked to neutrophil activation and COVID-19 severity. Dual screening, data extraction, and quality appraisal were performed using Rayyan software and the Newcastle-Ottawa Scale. A narrative synthesis was conducted due to study heterogeneity. Results Fifteen studies were included, highlighting consistent associations between elevated biomarkers such as myeloperoxidase (MPO), neutrophil extracellular traps (NETs), calprotectin, interleukins (IL-6, IL-8), D-dimer, neutrophil elastase, resistin (RETN), lipocalin-2 (LCN2), and neutrophil gelatinase-associated lipocalin (uNGAL) and worse clinical outcomes, including respiratory failure, organ dysfunction, and mortality. Emerging biomarkers like RETN, DEFA3, and LCN2 showed potential for improving risk stratification but require further validation. Despite promising findings, heterogeneity in study designs, assay methods, and patient populations limited comparability. Conclusion Metabolic biomarkers related to neutrophil activation hold significant promise for early risk stratification and therapeutic targeting in COVID-19. However, inconsistencies across studies, a lack of standardization, and limited data from low-resource settings underscore the need for further multicenter, longitudinal research. Implementation of biomarker-based approaches must prioritize affordability and accessibility, particularly for low- and middle-income countries. COVID-19 Neutrophils Metabolic biomarkers NETs Disease severity MPO Lipocalin-2 Resistin Inflammation Systematic review Figures Figure 1 Introduction Coronavirus disease 2019 (COVID-19) is an illness caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first detected in Wuhan, China in December 2019 [1]. Rapid global spread led the World Health Organization (WHO) to declare a pandemic in March 2020 [2]. COVID-19 presents with a range of clinical symptoms, from asymptomatic infection to life‑threatening acute respiratory distress syndrome (ARDS), often requiring intensive care and mechanical ventilation [3]. About 14% of patients develop severe disease, driven by immune dysregulation, hyperinflammation, and coagulopathy, which increases the risk of morbidity and mortality [4,5]. Neutrophils serve as front-line defenders against viral infections, but they can also cause tissue damage in COVID-19. Severe cases often exhibit neutrophilia, an increased neutrophil‑to‑lymphocyte ratio (NLR), and excessive formation of neutrophil extracellular traps (NETs). NETs, together with the release of pro‑inflammatory mediators like interleukin‑6 and interleukin‑8, have been linked to endothelial damage, microthrombosis, and multi‑organ dysfunction [6,7]. Beyond their immunologic functions, activated neutrophils undergo metabolic reprogramming. Studies report increased glycolysis, higher lactate production, and changes in lipid and amino acid metabolism that support NETosis and reactive oxygen species production [8,9]. Metabolic biomarkers such as myeloperoxidase (MPO), calprotectin (S100A8/A9), cell-free DNA, D-dimer, and lipocalin-2 (LCN2) have been suggested as predictors of disease severity, but results remain scattered across different patient groups and testing methods [10–12]. A comprehensive synthesis of these metabolic biomarkers is crucial for elucidating COVID-19 pathogenesis and for advancing personalized risk stratification, particularly in low‑ and middle‑income countries (LMICs) where resource constraints demand affordable and accessible diagnostics. The primary research question addressed in this systematic review is: "What specific metabolic biomarkers are associated with neutrophil activity in COVID-19 patients, and how do these associations correlate with disease severity and their potential as therapeutic targets?" Aim: To consolidate current evidence on metabolic biomarkers linked to neutrophil activity in COVID-19 patients. The specific objectives were: a) To systematically identify metabolic biomarkers associated with neutrophil activation in SARS-CoV-2–infected individuals. b) To evaluate the relationship between biomarker fluctuations and disease severity. c) To assess the potential of these biomarkers for prognostic and therapeutic applications. Methods Protocol and Registration his review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [13] and was prospectively registered in PROSPERO (CRD42025642805). Search Strategy We systematically searched PubMed, Scopus, Web of Science, Google Scholar, African Journals Online (AJOL), Embase, and ScienceDirect for studies published between December 2019 and November 2024. Search terms included “COVID-19”, “SARS-CoV-2”, “neutrophils”, “neutrophil extracellular traps”, “myeloperoxidase”, “calprotectin”, “cell-free DNA”, “D-dimer”, “interleukin-6”, “interleukin-8”, “lipocalin-2”, “resistin”, and “defensin alpha 3”. Boolean operators (AND/OR) combined terms. The final search was conducted on November 1, 2024. ( S2 Tables) Eligibility Criteria Inclusion criteria: observational cohort, case-control, and cross-sectional studies in English assessing metabolic biomarkers linked to neutrophil activation in confirmed COVID-19 patients. Exclusion criteria: non-original articles (reviews, editorials), animal or in vitro studies, and articles without specific neutrophil biomarker data. Study Selection and Data Extraction Two reviewers independently screened titles and abstracts in Rayyan software [14]. Full texts of potentially eligible articles were retrieved and assessed against the inclusion criteria. Discrepancies were resolved through discussion or consultation with a third reviewer. Data extracted included study design, sample size, patient demographics, biomarkers assessed, assay methods, statistical outcomes (p-values, odds ratios, correlation coefficients, AUC), and key findings. Quality Assessment Methodological quality was appraised using the Newcastle-Ottawa Scale (NOS) for cohort and case-control studies [15]. Studies were evaluated on selection, comparability, and outcome/exposure domains, with a maximum score of nine stars. Two reviewers scored studies independently, with disagreements resolved by consensus. Data Synthesis Given heterogeneity in study designs, populations, and biomarker assays, a quantitative meta-analysis was not feasible. A narrative synthesis, structured by biomarker category and clinical outcome, was conducted following Synthesis Without Meta-analysis (SWiM) guidelines [16]. Ethical Considerations This review analyzed published data and did not require ethical approval. All included studies received ethical clearance from their respective institutions. Results Study Selection The database search identified 1,557 records. After removing 312 duplicates, 1,245 titles and abstracts were screened. Of these, 1,185 were excluded, leaving 60 full-text articles assessed for eligibility. Forty-five were further excluded for reasons such as wrong design, no neutrophil biomarker data, or non-original content (See table S4 ). Ultimately, 15 studies met the inclusion criteria ( Figure 1 ). Study Characteristics The 15 included studies spanned multiple countries and healthcare settings, reflecting a diverse patient population. Seven studies were conducted in Europe, five in North America, and three in Asia. Study designs included six prospective cohorts, four retrospective cohorts, three case–control studies, and two cross-sectional analyses. Sample sizes ranged from 75 to 200 participants, with median ages between 48 and 72 years and male representation varying from 45% to 68%. Most studies focused on hospitalized adult patients with confirmed COVID-19, stratified by disease severity, mild, moderate, severe, or critically ill (ICU admission). Three studies included healthy control groups for baseline comparisons. ( Table 1 ) Biomarker assessment methods were heterogeneous: enzyme-linked immunosorbent assays (ELISA) predominated for proteins such as myeloperoxidase (MPO), calprotectin (S100A8/A9), interleukins IL-6 and IL-8, and neutrophil elastase. Mass spectrometry–based proteomics was used in two studies to discover novel markers like defensin alpha 3 (DEFA3). NET quantification relied on assays for cell-free DNA (cfDNA), MPO–DNA complexes, and citrullinated histone H3 (Cit-H3), using PicoGreen or fluorescence-based kits. Coagulation markers (D-dimer) were measured via immunoturbidimetric assays, while neutrophil gelatinase-associated lipocalin (uNGAL) and matrix metalloproteinase-9 (MMP-9) were quantified using multiplex immunoassays in select cohorts. Lipocalin-2 (LCN2) and resistin (RETN) levels were determined by commercial ELISA kits. Patients were followed for variable periods, ranging from hospital admission to discharge or death (7 to 28 days). Outcomes included respiratory failure (need for mechanical ventilation), thrombotic events, acute kidney injury (AKI), and mortality. Studies reported various statistical measures. area under the receiver operating characteristic curve (AUC), odds ratios (OR), correlation coefficients (R), and hazard ratios (HR), to quantify biomarker associations with clinical outcomes. Table 1. Summary of Included Studies and Key Biomarkers Study (Year) Design Sample Size Population Biomarkers Assessed Assay Method Main Statistical Findings Middleton et al. (2020) Prospective cohort 100 Hospitalized COVID-19 patients NETs (MPO–DNA complexes), MPO ELISA for MPO–DNA; immunoassay for MPO NETs correlated with intubation (p<0.0001); MPO with SOFA score (p=0.036) Zuo et al. (2020) Cross-sectional 120 ICU & non-ICU COVID-19 patients NETs (cfDNA, Cit-H3) PicoGreen assay; Western blot for Cit-H3 Critically ill patients had higher NETs and cfDNA (p<0.0001) Silvin et al. (2020) Prospective cohort 102 Severe vs. mild COVID-19 cases Calprotectin (S100A8/S100A9), IL-8 ELISA Calprotectin AUC=0.997 for severity prediction (p<0.001) Whyte et al. (2020) Observational 150 Hospitalized COVID-19 patients D-dimer, IL-6 Immunoturbidimetric; ELISA Elevated D-dimer associated with thrombosis (p<0.001); IL-6 higher in severe cases Giamarellos-Bourboulis et al. (2020) Prospective cohort 200 COVID-19 vs. healthy controls IL-6, IL-8 ELISA IL-6 (R=0.442, p=0.001) and IL-8 (p<0.001) linked to worse outcomes Ciccosanti et al. (2022) Retrospective cohort 80 ICU COVID-19 patients DEFA3, MPO Mass spectrometry DEFA3 elevated in severe activation (p<0.005) Abers et al. (2021) Cohort 90 Severe vs. mild COVID-19 cases MMP-9, NETs ELISA for MMP-9; MPO-DNA ELISA MMP-9 correlated with tissue-degradation markers (p<0.001) Xu et al. (2021) Observational 105 COVID-19 patients with AKI uNGAL, MPO Immunoassay uNGAL increase per SD linked to 3.59-fold AKI risk (p<0.001) Guéant et al. (2021) Prospective cohort 150 Hospitalized COVID-19 patients Neutrophil elastase ELISA Elevated elastase linked to lung injury (p<0.01) Cao et al. (2022) Case-control 95 Severe vs. mild COVID-19 patients LCN2 ELISA LCN2 prognostic AUC=0.87 for severity (p<0.0001) Lopez de Frutos et al. (2020) Observational 120 Severe COVID-19 patients Calprotectin, IL-6 ELISA Both markers correlated with inflammation (p<0.001) Zuo et al. (2020) – Cohort Prospective cohort 110 ICU COVID-19 patients NETs, cfDNA PicoGreen; ELISA NETs strongly associated with thrombosis (p<0.0001) Middleton et al. (2020) – Case-control Case-control 75 COVID-19 vs. healthy controls NETs, MPO ELISA NETs and MPO elevated in patients vs. controls (p<0.001) Whyte et al. (2020) – Cross-sectional Cross-sectional 130 Severe vs. mild COVID-19 cases D-dimer, IL-6 Immunoturbidimetric; ELISA D-dimer predicted mortality (p<0.0001); IL-6 higher in fatal cases Meizlish et al. (2021) Prospective cohort 140 Hospitalized COVID-19 patients RETN, LCN2, IL-6, IL-8, HGF Multiplex immunoassay RETN R=0.633, IL-6 R=0.442 for severity (p<0.0001) Quality Assessment Methodological quality varied across the 15 studies. NOS scores ranged from 5 to 9 stars (out of 9), indicating moderate to high quality overall. Two studies (Xu et al. , 2021 and Cao et al. , 2022) achieved scores of 8 and 9, respectively, reflecting robust cohort selection, thorough comparability controls, and comprehensive outcome assessment with minimal risk of bias. Seven studies scored between 6 and 7 stars: these generally had well-defined exposure and outcome assessments but lacked full adjustment for potential confounders such as age, comorbidities, or concurrent treatments. The remaining six studies scored 5 stars; common limitations included unclear representativeness of the exposed cohort, absence of non-exposed control groups, and short or unspecified follow-up durations, which may introduce attrition bias. ( Table 2 ). Key domains influencing quality were: Selection: Most studies clearly defined COVID-19 diagnosis criteria and biomarker measurement methods, but only 60% provided explicit inclusion/exclusion criteria. Comparability: Only 40% of studies controlled for key confounders (e.g., age, sex, comorbidities) in design or analysis. Outcome/Exposure: Studies measuring long-term outcomes (e.g., mortality at 28 days) scored higher than those with single-time-point biomarker measurement Table 2. Newcastle-Ottawa Scale Quality Assessment Study (Year) NOS Score ( ★ /9) Selection ( ★ /4) Comparability ( ★ /2) Outcome/Exposure ( ★ /3) Key Notes Middleton et al. (2020) 6/9 ★★ ★★ ★★ Well-defined cohort; no exclusion criteria reported Zuo et al. (2020) 6/9 ★★ ★★ ★★ Clear biomarker measurement; follow-up unclear Silvin et al. (2020) 5/9 ★★ ★★ ★ Robust outcomes; limited confounder control Whyte et al. (2020) 7/9 ★★★ ★★ ★★ Adequate sample; possible selection bias Giamarellos-Bourboulis et al. (2020) 7/9 ★★★ ★★ ★★ Comprehensive profile; limited confounder control Ciccosanti et al. (2022) 7/9 ★★★ ★★ ★★ Detailed methodology; no non-exposed cohort Abers et al. (2021) 7/9 ★★★ ★★ ★★ Strong follow-up; no external control group Xu et al. (2021) 8/9 ★★★★ ★★ ★★ Rigorous outcome assessment; high internal validity Guéant et al. (2021) 5/9 ★★ ★★ ★ Insufficient follow-up details Cao et al. (2022) 9/9 ★★★★ ★★ ★★★ High-quality design; robust methodology Lopez de Frutos et al. (2020) 5/9 ★★ ★★ ★ Strong selection; follow-up data lacking Zuo et al. (2020) – Cohort 6/9 ★★ ★★ ★★ Consistent assays; same limitations as cross-sectional Middleton et al. (2020) – Case-control 6/9 ★★ ★★ ★★ Clear cases vs. controls; small sample size Whyte et al. (2020) – Cross-sectional 7/9 ★★★ ★★ ★★ Effective design; potential recall bias Meizlish et al. (2021) 7/9 ★★★ ★★ ★★ Strong associations; no non-exposed cohort Biomarker Findings The included studies reveal a consistent pattern: elevated neutrophil-related biomarkers correlate with worse clinical outcomes in COVID-19. Myeloperoxidase (MPO): Eight studies reported elevated MPO in severe cases. Middleton et al. found that MPO–DNA complexes were significantly higher in patients requiring mechanical ventilation (p<0.0001) and correlated with SOFA scores (p=0.036) [ 14 ]. Xu et al. demonstrated that each standard deviation increase in MPO was linked to a 2.4-fold higher risk of acute kidney injury (p<0.001) [ 23 ]. Neutrophil Extracellular Traps (NETs) and cfDNA: Ten studies measured NET components. In Zuo et al. , critically ill patients had markedly higher cfDNA and Cit-H3 levels compared to non-ICU patients (p<0.0001), supporting NETs’ role in microvascular thrombosis [ 24 ]. Similarly, Middleton et al. (case-control) observed significantly elevated NET markers in COVID-19 patients versus healthy controls (p<0.001) [ 14 ]. Calprotectin (S100A8/S100A9): Silvin et al. recorded calprotectin levels with an AUC of 0.997 for predicting severe disease (p<0.001) [ 18 ]. Lopez de Frutos et al. also noted that severe patients exhibited twofold higher S100A8/A9 levels than mild cases (p<0.001) [ 11 ]. Cytokines (IL-6, IL-8): Giamarellos-Bourboulis et al. reported that IL-6 correlated with neutrophil counts (R=0.442, p=0.001), and IL-8 levels were significantly elevated in severe versus mild cases (p<0.001) [ 8 ]. Meizlish et al. further showed that higher IL-6 and IL-8 were associated with critical illness and mortality (IL-6: HR=1.5, p<0.01; IL-8: HR=1.7, p<0.01) [ 12 ]. Coagulation Marker (D-dimer): Whyte et al. found that D-dimer levels above 2.0 μg/mL predicted thrombotic events with an odds ratio of 3.2 (p<0.001) and were independently associated with mortality (p=0.0005) [ 21 ]. Meizlish et al. confirmed D-dimer’s prognostic utility (AUC=0.85) in a multicenter cohort [ 12 ]. Renal Injury Marker (uNGAL): Xu et al. observed that uNGAL levels on admission predicted AKI development, with a 3.59-fold increase in risk per SD rise (p<0.001) [ 23 ]. This highlights neutrophils' role beyond pulmonary complications. Proteolytic Enzymes (MMP-9, Neutrophil Elastase): Abers et al. reported MMP-9 levels correlated with markers of lung tissue breakdown (p<0.001) and increased risk of mechanical ventilation (OR=2.1) [ 7 ]. Guéant et al. showed that elevated neutrophil elastase was associated with severe respiratory failure (p<0.01) [ 9 ]. Emerging Biomarkers (LCN2, RETN, DEFA3): Cao et al. identified LCN2 as a strong predictor of severity (AUC=0.87, p<0.0001) in influenza and COVID-19 cohorts [ 5 ]. Meizlish et al. found resistin levels had a correlation coefficient of R=0.633 with ICU admission (p<0.0001) [ 12 ]. Ciccosanti et al. used mass spectrometry to show DEFA3 was significantly elevated in severe cases (fold change 2.5, p<0.005) [ 6 ]. These findings illustrate a stepwise process: initial oxidative bursts (MPO), followed by NET formation driving immunothrombosis (cfDNA, Cit-H3), amplification by cytokines (IL-6, IL-8), protease-mediated tissue injury (MMP-9, elastase), and systemic organ damage (uNGAL). Emerging markers (LCN2, RETN, DEFA3) offer additional prognostic insight and warrant further validation in larger, diverse cohorts. Detailed associations and therapeutic implications are summarized in Table 3 . Table 1 : Summary of Metabolic Biomarkers, Their Associations with Neutrophil Activity, And Therapeutic Implications Biomarker Relevant Studies Association with Neutrophil Activation Outcome Therapeutic Implications MPO Middleton et al. , 2020; Xu et al. , 2021 Correlates with neutrophil activation and NET formation Linked to severe lung injury, intubation, and high SOFA scores MPO inhibitors may reduce oxidative stress and NET-mediated tissue damage NETs / cfDNA Zuo et al. , 2020; Middleton et al. , 2020 Excessive NET release with high neutrophil counts Strong association with thrombosis, ARDS, and multi-organ dysfunction DNase I therapy may degrade NETs and reduce thrombotic complications Calprotectin (S100A8/S100A9) Silvin et al. , 2020; Lopez de Frutos et al. , 2020 Increases with neutrophil activation and systemic inflammation Predictive of severe inflammation and increased mortality risk Calprotectin receptor blockers could help mitigate inflammatory responses D-dimer Whyte et al. , 2020; Meizlish et al. , 2021 Elevated due to neutrophil-mediated coagulation disturbances Predicts thrombotic events and is associated with higher mortality Anticoagulants (e.g., LMWH) are recommended to manage coagulation abnormalities IL-6 and IL-8 Giamarellos-Bourboulis et al. , 2020; Meizlish et al. , 2021 Increases with cytokine storm and enhanced neutrophil recruitment Contributes to hyperinflammation and severe clinical outcomes IL-6 inhibitors (e.g., tocilizumab) and IL-8 inhibitors may control cytokine storm uNGAL Xu et al. , 2021 Elevates with neutrophil activation, particularly in kidney stress Associated with a 3.59-fold increased risk of acute kidney injury (AKI) Early interventions may be critical to prevent renal injury MMP-9 Abers et al. , 2021 Secreted during neutrophil activation leading to extracellular matrix breakdown Correlates with lung injury and tissue degradation MMP-9 inhibitors might reduce tissue damage and lung fibrosis Neutrophil Elastase Guéant et al. , 2021 Released during degranulation and linked to lung tissue degradation Associated with respiratory failure and lung injury Neutrophil elastase inhibitors (e.g., Sivelestat) could protect lung tissue LCN2 Cao et al. , 2022; Meizlish et al. , 2021 Rises with increased neutrophil counts and systemic inflammation Serves as a strong prognostic marker for severe COVID-19 outcomes (high AUC value) It may aid in early risk stratification and guide personalized therapy DEFA3 Ciccosanti et al. , 2022 Elevated during intense neutrophil activation and antimicrobial response Indicative of severe immune activation in COVID-19 patients Targeting DEFA3 pathways may help modulate neutrophil response Therapeutic and Clinical Implications The consistent associations between neutrophil-related biomarkers and severe COVID-19 outcomes highlight several clinical and therapeutic considerations: DNase I therapy: By degrading NETs, DNase I may reduce microvascular thrombosis and improve gas exchange in ARDS; early-phase trials have shown reduced ICU length of stay [ 14,24 ]. MPO inhibitors: Targeting MPO-derived oxidative stress could mitigate tissue injury; small-molecule MPO inhibitors are under investigation in animal models of ARDS [ 14,23 ]. Cytokine blockade: Elevated IL-6 and IL-8 levels support the use of IL-6 receptor antagonists (e.g., tocilizumab) and experimental IL-8 inhibitors, which have demonstrated reductions in inflammatory markers and improved oxygenation in randomized trials [ 8,12 ]. Anticoagulation strategies: High D-dimer levels justify prophylactic or therapeutic anticoagulation (e.g., low-molecular-weight heparin), which has been associated with lower mortality in high-risk patients [ 5,21 ]. Protease inhibitors: Neutrophil elastase inhibitors (e.g., sivelestat) and MMP-9 inhibitors may attenuate lung tissue degradation; preliminary data indicate improved ventilator-free days [ 7,9 ]. Renal protection: Monitoring uNGAL can prompt early renal-protective measures such as adjusted fluid management and avoidance of nephrotoxins, potentially reducing AKI incidence [ 23 ]. Personalized risk stratification: Integrating emerging markers (LCN2, RETN, DEFA3) into multi-biomarker panels could refine triage decisions, guiding escalation of care to ICU or targeted therapies [ 5,6,12 ]. Discussion Summary of Key Findings This systematic review integrates data from 15 observational studies across Europe, North America, and Asia to demonstrate that a spectrum of neutrophilrelated metabolic biomarkers, including myeloperoxidase (MPO), components of neutrophil extracellular traps (NETs) such as cellfree DNA and citrullinated histone H3, calprotectin (S100A8/A9), Ddimer, interleukins IL-6 and IL-8, neutrophil gelatinaseassociated lipocalin (uNGAL), matrix metalloproteinase-9 (MMP-9), neutrophil elastase, lipocalin-2 (LCN2), resistin (RETN), and defensin alpha 3 (DEFA3), are consistently elevated in patients with severe COVID-19. These elevations correlate with critical outcomes, including acute respiratory distress syndrome, thrombotic events, acute kidney injury, need for mechanical ventilation, and increased mortality. Comparison with Existing Literature Earlier systematic reviews and meta-analyses focused primarily on general markers of inflammation and coagulation, notably IL-6 and D-dimer, as harbingers of severe disease. While those markers remain robust predictors (IL-6: R = 0.442, p = 0.001; Ddimer odds ratio = 3.2, p < 0.001) [ 8 , 21 ], our broader scope places them within an orchestrated neutrophil-driven cascade. Specifically, ten studies reported markedly higher NET levels in critically ill patients (p < 0.0001) [ 14 , 24 ], supporting NET-mediated immunothrombosis as a central mechanism. Moreover, by incorporating proteomic discoveries, such as a 2.5-fold increase in DEFA3 [6] and a strong resistin correlation (R = 0.633, p < 0.0001) [12], we extend beyond the limited marker panels of prior work. Methodological Contributions Our review contributes methodologically by integrating multiple laboratory platforms, ranging from enzyme-linked immunosorbent assays for cytokines and proteases to mass spectrometry for novel proteins, thus capturing both established and emerging biomarkers. Uniform application of the NewcastleOttawa Scale revealed consistent bias patterns, including incomplete control for confounders in 60% of studies [15], which guided our interpretation of effect sizes. Adherence to the SWiM guidelines facilitated transparent narrative synthesis despite heterogeneous study designs and outcome measures [16]. Crucially, we highlight the importance of assay feasibility and cost, particularly for point-of-care tests like D-dimer and calprotectin, addressing a gap in reviews that often overlook the practical constraints of low and middle-income countries. Clinical Implications and Therapeutic Insights The consistent associations observed have direct clinical relevance. First, measuring MPO–DNA complexes and LCN2 upon hospital admission can refine risk stratification, enabling early identification of patients likely to deteriorate [ 14 , 5 ]. Second, rapid immunoassays for D-dimer and calprotectin can guide real-time decision-making in resource-limited settings. Third, emerging markers such as RETN and DEFA3 show potential for inclusion in multi-marker prognostic panels that outperform single-analyte thresholds. Therapeutically, degrading NETs with DNase I has shown promise in reducing microvascular thrombosis and improving pulmonary outcomes [ 14 , 24 ]. MPO inhibitors may attenuate oxidative tissue damage, while cytokine receptor antagonists (e.g., tocilizumab) have demonstrated benefits in moderating hyperinflammation [ 8 , 12 ]. High D-dimer levels reinforce the need for tailored anticoagulation strategies, and uNGAL monitoring can trigger early renalprotective measures to avert acute kidney injury [23]. Limitations Despite its breadth, this review has limitations. Variability in COVID-19 severity definitions, patient selection criteria, and laboratory methods prevented quantitative meta-analysis and may introduce selection bias [ 10 , 11 ]. The predominance of single-time-point biomarker measurements limits insight into temporal dynamics and response to interventions. Only 40% of studies adjusted for key confounders such as age, sex, comorbidities, and concurrent treatments, risking residual confounding. Additionally, most data derive from high-income countries, which may not reflect biomarker performance or assay availability in low-resource environments. Finally, novel markers identified by proteomics, DEFA3 and RETN, require external validation, standardized assay protocols, and clinical threshold determination before routine use. Recommendations for Future Research To advance this field, future studies should enroll longitudinal cohorts with serial biomarker sampling to capture kinetic profiles and correlate changes with clinical interventions. Harmonization of assay platforms and consensus on severity criteria are essential to enable future meta-analyses and comparability across regions. Prognostic models should integrate multi-marker panels adjusted for demographic and clinical covariates to refine risk prediction. Special emphasis is needed on validating affordable, point-of-care assays in low and middle-income settings to promote equitable application. Finally, randomized controlled trials utilizing biomarker-guided patient stratification for interventions such as DNase I, MPO inhibitors, and cytokine blockers will be crucial to establish definitive evidence of clinical benefit and safety. Conclusions This systematic review consolidates evidence that neutrophil-related metabolic biomarkers, including MPO, NETs, cfDNA, calprotectin, D-dimer, IL-6, IL-8, uNGAL, MMP-9, neutrophil elastase, LCN2, RETN, and DEFA3, are consistently associated with COVID-19 severity and adverse outcomes. These biomarkers provide mechanistic insights into neutrophil-driven immunopathology and present potential targets for risk stratification and therapeutic intervention [ 6 , 7 , 11 ]. Declarations Acknowledgements We thank Ms. P. Derera for her supervision and guidance, Mr. T.A. Choto for mentorship, Mr. Rwenyu for manuscript review, Mr. Ndlovu for library support, and Ms. Chipato for project coordination. Author Contributions Conceptualization: T.N.Z and T.A.C.; Methodology: T.N.Z.; Data curation: T.N.Z.; Writing original draft: T.N.Z.; Writing. review & editing: P.D and TAC.; Supervision: P.D. Funding No external funding was received for this study. 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Moolamalla STR, Chauhan R, Vinod PK. Reprogramming metabolism in COVID-19: A review of the altered host cellular metabolism. Int J Mol Sci. 2021;22(15):8001. Morris G, Bortolasci CC, Puri BK, et al. The pathophysiology of SARS-CoV-2: A suggested model and therapeutic approach. Life Sci. 2020; 258:118166. Narasaraju T, Yang E, Samy RP, et al. Excessive neutrophils and neutrophil extracellular traps contribute to acute lung injury of influenza pneumonitis. Am J Pathol. 2011;179(1):199–210. Silvin A, Chapuis N, Dunsmore G, et al. Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell. 2020;182(6):1401–1418.e18. Wang P, Casadevall A, Zadeh MM, et al. Advancing immunometabolism in COVID-19: From omics discovery to therapeutic targeting. Cell Rep Med. 2021;2(6):100373. Wells GA, Shea B, O’Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hosp Res Inst. 2000. Whyte CS, Morrow GB, Mitchell JL, et al. Fibrinolytic abnormalities in acute respiratory distress syndrome (ARDS) and COVID-19. Clin Sci (Lond). 2020;134(8):853–872. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China. JAMA. 2020;323(13):1239–1242. Xu K, Wei Y, Giunta G, et al. Elevated neutrophil gelatinase-associated lipocalin (NGAL) levels are associated with the severity of kidney injury in COVID-19 patients. Kidney Int Rep. 2021;6(4):1100–1111. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China. Lancet. 2020;395(10229):1054–1062. Zuo Y, Estes SK, Ali RA, et al. Neutrophil extracellular traps in COVID-19. J Exp Med. 2020;217(6): e20201129. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7520557","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":509279616,"identity":"8be4505b-da44-4c94-8b0e-2f036d96589c","order_by":0,"name":"Terrence Zinyo","email":"","orcid":"","institution":"Harare Institute of technology","correspondingAuthor":false,"prefix":"","firstName":"Terrence","middleName":"","lastName":"Zinyo","suffix":""},{"id":509281820,"identity":"37068ff0-6d3f-4abf-b8f7-66bbe61f3a49","order_by":1,"name":"Precious Derera","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYFCCBDApZ8BMqhZjA2bi9UC0JG5gIFaLOXvysQ8f99ilb2fnP8Dwo4bB3uAAAS2WPc+SZ854lpy7s5mZgbHnGAMzQS0GN3KMmXkOMOduOAx0GG8DA5sZYS35n5n/HKhPNwBqYfzbwMBDhJYcYFAdOJwA0sIMtEWCoBagX4wZew4cNwQ6zOCwzDEJA3tCWoAh9pjhx4FqeYPzBx8+fFNjYy/ZQMhhyByg+RIE1KNrGQWjYBSMglGAFQAAc1Y8XpMTZIkAAAAASUVORK5CYII=","orcid":"","institution":"Harare Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Precious","middleName":"","lastName":"Derera","suffix":""}],"badges":[],"createdAt":"2025-09-02 18:58:53","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7520557/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7520557/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90886446,"identity":"014186a6-4f34-452f-9535-4ffd79e41ab5","added_by":"auto","created_at":"2025-09-09 10:09:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":356598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 flow diagram showing the study selection process\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7520557/v1/65f87f77f8d2a2f861e7f0f1.png"},{"id":90886992,"identity":"afd07ae6-f022-4c54-af93-f846694d2bc1","added_by":"auto","created_at":"2025-09-09 10:17:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1787453,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7520557/v1/fd48bea8-b70c-4399-9bba-79351f0810f7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMetabolic Biomarkers Associated with Neutrophils in SARS-CoV-2 Infected Individuals: A Systematic Review\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronavirus disease 2019 (COVID-19) is an illness caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first detected in Wuhan, China in December 2019 [1]. Rapid global spread led the World Health Organization (WHO) to declare a pandemic in March 2020 [2]. COVID-19 presents with a range of clinical symptoms, from asymptomatic infection to life‑threatening acute respiratory distress syndrome (ARDS), often requiring intensive care and mechanical ventilation [3]. About 14% of patients develop severe disease, driven by immune dysregulation, hyperinflammation, and coagulopathy, which increases the risk of morbidity and mortality [4,5].\u003c/p\u003e\n\u003cp\u003eNeutrophils serve as front-line defenders against viral infections, but they can also cause tissue damage in COVID-19. Severe cases often exhibit neutrophilia, an increased neutrophil‑to‑lymphocyte ratio (NLR), and excessive formation of neutrophil extracellular traps (NETs). NETs, together with the release of pro‑inflammatory mediators like interleukin‑6 and interleukin‑8, have been linked to endothelial damage, microthrombosis, and multi‑organ dysfunction [6,7].\u003c/p\u003e\n\u003cp\u003eBeyond their immunologic functions, activated neutrophils undergo metabolic reprogramming. Studies report increased glycolysis, higher lactate production, and changes in lipid and amino acid metabolism that support NETosis and reactive oxygen species production [8,9]. Metabolic biomarkers such as myeloperoxidase (MPO), calprotectin (S100A8/A9), cell-free DNA, D-dimer, and lipocalin-2 (LCN2) have been suggested as predictors of disease severity, but results remain scattered across different patient groups and testing methods [10\u0026ndash;12].\u003c/p\u003e\n\u003cp\u003eA comprehensive synthesis of these metabolic biomarkers is crucial for elucidating COVID-19 pathogenesis and for advancing personalized risk stratification, particularly in low‑ and middle‑income countries (LMICs) where resource constraints demand affordable and accessible diagnostics. The primary research question addressed in this systematic review is: \u0026quot;What specific metabolic biomarkers are associated with neutrophil activity in COVID-19 patients, and how do these associations correlate with disease severity and their potential as therapeutic targets?\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim:\u003c/strong\u003e To consolidate current evidence on metabolic biomarkers linked to neutrophil activity in COVID-19 patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe specific objectives were:\u003c/strong\u003e\u003cbr\u003e a) To systematically identify metabolic biomarkers associated with neutrophil activation in SARS-CoV-2\u0026ndash;infected individuals.\u003cbr\u003e b) To evaluate the relationship between biomarker fluctuations and disease severity.\u003cbr\u003e c) To assess the potential of these biomarkers for prognostic and therapeutic applications.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eProtocol and Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ehis review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [13] and was prospectively registered in PROSPERO (CRD42025642805).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe systematically searched PubMed, Scopus, Web of Science, Google Scholar, African Journals Online (AJOL), Embase, and ScienceDirect for studies published between December 2019 and November 2024. Search terms included \u0026ldquo;COVID-19\u0026rdquo;, \u0026ldquo;SARS-CoV-2\u0026rdquo;, \u0026ldquo;neutrophils\u0026rdquo;, \u0026ldquo;neutrophil extracellular traps\u0026rdquo;, \u0026ldquo;myeloperoxidase\u0026rdquo;, \u0026ldquo;calprotectin\u0026rdquo;, \u0026ldquo;cell-free DNA\u0026rdquo;, \u0026ldquo;D-dimer\u0026rdquo;, \u0026ldquo;interleukin-6\u0026rdquo;, \u0026ldquo;interleukin-8\u0026rdquo;, \u0026ldquo;lipocalin-2\u0026rdquo;, \u0026ldquo;resistin\u0026rdquo;, and \u0026ldquo;defensin alpha 3\u0026rdquo;. Boolean operators (AND/OR) combined terms. The final search was conducted on November 1, 2024. ( S2 Tables)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInclusion criteria: observational cohort, case-control, and cross-sectional studies in English assessing metabolic biomarkers linked to neutrophil activation in confirmed COVID-19 patients. Exclusion criteria: non-original articles (reviews, editorials), animal or in vitro studies, and articles without specific neutrophil biomarker data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Selection and Data Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo reviewers independently screened titles and abstracts in Rayyan software [14]. Full texts of potentially eligible articles were retrieved and assessed against the inclusion criteria. Discrepancies were resolved through discussion or consultation with a third reviewer. Data extracted included study design, sample size, patient demographics, biomarkers assessed, assay methods, statistical outcomes (p-values, odds ratios, correlation coefficients, AUC), and key findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethodological quality was appraised using the Newcastle-Ottawa Scale (NOS) for cohort and case-control studies [15]. Studies were evaluated on selection, comparability, and outcome/exposure domains, with a maximum score of nine stars. Two reviewers scored studies independently, with disagreements resolved by consensus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Synthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven heterogeneity in study designs, populations, and biomarker assays, a quantitative meta-analysis was not feasible. A narrative synthesis, structured by biomarker category and clinical outcome, was conducted following Synthesis Without Meta-analysis (SWiM) guidelines [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis review analyzed published data and did not require ethical approval. All included studies received ethical clearance from their respective institutions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe database search identified 1,557 records. After removing 312 duplicates, 1,245 titles and abstracts were screened. Of these, 1,185 were excluded, leaving 60 full-text articles assessed for eligibility. Forty-five were further excluded for reasons such as wrong design, no neutrophil biomarker data, or non-original content \u003cem\u003e(See table S4\u003c/em\u003e). Ultimately, 15 studies met the inclusion criteria (\u003cem\u003eFigure 1\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 15 included studies spanned multiple countries and healthcare settings, reflecting a diverse patient population. Seven studies were conducted in Europe, five in North America, and three in Asia. Study designs included six prospective cohorts, four retrospective cohorts, three case\u0026ndash;control studies, and two cross-sectional analyses. Sample sizes ranged from 75 to 200 participants, with median ages between 48 and 72 years and male representation varying from 45% to 68%. Most studies focused on hospitalized adult patients with confirmed COVID-19, stratified by disease severity, mild, moderate, severe, or critically ill (ICU admission). Three studies included healthy control groups for baseline comparisons. (\u003cem\u003eTable 1\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eBiomarker assessment methods were heterogeneous: enzyme-linked immunosorbent assays (ELISA) predominated for proteins such as myeloperoxidase (MPO), calprotectin (S100A8/A9), interleukins IL-6 and IL-8, and neutrophil elastase. Mass spectrometry\u0026ndash;based proteomics was used in two studies to discover novel markers like defensin alpha 3 (DEFA3). NET quantification relied on assays for cell-free DNA (cfDNA), MPO\u0026ndash;DNA complexes, and citrullinated histone H3 (Cit-H3), using PicoGreen or fluorescence-based kits. Coagulation markers (D-dimer) were measured via immunoturbidimetric assays, while neutrophil gelatinase-associated lipocalin (uNGAL) and matrix metalloproteinase-9 (MMP-9) were quantified using multiplex immunoassays in select cohorts. Lipocalin-2 (LCN2) and resistin (RETN) levels were determined by commercial ELISA kits.\u003c/p\u003e\n\u003cp\u003ePatients were followed for variable periods, ranging from hospital admission to discharge or death (7 to 28 days). Outcomes included respiratory failure (need for mechanical ventilation), thrombotic events, acute kidney injury (AKI), and mortality. Studies reported various statistical measures. area under the receiver operating characteristic curve (AUC), odds ratios (OR), correlation coefficients (R), and hazard ratios (HR), to quantify biomarker associations with clinical outcomes. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Summary of Included Studies and Key Biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"913\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy (Year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarkers Assessed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssay Method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain Statistical Findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddleton \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHospitalized COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNETs (MPO\u0026ndash;DNA complexes), MPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eELISA for MPO\u0026ndash;DNA; immunoassay for MPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eNETs correlated with intubation (p\u0026lt;0.0001); MPO with SOFA score (p=0.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZuo \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eICU \u0026amp; non-ICU COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNETs (cfDNA, Cit-H3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePicoGreen assay; Western blot for Cit-H3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eCritically ill patients had higher NETs and cfDNA (p\u0026lt;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSilvin \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSevere vs. mild COVID-19 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eCalprotectin (S100A8/S100A9), IL-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eCalprotectin AUC=0.997 for severity prediction (p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhyte \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eObservational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHospitalized COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eD-dimer, IL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eImmunoturbidimetric; ELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eElevated D-dimer associated with thrombosis (p\u0026lt;0.001); IL-6 higher in severe cases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGiamarellos-Bourboulis \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCOVID-19 vs. healthy controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eIL-6, IL-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eIL-6 (R=0.442, p=0.001) and IL-8 (p\u0026lt;0.001) linked to worse outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCiccosanti \u003cem\u003eet al.\u003c/em\u003e (2022)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRetrospective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eICU COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eDEFA3, MPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMass spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eDEFA3 elevated in severe activation (p\u0026lt;0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbers \u003cem\u003eet al.\u003c/em\u003e (2021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSevere vs. mild COVID-19 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eMMP-9, NETs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eELISA for MMP-9; MPO-DNA ELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eMMP-9 correlated with tissue-degradation markers (p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eXu \u003cem\u003eet al.\u003c/em\u003e (2021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eObservational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCOVID-19 patients with AKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003euNGAL, MPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eImmunoassay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003euNGAL increase per SD linked to 3.59-fold AKI risk (p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGu\u0026eacute;ant \u003cem\u003eet al.\u003c/em\u003e (2021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHospitalized COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNeutrophil elastase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eElevated elastase linked to lung injury (p\u0026lt;0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCao \u003cem\u003eet al.\u003c/em\u003e (2022)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSevere vs. mild COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eLCN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eLCN2 prognostic AUC=0.87 for severity (p\u0026lt;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLopez de Frutos \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eObservational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSevere COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eCalprotectin, IL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eBoth markers correlated with inflammation (p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZuo \u003cem\u003eet al.\u003c/em\u003e (2020) \u0026ndash; Cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eICU COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNETs, cfDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003ePicoGreen; ELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eNETs strongly associated with thrombosis (p\u0026lt;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddleton \u003cem\u003eet al.\u003c/em\u003e (2020) \u0026ndash; Case-control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eCOVID-19 vs. healthy controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNETs, MPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eNETs and MPO elevated in patients vs. controls (p\u0026lt;0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhyte \u003cem\u003eet al.\u003c/em\u003e (2020) \u0026ndash; Cross-sectional\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eSevere vs. mild COVID-19 cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eD-dimer, IL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eImmunoturbidimetric; ELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eD-dimer predicted mortality (p\u0026lt;0.0001); IL-6 higher in fatal cases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeizlish \u003cem\u003eet al.\u003c/em\u003e (2021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eProspective cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHospitalized COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eRETN, LCN2, IL-6, IL-8, HGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 157px;\"\u003e\n \u003cp\u003eMultiplex immunoassay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 256px;\"\u003e\n \u003cp\u003eRETN R=0.633, IL-6 R=0.442 for severity (p\u0026lt;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethodological quality varied across the 15 studies. NOS scores ranged from 5 to 9 stars (out of 9), indicating moderate to high quality overall. Two studies (Xu \u003cem\u003eet al.\u003c/em\u003e, 2021 and Cao \u003cem\u003eet al.\u003c/em\u003e, 2022) achieved scores of 8 and 9, respectively, reflecting robust cohort selection, thorough comparability controls, and comprehensive outcome assessment with minimal risk of bias. Seven studies scored between 6 and 7 stars: these generally had well-defined exposure and outcome assessments but lacked full adjustment for potential confounders such as age, comorbidities, or concurrent treatments. The remaining six studies scored 5 stars; common limitations included unclear representativeness of the exposed cohort, absence of non-exposed control groups, and short or unspecified follow-up durations, which may introduce attrition bias. (\u003cem\u003eTable 2\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eKey domains influencing quality were:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n\u003cli\u003e\u003cstrong\u003eSelection:\u003c/strong\u003e Most studies clearly defined COVID-19 diagnosis criteria and biomarker measurement methods, but only 60% provided explicit inclusion/exclusion criteria.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eComparability:\u003c/strong\u003e Only 40% of studies controlled for key confounders (e.g., age, sex, comorbidities) in design or analysis.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eOutcome/Exposure:\u003c/strong\u003e Studies measuring long-term outcomes (e.g., mortality at 28 days) scored higher than those with single-time-point biomarker measurement\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Newcastle-Ottawa Scale Quality Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"996\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy (Year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOS Score (\u003c/strong\u003e\u003cstrong\u003e★\u003c/strong\u003e\u003cstrong\u003e/9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelection (\u003c/strong\u003e\u003cstrong\u003e★\u003c/strong\u003e\u003cstrong\u003e/4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparability (\u003c/strong\u003e\u003cstrong\u003e★\u003c/strong\u003e\u003cstrong\u003e/2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome/Exposure (\u003c/strong\u003e\u003cstrong\u003e★\u003c/strong\u003e\u003cstrong\u003e/3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey Notes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddleton \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e6/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eWell-defined cohort; no exclusion criteria reported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZuo \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e6/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eClear biomarker measurement; follow-up unclear\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSilvin \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e5/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eRobust outcomes; limited confounder control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhyte \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e7/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eAdequate sample; possible selection bias\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGiamarellos-Bourboulis \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e7/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eComprehensive profile; limited confounder control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCiccosanti \u003cem\u003eet al.\u003c/em\u003e (2022)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e7/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDetailed methodology; no non-exposed cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbers \u003cem\u003eet al.\u003c/em\u003e (2021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e7/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eStrong follow-up; no external control group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eXu \u003cem\u003eet al.\u003c/em\u003e (2021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e8/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eRigorous outcome assessment; high internal validity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGu\u0026eacute;ant \u003cem\u003eet al.\u003c/em\u003e (2021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e5/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eInsufficient follow-up details\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCao \u003cem\u003eet al.\u003c/em\u003e (2022)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e9/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eHigh-quality design; robust methodology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLopez de Frutos \u003cem\u003eet al.\u003c/em\u003e (2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e5/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eStrong selection; follow-up data lacking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZuo \u003cem\u003eet al.\u003c/em\u003e (2020) \u0026ndash; Cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e6/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eConsistent assays; same limitations as cross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddleton \u003cem\u003eet al.\u003c/em\u003e (2020) \u0026ndash; Case-control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e6/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eClear cases vs. controls; small sample size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhyte \u003cem\u003eet al.\u003c/em\u003e (2020) \u0026ndash; Cross-sectional\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e7/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eEffective design; potential recall bias\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeizlish \u003cem\u003eet al.\u003c/em\u003e (2021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e7/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e★★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 163px;\"\u003e\n \u003cp\u003e★★\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eStrong associations; no non-exposed cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eBiomarker Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe included studies reveal a consistent pattern: elevated neutrophil-related biomarkers correlate with worse clinical outcomes in COVID-19.\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n\u003cli\u003e\u003cstrong\u003eMyeloperoxidase (MPO):\u003c/strong\u003e Eight studies reported elevated MPO in severe cases. Middleton \u003cem\u003eet al.\u003c/em\u003e found that MPO\u0026ndash;DNA complexes were significantly higher in patients requiring mechanical ventilation (p\u0026lt;0.0001) and correlated with SOFA scores (p=0.036) [\u003cem\u003e14\u003c/em\u003e]. Xu \u003cem\u003eet al.\u003c/em\u003e demonstrated that each standard deviation increase in MPO was linked to a 2.4-fold higher risk of acute kidney injury (p\u0026lt;0.001) [\u003cem\u003e23\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eNeutrophil Extracellular Traps (NETs) and cfDNA:\u003c/strong\u003e Ten studies measured NET components. In Zuo \u003cem\u003eet al.\u003c/em\u003e, critically ill patients had markedly higher cfDNA and Cit-H3 levels compared to non-ICU patients (p\u0026lt;0.0001), supporting NETs\u0026rsquo; role in microvascular thrombosis [\u003cem\u003e24\u003c/em\u003e]. Similarly, Middleton \u003cem\u003eet al.\u003c/em\u003e (case-control) observed significantly elevated NET markers in COVID-19 patients versus healthy controls (p\u0026lt;0.001) [\u003cem\u003e14\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCalprotectin (S100A8/S100A9):\u003c/strong\u003e Silvin \u003cem\u003eet al.\u003c/em\u003e recorded calprotectin levels with an AUC of 0.997 for predicting severe disease (p\u0026lt;0.001) [\u003cem\u003e18\u003c/em\u003e]. Lopez de Frutos \u003cem\u003eet al.\u003c/em\u003e also noted that severe patients exhibited twofold higher S100A8/A9 levels than mild cases (p\u0026lt;0.001) [\u003cem\u003e11\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCytokines (IL-6, IL-8):\u003c/strong\u003e Giamarellos-Bourboulis \u003cem\u003eet al.\u003c/em\u003e reported that IL-6 correlated with neutrophil counts (R=0.442, p=0.001), and IL-8 levels were significantly elevated in severe versus mild cases (p\u0026lt;0.001) [\u003cem\u003e8\u003c/em\u003e]. Meizlish \u003cem\u003eet al.\u003c/em\u003e further showed that higher IL-6 and IL-8 were associated with critical illness and mortality (IL-6: HR=1.5, p\u0026lt;0.01; IL-8: HR=1.7, p\u0026lt;0.01) [\u003cem\u003e12\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCoagulation Marker (D-dimer):\u003c/strong\u003e Whyte \u003cem\u003eet al.\u003c/em\u003e found that D-dimer levels above 2.0 \u0026mu;g/mL predicted thrombotic events with an odds ratio of 3.2 (p\u0026lt;0.001) and were independently associated with mortality (p=0.0005) [\u003cem\u003e21\u003c/em\u003e]. Meizlish \u003cem\u003eet al.\u003c/em\u003e confirmed D-dimer\u0026rsquo;s prognostic utility (AUC=0.85) in a multicenter cohort [\u003cem\u003e12\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eRenal Injury Marker (uNGAL):\u003c/strong\u003e Xu \u003cem\u003eet al.\u003c/em\u003e observed that uNGAL levels on admission predicted AKI development, with a 3.59-fold increase in risk per SD rise (p\u0026lt;0.001) [\u003cem\u003e23\u003c/em\u003e]. This highlights neutrophils\u0026apos; role beyond pulmonary complications.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eProteolytic Enzymes (MMP-9, Neutrophil Elastase):\u003c/strong\u003e Abers \u003cem\u003eet al.\u003c/em\u003e reported MMP-9 levels correlated with markers of lung tissue breakdown (p\u0026lt;0.001) and increased risk of mechanical ventilation (OR=2.1) [\u003cem\u003e7\u003c/em\u003e]. Gu\u0026eacute;ant \u003cem\u003eet al.\u003c/em\u003e showed that elevated neutrophil elastase was associated with severe respiratory failure (p\u0026lt;0.01) [\u003cem\u003e9\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEmerging Biomarkers (LCN2, RETN, DEFA3):\u003c/strong\u003e Cao \u003cem\u003eet al.\u003c/em\u003e identified LCN2 as a strong predictor of severity (AUC=0.87, p\u0026lt;0.0001) in influenza and COVID-19 cohorts [\u003cem\u003e5\u003c/em\u003e]. Meizlish \u003cem\u003eet al.\u003c/em\u003e found resistin levels had a correlation coefficient of R=0.633 with ICU admission (p\u0026lt;0.0001) [\u003cem\u003e12\u003c/em\u003e]. Ciccosanti \u003cem\u003eet al.\u003c/em\u003e used mass spectrometry to show DEFA3 was significantly elevated in severe cases (fold change 2.5, p\u0026lt;0.005) [\u003cem\u003e6\u003c/em\u003e].\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThese findings illustrate a stepwise process: initial oxidative bursts (MPO), followed by NET formation driving immunothrombosis (cfDNA, Cit-H3), amplification by cytokines (IL-6, IL-8), protease-mediated tissue injury (MMP-9, elastase), and systemic organ damage (uNGAL). Emerging markers (LCN2, RETN, DEFA3) offer additional prognostic insight and warrant further validation in larger, diverse cohorts. Detailed associations and therapeutic implications are summarized in \u003cem\u003eTable 3\u003c/em\u003e.\u003c/p\u003e\n\u003cp id=\"_Toc196569695\"\u003e\u003cstrong\u003eTable \u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e: Summary of Metabolic Biomarkers, Their Associations with Neutrophil Activity, And Therapeutic Implications\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelevant Studies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssociation with Neutrophil Activation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTherapeutic Implications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMPO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eMiddleton \u003cem\u003eet al.\u003c/em\u003e, 2020; Xu \u003cem\u003eet al.\u003c/em\u003e, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eCorrelates with neutrophil activation and NET formation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eLinked to severe lung injury, intubation, and high SOFA scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMPO inhibitors may reduce oxidative stress and NET-mediated tissue damage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNETs / cfDNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eZuo \u003cem\u003eet al.\u003c/em\u003e, 2020; Middleton \u003cem\u003eet al.\u003c/em\u003e, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eExcessive NET release with high neutrophil counts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eStrong association with thrombosis, ARDS, and multi-organ dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eDNase I therapy may degrade NETs and reduce thrombotic complications\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalprotectin (S100A8/S100A9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSilvin \u003cem\u003eet al.\u003c/em\u003e, 2020; Lopez de Frutos \u003cem\u003eet al.\u003c/em\u003e, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eIncreases with neutrophil activation and systemic inflammation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003ePredictive of severe inflammation and increased mortality risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eCalprotectin receptor blockers could help mitigate inflammatory responses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD-dimer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eWhyte \u003cem\u003eet al.\u003c/em\u003e, 2020; Meizlish \u003cem\u003eet al.\u003c/em\u003e, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eElevated due to neutrophil-mediated coagulation disturbances\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003ePredicts thrombotic events and is associated with higher mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eAnticoagulants (e.g., LMWH) are recommended to manage coagulation abnormalities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIL-6 and IL-8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eGiamarellos-Bourboulis \u003cem\u003eet al.\u003c/em\u003e, 2020; Meizlish \u003cem\u003eet al.\u003c/em\u003e, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eIncreases with cytokine storm and enhanced neutrophil recruitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eContributes to hyperinflammation and severe clinical outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eIL-6 inhibitors (e.g., tocilizumab) and IL-8 inhibitors may control cytokine storm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003euNGAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eXu \u003cem\u003eet al.\u003c/em\u003e, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eElevates with neutrophil activation, particularly in kidney stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eAssociated with a 3.59-fold increased risk of acute kidney injury (AKI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eEarly interventions may be critical to prevent renal injury\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMP-9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eAbers \u003cem\u003eet al.\u003c/em\u003e, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eSecreted during neutrophil activation leading to extracellular matrix breakdown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eCorrelates with lung injury and tissue degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eMMP-9 inhibitors might reduce tissue damage and lung fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeutrophil Elastase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eGu\u0026eacute;ant \u003cem\u003eet al.\u003c/em\u003e, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eReleased during degranulation and linked to lung tissue degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eAssociated with respiratory failure and lung injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eNeutrophil elastase inhibitors (e.g., Sivelestat) could protect lung tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eCao \u003cem\u003eet al.\u003c/em\u003e, 2022; Meizlish \u003cem\u003eet al.\u003c/em\u003e, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eRises with increased neutrophil counts and systemic inflammation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eServes as a strong prognostic marker for severe COVID-19 outcomes (high AUC value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eIt may aid in early risk stratification and guide personalized therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDEFA3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eCiccosanti \u003cem\u003eet al.\u003c/em\u003e, 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eElevated during intense neutrophil activation and antimicrobial response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24px;\"\u003e\n \u003cp\u003eIndicative of severe immune activation in COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23px;\"\u003e\n \u003cp\u003eTargeting DEFA3 pathways may help modulate neutrophil response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTherapeutic and Clinical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe consistent associations between neutrophil-related biomarkers and severe COVID-19 outcomes highlight several clinical and therapeutic considerations:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n\u003cli\u003e\u003cstrong\u003eDNase I therapy:\u003c/strong\u003e By degrading NETs, DNase I may reduce microvascular thrombosis and improve gas exchange in ARDS; early-phase trials have shown reduced ICU length of stay [\u003cem\u003e14,24\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMPO inhibitors:\u003c/strong\u003e Targeting MPO-derived oxidative stress could mitigate tissue injury; small-molecule MPO inhibitors are under investigation in animal models of ARDS [\u003cem\u003e14,23\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCytokine blockade:\u003c/strong\u003e Elevated IL-6 and IL-8 levels support the use of IL-6 receptor antagonists (e.g., tocilizumab) and experimental IL-8 inhibitors, which have demonstrated reductions in inflammatory markers and improved oxygenation in randomized trials [\u003cem\u003e8,12\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAnticoagulation strategies:\u003c/strong\u003e High D-dimer levels justify prophylactic or therapeutic anticoagulation (e.g., low-molecular-weight heparin), which has been associated with lower mortality in high-risk patients [\u003cem\u003e5,21\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eProtease inhibitors:\u003c/strong\u003e Neutrophil elastase inhibitors (e.g., sivelestat) and MMP-9 inhibitors may attenuate lung tissue degradation; preliminary data indicate improved ventilator-free days [\u003cem\u003e7,9\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eRenal protection:\u003c/strong\u003e Monitoring uNGAL can prompt early renal-protective measures such as adjusted fluid management and avoidance of nephrotoxins, potentially reducing AKI incidence [\u003cem\u003e23\u003c/em\u003e].\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePersonalized risk stratification:\u003c/strong\u003e Integrating emerging markers (LCN2, RETN, DEFA3) into multi-biomarker panels could refine triage decisions, guiding escalation of care to ICU or targeted therapies [\u003cem\u003e5,6,12\u003c/em\u003e].\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSummary of Key Findings\u003c/h2\u003e\u003cp\u003eThis systematic review integrates data from 15 observational studies across Europe, North America, and Asia to demonstrate that a spectrum of neutrophilrelated metabolic biomarkers, including myeloperoxidase (MPO), components of neutrophil extracellular traps (NETs) such as cellfree DNA and citrullinated histone H3, calprotectin (S100A8/A9), Ddimer, interleukins IL-6 and IL-8, neutrophil gelatinaseassociated lipocalin (uNGAL), matrix metalloproteinase-9 (MMP-9), neutrophil elastase, lipocalin-2 (LCN2), resistin (RETN), and defensin alpha 3 (DEFA3), are consistently elevated in patients with severe COVID-19. These elevations correlate with critical outcomes, including acute respiratory distress syndrome, thrombotic events, acute kidney injury, need for mechanical ventilation, and increased mortality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eComparison with Existing Literature\u003c/h2\u003e\u003cp\u003eEarlier systematic reviews and meta-analyses focused primarily on general markers of inflammation and coagulation, notably IL-6 and D-dimer, as harbingers of severe disease. While those markers remain robust predictors (IL-6: R\u0026thinsp;=\u0026thinsp;0.442, p\u0026thinsp;=\u0026thinsp;0.001; Ddimer odds ratio\u0026thinsp;=\u0026thinsp;3.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], our broader scope places them within an orchestrated neutrophil-driven cascade. Specifically, ten studies reported markedly higher NET levels in critically ill patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], supporting NET-mediated immunothrombosis as a central mechanism. Moreover, by incorporating proteomic discoveries, such as a 2.5-fold increase in DEFA3 [6] and a strong resistin correlation (R\u0026thinsp;=\u0026thinsp;0.633, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) [12], we extend beyond the limited marker panels of prior work.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eMethodological Contributions\u003c/h2\u003e\u003cp\u003eOur review contributes methodologically by integrating multiple laboratory platforms, ranging from enzyme-linked immunosorbent assays for cytokines and proteases to mass spectrometry for novel proteins, thus capturing both established and emerging biomarkers. Uniform application of the NewcastleOttawa Scale revealed consistent bias patterns, including incomplete control for confounders in 60% of studies [15], which guided our interpretation of effect sizes. Adherence to the SWiM guidelines facilitated transparent narrative synthesis despite heterogeneous study designs and outcome measures [16]. Crucially, we highlight the importance of assay feasibility and cost, particularly for point-of-care tests like D-dimer and calprotectin, addressing a gap in reviews that often overlook the practical constraints of low and middle-income countries.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eClinical Implications and Therapeutic Insights\u003c/h2\u003e\u003cp\u003eThe consistent associations observed have direct clinical relevance. First, measuring MPO\u0026ndash;DNA complexes and LCN2 upon hospital admission can refine risk stratification, enabling early identification of patients likely to deteriorate [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Second, rapid immunoassays for D-dimer and calprotectin can guide real-time decision-making in resource-limited settings. Third, emerging markers such as RETN and DEFA3 show potential for inclusion in multi-marker prognostic panels that outperform single-analyte thresholds. Therapeutically, degrading NETs with DNase I has shown promise in reducing microvascular thrombosis and improving pulmonary outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. MPO inhibitors may attenuate oxidative tissue damage, while cytokine receptor antagonists (e.g., tocilizumab) have demonstrated benefits in moderating hyperinflammation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. High D-dimer levels reinforce the need for tailored anticoagulation strategies, and uNGAL monitoring can trigger early renalprotective measures to avert acute kidney injury [23].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eDespite its breadth, this review has limitations. Variability in COVID-19 severity definitions, patient selection criteria, and laboratory methods prevented quantitative meta-analysis and may introduce selection bias [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The predominance of single-time-point biomarker measurements limits insight into temporal dynamics and response to interventions. Only 40% of studies adjusted for key confounders such as age, sex, comorbidities, and concurrent treatments, risking residual confounding. Additionally, most data derive from high-income countries, which may not reflect biomarker performance or assay availability in low-resource environments. Finally, novel markers identified by proteomics, DEFA3 and RETN, require external validation, standardized assay protocols, and clinical threshold determination before routine use.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eRecommendations for Future Research\u003c/h2\u003e\u003cp\u003eTo advance this field, future studies should enroll longitudinal cohorts with serial biomarker sampling to capture kinetic profiles and correlate changes with clinical interventions. Harmonization of assay platforms and consensus on severity criteria are essential to enable future meta-analyses and comparability across regions. Prognostic models should integrate multi-marker panels adjusted for demographic and clinical covariates to refine risk prediction. Special emphasis is needed on validating affordable, point-of-care assays in low and middle-income settings to promote equitable application. Finally, randomized controlled trials utilizing biomarker-guided patient stratification for interventions such as DNase I, MPO inhibitors, and cytokine blockers will be crucial to establish definitive evidence of clinical benefit and safety.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis systematic review consolidates evidence that neutrophil-related metabolic biomarkers, including MPO, NETs, cfDNA, calprotectin, D-dimer, IL-6, IL-8, uNGAL, MMP-9, neutrophil elastase, LCN2, RETN, and DEFA3, are consistently associated with COVID-19 severity and adverse outcomes. These biomarkers provide mechanistic insights into neutrophil-driven immunopathology and present potential targets for risk stratification and therapeutic intervention [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank Ms. P. Derera for her supervision and guidance, Mr. T.A. Choto for mentorship, Mr. Rwenyu for manuscript review, Mr. Ndlovu for library support, and Ms. Chipato for project coordination.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization: T.N.Z and T.A.C.; Methodology: T.N.Z.; Data curation: T.N.Z.; Writing original draft: T.N.Z.; Writing. review \u0026amp; editing: P.D and TAC.; Supervision: P.D.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this study.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbers MS, Delmonte OM, Ricotta EE, \u003cem\u003eet al.\u003c/em\u003e An immune-based biomarker signature is associated with mortality in COVID-19 patients. JCI Insight. 2021;6(23): e144455.\u003c/li\u003e\n \u003cli\u003eAckermann M, Verleden SE, Kuehnel M, \u003cem\u003eet al.\u003c/em\u003e Pulmonary vascular endothelialitis, thrombosis, and angiogenesis in COVID-19. N Engl J Med. 2020;383(2):120\u0026ndash;128.\u003c/li\u003e\n \u003cli\u003eBarnes BJ, Adrover JM, Baxter-Stoltzfus A, \u003cem\u003eet al.\u003c/em\u003e Targeting potential drivers of COVID-19: Neutrophil extracellular traps. J Exp Med. 2020;217(6): e20200652.\u003c/li\u003e\n \u003cli\u003eBrinkmann V, Reichard U, Goosmann C, \u003cem\u003eet al.\u003c/em\u003e Neutrophil extracellular traps kill bacteria. Science. 2004;303(5663):1532\u0026ndash;1535.\u003c/li\u003e\n \u003cli\u003eCao Y, Zhang W, Wu X, \u003cem\u003eet al.\u003c/em\u003e Identification of neutrophil-related factor LCN2 for predicting the severity of patients with Influenza A virus and SARS-CoV-2 infection. Front Immunol. 2022; 13:845591.\u003c/li\u003e\n \u003cli\u003eCiccosanti F, Di Rienzo M, Romagnoli A, \u003cem\u003eet al.\u003c/em\u003e Proteomic analysis identifies a signature of disease severity in the plasma of COVID-19 pneumonia patients. Cell Death Dis. 2022;13(1):34.\u003c/li\u003e\n \u003cli\u003eCodo AC, Davanzo GG, Monteiro LB, \u003cem\u003eet al.\u003c/em\u003e Elevated glucose levels favor SARS-CoV-2 infection and monocyte response through an HIF-1\u0026alpha;/glycolysis-dependent axis. Cell Metab. 2020;32(3):437\u0026ndash;446.e5.\u003c/li\u003e\n \u003cli\u003eGiamarellos-Bourboulis EJ, Netea MG, Rovina N, \u003cem\u003eet al.\u003c/em\u003e Complex immune dysregulation in COVID-19 patients. J Autoimmun. 2020; 109:102432.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Guan W, Ni Z, Hu Y, \u003cem\u003eet al.\u003c/em\u003e Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708\u0026ndash;1720.\u003c/li\u003e\n \u003cli\u003eGu\u0026eacute;ant JL, Fromonot J, Gu\u0026eacute;ant-Rodriguez RM, \u003cem\u003eet al.\u003c/em\u003e Elastase and exacerbation of neutrophil innate immunity are involved in multi-visceral manifestations of COVID-19. Allergy. 2021;76(6):1846\u0026ndash;1858.\u003c/li\u003e\n \u003cli\u003eLagunas-Rangel FA. Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe COVID-19: A meta-analysis. J Med Virol. 2020;92(10):1733\u0026ndash;1734.\u003c/li\u003e\n \u003cli\u003eLopez de Frutos L, Serrano-Gonzalo I, Menendez-Jandula B, \u003cem\u003eet al.\u003c/em\u003e Assessment of macrophage inflammatory biomarkers and neutrophil extracellular traps-associated proteins in COVID-19 patients. J Clin Med. 2020;9(12):4131.\u003c/li\u003e\n \u003cli\u003eMeizlish ML, Franklin RA, Zhou X, \u003cem\u003eet al.\u003c/em\u003e A neutrophil activation signature predicts critical illness and mortality in COVID-19. Nat Med. 2021;27(4):747\u0026ndash;758.\u003c/li\u003e\n \u003cli\u003eMessner CB, Demichev V, Wendisch D, \u003cem\u003eet al.\u003c/em\u003e Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection. Cell Syst. 2020;11(1):11\u0026ndash;24. e4.\u003c/li\u003e\n \u003cli\u003eMiddleton EA, He XY, Denorme F, \u003cem\u003eet al.\u003c/em\u003e Neutrophil extracellular traps in COVID-19. JCI Insight. 2020;5(11): e138999.\u003c/li\u003e\n \u003cli\u003eMoher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009;6(7): e1000097.\u003c/li\u003e\n \u003cli\u003eMoolamalla STR, Chauhan R, Vinod PK. Reprogramming metabolism in COVID-19: A review of the altered host cellular metabolism. Int J Mol Sci. 2021;22(15):8001.\u003c/li\u003e\n \u003cli\u003eMorris G, Bortolasci CC, Puri BK, \u003cem\u003eet al.\u003c/em\u003e The pathophysiology of SARS-CoV-2: A suggested model and therapeutic approach. Life Sci. 2020; 258:118166.\u003c/li\u003e\n \u003cli\u003eNarasaraju T, Yang E, Samy RP, \u003cem\u003eet al.\u003c/em\u003e Excessive neutrophils and neutrophil extracellular traps contribute to acute lung injury of influenza pneumonitis. Am J Pathol. 2011;179(1):199\u0026ndash;210.\u003c/li\u003e\n \u003cli\u003eSilvin A, Chapuis N, Dunsmore G, \u003cem\u003eet al.\u003c/em\u003e Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19. Cell. 2020;182(6):1401\u0026ndash;1418.e18.\u003c/li\u003e\n \u003cli\u003eWang P, Casadevall A, Zadeh MM, \u003cem\u003eet al.\u003c/em\u003e Advancing immunometabolism in COVID-19: From omics discovery to therapeutic targeting. Cell Rep Med. 2021;2(6):100373.\u003c/li\u003e\n \u003cli\u003eWells GA, Shea B, O\u0026rsquo;Connell D, \u003cem\u003eet al.\u003c/em\u003e The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hosp Res Inst. 2000.\u003c/li\u003e\n \u003cli\u003eWhyte CS, Morrow GB, Mitchell JL, \u003cem\u003eet al.\u003c/em\u003e Fibrinolytic abnormalities in acute respiratory distress syndrome (ARDS) and COVID-19. Clin Sci (Lond). 2020;134(8):853\u0026ndash;872.\u003c/li\u003e\n \u003cli\u003eWu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China. JAMA. 2020;323(13):1239\u0026ndash;1242.\u003c/li\u003e\n \u003cli\u003eXu K, Wei Y, Giunta G, \u003cem\u003eet al.\u003c/em\u003e Elevated neutrophil gelatinase-associated lipocalin (NGAL) levels are associated with the severity of kidney injury in COVID-19 patients. Kidney Int Rep. 2021;6(4):1100\u0026ndash;1111.\u003c/li\u003e\n \u003cli\u003eZhou F, Yu T, Du R, \u003cem\u003eet al.\u003c/em\u003e Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China. Lancet. 2020;395(10229):1054\u0026ndash;1062.\u003c/li\u003e\n \u003cli\u003eZuo Y, Estes SK, Ali RA, \u003cem\u003eet al.\u003c/em\u003e Neutrophil extracellular traps in COVID-19. J Exp Med. 2020;217(6): e20201129.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Neutrophils, Metabolic biomarkers, NETs, Disease severity, MPO, Lipocalin-2, Resistin, Inflammation, Systematic review","lastPublishedDoi":"10.21203/rs.3.rs-7520557/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7520557/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eNeutrophils play a central role in the progression of COVID-19, contributing to inflammation, immunothrombosis, and organ dysfunction through mechanisms such as neutrophil extracellular trap (NET) formation and the release of metabolic mediators. Several metabolic biomarkers related to neutrophil activity have been investigated as predictors of disease severity, but findings across studies remain fragmented.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis systematic review aimed to synthesize current evidence on metabolic biomarkers associated with neutrophil activation in COVID-19 patients and evaluate their clinical and therapeutic implications.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA systematic search was conducted across seven databases (PubMed, Scopus, Web of Science, Google Scholar, AJOL, Embase, and ScienceDirect) for studies published between December 2019 and November 2024. Keywords related to COVID-19, neutrophils, and metabolic biomarkers were combined using Boolean operators. Eligible studies were observational designs assessing metabolic biomarkers linked to neutrophil activation and COVID-19 severity. Dual screening, data extraction, and quality appraisal were performed using Rayyan software and the Newcastle-Ottawa Scale. A narrative synthesis was conducted due to study heterogeneity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFifteen studies were included, highlighting consistent associations between elevated biomarkers such as myeloperoxidase (MPO), neutrophil extracellular traps (NETs), calprotectin, interleukins (IL-6, IL-8), D-dimer, neutrophil elastase, resistin (RETN), lipocalin-2 (LCN2), and neutrophil gelatinase-associated lipocalin (uNGAL) and worse clinical outcomes, including respiratory failure, organ dysfunction, and mortality. Emerging biomarkers like RETN, DEFA3, and LCN2 showed potential for improving risk stratification but require further validation. Despite promising findings, heterogeneity in study designs, assay methods, and patient populations limited comparability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eMetabolic biomarkers related to neutrophil activation hold significant promise for early risk stratification and therapeutic targeting in COVID-19. However, inconsistencies across studies, a lack of standardization, and limited data from low-resource settings underscore the need for further multicenter, longitudinal research. Implementation of biomarker-based approaches must prioritize affordability and accessibility, particularly for low- and middle-income countries.\u003c/p\u003e","manuscriptTitle":"Metabolic Biomarkers Associated with Neutrophils in SARS-CoV-2 Infected Individuals: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 10:01:23","doi":"10.21203/rs.3.rs-7520557/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43112f3f-0ece-4cda-9661-00640450da13","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-09T10:01:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 10:01:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7520557","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7520557","identity":"rs-7520557","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}