Oxidative Stress, Antioxidant Depletion, and DNA Damage in Post-COVID-19 Patients: Evidence of a Disrupted Redox Network and Loss of Age-Dependent Antioxidant Compensation

Oxidative Stress, Antioxidant Depletion, and DNA Damage in Post-COVID-19 Patients: Evidence of a Disrupted Redox Network and Loss of Age-Dependent Antioxidant Compensation | 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 Research Article Oxidative Stress, Antioxidant Depletion, and DNA Damage in Post-COVID-19 Patients: Evidence of a Disrupted Redox Network and Loss of Age-Dependent Antioxidant Compensation Nandini Dikshit, Surya Kant Tripathi, Jyoti Bajpai, Ajay Verma, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9253258/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The relationship between oxidative stress and viral infections is well established, yet data on the redox consequences of COVID-19 beyond the acute phase remain sparse. We conducted a comprehensive assessment of oxidative stress biomarkers, antioxidant defences, and DNA damage in post-COVID patients, and examined whether COVID-19 infection disrupts the normal architecture of the antioxidant network. Methods In this single-centre, cross-sectional, case-control study, 40 symptomatic post-COVID patients and 40 age- and sex-matched healthy controls were recruited from the Department of Respiratory Medicine, King George’s Medical University, Lucknow, India. Blood levels of lipid peroxidation (LPO), total antioxidant activity (TAA), superoxide dismutase (SOD), and glutathione reductase (GR) were measured. DNA damage was quantified using the alkaline comet assay. Data were analysed using Mann-Whitney U tests and Spearman rank correlations. Results Post-COVID patients showed profound antioxidant depletion: TAA was reduced by 60.8% (median 51.7 vs 224.3 mM; p < 0.001; Cohen’s d = 1.57), SOD by 34.0% (p < 0.001; d = 0.58), and GR by 35.0% (p < 0.001; d = 0.65). LPO was elevated but did not reach significance after correction for non-normality (p = 0.254), though it correlated significantly with radiographic severity (ρ = 0.403; p = 0.010) and was markedly elevated in patients with neurological involvement (2486.6 nmole/ml; p = 0.008). DNA damage was significantly increased across all comet parameters (% Tail DNA: +24.0%; p < 0.001; d = 0.82). A novel finding was that the physiological age-dependent increase in TAA observed in controls (ρ = 0.425; p = 0.006) was abolished in patients (ρ = −0.061; p = 0.707). Inter-marker correlation analysis revealed a rewiring of the antioxidant network, with breakdown of the normal LPO–TAA feedback relationship and emergence of SOD–TAA co-depletion. Conclusions Post-COVID patients exhibit severe antioxidant depletion, significant DNA damage, and disruption of the normal redox network architecture. These findings provide a biochemical rationale for antioxidant-targeted therapeutic strategies in post-COVID management. COVID-19 Post-COVID Oxidative stress Antioxidant depletion DNA damage Comet assay Lipid peroxidation Redox network Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected hundreds of millions of people worldwide since its emergence in Wuhan, China, in late 2019 [ 1 ]. While the acute phase of the disease has been extensively studied, a substantial proportion of survivors continue to experience persistent symptoms beyond 12 weeks, a condition termed post-COVID syndrome [ 2 ]. These sequelae encompass a broad clinical spectrum, including fatigue, dyspnoea, cognitive impairment, and multi-organ dysfunction, the pathophysiology of which remains incompletely understood. One of the principal mechanisms by which SARS-CoV-2 causes organ damage is through mitochondrial dysfunction, leading to overproduction of reactive oxygen species (ROS) [ 3 ]. Under physiological conditions, ROS are counterbalanced by a tightly regulated antioxidant defence system comprising enzymatic components—superoxide dismutase (SOD), catalase, and glutathione reductase (GR)—and non-enzymatic molecules including vitamins C and E, albumin, and uric acid, collectively reflected by total antioxidant activity (TAA) [ 4 ]. When this equilibrium is disrupted, oxidative stress ensues, leading to lipid peroxidation of cell membranes, protein oxidation, and ultimately DNA damage [ 5 ]. Although a clear correlation between oxidative stress markers and disease severity has been demonstrated for many viral infections, clinical data for SARS-CoV-2 remain limited [ 6 ]. Moreover, existing studies have predominantly focused on the acute phase, examining individual markers in isolation. No study to date has comprehensively assessed the interrelationship between multiple oxidative stress markers, antioxidant defences, and DNA damage in a single cohort of post-COVID patients, nor has the structural integrity of the antioxidant network itself been examined. We hypothesised that post-COVID patients would exhibit not only elevated oxidative damage and depleted antioxidant defences, but also a disruption of the normal correlational architecture of the redox system. To test this, we measured four oxidative stress biomarkers alongside DNA damage via the alkaline comet assay in 40 post-COVID patients and 40 healthy controls (Fig. 1 ). We further examined whether COVID-19 infection alters the age-dependent regulation of antioxidant capacity and the inter-marker correlational structure of the redox network. 2. Materials and Methods 2.1 Study Design and Participants This cross-sectional, comparative, case-control study was conducted at the Department of Respiratory Medicine, King George’s Medical University (KGMU), Lucknow, India, in collaboration with the Food Toxicology Laboratory, CSIR–Indian Institute of Toxicology Research (CSIR-IITR), Lucknow. The study was approved by the Institutional Ethics Committee (protocol code 107th ECM II B—Thesis/P8, approved 14 July 2021) and conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants. Forty symptomatic post-COVID patients were enrolled who met the following criteria: age ≥ 18 years; confirmed past history of COVID-19 infection (RT-PCR positive); at least two subsequent negative RT-PCR reports; and persistence of symptoms beyond 4 weeks from testing positive that could not be explained by an alternative diagnosis. Forty age- and sex-matched healthy controls with no history of COVID-19 infection or acute febrile illness in the preceding 3 months and no known comorbid conditions were recruited from the same demographic profile. 2.2 Clinical Assessment All patients underwent clinical evaluation including assessment of residual symptoms, resting oxygen saturation, and FiO2 requirement at the time of sample collection. Chest radiographs were scored on a scale of 0–18 according to the method described by Monaco et al. [ 7 ]. Disease severity during the acute phase was classified as mild, moderate, or severe based on WHO criteria. The presence of comorbidities and involvement of organ systems beyond the respiratory tract (CNS, CVS, GIT, renal, musculoskeletal) were recorded. 2.3 Sample Collection and Biochemical Assays Ten millilitres of venous blood was collected in EDTA vials and transported to the Food Toxicology Laboratory at CSIR-IITR within 2 hours of collection. Lipid peroxidation (LPO) was measured using the malondialdehyde (MDA) assay (Sigma-Aldrich kit MAK085) [ 8 ]. Total antioxidant activity (TAA) was assessed using the ABTS method. Superoxide dismutase (SOD) activity was determined by the method of Sun et al. (1998). Glutathione reductase (GR) was measured according to Dringen et al. (1996). 2.4 Comet Assay DNA damage was assessed using the alkaline comet assay (single-cell gel electrophoresis) according to Singh et al. (1988) [ 9 ]. Slides were scored using the Komet 5 image analysis system (Kinetic Imaging, Liverpool, UK) attached to a fluorescence microscope (Leica, Germany) with a 40× objective. Fifty randomly selected cells were scored per sample. Three parameters were recorded: percentage of tail DNA, olive tail moment (OTM, arbitrary units), and tail length (µm). 2.5 Statistical Analysis All data were tested for normality using the Shapiro-Wilk test. As all four biomarkers in the patient group showed significant departure from normality (all p < 0.001), non-parametric methods were used for primary analyses. Case-control comparisons were performed using the Mann-Whitney U test with rank-biserial correlation as the effect size measure. Cohen’s d was calculated for standardised effect size interpretation. Spearman rank correlation coefficients were used for all association analyses. Inter-marker correlations were computed separately for patients and controls to examine network-level changes. A p-value < 0.05 was considered statistically significant. 3. Results 3.1 Demographic and Clinical Profile The patient group comprised 32 males (80%) and 8 females (20%) with a mean age of 48.4 years (range 28–70). The control group comprised 27 males (67.5%) and 13 females (32.5%) with a mean age of 37.6 years (range 26–60). Among patients, 33 (82.5%) had experienced severe COVID, 4 (10%) moderate, and 3 (7.5%) mild disease. Twenty-three patients (57.5%) had comorbidities, with diabetes mellitus being the most common. The most prevalent residual symptoms were fatigue (85%), breathlessness (82.5%), ageusia (75%), and cough (62.5%). Twenty-two patients (55%) had extra-pulmonary system involvement, most commonly cardiovascular (n = 7) and renal (n = 7) followed by CNS (n = 4). 3.2 Oxidative Stress Biomarkers: Case-Control Comparison Descriptive statistics and case-control comparisons for all four biomarkers are presented in Table 1 and Fig. 2 . Given the significant departure from normality in all patient-group markers (Shapiro-Wilk p < 0.001 for all), results are presented as both mean ± SD and median (IQR), with Mann-Whitney U as the primary test. Table 1 Oxidative stress biomarkers in post-COVID patients versus healthy controls. Marker Patients (n = 40) Median [IQR] Controls (n = 40) Median [IQR] U p-value d Effect TAA (mM) 51.7 [44.9–76.5] 224.3 [205.2–264.1] 200 < 0.001 1.57 Large SOD (U/ml) 11.5 [6.7–22.8] 26.2 [19.2–37.1] 435 < 0.001 0.58 Medium GR (U/min/mg) 5.0 [2.8–12.1] 14.7 [9.5–23.6] 414 < 0.001 0.65 Medium LPO (nmole/ml) 696.5 [309.8–1369.1] 616.0 [245.4–1159.2] 919 0.254 0.44 Small Three of four biomarkers showed highly significant differences. TAA demonstrated the largest effect, with a 60.8% reduction in patients relative to controls (Cohen’s d = 1.57, large effect). SOD and GR were reduced by 34.0% and 35.0% respectively, both with medium effect sizes. LPO was elevated by 61.6% in mean values; however, extreme variability in the patient group (coefficient of variation = 120.6%) meant that the median difference was modest and the Mann-Whitney U test did not reach significance (p = 0.254). 3.3 DNA Damage All three comet assay parameters were significantly elevated in post-COVID patients (Table 2 , Fig. 3 ). Percentage tail DNA showed the largest effect (d = 0.82), representing a 24.0% increase in DNA fragmentation. Table 2 DNA damage parameters by alkaline comet assay. Parameter Patients (mean ± SE) Controls (mean ± SE) t p-value d % Tail DNA 12.50 ± 0.49 10.08 ± 0.44 3.675 < 0.001 0.82 OTM (arb. units) 1.26 ± 0.06 1.03 ± 0.05 2.945 0.004 0.66 Tail length (µm) 14.90 ± 0.36 13.18 ± 0.50 2.792 0.007 0.62 3.4 LPO and Clinical Correlates Although LPO did not differ significantly between groups overall, subgroup analyses revealed clinically meaningful associations (Fig. 4 ). LPO increased in a dose-dependent fashion with COVID severity (mild: 579.8; moderate: 707.8; severe: 1262.7 nmole/ml) and correlated significantly with chest radiograph severity score (Spearman ρ = 0.403; p = 0.010), demonstrating a 3.4-fold increase from the lowest to highest score category. LPO was significantly higher in patients with comorbidities (p = 0.032) and showed a striking gradient across organ systems, with patients exhibiting neurological sequelae showing the highest levels (2486.6 nmole/ml; p = 0.008)—over twice the cohort mean (Table 3 ). Table 3 Lipid peroxidation stratified by clinical and radiological variables. Clinical Variable LPO (nmole/ml) p-value Severity: Mild 579.8 — Severity: Moderate 707.8 — Severity: Severe 1262.7 0.032 X-ray score 0–6 518.3 — X-ray score 6–12 1041.6 — X-ray score 12–18 1744.2 0.010 With comorbidities 1156.0 — Without comorbidities 715.6 0.032 CNS involvement 2486.6 0.008 CVS involvement 1230.1 0.002 Renal involvement 1325.5 0.010 Musculoskeletal 1018.3 0.299 3.5 Disruption of Age-Dependent Antioxidant Compensation In healthy controls, TAA increased significantly with age (Spearman ρ = 0.425; p = 0.006), consistent with a physiological compensatory upregulation of antioxidant capacity (Fig. 5 A). This relationship was completely abolished in post-COVID patients (ρ = −0.061; p = 0.707; Fig. 5 B). No other marker showed a significant age-dependent relationship in either group (Table 4 ). Table 4 Spearman correlations between age and oxidative stress markers. Marker Patient ρ Patient p Control ρ Control p TAA -0.061 0.707 0.425 0.006 LPO -0.012 0.941 -0.025 0.878 SOD -0.064 0.695 -0.064 0.693 GR 0.030 0.852 0.025 0.878 3.6 Rewiring of the Antioxidant Network Inter-marker Spearman correlations computed separately for patients and controls revealed two notable shifts (Table 5 , Fig. 6 ). In controls, LPO and TAA showed a significant positive correlation (ρ = 0.344; p = 0.030), consistent with a homeostatic feedback loop. This relationship was absent in patients (ρ = −0.114; p = 0.484). Conversely, a new correlation between TAA and SOD emerged in patients (ρ = 0.444; p = 0.004) that was absent in controls (ρ = 0.157; p = 0.334), suggesting co-depletion of enzymatic and non-enzymatic antioxidant pools. Table 5 Inter-marker Spearman correlations in patients versus controls. Marker Pair Patient ρ p Control ρ p LPO vs TAA -0.114 0.484 0.344 0.030 LPO vs SOD -0.090 0.579 0.094 0.565 LPO vs GR -0.013 0.937 0.047 0.776 TAA vs SOD 0.444 0.004 0.157 0.334 TAA vs GR 0.143 0.378 0.126 0.440 SOD vs GR 0.129 0.427 0.030 0.854 3.7 Sex-Stratified Vulnerability Sex-stratified analysis revealed dimorphic patterns of oxidative damage (Fig. 7 ). Female patients showed a 108.7% increase in LPO compared to female controls, versus 47.4% in males. Conversely, males showed more severe TAA depletion (− 66.9%) compared to females (− 36.6%). SOD reduction was comparable between sexes. 4. Discussion This study provides the most comprehensive assessment to date of the oxidative stress–antioxidant balance in post-COVID patients, combining four biomarkers with DNA damage analysis and, for the first time, examining the structural integrity of the redox network itself. Our principal findings are fourfold: (i) profound and consistent antioxidant depletion across multiple pathways; (ii) significant DNA damage indicating genotoxic consequences; (iii) disruption of the physiological age-dependent antioxidant compensation; and (iv) rewiring of the inter-marker correlational architecture of the redox system. The most robust finding was the severe depletion of total antioxidant activity, which showed a large effect size (d = 1.57) and the highest statistical significance. This is consistent with the findings of Martín-Fernández et al. (2021), who reported lower total antioxidant capacity in acute COVID-19 patients [ 10 ], and extends these observations to the post-acute phase. The concurrent reduction in both enzymatic (SOD, GR) and non-enzymatic (TAA) antioxidant defences suggests exhaustion of the entire antioxidant system rather than dysfunction of a single pathway. An important methodological consideration is the non-significance of lipid peroxidation in the overall case-control comparison when appropriate non-parametric testing is applied. The extreme variability in patient LPO values (CV = 120.6%) renders the mean a poor summary statistic. However, the clinical relevance of LPO is preserved in subgroup analyses: it correlated significantly with radiographic severity (ρ = 0.403; p = 0.010) and was strikingly elevated in patients with neurological sequelae (2486.6 nmole/ml; p = 0.008). The latter finding is particularly noteworthy: patients with CNS involvement had LPO levels 2.15 times the cohort average, suggesting that the brain may be especially vulnerable to post-COVID lipid peroxidative damage. This aligns with evidence that the CNS is rich in polyunsaturated fatty acids and has limited antioxidant reserve [ 11 ]. Our demonstration of significant DNA damage via the comet assay provides direct evidence of genotoxic consequences of post-COVID oxidative stress. The 24% increase in tail DNA percentage (d = 0.82, large effect) indicates substantial DNA strand breakage persisting beyond the acute infection. While comet assays have documented DNA damage in other chronic diseases [ 12 ], ours is among the first to demonstrate this specifically in post-COVID patients. Perhaps the most novel findings relate to the network-level disruption of the antioxidant system. The abolition of the age–TAA relationship is striking: in healthy controls, antioxidant capacity increases with age, likely reflecting a compensatory response to age-related oxidative burden. This mechanism is entirely disrupted by COVID-19, suggesting that the virus overwhelms not merely the quantity but the regulatory architecture of the antioxidant system. The rewiring of inter-marker correlations further supports this: the normal LPO–TAA feedback loop breaks down, replaced by SOD–TAA co-depletion—a transition from compensatory to uncompensated oxidative stress. The sex-dimorphic pattern of oxidative damage deserves attention. The disproportionate LPO elevation in females (+ 108.7% vs + 47.4% in males) contrasts with the more severe TAA depletion in males (− 66.9% vs − 36.6%), suggesting sex-specific patterns of oxidative vulnerability that may inform personalised therapeutic approaches. Our study has several limitations. The sample size of 40 per group limits statistical power for subgroup analyses, particularly the severity subgroups where mild (n = 3) and moderate (n = 4) groups are small. The cross-sectional design precludes assessment of temporal dynamics. The extreme variability in patient LPO warrants caution in interpreting mean values. 5. Conclusions Post-COVID patients exhibit severe, multi-pathway antioxidant depletion, significant DNA damage, and fundamental disruption of the redox network architecture. The loss of age-dependent antioxidant compensation and the shift from compensatory to co-depleting inter-marker dynamics represent novel findings that advance our understanding of the pathophysiology of post-COVID syndrome. These data provide a biochemical rationale for antioxidant-targeted interventions and highlight the need for long-term monitoring of genotoxic consequences in COVID-19 survivors. Declarations Ethics Approval and Consent to Participate: This study was performed after approval from the Institutional Ethics Committee, King George’s Medical University, Lucknow, Uttar Pradesh, India (Ref. code: 107th ECM IIB-Thesis/P8; No. 840/Ethics/2021; dated 14/07/2021). All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the principles outlined in the Declaration of Helsinki. Consent to Participate: Informed consent was obtained from all individual participants included in the study. Participants were fully informed about the purpose of the research, the procedures involved, potential risks and benefits, and their right to withdraw at any time without penalty. Confidentiality and anonymity of participants was strictly maintained throughout the study. No personally identifiable information has been disclosed in this research. Consent for Publication: Not applicable. Clinical Trial Number: Not applicable. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of Interest: The authors declare that they have no competing financial or non-financial interests. Data Availability Statement: The data supporting the findings of this study are available upon request from the corresponding author. The data are not publicly available due to privacy/ethical restrictions. Acknowledgements: Nil. Author Contributions: N.D. wrote the original draft and conducted the investigation. S.K.T. conceptualized the study and supervised the research. J.B. reviewed and edited the manuscript. A.V. provided methodology and resources. K.M.A. supervised the laboratory investigation at CSIR-IITR. C.K. and I.J. performed the laboratory assays and data acquisition. S.K.V., S.K., R.A.S.K., R.G., A.S., D.B., and A.K. contributed to data collection, clinical evaluation, and critical review of the manuscript. All authors reviewed the manuscript and approved the final version. The authors declare that all data were generated in-house, that no paper mill was used and that no AI tool has been used for the generation of text or figures. References Cucinotta D, Vanelli M (2020) WHO declares COVID-19 a pandemic. Acta Bio Medica: Atenei Parmensis 91(1):157 National comprehensive guidelines for the management of Post Covid sequelae Ministry of Health and Family Welfare, Government of India Ntyonga-Pono MP (2020) COVID-19 infection and oxidative stress: an under-explored approach for prevention and treatment? Pan Afr Med J. ;35(Suppl 2) Pham-Huy LA, He H, Pham-Huy C (2008) Free radicals, antioxidants in disease and health. Int J Biomed Sci 4(2):89 Pizzino G, Irrera N, Cucinotta M et al (2017) Oxidative stress: harms and benefits for human health. Oxid Med Cell Longev 2017:8416763 Delgado-Roche L, Mesta F (2020) Oxidative stress as key player in severe acute respiratory syndrome coronavirus (SARS-CoV) infection. 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Mutagenesis 35(1):5–18 Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 05 Apr, 2026 Submission checks completed at journal 05 Apr, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9253258","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620608544,"identity":"359095a4-7e17-4bf9-85c0-a0031381712f","order_by":0,"name":"Nandini Dikshit","email":"","orcid":"","institution":"King George's Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nandini","middleName":"","lastName":"Dikshit","suffix":""},{"id":620608546,"identity":"c6467cbb-4175-4edb-a993-5d9d723683d0","order_by":1,"name":"Surya Kant Tripathi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYDACHgglA8SGD0B8PqK0HIBQxgYgPhspWswkQAIEtcj3HH72+OOOwzy67c3bKr/m2MmwMTA/fHQDjxaDs23mBgfPHOYxO3Os7LbstmSgw9iMjXPwaeEHuudgG1DLjRyz25LbmIFaeNik8WmR72f/BtFy/41ZseS2esJaGM72wGzhMWP8uO0wYS0GZ86USZw9kw70S1qxNOO24zxszAT8It+Tvk2icoe1nNnxwxs//txWbc/P3vzwMV6HgQBjA4RmBicFZkLKkbUw/iBG9SgYBaNgFIw4AACGx0fYdldu3QAAAABJRU5ErkJggg==","orcid":"","institution":"King George's Medical University","correspondingAuthor":true,"prefix":"","firstName":"Surya","middleName":"Kant","lastName":"Tripathi","suffix":""},{"id":620608552,"identity":"aa0e3e97-7bb2-4ff4-8d91-1808415437bb","order_by":2,"name":"Jyoti Bajpai","email":"","orcid":"","institution":"King George's Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jyoti","middleName":"","lastName":"Bajpai","suffix":""},{"id":620608553,"identity":"ccb9118e-9a60-4f53-93f9-5c674e710a1c","order_by":3,"name":"Ajay Verma","email":"","orcid":"","institution":"King George's Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ajay","middleName":"","lastName":"Verma","suffix":""},{"id":620608555,"identity":"58b8b6d5-1450-4ade-b829-45a2698636f8","order_by":4,"name":"Kausar Mahmood Ansari","email":"","orcid":"","institution":"Indian Institute of Toxicology Research","correspondingAuthor":false,"prefix":"","firstName":"Kausar","middleName":"Mahmood","lastName":"Ansari","suffix":""},{"id":620608556,"identity":"bcc30a84-36f1-4496-b80f-3f02e103de0a","order_by":5,"name":"Chamanpreet Kaur","email":"","orcid":"","institution":"Indian Institute of Toxicology Research","correspondingAuthor":false,"prefix":"","firstName":"Chamanpreet","middleName":"","lastName":"Kaur","suffix":""},{"id":620608557,"identity":"b2ed7756-0118-4b8c-81be-b2d0048d2bc9","order_by":6,"name":"Ishrat Jahan","email":"","orcid":"","institution":"Indian Institute of Toxicology Research","correspondingAuthor":false,"prefix":"","firstName":"Ishrat","middleName":"","lastName":"Jahan","suffix":""},{"id":620608558,"identity":"335c4230-d7eb-463b-b3a7-2367b7ca0abd","order_by":7,"name":"Sanjeev Kumar Verma","email":"","orcid":"","institution":"King George's Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sanjeev","middleName":"Kumar","lastName":"Verma","suffix":""},{"id":620608559,"identity":"f36092b7-001e-404f-86a0-4e5bc2e94a8c","order_by":8,"name":"Santosh Kumar","email":"","orcid":"","institution":"King George's Medical University","correspondingAuthor":false,"prefix":"","firstName":"Santosh","middleName":"","lastName":"Kumar","suffix":""},{"id":620608560,"identity":"354e3974-8b61-4d80-ba69-78d00f678a5f","order_by":9,"name":"R.A.S. Kushwaha","email":"","orcid":"","institution":"King George's Medical University","correspondingAuthor":false,"prefix":"","firstName":"R.A.S.","middleName":"","lastName":"Kushwaha","suffix":""},{"id":620608561,"identity":"2e7b40d0-138c-43e7-98c3-e3b3db47e2e1","order_by":10,"name":"Rajiv Garg","email":"","orcid":"","institution":"King George's Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rajiv","middleName":"","lastName":"Garg","suffix":""},{"id":620608564,"identity":"f9352109-b7a7-4fac-b4ac-12218737cb1b","order_by":11,"name":"Anand Srivastava","email":"","orcid":"","institution":"King George's Medical University","correspondingAuthor":false,"prefix":"","firstName":"Anand","middleName":"","lastName":"Srivastava","suffix":""},{"id":620608566,"identity":"9a5b75c7-ee89-46e2-b46f-cdef09e40a9e","order_by":12,"name":"Darshan Bajaj","email":"","orcid":"","institution":"King George's Medical University","correspondingAuthor":false,"prefix":"","firstName":"Darshan","middleName":"","lastName":"Bajaj","suffix":""},{"id":620608568,"identity":"bf7870f7-9ad3-4fba-bbf7-4039f1430877","order_by":13,"name":"Ankit Katiyar","email":"","orcid":"","institution":"King George's Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ankit","middleName":"","lastName":"Katiyar","suffix":""}],"badges":[],"createdAt":"2026-03-28 13:54:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9253258/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9253258/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107483193,"identity":"664b4988-ce09-4f8b-9930-9c2a9ec8f507","added_by":"auto","created_at":"2026-04-22 02:26:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":468446,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStudy flow diagram showing enrollment, clinical assessment, laboratory assays, and statistical analysis pipeline.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig1FlowDiagram.png","url":"https://assets-eu.researchsquare.com/files/rs-9253258/v1/1f107f5cff3c0f280f736770.png"},{"id":107483181,"identity":"50288a7c-29b2-4e35-8596-ecb3750b7048","added_by":"auto","created_at":"2026-04-22 02:26:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230259,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOxidative stress biomarkers in post-COVID patients (red) versus healthy controls (green). Error bars represent SEM. Significance brackets show Mann-Whitney p-values and Cohen’s d effect sizes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig2CaseControl.png","url":"https://assets-eu.researchsquare.com/files/rs-9253258/v1/a1dfce20928f4bfab684d9e5.png"},{"id":107254223,"identity":"32d653b0-ad57-4e67-8369-4cd70e461798","added_by":"auto","created_at":"2026-04-19 12:00:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":155189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDNA damage parameters (comet assay) in post-COVID patients versus controls. All three parameters were significantly elevated.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3DNAdamage.png","url":"https://assets-eu.researchsquare.com/files/rs-9253258/v1/69e48899a9fcc559687cb4b3.png"},{"id":107254214,"identity":"754999f5-4094-408b-9a7d-caca069d86f9","added_by":"auto","created_at":"2026-04-19 12:00:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":213469,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLipid peroxidation stratified by (A) COVID severity, (B) chest X-ray severity score, and (C) organ system involvement. Dashed line in (C) indicates cohort mean.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig4LPOClinical.png","url":"https://assets-eu.researchsquare.com/files/rs-9253258/v1/e846434b0d22ba0722d5df38.png"},{"id":107254217,"identity":"c8abd4d0-f10e-4e29-85bb-d2e4f927690e","added_by":"auto","created_at":"2026-04-19 12:00:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":317476,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDisruption of age-dependent antioxidant compensation. (A) Healthy controls show a significant positive correlation between age and TAA (ρ = 0.425, p = 0.006). (B) This relationship is abolished in post-COVID patients (ρ = −0.061, p = 0.707).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig5AgeTAA.png","url":"https://assets-eu.researchsquare.com/files/rs-9253258/v1/83803822cb2140afb8ba893a.png"},{"id":107254221,"identity":"ef89bae4-3522-4121-a949-58bdfad4dee0","added_by":"auto","created_at":"2026-04-19 12:00:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":221290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRewiring of the antioxidant network. (A) In controls, the LPO–TAA feedback loop is intact (green line). (B) In patients, this loop is broken and replaced by SOD–TAA co-depletion (red line). Line thickness proportional to correlation strength.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig6Network.png","url":"https://assets-eu.researchsquare.com/files/rs-9253258/v1/7b6c297f51f31eb6a3afbd84.png"},{"id":107254227,"identity":"76be0bbc-d4be-4f50-9cdd-a6bd325041a6","added_by":"auto","created_at":"2026-04-19 12:00:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":104983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSex-stratified percentage change in biomarkers (post-COVID vs sex-matched controls). Females show disproportionate LPO elevation; males show more severe TAA depletion.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig7SexStratified.png","url":"https://assets-eu.researchsquare.com/files/rs-9253258/v1/d02d99ee1f6e7814f4af18e4.png"},{"id":107485587,"identity":"4f854ee0-c97b-4056-87b5-c87817751d2a","added_by":"auto","created_at":"2026-04-22 02:35:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1884609,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9253258/v1/396c4710-ba7f-4105-98a8-d8266b544548.pdf"},{"id":107254219,"identity":"2b21986f-7358-48c3-8033-abb5efa142dd","added_by":"auto","created_at":"2026-04-19 12:00:38","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":25770,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9253258/v1/6fe477d5e6b666fc33b766e1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eOxidative Stress, Antioxidant Depletion, and DNA Damage in Post-COVID-19 Patients: Evidence of a Disrupted Redox Network and Loss of Age-Dependent Antioxidant Compensation\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected hundreds of millions of people worldwide since its emergence in Wuhan, China, in late 2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While the acute phase of the disease has been extensively studied, a substantial proportion of survivors continue to experience persistent symptoms beyond 12 weeks, a condition termed post-COVID syndrome [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These sequelae encompass a broad clinical spectrum, including fatigue, dyspnoea, cognitive impairment, and multi-organ dysfunction, the pathophysiology of which remains incompletely understood.\u003c/p\u003e \u003cp\u003eOne of the principal mechanisms by which SARS-CoV-2 causes organ damage is through mitochondrial dysfunction, leading to overproduction of reactive oxygen species (ROS) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Under physiological conditions, ROS are counterbalanced by a tightly regulated antioxidant defence system comprising enzymatic components\u0026mdash;superoxide dismutase (SOD), catalase, and glutathione reductase (GR)\u0026mdash;and non-enzymatic molecules including vitamins C and E, albumin, and uric acid, collectively reflected by total antioxidant activity (TAA) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. When this equilibrium is disrupted, oxidative stress ensues, leading to lipid peroxidation of cell membranes, protein oxidation, and ultimately DNA damage [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough a clear correlation between oxidative stress markers and disease severity has been demonstrated for many viral infections, clinical data for SARS-CoV-2 remain limited [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, existing studies have predominantly focused on the acute phase, examining individual markers in isolation. No study to date has comprehensively assessed the interrelationship between multiple oxidative stress markers, antioxidant defences, and DNA damage in a single cohort of post-COVID patients, nor has the structural integrity of the antioxidant network itself been examined.\u003c/p\u003e \u003cp\u003eWe hypothesised that post-COVID patients would exhibit not only elevated oxidative damage and depleted antioxidant defences, but also a disruption of the normal correlational architecture of the redox system. To test this, we measured four oxidative stress biomarkers alongside DNA damage via the alkaline comet assay in 40 post-COVID patients and 40 healthy controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We further examined whether COVID-19 infection alters the age-dependent regulation of antioxidant capacity and the inter-marker correlational structure of the redox network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Participants\u003c/h2\u003e \u003cp\u003eThis cross-sectional, comparative, case-control study was conducted at the Department of Respiratory Medicine, King George\u0026rsquo;s Medical University (KGMU), Lucknow, India, in collaboration with the Food Toxicology Laboratory, CSIR\u0026ndash;Indian Institute of Toxicology Research (CSIR-IITR), Lucknow. The study was approved by the Institutional Ethics Committee (protocol code 107th ECM II B\u0026mdash;Thesis/P8, approved 14 July 2021) and conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eForty symptomatic post-COVID patients were enrolled who met the following criteria: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; confirmed past history of COVID-19 infection (RT-PCR positive); at least two subsequent negative RT-PCR reports; and persistence of symptoms beyond 4 weeks from testing positive that could not be explained by an alternative diagnosis. Forty age- and sex-matched healthy controls with no history of COVID-19 infection or acute febrile illness in the preceding 3 months and no known comorbid conditions were recruited from the same demographic profile.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical Assessment\u003c/h2\u003e \u003cp\u003eAll patients underwent clinical evaluation including assessment of residual symptoms, resting oxygen saturation, and FiO2 requirement at the time of sample collection. Chest radiographs were scored on a scale of 0\u0026ndash;18 according to the method described by Monaco et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Disease severity during the acute phase was classified as mild, moderate, or severe based on WHO criteria. The presence of comorbidities and involvement of organ systems beyond the respiratory tract (CNS, CVS, GIT, renal, musculoskeletal) were recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sample Collection and Biochemical Assays\u003c/h2\u003e \u003cp\u003eTen millilitres of venous blood was collected in EDTA vials and transported to the Food Toxicology Laboratory at CSIR-IITR within 2 hours of collection. \u003cb\u003eLipid peroxidation (LPO)\u003c/b\u003e was measured using the malondialdehyde (MDA) assay (Sigma-Aldrich kit MAK085) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u003cb\u003eTotal antioxidant activity (TAA)\u003c/b\u003e was assessed using the ABTS method. \u003cb\u003eSuperoxide dismutase (SOD)\u003c/b\u003e activity was determined by the method of Sun et al. (1998). \u003cb\u003eGlutathione reductase (GR)\u003c/b\u003e was measured according to Dringen et al. (1996).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Comet Assay\u003c/h2\u003e \u003cp\u003eDNA damage was assessed using the alkaline comet assay (single-cell gel electrophoresis) according to Singh et al. (1988) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Slides were scored using the Komet 5 image analysis system (Kinetic Imaging, Liverpool, UK) attached to a fluorescence microscope (Leica, Germany) with a 40\u0026times; objective. Fifty randomly selected cells were scored per sample. Three parameters were recorded: percentage of tail DNA, olive tail moment (OTM, arbitrary units), and tail length (\u0026micro;m).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll data were tested for normality using the Shapiro-Wilk test. As all four biomarkers in the patient group showed significant departure from normality (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), non-parametric methods were used for primary analyses. Case-control comparisons were performed using the Mann-Whitney U test with rank-biserial correlation as the effect size measure. Cohen\u0026rsquo;s d was calculated for standardised effect size interpretation. Spearman rank correlation coefficients were used for all association analyses. Inter-marker correlations were computed separately for patients and controls to examine network-level changes. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic and Clinical Profile\u003c/h2\u003e \u003cp\u003eThe patient group comprised 32 males (80%) and 8 females (20%) with a mean age of 48.4 years (range 28\u0026ndash;70). The control group comprised 27 males (67.5%) and 13 females (32.5%) with a mean age of 37.6 years (range 26\u0026ndash;60). Among patients, 33 (82.5%) had experienced severe COVID, 4 (10%) moderate, and 3 (7.5%) mild disease. Twenty-three patients (57.5%) had comorbidities, with diabetes mellitus being the most common. The most prevalent residual symptoms were fatigue (85%), breathlessness (82.5%), ageusia (75%), and cough (62.5%). Twenty-two patients (55%) had extra-pulmonary system involvement, most commonly cardiovascular (n\u0026thinsp;=\u0026thinsp;7) and renal (n\u0026thinsp;=\u0026thinsp;7) followed by CNS (n\u0026thinsp;=\u0026thinsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Oxidative Stress Biomarkers: Case-Control Comparison\u003c/h2\u003e \u003cp\u003eDescriptive statistics and case-control comparisons for all four biomarkers are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Given the significant departure from normality in all patient-group markers (Shapiro-Wilk p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all), results are presented as both mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and median (IQR), with Mann-Whitney U as the primary test.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOxidative stress biomarkers in post-COVID patients versus healthy controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients (n\u0026thinsp;=\u0026thinsp;40) Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;40) Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTAA (mM)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.7 [44.9\u0026ndash;76.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224.3 [205.2\u0026ndash;264.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOD (U/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.5 [6.7\u0026ndash;22.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.2 [19.2\u0026ndash;37.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGR (U/min/mg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.0 [2.8\u0026ndash;12.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.7 [9.5\u0026ndash;23.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLPO (nmole/ml)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e696.5 [309.8\u0026ndash;1369.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e616.0 [245.4\u0026ndash;1159.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThree of four biomarkers showed highly significant differences. TAA demonstrated the largest effect, with a 60.8% reduction in patients relative to controls (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;1.57, large effect). SOD and GR were reduced by 34.0% and 35.0% respectively, both with medium effect sizes. LPO was elevated by 61.6% in mean values; however, extreme variability in the patient group (coefficient of variation\u0026thinsp;=\u0026thinsp;120.6%) meant that the median difference was modest and the Mann-Whitney U test did not reach significance (p\u0026thinsp;=\u0026thinsp;0.254).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 DNA Damage\u003c/h2\u003e \u003cp\u003eAll three comet assay parameters were significantly elevated in post-COVID patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Percentage tail DNA showed the largest effect (d\u0026thinsp;=\u0026thinsp;0.82), representing a 24.0% increase in DNA fragmentation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDNA damage parameters by alkaline comet assay.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e% Tail DNA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOTM (arb. units)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTail length (\u0026micro;m)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 LPO and Clinical Correlates\u003c/h2\u003e \u003cp\u003eAlthough LPO did not differ significantly between groups overall, subgroup analyses revealed clinically meaningful associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). LPO increased in a dose-dependent fashion with COVID severity (mild: 579.8; moderate: 707.8; severe: 1262.7 nmole/ml) and correlated significantly with chest radiograph severity score (Spearman ρ\u0026thinsp;=\u0026thinsp;0.403; p\u0026thinsp;=\u0026thinsp;0.010), demonstrating a 3.4-fold increase from the lowest to highest score category. LPO was significantly higher in patients with comorbidities (p\u0026thinsp;=\u0026thinsp;0.032) and showed a striking gradient across organ systems, with patients exhibiting neurological sequelae showing the highest levels (2486.6 nmole/ml; p\u0026thinsp;=\u0026thinsp;0.008)\u0026mdash;over twice the cohort mean (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLipid peroxidation stratified by clinical and radiological variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPO (nmole/ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity: Mild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e579.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity: Moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e707.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeverity: Severe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1262.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-ray score 0\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e518.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX-ray score 6\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1041.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX-ray score 12\u0026ndash;18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1744.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1156.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWithout comorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e715.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCNS involvement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2486.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVS involvement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1230.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenal involvement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1325.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMusculoskeletal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1018.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Disruption of Age-Dependent Antioxidant Compensation\u003c/h2\u003e \u003cp\u003eIn healthy controls, TAA increased significantly with age (Spearman ρ\u0026thinsp;=\u0026thinsp;0.425; p\u0026thinsp;=\u0026thinsp;0.006), consistent with a physiological compensatory upregulation of antioxidant capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). This relationship was completely abolished in post-COVID patients (ρ = \u0026minus;0.061; p\u0026thinsp;=\u0026thinsp;0.707; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). No other marker showed a significant age-dependent relationship in either group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman correlations between age and oxidative stress markers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatient p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.425\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLPO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Rewiring of the Antioxidant Network\u003c/h2\u003e \u003cp\u003eInter-marker Spearman correlations computed separately for patients and controls revealed two notable shifts (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In controls, LPO and TAA showed a significant positive correlation (ρ\u0026thinsp;=\u0026thinsp;0.344; p\u0026thinsp;=\u0026thinsp;0.030), consistent with a homeostatic feedback loop. This relationship was absent in patients (ρ = \u0026minus;0.114; p\u0026thinsp;=\u0026thinsp;0.484). Conversely, a new correlation between TAA and SOD emerged in patients (ρ\u0026thinsp;=\u0026thinsp;0.444; p\u0026thinsp;=\u0026thinsp;0.004) that was absent in controls (ρ\u0026thinsp;=\u0026thinsp;0.157; p\u0026thinsp;=\u0026thinsp;0.334), suggesting co-depletion of enzymatic and non-enzymatic antioxidant pools.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInter-marker Spearman correlations in patients versus controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker Pair\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLPO vs TAA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.344\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLPO vs SOD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLPO vs GR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTAA vs SOD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.444\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTAA vs GR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOD vs GR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Sex-Stratified Vulnerability\u003c/h2\u003e \u003cp\u003eSex-stratified analysis revealed dimorphic patterns of oxidative damage (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Female patients showed a 108.7% increase in LPO compared to female controls, versus 47.4% in males. Conversely, males showed more severe TAA depletion (\u0026minus;\u0026thinsp;66.9%) compared to females (\u0026minus;\u0026thinsp;36.6%). SOD reduction was comparable between sexes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides the most comprehensive assessment to date of the oxidative stress\u0026ndash;antioxidant balance in post-COVID patients, combining four biomarkers with DNA damage analysis and, for the first time, examining the structural integrity of the redox network itself. Our principal findings are fourfold: (i) profound and consistent antioxidant depletion across multiple pathways; (ii) significant DNA damage indicating genotoxic consequences; (iii) disruption of the physiological age-dependent antioxidant compensation; and (iv) rewiring of the inter-marker correlational architecture of the redox system.\u003c/p\u003e \u003cp\u003eThe most robust finding was the severe depletion of total antioxidant activity, which showed a large effect size (d\u0026thinsp;=\u0026thinsp;1.57) and the highest statistical significance. This is consistent with the findings of Mart\u0026iacute;n-Fern\u0026aacute;ndez et al. (2021), who reported lower total antioxidant capacity in acute COVID-19 patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and extends these observations to the post-acute phase. The concurrent reduction in both enzymatic (SOD, GR) and non-enzymatic (TAA) antioxidant defences suggests exhaustion of the entire antioxidant system rather than dysfunction of a single pathway.\u003c/p\u003e \u003cp\u003eAn important methodological consideration is the non-significance of lipid peroxidation in the overall case-control comparison when appropriate non-parametric testing is applied. The extreme variability in patient LPO values (CV\u0026thinsp;=\u0026thinsp;120.6%) renders the mean a poor summary statistic. However, the clinical relevance of LPO is preserved in subgroup analyses: it correlated significantly with radiographic severity (ρ\u0026thinsp;=\u0026thinsp;0.403; p\u0026thinsp;=\u0026thinsp;0.010) and was strikingly elevated in patients with neurological sequelae (2486.6 nmole/ml; p\u0026thinsp;=\u0026thinsp;0.008). The latter finding is particularly noteworthy: patients with CNS involvement had LPO levels 2.15 times the cohort average, suggesting that the brain may be especially vulnerable to post-COVID lipid peroxidative damage. This aligns with evidence that the CNS is rich in polyunsaturated fatty acids and has limited antioxidant reserve [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur demonstration of significant DNA damage via the comet assay provides direct evidence of genotoxic consequences of post-COVID oxidative stress. The 24% increase in tail DNA percentage (d\u0026thinsp;=\u0026thinsp;0.82, large effect) indicates substantial DNA strand breakage persisting beyond the acute infection. While comet assays have documented DNA damage in other chronic diseases [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], ours is among the first to demonstrate this specifically in post-COVID patients.\u003c/p\u003e \u003cp\u003ePerhaps the most novel findings relate to the network-level disruption of the antioxidant system. The abolition of the age\u0026ndash;TAA relationship is striking: in healthy controls, antioxidant capacity increases with age, likely reflecting a compensatory response to age-related oxidative burden. This mechanism is entirely disrupted by COVID-19, suggesting that the virus overwhelms not merely the quantity but the regulatory architecture of the antioxidant system. The rewiring of inter-marker correlations further supports this: the normal LPO\u0026ndash;TAA feedback loop breaks down, replaced by SOD\u0026ndash;TAA co-depletion\u0026mdash;a transition from compensatory to uncompensated oxidative stress.\u003c/p\u003e \u003cp\u003eThe sex-dimorphic pattern of oxidative damage deserves attention. The disproportionate LPO elevation in females (+\u0026thinsp;108.7% vs\u0026thinsp;+\u0026thinsp;47.4% in males) contrasts with the more severe TAA depletion in males (\u0026minus;\u0026thinsp;66.9% vs\u0026thinsp;\u0026minus;\u0026thinsp;36.6%), suggesting sex-specific patterns of oxidative vulnerability that may inform personalised therapeutic approaches.\u003c/p\u003e \u003cp\u003eOur study has several limitations. The sample size of 40 per group limits statistical power for subgroup analyses, particularly the severity subgroups where mild (n\u0026thinsp;=\u0026thinsp;3) and moderate (n\u0026thinsp;=\u0026thinsp;4) groups are small. The cross-sectional design precludes assessment of temporal dynamics. The extreme variability in patient LPO warrants caution in interpreting mean values.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003ePost-COVID patients exhibit severe, multi-pathway antioxidant depletion, significant DNA damage, and fundamental disruption of the redox network architecture. The loss of age-dependent antioxidant compensation and the shift from compensatory to co-depleting inter-marker dynamics represent novel findings that advance our understanding of the pathophysiology of post-COVID syndrome. These data provide a biochemical rationale for antioxidant-targeted interventions and highlight the need for long-term monitoring of genotoxic consequences in COVID-19 survivors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u0026nbsp;\u003c/strong\u003eThis study was performed after approval from the Institutional Ethics Committee, King George\u0026rsquo;s Medical University, Lucknow, Uttar Pradesh, India (Ref. code: 107th ECM IIB-Thesis/P8; No. 840/Ethics/2021; dated 14/07/2021). All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all individual participants included in the study. Participants were fully informed about the purpose of the research, the procedures involved, potential risks and benefits, and their right to withdraw at any time without penalty. Confidentiality and anonymity of participants was strictly maintained throughout the study. No personally identifiable information has been disclosed in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing financial or non-financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe data supporting the findings of this study are available upon request from the corresponding author. The data are not publicly available due to privacy/ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eN.D. wrote the original draft and conducted the investigation. S.K.T. conceptualized the study and supervised the research. J.B. reviewed and edited the manuscript. A.V. provided methodology and resources. K.M.A. supervised the laboratory investigation at CSIR-IITR. C.K. and I.J. performed the laboratory assays and data acquisition. S.K.V., S.K., R.A.S.K., R.G., A.S., D.B., and A.K. contributed to data collection, clinical evaluation, and critical review of the manuscript. All authors reviewed the manuscript and approved the final version. The authors declare that all data were generated in-house, that no paper mill was used and that no AI tool has been used for the generation of text or figures.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCucinotta D, Vanelli M (2020) WHO declares COVID-19 a pandemic. Acta Bio Medica: Atenei Parmensis 91(1):157\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational comprehensive guidelines for the management of Post Covid sequelae Ministry of Health and Family Welfare, Government of India\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNtyonga-Pono MP (2020) COVID-19 infection and oxidative stress: an under-explored approach for prevention and treatment? Pan Afr Med J. ;35(Suppl 2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePham-Huy LA, He H, Pham-Huy C (2008) Free radicals, antioxidants in disease and health. Int J Biomed Sci 4(2):89\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePizzino G, Irrera N, Cucinotta M et al (2017) Oxidative stress: harms and benefits for human health. Oxid Med Cell Longev 2017:8416763\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelgado-Roche L, Mesta F (2020) Oxidative stress as key player in severe acute respiratory syndrome coronavirus (SARS-CoV) infection. Arch Med Res 51(5):384\u0026ndash;387\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonaco CG, Zaottini F, Schiaffino S et al (2020) Chest x-ray severity score in COVID-19 patients on emergency department admission. Eur Radiol Exp 4(1):68\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyala A, Mu\u0026ntilde;oz MF, Arg\u0026uuml;elles S (2014) Lipid peroxidation: production, metabolism, and signaling mechanisms of malondialdehyde and 4-hydroxy-2-nonenal. Oxid Med Cell Longev 2014:360438\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFairbairn DW, Olive PL, O\u0026rsquo;Neill KL (1995) The comet assay: a comprehensive review. Mutat Res 339(1):37\u0026ndash;59\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;n-Fern\u0026aacute;ndez M, Aller R, Heredia-Rodr\u0026iacute;guez M et al (2021) Lipid peroxidation as a hallmark of severity in COVID-19 patients. Redox Biol 48:102181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŽarković N, Orehovec B, Milković L et al (2021) Preliminary findings on the association of the lipid peroxidation product 4-hydroxynonenal with the lethal outcome of aggressive COVID-19. Antioxidants 10(9):1341\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026oslash;ller P, Stopper H, Collins AR (2020) Measurement of DNA damage with the comet assay in high-prevalence diseases. Mutagenesis 35(1):5\u0026ndash;18\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"COVID-19, Post-COVID, Oxidative stress, Antioxidant depletion, DNA damage, Comet assay, Lipid peroxidation, Redox network","lastPublishedDoi":"10.21203/rs.3.rs-9253258/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9253258/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relationship between oxidative stress and viral infections is well established, yet data on the redox consequences of COVID-19 beyond the acute phase remain sparse. We conducted a comprehensive assessment of oxidative stress biomarkers, antioxidant defences, and DNA damage in post-COVID patients, and examined whether COVID-19 infection disrupts the normal architecture of the antioxidant network.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this single-centre, cross-sectional, case-control study, 40 symptomatic post-COVID patients and 40 age- and sex-matched healthy controls were recruited from the Department of Respiratory Medicine, King George\u0026rsquo;s Medical University, Lucknow, India. Blood levels of lipid peroxidation (LPO), total antioxidant activity (TAA), superoxide dismutase (SOD), and glutathione reductase (GR) were measured. DNA damage was quantified using the alkaline comet assay. Data were analysed using Mann-Whitney U tests and Spearman rank correlations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePost-COVID patients showed profound antioxidant depletion: TAA was reduced by 60.8% (median 51.7 vs 224.3 mM; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;1.57), SOD by 34.0% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; d\u0026thinsp;=\u0026thinsp;0.58), and GR by 35.0% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; d\u0026thinsp;=\u0026thinsp;0.65). LPO was elevated but did not reach significance after correction for non-normality (p\u0026thinsp;=\u0026thinsp;0.254), though it correlated significantly with radiographic severity (ρ\u0026thinsp;=\u0026thinsp;0.403; p\u0026thinsp;=\u0026thinsp;0.010) and was markedly elevated in patients with neurological involvement (2486.6 nmole/ml; p\u0026thinsp;=\u0026thinsp;0.008). DNA damage was significantly increased across all comet parameters (% Tail DNA: +24.0%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; d\u0026thinsp;=\u0026thinsp;0.82). A novel finding was that the physiological age-dependent increase in TAA observed in controls (ρ\u0026thinsp;=\u0026thinsp;0.425; p\u0026thinsp;=\u0026thinsp;0.006) was abolished in patients (ρ = \u0026minus;0.061; p\u0026thinsp;=\u0026thinsp;0.707). Inter-marker correlation analysis revealed a rewiring of the antioxidant network, with breakdown of the normal LPO\u0026ndash;TAA feedback relationship and emergence of SOD\u0026ndash;TAA co-depletion.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePost-COVID patients exhibit severe antioxidant depletion, significant DNA damage, and disruption of the normal redox network architecture. These findings provide a biochemical rationale for antioxidant-targeted therapeutic strategies in post-COVID management.\u003c/p\u003e","manuscriptTitle":"Oxidative Stress, Antioxidant Depletion, and DNA Damage in Post-COVID-19 Patients: Evidence of a Disrupted Redox Network and Loss of Age-Dependent Antioxidant Compensation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:00:33","doi":"10.21203/rs.3.rs-9253258/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T12:32:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T14:14:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16108505516395518139719452772794423930","date":"2026-04-16T12:07:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T06:52:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T03:25:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T03:24:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Naunyn-Schmiedeberg's Archives of Pharmacology","date":"2026-03-28T13:43:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"naunyn-schmiedebergs-archives-of-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nsap","sideBox":"Learn more about [Naunyn-Schmiedeberg's Archives of Pharmacology](https://www.springer.com/journal/210)","snPcode":"210","submissionUrl":"https://submission.nature.com/new-submission/210/3","title":"Naunyn-Schmiedeberg's Archives of Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0360a797-f70e-4c5c-bc85-8e2920ad47a1","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T06:39:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:00:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9253258","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9253258","identity":"rs-9253258","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}