Hepcidin dynamics predict outcomes in COVID-19 patients treated with interleukin inhibitors

Hepcidin dynamics predict outcomes in COVID-19 patients treated with interleukin inhibitors | 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 Hepcidin dynamics predict outcomes in COVID-19 patients treated with interleukin inhibitors Elena Buzzetti, Giuliano Bolondi, Andrea Ricci, Cinzia Garuti, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7792556/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 Introduction : iron metabolism and its key regulatory hormone, hepcidin, play a pivotal role in infection and inflammation. However, the prognostic value of iron related markers in SARS-CoV-2 infection remains poorly defined. Methods : We conducted a retrospective observational study examining a cohort of 30 patients admitted between March and April 2020 for COVID-19 infection. Serial measurements of hepcidin and other iron-related parameters were obtained at baseline, day 3, and day 7 after administration of anti-IL-1 receptor or anti-IL-6-receptor therapies. The primary outcome was in-hospital mortality. Results : Baseline iron and inflammatory markers did not differ significantly between survivors and non-survivors. Hepcidin levels correlated positively with C-reactive protein (r = 0.57, p < 0.001) and inversely with serum iron (r = − 0.3, p = 0.006) at baseline day 3 day post-treatment. Importantly, the percentage change in hepcidin from baseline to day 3 after anti-IL therapy was the only independent predictor of in-hospital mortality in multivariate analysis. A hepcidin decrease ≤ 39% showed a specificity of 85.7 (95% CI 42.1–99.6) and a sensitivity of 73.9 (95% CI, 51.6–89.8) for predicting mortality. Conclusions: Hepcidin dynamics are strongly associated with clinical outcomes in COVID-19. Monitoring hepcidin trends may provide a simple, reliable tool for risk stratification in patients treated with interleukin inhibitors, and could complement established biomarkers in infection management. COVID-19 hepcidin iron homeostasis prognostic biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The recent Coronavirus Disease 2019 (COVID-19), caused by the novel SARS-CoV2, has resulted in a major global health emergency, with more than 775 million confirmed cases worldwide as of early 2025. From the beginning of the pandemic in 2019 to date, COVID-19 has caused 7.4 million confirmed deaths [ 1 ], although the true toll may be higher when considering estimates of excess mortality [ 2 , 3 ]. In severe cases, COVID-19 can lead to hypoxic respiratory failure, acute respiratory distress syndrome (ARDS), cardiac events, disseminated intravascular coagulation (DIC), and multiorgan failure [ 4 , 5 ]. Although the precise pathogenesis remains incompletely defined, it is known that a redundant activation of inflammatory pathways, with excessive liberation of cytokines and inflammatory factors, plays a fundamental role in determining the disease severity and progression [ 6 – 8 ]. Elevated circulating markers of systemic inflammation, such as C reactive Protein (PCR), erythrocyte sedimentation rate, and ferritin, have been significantly correlated with symptom severity [ 9 , 10 ]. Ferritin is both an acute phase protein and the principal iron-storage protein, and its marked elevation in COVID-19 patients has prompted hypotheses regarding the impact of SARS-CoV2 infection on the complex iron regulatory pathways. More broadly, infections and inflammatory stimuli are known to influence body iron homeostasis. In COVID-19, hypoxia, inflammation, altered hepatic synthesis, and anaemia are common epiphenomena that can profoundly affect iron metabolism. The expression of hepcidin, a liver-derived peptide of the defensin family and the master regulator of systemic iron balance, is influenced by both stimulatory and inhibitory signals [ 11 , 12 ]. Inhibitory regulators are represented mainly by erythroferrone (ERFE) and ERFE-mediated pathways, which are activated by erythropoietic stress such as anaemia or hypoxia. These pathways, via renal erythropoietin production, suppress hepcidin expression, thereby enhancing iron mobilization from stores and dietary absorption. Conversely, hepcidin expression is upregulated by high iron levels(via the bone morphogenetic proteins (BMPs), endoplasmic reticulum (ER) stress through the activity of the transcription factor cyclic AMP response element-binding protein H (CREBH), and inflammation, primarily mediated by the Interleukin 6 (IL-6) pathway (9). During the COVID-19 pandemic, inhibitors of interleukin (IL) pathways, particularly targeting IL-1 (Anakinra) and IL-6 (Tocilizumab) receptors, were introduced as therapeutic options for patients with severe SARS-CoV2 infection, aiming to attenuate the inflammatory cascade activated by the virus [ 13 , 14 ]. On this basis, we investigated a cohort of patients admitted to our hospital for SARS-CoV2 infection during the first wave of the pandemic in Italy (March-April 2020) who were treated in medical wards and received immune-modulatory medications such as tocilizumab or anakinra. The aims of this explorative study were: (1) to describe iron-related parameters in such patient population; (2) to monitor changes in these markers following the administration of IL-receptors inhibitors; and (3) to assess the prognostic value of hepcidin and other iron biomarkers for clinical outcomes. The primary endpoint was in-hospital survival. Notably, very few studies have measured hepcidin in COVID-19, and none to date have reported its temporal trends. Demonstrating the clinical utility of hepcidin measurements in such context, could provide a novel, practical biomarker for monitoring disease progression and treatment response in viral infections. Material and methods Ethical approval This retrospective observational study was conducted within the Department of Medical and Surgical Science, in collaboration with the Infectious Disease Department of the University Hospital of Modena (Italy). The study includes data collected for previously published studies, approved by the Regional Ethics Committee of Emilia Romagna [15, 16]. Due to isolation precautions, all patients provided verbal rather than written informed consent. The study complies with the STROBE guidelines for observational research. Study population All consecutive patients aged ≥18 years admitted to the AOU Policlinico of Modena between March 12 and May 3, 2020, with symptomatic SARS-CoV-2 infection (confirmed by RT-PCR on nasopharyngeal swabs and/or by positive serology) were considered for inclusion. Of these, we selected patients who (1) received tocilizumab or anakinra after failing standard supportive care (SSC), and (2) had blood samples collected at three relevant time points. The selected time points were: Baseline (Time 1) : the last laboratory assessment prior to immunomodulatory drug administration; Day 3 (Time 2) : laboratory assessment approximately 3 days after treatment (range: +2 to +6 days); Day 7 (Time 3) : laboratory assessment approximately 7 days after treatment (range: +7 to +11 days). Tocilizumab was administered either intravenously at 8 mg/kg (two doses, 12 hours apart) or subcutaneously at 162 mg (two simultaneous doses). Anakinra was administered subcutaneously at 100 mg as a single dose. One of these agents was given to patients who did not respond to SSC within 3 days of hospital admission, depending on drug availability. Data collection Data were retrieved from electronic medical records and included demographic characteristics, medical history, and laboratory results. The PaO2/FiO2 ratio (P/F or Horowitz index) was calculated to a:sess the severity of respiratory distress, classified according to the Berlin definition of Acute Respiratory Distress Syndrome (ARDS): P/F ≥ 300 = mild; P/F 200−299 = moderate; P/F=100−199 = severe; P/F < 100 = very severe. The Sequential Organ Failure Assessment (SOFA) score prior to anti-IL treatment was also calculated as an additional index of disease severity. The primary outcome of the study was in-hospital death. Samples collection and measurements of IL-6, hepcidin, and iron parameters Blood samples were collected and centrifuged at 3,000 rpm for 10 minutes. The resulting serum was frozen and stored at -80°C until analysis. Serum Hepcidin-25 concentrations were measured using commercially available enzyme-linked immunoassorbent assay (ELISA) kits (DRG International Inc, Springfield, USA), based on the principle of competitive binding, following the manufacturer’s instructions. Briefly, endogenous Hepcidin-25 in patient samples competes with a Hepcidin-25-biotin conjugate for binding to the coated plates. Bound biotin is detected using streptavidin-peroxidase, and the colorimetric signal generated after substrate addition is inversely proportional to the Hepcidin-25 concentration. Serum iron, ferritin (SF), total iron binding capacity (TIBC), transferrin saturation (TSAT), IL-6, and other routine blood parameters were measured using automated biochemical methods in the centralised hospital laboratory. Statistical analysis All data were tested for normal distribution using the Shapiro–Wilk test. Continuous data were presented as mean ± standard deviation (SD) or range if normally distributed, or as median with interquartile range (IQR) or range if non-normally distributed. Categorical variables were presented as counts and percentages. Comparisons between categorical variables were performed by using the chi-square test or Fisher's exact test. Between-group comparisons of continuous variables were performed using the Student's t -test for normally distributed data, and the Mann-Whitney test for non-normally distributed data. A two-sided p-value < 0.05 was considered statistically significant. Paired t -tests were performed for trend analysis. A stepwise Cox regression analysis was used to examine the relationship between variables and the outcome of interest (in-hospital mortality). Variables significantly associated in univariate analysis with a statistical significance corresponding to a p-value < 0.1 were entered into the multivariate logistic regression model. Receiver operating characteristic (ROC) curve analysis, as well as comparison of ROC curves, was performed to evaluate the accuracy of parameters associated with mortality and to find optimal cut-offs. All analyses were performed using IBM SPSS (22.0, IBM, New York, USA) or MedCalc (MedCalc Software Ltd, Ostend, Belgium). Results In total, 30 patients were included, and their baseline characteristics are presented in Table 1. The mean age was 66 year s, and the majority were male (77%) and hypertensive (60%). Six patients (20%) had a pre-existing respiratory disease (chronic obstructive pulmonary disease, COPD, n = 1, obstructive sleep apnea syndrome, OSAS, n = 2, restrictive disease, n = 1, asthma, n = 1, Idiopathic pulmonary fibrosis, n = 1). All patients presented with dyspnea and fever, and 70% also reported cough. At the time of admission, all patients had respiratory failure, with a P/F < 400 (median 156, range 60-341). As per the inclusion criteria, all patients received an anti-IL monoclonal antibody after a median of 3 days (±3) from hospital admission due to clinical worsening despite standard supportive care (SSC). Three patients were treated with anakinra (anti IL-1R), while 27 received tocilizumab (anti IL-6-R). Six patients received anti IL-6R or anti IL-1R therapy after transfer to the ICU for further clinical deterioration, and 4 of them subsequently died. Characteristics according to clinical outcome Baseline characteristics by survival status are summarized in Table 2. Patients who died were significantly older (73.4 ± 8 years vs. 63.5 ± 14 years, p = 0.04) and more likely to be female ( 57% vs. 13% , p = 0.033). Non-survivors also had higher SOFA scores at admission (4 . 3 ± 1.6 vs. 2.3 ± 1.3 , p = 0.003), and elevated D-dimer levels (2070 μg/L , IQR 5530 vs. 1150 μg/L , IQR 1180 , p = 0.042) and LDH values ( 792 U/L , IQR 499 vs. 617 U/L , IQR 216, p = 0.015) compared with survivors. Inflammation and iron metabolism markers Baseline values of iron parameters, hepcidin, and inflammatory markers (Time 1, before anti-IL therapy) according to outcome are shown in Table 3. No significant differences were observed between the two groups, although there was a trend toward higher serum ferritin, and serum iron levels, and lower hepcidin levels in patients who did not survive. Similarly, no significant between-group differences were found for IL-6 levels or C-reactive protein (CRP). However, their trends differed: CRP values were higher in survivors, while IL-6 values were higher in non-survivors. Iron and inflammatory parameters were also assessed at Time 2 (Day 3) and Time 3 (Day 7) after immunomodulatory therapy. No significant between-group differences were observed at these time points (data not shown). Hepcidin positively correlated with CRP at both Time 0 and Time 1, and also when pooling measurements from the two time points (r = 0.57, p < 0.001; Figure 1). It was negatively correlated with serum iron (r = - 0.3, p = 0.006; Figure 2), but showed no significant correlation with P/F ratio, arterial PaO2 or IL-6. The longitudinal modifications of iron or inflammation parameters are reported in Figure 3, with intergroup differences evaluated as follows: - Hepcidin: survivors exhibited a greater reduction in hepcidin levels compared with non-survivors, particularly from Time 1 to Time 2 and from Time 1 to Time 3 (Figure 3a); - Serum iron and ferritin: these parameters showed opposite trends from Time 1 to Time 3, in both survivors and non-survivors, reflecting preserved iron “sensing” even in the group of patients who ultimately died (Figure 3b, and c) - CRP: levels decreased gradually in survivors, whereas in non-survivors they significantly increased from Time 1 to Time 2 (Figure 3d) - IL-6: levels increased in both groups from Time 1 to Time2, particularly in the group of patients who died (Figure 3e). Predictors of mortality Variables showing different trends between survivors and non-survivors, along with iron-related parameters and determinants previously described in the literature, were tested in a logistic regression model as predictors of the primary outcome (in-hospital death). Table 4 reports all the crude Odds Ratios (ORs) with 95% confidence interval (95%CI) for all tested predictors of the primary outcome. Only variables with p < 0.1 were included in the multivariable logistic regression model, and their adjusted p-values are reported. As shown in Table 4, SOFA score, baseline hepcidin levels, changes in hepcidin, CRP and IL-6 levels between Time 1-Time 2 and Time 1-Time 3 were associated with the outcome of interest in the univariate Cox regression analysis with a p value < 0.1. These variables were subsequently tested in multiple variable models. Among them, only the change in hepcidin values from time 1 to time 2 remained significantly associated with in-hospital death. Receiver operating characteristic (ROC) curve analysis showed that the percentage decrease in hepcidin from Time 1 to Time 2 had an area under the curve (AUC) of 0.83 for predicting mortality. Particularly, a cutoff value of ≤ 39% yielded a specificity of 85.71 % (95% CI: 42.1 - 99.6) and a sensitivity of 73.91 % (95% CI: 51.6 - 89.8). The Kaplan-Meier curves illustrate the difference in cumulative survival among patients according to a hepcidin percentage drop of >39% or ≤39% (Figure 4). Discussion In this pilot study, we investigated the dynamics of iron metabolism markers, particularly hepcidin, in patients with acute-phase COVID-19, assessing their potential prognostic relevance. Although the complexity of iron regulation during the pandemic made rigorous trial-based evaluations challenging, multiple observational studies have reported dysregulated iron metabolism in COVID-19. A large review [17] highlighted that reduced serum iron, along with elevated ferritin and hepcidin levels, was associated with greater disease severity, hospitalization, and mortality, though not consistently with oxygen requirements [18] [19, 20]. The inflammatory response in COVID-19 is believed to upregulate hepcidin via IL-6, resulting in hypoferremia and anemia of inflammation. Our findings suggest that this regulatory mechanism remains largely intact. The inverse relationship between serum iron and ferritin observed in our cohort supports a physiologically consistent inflammatory response [21]. In our population, patients who failed to respond to standard supportive care (SSC) within three days received anti-interleukin therapy (Time 1). Among them, those who exhibited a hepcidin reduction of ≤39% by Day 3 post-treatment (Time 2) had significantly higher mortality rates (Figure 4). While baseline hepcidin levels were only borderline significantly different between survivors and non-survivors (157 ± 66 ng/mL vs. 104 ± 24 ng/mL, p = 0.05), the trajectory of hepcidin over time provided a stronger predictive signal. Absolute hepcidin values have been proposed as mortality predictors, but prior studies have reported inconsistent results and variable cut-offs [18, 22, 23]. Our findings emphasize the prognostic superiority of hepcidin kinetics over single-point measurements or other iron/inflammatory markers. This suggests that dynamic changes in hepcidin may better reflect the host's capacity to regulate inflammation and iron homeostasis, critical elements for survival in acute infection. Persistent hepcidin elevation, reported in other studies up to 60 days post-symptom onset, may be driven by altered iron-sensing pathways, possibly responding to ongoing subclinical inflammation [24]. Interestingly, the pattern of iron metabolism and redox changes in COVID-19 resembles that observed in acute respiratory distress syndrome (ARDS), rather than in chronic conditions like COPD [25], underscoring the role of acute inflammation in disrupting iron homeostasis. Despite early descriptions of a "cytokine storm" in COVID-19, this phenomenon may have been overstated. Diagnostic criteria for hemophagocytic lymphohistiocytosis (HLH), such as hyperferritinemia, are fulfilled in fewer than 10% of cases [26]. Moreover, cytokine levels in COVID-19 are often lower than those seen in sepsis or ARDS [27, 28], and aside from IL-6, most cytokines show limited correlation with disease severity [29]. As for IL-6 levels in our cohort, they showed an increase right after the administration of the antagonist, as described in previous reports, likely due to saturation of IL-6 receptors [30]. Our study has two main limitations: a small sample size and a retrospective design. The former reflects the emergency setting during the early pandemic and the strict inclusion criteria requiring three serial measurements to reliably assess trends. While the limited sample size may reduce generalizability and complicate meta-analyses, it is consistent with other published studies, and our statistical analysis supports the robustness of the findings. The observational design, however, restricts causal inference, including the lack of a clear mechanistic explanation for the divergent trends between CRP and IL-6. Despite these limitations, our results support hepcidin as a promising biomarker in infection management. Current biomarkers, including CRP, procalcitonin, and mid-regional pro-adrenomedullin, have known limitations. Hepcidin, with its dual role as an iron regulator and acute-phase reactant, as well as its β-defensin-like antimicrobial properties [31, 32], may offer a valuable complement or alternative, especially in complex infectious contexts. In conclusion, hepcidin dynamics represent a promising and easily measurable biomarker to stratify risk in hospitalized COVID-19 patients treated with IL inhibitors, warranting further validation in larger cohorts. Hepcidin measurement could be especially useful when existing markers perform suboptimally, or as part of a multi-marker strategy to enhance prognostic accuracy. Future studies are essential to validate our findings and to further elucidate the multifaceted regulatory mechanisms of iron metabolism during infection, mechanisms likely more intricate than previously understood. 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Buzzetti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACdjB5AMozsGFgYIYweXBqYUZoYWw4YJCG0IJTD6oWhsMIGVxa+JmZjz34wXAncX7/4eOPPxScT9zOzp34uKKCQcYehxbJZrZ0wx6GZ4mNDccSgQ67nbizmXez4ZkzuB1mcJjHTIKH4XBiM2OPIVjLhsO82yQb23BrsT/M/03yD1BLGzP/R6CWc1At//DYwszDJg2ypYeNBxRiB6BaGnBrkTjMZiYtY/DMeAYPm+GMMwbJxkAtmw0bjknw8BzAEWLtzc8k31TckQWG2IMPFX/sZDecP7vxYUONjT17Aw5rIM7DYj0+9aNgFIyCUTAKCAAAAtJYqbZCViEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4462-7935","institution":"University of Modena and Reggio Emilia: Universita degli Studi di Modena e Reggio Emilia","correspondingAuthor":true,"prefix":"","firstName":"Elena","middleName":"","lastName":"Buzzetti","suffix":""},{"id":534657842,"identity":"bf8dac64-a3fb-47c8-a5d2-c18cd42b8b63","order_by":1,"name":"Giuliano Bolondi","email":"","orcid":"","institution":"Nuovo Ospedale Civile Sant'Agostino Estense di Baggiovara: Azienda Ospedaliero-Universitaria di Modena Ospedale Civile di Baggiovara","correspondingAuthor":false,"prefix":"","firstName":"Giuliano","middleName":"","lastName":"Bolondi","suffix":""},{"id":534657843,"identity":"88ffb1c9-90f2-43ad-81ef-519759c217d4","order_by":2,"name":"Andrea Ricci","email":"","orcid":"","institution":"University of Modena and Reggio Emilia: Universita degli Studi di Modena e Reggio Emilia","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Ricci","suffix":""},{"id":534657844,"identity":"0d9b4735-acc7-4e07-8c0d-7b716bf4f002","order_by":3,"name":"Cinzia Garuti","email":"","orcid":"","institution":"University of Modena and Reggio Emilia: Universita degli Studi di Modena e Reggio Emilia","correspondingAuthor":false,"prefix":"","firstName":"Cinzia","middleName":"","lastName":"Garuti","suffix":""},{"id":534657845,"identity":"bd850d3f-0623-4132-8ee7-21b49dffcb53","order_by":4,"name":"Elisa 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Emilia","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Mussini","suffix":""},{"id":534657848,"identity":"ceb85619-2d0f-4d63-97fe-5374ed5b5d92","order_by":7,"name":"Giovanni Guaraldi","email":"","orcid":"","institution":"University of Modena and Reggio Emilia: Universita degli Studi di Modena e Reggio Emilia","correspondingAuthor":false,"prefix":"","firstName":"Giovanni","middleName":"","lastName":"Guaraldi","suffix":""},{"id":534657849,"identity":"e135f8b3-ec3d-4dd0-9895-e0ee7e751b3c","order_by":8,"name":"Andrea Cossarizza","email":"","orcid":"","institution":"University of Modena and Reggio Emilia: Universita degli Studi di Modena e Reggio Emilia","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Cossarizza","suffix":""},{"id":534657850,"identity":"0cbe1043-63fe-4f41-bd75-f0ca4897a5fe","order_by":9,"name":"Antonello Pietrangelo","email":"","orcid":"","institution":"University of Modena and Reggio 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05:24:39","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135724,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7792556/v1/7898c52120bb71d1cb910b1e.html"},{"id":95264409,"identity":"6b0b604f-1d94-48f2-a628-31a46f37c46a","added_by":"auto","created_at":"2025-11-06 05:24:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11274,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between serum hepcidin (ng/mL) and serum CRP (mg/dL)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1BuzzettietalIAEM61025.png","url":"https://assets-eu.researchsquare.com/files/rs-7792556/v1/57190779005b469591aaa309.png"},{"id":95313402,"identity":"61953d73-7280-435f-b7f1-6bd63a24f5de","added_by":"auto","created_at":"2025-11-06 15:51:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between serum hepcidin (ng/mL) and serum iron (μg/dL)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2BuzzettietalIAEM61025.png","url":"https://assets-eu.researchsquare.com/files/rs-7792556/v1/4d1edaa84ae9045846be694e.png"},{"id":95313540,"identity":"75d1f4a3-0c69-4c52-bb73-c397a97a6d14","added_by":"auto","created_at":"2025-11-06 15:51:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81161,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in a) Hepcidin, b) Serum Iron, c) Serum ferritin, d) CRP, e) IL6 after the administration of anti-interleukin agent\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3Buzzettietal.IAEMpng.png","url":"https://assets-eu.researchsquare.com/files/rs-7792556/v1/d5cb235f439a38a35d074c43.png"},{"id":95313590,"identity":"0236e31a-741a-44b8-8c8e-830b39e6dceb","added_by":"auto","created_at":"2025-11-06 15:51:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative survival according to Hepcidin%Drop from T1 to T2\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4BuzzettietalIAEM61025.png","url":"https://assets-eu.researchsquare.com/files/rs-7792556/v1/f938c36b2b6fe9e020d4f71e.png"},{"id":97665436,"identity":"7df2ac53-61d8-42d2-8f2f-1bd2f6a53d4a","added_by":"auto","created_at":"2025-12-08 09:18:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":844416,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7792556/v1/7987f455-e46e-408c-8845-e49d4d8988e9.pdf"},{"id":95264411,"identity":"ccc0bc89-eeb0-4d9b-a541-1017be757ff5","added_by":"auto","created_at":"2025-11-06 05:24:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23015,"visible":true,"origin":"","legend":"","description":"","filename":"TablesIAEMBuzzettietal61025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7792556/v1/04afe31668dcaae85ef6c7ae.docx"}],"financialInterests":"","formattedTitle":"Hepcidin dynamics predict outcomes in COVID-19 patients treated with interleukin inhibitors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe recent Coronavirus Disease 2019 (COVID-19), caused by the novel SARS-CoV2, has resulted in a major global health emergency, with more than 775\u0026nbsp;million confirmed cases worldwide as of early 2025. From the beginning of the pandemic in 2019 to date, COVID-19 has caused 7.4\u0026nbsp;million confirmed deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], although the true toll may be higher when considering estimates of excess mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn severe cases, COVID-19 can lead to hypoxic respiratory failure, acute respiratory distress syndrome (ARDS), cardiac events, disseminated intravascular coagulation (DIC), and multiorgan failure [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although the precise pathogenesis remains incompletely defined, it is known that a redundant activation of inflammatory pathways, with excessive liberation of cytokines and inflammatory factors, plays a fundamental role in determining the disease severity and progression [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Elevated circulating markers of systemic inflammation, such as C reactive Protein (PCR), erythrocyte sedimentation rate, and ferritin, have been significantly correlated with symptom severity [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Ferritin is both an acute phase protein and the principal iron-storage protein, and its marked elevation in COVID-19 patients has prompted hypotheses regarding the impact of SARS-CoV2 infection on the complex iron regulatory pathways. More broadly, infections and inflammatory stimuli are known to influence body iron homeostasis. In COVID-19, hypoxia, inflammation, altered hepatic synthesis, and anaemia are common epiphenomena that can profoundly affect iron metabolism.\u003c/p\u003e\u003cp\u003eThe expression of hepcidin, a liver-derived peptide of the defensin family and the master regulator of systemic iron balance, is influenced by both stimulatory and inhibitory signals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Inhibitory regulators are represented mainly by erythroferrone (ERFE) and ERFE-mediated pathways, which are activated by erythropoietic stress such as anaemia or hypoxia. These pathways, via renal erythropoietin production, suppress hepcidin expression, thereby enhancing iron mobilization from stores and dietary absorption. Conversely, hepcidin expression is upregulated by high iron levels(via the bone morphogenetic proteins (BMPs), endoplasmic reticulum (ER) stress through the activity of the transcription factor cyclic AMP response element-binding protein H (CREBH), and inflammation, primarily mediated by the Interleukin 6 (IL-6) pathway (9).\u003c/p\u003e\u003cp\u003eDuring the COVID-19 pandemic, inhibitors of interleukin (IL) pathways, particularly targeting IL-1 (Anakinra) and IL-6 (Tocilizumab) receptors, were introduced as therapeutic options for patients with severe SARS-CoV2 infection, aiming to attenuate the inflammatory cascade activated by the virus [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOn this basis, we investigated a cohort of patients admitted to our hospital for SARS-CoV2 infection during the first wave of the pandemic in Italy (March-April 2020) who were treated in medical wards and received immune-modulatory medications such as tocilizumab or anakinra. The aims of this explorative study were: (1) to describe iron-related parameters in such patient population; (2) to monitor changes in these markers following the administration of IL-receptors inhibitors; and (3) to assess the prognostic value of hepcidin and other iron biomarkers for clinical outcomes. The primary endpoint was in-hospital survival. Notably, very few studies have measured hepcidin in COVID-19, and none to date have reported its temporal trends. Demonstrating the clinical utility of hepcidin measurements in such context, could provide a novel, practical biomarker for monitoring disease progression and treatment response in viral infections.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective observational study was conducted within the Department of Medical and Surgical Science, in collaboration with the Infectious Disease Department of the University Hospital of Modena (Italy). The study includes data collected for previously published studies, approved by the Regional Ethics Committee of Emilia Romagna [15, 16]. Due to isolation precautions, all patients provided verbal rather than written informed consent. The study complies with the STROBE guidelines for observational research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll consecutive patients aged ≥18 years admitted to the AOU Policlinico of Modena between March 12 and May 3, 2020, with symptomatic SARS-CoV-2 infection (confirmed by RT-PCR on nasopharyngeal swabs and/or by positive serology) were considered for inclusion.\u003c/p\u003e\n\u003cp\u003eOf these, we selected patients who (1) received tocilizumab or anakinra after failing standard supportive care (SSC), and (2) had blood samples collected at three relevant time points. The selected time points were:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eBaseline (Time 1)\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e the last laboratory assessment prior to immunomodulatory drug administration;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDay 3 (Time 2)\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e laboratory assessment approximately 3 days after treatment (range: +2 to +6 days);\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDay 7 (Time 3)\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e laboratory assessment approximately 7 days after treatment (range: +7 to +11 days).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTocilizumab was administered either intravenously at 8 mg/kg (two doses, 12 hours apart) or subcutaneously at 162 mg (two simultaneous doses). Anakinra was administered subcutaneously at 100 mg as a single dose. \u0026nbsp; One of these agents was given to patients who did not respond to SSC within 3 days of hospital admission, depending on drug availability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were retrieved from electronic medical records and included demographic characteristics, medical history, and laboratory results.\u003c/p\u003e\n\u003cp\u003eThe PaO2/FiO2 ratio (P/F or Horowitz index) was calculated to a:sess the severity of respiratory distress, classified according to the Berlin definition of Acute Respiratory Distress Syndrome (ARDS): \u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eP/F ≥ 300 = mild;\u003c/li\u003e\n \u003cli\u003eP/F 200−299 = moderate;\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;P/F=100−199 = severe;\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;P/F \u0026lt; 100 = very severe.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe Sequential Organ Failure Assessment (SOFA) score prior to anti-IL treatment was also calculated as an additional index of disease severity. The primary outcome of the study was in-hospital death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSamples collection and measurements of IL-6, hepcidin, and iron parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples were collected and centrifuged at 3,000 rpm for 10 minutes. The resulting serum was frozen and stored at -80°C until analysis.\u003c/p\u003e\n\u003cp\u003eSerum Hepcidin-25 \u0026nbsp;concentrations were measured using commercially available enzyme-linked immunoassorbent assay (ELISA) kits (DRG International Inc, Springfield, USA), based on the principle of competitive binding, following the manufacturer’s instructions. Briefly, endogenous Hepcidin-25 in patient samples competes with a Hepcidin-25-biotin conjugate for binding to the coated plates. Bound biotin is detected using streptavidin-peroxidase,\u0026nbsp;and the colorimetric signal generated after substrate addition is inversely proportional to the Hepcidin-25 concentration. \u0026nbsp; Serum iron, ferritin (SF), total iron binding capacity (TIBC), transferrin saturation (TSAT), IL-6, and other routine blood parameters were measured using automated biochemical methods in the centralised hospital laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were tested for normal distribution using the Shapiro–Wilk test. Continuous data were presented as mean ± standard deviation (SD) or range if normally distributed, or as median with interquartile range (IQR) or range if non-normally distributed. Categorical variables were presented as counts and percentages. Comparisons between categorical variables were performed by using the chi-square test or Fisher's exact test. Between-group comparisons of continuous variables were performed using the Student's \u003cem\u003et\u003c/em\u003e-test for normally distributed data, and the Mann-Whitney test for non-normally distributed data. A two-sided p-value \u0026lt; 0.05 was considered statistically significant. Paired \u003cem\u003et\u003c/em\u003e-tests were performed for trend analysis.\u003c/p\u003e\n\u003cp\u003eA stepwise Cox regression analysis was used to examine the relationship between variables and the outcome of interest (in-hospital mortality). Variables significantly associated in univariate analysis with a statistical significance corresponding to a p-value \u0026lt; 0.1 were entered into the multivariate logistic regression model. Receiver operating characteristic (ROC) curve analysis, as well as comparison of ROC curves, was performed to evaluate the accuracy of parameters associated with mortality and to find optimal cut-offs.\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using IBM SPSS (22.0, IBM, New York, USA) or MedCalc (MedCalc Software Ltd, Ostend, Belgium).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, 30 patients were included, and their baseline characteristics are presented in Table 1.\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003emean age\u003c/strong\u003e was \u003cstrong\u003e66 year\u003c/strong\u003es, and the majority were male (77%) and hypertensive (60%). Six patients (20%) had a pre-existing respiratory disease (chronic obstructive pulmonary disease, COPD, n = 1, obstructive sleep apnea syndrome, OSAS, n = 2, restrictive disease, n = 1, asthma, n = 1, Idiopathic pulmonary fibrosis, n = 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll patients presented with \u003cstrong\u003edyspnea and fever, and\u0026nbsp;\u003c/strong\u003e70% also reported cough. At the time of admission, all patients had respiratory failure, with a P/F \u0026lt; 400 (median 156, range 60-341).\u003c/p\u003e\n\u003cp\u003eAs per the inclusion criteria, all patients received an anti-IL monoclonal antibody after a median of 3 days (±3) from hospital admission due to clinical worsening despite standard supportive care (SSC). Three patients were treated with anakinra \u0026nbsp;(anti IL-1R), while 27 received tocilizumab (anti IL-6-R). Six patients received anti IL-6R or anti IL-1R therapy after transfer to the ICU for further clinical deterioration, and 4 of them subsequently died.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics according to clinical outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics by survival status are summarized in Table 2.\u003c/p\u003e\n\u003cp\u003ePatients who died were significantly older (73.4 ± 8 years vs. 63.5 ± 14 years, p = 0.04) and more likely to be female (\u003cstrong\u003e57% vs. 13%\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e p = 0.033). Non-survivors also had higher \u003cstrong\u003eSOFA scores at admission\u003c/strong\u003e(4\u003cstrong\u003e.\u003cstrong\u003e3 ± 1.6 vs. 2.3 ± 1.3\u003c/strong\u003e\u003c/strong\u003e, p = 0.003), and elevated \u003cstrong\u003eD-dimer levels (2070 μg/L\u003c/strong\u003e, IQR \u003cstrong\u003e5530\u003c/strong\u003e vs. \u003cstrong\u003e1150 μg/L\u003c/strong\u003e, IQR \u003cstrong\u003e1180\u003c/strong\u003e, p = 0.042) and \u003cstrong\u003eLDH values\u003c/strong\u003e (\u003cstrong\u003e792 U/L\u003c/strong\u003e, IQR \u003cstrong\u003e499\u003c/strong\u003e vs. \u003cstrong\u003e617 U/L\u003c/strong\u003e, IQR 216, p = 0.015) compared with survivors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInflammation and iron metabolism markers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline values of iron parameters, hepcidin, and inflammatory markers (Time 1, before anti-IL therapy) according to outcome are shown in Table 3. No significant differences were observed between the two groups, although there was a trend toward higher serum ferritin, and serum iron levels, and lower hepcidin levels in patients who did not survive. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, no significant between-group differences were found for IL-6 levels or C-reactive protein (CRP). However, their trends differed: CRP values were higher in survivors, while IL-6 values were higher in non-survivors. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIron and inflammatory parameters were also assessed at Time 2 (Day 3) and Time 3 (Day 7) after immunomodulatory therapy. No significant between-group differences were observed at these time points (data not shown). Hepcidin positively correlated with CRP at both Time 0 and Time 1, and also when pooling measurements from the two time points (r = 0.57, p \u0026lt; 0.001; Figure 1). It was negatively correlated with serum iron (r = - 0.3, p = 0.006; Figure 2), but showed no significant correlation with P/F ratio, arterial PaO2 or IL-6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe longitudinal modifications of iron or inflammation parameters are reported in Figure 3, with intergroup differences evaluated as follows:\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hepcidin: survivors exhibited a greater reduction in hepcidin levels compared with non-survivors, particularly from Time 1 to Time 2 and from Time 1 to Time 3 (Figure 3a);\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Serum iron and ferritin: these parameters showed opposite trends from Time 1 to Time 3, in both survivors and non-survivors, reflecting preserved \u0026nbsp;iron “sensing” even in the group of patients who ultimately died (Figure 3b, and c)\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;CRP: levels decreased gradually in survivors, whereas in non-survivors they \u0026nbsp;significantly increased from Time 1 to Time 2 (Figure 3d)\u003c/p\u003e\n\u003cp\u003e-\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;IL-6: levels increased in both groups from Time 1 to Time2, particularly in the group of patients who died (Figure 3e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors of mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Variables showing different trends between survivors and non-survivors, along with iron-related parameters and determinants previously described in the literature, were tested in a logistic regression model as predictors of the primary outcome (in-hospital death).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Table 4 reports all the crude Odds Ratios (ORs) with 95% confidence interval (95%CI) for all tested predictors of the primary outcome. Only variables with p \u0026lt; 0.1 were included in the multivariable logistic regression model, and their adjusted p-values are reported.\u003c/p\u003e\n\u003cp\u003eAs shown in Table 4, SOFA score, baseline hepcidin levels, changes in hepcidin, CRP and IL-6 levels between Time 1-Time 2 and Time 1-Time 3 were associated with the outcome of interest in the univariate Cox regression analysis with a p value \u0026lt; 0.1. These variables were subsequently tested in multiple variable models. Among them, only the change in hepcidin values from time 1 to time 2 remained significantly associated with in-hospital death.\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curve analysis showed that the percentage decrease \u0026nbsp;in hepcidin from Time 1 to Time 2 had an area under the curve (AUC) of 0.83 for predicting mortality. Particularly, a cutoff value of ≤ 39% yielded a specificity of 85.71 % (95% CI: 42.1 - 99.6) and a sensitivity of 73.91 % (95% CI: 51.6 - 89.8).\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier curves illustrate the difference in cumulative survival among patients according to a hepcidin percentage drop of \u0026gt;39% or ≤39% (Figure 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this pilot study, we investigated the dynamics of iron metabolism markers, particularly hepcidin, in patients with acute-phase COVID-19, assessing their potential prognostic relevance. Although the complexity of iron regulation during the pandemic made rigorous trial-based evaluations challenging, multiple observational studies have reported dysregulated iron metabolism in COVID-19. A large review [17] highlighted that reduced serum iron, along with elevated ferritin and hepcidin levels, was associated with greater disease severity, hospitalization, and mortality, though not consistently with oxygen requirements [18] [19, 20].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe inflammatory response in COVID-19 is believed to upregulate hepcidin via IL-6, resulting in hypoferremia and anemia of inflammation. Our findings suggest that this regulatory mechanism remains largely intact. The inverse relationship between serum iron and ferritin observed in our cohort supports a physiologically consistent inflammatory response [21].\u003c/p\u003e\n\u003cp\u003eIn our population, patients who failed to respond to standard supportive care (SSC) within three days received anti-interleukin therapy (Time 1). Among them, those who exhibited a hepcidin reduction of ≤39% by Day 3 post-treatment (Time 2) had significantly higher mortality rates (Figure 4). While baseline hepcidin levels were only borderline significantly different between survivors and non-survivors (157 ± 66 ng/mL vs. 104 ± 24 ng/mL, p = 0.05), the trajectory of hepcidin over time provided a stronger predictive signal.\u003c/p\u003e\n\u003cp\u003eAbsolute hepcidin values have been proposed as mortality predictors, but prior studies have reported inconsistent results and variable cut-offs [18, 22, 23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings emphasize the prognostic superiority of hepcidin kinetics over single-point measurements or other iron/inflammatory markers. This suggests that dynamic changes in hepcidin may better reflect the host's capacity to regulate inflammation and iron homeostasis, critical elements for survival in acute infection.\u003c/p\u003e\n\u003cp\u003ePersistent hepcidin elevation, reported in other studies up to 60 days post-symptom onset, may be driven by altered iron-sensing pathways, possibly responding to ongoing subclinical inflammation \u0026nbsp;[24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, the pattern of iron metabolism and redox changes in COVID-19 resembles that observed in acute respiratory distress syndrome (ARDS), rather than in chronic conditions like COPD [25], underscoring the role of acute inflammation in disrupting iron homeostasis.\u003c/p\u003e\n\u003cp\u003eDespite early descriptions of a \"cytokine storm\" in COVID-19, this phenomenon may have been overstated. Diagnostic criteria for hemophagocytic lymphohistiocytosis (HLH), such as hyperferritinemia, are fulfilled in fewer than 10% of cases [26]. Moreover, cytokine levels in COVID-19 are often lower than those seen in sepsis or ARDS [27, 28], \u0026nbsp;and aside from IL-6, most cytokines show limited correlation with disease severity [29].\u003c/p\u003e\n\u003cp\u003eAs for IL-6 levels in our cohort, they showed an increase right after the administration of the antagonist, as described in previous reports, likely due to saturation of IL-6 receptors [30].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study has two main limitations: a small sample size and a retrospective design. The former reflects the emergency setting during the early pandemic and the strict inclusion criteria requiring three serial measurements to reliably assess trends. While the limited sample size may reduce generalizability and complicate meta-analyses, it is consistent with other published studies, and our statistical analysis supports the robustness of the findings. The observational design, however, restricts causal inference, including the lack of a clear mechanistic explanation for the divergent trends between CRP and IL-6.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, our results support hepcidin as a promising biomarker in infection management. Current biomarkers, including CRP, procalcitonin, and mid-regional pro-adrenomedullin, have known limitations. Hepcidin, with its dual role as an iron regulator and acute-phase reactant, as well as its β-defensin-like antimicrobial properties [31, 32], may offer a valuable complement or alternative, especially in complex infectious contexts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, hepcidin dynamics represent a promising and easily measurable biomarker to stratify risk in hospitalized COVID-19 patients treated with IL inhibitors, warranting further validation in larger cohorts.\u003c/p\u003e\n\u003cp\u003eHepcidin measurement could be especially useful when existing markers perform suboptimally, or as part of a multi-marker strategy to enhance prognostic accuracy. Future studies are essential to validate our findings and to further elucidate the multifaceted regulatory mechanisms of iron metabolism during infection, mechanisms likely more intricate than previously understood.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ehttps://\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edata.who.int/dashboards/covid19/deaths\u003c/span\u003e\u003cspan address=\"http://data.who.int/dashboards/covid19/deaths\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePizzato M, Gerli AG, La Vecchia C, Alicandro G (2024) Impact of COVID-19 on total excess mortality and geographic disparities in Europe, 2020\u0026ndash;2023: a spatio-temporal analysis. 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Science 306:2090\u0026ndash;2093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1104742\u003c/span\u003e\u003cspan address=\"10.1126/science.1104742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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, hepcidin, iron homeostasis, prognostic biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7792556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7792556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eIntroduction\u003c/b\u003e: iron metabolism and its key regulatory hormone, hepcidin, play a pivotal role in infection and inflammation. However, the prognostic value of iron related markers in SARS-CoV-2 infection remains poorly defined.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e: We conducted a retrospective observational study examining a cohort of 30 patients admitted between March and April 2020 for COVID-19 infection. Serial measurements of hepcidin and other iron-related parameters were obtained at baseline, day 3, and day 7 after administration of anti-IL-1 receptor or anti-IL-6-receptor therapies. The primary outcome was in-hospital mortality.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e: Baseline iron and inflammatory markers did not differ significantly between survivors and non-survivors. Hepcidin levels correlated positively with C-reactive protein (r\u0026thinsp;=\u0026thinsp;0.57, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and inversely with serum iron (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.3, p\u0026thinsp;=\u0026thinsp;0.006) at baseline day 3 day post-treatment. Importantly, the percentage change in hepcidin from baseline to day 3 after anti-IL therapy was the only independent predictor of in-hospital mortality in multivariate analysis. A hepcidin decrease\u0026thinsp;\u0026le;\u0026thinsp;39% showed a specificity of 85.7 (95% CI 42.1\u0026ndash;99.6) and a sensitivity of 73.9 (95% CI, 51.6\u0026ndash;89.8) for predicting mortality.\u003c/p\u003e\u003cp\u003eConclusions:\u003c/p\u003e\u003cp\u003eHepcidin dynamics are strongly associated with clinical outcomes in COVID-19. Monitoring hepcidin trends may provide a simple, reliable tool for risk stratification in patients treated with interleukin inhibitors, and could complement established biomarkers in infection management.\u003c/p\u003e","manuscriptTitle":"Hepcidin dynamics predict outcomes in COVID-19 patients treated with interleukin inhibitors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-06 05:24:34","doi":"10.21203/rs.3.rs-7792556/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":"27f268aa-76d4-42b3-b239-3da5b35db9fb","owner":[],"postedDate":"November 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-03T17:50:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-06 05:24:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7792556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7792556","identity":"rs-7792556","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}