---
title: "Iron-related Parameters and Sepsis-associated Acute Kidney Injury — Source Content Not Available"
id: "frontiers-in-immunology-19-association-of-iron-related-parameters-with-the-development-of-sepsis"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-19-association-of-iron-related-parameters-with-the-development-of-sepsis"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1918644"
published_at: "2026-08-26T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Iron-related Parameters and Sepsis-associated Acute Kidney Injury — Source Content Not Available
## Provenance & Clinical Metadata
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- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1918644)
- **Published At:** 2026-08-26T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Source metadata indicate an article titled "Association of iron-related parameters with the development of sepsis-associated acute kidney injury" published in Frontiers in Immunology and accessible at the supplied URL. - The provided source content contains only website navigation, journal pages, and repeated site headers; the article abstract, methods, results, figures, and conclusions are not present. - No study population details, laboratory or clinical **iron-related parameters**, statistical analyses, or reported associations with **sepsis-associated acute kidney injury** are available in the supplied text. - Because key article sections are missing, no clinical findings, effect sizes, significance values, or recommendations can be summarized from this source. - The absence of article content prevents assessment of study design (prospective vs retrospective), sample size, inclusion/exclusion criteria, timing of measurements, or confounder adjustment. - Clinicians and researchers seeking to evaluate the evidence should retrieve the full article from Frontiers in Immunology, check the abstract and full text, and review methods, results, and limitations before applying findings to practice or research. - The supplied material allows only verified statements about the article title, journal, and URL; all other clinical details were not reported in the provided source.
## Clinical Analysis & Structured Key Points
Frontiers | Association of iron-related parameters with the development of sepsis-associated acute kidney injury ORIGINAL RESEARCH article Front. Immunol. , 26 August 2026 Sec. Inflammation Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1918644 Published in Frontiers in Immunology Inflammation 7 impact factor 11.3 citescore Editor & Reviewers Edited by S K Soohyun Kim Reviewed by S M SHANHUA MAO Q L Qi Liu Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Table 1 Comparison of baseline clinical characteristics between the SA-AKI and non-SA-AKI groups. View in article Table 2 Associations of iron-related parameters with the risk of SA-AKI. View in article Table 3 Proportions of SA-AKI across quartiles of iron-related parameters. View in article Table 4 Multivariable logistic regression analyses of the clinical model and the clinical model plus SF for SA-AKI. View in article Table 5 Discrimination and calibration of the two multivariable models for SA-AKI. View in article Table 6 Discriminatory performance of SF for identifying SA-AKI. View in article ORIGINAL RESEARCH article Front. Immunol. , 26 August 2026 Sec. Inflammation Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1918644 Association of iron-related parameters with the development of sepsis-associated acute kidney injury P A Pengyi An 1,2 † G G Gong Gu 1 † Z G Zhuang Ge 1 † Y D Yingying Dong 3 F L Feili Liu 3,4 X L Xuhan Liu 3 * C Y Chuwei Yang 1 * 1. Department of Emergency Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian, China 2. Department of Emergency Medicine, The People’s Hospital of Liaoning Province, Shenyang, China 3. Department of Endocrinology, Central Hospital of Dalian University of Technology, Dalian, China 4. The Second Affiliated Hospital of Dalian Medical University, Dalian, China See more Article metrics View details Abstract Objective: To investigate the associations of iron-related parameters, including serum ferritin (SF), serum iron (SI), and transferrin saturation (TSAT), with the development of sepsis-associated acute kidney injury (SA-AKI). Methods: This retrospective study included 808 patients with sepsis admitted to the Second Affiliated Hospital of Dalian Medical University between January 1, 2015 and September 30, 2024. Data were obtained from the Yidu Cloud database and the hospital electronic medical record system. Patients were classified according to whether acute kidney injury (AKI) was clinically diagnosed within 48 hours of admission. Group differences were assessed using appropriate parametric or nonparametric tests, and correlations using Spearman analysis. SF, SI, and TSAT were categorized into quartiles to examine trends in AKI incidence. Two multivariable logistic regression models—a clinical model and the clinical model plus SF—were constructed to evaluate factors associated with SA-AKI. Receiver operating characteristic (ROC) curves were used to assess discriminatory ability, and the areas under the curves (AUCs) were compared using the DeLong test. Model calibration was assessed using the Hosmer–Lemeshow test and calibration plots, and internal validation was performed using 1,000 bootstrap resamples. Results: Among the 808 patients, 310 (38.4%) had SA-AKI. In Model 2, higher SF remained associated with SA-AKI (OR 2.479 per 100-ng/mL increase, 95% CI 2.123,2.894, P < 0.001). Adding SF to the clinical model increased the AUC from 0.730 to 0.846 (95% CI 0.085,0.147, P < 0.001). Both models showed acceptable calibration, and bootstrap estimates were generally consistent with the original results. For SF alone, the AUC was 0.793 (95% CI 0.761, 0.826), and the optimal cutoff was 525.56 ng/mL, with a sensitivity of 0.710 and a specificity of 0.765. Conclusions: Higher SF was associated with SA-AKI and improved the discriminatory ability of the clinical model. SF may provide additional information for the clinical assessment and identification of SA-AKI in patients with sepsis. 1 Introduction Because of their high metabolic demand and marked susceptibility to redox imbalance, the kidneys are among the organs most vulnerable to injury during sepsis ( 1 ). The development of sepsis-associated acute kidney injury (SA-AKI) often marks a major turning point in the clinical course of sepsis and is closely associated with poor outcomes. A multinational cross-sectional study on acute kidney injury (AKI) epidemiology reported that sepsis is the leading precipitating factor for AKI and that SA-AKI accounts for 40.7% of AKI cases in the intensive care unit (ICU) ( 2 ). In addition, a meta-analysis including 189 studies showed that SA-AKI is strongly associated with poor prognosis, with mortality reaching 48%, while approximately 10% of survivors remain dependent on renal replacement therapy ( 3 ). At present, AKI is still diagnosed mainly on the basis of serum creatinine (SCr) and urine output. However, both are delayed functional markers and often become abnormal only after substantial kidney injury has already occurred. Identifying earlier and more sensitive parameters therefore remains clinically important. Increasing evidence suggests that disturbances in iron metabolism are involved in both the onset and progression of sepsis. Inflammatory activation can induce hepcidin overexpression, promoting intracellular iron sequestration and reducing circulating iron availability. Iron deficiency may impair erythropoiesis and oxygen delivery, and may also contribute to multiple organ dysfunction in sepsis through disrupted cellular metabolism and mitochondrial injury ( 4 – 7 ). At the same time, the kidney is a key organ in iron handling, and excess iron deposition may catalyze hydroxyl radical generation, thereby aggravating oxidative stress and lipid peroxidation, ultimately leading to tubular epithelial injury and necrosis ( 8 – 10 ). These processes are central to the pathogenesis of SA-AKI. It is therefore plausible that disordered iron metabolism is not merely a consequence of systemic inflammation in sepsis, but may also directly contribute to renal oxidative injury and cell death. Several clinical studies have shown that iron-related parameters are associated with prognosis in critically ill patients and in patients with sepsis. Serum ferritin (SF) and serum iron (SI) have been reported to be positively associated with in-hospital mortality in severe sepsis, whereas lower transferrin saturation (TSAT) has been linked to reduced survival ( 11 – 13 ). However, the relationship between iron metabolism and sepsis-associated kidney injury has not been clearly defined. In this context, the present retrospective study evaluated iron-related parameters measured within 48 hours after admission in patients with sepsis, including SF, SI, and TSAT, and examined their associations with SA-AKI. The findings may provide additional information for clinical assessment and further insight into the role of iron dysregulation in SA-AKI. 2 Materials and methods 2.1 Data source This retrospective study used data extracted from the Yidu Cloud electronic medical record system. We included patients who met the diagnostic criteria for sepsis and were admitted to the Second Affiliated Hospital of Dalian Medical University between January 1, 2015 and September 30, 2024. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Dalian Medical University (No. KY2024-242-01). 2.2 Selection criteria Sepsis was defined according to the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) published in 2016 ( 14 ). AKI was diagnosed according to the 2012 Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline for AKI ( 15 ). The inclusion criteria were as follows: (1) age ≥18 years; (2) diagnosis of sepsis based on the Sepsis-3 criteria; and (3) completion of the laboratory tests required for this study within 48 hours after admission. The exclusion criteria were as follows: (1) chronic kidney disease; (2) decompensated cirrhosis; (3) upper gastrointestinal bleeding; (4) malignant tumors; (5) hematologic diseases; (6) immune disorders; (7) prior organ transplantation; (8) other diseases associated with kidney injury; and (9) long-term regular use before admission or ongoing use at admission of medications that might affect renal function or iron metabolism ( Figure 1 ). Figure 1 Flowchart of patient selection and grouping When a preadmission SCr value was available in the electronic medical records, it was used as the baseline SCr. Patients with documented pre-existing abnormal SCr values, chronic kidney disease, or other known kidney diseases were excluded. When no historical SCr value was available, the sex-specific upper limit of the institutional laboratory reference range was used as the surrogate baseline SCr value: 104 μmol/L for adult males and 84 μmol/L for adult females, as measured using an enzymatic method. 2.3 Data collection The following baseline variables were collected: age, sex, medical history, infection site at admission (respiratory, urinary, abdominal, or other), mean arterial pressure (MAP), and Sequential Organ Failure Assessment (SOFA) score. Laboratory results obtained within 48 hours after admission were also collected, including routine blood indices [white blood cell count (WBC), neutrophil count (NEU), platelet count (PLT), red blood cell count (RBC), and hemoglobin (Hb)]; inflammatory markers [C-reactive protein (CRP) and procalcitonin (PCT)]; liver function indices [total bilirubin (TBil), alanine aminotransferase (ALT), and aspartate aminotransferase (AST)]; renal function indices [SCr and blood urea nitrogen (BUN)]; iron-related parameters [SF, SI, and TSAT]; tissue perfusion marker [lactate (LAC)]; and coagulation parameters [D-dimer, prothrombin time activity (PTA), fibrinogen (Fbg), and activated partial thromboplastin time (APTT)]. 2.4 Study outcome Patients were initially classified into the non-AKI and AKI groups according to whether AKI had been clinically diagnosed and documented in the medical records within the first 48 hours after admission. All patients classified as having AKI were subsequently reviewed by the investigators to confirm that they met the KDIGO SCr criteria for AKI. 2.5 Statistical analysis Statistical analyses were performed using IBM SPSS Statistics version 26.0. Continuous variables were assessed for normality. Normally distributed variables are presented as the mean ± standard deviation and were compared using the independent-samples t test. Non-normally distributed variables are presented as the median (Q1, Q3) and were compared using the Mann–Whitney U test. Categorical variables are presented as numbers and percentages [n (%)] and were compared using the chi-square test. Spearman correlation analysis was used to assess the associations between iron-related parameters and SA-AKI. SF, SI, and TSAT were further categorized into quartiles to examine trends in the incidence of SA-AKI across increasing quartiles. Variables included in the clinical model were selected according to clinical relevance and data availability. The linearity of continuous variables in the logit was assessed using the Box–Tidwell test. To account for multiple testing, a Bonferroni-adjusted significance threshold, calculated as 0.05 divided by the number of continuous variables examined, was applied. Continuous variables that did not satisfy the linearity assumption were natural-log transformed and reassessed before model fitting. Two multivariable logistic regression models were subsequently constructed to evaluate factors associated with SA-AKI. Model 1 included age, sex, hypertension, diabetes mellitus, coronary artery disease, stroke, infection-site indicators, WBC, PLT, PCT, LAC, D-dimer, and MAP. Model 2 included all variables in Model 1 with the addition of SF. Multicollinearity among the variables included in each model was assessed using the variance inflation factor (VIF), with a VIF <5 considered to indicate the absence of substantial multicollinearity. Logistic regression results are reported as ORs, 95% CI, and corresponding P values. Receiver operating characteristic (ROC) curves were constructed, and the areas under the curves (AUCs) with 95% CIs were calculated to assess the discriminatory ability of the iron-related parameters and the two multivariable models for identifying SA-AKI. The ROC curves for Model 1 and Model 2 were generated using the predicted probabilities derived from the corresponding models. The optimal cutoff values for the iron-related parameters were determined using the maximum Youden index. The AUCs of Model 1 and Model 2 were compared using the DeLong test. Model calibration was evaluated using the Hosmer–Lemeshow goodness-of-fit test and calibration plots comparing observed and predicted probabilities across deciles of predicted risk. Internal validation was performed using bootstrap resampling with 1,000 repetitions to assess the stability of the regression estimates. All statistical tests were two-sided, and a P < 0.05 was considered statistically significant, except when a Bonferroni-adjusted significance threshold was applied. 3 Results 3.1 Patient characteristics A total of 808 patients with sepsis were included, of whom 498 (61.6%) were assigned to the non-AKI group and 310 (38.4%) to the AKI group. Among the iron-related parameters, SF was significantly higher in the AKI group, whereas SI and TSAT were significantly lower (all P < 0.05) ( Table 1 ). Table 1 Variable Non-AKI group (n=498) AKI group (n=310) Z / χ 2 / t P Age 76 (65, 83) 78 (69, 84) -1.921 0.055 Sex Male 269 (54.0%) 170 (54.8%) Female 229 (46.0%) 140 (45.2%) Comorbidities Hypertension 251 (50.4%) 146 (47.1%) 0.835 0.361 Diabetes mellitus 179 (35.9%) 125 (40.3%) 1.561 0.212 Coronary artery disease 134 (26.9%) 101 (32.6%) 2.981 0.084 Stroke 147 (29.5%) 77 (24.8%) 2.088 0.148 None 118 (23.7%) 68 (21.9%) 0.334 0.563 Site of infection Respiratory tract 372 (74.7%) 220 (71.0%) 1.358 0.244 Urinary tract 230 (46.2%) 153 (49.4%) 0.770 0.380 Intra-abdominal 77 (15.5%) 35 (11.3%) 2.785 0.095 Other 34 (6.8%) 20 (6.5%) 0.043 0.835 Multiple sites 195 (39.2%) 111 (35.8%) 0.911 0.340 SOFA (score) 7.03 ± 3.11 8.40 ± 3.56 -5.604 <0.001 WBC (×10 9 /L) 11.32 (7.84,15.16) 12.95 (10.09,17.12) -4.539 <0.001 NEU (×10 9 /L) 9.59 (6.12,13.44) 11.11 (8.19,15.42) -4.492 <0.001 PLT (×10 9 /L) 198 (139,264) 175 (111,245) -3.221 0.001 RBC (×10 12 /L) 3.94 ± 0.70 3.58 ± 0.91 5.918 <0.001 Hb (g/L) 117.70 ± 22.58 105.33 ± 29.44 6.334 <0.001 CRP (mg/L) 81.29 (29.10,148.28) 114.50 (40.47,199.15) -3.651 <0.001 PCT (ng/mL) 0.46 (0.11,2.49) 2.36 (0.41,12.04) -8.507 <0.001 TBil (μmol/L) 15.6 (9.71, 22.46) 14.75 (10.58, 20.61) -0.318 0.750 ALT (U/L) 22.08 (13.00,38.46) 24.25 (14.00,46.83) -1.434 0.152 AST (U/L) 25.50 (18.20,45.35) 30.07 (18.00,67.85) -2.744 0.006 SCr (μmol/L) 62.95 (50.06,75.50) 194.69 (142.93,256.27) -23.766 <0.001 BUN (mmol/L) 6.00 (4.40,8.32) 17.60 (12.35,25.12) -20.398 <0.001 SF (ng/mL) 449.36 (377.85,522.76) 615.14 (512.22,713.02) -14.043 <0.001 SI (μmol/L) 13.37 (9.35,19.81) 8.43 (4.69,13.65) -10.127 <0.001 TSAT (%) 33.08 (24.52,43.41) 20.56 (14.35,34.29) -9.277 <0.001 LAC (mmol/L) 1.79 (1.38,2.66) 1.70 (1.34,2.79) -0.724 0.469 D-dimer (μg/mL) 1.69 (1.02,3.36) 2.64 (1.54,6.26) -7.175 <0.001 PTA (%) 82.36 ± 19.24 76.32 ± 18.77 4.379 <0.001 Fbg (g/L) 5.02 ± 1.97 5.00 ± 2.04 0.083 0.934 APTT (s) 39.30 (34.90,44.63) 39.90 (35.10,46.13) -1.353 0.176 MAP (mmHg) 93.94 ± 17.44 92.42 ± 19.73 1.115 0.265 Comparison of baseline clinical characteristics between the SA-AKI and non-SA-AKI groups. Other mainly included hematologic and lymphatic system infections, nervous system infections, bone and joint infections, and skin and soft tissue infections. 3.2 Correlations between iron-related parameters and SA-AKI Spearman correlation analysis showed that SF was positively correlated with AKI occurrence, whereas SI and TSAT were moderately negatively correlated with AKI occurrence. All correlations were statistically significant (all P < 0.05) ( Table 2 ). Table 2 Variable r 95% CI P SF 0.494 0.439, 0.546 <0.001 SI -0.356 -0.417, -0.293 <0.001 TSAT -0.327 -0.389, -0.262 <0.001 Associations of iron-related parameters with the risk of SA-AKI. 3.3 Associations of iron-related parameters with SA-AKI incidence According to the study by Sun et al. ( 16 ), patients were stratified into quartiles based on baseline SF, SI, and TSAT levels. The incidence of AKI increased progressively across SF quartiles ( P < 0.001). In contrast, AKI incidence decreased progressively across SI quartiles ( P < 0.001). A similar overall trend was observed for TSAT ( P < 0.001), although the difference between Q3 and Q4 was not statistically significant ( P = 0.195) ( Table 3 ). Table 3 Variable Q1 Q2 Q3 Q4 P SF 25 (12.4%) 44 (21.8%) 85 (42.1%) 156 (77.2%) <0.001 SI 143 (70.8%) 62 (30.7%) 60 (29.7%) 45 (22.3%) <0.001 TSAT 142 (70.3%) 67 (33.2%) 49 (24.3%) 52 (25.7%) # <0.001 Proportions of SA-AKI across quartiles of iron-related parameters. # P = 0.195 for the comparison between the Q3 and Q4 groups. 3.4 Multivariable logistic regression analyses The Box–Tidwell assessment indicated that PCT required natural-log transformation. After transformation, the continuous variables included in the final models satisfied the linearity assumption in the logit according to the Bonferroni-adjusted significance threshold. No substantial multicollinearity was detected in either model, with all VIF values below 5. In Model 1, age, stroke, intra-abdominal infection, WBC, natural-log-transformed PCT, and D-dimer were significantly associated with SA-AKI. After SF was added to the clinical model, SF was significantly associated with SA-AKI, with an OR of 2.479 per 100-ng/mL increase (95% CI 2.123,2.894, P < 0.001). Age, stroke, respiratory tract infection, intra-abdominal infection, natural-log-transformed PCT, and D-dimer also remained significantly associated with SA-AKI in Model 2, whereas WBC no longer reached statistical significance (P = 0.066) ( Table 4 ). Table 4 Variable Model 1 Model 2 OR 95% CI P OR 95% CI P Age 1.018 1.005,1.030 0.005 1.016 1.002,1.031 0.027 Sex 1.106 0.797,1.537 0.547 1.077 0.739,1.569 0.700 Comorbidities Hypertension 0.913 0.650,1.283 0.601 1.132 0.769,1.667 0.530 Diabetes mellitus 1.100 0.786,1.537 0.579 0.869 0.594,1.271 0.469 Coronary artery disease 1.277 0.905,1.803 0.164 1.110 0.748,1.647 0.603 Stroke 0.625 0.434,0.902 0.012 0.572 0.377,0.869 0.009 Site of infection Respiratory tract 0.756 0.503,1.135 0.177 0.609 0.382,0.970 0.037 Urinary tract 1.239 0.872,1.759 0.232 1.423 0.953,2.125 0.084 Intra-abdominal 0.390 0.231,0.658 <0.001 0.378 0.210,0.682 0.001 Other 0.780 0.408,1.492 0.452 0.695 0.329,1.466 0.339 WBC (×10 9 /L) 1.039 1.012,1.066 0.004 1.027 0.998,1.056 0.066 PLT (×10 9 /L) 0.999 0.998,1.001 0.307 1.000 0.998,1.002 0.886 ln[PCT (ng/mL)] 1.336 1.234,1.448 <0.001 1.339 1.223,1.466 <0.001 LAC (mmol/L) 1.006 0.938,1.080 0.862 0.981 0.907,1.061 0.632 D-dimer (μg/mL) 1.072 1.036,1.110 <0.001 1.064 1.024,1.104 0.001 MAP (mmHg) 1.001 0.993,1.010 0.750 1.004 0.995,1.014 0.380 SF (per 100 ng/mL) 2.479 2.123,2.894 <0.001 Multivariable logistic regression analyses of the clinical model and the clinical model plus SF for SA-AKI. PCT was natural-log transformed to satisfy the linearity assumption in the logit. The OR for SF was expressed per 100-ng/mL increase. For binary variables, the reference categories were male for sex and absence of the corresponding comorbidity or infection-site indicator. 3.5 Discrimination, calibration, and internal validation of the models Model 1 showed an AUC of 0.730 (95% CI 0.695,0.766, P < 0.001), whereas
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