Acute kidney injury (AKI) is one of the most common complications following aortic surgery and elevates the risks for: postoperative mortality, renal replacement therapy (RRT), prolonged intensive care unit (ICU) and hospital stay, higher medical costs, and a continued risk of death 10 years after surgery [1, 2, 3]. Early identification of high-risk AKI patients is crucial due to the condition’s rapid progression and poor prognosis. While serum creatinine and urine output are standard diagnostic markers, their limitations, their predictive value may be limited due to their delayed rise, and the fact that urine output may not be a reliable indicator during the polyuric phase [4, 5]. Therefore, alternative biomarkers based on the pathophysiological features of AKI for early risk identification are needed. In addition to some well-studied urinary biomarkers such as neutrophil gelatinase-associated lipocalin (NGAL), insulin-like growth factor binding protein-7 (IGFBP-7), and tissue inhibitor of metalloproteinase-2 (TIMP-2) [6, 7, 8, 9, 10, 11], blood biomarkers involved in the immune-inflammatory phase of AKI have also been identified and warrant further research.

Previous research has demonstrated that the innate immune response is closely linked to the pathogenesis of renal ischemia-reperfusion injuries [12]. Among the innate immune cells involved in this process, macrophages have been found to play a complex role throughout the development of AKI via their polarization to either a “pro-inflammatory” or “anti-inflammatory” phenotype [13]. Several animal studies have confirmed that macrophage polarization is involved in the initiation and repair of AKI, and influences the outcome [14, 15, 16]. Macrophage polarization can be induced by various cytokines such as interferon-gamma (IFN-$\gamma$) and interleukin-4 (IL-4), and polarized macrophages also release various inflammatory cytokines which exert specific effects [17, 18]. Therefore, the evaluation of macrophage polarization-related cytokines may also be valuable in predicting AKI following surgery. Previous studies have shown that macrophage polarization-related cytokines such as monocyte chemotactic protein-1 (MCP-1), interleukin-10 (IL-10), interleukin-6 (IL-6), and interleukin-1 receptor antagonist (IL-1RA) are associated with AKI in patients undergoing aortic surgery [19, 20].

Despite their potential, these biomarkers that were individually selected to predict AKI, have demonstrated limited sensitivity and specificity in clinical practice. Moreover, these existing studies were limited to specific aortic disease groups, lacking independent validation in broader populations. Therefore, utilizing a more comprehensive set of macrophage polarization-related cytokines in a cohort of patients with multiple aortic diseases, and integrating multiple cytokines into a simple model may improve the prediction of AKI risk assessment.

In this study, we conducted a systematic analysis of eight macrophage polarization-related cytokines in plasma samples from adult patients who underwent aortic surgery with cardiopulmonary bypass (CPB). We used all-subset regression to identify promising cytokines for inclusion in a model aimed at predicting postoperative AKI. In addition, we assessed the ability of each cytokine of interest to determine the risk of AKI as well as in-hospital outcomes.

Our study found that elevated levels of cytokines related to macrophage polarization, specifically IFN-$\gamma$ and IL-4, are significantly associates with AKI development in patients undergoing CPB surgery. The cytokines IFN-$\gamma$ and IL-4 were selected as predictors of AKI by our all-subset regression model. Compared to models reliant on clinical factors like age, hypertension, preoperative serum creatinine, and extended CPB time, our cytokine-based model demonstrated superior predictive accuracy and net clinical benefit for AKI. Furthermore, both IFN-$\gamma$ and IL-4 appear to function as independent risk factors for AKI, with higher levels of each cytokine correlating with more severe AKI and adverse composite outcomes.

AKI is a frequent complication following aortic surgery and is strongly linked to poor clinical outcomes, including the need for RRT and increased mortality. Current diagnostic measures for AKI, including serum creatinine levels, fall short in early detection of renal tubular injury due to their delayed elevation and low sensitivity [27]. A study involving 303 patients was designed to assess the correlation between AKI (as defined by serum creatinine) and diffuse histologic criteria for acute kidney injury based on renal biopsy. Remarkably, approximately one-third of patients meeting the histological AKI criteria failed to meet AKI diagnostic criteria. This was primarily because serum creatinine did not rise quickly enough to fulfill the definition of AKI [28].

Therefore, there is a considerable need for a reliable biomarker to predict early AKI, as timely intervention can lead to the prevention of multiple clinical complications. In recent years, several novel biomarkers for AKI have been evaluated in aortic surgery. The most studied biomarkers, derived from urine or plasma samples, are related to cell cycle arrest, such as NGAL, liver-type fatty acid-binding protein (L-FABP), IGFBP-7, TIMP-2, and cystatin C [7, 8, 9, 10, 11, 29, 30, 31, 32]. A few studies have evaluated the predictive ability of blood immune-inflammatory biomarkers such as plasma secretory leukocyte peptidase inhibitor (SLPI), pentraxin 3 (PTX3), IL1-RA, MCP-1, suppressor of tumorigenicity 2 (ST-2), IL-6, and IL-10 [19, 20, 33, 34]. However, these heterogeneous studies mainly evaluated the association between an individual or multiple selected cytokines and AKI based on a small number of samples and most were limited to a specific setting, with wide variations in predictive performance. As a result, it has been suggested that these studies may have introduced a selection bias that has undermined the ability of individual biomarkers to predict AKI.

Our study differs from previous research by aiming to develop and validate a novel robust predictive model specifically for detecting AKI in patients who underwent aortic surgery with CPB. We evaluated eight macrophage polarization-related cytokines (including IFN-$\gamma$, IL-4, IL-6, IL-10, IL-1RA, IL-1$\beta$, IL-12p40, and TNF-$\alpha$) in a cohort of 156 patients undergoing a diverse range of aortic surgery. As shown in Fig. 1, the Mann-Whitney analysis showed the concentrations of IFN-$\gamma$, IL-4, IL-6, IL-10, IL-1RA, IL-12p40 and TNF-$\alpha$ were significantly higher in the AKI group compared with the non-AKI group (p 0.001). In contrast, there was no difference in IL-1$\beta$ levels between the AKI and non-AKI groups (p = 0.216). Furthermore, univariate regression analysis also showed that these 7 cytokines (IFN-$\gamma$, IL-4, IL-6, IL-10, IL-1RA, IL-12p40 and TNF-$\alpha$) were significantly associated with AKI (p 0.05), whereas IL-1$\beta$ was not (p = 0.280) (Table 2). These 7 cytokines showed statistical significance between the AKI and non-AKI groups in univariate analysis, indicating that multiple macrophage polarization-related cytokines, rather than 1 or 2 cytokines, are involved in postoperative AKI.

To objectively select the most predictive combination of cytokines for estimating AKI among these 7 cytokines, we used all-subset regression, also known as best subset selection. It can fit all possible combination models of predictive variables and then select the best model under the condition of the existing variables according to the adjusted r-squared and BIC. The adjusted r-squared value reflects the explanatory power of the combination of predictors on the dependent variable, while the BIC value reflects the goodness of fit of the model. Given the high adjusted r-squared and the lowest BIC, IFN-$\gamma$ and IL-4 were selected to build the predictive model. Binary logistic regression was used to build the predictive model. The predictive model based on IFN-$\gamma$ and IL-4 yielded a powerful discriminative ability of AKI. Even after separating the entire population into subgroups based on age, sex, and type of surgery, positive results were still obtained from this model. Our model outperformed the standard clinical factor-based models in multiple subgroups including females, the elderly, and patients with a single surgical site, reflecting improved clinical applicability and reliability.

In clinical practice, the standard models for predicting postoperative AKI typically rely on patient clinical data. Several clinical characteristics of patients, such as age, preoperative serum creatinine, history of hypertension, poor preoperative cardiac functional status, and prolonged CPB time, have been considered risk factors for AKI after aortic surgery [22, 23, 24, 25, 26]. Zhang and his colleagues reported 4 clinical prediction models for AKI based on perioperative variables for patients with acute type A aortic dissection (ATAAD) undergoing Sun’s procedure, with the AUC ranging from 0.710 to 0.848 [35]. Kim and his colleagues [36] reported a simplified clinical score including 6 clinical variables (age, preoperative glomerular filtration rate, left ventricular ejection fraction, operation time, intraoperative urine output, and intraoperative use of furosemide) to stratify the risk of postoperative AKI in patients undergoing aortic surgery, with an AUC of 0.740. Although the variables of these prediction models were easy to collect, most of them lacked specificity, and their predictive power may largely depend on the richness of the variables. In our study, we compared a standard clinical factor-based prediction model using age, sex, hypertension history, NYHA III-IV, and prolonged CPB time to our cytokine-based prediction model described above. Consistent with the existing literature, our clinical factor-based model had an average predictive power with an AUC of 0.72, which was much lower than our cytokine-based prediction model with an AUC of 0.90. In addition, our cytokine model had many advantages over the clinical factor model in terms of patient reclassification and net clinical benefit, as well as model parsimony.

To the best of our knowledge, while both IFN-y and IL-4 are known to play contribute towards AKI pathophysiology, their predictive value in post-surgical AKI has not been previously documented in the existing literature. IFN-$\gamma$, the typical cytokine produced by classically activated macrophages (M1), has been implicated in CD4+ T cell-mediated ischemia-reperfusion injury [37]. While derived from alternatively activated macrophages (M2), IL-4 has been involved in tissue repair and recovery from ischemia-reperfusion injury and promoted renal tubular interstitial fibrosis in animal models [38, 39]. In a study by Moledina and colleagues, both IFN-$\gamma$ and IL-4 were elevated in the blood within 6 hours of surgery and were independently associated with both AKI and decreased one-year mortality rates following cardiac surgery [40]. Similarly, our study demonstrates for the first time that IFN-$\gamma$ and IL-4 were significantly increased by the first day following surgery and were independently associated with AKI after aortic surgery. Specifically, after adjustment for confounders, the second and third tertiles of IL-4 had a 5.32- and 68.05-fold greater risk of AKI, respectively, than the first tertile. In contrast, the risk of AKI in the second and third tertiles of IFN-$\gamma$ was 12.39-fold and 55.32-fold higher, than in the first tertile. In addition, we found that patients in the highest tertiles of IFN-$\gamma$ or IL-4 were at high risk for severe AKI, composite outcomes, longer ICU stays, longer hospital stays, and longer periods of mechanical ventilation. Furthermore, each cytokine had prognostic value for the prediction of increased risk of AKI and composite outcomes across age, sex, and surgery type groups. In conclusion, our results suggest that IL-4 and IFN-$\gamma$ not only independently predicted AKI after aortic surgery, but also increased morbidity and mortality in these patients. Thus, IL-4 and IFN-$\gamma$ are promising potential therapeutic targets for the treatment of AKI. Further large-scale studies are needed to confirm these findings in the future.

Since IFN-$\gamma$ and IL-4 were significantly associated with postoperative AKI in both adult aortic surgery and adult cardiac surgery, macrophage polarization-related cytokines may represent a common pathophysiological mechanism involved in postoperative AKI with CPB. Given the encouraging results of our study, we look forward to extending our study to patients undergoing cardiac surgery with CPB to predict AKI in the future.

There were some notable differences between our study and other studies reported in the literature on this subject. For example, the serum concentrations of IFN-$\gamma$ were significantly higher in patients with AKI after surgery in our study. However, in a study of the repair of congenital cardiac defects with CPB, children with AKI did not have significantly elevated serum IFN-$\gamma$ levels at 2, 12, and 24 hours after CPB compared with patients who did not develop AKI [41]. In addition, IFN-$\gamma$ and IL-4 levels strongly correlated with AKI severity in our study, whereas children with progressive AKI did not show a significant difference in IFN-$\gamma$ and IL-4 levels compared to children without progressive AKI after surgery in another cohort of patients with congenital heart disease [42]. These differences suggest that the immunoinflammatory process in postoperative AKI is complex and may differ between study populations and types of surgery. This may be due to the differences in the pathophysiology between children and adults, as well as the differences in operations between adult aortic and congenital heart surgery, that may influence the immunoinflammatory pathways that affect the various changes in blood cytokines. Additionally, cytokine concentrations may differ based on the time of sampling, sample source, and methods of measurement. Therefore, when employing these cytokines or models for the prediction of AKI in clinical practice, it is necessary to consider the conditions of their application, such as the appropriate objectives, specific types of surgery, the time of sampling, the sample source, and the methods of measurement.

This study has multiple limitations warranting discussion. First, its retrospective, observational, and single center nature potentially introduces selection bias, rendering the findings less generalizable. Since some selection bias may be inevitable, the results cannot be considered conclusive. Second, while we conducted internal validation using 1000 bootstrap samples, external validation from a broader array of hospitals is required to confirm the model’s robustness. Third, this study had a limited sample. Since the criteria for AKI are not uniform, we could have missed some patients who had subsequently developed AKI had we followed the Acute Kidney Injury Network guideline, which only takes into account a blood creatinine rise within 48 hours following surgery. Finally, our predictive model is limited because it is based on cytokines, whose assays may not be available in some hospitals.

In patients undergoing aortic surgery with CPB, we found a strong association between macrophage polarization-related cytokines and postoperative AKI. Our internally validated model, incorporating IFN-$\gamma$ and IL-4, reliably predicts AKI risks and facilitates AKI risk stratification in patients undergoing aortic surgery. Patients with high levels of IFN-$\gamma$ and IL-4 were more likely to experience delayed recovery, severe AKI, and adverse composite outcomes. Although promising, the model requires external validation from multi-center data for broader clinical application. Once confirmed, this predictive tool could offer clinicians valuable insights for early identification of patients at high risk for AKI development.

AKI, acute kidney injury; AIC, akaike information criterion; AKIN, acute kidney injury network; BIC, Bayesian information criterion; CPB, cardiopulmonary bypass; DCA, decision curve analysis; ICU, intensive care unit; IDI, integrated discrimination improvement; NGAL, neutrophil gelatinase-associated lipocalin; IGFBP-7, insulin-like growth factor binding protein-7; TIMP-2, tissue inhibitor of metalloproteinase-2; GFR, glomerular filtration rate; LVEF, left ventricular ejection fraction; NRI, net reclassification improvement; ROC, receiver operator characteristic curve; AUC, area under the curve; RRT, renal replacement therapy; IFN-$\gamma$, interferon-gamma; IL-4, interleukin-4; MCP-1, monocyte chemotactic protein-1; IL-10, interleukin-10; IL-6, interleukin-6; IL-1RA, interleukin-1 receptor antagonist; BMI, body mass index; IL-1$\beta$, interleukin-1$\beta$; IL-12p40, interleukin-12p40; TNF-$\alpha$, tumor necrosis factor-alpha; SLPI, serum leukocyte peptidase inhibitor; PTX3, pentraxin 3; ST-2, suppressor of tumorigenicity-2.

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

LQW and ZYC designed the research study. SZ participated in data collection. SZ, MJC, PFC, and DMZ analyzed the data. MJC drafted the article. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

This study was conducted after the acquisition of written informed consent from the participating patients and upon approval by the Ethics Committees of Fuwai Hospital (No. 2023-2005).

The authors wish to thank Zhongli Chen (Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease of China, Beijing, China) for her assistance in performing the statistical analysis for this work.

This work was supported by the National Natural Science Foundation of China (NO.: NSFC8210022135) and the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (NO.: 2021-1-I2M-016).

The authors declare no conflict of interest.