L-Carnitine, A new biomarker screened based on untargeted metabolomics, predict cardiac surgery-associated acute kidney injury: A prospective cohort study

L-Carnitine, A new biomarker screened based on untargeted metabolomics, predict cardiac surgery-associated acute kidney injury: A prospective cohort study | 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 L-Carnitine, A new biomarker screened based on untargeted metabolomics, predict cardiac surgery-associated acute kidney injury: A prospective cohort study Wenxiu Chen, Hao Zhang, Xiao Shen, Liang Hong, Hong Tao, Ming Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7206985/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2026 Read the published version in BMC Nephrology → Version 1 posted 9 You are reading this latest preprint version Abstract Background: This study aims to investigate the diagnostic significance of L-carnitine (LC) for the early detection of acute kidney injury associated with cardiac surgery (CSA-AKI). Methods: We collected clinical data and serum samples from 27 patients admitted to the Intensive Care Unit (ICU) of Nanjing Medical University Affiliated Nanjing Hospital between February 2024 and March 2024. Of these, 13 patients belonged to the CSA-AKI group, while 14 were in the non-CSA-AKI group. An untargeted metabolomic analysis was conducted, which identified LC as a differential metabolite. In addition, clinical data and serum samples were prospectively collected from patients undergoing cardiac surgery at Nanjing Medical University Affiliated Nanjing Hospital between May 2024 and July 2024. Serum samples were taken preoperatively (immediately upon entering the operating room) and postoperatively (immediately upon ICU admission). The concentrations of blood urea nitrogen (BUN), serum creatinine (Scr), neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and LC were assessed. Multivariate logistic regression analysis was used to find independent risk variables for CSA-AKI. Predictive performance of the biomarkers, the clinical model, and their combination were evaluated using the area under the receiver operating characteristic curve (AUC). Results: 170 patients in all who satisfied the inclusion requirements for cardiac surgery were included in the study. The incidence of CSA-AKI was 27.06%. Multivariate logistic regression analysis indicated that preoperative heart failure, vasopressor-inotropic score, and postoperative partial pressure of oxygen were independent risk factors for the development of CSA-AKI. Serum biomarker analysis showed significant differences in BUN, Scr, NGAL, and LC levels before and after cardiac surgery. After surgery, LC levels in patients with CSA-AKI were considerably lower than those in patients without CSA-AKI. Postoperative LC had a predictive ability with an AUC of 0.777 (95%CI: 0.697-0.857, P < 0.001). Incorporating postoperative LC into the clinical model can greatly enhance the model's predictive performance. Conclusion: Postoperative LC can effectively predict the occurrence of CSA-AKI, and when combined with the clinical prediction model, it demonstrates improved predictive performance for CSA-AKI. Cardiac Surgery-Associated Acute Kidney Injury Metabolomics L-Carnitine Clinical Prediction Model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Cardiac surgery-associated acute kidney injury (CSA-AKI) is a prevalent complication linked to higher rates of morbidity and mortality. Several factors may contribute to the occurrence and development of CSA-AKI, including ischemia-reperfusion injury, inflammation, hypoperfusion, and oxidative stress [ 1 ]. In clinical practice, traditional diagnostic markers like serum creatinine (Scr) and urine output are commonly employed for diagnosing AKI, which often results in delays in diagnosis and the subsequent implementation of prevention and treatment strategies for AKI [ 2 ]. While there are currently some novel and potentially useful biomarkers to be found, including cystatin C, and plasma neutrophil gelatinase-associated lipocalin (NGAL), the use of urinary tissue inhibitors of metalloproteinases-2 (TIMP-2), and the insulin-like growth factor binding protein 7 (IGFBP7) are only a few of the factors that are relevant for predicting AKI [ 3 ]. There is still controversy regarding the best biomarker for CSA-AKI monitoring. Early diagnosis of AKI can help initiate strategies to prevent severe kidney damage, therefore, finding early diagnostic markers for CSA-AKI is particularly important. After the development of genomes and proteomics, metabolomics is a relatively new field of study. As a branch of holistic science, it primarily studies the composition of all small-molecule metabolites within organisms and their dynamic changes under internal and external stimuli [ 4 ]. Among high-throughput analytical techniques, genomics represents the maximal potential of a cell, what could happen; transcriptomics indicates the developmental direction of a cell, what will happen; proteomics reflects the functional execution of a cell, what causes things to happen; while metabolomics reveals the ultimate functional state of a cell, what has already happened or is currently happening. Located downstream of the central dogma, metabolomics captures nearly all changes in genes and proteins at the metabolic level. Therefore, metabolomics serves as a bridge linking genotype with phenotype, as well as the microscopic with the macroscopic [ 5 ]. The integration of chromatography and mass spectrometry facilitates the complete workflow, from separating substances with chromatography to identifying them through mass spectrometry. In particular, the tandem use of liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry allows expert qualitative and quantitative analysis, significantly improving the coverage of metabolomic detection and providing more complete information on metabolites and their abundances [ 6 , 7 ]. However, there are relatively few metabolomic studies on blood in patients with CSA-AKI. Collected serum samples and performed untargeted metabolomic analysis, identifying L-carnitine (LC) as a differential metabolite. Carnitine, scientifically referred to as β-hydroxy-γ-N-trimethylammonium butyric acid, is a quaternary ammonium compound, with its biologically active stereoisomer being LC [ 8 ]. The liver, brain, and kidneys all produce LC, a branched-chain non-essential amino acid, which is produced from lysine and methionine [ 9 , 10 ]. Due to its ability to move activated long-chain fatty acids from the cytoplasm into mitochondria [ 11 ], LC is necessary for the process of β-oxidation. The kidney, due to its high energy demands and its key role in filtering toxic and non-toxic metabolites, is particularly susceptible to injury caused by ischemia, mitochondrial dysfunction, and oxidative stress. In healthy kidneys, renal tubular cells have a high concentration of mitochondria to fulfill the substantial ATP requirements needed for the reabsorption of significant amounts of ultrafiltrate and solutes, with mitochondrial function primarily depending on oxidative phosphorylation and fatty acid β-oxidation. Three types of AKI, nephrotoxic AKI, ischemia/reperfusion injury AKI, and cytokine-mediated or other pro-inflammatory models of AKI, are associated with mitochondrial metabolic disturbances and impaired free radical scavenging, encompassing nearly all causes of AKI in critically ill patients. In addition to serving as a fatty acid co-transporter, LC also safeguards mitochondrial function and maintains renal health by directly acting as an antioxidant. It functions as a free radical scavenger, boosts the body’s natural antioxidant defenses, and inhibits the buildup of lipid peroxidation products [ 13 , 14 ]. Research indicates that rat renal tubular cells that were pretreated with LC displayed lower transcriptional activity of nuclear factor-κB and reduced levels of tumor necrosis factor-α (TNF-α), intercellular adhesion molecule-1, and monocyte chemoattractant protein-1 when compared to untreated cells. The observed decrease in nephrotoxicity following LC treatment is believed to result from diminished downstream mitochondrial fragmentation, improved mitochondrial adaptation to elevated energy demands, and LC’s capacity to scavenge free radicals [ 12 , 15 ]. Furthermore, research has revealed that serum LC levels in children suffering from urinary tract infections were markedly lower compared to those in healthy children [ 16 ]. Combined the results of untargeted metabolomics, we therefore hypothesized that serum LC might be used as a biomarker for early diagnosis of CSA-AKI. We performed a prospective single-center cohort research on patients having cardiac surgery to assess the predictive ability of LC for CSA-AKI in order to verify the hypothesis. Methods and Materials Study design and participants The Department of Intensive Care Unit at Nanjing Medical University Affiliated Nanjing Hospital (Nanjing, China) carried out this prospective single-center research project. Patients were enrolled who underwent cardiac surgery with cardiopulmonary bypass and over 18 years old between February 2024 and March 2024. The exclusion criteria were as follows individuals under 18 years of age, those with advanced chronic kidney disease (CKD) prior to cardiac surgery (including chronic dialysis, kidney transplantation, or a preoperative estimated glomerular filtration rate [eGFR] <30 ml/min/1.73 m²), patients who experienced acute kidney injury (AKI) before the cardiac surgery, and individuals without serum creatinine (Scr) values recorded before and after the surgery. The untargeted metabolomics included thirteen CSA-AKI patients and fourteen non-CSA-AKI patients, the validation cohort was composed of 170 eligible patients. The research was conducted in compliance with the Declaration of Helsinki and received approval from the Regional Human Research Ethics Committee of Nanjing Medical University Affiliated Nanjing Hospital (KY20240123-01). All participants provided written informed consent. Sample collection Blood samples were collected at two points for all participants: immediately when the patients entered the operating room and immediately after the patients was admitted to the ICU, which we refer them as preoperative and postoperative. After collection, blood samples were left at room temperature for 60 minutes to allow clotting, then centrifuged at 3000rpm for 10 minutes at 4°C. Two hundred microlitres (200μL) of the supernatant (serum) was transferred into properly labeled 2mL centrifuge tubes. Following five minutes of rapid freezing in liquid nitrogen, the samples were kept at 80°C. Freeze-thaw cycles were repeatedly avoided. Sample measurement Untargeted metabolomics cohort: serum samples (preoperative and postoperative) from CSA-AKI patients (n=13) and age, gender-matched non-CSA-AKI controls (n=14) were thawed. Then vortex them for 10 seconds to mix thoroughly, and transfer 50μL of each serum sample into corresponding labeled centrifuge tubes. Add 300μL of 20% acetonitrile-methanol internal standard extraction solution, vortex for 3 minutes. Centrifuge at 12,000r/min for 10 minutes at 4 °C. Following centrifugation, transfer 200μL of the supernatant to a separately labeled centrifuge tube and store it in a -20°C freezer for 30 minutes. After centrifugation at 12000r/min for three minutes at 4°C, the mixture was left at 4°C. For the purpose of instrumental analysis, then transfer 180μL of the supernatant into the corresponding autosampler vial insert. In the validation cohort: LC, BUN, Scr, KIM-1, NGAL levels were measured by ELISA Kit (COIBO BIO, Shanghai, China) according to the manufacturer’s instructions. All samples were measured in duplicate. The detection ranges for LC, BUN, Scr, KIM-1, NGAL are 3.75 μmol/L-120 μmol/L, 0.75mmol/L-24mmol/L, 6.25 μmol/L-200 μmol/L, 0.25ng/mL-8ng/mL, 3.75ng/mL-120ng/mL. Outcome definition AKI is defined based on the KDIGO criteria, where its occurrence is indicated by either an increase in serum creatinine (Scr) of 26.5 mmol/L (0.3 mg/dl) within 48 hours, a 50% increase in Scr from the preoperative baseline within 7 days, or a urine output of less than 0.5 mL/kg/h for more than 6 consecutive hours. Statistical analysis We used R 4.4.1 and SPSS 25.0 for data analysis, and GraphPad Prism 9.5.1 for graphing. Continuous variables that follow a normal distribution are expressed as mean ± standard deviation(x ± s), while non-normally distributed continuous variables are expressed as median and interquartile range. Group comparisons were conducted using either an independent samples t-test or the Mann-Whitney U test. Using the chi-square test, categorical variables are shown as counts or percentages and compared. Multivariate logistic regression was used to examine independent risk variables associated with CSA-AKI. The predictive performance of the LC model and the LC combined clinical prediction model for CSA-AKI was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). Results Analysis of untargeted metabolomics In this part of the study, LC-MS/MS-based metabolomics was utilized to analyze 26 samples from CSA-AKI patients (categorized into AKI_pre and AKI_post groups) and 28 samples from non-CSA-AKI patients (classified into Non_AKI_pre and Non_AKI_post groups). Metabolite ion peaks were extracted from the metabolic profiles, resulting in a preliminary collection that identified 2,730 metabolites in positive ion mode and 2,240 metabolites in negative ion mode. There were 1,155 differentially expressed metabolites between the AKI_pre and Non_AKI_pre groups, and 451 differentially expressed metabolites between the AKI_post and Non_AKI_post groups. The extracted ion peaks were used to generate principle component analysis (PCA) plots, where each point represents a sample, allowing observation of sample clustering and dispersion. Figures 1 (A, B) illustrate the PCA maps for all groups (AKI_pre, AKI_post, Non_AKI_pre, Non_AKI_post) under both positive and negative ion modes. The tight clustering of quality control (QC) samples indicates a stable and reproducible instrumental analysis system, suggesting reliable experimental data that reflect biological variability among the samples. Figures 1 (C, D) illustrate the positional distribution and clustering patterns of the AKI_pre versus Non_AKI_pre groups and the AKI_post versus Non_AKI_post groups, respectively, reflecting their similarities and differences. To provide a clearer understanding of the metabolic profile differences among the groups, orthogonal partial least squares discrimination analysis (OPLS-DA) was conducted on the data.In contrast to PCA, OPLS-DA is a supervised discriminant analysis technique that employs partial least squares regression to create a model that correlates metabolite expression levels with sample categories. It then filters out noise unrelated to classification information, thereby enhancing the model’s effectiveness and interpretability for predicting sample categories. In the model, R2Y and Q2 serve as indicators of the explanation rate and prediction rate respectively, and are essential metrics for assessing the reliability of the model. Values of both greater than 0.1 indicate a relatively reliable model. In Figure 2A, the R2Y and Q2 values for the AKI_pre and Non_AKI_pre groups were 0.997 and 0.857, respectively. In Figure 2B, the R2Y and Q2 values for the AKI_post and Non_AKI_post groups were 0.97 and 0.543, respectively. Using the variable significance of the projection (VIP) values obtained from the above OPLS-DA models as a preliminary criterion, the screening of differentially expressed metabolites was carried out to find possible differential metabolites between groups. The use of univariate statistical analysis was used to confirm these contenders. Metabolites were regarded as statistically significant if they were found to be in the univariate analysis, with a VIP value > 1 and a P -value < 0.05. As shown in Figure 3A, based on these screening criteria, a total of 451 differential metabolites were identified between the AKI_post and Non_AKI_post groups, including 122 upregulated and 329 downregulated metabolites. Figure 3B presents the results of Pearson correlation analysis performed on the screened differential metabolites, visualized as a heatmap. Figure 3C shows the Z-score normalized profiles of the differential metabolites across different samples. A typical picture of the distribution of each differential metabolite between the AKI_post and Non_AKI_post groups is given by the Z-score plot. Figure 3D illustrates the top 20 metabolites that exhibit the highest VIP values in the OPLS-DA model. Based on the above screening results, carnitine was identified as a differential metabolite between the Non_AKI_post and AKI_post groups. Clinical characteristics of validation cohort There were 170 patients undergoing cardiac surgery included in this study, a detailed flow chart can be seen in Figure 4. All surgeries were elective and used CPB. 46 patients (27.06%) developed AKI. Table 1 summarizes the demographic, clinical and laboratory characteristics as well as the adverse outcomes of patients with and without AKI. Compared with Non-CSA-AKI patients, CSA-AKI patients were older, had a higher BMI, a higher smoking rate, a higher prevalence of preoperative heart failure, longer operative time, longer cardiopulmonary bypass duration, greater total intraoperative blood transfusion volume, less intraoperative urine output, higher preoperative BUN, higher preoperative Scr, higher preoperative uric acid, lower preoperative hemoglobin, and higher clinical scores (including APACHE II score, EuroScore, vasopressor score, and Cleveland score). Additionally, postoperative oxygen partial pressure was lower, and postoperative lactate levels were higher in CSA-AKI patients. In terms of biomarkers, CSA-AKI patients showed higher postoperative NGAL levels, lower preoperative KIM-1 levels, and lower postoperative LC levels (Table 1). Table 1 Patient Characteristics by AKI Status. Characteristics All (n=170) Non CSA-AKI（n=124） CSA-AKI （n=46） P value Demographics Age, yrs 62(54,69) 61(54,68) 67(58,73) 0.016 Male, n(%) 115(67.6) 85(68.5) 30(65.2) 0.820 BMI, kg/m 2 24.48±2.86 24.18±2.70 25.30±3.12 0.022 Medical history Hypertension, n(%) 90(52.9) 62(50) 28(60.9) 0.276 Diabetes, n(%) 38(22.4) 24(19.4) 14(30.4) 0.182 Smoke, n(%) 41(24.1) 24(19.4) 17(37.0) 0.029 Heart failure, n(%) 55(32.4) 25(20.2) 30(65.2) <0.001 CKD, n(%) 9(5.3) 4(3.2) 5(10.9) 0.111 EF(%) 62.0(56.0-64.0) 62.0(57.5-64.0) 61.0(49.0-64.0) 0.365 Operative variables Operative types, n(%) 0.906 CABG 54(31.8) 41(33.1) 13(28.3) Valve 76(44.7) 53(42.7) 23(50) CABG+Valve 23(13.5) 16(12.9) 7(15.2) Others 17(10) 14(11.3) 3(6.5) Operation time, h 4.3(3.8-5.0) 4.3(3.8-5.0) 4.8(4.0-5.3) 0.042 Cross-clamp time, min 83.6±35.8 81.2±35.5 90.0±36.1 0.157 CPB time, min 113.0 (87.0-140.0) 108.0 (85.5-136.0) 122.5 (104.0-148.0) 0.039 Fluid input, ml 2500.0 (2000.0-3000.0) 2300.0 (2000.0-3000.0) 2500.0 (2000.0-2700.0) 0.297 Blood input, ml 1250.0 (950.0-1625.0) 1200.0 (925.0-1550.0) 1387.5 (1050.0-1850.0) 0.022 Total output, ml 2210.0 (1850.0-2800.0) 2250.0 (1875.0-2850.0) 2200.0 (1832.0-2500.0) 0.642 Total output blood, ml 1100.0 (1000.0-1350.0) 1100.0 (1000.0-1300.0) 1200.0 (1000.0-1500.0) 0.079 Total output urine, ml 1000.0 (750.0-1400.0) 1100.0 (800.0-1500.0) 1000.0 (620.0-1200.0) 0.006 Autologous blood, ml 500.0 (250.0-500.0) 500.0 (250.0-500.0) 500.0 (250.0-500.0) 0.276 Organic blood, ml 500.0 (400.0-500.0) 500.0 (400.0-500.0) 500.0 (400.0-500.0) 0.884 Preoperative laboratory variables HDLC, mmol/L 1.0±0.2 1.0±0.2 1.0±0.2 0.692 LDLC, mmol/L 2.2(1.6-2.7) 2.2(1.6-2.8) 2.2(1.6-2.6) 0.617 ApoA1, g/L 1.3±0.2 1.3±0.2 1.3±0.2 0.569 ApoB, g/L 0.7(0.60-0.9) 0.8(0.61-1.0-0) 0.7(0.6-0.9) 0.297 Lpa, mg/L 166.0(72.0-405.0) 201.5(74.5-437.0) 144.3(70.0-321.0) 0.471 Total Cholesterol, mmol/L 4.0(3.4-4.7) 4.0(3.3-4.7) 4.0(3.4-4.5) 0.651 Triglycerides, mmol/L 1.3 (0.9-1.8) 1.2(0.9-1.8) 1.3(1.0-1.9) 0.412 Bilirubin, umol/L 9.6(7.4-15.4) 9.7(7.6-15.1) 9.3(7.4-17.7) 0.587 BUN, mmol/L 6.5(5.2-7.8) 6.3(5.0-7.8) 7.3 (5.9-8.4) 0.014 Scr, umol/L 73.7(61.3-83.0) 71.3 (58.7-81.0) 79.1(70.1-87.4) 0.005 Uric acid, umol/L 329.0 (274.0-409.0) 313.0 (264.5-389.5) 376.5 (303.0-458.0) 0.004 K, mmol/L 3.9±0.4 3.9±0.4 3.8±0.4 0.126 Na, mmol/L 139.7 (138.3-141.1) 139.7 (138.4-141.1) 139.7 (137.9-141.2) 0.790 P, mmol/L 1.2(1.1-1.3) 1.2(1.1-1.3) 1.3(1.1-1.3) 0.100 CL, mmol/L 104.8 (102.7-106.4) 104.9 (103.3-106.5) 104.1 (102.1-106.1) 0.185 WBC, *10^9/L 5.8(4.8-7.1) 5.8(4.7-7.2) 5.4(4.9-6.9) 0.835 Hb, g/L 133.0 (123.0-141.0) 134.0 (125.5-143.5) 129.5 (121.0-136.0) 0.025 Plt, *10^9/L 182.5 (152.0-224.0) 184.5 (156.5-224.0) 177.5 (138.0-225.0) 0.326 Clinical scoring APACHE II score 13.0(11.0-15.0) 12.0(11.0-14.5) 13.5(11.0-17.0) 0.040 EuroScore score 6.0(4.0-6.0) 5.0(4.0-6.0) 6.0(5.0-7.0) 0.004 Vasoactive-inotropic score 5.0(3.0-8.0) 3.0(2.0-5.0) 10.0(8.0-13.0) <0.001 Cleveland score 2.0(1.0-3.0) 2.0(1.0-2.0) 2.0(2.0-3.0) 0.011 Postoperative laboratory variables Postoperative WBC, *10^9/L 10.8(8.8-13.7) 10.8(8.8-13.7) 10.5(8.8-13.1) 0.585 Postoperative Hb, g/L 98.5±16.9 99.7±16.3 95.4±18.3 0.139 Postoperative Plt, *10^9/L 128.0 (104.0-161.0) 131.5 (106.50-168.0) 123.0 (103.0-150.0) 0.395 Postoperative NE, *10^9/L 9.8(8.1-12.3) 9.8(8.1-12.6) 9.4(7.8-11.9) 0.303 Postoperative LY, *10^9/L 0.5(0.4-0.7) 0.5(0.4-0.7) 0.5(0.4-0.7) 0.709 Postoperative PO2, mmHg 135.5 (110.4-166.6) 140.9 (118.0-170.5) 122.8 (101.1-154.7) 0.017 Postoperative PCO2, mmHg 31.7(28.5-35.2) 31.3(28.6-35.2) 32.3(27.5-35.2) 0.758 Postoperative Lac, mmol/L 1.6(0.9-2.3) 1.5(0.9-2.1) 2.0(1.2-2.7) 0.019 Postoperative BUN, U/L 6.7(5.3-8.4) 6.3(5.2-8.0) 7.5(6.6-10.0) <0.001 Postoperative Scr, U/L 70.6(59.6-86.5) 68.3(56.2-80.1) 89.1(69.2-108.8) <0.001 Biomarkers Preoperative NGAL, ng/ml 75.3(58.3-91.8) 75.0(57.8-89.3) 78.0(62.1-96.5) 0.177 Postoperative NGAL, ng/ml 147.9 (121.3-169.7) 141.7 (114.0-165.9) 157.8 (134.6-173.2) 0.021 Preoperative KIM-1, ng/ml 6.3(5.1-7.5) 6.4(5.3-7.5) 5.7(4.4 -7.1) 0.070 Postoperative KIM-1, ng/ml 11.6(9.6-12.7) 11.5(9.7-12.70) 11.7(9.4-12.9) 0.350 Preoperative LC, umol/L 131.7(118.9-144.6) 134.5(119.7-144.9) 129.1(88.3-141.4) 0.244 Postoperative LC, umol/L 69.5(57.2-86.1) 73.4(64.0-89.4) 47.7(17.2-71.5) <0.001 Outcomes CRRT, n(%) 1(0.6) 0(0) 1(2.2) 0.605 Death, n (%) 6(3.5) 4(3.2) 2(4.3) 1.000 Abbreviations: CSA-AKI, cardiac surgery-associated acute kidney injury; BMI, Body Mass Index; CKD, chronic kidney disease; EF, ejection fraction; CABG, coronary artery bypass grafting; CPB, cardiopulmonary bypass; HDLC, High-Density Lipoprotein Cholesterol; LDLC, Low-Density Lipoprotein Cholesterol; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B; Lpa, Lipoprotein a; BUN, Blood Urea Nitrogen; Scr, Serum Creatinine; K, Potassium; Na, Sodium; P, Phosphorus; CL, Chloride; WBC, white blood cell; Hb, Hemoglobin; Plt, Platelet; APACHE II, Acute Physiology and Chronic Health Analysis II; EuroScore, European System for Cardiac Operative Risk Evaluation; NE, Neutrophil; LY, Lymphocyte; Lac, Lactate; NGAL, neutrophil gelatinase-associated lipocalin; KIM-1, kidney injury molecule-1; LC, L-carnitine; CRRT, continuous renal replacement therapy. Table 1 was used to perform the multivariate logistic regression analysis, which included the variables that showed statistically significant differences in Table 1. The findings demonstrated that preoperative heart failure (OR 7.55, 95%CI 2.04-27.90, P = 0.002), vasoactive drug score (OR 1.65，95%CI 1.33-2.04, P < 0.001), and postoperative partial pressure of oxygen (OR 0.98, 95%CI 0.96-1.00, P = 0.027) were identified as independent risk factors for the development of CSA-AKI (Table 2). Table 2 Multivariate analysis of risk factors related with CSA-AKI Characteristics OR 95%CI P value Age 0.99 0.92-1.06 0.776 BMI 1.22 0.94-1.60 0.138 Smoke 2.14 0.57-8.01 0.260 Heart failure 7.55 2.04-27.90 0.002 Operation time 1.27 0.49-3.33 0.620 CPB time 0.99 0.97-1.01 0.409 Blood input 1.00 1.00-1.00 0.517 Total output urine 1.00 1.00-1.00 0.208 BUN 0.91 0.64-1.30 0.602 Scr 1.03 0.99-1.08 0.122 Uric acid 1.00 0.99-1.01 0.807 Hb 1.01 0.97-1.04 0.705 APACHE II score 1.09 0.91-1.31 0.329 EuroScore score 1.25 0.88-1.76 0.212 Vasoactive-inotropic score 1.65 1.33-2.04 <0.001 Cleveland score 1.24 0.64-2.43 0.521 Postoperative PO2 0.98 0.96-1.00 0.027 Postoperative Lac 1.05 0.65-1.70 0.853 Abbreviations: BMI, Body Mass Index; CPB, Cardiopulmonary Bypass; BUN, Blood Urea Nitrogen; Scr, Serum Creatinine; Hb, Hemoglobin; APACHE II, Acute Physiology and Chronic Health Evaluation II; EuroScore, European System for Cardiac Operative Risk Evaluation; Lac, Lactate. By analyzing the preoperative and postoperative levels of BUN, Scr, KIM-1, NGAL, and LC in patients with and without CSA-AKI, we found that patients in the CSA-AKI group had higher preoperative and postoperative BUN and Scr levels (Figure 5). Additionally, the postoperative NGAL level was higher in the CSA-AKI group ( P = 0.021), while the postoperative LC level was lower ( P < 0.001). The AUC results indicated that the predictive performance of the clinical model based on LR for CSA-AKI was 0.828 (95% CI 0.761-0.895, P < 0.001). The postoperative LC (postLC) showed a predictive performance of 0.777 (95% CI 0.697-0.857, P < 0.001) for CSA-AKI. When the LR-based clinical model was combined with postoperative LC for prediction, the AUC increased to 0.878 (95%CI 0.819-0.937, P < 0.001), indicating that the predictive ability of the model was further improved (Figure 6). This suggests that incorporating the biomarker LC can enhance the model's predictive performance. Discussion Currently, urine output and Scr levels are used to identify and diagnose AKI. However, Scr is not an ideal biomarker for AKI because changes in Scr levels lag behind the decline in glomerular filtration rate and may take up to 24-36 hours to show a significant increase after substantial kidney injury [17]. Clinically, several biomarkers have been investigated. For example, NGAL has proven to be an effective biomarker for patients undergoing cardiac surgery, as its expression rises earlier than that of traditional markers such as serum Scr [18]. KIM-1 demonstrates excellent predictive value for adult AKI with high sensitivity and specificity [19], while cystatin C holds greater clinical significance in predicting AKI following major surgeries [20]. The concept of ideal biomarkers is not new; early prediction and diagnosis are crucial for improving treatment outcomes and prognosis in patients with renal injury. Although various novel biomarkers have been identified and validated, none of them can specifically detect AKI to date [21]. As a result, the use of these AKI biomarkers in clinical settings continues to encounter obstacles. This is especially true for the early diagnosis of CSA-AKI, early detection of CSA-AKI could help improve outcomes in patients after cardiac surgery. In this study, we collected preoperative and postoperative serum samples from patients undergoing cardiac surgery and performed untargeted metabolomics analysis. We identified carnitine as a differential metabolic compound and used its biologically active form LC, as a potential biomarker, aiming to explore its early predictive value for CSA-AKI. Certain studies indicate that low serum levels of LC are linked to urinary tract infections in children. The activation of inflammatory mediators, changes in cytokines, and the generation of reactive oxygen species play a key role in the development of tissue damage following pyelonephritis. As a potent natural antioxidant, LC may protect cells and tissues from injury by inhibiting lipid peroxidation [16]. Vaseghi et al. discovered that acute pyelonephritis in children could be the cause of LC supplementation, which would dramatically decrease renal scarring [23]. Ramadan et al. noted that serum LC levels in children undergoing acute asthma attacks were considerably lower compared to those in the control group. The decrease in LC was shown to be related to the occurrence of asthma attacks in children, according to them. During asthma exacerbations, inflammatory cells release phospholipase A2 into the airways, which breaks down phosphatidylcholine, the primary component of pulmonary surfactant. The decreased serum LC levels seen during or after asthma attacks in children may result from reduced surfactant production during the episode and the subsequent use of body reserves to replenish it [24]. In our study, we collected preoperative and postoperative serum samples from patients having cardiac surgery and assessed their LC levels. Postoperative LC displayed a predictive ability for CSA-AKI, yielding an AUC of 0.777 (95%CI 0.697-0.857, P < 0.001). When the LR-based clinical model was combined with postoperative LC for prediction, the AUC increased to 0.878 (95%CI 0.819-0.937, P < 0.001), indicating that the predictive performance of the clinical model was further improved. Carnitine plays a role in β-oxidation, and its anti-inflammatory and antioxidant effects have been well documented [25]. During cardiac surgery, the use of CPB can lead to hypotension, reduced perfusion, and non-pulsatile blood flow, which may cause renal ischemia. Upon reperfusion after ischemia, reactive oxygen species (ROS) are released at rates exceeding the cells' capacity to neutralize and metabolize them, leading to reperfusion injury. Biomarkers of ischemia-reperfusion injury consist of nitric oxide, which is excessively produced during ischemia-reperfusion and transformed into free radicals; myeloperoxidase, released from neutrophil granules during phagocytosis; and malondialdehyde(MDA), which indicates the extent of lipid peroxidation or oxidative changes in lipids. By decreasing oxidative stress brought on by ischemia-reperfusion injury, LC might prevent cell death. This antioxidant effect may be due to LC’s direct scavenging of ROS or its enhancement of endogenous antioxidant defense mechanisms, such as lipid peroxidation inhibition [8]. Studies have shown that LC supplementation reduces MDA levels and significantly increases the activity of reduced/oxidized glutathione and glutathione peroxidase [26]. In their meta-analysis, Hadis Fathizadeh et al. found that LC supplementation reduces serum levels of inflammatory cytokines, including C-reactive protein (CRP), interleukin-6 (IL-6), TNF-α, and malondialdehyde (MDA), in both healthy individuals and those with specific diseases, while simultaneously increasing superoxide dismutase levels [27]. Research has indicated that mitochondrial dysfunction in sepsis is a key contributor to organ failure. The production of energy in the mitochondria relies on carnitine-mediated transport, which is facilitated by carnitine palmitoyltransferase 1. However, this process is impaired during sepsis, and LC supplementation has been shown to reduce mortality in septic patients [28-30]. Animal studies have found that polymyxins induce nephrotoxicity by acting on mitochondria and triggering permeability transition. LC prevents polymyxin-induced mitochondrial permeability transition in vitro; furthermore, when LC is used in combination with polymyxin, it demonstrates nephroprotective effects in mice treated with polymyxin [31]. Clinical and animal studies have also shown that carnitine deficiency following heat stress reduces ATP production, leading to an energy crisis that impairs recovery. Oral supplementation with LC prior to heat exposure helps maintain ATP production in renal tubular mitochondria and attenuates macrophage-mediated inflammation, thereby alleviating heat stress-induced AKI and subsequent kidney fibrosis [32]. For the first time, we conducted a prospective clinical study to investigate the expression levels of LC in patients undergoing cardiac surgery and validated its predictive capability for CSA-AKI, offering a new potential biomarker for the early diagnosis of CSA-AKI. However, this study also has several limitations. First, it was a single-center study, and future multicenter prospective studies with larger sample sizes are needed for further validation. Second, the clinical outcomes assessed in this study were limited to CRRT and mortality; additional outcomes should be included, or AKI should be stratified to allow subgroup analysis of high-risk CSA-AKI patients. Furthermore, the current study focused only on the single biomarker LC, and further research is needed to explore the underlying mechanisms associated with its protective effects. Conclusions To sum up, LC is capable of forecasting the incidence of CSA-AKI. Combining the early biomarker LC with a clinical prediction model effectively enhances the ability to predict CSA-AKI, helping clinicians identify high-risk patients at an early stage and implement timely clinical interventions to improve patient outcomes. Declarations Funding None. Ethics approval and consent to participate In line with the Declaration of Helsinki, this study received approval from the Ethics Committee of Nanjing Medical University Affiliated Nanjing Hospital (KY20240123-01). Participants or their legal representatives were asked to get informed consent. All the methods were conducted in compliance with the applicable guidelines and regulations outlined in the declaration. Authors' contributions Wenxiu Chen authored the primary manuscript text and created the figures and tables. Hao Zhang, Xiao Shen, Liang Hong, Hong Tao and Ming Chen collected the data. Study design was done by Cui Zhang and Wenkui Yu, who also used data interpretation and manuscript preparation. Declaration of Competing Interest All the authors declare that there is no competing interests. Availability of data and materials The datasets analyzed in this study are available from the corresponding author upon reasonable request. Acknowledgments We extend our heartfelt thanks to all the researchers and patients involved in this study. References Yun Xie Q, Guo B, Yang, et al. Tissue Inhibitor Metalloproteinase-2·IGF-Binding Protein 7 for the Prediction of Acute Kidney Injury following Cardiac Surgery. Cardiorenal Med. 2024;14(1):251–60. Chew STH, Hwang NC. Acute kidney injury after cardiac surgery: a narrative review of the literature. J Cardiothorac Vasc Anesth. 2019;33(4):1122–38. Schrezenmeier EV, Barasch J, Budde K, et al. Biomarkers in acute kidney injury: pathophysiological basis and clinical performance. Acta Physiol. 2017;219(3):554–72. Bauermeister A, Mannochio-Russo H, Costa-Lotufo LV, et al. Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol. 2022;20(3):143–60. Rego SM, Snyder MP. High Throughput Sequencing and Assessing Disease Risk. Cold Spring Harb Perspect Med. 2019; 9(1). Chen CJ, Lee DY, Yu J, et al. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Jan, 2026 Read the published version in BMC Nephrology → Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 19 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 05 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers invited by journal 01 Sep, 2025 Editor assigned by journal 25 Jul, 2025 Submission checks completed at journal 25 Jul, 2025 First submitted to journal 24 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7206985","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":517632993,"identity":"0a87d5ca-f4d9-45cb-8183-088fadd3a9bb","order_by":0,"name":"Wenxiu Chen","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenxiu","middleName":"","lastName":"Chen","suffix":""},{"id":517632994,"identity":"d96af643-7468-4b5a-b2c1-536f5273d7e4","order_by":1,"name":"Hao Zhang","email":"","orcid":"","institution":"Nanjing Medical 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1","display":"","copyAsset":false,"role":"figure","size":59978,"visible":true,"origin":"","legend":"\u003cp\u003ePCA analysis map.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7206985/v1/33f91389ca98b3f5bdb9fe4c.jpg"},{"id":91930614,"identity":"064a32fe-25e9-4d28-946e-ed167e01eb1f","added_by":"auto","created_at":"2025-09-23 02:26:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31532,"visible":true,"origin":"","legend":"\u003cp\u003eOPLS-DA analysis map.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7206985/v1/f3f5b37ae33856912100efc4.jpg"},{"id":91930554,"identity":"cd05f90d-d413-4fa0-9ef8-e1ed7bdc4848","added_by":"auto","created_at":"2025-09-23 02:26:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103765,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential metabolites between the AKI_post group and the Non_AKI_post group. \u003cstrong\u003eA.\u003c/strong\u003e The difference metabolites between the AKI_post and the Non_AKI_post groups were plotted in terms of volcano; \u003cstrong\u003eB. \u003c/strong\u003eThe differential metabolites were shown by the correlation heatmap; \u003cstrong\u003eC. \u003c/strong\u003eZ-score plot of differential metabolites; \u003cstrong\u003eD. \u003c/strong\u003eVIP value plot of differential metabolites.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7206985/v1/3fee4909e50b862386b05631.jpg"},{"id":91932291,"identity":"6bda8c08-f3d3-437b-8040-14715ec4c133","added_by":"auto","created_at":"2025-09-23 02:34:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61700,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of participants selection.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7206985/v1/5f6bd253868b91fabbeb04f8.jpg"},{"id":91930661,"identity":"0739afa2-6f46-4c08-8496-22690f4670ec","added_by":"auto","created_at":"2025-09-23 02:26:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":49154,"visible":true,"origin":"","legend":"\u003cp\u003eThe alterations in each biomarker prior to and following cardiac surgery.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7206985/v1/2e2422bd1dfd2241091c4d8e.jpg"},{"id":91930652,"identity":"9ea9a220-b60e-4398-98e2-13b3ac0fcf47","added_by":"auto","created_at":"2025-09-23 02:26:34","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70233,"visible":true,"origin":"","legend":"\u003cp\u003eThe AUC of postoperative LC, the LR clinical prediction model, and their combination for predicting CSA-AKI.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7206985/v1/4b64fd0d1da7fe8617ced076.jpg"},{"id":100070154,"identity":"ecbfbae7-084a-4eea-83d0-ecc9b7dcae36","added_by":"auto","created_at":"2026-01-12 16:16:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1130637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7206985/v1/d847ff0d-0967-43ff-9857-52ae80691727.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"L-Carnitine, A new biomarker screened based on untargeted metabolomics, predict cardiac surgery-associated acute kidney injury: A prospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiac surgery-associated acute kidney injury (CSA-AKI) is a prevalent complication linked to higher rates of morbidity and mortality. Several factors may contribute to the occurrence and development of CSA-AKI, including ischemia-reperfusion injury, inflammation, hypoperfusion, and oxidative stress [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In clinical practice, traditional diagnostic markers like serum creatinine (Scr) and urine output are commonly employed for diagnosing AKI, which often results in delays in diagnosis and the subsequent implementation of prevention and treatment strategies for AKI [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While there are currently some novel and potentially useful biomarkers to be found, including cystatin C, and plasma neutrophil gelatinase-associated lipocalin (NGAL), the use of urinary tissue inhibitors of metalloproteinases-2 (TIMP-2), and the insulin-like growth factor binding protein 7 (IGFBP7) are only a few of the factors that are relevant for predicting AKI [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. There is still controversy regarding the best biomarker for CSA-AKI monitoring. Early diagnosis of AKI can help initiate strategies to prevent severe kidney damage, therefore, finding early diagnostic markers for CSA-AKI is particularly important.\u003c/p\u003e\u003cp\u003eAfter the development of genomes and proteomics, metabolomics is a relatively new field of study. As a branch of holistic science, it primarily studies the composition of all small-molecule metabolites within organisms and their dynamic changes under internal and external stimuli [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among high-throughput analytical techniques, genomics represents the maximal potential of a cell, what could happen; transcriptomics indicates the developmental direction of a cell, what will happen; proteomics reflects the functional execution of a cell, what causes things to happen; while metabolomics reveals the ultimate functional state of a cell, what has already happened or is currently happening. Located downstream of the central dogma, metabolomics captures nearly all changes in genes and proteins at the metabolic level. Therefore, metabolomics serves as a bridge linking genotype with phenotype, as well as the microscopic with the macroscopic [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The integration of chromatography and mass spectrometry facilitates the complete workflow, from separating substances with chromatography to identifying them through mass spectrometry. In particular, the tandem use of liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry allows expert qualitative and quantitative analysis, significantly improving the coverage of metabolomic detection and providing more complete information on metabolites and their abundances [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, there are relatively few metabolomic studies on blood in patients with CSA-AKI. Collected serum samples and performed untargeted metabolomic analysis, identifying L-carnitine (LC) as a differential metabolite.\u003c/p\u003e\u003cp\u003eCarnitine, scientifically referred to as β-hydroxy-γ-N-trimethylammonium butyric acid, is a quaternary ammonium compound, with its biologically active stereoisomer being LC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The liver, brain, and kidneys all produce LC, a branched-chain non-essential amino acid, which is produced from lysine and methionine [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Due to its ability to move activated long-chain fatty acids from the cytoplasm into mitochondria [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], LC is necessary for the process of β-oxidation. The kidney, due to its high energy demands and its key role in filtering toxic and non-toxic metabolites, is particularly susceptible to injury caused by ischemia, mitochondrial dysfunction, and oxidative stress. In healthy kidneys, renal tubular cells have a high concentration of mitochondria to fulfill the substantial ATP requirements needed for the reabsorption of significant amounts of ultrafiltrate and solutes, with mitochondrial function primarily depending on oxidative phosphorylation and fatty acid β-oxidation. Three types of AKI, nephrotoxic AKI, ischemia/reperfusion injury AKI, and cytokine-mediated or other pro-inflammatory models of AKI, are associated with mitochondrial metabolic disturbances and impaired free radical scavenging, encompassing nearly all causes of AKI in critically ill patients. In addition to serving as a fatty acid co-transporter, LC also safeguards mitochondrial function and maintains renal health by directly acting as an antioxidant. It functions as a free radical scavenger, boosts the body\u0026rsquo;s natural antioxidant defenses, and inhibits the buildup of lipid peroxidation products [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Research indicates that rat renal tubular cells that were pretreated with LC displayed lower transcriptional activity of nuclear factor-κB and reduced levels of tumor necrosis factor-α (TNF-α), intercellular adhesion molecule-1, and monocyte chemoattractant protein-1 when compared to untreated cells. The observed decrease in nephrotoxicity following LC treatment is believed to result from diminished downstream mitochondrial fragmentation, improved mitochondrial adaptation to elevated energy demands, and LC\u0026rsquo;s capacity to scavenge free radicals [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Furthermore, research has revealed that serum LC levels in children suffering from urinary tract infections were markedly lower compared to those in healthy children [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Combined the results of untargeted metabolomics, we therefore hypothesized that serum LC might be used as a biomarker for early diagnosis of CSA-AKI.\u003c/p\u003e\u003cp\u003eWe performed a prospective single-center cohort research on patients having cardiac surgery to assess the predictive ability of LC for CSA-AKI in order to verify the hypothesis.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy design and participants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Department of Intensive Care Unit at Nanjing Medical University Affiliated Nanjing Hospital (Nanjing, China) carried out this prospective single-center research project. Patients were enrolled who underwent cardiac surgery with cardiopulmonary bypass and over 18 years old between February 2024 and March 2024. The exclusion criteria were as follows individuals under 18 years of age, those with advanced chronic kidney disease (CKD) prior to cardiac surgery (including chronic dialysis, kidney transplantation, or a preoperative estimated glomerular filtration rate [eGFR] \u0026lt;30 ml/min/1.73 m\u0026sup2;), patients who experienced acute kidney injury (AKI) before the cardiac surgery, and individuals without serum creatinine (Scr) values recorded before and after the surgery.\u0026nbsp;The\u0026nbsp;untargeted metabolomics\u0026nbsp;included thirteen CSA-AKI patients and fourteen non-CSA-AKI patients, the validation cohort was composed of 170 eligible patients.\u0026nbsp;The research was conducted in compliance with the Declaration of Helsinki and received approval from the Regional Human Research Ethics Committee of Nanjing Medical University Affiliated Nanjing Hospital\u0026nbsp;(KY20240123-01).\u0026nbsp;All participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples were collected at two points for all participants: immediately when the patients entered the operating room and immediately after the patients was admitted to the ICU, which we refer them as preoperative and postoperative. After collection, blood samples were left at room temperature for 60 minutes to allow clotting, then centrifuged at 3000rpm for 10 minutes at 4\u0026deg;C. Two hundred microlitres (200\u0026mu;L) of the supernatant (serum) was transferred into properly labeled 2mL centrifuge tubes. Following five minutes of rapid freezing in liquid nitrogen, the samples were kept at 80\u0026deg;C. Freeze-thaw cycles were repeatedly avoided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample measurement\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUntargeted metabolomics cohort: serum samples (preoperative and postoperative) from CSA-AKI patients (n=13) and age, gender-matched non-CSA-AKI controls (n=14) were thawed. Then vortex them for 10 seconds to mix thoroughly, and transfer 50\u0026mu;L of each serum sample into corresponding labeled centrifuge tubes. Add 300\u0026mu;L of 20% acetonitrile-methanol internal standard extraction solution, vortex for 3 minutes. Centrifuge at 12,000r/min for 10 minutes at 4 \u0026deg;C. Following centrifugation, transfer 200\u0026mu;L of the supernatant to a separately labeled centrifuge tube and store it in a -20\u0026deg;C freezer for 30 minutes. After centrifugation at 12000r/min for three minutes at 4\u0026deg;C, the mixture was left at 4\u0026deg;C. For the purpose of instrumental analysis, then transfer 180\u0026mu;L of the supernatant into the corresponding autosampler vial insert.\u003c/p\u003e\n\u003cp\u003eIn the validation cohort:\u0026nbsp;LC, BUN, Scr, KIM-1, NGAL levels were measured by ELISA Kit (COIBO BIO, Shanghai, China) according to the manufacturer\u0026rsquo;s instructions. All samples were measured in duplicate. The detection ranges for LC, BUN, Scr, KIM-1, NGAL are 3.75 \u0026mu;mol/L-120 \u0026mu;mol/L,\u0026nbsp;0.75mmol/L-24mmol/L, 6.25 \u0026mu;mol/L-200 \u0026mu;mol/L, 0.25ng/mL-8ng/mL, \u0026nbsp;3.75ng/mL-120ng/mL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOutcome definition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAKI is defined based on the KDIGO criteria, where its occurrence is indicated by either an increase in serum creatinine (Scr) of 26.5 mmol/L (0.3 mg/dl) within 48 hours, a 50% increase in Scr from the preoperative baseline within 7 days, or a urine output of less than 0.5 mL/kg/h for more than 6 consecutive hours.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used R 4.4.1 and SPSS 25.0 for data analysis, and GraphPad Prism 9.5.1 for graphing. Continuous variables that follow a normal distribution are expressed as mean \u0026plusmn; standard deviation(x \u0026plusmn; s), while non-normally distributed continuous variables are expressed as median and interquartile range. Group comparisons were conducted using either an independent samples t-test or the Mann-Whitney U test. Using the chi-square test, categorical variables are shown as counts or percentages and compared. Multivariate logistic regression was used to examine independent risk variables associated with CSA-AKI. The predictive performance of the LC model and the LC combined clinical prediction model for CSA-AKI was assessed using the area under the receiver operating characteristic (ROC) curve (AUC).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnalysis of untargeted metabolomics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this part of the study, LC-MS/MS-based metabolomics was utilized to analyze 26 samples from CSA-AKI patients (categorized into AKI_pre and AKI_post groups) and 28 samples from non-CSA-AKI patients (classified into Non_AKI_pre and Non_AKI_post groups). Metabolite ion peaks were extracted from the metabolic profiles, resulting in a preliminary collection that identified 2,730 metabolites in positive ion mode and 2,240 metabolites in negative ion mode.\u0026nbsp;There were 1,155 differentially expressed metabolites between the AKI_pre and Non_AKI_pre groups, and 451 differentially expressed metabolites between the AKI_post and Non_AKI_post groups. The extracted ion peaks were used to generate principle component analysis (PCA) plots, where each point represents a sample, allowing observation of sample clustering and dispersion.\u0026nbsp;Figures 1\u0026nbsp;(A,\u0026nbsp;B)\u0026nbsp;illustrate the PCA maps for all groups\u0026nbsp;(AKI_pre,\u0026nbsp;AKI_post,\u0026nbsp;Non_AKI_pre,\u0026nbsp;Non_AKI_post)\u0026nbsp;under both positive and negative ion modes.\u0026nbsp;The tight clustering of quality control (QC) samples indicates a stable and reproducible instrumental analysis system, suggesting reliable experimental data that reflect biological variability among the samples. Figures 1 (C, D) illustrate the positional distribution and clustering patterns of the AKI_pre versus Non_AKI_pre groups and the AKI_post versus Non_AKI_post groups, respectively, reflecting their similarities and differences.\u003c/p\u003e\n\u003cp\u003eTo provide a clearer understanding of the metabolic profile differences among the groups, orthogonal partial least squares discrimination analysis (OPLS-DA) was conducted on the data.In contrast to PCA, OPLS-DA is a supervised discriminant analysis technique that employs partial least squares regression to create a model that correlates metabolite expression levels with sample categories. It then filters out noise unrelated to classification information, thereby enhancing the model\u0026rsquo;s effectiveness and interpretability for predicting sample categories. In the model, R2Y and Q2 serve as indicators of the explanation rate and prediction rate respectively, and are essential metrics for assessing the reliability of the model. Values of both greater than 0.1 indicate a relatively reliable model. In Figure 2A, the R2Y and Q2 values for the AKI_pre and Non_AKI_pre groups were 0.997 and 0.857, respectively. In Figure 2B, the R2Y and Q2 values for the AKI_post and Non_AKI_post groups were 0.97 and 0.543, respectively.\u003c/p\u003e\n\u003cp\u003eUsing the variable significance of the projection (VIP) values obtained from the above OPLS-DA models as a preliminary criterion, the screening of differentially expressed metabolites was carried out to find possible differential metabolites between groups. The use of univariate statistical analysis was used to confirm these contenders. Metabolites were regarded as statistically significant if they were found to be in the univariate analysis, with a VIP value \u0026gt; 1 and a \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05. As shown in Figure 3A, based on these screening criteria, a total of 451 differential metabolites were identified between the AKI_post and Non_AKI_post groups, including 122 upregulated and 329 downregulated metabolites. Figure 3B presents the results of Pearson correlation analysis performed on the screened differential metabolites, visualized as a heatmap. Figure 3C shows the Z-score normalized profiles of the differential metabolites across different samples. A typical picture of the distribution of each differential metabolite between the AKI_post and Non_AKI_post groups is given by the Z-score plot. Figure 3D illustrates the top 20 metabolites that exhibit the highest VIP values in the OPLS-DA model. Based on the above screening results, carnitine was identified as a differential metabolite between the Non_AKI_post and AKI_post groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical characteristics of validation cohort\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were 170 patients undergoing cardiac surgery included in this study, a detailed flow chart can be seen in Figure 4. All surgeries were elective and used CPB. 46 patients (27.06%) developed AKI. Table 1 summarizes the demographic, clinical and laboratory characteristics as well as the adverse outcomes of patients with and without AKI. Compared with Non-CSA-AKI patients, CSA-AKI patients were older, had a higher BMI, a higher smoking rate, a higher prevalence of preoperative heart failure, longer operative time, longer cardiopulmonary bypass duration, greater total intraoperative blood transfusion volume, less intraoperative urine output, higher preoperative BUN, higher preoperative Scr, higher preoperative uric acid, lower preoperative hemoglobin, and higher clinical scores (including APACHE II score, EuroScore, vasopressor score, and Cleveland score). Additionally, postoperative oxygen partial pressure was lower, and postoperative lactate levels were higher in CSA-AKI patients. In terms of biomarkers, CSA-AKI patients showed higher postoperative NGAL levels, lower preoperative KIM-1 levels, and lower postoperative LC levels (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient Characteristics by AKI Status.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"121%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eCharacteristics\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003eAll\u003cbr\u003e(n=170)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003eNon CSA-AKI（n=124）\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003eCSA-AKI\u003cbr\u003e（n=46）\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u003cem\u003eP\u003c/em\u003e value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eDemographics\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eAge, yrs\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e62(54,69)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e61(54,68)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e67(58,73)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.016\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eMale, n(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e115(67.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e85(68.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e30(65.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.820\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e24.48\u0026plusmn;2.86\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e24.18\u0026plusmn;2.70\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e25.30\u0026plusmn;3.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.022\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003eMedical history\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eHypertension, n(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e90(52.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e62(50)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e28(60.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.276\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eDiabetes, n(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e38(22.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e24(19.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e14(30.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.182\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eSmoke, n(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e41(24.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e24(19.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e17(37.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.029\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eHeart failure, n(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e55(32.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e25(20.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e30(65.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eCKD, n(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e9(5.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e4(3.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e5(10.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.111\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eEF(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e62.0(56.0-64.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e62.0(57.5-64.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e61.0(49.0-64.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.365\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003eOperative variables\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eOperative types, n(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.906\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eCABG\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e54(31.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e41(33.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e13(28.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eValve\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e76(44.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e53(42.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e23(50)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eCABG+Valve\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e23(13.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e16(12.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e7(15.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eOthers\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e17(10)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e14(11.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e3(6.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eOperation time, h\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e4.3(3.8-5.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e4.3(3.8-5.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e4.8(4.0-5.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.042\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eCross-clamp time, min\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e83.6\u0026plusmn;35.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e81.2\u0026plusmn;35.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e90.0\u0026plusmn;36.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.157\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eCPB time, min\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e113.0\u003cbr\u003e(87.0-140.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e108.0\u003cbr\u003e(85.5-136.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e122.5\u003cbr\u003e(104.0-148.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.039\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eFluid input, ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e2500.0\u003cbr\u003e(2000.0-3000.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e2300.0\u003cbr\u003e(2000.0-3000.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e2500.0\u003cbr\u003e(2000.0-2700.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.297\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eBlood input, ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e1250.0\u003cbr\u003e(950.0-1625.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1200.0\u003cbr\u003e(925.0-1550.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1387.5\u003cbr\u003e(1050.0-1850.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.022\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eTotal output, ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e2210.0\u003cbr\u003e(1850.0-2800.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e2250.0\u003cbr\u003e(1875.0-2850.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e2200.0\u003cbr\u003e(1832.0-2500.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.642\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eTotal output blood, ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e1100.0\u003cbr\u003e(1000.0-1350.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1100.0\u003cbr\u003e(1000.0-1300.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1200.0\u003cbr\u003e(1000.0-1500.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.079\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eTotal output urine, ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e1000.0\u003cbr\u003e(750.0-1400.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1100.0\u0026nbsp;\u003cbr\u003e(800.0-1500.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1000.0\u003cbr\u003e(620.0-1200.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.006\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eAutologous blood, ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e500.0\u003cbr\u003e(250.0-500.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e500.0\u003cbr\u003e(250.0-500.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e500.0\u003cbr\u003e(250.0-500.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.276\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eOrganic blood, ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e500.0\u003cbr\u003e(400.0-500.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e500.0\u003cbr\u003e(400.0-500.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e500.0\u003cbr\u003e(400.0-500.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.884\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003ePreoperative laboratory variables\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eHDLC, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e1.0\u0026plusmn;0.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1.0\u0026plusmn;0.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1.0\u0026plusmn;0.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.692\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eLDLC, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e2.2(1.6-2.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e2.2(1.6-2.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e2.2(1.6-2.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.617\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eApoA1, g/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e1.3\u0026plusmn;0.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1.3\u0026plusmn;0.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1.3\u0026plusmn;0.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.569\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eApoB, g/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e0.7(0.60-0.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e0.8(0.61-1.0-0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e0.7(0.6-0.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.297\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eLpa, mg/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e166.0(72.0-405.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e201.5(74.5-437.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e144.3(70.0-321.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.471\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eTotal Cholesterol, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e4.0(3.4-4.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e4.0(3.3-4.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e4.0(3.4-4.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.651\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eTriglycerides, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e1.3 (0.9-1.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1.2(0.9-1.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1.3(1.0-1.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.412\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eBilirubin, umol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e9.6(7.4-15.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e9.7(7.6-15.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e9.3(7.4-17.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.587\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eBUN, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e6.5(5.2-7.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e6.3(5.0-7.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e7.3 (5.9-8.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.014\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eScr, umol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e73.7(61.3-83.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e71.3 (58.7-81.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e79.1(70.1-87.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.005\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eUric acid, umol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e329.0\u003cbr\u003e(274.0-409.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e313.0\u003cbr\u003e\u0026nbsp;(264.5-389.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e376.5\u003cbr\u003e(303.0-458.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.004\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eK, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e3.9\u0026plusmn;0.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e3.9\u0026plusmn;0.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e3.8\u0026plusmn;0.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.126\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eNa, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e139.7\u003cbr\u003e(138.3-141.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e139.7\u003cbr\u003e(138.4-141.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e139.7\u003cbr\u003e(137.9-141.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.790\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eP, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e1.2(1.1-1.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1.2(1.1-1.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1.3(1.1-1.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.100\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eCL, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e104.8\u003cbr\u003e(102.7-106.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e104.9\u003cbr\u003e(103.3-106.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e104.1\u003cbr\u003e(102.1-106.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.185\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eWBC, *10^9/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e5.8(4.8-7.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e5.8(4.7-7.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e5.4(4.9-6.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.835\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eHb, g/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e133.0\u003cbr\u003e(123.0-141.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e134.0\u003cbr\u003e(125.5-143.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e129.5\u003cbr\u003e(121.0-136.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.025\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePlt, *10^9/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e182.5\u003cbr\u003e(152.0-224.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e184.5\u003cbr\u003e(156.5-224.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e177.5\u003cbr\u003e(138.0-225.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.326\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003eClinical scoring\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eAPACHE II score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e13.0(11.0-15.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e12.0(11.0-14.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e13.5(11.0-17.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.040\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eEuroScore score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e6.0(4.0-6.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e5.0(4.0-6.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e6.0(5.0-7.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.004\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eVasoactive-inotropic score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e5.0(3.0-8.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e3.0(2.0-5.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e10.0(8.0-13.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eCleveland score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e2.0(1.0-3.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e2.0(1.0-2.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e2.0(2.0-3.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.011\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003ePostoperative laboratory variables\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative WBC, *10^9/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e10.8(8.8-13.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e10.8(8.8-13.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e10.5(8.8-13.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.585\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative Hb, g/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e98.5\u0026plusmn;16.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e99.7\u0026plusmn;16.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e95.4\u0026plusmn;18.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.139\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative Plt, *10^9/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e128.0\u003cbr\u003e(104.0-161.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e131.5\u003cbr\u003e(106.50-168.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e123.0\u003cbr\u003e(103.0-150.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.395\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative NE, *10^9/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e9.8(8.1-12.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e9.8(8.1-12.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e9.4(7.8-11.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.303\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative LY, *10^9/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e0.5(0.4-0.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e0.5(0.4-0.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e0.5(0.4-0.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.709\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative PO2, mmHg\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e135.5\u003cbr\u003e(110.4-166.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e140.9\u003cbr\u003e(118.0-170.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e122.8\u003cbr\u003e(101.1-154.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.017\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative PCO2, mmHg\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e31.7(28.5-35.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e31.3(28.6-35.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e32.3(27.5-35.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.758\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative Lac, mmol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e1.6(0.9-2.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1.5(0.9-2.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e2.0(1.2-2.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.019\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative BUN, U/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e6.7(5.3-8.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e6.3(5.2-8.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e7.5(6.6-10.0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative Scr, U/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e70.6(59.6-86.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e68.3(56.2-80.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e89.1(69.2-108.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003eBiomarkers\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePreoperative NGAL, ng/ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e75.3(58.3-91.8)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e75.0(57.8-89.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e78.0(62.1-96.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.177\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative NGAL, ng/ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e147.9\u003cbr\u003e(121.3-169.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e141.7\u003cbr\u003e(114.0-165.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e157.8\u003cbr\u003e(134.6-173.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.021\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePreoperative KIM-1, ng/ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e6.3(5.1-7.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e6.4(5.3-7.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e5.7(4.4 -7.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.070\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative KIM-1, ng/ml\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e11.6(9.6-12.7)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e11.5(9.7-12.70)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e11.7(9.4-12.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.350\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePreoperative LC, umol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e131.7(118.9-144.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e134.5(119.7-144.9)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e129.1(88.3-141.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.244\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003ePostoperative LC, umol/L\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e69.5(57.2-86.1)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e73.4(64.0-89.4)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e47.7(17.2-71.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 100px;\"\u003eOutcomes\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eCRRT, n(%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e1(0.6)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e0(0)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e1(2.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e0.605\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003eDeath, n (%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e6(3.5)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e4(3.2)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e2(4.3)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e1.000\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: CSA-AKI, cardiac surgery-associated acute kidney injury; BMI, Body Mass Index; CKD, chronic kidney disease; EF, ejection fraction; CABG, coronary artery bypass grafting; CPB, cardiopulmonary bypass; HDLC, High-Density Lipoprotein Cholesterol; LDLC, Low-Density Lipoprotein Cholesterol; ApoA1, Apolipoprotein A1; ApoB, Apolipoprotein B; Lpa, Lipoprotein a; BUN, Blood Urea Nitrogen; Scr, Serum Creatinine; K, Potassium; Na, Sodium; P, Phosphorus; CL, Chloride; WBC, white blood cell; Hb, Hemoglobin; Plt, Platelet; APACHE II, Acute Physiology and Chronic Health Analysis II; EuroScore, European System for Cardiac Operative Risk Evaluation; NE, Neutrophil; LY, Lymphocyte; Lac, Lactate; NGAL, neutrophil gelatinase-associated lipocalin; KIM-1, kidney injury molecule-1; LC, L-carnitine; CRRT, continuous renal replacement therapy.\u003c/p\u003e\n\u003cp\u003eTable 1 was used to perform the multivariate logistic regression analysis, which included the variables that showed statistically significant differences in Table 1. The findings demonstrated that preoperative heart failure (OR 7.55, 95%CI 2.04-27.90, \u003cem\u003eP\u003c/em\u003e = 0.002), vasoactive drug score (OR 1.65，95%CI 1.33-2.04, \u003cem\u003eP\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), and postoperative partial pressure of oxygen (OR 0.98, 95%CI 0.96-1.00, \u003cem\u003eP\u003c/em\u003e = 0.027) were identified as independent risk factors for the development of CSA-AKI (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate analysis of risk factors related with CSA-AKI\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003eCharacteristics\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003eOR\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e95%CI\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eAge\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e0.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.92-1.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.776\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eBMI\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.22\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.94-1.60\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.138\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eSmoke\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e2.14\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.57-8.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.260\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eHeart failure\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e7.55\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e2.04-27.90\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.002\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eOperation time\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.27\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.49-3.33\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.620\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eCPB time\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e0.99\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.97-1.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.409\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eBlood input\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e1.00-1.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.517\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eTotal output urine\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e1.00-1.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.208\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eBUN\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e0.91\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.64-1.30\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.602\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eScr\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.99-1.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.122\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eUric acid\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.99-1.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.807\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eHb\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.01\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.97-1.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.705\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eAPACHE II score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.91-1.31\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.329\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eEuroScore score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.25\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.88-1.76\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.212\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eVasoactive-inotropic score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.65\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e1.33-2.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003eCleveland score\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.24\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.64-2.43\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.521\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003ePostoperative PO2\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e0.98\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.96-1.00\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.027\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 151px;\"\u003ePostoperative Lac\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e1.05\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e0.65-1.70\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 139px;\"\u003e0.853\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: BMI, Body Mass Index; CPB, Cardiopulmonary Bypass; BUN, Blood Urea Nitrogen; Scr, Serum Creatinine; Hb, Hemoglobin; APACHE II, Acute Physiology and Chronic Health Evaluation II; EuroScore, European System for Cardiac Operative Risk Evaluation; Lac, Lactate.\u003c/p\u003e\n\u003cp\u003eBy analyzing the preoperative and postoperative levels of BUN, Scr, KIM-1, NGAL, and LC in patients with and without CSA-AKI, we found that patients in the CSA-AKI group had higher preoperative and postoperative BUN and Scr levels (Figure 5). Additionally, the postoperative NGAL level was higher in the CSA-AKI group (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.021), while the postoperative LC level was lower (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eThe AUC results indicated that the predictive performance of the clinical model based on LR for CSA-AKI was 0.828 (95% CI 0.761-0.895, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). The postoperative LC (postLC) showed a predictive performance of 0.777 (95% CI 0.697-0.857, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001) for CSA-AKI. When the LR-based clinical model was combined with postoperative LC for prediction, the AUC increased to 0.878 (95%CI 0.819-0.937, \u003cem\u003eP\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), indicating that the predictive ability of the model was further improved (Figure 6). This suggests that incorporating the biomarker LC can enhance the model\u0026apos;s predictive performance.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrently, urine output and Scr levels are used to identify and diagnose AKI. However, Scr is not an ideal biomarker for AKI because changes in Scr levels lag behind the decline in glomerular filtration rate and may take up to 24-36 hours to show a significant increase after substantial kidney injury [17]. Clinically, several biomarkers have been investigated. For example, NGAL has proven to be an effective biomarker for patients undergoing cardiac surgery, as its expression rises earlier than that of traditional markers such as serum Scr [18]. KIM-1 demonstrates excellent predictive value for adult AKI with high sensitivity and specificity [19], while cystatin C holds greater clinical significance in predicting AKI following major surgeries [20]. The concept of ideal biomarkers is not new; early prediction and diagnosis are crucial for improving treatment outcomes and prognosis in patients with renal injury. Although various novel biomarkers have been identified and validated, none of them can specifically detect AKI to date [21]. As a result, the use of these AKI biomarkers in clinical settings continues to encounter obstacles. This is especially true for the early diagnosis of CSA-AKI, early detection of CSA-AKI could help improve outcomes in patients after cardiac surgery. In this study, we collected preoperative and postoperative serum samples from patients undergoing cardiac surgery and performed untargeted metabolomics analysis. We identified carnitine as a differential metabolic compound and used its biologically active form LC, as a potential biomarker, aiming to explore its early predictive value for CSA-AKI.\u003c/p\u003e\n\u003cp\u003eCertain studies indicate that low serum levels of LC are linked to urinary tract infections in children. The activation of inflammatory mediators, changes in cytokines, and the generation of reactive oxygen species play a key role in the development of tissue damage following pyelonephritis. As a potent natural antioxidant, LC may protect cells and tissues from injury by inhibiting lipid peroxidation [16]. Vaseghi et al. discovered that acute pyelonephritis in children could be the cause of LC supplementation, which would dramatically decrease renal scarring [23]. Ramadan et al. noted that serum LC levels in children undergoing acute asthma attacks were considerably lower compared to those in the control group. The decrease in LC was shown to be related to the occurrence of asthma attacks in children, according to them. During asthma exacerbations, inflammatory cells release phospholipase A2 into the airways, which breaks down phosphatidylcholine, the primary component of pulmonary surfactant. The decreased serum LC levels seen during or after asthma attacks in children may result from reduced surfactant production during the episode and the subsequent use of body reserves to replenish it [24]. In our study, we collected preoperative and postoperative serum samples from patients having cardiac surgery and assessed their LC levels. Postoperative LC displayed a predictive ability for CSA-AKI, yielding an AUC of 0.777 (95%CI 0.697-0.857,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;\u0026nbsp;0.001).\u0026nbsp;When the LR-based clinical model was combined with postoperative LC for prediction,\u0026nbsp;the AUC increased to 0.878\u0026nbsp;(95%CI 0.819-0.937,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026lt;\u0026nbsp;0.001),\u0026nbsp;indicating that the predictive performance of the clinical model was further improved.\u003c/p\u003e\n\u003cp\u003eCarnitine plays a role in β-oxidation, and its anti-inflammatory and antioxidant effects have been well documented [25]. During cardiac surgery, the use of CPB can lead to hypotension, reduced perfusion, and non-pulsatile blood flow, which may cause renal ischemia. Upon reperfusion after ischemia, reactive oxygen species (ROS) are released at rates exceeding the cells' capacity to neutralize and metabolize them, leading to reperfusion injury. Biomarkers of ischemia-reperfusion injury consist of nitric oxide, which is excessively produced during ischemia-reperfusion and transformed into free radicals; myeloperoxidase, released from neutrophil granules during phagocytosis; and malondialdehyde(MDA), which indicates the extent of lipid peroxidation or oxidative changes in lipids. By decreasing oxidative stress brought on by ischemia-reperfusion injury, LC might prevent cell death.\u0026nbsp;This antioxidant effect may be due to LC’s direct scavenging of ROS or its enhancement of endogenous antioxidant defense mechanisms, such as lipid peroxidation inhibition [8]. Studies have shown that LC supplementation reduces MDA levels and significantly increases the activity of reduced/oxidized glutathione and glutathione peroxidase [26].\u0026nbsp;In their meta-analysis,\u0026nbsp;Hadis Fathizadeh et al. found that LC supplementation reduces serum levels of inflammatory cytokines,\u0026nbsp;including C-reactive protein\u0026nbsp;(CRP),\u0026nbsp;interleukin-6\u0026nbsp;(IL-6),\u0026nbsp;TNF-α,\u0026nbsp;and malondialdehyde\u0026nbsp;(MDA),\u0026nbsp;in both healthy individuals and those with specific diseases,\u0026nbsp;while simultaneously increasing superoxide dismutase levels\u0026nbsp;[27].\u0026nbsp;Research has indicated that mitochondrial dysfunction in sepsis is a key contributor to organ failure.\u0026nbsp;The production of energy in the mitochondria relies on carnitine-mediated transport,\u0026nbsp;which is facilitated by carnitine palmitoyltransferase 1.\u0026nbsp;However, this process is impaired during sepsis, and LC supplementation has been shown to reduce mortality in septic patients [28-30]. Animal studies have found that polymyxins induce nephrotoxicity by acting on mitochondria and triggering permeability\u0026nbsp;transition.\u0026nbsp;LC prevents polymyxin-induced mitochondrial permeability transition in vitro;\u0026nbsp;furthermore,\u0026nbsp;when LC is used in combination with polymyxin,\u0026nbsp;it demonstrates nephroprotective effects in mice treated with polymyxin\u0026nbsp;[31].\u0026nbsp;Clinical and animal studies have also shown that carnitine deficiency following heat stress reduces ATP production, leading to an energy crisis that impairs recovery. Oral supplementation with LC prior to heat exposure helps maintain ATP production in renal tubular mitochondria and attenuates macrophage-mediated inflammation, thereby alleviating heat stress-induced AKI and subsequent kidney fibrosis [32].\u003c/p\u003e\n\u003cp\u003eFor the first time, we conducted a prospective clinical study to investigate the expression levels of LC in patients undergoing cardiac surgery and validated its predictive capability for CSA-AKI, offering a new potential biomarker for the early diagnosis of CSA-AKI. However, this study also has several limitations. First, it was a single-center study, and future multicenter prospective studies with larger sample sizes are needed for further validation. Second, the clinical outcomes assessed in this study were limited to CRRT and mortality; additional outcomes should be included, or AKI should be stratified to allow subgroup analysis of high-risk CSA-AKI patients. Furthermore, the current study focused only on the single biomarker LC, and further research is needed to explore the underlying mechanisms associated with its protective effects.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTo sum up, LC is capable of forecasting the incidence of CSA-AKI. Combining the early biomarker LC with a clinical prediction model effectively enhances the ability to predict CSA-AKI, helping clinicians identify high-risk patients at an early stage and implement timely clinical interventions to improve patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn line with the Declaration of Helsinki, this study received approval from the Ethics Committee of Nanjing Medical University Affiliated Nanjing Hospital (KY20240123-01). Participants or their legal representatives were asked to get informed consent. All the methods were conducted in compliance with the applicable guidelines and regulations outlined in the declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWenxiu Chen authored the primary manuscript text and created the figures and tables. Hao Zhang, Xiao Shen, Liang Hong, Hong Tao and Ming Chen collected the data. Study design was done by Cui Zhang and Wenkui Yu, who also used data interpretation and manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare that there is no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our heartfelt thanks to all the researchers and patients involved in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYun Xie Q, Guo B, Yang, et al. Tissue Inhibitor Metalloproteinase-2\u0026middot;IGF-Binding Protein 7 for the Prediction of Acute Kidney Injury following Cardiac Surgery. Cardiorenal Med. 2024;14(1):251\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChew STH, Hwang NC. Acute kidney injury after cardiac surgery: a narrative review of the literature. J Cardiothorac Vasc Anesth. 2019;33(4):1122\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchrezenmeier EV, Barasch J, Budde K, et al. Biomarkers in acute kidney injury: pathophysiological basis and clinical performance. Acta Physiol. 2017;219(3):554\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBauermeister A, Mannochio-Russo H, Costa-Lotufo LV, et al. Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol. 2022;20(3):143\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRego SM, Snyder MP. High Throughput Sequencing and Assessing Disease Risk. 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Nutr Clin Pract. 2008;23:16\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarcovina SM, Sirtori C, Peracino A, et al. Translating the Basic Knowledge of Mitochondrial Functions to Metabolic Therapy: Role of L-Carnitine. Transl Res. 2013;161:73\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStanley CA. Carnitine Deficiency Disorders in Children. Ann N Y Acad. 2004;1033:42\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFontecha-Barriuso M, Lopez-Diaz AM, Guerrero-Mauvecin J, et al. Tubular Mitochondrial Dysfunction, Oxidative Stress, and Progression of Chronic Kidney Disease. Antioxidants. 2022;11:1356.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEstaphan S, Eissa H, Elattar S, et al. A Study on the Effect of Cimetidine and L-Carnitine on Myoglobinuric Acute Kidney Injury in Male Rats. 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Pharmacotherapy. 2020;40(9):913\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIoannis G, Fatouros I, Douroudos S, Panagoutsos, et al. Effects of L-carnitine on oxidative stress responses in patients with renal disease. Med Sci Sports Exerc. 2010;42(10):1809\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFathizadeh H, Milajerdi A, Reiner Z, et al. The effects of L-carnitine supplementation on indicators of inflammation and oxidative stress: a systematic review and meta-analysis of randomized controlled trials. J Diabetes Metab Disord. 2020;19(2):1879\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYahyapoor F. L-carnitine effects on clinical status and mortality rate in septic patients: a systematic literature review. J Fasting Health. 2021;9(1):14\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdollahi H, Abdolahi M, Sedighiyan M, et al. The effect of L-Carnitine on mortality rate in septic patients: a systematic review and meta-analysis on randomized clinical trials. Endocr Metab Immune Disord Drug Targets. 2021;21(4):673\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahdi Keshani B, Alikiaii G, Askari. The effects of L-carnitine supplementation on inflammatory factors, oxidative stress, and clinical outcomes in patients with sepsis admitted to the intensive care unit (ICU): study protocol for a double blind, randomized, placebo-controlled clinical trial. Trials. 2022;23(1):170.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSophia L, Samodelov Z, Gai F, De Luca, et al. L-carnitine co-administration prevents colistin-induced mitochondrial permeability transition and reduces the risk of acute kidney injury in mice. Sci Rep. 2024;14(1):16444.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHiroyasu Goto H, Nakashima K, Mori, et al. L-Carnitine pretreatment ameliorates heat stress-induced acute kidney injury by restoring mitochondrial function of tubular cells. Am J Physiol Ren Physiol. 2024;326(3):F338\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiac Surgery-Associated Acute Kidney Injury, Metabolomics, L-Carnitine, Clinical Prediction Model","lastPublishedDoi":"10.21203/rs.3.rs-7206985/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7206985/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: This study aims to investigate the diagnostic significance of L-carnitine (LC) for the early detection of acute kidney injury associated with cardiac surgery (CSA-AKI).\u003c/p\u003e\n\u003cp\u003eMethods: We collected clinical data and serum samples from 27 patients admitted to the Intensive Care Unit (ICU) of Nanjing Medical University Affiliated Nanjing Hospital between February 2024 and March 2024. Of these, 13 patients belonged to the CSA-AKI group, while 14 were in the non-CSA-AKI group. An untargeted metabolomic analysis was conducted, which identified LC as a differential metabolite. In addition, clinical data and serum samples were prospectively collected from patients undergoing cardiac surgery at Nanjing Medical University Affiliated Nanjing Hospital between May 2024 and July 2024. Serum samples were taken preoperatively (immediately upon entering the operating room) and postoperatively (immediately upon ICU admission). The concentrations of blood urea nitrogen (BUN), serum creatinine (Scr), neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and LC were assessed. Multivariate logistic regression analysis was used to find independent risk variables for CSA-AKI. Predictive performance of the biomarkers, the clinical model, and their combination were evaluated using the area under the receiver operating characteristic curve (AUC).\u003c/p\u003e\n\u003cp\u003eResults: 170 patients in all who satisfied the inclusion requirements for cardiac surgery were included in the study. The incidence of CSA-AKI was 27.06%. Multivariate logistic regression analysis indicated that preoperative heart failure, vasopressor-inotropic score, and postoperative partial pressure of oxygen were independent risk factors for the development of CSA-AKI. Serum biomarker analysis showed significant differences in BUN, Scr, NGAL, and LC levels before and after cardiac surgery. After surgery, LC levels in patients with CSA-AKI were considerably lower than those in patients without CSA-AKI. Postoperative LC had a predictive ability with an AUC of 0.777 (95%CI: 0.697-0.857, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001). Incorporating postoperative LC into the clinical model can greatly enhance the model's predictive performance.\u003c/p\u003e\n\u003cp\u003eConclusion: Postoperative LC can effectively predict the occurrence of CSA-AKI, and when combined with the clinical prediction model, it demonstrates improved predictive performance for CSA-AKI.\u003c/p\u003e","manuscriptTitle":"L-Carnitine, A new biomarker screened based on untargeted metabolomics, predict cardiac surgery-associated acute kidney injury: A prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 02:25:24","doi":"10.21203/rs.3.rs-7206985/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-07T10:07:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T11:58:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T00:30:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255880121837929084613788343652058173639","date":"2025-09-06T01:13:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318899994963886916968017180689642834114","date":"2025-09-02T04:57:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-02T02:53:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-25T13:31:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-25T13:30:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-07-24T15:08:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33870337-02da-4f19-8982-1f63ae414f9b","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:10:27+00:00","versionOfRecord":{"articleIdentity":"rs-7206985","link":"https://doi.org/10.1186/s12882-025-04593-3","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2026-01-06 15:59:15","publishedOnDateReadable":"January 6th, 2026"},"versionCreatedAt":"2025-09-23 02:25:24","video":"","vorDoi":"10.1186/s12882-025-04593-3","vorDoiUrl":"https://doi.org/10.1186/s12882-025-04593-3","workflowStages":[]},"version":"v1","identity":"rs-7206985","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7206985","identity":"rs-7206985","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}