Triglyceride Glucose Index: as an Effective predictor for Vancomycin-Induced Acute Kidney Injury

Triglyceride Glucose Index: as an Effective predictor for Vancomycin-Induced Acute Kidney Injury | 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 Triglyceride Glucose Index: as an Effective predictor for Vancomycin-Induced Acute Kidney Injury li yuan, Shuang Song, Liying Zhu, Xiaorun Zhang, Yijiao Mou, Maoxing Lei, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4207548/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives: The triglyceride glucose (TyG) index is dependable marker of insulin resistance (IR) . It was reported to be associated with cardiovascular diseases and acute kidney injury. However, The correlation between the TyG and vancomycin-induced acute kidney injury remains uncertain. The aim of this study was to investigated the association between the TyG and Vancomycin-Induced acute kidney injury(VI-AKI). Methods: We extracted clinical data of patients treated with vancomycin from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and divided them into four groups according to their TyG index tertiles. The primary outcome was VI-AKI. Cox proportional hazards regression and restricted cubic spline (RCS) analysis were employed to investigate the relationship between the TyG index and VI-AKI. Kaplan-Meier analysis was employed to assess the association between the TyG index and the occurrence of VI-AKI, as well as the 90-day mortality rate among patients who experienced VI-AKI. Results: . A total of 1071 participants were included, among whom 674 (62.9%) experienced VI-AKI. Stratifying by baseline TyG quartiles, it was observed that elevated TyG levels were correlated with a heightened risk of VI-AKI. Cox proportional hazards regression analysis indicates that the TyG index is a risk factor for VI-AKI (HR=1.33, 95%CI 1.20-1.47). The RCS model illustrated the linear relationship between higher TyG index and increased risk of VI-AKI(p for nonlinear=0.004). Kaplan-Meier analysis revealed an association between high TyG levels and an increased incidence of VI-AKI(p<0.001), but it did not correlate with the 90-day mortality rate among patients who experienced AKI(p=0.7). Conclusions: In participants treated with vancomycin, there is a significant relationship between high TyG levels and a higher incidence rate of VI-AKI. Triglyceride Glucose Index vancomycin acute kidney injury predictor Nephrotoxic drugs Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Acute kidney injury (AKI) is a heterogeneous clinical syndrome that has multiple aetiologies, variable pathogenesis and diverse outcomes. AKI impacts approximately 30–60% of critically ill patients and is correlated with acute morbidity and mortality( 1 ). One of the primary reasons for AKI occurrence in critically ill patients is the use of nephrotoxic drugs. The glycopeptide antibiotic vancomycin is used to treat serious infections caused by Gram-positive bacteria, such as Methicillin-resistant Staphylococcus aureus (MRSA), when other antibiotics are ineffective. However, vancomycin induced acute kidney injury (VI-AKI) is a significant reason for restricting its clinical use( 2 ). A systematic review and meta-analysis, involving 4033 patients, revealed that the administration of vancomycin is associated with a 2.5-fold higher risk of AKI( 3 ). Initially, The triglyceride glucose (TyG) index is an indicator used to assess insulin resistance. In recent years, the TyG index has been associated with the development and adverse prognosis of cardiovascular diseases. Laura et al. first proposed that there is a positive correlation between the TyG index and cardiovascular disease events, including coronary heart disease, heart failure, cerebrovascular disease, and peripheral artery disease, and this correlation is not influenced by confounding factors( 4 ). Furthermore, the TyG index has shown its reliability and convenience as a predictive marker for unfavorable outcomes in individuals with kidney disease( 5 ). In patients with type 2 diabetes who have undergone coronary angiography, elevated TyG levels are strongly linked to an increased occurrence of contrast-induced acute kidney injury (CI-AKI)( 6 ). However, there is currently insufficient research to confirm the association between TyG and VI-AKI. Therefore, we conducted a retrospective cohort study to investigate the prognostic significance of the TyG index for AKI in patients receiving vancomycin. 2. Materials and methods 2.1. Database The MIMIC-IV (Medical Information Mart for Intensive Care IV) version 2.2 is an open-access database that offers extensive clinical data from patients admitted to the Beth Israel Deaconess Medical Center spanning the years 2008 to 2019( 7 , 8 ). The database encompasses a range of clinical data including demographic details, vital signs, imaging studies, laboratory test outcomes, a comprehensive data dictionary, and documentation featuring International Classification of Diseases codes(ICD-9 and ICD-10).Additionally, it contains validated hourly physiological records from bedside monitors monitored by ICU nurses.As the health information from MIMIC-IV database was de-identified, patient consent was not required for its use. We have acquired Credentialing and Certification through training provided by PhysioNet in order to use the aforementioned databases(PhysioNet ID:12168208)( 9 ). 2.2. Study patients and Patient characteristics We included patients who received intravenous vancomycin in the ICU. Additionally,patients meeting any of the following three criteria will be excluded: ( 1 ): Age < 18 years old. ( 2 ): Not the first admission to ICU. ( 3 ): While taking vancomycin, concurrent use of other nephrotoxic drugs such as aminoglycoside antibiotics, antifungal medications, contrast agents, etc. ( 4 ): Missing data on triglycerides and glucose levels on the first day of ICU admission, as well as information on creatinine levels and urine output during vancomycin treatment. Using the official code and raw data, we placed it within pgAdmin 4 (version 7.1). SQL (Structured Query Language) is the standard language for interacting with relational databases like PostgreSQL, which is commonly managed by pgAdmin 4. We utilized SQL to extract patient data from pgAdmin 4, including: ( 1 ) Demographic characteristics: gender, age, bmi. ( 2 ) Complications: hypotension, hypertension, myocardial infarction (MI), heart failure (HF), diabetes, choronic kidney disease (CKD). All complications' diagnoses conform to either ICD-9 or ICD-10 coding standards. ( 3 ) The complete blood count and biochemical indicators on the first day of ICU admission. ( 4 ) Vancomycin’s duration hours and concentration. ( 5 ) medication history: While using vancomycin, patients concurrently receive piperacillin-tazobactam and vasopressor medications. The TyG index was calculated using the following formula: ln [fasting TG (mg/dl) ×fasting glucose (mg/dl)]/2. 2.3. Endpoints Our primary study endpoint is the occurrence of AKI during intravenous administration of vancomycin in patients. VI-AKI is defined as AKI occurring during the use of vancomycin, with AKI defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. This includes an increase in serum creatinine (SCr) to 1.5 times the baseline within the preceding 7 days; or an increase in SCr by ≥ 0.3 mg/dl within 48 hours; or urine output < 0.5 ml/kg/h for 6 hours or more( 10 ). The minimum SCr value obtained within the first 7 days of admission is considered the baseline SCr. If the SCr value before admission is unavailable, the initial SCr measurement at admission is used as the baseline. The secondary outcome is the mortality rate 90 days after the occurrence of VI-AKI. 2.4. Data processing and Statistical analyses All participants were divided into tertiles based on their TyG index levels. Using the 'compareGroups' R package, we conducted a Shapiro-Wilk test for normality. Continuous variables with a normal distribution are presented as mean (SD,standard deviation) and compared using one-way ANOVA. Non-normally distributed variables are presented as median (25%,75%) and compared using the Kruskal-Wallis test. Categorical variables are described as percentages and compared using the chi-square test( 12 ). We also show the patients’ sofa score distribution. We used Cox proportional hazards models to calculate the hazard ratio (HR) and the 95% confidence interval (CI) for the TyG index and the occurrence of VI-AKI across groups. (Model 1: unadjusted; Model 2: adjusted for age, gender, BMI; Model 3: adjusted for age, gender, BMI, hypotension, hypertension, MI, HF, CKD, diabetes, hemoglobin, platelet, wbc, neutrophils_abs, BUN, chloride, glycemia, sodium, cholesterol, HDL, LDL, INR, PT, ALT, AST, VC_concentration, VC_duration, VC + PT, VC + VA). Additionally, We utilized a 3-knots restricted cubic spline (RCS) to illustrate the potential nonlinear relationship between the TyG index and VI-AKI. The Kaplan-Meier survival analysis was used to estimate the incidence of AKI and 90-day mortality among groups stratified by the TyG index. Subgroup analyses were conducted to assess the consistency of the prognostic value of the TyG index within various subgroups. Subgroups were defined based on age (< 65 versus ≥ 65 years), gender (female versus male), BMI (< 30 versus ≥ 30 kg/m2), and the presence of specific medical histories such as diabetes, CKD, and a history of piperacillin-tazobactam and vasopressor medication use. 3. Results 3.1. Baseline characteristics and sofa scores We included a total of 1071 patients who met the criteria and received vancomycin, with a mean age of 62.3 ± 14.8 years. Of these, 61.8% were male. According to the TyG index, participants were divided into four groups based on quartiles(Q1: 8.40 [8.17;8.57]; Q2: 8.90 [8.79;9.02]; Q3: 9.36 [9.24;9.49]; Q4: 9.97 [9.78;10.2]), as shown in Table 1 . In the Q4 group, patients tended to have a lower age but a higher BMI. Additionally, a higher proportion of patients in this group had complications such as hypertension, hypotension, and diabetes. In terms of blood routine, this group exhibited elevated levels of WBC (white blood cell count), platelets, and absolute neutrophil count (P < 0.05). Furthermore, individuals in this group demonstrated Increased levels of sodium, cholesterol, LDL, and ALT, as well as decreased levels of INR and PT (P < 0.001). Table 1 Baseline characteristics of the study population by quartiles of the TyG index Q1 ( N = 268) Q2 (N = 268) Q3 (N = 267) Q4 (N = 268) p.overall TyG, [IQR] 8.40 [8.17;8.57] 8.90 [8.79;9.02] 9.36 [9.24;9.49] 9.97 [9.78;10.2] <0.001 age, [IQR] 67.3 [55.9;77.7] 63.0 [52.2;73.3] 62.9 [53.9;70.0] 61.6 [49.5;68.8] <0.001 gender, (%): 0.673 Female 109 (40.7%) 104 (38.8%) 95 (35.6%) 101 (37.7%) Male 159 (59.3%) 164 (61.2%) 172 (64.4%) 167 (62.3%) bmi, [IQR] 25.2 [21.9;29.3] 26.0 [23.0;29.9] 26.9 [23.7;32.5] 28.3 [24.2;33.6] <0.001 hypotension, (%) 122 (45.5%) 145 (54.1%) 149 (55.8%) 151 (56.3%) 0.042 hypertension, (%) 79 (29.5%) 102 (38.1%) 78 (29.2%) 110 (41.0%) 0.005 MI, (%) 43 (16.0%) 54 (20.1%) 52 (19.5%) 52 (19.4%) 0.615 HF, (%) 107 (39.9%) 87 (32.5%) 91 (34.1%) 75 (28.0%) 0.032 diabetes, (%) 60 (22.4%) 78 (29.1%) 111 (41.6%) 137 (51.1%) <0.001 CKD (%) 84 (31.3%) 58 (21.6%) 84 (31.5%) 70 (26.1%) 0.030 hemoglobin, [IQR] 9.00 [8.22;10.0] 9.35 [8.44;10.6] 9.01 [8.30;9.92] 8.95 [8.29;10.2] 0.017 platelet, [IQR] 194 [120;285] 213 [143;298] 219 [135;303] 224 [150;327] 0.049 RDW, [IQR] 16.4 [14.7;18.2] 16.1 [14.4;17.6] 16.2 [14.7;18.1] 15.9 [14.3;17.8] 0.068 WBC, [IQR] 9.23 [6.75;12.9] 10.6 [7.65;13.2] 10.6 [8.02;13.5] 11.2 [8.30;14.5] <0.001 neutrophils_abs, [IQR] 7.61 [4.67;12.2] 8.98 [5.71;13.1] 9.32 [5.88;12.9] 9.52 [5.70;12.5] 0.037 lymphocytes_abs, [IQR] 0.96 [0.65;1.43] 1.10 [0.73;1.58] 1.01 [0.68;1.46] 1.13 [0.71;1.69] 0.201 monocytes_abs, [IQR] 0.61 [0.37;0.88] 0.68 [0.42;0.99] 0.68 [0.40;0.93] 0.63 [0.38;0.96] 0.239 albumin, [IQR] 29.6 [25.6;34.0] 29.8 [26.6;33.4] 28.7 [24.7;32.4] 28.8 [25.2;33.0] 0.096 bicarbonate, [IQR] 25.1 [23.0;27.0] 24.8 [22.6;27.0] 25.0 [22.8;26.5] 24.3 [22.2;26.5] 0.141 BUN, [IQR] 8.90 [5.70;14.4] 10.2 [6.43;15.6] 11.1 [6.82;16.4] 10.8 [7.13;16.1] 0.009 calcium, [IQR] 2.11 [2.02;2.21] 2.12 [2.04;2.19] 2.10 [2.01;2.19] 2.11 [2.00;2.22] 0.476 chloride, Mean (SD) 103 (5.13) 102 (4.62) 101 (4.93) 103 (4.27) 0.003 creatinine, [IQR] 95.9 [63.5;200] 107 [70.7;166] 111 [71.3;223] 110 [68.7;202] 0.195 glycemia, [IQR] 6.50 [5.84;7.65] 7.09 [6.32;8.32] 7.96 [6.86;9.52] 8.91 [7.61;10.5] <0.001 sodium, Mean (SD) 138 (3.78) 139 (3.27) 138 (3.99) 140 (3.55) <0.001 potassium, [IQR] 4.13 [3.93;4.35] 4.11 [3.87;4.34] 4.11 [3.93;4.35] 4.13 [3.93;4.38] 0.529 triglyceride, [IQR] 0.79 [0.64;0.98] 1.26 [1.09;1.51] 1.85 [1.56;2.18] 3.10 [2.44;4.05] <0.001 cholesterol, [IQR] 2.75 [2.02;3.58] 3.06 [2.23;3.99] 3.08 [2.40;4.07] 3.55 [2.80;4.65] <0.001 HDL, [IQR] 0.91 [0.62;1.22] 0.80 [0.53;1.17] 0.75 [0.52;0.97] 0.73 [0.47;1.01] <0.001 LDL, [IQR] 1.37 [0.89;1.94] 1.68 [1.04;2.29] 1.58 [1.01;2.25] 1.63 [1.04;2.49] 0.001 INR, [IQR] 1.40 [1.22;1.79] 1.33 [1.15;1.64] 1.35 [1.21;1.64] 1.28 [1.16;1.47] <0.001 PT, [IQR] 15.4 [13.6;19.4] 14.6 [12.9;17.9] 14.7 [13.2;18.0] 14.0 [12.8;16.4] <0.001 ALT, [IQR] 24.4 [16.1;53.7] 36.1 [21.0;75.4] 37.6 [17.5;97.3] 40.5 [20.0;91.2] <0.001 AST, [IQR] 37.0 [23.9;64.2] 43.6 [27.1;91.3] 40.7 [24.5;96.8] 44.4 [27.1;105] 0.014 VC_concentration, [IQR] 16.7 [13.1;20.6] 16.9 [13.0;20.6] 17.3 [13.2;20.9] 16.9 [13.4;20.0] 0.896 VC_duration, [IQR] 145 [67.8;279] 140 [78.0;261] 163 [82.5;300] 173 [82.0;296] 0.180 VC + PT, (%) 81 (30.2%) 107 (39.9%) 109 (40.8%) 110 (41.0%) 0.026 VC + VA, (%) 29 (10.8%) 38 (14.2%) 37 (13.9%) 51 (19.0%) 0.058 VI-AKI, (%) 132 (49.3%) 163 (60.8%) 181 (67.8%) 198 (73.9%) <0.001 Table 1 Data are presented as mean ± standard deviation (SD) or median (IQR) for continuous variables and proportions (%) for categorical variables. Abbreviation：MI (Myocardial Infarction), HF (Heart Failure), CKD (Chronic Kidney Disease), RDW (Red Cell Distribution Width), WBC (White Blood Cell Count), BUN (Blood Urea Nitrogen), HDL (high-density lipoprotein), LDL (low-density lipoprotein), INR (International Normalized Ratio), PT (Prothrombin Time), ALT (Alanine Aminotransferase), AST (Aspartate Aminotransferase), VC+PT (Vancomycin and Piperacillin-tazobactam), VC+VA (Vancomycin and Vasoactive agents), VI-AKI (Vancomycin Induced Acute Kidney Injury). In Figure 1. A , we can observe a clear increase in the proportion of VI-AKI with the rise of the TyG index. Figure 1. B illustrates the SOFA scores across different systems among the groups, the Q4 group exhibited high SOFA scores. Moreover, Figure 1.C depicts a higher proportion of high cardiovascular scores in the Q4 group, while Figure 1.D illustrates a higher proportion of low renal scores in the Q1 group. 3.2. TyG index and endpoints Cox proportional hazard ratios (HR) for VI-AKI show in Table 2 . When the TyG index as a continuous variable, Cox proportional hazards analysis indicated a statistically significant association (P<0.001) between the risk of VI-AKI and the TyG index in all models(Model1: 1.36[95% CI 1.20–1.48]; Model2: 1.32[95% CI 1.20–1.47]; Model3: 1.28[95% CI 1.10–1.49]). Moreover, when considering the TyG index as a nominal variable, the highest quartile (Q4) of the TyG index exhibited a significant association with the risk of AKI in all models (Q1 vs. Q4 in Model 1: 2.00[95% CI 1.60–2.50]; Model 2: 1.98[95% CI 1.57–2.50]; Model 3: 1.58 [95% CI 1.20–2.00]). Except for the Q1 vs Q2 in Model 3, which was not statistically significant (p=0.1), the Cox proportional hazard ratios for all other were statistically meaningful(p<0.05). Fig 2 displays the restricted cubic splines regression model, illustrating a linear relationship between the TyG index and VI-AKI risk in models (P for non-linearity in unadjusted model=0.004 and adjusted model=0.224). Finally, we also conducted Kaplan-Meier analysis for TyG quartile groups with VI-AKI cumulative incidence and the 90-day mortality rate after VI-AKI occurrence ( Fig 3 ). We can observe a clear relationship between the cumulative incidence of VI-AKI and the groups(P<0.001), but there is no association with the 90-day mortality rate. Table 2 . Cox proportional hazard ratios (HR) for VI-AKI Model1 Model2 Model3 HR(95% CI) p-value HR(95% CI) p-value HR(95% CI) `p-value TyG index 1.36[95% CI 1.20–1.48] <0.001 1.32[95% CI 1.20–1.47] <0.001 1.28[95% CI 1.10–1.49] 0.001 Quartile Q1 Ref Ref Ref Q2 1.44[95% CI 1.14–1.80] 0.002 1.43[95% CI 1.13–1.80] 0.003 1.22[95% CI 0.96–1.56] 0.1 Q3 1.70[95% CI 1.35–2.13] <0.001 1.68[95% CI 1.33–2.11] <0.001 1.36[95% CI 1.06–1.75] 0.01 Q4 2.00[95% CI 1.60–2.50] <0.001 1.98[95% CI 1.57–2.50] <0.001 1.58 [95% CI 1.20–2.00] 0.001 3.2. Subgroup analysis To further substantiate the relationship between TyG and VI-AKI, we conducted a subgroup analysis,based on gender, age, BMI, Diabetes,CKD, VC+PT, VC+VA. In Figure 4, There is a significant relationship between TyG and VI-AKI for males (HR = 1.30, 95% CI 1.14-1.50), females (HR = 1.37, 95% CI 1.18-1.59), age<65 years (HR = 1.20, 95% CI 1.05-1.37), age ≥65 years (HR = 1.55, 95% CI 1.31-1.83)，BMI<30 (HR = 1.38, 95% CI 1.22-1.56), with diabetes (HR = 1.20 95% CI 1.02-1.47), without diabetes (HR = 1.53, 95% CI 1.32-1.77), with CKD (HR = 1.29, 95% CI 1.06-1.56), without CKD (HR = 1.35, 95% CI 1.20-1.52), Vancomycin with Piperacillin-tazobactam (HR = 1.32, 95% CI 1.11-1.57), Vancomycin without Piperacillin-tazobactam (HR = 1.32, 95% CI 1.16-1.50), Vancomycin without vasopressor agents (HR = 1.32, 95% CI 1.18-1.48). However, there is no association between TyG and VI-AKI in patients with BMI ≥ 30 or in patients who used vasopressor agents during vancomycin therapy. 4. Discussion Based on available information, This study marks the inaugural exploration into the correlation between the TyG index and VI-AKI. VI-AKI is a common and potentially lethal adverse reaction to the use of vancomycin in septic patients. Our research revealed that as the TyG index increased, patients using vancomycin had a higher risk of developing VI-AKI. This association remained reliable even after adjusting for other confounding factors. Furthermore, we found no association between TyG and the 90-day mortality rate following VI-AKI occurrence. This research extends the application scope of the TyG index, demonstrating its close association with adverse drug effects. It can serve as a clinical indicator for managing vancomycin usage. Vancomycin is a frequently utilized antibiotic in hospital, employed to treat skin infections, bloodstream infections, and other conditions caused by gram-positive bacteria such as Staphylococcus aureus and Streptococcus. Due to its optimal bactericidal effectiveness and relatively low cost, vancomycin is prescribed more frequently than any other antibiotic, constituting up to 35% of infections in hospitalized patients( 13 ). However, approximately 90% of vancomycin is metabolized through renal pathways, which may lead to the occurrence of vancomycin-induced acute kidney injury (VI-AKI) during its usage. The reported incidence of VI-AKI in the literature fluctuates between 5% and over 20%, influenced by the characteristics of the study populations. ( 14 – 16 ). Multiple randomized clinical trials have reported an increased risk of vancomycin-associated AKI (RR 2.45, P < .0001), which surpasses that of alternative second-line treatments for MRSA infections such as daptomycin or linezolid( 3 ). VI-AKI commonly arises within 7 days of vancomycin treatment initiation and typically resolves upon prompt discontinuation of the medication. Nevertheless, certain patients, particularly those with critical illnesses, may not fully regain renal function. VI-AKI correlates with extended hospitalization, higher rates of hospital readmission, and increased patient mortality( 17 , 18 ). Vancomycin enters tubular epithelial cells through two pathways: receptor-mediated endocytosis from urine and transporter-mediated secretion from the peritubular circulation( 19 ). Both mechanisms lead to the accumulation of the drug in the cytoplasm of tubular epithelial cells, exposing them and the surrounding interstitium to potentially nephrotoxic substances( 20 ). The pathophysiological mechanisms, as well as diagnostic and therapeutic strategies for VI-AKI, remain poorly elucidated despite the ongoing publication of an expanding body of evidence on these matters. The prevailing consensus posits that the principal mechanisms underlying VI-AKI involve heightened drug concentrations within renal tubules among individuals with nephrotoxic risk factors( 21 ).This, in turn, triggers oxidative stress, complement activation, inflammatory injury, mitochondrial dysfunction, and proximal renal tubule cell apoptosis( 15 ). Diabetes is a chronic disease that can continuously damage vascular endothelium. Cardiovascular and cerebrovascular diseases are common complications of diabetes, and renal function deterioration tends to progress over time in these patients( 22 ). Kim et al, through meta-analysis, discovered that diabetes increases the risk of vancomycin-associated AKI(OR 1.25, 95% CI: 1.10–1.42)( 23 ).Insulin resistance, characterized by diminished responsiveness to insulin despite elevated insulin levels in the bloodstream, is a systemic disorder impacting various insulin-regulating pathways and multiple organs( 24 ). Insulin signaling, mediated by insulin receptors located on tubular cells and podocytes in the kidney, plays a crucial role in renal hemodynamics, podocyte activity, and tubular function. Insulin resistance-related hyperinsulinemia is associated with metabolic syndrome, inflammation, and adipokine dysregulation, which can lead to glomerular injury. Insulin resistance-related glomerular endothelial cell damage, mesangial cell proliferation, and basement membrane thickening associated with oxidative stress ultimately lead to glomerulosclerosis and tubulointerstitial damage, resulting in renal dysfunction( 25 – 27 ). As a simple and stable clinical indicator, TyG index is more suitable for assessing insulin resistance compared to HOMA( 28 – 30 ). It is computed using the natural logarithm of fasting blood glucose and fasting blood triglycerides. The rationale behind this formula lies in the observation that insulin resistance often leads to elevated levels of triglycerides and glucose in individuals without pre-existing health conditions( 31 ). Chen et al. conducted a study involving 5484 non-diabetic participants and found that higher TyG levels were significantly associated with an increased prevalence of CKD( 32 ). Yang et al. highlight the TyG index as a robust and independent predictor of the incidence of AKI and poor renal outcomes in critically ill patients with heart failure( 33 ). Furthermore, The AUC of the ROC curve for contrast-induced acute kidney injury in patients with type 2 diabetes reached as high as 0.728 at a TyG value of 8.88. The corresponding sensitivity was notably high at 94.9%( 6 ). Reactive oxygen species (ROS) play a pivotal role in the mechanisms underlying this relationship between TyG and VI-AKI. Enhanced oxidative stress arises from the overproduction of ROS in the context of concomitant, insufficient antioxidant pathways. Renal ROS production is predominantly mediated by various NADPH oxidases (NOXs), while a defective antioxidant system and mitochondrial dysfunction may also contribute. Additionally, oxidative stress represents the primary mechanism of VI-AKI( 34 , 35 ). Our research uncovered a substantial correlation between TyG levels and VI-AKI, indicating that TyG may serve as a valuable marker for assessing the risk of AKI in patients treated with vancomycin. 5. Study Limitations This study has the following limitations. First, due to the observational nature of this study, a causal relationship between the TyG index and VI-AKI events cannot be fully established. Second, changes in patient TyG index during hospitalization were not monitored. Finally, multicenter studies with larger sample sizes are needed to further clarify the correlation between the TyG index and VI-AKI. 6. Conclusion In conclusion, this study unveils a strong association between TyG and VI-AKI, offering a novel indicator for risk stratification of VI-AKI during vancomycin usage. Declarations 7.1. Ethics approval and consent to participate MIMIC-IV database is publicly available anonymized database, approval for the ethical committee are not necessary. 7.2. Consent for publication Not appicable. 7.3. Availability of data and materials The datasets are available in the PhysioNet (https://physionet.org/content/mimiciv/2.2/). 7.4. Competing interests The authors declare that they have no competing interests. 7.5. Funding No funding was provided. 7.6. Author contributions YL designed the study and drafted the manuscript; SS and LY extracted the data; XR and YJ conducted data quality management and statistical analysis ; MX developed the website; WJ and ZT critically revised the manuscript. All authors contributed to the article and approved the submitted version. 7.7. Acknowledgements We thank all participants in the Nanjing First Hospital and Nanjing Medical University. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the authors used chatgpt3.5 in order to verify code and translate. After using this tool,the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication. References Hoste EAJ, Kellum JA, Selby NM, Zarbock A, Palevsky PM, Bagshaw SM, et al. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol. 2018 Oct;14(10):607–25. Kan WC, Chen YC, Wu VC, Shiao CC. 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Building Bivariate Tables: The compareGroups Package for R . J Stat Soft [Internet]. 2014 [cited 2023 Dec 3];57(12). Available from: http://www.jstatsoft.org/v57/i12/ Diallo OO, Baron SA, Abat C, Colson P, Chaudet H, Rolain JM. Antibiotic resistance surveillance systems: A review. Journal of Global Antimicrobial Resistance. 2020 Dec;23:430–8. Bamgbola O. Review of vancomycin-induced renal toxicity: an update. Therapeutic Advances in Endocrinology and Metabolism. Tantranont N, Luque Y, Hsiao M, Haute C, Gaber L, Barrios R, et al. Vancomycin-Associated Tubular Casts and Vancomycin Nephrotoxicity. Kidney International Reports. 2021 Jul;6(7):1912–22. Filippone E, Kraft W, Farber J. The Nephrotoxicity of Vancomycin. Clin Pharma and Therapeutics. 2017 Sep;102(3):459–69. Awdishu L, Le A, Amato J, Jani V, Bal S, Mills R, et al. Urinary Exosomes Identify Inflammatory Pathways in Vancomycin Associated Acute Kidney Injury. IJMS. 2021 Mar 10;22(6):2784. Morales-Alvarez MC. Nephrotoxicity of Antimicrobials and Antibiotics. Advances in Chronic Kidney Disease. 2020 Jan;27(1):31–7. Perazella MA. Drug-induced acute kidney injury: diverse mechanisms of tubular injury. Curr Opin Crit Care. 2019 Dec;25(6):550–7. Kwiatkowska E, Domański L, Dziedziejko V, Kajdy A, Stefańska K, Kwiatkowski S. The Mechanism of Drug Nephrotoxicity and the Methods for Preventing Kidney Damage. Int J Mol Sci. 2021 Jun 6;22(11):6109. Pais GM, Liu J, Zepcan S, Avedissian SN, Rhodes NJ, Downes KJ, et al. Vancomycin-Induced Kidney Injury: Animal Models of Toxicodynamics, Mechanisms of Injury, Human Translation, and Potential Strategies for Prevention. Pharmacotherapy. 2020 May;40(5):438–54. Akhtar M, Taha NM, Nauman A, Mujeeb IB, Al-Nabet ADMH. Diabetic Kidney Disease: Past and Present. Adv Anat Pathol. 2020 Mar;27(2):87–97. Kim JY, Yee J, Yoon HY, Han JM, Gwak HS. Risk factors for vancomycin‐associated acute kidney injury: A systematic review and meta‐analysis. Brit J Clinical Pharma. 2022 Sep;88(9):3977–89. Galicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB, et al. Pathophysiology of Type 2 Diabetes Mellitus. Int J Mol Sci. 2020 Aug 30;21(17):6275. Artunc F, Schleicher E, Weigert C, Fritsche A, Stefan N, Häring HU. The impact of insulin resistance on the kidney and vasculature. Nat Rev Nephrol. 2016 Dec;12(12):721–37. Gluba A, Mikhailidis DP, Lip GYH, Hannam S, Rysz J, Banach M. Metabolic syndrome and renal disease. Int J Cardiol. 2013 Apr 5;164(2):141–50. Yang J, Liu Z. Mechanistic Pathogenesis of Endothelial Dysfunction in Diabetic Nephropathy and Retinopathy. Front Endocrinol (Lausanne). 2022;13:816400. Unger G, Benozzi SF, Perruzza F, Pennacchiotti GL. Triglycerides and glucose index: a useful indicator of insulin resistance. Endocrinol Nutr. 2014 Dec;61(10):533–40. Vasques ACJ, Novaes FS, de Oliveira M da S, Souza JRM, Yamanaka A, Pareja JC, et al. TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract. 2011 Sep;93(3):e98–100. Khan SH, Sobia F, Niazi NK, Manzoor SM, Fazal N, Ahmad F. Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetol Metab Syndr. 2018;10:74. Alizargar J, Bai CH, Hsieh NC, Wu SFV. Use of the triglyceride-glucose index (TyG) in cardiovascular disease patients. Cardiovasc Diabetol. 2020 Dec;19(1):8, s12933-019-0982–2. Chen N, Ma LL, Zhang Y, Chu X, Dong J, Yan YX. Association of long-term triglyceride-glucose index patterns with the incidence of chronic kidney disease among non-diabetic population: evidence from a functional community cohort. Cardiovasc Diabetol. 2024 Jan 3;23(1):7. Yang Z, Gong H, Kan F, Ji N. Association between the triglyceride glucose (TyG) index and the risk of acute kidney injury in critically ill patients with heart failure: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023 Aug 31;22(1):232. Oktem F, Arslan MK, Ozguner F, Candir O, Yilmaz HR, Ciris M, et al. In vivo evidences suggesting the role of oxidative stress in pathogenesis of vancomycin-induced nephrotoxicity: protection by erdosteine. Toxicology. 2005 Nov 15;215(3):227–33. Jha JC, Banal C, Chow BSM, Cooper ME, Jandeleit-Dahm K. Diabetes and Kidney Disease: Role of Oxidative Stress. Antioxid Redox Signal. 2016 Oct 20;25(12):657–84. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4207548","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287791588,"identity":"9f557ae8-bdcd-4e75-833c-457f8dac3463","order_by":0,"name":"li yuan","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"li","middleName":"","lastName":"yuan","suffix":""},{"id":287791589,"identity":"b04d3c67-136a-46e7-a44a-ee54d4ab4361","order_by":1,"name":"Shuang Song","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Song","suffix":""},{"id":287791590,"identity":"675c8d95-fcec-4a27-9bd0-86ed9df57f6e","order_by":2,"name":"Liying Zhu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liying","middleName":"","lastName":"Zhu","suffix":""},{"id":287791591,"identity":"3e5bd2f6-c1cd-4aba-a59f-316fefa7ceba","order_by":3,"name":"Xiaorun Zhang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaorun","middleName":"","lastName":"Zhang","suffix":""},{"id":287791592,"identity":"5c007536-ab7e-4569-a840-f272c2e3b4d6","order_by":4,"name":"Yijiao Mou","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yijiao","middleName":"","lastName":"Mou","suffix":""},{"id":287791593,"identity":"432eab9f-43a2-44cd-a7d0-af3fe10e62ac","order_by":5,"name":"Maoxing Lei","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Maoxing","middleName":"","lastName":"Lei","suffix":""},{"id":287791594,"identity":"282c3a1a-a611-49dc-96e4-cc149bdd6c9e","order_by":6,"name":"Wenjing Wang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Wang","suffix":""},{"id":287791595,"identity":"1910516a-ed64-45b1-8778-43fed0f693f5","order_by":7,"name":"Zhen Tao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYFACxoYDCQY2PPzsDURq4GFgbnzwoCJNRrLnANFa2JsNH5w5bGNww4FILfb8B9skEtvO8zDcYGD88DGHKFvAWm7zMM5uYJacuY0YLYyNEC3MMgfYmHmJ0sLMCNJyjodNIoFYLWyMzQYJZw7w8BCv5Qxj44OEimQeCZ6DzcT5hb3/+IODPwzs7O2PNx/88JEYLUiAsYE09aNgFIyCUTAKcAMA6/k1MDrcpGgAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Tao","suffix":""}],"badges":[],"createdAt":"2024-04-02 14:58:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4207548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4207548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54321907,"identity":"9a40546d-31ff-4e74-897e-b37b15590629","added_by":"auto","created_at":"2024-04-08 19:40:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82918,"visible":true,"origin":"","legend":"\u003cp\u003eVI-AKI and sofa score distribution\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4207548/v1/744513557599effa2ec349c8.jpg"},{"id":54321905,"identity":"d795ffb9-5b59-4de9-8da9-26b57771f185","added_by":"auto","created_at":"2024-04-08 19:40:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Model1: unadjusted.\u003cstrong\u003eB\u003c/strong\u003e Model2: adjusted for age, gender, BMI.\u003cstrong\u003eC \u003c/strong\u003eModel3: adjusted for age, gender, BMI, hypotension, hypertension, MI, HF, CKD, diabetes, hemoglobin, platelet, wbc, neutrophils_abs, BUN, chloride, glycemia, sodium, cholesterol, HDL, LDL, INR, PT, ALT, AST, VC_concentration, VC_duration, VC+PT, VC+VA\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4207548/v1/b49382082cd358a6dc777840.jpg"},{"id":54322274,"identity":"20534a68-35a7-4828-a1b3-4dc25d714eef","added_by":"auto","created_at":"2024-04-08 19:48:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80580,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier event curves for VI-AKI and 90-day mortality rate after VI-AKI occurrence.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4207548/v1/a1dbc72066b0f2bc6b0ccb2a.jpg"},{"id":54321906,"identity":"aa6a295f-1972-4717-8d48-41d77adbbb97","added_by":"auto","created_at":"2024-04-08 19:40:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89923,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of hazard ratios for theVI-AKI in different subgroups\u003c/p\u003e\n\u003cp\u003eVC+PT: vancomycin and Piperacillin-tazobactam; VC+VA: vancomycin and vasopressor agents.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4207548/v1/ab3370cc84ee1a7a89a1f391.jpg"},{"id":62119635,"identity":"c6908d1b-eb94-4ac9-a9ba-6d4a4f3f2b64","added_by":"auto","created_at":"2024-08-09 13:39:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":893791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4207548/v1/9104c721-cb13-4b7a-bad6-d37e2054f89e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Triglyceride Glucose Index: as an Effective predictor for Vancomycin-Induced Acute Kidney Injury","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute kidney injury (AKI) is a heterogeneous clinical syndrome that has multiple aetiologies, variable pathogenesis and diverse outcomes. AKI impacts approximately 30\u0026ndash;60% of critically ill patients and is correlated with acute morbidity and mortality(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). One of the primary reasons for AKI occurrence in critically ill patients is the use of nephrotoxic drugs. The glycopeptide antibiotic vancomycin is used to treat serious infections caused by Gram-positive bacteria, such as Methicillin-resistant Staphylococcus aureus (MRSA), when other antibiotics are ineffective. However, vancomycin induced acute kidney injury (VI-AKI) is a significant reason for restricting its clinical use(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). A systematic review and meta-analysis, involving 4033 patients, revealed that the administration of vancomycin is associated with a 2.5-fold higher risk of AKI(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInitially, The triglyceride glucose (TyG) index is an indicator used to assess insulin resistance. In recent years, the TyG index has been associated with the development and adverse prognosis of cardiovascular diseases. Laura et al. first proposed that there is a positive correlation between the TyG index and cardiovascular disease events, including coronary heart disease, heart failure, cerebrovascular disease, and peripheral artery disease, and this correlation is not influenced by confounding factors(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Furthermore, the TyG index has shown its reliability and convenience as a predictive marker for unfavorable outcomes in individuals with kidney disease(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In patients with type 2 diabetes who have undergone coronary angiography, elevated TyG levels are strongly linked to an increased occurrence of contrast-induced acute kidney injury (CI-AKI)(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, there is currently insufficient research to confirm the association between TyG and VI-AKI. Therefore, we conducted a retrospective cohort study to investigate the prognostic significance of the TyG index for AKI in patients receiving vancomycin.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Database\u003c/h2\u003e \u003cp\u003eThe MIMIC-IV (Medical Information Mart for Intensive Care IV) version 2.2 is an open-access database that offers extensive clinical data from patients admitted to the Beth Israel Deaconess Medical Center spanning the years 2008 to 2019(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The database encompasses a range of clinical data including demographic details, vital signs, imaging studies, laboratory test outcomes, a comprehensive data dictionary, and documentation featuring International Classification of Diseases codes(ICD-9 and ICD-10).Additionally, it contains validated hourly physiological records from bedside monitors monitored by ICU nurses.As the health information from MIMIC-IV database was de-identified, patient consent was not required for its use. We have acquired Credentialing and Certification through training provided by PhysioNet in order to use the aforementioned databases(PhysioNet ID:12168208)(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study patients and Patient characteristics\u003c/h2\u003e \u003cp\u003eWe included patients who received intravenous vancomycin in the ICU. Additionally,patients meeting any of the following three criteria will be excluded: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e): Age\u0026thinsp;\u0026lt;\u0026thinsp;18 years old. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e): Not the first admission to ICU. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e): While taking vancomycin, concurrent use of other nephrotoxic drugs such as aminoglycoside antibiotics, antifungal medications, contrast agents, etc. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e): Missing data on triglycerides and glucose levels on the first day of ICU admission, as well as information on creatinine levels and urine output during vancomycin treatment.\u003c/p\u003e \u003cp\u003eUsing the official code and raw data, we placed it within pgAdmin 4 (version 7.1). SQL (Structured Query Language) is the standard language for interacting with relational databases like PostgreSQL, which is commonly managed by pgAdmin 4. We utilized SQL to extract patient data from pgAdmin 4, including: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Demographic characteristics: gender, age, bmi. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Complications: hypotension, hypertension, myocardial infarction (MI), heart failure (HF), diabetes, choronic kidney disease (CKD). All complications' diagnoses conform to either ICD-9 or ICD-10 coding standards. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The complete blood count and biochemical indicators on the first day of ICU admission. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Vancomycin\u0026rsquo;s duration hours and concentration. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) medication history: While using vancomycin, patients concurrently receive piperacillin-tazobactam and vasopressor medications.\u003c/p\u003e \u003cp\u003eThe TyG index was calculated using the following formula: ln [fasting TG (mg/dl) \u0026times;fasting glucose (mg/dl)]/2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Endpoints\u003c/h2\u003e \u003cp\u003eOur primary study endpoint is the occurrence of AKI during intravenous administration of vancomycin in patients. VI-AKI is defined as AKI occurring during the use of vancomycin, with AKI defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. This includes an increase in serum creatinine (SCr) to 1.5 times the baseline within the preceding 7 days; or an increase in SCr by \u0026ge;\u0026thinsp;0.3 mg/dl within 48 hours; or urine output\u0026thinsp;\u0026lt;\u0026thinsp;0.5 ml/kg/h for 6 hours or more(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The minimum SCr value obtained within the first 7 days of admission is considered the baseline SCr. If the SCr value before admission is unavailable, the initial SCr measurement at admission is used as the baseline.\u003c/p\u003e \u003cp\u003eThe secondary outcome is the mortality rate 90 days after the occurrence of VI-AKI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data processing and Statistical analyses\u003c/h2\u003e \u003cp\u003eAll participants were divided into tertiles based on their TyG index levels. Using the 'compareGroups' R package, we conducted a Shapiro-Wilk test for normality. Continuous variables with a normal distribution are presented as mean (SD,standard deviation) and compared using one-way ANOVA. Non-normally distributed variables are presented as median (25%,75%) and compared using the Kruskal-Wallis test. Categorical variables are described as percentages and compared using the chi-square test(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). We also show the patients\u0026rsquo; sofa score distribution.\u003c/p\u003e \u003cp\u003eWe used Cox proportional hazards models to calculate the hazard ratio (HR) and the 95% confidence interval (CI) for the TyG index and the occurrence of VI-AKI across groups. (Model 1: unadjusted; Model 2: adjusted for age, gender, BMI; Model 3: adjusted for age, gender, BMI, hypotension, hypertension, MI, HF, CKD, diabetes, hemoglobin, platelet, wbc, neutrophils_abs, BUN, chloride, glycemia, sodium, cholesterol, HDL, LDL, INR, PT, ALT, AST, VC_concentration, VC_duration, VC\u0026thinsp;+\u0026thinsp;PT, VC\u0026thinsp;+\u0026thinsp;VA). Additionally, We utilized a 3-knots restricted cubic spline (RCS) to illustrate the potential nonlinear relationship between the TyG index and VI-AKI. The Kaplan-Meier survival analysis was used to estimate the incidence of AKI and 90-day mortality among groups stratified by the TyG index. Subgroup analyses were conducted to assess the consistency of the prognostic value of the TyG index within various subgroups. Subgroups were defined based on age (\u0026lt;\u0026thinsp;65 versus \u0026ge;\u0026thinsp;65 years), gender (female versus male), BMI (\u0026lt;\u0026thinsp;30 versus \u0026ge;\u0026thinsp;30 kg/m2), and the presence of specific medical histories such as diabetes, CKD, and a history of piperacillin-tazobactam and vasopressor medication use.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline characteristics and sofa scores\u003c/h2\u003e \u003cp\u003eWe included a total of 1071 patients who met the criteria and received vancomycin, with a mean age of 62.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8 years. Of these, 61.8% were male. According to the TyG index, participants were divided into four groups based on quartiles(Q1: 8.40 [8.17;8.57]; Q2: 8.90 [8.79;9.02]; Q3: 9.36 [9.24;9.49]; Q4: 9.97 [9.78;10.2]), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the Q4 group, patients tended to have a lower age but a higher BMI. Additionally, a higher proportion of patients in this group had complications such as hypertension, hypotension, and diabetes. In terms of blood routine, this group exhibited elevated levels of WBC (white blood cell count), platelets, and absolute neutrophil count (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, individuals in this group demonstrated Increased levels of sodium, cholesterol, LDL, and ALT, as well as decreased levels of INR and PT (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population by quartiles of the TyG index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1 (\u003cem\u003eN\u0026thinsp;=\u0026thinsp;268)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;268)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;267)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;268)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep.overall\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.40 [8.17;8.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.90 [8.79;9.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.36 [9.24;9.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.97 [9.78;10.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.3 [55.9;77.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0 [52.2;73.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.9 [53.9;70.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.6 [49.5;68.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender, (%):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (38.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (64.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebmi, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.2 [21.9;29.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0 [23.0;29.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.9 [23.7;32.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.3 [24.2;33.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehypotension, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (54.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e151 (56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehypertension, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (29.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 (41.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMI, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediabetes, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (41.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137 (51.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehemoglobin, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.00 [8.22;10.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.35 [8.44;10.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.01 [8.30;9.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.95 [8.29;10.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194 [120;285]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213 [143;298]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219 [135;303]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e224 [150;327]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.4 [14.7;18.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.1 [14.4;17.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.2 [14.7;18.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.9 [14.3;17.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.23 [6.75;12.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6 [7.65;13.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.6 [8.02;13.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2 [8.30;14.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eneutrophils_abs, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.61 [4.67;12.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.98 [5.71;13.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.32 [5.88;12.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.52 [5.70;12.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elymphocytes_abs, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 [0.65;1.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 [0.73;1.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 [0.68;1.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13 [0.71;1.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonocytes_abs, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61 [0.37;0.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 [0.42;0.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 [0.40;0.93]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63 [0.38;0.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealbumin, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.6 [25.6;34.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.8 [26.6;33.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.7 [24.7;32.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.8 [25.2;33.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebicarbonate, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.1 [23.0;27.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8 [22.6;27.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0 [22.8;26.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.3 [22.2;26.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.90 [5.70;14.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2 [6.43;15.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.1 [6.82;16.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.8 [7.13;16.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecalcium, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.11 [2.02;2.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12 [2.04;2.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.10 [2.01;2.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11 [2.00;2.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echloride, Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (5.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (4.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (4.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103 (4.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecreatinine, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.9 [63.5;200]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 [70.7;166]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 [71.3;223]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 [68.7;202]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eglycemia, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.50 [5.84;7.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.09 [6.32;8.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.96 [6.86;9.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.91 [7.61;10.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esodium, Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (3.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140 (3.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epotassium, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.13 [3.93;4.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.11 [3.87;4.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.11 [3.93;4.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.13 [3.93;4.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etriglyceride, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 [0.64;0.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 [1.09;1.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.85 [1.56;2.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.10 [2.44;4.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echolesterol, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.75 [2.02;3.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.06 [2.23;3.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.08 [2.40;4.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.55 [2.80;4.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 [0.62;1.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 [0.53;1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 [0.52;0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73 [0.47;1.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37 [0.89;1.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68 [1.04;2.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58 [1.01;2.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.63 [1.04;2.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 [1.22;1.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 [1.15;1.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35 [1.21;1.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28 [1.16;1.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.4 [13.6;19.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.6 [12.9;17.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.7 [13.2;18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.0 [12.8;16.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.4 [16.1;53.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.1 [21.0;75.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.6 [17.5;97.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.5 [20.0;91.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0 [23.9;64.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.6 [27.1;91.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.7 [24.5;96.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.4 [27.1;105]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVC_concentration, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.7 [13.1;20.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.9 [13.0;20.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.3 [13.2;20.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.9 [13.4;20.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVC_duration, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e145 [67.8;279]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140 [78.0;261]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 [82.5;300]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e173 [82.0;296]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVC\u0026thinsp;+\u0026thinsp;PT, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (40.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110 (41.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVC\u0026thinsp;+\u0026thinsp;VA, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI-AKI, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (60.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181 (67.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (IQR) for continuous variables and proportions (%) for categorical variables.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003eAbbreviation：MI (Myocardial Infarction), HF (Heart Failure), CKD (Chronic Kidney Disease), RDW (Red Cell Distribution Width), WBC (White Blood Cell Count), BUN (Blood Urea Nitrogen), HDL (high-density lipoprotein), LDL (low-density lipoprotein), INR (International Normalized Ratio), PT (Prothrombin Time), ALT (Alanine Aminotransferase), AST (Aspartate Aminotransferase), VC+PT (Vancomycin and Piperacillin-tazobactam), VC+VA (Vancomycin and Vasoactive agents), VI-AKI (Vancomycin Induced Acute Kidney Injury).\u003c/p\u003e\n\u003cp\u003eIn \u003cstrong\u003eFigure 1. A\u003c/strong\u003e, we can observe a clear increase in the proportion of VI-AKI with the rise of the TyG index. \u003cstrong\u003eFigure 1. B\u003c/strong\u003e illustrates the SOFA scores across different systems among the groups, the Q4 group exhibited high SOFA scores. Moreover, \u003cstrong\u003eFigure 1.C\u003c/strong\u003e depicts a higher proportion of high cardiovascular scores in the Q4 group, while \u003cstrong\u003eFigure 1.D\u003c/strong\u003e illustrates a higher proportion of low renal scores in the Q1 group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2. TyG index and\u0026nbsp;endpoints\u003c/p\u003e\n\u003cp\u003eCox proportional hazard ratios (HR) for VI-AKI show in \u003cstrong\u003eTable 2\u003c/strong\u003e. When the TyG index as a continuous variable, Cox proportional hazards analysis indicated a statistically significant association (P\u0026lt;0.001) between the risk of VI-AKI and the TyG index in all models(Model1: 1.36[95% CI 1.20\u0026ndash;1.48]; Model2: 1.32[95% CI 1.20\u0026ndash;1.47]; Model3: 1.28[95% CI 1.10\u0026ndash;1.49]).\u003c/p\u003e\n\u003cp\u003eMoreover, when considering the TyG index as a nominal variable, the highest quartile (Q4) of the TyG index exhibited a significant association with the risk of AKI in all models (Q1 vs. Q4 in Model 1: 2.00[95% CI 1.60\u0026ndash;2.50]; Model 2: 1.98[95% CI 1.57\u0026ndash;2.50]; Model 3: 1.58 [95% CI 1.20\u0026ndash;2.00]). Except for the Q1 vs Q2 in Model 3, which was not statistically significant (p=0.1), the Cox proportional hazard ratios for all other were statistically meaningful(p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig 2\u003c/strong\u003e displays the restricted cubic splines regression model, illustrating a linear relationship between the TyG index and VI-AKI risk in models (P for non-linearity in unadjusted model=0.004 and adjusted model=0.224).\u003c/p\u003e\n\u003cp\u003eFinally, we also conducted Kaplan-Meier analysis for TyG quartile groups with VI-AKI cumulative incidence and the 90-day mortality rate after VI-AKI occurrence (\u003cstrong\u003eFig 3\u003c/strong\u003e). We can observe a clear relationship between the cumulative incidence of VI-AKI and the groups(P\u0026lt;0.001), but there is no association with the 90-day mortality rate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Cox proportional hazard ratios (HR) for VI-AKI\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.86861313868613%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.153284671532848%\" valign=\"top\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"top\"\u003e\n \u003cp\u003eHR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.008474576271187%\" valign=\"top\"\u003e\n \u003cp\u003eHR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.983050847457626%\" valign=\"top\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.915254237288135%\" valign=\"top\"\u003e\n \u003cp\u003eHR(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.10169491525424%\" valign=\"top\"\u003e\n \u003cp\u003e`p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003e1.36[95% CI 1.20\u0026ndash;1.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003e1.32[95% CI 1.20\u0026ndash;1.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.153284671532848%\" valign=\"top\"\u003e\n \u003cp\u003e1.28[95% CI 1.10\u0026ndash;1.49]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003eQuartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.153284671532848%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.153284671532848%\" valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003e1.44[95% CI 1.14\u0026ndash;1.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003e1.43[95% CI 1.13\u0026ndash;1.80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.153284671532848%\" valign=\"top\"\u003e\n \u003cp\u003e1.22[95% CI 0.96\u0026ndash;1.56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003e1.70[95% CI 1.35\u0026ndash;2.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003e1.68[95% CI 1.33\u0026ndash;2.11]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.153284671532848%\" valign=\"top\"\u003e\n \u003cp\u003e1.36[95% CI 1.06\u0026ndash;1.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003e2.00[95% CI 1.60\u0026ndash;2.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.510948905109489%\" valign=\"top\"\u003e\n \u003cp\u003e1.98[95% CI 1.57\u0026ndash;2.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.153284671532848%\" valign=\"top\"\u003e\n \u003cp\u003e1.58 [95% CI 1.20\u0026ndash;2.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.86861313868613%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.2. Subgroup analysis\u003c/p\u003e\n\u003cp\u003eTo further substantiate the relationship between TyG and VI-AKI, we conducted a subgroup analysis,based on gender, age, BMI, Diabetes,CKD, VC+PT, VC+VA. In Figure 4, There is a significant relationship between TyG and VI-AKI for males (HR = 1.30, 95% CI 1.14-1.50), \u0026nbsp;females (HR = 1.37, 95% CI 1.18-1.59), age\u0026lt;65 years (HR = 1.20, 95% CI 1.05-1.37), age \u0026ge;65 years (HR = 1.55, 95% CI 1.31-1.83)，BMI\u0026lt;30 (HR = 1.38, 95% CI 1.22-1.56), with diabetes (HR = 1.20 95% CI 1.02-1.47), without diabetes (HR = 1.53, 95% CI 1.32-1.77), with CKD (HR = 1.29, 95% CI 1.06-1.56), without CKD (HR = 1.35, 95% CI 1.20-1.52), Vancomycin with Piperacillin-tazobactam (HR = 1.32, 95% CI 1.11-1.57), Vancomycin without Piperacillin-tazobactam (HR = 1.32, 95% CI 1.16-1.50), Vancomycin without vasopressor agents (HR = 1.32, 95% CI 1.18-1.48). However, there is no association between TyG and VI-AKI in patients with BMI \u0026ge; 30 or in patients who used vasopressor agents during vancomycin therapy. \u0026nbsp;\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBased on available information, This study marks the inaugural exploration into the correlation between the TyG index and VI-AKI. VI-AKI is a common and potentially lethal adverse reaction to the use of vancomycin in septic patients. Our research revealed that as the TyG index increased, patients using vancomycin had a higher risk of developing VI-AKI. This association remained reliable even after adjusting for other confounding factors. Furthermore, we found no association between TyG and the 90-day mortality rate following VI-AKI occurrence. This research extends the application scope of the TyG index, demonstrating its close association with adverse drug effects. It can serve as a clinical indicator for managing vancomycin usage.\u003c/p\u003e \u003cp\u003eVancomycin is a frequently utilized antibiotic in hospital, employed to treat skin infections, bloodstream infections, and other conditions caused by gram-positive bacteria such as Staphylococcus aureus and Streptococcus. Due to its optimal bactericidal effectiveness and relatively low cost, vancomycin is prescribed more frequently than any other antibiotic, constituting up to 35% of infections in hospitalized patients(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, approximately 90% of vancomycin is metabolized through renal pathways, which may lead to the occurrence of vancomycin-induced acute kidney injury (VI-AKI) during its usage. The reported incidence of VI-AKI in the literature fluctuates between 5% and over 20%, influenced by the characteristics of the study populations. (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Multiple randomized clinical trials have reported an increased risk of vancomycin-associated AKI (RR 2.45, P\u0026thinsp;\u0026lt;\u0026thinsp;.0001), which surpasses that of alternative second-line treatments for MRSA infections such as daptomycin or linezolid(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). VI-AKI commonly arises within 7 days of vancomycin treatment initiation and typically resolves upon prompt discontinuation of the medication. Nevertheless, certain patients, particularly those with critical illnesses, may not fully regain renal function. VI-AKI correlates with extended hospitalization, higher rates of hospital readmission, and increased patient mortality(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVancomycin enters tubular epithelial cells through two pathways: receptor-mediated endocytosis from urine and transporter-mediated secretion from the peritubular circulation(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Both mechanisms lead to the accumulation of the drug in the cytoplasm of tubular epithelial cells, exposing them and the surrounding interstitium to potentially nephrotoxic substances(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The pathophysiological mechanisms, as well as diagnostic and therapeutic strategies for VI-AKI, remain poorly elucidated despite the ongoing publication of an expanding body of evidence on these matters. The prevailing consensus posits that the principal mechanisms underlying VI-AKI involve heightened drug concentrations within renal tubules among individuals with nephrotoxic risk factors(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).This, in turn, triggers oxidative stress, complement activation, inflammatory injury, mitochondrial dysfunction, and proximal renal tubule cell apoptosis(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiabetes is a chronic disease that can continuously damage vascular endothelium. Cardiovascular and cerebrovascular diseases are common complications of diabetes, and renal function deterioration tends to progress over time in these patients(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Kim et al, through meta-analysis, discovered that diabetes increases the risk of vancomycin-associated AKI(OR 1.25, 95% CI: 1.10\u0026ndash;1.42)(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).Insulin resistance, characterized by diminished responsiveness to insulin despite elevated insulin levels in the bloodstream, is a systemic disorder impacting various insulin-regulating pathways and multiple organs(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Insulin signaling, mediated by insulin receptors located on tubular cells and podocytes in the kidney, plays a crucial role in renal hemodynamics, podocyte activity, and tubular function. Insulin resistance-related hyperinsulinemia is associated with metabolic syndrome, inflammation, and adipokine dysregulation, which can lead to glomerular injury. Insulin resistance-related glomerular endothelial cell damage, mesangial cell proliferation, and basement membrane thickening associated with oxidative stress ultimately lead to glomerulosclerosis and tubulointerstitial damage, resulting in renal dysfunction(\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a simple and stable clinical indicator, TyG index is more suitable for assessing insulin resistance compared to HOMA(\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). It is computed using the natural logarithm of fasting blood glucose and fasting blood triglycerides. The rationale behind this formula lies in the observation that insulin resistance often leads to elevated levels of triglycerides and glucose in individuals without pre-existing health conditions(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Chen et al. conducted a study involving 5484 non-diabetic participants and found that higher TyG levels were significantly associated with an increased prevalence of CKD(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Yang et al. highlight the TyG index as a robust and independent predictor of the incidence of AKI and poor renal outcomes in critically ill patients with heart failure(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Furthermore, The AUC of the ROC curve for contrast-induced acute kidney injury in patients with type 2 diabetes reached as high as 0.728 at a TyG value of 8.88. The corresponding sensitivity was notably high at 94.9%(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Reactive oxygen species (ROS) play a pivotal role in the mechanisms underlying this relationship between TyG and VI-AKI. Enhanced oxidative stress arises from the overproduction of ROS in the context of concomitant, insufficient antioxidant pathways. Renal ROS production is predominantly mediated by various NADPH oxidases (NOXs), while a defective antioxidant system and mitochondrial dysfunction may also contribute. Additionally, oxidative stress represents the primary mechanism of VI-AKI(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Our research uncovered a substantial correlation between TyG levels and VI-AKI, indicating that TyG may serve as a valuable marker for assessing the risk of AKI in patients treated with vancomycin.\u003c/p\u003e"},{"header":"5. Study Limitations","content":"\u003cp\u003eThis study has the following limitations. First, due to the observational nature of this study, a causal relationship between the TyG index and VI-AKI events cannot be fully established. Second, changes in patient TyG index during hospitalization were not monitored. Finally, multicenter studies with larger sample sizes are needed to further clarify the correlation between the TyG index and VI-AKI.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn conclusion, this study unveils a strong association between TyG and VI-AKI, offering a novel indicator for risk stratification of VI-AKI during vancomycin usage.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e7.1. Ethics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMIMIC-IV database is publicly available anonymized database, approval for the ethical committee are not necessary.\u003c/p\u003e\n\u003cp\u003e7.2. Consent for publication\u003c/p\u003e\n\u003cp\u003eNot appicable.\u003c/p\u003e\n\u003cp\u003e7.3. Availability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets are available in the PhysioNet (https://physionet.org/content/mimiciv/2.2/).\u003c/p\u003e\n\u003cp\u003e7.4. Competing interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e7.5. Funding\u003c/p\u003e\n\u003cp\u003eNo funding was provided.\u003c/p\u003e\n\u003cp\u003e7.6. Author contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYL designed the study and drafted the manuscript; SS and LY extracted the data; XR and YJ conducted data quality management and statistical analysis ; MX developed the website; WJ and ZT critically revised the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e7.7. Acknowledgements\u003c/p\u003e\n\u003cp\u003eWe thank all participants in the Nanjing First Hospital and Nanjing Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the authors used chatgpt3.5 in order to verify code and translate. After using this tool,the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHoste EAJ, Kellum JA, Selby NM, Zarbock A, Palevsky PM, Bagshaw SM, et al. Global epidemiology and outcomes of acute kidney injury. Nat Rev Nephrol. 2018 Oct;14(10):607\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eKan WC, Chen YC, Wu VC, Shiao CC. Vancomycin-Associated Acute Kidney Injury: A Narrative Review from Pathophysiology to Clinical Application. IJMS. 2022 Feb 12;23(4):2052. \u003c/li\u003e\n\u003cli\u003eSinha Ray A, Haikal A, Hammoud KA, Yu ASL. Vancomycin and the Risk of AKI: A Systematic Review and Meta-Analysis. CJASN. 2016 Dec;11(12):2132\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-\u0026Iacute;\u0026ntilde;igo L, Navarro-Gonz\u0026aacute;lez D, Fern\u0026aacute;ndez-Montero A, Pastrana-Delgado J, Mart\u0026iacute;nez JA. The TyG index may predict the development of cardiovascular events. Eur J Clin Invest. 2016 Feb;46(2):189\u0026ndash;97. \u003c/li\u003e\n\u003cli\u003eYildirim F, Yildiz AB, Kanbay M. A promising tool: triglyceride-glucose index to stratify the risk of cardiovascular events in chronic kidney disease. Clin Kidney J. 2022 Sep;15(9):1653\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eQin Y, Tang H, Yan G, Wang D, Qiao Y, Luo E, et al. A High Triglyceride-Glucose Index Is Associated With Contrast-Induced Acute Kidney Injury in Chinese Patients With Type 2 Diabetes Mellitus. Front Endocrinol (Lausanne). 2020;11:522883. \u003c/li\u003e\n\u003cli\u003eJohnson, A, Bulgarelli L, Pollard. MIMIC-IV (version 2.2). PhysioNet. 2023; \u003c/li\u003e\n\u003cli\u003eJohnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023 Jan 3;10(1):1. \u003c/li\u003e\n\u003cli\u003eGoldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000 Jun 13;101(23):E215-220. \u003c/li\u003e\n\u003cli\u003eStevens PE, Levin A, Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013 Jun 4;158(11):825\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eBuuren SV, Groothuis-Oudshoorn K. \u003cstrong\u003emice\u003c/strong\u003e : Multivariate Imputation by Chained Equations in \u003cem\u003eR\u003c/em\u003e. J Stat Soft [Internet]. 2011 [cited 2023 Dec 3];45(3). Available from: http://www.jstatsoft.org/v45/i03/\u003c/li\u003e\n\u003cli\u003eSubirana I, Sanz H, Vila J. Building Bivariate Tables: The \u003cstrong\u003ecompareGroups\u003c/strong\u003e Package for \u003cem\u003eR\u003c/em\u003e. J Stat Soft [Internet]. 2014 [cited 2023 Dec 3];57(12). Available from: http://www.jstatsoft.org/v57/i12/\u003c/li\u003e\n\u003cli\u003eDiallo OO, Baron SA, Abat C, Colson P, Chaudet H, Rolain JM. Antibiotic resistance surveillance systems: A review. Journal of Global Antimicrobial Resistance. 2020 Dec;23:430\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eBamgbola O. Review of vancomycin-induced renal toxicity: an update. Therapeutic Advances in Endocrinology and Metabolism. \u003c/li\u003e\n\u003cli\u003eTantranont N, Luque Y, Hsiao M, Haute C, Gaber L, Barrios R, et al. Vancomycin-Associated Tubular Casts and Vancomycin Nephrotoxicity. Kidney International Reports. 2021 Jul;6(7):1912\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eFilippone E, Kraft W, Farber J. The Nephrotoxicity of Vancomycin. Clin Pharma and Therapeutics. 2017 Sep;102(3):459\u0026ndash;69. \u003c/li\u003e\n\u003cli\u003eAwdishu L, Le A, Amato J, Jani V, Bal S, Mills R, et al. Urinary Exosomes Identify Inflammatory Pathways in Vancomycin Associated Acute Kidney Injury. IJMS. 2021 Mar 10;22(6):2784. \u003c/li\u003e\n\u003cli\u003eMorales-Alvarez MC. Nephrotoxicity of Antimicrobials and Antibiotics. Advances in Chronic Kidney Disease. 2020 Jan;27(1):31\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003ePerazella MA. Drug-induced acute kidney injury: diverse mechanisms of tubular injury. Curr Opin Crit Care. 2019 Dec;25(6):550\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eKwiatkowska E, Domański L, Dziedziejko V, Kajdy A, Stefańska K, Kwiatkowski S. The Mechanism of Drug Nephrotoxicity and the Methods for Preventing Kidney Damage. Int J Mol Sci. 2021 Jun 6;22(11):6109. \u003c/li\u003e\n\u003cli\u003ePais GM, Liu J, Zepcan S, Avedissian SN, Rhodes NJ, Downes KJ, et al. Vancomycin-Induced Kidney Injury: Animal Models of Toxicodynamics, Mechanisms of Injury, Human Translation, and Potential Strategies for Prevention. Pharmacotherapy. 2020 May;40(5):438\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eAkhtar M, Taha NM, Nauman A, Mujeeb IB, Al-Nabet ADMH. Diabetic Kidney Disease: Past and Present. Adv Anat Pathol. 2020 Mar;27(2):87\u0026ndash;97. \u003c/li\u003e\n\u003cli\u003eKim JY, Yee J, Yoon HY, Han JM, Gwak HS. Risk factors for vancomycin‐associated acute kidney injury: A systematic review and meta‐analysis. Brit J Clinical Pharma. 2022 Sep;88(9):3977\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eGalicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB, et al. Pathophysiology of Type 2 Diabetes Mellitus. Int J Mol Sci. 2020 Aug 30;21(17):6275. \u003c/li\u003e\n\u003cli\u003eArtunc F, Schleicher E, Weigert C, Fritsche A, Stefan N, H\u0026auml;ring HU. The impact of insulin resistance on the kidney and vasculature. Nat Rev Nephrol. 2016 Dec;12(12):721\u0026ndash;37. \u003c/li\u003e\n\u003cli\u003eGluba A, Mikhailidis DP, Lip GYH, Hannam S, Rysz J, Banach M. Metabolic syndrome and renal disease. Int J Cardiol. 2013 Apr 5;164(2):141\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eYang J, Liu Z. Mechanistic Pathogenesis of Endothelial Dysfunction in Diabetic Nephropathy and Retinopathy. Front Endocrinol (Lausanne). 2022;13:816400. \u003c/li\u003e\n\u003cli\u003eUnger G, Benozzi SF, Perruzza F, Pennacchiotti GL. Triglycerides and glucose index: a useful indicator of insulin resistance. Endocrinol Nutr. 2014 Dec;61(10):533\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eVasques ACJ, Novaes FS, de Oliveira M da S, Souza JRM, Yamanaka A, Pareja JC, et al. TyG index performs better than HOMA in a Brazilian population: a hyperglycemic clamp validated study. Diabetes Res Clin Pract. 2011 Sep;93(3):e98\u0026ndash;100. \u003c/li\u003e\n\u003cli\u003eKhan SH, Sobia F, Niazi NK, Manzoor SM, Fazal N, Ahmad F. Metabolic clustering of risk factors: evaluation of Triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetol Metab Syndr. 2018;10:74. \u003c/li\u003e\n\u003cli\u003eAlizargar J, Bai CH, Hsieh NC, Wu SFV. Use of the triglyceride-glucose index (TyG) in cardiovascular disease patients. Cardiovasc Diabetol. 2020 Dec;19(1):8, s12933-019-0982\u0026ndash;2. \u003c/li\u003e\n\u003cli\u003eChen N, Ma LL, Zhang Y, Chu X, Dong J, Yan YX. Association of long-term triglyceride-glucose index patterns with the incidence of chronic kidney disease among non-diabetic population: evidence from a functional community cohort. Cardiovasc Diabetol. 2024 Jan 3;23(1):7. \u003c/li\u003e\n\u003cli\u003eYang Z, Gong H, Kan F, Ji N. Association between the triglyceride glucose (TyG) index and the risk of acute kidney injury in critically ill patients with heart failure: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023 Aug 31;22(1):232. \u003c/li\u003e\n\u003cli\u003eOktem F, Arslan MK, Ozguner F, Candir O, Yilmaz HR, Ciris M, et al. In vivo evidences suggesting the role of oxidative stress in pathogenesis of vancomycin-induced nephrotoxicity: protection by erdosteine. Toxicology. 2005 Nov 15;215(3):227\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eJha JC, Banal C, Chow BSM, Cooper ME, Jandeleit-Dahm K. Diabetes and Kidney Disease: Role of Oxidative Stress. Antioxid Redox Signal. 2016 Oct 20;25(12):657\u0026ndash;84. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride Glucose Index, vancomycin, acute kidney injury, predictor, Nephrotoxic drugs","lastPublishedDoi":"10.21203/rs.3.rs-4207548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4207548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003eThe triglyceride glucose (TyG) index is dependable marker of insulin resistance (IR) . It was reported to be associated with cardiovascular diseases and acute kidney injury.\u003c/p\u003e\n\u003cp\u003eHowever, The correlation between the TyG and vancomycin-induced acute kidney injury remains uncertain. The aim of this study was to investigated the association between the TyG and Vancomycin-Induced acute kidney injury(VI-AKI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We extracted clinical data of patients treated with vancomycin from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and divided them into four groups according to their TyG index tertiles. The primary outcome was VI-AKI. Cox proportional hazards regression and restricted cubic spline (RCS) analysis were employed to investigate the relationship between the TyG index and VI-AKI. Kaplan-Meier analysis was employed to assess the association between the TyG index and the occurrence of VI-AKI, as well as the 90-day mortality rate among patients who experienced VI-AKI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e. A total of 1071 participants were included, among whom 674 (62.9%) experienced VI-AKI. Stratifying by baseline TyG quartiles, it was observed that elevated TyG levels were correlated with a heightened risk of VI-AKI. Cox proportional hazards regression analysis indicates that the TyG index is a risk factor for VI-AKI (HR=1.33, 95%CI 1.20-1.47). The RCS model illustrated the linear relationship between higher TyG index and increased risk of VI-AKI(p for nonlinear=0.004). Kaplan-Meier analysis revealed an association between high TyG levels and an increased incidence of VI-AKI(p\u0026lt;0.001), but it did not correlate with the 90-day mortality rate among patients who experienced AKI(p=0.7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eIn participants treated with vancomycin, there is a significant relationship between high TyG levels and a higher incidence rate of VI-AKI.\u003c/p\u003e","manuscriptTitle":"Triglyceride Glucose Index: as an Effective predictor for Vancomycin-Induced Acute Kidney Injury","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 19:40:28","doi":"10.21203/rs.3.rs-4207548/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b43275f3-3759-4c9d-8b5f-35f8c9ac17a1","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-03T12:39:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 19:40:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4207548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4207548","identity":"rs-4207548","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}