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. 2025 Feb 19;47(1):2466818. doi: 10.1080/0886022X.2025.2466818

The triglyceride-glucose index and acute kidney injury risk in critically ill patients with coronary artery disease

Yi Zhang a,#, Gang Li a,#, Junjie Li b,#, Bohao Jian a, Keke Wang b, Jiantao Chen a, Jian Hou a, Jianbo Liao a, Zhuoming Zhou a,✉, Zhongkai Wu a,✉, Mengya Liang a,✉
PMCID: PMC11843639  PMID: 39972619

Abstract

Background

The triglyceride-glucose (TyG) index, proven a reliable and simple surrogate of insulin resistance, has shown potential associations with cardiovascular outcomes and renal diseases. This research delved into the utility of the TyG index in predicting the risk of acute kidney injury (AKI) in patients with coronary artery disease (CAD), an area not extensively covered in existing literature.

Methods

A cohort of patients with CAD was recruited from the Medical Information Mart for Intensive Care-IV database, and categorized into quartiles based on their TyG index. The primary outcome was AKI incidence, and the secondary outcome was renal replacement therapy (RRT). Scatterplot histograms, cox proportional hazards models, Kaplan-Meier survival curves, and restricted cubic splines were employed to investigate the association between the TyG index and the risk of AKI in patients with CAD.

Results

A total of 1,501 patients were enrolled in this study, predominantly male (61.56%), with a median age of 69.80 years. The AKI incidence was 67.22% among all patients, with the AKI stages increased with higher TyG levels (P for trend <0.001). The Kaplan-Meier survival analyses demonstrated statistically significant differences in AKI incidence and RRT application throughout the entire cohort, stratified by the TyG index quartiles (p < 0.001). Additionally, the restricted cubic spline analysis revealed a non-linear association between the TyG index and the risk of AKI (P for non-linear =0.637). Both multivariate Cox proportional hazards analyses (HR 1.62; 95% CI 1.15–2.27; p = 0.005) and multivariate logistic regression analyses (OR 2.16; 95% CI 1.18–3.94; p = 0.012) showed that the elevated TyG index was significantly related to AKI incidence. The association between TyG index and the risk of AKI is more significant in patients without diabetes (HR 1.27; 95% CI 1.14–1.42; p < 0.001), compared to patients with diabetes (P for interaction =0.013).

Conclusions

In summary, the TyG index emerged as a reliable predictor for the occurrence of AKI in CAD patients during ICU stay. Furthermore, it is also anticipated to serve as a valuable indicator for non-diabetic patients in predicting the incidence of AKI.

Keywords: Triglyceride-glucose index, insulin resistance, coronary heart disease, acute kidney injury, renal replacement therapy

Graphical abstract

graphic file with name IRNF_A_2466818_UF0001_C.jpg

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Introduction

Coronary artery disease (CAD) is considered a prominent contributor to worldwide cardiovascular morbidity and mortality [1,2], which imposes a substantial societal health burden. Acute kidney injury (AKI) is a common comorbidity that threatens the prognosis of CAD patients, especially in critically ill patients with CAD [3–5]. Glucose and lipid metabolism disorders are well-known risk factors for CAD and are also closely associated with the progression of kidney injury, because metabolic disturbance is involved in vasculopathy including atherosclerosis and microvessel lesions [6,7]. The interaction between cardiac dysfunction and kidney disease can create highly complex and challenging clinical scenarios, known as ‘cardiorenal syndrome (CRS)’. CRS is characterized by interrelated and deteriorated cycle of declining function in both organs [8,9] and metabolic disturbance may be the common driver of pathophysiology in CRS. Under these circumstances, both cardiac and renal issues should be addressed in the clinical management of CAD patients during ICU stay. However, the timely diagnosis of AKI remains challenging, since conventional measurements, such as urine output and serum creatinine, are not sensitive in the early stages of renal injury [10]. Alternative biomarkers such as urinary kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL) have been proposed, yet their application is limited by cost and availability, especially in resource-constrained settings [10]. As a complementary strategy, early identification of high-risk AKI groups among CAD patients in the ICU is crucial for improving their risk stratification, treatment management and prognosis.

Insulin Resistance (IR), a prominent hallmark of metabolic syndrome [11,12], plays a crucial role in the development of cardiorenal syndrome [13,14]. Although the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) index is widely used to assess IR, its utility is limited in subjects undergoing insulin therapy or those with complete loss of pancreatic beta-cell function [15]. As an alternative to HOMA-IR, the triglyceride-glucose (TyG) index, calculated using fasting triglyceride and fasting blood glucose levels, has been proposed and shown to be superior in assessing IR [16,17]. This simple, convenient, and low-cost alternative method does not require insulin quantification and can be used for all subjects, regardless of their insulin treatment status. Moreover, multiple studies have demonstrated that the TyG index is significantly associated with adverse cardiovascular events, including carotid artery plaque, arterial stiffness, acute coronary syndrome, myocardial infarction and heart failure [18–21]. Besides, patients with higher TyG index experience an elevated risk of hospitalization and mortality in CAD [22,23] or chronic kidney disease population [24–26]. Taken together, current evidence suggests a potential role for the TyG index in predicting the risk of AKI among CAD patients, whereas such association remains insufficiently explored in the current literature.

Herein, the primary objective of this research is to explore the intricate association between the TyG index and the susceptibility to AKI in critically ill patients with CAD. Further, we seek to investigate whether this association is affected by different subgroups, with a specific focus on diabetes, aiming to verify the stability of the TyG index for predicting AKI in different glucose metabolic states.

Methods

Data source and patient selection

The data analyzed in this study were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (https://mimic.mit.edu), which is a comprehensive and public database encompassing over 50,000 ICU admissions from Beth Israel Deaconess Medical Center, Boston, spanning from 2008 to 2019 [27]. One author (Yi Zhang) completed the Collaborative Institutional Training Initiative program and obtained the requisite certification for accessing the MIMIC-IV database (certification number: 59,829,466). A waiver of informed consent was granted for us by Beth Israel Deaconess Medical Center, as the database’s de-identified nature ensures patient confidentiality. Our study adhered rigorously to the principles outlined in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, ensuring comprehensive and transparent reporting of its observational study design, methods, and findings.

Inclusion criteria were established as follows: 1) patients (aged 18 years or older) diagnosed with CAD at admission and requiring ICU stay as recorded in the MIMIC-IV database, defined using the International Classification of Diseases, Ninth Revision, Clinical Modification codes 410–411 and Tenth Revision, Clinical Modification codes I20–I21 (source: https://icd.who.int/browse10/2019/en) (n = 31,970); 2) in the case of patients with multiple admissions, only their initial stay to the ICU was considered (n = 9,576); 3) baseline serum creatinine or urinary output, triglyceride and blood glucose levels during the first ICU admission should be available (n = 1,501).

Data collection

All data were extracted using the Structured Query Language by pgAdmin4 PostgreSQL tools (version 14.2). The extracted data included: 1) demographics: body mass index (BMI), age, gender; 2) vital signs: systolic blood pressure (SBP), diastolic blood pressure (DBP); 3) initial ICU lab tests: high-density lipoprotein, low-density lipoprotein, total cholesterol, triglyceride, blood glucose, serum creatinine, blood urea nitrogen, estimated Glomerular Filtration Rate (eGFR), urea protein, urea albumin, C reactive protein, procalcitonin, N-terminal pro-brain natriuretic peptide, hemoglobin A1c, and albumin; Notably, low-density lipoprotein levels were obtained as either directly measured values or calculated using the Friedewald formula, standardizing all results to mg/dL for consistency [28,29]. 3) medical histories: drinking, smoking, hyperlipidemia, hypertension, diabetes, congestive heart failure, atrial fibrillation, stroke, chronic pulmonary disease, and chronic kidney disease, coronary artery bypass grafting (CABG), and percutaneous coronary intervention (PCI); 4) medications: included the use of both oral glucose-lowering agents (e.g. metformin, sulfonylureas, DPP-4 inhibitors, SGLT-2 inhibitors) and injectable glucose-lowering agents (e.g. insulin, GLP-1 receptor agonists), as well as lipid-lowering agents, including statins and fibrates, administered within 48 h after ICU admission; 5) endpoint indicators: ICU and hospital stay specifics, and the use of RRT. The medical histories were defined using Ninth Revision and Tenth Revision, Clinical Modification codes. The follow-up period commenced on the ICU or hospital admission date and concluded when the endpoints of interest occurred. The TyG index was calculated using a logarithmic formula: ln [fasting triglyceride (mg/dl) × fasting blood glucose (mg/dl)/2] [17].

To effectively address missing values and uphold data integrity, the following strategies were implemented: 1) variables with over 30% missing values, including urea protein, urea albumin, C reactive protein, procalcitonin, N-terminal pro-brain natriuretic peptide, hemoglobin A1c, and albumin were excluded to avoid potential bias; 2) variables like high-density lipoprotein, and total cholesterol which had missing data between 25% and 30%, dummy variables were introduced to reduce bias from simply replacing missing values [30]; 3) the ‘mice’ package in R, which is widely recognized for its robust methods in handling missing data, was used to impute the variables with under 25% missing data to enhance the reliability of our analysis [31].

Outcomes of interest and clinical definition

The primary outcome of this study is the incidence of AKI in the ICU. CAD was defined as angina pectoris, acute myocardial infarction (AMI), acute coronary syndrome. In accordance with the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, AKI is established as an increase in serum creatinine level by ≥0.3 mg/dL above baseline within 48 h, or an increase in serum creatinine to ≥1.5 times the baseline within the prior 7 days, or urinary output is <0.5 mL/kg/h for 6 h or more [32]. To address missing baseline creatinine data, our approach ensured consistency with KDIGO guidelines while minimizing potential bias. The baseline serum creatinine was determined using the minimum of the serum creatinine values available within the 7 days before admission [33]. In cases where pre-admission serum creatinine values were unavailable, the initial serum creatinine measurement at admission was used as the baseline. The secondary outcome was the use of RRT. The follow-up started from the date of ICU admission and ended at AKI incidence or RRT utlization.

Statistical analysis

The study cohort was categorized into quartiles according to the TyG index. We evaluated baseline characteristics across these groups. Continuous variables were expressed as the means with standard deviation (SD) or medians with interquartile range (IQR), with their normality confirmed using the Shapiro-Wilk test and then compared by the Wilcoxon rank-sum test. Categorical data were expressed as numbers with proportions and compared by the Pearson chi-square test or Fisher exact test, if appropriate. A scatter histogram was plotted to demonstrate the distribution of the TyG index at different AKI stages. The incidence of AKI and the use of RRT among groups based on the TyG index were estimated using the Kaplan-Meier method and compared by the log-rank test. A restricted cubic splines model with 4 knots at the 5th, 35th, 65th, and 95th centiles was utilized to flexibly investigate the potential dose-response correlations between the TyG index and AKI incidence, adjusted for multiple models as mentioned following. Multivariable Cox proportional hazards models and logistic regression analysis were employed to analyze the relationship between the TyG index and AKI incidence. Additionally, multivariable logistic regression analysis was applied to explore the relationship between the TyG index and RRT utilization. These analyses were demonstrated through a comparative analysis between different groups and adjusted for multiple variables. To avoid overfitting the model because of multicollinearity among variables, we also calculated the variance inflation factor. Variables with variance inflation factor ≥5 were excluded. Finally, clinically relevant and prognosis-associated variables were enrolled in the multivariate model: model 1 was unadjusted; model 2 was adjusted for key demographics, including age and gender; model 3 was adjusted for the variables in Model 2 plus SBP, DBP, high-density lipoprotein, low-density lipoprotein, total cholesterol, smoking, drinking, hyperlipidemia, hypertension, diabetes, congestive heart failure, atrial fibrillation, stroke, chronic obstructive pulmonary disease, chronic kidney disease, CABG, PCI, glucose-lowering agents, and Lipid-lowering agents. The results were presented as hazard ratios (HRs) or odds ratios (ORs) with their 95% confidence intervals (95% CIs). The TyG index was incorporated into these models in both continuous and categorical forms, using the lowest quartile of the TyG index (Q1) as the baseline group for all four models. The P values for trends were obtained through the use of the quartile level as an ordinal variable. Additionally, subgroup analyses of AKI incidence were conducted with a Cox proportional hazards model, successively based on age (<65 versus ≥65 years), gender (female versus male), presence of hyperlipidemia, hypertension, diabetes mellitus, congestive heart failure, and chronic kidney disease. The interactions between the TyG index and variables used for stratification were examined with likelihood ratio tests. The results of subgroup analyses were presented with a forest plot. For further analysis, survival curves and histograms were plotted according to the presence of diabetes mellitus. All tests were two-sided, and P values less than 0.05 were considered significant. All statistical analyses were performed using R, version 4.2.2 (R Foundation for Statistical Computing, http://www.R-project.org).

Results

A total of 1,501 patients who met the selection criteria were finally enrolled in our study (Figure S1). Generally, the median age of the enrolled patients was 69.80 (IQR: 59.72, 80.45) years, and 924 (61.56%) were male. Among all the enrolled participants, the median TyG index value was 9.04 (IQR: 8.63, 9.55) (Table 1). The incidence of AKI was 67.22%.

Table 1.

Baseline characteristics of patients with CAD according to TyG index quartilesa.

Variables Total Q1 Q2 Q3 Q4 P value
(N = 1,501) (n = 374) (n = 377) (n = 380) (n = 370)
Demographics            
 BMI, kg/m2 28.87 (25.03, 33.13) 26.63 (23.44,30.36) 28.79 (24.54,31.99) 29.29 (25.17,33.55) 30.47 (26.86,36.13) <0.001
 Age, years 69.80 (59.72, 80.45) 74.12 (62.04, 83.80) 71.96 (62.25, 82.16) 69.21 (59.34, 79.19) 65.76 (56.90, 73.71) <0.001
 Gender, male, n (%) 924 (61.56) 226 (60.43) 225 (59.68) 242 (63.68) 231 (62.43) 0.658
Vital signs            
 SBP, mmHg 124.00 (109.00, 142.00) 125.00 (110.00,141.00) 123.00 (110.00,141.00) 125.50 (108.75,142.00) 124.00 (106.00,142.00) 0.699
 DBP, mmHg 70.00 (58.00, 82.00) 70.00 (59.25,81.75) 71.00 (59.00,82.00) 70.00 (59.00,83.25) 69.00 (57.00,83.00) 0.961
Laboratory parameters            
 High-density lipoprotein, mg/dL 41.00 (32.00, 51.00) 47.00 (38.00, 58.00) 43.00 (33.00, 54.00) 39.00 (31.00, 48.00) 35.00 (28.00, 42.75) <0.001
 Low-density lipoprotein, mg/dL 83.00 (59.00, 111.00) 79.00 (56.00,104.25) 84.00 (61.00,111.00) 85.00 (60.00,114.00) 81.00 (57.00,113.00) 0.185
 Total cholesterol, mg/dL 147.00 (118.00, 180.00) 140.00 (113.75, 170.25) 149.00 (120.75, 175.25) 149.00 (121.00, 185.00) 153.50 (118.25, 196.00) 0.002
 Triglyceride, mg/dL 118.00 (85.00, 170.00) 73.00 (58.00, 89.00) 107.00 (88.00, 122.00) 139.00 (114.75, 170.25) 220.00 (160.25, 308.50) <0.001
 Glucose, mg/dL 138.00 (112.00, 184.00) 109.00 (96.00, 126.00) 131.00 (113.00, 156.00) 151.00 (122.75, 187.00) 209.00 (158.00, 300.75) <0.001
 Serum creatinine, mg/dL 1.10 (0.80, 1.60) 1.00 (0.80, 1.40) 1.00 (0.80, 1.40) 1.10 (0.90, 1.80) 1.20 (0.90, 2.00) <0.001
 Blood urea nitrogen, mg/dL 21.00 (15.00, 34.00) 19.50 (15.00, 30.75) 20.00 (15.00, 32.00) 22.00 (16.00, 34.25) 24.00 (16.00, 38.00) <0.001
 eGFR (mL/min/1.73m2) 65.00 (41.00, 91.00) 71.00 (49.00, 97.75) 71.00 (46.00, 94.00) 64.50 (37.00, 88.25) 57.00 (34.25, 83.00) <0.001
 TyG index 9.04 (8.63, 9.55) 8.32 (8.10, 8.50) 8.85 (8.73, 8.95) 9.28 (9.14, 9.41) 10.00 (9.74, 10.41) <0.001
Medical history, n (%)            
 Drinking 60 (4.00) 13 (3.48) 15 (3.98) 18 (4.74) 14 (3.78) 0.837
 Smoking 362 (24.12) 69 (18.45) 88 (23.34) 103 (27.11) 102 (27.57) 0.012
 Hyperlipidemia 416 (27.71) 96 (25.67) 103 (27.32) 105 (27.63) 112 (30.27) 0.569
 Hypertension 844 (56.23) 211 (56.42) 213 (56.50) 214 (56.32) 206 (55.68) 0.996
 Diabetes mellitus 496 (33.04) 54 (14.44) 96 (25.46) 133 (35.00) 213 (57.57) <0.001
 Congestive heart failure 726 (48.37) 165 (44.12) 185 (49.07) 187 (49.21) 189 (51.08) 0.264
 Atrial fibrillation 482 (32.11) 138 (36.90) 134 (35.54) 122 (32.11) 133 (35.95) 0.682
 Stroke 182 (12.13) 48 (12.83) 52 (13.79) 40 (10.53) 42 (11.35) 0.516
 Chronic obstructive pulmonary disease 391 (26.05) 101 (27.01) 108 (28.65) 97 (25.53) 85 (22.97) 0.338
 Chronic kidney disease 376 (25.05) 86 (22.99) 76 (20.16) 110 (28.95) 104 (28.11) 0.014
 CABG 104 (6.93) 23 (6.15) 32 (8.49) 26 (6.84) 23 (6.22) 0.558
 PCI 465 (30.98) 103 (27.54) 122 (32.36) 128 (33.68) 112 (30.27) 0.285
Medications            
 Glucose-lowering agents* 578 (38.51) 135 (36.10) 148 (39.26) 152 (40.00) 143 (38.65) 0.713
 Lipid-lowering agents* 282 (18.79) 72 (19.25) 73 (19.36) 70 (18.42) 67 (18.11) 0.964
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Continuous variables were presented as median (IQR) or mean (SD) and confirmed the normality with the Shapiro-Wilk test, then compared by the Wilcoxon rank-sum test. Categorical variables were presented as numbers (%), and were analyzed by χ2 tests.

Abbreviations: BMI: body mass index; CABG: coronary artery bypass grafting; DBP: diastolic blood pressure; eGFR: estimated glomerular filtration rate; SBP: systolic blood pressure; PCI: percutaneous coronary intervention; TyG index: triglyceride glucose index.

a

TyG index: Q1: 3.98–8.63; Q2: 8.63–9.05; Q3: 9.05–9.55; Q4: 9.55–12.49.

Glucose-lowering agents*: included all oral glucose-lowering agents (e.g. metformin, sulfonylureas, DPP-4 inhibitors, SGLT-2 inhibitors) and injectable glucose-lowering agents (e.g. insulin, GLP-1 receptor agonists) administered within 48 h after ICU admission.

Lipid-lowering agents*: included all statins and fibrates used within 48 h after ICU admission.

Baseline characteristics of patients

The baseline characteristics of patients with CAD stratified according to the quartiles of the TyG index were briefly summarized in Table 1. Patients were divided into quartiles according to the ICU admission TyG index levels (quartile Q1: 3.98–8.63; Q2: 8.63–9.05; Q3: 9.05–9.55; Q4: 9.55–12.49). The median TyG index of the four groups were 8.32 (IQR: 8.10, 8.50), 8.85 (IQR: 8.73, 8.95), 9.28 (IQR: 9.14, 9.41), and 10.00 (IQR: 9.74, 10.41), respectively. Compared with the Q1 group, the Q4 group was more likely to have a younger age, higher levels of triglyceride and glucose, higher BMI, and a higher prevalence of smoking history, as well as a lower level of high-density lipoprotein. Additionally, there was an increase in serum creatinine and blood urea nitrogen levels and a decrease in eGFR across quartiles. The prevalence of diabetes and chronic kidney disease was also higher in the higher TyG index groups. No statistically significant differences were observed for other variables across the four groups.

Table S1 compared the baseline characteristics between AKI patients and non-AKI patients. Compared to non-AKI patients, AKI patients was older, and had a reduced level of high-density lipoprotein, low-density lipoprotein, total cholesterol, and eGFR, elevated levels of glucose, serum creatinine, and blood urea nitrogen. A higher prevalence of diabetes, congestive heart failure, atrial fibrillation, and chronic kidney disease was observed in the AKI group.

Events after ICU

As Table 2 revealed, compared with the Q1 group, the Q4 group had the highest ICU mortality and hospital mortality (p < 0.001 for both). Moreover, as TyG index quartiles increased, the length of stay in both the ICU and the hospital escalated (p < 0.001 for both). Patients in the AKI group faced significantly higher rates of both hospital and ICU mortality than those in the non-AKI group, with hospital mortality at 19.03% versus 5.28% (p < 0.001) and ICU mortality at 55.00% versus 34.76% (p < 0.001). Additionally, AKI patients experienced extended durations of stay in both the ICU and the hospital (length of hospital stay: 3.91 (2.75, 7.86) versus 11.79 (6.08, 20.10), p < 0.001; length of ICU stay: 1.32 (0.91, 2.10) versus 4.38 (2.32, 9.73), p < 0.001) (Table S2).

Table 2.

Events after admission according to TyG index quartilesa.

Events Total Q1 Q2 Q3 Q4 P value
(N = 1,501) (n = 374) (n = 377) (n = 380) (n = 370)
AKIb 1,009 (67.22) 222 (59.09) 249 (66.05) 257 (67.89) 281 (75.95) <0.001
ICU death 131 (8.73) 27 (7.22) 26 (6.90) 26 (6.84) 52 (14.05) <0.001
Hospital death 218 (14.52) 46 (12.30) 48 (12.73) 45 (11.84) 79 (21.35) <0.001
ICU stay 2.88 (1.51, 6.69) 2.37 (1.28,5.21) 2.74 (1.46,5.70) 2.81 (1.63,6.23) 3.98 (1.79,11.38) <0.001
Hospital stay 8.47 (3.99, 17.00) 7.17 (3.88,14.65) 7.85 (3.88,15.57) 9.07 (4.08,18.13) 10.76 (4.70,20.11) <0.001
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Continuous variables were presented as mean (IQR) or mean (SD) and confirmed the normality with the Shapiro-Wilk test, then compared by the Wilcoxon rank-sum test. Categorical variables were presented as numbers (%), and were analyzed by χ2 tests.

Abbreviations: AKI: acute kidney failure; ICU: intensive care unit.

a

TyG index: Q1: 3.98–8.63; Q2: 8.63–9.05; Q3: 9.05–9.55; Q4: 9.55–12.49.

b

AKI was defined in accordance with Kidney Disease: Improving Global Outcomes (KDIGO) guidelines as an increase in serum creatinine level by ≥0.3 mg/dL above baseline within 48 h, or an increase in serum creatinine to ≥ 1.5 times the baseline within the prior 7 days, or urinary output is <0.5 mL/kg/h for 6 h or more.

Association of TyG index and the risk of AKI

Notably, the AKI group exhibited a significantly higher TyG index than the non-AKI group (non-AKI: 8.95 (8.52, 9.41) versus AKI: 9.09 (8.67, 9.65); p < 0.001) (Table S1), and the progression of the AKI stages increased with the TyG index (P for trend <0.001, Figure 1). Furthermore, the incidence of AKI also increased progressively with increasing TyG index (59.09%, 66.05%, 67.89%, and 75.09% from Q1 to Q4, respectively, p < 0.001) (Table 3). We illustrate the cumulative incidence of AKI across different TyG index quartiles through the Kaplan-Meier survival analysis and the pairwise comparisons between groups are also presented. Obviously, patients in Quartile 4 had a significantly higher risk of developing AKI than those in other quartiles (p < 0.001, Figure 2(A)). The restricted cubic splines regression models demonstrated the dose-response relationship between the TyG index and AKI incidence in all models (P for non-linearity = 0.458, P for non-linearity = 0.202, and P for non-linearity = 0.637) (Figure 3). When the TyG index was considered as a continuous variable, Cox proportional hazards analyses showed a statistically significant association between the TyG index and the risk of AKI in model 1 (HR 1.22; 95% CI 1.13–1.33; p < 0.001), model 2 (HR 1.28; 95% CI 1.18–1.40; p < 0.001), and model 3 (HR 1.30; 95% CI 1.04–1.63; p < 0.001). When analyzing the TyG index as a categorical variable, Q3 and Q4 of the TyG index showed a significant association with the risk of AKI in each model, even in the fully adjusted model 3 (Q1 versus Q2: HR: 1.19, 95% CI, 0.99–1.43, p = 0.062; HR: 1.25, 95% CI, 1.04–1.50, p = 0.017; HR:1.62, 95% CI, 1.15–2.27, p = 0.005) (Table 3). Although the differences between adjacent quartiles (e.g. Q1/Q2) did not reach statistical significance, the P for trend (<0.001) was significant, indicating a clear and progressive increase in AKI risk across quartiles. Additionally, to prove the robustness of the association between the TyG index and AKI incidence, a sensitivity analysis was performed using multivariate logistic regression analyses (TyG index as a continuous variable: OR 1.57; 95%CI 1.13–2.18; p = 0.007; TyG index as a categorical variable: Q1 versus Q2: OR 1.28; 95% CI 0.88–1.87; p = 0.202; Q3: OR 1.56; 95% CI 1.01–2.42; p = 0.012; Q4: OR 2.16; 95% CI 1.18–3.94; p = 0.012) (Table 3). Both Cox proportional hazards analysis and logistic regression analysis suggested a consistent correlation between the TyG index and a higher AKI incidence, which remained robust when adjusted for potential confounders.

Figure 1.

Figure 1.

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The relationship between different AKI stages and TyG index in CAD patients admitted to the ICU. Error bars indicate 95% CIs. The black scatters correspond to the individual TyG index. AKI, acute kidney injury; CAD, coronary artery disease; CIs, confidence intervals; ICU, intensive care unit; TyG, triglyceride-glucose.

Table 3.

Cox proportional hazard ratios (HR) and multivariate logistic regression analyses of TyG index and AKI incidence.

  No. of Events/ Patients (%) b Model 1
 Model 2
 Model 3
  HR/OR (95% CI) P value HR/OR (95% CI) P value HR/OR (95% CI) P value
Cox proportional hazard ratios, HR (95% CI)    
Continuous variable per 1 unit 1,009/1,501 (67.22) 1.22 (1.13, 1.33) <0.001 1.28 (1.18, 1.40) <0.001 1.30 (1.04, 1.63) 0.021
Quartiles a              
 Q1 221/374 (59.09) 1.00 (reference)   1.00 (reference)   1.00 (Reference)  
 Q2 249/377 (66.05) 1.14 (0.95, 1.37) 0.151 1.16 (0.96, 1.39) 0.116 1.13 (0.89, 1.42) 0.299
 Q3 258/380 (67.89) 1.21 (1.01, 1.45) 0.037 1.27 (1.06, 1.52) 0.010 1.32 (1.02, 1.70) 0.043
 Q4 281/370 (75.95) 1.58 (1.32, 1.88) <0.001 1.72 (1.44, 2.06) <0.001 1.62 (1.15, 2.27) 0.005
 P for trend <0.001   <0.001   <0.001   0.005
               
Multivariate logistic regression, OR (95% CI)    
Continuous variable per 1 unit 1,009/1,501 (67.22) 1.45 (1.25, 1.68) <0.001 1.57 (1.34, 1.83) <0.001 1.57 (1.13, 2.18) 0.007
Quartiles a              
 Q1 221/374 (59.09) 1.00 (reference)   1.00 (reference)   1.00 (Reference)  
 Q2 249/377 (66.05) 1.35 (1.01, 1.81) 0.049 1.36 (1.01, 1.83) 0.046 1.28 (0.88, 1.87) 0.202
 Q3 258/380 (67.89) 1.46 (1.09, 1.97) 0.012 1.58 (1.17, 2.14) 0.003 1.56 (1.01, 2.42) 0.046
 Q4 281/370 (75.95) 2.19 (1.59, 3.00) <0.001 2.59 (1.87, 3.58) <0.001 2.16 (1.18, 3.94) 0.012
 P for trend <0.001   <0.001   <0.001   0.011
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Model 1 was unadjusted;.

Model 2 was adjusted for age, gender;.

Model 3 was adjusted for the variables in Model 2 plus SBP, DBP, high-density lipoprotein, low-density lipoprotein, total cholesterol, smoking, drinking, hyperlipidemia, hypertension, diabetes, congestive heart failure, atrial fibrillation, stroke, chronic obstructive pulmonary disease, chronic kidney disease, CABG, PCI, glucose-lowering agents, and lipid-lowering agents.

Abbreviations: AKI: acute kidney injury; CABG: coronary artery bypass grafting; CI: confidence interval; DBP: diastolic blood pressure; HR: hazard ratio; OR: odds ratio; PCI: percutaneous coronary intervention; SBP: systolic blood pressure; TyG index: triglyceride glucose index.

a

TyG index: Q1: 3.98–8.63; Q2: 8.63–9.05; Q3: 9.05–9.55; Q4: 9.55–12.49;.

b

The incidence of AKI increased progressively with increasing TyG index (59.09% vs. 66.05% vs. 67.89% vs. 75.09% for Q1 through Q4, respectively, p < 0.001).

Figure 2.

Figure 2.

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Kaplan–meier curves for cumulative incidence curve of (A) AKI incidence. (B) RRT utilization. (footnote TyG index quartiles: Q1: 3.98–8.63; Q2: 8.63–9.05; Q3: 9.05–9.55; Q4: 9.55–12.49). AKI, acute kidney injury; RRT, renal replacement therapy.

Figure 3.

Figure 3.

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Restricted cubic spline curves with knots at the 5th, 35th, 65th, and 95th percentiles for the TyG index hazard ratio. Heavy Central lines represent the estimated HRs, with shaded ribbons denoting 95%CIs. TyG index 9.04 was selected as the reference level represented by the vertical dotted lines. The horizontal dotted lines represent the HR of 1.0. (A) Model 1 was unadjusted. (B) Model 2 was adjusted for age and gender. (C) Model 3 was adjusted for the variables in model 2 and further adjusted for SBP, DBP, high-density lipoprotein, low-density lipoprotein, total cholesterol, smoking, drinking, hyperlipidemia, hypertension, diabetes, congestive heart failure, atrial fibrillation, stroke, chronic obstructive pulmonary disease, chronic kidney disease, CABG, PCI, glucose-lowering agents, and lipid-lowering agents. AKI, acute kidney injury; CABG, coronary artery bypass grafting; CI, confidence interval; HR, hazard ratio; DBP, diastolic blood pressure; HR, hazard ratio; OR, odds ratio; PCI, percutaneous coronary intervention; SBP, systolic blood pressure.

Association of TyG index and the risk of AKI across various subgroups

Subgroup analyses revealed distinct patterns between the TyG index and the risk of AKI in certain clinical subgroups, particularly among those with hyperlipidemia and diabetes mellitus. However, no significant interaction was observed across other subgroups such as gender, age, hypertension, congestive heart failure, and chronic kidney disease (Figure 4). We observed interactions between the TyG index and both the hyperlipidemia (P for interaction = 0.006) and diabetes subgroups (P for interaction = 0.013). Notably, the association between the TyG index and the risk of AKI appeared to be more prominent in patients without diabetes (HR 1.27; 95% CI 1.14–1.42; p < 0.001), compared with patients with diabetes (HR 1.02; 95% CI 0.89–1.17; p = 0.785). The survival curves and histogram plots also indicated that patients without diabetes were more likely to represent a non-linear relationship between TyG index and AKI incidence compared with patients with diabetes (Figure 5).

Figure 4.

Figure 4.

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Forest plots of hazard ratios for AKI incidence in different subgroups. Cox proportional hazards analysis evaluating prognostic implication of TyG index in various subgroups. HR was evaluated by 1-SD increase of the TyG index. AKI, acute kidney injury; HR, hazard ratio; CI, confidence interval.

Figure 5.

Figure 5.

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Subgroup analyses indicating significant interaction between the TyG index and patients with diabetes. (A) AKI incidence in patients with diabetes stratified by TyG index. (B) AKI incidence in patients without diabetes stratified by TyG index. (C) Hazard ratios (95%CIs) of AKI incidence in patients with diabetes stratified by TyG index. (D) Hazard ratios (95%CIs) of AKI incidence in patients without diabetes stratified by TyG index.

Error bars indicate 95% CIs. The first quartile is the reference. Hazard ratios and 95% CIs were unadjusted. AKI, acute kidney injury; CIs, confidence intervals; TyG, triglyceride-glucose.

Association of TyG index and the risk of RRT

Generally, the use of RRT increased with a higher TyG index (5.88%, 2.65%, 8.16%, and 16.59% from Q1 to Q4, respectively, P for trend <0.001). After adjustments were made in the multivariate logistic regression analyses, the TyG index was found to be significantly associated with RRT. For each unit growth in the TyG index, the use of RRT grew by 1.72-fold (OR 1.72; 95% CI 1.05–2.81; p = 0.030). After TyG index quartiles stratification, the utilization of RRT in the highest TyG index subgroup (Q4) increased by 3.00-fold compared with Q1 (OR 3.00; 95% CI 1.03–8.75; p = 0.045) (Table S3).

The Kaplan-Meier survival analysis was conducted to assess the effect of the TyG index on the use of RRT in the subset of patients with AKI. Patients with AKI in the highest quartile of the TyG index exhibited the greatest risk of RRT usage (p < 0.001, Figure 2(B)).

Discussion

To our knowledge, this study is the first to examine the relationship between the TyG index and AKI incidence in critically ill patients with CAD. The key findings in our study can be summarized as follows: 1) patients who developed AKI exhibited significantly higher TyG index levels compared to those who did not and the AKI stages progressed as the TyG index increased; 2) an elevated TyG index was a robust independent marker of increased AKI incidence in critically ill patients with CAD; 3) the association between TyG and AKI is more pronounced in non-diabetic patients than in those with diabetes. Importantly, the current study exhibits the application of TyG index in the risk stratification of AKI in critically ill CAD patients, which helps prevent AKI and improves prognosis.

TyG index, cardiovascular disease, and kidney disease risk

As a novel index for evaluating IR and metabolic disorders, TyG index has presented its convenient, repeatable prognostic value in cardiovascular diseases and kidney diseases. In recent years, numerous clinical evidence has highlighted the link between TyG index and cardiovascular and renal diseases across different populations. For instance, Zhang et al. suggested that in acute coronary syndrome patients with elevated TyG index undergoing emergency PCI experienced a higher risk of adverse cardiovascular outcomes, including patients without diabetes [11]. Similarly, Ye et al. reported that a higher TyG index corresponded to increased in-hospital and one-year mortality rates in patients with chronic kidney disease and CAD [25]. In the context of cardiovascular diseases, the TyG index was positively correlated with coronary artery calcification [34], coronary plaque progression [35], and subclinical myocardial injury [36]. This positive correlation distinctly accentuated the pivotal role of the TyG index in forecasting cardiovascular disease outcomes. Lei et al. pointed out a positive and independent association between the TyG index and chronic kidney disease in the elderly [37]. More strikingly, a retrospective study including 54,263 ICU patients first revealed that increased TyG index is related to a higher risk of AKI in critically ill patients [38]. However, it did not conduct a subgroup analysis on the disease context of critically ill patients and also did not display or discuss the temporal characteristics of AKI occurrence. And Yang et al. had made some progress in the heart failure patients and found that the TyG index is able to serve as a reliable and independent predictor of the incidence of AKI [26]. In spite of that, the association between the TyG index and AKI remains to be further explored and discussed in patients with cardiovascular diseases. Obviously, the incidence of AKI in ICU patients with CAD is significantly higher than that in the overall ICU population (67.22% vs 25.1%). Considering that heart failure only covered patients with advanced cardiovascular diseases, it is necessary to examine the predictive effect of TyG on AKI occurrence in a more classic population, which is more clinically valuable and benefits more people with cardiovascular diseases. Hence, we conduct this study including 1,501 critically ill patients with CAD and verify the function of the TyG index to predict AKI and the strong association between the TyG and RRT. Additionally, the TyG index statistics of different AKI stages also visually reveals the relationship between TyG and AKI progression, which has not been reported in previous studies. In general, the TyG index has the potential to be a convenient and robust biomaker to guide AKI risk stratification and treatment decisions in critically ill patients with CAD.

It is worth mentioning that CRS encompasses a range of disorders where cardiac and renal dysfunctions coexist in the clinic, significantly impacting patient outcomes [8,9]. According to the classification of CRS, type 1 CRS is particularly noteworthy as it is characterized by a swift deterioration of cardiac function leading directly to AKI. This subtype exemplifies how acute cardiac events can immediately compromise renal function, underscoring the necessity for swift identification and intervention to mitigate further organ damage. Previous studies revealed a significant portion developed AKI shortly after admission [39,40], with our research showing that 87.2% had AMI, with 66.0% developing AKI and 62.8% within 72 h of ICU among 1,501 critical ill CAD patients. It has also been shown that patients with AMI who undergo coronary angiography or PCI have up to a 20% risk of acute kidney injury [36,41]. However, the sudden drop in glomerular filtration rate observed in early AKI stages is usually reversible [42]. Traditional renal function indices and serum creatinine have limited performance in the early diagnosis of AKI [10]. Given the high mortality rate of patients with AMI who develop AKI [43], early identification and intervention in these high-risk patients is particularly critical to help slow the progression of AKI and improve overall prognosis, the TyG index holds promise as a predictor for high-risk AKI post-AMI. Our findings align with previous studies linking higher TyG index levels to increased risks of cardiovascular and renal complications, thereby validating the applicability of TyG index in critically ill CAD patients [11,25]. However, unlike studies in general CAD populations, where the predictive power of TyG index varied across patient subgroups, our study highlights its consistent association with AKI risk in an ICU setting, even after adjusting for critical confounders. Although the lack of a significant trend in the TyG index among CABG patients in our study may be partly due to the small sample size, previous studies have shown that the TyG index is a reliable predictor of major adverse cardiovascular events in patients undergoing CABG [44,45]. It is important to note that the relationship between TyG index and metabolic syndrome in CABG patients might be influenced by a complex interplay of factors such as preoperative medication use, comorbidities, and the nature of the surgery itself. Furthermore, the TyG index could impact therapeutic strategies beyond risk assessment, as interventions to improve insulin sensitivity may simultaneously reduce cardiovascular risk and decelerate kidney disease progression. This comprehensive approach is vital for patients concomitant with cardiac and renal disease with a more holistic and integrated treatment strategy.

Potential mechanisms

IR has been proven to be an important risk factor for the deterioration of both cardiac and kidney function [46–48]. As a surrogate for IR, TyG index makes itself a key predictor of cardiovascular and renal disease risk and progression. The pathophysiological basis of this relationship was rooted in factors such as endothelial dysfunction [48], oxidative stress [49], and systemic inflammation [50], which are further exacerbated in IR states.

The underlying mechanisms that IR prompted the pathological reaction between the heart and kidney may involve the following factors. IR also triggers inappropriate activation of the sympathetic nervous system and the renin-angiotensin-aldosterone system, leading to decreased renal perfusion and increased cardiac workload [51,52]. It also induced an inflammatory response through mitochondrial dysfunction and endoplasmic reticulum stress, leading to reactive oxygen species accumulation and cellular damage, impairing NO release, and causing endothelial dysfunction [53]. All the above processes ultimately lead to cardiac and renal insufficiency [54]. At the same time, IR increases free fatty acids and glucose [55,56]. On the one hand, it leads to cardiac and renal lipid accumulation and lipotoxicity [57,58]. On the other hand, IR can cause hyperglycemia, which also induces atherosclerosis and AKI [59,60]. Since impaired glucose homeostasis affects autonomic functions of the heart and kidneys, this risk persists even in non-diabetic patients. This is related to the phenomenon known as ‘hyperglycemic memory’, whereby hyperglycemic stress persists even after blood glucose returns to normal [61,62]. Future research should investigate the underlying mechanisms of TyG index in AKI development further, particularly exploring whether interventions targeting insulin resistance could reduce AKI risk.

Subgroup analysis

The TyG index, which merges triglyceride and fasting blood glucose levels, acts as a surrogate indicator of IR, theoretically reflecting the metabolic status of patients with diabetes mellitus more flexiblely. However, no significant difference in AKI incidence across TyG index levels in diabetes patients was observed in our study. Actually, we found similar results in other critically ill population studies [26,37], which showed a more positive association between elevated TyG index and risk of AKI in non-diabetics than in diabetics. Consistently, the role of TyG index in predicting adverse cardiovascular outcomes also seems to be more robust in non-diabetics than in diabetics [63,64]. For instance, a post-hoc analysis of the Kailuan cohort displayed that elevated TyG index is associated with the increased risk of myocardial infarction in non-diabetic people but not in diabetic people [20]. The considerable reasons associated with invalidity of TyG index included body adaptation, metabolic homeostasis and pharmaceuticals. Firstly, in non-diabetic patients, IR may be an emerging problem, which made them highly susceptible to IR and glucose level variations and resulted in acute changes in cardiac and renal function. However, individuals with diabetes might already suffer from chronic microvascular damage and structural kidney alterations, which may potentially mitigate the effects of acute changes. Besides, compared with non-diabetic patients, diabetes patients experienced chronic intermittent hyperglycemia or chronic persistent hyperglycemia, which meant a higher TyG index. The increase in the basic level of TyG index dissimulated the clinical significance of its differences. Concomitantly, because patients with diabetes are often treated with a variety of medications, including oral hypoglycemic drugs, which may somewhat eclipse the predictive capacity for AKI in the diabetic patients. As a result, the fast blood glucose parameter of diabetes patients might exhibit less variability than those without diabetes, which diminished the sensitivity of the TyG index in predicting AKI in the diabetic cohort. This explains why the association between the TyG index and AKI risk was more significant in non-diabetic patients, such interaction suggests the need to consider the specific metabolic context of the patient when using the TyG index.

Study limitations

Our study confirms that an elevated TyG index is a robust and independent marker of increased AKI incidence in critically ill patients with CAD. However, we have to acknowledge that our study has several limitations. Firstly, the retrospective and observational nature of this study precludes the establishment of definitive causal relationships. Secondly, the present study is confined to a single center with a relatively restricted number of participants and a short follow-up period, with no post-discharge follow-up data, restricting our ability to analyze long-term outcomes such as AKI recovery and CKD progression. Despite efforts to mitigate it through multivariable adjustments and subgroup analysis, potential data bias might persist due to lingering confounding factors. What’s more, we used dummy variables to handle data with 25%-30% missing values, which may introduce potential bias. However, we combined this approach with multiple imputation for variables with lower missing rates (below 25%) to further mitigate bias risk. Finally, our study failed to further investigate the dynamic change of the TyG index, as well as its association with AKI in CAD patients. Despite these limitations, our study introduces an innovative approach by extending the utility of the TyG index beyond traditional cardiovascular outcomes to predict AKI risk in an ICU-specific CAD population. This study underscores the need for further research that includes prospective, multi-center trials to corroborate our findings and examine the potential integration of the TyG index into clinical risk models for early AKI prediction. Such efforts could lead to the development of improved risk stratification protocols, enhancing preventive and therapeutic interventions in critically ill CAD patients.

Conclusion

In conclusion, this study extends the applicability of TyG index to predict AKI incidence in critically ill CAD patients. Furthermore, the TyG index, showing a stronger association with AKI risk in non-diabetic patients, is anticipated to be a valuable tool for risk stratification and managing AKI in critically ill CAD patients across various glucose metabolism states.

Supplementary Material

Supplementary_materials new.docx
IRNF_A_2466818_SM4774.docx (192.6KB, docx)

Acknowledgments

We thank the investigators of the Beth Israel Deaconess Medical Center for sharing the MIMIC-IV database.

Funding Statement

This work received support from several grants: the National Natural Science Foundation of China [grant numbers 82370271], and the National key R&D project of China [grant numbers 2023YFC2706200 and 1823YFC2706200] awarded Zhongkai Wu.

Authors’ contributions

All coauthors have contributed significantly and intellectually to the work and approved the article’s final version. The research was conceptualized and designed by Zhuoming Zhou. Yi Zhang took charge of the data collection and conducting statistical analysis. Gang Li and Junjie Li carried out visualization tasks. The manuscript’s primary drafting was done by Yi Zhang, with contributions from Bohao Jian, Jian Hou, Jiantao Chen, and Keke Wang. The project was overseen and supervised by Mengya Liang, Zhuoming Zhou, and Zhongkai Wu. Critical revisions for important intellectual content were by Mengya Liang, Zhuoming Zhou, and Zhongkai Wu.

Ethical approval and consent to participate

Given the nature of our study, the institutional review board of the First Affiliated Hospital of Sun Yat-sen University waived the need for ethic approval for this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data analyzed in this study were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (https://mimic.mit.edu). Our right to access the database and acquire the data was approved by the institutional review board of the Massachusetts Institute of Technology (Cambridge, MA, USA) after one of our authors (ZY) completed the Collaborative Institutional Training Initiative program of the National Institutes of Health and obtained the requisite certification for accessing the MIMIC-IV database (Record ID 59,829,466).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary_materials new.docx
IRNF_A_2466818_SM4774.docx (192.6KB, docx)

Data Availability Statement

The data analyzed in this study were sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (https://mimic.mit.edu). Our right to access the database and acquire the data was approved by the institutional review board of the Massachusetts Institute of Technology (Cambridge, MA, USA) after one of our authors (ZY) completed the Collaborative Institutional Training Initiative program of the National Institutes of Health and obtained the requisite certification for accessing the MIMIC-IV database (Record ID 59,829,466).

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