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. 2025 Apr 25;20(1):221.
doi: 10.1186/s13019-025-03455-1.

The application of the triglyceride-glucose-body mass index (TyG-BMI) in predicting acute kidney injury in diabetic patients following coronary artery bypass grafting surgery

Chen Li #  1 , Xiaobin Liu #  1 , Xingping Lv #  1 , Wei Zhou  1 , Guoliang Fan  1 , Feng Zhu  2
Affiliations

The application of the triglyceride-glucose-body mass index (TyG-BMI) in predicting acute kidney injury in diabetic patients following coronary artery bypass grafting surgery

Chen Li et al. J Cardiothorac Surg. .

Abstract

Background: The triglyceride-glucose-body mass index (TyG-BMI), a marker for insulin resistance, is recognized for its predictive role in cardiovascular and metabolic diseases, including kidney disease. we explored the TyG-BMI index's association with postoperative kidney injury in coronary artery bypass grafting (CABG) patients, who are at an elevated risk for such complications, underscoring its potential as a predictor for acute kidney injury (AKI).

Methods: This single-center, retrospective study included 126 patients. Patients were divided into AKI and non-AKI groups postoperatively according to the KDIGO classification criteria. Univariate logistic regression was used to screen for variables with significant differences (P < 0.01), and multiple multivariate regression models were constructed to analyze independent risk factors in the multivariate regression model and to analyze the value of TyG-BMI in predicting AKI in diabetic patients after CABG.

Results: Compared to the non-AKI group, the AKI group had statistically significant differences in preoperative fasting triglycerides, preoperative fasting glucose, preoperative and postoperative creatinine levels, ICU stay duration, and TyG-BMI levels (P < 0.05). Based on the results of univariate regression analysis, a multivariate logistic regression model A was constructed using all significant variables, and a multivariate logistic regression model 2 was constructed using significant variables other than TyG-BMI. ROC analysis showed that model 2 had better predictive performance than model 1 (AUC = 0.836 vs. 0.766). A positive correlation was observed between TyG-BMI and AKI occurrence (Spearman's correlation coefficient: R = 0.33, P = 0.00019).

Conclusion: Elevated TyG-BMI levels are closely associated with AKI in diabetic patients after CABG. TyG-BMI has potentially predictive value for AKI in diabetic patients after CABG and may play a crucial role in risk stratification in clinical practice.

Keywords: Acute kidney injury; Body mass index; Coronary artery bypass grafting; Insulin resistance; Triglyceride-glucose.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Approval for this study was obtained from the Committee on Ethics of Biomedical Research at Shanghai East Hospital, Tongji University School of Medicine, Shanghai (No. 2024-YS-043), with a waiver for individual patient consent granted. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). Given the observational nature of the study, individual patient consent was waived by the Ethics Committee. Consent for publication: All participating authors agree to publication of the article. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow diagram of patient selection
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Fig. 2
Forest plots for Multivariate Logistic Regression Model
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The Comparison of ROC curve for predicting AKI
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Fig. 4
The ROC curve of TyG-BMI and Crea
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Fit curve of Model2 for AKI
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Correlatioin betweenTyG-BMI and AKI
See this image and copyright information in PMC

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