Evidence map›Paper›PMID 40281638›Full record

ArticleJournal of cardiothoracic surgery2025

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, Xiaobin Liu, Xingping Lv, Wei Zhou, Guoliang Fan, Feng Zhu

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Article in Journal of cardiothoracic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 · Its place in the literature

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3 citing papers in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Chen Li *Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
Xiaobin Liu *Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
Xingping Lv *Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
Wei ZhouDepartment of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
Guoliang FanDepartment of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China.
Feng ZhuDepartment of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200120, China. alexzhujunchi@hotmail.com.

Funding

National Key R&D Program "Stem Cell and Transformation Research" Key Special Project 2019YFA0110601The Top-level Clinical Discipline Project of Shanghai Pudong PWYgf2021-03
6 · The paper itself

Abstract

backgroundThe 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).

methodsThis 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.

resultsCompared 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).

conclusionElevated 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.

Indexed as

Acute Kidney InjuryBlood GlucoseBody Mass IndexCoronary Artery BypassDiabetes MellitusPostoperative ComplicationsTriglyceridesAgedBiomarkersFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesRisk FactorsBiomarkersBlood GlucoseTriglyceridesAcute kidney injuryBody mass indexCoronary artery bypass graftingInsulin resistanceTriglyceride-glucose

Identifiers

PMID40281638
PMCPMC12023640

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