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ORIGINAL RESEARCH article

Front. Endocrinol.

Sec. Cardiovascular Endocrinology

This article is part of the Research TopicCardiovascular Risks in Cardiovascular-Kidney-Metabolic Syndrome: Mechanisms and TherapiesView all 9 articles

Antidiabetic and lipid-lowering therapy modify the association between triglyceride–glucose index and acute kidney injury in critically ill patients with coronary artery disease

Provisionally accepted
Yang  YuehangYang Yuehang1Linfeng  HeLinfeng He2Xueying  JiaoXueying Jiao3Hanshen  LuoHanshen Luo1Xiang  QiuXiang Qiu1Chuli  ShiChuli Shi1Li  DingLi Ding1*Jiawei  ShiJiawei Shi1*
  • 1Department of Cardiovascular Surgery, Huazhong University of Science and Technology Tongji Medical College Union Hospital, Wuhan, China
  • 2Department of Endocrinology and Metabolism, Nanchang University Second Affiliated Hospital, Nanchang, China
  • 3Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden

The final, formatted version of the article will be published soon.

Background Acute kidney injury (AKI) is a common and serious complication in critically ill coronary artery disease (CAD) patients. The triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance, has been linked to adverse cardiovascular and renal outcomes. However, whether antidiabetic drugs or lipid-lowering drugs modify its association with AKI in this population remains unclear. Methods This study retrospectively analyzed 2,517 critically ill CAD patients from the MIMIC-IV database. Patients were stratified according to the use of antidiabetic and lipid-lowering drugs. The primary endpoint was the occurrence of AKI during hospitalization. Multivariable logistic regression and restricted cubic spline (RCS) models were applied to examine the association between the TyG index and AKI risk. Subgroup analyses, sensitivity analyses, and independent external validation were performed to assess the robustness of the findings. Results The median age of patients was 69 years, and 68.06% were male. In the fully adjusted logistic regression model, a higher TyG index was significantly associated with an increased risk of AKI among patients without the use of antidiabetic drugs (OR 2.021, 95% CI 1.674–2.454) or lipid-lowering drugs (OR 1.912, 95% CI 1.648–2.228). With the use of antidiabetic drugs, this association remained significant but was attenuated (OR 1.480, 95% CI 1.190–1.853), with a significant interaction observed between the use of antidiabetic drugs and the TyG index in relation to AKI risk (P for interaction = 0.040). With the use of lipid-lowering drugs, the association between the TyG index and AKI risk was weakened (OR 1.445, 95% CI 0.934–2.307), but no significant interaction was found (P for interaction = 0.332). RCS analyses demonstrated a linear relationship between higher TyG index values and increased AKI risk. Similar results were confirmed in external validation. Conclusions In critically ill CAD patients, a higher TyG index was independently associated with an increased risk of AKI, whereas this association was attenuated in those with the use of antidiabetic or lipid-lowering drugs. These findings highlight the importance of incorporating metabolic risk assessment into the management of critically ill patients and underscore the potential of pharmacological interventions to improve renal outcomes.

Keywords: Triglyceride-glucose index, coronary heart disease, Acute Kidney Injury, Antidiabetic drugs, Lipid-lowering drugs

Received: 08 Sep 2025; Accepted: 04 Nov 2025.

Copyright: © 2025 Yuehang, He, Jiao, Luo, Qiu, Shi, Ding and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Li Ding, dinglimed@hust.edu.cn
Jiawei Shi, 15807298713@163.com

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