Association between glycemic variability and acute kidney injury incidence in patients with cerebral infarction: an analysis of the MIMIC-IV database

Yiming Hua; Ze Chen; Lele Cheng; Ning Ding; Yifei Xie; Hao Wu; Huaizhi Jing; Yu Xu; Yue Wu; Beidi Lan

doi:10.3389/fendo.2025.1615051

Association between glycemic variability and acute kidney injury incidence in patients with cerebral infarction: an analysis of the MIMIC-IV database

Front Endocrinol (Lausanne). 2025 Jun 12:16:1615051. doi: 10.3389/fendo.2025.1615051. eCollection 2025.

Authors

Yiming Hua^{1

2}, Ze Chen^{1

2

3}, Lele Cheng^{1

2

4}, Ning Ding^{1

2}, Yifei Xie^{1

2}, Hao Wu^{1

2}, Huaizhi Jing^{1

2}, Yu Xu^{1

2}, Yue Wu^{1

2}, Beidi Lan^{1

2

5}

Affiliations

¹ Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
² Key Laboratory of Molecular Cardiology, Key Laboratory of Environment, Genes Related to Diseases, Ministry of Education Xi'an Jiaotong University, Xi'an, Shaanxi, China.
³ Department of Vascular Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
⁴ Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
⁵ Department of Cardiovascular Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Abstract

Introduction: Glycemic variability (GV) is an increasingly important predictive indicator of vascular occlusion-related complications. Studies have demonstrated that a higher GV is associated with poor outcomes in patients with cerebral infarction (CI). The prognostic utility of GV in CI patients for predicting acute kidney injury (AKI) remains inadequately characterized. This investigation systematically examines the pathophysiological relationship between acute glycemic fluctuations and AKI development in CI populations, with particular emphasis on temporal patterns of glucose dysregulation.

Methods: This retrospective cohort analysis utilized data from the MIMIC-IV database, categorizing CI patients into quartiles based on GV metrics. Primary outcomes included AKI incidence and renal replacement therapy (RRT) initiation, with in-hospital mortality designated as the secondary endpoint. Analytical methodologies employed Kaplan-Meier survival curves with log-rank testing, multivariable-adjusted Cox proportional hazards regression, and logistic regression modeling to evaluate GV-AKI associations while controlling for critical confounders.

Results: The analytical cohort comprised 3,343 critically ill individuals extracted from the MIMIC-IV database. Kaplan-Meier curve analysis demonstrated progressively elevated cumulative risks of AKI development, RRT requirement, and in-hospital mortality among individuals with heightened GV. Following multivariable adjustment, logistic regression models and Cox proportional hazards analyses confirmed GV as an independent predictor of AKI progression, RRT dependency, and mortality risk in cerebral infarction patients.

Conclusion: This investigation identifies GV as an independent prognostic determinant for AKI development in cerebral infarction patients. GV demonstrates clinical utility as a biomarker for stratifying AKI risk in this population.

Keywords: ICU; MIMIC-IV; acute kidney injury; cerebral infarction; glycemic variability.

MeSH terms

Acute Kidney Injury* / blood
Acute Kidney Injury* / epidemiology
Acute Kidney Injury* / etiology
Aged
Blood Glucose* / analysis
Blood Glucose* / metabolism
Cerebral Infarction* / blood
Cerebral Infarction* / complications
Cerebral Infarction* / epidemiology
Databases, Factual
Female
Hospital Mortality
Humans
Incidence
Male
Middle Aged
Prognosis
Renal Replacement Therapy
Retrospective Studies
Risk Factors

Substances

Blood Glucose