Glucose-to-potassium ratio has a non-linear J-shaped association with acute kidney injury risk in traumatic brain injury

H Hao Wang (Division of Quantitative Sciences, Department of Oncology Johns Hopkins University School of Medicine Baltimore Maryland USA) Q Qing Dao X Xiaomei Li M Meng Shi M Meili Yang C Cui Gu J Jihua Wang

Abstract

Abstract Acute kidney injury (AKI) is a severe complication of traumatic brain injury (TBI) associated with poor prognosis. The glucose-to-potassium ratio (GPR), an emerging marker of metabolic stress, may play a role in post-TBI AKI, but its precise relationship is unclear. This retrospective cohort study investigated the association between early-admission GPR and AKI in 2,388 TBI patients from the MIMIC-IV database. Using multivariable logistic regression and restricted cubic spline (RCS) models, we explored both linear and non-linear associations. Of the patients, 56.7% (1,355/2,388) developed AKI. While a simple linear association between GPR and AKI was not significant after adjusting for confounders ( P = 0.20), we discovered a significant non-linear "J-shaped" relationship (P for non-linearity = 0.012). The risk of AKI increased steeply once GPR surpassed an inflection point of approximately 30. A random forest model incorporating multiple clinical variables demonstrated significantly superior predictive performance (AUC = 0.808) compared to traditional logistic regression (AUC = 0.785; DeLong test P = 0.025). These findings reveal that a high GPR (> 30) acts as an independent non-linear risk indicator for post-TBI AKI, Rather than a direct driver of injury, an elevated GPR represents a valuable early warning signal for metabolic decompensation, suggesting clinicians should closely monitor high-risk patients to facilitate early recognition of AKI.

Article Details

Volume / Issue Vol. 1, Issue 1
Published June 12, 2026
ISSN 2045-2322
Publisher Nature Portfolio

Journal Info

Scientific Reports

Nature Portfolio

ISSN: 2045-2322 Open Access Life Sciences

Authors (7)

H

Hao Wang

Division of Quantitative Sciences, Department of Oncology Johns Hopkins University School of Medicine Baltimore Maryland USA

Q

Qing Dao

X

Xiaomei Li

M

Meng Shi

M

Meili Yang

C

Cui Gu

J

Jihua Wang