What does this research mean for the field?

The HDL-to-LDL ratio is a promising predictive biomarker for contrast-induced acute kidney injury (CI–AKI). Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research investigates the impact of the HDL-to-LDL ratio on the development of contrast-induced acute kidney injury (CI–AKI) in patients.

February 25, 2026Open Access

Correlation between the high-density lipoprotein-to-low-density lipoprotein ratio and contrast-induced acute kidney injury: an analysis based on the in-hospital AKI electronic monitoring system

Puntos clave

This research investigates the impact of the HDL-to-LDL ratio on the development of contrast-induced acute kidney injury (CI–AKI) in patients.
Enrolled patients undergoing elective angiography from January 2019 to May 2024.
Divided participants into CI–AKI and non-CI–AKI groups based on outcomes.
Utilized machine learning models to analyze relationships and predict CI–AKI.
Performed restricted cubic splines and mediation analysis for deeper insights.
Conducted subgroup and propensity score matching analyses to verify findings.
300 out of 3,139 patients developed CI–AKI (9.56%).
Significant association between HDL-to-LDL ratio and CI–AKI in both adjusted and unadjusted logistic regression (P < 0.05).
The logistic regression model achieved the highest AUC of 0.749 for predicting CI–AKI.
SHAP analysis confirmed the importance of the HDL-to-LDL ratio in predicting acute kidney injury.

Resumen

Contrast-induced acute kidney injury (CI–AKI) is a common and serious complication that can occur following angiography procedures. Considering the proposed association between dyslipidemia and CI–AKI, this study aimed to investigate how the balance between the HDL-to-LDL ratio might influence the onset of CI–AKI. From January 2019 to May 2024, patients who underwent elective angiography and interventional therapy at Ningbo No. 2 Hospital were enrolled in the study and subsequently divided into the CI–AKI group and the non-CI–AKI group. The patients’ admission HDL-to-LDL ratios were calculated. Six machine learning models including logistic regression, random forest, gradient boosting decision tree, XGBoost, LightGBM, and CatBoost were employed, complemented by restricted cubic splines, and mediation analysis to examine the connection between HDL-to-LDL ratio and CI–AKI. Model interpretability was evaluated via SHAP analysis. Furthermore, subgroup analyses and propensity score matching analyses were performed to evaluate the robustness of the findings. Among the 3,139 enrolled patients in total, 300 individuals (9.56%) developed CI–AKI. Both unadjusted and adjusted logistic regression models showed a significant association between the HDL-to-LDL ratio and the occurrence of CI–AKI (all P < 0.05). In addition, restricted cubic spline analysis revealed that the HDL-to-LDL ratio followed an approximate L-shaped curve. Lasso regression identified the HDL-to-LDL ratio as an important predictor to include in the models. Six machine learning models were developed, with the logistic regression model achieving the highest AUC (0.749). SHAP analysis further confirmed the importance of HDL-to-LDL ratio in AKI prediction. These findings were further validated through propensity score matching and subgroup analyses, confirming their robustness. HDL-to-LDL ratio is a promising predictive biomarker for CI–AKI. Additional research to elucidate the influence of lipid metabolism on CI–AKI is required.

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