Untargeted urinary metabolomics reveals metabolic alterations and severity biomarkers in cardiac surgery-associated acute kidney injury.

INTRODUCTION

Cardiac surgery-associated acute kidney injury (CS-AKI) is a frequent, serious complication linked to increased morbidity, mortality, and long-term kidney dysfunction. Progress in early detection and effective interventions remains limited, underscoring the need for novel biomarkers and therapeutic targets. Given the kidneys' high metabolic activity, metabolomics offers a powerful strategy to identify such markers and provide mechanistic insights.

OBJECTIVES

Our goal is to investigate urinary metabolome alterations associated with CS-AKI.

METHODS

We performed untargeted amine/phenol metabolomic profiling of urine samples collected within four hours post-surgery from 55 cardiac surgery patients (26 non-AKI, 29 AKI), using high-performance chemical isotope labeling and liquid chromatography-mass spectrometry (LC-MS). Multivariate analyses were applied to assess group separation and to evaluate the diagnostic performance of key metabolites.

RESULTS

Urinary metabolomic profiles were significantly different between AKI and non-AKI patients. Of 1384 detected metabolites, 45 were differentially altered in AKI (defined by a fold change exceeding the mean ± 2 standard deviations, raw p < 0.05), with 25 metabolites annotated to known compounds. The most prominent metabolite, N-acetylputrescine (Log2 fold change = 1.95, raw p < 0.001, adjusted p = 0.01), demonstrated high discriminatory power with an area under the curve (AUC) of 0.881. Additionally, four other polyamine catabolites were elevated in AKI, suggesting upregulation of polyamine catabolism. Several metabolites, including carnosine, 5-hydroxyindoleacetic acid, and N-acetylputrescine, were significantly correlated with AKI severity.

CONCLUSIONS

Untargeted urinary metabolomics distinguished AKI from non-AKI after cardiac surgery and identified distinct metabolic signatures associated with AKI severity. This work underscores the value of urinary metabolomics for revealing mechanistic pathways and identifying candidate biomarkers that may aid early diagnosis and monitoring of CS-AKI.