Metabolomic Analysis for Biomarker Identification in Severe AKI in Critically Ill Patients

Background: Acute kidney injury (AKI) is a frequent complication linked to high morbidity and mortality. Metabolomics allows global metabolic profiling and is a promising tool for identifying early biomarkers of kidney dysfunction. Methods: We prospectively enrolled 124 ICU patients without evidence of AKI on admission. Untargeted serum metabolomic profiling was performed using gas chromatography-mass spectrometry (GC/MS), identifying 53 metabolites. Principal component analysis (PCA) and volcano plots were used to explore metabolic differences between patients who developed AKI. Logistic regression models were applied to evaluate associations between specific metabolites and AKI, adjusted for SOFA and Charlson scores. Results: PCA showed partial separation between AKI and non-AKI groups. The most discriminative metabolites were gluconic acid and valine. Volcano plot analysis showed lower levels of 1,5-anhydroglucitol and higher levels of gluconic acid, cysteine, myo-inositol, and 3,4-dihydroxybutanoic acid (log2-FC > 0.5, p < 0.05) in AKI patients. After adjusting for SOFA and Charlson scores, only gluconic acid and 3,4-dihydroxybutanoic acid remained significantly associated with AKI development (AUC 0.75, p<0.001). (Figure1) Conclusion: Elevated gluconic acid and 3,4-dihydroxybutanoic indicate early metabolic changes linked to oxidative stress and mitochondrial dysfunction and risk of AKI, suggesting their potential as early biomarkers for risk stratification and intervention. Funding: Government Support – Non-U.S.Figure 1. Exploratory and predictive analysis of the metabolomic profile in AKI. A. Principal component analysis (PLS-DA) of serum metabolites in patients with and without AKI. B. VIP scores showing top metabolites differentiating both groups. C. Volcano plot of significantly altered metabolites (fold change >0.5, p<0.05). D. ROC curves of key metabolites before and after adjustment for Charlson and SOFA.