Machine learning-driven multi-omics integration of urinary organic acids and ions enables precision risk stratification for calcium oxalate nephrolithiasis.

Background: Calcium oxalate (CaOx) nephrolithiasis is closely associated with metabolic dysregulation, while current risk assessment based on 24-h urine analysis is time-consuming and inconvenient. This study aimed to develop a noninvasive predictive model for CaOx stones using morning urine organic acid and inorganic ion profiles combined with machine learning, and to identify metabolomic biomarkers related to CaOx stone formation. Methods: A total of 232 CaOx stone formers and 238 healthy controls were enrolled. Organic acids and inorganic ions in morning urine were quantified by gas chromatography-mass spectrometry and ion chromatography, respectively. Participants were randomly divided into training and testing sets (8:2). Diagnostic models were constructed using random forest, support vector machine, logistic regression, and extreme gradient boosting, with 10-fold cross-validation for optimization. Model performance was evaluated using AUC, accuracy, sensitivity, specificity, F1-score, and G-mean. Differential metabolite screening based on Results: No significant differences in age or sex were observed between groups, whereas BMI was higher in the CaOx group ( Conclusion: This study integrates morning urine organic acid and inorganic ion profiling with machine learning to establish a predictive model for CaOx nephrolithiasis. Five urinary metabolites were identified as potential biomarkers, providing a convenient tool for risk assessment and new insights into CaOx stone pathogenesis.