Background: Employing advanced machine learning models, we aim to identify biomarkers for urolithiasis from 24-h metabolic urinary abnormalities and study their associations with urinary stone diseases. Methods: We retrospectively recruited 468 patients at Peking Union Medical College Hospital who were diagnosed with urinary stone disease, including renal, ureteral, and multiple location stones, and had undergone a 24-h urine metabolic evaluation. We applied machine learning methods to identify biomarkers of urolithiasis from the urinary metabolite profiles. In total, 148 (34.02%) patients were with kidney stones, 34 (7.82%) with ureter stones, and 163 (34.83%) with multiple location stones, all of whom had detailed urinary metabolite data. Our analyses revealed that the Random Forest algorithm exhibited the highest predictive accuracy, with AUC values of 0.809 for kidney stones, 0.99 for ureter stones, and 0.775 for multiple location stones. The Super Learner Ensemble Method also demonstrated high predictive performance with slightly lower AUC values compared to Random Forest. Further analysis using multivariate logistic regression identified significant features for each stone type based on the Random Forest method. Results: We found that 24-h urinary magnesium was positively associated with both kidney stones and multiple location stones (OR = 1.195 [1.06–1.3525] and 1.3258 [1.1814–1.4949]) due to its high correlation with urinary phosphorus, while 24-h urinary creatinine was a protective factor for kidney stones and ureter stones, with ORs of 0.9533 [0.9117–0.996] and 0.8572 [0.8182–0.8959]. eGFR was a risk factor for ureter stones and multiple location stones, with ORs of 1.0145 [1.0084–1.0209] and 1.0148 [1.0077–1.0223]. Conclusion: Machine learning techniques show promise in revealing the links between urological stone disease and 24-h urinary metabolic data. Enhancing the prediction accuracy of these models leads to improved dietary or pharmacological prevention strategies.

Background/Objectives: Stone composition reflects the urinary environment in which calculi form, whereas 24 h urine testing identifies modifiable lithogenic abnormalities. This study examined component-specific urinary metabolic profiles and evaluated whether routine clinical and urinary variables could distinguish common stone minerals. Methods: This retrospective study included 256 patients undergoing 297 stone surgeries, consolidated into 256 treatment episodes. Fourier-transform infrared spectroscopy identified stone minerals, which were linked to preoperative 24 h urine profiles. Calcium oxalate monohydrate (COM), calcium oxalate dihydrate (COD), carbonate apatite, and anhydrous uric acid were evaluated using multivariable logistic regression. Mutually exclusive calcium oxalate–calcium phosphate phenotypes, nested cross-validated prediction models, and unsupervised clustering were also examined. Results: COM, COD, carbonate apatite, and anhydrous uric acid were present in 78.5%, 73.0%, 39.5%, and 9.8% of episodes, respectively. Higher urine pH was associated with carbonate apatite (odds ratio [OR] per standard deviation, 1.78; 95% confidence interval [CI], 1.33–2.38), whereas lower pH was associated with anhydrous uric acid (OR, 0.26; 95% CI, 0.11–0.58). Higher urinary citrate was associated with COM, but phenotype analysis showed that hypocitraturia increased from 45.3% in calcium oxalate-only episodes to 60.7% in mixed calcium oxalate–calcium phosphate episodes and 89.5% in calcium phosphate episodes without calcium oxalate. Lower urine volume was associated with COD. Cross-validated areas under the curve ranged from 0.654 to 0.712, and metabolic clusters showed substantial mineral overlap. Conclusions: Urine pH provided the clearest component-specific signal, whereas citrate associations depended on accompanying minerals. Routine 24 h urine testing complemented but did not replace direct stone analysis.