Predictive patterns of lower urinary tract symptoms and bacteriuria in adults with type 2 diabetes

Background Numerous studies demonstrated the risk factors for urological complications in patients with diabetes before sodium–glucose co-transporter 2 inhibitor (SGLT2i) became commercially available. This study aimed to comprehensively investigate urological characteristics in patients with type 2 diabetes (T2DM) after SGLT2i became commercially available. Methods We examined 63 outpatients with T2DM suspected of bacteriuria based on urinary sediment examinations. Urine cultures were performed, and lower urinary tract symptoms (LUTS) were assessed via questionnaires. Patients with bacteriuria were assessed using ultrasonography to measure post-void residual volume (PVR). Utilizing demographic and laboratory data, a random forest algorithm predicted LUTS, bacteriuria, and symptomatic bacteriuria (SB). Results Thirty-two patients had LUTS and 31 had bacteriuria. High-density lipoprotein cholesterol level was crucial in predicting LUTS, while age was crucial in predicting bacteriuria. In predicting SB among patients with bacteriuria, creatinine level and estimated glomerular filtration rate were crucial. Our models had high predictive accuracy for LUTS (area under the curve [AUC] = 0.846), followed by bacteriuria (AUC = 0.770) and SB (AUC = 0.938) in receiver operating characteristic curve analysis. These predictors were previously reported as risk factors for urological complications. Although SGLT2i use was not an important predictor in our study, all SGLT2i users with bacteriuria had SB and exhibited higher PVR compared to non-SGLT2i users with bacteriuria. Conclusion This study’s random forest model highlighted distinct essential predictors for each urological condition. The predictors were consistent before and after SGLT2i became commercially available.