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. Supplementary Information: The online version contains supplementary material available at 10.1007/s13340-023-00687-1.

Internet of things-based approach for glycemic control in people with type 2 diabetes: A randomized controlled trial.

AIMS: The utilization of long-term effect of internet of things (IoT) on glycemic control is controversial. This trial aimed to examine the effect of an IoT-based approach for type 2 diabetes. MATERIALS AND METHODS: This randomized controlled trial enrolled 1,159 adults aged 20-74 years with type 2 diabetes with a HbA1c of 6.0-8.9% (42-74 mmol/mol), who were using a smartphone on a daily basis were randomly assigned to either the IoT-based approach group (ITG) or the control group (CTG). The ITG were supervised to utilize an IoT automated system that demonstrates a summary of lifelogging data (weight, blood pressure, and physical activities) and provides feedback messages that promote behavioral changes in both diet and exercise. The primary end point was a HbA1c change over 52 weeks. RESULTS: Among the patients, 581 were assigned to the ITG and 578 were in the CTG. The changes in HbA1c from baseline to the final measurement at 52 weeks [mean (standard deviation)] were -0.000 (0.6225)% in ITG and - 0.006 (0.6449)% in CTG, respectively (P = 0.8766). In the per protocol set, including ITG using the IoT system almost daily and CTG, excluding those using the application almost daily, the difference in HbA1c from baseline to 52 weeks were -0.098 (0.579)% and 0.027 (0.571)%, respectively (P = 0.0201). We observed no significant difference in the adverse event profile between the groups. CONCLUSIONS: The IoT-based approach did not reduce HbA1c in patients with type 2 diabetes. IoT-based intervention using data on the daily glycemic control and HbA1c level may be required to improve glycemic control.