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BACKGROUND: Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals. METHODS: An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values. RESULTS: Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones. CONCLUSIONS: These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.
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INTRODUCTION: Treatment of type 2 diabetes with sodium-glucose cotransporter 2 (SGLT2) inhibitors may result in genital fungal infections. We investigated possible risk factors for developing such infections among patients treated with the SGLT2 inhibitor dapagliflozin. METHODS: The Association of British Clinical Diabetologists (ABCD) collected data on patients treated with dapagliflozin in routine clinical practice from 59 diabetes centres. We assessed possible associations of patient's age, diabetes duration, body mass index, glycated haemoglobin, renal function, patient sex, ethnicity and prior genital fungal infection, urinary tract infection, urinary incontinence or nocturia, with the occurrence of ≥1 genital fungal infection within 26 weeks of treatment. RESULTS: 1049 out of 1116 patients (476 women, 573 men) were analysed. Baseline characteristics were, mean±SD, age 56.7±10.2years, BMI 35.5±6.9kg/m2 and HbA1c 9.4±1.5%. Only patient sex (13.2% women vs 3.3% men) and prior history of genital fungal infection (21.6% vs 7.3%) were found to be associated with occurrence of genital fungal infections after dapagliflozin treatment, adjusted OR 4.22 [95%CI 2.48,7.19], P<0.001 and adjusted OR 2.41 [95% CI 1.04,5.57], P=0.039, respectively. CONCLUSION: Women and patients with previous genital fungal infections had higher risks of developing genital fungal infections with dapagliflozin treatment.