Journal of Diabetes Science and Technology最新文献

Integration of direct electron transfer-type (DET-type) Burkholderia cepacia glucose dehydrogenase (BcGDH) with an extended gate field effect transistor (EGFET) transducer to measure glucose in human plasma is a promising approach to overcome technology limitations in commercial continuous glucose monitors (CGM). Sensors were fabricated using microwire electrodes and were characterized for selectivity against interferents, reversibility, stability, and validated ex vivo. DET-type EGFET sensors showed low signal bias against a variety of interfering compounds and demonstrated acute reversibility and stability, while also successfully measuring glucose ex vivo in human plasma with a limit of detection of 0.94 mM. The DET-type EGFET glucose sensor was operated ex vivo over a physiological concentration range, demonstrating the feasibility of using EGFET-based transduction of DET-BcGDH for future use in CGM applications.

Background: There is emerging interest in the application of foot temperature monitoring as means of diabetic foot ulcer (DFU) prevention. However, the variability in temperature readings of neuropathic feet remains unknown. The aim of this study was to analyze the long-term consistency of foot thermograms of diabetic feet at the risk of DFU.

Methods: A post-hoc analysis of thermal images of 15 participants who remained ulcer-free during a 12-month follow-up were unblinded at the end of the trial. Skin foot temperatures of 12 plantar, 15 dorsal, 3 lateral, and 3 medial regions of interests (ROIs) were derived on monthly thermograms. The temperature differences (∆Ts) of corresponding ROIs of both feet were calculated.

Results: Over the 12-month study period, out of the total 2026 plantar data points, 20.3% ROIs were rated as abnormal (absolute ∆T ≥ 2.2°C). There was a significant between-visit variability in the proportion of plantar ROIs with ∆T ≥ 2.2°C (range 7.6%-30.8%, chi-square test, P = .001). The proportion of patients presenting with hotspots (ROIs with ∆T ≥ 2.2°C), abnormal plantar foot temperature (mean ∆T of 12 plantar ROIs ≥ 2.2°C), and abnormal whole foot temperature (mean ∆T of 33 ROIs ≥ 2.2°C) varied between visits and showed no pattern (P > .05 for all comparisons). This variability was not related to the season of assessment.

Conclusions: Despite the high rate of hotspots on monthly thermograms, all feet remained intact. This study underscores a significant between-visit inconsistency in thermal images of neuropathic feet which should be considered when planning DFU-prevention programs for self-testing and behavior modification.

Background: The growing adoption of diabetes devices has highlighted the need for integrated platforms to consolidate data from various vendors and device types, enhancing the patient experience and treatment. This shift could pave the way for a transition from conventional outpatient diabetes clinics to advanced home monitoring and virtual care methods. Overall, we wished to empower individuals with diabetes and healthcare providers to interpret and utilize information from diabetes devices more effectively.

Methods: Stenopool integrates most diabetes devices for glucose monitoring and insulin administration in our clinic. The platform was initially developed with inspiration from open-source software, and the current version is a unique digital platform for managing and analyzing diabetes device data. The development process, outcomes, and status are described.

Results: Since November 2021, Stenopool has been used in our outpatient clinic to integrate over 30 different diabetes devices from around 7000 individuals. Data are primarily uploaded via wired connections, but also using semi-automated and automated cloud-to-cloud data transfers. The platform offers a streamlined workflow for healthcare providers and displays data from various glucose meter, insulin pump, and continuous glucose monitor (CGM) vendors on a single screen in a manner that healthcare providers can modify. A data warehouse with data from Stenopool and electronical health records is nearing completion, preparing the development of tools for population health management, quality assessment, and risk stratification of patients.

Conclusion: Using Stenopool, we aimed to enhance diabetes device data management, facilitate the future for virtual patient care pathways, and improve outcomes. This article outlines the platform's development process and challenges.

Background: The study conceptualizes the application of particle filters for meal detection and shape estimation, aimed at assisting patients with type 1 diabetes (T1D) in meal announcements. It concentrates on leveraging continuous glucose monitoring (CGM) and insulin pump data to detect meals, which will eventually enhance prior research that relied on accelerometer data for event detection.

Method: The research employs glucose appearance rate (RA) curves derived from 21 triple-tracer studies. These curves are normalized and adjusted based on population-level meal size distributions obtained from the National Health and Nutrition Examination Survey (NHANES). Furthermore, glucose response curves resulting from insulin, as reported in existing literature focusing on fast-acting insulin analogs, are integrated into the analysis. This normalization of the population level probability distribution facilitates individualized scaling, taking into account insulin sensitivity and carbohydrate-to-insulin ratios.

Results: Preliminary findings from the Tidepool data set suggest that the algorithm is effective in meal detection.

Conclusions: This innovative approach holds the potential to help individuals with T1D better manage their blood glucose levels by providing information regarding glucose response to meals and estimated meal sizes for closed-loop control. Future research can focus on enhancing key components of the algorithm and incorporating additional data types to improve its performance further.

Background and aims: Glycemic control is crucial for people with type 2 diabetes. However, only about half achieve the advocated HbA1c target of ≤7%. Identifying those who will probably struggle to reach this target may be valuable as they require additional support. Thus, the aim of this study was to develop a model to predict people with type 2 diabetes not achieving HbA1c target after initiating fast-acting insulin.

Methods: Data from a randomized controlled trial (NCT01819129) of participants with type 2 diabetes initiating fast-acting insulin were used. Data included demographics, clinical laboratory values, self-monitored blood glucose (SMBG), health-related quality of life (SF-36), and body measurements. A logistic regression was developed to predict HbA1c target nonachievers. A potential of 196 features was input for a forward feature selection. To assess the performance, a 20-repeated stratified 5-fold cross-validation with area under the receiver operating characteristics curve (AUROC) was used.

Results: Out of the 467 included participants, 98 (21%) did not achieve HbA1c target of ≤7%. The forward selection identified 7 features: baseline HbA1c (%), mean postprandial SMBG at all meals 3 consecutive days before baseline (mmol/L), sex, no ketones in urine, baseline albumin (g/dL), baseline low-density lipoprotein cholesterol (mmol/L), and traces of protein in urine. The model had an AUROC of 0.745 [95% CI = 0.734, 0.756].

Conclusions: The model was able to predict those who did not achieve HbA1c target with promising performance, potentially enabling early identification of people with type 2 diabetes who require additional support to reach glycemic control.

Background: Despite remarkable progress in diabetes technology, most systems still require estimating meal carbohydrate (CHO) content for meal-time insulin delivery. Emerging smartphone applications may obviate this need, but performance data in relation to patient estimates remain scarce.

Objective: The objective is to assess the accuracy of two commercial CHO estimation applications, SNAQ and Calorie Mama, and compare their performance with the estimation accuracy of people with type 1 diabetes (T1D).

Methods: Carbohydrate estimates of 53 individuals with T1D (aged ≥16 years) were compared with those of SNAQ (food recognition + quantification) and Calorie Mama (food recognition + adjustable standard portion size). Twenty-six cooked meals were prepared at the hospital kitchen. Each participant estimated the CHO content of two meals in three different sizes without assistance. Participants then used SNAQ for CHO quantification in one meal and Calorie Mama for the other (all three sizes). Accuracy was the estimate's deviation from ground-truth CHO content (weight multiplied by nutritional facts from recipe database). Furthermore, the applications were rated using the Mars-G questionnaire.

Results: Participants' mean ± standard deviation (SD) absolute error was 21 ± 21.5 g (71 ± 72.7%). Calorie Mama had a mean absolute error of 24 ± 36.5 g (81.2 ± 123.4%). With a mean absolute error of 13.1 ± 11.3 g (44.3 ± 38.2%), SNAQ outperformed the estimation accuracy of patients and Calorie Mama (both P > .05). Error consistency (quantified by the within-participant SD) did not significantly differ between the methods.

Conclusions: SNAQ may provide effective CHO estimation support for people with T1D, particularly those with large or inconsistent CHO estimation errors. Its impact on glucose control remains to be evaluated.

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.