Continuous Glucose Measurement Research Articles

Continuous glucose monitoring using wearable non-enzymatic sensors in a physiological environment

A novel non-enzymatic sensor has been developed for continuous glucose measurement in physiological body fluids using a Step-wise; amperometric method. Unlike; traditional nickel-based catalysts, this sensor overcomes challenges in biological and neutral pH environments. It utilizes a carbon fiber microelectrode modified with gold and nickel nanoparticles, reinforced by a biopolymer layer derived from quince seed mucilage (QSM). By applying a negative pretreatment potential step, hydroxide ions are locally generated, creating a partially alkaline environment that activates the nickel nanoparticles. Glucose concentration is determined by measuring the current at the electrocatalytic potential of nickel, which directly oxidizes glucose. To clean and reactivate the sensor, a positive pulse potential step is applied at the end of each cycle. The sensor exhibits high sensitivity (13.8 μA mM−1.mm−2) and a low limit of detection (11.3 μM) in neutral pH (7.4). These results demonstrate the promising performance of the sensor for continuous glucose measurement in physiological body fluids. Using eco-friendly and biocompatible QSM as a reinforcing layer enhances its potential for biomedical applications. Additionally, a wearable compact electronic module was designed and fabricated to apply the potential, read the sensor's output currents, and monitor and record the results. The module's performance in measuring glucose in blood plasma is comparable to that of a commercial glucometer.

Automated closed-loop insulin delivery for the management of type 1 diabetes during pregnancy: the AiDAPT RCT

Background There are over 2000 pregnancies annually in women with type 1 diabetes in the UK. Despite recent improvements in diabetes technology, most women cannot achieve and maintain the recommended pregnancy glucose targets. Thus, one in two babies experience complications requiring neonatal care unit admission. Recent studies demonstrate that hybrid closed-loop therapy, in which algorithms adjust insulin delivery according to continuous glucose measurements, is effective for managing type 1 diabetes outside of pregnancy, but efficacy during pregnancy is unclear. Objective To examine the clinical efficacy of hybrid closed-loop compared to standard insulin therapy in pregnant women with type 1 diabetes. Design A multicentre, parallel-group, open-label, randomised, controlled trial in pregnant women with type 1 diabetes. Setting Nine antenatal diabetes clinics in England, Scotland and Northern Ireland. Participants Pregnant women with type 1 diabetes and above-target glucose levels, defined as glycated haemoglobin A1c of ≥ 48 mmol/mol (6.5%) in early pregnancy. Interventions A hybrid closed-loop system compared to standard insulin delivery (via insulin pump or multiple daily injections) with continuous glucose monitoring. Outcome measures The primary outcome is the difference between the intervention and control groups in percentage time spent in the pregnancy glucose target range (3.5–7.8 mmol/l) as measured by continuous glucose monitoring from 16 weeks’ gestation until delivery. Secondary outcomes include overnight time in range, time above range (> 7.8 mmol/l), glycated haemoglobin A1c, safety outcomes (diabetic ketoacidosis, severe hypoglycaemia, adverse device events), psychosocial functioning obstetric and neonatal outcomes. Results The percentage of time that maternal glucose levels were within target range was higher with closed-loop than standard insulin therapy: 68.2 ± 10.5 in closed-loop and 55.6 ± 12.5 in the control group (mean‑adjusted difference 10.5 percentage points, 95% confidence interval 7.0 to 14.0; p < 0.001). Results were consistent in secondary outcomes, with less time above range (−10.2%, 95% confidence interval −13.8 to −6.6%; p < 0.001), higher overnight time in range (12.3%, 95% confidence interval 8.3 to 16.2%; p < 0.001) and lower glycated haemoglobin A1c (−0.31%, 95% confidence interval −0.50 to −0.12%; p < 0.002) all favouring closed-loop. The treatment effect was apparent from early pregnancy and consistent across clinical sites, maternal glycated haemoglobin A1c categories and previous insulin regimen. Maternal glucose improvements were achieved with 3.7 kg less gestational weight gain and without additional hypoglycaemia or total daily insulin dose. There were no unanticipated safety problems (six vs. five severe hypoglycaemia cases, one diabetic ketoacidosis per group) and seven device-related adverse events associated with closed-loop. There were no between-group differences in patient-reported outcomes. There was one shoulder dystocia in the closed-loop group and four serious birth injuries, including one neonatal death in the standard care group. Limitations Our results cannot be extrapolated to closed-loop systems with higher glucose targets, and our sample size did not provide definitive data on maternal and neonatal outcomes. Conclusions Hybrid closed-loop therapy significantly improved maternal glycaemia during type 1 diabetes pregnancy. Our results support National Institute for Health and Care Excellence guideline recommendations that hybrid closed-loop therapy should be offered to all pregnant women with type 1 diabetes. Future work Future trials should examine the effectiveness of hybrid closed-loop started before pregnancy, or as soon as possible after pregnancy confirmation. Trial registration This trial is registered as ISRCTN56898625. Funding This award was funded by the National Institute of Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 16/35/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 7. See the NIHR Funding and Awards website for further award information. Dexcom supplied the continuous glucose monitoring systems used by AiDAPT intervention- and control-arm participants at reduced cost.

A concept for human use of real-time and remote monitoring of diabetic subjects using intermittent scanned continuous glucose measurement

BackgroundFlash glucose monitoring systems like the FreeStyle Libre (FSL) sensor have gained popularity for monitoring glucose levels in people with diabetes mellitus. This sensor can be paired with an off-label converted real-time continuous glucose monitor (c-rtCGM) plus an ad hoc computer/smartphone interface for remote real-time monitoring of diabetic subjects, allowing for trend analysis and alarm generation.ObjectivesThis work evaluates the accuracy and agreement between the FSL sensor and the developed c-rtCGM system. As real-time monitoring is the main feature, the system's connectivity was assessed at 5-min intervals during the trials.MethodsOne week of glucose data were collected from 16 type 1 diabetic rats using the FSL sensor and the c-rtCGM. Baseline blood samples were taken the first day before inducing type 1 diabetes with streptozotocin. Once confirmed diabetic rats, FSL and c-rtCGM, were implanted, and to improve data matching between the two monitoring devices, the c-rtCGM was calibrated to the FSL glucometer readings. A factorial design 2 × 3^3 and a second-order regression was used to find the base values of the linear model transformation of the raw data obtained from the sensor. Accuracy, agreement, and connectivity were assessed by median absolute relative difference (Median ARD), range averaging times, Parkes consensus error grid analysis (EGA), and Bland–Altman analysis with a non-parametric approach.ResultsCompared to the FSL sensor, the c-rtCGM had an overall Median ARD of 6.58%, with 93.06% of results in zone A when calibration was not carried out. When calibration frequency changed from every 50 h to 1 h, the overall Median ARD improved from 6.68% to 2.41%, respectively. The connectivity evaluation showed that 95% of data was successfully received every 5 min by the computer interface.Conclusions and clinical importanceThe results demonstrate the feasibility and reliability of real-time and remote subjects with diabetes monitoring using the developed c-rtCGM system. Performing calibrations relative to the FSL readings increases the accuracy of the data displayed at the interface.

Accuracy and Feasibility of Real-time Continuous Glucose Monitoring in Critically Ill Patients After Abdominal Surgery and Solid Organ Transplantation.

Glycemia management in critical care is posing a challenge in frequent measuring and adequate insulin dose adjustment. In recent years, continuous glucose measurement has gained accuracy and reliability in outpatient and inpatient settings. The aim of this study was to assess the feasibility and accuracy of real-time continuous glucose monitoring (CGM) in ICU patients after major abdominal surgery. We included patients undergoing pancreatic surgery and solid organ transplantation (liver, pancreas, islets of Langerhans, kidney) requiring an ICU stay after surgery. We used a Dexcom G6 sensor, placed in the infraclavicular region, for real-time CGM. Arterial blood glucose measured by the amperometric principle (ABL 800; Radiometer, Copenhagen, Denmark) served as a reference value and for calibration. Blood glucose was also routinely monitored by a StatStrip bedside glucose meter. Sensor accuracy was assessed by mean absolute relative difference (MARD), bias, modified Bland-Altman plot, and surveillance error grid for paired samples of glucose values from CGM and acid-base analyzer (ABL). We analyzed data from 61 patients and obtained 1,546 paired glucose values from CGM and ABL. Active sensor use was 95.1%. MARD was 9.4%, relative bias was 1.4%, and 92.8% of values fell in zone A, 6.1% fell in zone B, and 1.2% fell in zone C of the surveillance error grid. Median time in range was 78%, with minimum (<1%) time spent in hypoglycemia. StatStrip glucose meter MARD compared with ABL was 5.8%. Our study shows clinically applicable accuracy and reliability of Dexcom G6 CGM in postoperative ICU patients and a feasible alternative sensor placement site.

An automatic glucose monitoring system based on periplasmic binding proteins for online bioprocess monitoring

Glucose is one of the most vital nutrients in all living organisms, so its monitoring is critical in healthcare and bioprocessing. Enzymatic sensors are more popular as a technology solution to meet the requirement. However, periplasmic binding proteins have been investigated extensively for their high sensitivity, enabling microdialysis sampling to replace existing complex and expensive glucose monitoring solutions based on enzymatic sensors. The binding proteins are used as optical biosensors by introducing an environment-sensitive fluorophore to the protein. The biosensor's construction, characterization, and potential application are well studied, but a complete glucose monitoring system based on it is yet to be reported. This work documents the development of the first glucose sensor prototype based on glucose binding protein (GBP) for automatic and continuous glucose measurements. The development includes immobilizing the protein into reusable chips and a low-cost solution for non-invasive glucose sampling in bioprocesses using microdialysis sampling technique. A program was written in LabVIEW to accompany the prototype for the complete automation of measurement. The sampling technique allowed glucose measurements of a few micromolar to 260 mM glucose levels. A thorough analysis of the sampling mode and the device's performance was conducted. The reported measurement accuracy was 81.78%, with an RSD of 1.83%. The prototype was also used in online glucose monitoring of E. coli cell culture. The mode of glucose sensing can be expanded to the measurement of other analytes by switching the binding proteins.

Continuous in-vitro physiological glucose sensing for co-cultured 3D tumour spheroids using SERS based nano particle sensors (SERS-gNPS)

Evaluation of glucose in cancerous tumours acts as a hall mark for cancer progression and thus serves as a major interest in these days. Here, we have developed a biocompatible, surface enhanced Raman spectroscopy-based glucose Nano Particle Sensor (SERS-gNPS) to measure glucose dynamically in 3D Co-cultured Colon Cancer Tumour Spheroids (3D-CCTS). 3D-CCTS were produced using polydimethylsiloxane (PDMS) based µ-well array chip. SERS-gNPS was fabricated by conjugating 4-Mercaptophenylboronic acid (4-MPBA) to 50 nm silver nanoparticles (AgNP) and used under confocal Raman spectroscopy to measure glucose. The calibration curve shows direct relationship between Raman peak intensity at 1078 cm-1 with increasing glucose levels. The sensor has a limit of detection of 0.1 mM and found to be linear in the physiological range (1 mM – 10 mM) of glucose with an average standard deviation of ± 5%. SERS-gNPS was applied for continuous glucose measurement in an in-vitro 3D-CCTS and 3D Mono-cultured fibroblast Spheroids (3D-MS). The results revealed that the 3D-CCTS have more prominent glucose gradient (∼1 mM) as compared to 3D-MS (∼0.4 mM) from core to peripheral regions at 3 days to 5 days of culture. SERS-gNPS provides a platform for accurate and real-time measurement of glucose, and offers an enhanced understanding of glucose distribution in the tumour spheroids.

Protocol for the combined cardiometabolic deep phenotyping and registry-based 20-year follow-up study of the Inter99 cohort

IntroductionThe population-based Inter99 cohort has contributed extensively to our understanding of effects of a systematic screening and lifestyle intervention, as well as the multifactorial aetiology of type 2 diabetes (T2D)...

Non-invasive glucose measurements : where do we stand?

The technologies used to measure blood glucose have significantly evolved the past few years, especially with the introduction of continuous interstitial glucose measurements, simplifying the management of the disease. More recently, there has been a lot of interest regarding some potential revolutionary methods, such as smartwatches, and glucose measurements in sweat, saliva, and even tears. In this article, we review the different technologies that are under development, and notice that although promising, they rest imprecise. False measurements can have fatal consequences for our patients. Nevertheless, these innovations are promising and have the potential to change the daily life of people with diabetes in the future.

(Invited) Cavitas Biosensors for Non-Invasive Monitoring of Blood Chemicals

More than ten years have passed since various wearable sensors such as wristband sensors have been developed and commercialized, but most of them are for the purpose of measuring physical information such as heart rate. In medical and healthcare science, wearable sensors that measure biological and chemical information from the body are required. Of course, biosensing of interstitial fluid chemicals is possible like Abbott's Libre, but there is a need for non-invasive and unrestrained sensing techniques, and the development of such wearable sensors will expand future market size.We have developed several cavitas biosensors targeting the chemical analytes in the human body cavities. So far, we have developed a soft contact lens-type glucose sensor, enabled continuous measurement of tear glucose, and reported its correlation with blood glucose levels. In addition, we fabricated a mouth guard type sensor with excellent aesthetics incorporating a small potentiostat and a Bluetooth transmitter (total weight: 1 g) and reported that it is possible to monitor oral information with some smart-watches with Bluetooth communication.In addition, we constructed an imaging system that converts blood-derived gas components into bio-moving images and succeeded in producing moving images of percutaneous gases from various skin surfaces. As a result, we found that transcutaneous gas can be stably and highly sensitively measured from the external auditory canal. As a new cavitas biosensor, we also fabricated headset-type gas sensors to monitor not only percutaneous ethanol gas after drinking alcohol, but also acetone gas (a lipid metabolite) from the ear canal, thus indicating the possibility of a new wearable biosensor.In this presentation, I will discuss the above research results and the possibility of future non-invasive wearable biosensors such as cavitas devices. Figure 1

T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus

Type 1 diabetes mellitus (T1D) patients face daily difficulties in keeping their blood glucose levels within appropriate ranges. Several techniques and devices, such as flash glucose meters, have been developed to help T1D patients improve their quality of life. Most recently, the data collected via these devices is being used to train advanced artificial intelligence models to characterize the evolution of the disease and support its management. Data scarcity is the main challenge for generating these models, as most works use private or artificially generated datasets. For this reason, this work presents T1DiabetesGranada, an open under specific permission longitudinal dataset that not only provides continuous glucose levels, but also patient demographic and clinical information. The dataset includes 257 780 days of measurements spanning four years from 736 T1D patients from the province of Granada, Spain. This dataset advances beyond the state of the art as one the longest and largest open datasets of continuous glucose measurements, thus boosting the development of new artificial intelligence models for glucose level characterization and prediction.

The role of continuous glucose monitoring in physical activity and nutrition management: perspectives on present and possible uses

[Purpose] Continuous glucose monitoring (CGM) is on the rise as the prevalence of obesity and diabetes increases. This review aimed to explore the use of CGM and its potential novel applications in physical activity and nutrition management.[Methods] We searched PubMed, Web of Science, and Wiley Online Library databases using the keywords ‘continuous glucose monitor,’ ‘nutrition,’ ‘physical activity,’ and ‘numerical modeling.’[Results] Continuous blood glucose measurement is useful for individuals with obesity and diabetes. Long-term blood glucose data allow for personalized planning of nutritional composition, meal timing, and physical activity type and intensity, as well as help prevent hypoglycemia and hyperglycemia. Thus, understanding the limitations of CGM is important for its effective use.[Conclusion] CGM systems are being increasingly used to monitor and identify appropriate blood glucose controlling interventions. Blood glucose level is influenced by various factors such as nutrient composition, meal timing, physical activity, circadian rhythm, and cortisol levels. Numerical modeling can be used to analyze the complex relationship between stress, sleep, nutrition, and physical activity, which affect blood glucose levels. In future, blood glucose, sleep, and stress data will be integrated to predict appropriate lifestyle levels for blood glucose management. This integrated approach improves glucose control and overall wellbeing, potentially reducing societal costs.

Investigation of The Effect of Combined Exercises on Glycemic Control in Individuals with Type 1 Diabetes

The aim of the present study was to investigate the effect of the combined physical exercise protocols on the blood glucose (BG) of children with T1D. The study was conducted over four weeks using self-controlled clinical trials. The first two weeks were control period and the last two weeks were the experimental period. A total of 6 exercise sessions were performed 3 days a week during the experimental period. The exercise program consisted of aerobic, stretching, and resistance exercises. Each exercise session was performed for 40 minutes. A total of 15 children with T1D, whose mean age was 13.3±SD, were included in the study. The blood glucose and insulin dose data of the participants were recorded in both periods through the continuous glucose measurement system and the registry. It was determined that aerobic exercises provided a significant decrease in blood glucose (p &lt; .05). There was no statistically significant difference between the mean BG values in the control (211.7±37.3) and experimental (208.7±40.4) periods (p &gt;0.05). On the other hand, a statistically significant decrease was found in the total daily insulin doses used by participants during the experimental (31.6±20.8 Unit) period (p &lt; 0.001). Physical exercises had an acute positive effect on blood glucose in children with T1D. However, the effect of exercise alone is not sufficient for chronic glycemic control in T1D. For chronic glycemic control, first of all, the injected insulin doses should be carefully regulated according to carbohydrates and exercise.

Improving the Accuracy of Continuous Blood Glucose Measurement Using Personalized Calibration and Machine Learning.

Despite tremendous developments in continuous blood glucose measurement (CBGM) sensors, they are still not accurate for all patients with diabetes. As glucose concentration in the blood is <1% of the total blood volume, it is challenging to accurately measure glucose levels in the interstitial fluid using CBGM sensors due to within-patient and between-patient variations. To address this issue, we developed a novel data-driven approach to accurately predict CBGM values using personalized calibration and machine learning. First, we scientifically divided measured blood glucose into smaller groups, namely, hypoglycemia (<80 mg/dL), nondiabetic (81-115 mg/dL), prediabetes (116-150 mg/dL), diabetes (151-181 mg/dL), severe diabetes (181-250 mg/dL), and critical diabetes (>250 mg/dL). Second, we separately trained each group using different machine learning models based on patients' personalized parameters, such as physical activity, posture, heart rate, breath rate, skin temperature, and food intake. Lastly, we used multilayer perceptron (MLP) for the D1NAMO dataset (training to test ratio: 70:30) and grid search for hyperparameter optimization to predict accurate blood glucose concentrations. We successfully applied our proposed approach in nine patients with type 1 diabetes and observed that the mean absolute relative difference (MARD) decreased from 17.8% to 8.3%.

Antiallergic Abdominal Belt for Human Glucose Level Measurement Using Microwave Active Sensor Antenna

In this paper, microwave active antenna sensor based MDBM meter is developed for continuous glucose measurement (CGM) in human body. MDBM meter consists of Antiallergic Abdominal belt with radar IC (RCWL0516) mixer IC (BFR520) and HSIS antenna sensor. Microwave active antenna sensor is H-shaped patch with I-shaped slot (HSIS), which is placed on human pancreas and acquires reflected microwave signals from dielectric materials in pancreas. Pancreas dielectric materials radiation property changes during insulin secretion. Existing CGM meter measure diabetic value using invasive sensor. Invasive sensor is prick type sensor and leads to infections. In human body, pricked sensor stays on body for 14 days. Non-invasive sensor based diabetic measurement meter never measures CGM, due to various problem such as sensor mounting on human body and skin crumbling effect. For above problems, MDBM meter is proposed. In MDBM meter, antenna receives reflected signals from dielectric minerals in pancreas through HSIS active sensor antenna process with microcontroller and predicts diabetic value based on machine learning. Return loss of HSIS antenna sensor is highly correlated with laboratory based diabetic measurement. MDBM meter based CGM is performed in 150 diabetic person with 410 samples data. From MDBM meter, fasting blood glucose test, accuracy is about 92.66%, glucose tolerance test, is about 91.33% accuracy, and random blood glucose test accuracy is about 91.88% were obtained. Proposed MDBM meter with HSIS antenna sensor measures fluctuating diabetic value with 91.85% accuracy, when compared to home based invasive glucometer measurement.

Geometry-Defined Response Time and Sensitivity for Microneedle-Based Amperometric Sensors

Smart, ultra-scaled, always-on wearable (and implantable) sensors are an exciting frontier of modern medicine. Among them, minimally invasive microneedles (MN) is an emerging technology platform for theragnostic applications. Compared to traditional continuous glucose measurement (CGM) devices, these MNs offer pain-less insertion and simple operation. These MN systems, however, rely on analyte diffusion from the interstitial fluid (ISF) to the sensing site, and thus, (a) introduce a substantial and intrinsic diffusion delay in sensor response, and (b) reduce the analyte concentration to which the sensor must respond. A diversity of experimental platforms has been proposed to improve performance, but their optimization relies on empirical iterative approaches. Here, we integrate the theory of transient flux balance and the biomimetic concepts from ion uptake by bacteria to derive a generalized physics-guided model for MN sensors. The framework suggests strategies to minimize response time and maximize extracted analyte concentration in terms of the geometric and physical properties of the system. Our results show that there exists an intrinsic trade-off between response time and extracted analyte concentration. Our model, validated against numerical simulations and experiment data, offers a predictive design framework that would significantly reduce the optimization time for MN-based sensor platforms.

1204-P: The Effect of Eating Behaviour, BMI, and Glycaemia in Women with Gestational Diabetes

Introduction: Maternal obesity and poor diet choices are often attributed as the cause of gestational diabetes. Maternal eating behaviour during pregnancy and the influence of this on the disease aetiology remains poorly understood. We aimed to compare eating behaviour in women with gestational diabetes to non-pregnant adults to determine which of the eating behaviour traits are associated with BMI as well as glucose metrics. Methods: Participants (total n=448) including men (n=67), non-pregnant women (n=181) and women with gestational diabetes during a singleton pregnancy (n=200; 29 weeks’ gestation; NICE / Covid-19 criteria) were recruited prospectively and completed a three-factor eating questionnaire (TFEQ-R18). Cross-sectional associations between continuous glucose measurement metrics and BMI with uncontrolled eating (UE), emotional eating (EE) and cognitive restraint (CR) were assessed using linear regression. Results: Women with gestational diabetes had significantly lower UE scores compared to men (53% vs 63%; p&amp;lt;0.001) and non-pregnant women (53% vs 65%; p&amp;lt;0.001), and lower EE scores compared to non-pregnant women (60% vs 71%; p&amp;lt;0.001). In women with gestational diabetes, only EE scores were significantly associated with BMI (Coeff 7.8; 95%CI 3.9-11.7; p&amp;lt;0.001), while UE and CR scores have no association. However, EE, UE and CR were not significantly associated with glucose metrics such as average glucose levels, time above range or time below range. Conclusions: Favourable eating behaviour was observed in women with gestational diabetes when compared to men and non-pregnant women. Data showing EE is strongly associated with BMI suggest that interventions which consider this may be more impactful for weight management in women with gestational diabetes and, as such, exploring this avenue may be an important aspect to address when considering new translational approaches. Disclosure L.C.Kusinski: None. P.Tobolska: None. D.Jones: None. N.Atta: None. L.M.Oude griep: None. F.M.Gribble: Other Relationship; Kallyope, Research Support; AstraZeneca, Eli Lilly and Company. C.L.Meek: Research Support; Dexcom, Inc. Funding Diabetes UK (17/0005712)

Integrated Device Based on a Sudomotor Nanomaterial for Sweat Detection

The compositions of sweat and blood are related. Therefore, sweat is an ideal noninvasive test body fluid that could replace blood for linear detection of many biomarkers, especially blood glucose. However, access to sweat samples remains limited to physical exercise, thermal stimulation, or electrical stimulation. Despite intensive research, a continuous, innocuous, and stable method for sweat stimulation and detection has not yet been developed. In this study, a nanomaterial for a sweat-stimulating gel based on the transdermal drug delivery system is presented, which transports acetylcholine chloride into the receptors of sweat glands to achieve the function of biological stimulation of skin sweating. The nanomaterial was applied to a suitable integrated sweat glucose detection device for noninvasive blood glucose monitoring. The total amount of evaporated sweat enabled by the nanomaterial is up to 35 μL·cm–2 for 24 h, and the device detects up to 17.65 μM glucose under optimal conditions, showing stable performance regardless of the user’s activity level. In addition, the in vivo test was performed and compared with several studies and products, which showed excellent detection performance and osmotic relationship. The nanomaterial and associated integrated device represent a significant advance in continuous passive sweat stimulation and noninvasive sweat glucose measurement for point-of-care applications.

EPS4.05 Exploring the practicalities and effectiveness of a screening tool in adult cystic fibrosis diabetes

Objectives: A screening tool for Cystic Fibrosis Diabetes (CFD) is in use at the Norfolk and Norwich University Hospital (NNUH) to improve cost-effectiveness and ensure appropriate allocation of limited time and resources within the CF team. The tool involves initial screening using 3-point oral glucose tolerance testing (OGTT) alongside HbA1c. When this is not possible, HbA1c is used in isolation to highlight patients for additional monitoring. The results are assessed and patients are categorised for further discussion, which may lead to testing using continuous glucose monitoring (CGM) or finger prick glucose measurements, or allocated to yearly re-screening. The aim of this audit was to explore the use and effectiveness of this tool.

Insulin detection in diabetes mellitus: challenges and new prospects.

Tremendous progress has been made towards achieving tight glycaemic control in individuals with diabetes mellitus through the use of frequent or continuous glucose measurements. However, in patients who require insulin, accurate dosing must consider multiple factors that affect insulin sensitivity and modulate insulin bolus needs. Accordingly, an urgent need exists for frequent and real-time insulin measurements to closely track the dynamic blood concentration of insulin during insulin therapy and guide optimal insulin dosing. Nevertheless, traditional centralized insulin testing cannot offer timely measurements, which are essential to achieving this goal. This Perspective discusses the advances and challenges in moving insulin assays from traditional laboratory-based assays to frequent and continuous measurements in decentralized (point-of-care and home) settings. Technologies that hold promise for insulin testing using disposable test strips, mobile systems and wearable real-time insulin-sensing devices are discussed. We also consider future prospects for continuous insulin monitoring and for fully integrated multisensor-guided closed-loop artificial pancreas systems.

Miniaturization of an Osmotic Pressure-Based Glucose Sensor for Continuous Intraperitoneal and Subcutaneous Glucose Monitoring by Means of Nanotechnology.

The Sencell sensor uses glucose-induced changes in an osmotic pressure chamber for continuous glucose measurement. A final device shall have the size of a grain of rice. The size limiting factor is the piezo-resistive pressure transducers inside the core sensor technology (resulting chamber volume: 70 µL. To achieve the necessary miniaturization, these pressure transducers were replaced by small (4000 × 400 × 150 nm³) nano-granular tunneling resistive (NTR) pressure sensors (chamber volume: 750 nL). For benchmark testing, we filled the miniaturized chamber with bovine serum albumin (BSA, 1 mM) and exposed it repeatedly to distilled water followed by 1 mM BSA solution. Thereafter, we manufactured sensors with glucose testing chemistry (ConcanavalinA/dextran) and investigated sensor performance with dynamic glucose changes between 0 and 300 mg/dL. Evaluation of the miniaturized sensors resulted in reliable pressure changes, both in the BSA benchmark experiment (30-35 mBar) and in the dynamic in vitro continuous glucose test (40-50 mBar). These pressure results were comparable to similar experiments with the previous larger in vitro sensors (30-50 mBar). In conclusion, the NTR pressure sensor technology was successfully employed to reduce the size of the core osmotic pressure chamber by more than 95% without loss in the osmotic pressure signal.