Periodontitis can lead to the development of atherosclerotic heart disease.

To assess the relation between periodontal disease and continuous glucose monitoring (CGM)-derived metrics, caspase-3 and carotid intima media thickness (CIMT) in adolescents with type 1 diabetes mellitus (T1DM).

This cross-sectional study included 115 participants with T1DM (15.91 ± 2.14 years). CIMT, CGM-derived metrics, periodontal probing depths (PPD) and amount of loss clinical attachment (CAL) were assessed. Serum caspase-3 levels were measured in T1DM participants compared with 40 healthy controls.

Periodontitis was found in 69.6 % of T1DM group. Serum caspase-3 levels were significantly elevated in T1DM participants compared with controls (median 8.4 versus 1.2 ng/mL; p < 0.001. Participants with periodontitis had higher percentage of nephropathy with elevated CIMT, caspase-3 levels, glucose management indicator (GMI) (7.5 ± 0.58 versus 7.1 ± 0.51 %; p = 0.047), coefficient of variation (CV) (36.4 ± 5.6 versus 33.2 ± 5.9 %; p = 0.008) and glycemic risk index while time in range (TIR) was significantly lower (58.5 ± 14.1 versus 69.7 ± 11.5 %; p = 0.002) compared with those without periodontitis. Participants using advanced hybrid close loop system had lower caspase 3 levels and CIMT compared with those on multiple daily injections. There were positive correlations between caspase-3 and GMI (r = 0.587), CV (r = 0.434), CIMT (r = 0.425), PPD (r = 0.952) and CAL (r = 0.739) while caspase-3 was negatively correlated to TIR(r = -0.481; p < 0.001 for all).

Periodontitis is prevalent among adolescents with T1DM and is linked to glycemic variability and poor metabolic control while associated with diabetic nephropathy and subclinical atherosclerosis. Elevated caspase-3 highlights the involvement of apoptosis in periodontal disease and subclinical atherosclerosis in T1DM.

Maintaining optimal glucose control is critical for postoperative care cardiac surgery patients. Continuous glucose monitoring (CGM) in this setting remains understudied. We evaluated the efficacy of CGM with a specialized titration protocol in cardiac surgery patients with type 2 diabetes (T2D) and prediabetes.

In this randomized-controlled trial, 54 cardiac surgery patients were randomized one day post-surgery, with 27 CGM and 25 point-of-care (POC) patients completing the study. The CGM group used Dexcom G6 with a CGM-specialized titration protocol, while the POC group used standard monitoring with blinded CGM. The primary outcome was time-in-range (TIR) 100-180 mg/dL for 7 days post-surgery. Secondary outcomes included various glycemic metrics and surgical outcomes. Multiple comparison adjustments were performed using false-discovery-rate (FDR).

Thirty-one (59.6%) had diabetes and 21 (40.4%) had prediabetes. While TIR 100-180 mg/dL showed no difference (74.7% vs. 71.6%, FDR-adjusted p = 0.376), the CGM group demonstrated improvements in TIR 70-180 mg/dL (83.8% vs. 75.8%, FDR-adjusted p = 0.026), time-in-tight-range (TITR) 100-140 mg/dL (46.3% vs. 36.3%, FDR-adjusted p = 0.018), and TITR 70-140 mg/dL (55.3% vs. 40.5%, FDR-adjusted p = 0.003). Both groups maintained very low rates of time below range (< 70 mg/dL: 0.03% vs. 0.18%, FDR-adjusted p = 0.109). The CGM group showed lower postoperative atrial fibrillation (AF) (18.8% vs. 55.6%, FDR-adjusted p = 0.04999).

While the primary outcome was not achieved, CGM with a specialized titration protocol demonstrated safe glycemic control with improvements in TIR 70-180 mg/dL and TITRs in cardiac surgery patients with T2D and prediabetes. The observed reduction in postoperative AF warrants further investigation.

ClinicalTrials.gov NCT06275971.

Sweat electrolyte analysis using potentiometric systems is a promising approach for continuous health monitoring. However, despite its potential, temperature-induced measurement errors remain a critical challenge, and, to our knowledge, no study has effectively addressed this issue for accurate potentiometric sensing during physiological activities. Here, we present a temperature-compensated flexible microsensor integrated with a wireless potentiometric measurement circuit for real-time sweat analysis. The wearable system features an array of microsensors for simultaneous detection of pH, Na+, K+, and skin temperature, enabling real-time dynamic temperature compensation. A PEDOT:PSS/graphene ion-to-charge transducer enhances sensitivity through superior electron acceptor properties and an expanded electroactive surface area. The incorporation of a Nafion top layer ensures 2-week-long stability by facilitating selective cation transport while mitigating sensor degradation. With temperature compensation, the wireless wearable device measures an accurate level of electrolytes under extreme temperature variations (8 to 56 °C), including outdoor exercises and exposure to dry saunas, to assess the necessity of temperature correction. This work collectively establishes a robust, high-performance platform for continuous monitoring of sweat biomarkers, thus advancing wearable diagnostic technology for personalized healthcare applications.

Traditional methods for sampling biological substance concentrations in blood are invasive and unsuitable for continuous monitoring. This paper presents a minimally invasive device equipped with optical waveguides on the surface of an acupuncture needle for measuring lactate concentration in subepidermal tissue, potentially in a continuous fashion. The device utilizes absorbance measurements, based on the correlation between absorbance and lactate concentration in tissue. The needle, with a diameter of 200 μm, exhibits good penetrability, particularly when coated with a biocompatible epoxy, which reduces insertion resistance. The experimental results demonstrate that the device can successfully measure the concentration of lactate solutions, with light intensity decreasing as the lactate concentration increases. However, challenges remain in optimizing the device to reduce optical losses and enhance measurement accuracy. Future work will focus on reducing optical loss, conducting animal experiments, and developing a miniaturized system for simultaneous measurement of multiple biological substances, aiming for practical applications in daily health monitoring.

This study compares the association between real-time continuous glucose monitoring (rtCGM) and intermittently- scanned CGM (isCGM) and glycemic control in individuals with type 1 diabetes mellitus (T1DM) in a real-world setting.

Using data from the Korean National Health Insurance Service Cohort, individuals with T1DM managed by intensive insulin therapy were followed at 3-month intervals for 2 years after the initiation of CGM. The glycosylated hemoglobin (HbA1c) levels and coefficients of variation (CVs) of rtCGM and isCGM users were compared using independent two-sample t-test and a linear mixed model.

The analyses considered 7,786 individuals (5,875 adults aged ≥19 years and 1,911 children and adolescents aged <19 years). Overall, a significant reduction in HbA1c level was observed after 3 months of CGM, and the effect was sustained for 2 years. The mean HbA1c level at baseline was higher in rtCGM users than in isCGM users (8.9%±2.7% vs. 8.6%±2.2%, P<0.001). However, from 3 to 24 months, rtCGM users had lower HbA1c levels than isCGM users at every time point (7.1%±1.2% vs. 7.5%±1.3% at 24 months, P<0.001 for all time points). In both adults and children, the greater reduction in HbA1c with rtCGM remained significant after adjusting for the baseline characteristics of the users. The CV also showed greater decrease with rtCGM than with isCGM.

In this large nationwide cohort study, the use of rtCGM was associated with a greater improvement in glycemic control, including HbA1c reduction, than the use of isCGM in both adults and children with T1DM.

Continuous glucose monitors (CGMs) are an increasingly prevalent electronic medical device used by patients with diabetes, offering several advantages over "finger sticks." There is a resulting rise in patients with CGMs seen in radiation oncology clinics. Manufacturers specify that CGMs should not be exposed to radiation (both diagnostic and therapeutic) due to the risk of device damage, creating challenges for patients and providers. We present a workflow for the management of CGMs in radiation oncology patients, beginning with systematic screening by providers and staff. We propose options for CGM management together with the device prescriber, including removal of the CGM or keeping it in place with periodic finger sticks to confirm the accuracy and offer guidance to radiation oncology providers and staff.

The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

The worldwide incidence of diabetes mellitus (DM), as a long-term metabolic condition, continues to rise, creating an urgent need for accurate and efficient diagnostic methods to identify and treat the disease early. Among various analytical technologies, electrochemical biosensors stand out for their exceptional attributes, including precise detection, selective response, quick results, and affordable implementation. The current review study examines the latest developments in electrochemical biosensor technology designed specifically for diabetes detection, emphasizing novel approaches in blood sugar monitoring and tracking key diabetes indicators, including HbA1c, insulin levels, and ketones. The discussion encompasses cutting-edge developments such as sensors incorporating nanomaterials, non-enzymatic detection systems, and portable monitoring devices, emphasizing how these innovations improve both technical capabilities and patient experience. This review also demonstrates how next-generation electrochemical biosensors could fundamentally change diabetes care and monitoring, leading to more widely available and accurate disease tracking methods.

Psychiatric disorders and internal diseases frequently co-occur, posing significant challenges due to overlapping symptoms, shared pathophysiological mechanisms, and increased healthcare burdens. Neuroinflammation has emerged as a central mechanism linking these conditions, driven by systemic inflammation, hypothalamic-pituitary-adrenal (HPA) axis dysregulation, and autonomic nervous system (ANS) imbalance. This review synthesizes current evidence on the role of neuroinflammation in comorbid conditions such as depression, anxiety, cardiovascular disease, and diabetes mellitus, emphasizing bidirectional relationships and shared inflammatory pathways. This analysis identifies gaps in longitudinal studies, biomarker validation, and the integration of multidisciplinary care models. Emerging therapeutic approaches, including IL-6 inhibitors, vagus nerve stimulation, and behavioral interventions, show promise but remain underexplored in combined applications. Furthermore, disparities in research representation limit the generalizability of findings and highlight the need for inclusive clinical trials. Addressing these gaps through precision medicine, advanced biomarker monitoring technologies, and equitable healthcare strategies could transform the management of these complex comorbidities. By advancing our understanding of neuroinflammatory mechanisms and promoting integrated interventions, this review underscores the need for a collaborative, patient-centered approach to improve outcomes and reduce the global burden of psychiatric and internal disease comorbidities.

The aim of this study is to evaluate the performance, comprehensiveness, reliability, and quality of English-language YouTube videos related to new glucose sensor devices.

In November 2024, a search was conducted on a computer using the keywords "glucose sensor devices," "continuous glucose monitor," "glucose sensor devices and Type 1 Diabetes Mellitus," and "glucose sensor devices and child." Based on the inclusion and exclusion criteria, 30 videos that met the research objectives were analyzed. Relevant URLs were recorded. For each video, the following information was collected.

Of the 30 videos analyzed, 40 % (n = 12) were presented or managed by healthcare professionals. The average values for performance features of the videos were: 172,257.166 ± 233,861.720 views, 170.866 ± 222.974 comments, and 1,643.133 ± 2,252.247 likes. Four videos did not receive any likes. 40 % of the videos contained good and useful information for viewers, while 60 % were of high quality.

This study demonstrates that detailed and reliable content in YouTube videos about glucose monitoring devices enhances quality. It is recommended that video content be regularly evaluated, and future research should be conducted using alternative measurement tools in different languages.

Continuous glucose monitoring (CGM) has significantly advanced diabetes management, evolving from early glucose testing methods to modern, FDA-approved systems. Despite its benefits, challenges related to data security, affordability, and awareness of CGM devices remain.

This article explores the historical development, current advancements, and ongoing challenges of CGM systems in diabetes management. It aims to provide insights into how these technologies have transformed patient care and highlight areas needing further improvement.

A comprehensive literature review was conducted, focusing on advancements in CGM technology. Sources included PubMed, Google Scholar, and recent guidelines and reviews on CGM systems and their impact on diabetes management.

The evolution from the Dextrostix test strip to modern CGM systems, including over-the-counter devices, has enhanced glucose monitoring and patient outcomes. Recent innovations, such as machine learning models for predicting glucose fluctuations, promise to improve diabetes management. However, issues like data security and device accessibility persist.

To maximize the benefits of CGM systems, addressing data security, improving affordability, and increasing awareness of CGM devices are crucial. Continued advancements in CGM technology and supportive policies are essential for enhancing diabetes care and patient outcomes globally.

The Glucose Management Indicator (GMI) is a biomarker of glycemic control which estimates hemoglobin A1c (HbA1c) based on the average glycemia recorded by continuous glucose monitoring sensors (CGMS). The GMI provides an immediate overview of the patient's glycemic control, but it might be biased by the patient's sensor wear adherence or by the sensor's reading errors. This study aims to evaluate the GMI's performance in the assessment of glycemic control and to identify the factors leading to erroneous estimates. In this study, 147 patients with type 1 diabetes, users of CGMS, were enrolled. Their GMI was extracted from the sensor's report and HbA1c measured at certified laboratories. The median GMI value overestimated the HbA1c by 0.1 percentage points (p = 0.007). The measurements had good reliability, demonstrated by a Cronbach's alpha index of 0.74, an inter-item correlation coefficient of 0.683 and an inter-item covariance between HbA1c and GMI of 0.813. The HbA1c and the difference between GMI and HbA1c were reversely associated (Spearman's r = -0.707; p < 0.001). The GMI is a reliable tool in evaluating glycemic control in patients with diabetes. It tends to underestimate the HbA1c in patients with high HbA1c values, while it tends to overestimate the HbA1c in patients with low HbA1c.

Patients with type 2 diabetes mellitus (T2DM) face elevated morbidity, mortality, and care costs. Digital self-monitoring of blood glucose (SMBG) can automatically upload data to apps, share the data with health care providers, reduce errors, and aid long-term diabetes management.

This study aimed to assess the effectiveness of digital diabetes management techniques based on digital SMBG on blood glucose in patients with T2DM at home.

A systematic search was conducted in PubMed, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, China Biomedical Literature Database, and Cochrane Library for articles published from the establishment of each database to December 25, 2023. Data were extracted independently by 2 researchers (YX and NX), and the risk of bias in individual trials was rated using the Cochrane risk-of-bias tool. A meta-analysis was conducted using RevMan 5.3 (Cochrane).

Twelve studies were included, involving 1669 participants. The meta-analysis found that in the digital diabetes management group, hemoglobin A1c (mean difference [MD] -0.52%, 95% CI -0.63% to -0.42%; P<.001), fasting blood sugar (MD -0.42, 95% CI -0.65 to -0.19 mmol/L; P<.001), 2-hour postprandial blood sugar (MD -0.64, 95% CI -0.97 to -0.32 mmol/L; P<.001), and BMI (MD -1.55, 95% CI -2.92 to -0.17 kg/m2; P=.03) were each improved compared to the control group.

Digital diabetes management has been shown to effectively improve blood glucose levels and BMI in individuals with T2DM in home settings. A key feature of successful digital health interventions is the frequent SMBG by patients, supported by dedicated health care professionals who provide timely, personalized, and responsive guidance.

PROSPERO CRD42024560431; https://tinyurl.com/yfam3nms.

Over the last decade, diabetes management tools such as continuous glucose monitors, automated insulin delivery systems, and connected insulin pens have experienced exponential growth. These technologies are more readily being adopted to manage diabetes due to increased availability. This mini-review provides information about recent innovations available in the United States for diabetes management to improve patient outcomes.

A systematic search was conducted using Medline, PubMed, ScienceDirect, and Embase databases, as well as the Cochrane Library to identify peer-reviewed articles published between 2014 and 2024, in English, and focused on treatment using technology in diabetes care.

Diabetes technology has significantly eased the burden of both glucose measurement and insulin delivery, which has, overall, improved diabetes management. Advancements in accuracy and glycemic outcomes have been demonstrated through rigorous clinical and observational trials, underscoring their potential to transform diabetes care. The literature suggests that the use of diabetes technologies promotes patient self-efficacy and enhances the quality of life for individuals with both type 2 and type 1 diabetes.

Diabetes technology has been shown to improve important aspects of diabetes care, from glycemic control to patient satisfaction and quality of life. It is important to assess the role of technology in type 1 and type 2 diabetes and individualize treatment goals and objectives.

Background: Modeling techniques in the field of pediatrics present unique challenges beyond traditional model limitations, and sometimes difficulties in faithfully simulating the condition's evolution over time. Objective: This study aimed to identify whether economic modeling approaches in diabetes in pediatric patients align with the recommendations of clinical practice guidelines and the latest scientific evidence. Methods: A literature review was performed in March 2023 to identify modeling-based economic evaluations in diabetes in pediatric patients. Data were extracted and synthesized from eligible studies. Clinical practice guidelines for diabetes were gathered to compare their alignment with modeling strategies. Two endocrinology specialists provided insights on the latest findings in diabetes that are not yet included in the guidelines. A multidisciplinary group of experts agreed on the relevant themes to conduct the comparative analysis: parameter informing on glycemic control, diabetic ketoacidosis/hypoglycemia, C-peptide as prognostic biomarker, metabolic memory, age at diagnosis, socioeconomic status, pediatric-specific sources of risk equations, and pediatric-specific sources of utilities/disutilities. Results: Nineteen modeling-based studies (7 de novo, 12 predesigned models) and 34 guidelines were selected. Hemoglobin A1c was the main parameter to model the glycemic control; however, guidelines recommend the usage of complementary measures (eg, time in range) which are not included in economic models. Eight models included diabetic ketoacidosis (42.1%), 16 included hypoglycemia (84.2%), 2 included C-peptide (1 of those as prognostic factor) (10.5%) and 1 included legacy effect (5.3%). Neither guidelines nor models included recent findings, such as age at diagnosis or socioeconomic status, as prognostic factors. The lack of pediatric-specific sources for risk equations and utility/disutility values were additional limitations. Discussion: Economic models designed for assessing interventions in diabetes in pediatric patients should be based on pediatric-specific data and include novel adjuvant glucose-monitoring metrics and latest evidence on prognostic factors (C-peptide, legacy effect, age at diagnosis, socioeconomic status) to provide a more faithful reflection of the disease. Conclusions: Economic models represent useful tools to inform decision making. However, further research assessing the gaps is needed to enhance evidence-based health economic modeling that best represents reality.

Continuous Glucose Monitoring sensors (CGMs) have revolutionized type 1 diabetes (T1D) management. In particular, in several cases, the retrospective analysis of CGM recordings allows clinicians to review and adjust patients' therapy. However, in this set-up, the artifacts that are often present in CGM data could lead to incorrect therapeutic actions. To mitigate this risk, we investigate how to detect one of the most common of these artifacts, the so-called pressure induced sensor attenuations, by means of anomaly detection algorithms. Specifically, these methods belong to the class of unsupervised techniques, which is particularly appealing since it does not require a labeled dataset, hardly available in practice. After having designed five features to highlight the anomalous state of the sensor, 8 different methods (e.g. Isolation Forest and Histogram-based Outlier Score) are assessed both in silico using the UVa/Padova Type 1 Diabetes Simulator and on real data of 36 subjects monitored for about 10 days. In the in silico scenario, the best results are achieved with Isolation Forest, which recognized the 74% of the failures generating on average only 2 false alerts during the whole monitoring time. In real data, Isolation Forest is confirmed to be effective in the detection of failures, achieving a recall of 55% and generating 3 false alarms in 10 days. By allowing to detect more than 50% of the artifacts while discarding only a few portions of correct data in several days of monitoring, the proposed approach could effectively improve the quality of CGM data used by clinicians to retrospectively evaluate and adjust T1D therapy.

Glucose is an essential molecule in energy metabolism. Dysregulated glucose metabolism, the defining feature of diabetes, requires active monitoring and treatment to prevent significant morbidity and mortality. Current technologies for intermittent and continuous glucose measurement are invasive. Noninvasive glucose measurement would eliminate this barrier toward making glucose monitoring more accessible, extending the benefits from people living with diabetes to prediabetes and the healthy.

A novel spectroscopy-based system for measuring glucose noninvasively was used in an exploratory, prospective, single-center clinical study (NCT06272136) to develop and test a machine learning-based computational model for continuous glucose monitoring without per-subject calibration. The study design blinded the development investigators to the validation analyses.

Twenty subjects were enrolled. Fifteen were used for the development set, and five in the validation set. All study participants were adults with insulin-treated diabetes and median glycated hemoglobin (HbA1c) of 7.3% (interquartile range [IQR] = 6.7-7.7). The computational model resulted in a mean absolute relative difference (MARD) of 14.5% and 96.5% of the paired glucose data points in the A plus B zones of the Diabetes Technology Society (DTS) error grid. The correlation between the average model sensitivity by wavelength and the spectrum of glucose was 0.45 (P < .001).

Our findings suggest that Raman spectroscopy coupled with advanced computational methods can enable continuous, noninvasive glucose measurement without per-subject invasive calibration.

Implantable sensors that can monitor analytes related to cognitive and physiological status have gained significant focus in recent years. We have developed an implantable biosensor to detect dehydroepiandrosterone sulfate (DHEA-S), a biomarker related to stress. The biosensor strategy was based on the principle of forced intercalation (FIT) aptamers designed to detect subtle intramolecular changes during aptamer-target binding events. By incorporating a steroid-specific fluorogenic aptamer into a hydrogel, the sensitivity and biostability of the FIT biosensor fiber were improved, which were essential for designing implantable sensors to monitor biomarker levels in the living body. The polyethylenimine-based hydrogel chosen for this study produced an optically transparent cross-linked network with optimal microstructure, physicochemical, and mechanical properties, making it suitable for optical biosensors. The in vitro studies showed that the biosensor fiber was successfully activated in human serum and skin analogue, providing a linear response to physiological concentrations of the steroid. We believe that this type of implantable platform can be effective in monitoring more complex biomarkers associated with physiological or psychological health.

Continuous glucose monitors (CGM) provide valuable insights about glycemic control that aid in diabetes management. However, interpreting metrics and charts and synthesizing them into linguistic summaries is often non-trivial for patients and providers. The advent of large language models (LLMs) has enabled real-time text generation and summarization of medical data. The objective of this study was to assess the strengths and limitations of using an LLM to analyze raw CGM data and produce summaries of 14 days of data for patients with type 1 diabetes. We first evaluated the ability of GPT-4 to compute quantitative metrics specific to diabetes found in an Ambulatory Glucose Profile (AGP). Then, using two independent clinician graders, we evaluated the accuracy, completeness, safety, and suitability of qualitative descriptions produced by GPT-4 across five different CGM analysis tasks. GPT-4 performed 9 out of the 10 quantitative metrics tasks with perfect accuracy across all 10 cases. The clinician-evaluated CGM analysis tasks had good performance across measures of accuracy [lowest task mean score 8/10, highest task mean score 10/10], completeness [lowest task mean score 7.5/10, highest task mean score 10/10], and safety [lowest task mean score 9.5/10, highest task mean score 10/10]. Our work serves as a preliminary study on how generative language models can be integrated into diabetes care through data summarization and, more broadly, the potential to leverage LLMs for streamlined medical time series analysis.

Investigate the knowledge, attitude, and practices (KAP) of type 2 diabetes patients regarding continuous glucose monitoring (CGM).

A cross-sectional study was undertaken at the First People's Hospital of Jiujiang City from Sep 20, 2023, to Dec 10, 2023.

A total of 633 patients with type 2 diabetes mellitus accessed the questionnaire link. Of these, 544 patients completed the questionnaires. After data cleaning, 493 questionnaires were included in the analysis, resulting in a response rate of 86% and an effective rate of 91%. Among the 493 participants, 66.9% were male, and 70.8% reported using continuous glucose monitoring (CGM). Median scores: knowledge 17 (14, 26), attitude 34 (32, 40), and practice 20 (17, 24). Positive correlations existed between knowledge and attitude (r = 0.562, P < 0.001), knowledge and practice (r = 0.653, P < 0.001), and attitude and practice (r = 0.661, P < 0.001). Logistic regression revealed that being male, participating in diabetes education, and possessing higher knowledge and attitude scores were independently associated with positive practices. Structural equation model (SEM) showed knowledge directly influenced attitude (β = 0.538, P = 0.010) and practice (β = 0.433, P = 0.010), while attitude directly influenced practice (β = 0.450, P = 0.010). Knowledge indirectly impacted practice through its influence on attitude (β = 0.242, P = 0.010).

Type 2 diabetes patients exhibited insufficient knowledge but positive attitudes and practices toward CGM. Recommends educational interventions to enhance knowledge, potentially improving CGM utilization and outcomes in this population. Regular and comprehensive diabetes education should be integrated into routine clinical practice to optimize self-management and overall patient outcomes.

Current glucose monitoring user interfaces (UIs) are problematic for people with Type 1 Diabetes Mellitus (T1DM) in maintaining recommended blood glucose levels effectively. However, there is a lack of in-depth investigation into this problem when these individuals interpret and make real-time decisions based on the glucose monitoring devices they use daily.

We aim to investigate problems associated with glucose monitoring UIs by observing users' interpretation and decision-making while reading their Continuous Glucose Monitoring (CGM), Flash Glucose Monitoring (Flash) or Self-monitoring of Blood Glucose (SMBG).

A mixed-method study was conducted. The Think Aloud protocol was used to capture participants' decision-making process while reading various device UIs. Their responses were evaluated using standard clinical guidance to assess their accuracy. Additionally, a survey was distributed to gather their perceptions of self-management practices.

Twenty-seven participants (17 patients and 10 carers) were recruited. Interpretation accuracy averaged 38.0% ± 11.1% for CGM, 39.5% ± 8.8% for Flash, and 33.3% ± 7.8% for SMBG group. Treatment action accuracy was 21.5% ± 15.6% for CGM, 21.2% ± 14.0% for Flash, and 18.0% ± 13.2% for SMBG group. Despite this, 75.0% of all participants expressed very high confidence in their self-management.

Interpreting and making decisions using glucose monitoring UIs remains significantly challenging for people with T1DM despite their self-perceived performance. Improving such UIs is crucial to reduce misinterpretation and help these individuals make better treatment decisions without relying on their potentially inaccurate interpretations.

This study aimed to assess the current state of self-monitoring of blood glucose (SMBG) in Chinese patients initiating premixed insulin and its influential factors.

This is a single-arm, multi-center, prospective real-world study enrolling a total of 8214 adult patients with type 2 diabetes mellitus (T2DM) newly initiated premixed insulin analogues. Each patient was followed up for 12 weeks, and the data related to SMBG was collected at week 1, week 4, week 8 and week 12, while data related to glycated hemoglobin were collected at week 1 and week 12. The primary outcome was the frequency of SMBG over 12 weeks.

At week 12, 83.3 % monitored blood glucose at least once, while 20.3 % of participants continued SMBG every week. The average monitoring frequency was 4.78 times/week over the first 4 weeks and 3.33 times/week over 12 weeks. The patients with a higher frequency of SMBG had better control of blood glucose.

This study found that most T2DM patients would take SMBG but the adherence decreased over time. The adherence to SMBG in Chinese T2DM patients was influenced by age, insulin dosage, education level, and diabetes duration. SMBG benefited the improvement of glycemic control.

Continuous Glucose Monitoring (CGM) provides a detailed representation of glucose fluctuations in individuals, offering a rich dataset for understanding glycemic control in diabetes management. This study explores the potential of Riemannian manifold-based geometric clustering to analyze and interpret CGM data for individuals with Type 1 Diabetes (T1D) and healthy controls (HC), aiming to enhance diabetes management and treatment personalization.

We utilized CGM data from publicly accessible datasets, covering both T1D individuals on insulin and HC. Data were segmented into daily intervals, from which 27 distinct glycemic features were extracted. Uniform Manifold Approximation and Projection (UMAP) was then applied to reduce dimensionality and visualize the data, with model performance validated through correlation analysis between Silhouette Score (SS) against HC cluster and HbA1c levels.

UMAP effectively distinguished between T1D on daily insulin and HC groups, with data points clustering according to glycemic profiles. Moderate inverse correlations were observed between SS against HC cluster and HbA1c levels, supporting the clinical relevance of the UMAP-derived metric.

This study demonstrates the utility of UMAP in enhancing the analysis of CGM data for diabetes management. We revealed distinct clustering of glycemic profiles between healthy individuals and diabetics on daily insulin indicating that in most instances insulin does not restore a normal glycemic phenotype. In addition, the SS quantifies day by day the degree of this continued dysglycemia and therefore potentially offers a novel approach for personalized diabetes care.

The development of minimally invasive continuous glucose monitoring systems (CGMs) has transformed diabetes management. CGMs have shown clinical significance by improving time in the euglycemic range, decreasing rates of hypoglycemia, and improving HbA1c. In Rwanda, CGMs are currently not in routine use, and no clinical studies of CGM use were identified in the literature.

To determine impact and feasibility of real-time CGM use among people living with T1D in Rwanda, through assessment of sensor usage, time in range, rates of hypo-and-hyperglycemia, HbA1c and rates of diabetes-related hospitalizations over time.

The Continuous Glucose Monitoring Among Patients with Type 1 Diabetes in Rwanda (CAPT1D) study is a single-arm prospective observational study conducted at the Rwandan Diabetes Association (RDA) clinic in Kigali, Rwanda, aiming to assess the impact and feasibility of CGM use in Rwanda. A cohort of 50 participants diagnosed with T1D were enrolled. Participants were at least 21 years old, undergoing multiple daily insulin therapy, and not currently pregnant. Phase I of the study was conducted over 12 months, using the Dexcom G6 CGM. Phase II and Phase III extended CGM use for an additional 6 months respectively, using the next generation, Dexcom G7 CGM. Here we report the quantitative results of the Phase I study.

Participants used the sensor for >80% of the time throughout the study period. A significant increase in time in range was observed within 3 months, and sustained over 12 months. HbA1c decreased significantly in 3 months and stayed lower throughout the 12-month period. Mean HbA1c levels decreased by 2.8% at 6 months (p<0.01) and 3.2% at 12 months (p<0.01) A total of 12 diabetes-related hospitalizations were reported during the study period. No cases of DKA or episodes of severe hypoglycemia occurred.

Significant and meaningful improvements in key glycemic indices indicate the potential feasibility and impact of CGM among people living with T1D in Rwanda. Future studies could be designed to include pre- and post-intervention analysis to determine the effectiveness in terms of complications and costs.

Deep-red fluorescence was implemented in this fully injectable, nonenzymatic glucose biosensor design to allow for better light penetration through the skin, particularly for darker skin tones. In this work, a novel method was developed to synthesize Cy5.5 labeled mannose conjugates (Cy5.5-mannobiose, Cy5.5-mannotriose, and Cy5.5-mannotetraose) to act as the fluorescent competing ligand in a competitive binding assay with the protein Concanavalin A acting as the recognition molecule. Using fluorescence anisotropy (FA) data, a computational model was developed to determine optimal concentration ratios of the assay components to allow for sensitive glucose measurements within the physiological range. The model was experimentally validated by measuring the glucose response via FA of the three Cy5.5-labeled mannose conjugates synthesized with Cy5.5-mannotetraose demonstrating the most sensitive response to glucose across the physiological range. The developed method may be broadly applied to a vast range of commercially available fluorescent dyes and opens up opportunities for glucose measurements using nonenzymatic assays.

Prediabetes, as an intermediary stage between normal glucose homeostasis and overt diabetes, affects an estimated 720 million individuals worldwide, highlighting the urgent need for proactive intervention strategies. Continuous glucose monitoring (CGM) emerges as a transformative tool, offering unprecedented insights into glycemic dynamics and facilitating tailored therapeutic interventions. This perspective scores the clinical significance of even slightly elevated fasting blood glucose levels and the critical role of early intervention. CGM technology provides real-time, continuous data on glucose concentrations, surpassing the constraints of conventional monitoring methods. Both retrospectively analyzed and real-time CGM systems offer valuable tools for glycemic management, each with unique strengths. The integration of CGM into routine care can detect early indicators of type 2 diabetes, inform the development of personalized intervention strategies, and foster patient engagement and empowerment. Despite challenges such as cost and the need for effective utilization through training and education, CGM's potential to revolutionize prediabetes management is evident. Future research should focus on refining CGM algorithms, exploring personalized intervention strategies, and leveraging wearable technology and artificial intelligence advancements to optimize glycemic control and patient well-being.

Background: In this study, we explored the potential of predicting dysglycemia in patients who need to continuously manage blood glucose levels using a non-invasive method via electrocardiography (ECG). Methods: The data were collected from patients with diabetes, and heart rate variability (HRV) features were extracted via ECG processing. A residual block-based one-dimensional convolution neural network model was used to predict dysglycemia. Results: The dysglycemia prediction results at each time point, including at the time of blood glucose measurement, 15 min prior to measurement, and 30 min prior to measurement, exhibited no significant differences compared with the blood glucose measurement values. This result confirmed that the proposed artificial intelligence model for dysglycemia prediction performed well at each time point. Additionally, to determine the optimal number of features required for predicting dysglycemia, 77 HRV features were individually eliminated in the order of decreasing importance with respect to the prediction accuracy; the optimal number of features for the model to predict dysglycemia was determined to be 12. The dysglycemia prediction results obtained 30 min prior to measurement, which exhibited the highest prediction range in this study, were as follows: accuracy = 90.5, sensitivity = 87.52, specificity = 92.74, and precision = 89.86. Conclusions: Furthermore, we determined that no significant differences exist in the blood glucose prediction results reported in previous studies, wherein various vital signs and blood glucose values were used as model inputs, and the results obtained in this study, wherein only ECG data were used to predict dysglycemia.

To evaluate the efficacy of a fixed-ratio combination of insulin glargine 100 U/mL plus lixisenatide (iGlarLixi) in people with type 2 diabetes (T2D) using derived time-in-range (dTIR).

Participant-level data from LixiLan-L, LixiLan-O and LixiLan-G were pooled and dTIR (70-180 mg/dL), derived time-above-range (> 180 mg/dL) and derived time-below-range (dTBR; < 70 mg/dL) were calculated from participant seven-point self-monitored blood glucose profiles.

This pooled analysis included data from 2420 participants receiving iGlarLixi (n = 1093), iGlar (n = 836), Lixi (n = 234) or a glucagon-like peptide-1 receptor agonist (GLP-1 RA) (n = 257). Numerically greater improvements in least square (LS) means dTIR were seen from baseline to end of treatment (EOT) with iGlarLixi (25.7%) versus iGlar (15.8%), Lixi (11.7%) or GLP-1 RA (16.2%). At EOT, the mean (standard deviation) dTBR was 0.71% ± 3.4%, 0.61% ± 3.2%, 0.08% ± 1.0% and 0.0% ± 0.0% for iGlarLixi, iGlar, Lixi and GLP-1 RA, respectively. In a subgroup analysis, participants aged younger than 65 years (n = 1690) and 65 years or older (n = 713) showed numerically greater improvements in LS means dTIR from baseline to EOT with iGlarLixi versus iGlar, Lixi or GLP-1 RA.

iGlarLixi achieved improvements in dTIR, with low dTBR values, providing further evidence to inform clinical outcomes with the use of iGlarLixi.

Despite the existence of an increasing array of digital technologies and tools for diabetes management, there are disparities in access to and uptake and use of continuous glucose monitoring (CGM) devices, particularly for those most at risk of poor diabetes outcomes.

This study aims to assess communication technology and CGM access, literacy, and use among patients receiving treatment for diabetes at an inner-city safety-net hospital.

A survey on digital technology ownership and use was self-administered by 75 adults with type 1 and type 2 diabetes at the diabetes clinic of Grady Memorial Hospital in Atlanta, Georgia. In-depth interviews were conducted with 16% (12/75) of these patient participants and 6 health care providers (HCPs) to obtain additional insights into the use of communication technology and CGM to support diabetes self-management.

Most participants were African American (66/75, 88%), over half (39/75, 52%) were unemployed or working part time, and 29% (22/75) had no health insurance coverage, while 61% (46/75) had federal coverage. Smartphone ownership and use were near universal; texting and email use were common (63/75, 84% in both cases). Ownership and use of tablets and computers and use and daily use of various forms of media were more prevalent among younger participants and those with type 1 diabetes, who also rated them as easier to use. Technology use specifically for diabetes and health management was low. Participants were supportive of a potential smartphone app for diabetes management, with a high interest in such an app helping them track blood sugar levels and communicate with their care teams. Younger participants showed higher levels of interest, perceived value, and self-efficacy for using an app with these capabilities. History of CGM use was reported by 56% (42/75) of the participants, although half (20/42, 48%) had discontinued use, above all due to the cost of the device and issues with its adhesive. Nonuse was primarily due to not being offered CGM by their HCP. Reasons given for continued use included convenience, improved blood glucose control, and better tracking of blood glucose. The in-depth interviews (n=18) revealed high levels of satisfaction with CGM by users and supported the survey findings regarding reasons for continued use. They also highlighted the value of CGM data to enhance communication between patients and HCPs.

Smartphone ownership was near universal among patients receiving care at an inner-city hospital. Alongside the need to address barriers to CGM access and continued use, there is an opportunity to leverage increased access to communication technology in combination with CGM to improve diabetes outcomes among underresourced populations.

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human's electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A + B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

Continuous glucose monitoring (CGM) can decrease hypoglycemic events and health care costs; however, barriers and facilitators that influence CGM use are unknown.

The purpose of this study was to evaluate hypoglycemic events and cost outcomes after CGM implementation and describe associated barriers and facilitators.

A mixed-methods study design was used to evaluate CGM implementation on 2 pulmonary units within an academic health center. Hypoglycemic events were evaluated before and after CGM implementation, and nurses were interviewed about facilitators and barriers that influence CGM use.

Hypoglycemic events decreased from a rate of 0.0906 per 1000 patient days to 0.0503 postimplementation, P < .0001. A $105 766 cost avoidance was recognized. Barriers and facilitators to CGM use are described.

Findings support CGM implementation, while uniquely contributing financial impact and device use barriers and facilitators. Hospitals may consider CGM use to improve timely identification and treatment of hypoglycemia.

Type 1 diabetes (T1D) severely impairs metabolic control and can lead to erectile dysfunction (ED) through hyperglycemia-induced vascular damage, autonomic neuropathy, and psychological distress. This review examines the role of continuous glucose monitoring (CGM) in ameliorating ED by addressing glucose variability and inflammation. A comprehensive analysis of studies and clinical trials was conducted to evaluate the impact of CGM on metabolic control, inflammatory responses, and vascular health in patients with T1D. Evidence suggests that CGM systems significantly stabilize blood glucose levels and reduce hyper- and hypoglycemic episodes that contribute to endothelial dysfunction and ED. CGM's real-time feedback helps patients optimize metabolic control, improve vascular health, and reduce inflammation. CGM has the potential to redefine ED management in patients with T1D by improving glycemic control and reducing the physiological stressors that cause ED, potentially improving quality of life and sexual health. Further research is warranted to explore the specific benefits of CGM for ED management.

Effective continuous glucose monitoring solutions require consistent sensor performance over the lifetime of the device, a manageable variance between devices, and the capability of high volume, low cost production. Here we present a novel and microfabrication-compatible method of depositing and stabilizing enzyme layers on top of planar electrodes that can aid in the mass production of sensors while also improving their consistency. This work is focused on the fragile biorecognition layer as that has been a critical difficulty in the development of microfabricated sensors. We test this approach with glucose oxidase (GOx) and evaluate the sensor performance with amperometric measurements of in vitro glucose concentrations. Spincoating was used to deposit a uniform enzyme layer across a wafer, which was subsequently immobilized via glutaraldehyde vapor crosslinking and patterned via liftoff. This yielded an approximately 300 nm thick sensing layer which was applied to arrays of microfabricated platinum electrodes built on blank wafers. Taking advantage of their planar array format, measurements were then performed in high-throughput parallel instrumentation. Due to their thin structure, the coated electrodes exhibited subsecond stabilization times after the bias potential was applied. The deposited enzyme layers were measured to provide a sensitivity of 2.3 ± 0.2 μA mM-1 mm-2 with suitable saturation behavior and minimal performance shift observed over extended use. The same methodology was then demonstrated directly on top of wireless CMOS potentiostats to build a monolithic sensor with similar measured performance. This work demonstrates the effectiveness of the combination of spincoating and vapor stabilization processes for wafer scale enzymatic sensor functionalization and the potential for scalable fabrication of monolithic sensor-on-CMOS devices.

Today, continuous glucose monitoring (CGM) is a standard diagnostic option for patients with diabetes, at least for those with type 1 diabetes and those with type 2 diabetes on insulin therapy, according to international guidelines. The switch from spot capillary blood glucose measurement to CGM was driven by the extensive and immediate support and facilitation of diabetes management CGM offers. In patients not using insulin, the benefits of CGM are not so well studied/obvious. In such patients, factors like well-being and biofeedback are driving CGM uptake and outcome. Apps can combine CGM data with data about physical activity and meal consumption for therapy adjustments. Personalized data management and coaching is also more feasible with CGM data. The same holds true for digitalization and telemedicine intervention ("virtual diabetes clinic"). Combining CGM data with Smart Pens ("patient decision support") helps to avoid missing insulin boluses or insulin miscalculation. Continuous glucose monitoring is a major pillar of all automated insulin delivery systems, which helps substantially to avoid acute complications and achieve more time in the glycemic target range. These options were discussed by a group of German experts to identify concrete gaps in the care structure, with a view to the necessary structural adjustments of the health care system.

Artificial pancreas requires data from multiple sources for accurate insulin dose estimation. These include data from continuous glucose sensors, past insulin dosage information, meal quantity and time and physical activity data. The effectiveness of closed-loop diabetes management systems might be hampered by the absence of these data caused by device error or lack of compliance by patients. In this study, we demonstrate the effect of output sequence length-driven generative and discriminative model selection in high quality data generation and augmentation. This novel generative adversarial network (GAN) based architecture automatically selects the generator and discriminator architecture based on the desired output sequence length. The proposed model is able to generate glucose, physical activity, meal information data for individual patients. The discriminative scores for Ohio T1DM (2018) dataset were 0.17 ±0.03 (Inputs: CGM, CHO, Insulin) and 0.15 ±0.02 (Inputs: CGM, CHO, Insulin, Heart Rate, Steps) and for Ohio T1D (2020) dataset was 0.16 ±0.02 (Inputs: CGM, CHO, Insulin) and 0.15 ±0.02 (Inputs: CGM, CHO, Insulin, acceleration). A mixture of generated and real data was used to test predictive scores for glucose forecasting models. The best RMSE and MARD achieved for OhioT1DM patients were 17.19 ±3.22 and 7.14 ±1.76 for PH=30 min with CGM, CHO, Insulin, heartrate and steps as inputs. Similarly, the RMSE and MARD for real+synthetic data were 15.63 ±2.57 and 5.86 ±1.69 respectively. Compared to existing generative models, we demonstrate that sequence length based architecture selection leads to better synthetic data generation for multiple output sequences (CGM, CHO, Insulin) and forecasting accuracy.

Recent advancements in the management of type 1 diabetes mellitus (T1DM) have significantly improved outcomes and quality of life for patients, particularly children. Technological innovations, such as continuous glucose monitoring (CGM) systems and insulin pump therapy, including hybrid closed-loop systems, have enhanced glycemic control by providing real-time data and automated insulin delivery. Ultrarapid-acting insulins and adjunctive pharmacotherapies, like sodium-glucose transport protein 2 (SGLT2) inhibitors and glucagon-like peptide 1 (GLP-1) receptor agonists, offer improved postprandial glucose management and reduced insulin requirements. Immunotherapy and beta-cell replacement therapies, including stem cell research and encapsulation devices, aim to preserve or restore endogenous insulin production. Digital health platforms and telemedicine have expanded access to education and support, fostering better self-management. Future directions in precision medicine, artificial intelligence, and microbiome research hold promise for personalized and potentially curative treatments. Collectively, these advances are transforming T1DM management, reducing disease burden, and enhancing the prospects for children with T1DM.

In healthcare, weight is often equated to and used as a marker for health. In examining nutrition and health status, there are many more effective markers independent of weight. In this article, we review practical and emerging clinical applications of technologies and tools used to collect non-weight-related data in nutrition assessment, monitoring, and evaluation in the outpatient setting. The aim is to provide clinicians with new ideas about various types of data to evaluate and track in nutrition care.

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1-15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives.

The prevalence of diabetes has increased worldwide. Therefore, interest in improving glycemic control for diabetes has grown, and continuous glucose monitoring (CGM) has recently received attention as an effective glycemic control method. This study developed and evaluated the effectiveness of an education program for pattern management using CGM based on Whittemore and Roy's middle-range theory of adapting to diabetes mellitus.

A quasi-experimental study was conducted on 50 adult patients with type 2 diabetes who visited the outpatient clinic of a university hospital. The experimental group was treated with a pattern management program using CGM for 12 weeks and six personalized education sessions were provided to the patients through face-to-face education and phone monitoring.

The frequency of diabetes-related symptoms in the experimental group decreased, and social support (t = 2.95, p = 0.005), perceived benefits (t = 3.72, p < 0.001) and self-care significantly increased (t = 6.09, p < 0.001). Additionally, the program was found to be effective in improving HbA1c (t = -3.83, p < 0.001), FBS (t = -2.14, p = 0.038), and HDL-C (t = 2.39, p = 0.021).

The educational program developed through this study can be implemented as a self-management approach for individuals with type 2 diabetes using CGM, aimed at enhancing glycemic control and preventing complications.

While insulin pumps remain the most common form of therapy for youths with type 1 diabetes (T1DM), they differ in the extent to which they utilize data from continuous glucose monitoring (CGM) and automate insulin delivery.

The aim of the study was to compare metabolic control in patients using different models of insulin pumps. This retrospective single-center study randomly sampled 30 patients for each of the following treatments: Medtronic 720G without PLGS (predictive low glucose suspend), Medtronic 640G or 740G with PLGS and Medtronic 780G. In the whole study group, we used CGM systems to assess patients' metabolic control, and we collected lipid profiles. In three groups of patients, we utilized CGM sensors (Guardian 3, Guardian 4, Libre 2 and Dexcom G6) to measure the following glycemic variability proxy values: time in range (TIR), time below 70 mg/dL (TBR), time above 180 mg/dL (TAR), coefficient of variation (CV) and mean sensor glucose.

Medtronic 640G or 740G and 780G users were more likely to achieve a target time in the target range 70-180 mg/dL (≥80%) [Medtronic 720G = 4 users (13.3%) vs. Medtronic 640G/740G = 10 users (33.3%) vs. Medtronic 780G = 13 users (43.3%); p = 0.0357)] or low glucose variability [Medtronic 720G = 9 users (30%) vs. Medtronic 640G/740G = 18 users (60%) vs. Medtronic 780G = 19 users (63.3%); p = 0.0175)].

Any integration between the insulin pump and CGM was associated with better glycemic control. More advanced technologies and artificial intelligence in diabetes help patients maintain better glycemia by eliminating various factors affecting postprandial glycemia.

The aim of this study was to compare the effectiveness of stand-alone intermittently scanned continuous glucose monitoring (isCGM) with or without a structured education programme and blood glucose monitoring (BGM) in adults with type 2 diabetes on multiple daily insulin injections (MDI).

In this 24 week randomised open-label multicentre trial, adults with type 2 diabetes on intensive insulin therapy with HbA1c levels of 58-108 mmol/mol (7.5-12.0%) were randomly assigned in a 1:1:1 ratio to isCGM with a structured education programme on adjusting insulin dose and timing according to graphical patterns in CGM (intervention group), isCGM with conventional education (control group 1) or BGM with conventional education (control group 2). Block randomisation was conducted by an independent statistician. Due to the nature of the intervention, blinding of participants and investigators was not possible. The primary outcome was change in HbA1c from baseline at 24 weeks, assessed using ANCOVA with the baseline value as a covariate.

A total of 159 individuals were randomised (n=53 for each group); 148 were included in the full analysis set, with 52 in the intervention group, 49 in control group 1 and 47 in control group 2. The mean (± SD) HbA1c level at baseline was 68.19±10.94 mmol/mol (8.39±1.00%). The least squares mean change (± SEM) from baseline HbA1c at 24 weeks was -10.96±1.35 mmol/mol (-1.00±0.12%) in the intervention group, -6.87±1.39 mmol/mol (-0.63±0.13%) in control group 1 (p=0.0367 vs intervention group) and -6.32±1.42 mmol/mol (-0.58±0.13%) in control group 2 (p=0.0193 vs intervention group). Adverse events occurred in 28.85% (15/52) of individuals in the intervention group, 26.42% (14/53) in control group 1 and 48.08% (25/52) in control group 2.

Stand-alone isCGM offers a greater reduction in HbA1c in adults with type 2 diabetes on MDI when education on the interpretation of graphical patterns in CGM is provided.

ClinicalTrials.gov NCT04926623.

This study was supported by Daewoong Pharmaceutical Co., Ltd.

Type 2 diabetes (T2D) is a prevalent chronic illness with many different options for treatment management. Continuous glucose monitors (CGM) offer physiological data that clinicians can access when making treatment decisions. However, the utility of CGM in management of T2D remains an active area of research. In our work, we demonstrate the feasibility of exploiting raw daily CGM data to estimate the physiological parameters of insulin sensitivity and beta-cell function that correlate with estimates derived from laboratory findings. We use a peak extraction algorithm to extract peaks from daily CGM data and implement a model-based approach to infer physiological parameters. We demonstrate that the inferred parameter estimates of insulin sensitivity and beta-cell function correlate to the ground truth measurements as determined by an oral glucose tolerance test (OGTT).

Insertable biosensor systems are medical diagnostic devices with two primary components: an implantable biosensor within the body and a wearable monitor that can remotely interrogate the biosensor from outside the body. Because the biosensor does not require a physical connection to the electronic monitor, insertable biosensor systems promise improved patient comfort, reduced inflammation and infection risk, and extended operational lifetimes relative to established percutaneous biosensor systems. However, the lack of physical connection also presents technical challenges that have necessitated new innovations in developing sensing chemistries, transduction methods, and communication modalities. In this review, we discuss the key developments that have made insertables a promising option for longitudinal biometric monitoring and highlight the essential needs and existing development challenges to realizing the next generation of insertables for extended-use diagnostic and prognostic devices.

Phosphorescence-based oxygen-sensing hydrogels are a promising platform technology for an upcoming generation of insertable biosensors that are smaller, softer, and potentially more biocompatible than earlier designs. However, much remains unknown about their long-term performance and biocompatibility in vivo. In this paper, we design and evaluate a range of hydrogel sensors that contain oxygen-sensitive phosphors stabilized by micro- and nanocarrier systems. These devices demonstrated consistently good performance and biocompatibility in young adult rats for over three months. This study thoroughly establishes the biocompatibility and long-term suitability of phosphorescence lifetime sensors in vivo, providing the groundwork for expansion of this platform technology into a family of small, unobtrusive biosensors for a range of clinically relevant metabolites.