Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

Continuous glucose monitoring with structured education in adults with type 2 diabetes managed by multiple daily insulin injections: a multicentre randomised controlled trial

AIMS/HYPOTHESIS

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS/INTERPRETATION

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.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04926623.

FUNDING

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

Long-Term Evaluation of Inserted Nanocomposite Hydrogel-Based Phosphorescent Oxygen Biosensors: Evolution of Local Tissue Oxygen Levels and Foreign Body Response

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.

Diabetes technology and sexual health: which role?

PURPOSE

The aim of this review is to evaluate the effects of new technology used in the management of diabetes mellitus (DM), including the use of continuous glucose monitoring (CGM) and the administration of insulin through continuous subcutaneous insulin infusion (CSII), on male and female sexual function.

METHODS

This narrative review was performed for all available prospective, retrospective and review articles, published up to June 2023 in PubMed. Data were extracted from the text and from the tables of the manuscript.

RESULTS

Sexual dysfunctions are an underestimated comorbidity of DM in both male and female. Although erectile dysfunction (ED) is recognized by the guidelines as a complication of DM, female sexual dysfunction (FSD) is poorly investigated in clinical setting. In addition to the complications of DM, the different types of therapies can also influence male and female sexual response. Furthermore, insulin therapy can be administered through multiple-daily injections (MDI) or a CSII. The new technologies in the field of DM allow better glycemic control which results in a reduction in the occurrence or aggravation of complications of DM. Despite this evidence, few data are available on the impact of new technologies on sexual dysfunctions.

CONCLUSIONS

The use of DM technology might affect sexual function due to the risk of a worse body image, as well as discomfort related to CSII disconnection during sexual activity. However, the use is related to an improved metabolic control, which, in the long-term associates to a reduction in all diabetes complications, including sexual function.

Measures of nutrition status and health for weight-inclusive patient care: A narrative review

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.

Accuracy of an Off-Label Transmitter and Data Manager Paired With an Intermittent Scanned Continuous Glucose Monitor in Adults With Type 1 Diabetes

BACKGROUND

This work evaluates the accuracy and agreement between the FreeStyle Libre sensor (FSL) and an off-label converted real-time continuous glucose monitor (c-rtCGM) device consisting of the MiaoMiao transmitter and the xDrip+ application which can be coupled to the FSL.

METHODS

Four weeks of glucose data were collected from 21 participants with type 1 diabetes using the c-rtCGM and FSL: two weeks with a single initial calibration (uncalibrated) and two weeks with a daily calibration (calibrated). Accuracy and agreement evaluation included mean absolute relative difference (MARD), the %20/20 rule, Bland-Altman plots, and the Consensus Error Grid analysis.

RESULTS

Values reported by the c-rtCGM system compared with the FSL resulted in an overall MARD of 12.06% and 84.71% of the results falling within Consensus Error Grid Zone A when the device is calibrated. For uncalibrated devices, an overall MARD of 17.49% was obtained. Decreased accuracy was shown in the hypoglycemic range and for rates of change greater than 2 mg/dL/min. The between-device bias also incremented with increasing glucose values.

CONCLUSION

Measurements recorded by the c-rtCGM were found to be accurate when compared with FSL data only when performing daily c-rtCGM device calibrations. High drops in accuracy and agreement between devices occurred when the c-rtCGM was not calibrated.

Global scientific trends on the islet transplantation in the 21st century: A bibliometric and visualized analysis

BACKGROUND

Islet transplantation (IT) has emerged as a significant research area for the treatment of diabetes mellitus and has witnessed a surge in scholarly attention. Despite its growing importance, there is a lack of bibliometric analyses that encapsulate the evolution and scientific underpinnings of this field. This study aims to fill this gap by conducting a comprehensive bibliometric analysis to delineate current research hotspots and forecast future trajectories within the IT domain with a particular focus on evidence-based medicine practices.

METHODS

This analysis scrutinized literature from January 1, 2000, to October 1, 2023, using the Web of Science Core Collection (WoSCC). Employing bibliometric tools such as VOSviewer, CiteSpace, and the R package "bibliometrix," we systematically evaluated the literature to uncover scientific trends and collaboration networks in IT research.

RESULTS

The analysis revealed 8388 publications from 82 countries, predominantly the United States and China. However, global cross-institutional collaboration in IT research requires further strengthening. The number of IT-related publications has increased annually. Leading research institutions in this field include Harvard University, the University of Alberta, the University of Miami, and the University of Minnesota. "Transplantation" emerges as the most frequently cited journal in this area. Shapiro and Ricordi were the most prolific authors, with 126 and 121 publications, respectively. Shapiro also led to co-citations, totaling 4808. Key research focuses on IT sites and procedures as well as novel therapies in IT. Emerging research hotspots are identified by terms like "xenotransplantation," "apoptosis," "stem cells," "immunosuppression," and "microencapsulation."

CONCLUSIONS

The findings underscore a mounting anticipation for future IT research, which is expected to delve deeper into evidence-based methodologies for IT sites, procedures, and novel therapeutic interventions. This shift toward evidence-based medicine underscores the field's commitment to enhancing the efficacy and safety of IT for diabetes treatment, signaling a promising direction for future investigations aimed at optimizing patient outcomes.

Leveraging Large Language Models to Analyze Continuous Glucose Monitoring Data: A Case Study

Continuous glucose monitors (CGM) provide patients and clinicians with valuable insights about glycemic control that aid in diabetes management. The advent of large language models (LLMs), such as GPT-4, has enabled real-time text generation and summarization of medical data. Further, recent advancements have enabled the integration of data analysis features in chatbots, such that raw data can be uploaded and analyzed when prompted. Studying both the accuracy and suitability of LLM-derived data analysis performed on medical time series data, such as CGM data, is an important area of research. 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. This study used simulated CGM data from 10 different cases. 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. We demonstrated that GPT-4 performs well across measures of accuracy, completeness, and safety when producing summaries of CGM data across all tasks. These results highlight the capabilities of using an LLM to produce accurate and safe narrative summaries of medical time series data. We highlight several limitations of the work, including concerns related to how GPT-4 may misprioritize highlighting instances of hypoglycemia and hyperglycemia. Our work serves as a preliminary study on how generative language models can be integrated into diabetes care through CGM analysis, and more broadly, the potential to leverage LLMs for streamlined medical time series analysis.

A novel questionnaire for evaluating digital tool use (DTUQ-D) among individuals with type 2 diabetes: exploring the digital landscape

Introduction

Effective healthcare currently incorporates a patient-centric system and accessible technology for patient self-management. This study aimed to develop and validate a novel questionnaire titled the Digital Tool Use Questionnaire for Diabetes (DTUQ-D) - a screening tool identifying the type, number, and frequency of digital tools used by Type 2 Diabetes Mellitus (T2DM) patients with within HMOs, online, and via applications.

Methods

The questionnaire was administered to two ethnic groups and both genders. A mixed-methods approach was used. In the qualitative phase, the questionnaire was developed through phone surveys of 29 T2DM patients, two endocrinologists and two technology experts. In the quantitative phase, involving 367 participants, convergent validity, construct validity, and reliability were examined.

Results

Findings indicated that the DTUQ-D is valid and reliable, successfully identifying digital tools utilized by T2DM patients, notwithstanding variations in factor structures between ethnic groups. This questionnaire provides a foundation for future research, offering a standardized approach to evaluating digital tool usage.

Discussion

The study enhances understanding of the role of digital tools in healthcare, especially for T2DM self-management. It also can be easily adapted to assess digital tool use for other illnesses by adjusting instructions and the wording of certain items.

The Association of Macronutrient Consumption and BMI to Exhaled Carbon Dioxide in Lumen Users: Retrospective Real-World Study

BACKGROUND

Metabolic flexibility is the ability of the body to rapidly switch between fuel sources based on their accessibility and metabolic requirements. High metabolic flexibility is associated with improved health outcomes and a reduced risk of several metabolic disorders. Metabolic flexibility can be improved through lifestyle changes, such as increasing physical activity and eating a balanced macronutrient diet. Lumen is a small handheld device that measures metabolic fuel usage through exhaled carbon dioxide (CO2), which allows individuals to monitor their metabolic flexibility and make lifestyle changes to enhance it.

OBJECTIVE

This retrospective study aims to examine the postprandial CO2 response to meals logged by Lumen users and its relationship with macronutrient intake and BMI.

METHODS

We analyzed deidentified data from 2607 Lumen users who logged their meals and measured their exhaled CO2 before and after those meals between May 1, 2023, and October 18, 2023. A linear mixed model was fitted to test the association between macronutrient consumption, BMI, age, and gender to the postprandial CO2 response, followed by a 2-way ANOVA.

RESULTS

The model demonstrated significant associations (P<.001) between CO2 response after meals and both BMI and carbohydrate intake (BMI: β=-0.112, 95% CI -0.156 to -0.069; carbohydrates: β=0.046, 95% CI 0.034-0.058). In addition, a 2-way ANOVA revealed that higher carbohydrate intake resulted in a higher CO2 response compared to low carbohydrate intake (F2,2569=24.23; P<.001), and users with high BMI showed modest responses to meals compared with low BMI (F2,2569=5.88; P=.003).

CONCLUSIONS

In this study, we show that Lumen's CO2 response is influenced both by macronutrient consumption and BMI. The results of this study highlight a distinct pattern of reduced metabolic flexibility in users with obesity, indicating the value of Lumen for assessing postprandial metabolic flexibility.

Real-world effectiveness of GLP-1 receptor agonist-based treatment strategies on "time in range" in patients with type 2 diabetes

Background: Diabetes affects millions of people worldwide annually, and several methods, including medications, are used for its management; glucagon-like peptide-1 receptor agonists (GLP-1RAs) are one such class of medications. The efficacy and safety of GLP-1RAs in treating type 2 diabetes mellitus (T2DM) have been assessed and have been shown to significantly improve time in range (TIR) in several clinical trials. However, presently, there is a lack of real-world evidence on the efficacy of GLP-1RAs in improving TIR. To address this, we investigated the effect of GLP-1RA-based treatment strategies on TIR among patients with T2DM in real-world clinical practice. Methods: This multicenter, retrospective, real-world study included patients with T2DM who had previously used a continuous glucose monitoring (CGM) system and received treatment with GLP-1RAs or oral antidiabetic drugs (OADs). Patients who received OADs served as controls and were matched in a 1:1 ratio to their GLP-1RA counterparts by propensity score matching. The primary endpoint was the TIR after 3-6 months of treatment. Results: According to propensity score matching, 202 patients were equally divided between the GLP-1RA and OAD groups. After 3-6 months of treatment, the TIR values for the GLP-1RA and OAD groups were 76.0% and 65.7%, respectively (p < 0.001). The GLP-1RA group displayed significantly lower time above range (TAR) and mean glucose values than the OAD group (p < 0.001). Subgroup analysis revealed that, compared with the administration of liraglutide, the administration of semaglutide and polyethylene glycol loxenatide (PEG-Loxe) significantly improved TIR over 3-6 months of treatment (p < 0.05). Conclusion: These real-world findings indicate that GLP-1RA-based treatment strategies could be superior to oral treatment strategies for improving TIR among patients with T2DM and that once-weekly GLP-1RA may be more effective than a once-daily GLP-1RA. Clinical trial registration: http://www.chinadrugtrials.org.cn/index.html, identifier number ChiCTR2300073697.

Identifying and mapping measures of medication safety during transfer of care in a digital era: a scoping literature review

BACKGROUND

Measures to evaluate high-risk medication safety during transfers of care should span different safety dimensions across all components of these transfers and reflect outcomes and opportunities for proactive safety management.

OBJECTIVES

To scope measures currently used to evaluate safety interventions targeting insulin, anticoagulants and other high-risk medications during transfers of care and evaluate their comprehensiveness as a portfolio.

METHODS

Embase, Medline, Cochrane and CINAHL databases were searched using scoping methodology for studies evaluating the safety of insulin, anticoagulants and other high-risk medications during transfer of care. Measures identified were extracted into a spreadsheet, collated and mapped against three frameworks: (1) 'Key Components of an Ideal Transfer of Care', (2) work systems, processes and outcomes and (3) whether measures captured past harms, events in real time or areas of concern. The potential for digital health systems to support proactive measures was explored.

RESULTS

Thirty-five studies were reviewed with 162 measures in use. Once collated, 29 discrete categories of measures were identified. Most were outcome measures such as adverse events. Process measures included communication and issue identification and resolution. Clinic enrolment was the only work system measure. Twenty-four measures captured past harm (eg, adverse events) and six indicated future risk (eg, patient feedback for organisations). Two real-time measures alerted healthcare professionals to risks using digital systems. No measures were of advance care planning or enlisting support.

CONCLUSION

The measures identified are insufficient for a comprehensive portfolio to assess safety of key medications during transfer of care. Further measures are required to reflect all components of transfers of care and capture the work system factors contributing to outcomes in order to support proactive intervention to reduce unwanted variation and prevent adverse outcomes. Advances in digital technology and its employment within integrated care provide opportunities for the development of such measures.

Continuous glucose monitoring metrics following sub-Tenon's injection of triamcinolone acetonide for diabetic macular edema

PURPOSE

This pilot study aims to comprehensively evaluate the effects of sub-Tenon's injection of triamcinolone acetonide (STTA) on glycemic control in patients with diabetic macular edema (DME) using professional continuous glucose monitoring (CGM).

METHODS

This retrospective study analyzed changes in glycemic control in 20 patients with type 2 mellitus and DME following single STTA (20 mg/0.5 mL) using The FreeStyle Libre Pro system. Professional CGM provides core CGM metrics such as the percentage of time that glucose levels fall within a target range and include the time in range (TIR) (70-180 mg/dL), time above range (TAR) (> 180 mg/dL), and time below range (TBR) (< 70 mg/dL). Outcome measures were the changes in CGM metrics (TIR, TAR and TBR) and the percentage of patients in whom TAR increased by at least 10 percentage points (ppt) 4 days before to 4 days after STTA administration.

RESULTS

The mean CGM metrics (TIR/TAR/TBR) were 75.5%/19.9%/4.4% 4 days before STTA and 73.7%/22.4%/3.5% 4 days after STTA; the metrics 4 days before and 4 days after STTA were not significantly different (P = 0.625 for TIR, P = 0.250 for TAR, and P = 0.375 for TBR). TAR increased by more than 10 ppt in four (20%) patients treated with sulfonylurea and/or insulin.

CONCLUSION

Although there were no significant changes in the CGM metrics, four patients developed CGM-measured hyperglycemia after STTA for DME.

Effects of Digitization of Self-Monitoring of Blood Glucose Records Using a Mobile App and the Cloud System on Outpatient Management of Diabetes: Single-Armed Prospective Study

BACKGROUND

In recent years, technologies promoting the digitization of self-monitoring of blood glucose (SMBG) records including app-cloud cooperation systems have emerged. Studies combining these technological interventions with support from remote health care professionals have reported improvements in glycemic control.

OBJECTIVE

To assess the use of an app-cloud cooperation system linked with SMBG devices in clinical settings, we evaluated its effects on outpatient management of diabetes without remote health care professional support.

METHODS

In this multicenter, open-label, and single-armed prospective study, 48 patients with diabetes (including type 1 and type 2) at 3 hospitals in Japan treated with insulin or glucagon-like peptide 1 receptor agonists and performing SMBG used the app-cloud cooperation system for 24 weeks. The SMBG data were automatically uploaded to the cloud via the app. The patients could check their data, and their attending physicians reviewed the data through the cloud prior to the patients' regular visits. The primary outcome was changes in glycated hemoglobin (HbA1c) levels.

RESULTS

Although HbA1c levels did not significantly change in all patients, the frequency of daily SMBG following applying the system was significantly increased before induction at 12 (0.60 per day, 95% CI 0.19-1.00; P=.002) and 24 weeks (0.43 per day, 95% CI 0.02-0.84; P=.04). In the subset of 21 patients whose antidiabetic medication had not been adjusted during the intervention period, a decrease in HbA1c level was observed at 12 weeks (P=.02); however, this significant change disappeared at 24 weeks (P=.49). The Diabetes Treatment Satisfaction Questionnaire total score and "Q4: convenience" and "Q5: flexibility" scores significantly improved after using the system (all P<.05), and 72% (33/46) patients and 76% (35/46) physicians reported that the app-cloud cooperation system helped them adjust insulin doses.

CONCLUSIONS

The digitization of SMBG records and sharing of the data by patients and attending physicians during face-to-face visits improved self-management in patients with diabetes.

TRIAL REGISTRATION

Japan Registry of Clinical Trials (jRCT) jRCTs042190057; https://jrct.niph.go.jp/en-latest-detail/jRCTs042190057.

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes

Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes - a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings ("virtual ward" and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.

Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial

BACKGROUND

Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring or continuous glucose monitoring devices, which require manual and visual interaction, thereby removing the focus of attention from the driving task. Hypoglycemia causes a decrease in attention, thereby challenging the safety of using such devices behind the wheel. Here, we present an investigation of a hands-free technology-a voice warning that can potentially be delivered via an in-vehicle voice assistant.

OBJECTIVE

This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception.

METHODS

We designed a voice warning and evaluated it in 3 studies. In all studies, participants received a voice warning while driving. Study 0 (n=10) assessed the feasibility of using a voice warning with healthy participants driving in a simulator. Study 1 (n=18) assessed the voice warning in participants with T1DM. Study 2 (n=20) assessed the voice warning in participants with T1DM undergoing hypoglycemia while driving in a real car. We measured participants' self-reported perception of the voice warning (with a user experience scale in study 0 and with acceptance, alliance, and trust scales in studies 1 and 2) and compliance behavior (whether they stopped the car and reaction time). In addition, we assessed technology affinity and collected the participants' verbal feedback.

RESULTS

Technology affinity was similar across studies and approximately 70% of the maximal value. Perception measure of the voice warning was approximately 62% to 78% in the simulated driving and 34% to 56% in real-world driving. Perception correlated with technology affinity on specific constructs (eg, Affinity for Technology Interaction score and intention to use, optimism and performance expectancy, behavioral intention, Session Alliance Inventory score, innovativeness and hedonic motivation, and negative correlations between discomfort and behavioral intention and discomfort and competence trust; all P<.05). Compliance was 100% in all studies, whereas reaction time was higher in study 1 (mean 23, SD 5.2 seconds) than in study 0 (mean 12.6, SD 5.7 seconds) and study 2 (mean 14.6, SD 4.3 seconds). Finally, verbal feedback showed that the participants preferred the voice warning to be less verbose and interactive.

CONCLUSIONS

This is the first study to investigate the feasibility of an in-vehicle voice warning for hypoglycemia. Drivers find such an implementation useful and effective in a simulated environment, but improvements are needed in the real-world driving context. This study is a kickoff for the use of in-vehicle voice assistants for digital health interventions.

Wearing the Lab: Advances and Challenges in Skin-Interfaced Systems for Continuous Biochemical Sensing

Continuous, on-demand, and, most importantly, contextual data regarding individual biomarker concentrations exemplify the holy grail for personalized health and performance monitoring. This is well-illustrated for continuous glucose monitoring, which has drastically improved outcomes and quality of life for diabetic patients over the past 2 decades. Recent advances in wearable biosensing technologies (biorecognition elements, transduction mechanisms, materials, and integration schemes) have begun to make monitoring of other clinically relevant analytes a reality via minimally invasive skin-interfaced devices. However, several challenges concerning sensitivity, specificity, calibration, sensor longevity, and overall device lifetime must be addressed before these systems can be made commercially viable. In this chapter, a logical framework for developing a wearable skin-interfaced device for a desired application is proposed with careful consideration of the feasibility of monitoring certain analytes in sweat and interstitial fluid and the current development of the tools available to do so. Specifically, we focus on recent advancements in the engineering of biorecognition elements, the development of more robust signal transduction mechanisms, and novel integration schemes that allow for continuous quantitative analysis. Furthermore, we highlight the most compelling and promising prospects in the field of wearable biosensing and the challenges that remain in translating these technologies into useful products for disease management and for optimizing human performance.

Functional sequelae after pancreatic resection for cancer

The morbidity and mortality of pancreatic cancer surgery has seen substantial improvement due to the standardization of surgical techniques, the optimization of perioperative multidisciplinary management and the organization of specialized care systems. The identification and treatment of postoperative functional and nutritional sequelae have thereby become major issues in patients who undergo pancreatic surgery. This review addresses the functional sequelae of pancreatic resection for cancerous and pre-cancerous lesions (excluding chronic pancreatitis). Its aim is to specify the prevalence and severity of sequelae according to the type of pancreatic resection and to document, where appropriate, the therapeutic management. Exocrine pancreatic insufficiency (ExPI) is observed in nearly one out of three patients at one year after surgery, and endocrine pancreatic insufficiency (EnPI) is present in one out of five patients after pancreatoduodenectomy (PD) and one out of three patients after distal pancreatectomy (DP). In addition, digestive functional disorders may appear, such as delayed gastric emptying (DGE), which affects 10 to 45% of patients after PD and nearly 8% after DP. Beyond these functional sequelae, pancreatic surgery can also induce nutritional and vitamin deficiencies secondary to a lack of uptake for certain vitamins or to the loss of absorption site in the duodenum. In addition to the treatment of ExPI with oral pancreatic enzymes, nutritional management is based on a high-calorie, high-protein diet with normal lipid intake in frequent small feedings, combined with vitamin supplementation adapted to monitored deficiencies. Better knowledge of the functional consequences of pancreatic cancer surgery can improve the overall management of patients.

Fabrication of conductive Ag/AgCl/Ag nanorods ink on Laser-induced graphene electrodes on flexible substrates for non-enzymatic glucose detection

An unusual strategy was designed to fabricate conductive patterns for flexible surfaces, which were utilized for non-enzymatic amperometric glucose sensors. The Ag/AgCl/Ag quasi-reference ink formulation utilized two reducing agents, NaBH[Formula: see text] and ethylene glycol. The parameters of the ink, such as sintering time and temperature, NaBH[Formula: see text] concentration, and layer number of coatings on flexible laser-induced graphene (LIG) electrodes were investigated. The conductive Ag/AgCl/Ag ink was characterized using electrochemical and surface analysis techniques. The electrocatalytic activity of Ag/AgCl/Ag NRs can be attributed to their high surface area, which offer numerous active sites for catalytic reactions. The selectivity and sensitivity of the electrodes for glucose detection were investigated. The XRD analysis showed (200) oriented AgCl on covered Ag NRs, and with the addition of NaBH[Formula: see text], the intensity of the peaks of the Ag NRs increased. The wide linear range of non-enzymatic sensors was attained from 0.003 to 0.18 mM and 0.37 to 5.0 mM, with a low limit of detection of 10 [Formula: see text]M and 20 [Formula: see text]M, respectively.The linear range of enzymatic sensor in real sample was determined from 0.040 to 0.097 mM with a detection limit of 50 [Formula: see text]M. Furthermore, results of the interference studies demonstrated excellent selectivity of the Ag/AgCl/Ag NRs/LIG electrode. The Ag/AgCl/Ag NRs on the flexible LIG electrode exhibited excellent sensitivity,long-time stablity,and reproducibility. The efficient electroactivity were deemed suitable for various electrochemical applications and biosensors.

A novel strategy for therapeutic drug monitoring: application of biosensors to quantify antimicrobials in biological matrices

Over the past few years, therapeutic drug monitoring (TDM) has gained practical significance in antimicrobial precision therapy. Yet two categories of mainstream TDM techniques (chromatographic analysis and immunoassays) that are widely adopted nowadays retain certain inherent limitations. The use of biosensors, an innovative strategy for rapid evaluation of antimicrobial concentrations in biological samples, enables the implementation of point-of-care testing (POCT) and continuous monitoring, which may circumvent the constraints of conventional TDM and provide strong technological support for individualized antimicrobial treatment. This comprehensive review summarizes the investigations that have harnessed biosensors to detect antimicrobial drugs in biological matrices, provides insights into the performance and characteristics of each sensing form, and explores the feasibility of translating them into clinical practice. Furthermore, the future trends and obstacles to achieving POCT and continuous monitoring are discussed. More efforts are necessary to address the four key 'appropriateness' challenges to deploy biosensors in clinical practice, paving the way for personalized antimicrobial stewardship.

Discordance Between Glycated Hemoglobin A1c and the Glucose Management Indicator in People With Diabetes and Chronic Kidney Disease

INTRODUCTION

Assessment of glucose exposure via glycated hemoglobin A1c (HbA1c) has limitations for interpretation in individuals with diabetes and chronic kidney disease (CKD). The glucose management indicator (GMI) derived from continuous glucose monitoring (CGM) data could be an alternative. However, the concordance between HbA1c measured in laboratory and GMI (HbA1c-GMI) is uncertain in individuals with CKD. The purpose of this study is to analyze this discrepancy.

MATERIAL AND METHOD

We performed a multicentric, retrospective, observational study. A group of individuals with diabetes and CKD (n = 170) was compared with a group of individuals with diabetes without CKD (n = 185). All individuals used an intermittently scanned continuous glucose monitoring (isCGM). A comparison of 14-day and 90-day glucose data recorded by the isCGM was performed to calculate GMI and the discordance between lab HbA1c and GMI was analyzed by a Bland-Altman method and linear regression.

RESULTS

HbA1c-GMI discordance was significantly higher in the CKD group versus without CKD group (0.78 ± 0.57 [0.66-0.90] vs 0.59 ± 0.44 [0.50-0.66]%, P < .005). An absolute difference >0.5% was found in 68.2% of individuals with CKD versus 42.2% of individuals without CKD. We suggest a new specific formula to estimate HbA1c from the linear regression between HbA1c and mean glucose CGM, namely CKD-GMI = 0.0261 × 90-day mean glucose (mg/L) + 3.5579 (r2 = 0.59).

CONCLUSIONS

HbA1c-GMI discordance is frequent and usually in favor of an HbA1c level higher than the GMI value, which can lead to errors in changes in glucose-lowering therapy, especially for individuals with CKD. This latter population should benefit from the CGM to measure their glucose exposure more precisely.

Testing the Real-World Accuracy of the Dexcom G6 Pro CGM During the Insulin-Only Bionic Pancreas Pivotal Trial

Continuous glucose monitors (CGMs) have transformed the way people with type 1 diabetes can self-monitor glucose levels. Past studies have evaluated the accuracy of CGMs in clinic-based studies, but few have analyzed their accuracy in real-world settings. The Insulin-Only Bionic Pancreas Trial provided the opportunity to assess real-world accuracy of the blinded Dexcom G6 Pro sensor over the first 48-60 h of wear using a blood glucose meter (BGM) as a comparator for 1073 CGM-BGM pairs across 53 participants. The mean absolute relative difference (MARD) was 11.0% over a median period of 50 h (range 47-79 h). The MARD was 13.6% in the first 12 h, 10.5% in hours 12-24, and 10.1% after the first 24 h. These results are comparable with accuracy shown previously with laboratory-based measurements and provide real-world evidence of Dexcom G6 Pro accuracy, which improved after the first 12 h and then remained stable thereafter. Clinical Trial Registry: clinicaltrials.gov; NCT04200313.

Hypoglycemic Effect of an Herbal Decoction (Modified Gangsimtang) in a Patient with Severe Type 2 Diabetes Mellitus Refusing Oral Anti-Diabetic Medication: A Case Report

There is growing interest in alternative therapies for type 2 diabetes mellitus (T2DM) because some patients refuse to receive conventional therapies. In East Asia, herbal medicines are often used to treat T2DM, and modified Gangsimtang (mGST) is prescribed to treat a condition called wasting thirst (), which resembles T2DM. This study reported the treatment of hyperglycemia using herbal medicines without oral hypoglycemic agents or insulin therapy. Case presentation: A 36-year-old man with obesity was diagnosed with T2DM four years prior to hospitalization and experienced blood glucose level reduction from 22.2-27.8 mmol/L (400-500 mg/dL) to 5.6-11.1 mmol/L (100-200 mg/dL) by using herbal medicines. He visited D Korean Medicine Hospital with chronic polydipsia and general weakness as chief complaints. He was diagnosed with T2DM on the basis of a hemoglobin A1c level of 11.7% and 2 h postprandial blood glucose level of >25.0 mmol/L (450 mg/dL). Moreover, he was diagnosed with a "dual deficiency of qi and yin" () because of ordinary symptoms (). During his 30-day inpatient treatment, the patient received mGST 120 mL thrice daily; as a result, his postprandial blood glucose level decreased from 25.3 mmol/L (455 mg/dL) to 8.6 mmol/L (154 mg/dL), polydipsia decreased (visual analog scale score decreased from six to one), and triglyceride levels decreased from 11.7 mmol/L (1031 mg/dL) to 2.0 mmol/L (174 mg/dL). Plasma glucose levels remained stable for 6 months after the treatment, and no adverse events were observed over 200 days. We administered an herbal decoction to decrease plasma glucose levels without using oral hypoglycemic agents or insulin. Conclusions: Herbal decoctions such as mGST can reduce hyperglycemia in patients with T2DM who refuse conventional therapy.

Glycaemic variability, assessed with continuous glucose monitors, is associated with diet, lifestyle and health in people without diabetes

Background

Continuous glucose monitors (CGMs) provide high-frequency information regarding daily glucose variation and are recognised as effective for improving glycaemic control in individuals living with diabetes. Despite increased use in individuals with non-diabetic blood glucose concentrations (euglycemia), their utility as a health tool in this population remains unclear.

Objectives

To characterise variation in time in range (TIR) and glycaemic variability in large populations without diabetes or impaired glucose tolerance; describe associations between CGM-derived glycaemic metrics and metabolic and cardiometabolic health traits; identify key diet and lifestyle factors associated with TIR and glycaemic variability.

Design

Glycaemic variability (coefficient of variation) and time spent in both the ADA secondary target range (TIRADA; 3.9-7.8 mmol/L) and a more stringent range (TIR3.9-5.6; 3.9-5.6 mmol/L) were calculated during free-living in PREDICT 1, PREDICT 2, and PREDICT 3 euglycaemic community-based volunteer cohorts. Associations between CGM derived glycaemic metrics, markers of cardiometabolic health, diet (food frequency questionnaire and logged diet records), diet-habits, and lifestyle were explored.

Results

Data from N=4135 participants (Mean SD; Age: 47 12 y; Sex: 83% Female, BMI: 27 6 kg/m2). Median glycaemic variability was 14.8% (IQR 12.6-17.6%), median TIRADA was 95.8% (IQR 89.6-98.6%) and TIR3.9-5.6 was 75.0% (IQR 64.6-82.8%). Greater TIR3.9-5.6 was associated with lower HbA1c, ASCVD 10y risk and HOMA-IR (all p < 0.05). Lower glycaemic variability was associated with lower % energy derived from carbohydrate (rs: 0.17, p < 0.01), ultra-processed foods (NOVA 4, % EI; rs: 0.12, p = 0.01) and a longer overnight fasting duration (rs: -0.10, p = 0.01).

Conclusions

A stringent TIR target provides sensitivity to detect changes in HOMA-IR, ASCVD 10 y risk and HbA1c that were not detected using ADA secondary targets. Associations among TIR, glycaemic variability, dietary intake (e.g. carbohydrate and protein) and habits (e.g. nocturnal fasting duration) highlight potential strategic targets to improve glycaemic metrics derived from continuous glucose monitors.

Flash Glucose Monitoring in Croatia: The Optimal Number of Scans per Day to Achieve Good Glycemic Control in Type 1 Diabetes

Background and Objectives: The purpose of this study is to determine the optimal number of scans per day required for attaining good glycemic regulation. Materials and Methods: The association of scanning frequency and glucometrics was analyzed according to bins of scanning frequency and bins of time in range (TIR) in the Croatian population of type 1 diabetes (T1DM) patients. Results: Intermittently scanned continuous glucose monitoring (isCGM) Libre users in Croatia performed on average 13 ± 7.4 scans per day. According to bins of scanning frequency, bin 5 with 11.2 ± 02 daily scans was sufficient for achieving meaningful improvements in glycemic regulation, while decreasing severe hypoglycemia required an increasing number of scans up to bin 10 (31 ± 0.9), yet with no effect on TIR improvement. When data were analyzed according to bins of TIR, an average of 16.3 ± 10.5 scans daily was associated with a TIR of 94.09 ± 3.49% and a coefficient of variation (CV) of 22.97 ± 4.94%. Improvement was shown between each successive bin of TIR but, of notice, the number of scans performed per day was 16.3 ± 10.5 according to TIR-based analysis and 31.9 ± 13.5 in bin 10 according to scan frequency analysis. Conclusions: In conclusion, an optimal average number of scans per day is 16.3 in order to achieve glucose stability and to minimize the burden associated with over-scanning.

Characteristics of glucose change in diabetes mellitus generalized through continuous wavelet transform processing: A preliminary study

BACKGROUND

The continuous glucose monitoring (CGM) system has become a popular evaluation tool for glucose fluctuation, providing a detailed description of glucose change patterns. We hypothesized that glucose fluctuations may contain specific information on differences in glucose change between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), despite similarities in change patterns, because of different etiologies. Unlike Fourier transform, continuous wavelet transform (CWT) is able to simultaneously analyze the time and fre-quency domains of oscillating data.

AIM

To investigate whether CWT can detect glucose fluctuations in T1DM.

METHODS

The 60-d and 296-d glucose fluctuation data of patients with T1DM (n = 5) and T2DM (n = 25) were evaluated respectively. Glucose data obtained every 15 min for 356 d were analyzed. Data were assessed by CWT with Morlet form (n = 7) as the mother wavelet. This methodology was employed to search for limited frequency glucose fluctuation in the daily glucose change. The frequency and enclosed area (0.02625 scalogram value) of 18 emerged signals were compared. The specificity for T1DM was evaluated through multiple regression analysis using items that demonstrated significant differences between them as explanatory variables.

RESULTS

The high frequency at midnight (median: 75 Hz, cycle time: 19 min) and middle frequency at noon (median: 45.5 Hz, cycle time: 32 min) were higher in T1DM vs T2DM (median: 73 and 44 Hz; P = 0.006 and 0.005, respectively). The area of the > 100 Hz zone at midnight to forenoon was more frequent and larger in T1DM vs T2DM. In a day, the lower frequency zone (15-35 Hz) was more frequent and the area was larger in T2DM than in T1DM. The three-dimensional scatter diagrams, which consist of the time of day, frequency, and area of each signal after CWT, revealed that high frequency signals belonging to T1DM at midnight had a loose distribution of wave cycles that were 17-24 min. Multivariate analysis revealed that the high frequency signal at midnight could characterize T1DM (odds ratio: 1.33, 95% confidence interval: 1.08-1.62; P = 0.006).

CONCLUSION

CWT might be a novel tool for differentiate glucose fluctuation of each type of diabetes mellitus using CGM data.

Millifluidic valves and pumps made of tape and plastic

There is growing interest in producing micro- and milli-fluidic technologies made of thermoplastic with integrated fluidic control elements that are easy to assemble and suitable for mass production. Here, we developed millifluidic valves and pumps made of acrylic layers bonded with double-sided tape that are simple and fast to assemble. We demonstrate that a layer of pressure-sensitive adhesive (PSA) is flexible enough to be deformed at relatively low pressures. A chemical treatment deposited on specific regions of the PSA prevents it from sticking to the thermoplastic, which enabled us to create three different types of valves in normally open or closed configurations. We characterized different aspects of their performance, their operating pressures, the cut-off pressure values to open or close the valves (for different configurations and sizes), and the flow rate and volume pumped by seven different micropumps. As an application, we implemented a glucose assay with integrated pumps and valves, automatically generating glucose dilutions and reagent mixing. The ability to create polymeric microfluidic control units made with tape paves the way for their mass manufacturing.

The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP

Machine learning has become a popular tool for learning models of complex dynamics from biomedical data. In Type 1 Diabetes (T1D) management, these models are increasingly been integrated in decision support systems (DSS) to forecast glucose levels and provide preventive therapeutic suggestions, like corrective insulin boluses (CIB), accordingly. Typically, models are chosen based on their prediction accuracy. However, since patient safety is a concern in this application, the algorithm should also be physiologically sound and its outcome should be explainable. This paper aims to discuss the importance of using tools to interpret the output of black-box models in T1D management by presenting a case-of-study on the selection of the best prediction algorithm to integrate in a DSS for CIB suggestion. By retrospectively "replaying" real patient data, we show that two long-short term memory neural networks (LSTM) (named p-LSTM and np-LSTM) with similar prediction accuracy could lead to different therapeutic decisions. An analysis with SHAP-a tool for explaining black-box models' output-unambiguously shows that only p-LSTM learnt the physiological relationship between inputs and glucose prediction, and should therefore be preferred. This is verified by showing that, when embedded in the DSS, only p-LSTM can improve patients' glycemic control.

GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks

Time series data generated by continuous glucose monitoring sensors offer unparalleled opportunities for developing data-driven approaches, especially deep learning-based models, in diabetes management. Although these approaches have achieved state-of-the-art performance in various fields such as glucose prediction in type 1 diabetes (T1D), challenges remain in the acquisition of large-scale individual data for personalized modeling due to the elevated cost of clinical trials and data privacy regulations. In this work, we introduce GluGAN, a framework specifically designed for generating personalized glucose time series based on generative adversarial networks (GANs). Employing recurrent neural network (RNN) modules, the proposed framework uses a combination of unsupervised and supervised training to learn temporal dynamics in latent spaces. Aiming to assess the quality of synthetic data, we apply clinical metrics, distance scores, and discriminative and predictive scores computed by post-hoc RNNs in evaluation. Across three clinical datasets with 47 T1D subjects (including one publicly available and two proprietary datasets), GluGAN achieved better performance for all the considered metrics when compared with four baseline GAN models. The performance of data augmentation is evaluated by three machine learning-based glucose predictors. Using the training sets augmented by GluGAN significantly reduced the root mean square error for the predictors over 30 and 60-minute horizons. The results suggest that GluGAN is an effective method in generating high-quality synthetic glucose time series and has the potential to be used for evaluating the effectiveness of automated insulin delivery algorithms and as a digital twin to substitute for pre-clinical trials.

Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes

Recent advancements in hybrid closed-loop systems, also known as the artificial pancreas (AP), have been shown to optimize glucose control and reduce the self-management burdens for people living with type 1 diabetes (T1D). AP systems can adjust the basal infusion rates of insulin pumps, facilitated by real-time communication with continuous glucose monitoring. Deep reinforcement learning (DRL) has introduced new paradigms of basal insulin control algorithms. However, all the existing DRL-based AP controllers require extensive random online interactions between the agent and environment. While this can be validated in T1D simulators, it becomes impractical in real-world clinical settings. To this end, we propose an offline DRL framework that can develop and validate models for basal insulin control entirely offline. It comprises a DRL model based on the twin delayed deep deterministic policy gradient and behavior cloning, as well as off-policy evaluation (OPE) using fitted Q evaluation. We evaluated the proposed framework on an in silico dataset generated by the UVA/Padova T1D simulator, and the OhioT1DM dataset, a real clinical dataset. The performance on the in silico dataset shows that the offline DRL algorithm significantly increased time in range while reducing time below range and time above range for both adult and adolescent groups. Then, we used the OPE to estimate model performance on the clinical dataset, where a notable increase in policy values was observed for each subject. The results demonstrate that the proposed framework is a viable and safe method for improving personalized basal insulin control in T1D.

A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials

BACKGROUND AND OBJECTIVE

Continuous glucose monitoring (CGM) sensors measure interstitial glucose concentration every 1-5 min for days or weeks. New CGM-based diabetes therapies are often tested in in silico clinical trials (ISCTs) using diabetes simulators. Accurate models of CGM sensor inaccuracies and failures could help improve the realism of ISCTs. However, the modeling of CGM failures has not yet been fully addressed in the literature. This work aims to develop a mathematical model of CGM gaps, i.e., occasional portions of missing data generated by temporary sensor errors (e.g., excessive noise or artifacts).

METHODS

Two datasets containing CGM traces collected in 167 adults and 205 children, respectively, using the Dexcom G6 sensor (Dexcom Inc., San Diego, CA) were used. Four Markov models, of increasing complexity, were designed to describe three main characteristics: number of gaps for each sensor, gap distribution in the monitoring days, and gap duration. Each model was identified on a portion of each dataset (training set). The remaining portion of each dataset (real test set) was used to evaluate model performance through a Monte Carlo simulation approach. Each model was used to generate 100 simulated test sets with the same size as the real test set. The distributions of gap characteristics on the simulated test sets were compared with those observed on the real test set, using the two-sample Kolmogorov-Smirnov test and the Jensen-Shannon divergence.

RESULTS

A six-state Markov model, having two states to describe normal sensor operation and four states to describe gap occurrence, achieved the best results. For this model, the Kolmogorov-Smirnov test found no significant differences between the distribution of simulated and real gap characteristics. Moreover, this model obtained significantly lower Jensen-Shannon divergence values than the other models.

CONCLUSIONS

A Markov model describing CGM gaps was developed and validated on two real datasets. The model describes well the number of gaps for each sensor, the gap distribution over monitoring days, and the gap durations. Such a model can be integrated into existing diabetes simulators to realistically simulate CGM gaps in ISCTs and thus enable the development of more effective and robust diabetes management strategies.

Narrative review of the role of technology in pediatric diabetes: from testing blood glucose to subcutaneous automated therapy and hope for cure

Background and Objective

Type 1 diabetes, the most common cause of diabetes in pediatrics, is defined by the hyperglycemia that results from the permanent autoimmune damage to the pancreas. The Diabetes Control and Complications Trial (DCCT) demonstrated that strict glycemic control targeting lower HbA1c goals can both delay the onset and progression of its complications that include diabetic neuropathy, nephropathy, retinopathy, and increased cardiovascular events. Our primary objective is to review the literature available regarding the technology applied for the treatment of diabetes, not only aiding patients' quality of life but addressing its effects on hypoglycemia and reduced risk of the long-term complications. It will synthesize the evolution of glucose monitoring devices; the development of insulin: from animal to recombinant engineering, smart insulin in the future; the development of algorithm-driven insulin delivery devices, the closed loop system/artificial pancreas; and the future utilization of technology to support islet cell transplant with the goal of a long-term cure. Emphasis will be made on what is known about the impact on its outcomes in children and adolescents.

Methods

A literature search was conducted using PubMed for publications from 1985 to present. Keywords used: type 1 diabetes, children, adolescents, pediatrics, continuous glucose monitoring (CGM), insulin pumps. Referenced articles include other reviews, current care guidelines as supported by cross sectional studies, cohort studies and randomized clinical trials.

Key Content and Findings

Understanding the pathophysiology of type 1 diabetes has led to the design of technology that facilitates glucose monitoring and insulin administration in a personalized manner. The current technology has improved outcomes and quality of life by decreasing hypoglycemic events and decreasing risk of long-term metabolic complications. Barriers remain, for children and adults, often driven by patient's preference as well as their understanding of the limitations of what they are wearing.

Conclusions

With the progressive evolution of this technology, it is now realistic to lower the burden of diabetes self-management while reducing hypoglycemia and risk of complications that otherwise impact daily life from academics, physical activity, career choices and even life expectancy.

DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions

Objective digital data is scarce yet needed in many domains to enable research that can transform the standard of healthcare. While data from consumer-grade wearables and smartphones is more accessible, there is critical need for similar data from clinical-grade devices used by patients with a diagnosed condition. The prevalence of wearable medical devices in the diabetes domain sets the stage for unique research and development within this field and beyond. However, the scarcity of open-source datasets presents a major barrier to progress. To facilitate broader research on diabetes-relevant problems and accelerate development of robust computational solutions, we provide the DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal data from wearable medical devices, including a total of 27,561 days of continuous glucose monitor data and 8,220 days of insulin pump data from 54 patients with diabetes. This dataset is useful for developing novel analytic solutions that can reduce the disease burden for people living with diabetes and increase knowledge on chronic condition management in outpatient settings.

Real-life 24-week changes in glycemic parameters among European users of flash glucose monitoring with type 1 and 2 diabetes and different levels of glycemic control

AIM

To evaluate real-life changes of glycemic parameters among flash glucose monitoring (FLASH) users who do not meet glycemic targets.

METHODS

De-identified data were obtained between 2014 and 2021 from patients using FLASH uninterrupted for a 24-week period. Glycemic parameters during first and last sensor use were examined in four identifiable groups: type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM) on basal-bolus insulin, T2DM on basal insulin, and T2DM without insulin treatment. Within each group, subgroup analyses were performed in persons with initial suboptimal glycemic regulation (time in range (TIR; 3.9-10 mmol/L) <70%, time above range (TAR; >10 mmol/L) >25%, or time below range (TBR; <3.9 mmol/L) >4%).

RESULTS

Data were obtained from 1,909 persons with T1DM and 1,813 persons with T2DM (1,499 basal-bolus insulin, 189 basal insulin, and 125 non-insulin users). In most of the performed analyses, both overall and in the various subgroups, significant improvements were observed in virtually all predefined primary (TIR) and secondary endpoints (eHbA1c, TAR, TBR and glucose variability).

CONCLUSIONS

24-weeks FLASH use in real life by persons with T1DM and T2DM with suboptimal glycemic regulation is associated with improvement of glycemic parameters, irrespective of pre-use regulation or treatment modality.

Drug Delivery Systems for Personal Healthcare by Smart Wearable Patch System

Smart wearable patch systems that combine biosensing and therapeutic components have emerged as promising approaches for personalized healthcare and therapeutic platforms that enable self-administered, noninvasive, user-friendly, and long-acting smart drug delivery. Sensing components can continuously monitor physiological and biochemical parameters, and the monitoring signals can be transferred to various stimuli using actuators. In therapeutic components, stimuli-responsive carrier-based drug delivery systems (DDSs) provide on-demand drug delivery in a closed-loop manner. This review provides an overview of the recent advances in smart wearable patch systems, focusing on sensing components, stimuli, and therapeutic components. Additionally, this review highlights the potential of fully integrated smart wearable patch systems for personalized medicine. Furthermore, challenges associated with the clinical applications of this system and future perspectives are discussed, including issues related to drug loading and reloading, biocompatibility, accuracy of sensing and drug delivery, and largescale fabrication.

Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study

BACKGROUND

Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations.

OBJECTIVE

This study aimed to propose a machine learning-based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns.

METHODS

Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis.

RESULTS

In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (P=.04), glycated hemoglobin level (P<.001), and time in range (P<.001) and thus had different management needs.

CONCLUSIONS

The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients' GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.

Continuous Glucose Monitoring in the Intensive Care Unit

Traditionally, the care of critically ill patients with diabetes or stress hyperglycemia in the intensive care unit (ICU) demands the use of continuous intravenous insulin (CII) therapy to achieve narrow glycemic targets. To reduce the risk of iatrogenic hypoglycemia and to achieve glycemic targets during CII, healthcare providers (HCP) rely on hourly point-of-care (POC) arterial or capillary glucose tests obtained with glucose monitors. The burden of this approach, however, was evident during the beginning of the pandemic when the immediate reduction in close contact interactions between HCP and patients with COVID-19 was necessary to avoid potentially life-threatening exposures. Taking advantage of the advancements in current diabetes technologies, including continuous glucose monitoring (CGM) devices integrated with digital health tools for remote monitoring, HCP implemented novel protocols in the ICU to care for patients with COVID-19 and hyperglycemia. We provide an overview of research conducted in the ICU setting with the use of initial CGM technology to current devices and summarize our recent experience in the ICU.

Association of Vibrotactile Biofeedback With Reduced Ergonomic Risk for Surgeons During Tonsillectomy

Importance

Work-related musculoskeletal disorders are common among otolaryngologists and can be associated with decreased productivity, missed workdays, and reduced quality of life. Ergonomic risk for surgeons is elevated during common otolaryngology procedures; current ergonomic interventions lack the ability to provide real-time feedback. The ability to quantify and mitigate ergonomic risk during surgery may reduce work-related musculoskeletal disorders.

Objective

To quantify the association of vibrotactile biofeedback with intraoperative ergonomic risk to surgeons during tonsillectomy.

Design, Setting, and Participants

This cross-sectional study was conducted between June 2021 and October 2021 at a freestanding tertiary care children's hospital and included 11 attending pediatric otolaryngologists. Data analysis was conducted from August to October 2021.

Interventions

Real-time quantification of ergonomic risk during tonsillectomy and the use of a vibrotactile biofeedback posture monitor.

Main Outcomes and Measures

Association of vibrotactile biofeedback with objective measures of ergonomic risk. Assessment tools included the Rapid Upper Limb Assessment, craniovertebral angle, and time spent in an at-risk posture.

Results

Eleven surgeons (mean [SD] age 42 [7] years; 2 women [18%]) performed 126 procedures with continuous posture monitoring in the presence (80 [63%]) and absence (46 [37%]) of vibrotactile biofeedback. No complications or delays associated with the device were reported. Intraoperative vibrotactile biofeedback was associated with improved Rapid Upper Limit Assessment neck, trunk, and leg scores by 0.15 (95% CI, 0.05-0.25), improved craniovertebral angle by 1.9 (95% CI, 0.32-3.40), and decreased overall time spent in an at-risk posture by 30% (95% CI, 22%-39%).

Conclusions and Relevance

The results of this cross-sectional study suggest that use of a vibrotactile biofeedback device to quantify and mitigate ergonomic risk for surgeons is feasible and safe while performing surgery. Vibrotactile biofeedback was associated with reduced ergonomic risk during tonsillectomy and may have a role in improving surgical ergonomics and preventing work-related musculoskeletal disorders.

Empowering People with Diabetes: Role of Continuous Glucose Monitor Systems

Continuous glucose monitoring in a person with diabetes who is embracing lifestyle changes can be greatly impactful in numerous ways. There are many factors that have been identified to affect blood glucose, and for someone who may be implementing the six pillars of lifestyle medicine, their blood sugar may require closer monitoring. Lifestyle medicine interventions may lead to improved glucose levels or even remission. The continuous glucose monitor allows people to see glucose levels, trends, and how rapidly their glucose levels are rising or dropping, empowering them to make connections with how they feel and how their actions are impacting their blood sugar, as well as giving information about how medications may need to be adjusted or deprescribed. CGM, when appropriately used, can help determine how to best manage diabetes, optimize outcomes, minimize risks, and empower the person and healthcare team.

The Evolution of Diabetes Technology - Options Towards Personalized Care

Advances in diabetes technology, especially in the last few decades, have transformed our ability to deliver care to persons with diabetes (PWD). Developments in glucose monitoring, especially continuous glucose monitoring systems (CGM), have revolutionized diabetes care and empowered our patients to manage their disease. CGM has also played an integral role in advancing automated insulin delivery systems. Currently available and upcoming advanced hybrid-closed loop systems aim to decrease patient involvement and are approaching the functionality of a fully automated artificial pancreas. Other advances, such as smart insulin pens and daily patch pumps, offer more options for patients and require less complicated and costly technology. Evidence to support the role of diabetes technology is growing, and PWD and clinicians must choose the right type of technology with a personalized strategy to manage diabetes effectively. Here, we review currently available diabetes technologies, summarize their individual features and highlight key patient factors to consider when creating a personalized treatment plan. We also address current challenges and barriers to the adoption of diabetes technologies.

Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective

Diabetes and its complications are seriously threatening the health and well-being of hundreds of millions of people. Glucose levels are essential indicators of the health conditions of diabetics. Over the past decade, concerted efforts in various fields have led to significant advances in glucose monitoring technology. In particular, the rapid development of continuous glucose monitoring (CGM) based on electrochemical sensing principles has great potential to overcome the limitations of self-monitoring blood glucose (SMBG) in continuously tracking glucose trends, evaluating diabetes treatment options, and improving the quality of life of diabetics. However, the applications of minimally invasive electrochemical CGM sensors are still limited owing to the following aspects: i) invasiveness, ii) short lifespan, iii) biocompatibility, and iv) calibration and prediction. In recent years, the performance of minimally invasive electrochemical CGM systems (CGMSs) has been significantly improved owing to breakthrough developments in new materials and key technologies. In this review, we summarize the history of commercial CGMSs, the development of sensing principles, and the research progress of minimally invasive electrochemical CGM sensors in reducing the invasiveness of implanted probes, maintaining enzyme activity, and improving the biocompatibility of the sensor interface. In addition, this review also introduces calibration algorithms and prediction algorithms applied to CGMSs and describes the application of machine learning algorithms for glucose prediction.

Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy

In diabetes prevention and care, invasiveness of glucose measurement impedes efficient therapy and hampers the identification of people at risk. Lack of calibration stability in non-invasive technology has confined the field to short-term proof of principle. Addressing this challenge, we demonstrate the first practical use of a Raman-based and portable non-invasive glucose monitoring device used for at least 15 days following calibration. In a home-based clinical study involving 160 subjects with diabetes, the largest of its kind to our knowledge, we find that the measurement accuracy is insensitive to age, sex, and skin color. A subset of subjects with type 2 diabetes highlights promising real-life results with 99.8% of measurements within A + B zones in the consensus error grid and a mean absolute relative difference of 14.3%. By overcoming the problem of calibration stability, we remove the lingering uncertainty about the practical use of non-invasive glucose monitoring, boding a new, non-invasive era in diabetes monitoring.

Accuracy of Flash Glucose Monitoring in Hemodialysis Patients With and Without Diabetes Mellitus

AIMS

Glucose and insulin metabolism are altered in hemodialysis patients, and diabetes management is difficult in these patients. We aimed to validate flash glucose monitoring (FGM) in hemodialysis patients with and without diabetes mellitus as an attractive option for glucose monitoring not requiring regular self-punctures.

METHODS

We measured interstitial glucose using a FreeStyle Libre device in eight hemodialysis patients with and seven without diabetes mellitus over 14 days and compared the results to simultaneously performed self-monitoring of capillary blood glucose (SMBG).

RESULTS

In 720 paired measurements, mean flash glucose values were significantly lower than self-measured capillary values (6.17±2.52 vs. 7.15±2.41 mmol/L, p=1.3 E-86). Overall, the mean absolute relative difference was 17.4%, and the mean absolute difference was 1.20 mmol/L. The systematic error was significantly larger in patients without vs. with diabetes (- 1.17 vs. - 0.82 mmol/L) and on dialysis vs. interdialytic days (-1.09 vs. -0.90 mmol/L). Compared to venous blood glucose (72 paired measurements), the systematic error of FGM was even larger (5.89±2.44 mmol/L vs. 7.78±7.25 mmol/L, p=3.74E-22). Several strategies to reduce the systematic error were evaluated, including the addition of +1.0 mmol/L as a correction term to all FGM values, which significantly improved accuracy.

CONCLUSIONS

FGM systematically underestimates blood glucose in hemodialysis patients but, taking this systematic error into account, the system may be useful for glucose monitoring in hemodialysis patients with or without diabetes.

Continuous Glucose Monitoring in Dogs and Cats: Application of New Technology to an Old Problem

In recent years, glucose monitoring has been revolutionized by the development of continuous glucose monitoring systems (CGMS), which are wearable non/minimally invasive devices that measure glucose concentration almost continuously for several consecutive d/wk. The Abbott FreeStyle Libre is the CGMS used most commonly. It has adequate clinical accuracy both in dogs and cats, even though the accuracy is lower in the hypoglycemic range. It allows an accurate identification of glycemic excursions occurring throughout the day as well as of glucose variations during consecutive days, enabling the clinician to make a more informed decision about the insulin dose and frequency of administration.

Diboronic-Acid-Based Electrochemical Sensor for Enzyme-Free Selective and Sensitive Glucose Detection

A diboronic acid anthracene-based fluorescent system for detecting blood glucose could be used for 180 days. However, there has not yet been a boronic acid immobilized electrode to selectively detect glucose in a signal-increased way. Considering malfunctions of sensors at high sugar levels, the electrochemical signal should be increased proportionally to the glucose concentration. Therefore, we synthesized a new diboronic acid derivative and fabricated the derivative-immobilized electrodes for the selective detection of glucose. We performed cyclic voltammetry and electrochemical impedance spectroscopy with an Fe(CN)₆^3-/4- redox pair for detecting glucose in the range of 0-500 mg/dL. The analysis revealed increased electron-transfer kinetics such as increased peak current and decreased semicircle radius of Nyquist plots as the glucose concentration increased. The cyclic voltammetry and impedance spectroscopy showed that the linear detection range of glucose was 40 to 500 mg/dL with limits of detection of 31.2 mg/dL and 21.5 mg/dL, respectively. We applied the fabricated electrode to detect glucose in artificial sweat and obtained 90% of the performance of the electrodes in PBS. Cyclic voltammetry measurements of other sugars such as galactose, fructose, and mannitol also showed linear increased peak currents proportional to the concentrations of the tested sugars. However, the slopes of the sugars were lower than that of glucose, indicating selectivity for glucose. These results proved the newly synthesized diboronic acid is a promising synthetic receptor for developing a long-term usable electrochemical sensor system.

Status of continuous glucose monitoring use and management in tertiary hospitals of China: a cross-sectional study

OBJECTIVE

This study aims to reveal the use and management status of continuous glucose monitoring (CGM) in tertiary hospitals in China and to determine the potential factors affecting the application of CGM, based on which more effective solutions would be produced and implemented.

DESIGN

An online, cross-sectional study was conducted from October 2021 to December 2021.

SETTING

Eighty-three tertiary hospitals in China were involved.

PARTICIPANTS

Eighty-three head nurses and 281 clinical nurses were obtained.

OUTCOME

Current condition of CGM use and management, the factors that hinder the use and management of CGM, scores of current CGM use and management, as well as their influencing factors, were collected.

RESULTS

Among the 83 hospitals surveyed, 57 (68.7%) hospitals used CGM for no more than 10 patients per month. Seventy-three (88.0%) hospitals had developed CGM standard operating procedures, but only 29 (34.9%) hospitals devised emergency plans to deal with adverse effects related to CGM. Comparably, maternal and children's hospitals were more likely to have a dedicated person to assign install CGM than general hospitals (52.2% vs 26.7%). As for the potential causes that hinder the use and management of CGM, head nurses' and nurses' perceptions differed. Head nurses perceived patients' limited knowledge about CGM (60.2%), the high costs of CGM and inaccessibility to medical insurance (59.0%), and imperfect CGM management systems (44.6%) as the top three factors. Different from head nurses, CGM operation nurses considered the age of CGM operators, the type of hospital nurses worked in, the number of patients using CGM per month and the number of CGM training sessions as potential factors (p<0.05).

CONCLUSIONS

The study provides a broad view of the development status of CGM in China. Generally speaking, the use and management of CGM in China are not yet satisfactory, and more efforts are wanted for improvement.

Prevalence of type 2 diabetes complications and its association with diet knowledge and skills and self-care barriers in Tabriz, Iran: A cross-sectional study

Background and Aims

Diabetes can lead to multiple complications that can reduce the quality of life, impose additional costs on the healthcare systems and ultimately lead to premature death. Proper self-care in diabetic patients can impede or delay the onset of diabetes complications. This study aimed to investigate diabetes complications and their association with diet knowledge, skills, and self-care barriers.

Methods

This was a cross-sectional study. A total of 1139 patients with Type 2 Diabetes Mellitus (T2DM) referring to health centers in Tabriz, Iran, were included from January to July 2019. Data were collected using two questionnaires: (1) a sociodemographic questionnaire and (2) a Personal Diabetes Questionnaire (PDQ). Data were analyzed using SPSS software version 22. χ ² test was used to examine the association between the socioeconomic and disease-related variables and the prevalence of diabetes complications. T-test was used to examine the association between diet knowledge and skills, self-care barriers, and the incidence of diabetes complications.

Results

In this study, 76.1% of patients had at least one complication, and 30.2% had a history of hospitalization due to diabetes complications during the past year. Approximately 49% and 43% were diagnosed with high blood pressure and hyperlipidemia, respectively. Cardiovascular disease was the most common diabetes complication (15.9%) and the cause of hospitalization (11.01%) in patients with diabetes. Barriers to diet adherence, blood glucose monitoring, and exercise were significantly associated with self-reported diabetes complications (p < 0.001). Our results showed no significant association between the number of complications and diet knowledge and skills (p = 0.44).

Conclusion

This study indicated that the prevalence of diabetes complications was higher among patients with more barriers to self-care. In light of these findings, taking appropriate measures to reduce barriers to self-care can prevent or delay the onset of diabetes complications.

Intermittent-scanned continuous glucose monitoring with low glucose alarms decreases hypoglycemia incidence in middle-aged adults with type 1 diabetes in real-life setting

OBJECTIVE

There is limited real-life data demonstrating that hypo-/hyperglycemic alarms added to continuous glucose monitoring (CGM) improve metabolic control in adults with type 1 diabetes (T1D). We evaluated the usefulness of switching from a flash or intermittent-scanned continuous glucose monitoring (is-CGM) device without low or higher glucose alarms to a is-CGM device with alarms to prevent hypoglycemia in adults with T1D.

METHODS

Individuals with T1D and fearful of hypoglycemia, prone to hypoglycemia unawareness, and/or experiencing severe hypoglycemia while using is-CGM Free Style Libre 1 (FSL1) were switched to FSL2 with individually-programmable low glucose alarms. The primary endpoint was the changes in % time below range (TBR%) <70 mg/dl [3.9 mmol/l] and <54 mg/dl [3.0 mmol/l] after 12 weeks on FSL2 compared with FSL1. Secondary endpoints were changes in % time in range (TIR% 70-180 mg/dl [3.9-10.0 mmol/l]), % time above range (TAR%) >180 [10.0 mmol/l], mean interstitial glucose, glycemic management indicator (GMI), interstitial glucose coefficient of variation (CV%), hemoglobin A1c, and sensor's scans/day.

RESULTS

We included 108 individuals (57.4 % men), aged 58.2 ± 17.3 [95 % CI: 55.0 to 61.5] years, with mean diabetes duration 25 ± 14.6 [95 % CI: 22.1 to 27.7] years. Among individuals, 40 (37.0 %) had hypoglycemia awareness with Clarke's score ≥4 and 19 (17.5 %) had a history of severe hypoglycemia. The median low glucose alarm threshold was 70 [IQR: 65-70] mg/dl (3.9 [IQR: 3.6-3.9] mmol/L). By comparison of first 12 weeks on FSL2 vs. last 12 weeks on FSL1, TBR% <70 mg/dl decreased from 4.5 ± 4.4 to 2.3 ± 2.8 % (p < 0.001), TBR% <54 mg/dl decreased from 1.4 ± 2.2 to 0.3 ± 0.9 % (p < 0.001). TIR% was not significantly different (51.5 ± 14.9 vs. 52.9 ± 16 % (p = 0.13)), nor was TAR% (43.8 ± 16.2 vs. 44.7 ± 16.5 % (p = 0.5)). CV% decreased from 39.4 ± 6.9 to 37.9 ± 6.1 % (p < 0.001). Those at risk for hypoglycemia (TBR >4 % and >1 %, respectively, at baseline) showed a significant decrease in the incidence of hypoglycemia <70 and <54 mg/dl (p < 0.0001). Patients' satisfaction with hypoglycemia alarms was high, since all individuals opted to pursue using individual alarm beyond the study period.

CONCLUSION

Switching from FSL1 to FSL2 with low glucose alarms reduced the frequency of hypoglycemia in middle-age adults with T1D, particularly in those who were prone to hypoglycemia awareness or severe hypoglycemia.

Association between fasting plasma glucose variability and maternal and infant outcomes in patients with hyperglycemia during pregnancy: a retrospective cohort study

Background

Blood glucose variability (GV) is believed to be closely related to the occurrence of adverse obstetric outcomes. However, few studies have investigated how the change in fasting plasma glucose (FPG) influenced on the adverse obstetric outcomes. This study mainly evaluated the relationship between FPG coefficient of variation (FPG-CV) and adverse outcomes in patients with gestational hyperglycemia and determine the ideal FPG-CV threshold for predicting maternal and infant outcomes.

Methods

We retrospective analyzed the data of 608 pregnant hyperglycemic patients in the Obstetrics Department of Shengjing Hospital Affiliated to China Medical University between June 2019 and December 2021 and followed up inpatients through the Hospital Information System (HIS). We collected the venous FPG from 24-28 weeks of pregnancy to delivery. Maternal and infant outcomes were based on the latest definitions. The chi-square test and logistic regression analysis were performed to evaluate the correlation between FPG-CV and adverse outcomes. Two multivariate binary logistic regression models were used to adjust for confounding factors. Stratified analysis was performed according to hemoglobin A1c (HbA1c) levels (<5.9% and ≥5.9%) and insulin injection (not used and used) in the third trimester of pregnancy. The receiver operating characteristic (ROC) curve was used to evaluate the prediction of FPG-CV on adverse outcomes.

Results

All patients were divided into four groups based on the quartile of FPG-CV. The proportion of FPG-SD and insulin injections differed among the groups (P<0.05). Among the outcomes, the highest incidence rate was 26.3% for large for gestational age (LGA), 8.7% for premature delivery. FPG-CV remains independently associated with low birth weight [odds ratio (OR) =1.086, P=0.007], preterm birth (OR =1.069, P=0.012), and preeclampsia (OR =1.180, P<0.001). FPG-CV can predict preeclampsia, with an area under the curve (AUC) of 0.725.

Conclusions

Our results suggest that patients with gestational hyperglycemia should undergo routine FPG monitoring from diagnosis to delivery. Also, the impact of blood glucose fluctuations on adverse outcomes should be considered in the clinical treatment. The rational application of hypoglycemic treatment can stabilize blood glucose levels, however, the effects of different regimens on GV and outcomes should be studied further.

Continuous Glucose Monitoring in Enterally Fed Children with Severe Central Nervous System Impairment

Children with severe central nervous system (CNS) impairment are at risk of developing various degrees of nutritional deficit that require long-term nutritional intervention. Interventions are most often implemented through enteral nutrition (EN) using commercially manufactured feeds administered via gastro/jejunostomy or nasogastric or nasojejunal tubes. The modality of feeding-continuous feeding or bolus feeding-is dependent on the function of the gastrointestinal tract, particularly the efficiency of gastric emptying. In the literature, the relationship between this type of nutrition and the occurrence of hyperglycaemia is often discussed. In addition, children with chronic neurological diseases are vulnerable to disorders of many mechanisms of neurohormonal counter-regulation related to carbohydrate management, and due to limited verbal and logical contact, it is difficult to recognise the symptoms of hypoglycaemia in such patients. We aimed to assess the carbohydrate metabolism in children with severe CNS impairment, with enteral nutrition delivered via nasogastric, nasoenteral, or percutaneous tubes, based on continuous glycaemic monitoring (CGM) and the measurement of glycated haemoglobin (HbA1c) levels.

MATERIALS AND METHODS

This prospective, observational study included nineteen patients (median (25-75 pc) age: 12.75 (6.17-15.55) years) with permanent CNS damage (Gross Motor Function Classification System V) receiving long-term tube enteral feeding, recruited from two paediatric university nutritional treatment centres. Patients with acute conditions and diagnosed diabetes were excluded. The nutritional status and nutritional support were analysed in all the inpatients in accordance with a uniform protocol. Using the CGM system (Medtronic iPro2), glycaemic curves were analysed, and in addition, HbA1C levels were determined in fourteen patients. CGM results were analysed using GlyCulator2.0. Statistical analysis was performed using the Statistica version 11 software (StatSoft Inc. Tulsa, OK, USA).

RESULTS

More than half (11/19; 58%) of the patients were undernourished (BMI < 3 pc for age and gender), with the stature age being significantly lower than calendar age (5 (4.5-9) vs. 12.75 (6.17-15.55) years; p = 0.0010). The actual caloric intake was 50 (37.7-68.8) kcal/kg (median; 25-75 pc). In patients fed using the bolus method, the number of calories consumed per day was statistically significantly higher than in children subjected to a continuous feeding supply (56.00 (41.00-75.00) vs. 33.40 (26.70-50.00) kcal/kg BW (body weight; p = 0.0159). Decreases in blood glucose levels below the alarm level (<70 mg/dL) were recorded in fifteen patients (78.9%), including two patients with episodes of clinically significant hypoglycaemia (<54 mg/dL). The minimum and maximum glycaemic values recorded in any individual CGM records were 67 mg/dL (median) (minimum: 41 mg/dL; maximum: 77 mg/dL) and 146 (minimum: 114 mg/dL; maximum: 180 g/dL), respectively, for the entire recording. The maximum percentage of glycaemic concentrations > 140 mg/dL (TAR 140) recorded overnight in children with BMI ≥ 3 amounted to 1.6% vs. 0% in undernourished patients (TAR 140: 0.0 (0.00-1.6%) vs. 0% (0.00-0.0%; p = 0.0375); the percentage of glycaemic concentrations <70 mg/dL in the entire recording was comparable (0.77% (0.13-2.2%) vs. 1.8% (0.5-14.4%) vs. p = 0.2629). There was a positive correlation between the mean daily glucose recorded using the CGM method and patients' BMI z-scores (R = 0.48, p = 0.0397). No statistically significant relationship was demonstrated between the occurrence of alarm hypoglycaemia events in the CGM records and undernutrition expressed by BMI z-scores (OR = 1.50 (95%CI: 0.16-13.75), the type of diet (for commercially manufactured OR = 0.36 (95%CI: 0.04-3.52), and the modality of diet delivery (for bolus feeding OR = 2.75 (95%CI: 0.28-26.61).

CONCLUSIONS

In children with chronic OU damage, enteral feeding is associated with a risk of hypoglycaemia, but further studies involving a larger number of patients are needed, and CGM might be a useful tool to estimate the metabolic adequacy of enteral nutritional support in terms of glucose control.