Glucose Monitoring Research Articles

Cutting-Edge Hydrogel Technologies in Tissue Engineering and Biosensing: An Updated Review.

Hydrogels, known for their unique ability to retain large amounts of water, have emerged as pivotal materials in both tissue engineering and biosensing applications. This review provides an updated and comprehensive examination of cutting-edge hydrogel technologies and their multifaceted roles in these fields. Initially, the chemical composition and intrinsic properties of both natural and synthetic hydrogels are discussed, highlighting their biocompatibility and biodegradability. The manuscript then probes into innovative scaffold designs and fabrication techniques such as 3D printing, electrospinning, and self-assembly methods, emphasizing their applications in regenerating bone, cartilage, skin, and neural tissues. In the realm of biosensing, hydrogels' responsive nature is explored through their integration into optical, electrochemical, and piezoelectric sensors. These sensors are instrumental in medical diagnostics for glucose monitoring, pathogen detection, and biomarker identification, as well as in environmental and industrial applications like pollution and food quality monitoring. Furthermore, the review explores cross-disciplinary innovations, including the use of hydrogels in wearable devices, and hybrid systems, and their potential in personalized medicine. By addressing current challenges and future directions, this review aims to underscore the transformative impact of hydrogel technologies in advancing healthcare and industrial practices, thereby providing a vital resource for researchers and practitioners in the field.

Continuous measurement of interstitial glycaemia in professional female UCI world tour cyclists undertaking a 9-day cycle training camp.

Nine cyclists (age: 26±5years, height: 168±5cm and mass 58.5±4.5kg) were observed using continuous glucose monitoring devices throughout a training camp. Interstitial glucose [iG] data were captured via the Abbott libre sense biosensor (Abbott Laboratories) and paired with the Supersapiens software (TT1 Products Inc.). [iG] data were split into time ranges, that is, overall (24-hourly), day-time (06:00-23:59), night-time (00:00-05:59) and exercise. [iG] data were stratified into percentage of time, below range ([TBR]<70mg/dl), in range ([TIR] 70-140mg/dl) and above range ([TAR]≥141mg/dl). Differences in diurnal and nocturnal data were analysed via repeated measures analysis of variance and paired t-tests where appropriate. p-value of ≤0.05 was accepted as significant. Riders spent an average of 3±1% TAR, 93±2% TIR and 8±3% TBR. Mean 24h [iG] was 93±2mg/dl with a coefficient of variation (CV) of 18±1%. Mean (day: 95±3 vs. night: 86±3mg/dl and p<0.001) and CV (day: 18±1 vs. night: 9±1% and p<0.001) in [iG] were higher during the day-time hours. TAR was greater during the day (day: 3±1 vs. night: 0±0% and p<0.001) but TBR and TIR were similar. Glucose levels below the clinical range may have implications for those without diabetes and warrants further investigation.

Sensitivity Approach for Blood Glucose Impedance Data Using the Geselowitz Theorem

The article presents a preclinical study focused on developing a non-invasive blood glucose monitoring device. Current glucose monitoring methods, such as capillary or venous tests, are inconvenient for frequent use, making continuous monitoring challenging. This study explores the BGP (Bruna Gabriela Pedro) model, which links electrical impedance to blood glucose levels, as a potential alternative. Using the Geselowitz Theorem and impedance spectroscopy in a 4-electrode configuration, the study investigates the relationship between blood conductivity and glucose concentration. Impedance spectra were recorded for five human blood samples with glucose concentrations between 106 and 188 mg/dL. These samples were housed in an insulating container with four stainless steel electrodes. Conductivity was calculated using the Geselowitz Theorem at 1.0, 3.9, and 24.9 kHz. A finite element model built in COMSOL was employed to assess electrode sensitivity, which had a maximum regression error of 3.75 %. The results demonstrated a sigmoid relationship between blood glucose concentration and conductivity, with impedance decreasing non-linearly as glucose levels increased. Sensitivity diminished for glucose concentrations exceeding 188 mg/dL. An increase of 82 mg/dL in glucose concentration led to conductivity changes of 1.14, 2.82, and 5.14 S/m at frequencies of 1.0, 3.9, and 24.9 kHz, respectively. The findings suggest that the BGP analytical model could be refined through the inclusion of additional data on glucose's impact on electrical impedance. These improvements could support the development of non-invasive glucose meters. The research holds significant clinical potential for the creation of a simple, cost-effective glucose monitoring device. Continuous glucose monitoring could enhance diagnostic accuracy and support better therapeutic decision-making, particularly in emergency care. The affordability and accessibility of such a device may benefit a broad patient base.

Comparison between a Flash Glucose Monitoring System and a Portable Blood Glucose Meter for Monitoring of Cats with Diabetic Ketosis or Ketoacidosis.

Cats with diabetic ketosis or ketoacidosis DK(A) require intensive glucose monitoring. The aim of this study was to assess the agreement between a portable blood glucose meter (PBGM) and a flash glucose monitoring system (FGMS; FreeStyle Libre 2.0 Abbott®) measuring interstitial glucose in cats with DK(A). Ten client-owned cats with naturally occurring DK(A) were prospectively enrolled. Glucose concentrations were assessed with both methods every 1-3 h until resolution of DK(A), and every 4-8 h thereafter. While the median FGMS measured glucose concentration (14.3 mmol/L) was significantly lower than the median PBGM measured glucose concentration (19 mmol/L) (p < 0.001), the overall correlation between the FGMS and PBGM was high (r = 0.88; p < 0.001). In the Parkes error grid analysis, 98.3% of measurements fell in zones A and B. Bland-Altman plot analysis demonstrated that in the low glycaemic range (BG < 5.5 mmol/L), 50% of FGMS measurements deviated more than ±0.83 mmol/L, and in the high glycaemic range (BG > 5.5 mmol/L), 81% of results deviated >15% from the PBGM measurements. There was significant inter-individual variation in the difference between glucose concentrations measured by the FGMS and PBGM (p < 0.001). In spite of being more easily tolerated and easier to use, currently this method cannot be recommended for routine monitoring of cats with DK(A).

Continuous Glucose Monitoring With Geriatric Principles in Older Adults With Type 1 Diabetes and Hypoglycemia: A Randomized Controlled Trial.

Continuous glucose monitoring (CGM) use in older adults with type 1 diabetes (T1D) has shown benefits. However, the impact of CGM use, coupled with simplified treatment regimens and personalized glycemic goals that are better suited for older patients with multiple comorbidities and hypoglycemia, is not known. Older adults (≥65 years) with T1D with hypoglycemia (two or more episodes of hypoglycemia [blood glucose <70 mg/dL for ≥20 min over 2 weeks]) who were CGM naïve or CGM users were randomized to intervention and control groups. The intervention consisted of the combined use of CGM with geriatric principles (i.e., adjusting goals based on overall health, and simplification of regimens based on CGM patterns and clinical characteristics) over 6 months. The control group received usual care by their endocrinologist. The primary end point was change in time when blood glucose was <70 mg/dL from baseline to 6 months. Cost-effectiveness was calculated using a health care sector perspective. We randomized 131 participants (aged 71 ± 5 years; 21% ≥75 years old) to the intervention (n = 68; CGM users = 33) or the control (n = 63; CGM users = 40) group. The median change in hypoglycemia from baseline to 6 months was -2·6% in the intervention group and -0.3% in the control group (median difference, -2.3% [95% CI -3.7%, -1.3%]; P < 0.001). This improvement was seen in both CGM naïve (-2.8%; 95% CI -5.6%, -0.8%) and CGM users (-1.2%; 95% CI -2.7%, -0.1%). The HbA1c did not differ between the groups (7.5% vs 7.3%). The intervention was cost-effective (incremental cost-effectiveness ratio $71,623 per quality adjusted life-year). In older adults with T1D and high risk of hypoglycemia, CGM use enhanced by geriatric principles can lower hypoglycemia without worsening glycemic control in a cost-effective fashion.

Effects of night-to-night variations in objectively measured sleep on blood glucose in healthy university students.

We examined associations between daily variations in objectively measured sleep and blood glucose in a sample of non-diabetic young adults to complement laboratory studies on how sleep affects blood glucose levels. 119 university students underwent sleep measurement using an Oura Ring 2 and continuous glucose monitoring (CGM) for up to 14 days. In 69 individuals who consumed a standardised diet across the study, multilevel models examined associations between sleep duration, timing and efficiency and daily CGM profiles. Separately, in 58 individuals, multilevel models were used to evaluate postprandial glycaemic responses to a test meal challenge on 7 days. Participants also underwent oral glucose tolerance testing once after a night of ad libitum sleep, and again following a night of sleep restriction by 1-2 hours relative to that individual's habitual sleep duration. Between-condition glucose and insulin excursions, HOMA-IR and Matsuda index were compared. Nocturnal sleep did not significantly influence following-day CGM profiles, postprandial glucose, or nocturnal mean glucose levels (all Ps>0.05). Longer sleep durations were associated with lower same-night glucose variability (all Ps<0.001). However, the range of variation of sugar levels was small and unlikely to be of functional significance. Considering naps in the analysis did not alter the findings. Sleep restriction by an average of 1.73 hours (SD=0.97) did not significantly impact excursions in glucose or insulin or insulin sensitivity the following morning (all Ps>0.05). Glucose handling in young, healthy adults may be more resilient to real-life fluctuations in sleep patterns than previously thought.

Correlation between insulin-like growth factor and complexity of glucose time series index in patients with newly diagnosed acromegaly: a PILOT study.

Acromegaly has a high risk of abnormal glucose metabolism. The complexity of the glucose time series index (CGI) is calculated from refined composite multi-scale entropy analysis of the continuous glucose monitoring (CGM) data. CGI is a new indicator of glucose imbalance based on ambulatory glucose monitoring technology, which allows for earlier response to glucose metabolism imbalance and correlates with patient prognosis. To compare the differences in glucose metabolic profile and CGI between acromegaly with normal glucose tolerance (NGT) and healthy subjects. Eight newly diagnosed patients with acromegaly (GH group) and eight age- and gender-matched healthy subjects (Control group) were included in this study. All participants underwent oral glucose tolerance test (OGTT) and 72-h CGM. A refined composite multi-scale entropy analysis was performed on the CGM data to calculate the CGI and we compare the differences in glycemic profiles and CGI between the two groups. After OGTT, compared with the control group, patients in the GH group had higher 2 h blood glucose (BG) (mmol/L) [GH vs control, 6.7 (6.1, 7.0) vs 5.2 (3.8, 6.3), P = 0.012], 3 h BG [5.1 (3.8, 6.5) vs 4.0 (3.4, 4.2), P = 0.046], mean BG [6.3 (6.1, 6.5) vs 5.5 (5.1, 5.9), P = 0.002], 2 h insulin (mU/L) [112.9 (46.8, 175.5) vs 34.1 (17.1, 55.6), P = 0.009], and 3 h insulin [26.8 (17.1, 55.4) vs 10.4 (4.2, 17.8), P = 0.016]. CGI was lower in the GH group [2.77 (1.92, 3.15) vs 4.2 (3.3, 4.8), P = 0.008]. Spearman's correlation analysis showed insulin-like growth factor (IGF) (r = -0.897, P < 0.001) and mean glucose (r = -0.717, P = 0.003) were significantly negatively correlated with CGI. Multiple linear stepwise regression showed that IGF-1 (r = -0.652, P = 0.028) was independent factor associated with CGI in acromegaly. IGF-1 was significantly associated with CGI, and CGI may serve as a novel marker to evaluate glucose homeostasis in acromegaly with normal glucose tolerance.

Various Approaches and Future Prospective for Therapies for Diabetes: A Review

The global prevalence of diabetes mellitus disease marked by increased blood glucose levels, has reached epidemic proportions. This review provides a comprehensive assessment of the current landscape of diabetes therapies and outlines future prospects for improving the management and treatment of this condition. The current scenario highlights a multifaceted approach to diabetes management, with a focus on lifestyle interventions, oral antidiabetic drugs, insulin therapy, and emerging technologies like continuous glucose monitoring and closed-loop systems. While these approaches have significantly improved the lives of people with diabetes, several challenges persist. These include medication adherence, hypoglycemia risk, and the financial burden of diabetes care. Innovative treatments that improve glucose regulation, improve cardiovascular health, and help patients lose weight—like SGLT-2 inhibitors and GLP-1 receptor agonists—have showed promise in treating these issues. The future prospects for diabetes therapies are exciting, with ongoing research into cell-based therapies, including pancreatic islet transplantation and stem cell-derived beta cells. Personalized medicine approaches, driven by genetics and artificial intelligence, hold the potential to optimize treatment regimens and predict individual responses to various therapies. Furthermore, advancements in telemedicine and digital health technologies will enhance patient self-management and remote monitoring.

Nutrition Management in Children Less than 5 Years of Age with Glycogen Storage Disease Type I: Survey Results.

Background: Nutrition management for GSD Type I (GSDI; OMIM #232200, 232220) is complex, with the goal being to maintain euglycemia while minimizing metabolic derangements. Management guidelines were published in 2002 and 2014. However, there is limited information on the nuances of nutrition management and the unique feeding challenges of children. Methods: A REDCap survey focusing on staffing and current practices in the nutrition management of children with GSD I who were <5 years of age was sent to the metabolic dietitian's listserv and GMDI membership in 8/2023. Results: There were 21 North American respondents. In 17/21 clinics (81%), Prosobee® was the primary choice for infant formula. Dietitians used different methods to determine hourly glucose needs. Fasting recommendations ranged from 1 to 3 h, and the use of nighttime continuous feeding was common. Cornstarch was started between 6 and 12 months of age. Most clinics did not use Glycosade® for children <5 years of age. Oral motor dysfunction, gagging, and lack of interest in food were common. Continuous glucose monitoring (CGM) devices were recommended in 20 clinics (95%). Most clinics followed patients on an outpatient basis. All clinics provided a hypoglycemia management plan; however, there was wide variability in practice. Conclusion: This survey highlights the variability in the care of individuals <5 years of age with GSD I. Updated guidelines are needed to help address the unique nutrition challenges in this age group.

Microglia Mediate Metabolic Dysfunction from Common Air Pollutants through NF-κB Signaling.

The prevalence of Type 2 Diabetes (T2D) poses a significant health challenge yet the contribution of air pollutants to T2D epidemics remains understudied. Several studies demonstrated a correlation between exposure to volatile organic compounds (VOCs) in indoor/outdoor environments, and T2D. Here, we conducted the first meta-analysis, establishing a robust association between exposure to benzene, a prevalent airborne VOC, and insulin resistance in humans across all ages. We utilized a controlled benzene exposure system, continuous glucose monitoring (CGM) approach and indirect calorimetry in mice, to investigate the underlying mechanisms. Following exposure, disruptions in energy homeostasis, accompanied by modifications in the hypothalamic transcriptome and alterations in insulin and immune signaling, were observed exclusively in males, leading to a surge in blood glucose levels. In agreement, RNA-sequencing of microglia reveals increased expression of genes associated with immune response and NF-κB signaling. Selective ablation of IKKβ in immune cells (Cx3cr1GFPΔIKK) or exclusively in microglia (Tmem119ERΔIKK) in adult mice alleviated benzene-induced gliosis, restored energy homeostasis, hypothalamic gene expression, and protected against hyperglycemia. We conclude that the microglial NF-κB pathway plays a critical role in chemical-induced metabolic disturbances, revealing a vital pathophysiological mechanism linking exposure to airborne toxicants and the onset of metabolic diseases.

Forecasting glucose values for patients with type 1 diabetes using heart rate data

Background:Type 1 Diabetes Mellitus (T1DM) is a chronic metabolic disease affecting millions of people worldwide. T1DM requires patients to continuously monitor their blood glucose levels. Due to pancreatic dysfunctions, patients use insulin injections to correct glucose values by synthetic insulin. Continuous Glucose Monitoring (CGM) is a system which includes an algorithm allowing to measure (and in some cases to predict) glucose levels at a frequent sampling time. This enable implementing advanced devices, including automated insulin pump delivery. Nevertheless, CGM still presents some limitations, including (i) the delay (time lag) in detecting change in glucose levels compared to the traditional blood glucose measurement, and (ii) the lack of a sufficient and acceptable time to accurately predict glucose values. Methods:We propose a framework based on a Gated Recurrent Unit (GRU) model to forecast both short- and long-term glucose values using heart rate (HR) and interstitial glucose (IG) values. The framework acquires HR and IG data and predicts glucose values with higher precision compared to state-of-the-art models. For training and testing the proposed framework, we used the OhioT1DM Dataset, which includes physiological data such as HR and IG values collected over an 8-week observation period. Additionally, we validated our framework using two other glucose datasets to ensure its generalizability across different HR and IG sampling frequencies. The proposed framework can be used to optimize the CGM system by incorporating patient HR measurements, thereby improving the prediction of short- and long-term glucose levels and reducing risks associated with conditions like hypoglycemia. Results:Experimental tests were conducted using HR and IG data from the OhioT1DM Dataset, as well as from two additional T1DM patient datasets. We analyzed 6 patients from Ohio dataset while we validated the algorithm on 23 patients coming from two different university hospitals (6 from the University of Catanzaro medical hospital and 17 gathered from a validated study at IRCCS San Matteo Hospital in Pavia) for a total number of 29 patients. Our framework demonstrates an improvement in forecasting IG values in terms of RMSE and MAE for different choice of prediction horizons (PH). In the case of a PH of 5, 10, 20, 30, and 60 min, we reach an RMSE of 5.0, 9.38, 15.27, 20.48, and 34.16 respectively. The framework is freely available as an open-source, with an example dataset on a GitHub repository (see https://github.com/rafgia/attention_to_glycemia). Conclusion:Our framework offers a promising solution for improving glucose level prediction and management in T1DM patients. By leveraging a GRU model and incorporating HR and IG values, we achieve more precise glucose level forecasting compared to state-of-the-art models. This approach not only enhances the accuracy of glucose predictions but also mitigates the risks associated with hypoglycemia.

Safety and tolerability of a low glycemic load dietary intervention in adults with cystic fibrosis: a pilot study.

To achieve and maintain adequate weight, people with cystic fibrosis (CF) May often consume energy-dense, nutrient-poor foods high in added sugars and refined carbohydrates; however, little is known about the glycemic and metabolic effects of dietary composition in this patient population. The objective of this pilot study was to investigate the safety and tolerability of a low glycemic load (LGL) diet in adults with CF and abnormal glucose tolerance (AGT). Ten adults with CF and AGT completed this prospective, open-label pilot study. Mean age was 27.0 ± 2.1 years, 64% were female, and all had pancreatic insufficiency. Each participant followed his/her typical diet for 2 weeks, then transitioned to a LGL diet via meal delivery service for 8 weeks. The primary outcome was change in weight from baseline to study completion, with safety established if no significant decline was noted. Other key safety outcomes included change in hypoglycemia measured by patient report and continuous glucose monitoring (CGM). Exploratory outcomes included changes in other CGM measures, body composition by dual energy X-ray absorptiometry (DXA), and patient reported outcomes. There were no significant changes in weight or in subjectively-reported or objectively-measured hypoglycemia. Favorable non-significant changes were noted in CGM measures of hyperglycemia and glycemic variability, DXA measures of fat mass, and gastrointestinal symptom surveys. A LGL dietary intervention was safe and well tolerated in adults with CF and AGT. These results lay the groundwork for future trials investigating the impact of low-glycemic dietary interventions on metabolic outcomes in the CF population.

Initiation of Intermittently Scanned Continuous Glucose Monitoring Is Associated With Reduced Hospitalization for Acute Diabetes Events and Cardiovascular Complications in Adults With Type 1 Diabetes.

We assessed the impact of intermittently scanned continuous glucose monitoring (isCGM) compared with blood glucose monitoring (BGM) on rates of hospitalization for metabolic and vascular complications of diabetes and on HbA1c levels for adults with type 1 diabetes. This retrospective study using data from the Swedish National Diabetes Register and the Swedish National Patient Register comprised adults with type 1 diabetes and an isCGM initiation date after 1 June 2017 and matched control individuals using BGM. Hospital admission rates were calculated per 100 person-years of follow-up. We identified 11,822 adults with type 1 diabetes and an isCGM index date after 1 June 2017 and HbA1c baseline values 3-8 months prior to the index date. Compared with 3,007 BGM users, isCGM users had a significantly lower relative risk of hospitalization for hypoglycemia (0.32; 95% CI 0.14, 0.74), diabetic ketoacidosis (0.55; 0.35, 0.87), stroke (0.48; 0.37, 0.64), acute myocardial infarction (0.64; 0.46, 0.91), atrial fibrillation (0.59; 0.38, 0.94), heart failure (0.25; 0.16, 0.39), peripheral vascular disease (0.21; 0.07, 0.62), kidney disease (0.48; 0.35, 0.66), or hospitalization for any reason (0.32; 0.29, 0.35). Compared with BGM users, change in mean HbA1c for isCGM users was -0.30% (-3.3 mmol/mol) at 6 months and -0.24% (-2.6 mmol/mol) at 24 months (both P < 0.001). This study shows that adults with type 1 diabetes in Sweden who initiate isCGM have significantly reduced hospitalization rates for acute diabetes events, kidney disease, and cardiovascular complications, along with improved glucose control, compared with BGM users.

Nuevas herramientas para el tratamiento de la diabetes tipo 1 en la edad pediátrica

In recent years, new resorces have been appearing in order to improve glycaemic control in patients with type 1 diabetes. These include new insulin analogues, glucose monitoring, smart insulin pens and hybrid close-loop systems. So, in these patients the percentage of time in range has increased, the episodes of hypoglycaemia or hyperglycaemia have decreased, and quality of life has improved. Finally, in the following years biological drugs such as teplizumab will be incorporated, which will be the first etiological treatment for type 1 diabetes.

Comparison of Glucose Measurement Techniques Using Venous and Capillary Blood Samples in Diabetics Regarding Patient Satisfaction

Aim: This study was conducted to compare the satisfaction of patients diagnosed with diabetes mellitus regarding blood glucose measurement based on the blood glucose measurement techniques (intravenous and capillary) used. Material and Method: A randomized controlled study was conducted with 110 patients with diabetes mellitus. Patients were randomized into the experimental group (n=55) and control group (n=55). Patient Information Form, Diabetes Fear of Injecting and Self-testing Questionnaire (D-FISQ), and Blood Glucose Measurement Patient Satisfaction Form were used in the study. The control group patients had their blood sugar measured using capillary blood from a finger prick. The experimental group patients had their blood glucose measured intravenously using venous blood, followed by capillary blood glucose measurement from a finger prick. Data analysis was conducted using chi-square test and independent t-test. Results: There were statistically significant differences in the satisfaction of diabetic patients with the blood glucose measurement technique between the use of venous blood and intravenous blood glucose measurement by finger prick (p

Correlation of Diabetes self-care adherence with glycemic measures: a cross-sectional health facility-based study in Kathmandu, Nepal

Self-care plays a key role in prevention of complications, and improvement of the quality of life of persons with diabetes (PWDs). This study has assessed adherence to self-care behavior and its correlation with glycemic measures among PWDs attending private health care facilities in Kathmandu, Nepal. A cross-sectional study was carried out using a semi-structured questionnaire. Socio-demographic information and recent fasting (FBG) and 2-hr post-prandial blood glucose (2-hr PBG) levels were recorded. Self-care inventory (SCI) 12-item version was used to assess adherence to diabetes self-care over the preceding four weeks. Self-care practice was measured in five domains: glucose monitoring; diabetes medication; diet; exercise; and preventative care. The overall adherence score was obtained by computing the average of seven items (items 1, 2, 4, 5, 6, 7, 14). Scores more than or equal to median score were considered as higher levels of self-care efficacy. Univariate linear regression analysis of the subscale scores and individual item-wise score with age, duration of diabetes (years), FBG and PPBG levels was carried out. Pearson’s coefficient (r) was reported for normally distributed variables. Among 385 PWDs, mean FBG was 146 mg/dl (±54.7), 2-hr PBG was 210 mg/dl (±82). Median score for overall adherence to diabetes self-care was 25 (IQR:21 to 29). Higher self-care efficacy was seen among 55% (n=212). Overall adherence to self-care was significantly and negatively correlated with age and glycemic measures. Self-care efficacy was mainly driven by adherence to diabetes medication. Adherence to preventative care, exercise and home monitoring of blood glucose was low. FBG was significantly and negatively correlated with glucose regulation (r=-0.16, P=0.001), diabetes medication and food regulation (r=-0.11, P=0.03), preventative care (-0.14, P=0.03) and overall adherence to treatment (-0.15, P=0.004). 2-hr PBG was significantly and negatively associated with all domains of self-care (r -0.14, P=0.04 to -0.21, P&lt;0.0001). Elderly persons, may face difficulty with regular physical exercise, dietary recommendations and overall adherence to self-care. Continuing diabetes education is imperative to motivate PWDs and caregivers regarding importance of self-care efficacy in glycemic regulation and diabetes care.

Intrapartum Care for People with Diabetes-Working towards Evidence-Based Management.

The consensus in the literature supports the need for careful monitoring and management of maternal blood glucose during labor to optimize neonatal outcomes. Guidelines generally recommend strict control of maternal blood glucose during labor, involving frequent checks, and the use of dextrose and insulin as needed. However, recent evidence has not consistently shown a strong association between strict control of blood glucose and a reduction in the rate of neonatal hypoglycemia. This raises questions about the extent to which intrapartum blood glucose control impacts neonatal hypoglycemia. This review aims to explore the literature on intrapartum maternal blood glucose management in individuals with pregestational or gestational diabetes, utilizing peer-reviewed journals and datasets, including PubMed, Google Scholar, and clinical guidelines. Observational studies, small sample sizes, variability in definitions of maternal hyperglycemia and neonatal hypoglycemia, and differences in measurement methods such as timing and thresholds for intervention limit the literature on this topic. Additionally, many studies may not fully account for confounding factors such as maternal body mass index, diet, and other comorbidities affecting blood glucose levels. These limitations underscore the need for a cautious interpretation of current findings and highlight the necessity for future research in this area. This review elaborates on the available data and summarizes evidence on managing labor in pregnancies complicated by diabetes. We also emphasize the need for further research to clarify the relationship between maternal blood glucose during labor and neonatal blood glucose. KEY POINTS: · The benefits of strict intrapartum blood glucose control are unclear.. · The optimal maternal blood glucose range to prevent neonatal hypoglycemia remains undefined.. · Additional research is necessary to understand the relationship between maternal and neonatal blood glucose..

First experiences of using the Dexcom ONE CGM system: results from a national survey on the perception of early adopters

Introduction: The availability of increasingly accurate and high-performing Continuous Glucose Monitoring (CGM) systems today allows for the adoption of selection criteria based not only on clinical profiles but also on the preferences of people with diabetes. Methods: The “Dexcom ONE Experience” study aimed to investigate the initial user experiences with the Dexcom ONE CGM system in the real lives of people with diabetes undergoing insulin therapy, specifically early adopters (i.e., patients who first in Italy adopted Dexcom ONE CGM who have used the device for at least 30 days). Empirical evidence was collected, focusing primarily on usability, satisfaction, and impact on quality of life (QoL) of the system, through an online survey. All survey participants were insulin-treated patients, as indicated by CGM device recommendations. Results: Analysis was conducted on 300 completed surveys. 93% of respondents consider the device useful for diabetes management; 91% find it helps in more effectively managing therapy, while 88% report an improvement in health. 86% find it easy to learn how to use, and 93% plan to continue using it in the coming months. 74% believe the system to be highly reliable. The most recognized and utilized functions are data visibility on smartphones/receivers and glycaemic trend visibility. 70% of respondents express being “very or extremely satisfied” with the Dexcom ONE device experience (scoring 4/5), and 90% note a “positive or extremely positive” impact on their QoL from using the device. Conclusions: In conclusion, usability and satisfaction levels have proven to be high among early adopters of the Dexcom ONE CGM system.

Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes

Background: Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices’ impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment. Objective: We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D). Methods: Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants’ CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D. Results: Our algorithm’s estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise ( P &gt; .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set ( P = .005) but not in the validation T2D data set ( P = .18). Conclusions: We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.

Relationship Between Average Glucose Levels and HbA1c Differs Across Racial Groups: A Substudy of the GRADE Randomized Trial.

To determine whether the relationship between average glucose (AG) levels and hemoglobin A1c (HbA1c) differs across racial/ethnic groups. We performed a prospective substudy of GRADE, a comparative effectiveness randomized trial conducted in 36 centers in the U.S. A total of 1,454 of the 5,047 participants in the GRADE cohort, including 534 non-Hispanic White (NHW), 389 non-Hispanic Black (NHB), and 327 Hispanic White patients and 204 patients of other racial/ethnic backgrounds, were included in the substudy. Continuous glucose monitoring (CGM) performed for 10 days was used to calculate AG10. Immediately after CGM, HbA1c and glycated albumin were measured. Fasting plasma glucose (FPG) and glucose area under the curve (AUC) were derived from a 75-g oral glucose tolerance test. The relationship between AG10 and HbA1c was significantly different for NHB compared with NHW patients and those of other racial/ethnic groups. HbA1c levels were 0.2-0.6 percentage points higher in NHB than in NHW patients for AG10 levels from 100 to 250 mg/dL. For an HbA1c of 7%, AG10 was 11 mg/dL higher for NHW than for NHB patients. Similar findings were observed across races for relationships of FPG and AUC with HbA1c and for glucose measurements with glycated albumin levels. Differences in the relationship between AG10 and HbA1c across racial groups remained after adjustments for any demographic or other differences between racial/ethnic subgroups. The relationship between several measures of glucose with HbA1c and glycated albumin consistently differed across races. These findings should be considered in setting treatment goals and diagnostic levels.