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This review discusses the potential value of tracking interstitial glucose with continuous glucose monitors (CGMs) in athletes, highlighting possible applications and important considerations in the collection and interpretation of interstitial glucose data. CGMs are sensors that provide real time, longitudinal tracking of interstitial glucose with a range of commercial monitors currently available. Recent advancements in CGM technology have led to the development of athlete-specific devices targeting glucose monitoring in sport. Although largely untested, the capacity of CGMs to capture the duration, magnitude, and frequency of interstitial glucose fluctuations every 1–15 min may present a unique opportunity to monitor fueling adequacy around competitive events and training sessions, with applications for applied research and sports nutrition practice. Indeed, manufacturers of athlete-specific devices market these products as a "fueling gauge," enabling athletes to "push their limits longer and get bigger gains." However, as glucose homeostasis is a complex phenomenon, extensive research is required to ascertain whether systemic glucose availability (estimated by CGM-derived interstitial glucose) has any meaning in relation to the intended purposes in sport. Whether CGMs will provide reliable and accurate information and enhance sports nutrition knowledge and practice is currently untested. Caveats around the use of CGMs include technical issues (dislodging of sensors during periods of surveillance, loss of data due to synchronization issues), practical issues (potential bans on their use in some sporting scenarios, expense), and challenges to the underpinning principles of data interpretation, which highlight the role of sports nutrition professionals to provide context and interpretation. [ABSTRACT FROM AUTHOR]

Aims: To study the effects of a bridging dose of U‐100 glargine (U‐100G) with the first dose of degludec in type 1 diabetes (T1D) patients transitioning from glargine to degludec, by comparing the glucose metrics 48 h before and after the transition. Materials and Methods: Patients with T1D on a stable U‐100G regimen and with glycated haemoglobin concentration <75 mmol/mol were randomized (double‐blind) to one dose of placebo or U‐100G with first dose of degludec, administered at 9:00 pm. Patients on once‐daily U‐100G at baseline received 50% of total U‐100G dose (bridging dose), while patients on twice‐daily U‐100G received 50% of the evening U‐100G dose. Participants wore a continuous glucose monitor during the study. Results: Forty participants were randomized, of whom 37 completed the study. The cohort was 65% male, the mean age was 47 years, duration of T1D 22 years, BMI 26 kg/m2, HbA1c 51 mmol/mol and total daily insulin dose 0.7 units/kg body weight. The bridging group included 19 participants (once‐daily U‐100G: n = 12; twice‐daily U‐100G: n = 7) and the placebo group included 18 participants (once‐daily U‐100G: n = 12; twice‐daily U‐100G: n = 6). Change in time in range (TIR) was not significantly different between the two treatment groups. In secondary analyses, among twice‐daily U‐100G users, TIR (3.9‐10 mmol/L) increased 8% in the bridging group in the 48 h after first dose of degludec compared to the preceding 48 h, while participants in the placebo group had a 9.5% decrease (p = 0.027). Conclusions: A subgroup of well‐controlled twice‐daily U‐100G users transitioning to degludec benefited from a 50% bridging dose of evening U‐100G with the first dose of degludec in a small pilot study. [ABSTRACT FROM AUTHOR]

Background: Telemedicine is often promoted as a possible solution to some of the challenges healthcare systems in many countries face, and an increasing number of studies evaluate the clinical effects. So far, the studies show varying results. Less attention has been paid to systemic factors, such as the context, implementation, and mechanisms of these interventions. Methods: This study evaluates the experiences of patients and health personnel enrolled in a pragmatic randomized controlled trial comparing telemedicine-based follow-up of chronic conditions with usual care. Patients in the intervention group received an individual treatment plan together with computer tablets and home telemonitoring devices to report point-of-care measurements, e.g., blood pressure, blood glucose or oxygen saturation, and to respond to health related questions reported to a follow-up service. In response to abnormal measurement results, a follow-up service nurse would contact the patient and consider relevant actions. We conducted 49 interviews with patients and 77 interviews with health personnel and managers at the local centers. The interview data were analyzed using thematic analysis and based on recommendations for conducting process evaluation, considering three core aspects within the process of delivering a complex intervention: (1) context, (2) implementation, and (3) mechanisms of impact. Results: Patients were mainly satisfied with the telemedicine-based service, and experienced increased safety and understanding of their symptoms and illness. Implementation of the service does, however, require dedicated resources over time. Slow adjustment of other healthcare providers may have contributed to the absence of reductions in the use of specialized healthcare and general practitioner (GP) services. An evident advantage of the service is its flexibility, yet this may also challenge cost-efficiency of the intervention. Conclusions: The implementation of a telemedicine-based service in primary healthcare is a complex process that is sensitive to contextual factors and that requires time and dedicated resources to ensure successful implementation. Trial registration: The trial was registered in www.clinicaltrials.gov (NCT04142710). Study start: 2019-02-09, Study completion: 2021-06-30, Study type: Interventional, Intervention/treatment: Telemedicine tablet and tools to perform measurements. Informed and documented consent was obtained from all subjects and next of kin participating in the study. [ABSTRACT FROM AUTHOR]

The article highlights the intersection of type 1 diabetes and dementia in aging populations, emphasizing the challenges faced by patients and caregivers. Topics discussed include the increased risk of cognitive decline in individuals with type 1 diabetes, the reluctance of patients to share diabetes management details, and strategies for healthcare providers to assist with diabetes management and support caregivers effectively.

Diabetes technology continues to evolve, advancing with our understanding of human biology and improving our ability to treat people with diabetes. Diabetes devices are broadly classified into the following categories: glucose sensors, insulin delivery devices, and digital health care technology (i.e., software and mobile applications). When supported by education and individually tailored, technology can play a key role in optimizing outcomes. Digital devices assist in diabetes management by tracking meals, exercise, sleep, and glycemic measurements in real time, all of which can guide physicians and other clinicians in their decision-making. Here, as people with diabetes and patient advocates, as well as diabetes specialists, primary care providers, and diabetes care and education specialists, we present our perspectives on the advances, benefits, and challenges of diabetes technology in primary care practices. [ABSTRACT FROM AUTHOR]

Background: Hyperglycemia has shown a negative association with cognitive dysfunction. We analyzed patients with high preoperative blood glucose level and hemoglobin A1c (HbA1c) level to determine the prevalence of postoperative delirium. Methods: We reviewed a database of 23,532 patients with diabetes who underwent non-cardiac surgery. Acute hyperglycemia was defined as fasting blood glucose > 140 mg/ dl or random glucose > 180 mg/dl within 24 h before surgery. Chronic hyperglycemia was defined as HbA1c level above 6.5% within three months before surgery. The incidence of delirium was compared according to the presence of acute and chronic hyperglycemia. Results: Of the 23,532 diabetic patients, 21,585 had available preoperative blood glucose level within 24 h before surgery, and 18,452 patients reported levels indicating acute hyperglycemia. Of the 8,927 patients with available HbA1c level within three months before surgery, 5,522 had levels indicating chronic hyperglycemia. After adjustment with inverse probability weighting, acute hyperglycemia was related to higher incidence of delirium (hazard ratio: 1.33, 95% CI [1.10,1.62], P = 0.004 for delirium) compared with controls without acute hyperglycemia. On the other hand, chronic hyperglycemia did not correlate with postoperative delirium. Conclusions: Preoperative acute hyperglycemia was associated with postoperative delirium, whereas chronic hyperglycemia was not significantly associated with postoperative delirium. Irrespective of chronic hyperglycemia, acute glycemic control in surgical patients could be crucial for preventing postoperative delirium. [ABSTRACT FROM AUTHOR]

Background: Heating of the arm and/or hand ("arterialization") is sometimes used in continuous glucose monitoring (CGM) performance studies with the reported aim of reducing differences between venous and capillary glucose concentrations. In this study, the effect of heating on venous glucose concentrations and CGM accuracy was investigated. Methods: A heating pad set to 50°C (122°F) was used with 20 participants to heat either the dominant or nondominant arm and hand. Venous and capillary samples were obtained every 15 min on both arms throughout each of three 6-h glucose challenges. CGM sensors were worn on each upper arm for each of the three visits. Results: Heating of the arm led to a median increase in venous glucose concentrations of +1.4%. No similar effect on capillary concentrations was observed. As a result, the median capillary to venous difference decreased from +5.9% in the nonheated arm to +4.2% in the heated arm. CGM accuracy observed in this study was affected by the selection of heated venous, nonheated venous, or capillary glucose concentrations as comparator data. The heating effect was more pronounced with rapidly decreasing glucose concentrations. Temperatures on the skin did not exceed 40°C (104°F). No adverse events or protocol deviations were associated with the use of the heating pad. Conclusions: Heating of the arm led to a small increase in venous glucose concentrations, but venous concentrations did not reach the level of capillary glucose concentrations. CGM accuracy observed in this study varied depending on the selected comparator data. This study was registered at the German Clinical Trials Register (DRKS00031197). [ABSTRACT FROM AUTHOR]

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are <70 mg/dL (3.9 mmol/L) and >300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems. [ABSTRACT FROM AUTHOR]

This article provides an executive summary of the Society of Critical Care Medicine's guidelines on glycemic control for critically ill children and adults in 2024. The guidelines recommend against intensive glucose control in critically ill children due to the increased risk of severe hypoglycemia. However, the use of a protocol with decision support tools is suggested for both adults and pediatric patients receiving insulin infusions. The article also references various studies and trials comparing intensive insulin therapy to conventional glucose control in different critical care situations, with varying results. Additionally, it mentions studies on the impact of glycemic control on neurodevelopmental outcomes in pediatric cardiac surgery patients and the use of computerized insulin dose calculators. [Extracted from the article]

Wearable glucose biosensors enable noninvasive glucose monitoring, thereby enhancing blood glucose management. In this work, we present a wearable biosensor based on carbon black nanoparticles (CBNPs) for glucose detection in human sweat. The biosensor consists of CBNPs, Prussian blue (PB), glucose oxidase, chitosan, and Nafion. The fabricated biosensor has a linear range of 5 µM to 1250 µM, sensitivity of 14.64 µA mM−1 cm−2, and a low detection potential (−0.05 V, vs. Ag/AgCl). The detection limit for glucose was calculated as 4.83 µM. This reusable biosensor has good selectivity and stability and exhibits a good response to glucose in real sweat. These results demonstrate the potential of our CBNP-based biosensor for monitoring blood glucose in human sweat. [ABSTRACT FROM AUTHOR]

Pancreatic islets are metabolically active micron-sized tissues responsible for controlling blood glucose through the secretion of insulin and glucagon. A loss of functional islet mass results in type 1 and 2 diabetes. Islet-on-a-chip devices are powerful microfluidic tools used to trap and study living ex vivo human and murine pancreatic islets and potentially stem cell-derived islet organoids. Devices developed over the past twenty years offer the ability to treat islets with controlled and dynamic microenvironments to mimic in vivo conditions and facilitate diabetes research. In this review, we explore the various islet-on-a-chip devices used to immobilize islets, regulate the microenvironment, and dynamically detect islet metabolism and insulin secretion. We first describe and assess the various methods used to immobilize islets including chambers, dam-walls, and hydrodynamic traps. We subsequently describe the surrounding methods used to create glucose gradients, enhance the reaggregation of dispersed islets, and control the microenvironment of stem cell-derived islet organoids. We focus on the various methods used to measure insulin secretion including capillary electrophoresis, droplet microfluidics, off-chip ELISAs, and on-chip fluorescence anisotropy immunoassays. Additionally, we delve into the various multiparametric readouts (NAD(P)H, Ca2+-activity, and O2-consumption rate) achieved primarily by adopting a microscopy-compatible optical window into the devices. By critical assessment of these advancements, we aim to inspire the development of new devices by the microfluidics community and accelerate the adoption of islet-on-a-chip devices by the wider diabetes research and clinical communities. [ABSTRACT FROM AUTHOR]

Digital health technologies are being utilized increasingly in the modern management of diabetes. These include tools such as continuous glucose monitoring systems, connected blood glucose monitoring devices, hybrid closed‐loop systems, smart insulin pens, telehealth, and smartphone applications (apps). Although many of these technologies have a solid evidence base, from the perspective of a person living with diabetes, there remain multiple barriers preventing their optimal use, creating a digital divide. In this article, we describe many of the origins of these barriers and offer recommendations on widening access to digital health technologies for underserved populations living with diabetes to improve their health outcomes. [ABSTRACT FROM AUTHOR]

The mean absolute relative difference (MARD) is a numerical metric that has been adopted by the diabetes technology community as the main indicator that describes the accuracy of a glucose sensor at a single point in time. The appropriateness of this adoption is questionable because there is limited evidence that MARD has meaningful clinical relevance in the current era of sensor technology. The calculation may be simple, but evaluation of MARD can be very complex because it is substantially impacted by the design of the data collection in an accuracy study. Factors that can influence the overall MARD include participant demographics such as type of diabetes and age, site of sensor wear, and the percentage of collected values in each glycemic range during the study that is, in turn, a function of the study design. MARD is only one of several important statistical metrics such as bias and precision that are relevant to assessing accuracy of a sensor. Furthermore, these analytic metrics convey little information about the safety and effectiveness of sensor use with an automated insulin delivery system or a standalone device. There are no clinical studies in people with diabetes (PWD) proving that MARD can accurately differentiate between a safe and unsafe sensor or between a more and less clinically effective sensor. Moreover, there are alternatives to MARD that can do this in a clinically meaningful way, which include error grid analyses and clinical studies in PWD. This review attempts to demythologize the status of MARD for the diabetes community in an effort to shift the focus from MARD to using clinically relevant assessments. [ABSTRACT FROM AUTHOR]

Significance: Many researchers have attempted to estimate blood glucose levels (BGLs) noninvasively using near-infrared (NIR) spectroscopy. However, the optical absorption change induced by blood glucose is weak in the NIR region and often masked by interference from other components such as water and hemoglobin. Aim: Instead of using direct optical absorption by glucose, this study proposes an index calculated from oxy- and deoxyhemoglobin signals that shows a good correlation with BGLs while using conventional visible and NIR spectroscopy. Approach: The metabolic index, which is based on tissue oxygen consumption, was derived through analytical methods and further verified and reproduced in a series of glucose challenge experiments. Blood glucose estimation units were prototyped by utilizing commercially available smart devices. Results: Our experimental results showed that the phase delay between the oxy-and deoxyhemoglobin signals in near-infrared spectroscopy correlates with BGL measured by a conventional continuous glucose monitor. The proposed method was also confirmed to work well with visible spectroscopy systems based on smart-phone cameras. The proposed method also demonstrated excellent repeatability in results from a total of 19 oral challenge tests. Conclusions: This study demonstrated the feasibility of non-invasive glucose monitoring using existing photoplethysmography sensors for pulse oximeters and smartwatches. Evaluating the proposed method in diabetic or unhealthy individuals may serve to further increase its practicality. [ABSTRACT FROM AUTHOR]

Background: Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to missing data. Methods: Two-week-long data from a continuous glucose monitor and a Fitbit activity tracker recording heart rate (HR) and step count in free-living patients with type 2 diabetes mellitus were used. The gap size distribution was fitted with a Planck distribution to test for missing not at random (MNAR) and a difference between distributions was tested with a Chi-squared test. Significant missing data dispersion over time was tested with the Kruskal–Wallis test and Dunn post hoc analysis. Results: Data from 77 subjects resulted in 73 cleaned glucose, 70 HR and 68 step count recordings. The glucose gap sizes followed a Planck distribution. HR and step count gap frequency differed significantly (p < 0.001), and the missing data were therefore MNAR. In glucose, more missing data were found in the night (23:00–01:00), and in step count, more at measurement days 6 and 7 (p < 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization. Conclusions: Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data. [ABSTRACT FROM AUTHOR]

Introduction: More than two-thirds of patients with type 1 diabetes (T1D) are overweight (OW) and/or obese (OB) in the USA and Western Europe, resulting in insulin resistance as in type 2 diabetes. None of the currently available glucagon like polypeptide 1 (GLP-1) analogs are approved for patients with T1D. A higher dose of semaglutide has been approved by the Food and Drug Administration (FDA) for subjects with body mass index (BMI) >27 kg/m2. We evaluated the real-world use of semaglutide in patients with T1D. Methods: This was a retrospective chart review study of 50 OW or OB patients with T1D who were initiated on semaglutide and followed for 1 year. The control group comprised of 50 computer-matched patients (for sex, race, weight, BMI, and diabetes duration) during a similar time period and were not on any weight loss medications. Results: Most patients (92%) were non-Hispanic white in both arms. Mean ± standard deviation (SD) age and duration of diabetes were 42 ± 11 and 27 ± 12 years, respectively. The continuous glucose monitors (CGM), insulin pump use, baseline BMI and body weight were also similar in the two groups. Baseline glycosylated hemoglobin (HbA1c) was insignificantly lower in the semaglutide group (7.6% vs. 8.2%, respectively; P = non-significant [NS]). Total daily insulin dose (TDD) and insulin dose per kg body weight were higher in the semaglutide group at baseline with no difference in basal or prandial insulin dose. There were significantly greater declines in mean (±SD), BMI (7.9% ± 2.6%), body weight (15.9 lbs ± 5.4 lbs), HbA1c, CGM glucose SD and coefficient of variation (CV), and increase in CGM time in range (TIR) in the semaglutide group compared to the control group with no difference in insulin dose changes, time above range (TAR), or time below range (TBR). Conclusions: We conclude that use of semaglutide in patients who are OW and/or OB with T1D was effective in lowering body weight and BMI, and improving glycemic metrics in this pilot real-world study. We strongly recommend performing prospective, large-randomized clinical trials with newer GLP-1 analogs like semaglutide and tirzepatide (twin-cretin) for subjects with T1D associated with OW and/or OB. [ABSTRACT FROM AUTHOR]

Background: We assessed changes in glycemic control and person-reported outcome measures (PROMs) with t:slim X2 insulin pump with Control-IQ technology use among historically minoritized youth who are least likely to access hybrid closed loop (HCL) technology. Methods: This single-arm, prospective pilot study enrolled 15 publicly insured, insulin pump-naïve, non-Hispanic Black youth ages 6 to <21 years with type 1 diabetes and hemoglobin A1c (HbA1c) ≥10% in a 6-month study of HCL use. The primary outcome was absolute change in time in range (TIR) (70–180 mg/dL). Secondary outcomes included other continuous glucose monitor metrics, PROMs, and diabetic ketoacidosis (DKA) incidence. Results: For 13 youth (median 14.8 years, 53.3% female, HbA1c 11.7%) who completed the study, baseline TIR of 12.3% (6.3–27.1%) increased 23.7%-points (16.9, 30.5%; P < 0.001) or 5.7 h per day. Percent time >250 mg/dL decreased 33.9%-points (−44.8, −23.1%; P < 0.001) or 8.1 h per day from a baseline of 69.4% (51.6, 84.0%). Median time in HCL was 78.3% (59.7, 87.3%). Youth received 10.1 (9.2, 11.9) boluses per day, 71.7% (63.8, 79.3%) of which were HCL-initiated autoboluses. Diabetes-specific quality of life increased among parents (P < 0.001) and youth (P = 0.004), and diabetes distress decreased in both groups (P < 0.001, P = 0.005). Improvements in glycemia did not correlate with any baseline youth or parent PROMs. DKA was high at baseline (67 episodes/100-person years) and did not increase during the intervention (72 episodes/100-person years, P = 0.78). Conclusion: Improvements in glycemic control and quality of life exceeding pivotal trial findings without increased safety risks among historically minoritized youth emphasize the need for equitable access to HCL systems. ClinicalTrials.gov: clinicaltrials.gov ID (NCT04807374). [ABSTRACT FROM AUTHOR]

Continuous glucose monitoring (CGM) values obtained from CGM systems using the same sensor but with different internal algorithms (the first- and third-generation FreeStyle Libre (1st-gen-libre and 3rd-gen-libre, respectively)) were compared. We used 19,819 paired and simultaneously measured CGM values of 13 patients with diabetes. The average CGM value was significantly higher (P < 0.0001) and the time below range (CGM value < 70 mg/dL) was significantly lower (P < 0.0001) with the 3rd-gen-libre than with the 1st-gen-libre. There was a significant correlation (P < 0.0001) between the CGM values of the 3rd-gen-libre (y-axis, mg/dL) and 1st-gen-libre (x-axis, mg/dL) using the following formula: y = 0.9728x + 10.024. On assessing the association between glycated hemoglobin (HbA1c (%), y-axis) and the average CGM values (x-axis, mg/dL) by applying the obtained equation to previously reported 1st-gen-libre data and converting it to 3rd-gen-libre data, we obtained the equation y = 0.02628x + 3.233, indicating that the glucose management indicator reported in the West may be underestimated compared with the laboratory-measured HbA1c in the Japanese population. Glucose values from the same sensor were found to be significantly different between readers with different algorithms, and the calculation of CGM-related indices may need to be individualized for each device. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Despite evidence that continuous glucose monitoring (CGM) use is associated with lower HbA1c among children with type 1 diabetes, uptake of this technology remains lower among those with difficulty accessing health care, including those from lower socioeconomic status backgrounds and racial and ethnic minorities. In this study, we sought to explore the impact of rural location in use of CGM technology to guide patient and provider decision making. RESEARCH DESIGN AND METHODS: In this retrospective study of electronic health record demographic and visits data from a single diabetes program from 1 January 2018 through 31 December 2021, we compared the odds of completing a visit with (+) and without (−) CGM interpretation between rural-urban commuting area (RUCA) designations. RESULTS: Among the 13,645 visits completed by 2,008 patients with type 1 diabetes younger than age 18 years, we found children living in small rural towns had 31% lower odds (6.3% of CGM+ visits, 8.6% of CGM− visits; adjusted odds ratio [aOR] 0.69, 95% CI 0.51–0.94) and those living in isolated rural towns had 49% lower odds (2.0% of CGM+ visits, 3.4% of CGM− visits; aOR 0.51, 95% CI 0.28–0.92) of completing a CGM-billed clinic visit compared with those living in urban areas (70.0% of CGM+ visits, 67.2% of CGM− visits). We also found significant differences in CGM-billed visits by neighborhood deprivation as well as race/ethnicity and insurance payor. CONCLUSIONS: Geographic location presents a meaningful barrier to access to care for patients living with type 1 diabetes. Further work is needed to identify and address the needs of children and families living in rural areas to improve the care of these patients. [ABSTRACT FROM AUTHOR]

[Display omitted] Monitoring blood glucose level is critical, since its abnormality leads to diabetes and causes death, even though glucose is essential for human living. Herein, the sensing study was performed on electrochemical nonenzymatic glucose sensors, which are composed of an Au nanocluster (AuNC) catalyst deposited on a metallic nanotube array (MeNTA) and polypyrrole nanowire (PPyNW). The AuNC was produced by irradiating a femtosecond pulse laser to the Au precursor solution, and it is a simple and facile method. The successful deposition of AuNC on both MeNTA and PPyNW was confirmed by means of the surface morphology and the Au content increase. On the exploration by cyclic voltammetry in alkaline condition, AuNC/MeNTA electrodes showed better performance than AuNC/PPyNW electrodes: The former was a remarkable electrocatalytic detector towards glucose oxidation with better sensitivity, lower detection limit, wider linear range, and longer-term stability without interference from potential interfering agents such as ascorbic acid, urea, NaCl, KCl, etc. Moreover, nonenzymatic AuNC/MeNTA electrodes exhibited high precision and accuracy in real human blood samples and, thus, can be a promising candidate in glucose sensing applications. [ABSTRACT FROM AUTHOR]

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

This study aimed to investigate the association between glucose dysregulation and delirium after non-cardiac surgery. Among a total of 203,787 patients who underwent non-cardiac surgery between January 2011 and June 2019 at our institution, we selected 61,805 with available preoperative blood glucose levels within 24 h before surgery. Patients experiencing glucose dysregulation were divided into three groups: hyperglycemia, hypoglycemia, and both. We compared the incidence of postoperative delirium within 30 days after surgery between exposed and unexposed patients according to the type of glucose dysregulation. The overall incidence of hyperglycemia, hypoglycemia, and both was 5851 (9.5%), 1452 (2.3%), and 145 (0.2%), respectively. The rate of delirium per 100 person-months of the exposed group was higher than that of the unexposed group in all types of glucose dysregulation. After adjustment, the hazard ratios of glucose dysregulation in the development of delirium were 1.35 (95% CI, 1.18–1.56) in hyperglycemia, 1.36 (95% CI, 1.06–1.75) in hypoglycemia, and 3.14 (95% CI, 1.27–7.77) in both. The subgroup analysis showed that exposure to hypoglycemia or both to hypo- and hyperglycemia was not associated with delirium in diabetic patients, but hyperglycemia was consistently associated with postoperative delirium regardless of the presence of diabetes. Preoperative glucose dysregulation was associated with increased risk of delirium after non-cardiac surgery. Our findings may be helpful for preventing postoperative delirium, and further investigations are required to verify the association and mechanisms for the effect we observed. [ABSTRACT FROM AUTHOR]

The ambulatory glucose profile (AGP) lacks sufficient statistical metrics and insightful graphs; indeed, it is missing important information on the temporal patterns of glucose variations. The AGP graph is difficult to interpret due to the overlapping metrics and fluctuations in glucose levels over 14 days. The objective of this proposed work is to overcome these challenges, specifically the lack of insightful information and difficulty in interpreting AGP graphs, to create a platform for decision assistance. The present work proposes 20 findings built from decision rules that were developed from a combination of AGP metrics and additional statistical metrics, which have the potential to identify patterns and insightful information on hyperglycemia and hypoglycemia. The "CGM Trace" webpage was developed, in which insightful metrics and graphical representations can be used to make inferences regarding the glucose data of any user. However, doctors (endocrinologists) can access the "Findings" tab for a summarized presentation of their patients' glycemic control. The findings were implemented for 67 patients' data, in which the data of 15 patients were collected from a clinical study and the data of 52 patients were gathered from a public dataset. The findings were validated by means of MANOVA (multivariate analysis of variance), wherein a p value of < 0.05 was obtained, depicting a strong significant correlation between the findings and the metrics. The proposed work from "CGM Trace" offers a deeper understanding of the CGM data, enhancing AGP reports for doctors to make treatment adjustments based on insightful information and hidden patterns for better diabetic management. [ABSTRACT FROM AUTHOR]

This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Understanding and monitoring silicon levels in the body is crucial due to its significant role in various physiological processes. Silicon, while often overshadowed by other minerals, plays a vital role in bone health, collagen formation, and connective tissue integrity. Moreover, recent research suggests its potential involvement in neurological health and the prevention of certain degenerative diseases. Investigating silicon levels becomes essential for a comprehensive understanding of its impact on overall health and well-being and paves the way for targeted interventions and personalized healthcare strategies. The approach presented in this paper is based on the integration of hyperspectral data and artificial intelligence techniques. The algorithm investigates the effectiveness of two distinct models utilizing SVMR and a perceptron independently. SVMR is employed to establish a robust regression model that maps input features to continuous glucose and silicon values. The study outlines the methodology, including feature selection, model training, and evaluation metrics. Experimental results demonstrate the algorithm's effectiveness at accurately predicting glucose and silicon concentrations and showcases its potential for real-world application in continuous glucose and silicon monitoring systems. [ABSTRACT FROM AUTHOR]

Diabetes, which is the seventh leading cause of death globally, necessitates real-time blood glucose monitoring, a process that is often invasive. A promising alternative is sweat glucose monitoring, which typically uses transition metals and their oxide nanomaterials as sensors. Despite their excellent surface-to-volume ratio, these materials have some drawbacks, including poor conductivity, structural collapse, and aggregation. As a result, selecting highly electroconductive materials and optimizing their nanostructures is critical. In this work, we developed a high-performance, low-cost, nonenzymatic sensor for sweat glucose detection, using the thermally grown native oxide of copper (CuNOx). By heating Cu foil at 160, 250, and 280 °C, we grew a native oxide layer of approximately 140 nm cupric oxide (CuO), which is excellent for glucose electrocatalysis. Using cyclic voltammetry, we found that our CuNOx sensors prepared at 280 °C exhibited a sensitivity of 1795 μA mM−1 cm−2, a linear range up to the desired limit of 1.00 mM for sweat glucose with excellent linearity (R2 = 0.9844), and a lower limit of detection of 135.39 μM. For glucose sensing, the redox couple Cu(II)/Cu(III) oxidizes glucose to gluconolactone and subsequently to gluconic acid, producing an oxidation current in an alkaline environment. Our sensors showed excellent repeatability and stability (remaining stable for over a year) with a relative standard deviation (RSD) of 2.48% and 4.17%, respectively, for 1 mM glucose. The selectivity, when tested with common interferants found in human sweat and blood, showed an RSD of 4.32%. We hope that the electrocatalytic efficacy of the thermally grown CuNOx sensors for glucose sensing can introduce new avenues in the fabrication of sweat glucose sensors. [ABSTRACT FROM AUTHOR]

Obesity or excessive weight gain is identified as the most important and significant risk factor in the development and progression of type 2 diabetes mellitus (DM) in all age groups. It has reached pandemic dimensions, making the treatment of obesity crucial in the prevention and management of type 2 DM worldwide. Multiple clinical studies have demonstrated that moderate and sustained weight loss can improve blood glucose levels, insulin action and reduce the need for diabetic medications. A combined approach of diet, exercise and lifestyle modifications can successfully reduce obesity and subsequently ameliorate the ill effects and deadly complications of DM. This approach also helps largely in the prevention, control and remission of DM. Obesity and DM are chronic diseases that are increasing globally, requiring new approaches to manage and prevent diabetes in obese individuals. Therefore, it is essential to understand the mechanistic link between the two and design a comprehensive approach to increase life expectancy and improve the quality of life in patients with type 2 DM and obesity. This literature review provides explicit information on the clinical definitions of obesity and type 2 DM, the incidence and prevalence of type 2 DM in obese individuals, the indispensable role of obesity in the pathophysiology of type 2 DM and their mechanistic link. It also discusses clinical studies and outlines the recent management approaches for the treatment of these associated conditions. Additionally, in vivo studies on obesity and type 2 DM are discussed here as they pave the way for more rigorous development of therapeutic approaches. [ABSTRACT FROM AUTHOR]

Introduction: Sufficiently high analytical quality of blood glucose monitoring systems (BGMS) is a prerequisite for efficient diabetes therapy. In this study we assessed system accuracy, measurement repeatability, intermediate measurement precision, user performance, and the influence of hematocrit on two CE-marked blood glucose monitoring systems. For one BGMS, measurement accuracy using venous samples was additionally investigated. Methods: Study procedures were based on the International Organization of Standardization (ISO) 15197:2013/EN ISO 15197:2015 ("ISO 15197"). User performance included data from 100 subjects who used one test strip lot, whereas for all other analyses three different reagent system lots were used. For system accuracy assessment, 100 capillary samples were measured in duplicate with each of three reagent system lots per system, resulting in 600 results per system. Results: CareSens S Fit and CareSens H Beat both fulfilled the ISO 15197 accuracy criteria with 97.5–100% of each test strip lot's results falling within ± 15 mg/dL or ± 15% of the results of the comparison method and 100% of results in consensus error grid (CEG) zone A for all three lots. User performance evaluation revealed sufficient accuracy in the hands of lay users although some handling errors were documented by study staff. Assessment of measurement repeatability and intermediate measurement precision is given by standard deviation (SD) (glucose levels < 100 mg/dL) and by coefficient of variation (CV) (glucose concentrations ≥ 100 mg/dL). SD was ≤ 4.1 mg/dL and CV ≤ 4.2% for measurement repeatability and SD was ≤ 2.2 mg/dL and CV ≤ 2.6% for intermediate measurement precision. In case of hematocrit influence, both BGMS complied with all three tested lots with the defined criteria. Conclusion: Both BGMS analyzed in this study fulfilled the required accuracy criteria of ISO 15197. They showed high precision, good performance in the hands of lay users, and the influence of hematocrit was acceptable in the labeled range. [ABSTRACT FROM AUTHOR]

Objective: To establish an accurate and robust calculation model for predicting hemoglobin A1c (HbA1c) for people with type 2 diabetes (T2D) by using the fewest discrete blood glucose values according to an irregular data set and propose an appropriate cost-effective and scientific scheme for routine blood glucose monitoring. Methods: By using two data sets obtained from 2017 to 2022, which involved 2432 people with T2D, ∼420,000 irregular blood glucose values, and 10,000 HbA1c values, multiple blood glucose monitoring schemes were designed and compared to find the optimal one. The data were structured and then fitted using a regularized extreme learning machine, and the results were evaluated on the basis of indicators such as mean absolute error (MAE), root mean square error, and the relevance analysis (R) value; the optimal scheme for routine blood glucose monitoring was determined by combining the accuracy and the cost and was compared with previous studies in terms of accuracy and stability. Results: Data fitting results for the chosen scheme: R = 0.8029 (P < 0.001), MAE = 0.3181% (95% confidence interval, 0.2666–0.3695%). Within the last 4 weeks before the prediction of HbA1c, a minimum of only seven fasting and seven postprandial blood glucose values are needed, of which are one fasting and one postprandial blood glucose values per 4 days. Compared with previous studies, the prediction model shows better accuracy and stability (P < 0.05), especially under the great glucose fluctuation group. Conclusion: A minimized calculation model for accurately and robustly predicting HbA1c using discrete self-monitoring of blood glucose data within 4 weeks for people with T2D has been established and provides a new reference for the design of a scheme for blood glucose monitoring. The diabetes care clinic of Peking University First Hospital (Registration Number: ChiCTR2300068139). [ABSTRACT FROM AUTHOR]

Background: Uptake of exercise in people with type 1 diabetes (T1D) is low despite significant health benefits. Fear of hypoglycemia is the main barrier to exercise. Continuous glucose monitoring (CGM) with predictive alarms warning of impending hypoglycemia may improve self-management of diabetes around exercise. Aim: To assess the impact of Dexcom G6 real-time CGM system with a predictive hypoglycemia alert function on the frequency, duration, and severity of hypoglycemia occurring during and after regular (≥150 min/week) physical activity in people with T1D. Methods: After 10 days of blinded run-in (Baseline), CGM was unblinded and participants randomized 1:1 to have the "urgent low soon" (ULS) alert switched "on" or "off" for 40 days. Participants then switched alerts "off" or "on," respectively, for a further 40 days. Physical activity, and carbohydrate and insulin doses were recorded. Results: Twenty-four participants (8 men, 16 women) were randomized. There was no difference in change from baseline of hypoglycemia <3.0 and <3.9 mmol/L with the ULS on or off during the 24 h after exercise. With ULS alert "on" time spent below 2.8 mmol/L compared with baseline was significantly (P = 0.04) lower than with ULS "off" in the 24 h after exercise. In mixed effects regression, timing of the exercise and baseline HbA1c independently affected risk of hypoglycemia during exercise; exercise timing also affected hypoglycemia risk after exercise. Conclusion: A CGM device with an ULS alert reduces exposure to hypoglycemia below 2.8 mmol/L overall and in the 24 h after exercise compared with a threshold alert. [ABSTRACT FROM AUTHOR]

Background: The purpose of this 6-month intervention pilot feasibility randomised trial was to test sending brief messages using mobile phones to promote self-management through taking medication as prescribed to people with type 2 diabetes. This was to inform the design and conduct of a future large-scale United Kingdom-based clinical trial and establish the feasibility of recruitment, the technology used, follow-up, and data collection. Methods: A multicentre individually randomised, controlled parallel group trial in primary care, recruiting adults (≥ 35 years) with type 2 diabetes in England. Consenting participants were randomly allocated to receive short message system text messages up to four times a week, or usual care, for a period of 6 months; messages contained behavioural change techniques targeting medication use. The primary outcome was the rate of recruitment to randomisation of participants to the trial with a planned rate of 22 participants randomised per month. The study also aimed to establish the feasibility of follow-up at 6 months, with an aim of retaining more than 80% of participants. Data, including patient-reported measures, were collected at baseline and the end of the 6-month follow-up period, and a notes review was completed at 24 months. Results: The trial took place between 26 November 2018 and 30 September 2019. In total 209 participants were randomly allocated to intervention (n = 103) or usual care (n = 106). The maximum rate of monthly recruitment to the trial was 60–80 participants per month. In total, 12,734 messages were sent to participants. Of these messages, 47 were identified as having failed to be sent by the service provider. Participants sent 2,864 messages to the automated messaging system. Baseline data from medical records were available for > 90% of participants with the exception of cholesterol (78.9%). At 6 months, a further HbA1c measurement was reported for 67% of participants. In total medical record data were available at 6 months for 207 (99.0%) of participants and completed self-report data were available for 177 (84.7%) of participants. Conclusion: The feasibility of a large-scale randomised evaluation of brief message intervention for people with type 2 diabetes appears to be high using this efficient design. Failure rate of sending messages is low, rapid recruitment was achieved among people with type 2 diabetes, clinical data is available on participants from routine medical records and self-report of economic measures was acceptable. Trial registration: ISCTRN ISRCTN13404264. Registered on 10 October 2018. [ABSTRACT FROM AUTHOR]

The study aimed to identify the most predictive factors for the development of type 2 diabetes. Using an XGboost classification model, we projected type 2 diabetes incidence over a 10-year horizon. We deliberately minimized the selection of baseline factors to fully exploit the rich dataset from the UK Biobank. The predictive value of features was assessed using shap values, with model performance evaluated via Receiver Operating Characteristic Area Under the Curve, sensitivity, and specificity. Data from the UK Biobank, encompassing a vast population with comprehensive demographic and health data, was employed. The study enrolled 450,000 participants aged 40–69, excluding those with pre-existing diabetes. Among 448,277 participants, 12,148 developed type 2 diabetes within a decade. HbA1c emerged as the foremost predictor, followed by BMI, waist circumference, blood glucose, family history of diabetes, gamma-glutamyl transferase, waist-hip ratio, HDL cholesterol, age, and urate. Our XGboost model achieved a Receiver Operating Characteristic Area Under the Curve of 0.9 for 10-year type 2 diabetes prediction, with a reduced 10-feature model achieving 0.88. Easily measurable biological factors surpassed traditional risk factors like diet, physical activity, and socioeconomic status in predicting type 2 diabetes. Furthermore, high prediction accuracy could be maintained using just the top 10 biological factors, with additional ones offering marginal improvements. These findings underscore the significance of biological markers in type 2 diabetes prediction. [ABSTRACT FROM AUTHOR]

Background: In diabetes, high blood glucose induces glucotoxicity, resulting in the further damage of pancreatic beta-cells and then precipitating diabetic complications. This study was aimed to investigate the relationship between glucotoxicity with the level of adipokines, diabetic cardiomyopathy, and hematological markers. Moreover, the study examined the potential modulatory effect of coenzyme Q10 (CoQ10) on the aforementioned markers associated with the sequelae of diabetes mellitus. Material and methods: Twenty-four male rats were randomly assigned to receive an injection of STZ to induce diabetes (n = 16) or to remain uninduced (n = 8). The hyperglycemic status was induced in fasting rats by single intraperitoneal injection of STZ (45 mg /kg b.w.) dissolved in citrate buffer (pH 4.5). Three days after STZ injection, rats were divided into three groups; Normal control group (A), Diabetic control group (B), and CoQ10- treated diabetic group (C). The group (C) was fed with the basal diet supplemented with 5 g of CoQ10 per kilogram of diet for three weeks after the diabetes induction. After 21 days, the blood and serum samples were taken to conduct biochemical analyses. Blood glucose was determined by Blood Glucose Monitoring System. Adipokines or cytokines were evaluated by ELISA from a serum sample. Cardiac myopathy biomarkers were estimated by UP-Converting Phosphor Immunoassay Analyzer, and hematological parameters were measured by automatic hematology analyzer. Results: In hyperglycemic rats, the level of fasting blood glucose, and serum level of resistin, omentin, TNF-α, and cardiomyopathy biomarkers significantly increased (P < 0.05). The treatment with CoQ10 significantly decreased the profile of adipokines and cardiomyopathy markers (cardiac enzymes and LPPLA2) in diabetic rats and also reduced glucose levels (P < 0.05). Lymphocyte percentages significantly decreased while significant increases were observed in granulocytes and MID percentages in hyperglycemic rats. Conclusion: Diabetic rats had higher serum levels of adipokines and cardiomyopathy markers. Among the hematological markers, GRA% and MID% increased while LYM% decreased. The profile of adipokines and cardiomyopathy markers improved when CoQ10 was supplemented. The study suggests that CoQ10 may have a beneficial effect on improving diabetic complications. [ABSTRACT FROM AUTHOR]

Diabetes patients need to monitor blood glucose all year round. In this article, a novel scheme is proposed for blood glucose detection. The proposed sensor is based on a U-shaped microfiber prepared using hydrogen-oxygen flame-heating technology, and then 3-aminopropyltriethoxysilane (APTES) and glucose oxidase (GOD) are successively coated on the surface of the U-shaped microfiber via a coating technique. The glucose reacts with the GOD of the sensor surface to produce gluconic acid, which changes the effective refractive index and then shifts the interference wavelength. The structure and morphology of the sensor were characterized via scanning electron microscope (SEM) and confocal laser microscopy (CLM). The experimental results show that the sensitivity of the sensor is as high as 5.73 nm/(mg/mL). Compared with the glucose sensor composed of the same material, the sensitivity of the sensor increased by 329%. The proposed sensor has a broad application prospect in blood glucose detection of diabetic patients due to the advantages of miniaturization, high sensitivity, and good stability. [ABSTRACT FROM AUTHOR]

This study examined the level of adherence to self-care behaviors among individuals with type 2 diabetes in Saudi Arabia and its connection with depression and demographic factors. A cross-sectional survey was conducted among diabetes patients using the Patient Health Questionnaire (PHQ-9) to measure depression and the Summary of Diabetes Self-Care Activities (SDSCA) to evaluate diabetes self-care activities. Among the 252 participants who completed the survey, 43.2% were older than 55 and 59% were men. The ordinal regression model showed an association between the PHQ-9 and SDSCA scores with an OR of 0.83 (95% CI: 0.71 to 0.96, p = 0.013). The PHQ-9 score was significantly associated with blood sugar monitoring (OR: 0.90 [95% CI: 0.82 to 0.99, p = 0.003]), exercise (OR: 0.88 [95% CI: 0.79 to 0.98, p = 0.002]), and diet (OR: 0.94 [95% CI: 0.85 to 1.03, p = 0.045]). Of all the diabetes-related factors, only a history of hospitalization and receiving diabetes education were found to be associated with improved self-care behaviors. In conclusion, a negative association was found between PHQ-9 scores and the SDSCA mean score and most daily diabetic self-care behavior components. [ABSTRACT FROM AUTHOR]

Background: Intrahepatic cholestasis of pregnancy (ICP) is an idiopathic disease of pregnancy. Little is known about how it specifically affects pregnancies resulting from in vitro fertilization (IVF). Our aim is to evaluate the impact of IVF on the perinatal outcomes of ICP. Methods: A retrospective study of 242 patients with intrahepatic cholestasis of pregnancy, comprising 36 conceived through IVF and 206 spontaneous conceptions (SC), enrolled between 2019 and 2021 was carried out. Data were analyzed from the medical archives of the Huazhong University of Science and Technology, Tongji Hospital. Results: Numerical values of transaminases (ALT, alanine aminotransferase; AST, aspartate aminotransferase) and serum total bile acid (TBA) are significantly lower in the IVF group than that in the spontaneous conceived group (p < 0.05). The incidence of gestational diabetes mellitus (GDM) was higher in the IVF group than in SC group (30.6% vs. 16%, p = 0.037). The cesarean section (CS) rates are higher in the IVF group (97.2% vs. 85.4%, p = 0.023). On the other hand, the prevalence of premature rupture of membranes (PROM) was higher in the SC group (10.7%) while none was reported in the IVF-ICP group. Other maternal comorbidities and neonatal outcomes were similar between the two groups. Conclusion: ICP patients who underwent IVF are more likely to suffer from GDM. Therefore, monitoring and management of blood glucose should be strengthened during pregnancy. Fortunately, IVF does not seem to worsen the progression or outlook of ICP, so sticking to standard management practices is recommended. [ABSTRACT FROM AUTHOR]

Introduction: Patients with diabetes mellitus monitor their blood glucose at home with monitors that require a drop of blood or use a continuous glucose monitoring device that implants a small needle in the body. However, both cause discomfort to the patients which may inhibit them for regular blood glucose checks. Photoplethysmogram (PPG) sensing technology is an approach for non-invasive blood glucose measurement and PPG sensors can be used to predict hypoglycaemic episodes. InChcek is a PPG-based non-invasive glucose monitor. However, its accuracy has not been checked yet. Hence, this study aimed to evaluate the accuracy of InCheck, a non-invasive glucose monitor for the estimation of blood glucose. Methods: In a tertiary care hospital, patients who came for blood glucose estimation were tested for blood glucose non-invasively on the InCheck device and then by the laboratory method (glucose oxidase-peroxidase). These two readings were compared. We used International Organization for Standardization (ISO) 15197:2013 (95% of values should be within ± 15 mg/dL of reference reading if reference glucose <100 mg/dL or within ± 15% of reference reading if reference glucose ≥100 mg/dL and 99% of the values should be within zones A and B in consensus error grid), and Surveillance Error Grid for analyzing the accuracy. Results: A total of 1223 samples were analyzed. There was a significant difference between the reference method glucose level (135 [Q1-Q3: 97 – 179] mg/dL) and monitor-measured glucose level (188.33 [Q1-Q3: 167.33-209.33] mg/dL) (P < 0.0001). A total of 18.5% of readings were following ISO 15197:2013 criteria and 67.25% of coordinates were within zone A and zone B of the consensus error grid. In the surveillance error grid analysis, about 29.4% of values were in the no-risk zone, 51.8% in slight risk, 18.6% in moderate risk, and 0.2% were in the severe risk zone. Conclusion: The accuracy of the InCheck device for the estimation of blood glucose by PPG signal is not following the recommended guidelines. Hence, further research is necessary for programming or redesigning the hardware and software for a better result from this optical sensor-based non-invasive home glucose monitor. [ABSTRACT FROM AUTHOR]

Introduction: Time in range (TIR), a metric of continuous glucose monitoring (CGM) provides better information regarding the individual's glycemic variability than a static measure like glycated hemoglobin (HbA1c). TIR is emerging as an independent risk factor for diabetic complications, both microvascular and macrovascular complications independent of HbA1c. Hence, this study evaluates the association between TIR and cardiac autonomic neuropathy (CAN) in type 2 diabetic patients. Materials and Methods: A total of 42 patients with type 2 diabetes mellitus were enrolled in this study and underwent a 3-day CGM using the "FreeStyle Libre Pro Flash Glucose Monitoring System Sensor" along with tests for CAN within the 3 days of attaching the CGM. Results: Out of 42 patients, 36 patients (85.7%) were diagnosed with CAN (early CAN 57.1% and definite CAN 28.6%) and the mean TIR was 64.4% ±23.5%. Out of those with TIR <70%, 42.9% were affected with definite CAN compared to only 14.3% among those with TIR >70%. Patients with more severe CAN were found to have a lower TIR (P = 0.115). Conclusion: The study found a high prevalence of cardiac autonomic neuropathy (CAN) of around 85.7% in type 2 diabetes patients. Lower TIR values were associated with a higher incidence of definite CAN (42.9% vs. 14.3% in TIR <70% vs. >70% groups). The findings suggest TIR is inversely associated with the presence and severity of cardiac autonomic neuropathy in type 2 diabetic patients and also a potential link between TIR and CAN severity. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Cinnamon zeylanicum (CZ) bark is widely used as supplement for diabetic management, there are concerns about its safety and potential toxicity at high doses, and limited evidence to support its efficacy. To investigate this further, this study examines the effects of Cinnamon zeylanicum aqueous extract (CZAE) on various factors in diabetic rats, including body weight, blood glucose level, biochemical parameters, histological changes, and gene expression. MATERIALS AND METHODS: In the study, 30 male Sprague-Dawley rats were divided into five groups of six animals each. Type 2 diabetes mellitus (T2DM) was induced in all groups except the negative control by a single intraperitoneal injection of streptozotocin (STZ). The rats in the intervention groups (Groups C, D, and E) were given CZAE at 1000, 1500, and 2000 mg/kg, dose respectively, for 28 days. The body weight and fasting blood glucose were monitored weekly, and their liver and renal profiles were analyzed. Histology was assessed with hematoxylin and eosin stain, and apoptotic gene expression was examined in liver and renal tissues. RESULTS: The body weight of rats in intervention groups increased compared to the control group. There was a significant decrease (P<0.001) in blood glucose levels. The extract significantly reduced (P<0.05) liver biochemical markers in the intervention groups compared to the control group. The histology of the liver & kidney improved (p<0.001) with upregulated Bcl-2 and down-regulated BAX genes in preventing apoptosis in the intervention groups. CONCLUSIONS: High doses of CZAE are safe and effective for T2DM. [ABSTRACT FROM AUTHOR]

Self-management behavior among diabetes patients is essential to monitor blood sugar levels, make necessary lifestyle changes, and help patients reduce their risk of complications from diabetes. We assessed the prevalence of metabolic syndrome (MS) and its association with self-management behavior and sociodemographic characteristics among 310 patients with type 2 diabetes mellitus (T2DM) attending different diabetes care centers in northern Saudi Arabia. We evaluated the self-management behaviors of patients with T2DM using a validated Arabic version of the Summary of Diabetes Self-Care Activities Scale. Regarding MS, we applied the National Cholesterol Education Program (NCEP) Adult Treatment Plan–3 (ATP–3) guidelines. A logistic regression analysis was used to identify the predictors of MS. We found that more than one-third (36.5%) of patients had MS according to the NCEP ATP-3 criteria. The prevalence of MS was significantly associated with unsatisfactory self-management behaviors. Regarding sociodemographic predictors for MS, we found a significant association between gender (ref: female: Adjusted OR (AOR) = 1.89, 95%CI = 1.17–2.95, p = 0.007) and body mass index (ref.: normal range: AOR = 2.98, 95%CI = 1.31–5.07, p = 0.003). Our findings suggest a tailor-made multifaceted intervention to improve the self-management behaviors of T2DM patients, which, in turn, can reduce MS. [ABSTRACT FROM AUTHOR]

A novel and self referenced multi-analyte Hybrid Plasmonic Dual Grating Biosensor is proposed. The grating structure is composed of gold and corrugations are filled with dielectric ZnS. A thin layer of ZnS with slit is spread over grating thus making it dual grating structure. The influence of convexing of slit walls is modeled by parameter alpha in analytical equation. The reflectance characteristics are rigorously investigated using wavelength interrogation method for different values of parameter alpha, it is observed that wavelength varies from 722.2 to 729.6 nm for surface refractive index in the slit varying from 1.3 to 1.4 respectively at alpha equal to 0. The surface sensitivity obtained is about 74 nm/RIU. The surface sensitivity is also investigated by intensity interrogation and is equal to 0.2637 R/RIU or 26.37% R/RIU. It is observed that for higher values of alpha, the sensor is ideal for sensing gaseous analytes while at lower values of alpha can be used for liquid analytes. Refractive index range of surface analyte is correlated with the refractive index of blood by using linear refractive index model and Gladstone-Dale law for blood. It is observed that for alpha equal to 0, the grating biosensor can be used for invasive blood glucose monitoring with very high sensitivity of about 0.02641 nm/mg/dl. Linear regression model relating blood glucose and salivary glucose is used. The proposed sensor can also be used for noninvasive salivary glucose monitoring with surface sensitivity of 0.7410 nm/mg/dl. [ABSTRACT FROM AUTHOR]

The body's glucose concentration is influenced by carbohydrate intake, insulin-induced carbohydrate reduction, and hepatic glycogen breakdown induced by stress hormones. This study investigated the potential of employing glucose fluctuations as a measure of stress by examining the relationship between heart rate variability (HRV) data and glucose levels during sleep in healthy subjects. In this cross-sectional study, a chest-worn electrocardiogram (ECG) and continuous glucose monitoring device (CGM) were respectively used to monitor the heart rate intervals and glucose fluctuations of five subjects (two males, three females) during sleep. A time-series correlation analysis was performed on the HRV data extracted from heart rate intervals and the corresponding glucose fluctuation data. The time-series analysis of ECG and CGM data collected from subjects during sleep (n = 25 nights) revealed a moderate negative correlation between glucose levels and HRV, with a cross-correlation coefficient of r = −0.453. Similar to HRV, changes in stress levels can be detected by observing glucose fluctuations, particularly during sleep when the impact of food intake can be eliminated. Our findings highlight a significant correlation between glucose levels and HRV, indicating that glucose fluctuations can be used as an indicator of autonomic nervous system activity in an exploratory study. • Glucose levels were analyzed to assess their correlation with heart rate variability (HRV). • A cross-sectional study using continuous glucose monitoring and chest-worn electrocardiogram devices to collect data during sleep. • Glucose fluctuations can serve as an indicator of autonomic nervous system activity. [ABSTRACT FROM AUTHOR]

Over the past two decades, there has been continuous advancement in the accuracy and complexity of continuous glucose monitoring devices. Continuous glucose monitoring provides valuable insights into blood glucose dynamics, and can record glucose fluctuations accurately and completely. Glycemic variability (GV) is a straightforward measure of the extent to which a patient's blood glucose levels fluctuate between high peaks and low nadirs. Many studies have investigated the relationship between GV and complications, primarily in the context of type 2 diabetes. Nevertheless, the exact contribution of GV to the development of diabetes complications remains unclear. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population‐based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. In the future, more longitudinal studies and trials are required to confirm the exact role of GV in the development of diabetes complications. The role of glycemic variability (GV) in the development of diabetes complications remains uncertain. In this literature review, we aimed to summarize the existing evidence regarding the measurement, target level, pathophysiological mechanisms relating GV and tissue damage, and population‐based studies of GV and diabetes complications. Additionally, we introduce novel methods for measuring GV, and discuss several unresolved issues of GV. [ABSTRACT FROM AUTHOR]

Patients with type 1 diabetes mellitus (T1D) require insulin administration at all times to maintain euglycemia and metabolic stability. Insulin administration in the perioperative period is complicated by fasting requirements and perioperative stressors that can change the patient's insulin needs. In addition, many anesthesia providers are not familiar with insulin dosing strategies and technology, such as insulin pumps and continuous glucose monitors (CGMs), that are commonly used by patients with T1D. Errors in perioperative insulin administration can lead to hypoglycemia, hyperglycemia, and diabetic ketoacidosis. This article reviews common errors of associated with the perioperative management of patients with T1D, including failure to assess and coordinate patient care preoperatively; failure to understand diabetes management and technology; failure to monitor blood glucose and recognize dysglycemia; and failure to appropriately administer basal insulin. [ABSTRACT FROM AUTHOR]

Diabetes increases the production of free radicals and inflammatory agents in the heart tissue and alters the expression of genes associated with the induction of apoptosis. Considering the importance of common cardiovascular disorders in diabetes, this study investigated the effect of eight weeks of aerobic exercise and crocin use, as well as tissue damage and oxidative stress caused by diabetes in the hearts of adult rats. Streptozotocin 50 mg/kg was injected as a single dose intraperitoneally to cause the diabetes. After 72 hours, a glucometer monitored blood glucose levels, and blood glucose above 250 mg/dl was considered diabetes. Continuous treadmill exercise was performed for eight weeks by placing the animal on the treadmill. Next, the animals were anesthetized, and samples were taken from the hearts and frozen in liquid nitrogen. Then, superoxide dismutase (SOD), glutathione peroxidase (GPx), and malondialdehyde (MDA) were measured in the cardiac tissue. Finally, the hearts of half of the animals were immediately immersed in a formalin solution for histological changes. According to our findings, diabetes increased lipid peroxidation, characterized by increased MDA levels in the control diabetes group and decreased SOD and GPx levels (P <0.05). It also changes the balance of expression of genes associated with apoptosis control, increased Bcl-2-associated X (Bax) expression, and decreased Bcl-2 expression (P <0.05). Also, we observed the induction of apoptosis in cardiac tissue. Using eight weeks of continuous exercise and administration of crocin significantly reduced blood sugar levels and lipid peroxidation and increased the activity of antioxidant enzymes and Bcl-2 gene expression compared to the diabetes control group. In addition, continuous exercise and crocin improved the oxidative stress parameters in the control group. This study showed that diabetes could cause oxidative stress and heart dysfunction. Moreover, simultaneously and separately, aerobic exercise with a treadmill and crocin administration can reduce these disorders and prevent apoptosis in the heart tissue. [ABSTRACT FROM AUTHOR]

The determination of the body's glucose level is a difficult job that has served as the foundation for substantial research efforts in biosensing applications. Recent advances in material engineering and synthesis methodologies have had a significant influence on the development of enzymatic and non‐enzymatic sensors for glucose detection that may be used in managing diabetes, food technology, and clinical, and bio‐processing applications. The intricate fabrication, detecting principles and phenomena involved, material alterations, and design to improve sensor performance. There is a great need for high‐efficiency, low‐cost blood glucose sensors that may be used on industrial and domestic scales. This review is primarily intended to shed light on the current state of knowledge about the use of graphene. The performance of non‐enzymatic glucose sensors is comprehensively compared with that of enzymatic electrochemical sensors based on graphene. This study will go over the research efforts that have been conducted to build biosensors employing nanostructures such as carbon nanotubes, graphene, metal nanoparticles, metal oxides, and metal sulfides. The manufacturing of the very sensitive enzyme‐free glucose biosensor will be thoroughly discussed. A thorough examination of the use of graphene as a co‐catalyst to improve the sensitivity of electrochemical sensors for glucose detection is also provided. [ABSTRACT FROM AUTHOR]

This study addresses the global challenge of effectively managing diabetes through self-care practices, including medication adherence, exercise, blood glucose monitoring, foot care, and diet. Our research aimed to evaluate self-care practices among type 2 diabetic patients, using the validated Summary of Diabetes Self-Care Activities scale (SDSCA), and to analyze associations with age, gender, education, and socioeconomic status. This cross-sectional study was conducted at District General Hospital Chilaw, Sri Lanka, and involved 187 participants. Data analysis employed SPSS version 25. The results highlighted suboptimal self-care behaviors and medication adherence, emphasizing the need for integrated interventions to enhance knowledge and self-care among "Type 2 Diabetes Mellitus" patients. [ABSTRACT FROM AUTHOR]

Background: Glycated hemoglobin measurements are a valuable tool for long-term blood glucose monitoring and the diagnosis of diabetes. Its widespread use has been made possible due to the development of new analytical methods with improved performances and standardization with reference materials. The aim of the present study was to evaluate the Trinity Biotech Premier Hb9210 analyzer for the measurement of HbA1c. Methods: The precision was assessed using the CLSI EP-15A3 and EP-10A3 protocols. The latter was also used to investigate linearity, carryover, and linear drift. The comparison study was performed between Premier Hb910 and Tosoh HLC-723 G8 through Passing–Bablok regression and the Bland-Altman plot. The Fleiss Kappa index was used to assess the degree of agreement. The interference of Hb variants was investigated using samples with Hb variants S, C, D, E, J, and Seville. Results: Within-run and between-run imprecision fell between 0.37% and 1.16%. No statistically significant nonlinearity, carry-over, and/or drift were observed. The resulting regression line of the Passing–Bablok analysis was y = 0.00 + 1.00x. The Pearson correlation coefficient was 0.997. In the Bland–Altman plot, the relative bias was 0.01%. The overall Fleiss Kappa index was 0.9. No interference from hemoglobin variants was observed. Conclusion: The Premier Hb9210 demonstrated a high degree of automation, reproducibility, good agreement, minimal carry-over effect, and excellent linearity across the wide range of HbA1c levels commonly found in diabetic patients and was not influenced by Hb variants. Premier Hb9210 can be used as an alternative to monitor glycemic status. Premier Hb9210 is not affected by common hemoglobin variants. Premier Hb9210 can correctly classify diabetic patients. [ABSTRACT FROM AUTHOR]

As a supplemental technique for disease diagnosis, expelled breath monitoring is becoming extremely prevalent. Nevertheless, because of the significant number of factors that must be considered, researchers are forced to create new algorithms for accurate data interpretations. In our study, a novel noninvasive breath biomarkers-based diabetic detection strategy is introduced by upcoming three main processes "pre-processing, feature extraction and diabetes detection". Raw data (breath signal) are initially pre-processed via data normalization to increase acquired breath signal quality. Most important features, such as "Improved empirical wavelet functions (IEWF), empirical mode decomposition (EMD), local mean decomposition (LMD), continuous wavelet transform (CWT) method, and entropy-based feature," are extracted from the pre-processed data. These attributes are fused together and are subsequently used to train the ensemble of classifiers in the diabetes detection stage. An ensemble of classifiers, including an "optimized quantum deep neural network (QDNN), a convolutional neural network (CNN), and a recurrent neural network (RNN)", is used to depict the diabetes detection phase. The CNN and RNN are trained using the retrieved fused features. The optimized QDNN receives the output of the CNN and RNN. The final detected result is provided by the optimized QDNN. A unique hybrid optimization model, Archimedes Principle with Enhanced Honey Badger Exploitation (APEHBE) algorithm, that combines the Archimedes Optimization Algorithm (ArchOA) and the Honey Badger Algorithm (HBA) is used to fine-tune the weight of QDNN. The effectiveness of the anticipated model is then verified by a comparison analysis. [ABSTRACT FROM AUTHOR]

Aims: Blood glucose control is central to the management of diabetes, and continuous glucose monitoring (CGM) improves glycemic control. We aimed to describe the perspectives of people with diabetes using CGM. Materials and methods: We performed a systematic review of qualitative studies. Results: Fifty‐four studies involving 1845 participants were included. Six themes were identified: gaining control and convenience (reducing pain and time, safeguarding against complications, achieving stricter glucose levels, and sharing responsibility with family); motivating self‐management (fostering ownership, and increasing awareness of glycemic control); providing reassurance and freedom (attaining peace of mind, and restoring social participation); developing confidence (encouraged by the endorsement of others, gaining operational skills, customizing settings for ease of use, and trust in the device); burdened with device complexities (bewildered by unfamiliar technology, reluctant to rely on algorithms, overwhelmed by data, frustrated with malfunctioning and inaccuracy, distressed by alerts, and bulkiness of machines interfering with lifestyle); and excluded by barriers to access (constrained by cost, lack of suppliers). Conclusions: CGM can improve self‐management and confidence in patients managing diabetes. However, the technical issues, uncertainty in readings, and cost may limit the uptake. Education and training from the health professionals may help to reduce the practical and psychological burden for better patient outcomes. [ABSTRACT FROM AUTHOR]