Impact of blood glucose on cognitive function in insulin resistance: novel insights from ambulatory assessment.

BACKGROUND/OBJECTIVES: Insulin resistance (IR)-related disorders and cognitive impairment lead to reduced quality of life and cause a significant strain on individuals and the public health system. Thus, we investigated the effects of insulin resistance (IR), and blood glucose fluctuations on cognitive function under laboratory and free-living conditions, using ecological momentary assessment (EMA). SUBJECTS/METHODS: Baseline assessments included neuropsychological tests and blood analysis. Individuals were classified as either insulin-sensitive (<2) or insulin-resistant (≥2), based on their Homeostatic Model Assessment (HOMA-IR) values. Continuous glucose monitoring (CGM) using a percutaneous sensor was performed for 1 week. Using multiple linear regression, we examined the effects of HOMA-IR and CGM metrics on cognitive domains. Working memory (WM) performance, which was assessed using EMA, 4 times a day for 3 consecutive days, was matched to short-term pre-task CGM metrics. Multilevel analysis was used to map the within-day associations of HOMA-IR, short-term CGM metrics, and WM. RESULTS: Analyses included 110 individuals (mean age 48.7 ± 14.3 years, 59% female, n = 53 insulin-resistant). IR was associated with lower global cognitive function (b = -0.267, P = 0.027), and WM (b = -0.316; P = 0.029), but not with executive function (b = -0.216; P = 0.154) during baseline. EMA showed that higher HOMA-IR was associated with lower within-day WM performance (ß = -0.20, 95% CI -0.40 to -0.00). CGM metrics were not associated with cognitive performance. CONCLUSIONS: The results confirm the association between IR and decrements in global cognitive functioning and WM, while no effects of CGM metrics were observed, making IR a crucial time point for intervention. Targeting underlying mechanisms (e.g., inflammation) in addition to glycemia could be promising to minimize adverse cognitive effects. Registered under https://drks.de/register/de identifier no. DRKS00022774.

Analytical Performance of the FreeStyle Libre 2 Glucose Sensor in Healthy Male Adults.

Continuous Glucose Monitoring (CGM) not only can be used for glycemic control in chronic diseases (e.g., diabetes), but is increasingly being utilized by individuals and athletes to monitor fluctuations in training and everyday life. However, it is not clear how accurately CGM reflects plasma glucose concentration in a healthy population in the absence of chronic diseases. In an oral glucose tolerance test (OGTT) with forty-four healthy male subjects (25.5 ± 4.5 years), the interstitial fluid glucose (ISFG) concentration obtained by a CGM sensor was compared against finger-prick capillary plasma glucose (CPG) concentration at fasting baseline (T0) and 30 (T30), 60 (T60), 90 (T90), and 120 (T120) min post OGTT to investigate differences in measurement accuracy. The overall mean absolute relative difference (MARD) was 12.9% (95%-CI: 11.8-14.0%). Approximately 100% of the ISFG values were within zones A and B in the Consensus Error Grid, indicating clinical accuracy. A paired t-test revealed statistically significant differences between CPG and ISFG at all time points (T0: 97.3 mg/dL vs. 89.7 mg/dL, T30: 159.9 mg/dL vs. 144.3 mg/dL, T60: 134.8 mg/dL vs. 126.2 mg/dL, T90: 113.7 mg/dL vs. 99.3 mg/dL, and T120: 91.8 mg/dL vs. 82.6 mg/dL; p < 0.001) with medium to large effect sizes (d = 0.57-1.02) and with ISFG systematically under-reporting the reference system CPG. CGM sensors provide a convenient and reliable method for monitoring blood glucose in the everyday lives of healthy adults. Nonetheless, their use in clinical settings wherein implications are drawn from CGM readings should be handled carefully.

Optical blood glucose non-invasive detection and its research progress.

Blood glucose concentration is an important index for the diagnosis of diabetes, its self-monitoring technology is the method for scientific diabetes management. Currently, the typical household blood glucose meters have achieved great success in diabetes management, but they are discrete detection methods, and involve invasive blood sampling procedures. Optical detection technologies, which use the physical properties of light to detect the glucose concentration in body fluids non-invasively, have shown great potential in non-invasive blood glucose detection. This article summarized and analyzed the basic principles, research status, existing problems, and application prospects of different optical glucose detection technologies. In addition, this article also discusses the problems of optical detection technology in wearable sensors and perspectives on the future of non-invasive blood glucose detection technology to improve blood glucose monitoring in diabetic patients.

Efficacy and Safety of Different Hybrid Closed Loop Systems for Automated Insulin Delivery in People With Type 1 Diabetes: A Systematic Review and Network Meta-Analysis.

AIMS: To compare the efficacy and safety of different hybrid closed loop (HCL) systems in people with diabetes through a network meta-analysis. METHODS: We searched MEDLINE, EMBASE, CENTRAL and PubMed for randomised clinical trials (RCTs) enrolling children, adolescents and/or adults with type 1 or type 2 diabetes, evaluating Minimed 670G, Minimed 780G, Control-IQ, CamAPS Fx, DBLG-1, DBLHU, and Omnipod 5 HCL systems against other types of insulin therapy, and reporting time in target range (TIR) as outcome. RESULTS: A total of 28 RCTs, all enrolling people with type 1 diabetes, were included. HCL systems significantly increased TIR compared with subcutaneous insulin therapy without continuous glucose monitoring (SIT). Minimed 780G achieved the highest TIR ahead of Control IQ (mean difference (MD) 5.1%, 95% confidence interval (95% CI) [0.68; 9.52], low certainty), Minimed 670G (MD 7.48%, 95% CI [4.27; 10.7], moderate certainty), CamAPS Fx (MD 8.94%, 95% CI [4.35; 13.54], low certainty), and DBLG1 (MD 10.69%, 95% CI [5.73; 15.65], low certainty). All HCL systems decreased time below target range, with DBLG1 (MD -3.69%, 95% CI [-5.2; -2.19], high certainty), Minimed 670G (MD -2.9%, 95% CI [-3.77; -2.04], moderate certainty) and Minimed 780G (MD -2.79%, 95% CI [-3.94; -1.64], high certainty) exhibiting the largest reductions compared to SIT. The risk of severe hypoglycaemia and diabetic ketoacidosis was similar to other types of insulin therapy. CONCLUSIONS: We show a hierarchy of efficacy among the different HCL systems in people with type 1 diabetes, thus providing support to clinical decision-making. TRIAL REGISTRATION: PROSPERO CRD42023453717.

Real-time continuous glucose monitoring vs. self-monitoring of blood glucose: cost-utility in South Korean type 2 diabetes patients on intensive insulin.

AIMS: This study investigated the cost-utility of real-time continuous glucose monitoring (rt-CGM) versus self-monitoring of blood glucose (SMBG) in people with type 2 diabetes (T2D) receiving intensive insulin therapy in South Korea. METHODS: The IQVIA Core Diabetes Model (CDM v9.5) was used, with clinical effectiveness data obtained from a large-scale real world study. Costs were obtained from South Korean sources and inflated to 2022 South Korean Won (KRW). A South Korean payer perspective was adopted over a lifetime horizon, with future costs and effects discounted at 4.5% per annum. Baseline characteristics included a mean baseline HbA1c level of 8.6% (71 mmol/mol), and a mean age of 64.4 years. A willingness-to-pay (WTP) threshold of KRW 46.0 million was used. RESULTS: Rt-CGM led to an increase of 0.683 quality-adjusted life years (QALYs) versus SMBG (7.526 QALYs for rt-CGM versus 6.843 QALYs for SMBG). An increase in costs of KRW 16.4 million (from KRW 90.4 million to KRW 106.8 million) was associated with rt-CGM. The incremental cost-utility ratio was KRW 24.0 million per QALY gained, significantly lower than the KRW 46 million threshold. CONCLUSIONS: For individuals with T2D managed by intensive insulin therapy in South Korea, rt-CGM is cost-effective relative to SMBG.

Hydrogel Capacitors Based on MoS2 Nanosheets and Applications in Glucose Monitoring.

Non-invasive/minimally invasive continuous monitoring of blood glucose and blood glucose administration have a high impact on chronic disease management in diabetic patients, but the existing technology is yet to achieve the above two purposes at the same time. Therefore, this study proposes a microfluidic microneedle patch based on 3D printing technology and an integrated control system design for blood glucose measurement, and a drug delivery control circuit based on a 555 chip. The proposed method provides an improved preparation of a PVA-PEG-MoS2 nanosheet hydrogel, making use of its dielectric properties to fabricate a microcapacitor and then embedding it in a microfluidic chip. When MoS2 nanosheets react with interstitial liquid glucose (and during the calibration process), the permittivity of the hydrogel is changed, resulting in changes in the capacitance of the capacitor. By converting the capacitance change into the square-wave period change in the output of the 555 chip with the control circuit design accordingly, the minimally invasive continuous measurement of blood glucose and the controlled release of hypoglycemic drugs are realized. In this study, the cross-linking structure of MoS2 nanosheets in hydrogel was examined using infrared spectroscopy and scanning electron microscopy (SEM) methods. Moreover, the critical doping mass fraction of MoS2 nanosheets was determined to be 2% via the measurement of the dielectric constant. Meanwhile, the circuit design and the relationship between the pulse cycle and glucose concentration is validated. The results show that, compared with capacitors in series, the microcapacitors embedded in microfluidic channels can be connected in parallel to obtain better linearized blood glucose measurement results.

Evaluating HbA1c-to-average glucose conversion with patient-specific kinetic models for diverse populations.

The discrepancy between estimated glycemia from HbA1c values and actual average glucose (AG) levels has significant implications for treatment decisions and patient understanding. Factors contributing to the gap include red blood cell (RBC) lifespan and glucose uptake into the RBC. Personalized models have been proposed to enhance AG prediction accuracy by considering interpersonal variation. This study contributes to our understanding of personalized models for estimating AG from HbA1c. Utilizing data from seven studies (340 participants), including Hispanic/Latino populations with or at risk of non-insulin-treated type 2 diabetes (T2D), we examined kinetic features across cohorts. Additionally, the study simulated scenarios to understand data requirements for improving accuracy. Personalized approaches improved agreement between AG estimations and CGM-AG, particularly with four or more weeks of training CGM data. A multiple linear regression model using kinetic parameters and added clinical features was shown to improve the accuracy of personalized models further. As CGM usage extends beyond type 1 diabetes, there is growing interest in leveraging CGM data for clinical decision-making. Patient-specific models offer a valuable tool for managing glycemic status in patients with discordant HbA1c and AG values.

Best practices and rationale for expanding Medicaid access to continuous glucose monitoring.

Numerous studies have demonstrated that use of continuous glucose monitoring (CGM) significantly improves overall glycemic control and reduces the frequency and severity of hypoglycemic events in individuals treated with intensive insulin, nonintensive insulin, and noninsulin therapies, with reductions in both all-cause and diabetes-related health care resource utilization and lower costs. However, implementation of CGM including prescribing and assessment of the ambulatory glucose profile to make clinical decisions in primary care settings is low. A recent pilot program was initiated at MetroHealth System (Cleveland, Ohio) to implement a CGM integration program for primary care offices throughout the system. Based on the experience and successes from this health system as well as current literature, rationale will be discussed to support the expansion of CGM to individuals enrolled in all Medicaid programs.

Long-Term Glycemic Control Improvement After the Home and Self-Care Program for Patients With Type 1 Diabetes: Real-World-Based Cohort Study.

BACKGROUND: The prevalence of type 1 diabetes (T1D) is increasing worldwide, with a much higher proportion of adult patients. However, achieving stable glycemic control is difficult in these patients. OBJECTIVE: After periodic implementation of structured education for patients with T1D through the Home and Self-Care Program, a pilot home health care project promoted by the Korean government, we evaluated the program's effects on glycemic control. METHODS: This study was conducted from April 2020 to March 2023. We analyzed 119 participants with T1D aged >15 years. Nursing and nutrition education were provided separately up to 4 times per year, with physician consultation up to 6 times per year. A distinguishing feature of this study compared with previous ones was the provision of remote support using a general-purpose smartphone communication app offered up to 12 times annually on an as-needed basis to enhance the continuity of in-person education effects. Patients were followed up on at average intervals of 3 months for up to 24 months. The primary end point was the mean difference in glycated hemoglobin (HbA1c) at each follow-up visit from baseline. For continuous glucose monitoring (CGM) users, CGM metrics were also evaluated. RESULTS: The mean HbA1c level of study participants was 8.6% at baseline (mean duration of T1D 10.02, SD 16.10 y). The HbA1c level reduction in participants who received at least 1 structured educational session went from 1.63% (SD 2.03%; P<.001; adjustment model=1.69%, 95% CI 1.24%-2.13% at the first follow-up visit) to 1.23% (SD 1.31%; P=.01; adjustment model=1.28%, 95% CI 0.78%-1.79% at the eighth follow-up visit). In the adjustment model, the actual mean HbA1c values were maintained between a minimum of 7.33% (95% CI 7.20%-7.46% at the first follow-up visit) and a maximum of 7.62% (95% CI 7.41%-7.82% at the sixth follow-up visit). Among CGM users, after at least 1 session, the mean time in the target range was maintained between 61.59% (adjusted model, 95% CI 58.14%-65.03% at the second follow-up visit) and 54.7% (95% CI 50.92%-58.48% at the eighth follow-up visit), consistently staying above 54.7% (corresponding to an HbA1c level of <7.6%). The mean time below the target range (TBR) also gradually improved to the recommended range (≤4% for TBR of <70 mg/dL and ≤1% for TBR of <54 mg/dL). CONCLUSIONS: The Home and Self-Care Program protocol for glycemic control in patients with T1D is effective, producing significant improvement immediately and long-term maintenance effects, including on CGM indexes.

Development of a three-dimensional scoring model for the assessment of continuous glucose monitoring data in type 1 diabetes.

INTRODUCTION: Despite the improvements in diabetes management by continuous glucose monitoring (CGM) it is difficult to capture the complexity of CGM data in one metric. We aimed to develop a clinically relevant multidimensional scoring model with the capacity to identify the most alarming CGM episodes and/or patients from a large cohort. RESEARCH DESIGN AND METHODS: Retrospective CGM data from 2017 to 2020 available in electronic medical records were collected from n=613 individuals with type 1 diabetes (total 82 114 days). A scoring model was developed based on three metrics; glycemic variability percentage, low blood glucose index and high blood glucose index. Values for each dimension were normalized to a numeric score between 0-100. To identify the most representative score for an extended time period, multiple ways to combine the mean score of each dimension were evaluated. Correlations of the scoring model with CGM metrics were computed. The scoring model was compared with interpretations of a clinical expert board (CEB). RESULTS: The dimension of hypoglycemia must be weighted to be representative, whereas the other two can be represented by their overall mean. The scoring model correlated well with established CGM metrics. Applying a score of ≥80 as the cut-off for identifying time periods with a 'true' target fulfillment (ie, reaching all targets for CGM metrics) resulted in an accuracy of 93.4% and a specificity of 97.1%. The accuracy of the scoring model when compared with the CEB was high for identifying the most alarming CGM curves within each dimension of glucose control (overall 86.5%). CONCLUSIONS: Our scoring model captures the complexity of CGM data and can identify both the most alarming dimension of glycemia and the individuals in most urgent need of assistance. This could become a valuable tool for population management at diabetes clinics to enable healthcare providers to stratify care to the patients in greatest need of clinical attention.

Microvolumetric determination of thrombomodulin based on competitive immunoreaction using a portable glucometer.

A new method of reducing the amount of reagent and sample for determination of thrombomodulin (TM) was developed based on competitive immunoreaction using a portable glucometer (PGM). Two types of nanocomposites, TM protein-modified magnetic nanoparticles (MNPs-TM) and TM antibody-/glucose oxidase-modified gold nanoparticles (Ab-GNPs-GOx), were prepared. Their binding product, MNPs-TM-Ab-GNPs-GOx, in the microvolumetric solution was used to catalyze the oxidation of glucose, leading to a decline of the glucose content. The TM-involved competitive immunoreaction had a negative effect on the generation of MNPs-/GNPs-based nanocomposites and inhibited the catalytic oxidation of glucose. The glucose content difference in the microvolumetric solution, which was revealed by a PGM, was in proportion to the logarithm of the TM concentration from 25 ng mL-1 to 2.5 µg mL-1. The limit of detection was 5.7 ng mL-1. Microvolumetric solution and a PGM were used in the measurement, which overcame some deficiencies of classical methods in chemo/biosensing, for example, special instrument, complicated measurement procedure, and high cost.

Recent Developments in Personal Glucose Meters as Point-of-Care Testing Devices (2020-2024).

Point-of-care testing (POCT) is a contemporary diagnostic approach characterized by its user-friendly nature, cost efficiency, environmental compatibility, and lack of reliance on professional experts. Therefore, it is widely used in clinical diagnosis and other analytical testing fields to meet the demand for rapid and convenient testing. The application of POCT technology not only improves testing efficiency, but also brings convenience and benefits to the healthcare industry. The personal glucose meter (PGM) is a highly successful commercial POCT tool that has been widely used not only for glucose analysis, but also for non-glucose target detection. In this review, the recent advances from 2020 to 2024 in non-glucose target analysis for PGMs as POCT devices are summarized. The signal transduction strategies for non-glucose target analysis based on PGMs, including enzymatic transduction, nanocarrier transduction (enzyme or glucose), and glucose consumption transduction are briefly introduced. Meanwhile, the applications of PGMs in non-glucose target analysis are outlined, encompassing biomedical, environmental, and food analysis, along with other diverse applications. Finally, the prospects of and obstacles to employing PGMs as POCT tools for non-glucose target analysis are discussed.

Improved Glycemic Control during a One-Week Adventure Camp in Adolescents with Type 1 Diabetes-The DIACAMP Study.

Adolescence remains a crucial age associated with diabetes distress in individuals living with type 1 diabetes (T1D). The Austrian organization "Diabär" regularly hosts a one-week adventure camp for adolescents (12-18 years) living with T1D. The camp focuses on "fun activities" without a structured educational protocol in order to minimize diabetes distress and increase diabetes management skills. In contrast to educational camps, training is kept to a minimum. However, attendees analyze the glycemic data of the previous day with their medical supervisor once daily during the camp. All subjects used a standardized real-time continuous glucose monitoring (CGM) system (DexcomG7) throughout the whole study. Glycemic metrics were prospectively analyzed during three periods: week 1 = home phase, week 2 = adventure camp, and week 3 = after the camp. Safety (time below range 1 [TBR1], 69-54 mg/dL, and time below range 2 [TBR2], <54 mg/dL) and efficacy (time in range [TIR], 70-180 mg/dL) were assessed by comparing the CGM data during weeks 1-3. The CGM data of 14 participants were analyzed. The TIR was higher during the camp week versus week 1 (70.4 ± 11.1% vs. 53.1 ± 20.2%; p = 0.001). The TBR1 significantly increased during camp compared to week 1 (2.5 ±1.7% vs. 1.3 ± 1.2%; p = 0.009), whereas the TBR2 did not differ. No serious adverse events occurred. This adventure camp without a main focus on education showed feasibility and safety in adolescents with T1D.

Insulinoma Unmasked: A Continuous Glucose Monitoring-Fueled Journey.

Insulinomas are rare functional neuroendocrine tumors that are usually indolent and small. Due to their rarity, there is often a delay in disease recognition and diagnosis, and small tumor size makes their localization challenging. Glucose monitoring and dietary modification with or without pharmacotherapy are crucial during diagnostics, and surgery is the only definite treatment. Continuous glucose monitoring (CGM) systems can be a valuable tool in managing insulinoma patients. We present three patients with confirmed endogenous hyperinsulinemic hypoglycemia undergoing tumor localization, medical treatment, and surgery while wearing a CGM system. By accurately depicting glucose fluctuations, CGM can help prevent hypoglycemia, decrease hypoglycemia unawareness, track hypoglycemia frequency, aid in medical therapy dose titration, and confirm a cure after surgery.

Using continuous glucose monitoring to measure and improve quality metrics: Updates on the Healthcare Effectiveness Data and Information Set 2024 Glucose Management Indicator measure.

Value-based diabetes care is a proactive approach to providing quality care to individuals with diabetes. This approach focuses on improving clinical outcomes rather than the volume of services provided. Implementation of value-based diabetes care requires an established set of standardized quality measures against which all stakeholders can assess and benchmark their performance. The National Committee for Quality Assurance recently added the Glucose Management Indicator to its Healthcare Effectiveness Data and Information Set. The Glucose Management Indicator can be used as a measure of glucose control. This article discusses the benefits of value-based care, the importance of diabetes quality measures, and how the rapidly increasing adoption of continuous glucose monitoring is impacting these measures while improving the lives of individuals with diabetes.

Impact of continuous glucose monitoring on emergency department visits and all-cause hospitalization rates among Medicaid beneficiaries with type 2 diabetes treated with multiple daily insulin or basal insulin therapy.

BACKGROUND: The increasing prevalence of diabetes in the United States continues to drive a steady rise in health care resource utilization, especially emergency department visits and all-cause hospitalizations, and the associated costs. OBJECTIVE: To investigate the impact of continuous glucose monitoring (CGM) on emergency department visits and all-cause hospitalizations among Medicaid beneficiaries with type 2 diabetes (T2D) treated with multiple daily insulin injections (MDIs) or basal insulin therapy (BIT) in a real-world setting. METHODS: In this retrospective, 12-month analysis, we used the Inovalon Insights claims dataset to evaluate the effects of CGM acquisition on emergency department visits and all-cause hospitalizations in the Managed Medicaid population. The analysis included 44,941 beneficiaries with T2D who were treated with MDIs (n = 35,367) or BIT (n = 9,574). Primary outcomes were changes in the number of emergency department visits and all-cause hospitalizations following 6 months after acquisition of CGM (post-index period) compared with 6 month prior to CGM acquisition (pre-index period). The first claim for CGM was the index date. Inclusion criteria were as follows: aged younger than 65 years, diagnosis of T2D, claims for short- or rapid-acting insulin (MDI group) or basal insulin (not rapid-acting) (BIT group), acquisition of a CGM device between January 1, 2017, and September 30, 2022, and continuous enrollment in their health plan throughout the pre-index and post-index periods. RESULTS: In the MDI group, all-cause inpatient hospitalization rates decreased from 3.25 to 2.29 events/patient-year (hazard ratio = 0.12; 95% CI = 0.11-0.13; P < 0.001) and emergency department visit rates decreased from 2.15 to 1.86 events/patient-year (hazard ratio = 0.52; 95% CI = 0.50-0.53; P < 0.001). In the BIT group, all-cause inpatient hospitalization rates decreased from 1.63 to 1.39 events/patient-year (hazard ratio = 0.11; 95% CI = 0.09-0.12; P < 0.001) and emergency department visit rates decreased from 1.60 to 1.43 events/patient-year (hazard ratio = 0.47; 95% CI = 0.44-0.50; P < 0.001). CONCLUSIONS: Acquisition of CGM is associated with significant reductions in emergency department visits and all-cause hospitalizations among people with T2D treated with MDIs or BIT.

Advances in diabetes technology within the digital diabetes ecosystem.

Ongoing innovations in glucose monitoring, insulin delivery, and telehealth technologies have created a digital diabetes ecosystem populated by connected tools and technologies that have been shown to improve clinical outcomes, lower costs, and reduce the burden of diabetes. Advances in connected continuous glucose monitoring devices, insulin pumps, and insulin pens have led to the development of automated insulin delivery systems that modulate insulin infusion based on sensor glucose data. Similar integrations of continuous glucose monitoring and connected blood glucose meter data into "smart" pens have lessened the guesswork of intensive insulin management for individuals who prefer traditional injection therapy. A growing number of health apps that can be accessed through smartphones and wearable devices provide information and advice that support individuals in adopting healthier lifestyles. The differences in features and functionality give users the ability to select the devices that best meet their unique requirements and preferences. This article reviews the most current digital diabetes technologies and discusses how the connectivity of these tools can create an overarching architecture of feedback mechanisms that monitor an individual's health status, motivate and enhance adherence to self-management, and provide advice and decision-support tools to clinicians as well as other members of the health care team to make living with diabetes more manageable.

A hybrid Transformer-LSTM model apply to glucose prediction.

The global prevalence of diabetes is escalating, with estimates indicating that over 536.6 million individuals were afflicted by 2021, accounting for approximately 10.5% of the world's population. Effective management of diabetes, particularly monitoring and prediction of blood glucose levels, remains a significant challenge due to the severe health risks associated with inaccuracies, such as hypoglycemia and hyperglycemia. This study addresses this critical issue by employing a hybrid Transformer-LSTM (Long Short-Term Memory) model designed to enhance the accuracy of future glucose level predictions based on data from Continuous Glucose Monitoring (CGM) systems. This innovative approach aims to reduce the risk of diabetic complications and improve patient outcomes. We utilized a dataset which contain more than 32000 data points comprising CGM data from eight patients collected by Suzhou Municipal Hospital in Jiangsu Province, China. This dataset includes historical glucose readings and equipment calibration values, making it highly suitable for developing predictive models due to its richness and real-time applicability. Our findings demonstrate that the hybrid Transformer-LSTM model significantly outperforms the standard LSTM model, achieving Mean Square Error (MSE) values of 1.18, 1.70, and 2.00 at forecasting intervals of 15, 30, and 45 minutes, respectively. This research underscores the potential of advanced machine learning techniques in the proactive management of diabetes, a critical step toward mitigating its impact.