Continuous Glucose Monitoring Research Articles

Continuous glucose monitoring profile in COVID-19 patients with and without diabetes receiving methylprednisolone.

Methylprednisolone is widely used during the COVID-19 epidemic. We aimed to evaluate the glucose profile of COVID-19 patients with and without diabetes receiving methylprednisolone. 36 patients with COVID-19 admitted to hospital were included: 17 with and 19 without diabetes. Methylprednisolone 40 mg was administered at about 9:00 a.m. Glucose levels were assessed by blinded intermittently scanned continuous glucose monitoring (isCGM) for an average of 6.8 ± 2.4 days. Excess hyperglycemia was defined as time above range (TAR) > 10.0 mmol/L (TAR>10.0) ≥ 25%, or TAR > 13.9 mmol/L (TAR>13.9) ≥ 10%. Glucose management indicator (GMI) was significantly higher than the admission glycated hemoglobin A1c (HbA1c) level in patients without diabetes [6.7 (6.1-7.0) % vs. 5.9 (5.9-6.1) %, P < 0.001], while no significant difference was found in patients with diabetes [9.0 (7.5-9.5) % vs. 8.9 (7.5-10.2) %, P > 0.05]. The difference between GMI and HbA1c (∆GMI-HbA1c) in patients without diabetes was significantly higher than in patients with diabetes [0.7 (0.2-1.0) % vs. -0.2 (-1.5-0.5) %, P = 0.005]. The circadian patterns of glucose were similar in the two groups. In patients without diabetes, excess hyperglycemia occurred in 31.6% (6/19) of participants, with 31.6% (6/19) having a TAR>10.0 ≥ 25%, while 21.1% (4/19) had a TAR>13.9 ≥ 10%. The impact of methylprednisolone on glycemia was more pronounced in COVID-19 patients without diabetes, compared to those with diabetes. A significant burden of methylprednisolone-induced hyperglycemia was observed in patients without diabetes.

Efficacy of Lisosan G (fermented wheat) on reactive hypoglycemia after bariatric surgery

Introductionpost-bariatric hypoglycemia (PBH) is considered a chronic complication after gastric bypass (RYGB) impacting roughly 30 % of patients. Current treatments often focus on nutritional interventions to reduce the frequency of episodes. This prospective study evaluated the effectiveness of Lisosan G (LG), a fermented wheat-based supplement added to the diet, in mitigating PBH episodes and elucidating its mechanism of action on the gut-pancreas axis. Methods:twenty subjects with PBH, who had undergone RYGB between 2015 and 2018, were enrolled. Subjects underwent clinical examination, blood test, and a 3-hour oral glucose load test (OGTT). Then, they were monitored for 2-weeks on a free diet with continuous glucose monitoring (CGM), which was extended for another 2-weeks after introduction of LG supplementation (5 g, twice daily) on the same diet. Finally, subjects repeated OGTT and blood test. PBH was defined as interstitial glucose ≤ 54 mg/dl. Resultsafter treatment, a marked reduction in PBH time was observed (75[23–113] vs 16 [0–33], minutes, p < 0.001). During OGTT, there was an increase in glucose nadir (44 ± 11 vs 56 ± 10, mg/dl, p = 0.038), and a significantly decrease in total GLP-1 AUC (7.6 ± 4.1 vs 6.5 ± 3.8, nmol/L*min, p = 0.043), in potentiation factor ratio (p = 0.037) and in total insulin AUC (57 ± 12 vs 49 ± 9, nmol/L*min, p = 0.043). ConclusionLG effectively reduces PBH frequency and duration, probably by attenuating GLP-1 concentrations and leading to a decrease in the second phase of insulin secretion in response to glucose. These findings underscore the promise of LG as a novel adjunct therapy for PBH, particularly when added to the diet, and emphasize the need for further exploration into its microbiota-modulating and anti-inflammatory effects.

Digital Medicine Tools and the Work of Being a Patient: A Qualitative Investigation of Digital Treatment Burden in Patients With Diabetes

ObjectiveTo understand the contribution of digital medicine tools (eg, continuous glucose monitoring systems, scheduling, and messaging applications) to treatment burden in patients with diabetes. Patients and MethodsBetween October and November 2023, we invited patients with type 1 or type 2 diabetes to participate in semistructured interviews. The interviewees completed the Treatment Burden Questionnaire as they reflected on how digital medicine tools affect their daily routines. A published taxonomy of treatment burden guided the qualitative content analysis of interview transcripts. ResultsIn total, 20 patients agreed to participate and completed interviews (aged 21-77 years, 55% female, 60% living with type 2 diabetes). We found 5 categories of tasks related to the use of digital medicine tools that patients had to complete (eg, calibrating continuous glucose monitors), 3 factors that made these tasks burdensome (eg, cost of device replacements), and 2 categories of consequences of burdensome tasks on patient wellbeing (eg, fatigue from device alarms). ConclusionPatients identified how digital medicine tools contribute to their treatment burden. The resulting digital burden taxonomy can be used to inform the design, implementation, and prescription of digital medicine tools including support for patients as they normalize them in their lives.

Continuous Glucose Monitoring With Remote Telemonitoring-enabled Virtual Educator Visits in Adults With Non-insulin Dependent Diabetes: A Randomized Trial

Continuous Glucose Monitoring With Remote Telemonitoring-enabled Virtual Educator Visits in Adults With Non-insulin Dependent Diabetes: A Randomized Trial

Association of the glucose patterns after a single non-standardized meal with the habitual diet composition and features of the daily glucose profile in individuals without diabetes

BackgroundThe postprandial glucose response (PPGR), contributing to the glycemic variability (GV), is positively associated with cardiovascular risk in people without diabetes, and can thus represent a target for cardiometabolic prevention strategies. ObjectiveThe study aimed to distinguish patterns of PPGR after a single non-standardized meal and to evaluate their relationship with the habitual diet and the daily glucose profile (DGP) in individuals at high-cardiometabolic risk. MethodsBaseline four-day continuous glucose monitoring (CGM) was performed in 159 adults recruited in the MEDGI-Carb trial. After a non-standardized breakfast, parameters of the PPGR were estimated by a mechanistic model: baseline glucose; amplitude – the magnitude of post-meal glucose concentrations; frequency – the velocity of post-meal glucose oscillations; damping – the rate of post-meal glucose decay. PPGR patterns were identified by cluster analysis. Differences between clusters and the relationship between PPGR parameters and individual features were explored by one-way ANOVA and correlation analysis, respectively. ResultsTwo patterns of PPGR emerged. Pattern A had higher baseline, amplitude, frequency, and damping than B. Individuals in cluster A compared to B had a higher energy (2,002±526 vs. 1,766±455 Kcal, p=0.025), protein (82±22 vs. 72±21 g, p=0.028) and fat (87±30 vs. 75±22 g, p=0.041), but not carbohydrate habitual intake. Pattern A associated with a higher average daily glucose (6.12±0.50 vs. 5.88±0.62 mmol/L, p=0.019) and lower GV (11.67±3.52 vs. 13.43±3.78%, p=0.010). Mean daily glucose correlated directly with baseline (rs=0.419, p<0.001) and amplitude (rs=0.189, p=0.022) of the PPGR, while DGP variability correlated directly with amplitude (rs=0.218, p=0.008), frequency (rs=-0.179, p=0.031) and damping (rs=-0.309, p<0.001). ConclusionsTwo PPGR patterns after a single non-standardized breakfast were identified in high-cardiometabolic risk individuals. The habitual diet was associated with the patterns and their dynamic parameters, which, in turn, could predict the individuals’ DGP. Our findings could support the implementation of dietary strategies targeting the PPGR to ameliorate the cardiometabolic risk profile.Clinical Trial ID: NCT03410719

The Application of Personalized Nutrition Counseling According to Glycemic Response in Obese Adults: A Randomized Dietary Intervention Study

This randomized clinical trial was conducted to investigate the effects of personalized nutrition counseling according to the blood glucose response and dietary intake, which can be measured using a flash continuous glucose monitoring (CGM) device, on weight changes and eating habits in obese adults. The participants of this study comprised obese adults over 30 years of age, which were randomly assigned to either the (1) personalized nutrition group (PN) or (2) control group (CON) with a study period of up to 12 weeks. Body weight, body mass index (BMI), body fat mass, body fat percentage, and waist-to-hip ratio (WHR) significantly decreased in the PN group when compared with the CON group (p < 0.05; p < 0.01). Based on the findings of the flash CGM, the PN group showed a significant decrease in both blood glucose levels, hemoglobin A1c, and time above range (p < 0.05). The levels of blood HbA1c and insulin were significantly decreased in both groups, but the PN group showed a greater decrease (HbA1c, p = 0.000; insulin, p = 0.000) than the CON group did (HbA1c, p = 0.001; insulin, p = 0.001). The blood triglyceride levels were significantly lowered only in the PN group (p = 0.026). It was confirmed that personalized nutrition counseling using a flash CGM device was effective in reducing body weight, abdominal fat and blood HbA1c, insulin, and triglyceride levels and improving meal quantity and eating habits in obese adults.

Analytical Accuracy of a Continuous Glucose Monitor in Adult Diabetic Ketoacidosis

Analytical Accuracy of a Continuous Glucose Monitor in Adult Diabetic Ketoacidosis

Cost-Utility of Real-Time Continuous Glucose Monitoring versus Self-Monitoring of Blood Glucose in People with Insulin-Treated Type 2 Diabetes in Spain.

Management of advanced type 2 diabetes (T2D) typically involves daily insulin therapy alongside frequent blood glucose monitoring, as treatments such as oral antidiabetic agents are therapeutically insufficient. Real-time continuous glucose monitoring (rt-CGM) has been shown to facilitate greater reductions in glycated hemoglobin (HbA1c) levels and improvements in patient satisfaction relative to self-monitoring of blood glucose (SMBG). This study aimed to investigate the cost-utility of rt-CGM versus SMBG in Spanish patients with insulin-treated T2D.. The analysis was conducted using the IQVIA Core Diabetes Model (CDM V9.5). Baseline characteristics of the simulated patient cohort and treatment efficacy data were sourced from a large-scale, United States-based retrospective cohort study. Costs were obtained from Spanish sources and inflated to 2022 Euros (EUR) where required. A remaining lifetime horizon (maximum 50 years) was used, alongside an annual discount rate of 3% for future costs and health effects. A willingness-to-pay (WTP) threshold of EUR 30,000 per quality-adjusted life year (QALY) was adopted, based on precedent across previous cost-effectiveness studies set in Spain. A Spanish payer perspective was adopted. Over patient lifetimes, rt-CGM yielded 9.933 QALYs, versus 8.997 QALYs with SMBG, corresponding to a 0.937 QALY gain with rt-CGM. Total costs in the rt-CGM arm were EUR 2347higher with rt-CGM versus SMBG (EUR 125,365 versus EUR123,017). The base case incremental cost-utility ratio was therefore EUR 2506 per QALY gained, substantially lower than the WTP threshold of EUR 30,000 per QALY. The analysis also projected a reduction in cumulative incidence of ophthalmic, renal, neurological, and cardiovascular events in rt-CGM users, with reductions of 16.03%, 13.07%, 7.34%, and 9.09%, respectively. Compared to SMBG, rt-CGM is highly likely to be a cost-effective intervention for patients living with insulin-treated T2D in Spain.

Factors associated with Glycemia Risk Index in a cohort of patients with type 1 Diabetes Mellitus and Latent Autoimmune Diabetes In Adults (LADA).

To analyze the degree of control based on classical glucometric parameters and Glycemia Risk Index (GRI) in real-life conditions in a cohort of patients with type 1 Diabetes Mellitus (DM) and Latent Autoimmune Diabetes in Adults (LADA) and to assess the factors that are associated with GRI. Cross-sectional study. 447 adult patients with type 1 DM and LADA users of Intermittent Continuous Glucose Monitoring (iCGM) with an adherence ≥ 70% were included. GRI was calculated with its Hypoglycemia (CHypo) and Hyperglycemia (CHyper) Components. Multivariate linear regression analysis was performed to evaluate the factors associated with GRI. Mean age 44.6 years (SD 13.7); 57.7% men; 83.9% type 1 DM; 16.1% LADA; time of evolution 20.6 years (SD 12.3). In patients with type 1 DM vs. LADA, differences were observed in relation to age [-11.1 years (SD 1.7)], age of onset [-21.9 years (DE 1.5)], time of evolution [11.7 years (DE 1.5)], treatment modality (p < 0.001), Time in Range (TIR) [-6.3% (SD 2.2)], Time Below Range (TBR) [1.9% (SD 0.6)], TBR level 1 (TBR1) [1.4% (SD 0.5)], Time Above Range (TAR) level 2 (TAR2) [4.7% (SD 1.3)], Coefficient of Variation (CV) [4.6% (SD 0.9)], GRI [11.3% (SD 2.8)], CHypo [1.3% (SD 0.5)] and CHyper [4.8% (SD 1.7)]. The variables that were independently associated with GRI were TIR (β = -1.34; CI 95% -1.43 to -1.25; p < 0.001), Glucose Management Indicator (GMI) (β = -5.82; CI 95% -7.59 to -4.05; p < 0.001), CV (β = 0.67; CI 95% 0.57 to 0.77; p < 0.001) and adherence to sensor usage (β = -0.16; CI 95% -1.27 to -0.06; p < 0.002). LADA present better control according to some glucometric parameters and a low GRI. However, the type of DM is not a factor that is independently associated with GRI.

Evaluation of the Effects of Non-Invasive Auricular Acupoint Stimulation on Postprandial Blood Glucose Regulation using a CGM Device

Background: Postprandial blood glucose regulation is critical for diabetes management. Non-invasive auricular acupoint stimulation may influence metabolic functions through the autonomic nervous system. This study aimed to evaluate its effects on postprandial glucose regulation using Continuous Glucose Monitoring (CGM). Methods: Thirteen healthy Japanese adults (10 males, 3 females) had 1.5 mm gold beads applied to six acupoints. Glucose levels were monitored over seven days using CGM. Key metrics included postprandial peak, 2-hour glucose levels, time to peak, and normalization time. Results: Auricular acupoint stimulation reduced time to glucose peak after breakfast (-14.6 min, p = 0.01) and glucose normalization time for breakfast and lunch combined (-20.8 min, p = 0.04). A significant reduction in 2-hour postprandial glucose was observed after lunch (-10.8 mg/dL, p = 0.03). No significant effect was found on peak glucose levels. Conclusion: Auricular acupoint stimulation may promote postprandial glucose normalization, particularly after breakfast and lunch, and could serve as a complementary approach to glucose management.

Use of Technology in the Management and Treatment of Prediabetes, A New Era: A Case Report Series

Introduction: The escalating prevalence of prediabetes has positioned it as a silent precursor to a looming public health crisis, particularly as it paves the way for type 2 diabetes mellitus (T2DM). This case series report explores the transformative role of technology, specifically the Sugar Fit’s Diabetes Reversal and Management Program (SDRMP), in the treatment and management of prediabetic patients. Methods: The case series follows the progress of two patients aged 31-44 with prediabetes utilizing the SDRMP to prevent the progression to diabetes. The interventions for both patients involved continuous glucose monitoring, personalized nutrition plans, fitness strategies, and behavioral modifications. The patients also engaged in regular communication with their life coaches through the Sugar Fit application, ensuring ongoing support and adherence to the program. Results: The results revealed significant improvements in glycemic control, lipid profiles, and overall well-being. The first patient, a 44-year-old male, demonstrated a successful return to normal glycemic control in 177 days through a comprehensive approach involving dietary changes, exercise, and lifestyle modifications. The second patient, a 31-year-old male, achieved normal glycemic control in a comparable timeframe, showcasing the effectiveness of the SDRMP. Discussion and Conclusion: These cases demonstrate that patients were able to adhere to the goals of care, preventing the progression of prediabetes to diabetes and improving insulin resistance. The study also underscores the potential impact of technology on public health outcomes, bridging the gap between traditional interventions and the evolving needs of a tech-savvy population. The synergy between healthcare expertise and technological innovation not only arrests the progression of prediabetes but also contributes to a broader societal shift towards metabolic well-being, paving the way for a healthier future. Future monitoring is essential to ensure the maintenance of lifestyle modifications and continued success in preventing diabetes.

Characterising impaired awareness of hypoglycaemia and associated risks through HypoA-Q: findings from a T1D Exchange cohort.

We aimed to: (1) externally validate the five-item Hypoglycaemia Awareness Questionnaire (HypoA-Q) impaired awareness subscale (HypoA-Q IA); (2) examine how impaired awareness of hypoglycaemia (IAH) relates to the risk of severe hypoglycaemia and level 2 hypoglycaemia; and (3) identify factors associated with IAH. Nationwide survey of T1D Exchange registrants was conducted to collect data on demographics, 6 month severe-hypoglycaemia history, hypoglycaemia awareness status (via HypoA-Q IA, the Gold instrument and the Clarke instrument) and continuous glucose monitor (CGM) measures. The Clarke hypoglycaemia awareness factor (Clarke-HAF) was calculated to exclude severe-hypoglycaemia history items. Analyses included Cronbach's α, Spearman correlations and logistic regression. Valid survey responses were collected from N=1580 adults with type 1 diabetes (median age, 44 years; 52% female participants; median HbA1c, 48 mmol/mol [6.5%]). Of these, 94% of participants were using CGMs and 69% were using hybrid closed-loop (HCL) systems; 30% had at least one severe-hypoglycaemia episode in the past 6 months. The HypoA-Q IA had satisfactory internal reliability (α=0.79) and construct validity. Higher HypoA-Q IA scores were independently associated with greater risk of severe hypoglycaemia (p<0.001), performing comparably to the Gold instrument and the Clarke-HAF instrument. HypoA-Q IA-determined IAH was independently associated with 88% higher odds of developing severe hypoglycaemia (p<0.001) and twofold higher odds for spending ≥1% of time in level 2 hypoglycaemia (p=0.011). Higher age and longer diabetes duration were associated with higher IAH risk (p<0.001). CGM and HCL use was associated with lower IAH risk (p<0.001). The HypoA-Q IA is a brief, valid and reliable tool for assessing IAH in today's technology-oriented era. IAH was independently associated with severe hypoglycaemia and level 2 hypoglycaemia in a cohort with high prevalence of advanced diabetes technology use and HbA1c within the recommended range. CGM and HCL use was related to lower IAH risk.

Effect of fenofibrate on residual beta cell function in adults and adolescents with newly diagnosed type 1 diabetes: a randomised clinical trial.

Fenofibrate, a peroxisome proliferator-activated receptor alpha agonist, shows some promise in alleviating beta cell stress and preserving beta cell function in preclinical studies of type 1 diabetes. The aim of this phase 2, placebo-controlled, double-blinded, randomised clinical trial was to investigate the efficacy and safety of fenofibrate in adults and adolescents with newly diagnosed type 1 diabetes. We enrolled 58 individuals (aged 16 to 40 years old) with newly diagnosed type 1 diabetes and randomised them to daily oral treatment with fenofibrate 160 mg or placebo for 52 weeks (in a block design with a block size of 4, assigned in a 1:1 ratio). Our primary outcome was change in beta cell function after 52 weeks of treatment, assessed by AUC for C-peptide levels following a 2h mixed-meal tolerance test. Secondary outcomes included glycaemic control (assessed by HbA1c and continuous glucose monitoring), daily insulin use, and proinsulin/C-peptide (PI/C) ratio as a marker of beta cell stress. We assessed outcome measures before and after 4, 12, 26 and 52 weeks of treatment. Blinding was maintained for participants, their healthcare providers and all staff involved in handling outcome samples and assessment. The statistical analyses for the primary outcome included 56 participants (n=27 in the fenofibrate group, after two withdrawals, and n=29 in the placebo group). We found no significant differences between the groups in either 2h C-peptide levels (mean difference of 0.08 nmol/l [95% CI -0.05, 0.23]), insulin use or glycaemic control after 52 weeks of treatment. On the contrary, the fenofibrate group showed a higher PI/C ratio at week 52 compared with placebo (mean difference of 0.024 [95% CI 0.000, 0.048], p<0.05). Blood lipidome analysis revealed that fenofibrate repressed pathways involved in sphingolipid metabolism and signalling at week 52 compared with placebo. The 52 week intervention evoked few adverse events and no serious adverse events. Follow-up in vitro experiments in human pancreatic islets demonstrated a stress-inducing effect of fenofibrate. Contrary to the beneficial effects of fenofibrate found in preclinical studies, this longitudinal, randomised, placebo-controlled trial does not support the use of fenofibrate for preserving beta cell function in individuals with newly diagnosed type 1 diabetes. EudraCT number: 2019-004434-41 FUNDING: This study was funded by the Sehested Hansens Foundation.

The benefits and accuracy of real-time continuous glucose monitoring in children and adolescents with type 1 diabetes attending a summer camp.

This study evaluated the usability, satisfaction, and accuracy of a real-time continuous glucose monitoring (rt-CGM) in children and adolescents with type 1 diabetes (T1D) attending a summer camp. Seven children and adolescents with T1D (camper) and 31 of healthcare providers (HCPs) participating in a 2-day summer camp in Kumamoto, Japan were enrolled. The usability and satisfaction were evaluated by tailored questionnaire. The accuracy of rt-CGM was evaluated using self-monitoring of blood glucose (BG) and sensor glucose (SG) values before or after (off camp) and during (on camp) the camp. The score of the usefulness of rt-CGM showed 3.29 ± 0.90 in campers and 4.23 ± 0.87 in HCPs (P = 0.017). The degree of recommendation score for rt-CGM was 3.29 ± 1.11 in campers and 4.23 ± 0.79 in HCPs (P = 0.013). Time in range (TIR) off camp was 45.9% and that on camp was 57.0%. Time above range (TAR) off camp was 53.4% and that on camp was 42.4%. The mean absolute relative difference (MARD) off camp was 19.7% ± 25.2%, whereas that on camp was 16.0% ± 14.8% (P = 0.367). Clinically acceptable zones of the error grid analyses were approximately 96% in total. Rt-CGM exhibited higher usability and recommendation scores in HCPs than those in campers. This may be related to relatively lower accuracy in rt-CGM. Overall usability and recommendation are clinically satisfactory, but due to relatively low accuracy, no decision should be made based on a single, non-verified SG value alone.

Evaluation of Glycemic Control in Patients With Diabetes by a Continuous Glucose Monitoring System During the Month of Ramadan.

Ramadan presents challenges for patients with type 2 diabetes mellitus (T2DM) who choose to fast. Maintaining good glycemic control through regular monitoring during fasting is crucial. This study aims to evaluate and compare glycemic fluctuations in fasting and non-fasting T2DM patients during Ramadan. We conducted a prospective comparative study on 39 T2DM patients, divided into fasting and non-fasting groups based on the International Diabetes Federation - Diabetes and Ramadan Alliance (IDF-DAR) Practical Guidelines 2021. Each patient wore an iPro®2 Continuous Glucose Monitoring System (CGMS) device (Medtronic plc, Dublin, Ireland),for five to seven days, with consultations scheduled before, during, and after Ramadan. Glycated hemoglobin (HbA1c) levels were measured before and after Ramadan. The fasting risk score was calculated using the DAR guidelines. The fasting group included 29 fasting patients with an average fasting risk score of 2.7 according to the DAR guidelines, while the non-fasting group had 10 non-fasting patients with an average score of 6.55. The average HbA1c was estimated by the CGMS at 7.36% for fasting patients and 7.1% for non-fasting patients. In the fasting group, 80% of patients experienced at least one episode of hyperglycemia, averaging 4.03 episodes per day, predominantly occurring during pre-dawn (suhoor) and post-sunset (iftar) periods. Hypoglycemia occurred in 24% of fasting patients, mainly before iftar. In contrast, the non-fasting group had a higher average of 6.8 hyperglycemic episodes per day, with 50% of the group also experiencing hypoglycemia. Notably, a significant improvement in HbA1c was observed in the non-fasting group after Ramadan (7.77 ± 0.89 vs. 9.13 ± 1.99, p=0.037). No significant changes in HbA1c, weight, or body mass index were found in the fasting group. Pre-Ramadan education, risk stratification, and continuous monitoring during Ramadan are essential to prevent metabolic complications in T2DM patients, whether fasting or non-fasting. CGMSs reveal a high prevalence of hyperglycemia, especially in non-fasting patients, underscoring the need for tailored treatment adjustments to achieve optimal glycemic control.

Machine Learning Algorithms Based on Time Series Pre-Clustering for Nocturnal Glucose Prediction in People with Type 1 Diabetes.

Background: Machine learning offers new options for glucose prediction and real-time glucose management. The aim of this study was to develop a machine learning-based algorithm that takes into account glucose dynamics patterns for predicting nocturnal glucose in individuals with type 1 diabetes. Methods: To identify glucose patterns, we applied a hierarchical clustering algorithm to real-time continuous glucose monitoring data obtained from 570 adult patients. Machine learning algorithms with or without pre-clustering were used for modeling. Results: Eight clusters without nocturnal hypoglycemia and six clusters with at least one low-glucose episode were identified by the cluster analysis. When forecasting time series without hypoglycemia with a prediction horizon (PH) of 15 or 30 min, gradient boosting trees (GBTs) with pre-clustering and random forest (RF) with pre-clustering outperformed algorithms based on medoids of time series clusters, the Holt model, and GBTs without pre-clustering. When forecasting time series with low-glucose episodes, a model based on the pre-clustering and GBTs provided the highest predictive accuracy at PH = 15 min, and a model based on RF with pre-clustering was the best at PH = 30 min. Conclusions: The results indicate that the clustering of glucose dynamics can enhance the efficacy of machine learning algorithms used for glucose prediction.

Characterizing the Impact of Physical Activity on Patients with Type 1 Diabetes Using Statistical and Machine Learning Models

Continuous glucose monitoring (CGM) represents a significant advancement in diabetes management, playing an important role in glycemic control for patients with type 1 diabetes (T1D). Despite their benefits, their performance is affected by numerous factors such as the carbohydrate intake, alcohol consumption, and physical activity (PA). Among these, PA could cause hypoglycemic episodes, which might happen after exercising. In this work, two main contributions are presented. First, we extend the performance evaluation of two glucose monitoring devices, Eversense and Free Style Libre (FSL), for measuring glucose concentrations during high-intensity PA and normal daily activity (NDA). The impact of PA is investigated considering (1) different glucose ranges (hypoglycemia, euglycemia, and hyperglycemia); and (2) four time periods throughout the day (morning, afternoon, evening, and night). Second, we evaluate the effectiveness of machine learning (ML) models, including logistic regression, K-nearest neighbors, and support vector machine, to automatically detect PA in T1D individuals using glucose measurements. The performance analysis showed significant differences between glucose levels obtained in the PA and NDA period for Eversense and FSL devices, specially in the hyperglycemic range and two time intervals (morning and afternoon). Both Eversense and FSL devices present measurements with large variability during strenuous PA, indicating that their users should be cautious. However, glucose recordings provided by monitoring devices are accurate for NDA, reaching similar values to capillary glucose device. Lastly, ML-based models yielded promising results to determine when an individual has performed PA, reaching an accuracy value of 0.93. The results can be used to develop an individualized data-driven classifier for each patient that categorizes glucose profiles based on the time interval during the day and according to if a patient performs PA. Our work contributes to the analysis of PA on the performance of CGM devices.

Nanomedicine Innovations for Diabetes Management: Revolutionizing Diagnosis, Treatment, and Monitoring

Diabetes mellitus, a widespread metabolic disease characterized by high blood sugar levels, affects many people globally. The limitations of conventional diagnostic and therapeutic approaches necessitate exploring innovative strategies. Nanotechnology shows remarkable potential for revolutionizing the field of diabetes theranostics (combined diagnosis and treatment) by enabling accurate diagnosis and precise treatment delivery. This article provides a comprehensive review of the latest advancements in nanomaterials for diagnosing and treating diabetes. It explores the applications of various nanomaterials, including inorganic and organic nanoparticles, nanocomposites, and nanostructured biosensors, in biomarker detection, glucose monitoring, insulin delivery, and addressing diabetes-related complications. The study focuses on the synthesis and functionalization of nanomaterials for diabetes, covering both traditional and environmentally friendly synthesis methods. This study looks into how nanomaterials can be used to carry natural antidiabetic extracts, recombinant insulin, and other antidiabetic drugs, to make them more bioavailable, targetable, and effective. However, the review also talks about the problems that come with using nanosensors to diagnose diabetes. It also looks at the newest developments in nanosensors for biomarker detection, implantable devices, and continuous glucose monitoring. Additionally, the review examines the potential of nanomaterials in the management of diabetic sequelae, including diabetic nephropathy, cardiovascular disorders, retinopathy, and wound healing. We underscore the significance of nanomaterials in islet transplantation, as they provide immunological protection and enhance the viability and efficacy of islets. This study provides useful insights into the prospects and challenges connected with the rapidly growing field of nanotechnology in diabetic theranostics through a comprehensive examination of the current landscape. Academics, clinicians, and stakeholders engaged in developing innovative nanomaterial-based approaches to accurately diagnose and effectively manage diabetes will find this resource highly helpful.

Identification of gut microbiome features associated with host metabolic health in a large population-based cohort

The complex relationship between the gut microbiome and host metabolic health has been an emerging research area. Several recent studies have highlighted the potential effects of the microbiome’s diversity, composition and metabolic production capabilities on Body Mass Index (BMI), liver health, glucose homeostasis and Type-2 Diabetes (T2D). The majority of these studies were constrained by relatively small cohorts, mostly focusing on individuals with metabolic disorders, limiting a comprehensive understanding of the microbiome’s role in metabolic health. Leveraging a large-scale, comprehensive cohort of nearly 9000 individuals, measured using Continuous Glucose Monitoring (CGM), Dual-energy X-ray absorptiometry (DXA) scan and liver Ultrasound (US) we examined the functional profile of the gut microbiome, and its relation to 38 metabolic health measures. We identified 145 unique bacterial pathways significantly correlated with metabolic health measures, with 86.9% of these showing significant associations with more than one metabolic health measure. Furthermore, 87,678 unique bacterial gene families were found to be significantly associated with at least one metabolic health measure. Notably, “key” bacterial pathways such as purine ribonucleosides degradation and anaerobic energy metabolism demonstrated multiple robust associations across various metabolic health measures, highlighting their potential roles in regulating metabolic processes. Our results remained largely unchanged after adjustments for nutritional habits and for BMI they were replicated in a geographically independent cohort. These insights pave the way for future research and potentially the development of microbiome-targeted interventions to enhance metabolic health.

Exploration of micro-miniature venturi pump for continuous glucose monitoring through interstitial fluid (ISF) extraction

Interstitial Fluid (ISF) is a naturally occurring body fluid that surrounds cells and tissues and contains certain markers useful for continuously monitoring glucose levels in the blood without constantly extracting it. Miniaturizing can reduce its cost and timescale. In this study, for continuous glucose monitoring the numerical analysis of a Polymethyl methacrylate (PMMA) based Micro-miniature venturi pump was carried out to extract Interstitial Fluid (ISF). The numerical study was carried out using COMSOL Multiphysics software by employing the k-ε turbulence model to obtain output as a vacuum at the throat of the pump. The micro-miniature venturi pump was numerically analyzed for several key parameters, including the dimensions of the throat, the convergent angle, and the divergent angle. This study obtained a minimum pressure of 49.41 kPa numerically and 70 kPa experimentally when an input pressure of 252 kPa of compressed air was given to the system. Further influence of turbulence kinetic energy and turbulence dissipation rate on throat pressure output have been notified. Notably, the pressure output reaches its minimum value of 49.41 kPa when the turbulent kinetic energy and the turbulent dissipation rate reach maximum values of 5.0 × 109 m2/s3 and 7000 m2/s2 respectively. As per the previous research findings, for continuous glucose monitoring through ISF extraction, a pressure level of 95 kPa is deemed adequate hence the designed micro-miniature venturi pump is suitable for this application.