Associations Between HbA1c and Glucose Time in Range Using Continuous Glucose Monitoring in Type 1 Diabetes: Cross-Sectional Population-Based Study.

INTRODUCTION: Continuous glucose monitoring (CGM) introduces novel indicators of glycemic control. METHODS: This cross-sectional study, based on the Swedish National Diabetes Register, examines 27,980 adults with type 1 diabetes. It explores the relationships between HbA1c (glycated hemoglobin) and various CGM-derived metrics, including TIR (time in range, representing the percentage of time within the range of 4-10 mmol/l for 2 weeks), TAR (time above range), TBR (time below range), mean glucose, standard deviation (SD), and coefficient of variation (CV). Pearson correlation coefficients and linear regression models were utilized for estimation. RESULTS: The analysis included 46% women, 30% on insulin pump, 7% with previous coronary heart disease and 64% with retinopathy. Mean ± SD values were age 48 ± 18 years, diabetes duration 25 ± 16 years, HbA1c 58.8 ± 12.8 mmol/mol, TIR 58.8 ± 19.0%, TAR 36.3 ± 20.0%, TBR 4.7 ± 5.4%, mean sensor glucose 9.2 ± 2.0 mmol/l, SD 3.3 ± 1.0 mmol/l, and CV 36 ± 7%. The overall association between HbA1c and TIR was - 0.71 (Pearson's r), with R2 0.51 in crude linear regression and 0.57 in an adjusted model. R2 values between HbA1c and CGM mean glucose were 0.605 (unadjusted) 0.619 (adjusted) and TAR (unadjusted 0.554 and fully adjusted 0.568, respectively), while fully adjusted R2 values were 0.458, 0.175 and 0.101 between HbA1c and CGM SD, CGM CV and TBR, respectively. CONCLUSIONS: This descriptive study demonstrates that the degree of association between HbA1c and new and readily available CGM-derived metrics, i.e., time in range (TIR), time above range (TAR), and CGM mean glucose, is robust in assessing the management of individuals with type 1 diabetes in clinical settings. Metrics from CGM that pertain to variability and hypoglycemia exhibit only weak correlations with HbA1c.

Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare.

Abstract: The patients of diabetes require to observe and control their glycemic profile through continuous glucose level monitoring. The blood glucose measurement is possible through invasive, minimally invasive and non-invasive methods. Invasive method is traditional method for instant glucose measurement where glucose is measured by taking blood samples from the body. However, the repeated finger pricking increases the risk of blood-related infections and trauma. Hence, the development of non-invasive real time device is essential for smart healthcare to manage glucose-insulin balance. The paper presents machine learning models for non-invasive glucose measurement. So, various machine learning algorithms including Logistic Regression, KNN, Gaussian Naive Bayes, Linear Regression, Multi-polynomial Regression, Neural Network, XGBoost, Decision Tree, Random Forest and Support Vector Machine are applied on two dataset which are PIDD (UCI repository) and iGLU dataset (iGLU device). The comparative analysis is carried out where accuracy, training time, recall, precision, f-1 score and AUC curve is measured for classification algorithms. For regression algorithms, measures like accuracy, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used for comparison purpose. Random forest with 84% accuracy and 68% recall, 76% precision and 72% f1-score for PIDD and Decision tree with 70% accuracy, 8% mean absolute error (MAE) and 8.5% root mean square error (RMSE) for iGLU dataset gives best results. Clark grid analysis has also been done where all the values fall under zone A which gives 100% accuracy and the device is useful for medication purpose. The proposed work has been also compared with similar methods and the proposed work has excellent results in terms of MAD, mARD, RMSE and AvgE. The device would be ideal as non-invasive solution for continuous glucose monitoring.

A Digital Therapeutic Allowing a Personalized Low-Glycemic Nutrition for the Prophylaxis of Migraine: Real World Data from Two Prospective Studies.

Migraine is a headache disorder associated with a high socioeconomic burden. The digital therapeutic sinCephalea provides an individualized low-glycemic diet based on continuous glucose measurement and is intended to provide a non-pharmacological migraine prophylaxis. We performed two prospective studies with migraine patients who used sinCephalea over a period of 16 weeks. The patients used a headache diary and recorded their migraine-related daily life impairments using the assessment tools HIT-6 and MIDAS for a pre versus post comparison. In addition, continuous glucose data of patients were compared to healthy controls. In both studies, patients reported a reduction of headache and migraine days as well as reductions in HIT-6 and MIDAS scores. More specifically, migraine days decreased by 2.40 days (95% CI [-3.37; -1.42]), HIT-6 improved by 3.17 points (95% CI [-4.63; -1.70]) and MIDAS by 13.45 points (95% CI [-22.01; -4.89]). Glucose data suggest that migraine patients have slightly increased mean glucose values compared to healthy controls, but drop into a glucose range that is below one's individual standard range before a migraine attack. In conclusion, sinCephalea is a non-pharmacological, digital migraine prophylaxis that induces a therapeutic effect within the range of pharmacological interventions.

PalatinoseTM (Isomaltulose) and Prebiotic Inulin-Type Fructans Have Beneficial Effects on Glycemic Response and Gut Microbiota Composition in Healthy Volunteers-A Real-Life, Retrospective Study of a Cohort That Participated in a Digital Nutrition Program.

It is well-appreciated that the diet is a crucial tool to counteract cardiometabolic disturbances due to its impact on blood glucose concentration and gut microbiome. This retrospective analysis aimed to examine whether the inclusion of isomaltulose and prebiotic inulin-type fructans (ITF) into the habitual diet has an impact on glycemic control and gut microbiota. Furthermore, we examined interindividual differences in glycemic response to sugar replacement with isomaltulose. We retrospectively analyzed data of 117 individuals who participated in a digital nutrition program including a 14-day continuous glucose measurement. Participants underwent six test days with sweetened drinks (isomaltulose vs. sucrose) consumed with their usual breakfasts and lunches. Dinner was supplemented with ITF for 11 days. Postprandial glycemia and 24 h-glycemic variability were determined following test meals and days, respectively. Fecal microbiota was analyzed by 16S rRNA sequencing before and after test phase. Meals with isomaltulose-sweetened drinks compared to meals with sucrose-sweetened drinks induced lower postprandial glycemia. Moreover, glucose oscillations over 24 h were lower on isomaltulose when compared to sucrose test days and improved further during ITF supplementation. Furthermore, ITF modulated gut microbiota composition beneficially. Responder analysis revealed that 72% of participants benefited from the sugar replacement with isomaltulose and that their gut microbiota differed from the low responders. Taken together, the incorporation of isomaltulose and ITF into the habitual diet was shown to be an effective strategy to improve glucose control and beneficially modulate gut microbiota, and thereby aid to maintain metabolic health. Data indicate interindividual differences in glycemic response to ingredients and suggest that gut microbiota might be somehow related to it.

A Digital Health Application Allowing a Personalized Low-Glycemic Nutrition for the Prophylaxis of Migraine: Proof-of-Concept Data from a Retrospective Cohort Study.

BACKGROUND: Migraine is a headache disorder with the highest socioeconomic burden. The aim of this study was to deliver the first proof-of-concept data of the potential role of an individual low-glycemic diet provided by a novel digital health application in the prophylaxis of migraine. METHODS: We analyzed data from a retrospective survey of individuals who participated in a digital nutrition program that provides dietary recommendations based on the individual analysis of continuous glucose measurement from an up to 14-day test phase. A total of 84 individuals completed the retrospective digital survey. The endpoints were changes in the number of migraine days, average duration of attacks, average pain severity, frequency of intake of pain medication, absenteeism, and presenteeism before and after program participation. RESULTS: The intraindividual comparisons of the endpoints before and after program participation revealed decreases in migraine frequency and other patient-relevant migraine parameters. Moreover, patients with a baseline migraine frequency of two and more migraine days per month and adherence to the dietary recommendations (n = 40) showed a mean reduction in migraine days by 33% with a 50%-responder rate of 38%. CONCLUSIONS: The data provides emerging evidence that an individualized low-glycemic diet based on continuous glucose measurement could be a promising approach for a diet-based, non-pharmacological migraine prophylaxis. However, future research is required to confirm the implied effectiveness.

Changes in Free-Living Glycemic Profiles after 12 Months of Lifestyle Intervention in Children with Overweight and with Obesity.

Previous studies demonstrated that hyperglycemic glucose concentrations are observed in children that are overweight or have obesity. The aim of this study was to evaluate the effect of a 12 month lifestyle intervention on free-living glycemic profiles in children that were overweight or had obesity, and the association of the alterations with changes in cardiovascular risk parameters. BMI z-score, free-living glycemic profiles, continuous overlapping net glycemic action (CONGA), and cardiovascular parameters were evaluated before and after a multidisciplinary lifestyle intervention, in 33 non-diabetic children that were overweight or had obesity. In children with a decrease in BMI z-score, the duration which glucose concentrations were above the high-normal threshold (6.7 mmol/L) and the glycemic variability decreased significantly. In these children, a decrease in median sensor glucose was associated with decreases in LDL-cholesterol, and systolic and diastolic blood pressure z-score. A decrease in BMI z-score was associated with a decrease in CONGA1, 2, and 4. In conclusion, the glycemic profiles in free-living conditions in children that were overweight improved in children with a decrease in BMI z-score after lifestyle intervention. In those children, changes in median sensor glucose concentrations were associated with changes in LDL-cholesterol and blood pressure z-scores. These results suggest that glucose homeostasis can improve after one year of lifestyle intervention and that these improvements are associated with improvements in cardiovascular health parameters.

[Technological Innovations in Diabetes Therapy]. / Technologische Innovationen in der Diabetes-Therapie.

Technological Innovations in Diabetes Therapy Abstract. In the last few years a whole array of technical innovations has dramatically increased treatment options for patients with diabetes mellitus. Capillary blood glucose measurements are increasingly replaced by continuous glucose monitoring. More and more insulin pump systems are linked up to continuous glucose monitoring, which thereby become ever more self-regulating. Novel ultra-long and ultra-short acting insulins have become available. There will soon be oral alternatives for several anti-diabetic treatments, which hitherto needed to be injected.

Interstitial glucose concentrations and hypoglycemia during 2 days of caloric deficit and sustained exercise: a double-blind, placebo-controlled trial.

Military personnel and some athlete populations endure short-term energy deficits from reduced energy intake and/or increased energy expenditure (EE) that may degrade physical and cognitive performance due to severe hypoglycemia (<3.1 mmol/l). The extent to which energy deficits alter normoglycemia (3.9-7.8 mmol/l) in healthy individuals is not known, since prior studies measured glucose infrequently, not continuously. The purpose of this study was to characterize the glycemic response to acute, severe energy deficit compared with fully fed control condition, using continuous glucose monitoring (CGM). For 2 days during a double-blind, placebo-controlled, crossover study, 23 volunteers (17 men/6 women; age: 21.3 ± 3.0 yr; body mass index: 25 ± 3 kg/m) increased habitual daily EE [2,300 ± 450 kcal/day [means ± SD)] by 1,647 ± 345 kcal/day through prescribed exercise (~3 h/day; 40-65% peak O2 consumption), and consumed diets designed to maintain energy balance (FED) or induce 93% energy deficit (DEF). Interstitial glucose concentrations were measured continuously by CGM (Medtronic Minimed). Interstitial glucose concentrations were 1.0 ± 0.9 mmol/l lower during DEF vs. FED (P < 0.0001). The percentage of time spent in mild (3.1-3.8 mmol/l) hypoglycemia was higher during DEF compared with FED [mean difference = 20.5%; 95% confidence interval (CI): 13.1%, 27.9%; P = 0.04], while time spent in severe (<3.1 mmol/l) hypoglycemia was not different between interventions (mean difference = 4.6%; 95% CI: -0.6%, 9.8%; P = 0.10). Three of 23 participants spontaneously reported symptoms (e.g., nausea) potentially related to hypoglycemia during DEF, and an additional participant reported symptoms during both interventions. These findings suggest that severe hypoglycemia rarely occurs in healthy individuals enduring severe, short-term energy deficit secondary to heavy exercise and inadequate energy intake.