Metrics for glycaemic control - from HbA1c to continuous glucose monitoring

Measures of nutrition status and health for weight-inclusive patient care: A narrative review

In healthcare, weight is often equated to and used as a marker for health. In examining nutrition and health status, there are many more effective markers independent of weight. In this article, we review practical and emerging clinical applications of technologies and tools used to collect non-weight-related data in nutrition assessment, monitoring, and evaluation in the outpatient setting. The aim is to provide clinicians with new ideas about various types of data to evaluate and track in nutrition care.

Innovative solution or cause for concern? The use of continuous glucose monitors in people not living with diabetes: A narrative review

AIMS

Continuous glucose monitors (CGMs) have expanded their scope beyond indicated uses for diabetes management and are gaining traction among people not living with diabetes (PNLD). CGMs track in time glucose levels and are proposed as tools for the early detection of abnormal glucose and a potential solution for its normalisation through behavioural change, particularly, diet personalisation and motivation of physical activity. This becomes relevance given the growing incidence of metabolic conditions, such as type 2 diabetes mellitus (T2DM). Clinical guidelines, however, do not recommend CGMs in contexts outside type 1 diabetes (T1DM) or insulin-treated T2DM. Therefore, there is a visible disconnect between the indicated and real-world usage of these medical devices. While the commercial market for CGMs in PNLD is expanding rapidly, a comprehensive and evidence-based evaluation of the devices' utility in this population has not been done. Therefore, this review aims to formulate a working model for CGM utility in PNLD as proposed by the 'health and wellness' market that advertises and distributes it to these individuals.

METHODS

We aim to critically analyse the available research addressing components of the working model, that is (1) detection of abnormal glucose; (2) behavioural change, and (3) metabolic health improvement.

RESULTS

We find a lack of consistent and high-quality evidence to support the utility of CGMs for these purposes. We identify significantly under-reserved areas including clinical benchmarks and scoring procedures for CGM measures, device acceptability, and potential adverse effects of CGMs on eating habits in PNLD. We also raise concerns about the robustness of available CGM research.

CONCLUSION

In the face of these research gaps, we urge for the commercial claims suggesting the utility of the device in PNLD to be labelled as misleading. We argue that there is a regulatory inadequacy that fuels 'off-label' CGM distribution and calls for the strengthening of post-market clinical follow-up oversight for CGMs. We hope this will help to avert the continued misinformation risk to PNLD and 'off-label' exacerbation of health disparities.

The complexity of glucose time series is associated with short- and long-term mortality in critically ill adults: a multi-center, prospective, observational study

BACKGROUND

The wealth of data taken from continuous glucose monitoring (CGM) remains to be fully used. We aimed to evaluate the relationship between a promising new CGM metric, complexity of glucose time series index (CGI), and mortality in critically ill patients.

METHODS

A total of 293 patients admitted to mixed medical/surgical intensive care units from 5 medical centers in Shanghai were prospectively included between May 2020 and November 2021. CGI was assessed using intermittently scanned CGM, with a median monitoring period of 12.0 days. Outcome measures included short- and long-term mortality.

RESULTS

During a median follow-up period of 1.7 years, a total of 139 (47.4%) deaths were identified, of which 73 (24.9%) occurred within the first 30 days after ICU admission, and 103 (35.2%) within 90 days. The multivariable-adjusted HRs for 30-day mortality across ascending tertiles of CGI were 1.00 (reference), 0.68 (95% CI 0.38-1.22) and 0.36 (95% CI 0.19-0.70), respectively. For per 1-SD increase in CGI, the risk of 30-day mortality was decreased by 51% (HR 0.49, 95% CI 0.35-0.69). Further adjustment for HbA1c, mean glucose during hospitalization and glucose variability partially attenuated these associations, although the link between CGI and 30-day mortality remained significant (per 1-SD increase: HR 0.57, 95% CI 0.40-0.83). Similar results were observed when 90-day mortality was considered as the outcome. Furthermore, CGI was also significantly and independently associated with long-term mortality (per 1-SD increase: HR 0.77, 95% CI 0.61-0.97).

CONCLUSIONS

In critically ill patients, CGI is significantly associated with short- and long-term mortality.

Association between glycemia risk index and arterial stiffness in type 2 diabetes

AIM

This study aims to investigate the association of glycemia risk index (GRI), a novel composite metric derived from continuous glucose monitoring (CGM), with arterial stiffness in patients with type 2 diabetes.

MATERIALS AND METHODS

A total of 342 adults with type 2 diabetes were enrolled between April and June 2023 from 11 communities in Shanghai, China. Medical examinations, including measurements of anthropometric parameters, blood pressure, and venous blood samples were conducted. Brachial-ankle pulse wave velocity (baPWV) was examined to evaluate arterial stiffness. All the participants underwent a 14 day CGM recording and GRI was calculated from the CGM data.

RESULTS

The mean age was 70.3 ± 6.8 years, and 162 (47.4%) were male. Participants with a higher baPWV had significantly higher levels of GRI and hyperglycemia component (both P for trend < 0.05). Linear regression revealed the significant positive linear associations of the GRI with baPWV in unadjusted or adjusted models (All P < 0.05). In the multivariable logistic analysis, each increase in the GRI quartile was associated with a 1.30-fold (95% CI 1.01-1.68, P for trend < 0.05) higher prevalence of increased arterial stiffness after adjustment for age, sex, BMI, diabetes duration, current smoking status, blood pressure, and lipid profile. Subgroup analyses showed that the association between the GRI quartiles and increased arterial stiffness was stronger among participants with a diabetes duration ≥15 years (P for interaction = 0.014).

CONCLUSION

Glycemia risk index assessed by continuous glucose monitoring is associated with increased arterial stiffness in type 2 diabetes.

Adaptive Biobehavioral Control: A Pilot Analysis of Human-Machine Coadaptation in Type 1 Diabetes

Background: While it is well recognized that an automated insulin delivery (AID) algorithm should adapt to changes in physiology, it is less understood that the individual would also have to adapt to the AID system. The adaptive biobehavioral control (ABC) method presented here attempts to compensate for this deficiency by including AID into an information cloud-based ecosystem. Methods: The Web Information Tool (WIT) implements the ABC concept via the following: (1) a Physiological Adaptation Module (PAM) that tracks metabolic changes and adapts AID parameters accordingly and (2) a Behavioral Adaptation Module (BAM) that provides information feedback. The safety of WIT (primary outcome) was assessed in an 8-week randomized, two-arm parallel pilot study. All participants used the Control-IQ® AID system enhanced with PAM, but only those in the Experimental group had access to BAM. Secondary glycemic outcomes were computed using the 2-week baseline period and the last 2 weeks of treatment. Results: Thirty participants with type 1 diabetes (T1D) completed all study procedures (17 female/13 male; age: 40 ± 14 years; HbA1c: 6.6% ± 0.5%). No severe hypoglycemia, DKA, or other serious adverse events were reported. Comparing the Experimental and Control groups, no significant difference was observed in time in range (70-180 mg/dL): 74.6% vs 73.8%, adjusted mean difference: 2.65%, 95% CI (-1.12%,6.41%), P = 0.161. Time in 70-140 mg/dL was significantly higher in the Experimental group: 50.7% vs 49.2%, 5.71% (0.44%,10.97%), P = 0.035, without increased time below range: 0.54% (-0.09%,1.17%), P = 0.089. Conclusion: The results demonstrate that it is safe to integrate an AID system into the WIT ecosystem. Validation in a full-scale study is ongoing.

Q-Score Complements the Time in Range in the Evaluation of Short-Term Glycemic Control

BACKGROUND AND AIMS

The Q-Score is a single-number composite metric that is constructed based on the following components: central glycemic tendency, hyperglycemia, hypoglycemia, and intra- and interday variability. Herein, we refined the Q-Score for the screening and analysis of short-term glycemic control using continuous glucose monitoring (CGM) profiles.

METHODS

Continuous glucose monitoring profiles were obtained from noninterventional, retrospective cross-sectional studies. The upper limit of the Q-Score component hyperglycemia' that is, the time above target range (TAR), was adjusted from 8.9 to 10 mmol/L (n = 1562 three-day-sensor profiles). A total of 302 people with diabetes mellitus treated with intermittent CGM for ≥14 days were enrolled. The time to stability was determined via correlation-based analysis.

RESULTS

There was a strong correlation between the Q-Scores of the two TARs, that is, 8.9 and 10 mmol/L (Q-ScoreTAR10 = -0.03 + 1.00 Q-ScoreTAR8.9, r = .997, p < .001). The times to stability of the Q-Score and TIR were 10 and 12 days, respectively. The Q-Score was correlated with fructosamine concentrations, the glucose management indicator (GMI), the time in range (TIR), and the glycemic risk index (GRI) (r = .698, .887, -.874, and .941), respectively. The number of Q-Score components above the target increased as the TIR decreased, from two (1.7 ± 0.9) in CGM profiles with a TIR between 70% and 80% to four (3.9 ± 0.5) in the majority of the CGM profiles with a TIR below 50%. A conversion matrix between the Q-Score and glycemic indices was developed.

CONCLUSIONS

The Q-Score is a tool for assessing short-term glycemic control. The Q-Score can be translated into clinician opinion using the GRI.

Investigation of the hypoglycemic mechanism of the ShenQi compound formula through metabonomics and 16S rRNA sequencing

Introduction: Herbal formulations are renowned for their complex biological activities, acting on multiple targets and pathways, as evidenced by in vitro studies. However, the hypoglycemic effect and underlying mechanisms of Shenqi Compound (SQ), a traditional Chinese herbal formula, remain elusive. This study aimed to elucidate the hypoglycemic effects of SQ and explore its mechanisms of action, focusing on intestinal flora and metabolomics. Methods: A Type 2 diabetes mellitus (T2DM) rat model was established through a high-fat diet, followed by variable glucose and insulin injections to mimic the fluctuating glycemic conditions seen in diabetes. Results: An eight-week regimen of SQ significantly mitigated hyperglycemia, inflammation, and insulin resistance in these rats. Notably, SQ beneficially modulated the gut microbiota by increasing populations of beneficial bacteria, such as Lachnospiraceae_NK4A136_group and Akkermansia, while reducing and inhibiting harmful strains such as Ruminococcus and Phascolarctobacterium. Metabolomics analyses revealed that SQ intervention corrected disturbances in Testosterone enanthate and Glycerophospholipid metabolism. Discussion: Our findings highlight the hypoglycemic potential of SQ and its mechanisms via modulation of the gut microbiota and metabolic pathways, offering a theoretical foundation for the use of herbal medicine in diabetes management.

Post-meal exercise under ecological conditions improves post-prandial glucose levels but not 24-hour glucose control

We investigated whether post-meal walking (PMW) improved post-prandial glucose and 24h glucose control under free-living conditions among physically inactive young women.

METHODS

Young women (Age: 20±1years; percent body fat: 28.2 ± 12%; BMI: 23.8 ± 4.2kg·m-1) completed a randomised crossover study to assess if PMW confers benefit. On the PMW day, women completed three bouts of brisk walks, and on the Control day they were instructed to follow normal habitual activities. Continuous glucose monitors captured post-prandial and 24h glucose, and physical activity monitors tracked physical activity throughout the study.

RESULTS

PMW walking increased total daily step count (Control = 9,159 ± 2,962 steps vs. PMW = 14,611±3,891 steps, p<0.001) and activity scores (Control=33.87±1.16 METs·h vs. PMW = 36.11±1.58 METs·h, p < 0.001). PMW led to lower 3h average post-prandial glucose (main effect of condition, p=0.011) and 3h post-prandial area under curve glucose responses (main effect of condition, p = 0.027) compared to the control condition. Post hoc analysis revealed the largest decline occurred after dinner (3h average glucose Control = 7.55±1.21 mmol/L vs. PMW = 6.71 ± 0.80mmol/L, p = 0.039), when insulin sensitivity is typically diminished. Despite improvements in post-prandial glucose control, this did not translate to improvements in 24h glucose control (p > 0.05).

CONCLUSION

Physically inactive and metabolically healthy young women, PMW improves post-prandial glucose but not 24h glucose control.

Predicting the Risk of Developing Type 1 Diabetes Using a One-Week Continuous Glucose Monitoring Home Test With Classification Enhanced by Machine Learning: An Exploratory Study

BACKGROUND

Detection of two or more autoantibodies (Ab) in the blood might describe those individuals at increased risk of developing type 1 diabetes (T1D) during the following years. The aim of this exploratory study is to propose a high versus low T1D risk classifier using machine learning technology based on continuous glucose monitoring (CGM) home data.

METHODS

Forty-two healthy relatives of people with T1D with mean ± SD age of 23.8 ± 10.5 years, HbA1c (glycated hemoglobin) of 5.3% ± 0.3%, and BMI (body mass index) of 23.2 ± 5.2 kg/m2 with zero (low risk; N = 21), and ≥2 (high risk; N = 21) Ab, were enrolled in an NIH (National Institutes of Health)-funded TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic features were extracted from two-hour post-SLMM CGM traces, compared across groups, and used in four supervised machine learning Ab risk status classifiers. Recursive Feature Elimination (RFE) algorithm was used for feature selection; classifiers were evaluated through 10-fold cross-validation, using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model.

RESULTS

The percent time of glucose >180 mg/dL (T180), glucose range, and glucose CV (coefficient of variation) were the only significant differences between the glycemic features in the two groups with P values of .040, .035, and .028 respectively. The linear SVM (Support Vector Machine) model with RFE features achieved the best performance of classifying low-risk versus high-risk individuals with AUC-ROC = 0.88.

CONCLUSIONS

A machine learning technology, combining a potentially self-administered one-week CGM home test, has the potential to reliably assess the T1D risk.

Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities

OBJECTIVE

Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid.

METHODS

Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources.

SIGNIFICANCE

These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.

Factors associated with nocturnal and diurnal glycemic variability in patients with type 2 diabetes: a cross-sectional study

PURPOSE

There is little information on factors that influence the glycemic variability (GV) during the nocturnal and diurnal periods. We aimed to examine the relationship between clinical factors and GV during these two periods.

METHODS

This cross-sectional study included 134 patients with type 2 diabetes. 24-h changes in blood glucose were recorded by a continuous glucose monitoring system. Nocturnal and diurnal GV were assessed by standard deviation of blood glucose (SDBG), coefficient of variation (CV), and mean amplitude of glycemic excursions (MAGE), respectively. Robust regression analyses were performed to identify the factors associated with GV. Restricted cubic splines were used to determine dose-response relationship.

RESULTS

During the nocturnal period, age and glycemic level at 12:00 A.M. were positively associated with GV, whereas alanine aminotransferase was negatively associated with GV. During the diurnal period, homeostatic model assessment 2-insulin sensitivity (HOMA2-S) was positively associated with GV, whereas insulin secretion-sensitivity index-2 (ISSI2) was negatively associated with GV. Additionally, we found a J-shape association between the glycemic level at 12:00 A.M. and MAGE, with 9.0 mmol/L blood glucose level as a cutoff point. Similar nonlinear associations were found between ISSI2 and SDBG, and between ISSI2 and MAGE, with ISSI2 value of 175 as a cutoff point.

CONCLUSION

Factors associated with GV were different between nocturnal and diurnal periods. The cutoff points we found in this study may provide the therapeutic targets for beta-cell function and pre-sleep glycemic level in clinical practice.

Marjorie: Visualizing Type 1 Diabetes Data to Support Pattern Exploration

In this work we propose Marjorie, a visual analytics approach to address the challenge of analyzing patients' diabetes data during brief regular appointments with their diabetologists. Designed in consultation with diabetologists, Marjorie uses a combination of visual and algorithmic methods to support the exploration of patterns in the data. Patterns of interest include seasonal variations of the glucose profiles, and non-periodic patterns such as fluctuations around mealtimes or periods of hypoglycemia (i.e., glucose levels below the normal range). We introduce a unique representation of glucose data based on modified horizon graphs and hierarchical clustering of adjacent carbohydrate or insulin entries. Semantic zooming allows the exploration of patterns on different levels of temporal detail. We evaluated our solution in a case study, which demonstrated Marjorie's potential to provide valuable insights into therapy parameters and unfavorable eating habits, among others. The study results and informal feedback collected from target users suggest that Marjorie effectively supports patients and diabetologists in the joint exploration of patterns in diabetes data, potentially enabling more informed treatment decisions. A free copy of this paper and all supplemental materials are available at https://osf.io/34t8c/.

Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?

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.

Combining uncertainty-aware predictive modeling and a bedtime Smart Snack intervention to prevent nocturnal hypoglycemia in people with type 1 diabetes on multiple daily injections

OBJECTIVE

Nocturnal hypoglycemia is a known challenge for people with type 1 diabetes, especially for physically active individuals or those on multiple daily injections. We developed an evidential neural network (ENN) to predict at bedtime the probability and timing of nocturnal hypoglycemia (0-4 vs 4-8 h after bedtime) based on several glucose metrics and physical activity patterns. We utilized these predictions in silico to prescribe bedtime carbohydrates with a Smart Snack intervention specific to the predicted minimum nocturnal glucose and timing of nocturnal hypoglycemia.

MATERIALS AND METHODS

We leveraged free-living datasets collected from 366 individuals from the T1DEXI Study and Glooko. Inputs to the ENN used to model nocturnal hypoglycemia were derived from demographic information, continuous glucose monitoring, and physical activity data. We assessed the accuracy of the ENN using area under the receiver operating curve, and the clinical impact of the Smart Snack intervention through simulations.

RESULTS

The ENN achieved an area under the receiver operating curve of 0.80 and 0.71 to predict nocturnal hypoglycemic events during 0-4 and 4-8 h after bedtime, respectively, outperforming all evaluated baseline methods. Use of the Smart Snack intervention reduced probability of nocturnal hypoglycemia from 23.9 ± 14.1% to 14.0 ± 13.3% and duration from 7.4 ± 7.0% to 2.4 ± 3.3% in silico.

DISCUSSION

Our findings indicate that the ENN-based Smart Snack intervention has the potential to significantly reduce the frequency and duration of nocturnal hypoglycemic events.

CONCLUSION

A decision support system that combines prediction of minimum nocturnal glucose and proactive recommendations for bedtime carbohydrate intake might effectively prevent nocturnal hypoglycemia and reduce the burden of glycemic self-management.

Safety and Efficacy of Sustained Automated Insulin Delivery Compared With Sensor and Pump Therapy in Adults With Type 1 Diabetes at High Risk for Hypoglycemia: A Randomized Controlled Trial

OBJECTIVE

Assess the safety and efficacy of automated insulin delivery (AID) in adults with type 1 diabetes (T1D) at high risk for hypoglycemia.

RESEARCH DESIGN AND METHODS

Participants were 72 adults with T1D who used an insulin pump with Clarke Hypoglycemia Perception Awareness scale score >3 and/or had severe hypoglycemia during the previous 6 months confirmed by time below range (TBR; defined as sensor glucose [SG] reading <70 mg/dL) of at least 5% during 2 weeks of blinded continuous glucose monitoring (CGM). Parallel-arm, randomized trial (2:1) of AID (Tandem t:slim ×2 with Control-IQ technology) versus CGM and pump therapy for 12 weeks. The primary outcome was TBR change from baseline. Secondary outcomes included time in target range (TIR; 70-180 mg/dL), time above range (TAR), mean SG reading, and time with glucose level <54 mg/dL. An optional 12-week extension with AID was offered to all participants.

RESULTS

Compared with the sensor and pump (S&P), AID resulted in significant reduction of TBR by -3.7% (95% CI -4.8, -2.6), P < 0.001; an 8.6% increase in TIR (95% CI 5.2, 12.1), P < 0.001; and a -5.3% decrease in TAR (95% CI -87.7, -1.8), P = 0.004. Mean SG reading remained similar in the AID and S&P groups. During the 12-week extension, the effects of AID were sustained in the AID group and reproduced in the S&P group. Two severe hypoglycemic episodes occurred using AID.

CONCLUSIONS

In adults with T1D at high risk for hypoglycemia, AID reduced the risk for hypoglycemia more than twofold, as quantified by TBR, while improving TIR and reducing hyperglycemia. Hence, AID is strongly recommended for this specific population.

Simulation-driven optimization of insulin therapy profiles in a commercial hybrid closed-loop system

BACKGROUND

Automated insulin delivery (AID) has represented a breakthrough in managing type 1 diabetes (T1D), showing safe and effective glucose control extensively across the board. However, metabolic variability still poses a challenge to commercial hybrid closed-loop (HCL) solutions, whose performance depends on customizable insulin therapy profiles. In this work, we propose an Identification-Replay-Optimization (IRO) approach to optimize gradually and safely such profiles for the Control-IQ AID algorithm.

METHODS

Closed-loop data are generated using the full adult cohort of the UVA/Padova T1D simulation platform in diverse glycemic scenarios. For each subject, daily records are processed and used to estimate a personalized model of the underlying insulin-glucose dynamics. Every two weeks, all identified models are integrated into an optimization procedure where daily basal and bolus profiles are adjusted so as to minimize the risks for hypo- and hyperglycemia. The proposed strategy is tested under different scenarios of metabolic and behavioral variability in order to evaluate the efficacy and convergence of the proposed strategy. Finally, glycemic metrics between cycles are compared using paired t-tests with p<0.05 as the significance threshold.

RESULTS

Simulations reveal that the proposed IRO approach was able to improve glucose control over time by safely mitigating the risks for both hypo- and hyperglycemia. Furthermore, smaller changes were recommended at each cycle, indicating convergence when simulation conditions were maintained.

CONCLUSIONS

The use of reliable simulation-driven tools capable of accurately reproducing field-collected data and predicting changes can substantially shorten the process of optimizing insulin therapy, adjusting it to metabolic changes and leading to improved glucose control.

1-Amino-1-deoxy-d-fructose ("fructosamine") and its derivatives: An update

1-Amino-1-deoxy-d-fructose (fructosamine, FN) derivatives are omnipresent in all living organisms, as a result of non-enzymatic condensation and Amadori rearrangement reactions between free glucose and biogenic amines such as amino acids, polypeptides, or aminophospholipids. Over decades, steady interest in fructosamine was largely sustained by its role as a key intermediate structure in the Maillard reaction that is responsible for the organoleptic and nutritional value of thermally processed foods, and for pathophysiological effects of hyperglycemia in diabetes. New trends in fructosamine research include the discovery and engineering of FN-processing enzymes, development of advanced tools for hyperglycemia monitoring, and evaluation of the therapeutic potential of both fructosamines and FN-recognizing proteins. This article covers developments in the field of fructosamine and its derivatives since 2010 and attempts to ascertain challenges in future research.

Performance Effect of Adjusting Insulin Sensitivity for Model-Based Automated Insulin Delivery Systems

BACKGROUND

Model predictive control (MPC) has become one of the most popular control strategies for automated insulin delivery (AID) in type 1 diabetes (T1D). These algorithms rely on a prediction model to determine the best insulin dosing every sampling time. Although these algorithms have been shown to be safe and effective for glucose management through clinical trials, managing the ever-fluctuating relationship between insulin delivery and resulting glucose uptake (aka insulin sensitivity, IS) remains a challenge. We aim to evaluate the effect of informing an AID system with IS on the performance of the system.

METHOD

The University of Virginia (UVA) MPC control-based hybrid closed-loop (HCL) and fully closed-loop (FCL) system was used. One-day simulations at varying levels of IS were run with the UVA/Padova T1D Simulator. The AID system was informed with an estimated value of IS obtained through a mixed meal glucose tolerance test. Relevant controller parameters are updated to inform insulin dosing of IS. Performance of the HCL/FCL system with and without information of the changing IS was assessed using a novel performance metric penalizing the time outside the target glucose range.

RESULTS

Feedback in AID systems provides a certain degree tolerance to changes in IS. However, IS-informed bolus and basal dosing improve glycemic outcomes, providing increased protection against hyperglycemia and hypoglycemia according to the individual's physiological state.

CONCLUSIONS

The proof-of-concept analysis presented here shows the potentially beneficial effects on system performance of informing the AID system with accurate estimates of IS. In particular, when considering reduced IS, the informed controller provides increased protection against hyperglycemia compared with the naïve controller. Similarly, reduced hypoglycemia is obtained for situations with increased IS. Further tailoring of the adaptation schemes proposed in this work is needed to overcome the increased hypoglycemia observed in the more resistant cases and to optimize the performance of the adaptation method.

Key indices of glycaemic variability for application in diabetes clinical practice

Near normal glycaemic control in diabetes consists to target daily glucose fluctuations and quarterly HbA1c oscillations in addition to overall glucose exposure. Consequently, the prerequisite is to define simple, and mathematically undisputable key metrics for the short- and long-term variability in glucose homeostasis. As the standard deviations (SD) of either glucose or HbA1c are dependent on their means, the coefficient of variation (CV = SD/mean) should be applied instead as it that avoids the correlation between the SD and mean values. A CV glucose of 36% is the most appropriate threshold between those with stable versus labile glucose homeostasis. However, when near normal mean glucose concentrations are achieved a lower CV threshold of <27 % is necessary for reducing the risk for hypoglycaemia to a minimal rate. For the long-term variability in glucose homeostasis, a CVHbA1c < 5 % seems to be a relevant recommendation for preventing adverse clinical outcomes.

The Effect of Insulin Degludec Versus Insulin Glargine U100 on Glucose Metrics Recorded During Continuous Glucose Monitoring in People With Type 1 Diabetes and Recurrent Nocturnal Severe Hypoglycemia

AIM

Comparing continuous glucose monitoring (CGM)-recorded metrics during treatment with insulin degludec (IDeg) versus insulin glargine U100 (IGlar-100) in people with type 1 diabetes (T1D) and recurrent nocturnal severe hypoglycemia.

MATERIALS AND METHODS

This is a multicenter, two-year, randomized, crossover trial, including 149 adults with T1D and minimum one episode of nocturnal severe hypoglycemia within the last two years. Participants were randomized 1:1 to treatment with IDeg or IGlar-100 and given the option of six days of blinded CGM twice during each treatment. CGM traces were reviewed for the percentage of time-within-target glucose range (TIR), time-below-range (TBR), time-above-range (TAR), and coefficient of variation (CV).

RESULTS

Seventy-four participants were included in the analysis. Differences between treatments were greatest during the night (23:00-06:59). Treatment with IGlar-100 resulted in 54.0% vs 49.0% with IDeg TIR (70-180 mg/dL) (estimated treatment difference [ETD]: -4.6%, 95% confidence interval [CI]: -9.1, -0.0, P = .049). TBR was lower with IDeg at level 1 (54-69 mg/dL) (ETD: -1.7% [95% CI: -2.9, -0.5], P < .05) and level 2 (<54 mg/dL) (ETD: -1.3% [95% CI: -2.1, -0.5], P = .001). TAR was higher with IDeg compared with IGlar-100 at level 1 (181-250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.3], P < .05) and level 2 (> 250 mg/dL) (ETD: 4.0% [95% CI: 0.8, 7.2], P < .05). The mean CV was lower with IDeg than that with IGlar-100 (ETD: -3.4% [95% CI: -5.6, -1.2], P < .05).

CONCLUSION

For people with T1D suffering from recurrent nocturnal severe hypoglycemia, treatment with IDeg, compared with IGlar-100, results in a lower TBR and CV during the night at the expense of more TAR.

Assessment of Meal Anticipation for Improving Fully Automated Insulin Delivery in Adults With Type 1 Diabetes

OBJECTIVE

Meals are a consistent challenge to glycemic control in type 1 diabetes (T1D). Our objective was to assess the glycemic impact of meal anticipation within a fully automated insulin delivery (AID) system among adults with T1D.

RESEARCH DESIGN AND METHODS

We report the results of a randomized crossover clinical trial comparing three modalities of AID systems: hybrid closed loop (HCL), full closed loop (FCL), and full closed loop with meal anticipation (FCL+). Modalities were tested during three supervised 24-h admissions, where breakfast, lunch, and dinner were consumed per participant's home schedule, at a fixed time, and with a 1.5-h delay, respectively. Primary outcome was the percent time in range 70-180 mg/dL (TIR) during the breakfast postprandial period for FCL+ versus FCL.

RESULTS

Thirty-five adults with T1D (age 44.5 ± 15.4 years; HbA1c 6.7 ± 0.9%; n = 23 women and n = 12 men) were randomly assigned. TIR for the 5-h period after breakfast was 75 ± 23%, 58 ± 21%, and 63 ± 19% for HCL, FCL, and FCL+, respectively, with no significant difference between FCL+ and FCL. For the 2 h before dinner, time below range (TBR) was similar for FCL and FCL+. For the 5-h period after dinner, TIR was similar for FCL+ and FCL (71 ± 34% vs. 72 ± 29%; P = 1.0), whereas TBR was reduced in FCL+ (median 0% [0-0%] vs. 0% [0-0.8%]; P = 0.03). Overall, 24-h control for HCL, FCL, and FCL+ was 86 ± 10%, 77 ± 11%, and 77 ± 12%, respectively.

CONCLUSIONS

Although postprandial control remained optimal with hybrid AID, both fully AID solutions offered overall TIR >70% with similar or lower exposure to hypoglycemia. Anticipation did not significantly improve postprandial control in AID systems but also did not increase hypoglycemic risk when meals were delayed.

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BACKGROUND

A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.

METHODS

We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.

RESULTS

The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.

CONCLUSION

The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

The Tandem Control-IQ advanced hybrid system improves glycemic control in children under 18 years of age with type 1 diabetes and night rest in caregivers

OBJECTIVE

To determine the impact of switching from the predictive low glucose suspend (PLGS) system to the advanced hybrid Tandem Control-IQ system on glucometrics and glycosylated haemoglobin (HbA1c) at one year. To assess the impact on the quality of life perceived by parents.

METHOD

Prospective study in 71 patients aged 6-18 years with type 1 diabetes (DM1), in treatment with PLGS, who switched to an advanced hybrid system. Glucometric data were collected before the change, at 4 and 8 weeks, and at one year of use; HbA1c before the change and after one year. The Diabetes Impact and Devices Satisfaction (DIDS) questionnaire was used at weeks 4 and 8.

RESULTS

An increase in time in range (TIR) was observed with a median of 76% (P<.001) at 4 weeks, which was maintained after one year (+8% in the total group). Overall, 73.24% of patients achieved a TIR above 70%. The subgroup with an initial TIR of less than 56% increased it by 14.4%. After one year there was a 0.3% reduction in HbA1c. Level 1 hypoglycaemia, level 1 and level 2 hyperglycaemia, mean glucose (GM) and coefficient of variation (CV) decreased. Auto mode stayed on 97% of the time and no dropouts occurred. Caregivers had a perception of better glycaemic control and less need to monitor blood glucose variations during the night. None of them would switch back to the previous system and they feel safe with the new system.

CONCLUSIONS

The Tandem Control-IQ advanced hybrid system was shown to be effective one year after its implementation with improvement in all glucometric parameters and HbA1c, as well as night-time rest in caregivers.

Predicting Immunological Risk for Stage 1 and Stage 2 Diabetes Using a 1-Week CGM Home Test, Nocturnal Glucose Increments, and Standardized Liquid Mixed Meal Breakfasts, with Classification Enhanced by Machine Learning

Background: Predicting the risk for type 1 diabetes (T1D) is a significant challenge. We use a 1-week continuous glucose monitoring (CGM) home test to characterize differences in glycemia in at-risk healthy individuals based on autoantibody presence and develop a machine-learning technology for CGM-based islet autoantibody classification. Methods: Sixty healthy relatives of people with T1D with mean ± standard deviation age of 23.7 ± 10.7 years, HbA1c of 5.3% ± 0.3%, and body mass index of 23.8 ± 5.6 kg/m2 with zero (n = 21), one (n = 18), and ≥2 (n = 21) autoantibodies were enrolled in an National Institutes of Health TrialNet ancillary study. Participants wore a CGM for a week and consumed three standardized liquid mixed meals (SLMM) instead of three breakfasts. Glycemic outcomes were computed from weekly, overnight (12:00-06:00), and post-SLMM CGM traces, compared across groups, and used in four supervised machine-learning autoantibody status classifiers. Classifiers were evaluated through 10-fold cross-validation using the receiver operating characteristic area under the curve (AUC-ROC) to select the best classification model. Results: Among all computed glycemia metrics, only three were different across the autoantibodies groups: percent time >180 mg/dL (T180) weekly (P = 0.04), overnight CGM incremental AUC (P = 0.005), and T180 for 75 min post-SLMM CGM traces (P = 0.004). Once overnight and post-SLMM features are incorporated in machine-learning classifiers, a linear support vector machine model achieved the best performance of classifying autoantibody positive versus autoantibody negative participants with AUC-ROC ≥0.81. Conclusion: A new technology combining machine learning with a potentially self-administered 1-week CGM home test can help improve T1D risk detection without the need to visit a hospital or use a medical laboratory. Trial registration: ClinicalTrials.gov registration no. NCT02663661.

What is Glycaemic Variability and which Pharmacological Treatment Options are Effective? A Narrative Review

Glycated haemoglobin is currently used for diagnosis and follow-up of diabetes mellitus. However, it has important limitations; as it only reflects the average glycaemia over the last 3 months, it does not allow the identification of crucial events, such as episodes of hypoglycaemia and hyperglycaemia. Strict control of hyperglycaemia can result in severe hypoglycaemia that can be life threatening and can have important sequelae. Recently, the concept of glycaemic variability has been developed to provide information about the magnitude of glycaemic excursions and the duration of these fluctuations. This new approach has the potential to improve outcomes, decrease the risk of hypoglycaemia, and decrease cardiovascular risk. This review describes the most commonly prescribed non-insulin anti-diabetic drugs for diabetes management, their mechanism of action, and the existing evidence about their effectiveness in improving glycaemic variability and diabetes control.

Delivering Biopsychosocial Health Care Within Routine Care: Spotlight-AQ Pivotal Multicenter Randomized Controlled Trial Results

BACKGROUND

Annual national diabetes audit data consistently shows most people with diabetes do not consistently achieve blood glucose targets for optimal health, despite the large range of treatment options available.

AIM

To explore the efficacy of a novel clinical intervention to address physical and mental health needs within routine diabetes consultations across health care settings.

METHODS

A multicenter, parallel group, individually randomized trial comparing consultation duration in adults diagnosed with T1D or T2D for ≥6 months using the Spotlight-AQ platform versus usual care. Secondary outcomes were HbA1c, depression, diabetes distress, anxiety, functional health status, and healthcare professional burnout. Machine learning models were utilized to analyze the data collected from the Spotlight-AQ platform to validate the reliability of question-concern association; as well as to identify key features that distinguish people with type 1 and type 2 diabetes, as well as important features that distinguish different levels of HbA1c.

RESULTS

n = 98 adults with T1D or T2D; any HbA1c and receiving any diabetes treatment participated (n = 49 intervention). Consultation duration for intervention participants was reduced in intervention consultations by 0.5 to 4.1 minutes (3%-14%) versus no change in the control group (-0.9 to +1.28 minutes). HbA1c improved in the intervention group by 6 mmol/mol (range 0-30) versus control group 3 mmol/mol (range 0-8). Moderate improvements in psychosocial outcomes were seen in the intervention group for functional health status; reduced anxiety, depression, and diabetes distress and improved well-being. None were statistically significant. HCPs reported improved communication and greater focus on patient priorities in consultations. Artificial Intelligence examination highlighted therapy and psychological burden were most important in predicting HbA1c levels. The Natural Language Processing semantic analysis confirmed the mapping relationship between questions and their corresponding concerns. Machine learning model revealed type 1 and type 2 patients have different concerns regarding psychological burden and knowledge. Moreover, the machine learning model emphasized that individuals with varying levels of HbA1c exhibit diverse levels of psychological burden and therapy-related concerns.

CONCLUSION

Spotlight-AQ was associated with shorter, more useful consultations; with improved HbA1c and moderate benefits on psychosocial outcomes. Results reflect the importance of a biopsychosocial approach to routine care visits. Spotlight-AQ is viable across health care settings for improved outcomes.

Clinically-Similar Clusters of Daily CGM Profiles: Tracking the Progression of Glycemic Control Over Time

BACKGROUND

The adoption of CGM results in vast amounts of data, but their interpretation is still more art than exact science. The International Consensus on Time in Range (TIR) proposed the widely accepted TIR system of metrics, which we now take forward by introducing a finite and fixed set of clinically-similar clusters (CSCs), such that the TIR metrics of the daily CGM profiles within a cluster are homogeneous.

METHODS

CSC definition and validation used 204,710 daily CGM profiles in health, type 1 and type 2 diabetes (T1D, T2D), on different treatments. The CSCs were defined using 23,916 daily CGM profiles (Training set), and the final fixed set of CSCs was obtained using another 37,758 profiles (Validation set). The Testing set (143,036 profiles) was used to establish the robustness and generalizability of the CSCs.

RESULTS

The final set of CSCs contains 32 clusters. Any daily CGM profile was classifiable to a single CSC which approximated common glycemic metrics of the daily CGM profile, as evidenced by regression analyses with 0 intercept (R-squares≥0.83, e.g., correlation≥0.91), for all TIR and several other metrics. The CSCs distinguished CGM profiles in health, T2D, and T1D on different treatments, and allowed tracking of the daily changes in a person's glycemic control over time.

CONCLUSION

Daily CGM profiles can be classified into one of 32 prefixed CSCs, which enables a host of applications, e.g. tabulated data interpretation and algorithmic approaches to treatment, database indexing, pattern recognition, and tracking disease progression.

Controllable preparation of silver-doped hollow carbon spheres and its application as electrochemical probes for determination of glycated hemoglobin

Silver-doped hollow carbon spheres (Ag@HCS) were firstly introduced as electrochemical probes for glycated hemoglobin (HbA_1c) sensing at a molecularly imprinted polymer (MIP)-based carbon cloth (CC) electrode. Herein, Ag@HCS was prepared using one-pot polymerization of resorcinol and formaldehyde with AgNO₃ on the SiO₂ template, subsequent carbonization, and template removal. Furthermore, poly-aminophenylboronic acid (PABA) as the MIP film was used as a sensing platform for recognition of HbA_1c, which captured the Ag@HCS probe by binding of HbA_1c with aptamer modified on the probe surface. Due to regular geometry, large specific surface area, superior electrical conductivity, and highly-dispersed Ag, the prepared Ag@HCS probe provided an amplified electrochemical signal based on the Ag oxidation. By use of the sandwich-type electrochemical sensor, the ultrahigh sensitivity of 4.365 μA (μg mL^-1)^-1 cm^-2 and a wide detection range of 0.8-78.4 μg mL^-1 for HbA_1c detection with a low detection limit of 0.35 μg mL^-1 were obtained. Excellent selectivity was obtained due to the specific binding between HbA_1c and PABA-based MIP film. The fabricated electrochemical sensing platform was also implemented successfully for the determination of HbA_1c concentrations in the serum of healthy individuals.

Research progress in the application of time in range: more than a percentage

ABSTRACT

Glucose monitoring is an important part of medical care in diabetes mellitus, which not only helps assess glycemic control and treatment safety, but also assists with treatment adjustment. With the development of continuous glucose monitoring (CGM), the use of CGM has increased rapidly. With the wealth of glucose data produced by CGM, new metrics are greatly needed to optimally evaluate glucose status and guide the treatment. One of the parameters that CGM provides, time in range (TIR), has been recognized as a key metric by the international consensus. Before the adoption of TIR in clinical practice, several issues including the minimum length of CGM use, the setting of the target range, and individualized TIR goals are summarized. Additionally, we discussed the mounting evidence supporting the association between TIR and diabetes-related outcomes. As a novel glucose metric, it is of interest to compare TIR with other conventional glucose markers such as glycated hemoglobin A1c. It is anticipated that the use of TIR may provide further information on the quality of glucose control and lead to improved diabetes management.

Hypoglycemia and glycemic variability of people with type 1 diabetes with lower and higher physical activity loads in free-living conditions using continuous subcutaneous insulin infusion with predictive low-glucose suspend system

INTRODUCTION

Maintaining glycemic control during and after physical activity (PA) is a major challenge in type 1 diabetes (T1D). This study compared the glycemic variability and exercise-related diabetic management strategies of adults with T1D achieving higher and lower PA loads in nighttime-daytime and active- sedentary behavior hours in free-living conditions.

RESEARCH DESIGN AND METHODS

Active adults (n=28) with T1D (ages: 35±10 years; diabetes duration: 21±11 years; body mass index: 24.8±3.4 kg/m²; glycated hemoglobin A1c: 6.9±0.6%) on continuous subcutaneous insulin delivery system with predictive low glucose suspend system and glucose monitoring, performed different types, duration and intensity of PA under free-living conditions, tracked by accelerometer over 14 days. Participants were equally divided into lower load (LL) and higher load (HL) by median of daily counts per minute (61122). Glycemic variability was studied monitoring predefined time in glycemic ranges (time in range (TIR), time above range (TAR) and time below range (TBR)), coefficient of variation (CV) and mean amplitude of glycemic excursions (MAGE). Parameters were studied in defined hours timeframes (nighttime-daytime and active-sedentary behavior). Self-reported diabetes management strategies were analysed during and post-PA.

RESULTS

Higher glycemic variability (CV) was observed in sedentary hours compared with active hours in the LL group (p≤0.05). HL group showed an increment in glycemic variability (MAGE) during nighttime versus daytime (p≤0.05). There were no differences in TIR and TAR across all timeframes between HL and LL groups. The HL group had significantly more TBR during night hours than the LL group (p≤0.05). Both groups showed TBR above recommended values. All participants used fewer post-PA management strategies than during PA (p≤0.05).

CONCLUSION

Active people with T1D are able to maintain glycemic variability, TIR and TAR within recommended values regardless of PA loads. However, the high prevalence of TBR and the less use of post-PA management strategies highlights the potential need to increase awareness on actions to avoid glycemic excursions and hypoglycemia after exercise completion.

Modeling risk of hypoglycemia during and following physical activity in people with type 1 diabetes using explainable mixed-effects machine learning

BACKGROUND

Physical activity (PA) can cause increased hypoglycemia (glucose <70 mg/dL) risk in people with type 1 diabetes (T1D). We modeled the probability of hypoglycemia during and up to 24 h following PA and identified key factors associated with hypoglycemia risk.

METHODS

We leveraged a free-living dataset from Tidepool comprised of glucose measurements, insulin doses, and PA data from 50 individuals with T1D (6448 sessions) for training and validating machine learning models. We also used data from the T1Dexi pilot study that contains glucose management and PA data from 20 individuals with T1D (139 session) for assessing the accuracy of the best performing model on an independent test dataset. We used mixed-effects logistic regression (MELR) and mixed-effects random forest (MERF) to model hypoglycemia risk around PA. We identified risk factors associated with hypoglycemia using odds ratio and partial dependence analysis for the MELR and MERF models, respectively. Prediction accuracy was measured using the area under the receiver operating characteristic curve (AUROC).

RESULTS

The analysis identified risk factors significantly associated with hypoglycemia during and following PA in both MELR and MERF models including glucose and body exposure to insulin at the start of PA, low blood glucose index 24 h prior to PA, and PA intensity and timing. Both models showed overall hypoglycemia risk peaking 1 h after PA and again 5-10 h after PA, which is consistent with the hypoglycemia risk pattern observed in the training dataset. Time following PA impacted hypoglycemia risk differently across different PA types. Accuracy of hypoglycemia prediction using the fixed effects of the MERF model was highest when predicting hypoglycemia during the first hour following the start of PA (AUROCVALIDATION = 0.83 and AUROCTESTING = 0.86) and decreased when predicting hypoglycemia in the 24 h after PA (AUROCVALIDATION = 0.66 and AUROCTESTING = 0.68).

CONCLUSION

Hypoglycemia risk after the start of PA can be modeled using mixed-effects machine learning to identify key risk factors that may be used within decision support and insulin delivery systems. We published the population-level MERF model online for others to use.

Real world use of hybrid-closed loop in children and young people with type 1 diabetes mellitus-a National Health Service pilot initiative in England

AIMS

Hybrid closed-loop (HCL) systems are characterised by integrating continuous glucose monitoring (CGM) with insulin pumps that automate insulin delivery via specific algorithms and user-initiated insulin delivery. The aim of the study was to evaluate effectiveness of HCLs on HbA1c, time-in-range (TIR), hypoglycaemia frequency and quality of life measures in children and young people (CYP) with T1D, and their carers.

METHODS

Patients were recruited prospectively into the National Health Service (NHS) England real-world HCL observational study from the 1st of August 2021 to the 10th of December 2022 from eight paediatric diabetes centres in England.

RESULTS

There were 251 CYP (147 males, 58%) with T1DM recruited with a mean age at recruitment of 12.3 (SD 3.5) (range 2-19) years. Eighty nine per cent of the CYP were of white ethnicity, 3% Asian, 4% black and 3% mixed ethnicity, and 1% were recorded as others. The HCL systems used in the study were: (1) Tandem Control-IQ AP system, which uses the Tandem t:slim X2 insulin pump (Tandem Diabetes Care, San Diego, CA) with the Dexcom G6® CGM (Dexcom, San Diego, CA) sensor; (2) Medtronic MiniMed™ 780G (Medtronic, Northridge, CA) and (3) CamAPS FX (CamDiab, Cambridge, UK.) All systems were fully funded by the national health service.

CONCLUSIONS

The results of the NHS England Closed Loop Study in Children and Young People showed improvements in glycaemic control, TIR, frequency of hypoglycaemia, hypoglycaemia fear and quality of sleep for children and young people when using HCL for 6 months. Hypoglycaemia fear and quality of sleep were also improved for their parents and carers at 6 months.

Antihyperglycemic Effects of Annona cherimola Miller and the Flavonoid Rutin in Combination with Oral Antidiabetic Drugs on Streptozocin-Induced Diabetic Mice

Ethanolic extract obtained from Annona cherimola Miller (EEAc) and the flavonoid rutin (Rut) were evaluated in this study to determine their antihyperglycemic content, % HbA1c reduction, and antihyperlipidemic activities. Both treatments were evaluated separately and in combination with the oral antidiabetic drugs (OADs) acarbose (Aca), metformin (Met), glibenclamide (Gli), and canagliflozin (Cana) in acute and subchronic assays. The evaluation of the acute assay showed that EEAc and Rut administered separately significantly reduce hyperglycemia in a manner similar to OADs and help to reduce % HbA1c and hyperlipidemia in the subchronic assay. The combination of EEAc + Met showed the best activity by reducing the hyperglycemia content, % HbA1c, Chol, HDL-c, and LDL-c. Rutin in combination with OADs used in all treatments significantly reduced the hyperglycemia content that is reflected in the reduction in % HbA1c. In relation to the lipid profiles, all combinate treatments helped to avoid an increase in the measured parameters. The results show the importance of evaluating the activity of herbal remedies in combination with drugs to determine their activities and possible side effects. Moreover, the combination of rutin with antidiabetic drugs presented considerable activity, and this is the first step for the development of novel DM treatments.

THE ASSESSMENT OF COMPENSATION OF CARBOHYDRATE METABOLISM IN PATIENTS WITH TYPE 2 DIABETES MELLITUS WITH METABOLIC SYNDROME BEYOND THE LIMITS OF GLYCATED HEMOGLOBIN

OBJECTIVE

The aim: To investigate glycemic variability in type 2 diabetes patients with metabolic syndrome (MS) and to assess its effect on diabetes compensation.

PATIENTS AND METHODS

Materials and methods: We used traditional indicators of glycemia variability according to the recommendations of the American Diabetes Association Professional Practice Committee. We proved that patients with type 2 diabetes mellitus with MS reliably have worse CGM indicators: Time in range TIR: (3.9-10.0 mmol/l) - 53.30±5.90%; Time above range (TAR): (time above range) (>10.1 mmol/l) - 43.33±5.96%; Time above TAR range (>13.9 mmol/l) - 22.1±3.91%; Glucose Variability СV - 44.10±4.89% compared to patients with type 2 diabetes without MS, which proves the negative effect of insulin resistance on compensation of diabetes.

RESULTS

Results: Determination of the level of EI in the blood, calculation of the Caro index, HOMA-IR are informative for verifying the presence of IR in patients with type 2 diabetes with MS. For optimal diabetes control, in addition to HbA1c, we must consider CGM data and % Time in range (TIR).

CONCLUSION

Conclusions: TIR should be used as a target point and an indicator of glycemic control in routine clinical practice. TIR provides accurate data on a patient's glycemic status and helps better control diabetes.

Evaluation of a Web-Based Simulation Tool for Self-Management Support in Type 1 Diabetes: A Pilot Study

OBJECTIVE

To develop and evaluate a novel Web-based Simulation Tool (WST) that brings simulation technologies to people with Type 1 Diabetes (T1D), enabling unique patient-data interactions seamlessly on a daily basis.

METHODS

A pilot clinical trial was conducted to assess system usability. The study consisted of one week of observation (Phase 1) and four weeks of interaction with WST (Phase 2). Responses to Technology Acceptance (TA) and Diabetes Distress Scale (DDS) questionnaires were collected, and follow-up interviews were conducted after Phase 2.

RESULTS

Fifteen participants with T1D using Control-IQ technology (age: 36 ± 13 years, HbA1c: 6.5% ± 0.7%) completed all study procedures. Generated simulation models achieved a median Mean Absolute Relative Difference (MARD) of 6.8% [interquartile range, IQR: 5.1%, 9.1%]. A decrease in expected benefits (likely explained by issues with the third-party data collection system) and an increase in expected burdens were observed. On a 1-5 scale, ease of use, trust, and usefulness scores were 3 [3,4], 4 [3,4], and 4 [3,4], respectively. Time below 70 mg/dL decreased between Phases 1 and 2 (1.6% [0.7%,3.7%] vs 0.8% [0.5%,3.0%]). A reduction in mean emotional burden was also observed (2.5 ± 1.1 vs 2.1 ± 0.8).

CONCLUSIONS

Results indicate that there was a learning curve to WST, but also that most participants trusted the system and found it useful in their diabetes care.

SIGNIFICANCE

Simulation technologies like WST could be used by educators and patients to facilitate diabetes self-management, leading to better diabetes literacy and reducing associated distress.

Continuous Glucose Monitoring Time Series Data Analysis: A Time Series Analysis Package for Continuous Glucose Monitoring Data

The R package Continuous Glucose Monitoring Time Series Data Analysis (CGMTSA) was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to (1) enable more accurate missing data imputation and outlier identification; (2) calculate recommended CGM metrics as well as key time series parameters; (3) plot interactive and three-dimensional graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps. The program is available for Linux, Windows, and Mac at GitHub.

Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data

The advancement of technology in the field of glycemic control has led to the widespread use of continuous glucose monitoring (CGM), which can be nowadays obtained from wearable devices equipped with a minimally invasive sensor, that is, transcutaneous needle type or implantable, and a transmitter that sends information to a receiver or smart device for data storage and display. This work aims to review the currently available software packages and tools for the analysis of CGM data. Based on the purposes of this work, 12 software packages have been identified from the literature, published until December 2021, namely: GlyCulator, EasyGV (Easy Glycemic Variability), CGM-GUIDE^© (Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation), GVAP (Glycemic Variability Analyzer Program), Tidepool, CGManalyzer, cgmanalysis, GLU, CGMStatsAnalyser, iglu, rGV, and cgmquantify. Comparison of available software packages and tools has been done in terms of main characteristics (i.e., publication year, presence of a graphical user interface, availability, open-source code, number of citations, programming language, supported devices, supported data format and organization of the data structure, documentation, presence of a toy example, video tutorial, data upload and download, measurement-units conversion), preprocessing procedures, data display options, and computed metrics; also, each of the computed metrics has been analyzed in terms of its adherence to the American Diabetes Association (ADA) 2017 international consensus on CGM data analysis and the ADA 2019 international consensus on time in range. Eventually, the agreement between metrics computed by different software and tools has been investigated. Based on such comparison, usability and complexity of data management, as well as the possibility to perform customized or patients-group analyses, have been discussed by highlighting limitations and strengths, also in relation to possible different user categories (i.e., patients, clinicians, researchers). The information provided could be useful to researchers interested in working in the diabetic research field as to clinicians and endocrinologists who need tools capable of handling CGM data effectively.

Continuous glucose monitoring and metrics for clinical trials: an international consensus statement

Randomised controlled trials and other prospective clinical studies for novel medical interventions in people with diabetes have traditionally reported HbA1c as the measure of average blood glucose levels for the 3 months preceding the HbA1c test date. The use of this measure highlights the long-established correlation between HbA1c and relative risk of diabetes complications; the change in the measure, before and after the therapeutic intervention, is used by regulators for the approval of medications for diabetes. However, with the increasing use of continuous glucose monitoring (CGM) in clinical practice, prospective clinical studies are also increasingly using CGM devices to collect data and evaluate glucose profiles among study participants, complementing HbA1c findings, and further assess the effects of therapeutic interventions on HbA1c. Data is collected by CGM devices at 1-5 min intervals, which obtains data on glycaemic excursions and periods of asymptomatic hypoglycaemia or hyperglycaemia (ie, details of glycaemic control that are not provided by HbA1c concentrations alone that are measured continuously and can be analysed in daily, weekly, or monthly timeframes). These CGM-derived metrics are the subject of standardised, internationally agreed reporting formats and should, therefore, be considered for use in all clinical studies in diabetes. The purpose of this consensus statement is to recommend the ways CGM data might be used in prospective clinical studies, either as a specified study endpoint or as supportive complementary glucose metrics, to provide clinical information that can be considered by investigators, regulators, companies, clinicians, and individuals with diabetes who are stakeholders in trial outcomes. In this consensus statement, we provide recommendations on how to optimise CGM-derived glucose data collection in clinical studies, including the specific glucose metrics and specific glucose metrics that should be evaluated. These recommendations have been endorsed by the American Association of Clinical Endocrinologists, the American Diabetes Association, the Association of Diabetes Care and Education Specialists, DiabetesIndia, the European Association for the Study of Diabetes, the International Society for Pediatric and Adolescent Diabetes, the Japanese Diabetes Society, and the Juvenile Diabetes Research Foundation. A standardised approach to CGM data collection and reporting in clinical trials will encourage the use of these metrics and enhance the interpretability of CGM data, which could provide useful information other than HbA1c for informing therapeutic and treatment decisions, particularly related to hypoglycaemia, postprandial hyperglycaemia, and glucose variability.

In-hospital glycemic variability and all-cause mortality among patients hospitalized for acute heart failure

BACKGROUND

High glycemic variability (GV) is a poor prognostic marker in cardiovascular diseases. We aimed to investigate the association of GV with all-cause mortality in patients with acute heart failure (HF).

METHODS

The Korean Acute Heart Failure registry enrolled patients hospitalized for acute HF from 2011 to 2014. Blood glucose levels were measured at the time of admission, during hospitalization, and at discharge. We included those who had 3 or more blood glucose measurements in this study. Patients were divided into two groups based on the coefficient of variation (CoV) as an indicator of GV. Among survivors of the index hospitalization, we investigated all-cause mortality at 1 year after discharge.

RESULTS

The study analyzed 2,617 patients (median age, 72 years; median left-ventricular ejection fraction, 36%; 53% male). During the median follow-up period of 11 months, 583 patients died. Kaplan-Meier curve analysis revealed that high GV (CoV > 21%) was associated with lower cumulative survival (log-rank P < 0.001). Multivariate Cox proportional analysis showed that high GV was associated with an increased risk of 1-year (HR 1.56, 95% CI 1.26-1.92) mortality. High GV significantly increased the risk of 1-year mortality in non-diabetic patients (HR 1.93, 95% CI 1.47-2.54) but not in diabetic patients (HR 1.19, 95% CI 0.86-1.65, P for interaction = 0.021).

CONCLUSIONS

High in-hospital GV before discharge was associated with all-cause mortality within 1 year, especially in non-diabetic patients with acute HF.

Feasibility of Using a Factory-Calibrated Continuous Glucose Monitoring System to Diagnose Type 2 Diabetes

Context: Plasma glucose or A1C criteria can be used to establish the diagnosis of type 2 diabetes (T2D). Objective: We examined whether continuous glucose monitoring (CGM) data from a single 10-day wear period could form the basis of an alternative diagnostic test for T2D. Design: We developed a binary classification diagnostic CGM (dCGM) algorithm using a dataset of 716 individual CGM sensor sessions from 563 participants with associated A1C measurements from seven clinical trials. Data from 470 participants were used for training and 93 participants for testing (49 normoglycemic [A1C <5.7%], 27 prediabetes, and 17 T2D [A1C ≥6.5%] not using pharmacotherapy). dCGM performance was evaluated against the accompanying A1C measurement, which was assumed to provide the correct diagnosis. Results: The dCGM algorithm's overall sensitivity, specificity, positive predictive value, and negative predictive value were 71%, 93%, 71%, and 93%, respectively. At other clinically relevant A1C thresholds, dCGM specificity among normoglycemic participants was 98% (48/49 correctly classified), and for participants with suboptimally controlled diabetes (A1C ≥7%, above the American Diabetes Association recommended A1C goal) the sensitivity was 100% (8/8 participants correctly diagnosed with T2D). Conclusions: Classifications based on the dCGM algorithm were in good agreement with traditional methods based on A1C. The dCGM algorithm may provide an alternative method for screening and diagnosing T2D, and warrants further investigation.

Effects of break in sedentary behaviour on blood glucose control in diabetic patients. Systematic review

INTRODUCTION

One of the main goals of prescribing physical activity for people with type 2 diabetes is to reduce hyperglycaemia, as it is a risk factor for the development of chronic complications. As less time spent each day in sedentary behaviour would lead to higher glucose consumption by skeletal muscle tissue, this could have significant positive effects on blood glucose control parameters. For this reason, the aim of this study was to analyse the information from different protocols for breaking sedentary behaviour and the association with blood glucose control parameters in patients with type 2 diabetes.

MATERIAL AND METHODS

A systematic search was carried out for randomised controlled studies on this topic published in the scientific literature. The following databases were considered: PubMed, Cochrane, EBSCO, WoS, ScienceDirect and Medline.

RESULTS

24 studies were identified and analysed using the COVIDENCE platform. Seven articles were selected for the final analysis, comprising 138 patients. The results show that breaks in sedentary behaviour with light physical activity in people with type 2 diabetes are effective in reducing insulin resistance, the area under the glucose curve, fasting and postprandial blood glucose, and blood glucose variability.

CONCLUSIONS

Acute interruption of sedentary behaviour, through light-intensity and short-duration exercise, can improve blood glucose indicators in patients with type 2 diabetes, including short term blood glucose variability.

Antidiabetic and Toxicological Effects of the Tea Infusion of Summer Collection from Annona cherimola Miller Leaves

Annona cherimola Miller (Ac) is a plant used in Mexican traditional medicine for the treatment of diabetes. In this work, the tea infusion extracts obtained from 1.5 g of leaf powder from Ac collected in May (AcMa), June (AcJun), July (AcJul), and August (AcAu) were evaluated on streptozocin-induced diabetic (STID) mice and for subchronic toxicity in STID and non-diabetic (ND) mice. In addition, extracts were subjected to high-performance liquid chromatography with diode array detection (HPLC-DAD). Results showed that the tea infusion extract of the sample collected in August (AcAu) exhibited the most significant antihyperglycemic activity during all acute assays. The analysis of the extracts (AcMa, AcJu, AcJul, and AcAu) by HPLC-DAD revealed that flavonoid glycosides, rutin, narcissin, and nicotiflorin were the major components. In addition, the sample AcAu contained the best concentration of flavonoids. In the case of subchronic oral toxicity, the AcAu sample did not cause mortality in STID mice, and histopathological analysis revealed significant improvement in the changes associated with diabetes in the liver and kidneys. These findings suggest that the Ac leaves collected in August may be a source of flavonoids such as rutin, with antidiabetic potential. In addition, these findings support the use of Ac to treat diabetes in traditional medicine.

Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults

It is often assumed that healthy people have the genuine ability to maintain tight blood glucose regulation. However, a few recent studies revealed that glucose dysregulation such as hyperglycemia may occur even in people who are considered normoglycemic by standard measures and were more prevalent than initially thought, suggesting that more investigations are needed to fully understand the within-day glucose dynamics of healthy people. In this paper, we conducted an analysis on a multi-modal dataset to examine the relationships between glycemic variability when people were awake and that when they were sleeping. The interstitial glucose levels were measured with a wearable continuous glucose monitoring (CGM) technology FreeStyle Libre 2 at every 15 min interval. In contrast to the traditional single-time-point measurements, the CGM data allow the investigation into the temporal patterns of glucose dynamics at high granularity. Sleep onset and offset timestamps were recorded daily with a Fitbit Charge 3 wristband. Our analysis leveraged the sleep data to split the glucose readings into segments of awake-time and in-sleep, instead of using fixed cut-off time points as has been done in existing literature. We combined repeated measure correlation analysis and quantitative association rules mining, together with an original post-filtering method, to identify significant and most relevant associations. Our results showed that low overall glucose in awake time was strongly correlated to low glucose in subsequent sleep, which in turn correlated to overall low glucose in the next day. Moreover, both analysis techniques identified significant associations between the minimal glucose reading in sleep and the low blood glucose index the next day. In addition, the association rules discovered in this study achieved high confidence (0.75–0.88) and lift (4.1–11.5), which implies that the proposed post-filtering method was effective in selecting quality rules.

Essential Continuous Glucose Monitoring Metrics: The Principal Dimensions of Glycemic Control in Diabetes

Background: With the proliferation of continuous glucose monitoring (CGM), a number of metrics were developed to assess quality of glycemic control. Many of them are highly correlated. Thus, we aim to identify the principal dimensions of glycemic control-a minimal set of metrics, necessary and sufficient for comprehensive assessment of diabetes management. Methods: Seventy-five thousand five hundred sixty-three 2-week CGM profiles recorded in six studies by 790 individuals with type 1 or type 2 diabetes were used to compute mean glucose (MG), percentage time >180 mg/dL (TAR), >250 mg/dL (TAR2), <70 mg/dL (TBR), <54 mg/dL (TBR2), and coefficient of variation (CV). The true dimensionality of the glycemic-metric space was identified in a training set (53,380 profiles) and validated in an independent test set (22,183 profiles). Results: Correlation analysis identified two blocks of metrics-(MG, TAR, TAR2) and (TBR, TBR2, CV)-each with high internal correlation, but insignificant between-block correlation, suggesting that the true dimensionality of the glycemic-metric space is 2. Principal component analysis confirmed two essential metrics quantifying exposure to hyperglycemia (i.e., treatment efficacy) and risk for hypoglycemia (i.e., treatment safety), and explaining ∼90% of the variance in the training and test data. Conclusion: Two essential metrics, treatment efficacy and treatment safety, are necessary and sufficient to characterize glycemic control in diabetes. Thus, quantitatively, diabetes treatment optimization is reduced to a two-dimensional problem, meaning that minimizing both exposure to hyperglycemia and risk for hypoglycemia will lead to improvement in any other metric of glycemic control.

Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes

BACKGROUND

The increasing prevalence of type 2 diabetes mellitus (T2D) and specialist shortage has caused a healthcare gap that can be bridged by a decision support system (DSS). We investigated whether a diabetes DSS can improve long- and/or short-term glycemic control.

METHODS

This is a retrospective observational cohort study of the Diabetiva program, which offered a patient-tailored DSS using Karlsburger Diabetes-Management System (KADIS) once a year. Glycemic control was analyzed at baseline and after 12 months in 452 individuals with T2D. Time in range (TIR; glucose 3.9-10 mmol/L) and Q-Score, a composite metric developed for analysis of continuous glucose profiles, were short-term and HbA1c long-term measures of glycemic control. Glucose variability (GV) was also measured.

RESULTS

At baseline, one-third of patients had good short- and long-term glycemic control. Q-Score identified insufficient short-term glycemic control in 17.9% of patients with HbA1c <6.5%, mainly due to hypoglycemia. GV and hyperglycemia were responsible in patients with HbA1c >7.5% and >8%, respectively. Application of DSS at baseline improved short- and long-term glycemic control, as shown by the reduced Q-Score, GV, and HbA1c after 12 months. Multiple regression demonstrated that the total effect on GV resulted from the single effects of all influential parameters.

CONCLUSIONS

DSS can improve short- and long-term glycemic control in individuals with T2D without increasing hypoglycemia. The Q-Score allows identification of individuals with insufficient glycemic control. An effective strategy for therapy optimization could be the selection of individuals with T2D most at need using the Q-Score, followed by offering patient-tailored DSS.

Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying

Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the multi-factored and chronic disease nature of diabetes. To control glucose levels in a safe range and lessen abnormal glucose variability efficiently and economically, an intelligent prediction of glucose is demanding. A glucose trajectory prediction system based on subcutaneous interstitial continuous glucose monitoring data and deep learning models for ensuing glucose trajectory was constructed, followed by the application of personalised prediction models on one participant with type 2 diabetes in a community. The predictive accuracy was then assessed by RMSE (root mean square error) using blood glucose data. Changes in glycaemic parameters of the participant before and after model intervention were also compared to examine the efficacy of this intelligence-aided health care. Individual Recurrent Neural Network model was developed on glucose data, with an average daily RMSE of 1.59 mmol/L in the application segment. In terms of the glucose variation, the mean glucose decreased by 0.66 mmol/L, and HBGI dropped from 12.99 × 10² to 9.17 × 10². However, the participant also had increased stress, especially in eating and social support. Our research presented a personalised care system for people with diabetes based on deep learning. The intelligence-aided health management system is promising to enhance the outcome of diabetic patients, but further research is also necessary to decrease stress in the intelligence-aided health management and investigate the stress impacts on diabetic patients.