Glucose variability

Glycemic Variability and Control by CGM in Healthy Older and Young Adults and Their Relationship With Diet

Continuous glucose monitoring (CGM) might be beneficial for investigating healthy aging since high glycemic variability may increase protein glycation, oxidative stress, and inflammation, resulting in vascular damage. Additionally, CGM data on the risks for hypoglycemia and hyperglycemia are scarce, have not been analyzed by individual day and night blocks, and have not been related to diet. Therefore, this study aimed to compare glucose parameters of healthy older and young adults and the relationship with diet. Participants were 34 young (age 20-35 years) and 27 older volunteers (age 60-75 years) with a normal glycated hemoglobin A1c less than 39 mmol/mol hemoglobin, free of disorders and medication. Twenty-four CGM-derived glucose parameters measured over 5 consecutive days were analyzed for whole days and for individual daytime and nighttime blocks. Dietary intake was determined by 3-day dietary record. Neither intraday nor interday glycemic variability differed between the healthy age groups. Glycemic control was good in both age groups, but somewhat poorer in older adults. The risk of hyperglycemia was higher and of hypoglycemia lower in older adults. During the daytime, mean and minimum glucose were higher in older adults. During the nighttime, age group differences were small. The carbohydrate intake correlated positively with glycemic variability in both age groups. The protein intake correlated positively with the hypoglycemic risk in young adults, but negatively in older adults. Results suggest that healthy aging does not increase glycemic variability and the risk of hypoglycemia. The effect of diet on hypoglycemic and hyperglycemic risk might change with aging.

A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in target

AIM

This study aims to predict risk factors for hypoglycemia in patients with type 2 diabetes mellitus (T2DM) using continuous glucose monitoring (CGM) and with time in range (TIR) > 70%.

METHODS

Data from 111 patients with T2DM who underwent CGM with TIR > 70% were analyzed. A hypoglycemia episode was defined as CGM-detected glucose < 3.9mmol/L sustained for at least 5 min. Logistic regression analysis was performed to examine the relationship between hypoglycemia and mean blood glucose (MBG), glycemic variability (GV) metrics [including mean amplitude of glucose excursion (MAGE), largest amplitude of glycemic excursion (LAGE), mean of daily difference (MODD), coefficient of variation (CV), standard deviation (SD)], and low blood glucose index (LBGI). A nomogram model was constructed, and its diagnostic performance was assessed. Data were bootstrapped 1000 times for internal validation, and a calibration curve was drawn to evaluate the model's predictive ability. Decision curve analysis was performed to assess its clinical usefulness.

RESULTS

Among the 111 included patients, 53 experienced hypoglycemic event during wearing CGM (47.75%). GV metrics were higher in hypoglycemia group, while MBG was lower. The multivariable logistic regression analysis showed that the MBG, GV metrics, LBGI were independently associated with hypoglycemia. The receiver operating characteristics (ROC) analysis indicated that the area under the curve (AUC) for the MBG-SD-LBGI model was 0.93 (95% CI = 0.88-0.97). The calibration curve showed good consistency between the predicted and observed probabilities. Decision curve analysis demonstrated strong clinical applicability.

CONCLUSION

This study demonstrates a significant correlation between CGM metrics and hypoglycemia in patients with T2DM who achieved TIR > 70%. These findings suggest that CGM metrics can predict the risk of hypoglycemia in T2DM patients with a TIR > 70%, and the nomogram developed from these metrics holds strong potential for clinical application.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

METHODS

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

RESULTS

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

CONCLUSION

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

Perioperative Glycemic Variability Influences Infection Rates Differently Following Revision Hip and Knee Arthroplasty

BACKGROUND

Recent investigations have determined that abnormal postoperative glycemia following primary total joint arthroplasty is associated with adverse events. Our study aimed to determine if hyperglycemia and glycemic variability following aseptic revision total joint arthroplasty were associated with periprosthetic joint infection (PJI) within two years postoperatively.

METHODS

A retrospective review was performed of 2,208 patients within a single institution undergoing aseptic revision total joint arthroplasty from 2012 to 2019. Postoperative glucose values were recorded. Glycemic variability was measured via three parameters: coefficient of variation, mean amplitude of glycemic excursions, and J-index. Logistic regression analyses were performed to examine associations with PJI at 90-day, 1-, and 2-year follow-up.

RESULTS

In revision hips, all glycemic measures were not associated with PJI at any time point in logistic regression analyses, except for the mean amplitude of glycemic excursions, which predicted PJI at one year (P = 0.045); body mass index was the only factor associated with PJI at all timepoints in all models. In revision knees, all glycemic measures were not associated with PJI at any timepoint in logistic regression analyses; however, PJI rates differed between diabetics and nondiabetics at all time points (P < 0.05).

CONCLUSIONS

Our findings illustrate that decreasing preoperative body mass index and postoperative glycemic variability may be critical in reducing PJI rates in revision hips. Furthermore, patients who have diabetes should be counseled that they remain at higher risk of PJI regardless of perioperative glucose control after revision knee surgery.

Association between various blood glucose variability-related indicators during early ICU admission and 28-day mortality in non-diabetic patients with sepsis

BACKGROUND

Various blood glucose (BG) variability-related indexes have been widely used to assess glycemic control and predict glycemic risks, but the association between BG variations and prognosis in non-diabetic patients with sepsis remains unclear.

METHODS

The single-center retrospective cohort study included 7,049 non-diabetic adults with sepsis who had at least 3 records of bedside capillary point of care BG testing during the first day after ICU admission from MIMIC-IV database (2008 to 2019). Coefficient of variation and standard deviation of glucose (i.e., GluCV and GluSD), M-value, J-index, high blood glucose index (HBGI), and low blood glucose index (LBGI) were used to describe glucose variability, quality of glycemic control, and glycemic risk of patients with sepsis. The dose-response relationship between BG variability-related indexes and mortality was explored using multivariate logistic regression with restricted cubic spline (RCS) function. If the dose-response curve presented a J-shape with a specific threshold value, a linear threshold function instead of RCS would be employed.

RESULTS

There is a J-shaped relationship between hospital mortality risk and glucose variability-related indexes in ICU patients with sepsis. The mortality risk remained relatively stable below the threshold of these indexes. However, over the threshold, the 28-day mortality risk increased by 2.82% (95% CI: 1.80-3.85%), 1.13% (95% CI: 0.66-1.60%), 1.96% (95% CI: 0.98-2.95%), 1.37% (95% CI: 0.57-2.16%), 11.19% (95% CI: 6.56-15.98%) and 39.04% (95% CI: 29.86-48.81%) for each unit increases in GluCV, GluSD, M-value, J-index, LBGI and HBGI, respectively. The effects of LBGI and HBGI on 7-day and 14-day mortality were more pronounced.

CONCLUSIONS

High levels of GluCV, GluSD, M-value, J-index, HBGI, and LBGI on the first day of ICU admission were important risk markers of hospital mortality among non-diabetic patients with sepsis.

An Update on the iglu Software Package for Interpreting Continuous Glucose Monitoring Data

Background: Continuous glucose monitors (CGMs) are increasingly used to provide detailed quantification of glycemic control and glucose variability. An open-source R package iglu has been developed to assist with automatic CGM metrics computation and data visualization, providing a comprehensive list of implemented CGM metrics. Motivated by the recent international consensus statement on CGM metrics and recommendations from recent reviews of available CGM software, we present an updated version of iglu with improved accessibility and expanded functionality. Methods: The functionality was expanded to include automated computation of hypo- and hyperglycemia episodes with corresponding visualizations, composite metrics of glycemic control (glycemia risk index and personal glycemic state), and glycemic metrics associated with postprandial excursions. The algorithm for mean amplitude of glycemic excursions has been updated for improved accuracy, and the corresponding visualization has been added. Automated hierarchical clustering capabilities have been added to facilitate statistical analysis. Accessibility was improved by providing support for the automatic processing of common data formats, expanding the graphical user interface, and providing mirrored functionality in Python. Results: The updated version of iglu has been released to the Comprehensive R Archive Network (CRAN) as version 4. The corresponding Python wrapper has been released to the Python Package Index (PyPI) as version 1. The new functionality has been demonstrated using CGM data from 19 subjects with prediabetes and type 2 diabetes. Conclusions: An updated version of iglu provides comprehensive and accessible software for analyses of CGM data that meets the needs of researchers with varying levels of programming experience. It is freely available on CRAN and on GitHub at https://github.com/irinagain/iglu.

Divergent hypoglycemic and hyperglycemic responses to the components of evening meals. A general adult population study in individuals without diabetes (AEGIS study)

BACKGROUND AND AIM

Few real-life studies have analyzed the glycemic response to nutrients in individuals without diabetes. We investigated the glycemic response to evening meals in relation to individual characteristics, nutrient components, and preprandial and postprandial routines.

METHODS

A cross-sectional study of 489 individuals without diabetes from a randomly selected general adult population (310 women, median age 46 years, range 18-84 years) was conducted using a continuous glucose monitoring device for 7 days. The study recorded the participants' glycemic profile at 6 h after dinner, the food consumed at dinner, the fasting duration before dinner, and the duration between the end of dinner and going to bed. Principal component analysis and multilevel functional data analysis were used to interpret the data.

RESULTS

On average, a postprandial glycemic peak was observed at 45 min, followed by a decline to baseline levels from 90 min onwards. Older age, higher body mass index, and large meals (especially those high in starch and dairy products) were all significantly associated with higher glucose levels throughout the 6 h after dinner. The fruit component was associated with a higher initial glycemic peak, followed by a lowering glycemic effect thereafter (p < 0.001). The alcohol component was associated with an initial hypoglycemic effect (p = 0.006). The participants who fasted longer before dinner had higher postprandial glycemic peaks (p = 0.001), and those who went to bed later had higher postprandial glucose levels than those who went to bed earlier (p = 0.003).

CONCLUSIONS

The participants' characteristics, nutrient components, and pre- and post-dinner routines have divergent effects on post-dinner glycemic response.

Evaluation of Glycemic Variability and Discharge Outcomes in Patients with Ischemic Stroke Following Thrombolysis

Background and Purpose

Hyperglycemia following acute ischemic stroke (AIS) is associated with adverse outcomes including, hemorrhagic conversion and increased length of stay; however, the impact of glycemic variability is largely unknown. This study aims to evaluate the effect of glycemic variability on discharge outcomes in patients treated with alteplase for AIS.

Methods

A retrospective review of ischemic stroke patients who presented within 4.5 hours from symptom onset and received alteplase was completed. Patients hospitalized for at least 48 hours were included. Glycemic variability was measured using J-index. Groups were defined by normal or abnormal J-indices. Logistic regression models were developed to determine odds ratios for select clinical characteristics, NIHSS score, mRS, and disposition at discharge.

Results

Of the 229 patients, 97 (42%) had an abnormal J-index. In the univariate analysis, abnormal J-index was associated with worse outcomes in terms of NIHSS score, mRS, and discharge disposition compared to a normal J-index. In the unadjusted multivariate analysis, abnormal J-index was associated with higher odds of unfavorable mRS (3-6) at discharge (OR 2.1; 95% CI 1.2 - 3.5, P = .009). In the adjusted multivariate analysis, patients with an abnormal J-index had higher odds of hemorrhagic transformation (OR 5.7; 95% CI 2.1 - 15.6, P < .0001). There was no difference in mortality.

Conclusion

Glycemic variability with abnormal J-index following AIS is associated with adverse functional outcomes at discharge and increased odds of hemorrhagic conversion in patients treated with alteplase. Additional studies validating glycemic variability indices post-ischemic stroke are needed to determine the full clinical impact.

Association of dipeptidyl peptidase-4 inhibitor initiation at glycated haemoglobin <7.5% with reduced major clinical events mediated by low glycated haemoglobin variability

AIM

Therapeutic inertia, hypoglycaemia and poor treatment persistence can lead to glycaemic fluctuation and poor outcomes in type 2 diabetes (T2D). We compared glycated haemoglobin (HbA1c) variability, insulin initiation, severe hypoglycaemia and clinical events in patients with T2D initiated dipeptidyl peptidase-4 inhibitors (DPP4is) at low versus high HbA1c thresholds.

METHODS

Using territory-wide electronic medical records in Hong Kong, we curated a propensity score-matched cohort of patients initiated DPP4i at HbA1c <7.5% versus ≥7.5% in 2007-2019. We expressed the HbA1c variability score (HVS) as a proportion of HbA1c varied by ≥0.5% compared with preceding values. We used the Cox model to compare the risks of insulin initiation and clinical outcomes, adjusted for time-varying variables between the two groups. Mediation analysis estimated the effects of HbA1c variability on outcomes.

RESULTS

Among 6874 insulin-naïve patients who initiated DPP4i, 88.7% were treated with metformin and 79.6% with sulphonylureas at baseline (54.9% men; mean age 65.2 ± 11.4 years). After a median follow-up of 4.6 years, compared with the high-threshold plus high-HVS group (≥50%), the low-threshold plus low-HVS (<50%) group had reduced hazard ratios (95% confidence interval) of insulin initiation (0.35, 0.31-0.40), severe hypoglycaemia (0.38, 0.34-0.44), major adverse cardiovascular endpoints (0.76, 0.66-0.88), heart failure (0.42, 0.36-0.49), end-stage kidney disease (0.65, 0.36-0.49) and mortality (0.45, 0.35-0.57). Reduced HbA1c variability explained 31.1%-81.2% of the effect size of DPP4i initiation at HbA1c <7.5% versus ≥7.5% on outcomes.

CONCLUSIONS

In Chinese patients with T2D, avoiding therapeutic inertia with intensified glycaemic control at HbA1c <7.5% using drugs with low risk of hypoglycaemia and good tolerability, such as DPP4i, delayed insulin treatment, reduced HbA1c variability and improved clinical events.

Relationship between EEG spectral power and dysglycemia with neurodevelopmental outcomes after neonatal encephalopathy

OBJECTIVE

We investigated how electroencephalography (EEG) quantitative measures and dysglycemia relate to neurodevelopmental outcomes following neonatal encephalopathy (NE).

METHODS

This retrospective study included 90 neonates with encephalopathy who received therapeutic hypothermia. EEG absolute spectral power was calculated during post-rewarming and 2-month follow-up. Measures of dysglycemia (hypoglycemia, hyperglycemia, and glycemic lability) and glucose variability were computed for the first 48 h of life. We evaluated the ability of EEG and glucose measures to predict neurodevelopmental outcomes at ≥ 18 months, using logistic regressions (with area under the receiver operating characteristic [AUROC] curves).

RESULTS

The post-rewarming global delta power (average all electrodes), hyperglycemia and glycemic lability predicted moderate/severe neurodevelopmental outcome separately (AUROC = 0.8, 95%CI [0.7,0.9], p < .001) and even more so when combined (AUROC = 0.9, 95%CI [0.8,0.9], p < .001). After adjusting for NE severity and magnetic resonance imaging (MRI) brain injury, only global delta power remained significantly associated with moderate/severe neurodevelopmental outcome (odds ratio [OR] = 0.9, 95%CI [0.8,1.0], p = .04), gross motor delay (OR = 0.9, 95%CI [0.8,1.0], p = .04), global developmental delay (OR = 0.9, 95%CI [0.8,1.0], p = .04), and auditory deficits (OR = 0.9, 95%CI [0.8,1.0], p = .03).

CONCLUSIONS

In NE, global delta power post-rewarming was predictive of outcomes at ≥ 18 months.

SIGNIFICANCE

EEG markers post-rewarming can aid prediction of neurodevelopmental outcomes following NE.

Role and molecular mechanism of Salvia miltiorrhiza associated with chemical compounds in the treatment of diabetes mellitus and its complications: A review

Diabetes mellitus (DM) is one of the most prevalent diseases worldwide, greatly impacting patients' quality of life. This article reviews the progress in Salvia miltiorrhiza, an ancient Chinese plant, for the treatment of DM and its associated complications. Extensive studies have been conducted on the chemical composition and pharmacological effects of S miltiorrhiza, including its anti-inflammatory and antioxidant activities. It has demonstrated potential in preventing and treating diabetes and its consequences by improving peripheral nerve function and increasing retinal thickness in diabetic individuals. Moreover, S miltiorrhiza has shown effectiveness when used in conjunction with angiotensin-converting enzyme inhibitors, angiotensin receptor blockers (ARBs), and statins. The safety and tolerability of S miltiorrhiza have also been thoroughly investigated. Despite the established benefits of managing DM and its complications, further research is needed to determine appropriate usage, dosage, long-term health benefits, and safety.

Appropriate glycemic management protects the germline but not the uterine environment in hyperglycemia

Emerging evidence indicates that parental diseases can impact the health of subsequent generations through epigenetic inheritance. Recently, it was shown that maternal diabetes alters the metaphase II oocyte transcriptome, causing metabolic dysfunction in offspring. However, type 1 diabetes (T1D) mouse models frequently utilized in previous studies may be subject to several confounding factors due to severe hyperglycemia. This limits clinical translatability given improvements in glycemic control for T1D subjects. Here, we optimize a T1D mouse model to investigate the effects of appropriately managed maternal glycemic levels on oocytes and intrauterine development. We show that diabetic mice with appropriate glycemic control exhibit better long-term health, including maintenance of the oocyte transcriptome and chromatin accessibility. We further show that human oocytes undergoing in vitro maturation challenged with mildly increased levels of glucose, reflecting appropriate glycemic management, also retain their transcriptome. However, fetal growth and placental function are affected in mice despite appropriate glycemic control, suggesting the uterine environment rather than the germline as a pathological factor in developmental programming in appropriately managed diabetes.

Short-term glycemic variability in non-diabetic, non-obese dogs assessed by common glycemic variability indices

Glycemic variability (GV) refers to swings in blood glucose levels and is an emerging measure of glycemic control in clinical practice. It is associated with micro- and macrovascular complications and poor clinical outcomes in diabetic humans. Although an integral part of patient assessment in human patients, it is to a large extent neglected in insulin-treated diabetic dogs. This prospective pilot study was performed to describe canine within-day GV in non-diabetic dogs with the aim to provide a basis for the interpretation of daily glucose profiles, and to promote GV as an accessible tool for future studies in veterinary medicine. Interstitial glucose concentrations of ten non-diabetic, non-obese beagles were continuously measured over a 48-h period using a flash glucose monitoring system. GV was assessed using the common indices MAGE (mean amplitude of glycemic excursion), GVP (Glycemic variability percentage) and CV (coefficient of variation). A total of 2260 sensor measurements were obtained, ranging from 3.7 mmol/L (67 mg/dL) to 8.5 mmol/L (153 mg/dL). Glucose profiles suggested a meal-dependent circadian rhythmicity with small but significant surges during the feeding periods. No differences in GV indices were observed between day and night periods (p > 0.05). The MAGE (mmol/L), GVP (%) and CV (%) were 0.86 (± 0.19), 7.37 (± 1.65), 6.72 (± 0.89) on day one, and 0.83 (± 0.18), 6.95 (± 1.52), 6.72 (± 1.53) on day two, respectively. The results of this study suggest that GV is low in non-diabetic dogs and that glucose concentrations are kept within narrow ranges.

Continuous Glucose Monitoring in Pregnancy

Diabetes mellitus in pregnancy is associated with adverse maternal and neonatal outcomes. Optimal glycemic control is associated with improved outcomes. Continuous glucose monitoring is a less invasive alternative to blood glucose measurements. Two types of continuous glucose monitoring are available in the market: real time and intermittently scanned. Continuous glucose monitoring is gaining popularity and is now recommended by some societies for glucose monitoring in pregnant women. In this review, we discuss the differences between the two types of continuous glucose monitoring, optimal treatment goals, and whether there is an improvement in maternal or neonatal outcomes.

Glycemic Variability As a Prognostic Factor for Mortality in Patients With Critical Illness: A Systematic Review and Meta-Analysis

OBJECTIVES

To perform a systematic review and meta-analysis to evaluate the association of various measures of glycemic variability, including time-domain and complexity-domain, with short-term mortality in patients with critical illness.

DATA SOURCES

We searched Embase Classic +, MEDLINE, and the Cochrane Database of Systematic Reviews from inception to November 3, 2023.

STUDY SELECTION

We included English language studies that assessed metrics of glycemic variation or complexity and short-term mortality in patients admitted to the ICU.

DATA EXTRACTION

Two authors performed independent data abstraction and risk-of-bias assessments. We used a random-effects model to pool binary and continuous data and summarized estimates of effect using odds ratios and mean difference. We used the Quality in Prognosis Studies tool to assess risk of bias and the Grading of Recommendations, Assessment, Development and Evaluations to assess certainty of pooled estimates.

DATA SYNTHESIS

We included 41 studies (n = 162,259). We demonstrate that increased sd, coefficient of variance, glycemic lability index, and decreased time in range are probably associated with increased mortality in critically ill patients (moderate certainty) and that increased mean absolute glucose, mean amplitude of glycemic excursion, and detrended fluctuation analysis may be associated with increased mortality (low certainty).

CONCLUSIONS

We found a consistent association between increased measures of glycemic variability and higher short-term mortality in patient with critical illness. Further research should focus on standardized measurements of glycemic variation and complexity, along with their utility as therapeutic targets and prognostic markers.

Evaluation of association between time in range, a continuous glucose monitoring metric, and cardiac autonomic neuropathy in type 2 diabetes patients

Introduction

Time in range (TIR), a metric of continuous glucose monitoring (CGM) provides better information regarding the individual's glycemic variability than a static measure like glycated hemoglobin (HbA1c). TIR is emerging as an independent risk factor for diabetic complications, both microvascular and macrovascular complications independent of HbA1c. Hence, this study evaluates the association between TIR and cardiac autonomic neuropathy (CAN) in type 2 diabetic patients.

Materials and Methods

A total of 42 patients with type 2 diabetes mellitus were enrolled in this study and underwent a 3-day CGM using the "FreeStyle Libre Pro Flash Glucose Monitoring System Sensor" along with tests for CAN within the 3 days of attaching the CGM.

Results

Out of 42 patients, 36 patients (85.7%) were diagnosed with CAN (early CAN 57.1% and definite CAN 28.6%) and the mean TIR was 64.4% ±23.5%. Out of those with TIR <70%, 42.9% were affected with definite CAN compared to only 14.3% among those with TIR >70%. Patients with more severe CAN were found to have a lower TIR (P = 0.115).

Conclusion

The study found a high prevalence of cardiac autonomic neuropathy (CAN) of around 85.7% in type 2 diabetes patients. Lower TIR values were associated with a higher incidence of definite CAN (42.9% vs. 14.3% in TIR <70% vs. >70% groups). The findings suggest TIR is inversely associated with the presence and severity of cardiac autonomic neuropathy in type 2 diabetic patients and also a potential link between TIR and CAN severity.

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.

Comparative Effectiveness of Oral Hypoglycemic Agents for Glycemic Control and Glycemic Variability in Patients with Type 2 Diabetes Mellitus: Using Flash Glucose Monitoring

AIM

The study aimed to compare the effectiveness of oral hypoglycemic agents (OHAs) as monotherapy, dual and quadruple therapy for glycemic control (GC) and glycemic variability (GV) in patients with type 2 diabetes (T2DM) using flash glucose monitoring system (FGM).

BACKGROUND

Diabetes management largely relies on HbA1c monitoring. Glycemic variability has been an evolving glycemic target for preventing complications related to type 2 diabetes mellitus.

OBJECTIVE

The purpose of the study was to compare glycemic control measures and glycemic variability measures among study groups and to study the relationships between GC and GV indices.

METHODS

Retrospectively, FGM data were collected from 50 T2DM patients. The patients were classified based on prescribed number of OHAs as monotherapy [group 1: Dipeptidyl peptidase- 4 (DPP-4) inhibitors (n=10), group 2: Sodium-glucose co-transporter-2 (SGLT2) inhibitors (n=10), group 3: Sulphonylureas (n=10), group 4: Dual therapy (n=10), and group 5: Quadruple therapy (n=10)]. Measures of GC and GV were evaluated.

RESULTS

Significant differences between study groups were observed in GC and GV measurements. The SGLT2 inhibitors monotherapy group demonstrated optimal GC [eA1c (%): 6.5 ± 2.2; MBG: 140.80 ± 63.94; TIR: 60.60 ± 19.96] and GV (SD: 42.38 ± 34.57; CV: 27.85 ± 6.68; MAGE: 96.76 ± 52.47; MODD: 33.96 ± 22.91) in comparison to other study groups. On using Pearson correlation analysis, mean blood glucose (MBG) and mean amplitude of glycemic excursion (MAGE) showed moderate correlation (r = 0.742)(r2 = 0.551), depicting distinct glucose variabilities at the same mean blood glucose levels.

CONCLUSION

The monotherapy group of SGLT2 inhibitors demonstrated glucose-lowering effects with reduced glycemic variability. Hence, optimum glycemic control is associated with decreased glycemic variability.

How to Measure Glycemic Variability? A Literature Review

Optimal glycemic control without the presence of diabetes-related complications is the primary goal for adequate diabetes management. Recent studies have shown that hemoglobin A1c level cannot fully evaluate diabetes management as glycemic fluctuations are demonstrated to have a major impact on the occurrence of diabetes-related micro- and macroangiopathic comorbidities. The use of continuous glycemic monitoring systems allowed the quantification of glycemic fluctuations, providing valuable information about the patients' glycemic control through various indicators that evaluate the magnitude of glycemic fluctuations in different time intervals. This review highlights the significance of glycemic variability by describing and providing a better understanding of common and alternative indicators available for use in clinical practice.

Visit-to-Visit Glucose Variability, Cognition, and Global Cognitive Decline: The Multi-Ethnic Study of Atherosclerosis

CONTEXT

Higher visit-to-visit glucose variability (GV) is associated with dysglycemia and type 2 diabetes (T2D), key risk factors for cognitive decline.

OBJECTIVE

Evaluate the association of GV with cognitive performance and decline in racially/ethnically diverse older populations with and without T2D.

METHODS

We calculated the standard deviation of glucose (SDG), average real variability (ARV), and variability independent of the mean (VIM) among 4367 Multi-Ethnic Study of Atherosclerosis participants over 6 clinical examinations. Participants completed a cognitive assessment at the fifth examination, and a subset completed a second assessment 6 years later. We used multivariable linear regression to estimate the association of intraindividual GV with cognitive test scores after adjustments for cardiovascular risk factors and mean glucose level over the study period.

RESULTS

Two-fold increments in the VIM and SDG were associated with worse Cognitive Abilities Screening Instrument (CASI) performance, while two-fold increments in VIM and ARV were associated with worse Digit Symbol Coding test score. GV measures were not associated with change in CASI performance among 1834 participants with repeat CASI data 6 years later. However, among 229 participants with incident T2D, the SDG and VIM were associated with decline in CASI (-1.7 [95% CI: -3.1, -0.3] and -2.1 [-3.7, -0.5] points, respectively). In contrast, single-timepoint glucose and HbA1c were not associated with CASI decline among participants with or without incident T2D.

CONCLUSION

Higher visit-to-visit GV over 16 to 18 years is associated with worse cognitive performance in the general population, and with modest global cognitive decline in participants with T2D.

Improved glycemic control after the use of flash glucose monitoring accompanied by improved treatment satisfaction in patients with non-insulin-treated type 2 diabetes: A post-hoc analysis of a randomized controlled trial

AIMS

In our previously reported randomized controlled trial in patients with noninsulin-treated type 2 diabetes, the use of flash glucose monitoring (FGM) improved glycated hemoglobin (HbA1c), and the improvement was sustained after the cessation of glucose monitoring. In this post-hoc analysis, we examined data from our trial to identify the factors that influenced FGM efficacy.

METHODS

We analyzed data for 48 of 49 participants of the FGM group who completed the trial to clarify the changes in various parameters and factors related to HbA1c improvement with the use of FGM.

RESULTS

Analyses of the FGM data during the 12-week FGM provision period showed that the weekly mean blood glucose levels considerably decreased as early as at 1 week compared with the baseline values, and this decline continued for 12 weeks. An enhancement in the Diabetes Treatment Satisfaction Questionnaire regarding "willingness to continue the current treatment" score was significantly associated with the improvement in HbA1c at 12 (p = 0.009) and 24 weeks (p = 0.012).

CONCLUSIONS

Glycemic control was improved soon after FGM initiation, accompanied by improved satisfaction with continuation of the current treatment in patients with noninsulin-treated type 2 diabetes.

The effect of sodium-glucose cotransporter 2 inhibitors as an adjunct to insulin in patients with type 1 diabetes assessed by continuous glucose monitoring: A systematic review and meta-analysis

AIMS

Patients undergoing insulin-based therapy for type 1 diabetes often experience poor glycemic control characterized by significant fluctuations. This study was undertaken to analyze the effect of sodium-glucose cotransporter 2 inhibitors (SGLT2Is), as an adjunct to insulin, on time in range (TIR) and glycemic variability in patients with type 1 diabetes, using continuous glucose monitoring (CGM). In addition, we examined which type of SGLT2I yielded a superior effect compared to others.

METHODS

We conducted a comprehensive search of PubMed, EMBASE, the Cochrane Library, Web of Science, and clinical trial registry websites, retrieving all eligible randomized clinical trials (RCTs) published up until February 2023. We analyzed the mean TIR, mean amplitude of glucose excursions (MAGE), mean daily glucose (MDG), diabetic ketoacidosis (DKA), standard deviation (SD), total insulin dose, and severe hypoglycemia to evaluate the efficacy and safety of SGLT2Is. A random-effects model was also employed.

RESULTS

This study encompassed 15 RCTs. The meta-analysis revealed that the use of SGLT2Is as an adjuvant therapy to insulin led to a significant increase in TIR (MD = 10.78, 95%CI = 9.33-12.23, I2 = 42 %, P < 0.00001) and a decrease in SD (MD = -0.38, 95%CI = -0.50 to -0.26, I2 = 0 %, P < 0.00001), MAGE (MD = -0.92, 95%CI = -1.17 to -0.67, I2 = 19 %, P < 0.00001), MDG(MD = -1.01, 95%CI = -1.32 to -0.70, I2 = 48 %, P < 0.00001), and total insulin dose (MD = -5.81, 95%CI = -7.81 to -3.82, I2 = 32 %, P < 0.00001). No significant increase was observed in the rate of severe hypoglycemia (RR = 1.04, 95 % CI = 0.76-1.43, P = 0.80). However, SGLT2I therapy was associated with increased DKA occurrence (RR = 2.79, 95 % CI = 1.42-5.48; P = 0.003, I2 = 16 %). In addition, the subgroup analyses based on the type of SGLT2Is revealed that dapagliflozin might exhibit greater efficacy compared to other SGLT2Is across most outcomes.

CONCLUSIONS

SGLT2Is exhibited a positive effect on improving blood glucose level fluctuations. Subgroup analysis showed that dapagliflozin appeared to have more advantages. However, giving due consideration to preventing adverse effects, particularly DKA, is paramount.

REGISTRATION

Prospero CRD42023408276.

Glucose variability: a new risk factor for cardiovascular disease

AIMS AND DATA SYNTHESIS

Glucose variability (GV) is increasingly considered an additional index of glycemic control. Growing evidence indicates that GV is associated with diabetic vascular complications, thus being a relevant point to address in diabetes management. GV can be measured using various parameters, but to date, a gold standard has not been identified. This underscores the need for further studies in this field also to identify the optimal treatment.

CONCLUSIONS

We reviewed the definition of GV, the pathogenetic mechanisms of atherosclerosis, and its relationship with diabetic complications.

Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis

Introduction

Continuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques.

Methods

In this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta-cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range.

Results

Distinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range.

Discussion

Our findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of--and provides opportunities for--taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data.

Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis

Continuous glucose monitoring systems (CGM) are a very useful tool to understand the behaviour of glucose in different situations and populations. Despite the widespread use of CGM systems in both clinical practice and research, our understanding of the reproducibility of CGM data remains limited. The present work examines the reproducibility of the results provided by a CGM system in a random sample of a free-living adult population, from a functional data analysis approach. Functional intraclass correlation coefficients (ICCs) and their 95% confidence intervals (CI) were calculated to assess the reproducibility of CGM results in 581 individuals. 62% were females 581 participants (62% women) mean age 48 years (range 18-87) were included, 12% had previously been diagnosed with diabetes. The inter-day reproducibility of the CGM results was greater for subjects with diabetes (ICC 0.46 [CI 0.39-0.55]) than for normoglycaemic subjects (ICC 0.30 [CI 0.27-0.33]); the value for prediabetic subjects was intermediate (ICC 0.37 [CI 0.31-0.42]). For normoglycaemic subjects, inter-day reproducibility was poorer among the younger (ICC 0.26 [CI 0.21-0.30]) than the older subjects (ICC 0.39 [CI 0.32-0.45]). Inter-day reproducibility was poorest among normoglycaemic subjects, especially younger normoglycaemic subjects, suggesting the need to monitor some patient groups more often than others.

Machine Learning-Based Time in Patterns for Blood Glucose Fluctuation Pattern Recognition in Type 1 Diabetes Management: Development and Validation Study

BACKGROUND

Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations.

OBJECTIVE

This study aimed to propose a machine learning-based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns.

METHODS

Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis.

RESULTS

In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (P=.04), glycated hemoglobin level (P<.001), and time in range (P<.001) and thus had different management needs.

CONCLUSIONS

The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients' GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians.

The effects of long-term cumulative HbA1c exposure on the development and onset time of dementia in the patients with type 2 diabetes mellitus: hospital based retrospective study (2005-2021)

AIMS

We examine cumulative effect of long-term glycemic exposure in patients with type 2 diabetes mellitus (T2DM) on the development of dementia.

METHODS

The study involved 20,487 records of patients with T2DM identified in the electronic medical record at Severance Hospital, Korea. Cumulative HbA1c (AUC_HbA1c) and mean HbA1c over time (HbA1c_avg) as measures of long-term glycemic exposure were compared for the development of dementia and the time to dementia.

RESULTS

AUC_HbA1c and HbA1c_avg were significantly higher in patients who later developed dementia than in those who did not dementia (AUC_HbA1c: 56.2 ± 26.4 vs. 52.1 ± 26.1 %*Year; HbA1c_avg: 7.0 ± 1.0 vs. 7.3 ± 1.0 %). Odds ratio of dementia increased when HbA1c_avg was 7.2% (55 mmol/mol) or above, and when AUC_HbA1c was 42 %*Year (e.g., HbA1c 7.0% maintained for 6 years) or above. Among those who developed dementia, as HbA1c_avg increased, the time to dementia onset decreased (β = -380.6 days, 95% confidence interval [CI]: -416.2 to -345.0).

CONCLUSIONS

Our results indicate poorly controlled T2DM was associated with an increased risk of developing dementia, as measured by AUC_HbA1c and HbA1c_avg. Higher cumulative glycemic exposure may lead to developing dementia in a shorter time.

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.

Effects of structured Ramadan Nutrition Plan on glycemic control and variability using continuous glucose monitoring in individuals with type 2 diabetes: A pilot study

BACKGROUND AND AIMS

Continuous glucose monitoring (CGM) has been increasingly used in recent years to evaluate glycemic control and variability in individuals with diabetes observing Ramadan fasting. However, the effectiveness of the Ramadan Nutrition Plan (RNP) in individuals with type 2 diabetes (T2D) using CGM-derived measures has not been investigated. The study aimed to evaluate the effects of structured RNP versus standard care using CGM in individuals with T2D.

METHODS

This parallel non-randomized interventional study with patients' preference design involved 21 individuals with T2D (mean age: 49 ± 10 years, BMI: 30.0 ± 6.2 kg/m2). Participants chose to receive either structured RNP (sRNT; structured Ramadan Nutrition Therapy group; n = 14) or standard care (SC; n = 7). Participants wore CGM 5 days before Ramadan and during Ramadan. CGM-derived measures of glycemic variability were calculated using Glyculator version 2.0.

RESULTS

Compared to the SC group, the sRNT group significantly reduced their fasting blood glucose levels, HbA1c, total cholesterol, diastolic blood pressure, and increased dietary fiber intake. CGM data showed the sRNT group had significantly lower average sensor glucose, peak sensor value, estimated A1c, percentage and duration of time-above-range, J-index, mean amplitude of glycemic excursion (MAGE), and continuous overall net glycemic action (CONGA); and a significantly higher percentage of time-in-range (TIR).

CONCLUSIONS

The structured RNP significantly improved clinical outcomes, glycemic control and variability in individuals with T2D. The study highlights the importance of utilizing CGM sensor data to monitor glycemic excursions during Ramadan fasting. Adequately powered randomized controlled trials are needed to confirm the findings.

Clinical relevance of glucose metrics during the early brain injury period after aneurysmal subarachnoid hemorrhage: An opportunity for continuous glucose monitoring

Hyperglycaemia, hypoglycaemia and higher glucose variability during the Early Brain Injury (EBI) period of aneurysmal subarachnoid hemorrhage (aSAH) have been associated with poor clinical outcome. However, it is unclear whether these associations are due to direct glucose-driven injury or if hyperglycaemia simply acts as a marker of initial severity. Actually, strict glucose control with intensive insulin therapy has not been demonstrated as an effective strategy for improving clinical outcomes after aSAH. Currently published studies describing an association between hyperglycaemia and prognosis in aSAH patients have been based on isolated glucose measurements and did not incorporate comprehensive dynamic evaluations, such as those derived from subcutaneous continuous glucose monitoring devices (CMG). Arguably, a more accurate knowledge on glycaemic patterns during the acute phase of aSAH could increase our understanding of the relevance of glycaemia as a prognostic factor in this disease as well as to underpin its contribution to secondary focal and diffuse brain injury. Herein, we have summarized the available evidence on the diagnostic and prognostic relevance of glucose metrics during the acute phase of cerebrovascular diseases, focusing in the EBI period after aSAH. Overall, obtaining a more precise scope of acute longitudinal glucose profiles could eventually be useful for improving glucose management protocols in the setting of acute aSAH and to advance toward a more personalized management of aSAH patients during the EBI phase.

Glycemic Variability in Type 1 Diabetes Mellitus Pregnancies—Novel Parameters in Predicting Large-for-Gestational-Age Neonates: A Prospective Cohort Study

Pregnancies with type 1 diabetes mellitus (T1DM) have a high incidence of large-for-gestational-age neonates (LGA) despite optimal glycemic control. In recent years, glycemic variability (GV) has emerged as a possible risk factor for LGA, but the results of the conducted studies are unclear. This study analyzed the association between GV and LGA development in pregnancies with T1DM. This was a prospective cohort study of patients with T1DM who used continuous glucose monitoring (CGM) during pregnancy. Patients were followed from the first trimester to birth. GV parameters were calculated for every trimester using the EasyGV calculator. The main outcomes were LGA or no-LGA. Logistic regression analysis was used to assess the association between GV parameters and LGA. In total, 66 patients were included. The incidence of LGA was 36%. The analysis extracted several GV parameters that were significantly associated with the risk of LGA. The J-index was the only significant parameter in every trimester of pregnancy (odds ratios with confidence intervals were 1.33 (1.02, 1.73), 3.18 (1.12, 9.07), and 1.37 (1.03, 1.82), respectively. Increased GV is a risk factor for development of LGA. The J-index is a possible novel GV parameter that may be assessed in all three trimesters of pregnancy together with glycated hemoglobin and time-in-range.

Discrepancies in glycemic metrics derived from different continuous glucose monitoring systems in adult patients with type 1 diabetes mellitus

BACKGROUND

Continuous glucose monitoring systems have been widely used but discrepancies among various brands of devices are rarely discussed. This study aimed to explore differences in glycemic metrics between FreeStyle Libre (FSL) and iPro2 among adults with type 1 diabetes mellitus (T1DM).

METHODS

Participants with T1DM and glycosylated hemoglobin of 7%-10% were included and wore FSL and iPro2 for 2 weeks simultaneously. Datasets collected on the insertion and detachment day, and those with insufficient quantity (<90%) were excluded. Agreements of measurement accuracy and glycemic metrics were evaluated.

RESULTS

A total of 40 498 paired data were included. Compared with the values from FSL, significantly higher median value was observed in iPro2 (147.6 [106.2, 192.6] vs. 144.0 [100.8, 192.6] mg/dl, p < 0.001) and the largest discordance was observed in hypoglycemic range (median absolute relative difference with iPro2 as reference value: 25.8% [10.8%, 42.1%]). Furthermore, significant differences in glycemic metrics between iPro2 and FSL were also observed in time in range (TIR) 70-180 mg/dl (TIR, 62.8 ± 12.4% vs. 58.8 ± 12.3%, p = 0.004), time spent below 70 mg/dl (4.4 [1.8, 10.9]% vs. 7.2 [5.4, 13.3]%, p < 0.001), time spent below 54 mg/dl (0.9 [0.3, 4.0]% vs. 2.6 [1.3, 5.6]%, p = 0.011), and coefficient of variation (CV, 38.7 ± 8.5% vs. 40.9 ± 9.3%, p = 0.017).

CONCLUSIONS

During 14 days of use, FSL and iPro2 provided different estimations on TIR, CV, and hypoglycemia-related parameters, which needs to be considered when making clinical decisions and clinical trial designs.

Identifying Continuous Glucose Monitoring Data Using Machine Learning

Background and Aims: The recent increase in wearable devices for diabetes care, and in particular the use of continuous glucose monitoring (CGM), generates large data sets and associated cybersecurity challenges. In this study, we demonstrate that it is possible to identify CGM data at an individual level by using standard machine learning techniques. Methods: The publicly available REPLACE-BG data set (NCT02258373) containing 226 adult participants with type 1 diabetes (T1D) wearing CGM over 6 months was used. A support vector machine (SVM) binary classifier aiming to determine if a CGM data stream belongs to an individual participant was trained and tested for each of the subjects in the data set. To generate the feature vector used for classification, 12 standard glycemic metrics were selected and evaluated at different time periods of the day (24 h, day, night, breakfast, lunch, and dinner). Different window lengths of CGM data (3, 7, 15, and 30 days) were chosen to evaluate their impact on the classification performance. A recursive feature selection method was employed to select the minimum subset of features that did not significantly degrade performance. Results: A total of 40 features were generated as a result of evaluating the glycemic metrics over the selected time periods (24 h, day, night, breakfast, lunch, and dinner). A window length of 15 days was found to perform the best in terms of accuracy (86.8% ± 12.8%) and F1 score (0.86 ± 0.16). The corresponding sensitivity and specificity were 85.7% ± 19.5% and 87.9% ± 17.5%, respectively. Through recursive feature selection, a subset of 9 features was shown to perform similarly to the 40 features. Conclusion: It is possible to determine with a relatively high accuracy if a CGM data stream belongs to an individual. The proposed approach can be used as a digital CGM "fingerprint" or for detecting glycemic changes within an individual, for example during intercurrent illness.

Complications of Diabetes and Metrics of Glycemic Management Derived From Continuous Glucose Monitoring

CONTEXT

Although glycated hemoglobin A1c is currently the best parameter used clinically to assess risk for the development of diabetes complications, it does not provide insight into short-term fluctuations in glucose levels. This review summarizes the relationship between continuous glucose monitoring (CGM)-derived metrics of glycemic variability and diabetes-related complications.

EVIDENCE ACQUISITION

PubMed and Embase databases were searched from January 1, 2010 to August 22, 2020, using the terms type 1 diabetes, type 2 diabetes, diabetes-related microvascular and macrovascular complications, and measures of glycaemic variability. Exclusion criteria were studies that did not use CGM and studies involving participants who were not diabetic, acutely unwell (post stroke, post surgery), pregnant, or using insulin pumps.

EVIDENCE SYNTHESIS

A total of 1636 records were identified, and 1602 were excluded, leaving 34 publications in the final review. Of the 20 852 total participants, 663 had type 1 diabetes (T1D) and 19 909 had type 2 diabetes (T2D). Glycemic variability and low time in range (TIR) showed associations with all studied microvascular and macrovascular complications of diabetes. Notably, higher TIR was associated with reduced risk of albuminuria, retinopathy, cardiovascular disease mortality, all-cause mortality, and abnormal carotid intima-media thickness. Peripheral neuropathy was predominantly associated with standard deviation of blood glucose levels (SD) and mean amplitude of glycemic excursions (MAGE).

CONCLUSION

The evidence supports the association between diabetes complications and CGM-derived measures of intraday glycemic variability. TIR emerged as the most consistent measure, supporting its emerging role in clinical practice. More longitudinal studies and trials are required to confirm these associations, particularly for T1D, for which there are limited data.

Large-Scale Data Analysis for Glucose Variability Outcomes with Open-Source Automated Insulin Delivery Systems

Open-source automated insulin delivery (AID) technologies use the latest continuous glucose monitors (CGM), insulin pumps, and algorithms to automate insulin delivery for effective diabetes management. Early community-wide adoption of open-source AID, such as OpenAPS, has motivated clinical and research communities to understand and evaluate glucose-related outcomes of such user-driven innovation. Initial OpenAPS studies include retrospective studies assessing high-level outcomes of average glucose levels and HbA1c, without in-depth analysis of glucose variability (GV). The OpenAPS Data Commons dataset, donated to by open-source AID users with insulin-requiring diabetes, is the largest freely available diabetes-related dataset with over 46,070 days’ worth of data and over 10 million CGM data points, alongside insulin dosing and algorithmic decision data. This paper first reviews the development toward the latest open-source AID and the performance of clinically approved GV metrics. We evaluate the GV outcomes using large-scale data analytics for the n = 122 version of the OpenAPS Data Commons. We describe the data cleaning processes, methods for measuring GV, and the results of data analysis based on individual self-reported demographics. Furthermore, we highlight the lessons learned from the GV outcomes and the analysis of a rich and complex diabetes dataset and additional research questions that emerged from this work to guide future research. This paper affirms previous studies’ findings of the efficacy of open-source AID.

The Effect of Coronavirus Disease-19 Pandemic Lockdown and the Overlapping Ramadan Fasting Period on Glucose Control in Insulin-Treated Patients With Diabetes: A Flash Glucose Monitoring Study

Background

A strict lockdown was enforced during coronavirus disease (COVID-19) pandemic in many countries including the UAE. Lockdown period overlapped with Ramadan which is accompanied by its own drastic changes in lifestyle that include meal timings.

Aims

We report the impact of COVID-19 lockdown (between 22/3/2020 and 24/6/2020) on glucose control pre- and postlockdown and during Ramadan, in patients with type 1 diabetes (T1D) and type 2 diabetes (T2D) on insulin therapy.

Methods

A number of twenty-four patients (19 men, 6 women) who were monitoring their glucose levels using flash glucose monitoring (FGM) and remotely connected to the diabetes clinic in Imperial College London Diabetes Centre (ICLDC), Abu Dhabi, UAE were included. Using the international consensus on the use of continuous glucose monitoring guidelines, analyses of data were performed on glucose management indicator (GMI), time in range (TIR), time in hyperglycemia, time in hypoglycemia, low blood glucose index (LBGI) and high blood glucose index (HBGI). Variables were calculated for each period: 30 days before lockdown 14/2/2020-14/3/2020, 30 days into lockdown and pre-Ramadan 20/3/2020-18/4/2020, and 30 days into lockdown and Ramadan 24/4/2020-23/5/2020, using cgmanalysis package in R-studio software.

Results

Mean average glucose (MAG) remained steady before and during lockdown, and no significant differences were observed in TIR, time in hypoglycemia, and LBGI between prelockdown and lockdown periods. However, there was a statistically significant difference in GMI and percentage of time in hyperglycemia (>10.0 mmol/L) between Ramadan and pre-Ramadan during the lockdown period in p = 0.007, 0.006, and 0.004, respectively. Percentage of TIR (3.9-10.0 mmol/L) was significantly lower in Ramadan as compared to pre-Ramadan (50.3% vs. 56.1%; p = 0.026). Mean absolute glucose (MAG) (182.0 mmol/L vs. 166.6 mmol/L, p = 0.007) and HBGI (10.2 (6.8, 14.8) vs. 11.9 (7.9, 17.8), p = 0.037) were significantly higher in Ramadan compared to pre-Ramadan period. There was no statistically significant difference in percentage of time in hypoglycemia (<3.9 mmol/L) and LBGI between Ramadan and pre-Ramadan periods.

Conclusion

The lockdown period had no significant effects in the markers of glycemic control in the population studied. However, Ramadan fasting period embedded within this time was associated with several changes that include increase in GMI, HBGI, and glycemic variability similar to what has been reported in other Ramadan studies.

Glycaemia dynamics in gestational diabetes mellitus

Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.

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.

Insulin glargine 300 U/ml for the treatment of feline diabetes mellitus

OBJECTIVES

The study aimed to evaluate the efficacy and safety of insulin glargine 300 U/ml (IGla-U300) in cats with variable duration of diabetes mellitus (DM).

METHODS

Thirteen client-owned cats with DM completed a prospective clinical trial. Four cats were highly suspected of hypersomatotropism and excluded from the insulin efficacy evaluation. All cats were treated with IGla-U300 SC at a starting dosage of 0.5 U/kg q12h and fed with a low carbohydrate diet. Cats were monitored for 8 weeks with a once-weekly at-home 16 h blood glucose curve (BGC) and a questionnaire evaluating the presence of DM-related clinical signs. In-clinic evaluations, including serum fructosamine measurement, were scheduled within 3 days of the first, third, sixth and eighth BGC. Glycemic variability was assessed by calculating the SD of each BGC.

RESULTS

Excluding four cats suspected of hypersomatotropism, at the time of the eighth BGC, improved or absent polyuria, polydipsia, polyphagia, weight loss, lethargy and improved or normal general demeanor were reported in 8/9 (88%), 8/9 (88%), 7/9 (77%), 7/9 (77%), 7/9 (77%) and 8/9 (88%) cats, respectively. Two cats achieved remission after 29 and 53 days. Another two cats went into remission after the end of the study (days 82 and 96). All cats that achieved remission were newly diagnosed diabetics. Median (range) serum fructosamine concentration significantly decreased when comparing the time of enrollment (604 [457-683] µmol/l) with the eighth week of treatment (366 [220-738] µmol/l) (P = 0.02). In all 13 cats, biochemical hypoglycemia (blood glucose <60 mg/dl; <3.3 mmol/l) was detected in 13/104 (12.5%) BGCs, while clinical signs suggesting hypoglycemic episodes were not reported. Glycemic variability was significantly lower at the fifth BGC when comparing cats that achieved remission with cats that did not achieve remission (P = 0.02).

CONCLUSIONS AND RELEVANCE

IGla-U300 seems effective and safe for the treatment of feline diabetes, but more long- term and comparative clinical trials are needed.

Intraoperative Glycemic Variability and Mean Glucose are Predictors for Postoperative Delirium After Cardiac Surgery: A Retrospective Cohort Study

Purpose

Postoperative delirium (POD) is a common but serious complication after cardiac surgery and is associated with various short- and long-term outcomes. In this study, we investigated the effects of intraoperative glycemic variability (GV) and other glycemic variables on POD after cardiac surgery.

Patients and Methods

A retrospective single-center cohort analysis was conducted using data from electronic medical record from 2018 to 2020. A total of 705 patients undergoing coronary artery bypass graft surgery and/or valve surgery, and/or aortic replacement surgery were included in the analysis. Intraoperative GV was assessed with a coefficient of variation (CV), which was defined as the standard deviation of five intraoperative blood glucose measurements divided by the mean. POD assessment was performed three times a day in the ICU and twice a day in the ward until discharge by trained medical staff. POD was diagnosed if any of the Confusion Assessment Method for the Intensive Care Unit was positive in the ICU, and the Confusion Assessment Method was positive in the ward. Multivariable logistic regression was used to identify associations between intraoperative GV and POD.

Results

POD occurred in 306 (43.4%) patients. When intraoperative glycemic CV was compared as a continuous variable, the delirium group had higher intraoperative glycemic CV than the non-delirium group (22.59 [17.09, 29.68] vs 18.19 [13.00, 23.35], p < 0.001), and when intraoperative glycemic CV was classified as quartiles, the incidence of POD increased as intraoperative glycemic CV quartiles increased (first quartile 29.89%; second quartile 36.67%; third quartile 44.63%; and fourth quartile 62.64%, p < 0.001). In the multivariable logistic regression model, patients in the third quartile of intraoperative glycemic CV were 1.833 times (OR 1.833, 95% CI: 1.132–2.967, p = 0.014), and patients in the fourth quartile of intraoperative glycemic CV were 3.645 times (OR 3.645, 95% CI: 2.235–5.944, p < 0.001) more likely to develop POD than those in the first quartile of intraoperative glycemic CV.

Conclusion

Intraoperative blood glucose fluctuation, manifested by intraoperative GV, is associated with POD after cardiac surgery. Patients with a higher intraoperative GV have an increased risk of POD.

Insulin Degludec Versus Insulin Glargine on Glycemic Variability in Diabetic Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND/AIMS

Currently, glycemic variability has more deleterious effects than sustained hyperglycemia and is closely associated with acute and chronic complications of diabetes. Reducing glycemic excursion is becoming another vital goal of glycemic control in clinical practice. This study aimed to determine whether insulin degludec (IDeg) or insulin glargine (IGla) was more beneficial for reducing glycemic fluctuations.

MATERIALS AND METHODS

This research was constructed according to the PRISMA guidelines. We searched eight databases and ClinicalTrials.gov from their inception to 30 November 2021. All randomized controlled trials comparing the efficacy of glucose variability between IDeg and IGla in diabetic patients were included.

RESULTS

Fourteen trials with 8,683 participants were included. In patients with T1DM, IDeg was associated with a lower mean (MD: -16.25, 95% CI -29.02 to -3.07, P = 0.01) and standard deviation (P = 0.03) compared to IGla in fasting blood glucose (FBG); in people with T2DM, IDeg was related to a lower mean of FBG versus insulin glargine 100 U/ml (IGla100) (P <0.001) and had a more extended time in the range (TIR) than IGla100 (SMD: 0.15, 95% CI 0.02 to 0.27, P = 0.02) but not longer than insulin glargine 300 U/ml (IGla300). Moreover, IDeg had a lower coefficient of variation of FBG than IGla (P = 0.0254). For other indicators of glycemic variability, namely, standard deviation of blood glucose for 24 h, the mean of 24-h blood glucose, mean amplitude of glycemic excursion, the coefficient of variation for 24 h, the mean of daily differences, area under the glucose curve, and M-value, no significant differences were identified between IDeg and IGla, regardless of T1DM or T2DM.

CONCLUSIONS

Based on the current studies, there was comparable efficacy between IDeg and IGla from multiple aspects of glycemic variability, regardless of T1DM or T2DM. However, IDeg may be superior to IGla in reducing FBG variability in T1DM and T2DM. Nonetheless, due to the limitations of the original studies, it is still unclear whether IDeg is superior to both IGla100 and IGla300. In T2DM, IDeg had more extended TIR than IGla100 but not longer than IGla300. Additionally, more well-designed randomized controlled trials comparing IDeg with IGla300 for different indicators of glycemic variability are still warranted.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO, CRD42021283203.

SGLT2 inhibitor ipragliflozin exerts antihyperglycemic effects via the blood glucose-dependent increase in urinary glucose excretion in type 2 diabetic mice

This study investigated the antihyperglycemic effects of the sodium-glucose cotransporter 2 (SGLT2) inhibitor ipragliflozin via the blood glucose-dependent increase in urinary glucose excretion in KK/A^y type 2 diabetic mice. In oral glucose tolerance tests (glucose load: 1, 2, or 4 g/kg) in 24-h-fasted mice, blood glucose levels increased in a glucose-loading dose-dependent manner. Oral administration of ipragliflozin (1 mg/kg) significantly inhibited the increase in blood glucose concomitant with urinary glucose excretion. To investigate the effects of ipragliflozin under low blood glucose conditions, blood glucose level and urinary glucose excretion were examined under fasting conditions in diabetic mice that had prefasted for 0, 6, 12, 18, or 24 h. Ipragliflozin significantly lowered blood glucose levels in mice that had prefasted for 0, 6, or 12 h, but not 18 h or more. Blood glucose level was well correlated with ipragliflozin-induced antihyperglycemic and urinary glucose excretion effects, suggesting that these effects occur in a blood glucose-dependent manner. Thus, in a hyperglycemic state, ipragliflozin exerts a potent antihyperglycemic effect and marked increases in urinary glucose excretion; however, in a non-hyperglycemic or hypoglycemic state, the hypoglycemic effect is weak. Ipragliflozin may therefore be a useful antidiabetic agent for normalizing daily blood glucose fluctuations in type 2 diabetes.

Significance of day-to-day glucose variability in patients after acute coronary syndrome

Background

Several studies have recently addressed the importance of glycemic variability (GV) in patients with acute coronary syndrome (ACS). Although daily GV measures, such as mean amplitude of glycemic excursions, are established predictors of poor prognosis in patients with ACS, the clinical significance of day-to-day GV remains to be fully elucidated. We therefore monitored day-to-day GV in patients with ACS to examine its significance.

Methods

In 25 patients with ACS, glucose levels were monitored for 14 days using a flash continuous glucose monitoring system. Mean of daily differences (MODD) was calculated as a marker of day-to-day GV. N-terminal pro-brain natriuretic peptide (NT-proBNP) was evaluated within 4 days after hospitalization. Cardiac function (left ventricular end-diastolic volume, left ventricular ejection fraction, stroke volume) was assessed by echocardiography at 3–5 days after admission and at 10–12 months after the disease onset.

Results

Of the 25 patients, 8 (32%) were diagnosed with diabetes, and continuous glucose monitoring (CGM)-based MODD was high (16.6 to 42.3) in 17 patients (68%). Although MODD did not correlate with max creatine kinase (CK), there was a positive correlation between J-index, high blood glucose index, and NT-proBNP (r = 0.83, p < 0.001; r = 0.85, p < 0.001; r = 0.41, p = 0.042, respectively).

Conclusions

In patients with ACS, MODD was associated with elevated NT-proBNP. Future studies should investigate whether day-to-day GV in ACS patients can predict adverse clinical events such as heart failure.

Glycemic variability of acute stroke patients and clinical outcomes: a continuous glucose monitoring study

Introduction:

Glycemic variability (GV) has been associated with worse prognosis in critically ill patients. We sought to evaluate the potential association between GV indices and clinical outcomes in acute stroke patients.

Methods:

Consecutive diabetic and nondiabetic, acute ischemic or hemorrhagic stroke patients underwent regular, standard-of-care finger-prick measurements and continuous glucose monitoring (CGM) for up to 96 h. Thirteen GV indices were obtained from CGM data. Clinical outcomes during hospitalization and follow-up period (90 days) were recorded. Hypoglycemic episodes disclosed by CGM but missed by finger-prick measurements were also documented.

Results:

A total of 62 acute stroke patients [48 ischemic and 14 hemorrhagic, median NIHSS score: 9 (IQR: 3–16) points, mean age: 65 ± 10 years, women: 47%, nondiabetic: 79%] were enrolled. GV expressed by higher mean absolute glucose (MAG) values was associated with a lower likelihood of neurological improvement during hospitalization before and after adjusting for potential confounders (OR: 0.135, 95% CI: 0.024–0.751, p = 0.022). There was no association of GV indices with 3-month clinical outcomes. During CGM recording, 32 hypoglycemic episodes were detected in 17 nondiabetic patients. None of these episodes were identified by the periodic blood glucose measurements and therefore they were not treated.

Conclusions:

Greater GV of acute stroke patients may be related to lower odds of neurological improvement during hospitalization. No association was disclosed between GV indices and 3-month clinical outcomes.

Evaluating perioperative glycemic status after different types of pancreatic surgeries via continuous glucose monitoring system: a pilot study

BACKGROUND

Perioperative glycemic status after pancreatic surgery has never been described. However, it's essential for optimal perioperative glucose management and understanding the pathogenesis of new-onset diabetes mellitus (NODM) after pancreatectomy. Continuous glucose monitoring (CGM) system provides us a helpful tool for closely monitoring and studying perioperative glucose change. This study tried to describe and compare perioperative glucose level and glycemic variability between different types of pancreatic surgeries via CGM device.

METHODS

This study was designed as a prospective observational study. Eighteen patients were enrolled and were grouped by different types of surgery received: control group (CTRL), pancreaticoduodenectomy (PD), distal pancreatectomy (DP), and total pancreatectomy (TP). CGM devices were implanted and initiated right after the surgery. Mean glucose value (MGV), coefficient of variation (CV), mean of daily difference (MODD), continuous overall net glycemic action (CONGA), and time above range (TAR)/time below range (TBR) was compared between groups to assess glucose level and glycemic variability.

RESULTS

TP showed the highest MGV and CV among all groups (P<0.001), while CTRL showed the lowest (P<0.001). PD and DP had similar MGV and CV lower than TP but higher than CTRL (P<0.001). TP had the highest MODD and CONGA, CTRL had the lowest, but no significant differences were found between groups. TP had the highest TAR (24.29%) and the lowest TBR (1.28%), while the control group showed the opposite. The differences in TAR/TBR between groups were all significant (P<0.05).

CONCLUSIONS

TP had the highest mean glucose level and the greatest glycemic variability. PD and DP had similar results: a higher mean glucose level than control but lower than TP. For glycemic variability, PD and DP seemed to have a near-normal result resembling the control group. CGM is useful for glucose monitoring in the perioperative management of pancreatic surgery.

Association of sleep quality with glycemic variability assessed by flash glucose monitoring in patients with type 2 diabetes

BACKGROUND

Deterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D).

METHODS

We recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9-10 mmol/L (TIR_3.9-10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose.

RESULTS

HbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR_3.9-10 was decreased (p for trend  <  0.05), but not MODD (p for trend  =  0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r =  0.322, 0.361, 0.308 and 0.354, respectively, p  <  0.001) and were inversely correlated with TIR_3.9-10 (r  =  - 0.386, p  <  0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β  =  0.251, t  =  2.112, p  =  0.041), CV (β  =  0.286, t  =  2.207, p  =  0.033), MAGE (β  =  0.323, t  =  2.489, p  =  0.018), and TIR_3.9-10 (β  =  - 0.401, t  =  - 3.930, p  <  0.001) but not for MODD (β  =  0.188, t  =  1.374, p  =  0.177).

CONCLUSIONS

Increased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D.

Retrospective study of glycemic variability, BMI, and blood pressure in diabetes patients in the Digital Twin Precision Treatment Program

The objective of this retrospective observational cohort study was to measure glycemic variability and reductions in body mass index (BMI), blood pressure (BP), and use of antihypertensive medications in type 2 diabetes (T2D) patients participating in the digital twin-enabled Twin Precision Treatment (TPT) Program. Study participants included 19 females and 45 males with T2D who chose to participate in the TPT Program and adhered to program protocols. Nine additional enrollees were excluded due to major program non-adherence. Enrollees were required to have adequate hepatic and renal function, no myocardial infarction, stroke, or angina ≤ 90 days before enrollment, and no history of ketoacidosis or major psychiatric disorders. The TPT program uses Digital Twin technology, machine learning algorithms, and precision nutrition to aid treatment of patients with T2D. Each study participant had ≥ 3 months of follow-up. Outcome measures included glucose percentage coefficient of variation (%CV), low blood glucose index (LBGI), high blood glucose index (HBGI), systolic and diastolic BP, number of antihypertensive medications, and BMI. Sixty-four patients participated in the program. Mean (± standard deviation) %CV, LBGI, and HBGI values were low (17.34 ± 4.35, 1.37 ± 1.37, and 2.13 ± 2.79, respectively) throughout the 90-day program. BMI decreased from 29.23 ± 5.83 at baseline to 27.43 ± 5.25 kg/m2. Systolic BP fell from 134.72 ± 17.73 to 124.58 ± 11.62 mm Hg. Diastolic BP decreased from 83.95 ± 10.20 to 80.33 ± 7.04 mm Hg. The percent of patients taking antihypertensive medications decreased from 35.9% at baseline to 4.7% at 90 days. During 90 days of the TPT Program, patients achieved low glycemic variability and significant reductions in BMI and BP. Antihypertensive medication use was eliminated in nearly all patients. Future research will focus on randomized case-control comparisons.

Metabolic and sympathovagal effects of bolus insulin glulisine versus basal insulin glargine therapy in people with type 2 diabetes: A randomized controlled study

AIMS/INTRODUCTION

This study compares the effects of two different insulin regimens - basal versus bolus insulin - on metabolic and cardiovascular autonomic function in Japanese participants with type 2 diabetes.

MATERIALS AND METHODS

Participants were randomly assigned to groups for therapy with insulin glulisine (IGlu) or insulin glargine (IGla). The primary efficacy end-point was glycemic variability, including M-values, mean of glucose levels, and a blood glucose profile of seven time points before and after the intervention. The secondary end-points included pleiotropic effects, including endothelial and cardiac autonomic nerve functions.

RESULTS

Blood glucose levels at all time points significantly decreased in both groups. Post-lunch, post-dinner, and bedtime blood glucose levels were significantly lower in the IGlu group than in the IGla group. Nadir fasting blood glucose levels at the end-point were significantly lower in the IGla group than in the IGlu group. The M-value and mean blood glucose levels were significantly decreased from baseline in both groups, although the former was significantly lower in the IGlu group than in the IGla group. IGla, but not IGlu, was found to elevate 24-h parasympathetic tone, especially during night-time, and it decreased 24-h sympathetic nerve activity, especially at dawn.

CONCLUSIONS

Both IGlu and IGla regimens reduced glucose variability, with IGlu bringing a greater reduction in M-value. IGla, but not IGlu, increased parasympathetic tone during night-time and decreased sympathetic nerve activity at dawn. These findings shed light on the previously unrecognized role of night-time basal insulin supplementation on sympathovagal activity in type 2 diabetes patients.

Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept

INTRODUCTION

Diabetes prevalence continues to grow and there remains a significant diagnostic gap in one-third of the US population that has pre-diabetes. Innovative, practical strategies to improve monitoring of glycemic health are desperately needed. In this proof-of-concept study, we explore the relationship between non-invasive wearables and glycemic metrics and demonstrate the feasibility of using non-invasive wearables to estimate glycemic metrics, including hemoglobin A1c (HbA1c) and glucose variability metrics.

RESEARCH DESIGN AND METHODS

We recorded over 25 000 measurements from a continuous glucose monitor (CGM) with simultaneous wrist-worn wearable (skin temperature, electrodermal activity, heart rate, and accelerometry sensors) data over 8-10 days in 16 participants with normal glycemic state and pre-diabetes (HbA1c 5.2-6.4). We used data from the wearable to develop machine learning models to predict HbA1c recorded on day 0 and glucose variability calculated from the CGM. We tested the accuracy of the HbA1c model on a retrospective, external validation cohort of 10 additional participants and compared results against CGM-based HbA1c estimation models.

RESULTS

A total of 250 days of data from 26 participants were collected. Out of the 27 models of glucose variability metrics that we developed using non-invasive wearables, 11 of the models achieved high accuracy (<10% mean average per cent error, MAPE). Our HbA1c estimation model using non-invasive wearables data achieved MAPE of 5.1% on an external validation cohort. The ranking of wearable sensor's importance in estimating HbA1c was skin temperature (33%), electrodermal activity (28%), accelerometry (25%), and heart rate (14%).

CONCLUSIONS

This study demonstrates the feasibility of using non-invasive wearables to estimate glucose variability metrics and HbA1c for glycemic monitoring and investigates the relationship between non-invasive wearables and the glycemic metrics of glucose variability and HbA1c. The methods used in this study can be used to inform future studies confirming the results of this proof-of-concept study.

Glucodensities: A new representation of glucose profiles using distributional data analysis

Biosensor data have the potential to improve disease control and detection. However, the analysis of these data under free-living conditions is not feasible with current statistical techniques. To address this challenge, we introduce a new functional representation of biosensor data, termed the glucodensity, together with a data analysis framework based on distances between them. The new data analysis procedure is illustrated through an application in diabetes with continuous-time glucose monitoring (CGM) data. In this domain, we show marked improvement with respect to state-of-the-art analysis methods. In particular, our findings demonstrate that (i) the glucodensity possesses an extraordinary clinical sensitivity to capture the typical biomarkers used in the standard clinical practice in diabetes; (ii) previous biomarkers cannot accurately predict glucodensity, so that the latter is a richer source of information and; (iii) the glucodensity is a natural generalization of the time in range metric, this being the gold standard in the handling of CGM data. Furthermore, the new method overcomes many of the drawbacks of time in range metrics and provides more in-depth insight into assessing glucose metabolism.