A novel approach to continuous glucose analysis utilizing glycemic variation

Effects of pre-exercise snack bars with low- and high-glycemic index on soccer-specific performance: An application of continuous glucose monitoring

This study aimed to investigate the effects of pre-exercise snack bars with different glycemic indices (GI) on soccer-specific performance. In a randomised crossover study design, 12 recreational soccer players consumed either low- or high-GI snack bars 1 h before 25 min small-sided game (SSG) training. Following the SSG training, the players' passing abilities were assessed using the Loughborough Soccer Passing Test (LSPT), followed by aerobic endurance capacities YOYO Intermittent Recovery Test Level 1 (YYIRT), respectively. Continuous glucose monitors (CGM) were used to track the glycemic response during SSG training and all tests. The result showed that participants' performance was significantly better in the low-GI trial compared with the high-GI trial for the LSPT movement (58.27   ±   10.99 vs. 62.27   ±   7.63 s, p < 0.05), LSPT total (74.64   ±   22.66 vs. 83.18   ±   18.29 s, p < 0.05), and YYIRT (1196   ±   657 vs. 993   ±   536 m, p < 0.01). The CGM data indicated a lower mean (6.2   ±   0.7 vs. 7.1   ±   0.6 mmol/L, p < 0.01) and lower glycemic variability in postprandial interstitial glucose levels in the low-GI trial, compared with the high-GI trial. In conclusion, pre-exercise low-GI snacks could result in more stable glycemic responses and enhance soccer-specific performance.

Cooking Increased the Postprandial Glycaemic Response but Enhanced the Preload Effect of Air-Dried Jujube

Randomised controlled trials involving healthy participants were conducted to investigate the impact of cooking and ingestion patterns on the physiological response and preloading effect of air-dried jujube (AJ). The participants' postprandial glycaemic and insulinemic responses were tested after ingestion of cooked or uncooked air-dried jujube containing 50 g (as a sole food source) or 15 g (as a preload food prior to a rice meal) of available carbohydrates. Compared with the uncooked AJ, the cooked air-dried jujube (CAJ) induced a 34.5% higher glycaemic peak, 57.1% greater glycaemic variability, and a 159.1% larger negative area under the glycaemic response curve when ingested as the only food in a meal. When eaten as a preload prior to a rice meal, the CAJ reduced the postprandial glycaemic peak by 25.17%. The CAJ preload enhanced insulin production in the 15 min after preloading but did not increase the total amount of postprandial insulin secretion. The result suggests that when taken as preload, the loose matrix of cooked fruits may exhibit glycaemic benefits by eliciting early insulin production and may therefore be conducive to the blood glucose management of a carbohydrate-laden meal.

Are standardized conditions needed for correct CGM data interpretation in subjects at early stages of glucose intolerance?

AIM

The present study comparatively evaluated glucose variability (GV) parameters derived from both continuous glucose monitoring (CGM) performed under standard conditions for a 24-h period and under usual everyday conditions for a 14-day period in a high-risk population without diabetes.

METHODS AND RESULTS

Seventy five subjects: 14 with normal glucose tolerance (NGT; mean age 43.6 ± 10.7 years; BMI 30.5 ± 6.9 kg/m2), 19 with high 1-h postload glucose > 8.6 mmol/l (1hrOGTT; mean age 45.6 ± 8.9 years; BMI 33.7 ± 6.9 kg/m2), and 42 with isolated impaired glucose tolerance (iIGT; mean age 47.6 ± 11.8 years; BMI 31.0 ± 6.5 kg/m2), were enrolled. An OGTT was performed. CGM was performed with blinded FreeStyleLibrePro for 24 h under standard conditions and for the rest of the 14-day period under usual everyday conditions. GV parameters derived from both periods were compared. There was a significant increase in GV with worsening of glucose tolerance from NGT, to 1hrOGTT and iIGT, independently of the conditions. Our findings showed moderate to strong correlations among GV indices between the studied periods in the cohort and in the 1hrOGTT and iIGT groups. However, a significant difference was found in some of the GV parameters between the analyzed periods.

CONCLUSION

The trend in GV is independent of the conditions, under which CGM is performed, in subjects at early stages of glucose intolerance. Although its measurements to some extend differ in standard and everyday conditions, there is no need of standardized conditions for correct interpretation of GV indices in this population.

From Stability to Variability: Classification of Healthy Individuals, Prediabetes, and Type 2 Diabetes Using Glycemic Variability Indices from Continuous Glucose Monitoring Data

Objective: This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7%/39 mmol/mol) using metrics derived from continuous glucose monitoring (CGM). In addition, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns. Methods: Data from five distinct studies, each featuring at least two days of CGM, were pooled. Participants included individuals classified as healthy, with prediabetes, or with type 2 diabetes mellitus (T2DM). Various CGM indices were extracted and compared across groups. The data set was split 70/30 for training and testing two classification models (XGBoost/Logistic Regression) to differentiate between prediabetes or dysglycemia and the healthy group. Results: The analysis included 836 participants (healthy: n = 282; prediabetes: n = 133; T2DM: n = 432). Across all CGM indices, a progressive shift was observed from the healthy group to those with diabetes (P < 0.001). Statistically significant differences (P < 0.01) were noted in mean glucose, time below range, time above 140 mg/dl, mobility, multiscale complexity index, and glycemic risk index when transitioning from health to prediabetes. The XGBoost models achieved the highest receiver operating characteristic area under the curve values on the test data set ranging from 0.91 [confidence interval (CI): 0.87-0.95] (prediabetes identification) to 0.97 [CI: 0.95-0.98] (dysglycemia identification). Conclusion: Our findings demonstrate a gradual deterioration of glucose homeostasis and increased glycemic variability across the spectrum from normo- to dysglycemia, as evidenced by CGM metrics. The performance of CGM-based indices in classifying healthy individuals and those with prediabetes and diabetes is promising.

Do Metrics of Temporal Glycemic Variability Reveal Abnormal Glucose Rates of Change in Type 1 Diabetes?

BACKGROUND

We aimed to identify the normal range of glucose rates of change (RoC) observed in health and assess whether existing metrics of temporal glycemic variability (GV-timing), such as mean absolute glucose change (MAG) and continuous overlapping net glycemic action (CONGA), are predictive of abnormally rapid RoC in type 1 diabetes (T1D).

METHODS

We identified the normal range of RoC over one-hour intervals from continuous glucose monitoring (CGM) data of healthy individuals. Rapidly rising glucose was defined as RoC values above percentiles 99% (level 1, L1) or 99.9% (level 2, L2), and rapidly falling glucose as below 1% (L1) or 0.1% (L2). The percentage of time these thresholds are exceeded in a given individual is referred to as time in fluctuation (TIF). In a separate CGM dataset of 736 T1D individuals, we calculated TIF-L1 and TIF-L2, and compared them against corresponding values of MAG and CONGA.

RESULTS

The extremum percentiles of RoC observed in health are 0.1%: -80 mg/dL/h, 1%: -50 mg/dL, 99%: +56 mg/dL/h, and 99.9%: +89 mg/dL/h. The T1D individuals spend significantly more TIF at rates exceeding these thresholds (TIF-L1: median, 16.7% [interquartile range, 12.7-21.5], TIF-L2: 5.0% [3.1-7.8]) than healthy individuals (TIF-L1: 1.4% [0.6-2.8], TIF-L2: 0.0% [0.0-0.2]). Both MAG and CONGA are highly correlated with TIF-L1 and TIF-L2 (r > .95 in each pairwise comparison).

CONCLUSIONS

Individuals with T1D spend significant time with glucose RoC exceeding those observed in health. Existing GV-timing metrics are strongly correlated with time with abnormal RoC. Incorporation of a GV-timing metric in clinical practice is recommended.

Time-Restricted Eating in Adults With Metabolic Syndrome : A Randomized Controlled Trial

BACKGROUND

Time-restricted eating (TRE), limiting daily dietary intake to a consistent 8 to 10 hours without mandating calorie reduction, may provide cardiometabolic benefits.

OBJECTIVE

To determine the effects of TRE as a lifestyle intervention combined with current standard-of-care treatments on cardiometabolic health in adults with metabolic syndrome.

DESIGN

Randomized controlled trial. (ClinicalTrials.gov: NCT04057339).

SETTING

Clinical research institute.

PARTICIPANTS

Adults with metabolic syndrome including elevated fasting glucose or hemoglobin A1c (HbA1c; pharmacotherapy allowed).

INTERVENTION

Participants were randomly assigned to standard-of-care (SOC) nutritional counseling alone (SOC group) or combined with a personalized 8- to 10-hour TRE intervention (≥4-hour reduction in eating window) (TRE group) for 3 months. Timing of dietary intake was tracked in real time using the myCircadianClock smartphone application.

MEASUREMENTS

Primary outcomes were HbA1c, fasting glucose, fasting insulin, homeostasis model assessment of insulin resistance, and glycemic assessments from continuous glucose monitors.

RESULTS

108 participants from the TIMET study completed the intervention (89% of those randomly assigned; 56 women, mean baseline age, 59 years; body mass index of 31.22 kg/m2; eating window of 14.19 hours). Compared with SOC, TRE improved HbA1c by -0.10% (95% CI, -0.19% to -0.003%). Statistical outcomes were adjusted for age. There were no major adverse events.

LIMITATION

Short duration, self-reported diet, potential for multiple elements affecting outcomes.

CONCLUSION

Personalized 8- to 10-hour TRE is an effective practical lifestyle intervention that modestly improves glycemic regulation and may have broader benefits for cardiometabolic health in adults with metabolic syndrome on top of SOC pharmacotherapy and nutritional counseling.

PRIMARY FUNDING SOURCE

National Institutes of Health.

AGATA: A Toolbox for Automated Glucose Data Analysis

BACKGROUND

Analyzing continuous glucose monitoring (CGM) data is a mandatory step for multiple purposes spanning from reporting clinical trial outcomes to developing new algorithms for diabetes management. This task is repetitive, and scientists struggle in computing literature glucose control metrics and waste time in reproducing possibly complex plots and reports. For this reason, to provide the diabetes technology community a unified tool, here we present Automated Glucose dATa Analysis (AGATA), an automated glucose data analysis toolbox developed in MATLAB/Octave.

METHODS

Automated Glucose dATa Analysis is an open-source software program to visualize and preprocess CGM data, compute glucose control metrics, detect adverse events, evaluate the effectiveness of users' prediction algorithms, and compare study arms. Automated Glucose dATa Analysis can be used as a standalone computer application accessible through a dedicated graphical user interface, particularly suitable for clinicians, or by integrating its functionalities in user-defined MATLAB/Octave scripts, which fits the need of researchers and developers. To demonstrate its features, we used AGATA to analyze CGM data of two subjects extracted from a publicly available data set of individuals with type one diabetes. Finally, AGATA's features are compared against those of 12 noncommercial software programs for CGM data analysis.

RESULTS

Using AGATA, we easily preprocessed, analyzed, and visualized CGM data in a handy way, in compliance with the requirements and the standards defined in the literature. Compared to the other considered software programs, AGATA offers more functionalities and capabilities.

CONCLUSION

Automated Glucose dATa Analysis is easy to use and reduces the burden of CGM data analysis. It is freely available in GitHub at https://github.com/gcappon/agata.

Time With Rapid Change of Glucose

BACKGROUND

A variety of metrics are used to describe glycemic variation, some of which may be difficult to comprehend or require complex strategies for smoothing of the glucose curve. We aimed to describe a new metric named time with rapid change of glucose (TRC), which is presented as percentage of time, similar to time above range (TAR), time in range (TIR), and time below range (TBR).

METHOD

We downloaded glucose data for 90 days from 159 persons with type 1 diabetes using the Abbott Freestyle Libre version 1. We defined TRC as the proportion of time (%) with an absolute rate of change of glucose > 1.5 mmol/L/15 minutes (1.8mg/dL/min) corresponding to a minimum rate of change for glucose in the 3.9-10.0 mmol/L (70-180 mg/dL) range within 1 hour. TRC is related to the other glucose variability metrics: CV within day (CVw) and mean amplitude of glycemic excursion (MAGE).

RESULTS

The more than 1.27 million glucose rates were t-location scale distributed with SD 0.91 mmol/L/15 min (1.1 mg/dL/15 min). The median TRC was 6.9% (IQR 4.5%-9.5%). The proportion of TRC with positive slope was 3.9% (2.6%-5.3%) and significantly higher than the proportion with negative slope 2.8% (1.5%-4.4%) P < .001. TRC correlated with CVw and MAGE (Spearman's correlation coefficient .56 and .65, respectively, P < .001).

CONCLUSION

TRC is proposed as an easily perceived metric to compare the performance of hybrid or fully automated closed-loop insulin delivery systems to obtain glucose homeostasis.

Assessing glycemic variability in critically ill patients: A prospective cohort study comparing insulin infusion therapy with insulin sliding scale

Glycemic variability (GV) has been associated with an increased mortality rate among critically ill patients. The clinical outcomes of having less GV even with slight hyperglycemia are better than those having tight glycemic control but higher GV. Insulin infusion remains the preferred method to control stress hyperglycemia in critically ill patients. However, its impacts on GV and clinical outcomes in critically ill patients still need further investigation. This study intended to evaluate the impact of insulin infusion therapy (IIT) compared to the insulin sliding scale (ISS) on the extent of GV and explore its impact on the clinical outcomes for critically ill patients. A prospective, single-center observational cohort study was conducted at a tertiary academic hospital in Saudi Arabia between March 2021 and November 2021. The study included adult patients admitted to ICUs who received insulin for stress hyperglycemia management. Patients were categorized into two groups based on the regimen of insulin therapy during ICU stay (IIT versus ISS). The primary outcome was the GV between the two groups. Secondary outcomes were ICU mortality, the incidence of hypoglycemia, and ICU length of stay (LOS). A total of 381 patients were screened; out of them, eighty patients met the eligibility criteria. The distribution of patients having diabetes and a history of insulin use was similar between the two groups. The GV was lower in the IIT group compared to the ISS group using CONGA (- 0.65, 95% CI [- 1.16, - 0.14], p-value = 0.01). Compared with ISS, patients who received IIT had a lower incidence of hypoglycemia that required correction (6.8% vs 2.77%; p-value = 0.38). In contrast, there were no significant differences in ICU LOS and ICU mortality between the two groups. Our study demonstrated that the IIT is associated with decreased GV significantly in critically ill patients without increasing the incidence of severe hypoglycemia. There is no survival benefit with the use of the IIT. Further studies with larger sample size are required to confirm our findings and elaborate on IIT's potential effect in reducing ICU complications in critically ill patients.

Timing and Nutrient Type of Isocaloric Snacks Impacted Postprandial Glycemic and Insulinemic Responses of the Subsequent Meal in Healthy Subjects

The aim of the study was to explore the impact of both the macronutrient composition and snacking timing on the postprandial glycemic insulinemic responses and food intake. Seventeen healthy female volunteers completed the randomized crossover trials. The volunteers were provided a standard breakfast and lunch at 8:00 and 13:00, respectively, and an ad libitum dinner at 18:00. Provided at either 10:30 (midmorning) or 12:30 (preload), the glycemic effects of the three types of 70 kcal snacks, including chicken breast (mid-C and pre-C), apple (mid-A and pre-A), and macadamia nut (mid-M and pre-M), were compared with the non-snack control (CON), evaluated by continuous glucose monitoring (CGM). The mid-M showed increased insulin resistance after lunch compared with CON, while the pre-M did not. The pre-A stabilized the glycemic response in terms of all variability parameters after lunch, while the mid-A had no significant effect on postprandial glucose control. Both the mid-C and pre-C improved the total area under the glucose curve, all glycemic variability parameters, and the insulin resistance within 2 h after lunch compared with CON. The pre-C attained the lowest energy intake at dinner, while the mid-A and the mid-M resulted in the highest. In conclusion, the chicken breast snack effectively stabilized postprandial glycemic excursion and reduced insulin resistance while the macadamia snack did not, regardless of ingestion time. Only as a preload could the apple snack mitigate the glucose response after the subsequent meal.

Association of physical activity metrics with glucose variability in people with type 1 diabetes: A cross‐sectional study

This study aims to investigate the association of physical activity metrics with measures of glucose variability in people with type 1 diabetes. From August 2019 to January 2022, people with type 1 diabetes, attending clinics or participating in ongoing research at the Dasman Diabetes Institute in Kuwait, were invited to participate in the study. Physical activity was measured over a 7‐day period using a wrist‐worn accelerometer, and glucose variability data were measured by continuous glucose monitoring (CGM) of the same period. Three hundred and eleven participants were recruited (age 33 (10) years, BMI 27(5) kg/m2 and n  = 311 (169 female and 142 male)). Overall physical activity levels were not associated with any measure of glucose variability. The intensity gradient, which measures the distribution of physical activity intensity, was negatively associated with mean glucose (−1.01(−0.28, −1.74) and p  = 0.007), CONGA (−1.00(−0.28, −1.72) and p  = 0.007), J‐index (−11.7(−2.23, 21.2) and p  = 0.016), HBGI (−2.73(−0.44, −5.02) and p  = 0.020), GRADE (−2.27(−0.59, −3.95), p  = 0.009) and GRADE – euglycaemia (−4.26(−0.46, −8.06) and p  = 0–029) and the M‐value (−4.41 (−0.05, −8.77) and p  = 0.049). Overall physical activity remains important, but it may be worth recommending people with type 1 diabetes to spend proportionately more of their day doing moderate to higher intensity physical activity, although this remains to be confirmed in an appropriately designed trial.

Physical activity is recommended to people with type 1 diabetes due to its broad health benefits. The relationship between physical activity and glucose variability is unclear. The current study shows that overall physical activity levels are not associated with measures of glucose variability, but spending proportionately more of their day doing moderate to higher intensity physical activity was associated with better glucose variability.

Effects of Digitization of Self-Monitoring of Blood Glucose Records Using a Mobile App and the Cloud System on Outpatient Management of Diabetes: Single-Armed Prospective Study

BACKGROUND

In recent years, technologies promoting the digitization of self-monitoring of blood glucose (SMBG) records including app-cloud cooperation systems have emerged. Studies combining these technological interventions with support from remote health care professionals have reported improvements in glycemic control.

OBJECTIVE

To assess the use of an app-cloud cooperation system linked with SMBG devices in clinical settings, we evaluated its effects on outpatient management of diabetes without remote health care professional support.

METHODS

In this multicenter, open-label, and single-armed prospective study, 48 patients with diabetes (including type 1 and type 2) at 3 hospitals in Japan treated with insulin or glucagon-like peptide 1 receptor agonists and performing SMBG used the app-cloud cooperation system for 24 weeks. The SMBG data were automatically uploaded to the cloud via the app. The patients could check their data, and their attending physicians reviewed the data through the cloud prior to the patients' regular visits. The primary outcome was changes in glycated hemoglobin (HbA1c) levels.

RESULTS

Although HbA1c levels did not significantly change in all patients, the frequency of daily SMBG following applying the system was significantly increased before induction at 12 (0.60 per day, 95% CI 0.19-1.00; P=.002) and 24 weeks (0.43 per day, 95% CI 0.02-0.84; P=.04). In the subset of 21 patients whose antidiabetic medication had not been adjusted during the intervention period, a decrease in HbA1c level was observed at 12 weeks (P=.02); however, this significant change disappeared at 24 weeks (P=.49). The Diabetes Treatment Satisfaction Questionnaire total score and "Q4: convenience" and "Q5: flexibility" scores significantly improved after using the system (all P<.05), and 72% (33/46) patients and 76% (35/46) physicians reported that the app-cloud cooperation system helped them adjust insulin doses.

CONCLUSIONS

The digitization of SMBG records and sharing of the data by patients and attending physicians during face-to-face visits improved self-management in patients with diabetes.

TRIAL REGISTRATION

Japan Registry of Clinical Trials (jRCT) jRCTs042190057; https://jrct.niph.go.jp/en-latest-detail/jRCTs042190057.

Genome-wide association studies and polygenic risk score phenome-wide association studies across complex phenotypes in the human phenotype project

BACKGROUND

Genome-wide association studies (GWASs) associate phenotypes and genetic variants across a study cohort. GWASs require large-scale cohorts with both phenotype and genetic sequencing data, limiting studied phenotypes. The Human Phenotype Project is a longitudinal study that has measured a wide range of clinical and biomolecular features from a self-assignment cohort over 5 years. The phenotypes collected are quantitative traits, providing higher-resolution insights into the genetics of complex phenotypes.

METHODS

We present the results of GWASs and polygenic risk score phenome-wide association studies with 729 clinical phenotypes and 4,043 molecular features from the Human Phenotype Project. This includes clinical traits that have not been previously associated with genetics, including measures from continuous sleep monitoring, continuous glucose monitoring, liver ultrasound, hormonal status, and fundus imaging.

FINDINGS

In GWAS of 8,706 individuals, we found significant associations between 169 clinical traits and 1,184 single-nucleotide polymorphisms. We found genes associated with both glycemic control and mental disorders, and we quantify the strength of genetic signals in serum metabolites. In polygenic risk score phenome-wide association studies for clinical traits, we found 16,047 significant associations.

CONCLUSIONS

The entire set of findings, which we disseminate publicly, provides newfound resolution into the genetic architecture of complex human phenotypes.

FUNDING

E.S. is supported by the Minerva foundation with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation.

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.

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.

Association of scan frequency with CGM-derived metrics and influential factors in adults with type 1 diabetes mellitus

Introduction

This study aimed to investigate the association between scan frequency and intermittently scanned continuous glucose monitoring (isCGM) metrics and to clarify the factors affecting scan frequency in adults with type 1 diabetes mellitus (T1D).

Methods

We enrolled adults with T1D who used FreeStyle® Libre. Scan and self-monitoring of blood glucose (SMBG) frequency and CGM metrics from the past 90-day glucose data were collected. The receiver operating characteristic curve was plotted to obtain the optimal cutoff values of scan frequency for the target values of time in range (TIR), time above range (TAR), and time below range (TBR).

Results

The study was conducted on 211 adults with T1D (mean age, 50.9 ± 15.2 years; male, 40.8%; diabetes duration, 16.4 ± 11.9 years; duration of CGM use, 2.1 ± 1.0 years; and mean HbA1c, 7.6 ± 0.9%). The average scan frequency was 10.5 ± 3.3 scan/day. Scan frequency was positively correlated with TIR and negatively correlated with TAR, although it was not significantly correlated with TBR. Scan frequency was positively correlated with the hypoglycemia fear survey-behavior score, while it was negatively correlated with some glycemic variability metrics. Adult patients with T1D and good exercise habits had a higher scan frequency than those without exercise habits. The AUC for > 70% of the TIR was 0.653, with an optimal cutoff of 11 scan/day.

Conclusions

In real-world conditions, frequent scans were linked to improved CGM metrics, including increased TIR, reduced TAR, and some glycemic variability metrics. Exercise habits and hypoglycemia fear-related behavior might affect scan frequency. Our findings could help healthcare professionals use isCGM to support adults with T1D.Clinical Trial Registry No. UMIN000039376.

Not so fast: Paradoxically increased variability in the glucose tolerance test due to food withdrawal in continuous glucose-monitored mice

OBJECTIVE

This study was performed to determine the effect of fasting on reproducibility of the glucose tolerance test. Due to individual variation in animal feeding behaviors, fasting animals prior to metabolic and behavioral experiments is widely held to reduce inter-subject variation in glucose and metabolic parameters of preclinical rodent models. Reducing variability is especially important for studies where initial metabolite levels can influence the magnitude of experimental interventions, but fasting also imposes stress that may distort the variables of interest. One such intervention is the glucose tolerance test (GTT) which measures the maximum response and recovery following a bolus of exogenous glucose. We sought to investigate how fasting affects the response of individual mice to a GTT.

METHODS

Using simultaneous continuous glucose monitoring (CGM) and indirect calorimetry, we quantified blood glucose, physical activity, body temperature, metabolic rates, and food consumption levels on a minute-to-minute basis in adult male mice for 4 weeks. We tested the effects of a 4-h or 18-h fast on the GTT to examine the effect of food withdrawal in light or dark photoperiods. Studies were also performed with 4-h fasting in additional mice without implanted CGM probes.

RESULTS

Contrary to our expectations, a 4-h fast during the light photoperiod promotes a paradoxical increase in inter-animal variation in metabolic rate, physical activity, body temperature, glycemia, and glucose tolerance. This hyperglycemic and hyper-metabolic phenotype promotes increased corticosterone levels and is consistent with a behavioral stress response to food deprivation, even in well-fed mice. We find that mice undergoing an 18-h fast entered torpor, a hibernation-like state. In addition to low body temperature and metabolic rate, torpor is also associated with glucose levels 56 mg/dl lower than those seen in mice with ad libitum access to food. Moreover, the time spent in torpor affects the response to a GTT.

CONCLUSION

Our results suggest fasting mice before glucose tolerance testing, and perhaps other experiments, can have the opposite of the intended effect where fasting can increase, rather than decrease, experimental variability.

24-h glucose level and variability in response to carbohydrate overfeeding assessed using continuous glucose monitoring system are associated with daily carbohydrate intake

BACKGROUND & AIMS

The quantity gap between daily and loaded carbohydrates may affects blood glucose response to carbohydrate intake; however, no study has investigated the difference in 24-h span. This study aimed to determine differences in the 24-h glucose levels and variability in response to single-day carbohydrate overfeeding based on daily carbohydrate intake in healthy Japanese men.

METHODS

Twenty male college students completed a 3-day dietary record and were divided into two groups based on whether their daily carbohydrate intake exceeded the median intake (H-CHO) or not (L-CHO). Thereafter, the participants consumed a high-carbohydrate diet (carbohydrate 8.1 g/kg/d) for 1 day. The 24-h glucose levels and glucose variability (CONGA1) were measured using a continuous glucose monitoring system.

RESULTS

The mean daily carbohydrate intakes in the L-CHO and H-CHO groups were 3.9 ± 0.5 and 5.8 ± 0.6 g/kg/d, respectively (p < 0.001). The peak 24-h glucose level was not differ between the L-CHO group and the H-CHO group (8.0 ± 0.8 vs. 8.0 ± 1.0; p = 0.886). The mean 24-h glucose level was higher in the L-CHO group than in the H-CHO group (6.0 ± 0.3 vs. 5.6 ± 0.3 mmol/L; p = 0.010). The CONGA1 was higher in the L-CHO group than in the H-CHO group (5.40 ± 0.41 vs. 4.95 ± 0.25; p = 0.008).

CONCLUSIONS

Mean glucose level and glucose variability in response to carbohydrate overfeeding were high in the individuals with small daily carbohydrate intake. These findings suggest that the large quantity gap between daily and loaded carbohydrates cause worse glucose control during carbohydrate overfeeding.

Exploring the risk of glycemic variability in non-diabetic depressive individuals: a cross-sectional GlyDep pilot study

Background

Data on the correlation between glycemic variability and depression in nondiabetic patients remain limited. Considering the link between increased glycemic variability and cardiovascular risks, this relationship could be significant in depressed patients.

Methods

In this single-center pilot study, we utilized Flash Glucose Monitoring (Abbott Libre Pro) to study glycemic variability. The CES-D (Center for Epidemiological Studies- Depression) scale was employed to measure depression levels. Based on CES-D scores, patients were classified into two groups: those with scores ≥ 33 and those with scores < 33. We analyzed various glycemic variability indices, including HBGI, CONGA, ADDR, MAGE, MAG, LI, and J-Index, employing the EasyGV version 9.0 software. SPSS (version 28) facilitated the data analysis.

Results

We screened patients with depression visiting the department of psychiatry, FGM was inserted in eligible patients of both the groups which yielded a data of 196 patient-days (98 patient-days for CES-D ≥ 33 and 98 patient-days for CES-D < 33). The glycemic variability indices CONGA (mg/dl), (76.48 ± 11.9 vs. 65.08 ± 7.12) (p = 0.048), MAGE (mg/dl) (262.50 ± 25.65 vs. 227.54 ± 17.72) (p = 0.012), MODD (mg/dl) (18.59 ± 2.77 vs. 13.14 ± 2.39) (p = 0.002), MAG(mg/dl) (92.07 ± 6.24vs. 63.86 ± 9.38) (p = <0.001) were found to be significantly higher in the CES-D ≥ 33 group.

Conclusion

Patients with more severe depressive symptoms, as suggested by CES-D ≥ 33, had higher glycemic variability.

To Use or Not to Use a Self-monitoring of Blood Glucose System? Real-world Flash Glucose Monitoring Patterns Using a Cluster Analysis of the FGM-Japan Study

Objective This study investigated self-monitoring of blood glucose (SMBG) adherence and flash glucose monitoring patterns using a cluster analysis in Japanese type 1 diabetes (T1D) patients with intermittently scanned continuous glucose monitoring (isCGM). Methods We measured SMBG adherence and performed a data-driven cluster analysis using a hierarchical clustering in T1D patients from Japan using the FreeStyle Libre system. Clusters were based on three variables (testing glucose frequency and referred Libre data for hyperglycemia or hypoglycemia). Patients We enrolled 209 participants. Inclusion criteria were patients with T1D, duration of isCGM use ≥3 months, age ≥20 years old, and regular attendance at the collaborating center. Results The rate of good adherence to SMBG recommended by a doctor was 85.0%. We identified three clusters: cluster 1 (low SMBG test frequency but high reference to Libre data, 17.7%), cluster 2 (high SMBG test frequency but low reference to Libre data, 34.0%), and cluster 3 (high SMBG test frequency and high reference to Libra data, 48.3%). Compared with other clusters, individuals in cluster 1 were younger, those in cluster 2 had a shorter Libre duration, and individuals in cluster 3 had lower time-in-range, higher severe diabetic distress, and high intake of snacks and sweetened beverages. There were no marked differences in the incidence of diabetic complications and rate of wearing the Libre sensor among the clusters. Conclusion We stratified the patients into three subgroups with varied clinical characteristics and CGM metrics. This new substratification might help tailor diabetes management of patients with T1D using isCGM.

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.

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.

The Relationship Between Dietary Patterns and Glycemic Variability in People with Impaired Glucose Tolerance

BACKGROUND

Diurnal glucose fluctuations are increased in prediabetes and might be affected by specific dietary patterns.

OBJECTIVES

The present study assessed the relationship between glycemic variability (GV) and dietary regimen in people with normal glucose tolerance (NGT) and impaired glucose tolerance (IGT).

METHODS

Forty-one NGT (mean age: 45.0 ± 9.0 y, mean BMI: 32.0 ± 7.0 kg/m2) and 53 IGT (mean age: 48.4 ± 11.2 y, mean BMI: 31.3 ± 5.9 kg/m2) subjects were enrolled in this cross-sectional study. The FreeStyleLibre Pro sensor was used for 14 d, and several parameters of GV were calculated. The participants were provided with a diet diary to record all meals. ANOVA analysis, Pearson correlation, and stepwise forward regression were performed.

RESULTS

Despite no difference in diet patterns between the 2 groups, GV parameters were higher in IGT than in NGT. GV worsened with an increase in overall daily carbohydrate and refined grain consumption and improved with the increase in whole grain intake in IGT. GV parameters were positively related [r = 0.14-0.53; all P < 0.02 for SD, continuous overall net glycemic action 1 (CONGA1), J-index, lability index (LI), glycemic risk assessment diabetes equation, M-value, and mean absolute glucose (MAG)], and low blood glucose index (LBGI) inversely (r = -0.37, P = 0.006) related to the total percentage of carbohydrate, but not to the distribution of carbohydrate between the main meals in the IGT group. A negative relationship existed between total protein consumption and GV indices (r = -0.27 to -0.52; P < 0.05 for SD, CONGA1, J-index, LI, M-value, and MAG). The total EI was related to GV parameters (r = 0.27-0.32; P < 0.05 for CONGA1, J-index, LI, and M-value; and r = -0.30, P = 0.028 for LBGI).

CONCLUSIONS

The primary outcome results showed that insulin sensitivity, calories, and carbohydrate content are predictors of GV in individuals with IGT. Overall, the secondary analyses suggested that carbohydrate and daily consumption of refined grains might be associated with higher GV, whereas whole grains and daily protein intake were related to lower GV in people with IGT.

Continuous glucose monitoring-based composite metrics: a review and assessment of performance in recent-onset and long-duration type 1 diabetes

This study examined correlations between continuous glucose monitoring (CGM)-based composite metrics and standard glucose metrics within CGM data sets from individuals with recent-onset and long-duration type 1 diabetes. First, a literature review and critique of published CGM-based composite metrics was undertaken. Second, composite metric results were calculated for the two CGM data sets and correlations with six standard glucose metrics were examined. Fourteen composite metrics met selection criteria; these metrics focused on overall glycemia (n = 8), glycemic variability (n = 4), and hypoglycemia (n = 2), respectively. Results for the two diabetes cohorts were similar. All eight metrics focusing on overall glycemia strongly correlated with glucose time in range; none strongly correlated with time below range. The eight overall glycemia-focused and two hypoglycemia-focused composite metrics were all sensitive to automated insulin delivery therapeutic intervention. Until a composite metric can adequately capture both achieved target glycemia and hypoglycemia burden, the current two-dimensional CGM assessment approach may offer greatest clinical utility.

Factors associated with hemoglobin glycation index in adults with type 1 diabetes mellitus: The FGM-Japan study

AIMS/INTRODUCTION

The discrepancy between HbA1c and glucose exposure may have significant clinical implications; however, the association between the hemoglobin glycation index (HGI) and clinical parameters in type 1 diabetes remains controversial. This study aimed to find the factors associated with HGI (laboratory HbA1c - predicted HbA1c derived from the continuous glucose monitoring [CGM]).

MATERIALS AND METHODS

We conducted a cross-sectional study of adults with type 1 diabetes (n = 211, age 50.9 ± 15.2 years old, female sex = 59.2%, duration of CGM use = 2.1 ± 1.0 years). All subjects wore the CGM for 90 days before HbA1c measurement. Data derived from the FreeStyle Libre sensor were used to calculate the glucose management indicator (GMI) and glycemic variability (GV) parameters. HGI was defined as the difference between the GMI and the laboratory HbA1c levels. The participants were divided into three groups according to the HGI tertile (low, moderate, and high). Multivariate regression analyses were performed.

RESULTS

The female sex ratio, HbA1c, and % coefficient of variation (%CV) significantly increased over the HGI tertile, while eGFR and Hb decreased over the HGI tertile. In multivariate analysis, the factors associated with HGI were %CV and eGFR, after adjusting for HbA1c level and sex (R²  = 0.44).

CONCLUSIONS

This study demonstrated that HGI is associated with female sex, eGFR, and some glycemic variability indices, independently of HbA1c. Minimizing glycemic fluctuations might reduce HGI. This information provides diabetic health professionals and patients with personalized diabetes management for adults with type 1 diabetes.

Apple preload increased postprandial insulin sensitivity of a high glycemic rice meal only at breakfast

PURPOSE

The possible impact of preload food on insulin sensitivity has yet been reported. This study aimed to investigate the glycemic and insulinemic effect of an apple preload before breakfast, lunch and early supper, based on high glycemic index (GI) rice meals.

METHODS

Twenty-three healthy participants in Group 1 and 14 participants in Group 2 were served with the reference meal (white rice containing 50 g of available carbohydrate) or experimental meals (apple preload and rice, each containing 15 and 35 g of available carbohydrate). The meals were either served at 8:00 for breakfast, 12:30 for lunch or 17:00 for early supper to explore the possible effect of time factor. The group 1 assessed the postprandial and subsequent-meal glycemic effect of the test meals by continuous glucose monitoring (CGM), along with subjective appetite; The group 2 further investigated the glycemic and insulin effect by blood collection.

RESULTS

The apple preload lowered the blood glucose peak value by 33.5%, 31.4% and 31.0% in breakfast, lunch and supper, respectively, while increased insulin sensitivity by 40.5% only at breakfast, compared with the rice reference. The early supper resulted significantly milder glycemic response than its breakfast and lunch counterparts did. The result of CGM tests was consistent with that of the fingertip blood tests.

CONCLUSION

Apple preload performed the best at breakfast in terms of enhancing the insulin sensitivity. The preload treatment could effectively attenuate postprandial GR without increasing the area under insulin response curve in any of the three meals.

The Use of Extreme Value Statistics to Characterize Blood Glucose Curves and Patient Level Risk Assessment of Patients With Type I Diabetes

OBJECTIVE

Characterizing blood glucose curves and providing precise patient level risk assessment of hyperglycemia using extreme value statistics and comparing these assessments with traditional indicators of glycemic variability which are not designed to specifically capture the risk of hyperglycemia.

RESEARCH DESIGN AND METHODS

One year return level (blood glucose level exceeded exactly once every year on average) and probability of exceeding and expected time spent above certain thresholds (600 and 400 mg/dL) per year were calculated. As a comparison, traditional metrics for glycemic variability were determined too. The effect of body mass index on extremes was also investigated using non-stationary models. Metrics were calculated on a dataset containing 170.8 patient-years of measurements of 226 patients.

RESULTS

Nine high-risk patients were identified with the novel metrics: their estimated time spent above 600 mg/dL per year were above 2 hours. These patients were at moderate risk according to the traditional metrics. Higher body mass index was associated with more extreme glucose levels.

CONCLUSIONS

Through these estimates it is possible to assess each patient's individual clinical risk of hyperglycemia even beyond the observed blood glucose levels and detection limits. Additionally, it allows the assessment of the impact of clinical characteristics and treatments on blood glucose control in a novel, mathematically well-founded and potentially clinically more useful way than the already existing indicators.

Effects of accumulated versus continuous individualized exercise on postprandial glycemia in young adults with obesity

BACKGROUND

Elevated postprandial glucose (PPG) is an independent risk factor for cardiovascular disease. Post-meal exercise effectively reduces PPG concentrations. However, the effect of accumulated versus continuous post-meal exercise on PPG control remains unclear. This study aimed to investigate the effects of individualized accumulated or continuous exercise on PPG in young adults with obesity.

METHODS

Twenty young adults with obesity (11 males) completed three 4-h randomized crossover trials with 6-14-day washout periods: (1) sitting (SIT), (2) one 30-min walking bout (CONT), and (3) three 10-min walking bouts separated by 20-min resting (ACCU). Walking was initiated 20 min before individual PPG peak after breakfast, which was predetermined by continuous glucose monitoring. Blood samples were collected at 15-30 min intervals, and the 24-h glucose was monitored via continuous glucose monitoring.

RESULTS

The 4-h PPG incremental area under the curve (iAUC) was 12.1%±30.9% and 21.5%±21.5% smaller after CONT (P = 0.022) and ACCU (P < 0.001), respectively, than after SIT. PPG concentrations were lower during CONT at 30-60 min and during ACCU at 30-105 min after breakfast than during SIT (all P < 0.05). The 4-h plasma insulin and C-peptide iAUC, and mean amplitude of glycemic excursions were lower after CONT and ACCU than after SIT (all P < 0.05).

CONCLUSIONS

Both continuous and accumulated exercises reduced PPG, insulin, and C-peptide concentrations and improved glucose fluctuations. Accumulated exercise maintained lower PPG concentrations for a longer time than continuous exercise in young adults with obesity.

CLINICAL TRIAL INFORMATION

Clinical trial registration No. ChiCTR 2000035064, URL: http://www.chictr.org.cn/showproj.aspx?proj=56584; (registered July 29, 2020).HIGHLIGHTS Both continuous and accumulated walking lowered post-meal glucose, insulin and C-peptide levels and improved glucose fluctuation.Postprandial glucose was kept lower for a longer time in accumulated than continuous walking.Accumulated post-meal exercise (e.g. three 10-min bouts of walking) could be recommended as a feasible and practical alternative protocol for postprandial glucose control, especially for those who have difficulty performing sufficient exercise in one session.

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.

Correlations between parameters of glycaemic variability and foetal growth, neonatal hypoglycaemia and hyperbilirubinemia in women with gestational diabetes

The diagnosis of gestational diabetes mellitus (GDM) is important to prevent maternal and neonatal complications. This study aimed to investigate the feasibility of parameters of glycaemic variability to predict neonatal complications in women with GDM. A retrospective study was conducted on pregnant women tested positive at the oral glucose tolerance test (OGTT) during 16-18 or 24-28 weeks of gestation. Glycaemic measures were extracted from patients' glucometers and expanded to obtain parameters of glycaemic variability. Data on pregnancy outcomes were obtained from clinical folders. Descriptive group-level analysis was used to assess trends in glycaemic measures and foetal outcomes. Twelve patients were included and analysed, accounting for 111 weeks of observations. The analysis of trends in parameters of glycaemic variability showed spikes of glycaemic mean, high blood glucose index and J-index at 30-31 weeks of gestation for cases with foetal macrosomia, defined as foetal growth >90° percentile, neonatal hypoglycaemia and hyperbilirubinemia. Specific trends in parameters of glycaemic variability observed at third trimester correlate with foetal outcomes. Further research is awaited to provide evidence that monitoring of glycaemic variability trends could be more clinically informative and useful than standard glycaemic checks to manage women with GDM at delivery.

A New Analysis Tool for Continuous Glucose Monitor Data

BACKGROUND

With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools.

METHODS

In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual's CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies.

RESULTS

In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability.

CONCLUSIONS

We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

Effect of Hydroxychloroquine on Glycemic Variability in Type 2 Diabetes Patients Uncontrolled on Glimepiride and Metformin Therapy

Background and Aims

No study to date has assessed the effect of hydroxychloroquine on various parameters of glycaemic variability. To assess the effect of hydroxychloroquine on glycaemic variability in type 2 diabetes patients uncontrolled on glimepiride and metformin.

Methods

A total of 30 T2DM patients aged 18-65 years uncontrolled on glimepiride and metformin therapy with HbA1c 7.5-10% (58-86 mmol/mol) were given adjunctive hydroxychloroquine 400 mg during the 12-week study period. The glycaemic variability parameters such as standard deviation of 24 hours of blood glucose, mean of daily differences (MODD) and mean amplitude of glycaemic excursion (MAGE) were assessed by continuous glucose monitoring system (CGMS) data at baseline and at 12 weeks after the addition of hydroxychloroquine 400 mg. Efficacy was assessed by change in fasting, postprandial plasma glucose and HbA1c from baseline to 12 weeks of addition of 400 mg hydroxychloroquine.

Results

There was a significant reduction in all parameters of glycaemic variability including MAGE, MODD, standard deviation of 24-hour blood glucose and average blood glucose as well as a significant reduction in fasting, postprandial blood glucose and glycated haemoglobin post 12 weeks of adjunctive treatment with hydroxychloroquine. At the end of 12 weeks of adjunctive treatment with hydroxychloroquine, there was a significant improvement in the percentage of time spent in the target glucose range of 3.9-8.3 mmol/L (70-150 mg/dL).

Conclusion

The addition of hydroxychloroquine in uncontrolled diabetes significantly reduces all glycaemic parameters including all parameters of glycaemic variability and hence can be an effective add-on to patients uncontrolled on glimepiride and metformin therapy.

The characteristics of postprandial glycemic response patterns to white rice and glucose in healthy adults: Identifying subgroups by clustering analysis

OBJECTIVES

Large interpersonal variability in postprandial glycemic response (PGR) to white rice has been reported, and differences in the PGR patterns during the oral glucose tolerance test (OGTT) have been documented. However, there is scant study on the PGR patterns of white rice. We examined the typical PGR patterns of white rice and glucose and the association between them.

MATERIALS AND METHODS

We analyzed the data of 3-h PGRs to white rice (WR) and glucose (G) of 114 normoglycemic female subjects of similar age, weight status, and same ethnic group. Diverse glycemic parameters, based on the discrete blood glucose values, were calculated over 120 and 180 min. K-means clustering based on glycemic parameters calculated over 180 min was applied to identify subgroups and representative PGR patterns. Principal factor analysis based on the parameters used in the cluster analysis was applied to characterize PGR patterns. Simple correspondence analysis was performed on the clustering categories of WR and G.

RESULTS

More distinct differences were found in glycemic parameters calculated over 180 min compared with that calculated over 120 min, especially in the negative area under the curve and Nadir. We identified four distinct PGR patterns to WR (WR1, WR2, WR3, and WR4) and G (G1, G2, G3, and G4), respectively. There were significant differences among the patterns regard to postprandial hyperglycemia, hypoglycemic, and glycemic variability. The WR1 clusters had significantly lower glycemic index (59 ± 19), while no difference was found among the glycemic index based on the other three clusters. Each given G subgroup presented multiple patterns of PGR to WR, especially in the largest G subgroup (G1), and in subgroup with the greatest glycemic variability (G3).

CONCLUSION

Multiple subgroups could be classified based on the PGR patterns to white rice and glucose even in seemingly homogeneous subjects. Extending the monitoring time to 180 min was conducive to more effective discrimination of PGR patterns. It may not be reliable to extrapolate the patterns of PGR to rice from that to glucose, suggesting a need of combining OGTT and meal tolerance test for individualized glycemic management.

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.

A Conditional Generative Adversarial Network for Synthesis of Continuous Glucose Monitoring Signals

This report describes how a Conditional Generative Adversarial Network (CGAN) was used to synthesize realistic continuous glucose monitoring systems (CGM) from healthy individuals and individuals with type 1 diabetes over a range of different HbA1c levels. The results showed that even though the CGAN generated data, did not perfectly reflect real world CGM, many of the important features were captured and reflected in the synthetic signals. It is briefly discussed how heterogenous data sources constitutes a challenge for comparison of predictive CGM models. Therefore 40,000 CGM days were generated by the trained CGAN, equivalent to 940,000 hours of synthetic CGM measurements. These data have been made available in a public database, which can be used as a reference in future studies.

Classification of Gastroparesis from Glycemic Variability in Type 1 Diabetes: A Proof-of-Concept Study

BACKGROUND AND OBJECTIVE

Delayed gastric emptying is a substantial challenge for people with diabetes, affecting quality of life and blood glucose regulation. The complication is underdiagnosed, and current diagnostic tests are expensive or time consuming or have modest accuracy. The assessment of glycemic variations has potential use in gastroparesis screening. The aim of this study was to investigate the differences in glycemic variability between type 1 diabetes patients with gastroparesis and without a diagnosis of gastroparesis and the potential for using a classification model to differentiate between groups.

METHODS

Continuous glucose monitoring (CGM) from 425 patients with diabetes was included in the analytic cohort, including 16 patients with a diagnosis of gastroparesis and 409 without a known gastroparesis diagnosis. Sixteen features (9 daytime features and 7 nighttime features) describing glucose dynamics were extracted to assess differences between patients with and without a diagnosis of gastroparesis. A logistic regression model was trained using forward selection and cross-validation.

RESULTS

In total, 3 features were included in the model utilizing forward selection of features and cross-validation: mean absolute glucose (MAG), span, and standard deviation during the night. The Receiver operating characteristic (ROC) AUC for the classification model was 0.76.

CONCLUSIONS

Gastroparesis seems to have an impact on glucose variability, especially during the night. Moreover, CGM could possibly be used as a part of the screening process for delayed gastric emptying, but more studies are needed to determine a realistic accuracy.

Effects of Variability in Glycemic Indices on Longevity in Chinese Centenarians

Background

Large fluctuations in blood glucose levels greatly impact the health and life span of elderly individuals. This study describes the characteristics of variability in glycemic indices in centenarians with the aim of emphasizing the importance of glycemic variability in elderly people.

Methods

We recruited individuals from Rugao City, Jiangsu Province, China from April 2020 to May 2021. The study cohort included 60 centenarians and 60 first-generation offspring, as well as 20 randomly selected non-cohabitant control individuals aged 60–80 years. A FreeStyle Libre H (hospital version) continuous glucose monitoring (CGM) device (Abbott Ireland UK) was used to measure glycemic variability. The indices measured included the time in target glucose range (TIR), time below target glucose range (TBR), time above target glucose range (TAR), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), coefficient of variation (CV), standard deviation of blood glucose (SDBG), continuous overlapping net glycemic action (CONGA), glucose management indicator (GMI) and estimated glycated hemoglobin (eHbA1c). Logistic regression was used to analyze the association between glycemic variability and longevity.

Results

Mean blood glucose (MBG), eHbA1c, GMI, mean fasting plasma glucose (M-FPG) and CONGA were lower in the centenarian group (p all < 0.05). PPGE-2 was higher in the control group than that measured in the centenarian and first-generation offspring groups (p < 0.05). There were no differences between the groups in MAGE, MODD, MAG, or TIR (p > 0.05). The risk of not achieving longevity increased with each one unit increase in MBG by 126% [2.26 (1.05–4.91)], eHbA1c by 67% [1.67 (1.03–2.72)], GMI by 568% [6.68 (1.11–40.30)], M-FPG by 365% [4.65 (1.57–13.75)], M-PPG1h by 98% [1.98 (1.18–3.31)], CONGA1 by 102% [2.02 (1.01–4.06)], Li by 200% [3.00 (1.04–8.61)], and PPGE-2 by 150% [2.50 (1.39–4.50)]. However, the risk of achieving longevity decreased with each unit increase of LBGI by 53% [0.47 (0.28–0.80)], ADRR by 60% [0.40 (0.18–0.86)], and TBR by 11% [0.89 (0.80–0.98)].

Conclusion

Fluctuation in blood glucose levels in centenarians is relatively small. Maintaining an average blood glucose level and keeping blood glucose fluctuations in the normal range is conducive to longevity.

Discordance in the reduction rate between glycated albumin and glycated hemoglobin levels in type 2 diabetes patients receiving SGLT2 inhibitors

AIMS

Although the difference in HbA_1c reduction between sodium-glucose cotransporter 2 (SGLT2) inhibitors and other oral glucose-lowering agents is relatively small, SGLT2 inhibitors exhibit beneficial cardiorenal protection. This study was based on the hypothesis that changes of HbA_1c in patients treated with SGLT2 inhibitors may not accurately reflect an improved glycemic profile.

METHODS

Two studies were conducted: 1) a retrospective cohort study of 3039 patients administered with either an SGLT2 or a dipeptidyl peptidase-4 (DPP4) inhibitor for 12 months comparing the changes in glycated albumin (GA) and HbA_1c levels and 2) a pilot study of 10 patients whose glycemic dynamics were evaluated using flash glucose monitoring at baseline and 2 months after treatment with an SGLT2 inhibitor.

RESULTS

SGLT2 inhibitors reduced GA more markedly than HbA_1c in both studies. DPP4 inhibitors decreased both GA and HbA_1c to a comparable degree. The mean glucose levels and glycemic standard deviation were significantly reduced after treatment with an SGLT2 inhibitor, in concordance with GA decline, although the lowering of HbA_1c was marginal.

CONCLUSIONS

Changes in HbA_1c levels underestimated the glucose-lowering effect and the diminished glycemic fluctuation induced by SGLT2 inhibitors. Thus, the distinct biomarker roles of GA and HbA_1c should be reevaluated.

Insulin secretion and action affect glucose variability in the early stages of glucose intolerance

AIMS

Since it is unknown whether glucose variability (GV) is increased and whether this is related to worsening of insulin secretion and action in prediabetes, we have assessed insulin secretion and sensitivity, and daily GV in early stages of dysglycemia.

MATERIALS AND METHODS

Twenty subjects with normal glucose tolerance (NGT; age 45.0 ± 9.5 years; BMI 31.1 ± 6.4 kg/m2), 25 with NGT and 1hrOGTT>8.6 mmol/L (1hrOGTT; 45.7 ± 8.5 years; 32.4 ± 7.0 kg/m2), and 59 with isolated impaired glucose tolerance (iIGT; 47.7 ± 11.2 years; 31.3 ± 6.1 kg/m2) underwent OGTT and MMTT. CGM was performed with blinded FreeStyle Libre Pro for 24 h under standard conditions. Parameters of beta-cell function, insulin sensitivity and GV were calculated.

RESULTS

Overall insulin secretion and action as well as GV progressively worsened across glucose tolerance categories. On a matrix analysis, GV parameters were inversely related to ISSI-2; r = -0.37 to -0.52; p < 0.0001; and IGI; r = -0.28 to -0.48; p < 0.0001 for CV, SD, J-index, LI, HBGI and MAGE. Insulin secretion (IGI) and b-cell function (ISSI-2) emerged as independent contributors to GV in early stage of dysglycemia accounting for about 16%-38% of its variability.

CONCLUSIONS

Our results show that daily GV worsens already with mild impairment of glucose tolerance. The increase in GV is inversely related to insulin secretion and action.

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.

Prospectively evaluating maternal and fetal outcomes in the era of CFTR modulators: the MAYFLOWERS observational clinical trial study design

Introduction

Therapeutic advances have markedly increased life expectancy for those with cystic fibrosis (CF), resulting in a median predicted survival over 50 years. Consequently, people with CF (pwCF) are living through their reproductive years and the rate of pregnancy is rapidly rising. Despite the increased relevance of this topic, multicentre studies investigating the association between maternal health and choices made during pregnancy on maternal and fetal outcomes do not exist. Furthermore, there are very limited data on the outcomes following CF transmembrane conductance regulator (CFTR) modulator use during pregnancy and lactation.

Methods and analysis

Maternal and Fetal Outcomes in the Era of Modulators (MAYFLOWERS) is a prospective, multicentre observational clinical trial which will enrol approximately 285 pregnant pwCF including those who are modulator ineligible and those who choose to continue or discontinue CFTR modulator therapy during pregnancy and lactation. The primary aim of this 35-month study is to assess whether lung function changes during pregnancy differ based on the continued use of modulators or other factors such as pre-existing comorbid conditions. Secondary objectives include evaluation of pregnancy related and obstetrical complications and changes in mental health.

Ethics and dissemination

The design of this study required special consideration of study burden on pregnant and lactating people with chronic illness in the setting of a substantial number of unanswered questions under these conditions. MAYFLOWERS is the first prospective clinical trial examining pregnancy in CF; the outcomes will guide providers on pregnancy management in pwCF and others with chronic respiratory disease.

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.

Utility of Continuous Glucose Monitoring vs Meal Study in Detecting Hypoglycemia After Gastric Bypass

CONTEXT

Gastric bypass (GB) increases postprandial glucose excursion, which in turn can predispose to the late complication of hypoglycemia. Diagnosis remains challenging and requires documentation of symptoms associated with low glucose and relief of symptom when glucose is normalized (Whipple triad).

OBJECTIVE

To compare the yield of mixed meal test (MMT) and continuous glucose monitoring system (CGMS) in detecting hypoglycemia after GB.

SETTING

The study was conducted at General Clinical Research Unit, Cincinnati Children's Hospital (Cincinnati, OH, USA).

METHODS

Glucose profiles were evaluated in 15 patients with documented recurrent clinical hypoglycemia after GB, 8 matched asymptomatic GB subjects, and 9 healthy weight-matched nonoperated controls using MMT in a control setting and CGMS under free-living conditions.

RESULTS

Patients with prior GB had larger glucose variability during both MMT and CGMS when compared with nonsurgical controls regardless of their hypoglycemic status. Sensitivity (71 vs 47%) and specificity (100 vs 88%) of MMT in detecting hypoglycemia was superior to CGMS.

CONCLUSIONS

Our findings indicate that a fixed carbohydrate ingestion during MMT is a more reliable test to diagnose GB-related hypoglycemia compared with CGMS during free-living state.

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.

Continuous Glucose Monitoring and HbA1c in Cystic Fibrosis: Clinical Correlations and Implications for CFRD Diagnosis

CONTEXT

The clinical utility and implications of continuous glucose monitoring (CGM) in cystic fibrosis (CF) are unclear.

OBJECTIVE

We examined the correlation between CGM measures and clinical outcomes in adults with CF, investigated the relationship between hemoglobin A1c (HbA1c) and CGM-derived average glucose (AG), and explored CGM measures that distinguish cystic fibrosis-related diabetes (CFRD) from normal and abnormal glucose tolerance.

METHODS

This prospective observational study included 77 adults with CF who had CGM and HbA1c measured at 2 to 3 time points 3 months apart.

RESULTS

Thirty-one of the 77 participants met American Diabetes Association-recommended diagnostic criteria for CFRD by oral glucose tolerance testing and/or HbA1c. In all participants, CGM measures of hyperglycemia and glycemic variability correlated with nutritional status and pulmonary function. HbA1c was correlated with AG (R2 = 0.71, P < 0.001), with no significant difference between this regression line and that previously established in type 1 and type 2 diabetes and healthy volunteers. Cutoffs of 17.5% time > 140 mg/dL and 3.4% time > 180 mg/dL had sensitivities of 87% and 90%, respectively, and specificities of 95%, for identifying CFRD. Area under the curve and percent of participants correctly classified with CFRD were higher for AG, SD, % time > 140, > 180, and > 250 mg/dL than for HbA1c.

CONCLUSION

CGM measures of hyperglycemia and glycemic variability are superior to HbA1c in distinguishing those with and without CFRD. CGM-derived AG is strongly correlated with HbA1c in adults with CF, with a similar relationship to other diabetes populations. Future studies are needed to investigate CGM as a diagnostic and screening tool for CFRD.

An Interactive Web Application for the Statistical Analysis of Continuous Glucose Monitoring Data in Epidemiological Studies

MOTIVATION

Continuous glucose monitoring (CGM) systems are an essential part of novel technology in diabetes management and care. CGM studies have become increasingly popular among researchers, healthcare professionals, and people with diabetes due to the large amount of useful information that can be collected using CGM systems. The analysis of the data from these studies for research purposes, however, remains a challenge due to the characteristics and large volume of the data.

RESULTS

Currently, there are no publicly available interactive software applications that can perform statistical analyses and visualization of data from CGM studies. With the rapidly increasing popularity of CGM studies, such an application is becoming necessary for anyone who works with these large CGM datasets, in particular for those with little background in programming or statistics. CGMStatsAnalyser is a publicly available, user-friendly, web-based application, which can be used to interactively visualize, summarize, and statistically analyze voluminous and complex CGM datasets together with the subject characteristics with ease.

Significance of Glycemic Variability in Diabetes Mellitus

The goal of diabetes treatment is to maintain good glycemic control, prevent the development and progression of diabetic complications, and ensure the same quality of life and life expectancy as healthy people. Hemoglobin A1c (HbA1c) is used as an index of glycemic control, but strict glycemic control using HbA1c as an index may lead to severe hypoglycemia and cardiovascular death. Glycemic variability (GV), such as excessive hyperglycemia and hypoglycemia, is associated with diabetic vascular complications and has been recognized as an important index of glycemic control. Here, we reviewed the definition and evaluated the clinical usefulness of GV, and its relationship with diabetic complications and therapeutic strategies to reduce GV.

Impact of isoenergetic intake of irregular meal patterns on thermogenesis, glucose metabolism, and appetite: a randomized controlled trial

BACKGROUND

Evidence is emerging that interdaily meal pattern variability potentially affects response such as thermic effect of food (TEF), macronutrient metabolism, and appetite.

OBJECTIVES

To investigate the effect of irregular meal pattern on TEF, glucose, insulin, lipid profile, and appetite regulation in women who are overweight or with obesity and confirmed insulin resistance.

DESIGN

In a randomized crossover trial, 9 women [mean ± SD BMI (in kg/m2): 33.3 ± 3.1] with confirmed insulin resistance consumed a regular (14 d; 6 meals/d) and an irregular (14 d; 3-9 meals/d) meal pattern separated by a 14-d washout interval. Identical foods were provided during the interventions, and at the start and end of each meal pattern, participants attended the laboratory after an overnight fast. Energy expenditure, glucose, insulin, lipids, adiponectin, leptin, glucagon-like peptide 1 (GLP-1), peptide YY (PYY), and ghrelin were measured at baseline and for 3 h after consumption of a test drink, after which an ad libitum test meal was offered. Subjective appetite ratings were recorded before and after the test drink, after the ad libitum meal, and during the intervention. Continuous interstitial glucose monitoring was undertaken for 7 consecutive days during each intervention.

RESULTS

TEF (over 3 h) was significantly lower postirregular intervention compared with postregular (97.7 ± 19.2 kJ*3 h in postregular visit and 76.7 ± 35.2 kJ*3 h in postirregular visit, paired t test, P = 0.048). Differences in HOMA-IR between the 2 interventions (3.3 ± 1.7 and 3.6 ± 1.6 in postregular and postirregular meal pattern, respectively) were not significant. Net incremental AUC for GLP-1 concentrations (over 3 h) for the postregular meal pattern were higher (864.9 ± 456.1 pmol/L*3 h) than the postirregular meal pattern (487.6 ± 271.7 pmol/L*3 h, paired t test, P = 0.005).

CONCLUSIONS

Following a 14-d period of an irregular meal pattern, TEF was significantly less than following a regular meal pattern, potentially compromising weight management if sustained long term. This study was registered at www.clinicaltrials.gov as NCT02582606.

Cross-Sectional and Individual Relationships between Physical Activity and Glycemic Variability

Introduction/Purpose

Overweight or obese adults spend more time sedentary and less time performing physical activity (PA) and are at an increased risk for developing impaired glycemic health. Free-living environments may provide insight into glycemic health in addition to clinical assessments. The purpose of this study was to examine the relationship between PA and glycemic health assessed by continuous glucose monitoring (CGM).

Methods

Twenty-eight overweight or obese adults each wore an accelerometer and CGM over the same 7 consecutive days. Average daily time (minutes and metabolic-equivalent minutes (MET-minutes)) and associated energy expenditure performing light (LPA), moderate-to-vigorous (MVPA), total PA, and standard deviation (SD) across days were calculated. Average daily 24-h and waking glycemia, mean glucose concentration, glycemic variability measured as the continuous overlapping net glycemic action, mean amplitude of glycemic excursions, and mean of daily difference were assessed.

Results

LPA MET-minutes per day was positively associated with 24-h and waking glycemia time-in-range and negatively associated with 24-h and waking time in hyperglycemia. Total PA time and the SD of MVPA and total PA time were negatively associated with 24-h mean glucose concentration. Individual-level analysis identified that most participants (50%-71%) expressed negative associations between LPA and MVPA time with 24-h mean glucose concentration, mean amplitude of glycemic excursion, and 4-h continuous overlapping net glycemic action.

Conclusions

Expectedly, greater total PA time and intensity-specific PA time were associated with lower 24-h and waking mean glucose concentration, greater glycemia time-in-range, and less time in hyperglycemia. The relationship between glucose concentrations and PA time SD was unexpected, whereas most participants expressed hypothesized relationships, which necessitates further exploration.