Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups

The effect of different times of day for exercise on blood glucose fluctuations

AIMS

This study aims to explore blood glucose variations before and after short-term intensive exercise in the morning or afternoon of a day and the trend of blood glucose fluctuations during exercise in patients with T2DM (type 2 diabetes, T2DM).

METHODS

Blood glucose variations of Fouty during morning exercise 8:00-12:00 hours and twenty during afternoon exercise 14:30-18:30 hours). Patients with T2DM discharged from the hospital were analyzed retrospectively, with the baseline data checked through the medical record system before intervention. We were asked to perform seven times of treadmill aerobic exercise, which lasted for 30 minutes with incremental intensity for each time, for two weeks under the supervision of the Continuous Glucose Monitor (CGM) and the heart rate armband. The exercise intensity has been adjusted by the clinicians and specialist nurses from the Department of Diabetes Mellitus according to the blood glucose levels and heart rate curves during exercise; data including the height, weight, body mass index (BMI), waist-to-hip ratio, fasting blood glucose, glycosylated hemoglobin, in-exercise CGM-measured blood glucose value/min, and after-exercise fingertip blood glucose value of patients with T2DM were collected after the intensive exercise (2 weeks). SPSS 22.0 and GraphPad Prism 7 were adopted for statistical analysis using the T-test and ANOVA.

RESULT

No difference was observed in the baseline data between the morning and afternoon exercise groups before intervention; compared to the morning exercise group, the fasting C-peptide value (2.15±0.97 vs. 1.53±0.46) in the afternoon exercise group was higher than that in the morning exercise group, with a superior (p=0.029) effect after two weeks of intervention, exhibiting a significant difference in the results. According to the results of repeated variance ANOVA analysis, the time for the appearance of significant improvement in blood glucose in the afternoon exercise group was 5 minutes earlier (11th minute vs 1 minute)than that in the morning exercise group (15th minute vs 1 min); significant differences were observed in both time (p=0.048 vs p<0.01) between the two groups on exercise days, as revealed by the results of bivariate ANOVA; in comparison to the morning exercise group (7.42±1.68), there was a significant difference (p=0.049)in the mean blood glucose between the two groups 25 min after patients with T2DM in the afternoon exercise group (6.25±1.53) started to exercise; in addition, a significant statistical difference (p=0.021) was revealed in the CGM-measured hourly the mean blood glucose on exercise days between the morning(8.18±1.88) and afternoon exercise (6.75±1.40)groups at 4:00 pm in week one and two w.

CONCLUSIONS

Glycaemic improvement in the short-term intensive afternoon exercise group may be superior to that of the morning exercise group, which may be related to greater fasting C-peptide secretion and longer effective exercise duration. The time to exercise is a factor affecting blood glucose variations during exercise. However, significant variations in the level of blood glucose during exercise must be further observed through exercise intervention over a more extended period.

Cross-sectional association of continuous glucose monitoring-derived metrics with cerebral small vessel disease in older adults with type 2 diabetes

AIM

To examine cross-sectional associations between continuous glucose monitoring (CGM)-derived metrics and cerebral small vessel disease (SVD) in older adults with type 2 diabetes.

MATERIALS AND METHODS

In total, 80 patients with type 2 diabetes aged ≥70 years were analysed. Participants underwent CGM for 14 days. From the CGM data, we derived mean sensor glucose, percentage glucose coefficient of variation, mean amplitude of glucose excursion, time in range (TIR, 70-180 mg/dl), time above range (TAR) and time below range metrics, glycaemia risk index and high/low blood glucose index. The presence of cerebral SVD, including lacunes, microbleeds, enlarged perivascular spaces and white matter hyperintensities, was assessed, and the total number of these findings comprised the total cerebral SVD score (0-4). Ordinal logistic regression analyses were performed to examine the association of CGM-derived metrics with the total SVD score.

RESULTS

The median SVD score was 1 (interquartile range 0-2). Higher hyperglycaemic metrics, including mean sensor glucose, TAR >180 mg/dl, TAR >250 mg/dl, and high blood glucose index and glycaemia risk index, were associated with a higher total SVD score. In contrast, a higher TIR (per 10% increase) was associated with a lower total SVD score (odds ratio 0.73, 95% confidence interval 0.56-0.95). Glycated haemoglobin, percentage glucose coefficient of variation, mean amplitude of glucose excursions, time below range and low blood glucose index were not associated with total cerebral SVD scores.

CONCLUSIONS

The hyperglycaemia metrics and TIR, derived from CGM, were associated with cerebral SVD in older adults with type 2 diabetes.

Effect of glycemic variability on infectious outcomes in critically Ill burn patients

After acute burn injury, patients experience a hypermetabolic state often complicated by a stress-induced hyperglycemia. Recent research points towards glycemic variability as a contributing factor in adverse outcomes in critically ill patients. In burn patients, greater glycemic variability has been associated with increased rates of mortality and sepsis. However, no studies to date have examined the impact of glycemic variability on rates of infection in this population or determined which measure may be most useful. Infection, and subsequent sepsis, remains the leading contributor to morbidity and mortality after burn injury. The primary objective of this study is to evaluate the relationship between different measures of glycemic variability and infectious complications in burn patients. This retrospective study included patients admitted to a single American Burn Association-verified burn center between January 1, 2020 and December 31, 2020 with burn or inhalation injury. The primary outcome was a composite of autograft loss, mortality, and proven infection. Secondary outcomes included hospital length of stay and a further analysis of the proven infection component of the composite primary outcome. In addition to mean glucose, several different measures of glycemic variability were used for comparison, including standard deviation, coefficient of variation, mean amplitude of glycemic excursions, and J-index. Outcomes were analyzed using multiple logistic regression analysis while controlling for revised Baux score. A quantile analysis was performed to do determine the optimal mean threshold. Three hundred and ninety-two patients were admitted and screened for inclusion during the study period. Most patients were excluded due to a LOS less than 72 h. 112 patients were included in the study. Of the 112 patients, 22.3% experienced an infectious complication (25 patients with 28 complications). Mean glucose (OR 1.024; 95% CI 1.004-1.045) and J-index (OR 1.044; 95% CI 1.003-1.087) were associated with occurrence of infectious complications. Regarding target mean glucose threshold, a daily mean glucose above 150 mg/dL showed the strongest association with infectious complications (OR 3.634; 95% CI 1.008-13.101). Mean glucose, standard of deviation, and J-index were all independently associated with proven infection.

Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes

BACKGROUND

The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE).

METHODS

Eligible participants are between 21 and 80 years of age diagnosed with moderately controlled T2D (HbA1c: 6.0 to 8.0%) and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: (1) Personalized, (2) Standardized, or (3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrient targets to meet Mediterranean diet guidelines, in addition to 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same educational content as those in the UCC arm, following the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, in addition to real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app.

DISCUSSION

The DiaTeleMed Study aims to address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05046886. Registered on September 16, 2021.

Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring

Aim: The aim of this study was to develop and validate a prediction model based on continuous glucose monitoring (CGM) data to identify a week-to-week risk profile of excessive hypoglycemia. Methods: We analyzed, trained, and internally tested two prediction models using CGM data from 205 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach (XGBoost) combined with feature engineering deployed on the CGM signals was utilized to predict excessive hypoglycemia risk defined by two targets (time below range [TBR] >4% and the upper TBR 90th percentile limit) of TBR the following week. The models were validated in two independent cohorts with a total of 253 additional patients. Results: A total of 61,470 weeks of CGM data were included in the analysis. The XGBoost models had an area under the receiver operating characteristic curve (ROC-AUC) of 0.83-0.87 (95% confidence interval; 0.83-0.88) in the test dataset. The external validation showed ROC-AUCs of 0.81-0.90. The most discriminative features included the low blood glucose index, the glycemic risk assessment diabetes equation (GRADE), hypoglycemia, the TBR, waveform length, the coefficient of variation and mean glucose during the previous week. This highlights that the pattern of hypoglycemia combined with glucose variability during the past week contains information on the risk of future hypoglycemia. Conclusion: Prediction models based on real-world CGM data can be used to predict the risk of hypoglycemia in the forthcoming week. The models showed good performance in both the internal and external validation cohorts.

Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes

Background

The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately-controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE).

Methods

Eligible participants are between 21 to 80 years of age diagnosed with moderately-controlled T2D (HbA1c: 6.0-8.0%), and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: 1) Personalized, 2) Standardized, or 3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrients targets to meet Mediterranean diet guidelines plus 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same education content as UCC on the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, plus real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app.

Discussion

The DiaTeleMed study will address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level.

Trial registration

The DiaTeleMed Study is registered with ClinicalTrials.gov (Identifier: NCT05046886).

Plant-Based Meat Analogs and Their Effects on Cardiometabolic Health: An 8-Week Randomized Controlled Trial Comparing Plant-Based Meat Analogs With Their Corresponding Animal-Based Foods

BACKGROUND

With the growing popularity of plant-based meat analogs (PBMAs), an investigation of their effects on health is warranted in an Asian population.

OBJECTIVES

This research investigated the impact of consuming an omnivorous animal-based meat diet (ABMD) compared with a PBMAs diet (PBMD) on cardiometabolic health among adults with elevated risk of diabetes in Singapore.

METHODS

In an 8-wk parallel design randomized controlled trial, participants (n = 89) were instructed to substitute habitual protein-rich foods with fixed quantities of either PBMAs (n = 44) or their corresponding animal-based meats (n = 45; 2.5 servings/d), maintaining intake of other dietary components. Low-density lipoprotein (LDL) cholesterol served as primary outcome, whereas secondary outcomes included other cardiometabolic disease-related risk factors (e.g. glucose and fructosamine), dietary data, and within a subpopulation, ambulatory blood pressure measurements (n = 40) at baseline and postintervention, as well as a 14-d continuous glucose monitor (glucose homeostasis-related outcomes; n = 37).

RESULTS

Data from 82 participants (ABMD: 42 and PBMD: 40) were examined. Using linear mixed-effects model, there were significant interaction (time × treatment) effects for dietary trans-fat (increased in ABMD), dietary fiber, sodium, and potassium (all increased in PBMD; P-interaction <0.001). There were no significant effects on the lipid-lipoprotein profile, including LDL cholesterol. Diastolic blood pressure (DBP) was lower in the PBMD group (P-interaction=0.041), although the nocturnal DBP dip markedly increased in ABMD (+3.2% mean) and was reduced in PBMD (-2.6%; P-interaction=0.017). Fructosamine (P time=0.035) and homeostatic model assessment for β-cell function were improved at week 8 (P time=0.006) in both groups. Glycemic homeostasis was better regulated in the ABMD than PBMD groups as evidenced by interstitial glucose time in range (ABMD median: 94.1% (Q1:87.2%, Q3:96.7%); PBMD: 86.5% (81.7%, 89.4%); P = 0.041). The intervention had no significant effect on the other outcomes examined.

CONCLUSIONS

An 8-wk PBMA diet did not show widespread cardiometabolic health benefits compared with a corresponding meat based diet. Nutritional quality is a key factor to be considered for next generation PBMAs. This trial was registered at https://clinicaltrials.gov/as NCT05446753.

Diet and Meal Pattern Determinants of Glucose Levels and Variability in Adults with and without Prediabetes or Early-Onset Type 2 Diabetes: A Pilot Study

This observational pilot study examined the association between diet, meal pattern and glucose over a 2-week period under free-living conditions in 26 adults with dysglycemia (D-GLYC) and 14 with normoglycemia (N-GLYC). We hypothesized that a prolonged eating window and late eating occasions (EOs), along with a higher dietary carbohydrate intake, would result in higher glucose levels and glucose variability (GV). General linear models were run with meal timing with time-stamped photographs in real time, and diet composition by dietary recalls, and their variability (SD), as predictors and glucose variables (mean glucose, mean amplitude of glucose excursions [MAGE], largest amplitude of glucose excursions [LAGE] and GV) as dependent variables. After adjusting for calories and nutrients, a later eating midpoint predicted a lower GV (β = -2.3, SE = 1.0, p = 0.03) in D-GLYC, while a later last EO predicted a higher GV (β = 1.5, SE = 0.6, p = 0.04) in N-GLYC. A higher carbohydrate intake predicted a higher MAGE (β = 0.9, SE = 0.4, p = 0.02) and GV (β = 0.4, SE = 0.2, p = 0.04) in N-GLYC, but not D-GLYC. In summary, our data suggest that meal patterns interact with dietary composition and should be evaluated as potential modifiable determinants of glucose in adults with and without dysglycemia. Future research should evaluate causality with controlled diets.

A low-glucose eating pattern is associated with improvements in glycemic variability among women at risk for postmenopausal breast cancer: an exploratory analysis

Background

High glycemic variability (GV) is a biomarker of cancer risk, even in the absence of diabetes. The emerging concept of chrononutrition suggests that modifying meal timing can favorably impact metabolic risk factors linked to diet-related chronic disease, including breast cancer. Here, we examined the potential of eating when glucose levels are near personalized fasting thresholds (low-glucose eating, LGE), a novel form of timed-eating, to reduce GV in women without diabetes, who are at risk for postmenopausal breast cancer.

Methods

In this exploratory analysis of our 16-week weight loss randomized controlled trial, we included 17 non-Hispanic, white, postmenopausal women (average age = 60.7 ± 5.8 years, BMI = 34.5 ± 6.1 kg/m2, HbA1c = 5.7 ± 0.3%). Participants were those who, as part of the parent study, provided 3-7 days of blinded, continuous glucose monitoring data and image-assisted, timestamped food records at weeks 0 and 16. Pearson's correlation and multivariate regression were used to assess associations between LGE and GV, controlling for concurrent weight changes.

Results

Increases in LGE were associated with multiple unfavorable measures of GV including reductions in CGM glucose mean, CONGA, LI, J-Index, HBGI, ADDR, and time spent in a severe GV pattern (r = -0.81 to -0.49; ps < 0.044) and with increases in favorable measures of GV including M-value and LBGI (r = 0.59, 0.62; ps < 0.013). These associations remained significant after adjusting for weight changes.

Conclusion

Low-glucose eating is associated with improvements in glycemic variability, independent of concurrent weight reductions, suggesting it may be beneficial for GV-related disease prevention. Further research in a larger, more diverse sample with poor metabolic health is warranted.Clinical trial registration: ClinicalTrials.gov, NCT03546972.

Metabolic health tracking using Ultrahuman M1 continuous glucose monitoring platform in non- and pre-diabetic Indians: a multi-armed observational study

Continuous glucose monitoring (CGM) device adoption in non- and pre-diabetics for preventive healthcare has uncovered a paucity of benchmarking data on glycemic control and insulin resistance for the high-risk Indian/South Asian demographic. Furthermore, the correlational efficacy between digital applications-derived health scores and glycemic indices lacks clear supportive evidence. In this study, we acquired glycemic variability (GV) using the Ultrahuman (UH) M1 CGM, and activity metrics via the Fitbit wearable for Indians/South Asians with normal glucose control (non-diabetics) and those with pre-diabetes (N = 53 non-diabetics, 52 pre-diabetics) for 14 days. We examined whether CGM metrics could differentiate between the two groups, assessed the relationship of the UH metabolic score (MetSc) with clinical biomarkers of dysglycemia (OGTT, HbA1c) and insulin resistance (HOMA-IR); and tested which GV metrics maximally correlated with inflammation (Hs-CRP), stress (cortisol), sleep, step count and heart rate. We found significant inter-group differences for mean glucose levels, restricted time in range (70-110 mg/dL), and GV-by-SD, all of which improved across days. Inflammation was strongly linked with specific GV metrics in pre-diabetics, while sleep and activity correlated modestly in non-diabetics. Finally, MetSc displayed strong inverse relationships with insulin resistance and dysglycemia markers. These findings present initial guidance GV data of non- and pre-diabetic Indians and indicate that digitally-derived metabolic scores can positively influence glucose management.

Glycemic variability in diagnosis of gestational diabetes as predictor of pharmacological treatment

INTRODUCTION

To establish whether glycemic variability (GV) parameters used when gestational diabetes mellitus (GDM) has been diagnosed could help predict the probability that a patient will need pharmacological treatment, and to analyze the link of these parameters to the development of maternal-fetal complications.

MATERIALS AND METHODS

A prospective study of 87 women with GDM who underwent retrospective continuous glucose monitoring (CGM) for six days between weeks 26 and 32 of gestation, following diagnosis. The mean glycemia levels and GV variables were analyzed together with their link to maternal-fetal complications, and the need for pharmacological treatment. ROC (receiver operating characteristic) curves were developed to determine validity to detect the need for pharmacological treatment.

RESULTS

Patients with higher mean glycemia (p < 0.001) and continuous overlapping of net glycemic action in a period of n-hours (CONGAn) (p = 0.001) required pharmacological treatment. The ROC curves showed cut-off points of 98.81 mg/dL for mean glycemia, and 86.70 mg/dL for CONGAn, with 83.3% sensitivity and 67.8% specificity for both parameters. No relation between the GV parameters and development of maternal-fetal complications was observed.

CONCLUSIONS

The use of CGM, once GDM is diagnosed, enables us to identify those patients who would benefit from closer monitoring during gestation, and facilitate a speedier take-up of pharmacological treatment. However, prospective studies involving a higher number of patients are needed, as well as a cost assessment for recommending the use of CGM following GDM diagnosis.

Difference on Glucose Profile From Continuous Glucose Monitoring in People With Prediabetes vs. Normoglycemic Individuals: A Matched-Pair Analysis

INTRODUCTION

Comprehensive characteristics of the glycemic profile for prediabetes derived by continuous glucose monitoring (CGM) are unknown. We evaluate the difference of CGM profiles between individuals with prediabetes and normoglycemic individuals, including the response to oral glucose tolerance test (OGTT).

METHODS

Individuals with prediabetes matched for age, sex, and BMI with normoglycemic individuals were instructed to use professional CGM for 1 week. OGTT was performed on the second day. The primary outcomes were percentages of glucose readings time below range (TBR): <54 or <70 mg/dL, time in range (TIR): 70 to 180 mg/dL, and time above range (TAR): >180 or >250 mg/dL. Area under the curve (AUC) was calculated following the OGTT. Glucose variability was depicted by coefficient of variation (CV), SD, and mean amplitude of glucose excursion (MAGE). Wilcoxon sign-ranked test, McNemar mid P-test and linear regression models were employed.

RESULTS

In all, 36 participants (median age 51 years; median body mass index [BMI] = 26.4 kg/m2) formed 18 matched pairs. Statistically significant differences were observed for 24-hour time in range (TIR; median 98.5% vs. 99.9%, P = .013), time above range (TAR) >180 mg/dl (0.4% vs. 0%, P = .0062), and 24-hour mean interstitial glucose (113.8 vs. 108.8 mg/dL, P = .0038) between people with prediabetes compared to normoglycemic participants. Statistically significant differences favoring the normoglycemic group were found for glycemic variability indexes (median CV 15.2% vs. 11.9%, P = .0156; median MAGE 44.3 vs. 33.3 mg/dL, P = 0.0043). Following OGTT, the AUC was significantly lower in normoglycemic compared to the prediabetes group (median 18615.3 vs. 16370.0, P = .0347 for total and 4666.5 vs. 2792.7, P = .0429 for incremental 2-hour post OGTT).

CONCLUSION

Individuals with prediabetes have different glucose profiles compared to normoglycemic individuals. CGM might be helpful in individuals with borderline glucose values for a more accurate reclassification.

Neuromuscular electrical stimulation changes glucose, but not its variability in type 2 diabetes: a randomized clinical trial

Neuromuscular electrical stimulation (NMES) can be an alternative to conventional exercising. This randomized clinical trial evaluated the effect of NMES in type 2 diabetes patients. Twenty-eight individuals with type 2 diabetes were assigned to NMES (n=14) or NMES-placebo (n=14) applied to knee extensor muscles for 60 minutes. Glucose variability, microvascular function and endothelial function were evaluated through continuous glucose monitoring system, near infrared spectroscopy and flow-mediated dilatation, respectively. Glucose levels (mg/dl) decreased 2h (184 ± 11 vs 223 ±15), 3h (179 ± 12 vs 219 ±14) and 4h (177 ± 12 vs 212 ±12) after NMES, in comparison to NMES-placebo. No differences in glucose variability were found: coefficient of variation (%) at 0-6h (11.4±1.3 vs 11.4±1.2), 6-12h (9.8±1.0 vs 11.6±1.6), 12-18h (15.5±2.0 vs 11.4±2.1), 18-24h (12.8±2.3 vs 10.0±1.6); standard deviation (mg/dl) at 0-6h (21.6±2 vs 24.6±3.5), 6-12h (19.5±1.8 vs 20.3±2.8), 12-18h (29.9±3.5 vs 21.3±2.8),18-24h (22.8±4.1 vs 16.6±2.0) and mean amplitude of glycemic excursions (mg/dl) 54.9±25.0 vs 70.3±35.7. Endothelial and microvascular functions did not change. In conclusion, one acute NMES session was strong enough to trigger glucose reduction in individuals with type 2 DM, but it failed to induce any significant change in glucose variability, endothelial and microvascular functions.

Continuous glucose monitoring captures glycemic variability in obesity after sleeve gastrectomy: A prospective cohort study

Objective

HbA1c is an insensitive marker for assessing real-time dysglycemia in obesity. This study investigated whether 1-h plasma glucose level (1-h PG) ≥155 mg/dL (8.6 mmol/L) during an oral glucose tolerance test (OGTT) and continuous glucose monitoring (CGM) measurement of glucose variability (GV) better reflected dysglycemia than HbA1c after weight loss from metabolic and bariatric surgery.

Methods

This was a prospective cohort study of 10 participants with type 2 diabetes compared with 11 participants with non-diabetes undergoing sleeve gastrectomy (SG). At each research visit; before SG, and 6 weeks and 6 months post-SG, body weight, fasting lipid levels, and PG and insulin concentrations during an OGTT were analyzed. Mean amplitude of glycemic excursions (MAGE), a CGM-derived GV index, was analyzed.

Results

The 1-h PG correlated with insulin resistance markers, triglyceride/HDL ratio and triglyceride glucose index in both groups before surgery. At 6 months, SG caused 22% weight loss in both groups. Despite a reduction in HbA1c by 3.0 ± 1.3% in the diabetes group (p < 0.01), 1-h PG, and MAGE remained elevated, and the oral disposition index, which represents pancreatic β-cell function, remained reduced in the diabetes group when compared to the non-diabetes group.

Conclusions

Elevation of GV markers and reduced disposition index following SG-induced weight loss in the diabetes group underscores persistent β-cell dysfunction and the potential residual risk of diabetes complications.

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 and hypoglycemia before and after Roux-en-Y Gastric Bypass and Sleeve Gastrectomy - A cohort study of females without diabetes

BACKGROUND

Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) lead to lower fasting glucose concentrations, but might cause higher glycemic variability (GV) and increased risk of hypoglycemia. However, it has been sparsely studied in patients without preoperative diabetes under normal living conditions.

OBJECTIVES

To study 24-hour interstitial glucose (IG) concentrations, GV, the occurrence of hypoglycemia and dietary intake before and after laparoscopic RYGB and SG in females without diabetes.

SETTING

Outpatient bariatric units at a community and a university hospital.

METHODS

Continuous glucose monitoring and open-ended food recording over 4 days in 4 study periods: at baseline, during the preoperative low-energy diet (LED) regimen, and at 6 and 12 months postoperatively.

RESULTS

Of 47 patients included at baseline, 83%, 81%, and 79% completed the remaining 3 study periods. The mean 24-hour IG concentration was similar during the preoperative LED regimen and after surgery and significantly lower compared to baseline in both surgical groups. GV was significantly increased 6 and 12 months after surgery compared to baseline. The self-reported carbohydrate intake was positively associated with GV after surgery. IG concentrations below 3.9 mmol/L were observed in 14/25 (56%) of RYGB- and 9/12 (75%) of SG-treated patients 12 months after surgery. About 70% of patients with low IG concentrations also reported hypoglycemic symptoms.

CONCLUSIONS

The lower IG concentration in combination with the higher GV after surgery, might create a lower margin to hypoglycemia. This could help explain the increased occurrence of hypoglycemic episodes after RYGB and SG.

Postbariatric hypoglycemia, abdominal pain and gastrointestinal symptoms after Roux-en-Y gastric bypass explored by continuous glucose monitoring

BACKGROUND

Abdominal pain and postbariatric hypoglycemia (PBH) are common after bariatric surgery.

OBJECTIVES

This study aimed to explore the potential relationship between abdominal pain, gastrointestinal symptoms, and PBH more than a decade after Roux-en-Y gastric bypass (RYGB) and whether continuous glucose monitoring (CGM) with dietary intervention has an educational role in reducing symptoms.

SUBJECTS

At two public hospitals in Norway (one University Hospital) 22 of 46 invited patients who reported abdominal pain more than weekly took part. Recruited from a prospective follow-up study of 546 patients 14.5 years after RYGB.

METHODS

They used a CGM for two 14-day periods, with a dietary intervention between periods. The Gastrointestinal Symptom Rating Scale (GSRS) and the Dumping Severity Score (DSS) questionnaires were completed at the start and end of the study.

RESULTS

The 22 women had preoperative age 39.6 ± 7.7 years and body mass index (BMI) 42.0 ± 4.0 kg/m2, present age 54.6 ± 7.7 years and BMI 29.8 ± 4.8 kg/m2. The total GSRS score and DSS of early dumping decreased after the diet intervention. The number of events with Level 1 (<3.9 mmol/L) or Level 2 (<3.0 mmol/L) hypoglycemia did not change in the second period. Half of the patients had fewer, three had unchanged, and eight had more frequent events with Level 1 hypoglycemia after the intervention. Ten patients had Level 2 hypoglycemia.

CONCLUSION

Though inconclusive findings, a personalized dietary intervention reduces GSRS. This intervention was accompanied by lower mean absolute glucose in patients with recurrent abdominal pain after bariatric surgery. However, further studies are needed to explore the benefits of CGM in this setting.

Glycaemic variability, assessed with continuous glucose monitors, is associated with diet, lifestyle and health in people without diabetes

Background

Continuous glucose monitors (CGMs) provide high-frequency information regarding daily glucose variation and are recognised as effective for improving glycaemic control in individuals living with diabetes. Despite increased use in individuals with non-diabetic blood glucose concentrations (euglycemia), their utility as a health tool in this population remains unclear.

Objectives

To characterise variation in time in range (TIR) and glycaemic variability in large populations without diabetes or impaired glucose tolerance; describe associations between CGM-derived glycaemic metrics and metabolic and cardiometabolic health traits; identify key diet and lifestyle factors associated with TIR and glycaemic variability.

Design

Glycaemic variability (coefficient of variation) and time spent in both the ADA secondary target range (TIRADA; 3.9-7.8 mmol/L) and a more stringent range (TIR3.9-5.6; 3.9-5.6 mmol/L) were calculated during free-living in PREDICT 1, PREDICT 2, and PREDICT 3 euglycaemic community-based volunteer cohorts. Associations between CGM derived glycaemic metrics, markers of cardiometabolic health, diet (food frequency questionnaire and logged diet records), diet-habits, and lifestyle were explored.

Results

Data from N=4135 participants (Mean SD; Age: 47 12 y; Sex: 83% Female, BMI: 27 6 kg/m2). Median glycaemic variability was 14.8% (IQR 12.6-17.6%), median TIRADA was 95.8% (IQR 89.6-98.6%) and TIR3.9-5.6 was 75.0% (IQR 64.6-82.8%). Greater TIR3.9-5.6 was associated with lower HbA1c, ASCVD 10y risk and HOMA-IR (all p < 0.05). Lower glycaemic variability was associated with lower % energy derived from carbohydrate (rs: 0.17, p < 0.01), ultra-processed foods (NOVA 4, % EI; rs: 0.12, p = 0.01) and a longer overnight fasting duration (rs: -0.10, p = 0.01).

Conclusions

A stringent TIR target provides sensitivity to detect changes in HOMA-IR, ASCVD 10 y risk and HbA1c that were not detected using ADA secondary targets. Associations among TIR, glycaemic variability, dietary intake (e.g. carbohydrate and protein) and habits (e.g. nocturnal fasting duration) highlight potential strategic targets to improve glycaemic metrics derived from continuous glucose monitors.

Serum Cytokines and Growth Factors in Subjects with Type 1 Diabetes: Associations with Time in Ranges and Glucose Variability

The detrimental effect of hyperglycemia and glucose variability (GV) on target organs in diabetes can be implemented through a wide network of regulatory peptides. In this study, we assessed a broad panel of serum cytokines and growth factors in subjects with type 1 diabetes (T1D) and estimated associations between concentrations of these molecules with time in ranges (TIRs) and GV. One hundred and thirty subjects with T1D and twenty-seven individuals with normal glucose tolerance (control) were included. Serum levels of 44 cytokines and growth factors were measured using a multiplex bead array assay. TIRs and GV parameters were derived from continuous glucose monitoring. Subjects with T1D compared to control demonstrated an increase in concentrations of IL-1β, IL-1Ra, IL-2Rα, IL-3, IL-6, IL-7, IL-12 p40, IL-16, IL-17A, LIF, M-CSF, IFN-α2, IFN-γ, MCP-1, MCP-3, and TNF-α. Patients with TIR ≤ 70% had higher levels of IL-1α, IL-1β, IL-6, IL-12 p70, IL-16, LIF, M-CSF, MCP-1, MCP-3, RANTES, TNF-α, TNF-β, and b-NGF, and lower levels of IL-1α, IL-4, IL-10, GM-CSF, and MIF than those with TIR > 70%. Serum IL-1β, IL-10, IL-12 p70, MCP-1, MCP-3, RANTES, SCF, and TNF-α correlated with TIR and time above range. IL-1β, IL-8, IL-10, IL-12 p70, MCP-1, RANTES, MIF, and SDF-1α were related to at least one amplitude-dependent GV metric. In logistic regression models, IL-1β, IL-4, IL-10, IL-12 p70, GM-CSF, HGF, MCP-3, and TNF-α were associated with TIR ≤ 70%, and MIF and PDGF-BB demonstrated associations with coefficient of variation values ≥ 36%. These results provide further insight into the pathophysiological effects of hyperglycemia and GV in people with diabetes.

Effects of reducing free sugars on 24-hour glucose profiles and glycemic variability in subjects without diabetes

Background

The Western diet, especially beverages and high processed food products, is high in sugars which are associated with the development of obesity and diabetes. The reduction of refined carbohydrates including free and added sugars improves glycemic control in individuals with diabetes, but the data regarding effects in subjects without diabetes are limited.

Objective

This study aimed to evaluate the effects of reducing free sugar intake on 24-h glucose profiles and glycemic variability using continuous glucose monitoring (CGM).

Methods

In the randomized controlled study, 21 normal weight and overweight/obese subjects (BMI 18-40 kg/m2) without diabetes were assigned to a 4-week reduced-sugar (RS) diet or control diet after a 2-week baseline phase. During the baseline phase, all participants were advised not to change their habitual diet. During the intervention phase, RS participants were asked to avoid added sugar and white flour products, whereas participants of the control group were requested to proceed their habitual diet. Anthropometric parameters and HbA1c were assessed before and at the end of the intervention phase. Interstitial glucose was measured using continuous glucose monitoring (CGM), and the food intake was documented by dietary records for 14 consecutive days during the baseline phase and for the first 14 consecutive days during the intervention phase. Mean 24-h glucose as well as intra- and inter-day indices of glucose variability, i.e., standard deviation (SD) around the sensor glucose level, coefficient of variation in percent (CV), mean amplitude of glucose excursions (MAGE), continuous overlapping net glycemic action (CONGA), and mean absolute glucose (MAG), were calculated for the baseline and intervention phases.

Results

During the intervention, the RS group decreased the daily intake of sugar (i.e., -22.4 ± 20.2 g, -3.28 ± 3.61 EN %), total carbohydrates (-6.22 ± 6.92 EN %), and total energy intake (-216 ± 108 kcal) and increased the protein intake (+2.51 ± 1.56 EN %) compared to the baseline values, whereby this intervention-induced dietary changes differed from the control group. The RS group slightly reduced body weight (-1.58 ± 1.33 kg), BMI, total fat, and visceral fat content and increased muscle mass compared to the baseline phase, but these intervention-induced changes showed no differences in comparison with the control group. The RS diet affected neither the 24-h mean glucose levels nor intra- and inter-day indices of glucose variability, HbA1c, or diurnal glucose pattern in the within- and between-group comparisons.

Conclusion

The dietary reduction of free sugars decreases body weight and body fat which may be associated with reduced total energy intake but does not affect the daily mean glucose and glycemic variability in individuals without diabetes.

Clinical trial registration

German Clinical Trials Register (DRKS); identifier: DRKS00026699.

Longitudinal MRI Shows Progressive Decline in Pancreas Size and Altered Pancreas Shape in Type 1 Diabetes

CONTEXT

Individuals with type 1 diabetes (T1D) have a smaller pancreas, but longitudinal changes in pancreas size and shape are unclear.

OBJECTIVE

We monitored changes in pancreas size and shape after diagnosis with T1D.

DESIGN

We conducted a prospective cohort study at an academic medical center between 2014 and 2022.

PATIENTS AND HEALTHY CONTROLS

Individuals with T1D (n = 91) or controls (n = 90) underwent magnetic resonance imaging (MRI) of the pancreas, including longitudinal MRI in 53 individuals with new-onset T1D.

INTERVENTION

Interventions included MRI and continuous glucose monitoring (CGM).

MAIN OUTCOME MEASURES

Pancreas size and shape were measured from MRI. For participants who used CGM, measures of glycemic variability were calculated.

RESULTS

On longitudinal imaging, pancreas volume and pancreas volume index normalized for body weight declined during the first year after diagnosis. Pancreas volume index continued to decline through the fifth year after diagnosis. A cross-sectional study of individuals with diabetes duration up to 60 years demonstrated that pancreas size in adults negatively correlated with age and disease duration, whereas pancreas volume and pancreas volume index remained stable in controls. Pancreas volume index correlated inversely with low blood glucose index, a measure of risk for hypoglycemia. Pancreas shape was altered in individuals with T1D and further diverged from controls over the first 5 years after diagnosis. Pancreas size and shape are altered in nondiabetic individuals at genetic risk for T1D. Combined pancreas size and shape analysis better distinguished the pancreas of individuals with T1D from controls than size alone.

CONCLUSIONS

Pancreas size declines most rapidly near the clinical diagnosis of T1D and continues to decline throughout adulthood. Declines in pancreas size are accompanied by changes in pancreas shape.

Efficacy and safety of closed-loop control system for type one diabetes in adolescents a meta analysis

This meta-analysis compares the efficacy and safety of Closed-Loop Control (CLC) to Sensor-Augmented Insulin Pump (SAP) for adolescent patients with Type 1 Diabetes Mellitus (T1DM). Eleven randomized-controlled trials were included with a total of 570 patients, from a total of 869 articles found adhering to PRISMA guidelines. The efficacy of the therapies were evaluated from the day, night and during physical activities monitoring of the of the mean blood glucose (BG), Time In Range (TIR), and Standard Deviation (SD) of the glucose variability. The safety measure of the therapies, was assessed from the day and night recording of the hypoglycemic and hyperglycemic events occurred. Pooled results of comparison of mean BG values for day, night and physical activities, - 4.33 [- 6.70, - 1.96] (P = 0.0003), - 16.61 [- 31.68, - 1.54] (P = 0.03) and - 8.27 [- 19.52, 2.99] (P = 0.15). The monitoring for day, night and physical activities for TIR - 13.18 [- 19.18, - 7.17] (P < 0.0001), - 15.36 [- 26.81, - 3.92] (P = 0.009) and - 7.39 [- 17.65, 2.87] (P = 0.16). The day and night results of SD of glucose variability was - 0.40 [- 0.79, - 0.00] (P = 0.05) and - 0.86 [- 2.67, 0.95] (P = 0.35). These values shows the superiority of CLC system in terms of efficacy. The safety evaluation, of the day, night and physical activities observations of average blood glucose goal hypoglycemic events - 0.54 [- 1.86, 0.79] (P = 0.43), 0.04 [- 0.20, 0.27] (P = 0.77) and 0.00 [- 0.25, 0.25] (P = 1.00) and hyperglycemic events - 0.04 [- 0.20, 0.27] (P = 0.77), - 7.11 [- 12.77, - 1.45] (P = 0.01) and - 0.00 [- 0.10, 0.10] (P = 0.97), highlights the commendable safety factor of CLC. The CLC systems can be considered as an ideal preference in the management of adolescents with type 1 diabetes to be used during a 24 h basis.

Continuous Glucose Monitoring in Patients Following Simultaneous Pancreas-Kidney Transplantation: Time in Range and Glucose Variability

Simultaneous pancreas-kidney transplantation (SPKT) can improve long-term patient survival and restore endogenous insulin secretion in recipients with type 1 diabetes (T1D). There are currently few data on glucose fluctuations assessed by continuous glucose monitoring (CGM) after SPKT. Aim: to evaluate CGM-derived time in range (TIR) and glucose variability (GV) in patients with T1D and functioning pancreatic grafts after SPKT. Fifty-four CGM recordings from 43 patients, 15 men and 28 women, aged 34 (31; 39) years were analyzed. Time since SKPT was up to 1 year (group 1, n = 13), from 1 to 5 years (group 2, n = 15), and from 5 to 12 years (group 3, n = 26). TIR (3.9-10 mmol/L), Time Above Range (TAR), Time Below Range (TBR), and GV parameters were estimated. There were no differences in mean glucose (5.5 [5.1; 6.2], 5.9 [5.4; 6.2], and 5.9 [5.6; 6.7] mmol/L), TIR (97.6 [92.8-99.1], 97.2 [93.2; 99.1], and 97.5 [93.4; 99]%); TAR (0, 1.8 [1.3; 3.7], and 2.5 [2; 5]%), TBR (5 [3.3; 12.7], 4.1 [2.2; 10.1], and 3.5 [1.3; 6.5]%) and GV parameters between three groups (all p > 0.05). Thus, recipients with functioning pancreatic grafts demonstrate remarkably high TIR and low GV after SPKT.

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.

Continuous Glucose Monitoring Reveals Perioperative Hypoglycemia in Most Patients With Diabetes Undergoing Major Surgery: A Prospective Cohort Study

OBJECTIVE

To investigate the frequency and duration of hypo- and hyperglycemia, assessed by continuous glucose monitoring (CGM) during and after major surgery, in departments with implemented diabetes care protocols.

SUMMARY BACKGROUND DATA

Inadequate glycemic control in the perioperative period is associated with serious adverse events, but monitoring currently relies on point blood glucose measurements, which may underreport glucose excursions.

METHODS

Adult patients without (A) or with diabetes [non-insulin-treated type 2 (B), insulin-treated type 2 (C) or type 1 (D)] undergoing major surgery were monitored using CGM (Dexcom G6), with an electrochemical sensor in the interstitial fluid, during surgery and for up to 10 days postoperatively. Patients and health care staff were blinded to CGM values, and glucose management adhered to the standard diabetes care protocol. Thirty-day postoperative serious adverse events were recorded. The primary outcome was duration of hypoglycemia (glucose <70 mg/dL). Clinicaltrials.gov: NCT04473001.

RESULTS

Seventy patients were included, with a median observation time of 4.0 days. CGM was recorded in median 96% of the observation time. The median daily duration of hypoglycemia was 2.5 minutes without significant difference between the 4 groups (A-D). Hypoglycemic events lasting ≥15 minutes occurred in 43% of all patients and 70% of patients with type 1 diabetes. Patients with type 1 diabetes spent a median of 40% of the monitoring time in the normoglycemic range 70 to 180 mg/dL and 27% in the hyperglycemic range >250 mg/dL. Duration of preceding hypo- and hyperglycemia tended to be longer in patients with serious adverse events, compared with patients without events, but these were exploratory analyses.

CONCLUSIONS

Significant duration of both hypo- and hyperglycemia was detected in high proportions of patients, particularly in patients with diabetes, despite protocolized perioperative diabetes management.

The impact of physical inactivity on glucose homeostasis when diet is adjusted to maintain energy balance in healthy, young males

BACKGROUND & AIMS

It is unclear if dietary adjustments to maintain energy balance during reduced physical activity can offset inactivity-induced reductions in insulin sensitivity and glucose disposal to produce normal daily glucose concentrations and meal responses. Therefore, the aim of the present study was to examine the impact of long-term physical inactivity (60 days of bed rest) on daily glycemia when in energy balance.

METHODS

Interstitial glucose concentrations were measured using Continuous Glucose Monitoring Systems (CGMS) for 5 days before and towards the end of bed rest in 20 healthy, young males (Age: 34 ± 8 years; BMI: 23.5 ± 1.8 kg/m2). Energy intake was reduced during bed rest to match energy expenditure, but the types of foods and timing of meals was maintained. Fasting venous glucose and insulin concentrations were determined, as well as the change in whole-body glucose disposal using a hyperinsulinemic-euglycemic clamp (HIEC).

RESULTS

Following long-term bed rest, fasting plasma insulin concentration increased 40% (p = 0.004) and glucose disposal during the HIEC decreased 24% (p < 0.001). Interstitial daily glucose total area under the curve (tAUC) from pre-to post-bed rest increased on average by 6% (p = 0.041), despite a 20 and 25% reduction in total caloric and carbohydrate intake, respectively. The nocturnal period (00:00-06:00) showed the greatest change to glycemia with glucose tAUC for this period increasing by 9% (p = 0.005). CGMS measures of daily glycemic variability (SD, J-Index, M-value and MAG) were not changed during bed rest.

CONCLUSIONS

Reduced physical activity (bed rest) increases glycemia even when daily energy intake is reduced to maintain energy balance. However, the disturbance to daily glucose homeostasis was much more modest than the reduced capacity to dispose of glucose, and glycemic variability was not negatively affected by bed rest, likely due to positive mitigating effects from the contemporaneous reduction in dietary energy and carbohydrate intake.

CLINICAL TRIALS RECORD

NCT03594799 (registered July 20, 2018) (https://clinicaltrials.gov/ct2/show/NCT03594799).

The Use of Continuous Glucose Monitors in Sport: Possible Applications and Considerations

This review discusses the potential value of tracking interstitial glucose with continuous glucose monitors (CGMs) in athletes, highlighting possible applications and important considerations in the collection and interpretation of interstitial glucose data. CGMs are sensors that provide real time, longitudinal tracking of interstitial glucose with a range of commercial monitors currently available. Recent advancements in CGM technology have led to the development of athlete-specific devices targeting glucose monitoring in sport. Although largely untested, the capacity of CGMs to capture the duration, magnitude, and frequency of interstitial glucose fluctuations every 1-15 min may present a unique opportunity to monitor fueling adequacy around competitive events and training sessions, with applications for applied research and sports nutrition practice. Indeed, manufacturers of athlete-specific devices market these products as a "fueling gauge," enabling athletes to "push their limits longer and get bigger gains." However, as glucose homeostasis is a complex phenomenon, extensive research is required to ascertain whether systemic glucose availability (estimated by CGM-derived interstitial glucose) has any meaning in relation to the intended purposes in sport. Whether CGMs will provide reliable and accurate information and enhance sports nutrition knowledge and practice is currently untested. Caveats around the use of CGMs include technical issues (dislodging of sensors during periods of surveillance, loss of data due to synchronization issues), practical issues (potential bans on their use in some sporting scenarios, expense), and challenges to the underpinning principles of data interpretation, which highlight the role of sports nutrition professionals to provide context and interpretation.

Relationship between intra-individual variability in nutrition-related lifestyle behaviors and blood glucose outcomes under free-living conditions in adults without type 2 diabetes

AIMS

This study determined the relationship between intra-individual variability in day-to-day nutrition-related lifestyle behaviors (meal timing, eating window, food intake, movement behaviors, sleep conditions, and body weight) and glycemic outcomes under free-living conditions in adults without type 2 diabetes.

METHODS

We analyzed 104 adults without type 2 diabetes. During the 7-day measurement period, dietary intake, movement behaviors, sleep conditions, and glucose outcomes were assessed. Daily food intake was assessed using a mobile-based health application. Movement behaviors and sleep conditions were assessed using a tri-axial accelerometer. Meal timing was assessed from the participant's daily life record. Blood glucose levels were measured continuously using a glucose monitor. Statistical analyses were conducted using a linear mixed-effects model, with mealtime, food intake, body weight, movement behaviors, and sleep conditions as fixed effects and participants as a random effect.

RESULTS

Dinner time and eating window were positively significantly correlated with mean (dinner time, p = 0.003; eating window, p = 0.001), standard deviation (SD; both at p < 0.001), and maximum (both at p < 0.001) blood glucose levels. Breakfast time was negatively associated with glucose outcomes (p < 0.01). Sedentary time was positively significantly associated with blood glucose SD (p = 0.040). Total sleep time was negatively significantly correlated with SD (p = 0.035) and maximum (p = 0.032) blood glucose levels. Total daily energy intake (p = 0.001), carbohydrate intake (p < 0.001), and body weight (p < 0.05) were positively associated with mean blood glucose levels.

CONCLUSION

Intra-individual variations in nutrition-related lifestyle behaviors, especially morning and evening body weight, and food intake, were associated with mean blood glucose levels, and a long sedentary time and total sleep time were associated with glucose variability. Earlier dinner times and shorter eating windows per day resulted in better glucose control.

The efficacy of intraoperatie continuous glucose monitoring in patients undergoing liver transplantation: a study protocol for a prospective randomized controlled superiority trial

BACKGROUND

The high incidence of intraoperative glucose dysregulations in liver transplantation (LT) is related to the lack of highly orchestrated control of intraoperative blood glucose. Glucose monitoring based on a single arterial blood gas test can only provide a simple glucose profile and is insufficient in monitoring intraoperative glycemic variability (GV), which is not conducive to controlling GV and may have a lag in the management of hyper/hypoglycemia. Continuous glucose monitor (CGM), which has been successfully applied in the management of chronic disease in diabetes, provides more detailed blood glucose records and reflect GV parameters such as coefficient of variation (CV%). However, its effectiveness and accuracy for guiding blood glucose management in major surgeries remains unclear.

METHODS

This is a single-center, randomized, controlled, superiority trial. One hundred and eighty patients scheduled for orthotopic LT will be recruited and randomized into two groups. All patients are monitored for intraoperative glucose using CGM combined with arterial blood gas (ABG). In the intervention group (group CG), ABG will be performed when CGM value is < 6.1 mmol/L or > 10.0 mmol/L, or the rate of change of CGM value > 1.67 mmol/(L·min). In the control group (group G), intraoperative ABG tests will be performed every 2 h, and the frequency of ABG tests will be adjusted based on the previous arterial glucose result. Patients in both groups will have their blood glucose adjusted according to arterial glucose values and a uniform protocol. Surgical and other anesthetic management is completed according to standard LT practices.

DISCUSSION

This study intends to investigate the effectiveness of CGM-based intraoperative glucose management and its impact on the prognosis of LT patients by comparing the GV, mean glucose values, and the incidence of hypo/hypoglycemic events guided by the above two glucose monitoring methods.

TRIAL REGISTRATION

This study is registered at www.chictr.org.cn on January 4, 2022, under the registration number ChiCTR2200055236.

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.

Regimen comprising GLP-1 receptor agonist and basal insulin can decrease the effect of food on glycemic variability compared to a pre-mixed insulin regimen

BACKGROUND

Increasing evidence suggests that glucagon-like peptide 1 (GLP-1) receptor agonists (RA) can stabilize glycemic variability (GV) and interfere with eating behavior. This study compared the impact of insulin, GLP-1 RA, and dietary components on GV using professional continuous glucose monitoring (CGM).

METHODS

Patients with type 2 diabetes underwent CGM before and after switching from a twice-daily pre-mixed insulin treatment regimen to a GLP-1 RA (liraglutide) plus basal insulin regimen. The dietary components were recorded and analyzed by a certified dietitian. The interactions between the medical regimen, GV indices, and nutrient components were analyzed.

RESULTS

Sixteen patients with type 2 diabetes were enrolled in this study. No significant differences in the diet components and total calorie intake between the two regimens were found. Under the pre-mixed insulin regimen, for increase in carbohydrate intake ratio, mean amplitude of glucose excursion (MAGE) and standard deviation (SD) increased; in contrast, under the new regimen, for increase in fat intake ratio, MAGE and SD decreased, while when the protein intake ratio increased, the coefficient of variation (CV) decreased. The impact of the food intake ratio on GV indices disappeared under the GLP-1 RA regimen. After switching to the GLP-1 RA regimen, the median MAGE, SD, and CV values decreased significantly. However, the significant difference in GV between the two regimens decreased during the daytime.

CONCLUSION

A GLP-1 RA plus basal insulin regimen can stabilize GV better than a regimen of twice-daily pre-mixed insulin, especially in the daytime, and can diminish the effect of food components on GV.

Glucose variability: A physiological correlate of eating disorder behaviors among individuals with binge-spectrum eating disorders

OBJECTIVES

Elevated glucose variability may be one mechanism that increases risk for significant psychological and physiological health conditions among individuals with binge-spectrum eating disorders (B-EDs), given the impact of eating disorder (ED) behaviors on blood glucose levels. This study aimed to characterize glucose variability among individuals with B-EDs compared with age-matched, sex-matched, and body mass index-matched controls, and investigate the association between frequency of ED behaviors and glucose variability.

METHODS

Participants were 52 individuals with B-EDs and 22 controls who wore continuous glucose monitors to measure blood glucose levels and completed ecological momentary assessment surveys to measure ED behaviors for 1 week. Independent samples t-tests compared individuals with B-EDs and controls and multiple linear regression models examined the association between ED behaviors and glucose variability.

RESULTS

Individuals with B-EDs demonstrated numerically higher glucose variability than controls (t = 1.42, p = .08, d = 0.43), although this difference was not statistically significant. When controlling for covariates, frequency of ED behaviors was significantly, positively associated with glucose variability (t = 3.17, p = .003) with medium effect size (f2  = 0.25). Post hoc analyses indicated that binge eating frequency was significantly associated with glucose variability, while episodes of 5+ hours without eating were not.

DISCUSSION

Glucose variability among individuals with B-EDs appears to be positively associated with engagement in ED behaviors, particularly binge eating. Glucose variability may be an important mechanism by which adverse health outcomes occur at elevated rates in B-EDs and warrants future study.

PUBLIC SIGNIFICANCE

This study suggests that some individuals with binge ED and bulimia nervosa may experience elevated glucose variability, a physiological symptom that is linked to a number of adverse health consequences. The degree of elevation in glucose variability is positive associated with frequency of eating disorder behaviors, especially binge eating.

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

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

Continuous glucose monitoring for detection of glycemic variability, hypoglycemia, and hyperglycemia in women with eating disorders

Background

The aim of this study was to investigate the relationships between hypoglycemia, hyperglycemia, glycemic variability (GV), and eating behavior by measuring daily glucose levels through an intermittently scanned continuous glucose monitoring (isCGM) system in outpatients classified according to eating disorder subtypes.

Methods

We analyzed data for 18 patients (four ANR, nine ANBP, and five BN cases). A FreeStyle Libre Pro® device was attached to the posterior aspect of the upper arm for glucose monitoring. This device conducted measurements every 15 min for five consecutive days. We estimated the mean amplitude of glycemic excursions (MAGE), hypoglycemia, and hyperglycemia.

Results

The mean glucose levels were 91.1 ± 2.2 mg/dL in the ANR group, 94.8 ± 7.5 mg/dL in the ANBP group, and 87.1 ± 8.0 mg/dL in the BN group (P = 0.174). The overall mean MAGE index was 52.8 ± 20.5 mg/dL. The mean MAGE values according to the subtypes were 42.2 ± 5.6 mg/dL in the ANR group, 57.4 ± 23.7 mg/dL in the ANBP group, and 53.0 ± 21.8 mg/dL in the BN group (P = 0.496). Over the course of five days, the frequency of hypoglycemia was as follows: three occurrences in the ANBP group, five occurrences in the BN group, and no occurrences in the ANR group (P = 0.016). Moreover, the occurrence of hypoglycemia was statistically significantly higher in the BN group than in the ANR group (P = 0.013). In the BN group, the frequency of hypoglycemia was highest between 2 and 6 AM, while hypoglycemia was observed throughout the day in the ANBP group. The frequency of hyperglycemia was one occurrence in the ANR group, one occurrence in the BN group, and zero occurrences in the ANBP group (P = 0.641).

Conclusions

Varying GV, hypoglycemia, and hyperglycemia were observed in all subtypes of eating disorders. Our findings suggest that eating behaviors such as binge eating and purging are associated with GV and hypoglycemia. We showed the importance of developing different nutritional approaches tailored to the subtype of eating disorder to prevent hypoglycemia. Additional studies are needed to explore the relationship between glucose levels and eating behaviors in patients with eating disorders.

Neuromuscular Electrical Stimulation during Hemodialysis Suppresses Postprandial Hyperglycemia in Patients with End-Stage Diabetic Kidney Disease: A Crossover Controlled Trial

Hemodialysis patients with diabetic kidney disease (DKD) experience blood glucose fluctuations owing to insulin removal. We evaluated the effects of single and long-term application of neuromuscular electrical stimulation (NMES) during hemodialysis on glycemic control. This trial was conducted in two stages: Stage 1, following a crossover design and 4 week washout period, eleven outpatients with DKD either underwent a single bout of NMES for 30 min (NMES period) or rested (control period) after receiving nutritional support during hemodialysis; Stage 2, following a crossover design and 4 week washout period, each participant received the intervention for 12 weeks. NMES was administered for 30 min at the maximum tolerable intensity. The mean subcutaneous glucose concentration and mean amplitude of glycemic excursion (MAGE) were determined by flash glucose monitoring for 24 h. Changes in glycoalbumin and MAGE before and after NMES initiation were evaluated. The mean blood glucose level and MAGE after a single bout of NMES were significantly lower than those after rest. Glycoalbumin levels and echo intensity of the rectus femoris tended to decrease, but not significantly by ANOVA due to a lack in statistical power after the dropout of three patients. NMES in end-stage DKD decreased blood glucose levels during and after hemodialysis.

Ambulatory Glucose Profile According to Different Phases of the Menstrual Cycle in Women Living With Type 1 Diabetes

CONTEXT

Some women living with type 1 diabetes complain of changes in glucose values according to the different phases of menstruation.

OBJECTIVE

To evaluate this variability through continuous glucose monitoring (CGM) data in type 1 diabetes patients.

DESIGN

Observational study.

SETTING

Ambulatory data, recruitment in 2 centers in the Paris region.

PATIENTS

Twenty-four women with type 1 diabetes having spontaneous menstrual cycles.

INTERVENTION

Collection of CGM data for 62 spontaneous menstrual cycles, with evaluation of five 3-day phases during each cycle: (1) early follicular (menstruations), (2) mid-follicular, (3) peri-ovulatory, (4) mid-luteal, and (5) late luteal.

MAIN OUTCOME MEASURE

Time in range (TIR, prespecified).

RESULTS

TIR decreased for each consecutive phase (61 ± 18%; 59 ± 18%; 59 ± 20%; 57 ± 18%; and 55 ± 20%, P = 0.02). The linear mixed model highlighted a decrease in TIR in the mid-luteal (P = 0.03) and late luteal (P < 0.001) phases compared with the early follicular phase. Time above range was significantly higher during the late luteal phase than the early follicular phase (P = 0.003). Time below range was significantly higher during the mid-follicular phase than in the early follicular phase.

CONCLUSION

In most of the study population, glucose levels rose linearly throughout the menstrual cycle, reaching a maximum in the late luteal phase. A sharp decrease was seen for most participants at the beginning of menstrual bleeding. This should be taken into consideration in daily care of type 1 diabetes patients to avoid hypoglycemia.

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.

Effect of taurine on glycaemic, lipid and inflammatory profile in individuals with type 2 diabetes: study protocol of a randomised trial

Type 2 diabetes mellitus (T2DM) is characterised by chronic hyperglycaemia. Despite the efficacy of conventional pharmacotherapy, some individuals do not reach glycaemic goals and require adjuvant therapies. Taurine, a semi-essential amino acid, decreases blood glucose and cholesterol levels in rodents and humans. However, glycated hemoglobin (HbA1c) has not been evaluated in randomised controlled trials after taurine treatment for more than 12 weeks. This study aims to evaluate the effect of taurine administration on glycaemic, lipid, inflammatory, anthropometric and dietary parameters in individuals with T2DM. A randomised, double-blind, placebo-controlled clinical trial will be conducted at the Clinical Research Center of a tertiary public hospital. Participants with T2DM (n 94) will be recruited and randomised to receive 3 g of taurine or placebo, twice/day, orally, for 12 weeks. Blood samples will be collected before and after 12 weeks of treatment, when HbA1c, fasting glucose, insulin, albuminuria, creatinine, total cholesterol and fractions, triglycerides, C-reactive protein, TNF-α, IL 1, 4, 5, 6, 10 and 13 will be evaluated. Anthropometric parameters and 24-hour food recall will also be evaluated. The study will evaluate the effect of taurine treatment on biochemical and anthropometric parameters in individuals with T2DM. These results will guide the decision-making to indicate taurine treatment as an adjunct in individuals with T2DM who have not reached their glycaemic goal.

The Effects of Imeglimin on the Daily Glycemic Profile Evaluated by Intermittently Scanned Continuous Glucose Monitoring: Retrospective, Single-Center, Observational Study

INTRODUCTION

Imeglimin is a novel antidiabetic drug that amplifies glucose-stimulated insulin secretion (GSIS) and improves insulin sensitivity. Several randomized clinical studies have shown the efficacy of imeglimin for glycemic control in patients with type 2 diabetes (T2D). We aimed to evaluate the short-term effects and safety of imeglimin in terms of glycemic control, as assessed by intermittently scanned continuous glucose monitoring (isCGM).

METHODS

This retrospective and observational study of 32 patients who were administered imeglimin in addition to existing treatment regimens was designed to evaluate glycemic profiles. The patients were monitored for more than 4 weeks, including the day of starting imeglimin. The changes in glycemic indices, including mean glucose level, coefficient of variation (CV), time in range (TIR) and time above range (TAR), before and after imeglimin administration were analyzed, and data on adverse effects were collected by interview.

RESULTS

Imeglimin administration significantly improved the mean values of glucose (from 159.0 ± 27.5 mg/dL to 141.7 ± 22.1 mg/dL; p < 0.001), TIR (from 67.9 ± 17.0% to 79.5 ± 13.3%; p < 0.001) and TAR (from 29.4 ± 17.5% to 17.9 ± 13.7%; p < 0.001) and tended to improve CV (from 29.0 ± 6.1 to 27.4 ± 5.58; p = 0.058). The curves of 24-h mean glucose level for all 32 subjects were shifted downward from the baseline after imeglimin administration. The high mean glucose level, high TAR, low TIR, low body mass index and low C-peptide were related to the efficacy of imeglimin for glycemic control. The main adverse effects were gastrointestinal disorders, and the incidence of hypoglycemia was increased in cases receiving a combination of imeglimin plus insulin or a glinide agent.

CONCLUSION

Imeglimin clearly shifted the daily glucose profile into an appropriate range in Japanese T2D patients, indicating improvement of short-term glycemic control. Imeglimin is thought to be a promising therapeutic agent for T2D patients, especially those with a low insulin secretory capacity, which is a common phenotype in East-Asian subjects with glucose intolerance.

Insulin resistance is associated with an unfavorable outcome among non-diabetic patients with isolated moderate-to-severe traumatic brain injury – A propensity score-matched study

Background

Hyperglycemia is an independent risk factor for the poor prognosis in patients with traumatic brain injury (TBI), and stress-induced impaired insulin function is the major factor of hyperglycemia in non-diabetic patients with TBI. Several types of research suggested that insulin resistance (IR) is related to the poor prognosis of neurocritical ill patients; here we focused on the role of IR in non-diabetic patients after TBI.

Methods

We performed a prospective observational study with the approval of the Ethics Committee of our institute. IR was accessed via the update Homeostasis Model Assessment (HOMA2) of IR, a computer-calculated index by glucose and insulin level. HOMA2 ≥ 1.4 was considered as the threshold of IR according to the previous studies. The glycemic variability (GV) indices were calculated by fingertip blood glucose concentration at an interval of 2 h within 24 h to explore the relationship between IR and GV. The outcome was the 6-month neurological outcome evaluated with the Glasgow outcome scale.

Results

A total of 85 patients with isolated moderate-to-severe TBI (admission GCS ≤ 12) were finally included in our study, 34 (40%) were diagnosed with IR with HOMA2 ≥ 1.4. After propensity score matching (PSM), 22 patients in IR group were matched to 34 patients in non-IR group. Patients with IR suffered increased systemic glycemic variation after isolated moderate-to-severe TBI. IR was a significant factor for the poor prognosis after TBI (OR = 3.25, 95% CI 1.03–10.31, p = 0.041).

Conclusions

The IR estimated by HOMA2 was associated with greater GV and an unfavorable outcome after isolated moderate-to-severe TBI. Ameliorating impaired insulin sensitivity may be a potential therapeutic strategy for the management of TBI patients.

Safety of Intranasal Insulin in Type 2 Diabetes on Systemic Insulin: A Double-Blinded Placebo-Controlled Sub-Study of Memaid Trial

AIMS

To determine safety of intranasal insulin (INI) in MemAID trial participants with diabetes treated with systemic insulins.

MATERIALS AND METHODS

This randomized, double-blinded trial consisted of 24-week INI or placebo treatment once daily and 24-week follow-up. Safety outcomes were: 1) Short-term effects on glycemic variability, hypoglycemic episodes on continuous glucose monitoring (CGM) at baseline and on-treatment. 2) Long-term effects on glucose metabolism and weight on INI/placebo treatment and post-treatment follow-up. Of 86 screened subjects, 14 were randomized, 9 (5 INI, 4 Placebo) completed CGM at baseline and on-treatment, and 5 (2 INI, 3 Placebo) completed treatment and follow-up.

RESULTS

INI was safe and was not associated with serious adverse events, hypoglycemic episodes or weight gain. INI administration did not acutely affect capillary glucose. Glycemic variability on CGM decreased with INI, compared to baseline. On INI treatment, there was a long-term trend toward lower HbA1c, plasma glucose and insulin. No interactions with subcutaneous insulins were observed.

CONCLUSIONS

INI is safe in older people with diabetes treated with systemic insulins, and it is not associated with adverse events, hypoglycemia or weight gain. Future studies are needed to determine whether INI administration can reduce glycemic variability, improve insulin sensitivity and thus potentially lessen diabetes burden in this population.

Aging of glucose profiles in an adult population without diabetes

AIMS

This study aimed to determine the effect of aging on glucose profiles in a population without diabetes.

METHODS

We investigated the evolution of glucose profiles in an adult population without diabetes using continuous glucose monitoring (CGM) in two periods separated by 5 years. Anthropometrics, laboratory tests (HbA1c, fasting blood glucose) and CGM data (mean glycemia level, coefficient of variation, time in range) were measured in both periods to study the change in values over time.

RESULTS

125 participants (68% women) mean age 43.1 ± 12.4 years and classified as normoglycemic at baseline were included. Of the total population 15.2% had worsened glycemic status after 5 years, age and baseline glucose values (HbA1c and percentage of values above 175 mg/dL) were the variables related with this change. Related to CGM, we found that after 5 years there was a decrease in the percentage of values between 70 and 99 mg/dl (45.0% to 38.7%, p = 0.002) and an increase in the 100-139 mg/dL range (52.9% to 57.5% p = 0.016).

CONCLUSIONS

Our results indicate that in an adult population without diabetes there are changes in glucose profiles with aging highlighting the reduction of blood glucose values below 100 mg/dL.

Maintenance of plasma glucose variability after an acute session of aerobic exercise despite changes in insulin and glucagon-like peptide-1 levels in type 2 diabetes

Objective

The present study aimed to evaluate glucose variability and hormonal responses during and after an aerobic exercise session performed after breakfast in type 2 diabetes patients treated with metformin.

Methods

In this quasi-experimental study individuals underwent clinical and laboratory evaluations and maximal exercise test. After two weeks an aerobic exercise session (30 minutes at 60%-70% of the peak heart rate) was performed. At rest, during and after the exercise session, glucose variability (mean amplitude glucose excursions, glucose coefficient of variation, and glucose standard deviation) and levels of plasma glucose, insulin, glucagon, and glucagon-like-peptide-1 were evaluated.

Results

Thirteen patients were enrolled in the study. Plasma glucose increased at 15 minutes during the exercise session (244.6 ± 61.9 mg/dL), and decreased at 60 min after exercise (195.6 ± 50.0 mg/dL). Glucose variability did not show any difference before and after exercise. Insulin levels at 15 min [27.1 μU/mL (14.2-42.1)] and 30 min [26.3 μU/mL (14.6-37.4)] during the exercise were higher than those at fasting [11.2 μU/mL (6.7-14.9)] but decreased 60 minutes after exercise (90 minutes) [16.6 μU/mL (8.7-31.7)]. Glucagon levels did not show any difference. GLP-1 levels increased at 30 min [7.9 pmol/L (7.1-9.2)] during exercise and decreased 60 min after exercise (90 minutes) [7.7 pmol/L (6.8-8.5)].

Conclusion

Subjects with type 2 diabetes presented expected changes in insulin, glucagon and GLP-1 levels after breakfast and a single aerobic exercise session, not accompanied by glycemic variability changes.

Reversibility of brain glucose kinetics in type 2 diabetes mellitus

AIMS/HYPOTHESIS

We have previously shown that individuals with uncontrolled type 2 diabetes have a blunted rise in brain glucose levels measured by ¹H magnetic resonance spectroscopy. Here, we investigate whether reductions in HbA_1c normalise intracerebral glucose levels.

METHODS

Eight individuals (two men, six women) with poorly controlled type 2 diabetes and mean ± SD age 44.8 ± 8.3 years, BMI 31.4 ± 6.1 kg/m² and HbA_1c 84.1 ± 16.2 mmol/mol (9.8 ± 1.4%) underwent ¹H MRS scanning at 4 Tesla during a hyperglycaemic clamp (~12.21 mmol/l) to measure changes in cerebral glucose at baseline and after a 12 week intervention that improved glycaemic control through the use of continuous glucose monitoring, diabetes regimen intensification and frequent visits to an endocrinologist and nutritionist.

RESULTS

Following the intervention, mean ± SD HbA_1c decreased by 24.3 ± 15.3 mmol/mol (2.1 ± 1.5%) (p=0.006), with minimal weight changes (p=0.242). Using a linear mixed-effects regression model to compare glucose time courses during the clamp pre and post intervention, the pre-intervention brain glucose level during the hyperglycaemic clamp was significantly lower than the post-intervention brain glucose (p<0.001) despite plasma glucose levels during the hyperglycaemic clamp being similar (p=0.266). Furthermore, the increases in brain glucose were correlated with the magnitude of improvement in HbA_1c (r = 0.71, p=0.048).

CONCLUSION/INTERPRETATION

These findings highlight the potential reversibility of cerebral glucose transport capacity and metabolism that can occur in individuals with type 2 diabetes following improvement of glycaemic control. Trial registration ClinicalTrials.gov NCT03469492.

Associations between continuous glucose monitoring-derived metrics and HbA1c in patients with type 2 diabetes mellitus

AIMS

The aim of this study was to define the relationship between time in range (TIR) and hemoglobin A1c (HbA1c) levels in patients with type 2 diabetes mellitus (T2DM).

METHODS

The glycemic profile of 999 Japanese patients was analyzed with FreeStyle Libre Pro Continuous Glucose Monitoring (FLP-CGM) while they continued their prescribed glucose-lowering medications. FLP-CGM data recorded over 8 consecutive days were analyzed.

RESULTS

The regression model for HbA1c on TIR was HbA1c = 9.4966-0.0309 × TIR. The predicted HbA1c level for TIR of 70% was 7.33% and is higher than reports subjecting mostly T1DM. The TIR corresponding to HbA1c 7.0% was 80.64%. The patients with low TIR tended to have long duration of diabetes, used high dose of daily insulin, high body mass index, high HbA1c, liver dysfunction and high triglyceride. Relatively higher percentages of patients of this group used sulfonylureas, glucagon like peptide-1 receptor agonists and insulin.

CONCLUSIONS

Our data showed predicted HbA1c corresponding to TIR is largely depends on study population, thus is not uniform. Our results provide new insights on the management of T2DM. However, caution should be exercised in extending the HbA1C-TIR relationship using FLP-CGM to any other sensors since there could be a risk of hypoglycemia in doing so.

Glucose variability and predicted cardiovascular risk after gastrectomy

PURPOSE

To investigate the correlation between glycemic trends and cardiovascular risk after gastrectomy for gastric cancer.

METHODS

We enrolled 105 gastric cancer patients who underwent gastrectomy at our hospital between October 2017 and July 2020. Postoperative glucose concentrations, trends, and patterns were recorded using a continuous glucose monitoring (CGM) device. Cardiovascular risk was calculated using the Framingham stroke risk profile score (FSRPS), the Framingham risk score (FRS), and the Suita score. We examined the correlations between glycemic variability and cardiovascular risk scores.

RESULTS

There were significant differences in the standard deviation (SD) of glucose levels between the high and low FSRPS groups (p = 0.049), the high and low FRS groups (p = 0.011), and the high and low Suita score groups (p = 0.044). The SD of glucose levels was significantly higher in patients with diabetes mellitus (DM) (p < 0.001) and those who underwent total gastrectomy (TG) (p = 0.017). Additionally, the CGM data available for 38 patients 1 year post-gastrectomy were analyzed for glucose level dynamics, and the SD was found to be significantly higher than that at 1 month (p < 0.001).

CONCLUSION

Our findings suggest that long-term follow-up and therapeutic strategies tailored to glycemic trends may be necessary for gastric cancer patients after gastrectomy, especially those with DM and those who have undergone TG, to prevent cardiovascular events.