Use of continuous glucose monitoring in normoglycemic, insulin-resistant women

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.

Meal Timing and Sleeping Energy Metabolism

There is a physiological link between sleep and eating. Insufficient sleep is a risk factor for overeating and excess body weight gain, and molecules such as orexin and insulin play a role in the control of sleep and energy intake. The effects of dietary timing on sleep and energy metabolism were examined in this review. First, we examined sleep energy metabolism and sleep quality under time-restricted eating, including skipping breakfast or dinner. Second, the mechanisms, benefits, and translational potential of the effects of time-restricted diets on sleep were discussed. Time-restricted eating under controlled conditions, in which daily caloric intake was kept constant, affected the time course of energy metabolism but did not affect total energy expenditure over 24 h. In free-living conditions, time-restricted eating for extended durations (4-16 weeks) decreased energy intake and body weight, and the effects of early time-restricted eating were greater than that of midday time-restricted eating. Although assessment of sleep by polysomnographic recording remains to be performed, no negative effects on the subjective quality of sleep have been observed.

The effect on glycaemic control of low-volume high-intensity interval training versus endurance training in individuals with type 2 diabetes

AIM

To evaluate whether high-intensity interval training (HIIT) with a lower time commitment can be as effective as endurance training (END) on glycaemic control, physical fitness and body composition in individuals with type 2 diabetes.

MATERIALS AND METHODS

A total of 29 individuals with type 2 diabetes were allocated to control (CON; no training), END or HIIT groups. Training groups received 3 training sessions per week consisting of either 40 minutes of cycling at 50% of peak workload (END) or 10 1-minute intervals at 95% of peak workload interspersed with 1 minute of active recovery (HIIT). Glycaemic control (HbA1c, oral glucose tolerance test, 3-hour mixed meal tolerance test with double tracer technique and continuous glucose monitoring [CGM]), lipolysis, VO₂ peak and body composition were evaluated before and after 11 weeks of intervention.

RESULTS

Exercise training increased VO₂ peak more in the HIIT group (20% ± 20%) compared with the END group (8% ± 9%) despite lower total energy expenditure and time usage during the training sessions. HIIT decreased whole body and android fat mass compared with the CON group. In addition, visceral fat mass, HbA1c, fasting glucose, postprandial glucose, glycaemic variability and HOMA-IR decreased after HIIT. The reduced postprandial glucose in the HIIT group was driven primarily by a lower rate of exogenous glucose appearance. In the CON group, postprandial lipolysis was augmented over the 11-week control period.

CONCLUSIONS

Despite a ~45% lower training volume, HIIT resulted in similar or even better improvements in physical fitness, body composition and glycemic control compared to END. HIIT therefore appears to be an important time-efficient treatment for individuals with type 2 diabetes.

Effect of breakfast skipping on diurnal variation of energy metabolism and blood glucose

Epidemiological studies suggest an association between breakfast skipping and body weight gain, insulin resistance or type 2 diabetes. Time when meal is consumed affects postprandial increase in energy expenditure and blood glucose, and breakfast skipping may reduce 24 h energy expenditure and elevate blood glucose level. The present study evaluated the effect of breakfast skipping on diurnal variation of energy metabolism and blood glucose. The skipped breakfast was compensated by following big meals at lunch and supper. In a randomized repeated-measure design with or without breakfast, eight males stayed twice in a room-size respiratory chamber. Blood glucose was recorded with a continuous glucose monitoring system. Breakfast skipping did not affect 24 h energy expenditure, fat oxidation and thermic effect of food, but increased overall 24 h average of blood glucose (83 ± 3 vs 89 ± 2 mg/dl, P < 0.05). Unlike 24 h glucose level, 24 h energy expenditure was robust when challenged by breakfast skipping. These observations suggest that changes in glucose homeostasis precede that of energy balance, in the potential sequence caused by breakfast skipping, if this dietary habit has any effect on energy balance.:

Acute effect of late evening meal on diurnal variation of blood glucose and energy metabolism

SUMMARY

OBJECTIVE

The notion that late evening meal promotes weight gain is popular, and it may also elicit postprandial hyperglycemia, since glucose tolerance decreases during midnight. Diabetic patients with night-eating symptoms, compared with patients without night-eating behaviors, are more likely to be obese and to have elevated A1c. However, epidemiological analysis adjusted for difference in total energy intake did not identify nighttime eating as the risk of obesity. The present study evaluated the effect of a single loading of late evening meal on diurnal variation of blood glucose and 24-h energy expenditure.

METHODS

Ten young adults stayed twice in a room-size respiratory chamber for 24 h, in a randomized repeated-measures design. After the entrance to the chamber at 1700 h, the subjects took normal (1900 h) or late (2230 h) evening meal, breakfast and lunch, and remained in the chamber until 1700 h. Time course of blood glucose was measured by continuous glucose monitoring system.

RESULTS

Late evening meal enhanced postprandial blood glucose response to the evening meal and the subsequent breakfast. Overall 24 h average blood glucose level was also elevated by late evening meal. Late evening meal shifted postprandial increase in energy expenditure toward late at night, but overall 24 h energy expenditure remained almost identical in the two dietary conditions.

CONCLUSIONS

The present study under controlled sedentary condition supports the notion that a single loading of late evening meal enhances average blood glucose over 24 h, but does not support that late evening meal reduces 24 h energy expenditure.

Lowering physical activity impairs glycemic control in healthy volunteers

INTRODUCTION

Postprandial glucose (PPG) is an independent predictor of cardiovascular events and death, regardless of diabetes status. Whereas changes in physical activity produce changes in insulin sensitivity, it is not clear whether changes in daily physical activity directly affect PPG in healthy free-living persons.

METHODS

We used continuous glucose monitors to measure PPG and PPG excursions (ΔPPG, postmeal - premeal blood glucose) at 30-min increments after meals in healthy habitually active volunteers (n = 12, age = 29 ± 1 yr, body mass index = 23.6 ± 0.9 kg·m(-2), VO2max = 53.6 ± 3.0 mL·kg(-1)·min(-1)) during 3 d of habitual (≥10,000 steps per day) and reduced (<5000 steps per day) physical activity. Diets were standardized across monitoring periods, and fasting-state oral glucose tolerance tests (OGTT) were performed on the fourth day of each monitoring period.

RESULTS

During 3 d of reduced physical activity (12,956 ± 769 to 4319 ± 256 steps per day), PPG increased at 30 and 60 min after a meal (6.31 ± 0.19 to 6.68 ± 0.23 mmol·L(-1) and 5.75 ± 0.16 to 6.26 ± 0.28 mmol·L(-1), P < 0.05 relative to corresponding active time point), and ΔPPG increased by 42%, 97%, and 33% at 30, 60, and 90 min after a meal, respectively (P < 0.05). Insulin and C-peptide responses to the OGTT increased after 3 d of reduced activity (P < 0.05), and the glucose response to the OGTT did not change significantly.

CONCLUSIONS

Thus, despite evidence of compensatory increases in plasma insulin during an OGTT, ΔPPG assessed by continuous glucose monitoring systems increased markedly during 3 d of reduced physical activity in otherwise healthy free-living individuals. These data indicate that daily physical activity is an important mediator of glycemic control, even among healthy individuals, and reinforce the utility of physical activity in preventing pathologies associated with elevated PPG.

Glycaemic control is improved by 7 days of aerobic exercise training in patients with type 2 diabetes

AIMS/HYPOTHESIS

Cardiovascular events and death are better predicted by postprandial glucose (PPG) than by fasting blood glucose or HbA(1c). While chronic exercise reduces HbA(1c) in patients with type 2 diabetes, short-term exercise improves measures of insulin sensitivity but does not consistently alter responses to the OGTT. The purpose of this study was to determine whether short-term exercise training improves PPG and glycaemic control in free-living patients with type 2 diabetes, independently of the changes in fitness, adiposity and energy balance often associated with chronic exercise training.

METHODS

Using continuous glucose monitors, PPG was quantified in previously sedentary patients with type 2 diabetes not using exogenous insulin (n = 13, age 53 ± 2 years, HbA(1c) 6.6 ± 0.2% (49.1 ± 1.9 mmol/mol)) during 3 days of habitual activity and during the final 3 days of a 7 day aerobic exercise training programme (7D-EX) which does not elicit measurable changes in cardiorespiratory fitness or body composition. Diet was standardised across monitoring periods, with modifications during 7D-EX to offset increases in energy expenditure. OGTTs were performed on the morning following each monitoring period.

RESULTS

7D-EX attenuated PPG (p < 0.05) as well as the frequency, magnitude and duration of glycaemic excursions (p < 0.05). Conversely, average 24 h blood glucose did not change, nor did glucose, insulin or C-peptide responses to the OGTT.

CONCLUSIONS/INTERPRETATION

7D-EX attenuated glycaemic variability and PPG in free-living patients with type 2 diabetes but did not significantly alter responses to the laboratory-based OGTT. These effects appeared to be independent of changes in fitness, body composition or energy balance. ClinicalTrials.gov numbers: NCT00954109 and NCT00972452.

FUNDING

This project was funded by the University of Missouri Institute for Clinical and Translational Sciences (CRM), NIH grant T32 AR-048523 (CRM), Diabetes Action Research and Education Foundation (JPT). Medtronic supplied CGMS sensors at a discounted rate.