In adult patients with type 1 diabetes healthy lifestyle associates with a better cardiometabolic profile.

BACKGROUND/AIMS: Little is known about lifestyle habits of adults with type 1 diabetes (T1D) and their association with cardiometabolic risk (CMR) factors. The aims of the present study were to determine the prevalence of adults with T1D who adopted a healthy lifestyle and to explore the association between a healthy lifestyle and the cardiometabolic profile. METHODS AND RESULTS: This is a cross-sectional analysis of 115 adults with T1D. Participants wore a motion sensor and completed a 3-day food record. The following CMR factors were assessed: body mass index, waist circumference, body composition (iDXA), glycated hemoglobin, lipids and blood pressure. Insulin resistance was estimated (estimated glucose disposal rate). Participants were classified according to the number of healthy lifestyle habits adopted (ranging from 0 to 3): regular physical activity (physical activity level ≥1.7), good diet quality (Canadian Healthy Eating Index score >80) and none-smoking status. The proportion of participants who adopted 3, 2, 1 or 0 lifestyle habits were 11%, 30%, 37%, and 23%, respectively. As the number of healthy lifestyle habits adopted increased, participants had significantly lower body mass index, waist circumference, body fat, total cholesterol, non-HDL-cholesterol, triglycerides and systolic blood pressure (p < 0.05). In addition, a trend for lower estimated insulin resistance was observed (p = 0.06). For each increase of one healthy lifestyle habit, body mass index decreased by 1.9 kg/m(2), waist circumference by 4.0 cm for men and 4.8 cm for women and trunk fat by 3.6% for men and 4.1% for women. CONCLUSIONS: These results suggest the importance of a healthy lifestyle among adults with T1D in order to control CMR factors.

Are bedtime nutritional strategies effective in preventing nocturnal hypoglycaemia in patients with type 1 diabetes?

Hypoglycaemia remains the major limiting factor for adequate diabetes control for patients with type 1 diabetes (T1D), especially during the night-time. Although nutritional strategies for nocturnal hypoglycaemia (NH) prevention are regularly suggested in clinical practice, there is no evidence-based recommendation for the usefulness and optimal composition of a bedtime snack. The aim of this narrative review was to analyse the current state of knowledge on nutritional strategies to prevent NH in individuals with T1D. A literature search was conducted, using PubMed and Medline (1946 to 2013); 16 studies were retrieved. Overall, the level of evidence was low. Results indicated that a calibrated bedtime snack based on bedtime blood glucose (BG) level could be effective to reduce NH occurrence for patients treated with human or animal insulin (short-acting combined with lente, ultralente and/or intermediate-acting insulin), but there is no evidence for patients treated with insulin analogues as part of multiple daily injections or insulin pump regimen. Some evidence suggests that including uncooked cornstarch or alanine in the bedtime snack composition could provide some benefits for the prevention of NH. Individualized recommendations of a bedtime snack intake for patients or situations at high risk for NH (long standing diabetes, hypoglycaemia unawareness, prior physical activity, alcohol consumption, bedtime BG close to hypoglycaemia threshold) appear as a prudent recommendation. On the basis of the available evidence, a bedtime snack cannot be recommended systematically but it might be useful if prescribed in an individualized fashion; further research is needed to evaluate these strategies.

Accuracy of energy expenditure estimation by activity monitors differs with ethnicity.

The aim of this project is to explore the accuracy of 2 activity monitors (SenseWear Armband & Actical) to estimate energy expenditure during rest and light to moderate intensity exercises in 2 ethnic groups. 18 Caucasian and 20 Black adults (age: 26.8±5.2 years; body mass index: 23.9±3.0 kg/m(2)) wore the 2 devices simultaneously during 3 standardised activities: 30-min rest, 45-min of treadmill at 40% of their V˙O2peak and 45-min of stationary cycling at 50% of their V˙O2peak. Energy estimated with the 2 devices was compared to indirect calorimetry measurements. Both devices overestimated energy expenditure during rest (SenseWear: 36% in Black vs. 16% in Caucasian; Actical: 26% vs. 11%, p<0.01 between groups) and treadmill (SenseWear: 50% vs. 25%; Actical: 67% vs. 32%, p<0.01 between groups). Both devices significantly underestimated energy expenditure during stationary cycling (SenseWear: 24% vs. 26%; Actical: 58% vs. 70%, p=NS between groups). Equations used to estimate energy expenditure from accelerometer data is less precise among Black adults than Caucasian adults. Ethnic-specific formulas are probably required.

Physical activity level and body composition among adults with type 1 diabetes.

AIMS: Physical activity is part of a healthy lifestyle and contributes to prevent weight gain and cardiometabolic disorders. Adults with Type 1 diabetes are at risk of weight gain attributable to various factors, including a high prevalence of sedentary lifestyle related to fear of exercise-induced hypoglycaemia. This project aims to observe the association between physical activity level and body composition in adults with Type 1 diabetes. METHODS: Cross-sectional study; 75 adults with and 75 adults without diabetes (52% men; 41.8 ± 11.8 years old) wore a motion sensor for 1 week and performed a cardiorespiratory fitness test on an ergocycle (VO(2peak)). Body composition was assessed by dual energy X-ray absorptiometry and circumferences measures. RESULTS: Mean body composition was not different between the two groups. VO(2peak) was lower among the group with diabetes than the control subjects (29.3 ± 9.2 vs. 33.5 ± 9.0 ml kg(-1) min(-1); P = 0.005), but their physical activity level (ratio total/resting energy expenditure) was similar (1.68 ± 0.37 vs. 1.65 ± 0.26; P = 0.572). In both groups, having an active lifestyle (physical activity level ≥ 1.7) compared with a more sedentary lifestyle (physical activity level < 1.7) was associated with lower BMI and percentage of total and truncal fat mass (P ≤ 0.030 to P ≤ 0.001). Among adults with diabetes, physical activity level was not associated with diabetes treatment (insulin doses) and control (HbA(1c) and hypoglycaemia) or cardiovascular risk factors (blood pressure and lipid profile). CONCLUSION: As in the population without diabetes, an active lifestyle is associated with a better body composition profile in adults with Type 1 diabetes.

Accuracy of the SenseWear Armband™ during ergocycling.

The present study aims to show the accuracy of a portable motion sensor, the SenseWear Armband, for the estimation of energy expenditure vs. energy expenditure measured by indirect calorimetry during ergocycling. 31 healthy adults (52% women; age: 26.7±6.3 years; Body Mass Index: 23.9±3.3 kg/m2) completed a 45-min ergocycling session at 50% of their VO2(peak). Despite a significant underestimation of 18.7±13.2 kcal during the first 10 min of the activity (T=5.06; p<0.001), we observed an overall good agreement between energy expenditure estimated by the SenseWear Armband during ergocycling and indirect calorimetry (260.3±80.1 vs. 287.8±97.1 kcal, respectively) (T=-2.148; p=0.04) and a significant intra-class correlation (r=0.81; p<0.001). The results of the present study indicate that the SenseWear Armband underestimated energy expenditure during a 45-min ergocycling session at a 50% VO2(peak) intensity, mainly during the first 10 min. Underestimation at the onset of the activity warrants further research.

Post-breakfast closed-loop glucose control is improved when accompanied with carbohydrate-matching bolus compared to weight-dependent bolus.

AIM: We compared post-breakfast closed-loop glucose control either matched with a carbohydrate-matching bolus or a weight-dependent bolus. METHODS: Twelve adults with type 1 diabetes consumed a 75 g CHO breakfast on two occasions. In random order, the breakfast was accompanied by a full carbohydrate-matching insulin bolus (8.30 U [7.50 U-11.50 U]) or a partial weight-dependent insulin bolus (0.047 U/kg; 3.45 U [2.95 U-3.75 U]). Postprandial glucose was regulated by sensor-responsive insulin and glucagon delivery. RESULTS: Glucose control after the weight-dependent bolus was safe and feasible (glucose values returned to pre-prandial levels after 5 h). However, 5-hr incremental area under the curve and percentage of time above 10 mmol/L were lower after the full bolus compared to the partial bolus (IAUC, 2.1 [0.8-4.2] mmol/L/hr vs 8.3 [6.5-11.4] mmol/L/hr; time in hyperglycaemia, 24% [6%-29%] vs 50% [25%-63%]; P < 0.001). CONCLUSIONS: Post-breakfast closed-loop glucose control without carbohydrate counting, but based on weight-dependent bolus is feasible but a carbohydrate-matching bolus provides better glucose control. CLINICAL TRIAL REGISTRY: NCT01519102.

Carbohydrate counting accuracy and blood glucose variability in adults with type 1 diabetes.

AIMS: Carbohydrate counting is an established approach used by patients with type 1 diabetes to improve their glycemic control. The aims of this study were to evaluate, in real life conditions, the accuracy of meal carbohydrate estimate and its impact on glycemic variability. METHODS: In this cross-sectional study, we observed the ability of 50 adults (48% women) with type 1 diabetes (age: 42.7±11.1 years); diabetes duration: 21.4±12.7 years; HbA1c: 7.2±1.2% (60±10 mmol/mol) to accurately estimate carbohydrates by analyzing 72-h food records and their corresponding 72-h blood glucose excursions using a continuous glucose monitor. RESULTS: The mean meal carbohydrate difference, between the patients' estimates and those assessed by a dietitian using a computerized analysis program, was 15.4±7.8 g or 20.9±9.7% of the total CHO content per meal (72.4±34.7 g per meal). Sixty-three percent of the 448 meals analyzed were underestimated. Greater differences in CHO's estimates predicted higher glycemic variability, as measured by the MAGE index and glucose standard deviation, and decreased time with glucose values between 4 and 10 mmol/L (R²=0.110, 0.114 and 0.110, respectively; P<0.05). CONCLUSION: Inaccurate carbohydrate counting is frequent and associated with higher daily blood glucose variability in adults with type 1 diabetes.

The Barriers to Physical Activity in Type 1 Diabetes (BAPAD-1) scale: predictive validity and reliability.

AIM: Perceived barriers are one determinant of physical activity. Depending on the study population, these barriers can vary. The aim of this study was to assess the reliability and predictive validity of the 'Barriers to Physical Activity in Type 1 Diabetes' (BAPAD-1) scale, developed by Dubé et al. METHODS: A total of 77 adults (48% women; age: 43.5±10.4; body mass index: 25.2±4.3kg/m(2); HbA(1c): 7.6±1.3%) with type 1 diabetes completed the questionnaire and an evaluation of their physical activity using an accelerometer (8.4±1.2 days) and cardiorespiratory fitness assessment (VO(2)(peak)). To evaluate the temporal stability of the questionnaire, a subgroup of 17 participants answered the BAPAD-1 scale on both visits required by the protocol (10±4 days). RESULTS: The BAPAD-1 scale showed good internal validity with an inter-items correlation coefficient (Cronbach's correlation) of 0.85. The intraclass correlation coefficient for the two times the scales were completed was 0.80. The BAPAD-1 score was negatively correlated with both physical activity energy expenditure (r=-0.25; P=0.03) and VO(2)(peak) adjusted for gender and age (r=-0.27; P=0.02). CONCLUSION: The BAPAD-1 scale is a reliable and valid tool for assessing salient barriers to physical activity. In future, this scale could be used to describe the factors accounting for physical activity, and for planning interventions aimed at promoting physical activity among adults with type 1 diabetes.