OBJECTIVETo investigate the effect of flexible intensive insulin therapy (FIIT) and an automated bolus calculator (ABC) in a Danish type 1 diabetes population treated with multiple daily injections. Furthermore, to test the feasibility of teaching FIIT in a 3-h structured course.RESEARCH DESIGN AND METHODSThe BolusCal Study was a 16-week randomized, controlled, open-label, three-arm parallel, clinical study of 51 adults with type 1 diabetes. Patients aged 18–65 years in poor metabolic control (HbA1c 8.0–10.5%) were randomized to the Control (n = 8), CarbCount (n = 21), or CarbCountABC (n = 22) arm. During a 3-h group teaching, the Control arm received FIIT education excluding carbohydrate counting. CarbCount patients were taught FIIT and how to count carbohydrates. CarbCountABC group teaching included FIIT and carbohydrate counting and patients were provided with an ABC.RESULTSAt 16 weeks, the within-group change in HbA1c was −0.1% (95% CI −1.0 to 0.7%; P = 0.730) in the Control arm, −0.8% (−1.3 to −0.3%; P = 0.002) in the CarbCount arm, and −0.7% (−1.0 to −0.4%; P < 0.0001) in the CarbCountABC arm. The difference in change in HbA1c between CarbCount and CarbCountABC was insignificant. Adjusting for baseline HbA1c in a regression model, the relative change in HbA1c was −0.6% (−1.2 to 0.1%; P = 0.082) in CarbCount and −0.8% (−1.4 to −0.1%; P = 0.017) in CarbCountABC. Treatment satisfaction measured by the Diabetes Treatment Satisfaction Questionnaire (status version) improved in all study arms, but the improvement was significantly greater in CarbCountABC.CONCLUSIONSFIIT and carbohydrate counting were successfully taught in 3 h and improved metabolic control and treatment satisfaction. Concurrent use of an ABC improved treatment satisfaction further.

Matching meal insulin to carbohydrate intake, blood glucose, and activity level is recommended in type 1 diabetes management. Calculating an appropriate insulin bolus size several times per day is, however, challenging and resource demanding. Accordingly, there is a need for bolus calculators to support patients in insulin treatment decisions. Currently, bolus calculators are available integrated in insulin pumps, as stand-alone devices and in the form of software applications that can be downloaded to, for example, smartphones. Functionality and complexity of bolus calculators vary greatly, and the few handfuls of published bolus calculator studies are heterogeneous with regard to study design, intervention, duration, and outcome measures. Furthermore, many factors unrelated to the specific device affect outcomes from bolus calculator use and therefore bolus calculator study comparisons should be conducted cautiously. Despite these reservations, there seems to be increasing evidence that bolus calculators may improve glycemic control and treatment satisfaction in patients who use the devices actively and as intended.

The aim of the present study was to assess the effects of a high carbohydrate diet (HCD) vs a low carbohydrate diet (LCD) on glycaemic variables and cardiovascular risk markers in patients with type 1 diabetes. Ten patients (4 women, insulin pump-treated, median ± standard deviation [s.d.] age 48 ± 10 years, glycated haemoglobin [HbA1c] 53 ± 6 mmol/mol [7.0% ± 0.6%]) followed an isocaloric HCD (≥250 g/d) for 1 week and an isocaloric LCD (≤50 g/d) for 1 week in random order. After each week, we downloaded pump and sensor data and collected fasting blood and urine samples. Diet adherence was high (225 ± 30 vs 47 ± 10 g carbohydrates/d; P < .0001). Mean sensor glucose levels were similar in the two diets (7.3 ± 1.1 vs 7.4 ± 0.6 mmol/L; P = .99). The LCD resulted in more time with glucose values in the range of 3.9 to 10.0 mmol/L (83% ± 9% vs 72% ± 11%; P = .02), less time with values ≤3.9 mmol/L (3.3% ± 2.8% vs 8.0% ± 6.3%; P = .03), and less glucose variability (s.d. 1.9 ± 0.4 vs 2.6 ± 0.4 mmol/L; P = .02) than the HCD. Cardiovascular markers were unaffected, while fasting glucagon, ketone and free fatty acid levels were higher at end of the LCD week than the HCD week. In conclusion, the LCD resulted in more time in euglycaemia, less time in hypoglycaemia and less glucose variability than the HCD, without altering mean glucose levels.

Aims To compare the effects of a low carbohydrate diet (LCD < 100 g carbohydrate/d) and a high carbohydrate diet (HCD > 250 g carbohydrate/d) on glycaemic control and cardiovascular risk factors in adults with type 1 diabetes. Materials and methods In a randomized crossover study with two 12‐week intervention arms separated by a 12‐week washout, 14 participants using sensor‐augmented insulin pumps were included. Individual meal plans meeting the carbohydrate criteria were made for each study participant. Actual carbohydrate intake was entered into the insulin pumps throughout the study. Results Ten participants completed the study. Daily carbohydrate intake during the two intervention periods was (mean ± standard deviation) 98 ± 11 g and 246 ± 34 g, respectively. Time spent in the range 3.9‐10.0 mmol/L (primary outcome) did not differ between groups (LCD 68.6 ± 8.9% vs. HCD 65.3 ± 6.5%, P = 0.316). However, time spent <3.9 mmol/L was less (1.9 vs. 3.6%, P < 0.001) and glycaemic variability (assessed by coefficient of variation) was lower (32.7 vs. 37.5%, P = 0.013) during LCD. No events of severe hypoglycaemia were reported. Participants lost 2.0 ± 2.1 kg during LCD and gained 2.6 ± 1.8 kg during HCD (P = 0.001). No other cardiovascular risk factors, including fasting levels of lipids and inflammatory markers, were significantly affected. Conclusions Compared with an intake of 250 g of carbohydrate per day, restriction of carbohydrate intake to 100 g per day in adults with type 1 diabetes reduced time spent in hypoglycaemia, glycaemic variability and weight with no effect on cardiovascular risk factors.

In summary, the currently available literature does not provide sufficient evidence to definitively determine the effects of advanced carbohydrate counting on HbA(1c), psychosocial measures, weight or hypoglycaemic events. Nevertheless, the method still appears preferable to other insulin dosing procedures, which justifies continued use and inclusion of advanced carbohydrate counting in clinical guidelines.

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The LCD reduces the treatment effect of glucagon on mild hypoglycemia. Carbohydrate intake should be considered when low-dose glucagon is used to correct hypoglycemia.

People with Type 1 diabetes initiating advanced carbohydrate counting obtained significantly greater HbA reductions when guided by an automated bolus calculator (NCT02084498).