Glucose Variability in Patients With Type 1 Diabetes With a Sucrose-added Diet

INTRODUCTION:

The continuous glucose monitoring system (CGMS) offers improved glycemic control for patients with type 1 diabetes, because may reduce asymptomatic hypoglycemic and postprandial hyperglycemia events (1, 2).

Glycemic disorder may be estimated as a whole from the determination of glycosylated hemoglobin (HbA1c) level, which integrates both hypoglycemic and hyperglycemia episodes (3). Glycemic variability is a complex phenomenon that includes both intraday and interday variability (3, 4).

The mean amplitude of glycemic excursion (MAGE) is an index for assessing the intraday glycemic variability. The calculation of the MAGE is obtained by measuring the arithmetic mean of the differences between consecutive peaks and nadirs provided that the differences are greater than one standard deviation (SD) of the mean glucose value (5).

The continuous overall net glycemic action (CONGA) index is an indicator of within-day glucose variability. After the first n number of hours of observation, the investigators may calculate the difference between current observation and observation in the previous n hours. CONGA is defined as the SD of the recorded differences. The frequently used index CONGA1, CONGA2 and CONGA4, coincide with observations lasting 1, 2, or 4 h and they are, therefore, expressions of glucose variability within these intervals (6).

The mean of daily differences (MODD) remains the sole index for estimating interday glycemic variability. This index is calculated as the mean of the absolute differences between glucose values at the same time on two consecutive days (3, 7).

Some of these indexes are complex or impossible to obtain with self-monitoring of blood glucose (SMBG). Thus, the CGMS allows to the calculation of scores (7).

Another aspect of diabetes treatment is the nutritional counseling. According to individuals with type 1 diabetes, diet adherence is one of the more difficult aspects of treatment (8, 9). However, nutrition therapy is essential in the management of diabetes, and meal-planning strategies for type 1 diabetes emphasize the relationship between prandial insulin dose selection and the anticipated amount of carbohydrates to be consumed (10, 11).

Carbohydrate counting is a meal-planning method that focuses on carbohydrates, and the American Diabetes Association recommends meal plans based on carbohydrate counting as a key strategy to achieving glycemic control (11).

There are two levels of carbohydrate counting. At the basic level, individuals must eat a consistent amount of carbohydrates at meals. It is useful to understand the effect of food and medication and to identify normal portion sizes, considering that one serving is equal to 15g of carbohydrates. The advanced level includes pattern management and understanding how to use insulin-to-carbohydrate ratios. Carbohydrate counting requires the ability to determine the amount of carbohydrates in each food, and it may promote weight gain when patients don't pay attention to their food choices (10, 12).

Our previous study has shown that patients with type 1 diabetes who began carbohydrate counting, especially the advanced method, increased their calories and carbohydrates intake (13).

Sucrose is a very attractive source of carbohydrate, and sweetness is considered one of the most powerful determinants of food consumption (14). Patients with diabetes type 1 appear to be especially prone to taste disorders (8).

Our previous study has shown that patients with type 1 diabetes's habitual intake sweets more than twice a week, despite hypoglycemia (13).

The use of CSII associated with the advanced carbohydrate counting may increase the calories and carbohydrate intake (15-18).

Previous studies have showed that the use of CSII with CGMS improve glycemic control, However, no study has evaluated the glucose variability in patients with type 1 diabetes in a diet with foods containing moderate quantity of sucrose.

The goal of this study is to evaluate and compare the influence of sucrose-free diet or sucrose-added diet on glucose variability in patients with type 1 diabetes, treated with CSII and CGMS.

EXPERIMENTAL DESIGN:

STUDY DESIGN:

This is a single-blind, two-way, crossover design in with patients with type 1 diabetes will be recruiting through poster advertisements or invited during routine medical appointment at the Clementino Fraga Filho University Hospital, Brazil by an investigator.

The sample size and selection by convenience. All participants will signed an informed consent.

Selected volunteers will be instruct to go to the hospital for anthropometric measurements, and insert the glucose sensor iPro2™ Professional CGMS system (Medtronic, Inc™). In addition, all volunteers will be receiving instructions to fill a 7-day food diary, and register their self-capillary blood glucose (four times/day).

In the same day, volunteers will receive a control diet (a diet with a little quantity of sugars) or test diet (with foods containing moderate quantity of sucrose in composition) during two-days each. These diets are composed by two menus: control diet for Tuesday and Wednesday, and test diet for Thursday and Friday.

If the volunteers consume the menus correctly during Tuesday to Friday, the diet on weekends is free. However, if a meal was consumed improperly (eg. more or less amounts, or inadequate replacements), Saturday or Sunday should be used to follow the menu not completed properly.

The researcher will contact daily in order to clarify possible doubts, and verify adherence to the diets.

After one-week, each volunteer will return for download CGMS, and in order to deliver the 7-day food diary.

INTERVENTION:

Percent energy from macronutrients was similar in both diets, and these were prescribed according to the American Diabetes Association guidelines (11).

Dietary energy content of: 50.7% and 54.6% carbohydrates; 21.5% and 21.2% protein; 27.7% and 23.42% total fat; 5.0% and 4.9% saturated fatty acids; 15.1% and 12.8% monounsaturated fatty acids; 7.3 and 5.9% polyunsaturated fatty acids; for control and test diets, respectively. Both diets were composed by 41g total dietary fiber.

Control diet has little quantity of sugars (30.4g/day, corresponding to 12.7% of total carbohydrates and 6.3% of total dietary energy intake), and test diet has foods containing moderate quantity of sucrose in composition (81.2g/day, corresponding to 30.2% of total carbohydrates and 16.5% of total dietary energy intake).

These diets are composed by two menus: control diet for Tuesday and Wednesday, and test diet for Thursday and Friday. The diet on weekends is free, if the volunteers consume the menus correctly during Tuesday to Friday. However, if a meal was consumed improperly (eg. more or less amounts, or inadequate replacements), Saturday or Sunday should be used to follow the menu not completed properly.

DIETARY ASSESSMENT:

Dietary intake will be evaluated from. All dietary records will be analyzed using a local nutritional software.

During those seven days, all foods and drinks consumed will have to be documented to allow quantitative estimation of dietary intake. Data were then entered into the DietPró 5.5i nutrition software (version 2010, Brazil) to convert the amount of food eaten into individual nutrients and the mean daily energy and nutrient intake for each patient will be calculated.

ANTHROPOMETRIC ASSESSMENT:

Body mass index (BMI) will be calculated as body weight in kilograms divided by the square of height in meters.

Waist circumference will be determined as the average of two measurements calculated to the nearest 0.1cm midway between the lower rib margin and the ilial crest after a normal expiration.

STATISTICAL:

Statistical analyses will be performed in SPSS software (version 20.0; SPSS Inc, Chicago, IL, USA). A p-value <0.05 will be consider statistically significant.

Qualitative variables were described as frequency, whereas quantitative variables will be describe as the mean ± standard deviations (SD) and 95% CI.

The Mann-Whitney test will be used for between-group comparison and the Wilcoxon test will be used to compare the effects of tests in each group. Spearman correlation and linear regression will be used to evaluate interactions.

Time course of glycemic datas will be analyzed with repeated measures analysis of variance two-way ANOVA.

The glycemic variability (MAGE, CONGA and MODD) will be calculated according to described by Kovatchev et al (3, 7).