Diabetes, Gestational
Conditions
Keywords
gestational diabetes, carbohydrate distribution, high/low carbohydrate, glycemic variability, MAGE, continuous glucose monitoring, Pregnancy in Diabetics
Brief summary
This study aims to investigate whether high-morning carbohydrate intake (HMK) compared with low-morning carbohydrate intake (LMK) affects glycemic variability in GDM patients based on Continuous glucose monitoring (CGM). High carbohydrate morning intake is expected to reduce hyperglycemic episodes and stabilize blood glucose compared with low morning carbohydrate intake.
Detailed description
Background: Women with GDM have an increased risk of macrosomia, cesarean section, birth defects and long term complications such as an increased risk, in both mother and child, to develop type 2 diabetes. According to Invitro and invivo studies of type 1 and 2 diabetes, great variations in blood glucose levels caused more complications than constantly elevated glucose levels. This study, therefore, intends to use Continuous glucose monitoring (CGM) for day-to-day monitoring of glycemic variability, including frequency, duration, and magnitude of hyperglycaemic fluctuations. Carbohydrate is the macronutrient that has the greatest impact on postprandial blood glucose response. Despite this, there is a current lack of evidence of how the carbohydrate intake should be distributed throughout a day. This study aims to investigate whether high-morning carbohydrate intake (HMK) compared to low-morning carbohydrate intake (LMK) affects glycemic variability in GDM patients. Design: Randomized crossover intervention study comparing two intervention diets; high-morning carbohydrate intake (HMK) versus low-morning carbohydrate intake (LMK) each of 3 days duration with four-day washout. Diet intervention: Both intervention diets have the same calorie content and contain the same amounts of protein, carbohydrate and fat for the individual patient, but the distribution of carbohydrate and energy differs throughout the day. Dietary intake will be estimated through 24-hour recall interview by trained dietitians. Estimation of actual intake is validated by photos of every main meal. All data will be collected and stored in RedCap to secure data checks. Statistics Analysis and sample size: Power calculation on primary outcome MAGE- estimates 15 patients for inclusion with a power of 80%, SD 0,6mmol/l, a significance level of 0,05 and a MIREDIF of 0,5 mmol/l. 15 persons include an expected dropout rate at 20%. Non-parametric tests will be used for the secondary and primary outcome. Perspective: A future perspective of this study is to improve the current treatment in regards to nutritional recommendations. Thus, the study could potentially contribute with the knowledge that would clarify the carbohydrate recommendations and improve the glycemic control of patients with GDM and therefore be beneficial to patients' future treatment and prevent complications and development of type 2 diabetes in the child.
Interventions
BEHAVIORALHigh/low carbohydrate distribution
A total of 2x3 days, were the patient follow a detailed diet plan. For 3 days they follow a diet plan where the majority of the carbohydrates are located on either the first part of the day(HMK) or the last part of the day(LMK). 4 days of washout are placed between the two interventions. They will not receive food but will be guided by a trained dietitian and the use of a meal plan.
Sponsors
University of Copenhagen
University of Aarhus
Study design
Allocation
RANDOMIZED
Intervention model
CROSSOVER
Primary purpose
TREATMENT
Masking
NONE
Intervention model description
Each patient will follow the HMK and LMK diet in a period of 2x3 days in randomized order with a four day washout between. The order of the two diets will be assigned randomly.
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Gestational diabetes mellitus diagnosed according to current WHO criteria for a 2-hour oral glucose tolerance test (OGTT) \> 8.5 mmol/l * Non-insulin depending * Adult 18+ years * Gestational age weeks 30-36 at start of inclusion
Exclusion criteria
* Diagnosed with celiac disease * Received bariatric surgery * Diagnosed eating disorder * Insulin-dependent diabetes at trial start * Known with type 2 diabetes before pregnancy * Children under 18 years * Starting up in insulin during the intervention period * Diagnosed with lactose intolerance * Goes into labor before the intervention is completed
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| mean amplitude of glucose excursions (MAGE) | 6 days | An index for glycemic variability assessment MAGE is the average variation in amplitude and is calculated as the mean of absolute value differences between adjacent glucose peaks and valleys, where the differences exceed 1 Standard Deviation (SD) from the mean. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Coefficient of variation | 6 days | Coefficient of variation |
| MBG | 6 days | The average blood glucose, calculated for each two intervention periods using CGM data. |
| Gastric inhibitory polypeptide (GIP) | 1 hour*2 | Gastric inhibitory polypeptide difference in 1 hour postprandial response |
| C-peptide | 11 days | Changes in C-peptide according to carbohydrate distribution |
| Glucagon-like-peptide 1 (GLP1) | 1 hour *2 | glucagon-like-peptide 1, difference in 1 hour postprandial response |
Other
| Measure | Time frame | Description |
|---|---|---|
| 3-hydroxy-butyrate | 11 days | To assess ketonemia |
Countries
Denmark
Outcome results
None listed