Insulin Resistance, Type 1 Diabetes
Conditions
Keywords
insulin resistance, type 1 diabetes, euglycaemic clamp, breath test
Brief summary
Insulin resistance can be assessed by the euglycaemic clamp technique. To date, this is the golden standard, but it is not suited for clinical practice. A 13C glucose breath test will be tested as a valid alternative. The curve of the exhaled 13C CO2 as a function of glucose metabolism can be correlated to the curve of the glucose disposal rate obtained via the clamp technique.
Detailed description
The hyperinsulinemic-euglycemic clamp technique is the golden standard to assess insulin resistance in type 1 diabetes subjects. The plasma insulin concentration is acutely raised and maintained at 100 μU/ml by a continuous infusion of insulin. Meanwhile, the plasma glucose concentration is held constant at basal levels by a variable glucose infusion. When the steady-state is achieved, the glucose infusion rate equals glucose uptake by all the tissues in the body and is therefore a measure of tissue insulin sensitivity. These data will be compared with the results of a 13C glucose breath test. Breath tests using 13C substrates are based on the principle that 13C CO2 in the exhaled breath can be measured as a metabolic tracer. Breath testing has a major advantage over the clamp test in that it can be performed non-invasively and repeatedly without necessary supervision by medical staff.
Interventions
clamp test (golden standard) to determine insulin resistance
13C glucose breath test to compare with the golden standard
Sponsors
Study design
Eligibility
Inclusion criteria
* Adult type 1 diabetes patients * 25/50 subjects with confirmed NAFLD (using ultrasound criteria)
Exclusion criteria
* pregnancy * gastric bypass surgery * cirrhosis * secondary cause of liver steatosis present * any cause which makes a 4-hour clamp impossible
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Correlation of the 13C breath test with the golden standard | one day | sensitivity and specificity analysis using linear regression |
Countries
Belgium