Diabetes Mellitus, Type 2, Diet Habit, Diet, Healthy, Pre-diabetes
Conditions
Brief summary
With this study, researchers want to conduct ambulatory studies in which people (healthy, with T2D, or at-risk of T2D) will consume a variety of pre-set and conventional meals in free-living conditions while wearing one or more continuous glucose monitors (CGMs) and, to assess physical activity, a smart watch. With data from these devices, researchers will develop algorithms that can predict the content of a meal.
Detailed description
Poor diet contributes to more than half of premature deaths related to cardiovascular and metabolic disease, including type 2 diabetes (T2D). At present, the number of adults developing T2D continues to rise, with over 30 million Americans living with T2D. Another 80 million are currently at-risk of progressing from pre-diabetes to T2D. Improving food choices remains a cornerstone of modern diabetes care and can decrease the risk of progression to T2D. However, at present, achieving timely and appropriate lifestyle change in adults with or at-risk of T2D is challenging. Conventional methods to record meal choice and track nutritional composition can be inaccurate (e.g., estimating protein content of a meal) and burdensome (i.e., individuals must manually enter information into a food diary). Interestingly, the blood glucose profile after a meal depends not only on the carbohydrate content but also on the amount of fat, protein, and fiber; as an example, adding fat and protein to carbohydrates generally leads to smaller increases and slower decreases in achieved glucose levels, lowering risk. This suggests that the shape of the glucose response to a meal may have the potential to indicate meal content. A unique opportunity to exploit this information is to use one or more continuous glucose monitors (CGMs). A CGM is a small sensor that attaches to the skin and measures glucose continuously every 1-15 minutes, making it possible to automatically record the glucose responses to meals. Researchers anticipate that findings will help clinicians provide new information to support positive behavior change to reduce the risk of or progression from pre-diabetes to T2D, and make it easier for patients to passively and accurately track nutritional components of their diet, potentially leading to healthier diets and improved health.
Interventions
None listed
Sponsors
Sansum Diabetes Research Institute
Texas A&M University
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Adults ≥ 18 years of age at enrollment visit. 2. Ability to walk, sit down and stand up independently. 3. Exclusive and continuous use, for the up to 14 day participation period, of a study-compatible smart phone, as well as ability to use a smart phone with sufficient proficiency to engage in study activities. 4. Based on the research staff's judgment, participant must have a good understanding, ability, and willingness to adhere to the protocol, including performance of self-monitored data collection during the free-living portion of the study. 5. Live or work within range of the study's meal delivery service. 6. Able to speak and read English sufficiently to engage in study activities. 7. Ability to refrigerate provided meals.
Exclusion criteria
1. Under 18 years of age. 2. Type 1 diabetes or a history of diabetic ketoacidosis. 3. Type 2 diabetes treated with oral medicines (other than Metformin) or any injectable GLP-1 receptor agonist or insulin. 4. Life expectancy \< 12 months. 5. Any active clinically significant physical or mental disease or disorder that, in the investigator's opinion, could interfere with the participation in the study. 6. History of major surgical procedures involving the stomach potentially affecting absorption of trial product (e.g., subtotal and total gastrectomy, sleeve gastrectomy, gastric bypass surgery). 7. Renal impairment, defined as estimated glomerular filtration rate \<60 mL/min/1.73 m2 as per Chronic Kidney Disease Epidemiology Collaboration formula. 8. Known or suspected abuse of alcohol, narcotics, or illicit drugs. 9. Language and/or technology barriers precluding comprehension of study activities and informed consent. 10. Any food allergies that, in the investigator's opinion, could interfere with participation in the study. 11. Pregnant (self-reported). 12. Current participation in other trials involving medications or devices.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility of measuring meal quantity and composition using CGMs | up to 14 days | Unit of measure: Correlation and regression error in estimating meal composition from post-prandial glucose measurements |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility of measuring impact of physical activity on estimations of meal composition using CGMs and smart watches | up to 14 days | Unit of measure: Correlation and regression error in estimating meal composition from post-prandial glucose measurements and physical activity data |
| Feasibility of measuring impact of gut microbiota on estimations of meal composition using CGMs | up to 14 days | Unit of measure: Correlation and regression error in estimating meal composition from post-prandial glucose measurements and identification of active gut microbiome pathways |
Countries
United States
Outcome results
None listed