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| Name | Class |
|---|---|
| Sansum Diabetes Research Institute | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Persons with Diabetes Mellitus, Type 2 | Venous blood draw of fasting HbA1c greater than or equal to 6.5% | ||
| Persons with Pre-diabetes | Venous blood draw of fasting HbA1c greater than or equal to 5.7% and less than 6.5% | ||
| Persons without Pre-diabetes or Diabetes Mellitus, Type 2 | Venous blood draw of fasting HbA1c less than 5.7% |
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| Measure | Description | Time Frame |
|---|---|---|
| Feasibility of measuring meal quantity and composition using CGMs | Unit of measure: Correlation and regression error in estimating meal composition from post-prandial glucose measurements | up to 14 days |
| Measure | Description | Time Frame |
|---|---|---|
| Feasibility of measuring impact of physical activity on estimations of meal composition using CGMs and smart watches | Unit of measure: Correlation and regression error in estimating meal composition from post-prandial glucose measurements and physical activity data | up to 14 days |
| Feasibility of measuring impact of gut microbiota on estimations of meal composition using CGMs |
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Inclusion Criteria:
Exclusion Criteria:
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Adults without or with type 2 diabetes, or at high risk of developing type 2 diabetes and who currently use a compatible smartphone will be enrolled.
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| Name | Affiliation | Role |
|---|---|---|
| Kristin Castorino, DO | Sansum Diabetes Research Institute | Principal Investigator |
| Bobak J Mortazavi, PhD | Texas A&M University | Principal Investigator |
| Ricardo Gutierrez-Osuna, PhD | Texas A&M University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sansum Diabetes Research Institute | Santa Barbara | California | 93105 | United States | ||
| Texas A&M University |
All de-identified individual participant data (IDP) that underlie results in publications.
Data will be made available after the primary publication of each analysis.
Data Sharing Agreements will be formulated by a committee of study investigators and community and industry partners.
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| D018149 | Glucose Intolerance |
| D005247 | Feeding Behavior |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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Unit of measure: Correlation and regression error in estimating meal composition from post-prandial glucose measurements and identification of active gut microbiome pathways |
| up to 14 days |
| College Station |
| Texas |
| 77843 |
| United States |
| D004700 | Endocrine System Diseases |
| D006943 | Hyperglycemia |
| D001522 | Behavior, Animal |
| D001519 | Behavior |