Models of Nutrition From Continuous Glucose Monitors

Texas A&M University45 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Diabetes Mellitus, Type 2 Pre-diabetes Diet Habit Diet, Healthy

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Locations (2)

Sansum Diabetes Research Institute

Santa Barbara, California, United States

Texas A&M University

College Station, Texas, United States

Outcomes

Primary Outcomes

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

Time frame: up to 14 days

Secondary Outcomes

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

Time frame: up to 14 days

Feasibility of measuring impact of gut microbiota on estimations of meal composition using CGMs

Unit of measure: Correlation and regression error in estimating meal composition from post-prandial glucose measurements and identification of active gut microbiome pathways

Time frame: up to 14 days

Data from ClinicalTrials.gov

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