Models of Nutrition From Continuous Glucose Monitors

SCH: INT: Personalized Models of Nutrition Intake From Continuous Glucose Monitors

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.

Study Overview

• Status: Completed
• Conditions: Diabetes Mellitus, Type 2; Diet, Healthy; Diet Habit; Pre-diabetes

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.

• Study Type: Observational
• Enrollment (Actual): 45

Contacts and Locations

Study Locations

• United States
  • California
    • Santa Barbara, California, United States, 93105
      • Sansum Diabetes Research Institute
  • Texas
    • College Station, Texas, United States, 77843
      • Texas A&M University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

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.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort
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%

What is the study measuring?

Primary Outcome Measures

Outcome Measure	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

Secondary Outcome Measures

Outcome Measure	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	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

Collaborators and Investigators

• Sponsor: Texas A&M University
• Collaborators: Sansum Diabetes Research Institute
• Investigators: Principal Investigator: Bobak J Mortazavi, PhD, Texas A&M University; Principal Investigator: Ricardo Gutierrez-Osuna, PhD, Texas A&M University; Principal Investigator: Kristin Castorino, DO, Sansum Diabetes Research Institute

Study record dates

Study Major Dates

• Study Start (Actual): September 17, 2021
• Primary Completion (Actual): August 1, 2024
• Study Completion (Actual): August 1, 2024

Study Registration Dates

• First Submitted: July 27, 2021
• First Submitted That Met QC Criteria: July 27, 2021
• First Posted (Actual): August 5, 2021

Study Record Updates

• Last Update Posted (Actual): August 21, 2024
• Last Update Submitted That Met QC Criteria: August 19, 2024
• Last Verified: August 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Glucose Metabolism Disorders; Metabolic Diseases; Endocrine System Diseases; Diabetes Mellitus; Diabetes Mellitus, Type 2; Prediabetic State
• Other Study ID Numbers: IIS2014475

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: All de-identified individual participant data (IDP) that underlie results in publications.
• IPD Sharing Time Frame: Data will be made available after the primary publication of each analysis.
• IPD Sharing Access Criteria: Data Sharing Agreements will be formulated by a committee of study investigators and community and industry partners.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; ANALYTIC_CODE

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No