Precision Diets for Diabetes Prevention

Diabetes Mellitus, Type 2 Clinical Trial

Official title:

Precision Diets for Diabetes Prevention

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03919877
• Other study ID #: 43883
• Secondary ID: 5RM1HG007735-09
• Status: Recruiting
• Phase: N/A
• Start date: May 24, 2018
• Est. completion date: December 2024

Study information

• Verified date: March 2024
• Source: Stanford University
• Contact: Alessandra Celli, PhD
• Phone: (650) 725-8491
• Email: alessandra.celli[@]stanford.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

With this study the investigators want to understand the physiological differences for people developing pre-diabetes and diabetes. The investigators hypothesize that different individuals go through different paths in the development of the disease. By understanding the personal mechanism for developing disease, the investigators will find a personalized approach to prevent that development. The investigators are also hoping to be able to find a biomarker that will pinpoint to the particular defect and thus, diagnose the problem at an earlier stage and have the information to give personalized diet recommendations to prevent the development of diabetes more effectively.

Description:

At present, individuals with prediabetes or diabetes are grouped together as a single entity, but almost certainly they represent a mix of different gene-environment interactions that lead to one of four dominant physiologic mechanisms underlying their dysglycemia. 1- liver insulin resistance, 2- muscle insulin resistance, 3- impaired insulin secretion, 4- impaired incretin hormone secretion. Gaps that we are addressing here are extremely important - first, we will define a composite biomarker to identify different subphenotypes of prediabetes based on the four known physiologic mechanisms that contribute differentially in each individual to glucose elevations, which we hypothesize will also be reflected in their "glucotype". Importantly, because both continuous glucose monitor and administration of standardized meal testing and metabolic tests are not practical in the clinic, the development of a composite biomarker comprised of select multi-omics measures and clinical variables will enable clinicians and possibly patients (without clinician) to easily identify the specific diet that will yield optimal health results.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 100
• Est. completion date: December 2024
• Est. primary completion date: December 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Be 18 years of age or older;

• Not be pregnant, if female;

Exclusion Criteria:

• Have major organ disease, hypertension defined as >160/100, pregnant/lactating, diabetogenic medications, malabsorptive disorders like celiac sprue, others, heavy alcohol use, use of weight loss medications or specific diets, weight change > 2 kg in the last three weeks, history of bariatric surgery.

• Any medical condition that physicians believe would interfere with study participation or evaluation of results.

• Mental incapacity a nd/or cognitive impairment on the part of the patient that would preclude adequate understanding of, or cooperation with, the study protocol.

Related Conditions & MeSH terms

• Diabetes Mellitus
• Diabetes Mellitus, Type 2
• Insulin Resistance
• Pre Diabetes
• Prediabetic State

Intervention

Other:
• Dietary
Dietary counseling based on results of CGM analyses.

Locations

Country	Name	City	State
United States	Stanford University	Stanford	California

Sponsors (2)

Lead Sponsor	Collaborator
Stanford University	National Human Genome Research Institute (NHGRI)

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change in glycemic control as measured by change blood sugar values	Change in glycemic control measured from baseline through all phases of study. Glycemic control is derived from continuous glucose monitor (CGM) data and expressed in milligrams/deciliter.	Three years
Primary	Classification of metabolic subphenotype	Classify metabolic subphenotype in individuals without diabetes using a machine learning algorithm applied to the glucose time-series response generated by a 16-point (blood draws) OGTT done in the clinical research center and at home (using CGM)	Four years
Secondary	Change in area under the curve (AUC) of blood glucose level	Measured from baseline through all phases of the study.	Three years