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| ID | Type | Description | Link |
|---|---|---|---|
| 5RM1HG007735-09 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Human Genome Research Institute (NHGRI) | NIH |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Optimizing Diet for Glycemic Control | Other | Phase 1: Metabolic testing will include 3 metabolic tests:
Phase 2: Participants follow their own diet while using the CGM. Participants are provided with 5-10 standardized foods to test during this phase. Phase 3: Participants are provided with additional standardized foods and counseled to continue their own diet during this phase. Phase 4: Participants are counseled on reducing or limiting the foods that caused glucose spikes and they are also counseled on macronutrient composition of their diet based on lipid profile. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Dietary | Other | Dietary counseling based on results of CGM analyses. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Change in Glycemic Control as Measured by Change Blood Sugar Values | Change in glycemic control measured from baseline through all phases of study, stratified according food type and metabolic sub-type. Glycemic control is derived from continuous glucose monitor (CGM) data and expressed in milligrams/deciliter. | Assessed at a meal (2 to 6 weeks after baseline), starting just prior eating, for a period of 3 hours |
| Area Under the Receiver Operating Characteristic (ROC) Curve - 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) oral glucose tolerance testing (OGTT) done in the clinical research center and at home (using CGM). Participants were categorized as insulin sensitive (IS) if teady state plasma glucose (SSPG) was <120 mg dl-1 and insulin resistant (IR) if their SSPG was ≥120 mg dl-1. For this analysis, disposition index (DI) < 1.58 indicates dysfunctional β-cell function, whereas DI ≥ 1.58 indicates normal β-cell function. | Baseline (Day 1) |
| Measure | Description | Time Frame |
|---|---|---|
| Change in Area Under the Curve (AUC) of Blood Glucose Level | Measured from baseline through all phases of study, from continuous glucose monitor (CGM) data, and stratified according food type and metabolic sub-type. | Assessed at a meal (2 to 6 weeks after baseline), starting just prior eating, for a period of 3 hours |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Michael P Snyder, PhD | Stanford University | Principal Investigator |
| Tracey McLaughlin, MD | Stanford University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Stanford University | Stanford | California | 94304 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40467897 | Result | Wu Y, Ehlert B, Metwally AA, Perelman D, Park H, Brooks AW, Abbasi F, Michael B, Celli A, Bejikian C, Ayhan E, Lu Y, Lancaster SM, Hornburg D, Ramirez L, Bogumil D, Pollock S, Wong F, Bradley D, Gutjahr G, Rangan ES, Wang T, McGuire L, Venkat Rangan P, Raeder H, Shipony Z, Lipson D, McLaughlin T, Snyder MP. Individual variations in glycemic responses to carbohydrates and underlying metabolic physiology. Nat Med. 2025 Jul;31(7):2232-2243. doi: 10.1038/s41591-025-03719-2. Epub 2025 Jun 4. | |
| 39715896 |
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| ID | Title | Description |
|---|---|---|
| FG000 | Optimizing Diet for Glycemic Control | Participants underwent metabolic testing, then ate a variety of foods to assess their impact on blood sugars. |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
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| ID | Title | Description |
|---|---|---|
| BG000 | Optimizing Diet for Glycemic Control | Participants underwent metabolic testing, then ate a variety of foods to assess their impact on blood sugars. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Change in Glycemic Control as Measured by Change Blood Sugar Values | Change in glycemic control measured from baseline through all phases of study, stratified according food type and metabolic sub-type. Glycemic control is derived from continuous glucose monitor (CGM) data and expressed in milligrams/deciliter. | Participants with all metabolic testing results, omics, CGM, and meal data | Posted | Mean | Standard Error | mg/dL | Assessed at a meal (2 to 6 weeks after baseline), starting just prior eating, for a period of 3 hours |
|
Up to 3 months
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Optimizing Diet for Glycemic Control | Participants underwent metabolic testing, then ate a variety of foods to assess their impact on blood sugars. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Michael Snyder, Ph.D. | Stanford University | (650) 723-4668 | mpsnyder@stanford.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Feb 28, 2023 | Dec 18, 2025 | Prot_000.pdf |
| SAP | No | Yes | No | Statistical Analysis Plan | Mar 11, 2026 | Mar 11, 2026 | SAP_001.pdf |
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| ID | Term |
|---|---|
| D018149 | Glucose Intolerance |
| D007333 | Insulin Resistance |
| D003924 | Diabetes Mellitus, Type 2 |
| ID | Term |
|---|---|
| D006943 | Hyperglycemia |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| ID | Term |
|---|---|
| D004032 | Diet |
| ID | Term |
|---|---|
| D009747 | Nutritional Physiological Phenomena |
| D000066888 | Diet, Food, and Nutrition |
| D010829 | Physiological Phenomena |
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| Oral Food Challege | Other | Participants ate a variety of foods, to assess their impact on blood sugars. |
|
| Metwally AA, Perelman D, Park H, Wu Y, Jha A, Sharp S, Celli A, Ayhan E, Abbasi F, Gloyn AL, McLaughlin T, Snyder MP. Prediction of metabolic subphenotypes of type 2 diabetes via continuous glucose monitoring and machine learning. Nat Biomed Eng. 2025 Aug;9(8):1222-1239. doi: 10.1038/s41551-024-01311-6. Epub 2024 Dec 23. |
| 40500312 | Derived | Park H, Metwally AA, Delfarah A, Wu Y, Perelman D, Mayer C, McGinity C, Rodgar M, Celli A, McLaughlin T, Mignot E, Snyder M. High-resolution lifestyle profiling and metabolic subphenotypes of type 2 diabetes. NPJ Digit Med. 2025 Jun 11;8(1):352. doi: 10.1038/s41746-025-01728-6. |
| Participants |
|
| Age, Continuous | Mean | Standard Deviation | years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Ethnicity (NIH/OMB) | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Count of Participants | Participants |
|
Participants underwent metabolic testing, then ate a variety of foods to assess their impact on blood sugars. |
| OG002 | Insulin-sensitive - Pasta | Participants underwent metabolic testing, then ate a variety of foods to assess their impact on blood sugars. |
| OG003 | Insulin-resistant - Pasta | Participants underwent metabolic testing, then ate a variety of foods to assess their impact on blood sugars. |
|
|
|
| Primary | Area Under the Receiver Operating Characteristic (ROC) Curve - 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) oral glucose tolerance testing (OGTT) done in the clinical research center and at home (using CGM). Participants were categorized as insulin sensitive (IS) if teady state plasma glucose (SSPG) was <120 mg dl-1 and insulin resistant (IR) if their SSPG was ≥120 mg dl-1. For this analysis, disposition index (DI) < 1.58 indicates dysfunctional β-cell function, whereas DI ≥ 1.58 indicates normal β-cell function. | Participants who had OGTT performed | Posted | Number | 95% Confidence Interval | Proportion of accurate classifications | Baseline (Day 1) |
|
|
|
| Secondary | Change in Area Under the Curve (AUC) of Blood Glucose Level | Measured from baseline through all phases of study, from continuous glucose monitor (CGM) data, and stratified according food type and metabolic sub-type. | Participants with all metabolic testing results, omics, CGM, and meal data | Posted | Mean | Standard Deviation | mg*min/dL | Assessed at a meal (2 to 6 weeks after baseline), starting just prior eating, for a period of 3 hours |
|
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| 0 |
| 115 |
| 0 |
| 115 |
| 0 |
| 115 |
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| D006946 | Hyperinsulinism |
| D003920 | Diabetes Mellitus |
| D004700 | Endocrine System Diseases |
| Title | Measurements |
|---|---|
|
The a priori threshold for statistical significance is < 0.05. |
| Other |