Ignite Creation Date:
2025-12-24 @ 6:31 PM
Ignite Modification Date:
2025-12-28 @ 8:25 AM
Study NCT ID:
NCT05701657
Status:
RECRUITING
Last Update Posted:
2025-07-14
First Post:
2023-01-11
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Nutrition for Precision Health, Powered by the All of Us
Sponsor:
RTI International
Organization:
Study Overview
Official Title:
Nutrition for Precision Health, Powered by the All of Us Research Program
Status:
RECRUITING
Status Verified Date:
2025-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The goal of this investigational study is to develop algorithms that predict human response to foods. The main question it aims to answer are:
* How does varying foods and eating patterns impact one's biological and physiological responses?
* In what ways can novel dietary assessment measures be used to improve dietary assessments and to prescribe assessments to people in future research with increased precision?
* Can artificial intelligence and machine learning techniques be combined to prescribe foods and eating patterns to individuals for optimization of their health?
There are 3 Modules participants may take part in:
* Module 1- A participant's dietary intake and accompanying nutritional status, biological and other measures will be observed over 10 days, as well as physiological responses to a liquid mixed meal tolerance test will be measured.
* Module 2- Participants will undergo three controlled dietary interventions provided for 14-days each and separated by washout periods of at least 14 days. Physiological responses following a diet-specific meal test will be measured.
* Module 3- Participants will undergo the same three dietary interventions during the same 14 day periods as Module 2 while being studied in-residence. Physiological responses following a liquid mixed meal tolerance test and a diet-specific meal test will be measured.
* How does varying foods and eating patterns impact one's biological and physiological responses?
* In what ways can novel dietary assessment measures be used to improve dietary assessments and to prescribe assessments to people in future research with increased precision?
* Can artificial intelligence and machine learning techniques be combined to prescribe foods and eating patterns to individuals for optimization of their health?
There are 3 Modules participants may take part in:
* Module 1- A participant's dietary intake and accompanying nutritional status, biological and other measures will be observed over 10 days, as well as physiological responses to a liquid mixed meal tolerance test will be measured.
* Module 2- Participants will undergo three controlled dietary interventions provided for 14-days each and separated by washout periods of at least 14 days. Physiological responses following a diet-specific meal test will be measured.
* Module 3- Participants will undergo the same three dietary interventions during the same 14 day periods as Module 2 while being studied in-residence. Physiological responses following a liquid mixed meal tolerance test and a diet-specific meal test will be measured.
Detailed Description:
The NPH study design includes three modules: Module 1, a study of participants' dietary intake; Module 2, a randomized crossover trial with three dietary interventions among participants in a community-dwelling setting; and Module 3, a similar trial to Module 2 except with participants in residence, all detailed in the following text. Briefly, Module 1 will observe participants for 8-10 days while consuming their usual diet. Modules 2 and 3 will include 3 dietary interventions, each administered for 14 days, followed by a washout period between each diet. The study will be conducted at multiple sites managed by 6 clinical centers throughout the United States.
An interdisciplinary team of clinical nutrition research experts will evaluate dietary assessment methods, microbial metagenomics and metatranscriptomics, targeted and untargeted metabolomics, computational and data science to advance our current understanding of nutrition which will inform personalized nutrition recommendations. The resulting artificial intelligence (AI) algorithms to predict individual responses to food will provide a foundation to create customized dietary approaches to optimize short- and long-term health.
An interdisciplinary team of clinical nutrition research experts will evaluate dietary assessment methods, microbial metagenomics and metatranscriptomics, targeted and untargeted metabolomics, computational and data science to advance our current understanding of nutrition which will inform personalized nutrition recommendations. The resulting artificial intelligence (AI) algorithms to predict individual responses to food will provide a foundation to create customized dietary approaches to optimize short- and long-term health.
Study Oversight
Has Oversight DMC:
True
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?:
Secondary ID Infos
| Secondary ID | Type | Domain | Link | View |
|---|---|---|---|---|
| UG1HD107692 | NIH | None | https://reporter.nih.gov/quic… | View |
| U24HD107676 | NIH | None | https://reporter.nih.gov/quic… | View |
| UG1HD107691 | NIH | None | https://reporter.nih.gov/quic… | View |
| UG1HD107697 | NIH | None | https://reporter.nih.gov/quic… | View |
| UG1HD107688 | NIH | None | https://reporter.nih.gov/quic… | View |
| UG1HD107696 | NIH | None | https://reporter.nih.gov/quic… | View |
| UG1HD107711 | NIH | None | https://reporter.nih.gov/quic… | View |
| U54TR004279 | NIH | None | https://reporter.nih.gov/quic… | View |
| U24CA268153 | NIH | None | https://reporter.nih.gov/quic… | View |
| U24DK131617 | NIH | None | https://reporter.nih.gov/quic… | View |
| U24CA268228 | NIH | None | https://reporter.nih.gov/quic… | View |
| U24OD023121 | NIH | None | https://reporter.nih.gov/quic… | View |
| OT2OD035404 | NIH | None | https://reporter.nih.gov/quic… | View |
| AOD22022001 | OTHER_GRANT | National Institutes of Health (NIH) Inter Agency Agreement | View | |
| OT2OD030043 | NIH | None | https://reporter.nih.gov/quic… | View |