N=1 Trials of Individual Variability in Post-prandial Glycemic Responses to Diets of Varying Macronutrient Composition

Not Applicable

Active, not recruiting

• Conditions: Personalized Nutrition
• Interventions: Dietary Supplement: High Protein Diet; Dietary Supplement: High Carbohydrate-Low Glycemic Index Diet; Dietary Supplement: High Carbohydrate-High Glycemic Index Diet; Dietary Supplement: High Fat Diet

• Registration Number: NCT05402085
• Lead Sponsor: National University of Singapore

Brief Summary

The key objective of this study is to identify the most suitable diet (i.e. high protein, high fat, low GI, high GI) for an individual. Importantly, we further seek to identify the biological determinants of inter-individual variability and to understand how these determinants affect blood glucose. The deep metabolic phenotyping, multi-omics profiling of each subject and fine-mapping of their glycemic responses to different diets will allow us to obtain preliminary data on the mechanistic basis underlying inter-individual dietary glycemic response. Data from this study will form the basis of large clinical trials, the development of novel foods, and/or novel technologies to alter the gut micro-biome for optimal blood glucose control.

Detailed Description

Diet plays a large role in determining our blood glucose levels, which in turn, can affect our risk of diabetes mellitus and heart disease. Traditionally, dietary recommendations are made for populations or groups of people. There is increasing recognition that each of us is an individual, with our own genetic background, physiology, and lifestyle. Each of these affects the way we digest and use the nutrients in foods that we consume. Recent studies have shown that different individuals consuming the same meal have very different glycaemic responses. The optimal diet for one person may not be the optimal diet for another. This could explain the controversies around our attempts to define the best diet for the population - there simply isn't one diet that is optimal for everybody. In our study, we will utilize an n-of-1 study design where each person receives all 3 diets one after another in a random sequence. We will measure blood glucose using a device that measures the interstitial blood glucose every 15 minutes for 2 weeks. The glycaemic effects of each diet will then be compared with the control diet in the same individual such that each person serves as his/her own control. The response is thus individualized.

Study Timeline

• Study Start: 2020-09-03
• Study First Submitted: 2022-05-23
• Study First Submitted QC: 2022-05-27
• Study First Posted: 2022-06-02
• Primary Completion: 2022-10-11
• Status Verified: 2024-11
• Last Update Submitted: 2024-11-13
• Last Update Posted: 2024-11-15
• Study Completion: 2024-12-31

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: Male
• Target Recruitment: 120

Inclusion Criteria

• Ability to give informed consent
• 21 to 60 years of age (inclusive) at screening
• Race must be Chinese or Indian or Malay
• Overtly healthy males, as determined by medical history, physical examination and laboratory results within normal reference range for the population or investigator site, or results with acceptable deviations that are judged to be not clinically significant by the investigator
• Males with stable medical problems that, in the investigator's opinion, will not significantly alter the performance of the biomarker panel, will not place the subject at increased risk by participating in the study, and will not interfere with interpretation of the data.
• Not on any regular medications (western / traditional medicine)
• Nutritional supplements with established chemical composition that can be ascertained and clearly recorded is acceptable. Participants have to stop taking nutritional supplements at least 2 weeks before the start of study period.
• Reliable and willing to make themselves available for the duration of the study and are willing to follow study procedures.

Exclusion Criteria

• Female

• A current smoker, or has smoked in the past 2 years

• History or presence of current lipid and cardiovascular disorders, respiratory, hepatic, renal, gastrointestinal, endocrine, lipid disorder, haematological, malignancy or neurological disorders capable of significantly altering the performance of the biomarker panel; or of interfering with the interpretation of data

• History of food allergies to test foods

• Regular use of medication that may affect glucose metabolism (e.g. steroids)

• History of type 1/type 2 diabetes and use of anti-diabetic medications in the past

• History of regular use of aspirin or vitamin C (both can affect glucose readings on CGM)

• Regularly use known drugs or abuse within 3 years

• Known or ongoing psychiatric disorders within 3 years

• Have donated blood of more than 500 mL within 4 weeks of study enrolment

• Have an average weekly alcohol intake that exceeds 21 units per week (males) and 14 units per week (females):

  • 1 unit = 12 oz or 360 mL of beer;
  • 5 oz or 150 mL of wine;
  • 1.5 oz or 45 mL of distilled spirits

• Uncontrolled hypertension (blood pressure [BP] >160/100mmHg)

• Active infection requiring systemic antiviral or antimicrobial therapy that will not be completed prior to Study Day 1

• Treatment with any investigational drug, or biological agent within one (1) month of screening or plans to enter into an investigational drug/ biological agent study during the duration of this study

• Treatment with any investigational drug, or biological agent within one (1) month of screening or plans to enter into an investigational drug/ biological agent study during the duration of this study

• History of bleeding diathesis or coagulopathy

• Any of the following laboratory values at screening:

Fasting glucose >=126mg/dL(>=7mmol/L) or 2 hour post-prandial glucose >=200mg/dL (>=11.1mmol/L)

• Clinically significant (as determined by investigator) abnormalities on laboratory examination that will increase risk to the patient or interfere with data integrity
• Have any other conditions, which, in the opinion of the Investigator would make the subject unsuitable for inclusion, or could interfere with the subject participating in or completing the study
• Significant change in weight (+/- 5%) during the past month
• Any hospitalization or surgery during the 6 months before enrolment in study
• Participants with antibiotic use in past 2 months

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: CROSSOVER

Arm && Interventions

Group	Intervention	Description
High Protein Diet	High Protein Diet	Diet consisting of 40% carbohydrate, 40% protein, 20% fat, with Glycemic Index \~55-65.
High Carbohydrate-Low Glycemic Index Diet	High Carbohydrate-Low Glycemic Index Diet	Diet consisting of 60% carbohydrate, 20% fat, 20% protein, with Glycemic Index \~45-50.
High Carbohydrate-High Glycemic Index Diet	High Carbohydrate-High Glycemic Index Diet	Diet consisting of 60% carbohydrate, 20% fat, 20% protein.
High Fat Diet	High Fat Diet	Diet consisting of 40% carbohydrate, 40% fat (25% monounsaturated fatty acids), 20% protein, with Glycemic Index \~55-65.

Primary Outcome Measures

Name	Time	Method
Inter-individual differences in glycemic response to various meal types.	14 days	To quantify inter-individual differences in glycemic response to high carbohydrate-high glycemic index, high carbohydrate-low glycemic index, high-protein and high-fat diets using continuous glucose monitoring.

Secondary Outcome Measures

Name	Time	Method
Correlation of metagenomic profile to inter-individual glycemic response differences from various meal types.	14 days	Correlation of metagenomic profile from shotgun sequencing of DNA to differences in inter-individual glycemic response of individuals from various meal types (primary outcome).
Correlation of metabolome profile to inter-individual glycemic response differences from various meal types.	14 days	Correlation of metabolome profile from blood samples to differences in inter-individual glycemic response of individuals from various meal types (primary outcome).
Correlation of sleep score quality to different glycemic responses from various meal types.	14 days	Correlation of sleep score quality derived from Fitbit active watch to different glycemic responses from various meal types (primary outcome).
Correlation of number of step counts from physical activity to different glycemic responses from various meal types.	14 days	Correlation of number of step counts from physical activity derived from Fitbit active watch to different glycemic responses from various meal types (primary outcome).

Trial Locations

Locations (1)

National University of Singapore; 🇸🇬 Singapore, Singapore