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The Effect on Metabolism, Food Intake and Preferences of a Knockout Gene Variant Involved in Carbohydrate Metabolism

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ClinicalTrials.gov Identifier: NCT05375656
Recruitment Status : Completed
First Posted : May 17, 2022
Last Update Posted : May 31, 2022
Sponsor:
Collaborators:
The Novo Nordisk Foundation Center for Basic Metabolic Research
Department of Food Science, University of Copenhagen
Steno Diabetes Center Greenland
Information provided by (Responsible Party):
Steno Diabetes Center Copenhagen

Brief Summary:

Around 10% has type 2 diabetes in Greenland, despite being a practically unknown disease only six decades ago. The drastic increase is of great concern, especially considering the transition that have occurred during the same decades going from a fisher-hunter lifestyle towards a more western lifestyle. Today, traditional marine foods are still increasingly being replaced by imported foods high in refined sugar (sucrose) and starch. Furthermore, recent studies discovered that the Greenlandic population harbors a different genetic architecture behind type 2 diabetes. Hence, obtaining more knowledge on interactions between lifestyle, genetics, and metabolism is therefore crucial in order to ameliorate the growing curve, or maybe even turn it around.

Sucrose intolerance is in general rare; however, it is a common condition in Greenland and other Inuit populations. Here it is caused by a genetic variant in the sucrase-isomaltase (SI) gene, resulting in complete loss of enzyme function and hence an inability to digest sucrose and some of the glycosidic bonds in starch, both carbohydrates that are not part of the traditional Inuit diet. A recent, unpublished study found the variant to be associated with lower BMI, body fat percentage, bodyweight, and lipid levels independent of the lower intake of refined sugar. This might be explained by differences in the metabolism of carbohydrates and in the gut microbiota. The healthier phenotype was confirmed by a SI knockout mouse model, which furthermore interestingly indicated that the variant might alter food and taste preferences.

It is anticipated that the drastic increase in type 2 diabetes in Greenland can be explained at least partly by the complex interaction between lifestyle and genetics. Therefore, the aim is to investigate if metabolic and microbial differences can explain the healthier phenotype of the homozygous carriers of the SI variant than wildtype individuals amd perform a 3-day cross-over dietary intervention using assigning subjects to a traditional Greenlandic diet and a Western diet. Moreover, the aim is to assess whether their food and taste preferences are different. The study will help us to understand the complex interactions between lifestyle, behavior, genetics, the microbiota and the host metabolism.


Condition or disease Intervention/treatment Phase
Diabetes Mellitus, Type 2 Metabolic Disease Sucrose Intolerance Congenital Sucrase Isomaltase Deficiency Other: Cross-over study Not Applicable

Detailed Description:

In this human study, effects of the SI knockout variant on metabolism, dietary habits and food preferences will be quantified. The study will be unique by being the first assessing the effect of a complete loss of SI function, which it is only feasible in Arctic populations.

Differences between homozygous (HO) carriers and heterozygous (HE)/wildtype (WT) individuals are suspected to be large on a carbohydrate-rich diet and small on a traditional diet. The following hypotheses will be addressed:

HO carriers metabolize carbohydrates differently than HE+WT individuals:

  1. HO have a lower glycemic variability on their habitual diet than WT+HE.
  2. HO have a lower glycemic variability on a starch and sucrose rich diet than WT+HE.
  3. HO have a glycemic variability similar to WT+HE on a traditional diet low in carbohydrates.

    HO carriers have different food preferences than HE+WT individuals:

  4. HO have a lower sweet taste preference compared to WT+HE.
  5. HO perceive iso-intense solutions of sucrose, fructose, and glucose differently in sweet taste intensity and WT+HE will perceive them iso-intense.
  6. HO consume less high-sugar-low-fat foods than WT+HE.
  7. HO have similar intake and preference for high-sugar-high-fat foods as WT+HE.

    HO carriers have a microbiota different from HE+WT individuals:

  8. Diversity and abundance of starch-fermenting bacteria is higher in HO than in WT+HE and the abundance of Parabacteroides is lower.
  9. The increase in starch-fermenting bacteria as well as fecal and circulating levels of short chain fatty acids is larger for HO than in WT+HE on a starch and sucrose rich diet.
  10. A diet low in carbohydrates will alter the microbiota similarly for HO and WT+HE.

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Study Type : Interventional  (Clinical Trial)
Actual Enrollment : 38 participants
Allocation: Randomized
Intervention Model: Crossover Assignment
Intervention Model Description:

A cross-over design will be applied to the intervention. Participants will be randomized to first receive either a diet high in starch and relatively high in sucrose, resembling a western diet, or a diet low in carbohydrate with many marine foods, resembling a traditional Inuit diet. There will be a wash out period of 7 days between the two diets.

Each participant will throw a dice in order to randomize the order of which the participants receive the two intervention diets.

Masking: Triple (Participant, Care Provider, Outcomes Assessor)
Masking Description: The study will be blinded with respect to the genotype of the participants for everyone involved in the study except for the investigator. The dietary intervention will not be blinded.
Primary Purpose: Prevention
Official Title: The Effect on Metabolism, Food Intake and Preferences of a Knockout Gene Variant Involved in Carbohydrate Metabolism
Actual Study Start Date : January 8, 2022
Actual Primary Completion Date : May 7, 2022
Actual Study Completion Date : May 7, 2022


Arm Intervention/treatment
Active Comparator: Traditional Inuit Diet

The traditional Inuit diet will consist of local foods, being primarily of animal origin, e.g. fish, marine mammals, caribou, and lamb. The diet will be supplemented with eggs, potatoes, and berries, and/or other foods low in starch and with no sucrose content. The diet will therefore have a high content of fat and protein, a low content of carbohydrate and no content of sucrose.

The participants will receive foods that will cover at least 100% of their energy requitement. Each participant will throw a dice in order to randomize the order of which the participants receive the two intervention diets.

Other: Cross-over study
Traditional Inuit Diet and Western Diet.

Experimental: Western Carbohydrate-Rich Diet

The Western diet will have high amounts of grain products, e.g. bread, pasta, rice, as well as fruits and vegetables and some foods with a high sucrose content, e.g. cake and sweet snacks and/or drinks, and cereal products with added sucrose. The diet will have a low amount of meat. Hence, the diet will be high in carbohydrates, starch, and some sucrose and have a lower content of protein and fat.

The participants will receive foods that will cover at least 100% of their energy requitement. Each participant will throw a dice in order to randomize the order of which the participants receive the two intervention diets.

Other: Cross-over study
Traditional Inuit Diet and Western Diet.




Primary Outcome Measures :
  1. Glycemic variability during Western diet [ Time Frame: During the 3 days of intervention with Western diet. ]
    Glycemic variability will be measured by mean amplitude of glycemic excursions (MAGE) during the whole study period, i.e. during both Western and Inuit diet and the wash-out period in between.

  2. Glycemic variability during Inuit diet [ Time Frame: During the 3 days of intervention with Inuit diet. ]
    Glycemic variability will be measured by mean amplitude of glycemic excursions (MAGE) during the whole study period, i.e. during both Western and Inuit diet and the wash-out period in between.


Secondary Outcome Measures :
  1. Sweet Bias Score [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    As a food reward measure, explicit liking for foods with sweet relative to savory taste will be assessed using the Leeds Food Preference Questionnaire. A sweet bias score will be estimated, where a positive score indicates higher preference for sweet relative to savoury foods and a negative score indicates higher preference for savoury foods.

  2. Fat Bias Score [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    As a food reward measure, explicit liking for foods with high-fat relative to low-fat content will be assessed using the Leeds Food Preference Questionnaire. A fat bias score will be estimated, where a positive score indicates higher preference for high-fat relative to low-fat foods and a negative score indicates higher preference for low-fat foods.

  3. High-fat savory preference [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    As a food reward measure, explicit liking for high-fat savory foods will be assessed in the Leeds Food Preference Questionnaire. The average rating from 0-100 on the visual analogue scale is calculated for all four foods within the food category.

  4. Low-fat savory preference [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    As a food reward measure, explicit liking for low-fat savory foods will be assessed in the Leeds Food Preference Questionnaire. The average rating from 0-100 on the visual analogue scale is calculated for all four foods within the food category.

  5. High-fat sweet preference [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    As a food reward measure, explicit liking for high-fat sweet foods will be assessed in the Leeds Food Preference Questionnaire. The average rating from 0-100 on the visual analogue scale is calculated for all four foods within the food category.

  6. Low-fat sweet preference [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    As a food reward measure, explicit liking for low-fat sweet foods will be assessed in the Leeds Food Preference Questionnaire. The average rating from 0-100 on the visual analogue scale is calculated for all four foods within the food category.

  7. Implicit wanting score: High-fat savory foods [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]

    As a food reward measure, implicit wanting for high-fat savory foods will be assessed using the Leeds Food Preference Questionnaire.

    The 'implicit wanting' score is calculated based on a combination of reaction time and choice or non-choice of foods in the forced choice paradigm. A positive score indicates a higher preference for this food category compared to the other food categories, and a negative score indicates a lower preference for this food category.


  8. Implicit wanting score: Low-fat savory foods [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]

    As a food reward measure, implicit wanting for low-fat savory foods will be assessed using the Leeds Food Preference Questionnaire.

    The 'implicit wanting' score is calculated based on a combination of reaction time and choice or non-choice of foods in the forced choice paradigm. A positive score indicates a higher preference for this food category compared to the other food categories, and a negative score indicates a lower preference for this food category.


  9. Implicit wanting score: High-fat sweet foods [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]

    As a food reward measure, implicit wanting for high-fat sweet foods will be assessed using the Leeds Food Preference Questionnaire.

    The 'implicit wanting' score is calculated based on a combination of reaction time and choice or non-choice of foods in the forced choice paradigm. A positive score indicates a higher preference for this food category compared to the other food categories, and a negative score indicates a lower preference for this food category.


  10. Implicit wanting score: Low-fat sweet foods [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]

    As a food reward measure, implicit wanting for low-fat sweet foods will be assessed using the Leeds Food Preference Questionnaire.

    The 'implicit wanting' score is calculated based on a combination of reaction time and choice or non-choice of foods in the forced choice paradigm. A positive score indicates a higher preference for this food category compared to the other food categories, and a negative score indicates a lower preference for this food category.


  11. Habitual diet [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    Habitual dietary intake will be assessed using a food frequency questionnaire. Macronutrient composition and content of sugar will be assessed as well as characterization of differences in food choice with respect to sweet foods and foods rich in starch. Intake will be expressed in g/day as well as E%.

  12. Intake in a snacking test meal [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    Using an ad libitum snacking test meal, preferences will be assessed for sweet-taste and content of sucrose and fat as well as other sweeteners than sucrose, e.g. honey.

  13. Sucrose sweetness sensitivity [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    Ability to taste a difference between iso-intense solutions of sucrose and fructose+glucose using a 2-alternative forced choice test

  14. Sweet liking [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    Hedonic rating of liking of iso-intense solutions of sucrose, fructose, glucose and fructose+glucose using a visual analogue scale (0-100 mm)

  15. Perceived intensity of sugars [ Time Frame: Baseline (to assess differences between genotypes, independent of the intervention) ]
    Hedonic rating of perceived intensity of iso-intense solutions of sucrose, fructose, glucose and fructose+glucos using a visual analogue scale (0-100 mm)

  16. Plasma lipids [ Time Frame: The day before and the day after each dietary intervention period. ]
    Changes in fasting plasma measures of VLDL-cholesterol, LDL-cholesterol, HDL-cholesterol, total cholesterol, remnant cholesterol, and triglycerides

  17. Serum insulin [ Time Frame: The day before and the day after each dietary intervention period. ]
    Changes in serum insulin. Fasting sample.

  18. Plasma CRP [ Time Frame: The day before and the day after each dietary intervention period. ]
    Changes in plasma CRP. Fasting sample.

  19. Plasma acetate [ Time Frame: The day before and the day after each dietary intervention period. ]
    Changes in plasma acetate. Fasting sample.

  20. Plasma propionate [ Time Frame: The day before and the day after each dietary intervention period. ]
    Changes in plasma propionate. Fasting sample.

  21. Plasma butyrate [ Time Frame: The day before and the day after each dietary intervention period. ]
    Changes in plasma butyrate. Fasting sample.

  22. HbA1c [ Time Frame: Baseline ]
    Fasting glycated hemoglobin

  23. Fecal acetate [ Time Frame: Before and on the last day or on the day after each dietary intervention period. ]
    Changes in fecal acetate.

  24. Fecal propionate [ Time Frame: Before and on the last day or on the day after each dietary intervention period. ]
    Changes in fecal propionate.

  25. Fecal butyrate [ Time Frame: Before and on the last day or on the day after each dietary intervention period. ]
    Changes in fecal butyrate.

  26. Fecal pH [ Time Frame: Before and on the last day or on the day after each dietary intervention period. ]
    pH of fecal samples.

  27. Changes in gut microbiota composition [ Time Frame: Before and on the last day or on the day after each dietary intervention period. ]
    Changes in gut microbiota composition between baseline and end of each dietary intervention period. Microbiota composition is measured by genome sequencing fecal samples.

  28. Baseline gut microbiota composition [ Time Frame: Before intervention (baseline). ]
    Characterization of the gut microbiota composition. Microbiota composition is measured by genome sequencing fecal samples.

  29. Fecal carbohydrates [ Time Frame: Before and on the last day or on the day after each dietary intervention period. ]
    Content of carbohydrates in fecal samples and changes in this during the intervention periods.

  30. Glycemic variability during habitual diet [ Time Frame: Measured during 7 days of wash-out ]
    Glycemic variability will be measured by mean amplitude of glycemic excursions (MAGE)


Other Outcome Measures:
  1. Body weight [ Time Frame: Baseline (participant characteristics) ]
    Weight (kg). Measured when the participant is wearing light underwear.

  2. Height [ Time Frame: Baseline (participant characteristics) ]
    Height (cm). Measured when the participant is not wearing shoes.

  3. Hip circumference [ Time Frame: Baseline (participant characteristics) ]
    Hip circumference (cm). Measured when the participant is wearing light underwear.

  4. Waist circumference [ Time Frame: Baseline (participant characteristics) ]
    Waist circumference (cm). Measured when the participant is wearing light underwear.

  5. Body composition [ Time Frame: Baseline (participant characteristics) ]
    Body fat percentage measured using a Tanita body composition analyser.

  6. Plasma lipodomics. [ Time Frame: The day before and the day after each dietary intervention period. ]
    For future analyses

  7. Plasma metabolomics. [ Time Frame: The day before and the day after each dietary intervention period. ]
    For future analyses

  8. Plasma proteomics. [ Time Frame: The day before and the day after each dietary intervention period. ]
    For future analyses

  9. Fecal lipodomics [ Time Frame: The day before and the day after each dietary intervention period. ]
    For future analyses

  10. Fecal metabolomics. [ Time Frame: The day before and the day after each dietary intervention period. ]
    For future analyses

  11. Fecal proteomics. [ Time Frame: The day before and the day after each dietary intervention period. ]
    For future analyses



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   18 Years to 80 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Homozygous carriers of the c.273_274delAG variant in the SI-gene (cases)
  • Homozygous non-carriers of the c.273_274delAG variant in the SI-gene (controls)

Exclusion Criteria:

  • Diagnosis of diabetes or pharmacological treatment of diabetes.
  • Gastrointestinal diseases such as inflammatory bowel disease, gastrointestinal cancer, and ulcer. Persons with mild gastrointestinal problems are not excluded, e.g. persons with lactose-intolerance who normally do not have any gastrointestinal problems.
  • Homozygous carriers of the TBC1D4 risk variant p.Arg684Ter.
  • Lack of compliance with the procedures in the study protocol, judged by Investigator.
  • For the homozygous carriers of the c.273_274delAG variant: rise in blood glucose in an oral sucrose tolerance test.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05375656


Locations
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Greenland
Maniitsoq Healthcare Center
Maniitsoq, Greenland
Pikialaarfik, Greenland Institute of Natural Resources
Nuussuaq, Greenland, 3905
Sponsors and Collaborators
Steno Diabetes Center Copenhagen
The Novo Nordisk Foundation Center for Basic Metabolic Research
Department of Food Science, University of Copenhagen
Steno Diabetes Center Greenland
Investigators
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Principal Investigator: Marit E Jørgensen, Prof. Steno Diabetes Center Greenland
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Responsible Party: Steno Diabetes Center Copenhagen
ClinicalTrials.gov Identifier: NCT05375656    
Other Study ID Numbers: F15319-03
First Posted: May 17, 2022    Key Record Dates
Last Update Posted: May 31, 2022
Last Verified: May 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Steno Diabetes Center Copenhagen:
Inuit
Greenlandic diet
Diabetes
Microbiota
Glucose variability
Food preference
SI
Additional relevant MeSH terms:
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Diabetes Mellitus, Type 2
Metabolic Diseases
Diabetes Mellitus
Glucose Metabolism Disorders
Endocrine System Diseases