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

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 A recent, unpublished study found the variant to be associated with lower BMI, body fat percentage, bodyweight, and lipid levels independent of the lower intake of refined sugar.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.

Study Overview

• Status: Completed
• Conditions: Diabetes Mellitus, Type 2; Metabolic Disease; Sucrase Isomaltase Deficiency; Sucrose Intolerance Congenital
• Intervention / Treatment: Other: Cross-over study

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.

• Study Type: Interventional
• Enrollment (Actual): 38
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Greenland
  • Maniitsoq, Greenland
    • Maniitsoq Healthcare Center
  • Nuussuaq, Greenland, 3905
    • Pikialaarfik, Greenland Institute of Natural Resources

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 80 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Randomized
• Interventional Model: Crossover Assignment
• Masking: Triple

Number of Arms

2

Arms and Interventions

Participant Group / 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Glycemic variability during 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.	During the 3 days of intervention with Western diet.
Glycemic variability during 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.	During the 3 days of intervention with Inuit diet.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sweet Bias Score	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.	Baseline (to assess differences between genotypes, independent of the intervention)
Fat Bias Score	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.	Baseline (to assess differences between genotypes, independent of the intervention)
High-fat savory preference	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.	Baseline (to assess differences between genotypes, independent of the intervention)
Low-fat savory preference	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.	Baseline (to assess differences between genotypes, independent of the intervention)
High-fat sweet preference	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.	Baseline (to assess differences between genotypes, independent of the intervention)
Low-fat sweet preference	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.	Baseline (to assess differences between genotypes, independent of the intervention)
Implicit wanting score: High-fat savory foods	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.	Baseline (to assess differences between genotypes, independent of the intervention)
Implicit wanting score: Low-fat savory foods	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.	Baseline (to assess differences between genotypes, independent of the intervention)
Implicit wanting score: High-fat sweet foods	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.	Baseline (to assess differences between genotypes, independent of the intervention)
Implicit wanting score: Low-fat sweet foods	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.	Baseline (to assess differences between genotypes, independent of the intervention)
Habitual diet	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%.	Baseline (to assess differences between genotypes, independent of the intervention)
Intake in a snacking test meal	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.	Baseline (to assess differences between genotypes, independent of the intervention)
Sucrose sweetness sensitivity	Ability to taste a difference between iso-intense solutions of sucrose and fructose+glucose using a 2-alternative forced choice test	Baseline (to assess differences between genotypes, independent of the intervention)
Sweet liking	Hedonic rating of liking of iso-intense solutions of sucrose, fructose, glucose and fructose+glucose using a visual analogue scale (0-100 mm)	Baseline (to assess differences between genotypes, independent of the intervention)
Perceived intensity of sugars	Hedonic rating of perceived intensity of iso-intense solutions of sucrose, fructose, glucose and fructose+glucos using a visual analogue scale (0-100 mm)	Baseline (to assess differences between genotypes, independent of the intervention)
Plasma lipids	Changes in fasting plasma measures of VLDL-cholesterol, LDL-cholesterol, HDL-cholesterol, total cholesterol, remnant cholesterol, and triglycerides	The day before and the day after each dietary intervention period.
Serum insulin	Changes in serum insulin. Fasting sample.	The day before and the day after each dietary intervention period.
Plasma CRP	Changes in plasma CRP. Fasting sample.	The day before and the day after each dietary intervention period.
Plasma acetate	Changes in plasma acetate. Fasting sample.	The day before and the day after each dietary intervention period.
Plasma propionate	Changes in plasma propionate. Fasting sample.	The day before and the day after each dietary intervention period.
Plasma butyrate	Changes in plasma butyrate. Fasting sample.	The day before and the day after each dietary intervention period.
HbA1c	Fasting glycated hemoglobin	Baseline
Fecal acetate	Changes in fecal acetate.	Before and on the last day or on the day after each dietary intervention period.
Fecal propionate	Changes in fecal propionate.	Before and on the last day or on the day after each dietary intervention period.
Fecal butyrate	Changes in fecal butyrate.	Before and on the last day or on the day after each dietary intervention period.
Fecal pH	pH of fecal samples.	Before and on the last day or on the day after each dietary intervention period.
Changes in gut microbiota composition	Changes in gut microbiota composition between baseline and end of each dietary intervention period. Microbiota composition is measured by genome sequencing fecal samples.	Before and on the last day or on the day after each dietary intervention period.
Baseline gut microbiota composition	Characterization of the gut microbiota composition. Microbiota composition is measured by genome sequencing fecal samples.	Before intervention (baseline).
Fecal carbohydrates	Content of carbohydrates in fecal samples and changes in this during the intervention periods.	Before and on the last day or on the day after each dietary intervention period.
Glycemic variability during habitual diet	Glycemic variability will be measured by mean amplitude of glycemic excursions (MAGE)	Measured during 7 days of wash-out

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Body weight	Weight (kg). Measured when the participant is wearing light underwear.	Baseline (participant characteristics)
Height	Height (cm). Measured when the participant is not wearing shoes.	Baseline (participant characteristics)
Hip circumference	Hip circumference (cm). Measured when the participant is wearing light underwear.	Baseline (participant characteristics)
Waist circumference	Waist circumference (cm). Measured when the participant is wearing light underwear.	Baseline (participant characteristics)
Body composition	Body fat percentage measured using a Tanita body composition analyser.	Baseline (participant characteristics)
Plasma lipodomics.	For future analyses	The day before and the day after each dietary intervention period.
Plasma metabolomics.	For future analyses	The day before and the day after each dietary intervention period.
Plasma proteomics.	For future analyses	The day before and the day after each dietary intervention period.
Fecal lipodomics	For future analyses	The day before and the day after each dietary intervention period.
Fecal metabolomics.	For future analyses	The day before and the day after each dietary intervention period.
Fecal proteomics.	For future analyses	The day before and the day after each dietary intervention period.

Collaborators and Investigators

• Sponsor: Steno Diabetes Center Copenhagen
• Collaborators: The Novo Nordisk Foundation Center for Basic Metabolic Research; Department of Food Science, University of Copenhagen; Steno Diabetes Center Greenland
• Investigators: Principal Investigator: Marit E Jørgensen, Prof., Steno Diabetes Center Greenland

Study record dates

Study Major Dates

• Study Start (Actual): January 8, 2022
• Primary Completion (Actual): May 7, 2022
• Study Completion (Actual): May 7, 2022

Study Registration Dates

• First Submitted: September 27, 2021
• First Submitted That Met QC Criteria: May 10, 2022
• First Posted (Actual): May 17, 2022

Study Record Updates

• Last Update Posted (Actual): May 31, 2022
• Last Update Submitted That Met QC Criteria: May 25, 2022
• Last Verified: May 1, 2022

More Information

Terms related to this study

• Keywords: Diabetes; Microbiota; Food preference; Glucose variability; Inuit; Greenlandic diet; SI
• Additional Relevant MeSH Terms: Glucose Metabolism Disorders; Endocrine System Diseases; Diabetes Mellitus; Diabetes Mellitus, Type 2; Metabolic Diseases
• Other Study ID Numbers: F15319-03

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No