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 A recent, unpublished study found the variant to be associated with lower BMI, body fat percentage, bodyweight, and lipid levels independent of the lower intake of refined sugar.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.
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
Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
TRIPLE
Enrollment
38
Traditional Inuit Diet and Western Diet.
Maniitsoq Healthcare Center
Maniitsoq, Greenland
Pikialaarfik, Greenland Institute of Natural Resources
Nuussuaq, Greenland
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.
Time frame: 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.
Time frame: During the 3 days of intervention with Inuit diet.
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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%.
Time frame: 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.
Time frame: 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
Time frame: 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)
Time frame: 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)
Time frame: 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
Time frame: The day before and the day after each dietary intervention period.
Serum insulin
Changes in serum insulin. Fasting sample.
Time frame: The day before and the day after each dietary intervention period.
Plasma CRP
Changes in plasma CRP. Fasting sample.
Time frame: The day before and the day after each dietary intervention period.
Plasma acetate
Changes in plasma acetate. Fasting sample.
Time frame: The day before and the day after each dietary intervention period.
Plasma propionate
Changes in plasma propionate. Fasting sample.
Time frame: The day before and the day after each dietary intervention period.
Plasma butyrate
Changes in plasma butyrate. Fasting sample.
Time frame: The day before and the day after each dietary intervention period.
HbA1c
Fasting glycated hemoglobin
Time frame: Baseline
Fecal acetate
Changes in fecal acetate.
Time frame: Before and on the last day or on the day after each dietary intervention period.
Fecal propionate
Changes in fecal propionate.
Time frame: Before and on the last day or on the day after each dietary intervention period.
Fecal butyrate
Changes in fecal butyrate.
Time frame: Before and on the last day or on the day after each dietary intervention period.
Fecal pH
pH of fecal samples.
Time frame: 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.
Time frame: 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.
Time frame: Before intervention (baseline).
Fecal carbohydrates
Content of carbohydrates in fecal samples and changes in this during the intervention periods.
Time frame: 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)
Time frame: Measured during 7 days of wash-out
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