Diet and dietary changes among people with type 2
diabetes in the clinical intervention study Fiberdia
Master Thesis by
Kristine Duus Molven
Department of Nutrition Faculty of Medicine
University of Oslo
May 2018
Diet and dietary changes among people with type 2 diabetes in the clinical
intervention study Fiberdia
Master thesis by Kristine Duus Molven
Supervisors:
Anne-Marie Aas Monica Hauger Carlsen
Eline Birkeland
Department of Nutrition Faculty of Medicine
UNIVERSITY OF OSLO
May 2018
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© Kristine Duus Molven 2018
Diet and dietary changes among people with type 2 diabetes in the clinical intervention study Fiberdia
http://www.duo.uio.no/
Print: Reprosentralen, University of Oslo
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Acknowledgements
This master thesis is a part of the Fiberdia study, carried out at Oslo University Hospital, Aker, in collaboration with the Department of Nutrition at the Faculty of Medicine, University of Oslo.
First of all, I would like to thank my main supervisor Anne-Marie Aas and my co-supervisors Monica Hauger Carlsen and Eline Birkeland. I am very grateful that you allowed me to take part in this project and for providing valuable feedback on my thesis. Anne-Marie; thank you for the great help you have provided during this study and for constantly being so positive.
Monica; thank you for always having the door open and for helping me with KBS. Eline;
thank you for warmly welcoming me and for taking time to share your scientific as well as linguistic knowledge with me.
I am also indebted to the Type 2 diabetes research group at Aker for letting me take part in your study group this year. It has been very inspiring to participate in such an experienced environment. In addition, I would like to thank the Division of Nutrition Epidemiology at the Department of Nutrition for opening up your group for us master students and Nina Nordberg for teaching me how to scan the FFQs.
Many, many thanks to all the participants in Fiberdia for contributing to scientific knowledge!
Without you, this master thesis would never have been possible. Moreover, I extend my gratitude to the kind study nurses Åse Halsne, Gøril Vinje and Gro Boezelijn at Aker who helped with the comprehensive data collection in Fiberdia.
To my fellow students – thanks a lot for sharing lunches, laughter and frustrations, for helpful conversations and for moral support. I am also grateful to my family and friends. Thank you for reassuring words and for letting me know that studying is only one side of life. I would like to thank my mum for caring for me and for constantly encouraging me. A special thank you to my dad for always listening and for giving me valuable feedback through all my years as a pupil and later as a student. I have learned a lot from you.
Last, but not least, I want to thank my dear Eivind for always believing in me, making me laugh and supporting me through this whole year when working with my master thesis.
Oslo, May 2018 Kristine Duus Molven
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Abstract
Background: Type 2 diabetes (T2D) is a metabolic, chronic disease with a substantial contribution to morbidity and mortality all over the world. Diet is one of the cornerstones in management of the disease, and prebiotic fibres may contribute to changes in energy intake.
Still, there is limited research in the field of dietary intake among people with T2D and few studies have evaluated the effect of prebiotic supplementation on dietary intake.
Objective: To describe the diet among the participants in the Norwegian cross-over study Fiberdia, and to investigate whether the participants’ diet changed during an intervention period with prebiotic fibres.
Subjects and methods: This master thesis was a sub-study to Fiberdia; a cross-over study of people with T2D where each participant was randomized to consume 16 g/d of prebiotics or placebo during six weeks and then, after a washout period, switch to the intervention that the individual did not receive the first time. During the study, the participants returned a food frequency questionnaire (FFQ) at baseline, after test period 1 and after test period 2. Baseline food intake (n = 31) was compared with Norwegian dietary recommendations and with the dietary intake of the general population reported in Norkost 3. Dietary intake reported after the prebiotic period was compared with dietary intake reported in the placebo period (n = 25).
Results: The participants in Fiberdia had a diet characterized by a low intake of
carbohydrates, with a complementary high intake of protein and fat. They reported a high intake of fibre and a low intake of added sugar. Overall, the adherence to dietary
recommendations was varying. The participants made somewhat healthier food choices than the general population. There was no significant difference in energy intake, but a
significantly lower intake of total fat (78 g/d vs. 89 g/d: P = 0.03 and 34 E % vs. 37 E %: P = 0.02) and monounsaturated fat (29 g/d vs. 34 g/d: P = 0.03 and 13 E % vs. 14 E %: P = 0.03) was found after the period with prebiotic fibres compared with the placebo period.
Conclusion: The diet of the Fiberdia participants was characterized by varying compliance with dietary recommendations, but with slightly healthier food choices than in the general population. Supplementation with prebiotic fibres did not seem to change the energy intake significantly compared with placebo, but needs to be investigated further.
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Table of contents
Acknowledgements ... III Abstract ... V Table of contents ... VII List of abbreviations ... IX List of tables and figures ... X List of appendices ... XI
1 Background ... 1
1.1 Diabetes mellitus ... 1
Definition and aetiology ... 1
Epidemiology ... 2
Health impact ... 3
Treatment and management ... 4
1.2 Fibre ... 8
Carbohydrates and fibre ... 8
Prebiotics ... 9
Health impact ... 11
1.3 Diabetes mellitus and fibre ... 13
The Fiberdia study ... 14
1.4 Dietary assessment ... 15
Food frequency questionnaire ... 16
Measurement errors ... 16
National dietary surveys ... 17
1.5 Rationale behind the master thesis ... 18
2 Objectives ... 19
3 Methods ... 20
3.1 Study population ... 20
3.2 Study design ... 21
3.3 Data collection ... 23
Anthropometric measurements ... 23
Dietary measurement ... 23
3.4 Statistical analyses ... 26
3.5 Ethics ... 27
4 Results ... 28
4.1 FFQs and data cleaning ... 28
VIII
4.2 Diet among participants in the Fiberdia study ... 28
Study population ... 28
Energy and macronutrients ... 29
Micronutrients ... 31
Food items and food groups ... 33
Comparison with Norkost 3 ... 35
4.3 Dietary changes during the Fiberdia study ... 37
Study population ... 37
Changes in energy and macronutrients ... 38
5 Discussion ... 42
5.1 Main findings ... 42
5.2 Methodological considerations ... 42
Study population ... 42
Study design ... 44
5.3 Diet among participants in the Fiberdia study ... 49
Energy and macronutrients ... 49
Micronutrients ... 53
Food items and food groups ... 54
Comparison with Norkost 3 ... 55
5.4 Dietary changes during the Fiberdia study ... 58
Compliance ... 58
Energy and macronutrients ... 58
Challenges and limitations ... 60
6 Conclusion ... 63
7 Future perspectives ... 64
References ... 65
Appendices ... 72
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List of abbreviations
AR Average requirement
BG Baseline group in Fiberdia BMI Body mass index
CG Completer group in Fiberdia CVD Cardiovascular disease
DNSG Diabetes and Nutrition Study Group DP Degree of polymerization
FFQ Food frequency questionnaire FOS Fructooligosaccharides GI Gastrointestinal
GLP-1 Glucagon-like peptide-1 GP General practitioner
HbA1c Haemoglobin A1c (glycated haemoglobin) ITF Inulin-type fructans
KBS Kostberegningssystem
MJ Megajoule
NCGD Norwegian Clinical Guidelines for Diabetes NDH Norwegian Directorate of Health
PAL Physical activity level PYY Peptide YY
P25 25th percentile P75 75th percentile
RCT Randomized clinical trial
RI Recommended intake
SCFA Short-chain fatty acid SD Standard deviation
SPSS Statistical Package for the Social Sciences T1D Type 1 diabetes
T2D Type 2 diabetes
WHO World Health Organization
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List of tables and figures
Table 1. Inclusion and exclusion criteria in Fiberdia………. 20 Table 2. Baseline characteristics of the baseline group in Fiberdia (n = 31) ……….28 Table 3. Estimated daily intake of energy and macronutrients per person from baseline group
in Fiberdia (n = 31) ………. 30
Table 4. Estimated daily intake of micronutrients in absolute amounts per person from
baseline group in Fiberdia (n = 31) ……….…… 31 Table 5. Estimated intake of micronutrients per MJ per person from baseline group in
Fiberdia (n = 31) .……… 32
Table 6. Estimated intake of selected food items and groups in grams per day from baseline group in Fiberdia (n = 31) ………...……… 34 Table 7. Baseline characteristics of the completer group in Fiberdia (n = 25) ………….…. 37 Table 8. Estimated daily intake of energy and macronutrients per person from completer group in Fiberdia (n = 25), at baseline and after prebiotic and placebo periods ………. 40 Table 9. Estimated intake of energy per person from completer group in Fiberdia, sorted according to which order they received fibre and placebo ………. 41 Figure 1. Flow chart of study participants in Fiberdia .………... 21 Figure 2. Overview of study design in Fiberdia……….22 Figure 3. Age distribution of participants in baseline group in Fiberdia (n = 31) …….…… 29 Figure 4. Number and percentage of the study sample (n = 31) adhering to Norwegian
recommendations for macronutrients ………. 30
Figure 5. Number and percentage of the study sample (n = 31) adhering to Norwegian age- and gender-based recommendations for micronutrients ………. 32 Figure 6. Number and percentage of the study sample (n = 31) adhering to Norwegian food based recommendations .………. 35 Figure 7. Food sources to fibre in baseline group in Fiberdia (n=31) .……….. 35 Figure 8. Food sources to saturated fat in baseline group in Fiberdia (n=31).……….. 35 Figure 9. Correlation between weight change and change in energy intake in the first test
period in Fiberdia (n = 25)……….. 41
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List of appendices
Appendix I. Written consent – study information given to the participants in Fiberdia.
Appendix II. Food frequency questionnaire used in Fiberdia.
Appendix III. Guidelines for data cleaning and missing values that were used when handling incomplete FFQs during the master thesis.
Appendix IV. Data from the national dietary survey, Norkost 3.
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1 Background
1.1 Diabetes mellitus
Definition and aetiology
Diabetes mellitus is a chronic metabolic disease where the hallmark is high blood glucose.
According to causality and pathogenesis the disorder can be divided into several subgroups (1) (p. 678). The two most common forms are type 1 diabetes (T1D) and type 2 diabetes (T2D) (2). T1D is an autoimmune-mediated disease where the pancreatic beta-cells are destructed by an inappropriate attack from the body’s own defence system. People with T1D have to use exogenous insulin for the rest of their lives in order to survive (2).
T2D is characterized by progressive loss of beta-cell insulin secretion and is associated with insulin resistance and obesity (2). The autoimmune destruction of beta-cells characteristic for T1D does not occur in T2D. At least initially, many individuals with T2D do not need insulin treatment, but can be treated by diet, exercise and oral medication (2). T2D comprises the majority (around 90 %) of diabetes cases in the world (3). The underlying pathophysiology of T2D is not yet fully known, but well-known risk factors for developing T2D are lifestyle factors like overweight/obesity, unhealthy diet, smoking and physical inactivity in
combination with an underlying genetic/ethnic susceptibility and increasing age (2, 4, 5). The majority of patients tend to be overweight or obese, or have an increased percentage of body fat distributed in the abdominal region (2).
Diabetes can be diagnosed on the basis of several criteria. Norway is following the World Health Organization’s (WHO) current recommendations for the diagnostic criteria, which are that at least one of the following conditions must be fulfilled (5, 6):
Glycated haemoglobin (HbA1c) ≥ 6.5 % (48 mmol/mol).
Fasting plasma glucose ≥ 7.0 mmol/l (126 mg/dl).
Plasma glucose ≥ 11.1 mmol/l (200 mg/dl) 2 hours after initiation of an oral glucose tolerance test.
In addition, a repeated test is required to confirm the diagnosis, but if the patient has typical clinical signs and symptoms related to diabetes and a random plasma glucose above 11.1
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mmol/l (200 mg/dl), a repeated test is not necessary (5, 6). HbA1c analysis should be the first choice for diagnostic testing as it reflects the average plasma glucose level over the preceding 8-12 weeks (6, 7). Once the diagnosis is set, the doctor should clarify which type of diabetes the patient is suffering from (6).
T1D most commonly affects children and young adults, while T2D usually affects persons older than 30 years at diagnosis (1) (p. 678). However, it is important to be aware that both diseases can occur in all age groups, and T2D is now also occurring in young adults and children (1) (p. 678). T1D can be recognized by the presence of one or more autoimmune markers (autoantibodies in blood sample), which are seen in 85-90 % of the patients with T1D (1) (p. 677). For T2D, a highly specific indicator is still lacking (8).
In addition to T1D and T2D, other types of diabetes exist. Gestational diabetes mellitus is a transient condition, diagnosed in some women during pregnancy (1) (p. 679). It usually resolves after delivery, but it is associated with increased long-term risk of developing T2D (8). The other types of diabetes can roughly be classified as having a genetic, exocrine
pancreatic, endocrine or drug-induced aetiology (8). Notably, a recent paper by Ahlqvist et al.
proposed a new stratification of diabetes into five major types (9). The authors suggested that their classification may improve and individualise treatments and also serve to identify – at the time of diagnosis – those individuals who have increased risk of complications. However, more research is needed to confirm whether this new classification scheme represents a significant step towards precision medicine in diabetes.
Epidemiology
WHO’s Global Report on diabetes from 2016 claims that the number of adults (> 18 years) living with diabetes worldwide has nearly quadrupled since 1980 (5). This number is now estimated to 422 million people, and it will likely continue to increase unless preventive measures are taken. Population growth and ageing are contributing factors to the increase, but not solely responsible for it. According to estimates from the International Diabetes
Federation the diabetes prevalence worldwide will be 10.4 % by year 2040, i.e. one in 10 adults (3). In Europe, it is estimated that the current diabetes prevalence is around 9 %, which corresponds to approximately 60 million people (4).
3 In Norway, diabetes is one of the most common chronic diseases, but it is not known exactly how many people that live with diagnosed or undiagnosed diabetes (10). Still, by combining data from different studies and databases including the Norwegian Prescription Database, it has been estimated that 216 000 Norwegians have T2D, which corresponds to a prevalence around 4.1 % (10). In addition, it is assumed that for every 100 persons diagnosed with diabetes, there may be 50-100 unknown cases. This means that the number of people living with diabetes in Norway might be as high as 300 000-400 000 (11).
Overall, diabetes is probably one of the most challenging health problems in the 21st century and a major global disease burden, resulting in increased morbidity and mortality (4). It is estimated that 12 % of global healthcare costs are used to treat diabetes and related complications (3).
Health impact
Diabetes is a chronic disease which can cause complications in many organs of the body. The disorder therefore leads to significant morbidity and increases the overall risk of dying
prematurely, especially if not well managed (5). Diabetic complications can involve acute events like hyperglycaemia that may trigger diabetic ketoacidosis, or treatment-induced hypoglycaemia that can result in seizures and loss of consciousness (5). Complications can also develop over years or decades with negative impact on kidneys, eyes, heart, blood vessels and nerves (12). These long-term complications are often divided into macro- and microvascular pathologies. To prevent long-term complications, good control of glycaemia, lipidaemia and blood pressure is important (13, 14). The number of people living with diabetic macro- and microvascular complications is likely to increase along with increasing prevalence of the disease.
Macrovascular complications
Macrovascular complications are those that affect the large blood vessels and are commonly seen in diabetes. In general, diabetes alone is a major and independent risk factor for
cardiovascular disease (CVD) (12). Also, other major risk factors of CVD are often present in the diabetic patient, in particular hypertension, dyslipidaemia, overweight and/or central obesity, which are often classified together as the “metabolic syndrome” (12). Macrovascular
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complications are frequently associated with premature mortality, and in fact around 50 % of people with diabetes die from CVD (15).
Microvascular complications
While macrovascular complications are the leading cause of death among patients with diabetes, microvascular complications account for the excess morbidity (13). Microvascular complications affect the small blood vessels, especially in the eyes, kidneys and nerves (12).
The pathophysiology is not entirely understood, but duration of diabetes and degree of diabetic control are the major risk factors for developing microvascular disease (12).
Microvascular complications are very common among people with diabetes. Neuropathy affects up to 50 % of diabetic patients, and lower limb amputations can be a consequence of the disease (13). Rates of amputations are typically 10 to 20 times higher in diabetes cohorts than in nondiabetic populations (5). Diabetic nephropathy is the single leading cause of end- stage renal disease worldwide, and it is estimated that 10-20 % of people with diabetes
eventually die of kidney failure (13, 15). In addition, diabetic retinopathy is the most common cause of blindness in many Western countries (12).
Treatment and management
Effective tools are available to manage T2D. A major part of them corresponds to those approaches people are encouraged to follow in order to prevent getting the disease in the first place. Risk factors such as ethnicity, age and genetics are not changeable, but other risk factors are highly modifiable. Therefore, an important strategy in both prevention and
treatment of T2D is lifestyle modification, and key elements are dietary habits, weight control and physical activity (5, 7, 16) (p. 808).
Managing T2D
People with T2D can live long and healthy lives, but it is important that their diabetes is detected early and managed well in order to prevent complications and premature death (5). A combination of diet, physical activity and, if necessary, medication, is the basic principle, where the main goal is to achieve a good metabolic control (6). It is also important to control lipids and blood pressure in order to reduce the risk of CVD and other complications.
Moreover, regular screening and early intervention for complications related to the eyes, kidneys and feet are important (5).
5 The Norwegian Clinical Guidelines for Diabetes (NCGD), compiled by the Norwegian
Directorate of Health (NDH), recommends an HbA1c level around 7 % (53 mmol/mol) as the general treatment goal, but for some people an adjusted goal depending on their age, response to lifestyle adjustments, duration of the disease and risk of hypoglycaemia can be necessary (6). Some people with T2D can achieve their target blood sugar levels with lifestyle
adjustments alone, but for many, treatment with oral medications is also necessary and insulin injections may eventually be needed.
If treatment with medication is necessary, the first-line medication is metformin, an oral prescription drug (6). If metformin is not sufficient to reach the glycaemic target, according to NCGD there is no specific order with respect to “what to use next”. It is, however, suggested to choose between sulphonylurea, dipeptidyl peptidase 4 (DPP-4) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, sodium-glucose cotransporter-2 (SGLT-2) inhibitors and basal insulin. These drugs can be combined or used alone, depending on how the patient responds to them. Overall, it is important to tailor T2D treatment to obtain the best glycaemic control.
NCGD recommend that follow-up of patients with T2D primarily should take place at their general practitioner (GP) (6). This is different from patients with T1D, who according to the guidelines should get interdisciplinary follow-up in the specialist health service with at least one consultation yearly (6). A problem regarding follow-up routines of T2D patients is that they can differ from one GP to another, depending on the GP’s personal interest in diabetes and care for the patient (17, 18). Similarly, the patients’ experience of lifestyle guidance and motivation may differ depending on the GP.
Dietary guidelines
As mentioned, diet is a cornerstone in the management of diabetes. The diet, and especially carbohydrate intake, is essential for glucose control because it is mainly responsible for the postprandial glucose response and plays a central role in managing metabolic control and preventing diabetes complications (16, 19). Also, it is even more important to try to achieve a beneficial diet because of the increased risk for CVD. Hence, people with T2D should be encouraged to obtain weight reduction or weight stabilisation if overweight/obese (16). There is no optimal mix of macronutrients, or a “one-size-fits-all” eating pattern that is appropriate
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for all people with diabetes (19, 20). However, there are evidence-based nutrition guidelines that can be used in nutrition guidance by health care professionals.
American and European dietary guidelines
The position statement “Nutrition Therapy Recommendations for the Management of Adults With Diabetes” was published by the American Diabetes Association in 2013 (19). At large, Americans with diabetes are generally recommended to follow the same advice given to the general U.S. population (19, 21). It is also specified in the position statement that people with diabetes are recommended to consume at least the amount of fibre advised for the general public (about 25 g/day for adult women and 38 g/day for adult men) and less than 10 E % from saturated fat (19). Correspondingly, the position statement specifies the importance of energy balance, physical activity and weight loss if overweight or obese (19).
In March 2018, Diabetes UK published a revised version of “Evidence-based nutrition guidelines for the prevention and management of diabetes” (22). Also Diabetes UK regard weight reduction, physical activity and diet as cornerstones in diabetes management. Their current recommendations include more food-based guidelines than earlier, trying to highlight the importance of foods, rather than focusing on individual nutrients. Key features in the guidelines are to eat more whole grains, fruit and vegetables, fish (two portions of fatty fish each week), nuts and legumes, and to eat less red and processed meat, refined carbohydrates and sugar-sweetened beverages. It is also recommended to decrease salt intake (< 6 g/d) and to limit the alcohol intake (< 14 units/week), whereas saturated fats should be replaced with unsaturated fats.
In 2004, the Diabetes and Nutrition Study Group (DNSG) of the European Association for the Study of Diabetes published an article about nutritional management of patients with diabetes (23). This paper was based on the best available evidence from the scientific literature and the collective clinical experience of members of the group. Some of the main recommendations are as follows:
Carbohydrate intake should range between 45-60 % of total energy intake. Dietary fibre intake should ideally be more than 40 g/day (or 20 g/1000 kcal/day). In patients with no evidence of nephropathy, protein intake may provide 10-20 % of total energy intake. Total fat intake should not contribute with more than 35 % of total energy intake, whereas saturated
7 and trans-unsaturated fatty acids should provide less than 10 % of total daily energy intake. In addition, DNSG emphasizes the importance of energy balance and body weight. For those patients who are overweight (body mass index (BMI) > 25 kg/m2), energy intake should be reduced and energy expenditure increased so that BMI moves towards the recommended range (18.5-25 kg/m2).
DNSG is currently working with an updated version of the guidelines (AM Aas, personal communication). In the meantime, the article from 2004 remains the existing dietary recommendations from the group.
Norwegian dietary guidelines
In Norway, NCGD recommend people with diabetes to maintain a diet in line with
Norwegian dietary advice to the general population (6). Hallmarks in those guidelines are a diet with plenty of vegetables and legumes, a daily intake of fruit, berries and whole-grain products, low-fat variants of dairy products, more fish and seafood than red meat, and lean parts of meat instead of processed meat (24, 25). In addition, NDH recommends to choose oils and margarines, nuts and kernels as fat sources and to reduce the intake of sugar and sodium. Besides, for people with diabetes it is important with individual adaption concerning food with high impact on blood glucose, such as saccharides and starch, and individual or group-based counselling by professionals (26).
In general, evidence-based nutrition guidelines for diabetes developed by countries, continents or health organizations are fairly equal to each other, and they are also fairly similar to those recommendations developed for the general population. The main purposes of such general guidelines are to contribute to an overall good development and function of the body, and to reduce the risk of diet-associated diseases such as CVD and some forms of cancer (21, 25, 27) (p. 19).
Overall, individuals with diabetes often receive more nutritional advice than the rest of the population because diet is such as an important element in managing the disease. Yet, many people with diabetes experience that deciding what to eat and following a food plan is the hardest part of their diabetes treatment (16). Dietary guidance in diabetes should therefore be based on the general guidelines and adjusted to each individual’s metabolic status (lipid profile, renal function, body weight etc.) and personal preferences (19).
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1.2 Fibre
Carbohydrates and fibre
The main dietary sources of energy are the macronutrients carbohydrates, protein and fat (27) (p. 186). Another source of energy included when calculating total energy intake is alcohol, but usually alcohol only contributes marginally (2-6 %) to the total energy intake for most people (27) (p. 311). The energy content of a mixed diet is calculated based on 17 kJ/g protein and available carbohydrate, 37 kJ/g fat and alcohol is considered to yield 29 kJ/g.
Chemically, carbohydrates can be classified based on their molecular size and monomeric composition. The three main carbohydrate groups are divided into sugars (mono- and
disaccharides), oligosaccharides (3-9 monomers) and polysaccharides (10 or more monomers) (27) (p. 250).
Nutritionally, carbohydrates can be divided into two main categories (27) (p. 250). The first includes carbohydrates that are digested and absorbed in the human small intestine and are metabolised in the cells of the body. This category can be referred to as “glycaemic
carbohydrates”, and the main glycaemic carbohydrates are glucose and fructose
(monosaccharides), sucrose and lactose (disaccharides), maltooligosaccharides and starch (polysaccharides). The other main category includes carbohydrates that are passed on to the colon and form substrates for the microbiota there (27) (p. 250). Generally, the non-digestible carbohydrates are referred to as “dietary fibre”, and the main categories of non-digestible carbohydrates are non-starch polysaccharides, resistant oligosaccharides and resistant starch.
Lignin is associated with the naturally occurring plant dietary fibre polysaccharides and comes under the definition of dietary fibre, even though it is not correctly a carbohydrate, but only a related substance (27) (p. 250-254).
Originally, fibre was defined as the components of plants that resist human digestive enzymes (28). However, after obtaining increasing knowledge about fibre and due to developments in the food industry, several alternative definitions and terms have been put forward. In 2009, the Codex Alimentarius Commission (established by the Food and Agriculture Organization of the United Nations and WHO) introduced a new definition of fibre (29):
“Dietary fibre means carbohydrate polymers1 with 10 or more monomeric units2, which are not hydrolysed by the endogenous enzymes in the small intestine of humans and belong to the following categories:
9 1. Edible carbohydrates polymers naturally occurring in the food as consumed.
2. Carbohydrate polymers, which have been obtained from food raw material by physical, enzymatic or chemical means and which have been shown to have a physiological effect of benefit to health as demonstrated by generally accepted scientific evidence to competent authorities,
3. Synthetic carbohydrate polymers, which have been shown to have a physiological effect of benefit to health as demonstrated by generally accepted scientific evidence to competent authorities.
Footnote 1: When derived from a plant origin, dietary fibre may include fractions of lignin and/or other compounds associated with polysaccharides in the plant cell walls. These compounds also may be measured by certain analytical method(s) for dietary fibre.
Footnote 2: Decision on whether to include carbohydrates of 3 to 9 monomeric units should be left up to national authorities.”
Norway is following the EU standards, which have decided to include carbohydrates of 3 to 9 monomeric units to the definition of dietary fibre, as commented in footnote 2 above (30-32).
Prebiotics
The prebiotic concept was introduced in the mid-1990s by Glenn R. Gibson and Marcel B.
Roberfroid (33). At that time, a prebiotic was defined as “a non-digestible food ingredient that beneficially affects the host by selectively stimulating the growth and/or activity of one or a limited number of bacteria in the colon, and thus improves host health”. In 2004, an article by Gibson et al. established three criteria for classifying a food ingredient as a prebiotic: 1) It resists gastric acidity, endogenous enzymes and absorption in the human gut; 2) It is fermented by specific genera/species of the intestinal microbiota; and 3) It stimulates the growth and/or activity of intestinal bacteria that confer health benefits to the host (34).
Since the mid-1990s, the prebiotic concept has evolved and several definitions and terms of prebiotics have been put forward over the years (35). In order to make a consensus definition of prebiotics with clarification on specificity, mechanisms, health attributes and relevance, a panel of experts in microbiology, nutrition and clinical research was convened by the International Scientific Association for Probiotics and Prebiotics in December 2016. Hence, the concept of prebiotics was updated and the newest definition of prebiotic was presented in
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2017: “a substrate that is selectively utilized by host microorganisms conferring a health benefit” (35).
The definitions of fibre and prebiotics overlap partially. One group of carbohydrates that meet the criteria for both fibre and prebiotics are inulin-type fructans (ITF), which also are among the most studied prebiotic compounds (36, 37). With regard to ITFs, the generic terms
encountered most frequently are inulin, oligofructose and fructooligosaccharides (FOS). Most often oligofructose and FOS seem to be used as synonyms. The ITFs are generally classified into subgroups based on the degree of polymerization (DP), and the generic names inulin and FOS reflect the DP-based subgrouping (34). Specifically, inulin is polymers of fructose with longer chains (DP of 2-60), whereas FOS has shorter chains (DP of 2-8), although the exact dividing line of DPs are blurred (34, 38). Other well-accepted prebiotics are galacto-
oligosaccharides (35), but they will not be considered in more detail here as they were not a part of the intervention of this master thesis.
Chemically, ITFs are a chain of fructose-molecules bound by β-2-1 linkages, typically with a terminal α-linked glucose molecule (33, 36, 39). Because of the β-2-1-configuration they resist enzymatic hydrolysis in the upper gastrointestinal (GI) tract, and have been classified as
“non-digestible” saccharides (33, 36). ITFs are natural components of wheat and several edible fruits and vegetables like onion, banana, garlic and leek (36). Industrially, ITFs are often extracted from plants (chicory most used), and FOS can be produced by enzymatic hydrolysis of inulin (36).
Over time, our knowledge of dietary fibre has increased substantially, both in terms of their physiological effects and with respect to chemical analysis. For many years, analytical determination of dietary fibre in foods did not include low molecular weight fibres like inulin and FOS (40). New methods that also can measure these substances have been developed.
Generally, FOS and inulin make up only a small part of the non-digestible carbohydrates in Nordic diets (27) (p. 255), so it will usually not make a huge difference in the estimation of total fibre intake whether they are included or not. Still, there is a need of international consensus on the preferred analytical methods for dietary fibre before food composition databases can be updated with new values of dietary fibre (40).
The energy content in dietary fibre can be estimated by the conversion factor 8 kJ/g.
However, the amount of energy in fibre varies with type of fibre, and the value of 8 kJ/g is an
11 estimated average value for all types of fibre (27, 31) (p. 187). There are some studies that have tried to determine the energy contribution from inulin and FOS in human subjects by the use of different methodologies, but the results are varying (41). Though, as an indication, the energy factor for inulin and FOS has been estimated to be 4.2-8.4 kJ/g or 25-50 % of the energy from digested fructose molecule (33, 37, 41).
Health impact
Over the years, there has been an increasing interest in investigating functionality of fibre and prebiotics because of their possible positive health effects, and different types of studies (in vitro, animal, clinical) have been carried out.
Gut microbiota
The different compartments of the GI tract are inhabited by populations of microorganisms where the colon is by far the most colonized section (38). The organisms are collectively known as the microbiota and have an enormous impact on the health of the host (42). One of the primary characteristics of fibre and prebiotics is that they are fermented in the colon and could modulate the gut microbiota by increasing the number and/or activity of specific bacteria already resident in the colon (43). Prebiotic’s quality to alter the composition of the gut microbiota have been demonstrated in both human feeding trials, animal studies and in vitro models (44). The results show a particular increase in bifido- and lactobacilli-bacteria, which both are strains regarded to have a beneficial impact on intestinal health (44). Because fermentation of fibres leads to an increase in bacterial mass and consequently faecal mass, fibres also have a stool bulking effect (43, 45).
Short-chained fatty acids
The colonic and mucosal microbiota contributes, together with the human gut, to the barrier that prevents pathogenic bacteria from invading the GI tract (43). Fermentation of fibre and prebiotics in the colon produces short-chained fatty acids (SCFA) like acetate, butyrate and propionate, in addition to lactate (43). This fermentation reduces luminal and faecal pH, which contributes to inhibiting the growth of pathogenic organisms (43, 45). At the same time, colonic epithelial cells preferentially use butyrate as an energy source, and butyrate is considered to be a key nutrient in the metabolic activity and growth of colonocytes (43).
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Immune modulation
Current evidence suggests that prebiotics can have positive effects on the immune system (44). However, it is not known whether these effects are directly caused by stimulation of immune-modulatory bacteria or are indirectly a result from production of SCFA, which are known to have immunomodulatory properties (44). More studies are needed to determine to what extent prebiotic supplementation influences different aspects of immunity in humans.
However, it is known that a well-balanced gut microbiota prevent colonization of the gut by pathogens, thus being essential for the immune-barrier in the GI (42).
Blood glucose, insulin response and appetite regulation
It has been demonstrated that G-protein-coupled receptors expressed throughout the gut can bind SCFA and that further signalling pathways then lead to induced production of the gut- derived satiety hormones GLP-1 and peptide YY (PYY) (46-49). Both GLP-1 and PYY can inhibit gastric emptying and reduce appetite, but GLP-1 can also stimulate insulin production and secretion (48, 49). Taken together, carbohydrates with prebiotic effects can have an impact on postprandial glucose and insulin response, appetite control and body weight (43).
Some feeding trials in healthy subjects have demonstrated that prebiotics can promote satiety, reduce food intake and increase plasma concentrations of GLP-1 and PYY (47, 50, 51).
However, systematic reviews tend to report no statistically observable differences on the mentioned parameters or inconsistent results (52-54). More and better studies are needed to determine the real impact of prebiotics and fibre on glucose homeostasis and appetite regulation.
The food and drink industry uses prebiotics because of their low energy content and the fact that prebiotics can be used as sweeteners and substitutes for sucrose (44). If convincing claims could be made about positive health effects of prebiotics, they would probably become even more popular in the food industry.
Safety
An article from 1999 by Carabin and colleagues concluded that inulin and oligofructose demonstrated no evidence of toxicity based on toxicological and clinical studies (41).
Likewise to date, a tolerable upper intake level for fibre has not been established (27) (p.
272). However, certain types of fibre and amounts can cause bloating, gas, abdominal
13 discomfort or undesirable changes in bowel movements, but these effects are just “symptoms”
of bacterial fermentation of fibre, not an indication of toxicity, and it is highly variable among individuals how well fibre intake is tolerated (55). Several clinical studies have reported that an intake up to 20-30 gram of inulin and/or FOS per day as supplements is well tolerated among adults (41).
Exceptionally, in people with gastrointestinal (GI) disorders like inflammatory bowel disease and irritable bowel syndrome, prebiotic supplementation may have a negative impact on symptoms (39). A diet low in fermentable oligo-, di-, and monosaccharides, and polyols (FODMAP), which also means a diet low in ITFs, has shown positive impact on symptoms in irritable bowel syndrome (39). The number of clinical trials is limited and therefore more studies are needed to see the effect of prebiotics in GI disorders and, in particular, on their use in conjunction with the low FODMAP diet.
1.3 Diabetes mellitus and fibre
It is well-known that balancing energy intake and energy expenditure is of importance for the prevention of T2D, but the role of specific dietary factors in the aetiology of the disease has been less studied. There are many theories concerning the relationship between fibre intake and T2D, and several studies consistently indicate that higher consumption of fibre can contribute to the prevention of T2D (55-57). Fibre and prebiotics can also have a positive effect in individuals that already have been diagnosed with T2D (22, 46, 58-60)
The benefits of fibre and prebiotics in diabetes can be due to several properties, among them the debated ability to regulate blood glucose, appetite and weight. In addition, the relationship probably depends on many factors, for example type of fibre consumed, dose of fibre, the individual itself (e.g. gender, BMI, age) and duration of fibre intake (58). It is hypothesized that dietary fibres also, by forming a viscous solution in the stomach, can delay gastric emptying and physically inhibit the absorption of macronutrients (mainly carbohydrates) in the lumen of the small intestine. The rate of glucose absorption will therefore decrease, which further will reduce the postprandial plasma glucose increase (45, 58).
Several studies have shown that the gut microbiota in people with overweight and T2D is characterized by a moderate degree of dysbiosis compared to healthy, normal-weighted individuals (61-65). Dietary fibre may have a beneficial impact on the microbiota in these
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patients. A newly published article by Zhao et al. showed that patients with T2D improved their HbA1c levels, partly via increased GLP-1 production, when their gut microbiota was manipulated by dietary fibres (46).
A randomized controlled trial (RCT) from 2013 showed that two months with inulin
supplementation of 10 g/d may improve glycaemic control in women with T2D (59), while a cross-over study concluded that 15 g/d FOS as dietary supplementation for 20 days showed no major effect on blood glucose in patients with T2D (66). An RCT from 2016 investigated the link between intestinal permeability, glucose tolerance and intestinal microbiota in human with T2D (67). The authors concluded that their data did not provide evidence for a positive role of prebiotics in the treatment of T2D. A systematic review from 2010, found that whole- grain consumption was not associated with improvements in glycaemic control in T2D, but seemed to reduce systemic inflammation (20).
Overall, there is a knowledge gap regarding the role of different dietary fibres in the
prevention and treatment of T2D. More studies are needed to investigate the role of different dietary fibres and to reveal the underlying mechanisms for the observed positive effect of dietary fibres in observational and interventional studies in patients with T2D.
The Fiberdia study
Fiberdia is a Norwegian double-blinded RCT, with cross-over design, with the primary aim to compare the effects of prebiotics (inulin and FOS) versus placebo on microbiota, blood glucose regulation and appetite in people with T2D (68, 69). The primary outcome measure is changes in plasma GLP-1 in response to a standardized test meal before and after six weeks of ingestion of prebiotic fibres (FOS and inulin) or placebo (maltodextrin), compared in a cross- over manner where the participants are their own controls. To our knowledge, there is no other study that has investigated the effect of prebiotic fibres on GLP-1 levels in patients with T2D.
To date, other data from Fiberdia are not completely analysed and no articles have been published from the study.
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1.4 Dietary assessment
In general, most people vary their intake of food and beverages from day to day and from season to season, and it can be hard to remember exactly what has been consumed each day.
Collecting valid dietary data is therefore challenging. A wide variety of methods for assessing dietary intake exist, but no method measures the diet 100 % exactly. Which method that is most appropriate to apply in a given research study, depends on the field of application and available resources. Assessment methods can either be prospective or retrospective (70) (p.
62). They can also be divided into open and closed methods. Food trends, food types and preferences are in constant change, and these changes can be captured with open methods because the respondent/interviewer writes down everything that has been eaten during a day (71) (p. 28). A closed method means that the respondent only can choose between already predetermined varieties of food types.
Prospective methods do not rely on long-term memory as the actual intake is recorded when it is consumed. The methods include weighted and unweighted food intake records, duplicate portion (subjects weigh and put aside a duplicate portion of all foods they have eaten for subsequent chemical analysis of the food) and recording by electronic devices such as mobile phones (70) (p. 62). Prospective methods are generally associated with high validity and accuracy, but also require much effort and resources both for the participant and for those who are doing the interviews and data handling (72). Another disadvantage is that the dietary behaviours can be altered due to the knowledge that foods and amounts are going to be recorded (72).
In retrospective methods, the subjects recall their consumption and the methods are therefore dependent on the respondents’ memory (71) (p. 28). Retrospective methods include 24-h dietary recall, which is a structured interview to obtain detailed information about all foods and beverages consumed during the previous day (past 24 hours), and diet history where the respondent is questioned about typically consumed food, portion sizes and frequency of food consumption over the recent past (73) (p. 136-139). Both 24-h dietary recall and diet history are open methods. Retrospective methods also include food frequency questionnaires (FFQ).
This was the method used to collect dietary data in the Fiberdia trial and it is therefore more thoroughly described underneath.
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Food frequency questionnaire
Food frequency questionnaire (FFQ) is a retrospective method designed to assess usual eating habits during recent months or years and is one of the most frequently used dietary
assessment methods in epidemiological studies (73, 74) (p. 134). The questionnaire is often self-administered and contains multiple questions about frequency of consumption and, usually, portion size of different foods (71) (p. 30). FFQ is mainly a closed method with predetermined alternatives of food types and amounts, but some questionnaires have an open box in the end in which the respondent can write down food types that were not mentioned in the questionnaire, but that he/she normally eats (73) (p. 52 and 135). Different forms can be made depending on the requirements of the study, for example a form covering everything that a person eats during the time period of interest or a form intended to cover only a person’s fruit and vegetable intake (71) (p. 30). When using FFQ as the dietary assessment method, it is important to be aware that day-to-day variation in the diet is not captured.
Instead, the aim is to give an estimate of habitual intake which can be used at the group level (70) (p. 63). A food propensity questionnaire is an FFQ without portion sizes. It only captures frequencies of consumption and are developed to be used as a covariate in a statistical model to enhance the estimation of usual intake from 24-h dietary recall (75).
The main advantages of FFQs are their possibility to be used in population samples that are geographically widespread, that they put a small burden on the participants, and that they can be time-saving and easy to administer as there is no need of an interviewer for each
participant. However, development and validation of the FFQ requires a lot of work in advance (73) (p. 136).
Measurement errors
Measurement errors always have to be taken into consideration when interpreting results from dietary studies. The two main types of measurement errors are generally called “random” and
“systematic” errors (71) (p. 43). Random errors are fluctuations in the measured data in unsystematic directions, so that the mean of repeated measures approaches the true value.
Contrary, systematic errors produce consistent errors so that the average value of repeated measurements does not approach the true value (76) (p. 287). A random error can be if the subject accidentally ticks off another frequency on a food item than he/she intended to tick off in the FFQ. A systematic error can for example occur if standardized FFQs are used
17 repeatedly and an important food item is missing, so that it would not be measured for those subjects consuming that food item (76) (p. 288).
In the case of FFQs, recall bias is a serious limitation which can lead to imprecision in the estimates of food consumption because people do not remember exactly what and how much they have eaten (73) (p. 136). Misinterpretations related to the questionnaire can also occur, and under- and over-reporting of energy intake is a common problem (73) (p. 144). Another challenge is FFQs that are not filled out correctly. When calculating dietary intake from FFQ there is a chain of arithmetic operations and data aggregation, and if some data are missing the numbers will be wrongly calculated (77). Missing answers to individual food items can be referred to as “item non-response", and is often a problem especially when there is no contact between respondent and researcher (77). Overall, it is important to be aware that FFQ and other dietary assessment methods can have a large degree of measurement errors, which can lead to biased estimates (73) (p. 141).
One way to reduce measurement errors, is to carry out a validation study. Validity is a term of the degree to which a method measures what it is meant to measure in a true and accurate way (73) (p. 242). In a dietary validation study, the test method is compared with a reference method which is regarded as better (as no diet method measures the true intake perfectly), in order to estimate the association between the subjects’ “true” intake (measured by the reference method) and the subject’s dietary intake measured by the test method (73, 78) (p.
256). The results from a dietary validation study are useful for determining the error associated with the test method, to decide whether or not the diet is satisfactorily measured and to revise the design of the test method if necessary (73) (p. 266-267).
Another way of evaluating the dietary assessment method to be used, is to carry out a reproducibility test (71) (p. 42). In that case, the identical dietary assessment method is repeated for the same subjects within a given time span. The aim is to evaluate the consistency of the assessment method.
National dietary surveys
Over the years, several nation-wide dietary surveys have been conducted in Norway with the aim to describe the diet and meal patterns of the population, assess nutritional content of the diet, and evaluate dietary habits and other health habits in relation to socio-demographic
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variables (79). The methods have varied. Both FFQs, 24-h recalls, four-day diet record and web-based FFQs have been employed (80). The surveys have been carried out among adults, 4th and 8th graders, and parents responding on behalf of their children aged 6, 12 and 24 months and 4 years (79, 80).
The latest national dietary survey among adults, was Norkost 3, and was conducted in Norway through 2010 and 2011 with the main aim to describe dietary intake in the general Norwegian population (81). A total of 1787 men and women between the age of 18 and 70 years answered two telephone-administered 24-h dietary recalls and a food propensity questionnaire.
1.5 Rationale behind the master thesis
There is no doubt that diet constitutes an essential part in T2D management (4, 5, 7, 16).
Several nutritional components may have beneficial effects in managing the disease, and fibre is one of them (6, 19, 23). Yet, few studies have evaluated the diet of people with T2D and how well they adhere to the recommendations. The studies that exist, many of an earlier date, have shown “poor compliance” with dietary recommendations (82-89), even though
individuals with diabetes often receive more nutritional advice than the rest of the population.
Also, there are little data available on dietary intake among people with T2D in Norway.
In the Fiberdia trial, the subjects were instructed not to change their habitual diet and physical activity pattern during the 3 months trial period. This was to avoid interference of other dietary components or body weight change on the main outcome measurements. At the same time, it was hypothesized that the prebiotic fibres would reduce appetite. Accordingly, there was a risk that the subjects would reduce their energy intake despite the instructions. Thus, it was necessary to assess dietary intake and body weight before and after the two intervention periods.
In addition, it was considered useful to map the background diet of the participants. Ideally, the participants’ habitual intake of prebiotic fibres should have been measured, but because that is not feasible with available methods, the intake of dietary fibres was measured. It was also desirable to compare the participants’ habitual diet against dietary recommendations and to compare their diet with the general Norwegian population.
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2 Objectives
In this study, we hypothesized that the intake of dietary fibre is lower than recommended for people with T2D and that their diet do not differ from that of the general Norwegian
population. Also, we hypothesized that the diet and intake of energy do not change during the intervention with prebiotic fibres.
The primary objective of this master thesis was to describe the diet among the Fiberdia participants.
The secondary objective was to analyse whether the participants` diet changed during the 6- week intervention period with prebiotic fibres compared with the placebo period, using energy intake as primary endpoint.
Based on hypothesises and objectives, the following research questions were put forward:
1) How is the diet of Fiberdia participants compared with the current dietary recommendations for the general population?
2) How is the participants` diet compared with the diet of the general population assessed in the large national food consumption survey Norkost 3?
3) Does the participants` energy intake change during the 6-week period with prebiotic fibres compared with the placebo period?
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3 Methods
3.1 Study population
Patients with T2D were recruited to Fiberdia in several ways. Some heard about the study when they participated at an introduction course about T2D held at Oslo University Hospital, Aker, while others were recruited through their GP. Others read about the study on webpages or Facebook pages, or in the magazine of the Norwegian Diabetes Association. Leaflets were distributed to local outpatient clinics, GPs and pharmacies.
Table 1 shows inclusion and exclusion criteria in Fiberdia. During the initial screening, potential participants were asked about their intake of main fibre sources (vegetables and fruits, bread and grain products). Based on this, their fibre intake was estimated. Alcohol consumption was estimated in a similar way. No one reported a higher intake than the
exclusion criterion for fibre or alcohol. However, these two food-based criteria were not used to exclude people later in the study.
Information about the study was given both written and orally, and each patient provided written consent before he/she was enrolled (Appendix I). The participants could withdraw from the study at any time, and without specifying a reason. All participants were recruited and completed the study between autumn 2015 and winter 2018. The flow chart of study participants in Fiberdia is shown in Figure 1.
Table 1. Inclusion and exclusion criteria in Fiberdia.
21 Figure 1. Flow chart of study participants in Fiberdia.
3.2 Study design
Fiberdia is a cross-over study where each participant was randomized to consume either 16 g prebiotics (50/50 mixture of FOS and inulin – Orafti®Synergy1) or 16 g placebo
(maltodextrin) daily during six weeks, illustrated in Figure 2. Thereafter, they had at least a four-week washout period before the person underwent six additional weeks with the intervention he/she did not receive initially. In some cases due to holidays or sickness, the washout period was extended.
The two intervention periods were equal in procedure. Both the taste and appearance of maltodextrin and prebiotics were similar, and were supplied by BENEO GmbH (Germany) as dry white powders in identical sachets. Each sachet contained 8 g powder, and the participants were instructed to consume it the preferred way, for example in a cup of water, over food or mixed in yoghurt. Because a suddenly increased intake of prebiotics could cause symptoms, the participants should consume only one sachet (8 g) per day the first week of each test period. Thereafter, consumption should increase to two sachets each day (16 g), which lasted the remaining period of five weeks.
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During the study, the participants went through a test day before and after each intervention period, altogether four test days that started in the morning. Every test day included
anthropometric measurements, fasting blood samples and blood samples at 10 time points from 0 to 180 minutes after a meal-challenge test, and an ad libitum lunch to test appetite. A faecal sample was collected at home within 24 h before the test days, frozen and brought to the research unit each test day. Every individual also returned a total of three FFQs, which are the main basis for this master thesis (Appendix II). Measurements other than FFQ data and body weight are not discussed in further details here as they are not within the scope of this thesis.
Figure 2. Overview of study design in Fiberdia.
Randomization and blinding
A statistician not directly involved in the study made the final randomization list. The participants were randomized to start with “A” or “B”, but neither the participant nor the study administrators knew the identity of A and B. The company in Germany delivered the sachets with powder marked with “A” and “B”, together with a sealed envelope with the key for A and B. An independent nurse, not involved in the study, was the only one with the access to the randomization list and prepared the bags with A and B to the participants. The supplements were delivered in identical sachets (not labelled A or B), and no one except the independent nurse, knew in which order each participant received treatment. The
randomization list and the envelope with the key for A and B were not opened until all participants had completed the intervention periods. Accordingly, both the participants and project personnel were blinded with regard to who received which substance and in which order.
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3.3 Data collection
Anthropometric measurements
Body weight and body composition were measured by Tanita body composition analyzer using Bioelectrical Impedance Analysis (BC-418 MA) (90). Analyses were performed with bare feet and indoor clothes, and one kilogram was always subtracted for clothes worn.
Height was measured with the participant standing without shoes on test day 1. BMI (kg/m2) was used to classify the participants into categories of underweight (< 18.5 kg/m2), normal weight (BMI 18.5-24.9 kg/m2), overweight (BMI 25-29.9 kg/m2) and obese (BMI ≥ 30 kg/m2), according to WHO’s definition (91). BMI was calculated automatically by the Tanita analyzer.
Dietary measurement
Dietary data from each participant were assessed by a self-administered, paper-based optical mark readable FFQ assessing total diet, from before the first intervention period (considered as baseline values) and after both intervention periods (test day 2 and 4). The participants were asked to recall the last six weeks and fill in the questionnaire as an average of their eating habits during that period. The form was estimated to take around one hour to complete.
To avoid poor compliance because of repeatedly being exposed to the burden of filling in the same FFQ, no FFQ was administered on the third test day. The first FFQ response was used as baseline comparison also for the second intervention period. The FFQs were completed at home prior to test day 1, 2, and 4. They were handed in during the test day and a dietician briefly looked through them to see if they were filled out correctly. If inaccuracies and/or item non-responses were discovered, the participant got guidance from the dietician to complete the FFQ correctly.
The 14-page FFQ included questions about 236 food items that were grouped according to the Norwegian meal pattern. The options regarding frequency of consumption varied from
“several times a day” to “once a month”. In addition, the option “never/rarely” was always present. Portion sizes were given as household measures (litre, dl, tablespoon, teaspoon), or in natural units such as slices of bread, number of carrots or pieces of chocolate. One of the fourteen pages regarded dietary supplement intake, and requested usage and portion size of 17 types of dietary supplement, together with an open-ended box to write down supplements that
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were not mentioned in the list. There was also an open-ended box on the last page, inquiring about any other frequently consumed foods not included in the questionnaire.
Optical scanning and nutrition calculation database
The FFQs were manually checked for errors, and then computer-scanned. Subsequently, image files were translated into data files using the Cardiff Teleform 2006 Software. The dietary information was converted into daily energy and nutrient intake by the Norwegian food database software “Kostberegningssystem” (KBS), a program developed by the
Department of Nutrition, University of Oslo, Norway. The current food database (AE-14) is primarily based on the official Norwegian food composition table (version from 2014 and 2015, www.matvaretabellen.no), but has been expanded to include about 3000 food items.
The KBS food database is updated on a regular basis (MH Carlsen, personal communication).
Answers to open-ended questions required separately coding in Notepad and import of data to KBS. Estimations of energy, nutrient and food and beverage intakes were subsequently done in KBS, exported to an excel-file and further processed by the statistic program Statistical Package for the Social Sciences (SPSS). The same person (KD Molven) conducted verifying and scanning, with guidance from an academic supervisor (MH Carlsen).
Data cleaning
A set of guidelines for handling incomplete FFQs were designed during this master thesis.
They were based on the guidelines that are recommended in “Handbook for scanning – questionnaires and diet records” (in Norwegian: Brukerveiledning for skanning – av spørreskjema og kostdagbøker) by Jannicke Fredriksen (92), and supplemented with guidelines for some cases that were not mentioned in the handbook (Appendix III). When corrections had to be made, correction fluid was used to wipe out inaccurate alternatives and then a pen to tick the “correct” answer, which made it easier and less time-consuming to scan the forms afterwards. In addition, the implemented changes were plotted in a separate Excel- file for archival purposes.
Under- and over-reporting
According to data from Black et al., also called the Goldberg method, it is estimated that when using dietary methods assessing habitual diet (such as FFQs or diet history), participants with a ratio between energy intake (EI) and basal metabolic rate (BMR) under 1.10 probably
25 under-reports their energy intake (93). Participants with EI/BMR ratio over 2.19 probably over-report their energy intake. These values are based on a physical activity level (PAL) value of 1.55, a value that is recommended for universal application when the activity level of the population is not known (93). To evaluate if participants under- or over-reported their energy intake, BMR were calculated from standard formulas based on age, gender, height and weight (94). A ratio between EI and BMR was then computed for each individual, and cut- offs for EI/BMR from Black et al. were used to classify the participants into under-, acceptable and over-reporters (93).
Validity of FFQ
The FFQ used in Fiberdia was based on former FFQs developed at the Department of Nutrition, University of Oslo, designed and validated to capture the habitual food intake among adults during the previous year (78, 95, 96).
Compliance
The participants were encouraged to eat their normal diet through the test period and only add the supplement they had been provided. To verify compliance, the participants were asked to keep and return unused sachets after each intervention period. If a participant took less than 50 % of the distributed supplement, he/she was characterized as non-compliant and excluded from the analyses. The participants were also instructed to avoid intentional weight changes throughout the test period. Therefore, weight measures during test days were used to examine whether they actually had been weight stable throughout the whole study period or if they had gained/lost weight.
Dietary recommendations and Norkost 3
The dietary results from Fiberdia were compared with the Norwegian dietary guidelines from 2014, which includes recommended intake (RI) ranges for macronutrients with upper and lower limits for certain nutrients, RI for micronutrients and quantitative guidelines for some food groups (25). RI for micronutrients are both given as recommended intake per MJ, useful when planning a diet for a mixed group, and as age- and gender-based recommended total intake per day (25). Both age and gender were taken into consideration when the proportion of subjects that adhered to each micronutrient recommendation was calculated.
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Additionally, dietary data from Fiberdia were compared with results from Norkost 3. No other information than the report published by NDH (81) was used due to limited resources and different methodology between Fiberdia and Norkost 3. Because of no access to individual Norkost 3 dietary data, it was not possible to compare our data with a more suitable age- and gender-matched reference group from Norkost 3.
3.4 Statistical analyses
All statistical analyses were conducted using IBM SPSS Statistics (version 25). Appropriate statistical methods were selected based on cross-over design, continuous or categorical variables and whether the data were normally or skewed distributed. All statistical tests were two-tailed, and P-values lower than 0.05 were considered statistically significant. No P-values were corrected for multiple comparisons.
Examination of data
Patient characteristics and food intake were described by descriptive statistics. All continuous variables were checked for normality by histograms, Q-Q-plots and Shapiro-Wilk test. Means and standard deviation (SD) were presented when the criteria for normal distribution were fulfilled/the data were judged approximately normal. For variables with a non-normal distribution, the median with 25th and 75th percentiles were presented. When examining differences between placebo and fibre-period, paired-samples t-test was performed for
variables with normal distribution. For variables with a non-normal distribution, the Wilcoxon signed rank test was applied. The relationship between different continuous variables was evaluated using scatterplot and Pearson’s correlation coefficient (r).
Statistical analyses were not applicable when comparing the participants` diet with data from Norkost 3 because of different methodology and no access to individual N3 dietary data.
Thus, only simple comparisons between study results were done. Data from Norkost 3 were only given as mean (SD) in their report. Therefore, in order to make a better comparison with results from this present study, both mean and median were presented in the table describing intake of food items and groups, although our data had a non-normal distribution.