Postprandial inflammation-

(1)

Postprandial inflammation-

Effect of meals with four different dairy products with equal amount of fat on inflammatory markers

Master thesis by Ingvild Hansen Ivan

Supervisors: Stine M. Ulven, Kirsten B. Holven

Department of Nutrition Faculty of Medicine

UNIVERSITY OF OSLO

May 2018

(2)

II

Postprandial inflammation - Effect of meals with four different dairy products with equal amount of fat on inflammatory markers

Author: Ingvild Hansen Ivan

Supervisors: Stine M. Ulven, Kirsten B. Holven http://www.duo.uio.no/

(3)

III

Acknowledgements

This master thesis has been conducted at the Department of Nutrition, University of Oslo 2017-2018.

I would like to express my gratitude to my supervisors, Stine Marie Ulven and Kirsten Bjørklund Holven for their guidance, patience, feedback and knowledge during the last year.

A special thanks to Stine for always leaving the door open for short and long conversations and questions, and for being encouraging and supporting until the very end of this project. I would also like to thank Patrik Hansson for including me in his work, and for being

supportive and always helpful.

My gratitude also goes to Navida A. Sheikh for being the most patient and warmhearted laboratory instructor that two amateurs could ever wish for, and to Karina Karlsen for being my lab-partner in crime through many challenging and cheerful times.

Last I would like to thank “B-gjengen”, Siri and Kristiane, for all the motivation, encouragement and laughter we have shared.

Oslo, May 2018 Ingvild Hansen Ivan

(4)

IV

Abstract

Background: Postprandial inflammation occurs after intake high-fat meals, especially those rich in saturated fat. A persistent low-grade inflammatory state has adverse health effects.

Dairy products are usually the main source of saturated fat in the general population, and as a high intake of saturated fat is believed to increase the risk of cardiovascular disease, low-fat dairy products are usually recommended. The association between intake of dairy products and the risk of cardiovascular disease is however not established. Studies have found both neutral and inverse associations between intake of dairy products and cardiovascular disease, and dairy matrix effect of the different compounds in dairy products may have beneficial effects.

Aim: This thesis aims to investigate whether intake of meals with similar amount of dairy fat derived from four different dairy products leads to changes in serum concentrations of

inflammatory markers in normal weight and overweight subjects.

Subjects and method: A postprandial single meal trial with a randomized cross-over design was performed with 19 normal weight subjects and 12 overweight subjects. The intervention consisted of four isocaloric high-fat meals with the same fat amount, different dairy products, eaten at separate occasions under controlled conditions. The dairy products used in the study were butter, cheese, whipped cream and sour cream. Fasting and postprandial blood samples were obtained at 0h and 6h and analyzed for ICAM-1, VCAM-1, E-selectin, TNF-R1 and TNF-R2 using ELISA measurements.

Results: In normal weigh subjects, intake of sour cream led to an increase in serum level of ICAM-1 (p=0.007) while intake of cheese led to a decrease in serum level of TNF-R2

(p=0.006). Intake of cheese led to significant greater decrease in TNF-R2 than intake of butter (p=0.014) and sour cream (0.044). In overweight subjects, intake of whipped cream led to an increase in serum levels of E-selectin (p=0.023). Intake of butter led to a significant greater decrease in TNF-R1 in the overweight group than in the normal weight group (p=0.012).

Conclusion: The findings of this thesis are that different dairy products to some extent influences postprandial inflammation differently. However, the risk of CVD related to intake of different dairy products cannot be determined based on the results from this study.

(5)

V

List of tables

Tabell 1: List of materials used in the study. ... 10

Tabell 2: Meal composition and nutritional content of the test meals. ... 14

Table 3: Calculation of intra-assay CV ... 17

Table 4: Calculation of inter-assay CV ... 17

Table 5: Details of the ELISA procedures conducted in the study. ... 19

Tabell 6: Baseline characteristics ... 22

Table 7: Fasting serum concentrations of circulating inflammatory markers. ... 23

Table 8: Postprandial changes in serum concentrations of inflammation markers. ... 24

Tabell 9: Summarized results of changes in inflammation markers found in other postprandial studies ... 36

(8)

VIII

List of figures

Figure 1: The different components involved in formation of atherosclerosis. Illustration is reused with permission. Originally published by Springer Nature(1). ... 2 Figur 2: Illustration of the adhesion molecules involved in leukocyte attachment to

endothelial cells. Based on free images from Servier Medical Art (Creative Commons

Attribution License, creativecommons.org/licenses/by/3.0/). ... 4 Figure 3: The course and design of the study. ... 13 Figure 4: Pictures of the test meals. Upper left: butter; upper right: cheese; lower left:

whipped cream; lower right: sour cream. ... 14 Figure 5: Principles of ELISA. From R&D systems... 16 Figure 6: Color change of the samples after adding substrate (left) and after adding stop solution (right). Private pictures. ... 16 Figure 7: 10 standard curves generated from the same samples of ICAM-1 analyzed on 10 different plates ... 18 Figure 8: Flow chart of the inclusion and exclusion of participants. ... 21 Figure 9: Serum concentrations of ICAM-1 at 0h and 6h. Data is presented as median with IQR. ... 25 Figure 10: Serum concentrations of VCAM-1 at 0h and 6h. Data is presented as median with IQR. ... 25 Figure 11: Serum concentrations of E-selectin at 0h and 6h. Data is presented as median with IQR. ... 26 Figure 12: Serum concentrations of TNF-R1 at 0h and 6h. Data is presented as median with IQR. ... 27 Figure 13: Serum concentrations of TNF-R2 at 0h and 6h. Data is presented as median with IQR. ... 27 Figure 14: Serum concentrations of hsCRP at 0h and 6h. Data is presented as median with IQR. ... 28 Figur 15: Differences in postprandial change in serum levels of TNF-R1 after each of the test meals between the normal weight and the overweight group. Data is presented as median with IQR. ... 28

(9)

IX

Abbreviations

BMI Body mass index CHD Coronary heart disease

hsCRP High sensitive C-reactive protein CV Coefficient of variation

CVD Cardiovascular disease

ICAM-1 Intracellular adhesion molecule 1 IFN-γ Interferon gamma

IL Interleukin

LDL-C Low density lipoprotein cholesterol MCP-1 Monocyte chemoattractant protein 1 MUFA Monounsaturated fatty acids

PBMC Peripheral blood mononuclear cells PUFA Polyunsaturated fatty acids

SFA Saturated fatty acid TG Triglycerides

TNF Tumor necrosis factor

TNF-R Tumor necrosis factor receptor TNF-R1 Tumor necrosis factor receptor 1 TNF-R2 Tumor necrosis factor receptor 2 USFA Unsaturated fatty acids

(10)

X

VCAM-1 Vascular cell adhesion molecule 1 WC Waist circumference

(11)

1

1 Background

1.1 Cardiovascular disease

Cardiovascular diseases (CVD) are the main causes of mortality globally. CVD is a collective term for disorders of the heart and blood vessels, and includes coronary heart disease (CHD), cerebrovascular disease, peripheral arterial disease, rheumatic heart disease, congenital heart disease, deep vein thrombosis and pulmonary embolism (2). The majority of deaths are caused by CHDs and strokes (3). In Norway, the incidence of CVDs have decreased

considerably during the last 20-30 years, but CVDs are still the main cause of death amongst the population when all ages are combined (4). Unfortunately, the decrease in myocardial infarcts amongst the older population is not observed in the age range between 25-44 years (4). At the same time, an increase in social inequalities caused by a steep decrease in mortality among the higher educated population is observed (5). This emphasizes the need for

preventive measures to be made, despite the decreased mortality rate in total. It is well known that the risk of CVD is closely related to lifestyle. High blood pressure, high blood cholesterol and smoking increases the risk of the disease, and an observed decrease in these risk factors amongst the total population is believed to explain the decrease in the total mortality rate (6).

Effective drugs to treat high blood pressure and high blood cholesterol are commonly used amongst the population. In addition, changes in lifestyle have proven to be of great

importance when it comes to minimizing the risk of CVD. Along with tobacco cessation, measures like increased physical activity, lowering the intake of alcohol, and lowering the intake of saturated fat, sugar and salt while increasing the amount of unsaturated fat, fruit and vegetables will prevent the risk of CVD (7).

The underlying cause of CHD and stroke is atherosclerosis (8). The process of developing atherosclerosis involves several factors. Endothelial dysfunction caused by chronic exposure to e.g. tobacco toxins, high blood pressure or hyperlipidemia leads to disturbances in

permeability and viscosity of the arterial wall. Retention and accumulation of low density lipoprotein (LDL)-particles between the endothelial cells and the tunica intima initiate an immune response characterized by increased recruitment and transmigration of leukocytes.

Macrophages, which make up the majority of the immune cells, ingest LDL-particles through phagocytosis and further develop into foam cells filled with cholesterol-containing lipid

(12)

2

droplets. Smooth muscle cells start migrating from the tunica media to the tunica intima of the arterial wall and produce a fibrous cap covering the accumulated foam cells. The thickening of the arterial wall will eventually lead to the obstruction of blood flow and associated clinical manifestations (1, 9).

1.2 Chronic inflammation and atherosclerosis

Inflammation is a physiological response to infection or tissue damage, and is an essential part of disease prevention. The response is carefully regulated to prevent damage of functional tissue. Negative feedback mechanisms normally ensure that the inflammatory response resolves rapidly after the injurious stimuli is eliminated. However, if the

mechanisms controlling the inflammatory response are disrupted, a pathological condition with adverse effects can occur (10). The presence of inflammatory molecules in the arterial wall is a prerequisite for developing atherosclerotic plaques. Immune mechanisms in interaction with metabolic risk factors like excessive LDL-cholesterol initiate the

development of atherosclerotic lesions and contributes to the progression of the disease (11).

A range of inflammatory components have been studied in relation to the risk of vascular disease (12). Elevated levels of cytokines and the acute phase protein C-reactive protein (CRP) have been linked to CVD in several studies, either as a cause of the disease or as a predictive marker (11, 13-16). Several diseases, like rheumatoid arthritis, psoriasis, Crohn´s disease and atopic dermatitis, are characterized by a chronic elevated systemic inflammation.

Figure 1: The different components involved in formation of atherosclerosis. Illustration is reused with permission. Originally published by Springer Nature(1).

(13)

3 Patients with some of these diseases are known to have shorter life expectancy compared with the regular population, due to cardiovascular events (17, 18). Chronic systemic inflammation has also been observed in humans without clinical symptoms of disease. This can be termed as a low-grade inflammatory state, and is believed to have adverse effects, despite the lack of symptoms.

1.3 Adhesion molecules and TNF alpha receptors and CVD risk

For leukocytes to be able to migrate through the vessel wall while circulating in the blood stream, the cells need to attach to the endothelial cells. This will occur in a process where leukocytes adhere to specific molecules present on the endothelial cells and roll along the vessel lining until they eventually attach firmly (19). Several adhesion molecules are involved in this process (20). The immunoglobulin-like molecules intracellular adhesion molecule 1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) function as ligands to integrins present on the cell surface of leukocytes. They are expressed on endothelial cells and are upregulated as a response to pro-inflammatory cytokines. Stimulation of the endothelial cells by cytokines also induces the expression of E-selectin, which is a selectin only present on activated endothelium. When leukocytes are approaching activated endothelium, E-selectin binds weakly to selectin ligands on the leukocytes. Chemokines are also expressed on the endothelium and bind to chemokine receptors on the leukocyte as the leukocyte “rolls” from selectin to selectin. Activation of chemokine receptors on the surface of leukocytes by ligand binding activates an intracellular signaling cascade that makes the integrins increase its affinity for ICAM-1/VCAM-1. The binding of integrins to ICAM-1/VCAM-1 leads to a firm adhesion of the leukocyte to the endothelium, and to transmigration through the vessel wall (21).

Adhesion molecules have been associated with the risk of CVD in several studies, but contradictory results exist, in particular among healthy subjects (22-30). While ICAM-1 and E-selectin have been able to predict future CVD events in healthy individuals (22), other studies have found that elevated levels of ICAM-1 predict future myocardial infarct only in healthy men (23). In another study, ICAM-1, but not VCAM-1 was found to predict CVD (24). A recent study found no association between ICAM-1 and VCAM-1 and CVD in healthy individuals (25). On the other hand, higher concentrations of ICAM-1, VCAM-1 and

(14)

4

E-selectin was found in hypertensive patients with and without hypercholesterolemia than in normotensive controls (26).

The pro-inflammatory cytokine tumor necrosis factor alpha (TNF-α) is in large part excreted by macrophages as a response to injury or pathogens and executes its function through binding to the two receptors TNF-receptor 1 (TNF-R1) and TNF-receptor 2 (TNF-R2) (31).

TNF-R1 is expressed on almost all cell types and is activated by both the soluble form and the membrane bound form of TNF-α. TNF-R2 is expressed primarily on cells of the immune system and on endothelial cells, and executes its function only when binding to membrane bound TNF-α (32). TNF-α, through activation of it receptor, is known to induce the

expression of adhesion molecules, and is therefore essential for proper adhesion of leukocytes to endothelial cells (33).

Several studies have found associations between TNF-receptors (TNF-Rs) and the risk of CVD. TNF-R2 has been associated with CVD in middle-aged to older men and women (27).

TNF-R1 was associated with CVD mortality in two cohorts of elderly (29) and with increased risk of CVD in healthy women (30). TNF-R1 and TNF-R2 was associated with CVD

Figur 2: Illustration of the adhesion molecules involved in leukocyte attachment to endothelial cells. Based on free images from Servier Medical Art (Creative Commons Attribution License, creativecommons.org/licenses/by/3.0/).

(15)

5 independent of established CVD risk factors in women in a recent study, while only TNF-R1 was associated with CVD when looking at the genders combined (28).

1.4 Low-grade inflammation and overweight

Overweight and obesity is associated with a low-grade inflammation state, and there is reason to believe that inflammation contributes largely to the development of obesity-related

metabolic dysfunction (34, 35). Obese individuals are known to have increased risk of CVD and diabetes 2, which is caused by a disrupted regulation of lipid and glucose homeostasis (36). The disruption of this regulation may be a result of the higher concentrations of inflammatory mediators (37, 38) . Obesity is characterized by a body mass index above 30kg/m² and usually by increased amount of visceral adipose tissue. Adipocytes synthesize a range of hormones, cytokines and inflammatory molecules (39). Proliferation and increased size of adipocytes, especially in the abdominal area, causes a disturbance in the secretion of these substances (40). As adipose tissue expands, a larger proportion of other cell populations become present within the tissue. Among these cells are T-lymphocytes and macrophages, which probably contributes to the local and systemic inflammatory environment seen with increased visceral adipose tissue (41, 42). The hormone adiponectin is secreted by adipocytes in a normal state, and appears to have anti-inflammatory and insulin-sensitizing effects.

Obesity, however, leads to a decreased secretion of adiponectin (43, 44). Pro-inflammatory cytokines and chemokines are secreted by both adipocytes and macrophages. Examples of these are TNF-α, IL-6, IL-8 and MCP-1, all of which are suggested to increase the risk of disease (45, 46). Diet change and weight loss have been found to reduce the concentration of pro-inflammatory markers and to reduce the risk of disease (47-50).

1.5 Postprandial Inflammation after intake of high-fat meals

Triglycerides (TGs) are known to increase after a meal, and are believed to be a risk factor for CVD (51). The mechanisms behind this increased risk have recently been suggested to be its activation of inflammatory mediators (52).

In addition to increased lipids, inflammatory markers also rise in blood after intake of a meal.

An energy dense diet with a chronic high intake of fat is believed to be a stimulus for chronic

(16)

6

inflammation (53, 54). When we eat, we will normally have an increase in several

inflammation markers, and most people will be in a postprandial inflammation state during the whole day. The postprandial inflammatory response is believed to be larger after intake of some nutrients than others. Understanding the difference in the inflammatory effect of

different nutrients could be useful in order to determine the risk of CVD, and to be able to provide effective and preventive dietary advices. High-fat meals have proven to give an increase in a range of inflammatory markers in the postprandial phase, as presented in two reviews (55, 56). A high content of saturated fatty acids (SFA) have been found to be particularly pro-inflammatory in postprandial studies. A study including healthy men found that a high-fat/high-energy meal led to increased IL-8 postprandially compared to a low-fat control meal. They study also observed an upregulation of gene expression of IL-8 and ICAM-1 in peripheral blood mononuclear cells (PBMC) (57). Another study in healthy men found that a butter breakfast with high amount of SFA led to a higher expression of mRNA TNF-α in PBMCs than an olive oil breakfast and a walnut breakfasts with high amount of unsaturated fatty acids (USFA). The butter and the olive oil breakfasts led to higher IL-6 mRNA level than after the walnut breakfast. However, no changes in plasma concentrations of these markers were observed after any of the meals (58). A study in 24 overweight subjects observed a larger increase in serum concentrations of IL-6, IFN-γ and MCP-1 after a high SFA-meal than after a high USFA-meal. The SFA-rich meal also increased more the IL-1beta mRNA level in leukocytes compared to the USFA-rich meal (59). In contrast, intake of a cheese meal induced a lower inflammatory response, seen by a smaller increase in postprandial CRP, compared to intake of a vegan meal, although the fat content (approximately 57 g total fat/19 g SFA) were the same in both meals (60).

1.6 Dairy products and cardiovascular disease risk

A large amount of evidence links a high intake of SFA to increased risk of CVD, and for this reason the dietary guidelines commonly advise that SFA make up no more than 10 % of a person’s total daily energy intake (61). Dairy products are usually the main sources of SFA in the diet amongst the general population, and in order not to exceed the recommended amount of fat it is advised to eat mainly the low-fat dairy products. It has been assumed that the SFA in dairy products contribute to a rise in the blood lipids, and that the low-fat variants would be beneficial compared to the full-fat products when it comes to cardiovascular health. However,

(17)

7 the link between dairy products and CVD is being debated, as studies on the field have shown inconclusive results when it comes to linking dairy products to the risk of CVD. It has been suggested that neither the intake of low-fat nor full-fat dairy products has any effects on LDL cholesterol concentrations in healthy individuals (62). Other reviews have found no

associations, or inverse associations, between the intake of dairy products and all-cause mortality, CHD and CVD (63, 64). One review found an inverse association of the intake of total dairy products and the risk of elevated blood pressure. When looking at only low-fat dairy intake the inverse association was found to be larger than for the total intake, while they found no association between high-fat dairy intake and risk of elevated blood pressure (65).

Another review found an inverse association of milk and cheese consumption with the risk of stroke, but an increased risk of stroke associated with intake of high-fat milk. They observed no associations for yoghurt, butter or total dairy (66). A review evaluating cheese

consumption and the risk of CVD found no association between the intake of cheese and CVD risk (67) while another review found lower levels of cholesterol after cheese

consumption than after consumption of butter (68). Two reviews examining the link between dairy intake and inflammatory markers have also been conducted. One of them, Bordoni et al., included 52 studies that either had examined the inflammatory response after intake of dairy products as their primary aim or studies that examined the inflammatory response after intake of other foods, but in which dairy products had been used as control meals (69). The other review, Labonté et al., included eight randomized control trials where the impact of dairy products on serum concentration of inflammatory markers had been assessed (70). Both reviews suggested that there is either no association or a negative association between both low-fat and full-fat dairy products and elevated concentrations of inflammation markers. They also suggested that the inverse association was stronger for fermented products than for dairy products as a total. A small number of clinical studies have compared SFA-rich dairy

products with other SFA-rich dairy products. One study found that the postprandial response in plasma lipids differed after each of the dairy products, even though they contain the same amount of SFAs (71). Another study found no difference in the response in inflammation markers after different full fat meals containing similar amount of fat (72).

The conflicting results may be explained by the wide variety in dairy products used in the different studies. Both low-fat variants and full-fat variants, in addition to fermented products and non-fermented products have been examined. There are considerable differences in nutrient composition between dairy products based on the production method. Several

(18)

8

constituents of dairy have been found to have effects on cardiovascular health, and studies have revealed that the effect of single constituents may be different based on the other constituents present in the product. Studies have examined if there is a difference in the outcome after ingesting a whole dairy product compared to ingesting a single nutrient, and results from several studies suggest that a dairy matrix effect exists (73). To be able to fully understand the impact of the constituents of dairy on risk factors for CVD, studies need to be conducted containing whole dairy foods in order to observe the actual effect of the products as a whole. As the low-grade inflammatory state seen after intake of high-fat meals could contribute to an atherogenic environment, determining the impact of high-fat dairy products on inflammation markers will be of great interest.

(19)

9

2 Aim of the study

To further understand if the dairy matrix effect exists, and if different dairy products with similar fat content may influence postprandial lipids and inflammation differently, a

randomized controlled crossover meal study was performed at Department of Nutrition, UiO in 2016-2017. The overall aim of this thesis was to investigate the relationship between dairy products and the postprandial inflammatory response 6 hours after intake of four single meals containing different dairy products with the same amount of fat in lean and overweight subjects.

2.1 Specific objectives

The specific objectives of this thesis are the following:

- To investigate whether intake of a meal with similar amount of dairy fat derived from butter, cheese, whipped cream or sour cream leads to changes in serum concentrations of ICAM-1, VCAM-1, E-selectin, TNF-R1 or TNF-R2 in normal weight individuals, and if the changes are different between the meals

- To investigate whether intake of a meal with similar amount of dairy fat derived from butter, cheese, whipped cream or sour cream leads to changes in serum concentrations of ICAM-1, VCAM-1, E-selectin, TNF-R1 or TNF-R2 in overweight individuals, and if the changes are different between the meals

- To investigate whether any postprandial changes in inflammatory markers differ between normal weight individuals and overweight individuals

(20)

10

3 Method and study design

Tabell 1: List of materials used in the study.

Equipment Distributors Country

1.8ml Cryo tubes NUNC Thermo Fisher Scientific Denmark

8.5 ml silica gel tubes Becton Dickinson Vacutainer Systems USA

Finnpipette Thermo Electron Corporation Finland

Finntip Flex 1000 Sterile R&D Systems USA

Finntip Flex 1200 R&D Systems USA

Finntip 300 Sterile R&D Systems USA

Finntip Flex 1200 Refill Kit Thermo Electron Corporation Finland

Falcon® 15ml High-Clarity Polypropylene Conical Tube Corning Incorporated Mexico Falcon® 50ml High-Clarity Polypropylene Conical Tube Corning Incorporated Mexico Falcon® 50ml Polypropylene Conical Tube Corning Incorporated Mexico

Integral pipetboy2 Sveits

Serological pipette 10ml Sarstedt AG & Co Sveits

MegaBlock 96 Well, 2.2ml, PP Sarstedt AG & Co Sveits

Costar® Assay Plate, 96 Well Corning Incorporated USA

Kit

DuoSet Human ICAM-1/CD54 R&D Systems USA

DuoSet Human VCAM-1/CD106 R&D Systems USA

Quantikine ELISA Human E-Selectin/CD62E R&D Systems USA

Quantikine ELISA Human TNF RI/TNFRSF1A R&D Systems USA

Quantikine ELISA Human TNF RII/TNFRSF1B R&D Systems USA

Chemicals

Dulbecco's Phosphate Buffered Saline (DPBS) Sigma-Aldrich Co UK

Tween 20 Merck KGaA Germany

PBS Tablets (Phosphate-Buffered Saline) Gibco® by Life Technologies Corporation UK

Bovine Serum Albumin Fraction V GE Healthcare Bio-Sciences USA

Color reagent A Stabilized Peroxide Solution R&D Systems USA Color reagent B Stabilized Chromogen Solution R&D Systems USA

Stop Solution R&D Systems USA

Instruments

Synergy H1 microplate reader BioTek USA

Wellwash 4 MK 2 Thermo Electron Corporation Finland

Software

Gen5 2.06 BioTek USA

SPSS Statistics 25 IBM USA

Excel 2016 for Windows Microsoft USA

EndNote X8 Clarivate Analytics USA

(21)

11

3.1 Subjects

Subjects were recruited through advertisement in local newspapers and through flyers, posters and e-mails at the University of Oslo between June 2016 and January 2017. Healthy normal weight subjects and overweight subjects were included in the study. Subjects were included in the normal weight group if they had a Body Mass Index (BMI) between 18.5 – 24.9 kg/m² and a waist circumference (WC) of < 94 cm for men and < 80 cm for women. They were included in the overweight group if they had a BMI of ≥ 25 kg/m² and a waist circumference of ≥ 94 cm for men and ≥ 80 cm for women. The exclusion criteria were triglycerides ≥ 1.3 mmol/L in the normal weight group. In addition, exclusion criteria for all were CRP > 10 mg/L, total cholesterol > 6.1 mmol/L for subjects between 18-29 years, > 6.9 mmol/L for subjects between 30-49 years and > 7.8 mmol/L for subjects ≥ 50 years old, and blood pressure > 160/100 mm Hg. They were also excluded if they reported to have had a weight change of ± 5 % of body weight during the last three months, comorbidities including diabetes type I and II (blood glucose ≥ 7 mmol/L fasting), CHD, haemophilia, anaemia (hemoglobin < 120 gram/L), gastro intestinal disease and hyperthyroidism (TSH > 4 mU/L), if they were pregnant/lactating, if they were allergic or intolerant to gluten, milk protein and/or lactose, if they reported use of medications affecting lipid metabolism or inflammation, if they were receiving hormone treatment (stabile dose of contraception or thyroxin for the last three months excepted), if they were unwilling to seponate omega-3 rich supplements four weeks prior to the screening and during the study period, if they had donated blood two months prior to test day 1 or during the study period, if they were smoking tobacco and if they had an excessive regular alcohol consumption (> 40 grams per day) prior to the test day.

3.2 Study design

An illustration of the course and design of the study is presented in figure 3. Subjects who wanted to participate in the study were interviewed by telephone to see if they matched the criteria before they proceeded to a screening visit at the Department of Nutrition, University of Oslo. The subjects were asked to fill in a food frequency questionnaire (FFQ) before coming to the screening visit in order to provide characteristic information about their habitual food intake. Blood samples and anthropometric measurements, e.g. height, weight, waist and hip circumference and body composition (Bioimpedance), were measured at the screening visit and further questions were asked to examine whether the subjects were eligible

(22)

12

to proceed to the intervention. The design of the study was a postprandial single meal trial with a randomized controlled cross-over intervention. The intervention consisted of four isocaloric high-fat meals with the same amount of fat, but from different dairy products eaten at separate occasions under controlled conditions. All participants ate all of the meals and served as their own controls. Blocked randomization was used to divide the participants into four groups receiving the test meals in the following orders; group 1: ABCD, group 2: BCDA, group 3: CDAB, group 4: DABC, similar as described by Ottestad et al (74). Between each intervention day there was a wash out period of at least two weeks for men and four weeks for women to minimize any hormonal changes due to women’s menstrual cycles. Participants were advised and encouraged to maintain their habitual diet and physical activity level during the whole study period. In order to standardize all participants’ nutritional and physical status before the screening visit and each test day, they were asked to consume a low-fat meal on the evening before a test day, not to consume any alcohol and to avoid strenuous exercise the day before a test day, not to consume any dietary supplements after the initial telephone interview nor four weeks before and during the whole study period, and fast for 12 hours before a test day, e.g. eat the last meal of the day 12 hours before check-in time of the test day. When arriving at the Department of Nutrition in the morning of each test meal day, the participants had the fasting (0-hour) blood sample taken before receiving the test meal. After ingesting the meal the participants stayed at the department until the 6-hour blood sample was taken. They were told not to ingest other than water and to engage in a minimum of physical activity until the end of the intervention day.

(23)

13

Test meals

The dairy products used in the four different test meals were butter (Smør 82% fat), cheese (Gräddost 38% fat), whipped cream (Kremfløte 38% fat) and sour cream (Seterrømme 35%

fat), all from the assortment of TINE. The dairy products were given as topping on three slices of bread, and 20 grams of jam was added to all meals to make them easier edible (figure 4). The meals were practically isocaloric and the amount of saturated fat was approximately the same. The exact nutrient composition of the meals is presented in the table below (table 2).

Figure 3: The course and design of the study.

(24)

14

Tabell 2: Meal composition and nutritional content of the test meals.

Butter Cheese Whipped cream Sour cream Meal composition

White toast bread, g

Raspberry jam, g 20 20 20 20

Butter (82 % fat), g 52 - - -

Cheese (38 % fat), g - 113 - -

Whipped cream (38 % fat), g - - 113 -

Sour cream (35 % fat), g - - - 123

Nutritional data

Energy, kJ 2614.1 2981.4 2712.4 2726.2

Energy, kcal 628.8 715.3 652.1 655.1

Carbohydrates, g 45.3 45.1 48.4 48.6

Carbohydrates, E% 30.5 26.6 31.3 31.3

Protein, g 8.8 30.0 10.9 11.2

Protein, E% 5.7 17.2 6.9 7.0

Fat, g 44.8 45.1 45.1 45.2

Fat, E% 63.7 56.2 61.8 61.6

Calcium, mg 9.0 678.0 85.0 94.0

kJ, kilo Joule; kcal, kilo calories; E%, energy percent

84 84 84 84

Figure 4: Pictures of the test meals. Upper left: butter; upper right: cheese; lower left: whipped cream; lower right: sour cream.

(25)

15

3.3 Ethics

The research has been approved by REK with the number 2016/418. Signed written consent to participate in the study was collected from all subjects before they were included them. The study was registered at www.clinicaltrials.gov as NCT02836106.

3.4 Blood Sampling

Fasting and postprandial venous blood samples were collected in 8.5 ml silica gel tubes (Becton Dickinson Vacutainer Systems, USA) and kept at room temperature for at least 30 minutes before being centrifuged at 1500g for 15 minutes. Serum was obtained from the gel tubes and immediately stored at -80 C in 1.8ml cryo-tubes (NUNC Thermo Fisher Scientific, Denmark) until analysis of inflammatory markers. 8.5 ml silica gel tubes containing fasting and postprandial venous blood samples were sent to Fürst Medical Laboratory for analyzing of triglycerides and high sensitive CRP (hsCRP) in serum.

3.5 ELISA measurements

Enzyme-linked immunosorbent assay (ELISA) was used to analyze the level of ICAM-1, VCAM-1, E-selectin, TNF-R1 and TNF-R2 in serum. ELISA is an analytical method designed to detect and quantify substances by combining enzyme-linked antibodies and antigens to create a color change in a solution. By reading the absorbance of the colored solutions, the concentration of the molecule of interest can be calculated. There are several types of ELISA with some differences in approach and quality. For the biomarkers measured in this study, sandwich-ELISA has been used due to its high sensitivity. Sandwich-ELISA is conducted by coating the wells of a micro plate with a capture antibody specific for the protein being measured. The specimen is added to the wells, whereupon the protein of interest will bind to the capture antibody and be fastened along with the antibody to the wells. A process of washing the micro plate with a suitable washing buffer ensures that nothing other than the antibody-antigen complex is left in the wells. Next, an enzyme conjugated antibody specific for the protein of interest is added, and a “sandwich” is created with the protein of interest placed in the middle of the two antibodies. The washing process is repeated to ensure that no enzymes other that the ones connected to the antigen-antibody complex is left in the well. The addition of a colored substrate creates a color change proportional with the amount

(26)

16

of enzyme left in the wells. A stop solution is added to end the enzymatic process and creates an additional color change. The absorbance of the solutions can then be read by a plate reader.

A high absorbance equals a high amount of the protein of interest, and the exact amount is being calculated by creating a standard curve based on the absorbance of standard samples with known concentrations (figure 5-6).

Figure 5: Principles of ELISA. From R&D systems.

Figure 6: Color change of the samples after adding substrate (left) and after adding stop solution (right). Private pictures.

(27)

17

Precision of ELISA measurements

The precision and reproducibility of the laboratory results were determined on a series of test assays before performing the analyses of the study samples. This was done by measuring the coefficient of variation (CV) for samples within a plate (intra-assay) (table 3) and for assays done on different plates (inter-assays) (table 4, figure 7), using an ICAM-1 DuoSet (R&D systems, USA). The CV was calculated by dividing the standard deviation of the sample replicates on the average concentration of the sample replicates, and was presented as percent.

A CV% below 10 was considered to be adequate precision. For determining intra-assay precision, two samples of ICAM-1 with known concentrations were tested 16 times on one micro plate. For determining inter-assay precision, duplicates of seven different samples of ICAM-1 were tested on 10 different micro plates.

Table 3: Calculation of intra-assay CV

Table 4: Calculation of inter-assay CV

Sample 1 (500pg/ml) Sample 2 (800pg/ml) Average CV%

Replicates 16 16

Std.dev 40.4 68.2

Mean (pg/ml) 554.3 802.5

CV% 7.3 8.5 7.9

Sample 1 (2000pg/ml)

Sample 2 (1000pg/ml)

Sample 3 (500pg/ml)

Sample 4 (250pg/ml)

Sample 5 (125pg/ml)

Sample 6 (62,5pg/ml)

Sample 7 (31,25pg/ml)

Average CV%

Plates 10 10 10 10 10 10 10

Replicates per

plate 2 2 2 2 2 2 2

Std.dev 215.2 70.4 22.6 12.0 5.9 4.5 3.5

Mean (pg/ml) 1976.2 1039.9 486.6 248.7 127.7 67.3 35.6

CV% 10.89 6.77 4.65 4.82 4.64 6.72 9.8 6.9

(28)

18

Figure 7: 10 standard curves generated from the same samples of ICAM-1 analyzed on 10 different plates

Measurement of inflammation markers

All ELISA`s were carried out using commercial ELISA kits (R&D systems, USA).

Concentrations of ICAM-1 and VCAM-1 were assayed using DuoSet kits. Concentrations of E-selectin, TNF RI and TNF RII were assayed using Quantikine kits. All procedures were done according to the manufacturer’s instructions (see appendices). Samples were marked with visits and time point (0h or 6h), but it was not known which meal the participants had been given at what time when analyzing the samples. All the standards and samples were analyzed in duplicates. Washing was conducted by filling 400µl of wash buffer (R&D systems) in each well three times (four times for E-selectin) using a Wellwash 4 MK 2 auto- washer (Thermo Electron Corporation) before blotting the plates against suitable paper towels. Standards with known concentration were analyzed on each single plate and the absorbance of the standard samples were used to create a four parameter logistic graph using the software Gen5 2.06. The absorbance was read by a Synergy H1 microplate reader

(BioTek). Readings at 540nm were subtracted from readings at 450nm to correct for optical imperfections in the plate. The average zero standard absorbance was subtracted from all readings.

(29)

19

Table 5: Details of the ELISA procedures conducted in the study.

3.6 Statistical analyses

The data were analyzed using IBM SPSS Statistics 25. The test meals were coded as meal 1, meal 2, meal 3 and meal 4 during the statistical analyses of ICAM-1, VCAM-1, E-selectin and TNF-R1/2, and the randomization key was reviled after the completion of all statistical analyses. Only data from participants who completed all four interventions were included in the statistical analyzes. Non-parametric tests were used for all analyses due to lack of normal distribution of the values. A p-value below 0.05 was considered significant. The Mann-

Whitney U test was used to examine the differences between the normal weight group and the overweight group. The Wilcoxon Signed Ranks test was used to examine the change in inflammation markers from 0h to 6h within each test meal. Friedman’s ANOVA was used to examine whether there existed a difference in change in inflammation markers from 0h to 6h between any of the test meals. If Friedman’s ANOVA gave a significant result, Wilcoxon Signed Ranks test was used to examine between which of the separate meals a difference could be detected. The results are presented as median with 25 and 75 percentiles.

Correlations between postprandial responses of biomarkers were tested using Spearman’s Rank Order Correlation. Correlations were tested between variables where a change with significance level p=0.05 or below had been detected in either the normal weight or the overweight group. The study was initially designed to examine changes in TG. Based on

Mole cule Catalogue # from R&D

systems Amount of se rum Dilution

1:250

Reagent Diluent (R&D systems) 1:2000

Reagent Diluent (R&D systems) 1:10

Calibrator Diluent (R&D systems) 1:10

Calibrator Diluent (R&D systems)

TNF-R2 DRT200 50µl

E-selectin DSLE00 30µl

TNF-R1 DRT100 50µl

ICAM-1 DY720 20µl

VCAM-1 DY809 20µl

(30)

20

previous postprandial studies, a sample of 18 subjects was found to be sufficient to detect changes in TG (75-77).

(31)

21

4 Results

A total of 31 men and women between 18-70 years completed the intervention, with 19 of them being included in the normal weight group and 12 included in the overweight group.

Figure 8: Flow chart of the inclusion and exclusion of participants.

(32)

22

4.1 Baseline characteristics

Baseline characteristics of the two study groups are presented in table 5. The normal weight group consisted largely of women, while the overweight group had an even distribution of women and men. The overweight group had a larger amount of visceral adipose tissue than the normal weight group.

Median Median

Subjects (n) 19 12

Female n (%) 15 (79) 6 (50)

Age (years) 32 25 , 34 52 35.5 , 59.8

BMI (kg/m²) 22.3 20.8 , 24.4 30.3 26.3 , 31.3

Lean (kg) 46.0 41.7 , 51.3 53.5 45.9 , 68.1

Fat (kg) 17.1 13.4 , 22.4 29.4 28.2 , 34.1

Fat % 29 22.5 , 36.4 37.7 33.4 , 41.3

VAT (g) 221 100 , 336 1428 828 , 1848

Hip circumference 96 92 , 100 111 104 , 113

Systolic blood pressure (mmHg) 111 107 , 117 122 116 , 128

Diastolic blood pressure (mmHg) 68 61 , 79 73 63 , 79

Glucose (mmol/l) 4.6 4.5 , 4.9 5.4 4.9 , 5.7

Insulin (pmol/l) 37.0 28.0 , 68.0 74* 39.0 , 90.0

TG (mmol/l) 0.81 0.71 , 0.96 1.10 0.74 , 1.49

TC (mmol/l) 4.60 4.30 , 5.00 5.40 4.78 , 6.08

HDL-C (mmol/l) 1.60 1.40 , 2.00 1.55 1.30 , 1.95

LDL-C (mmol/l) 2.60 2.00 , 3.10 3.65 2.95 , 4.23

Apo A1 (g/l) 1.50 1.50 , 1.70 1.65 1.40 , 1.90

Apo B (g/l) 0.80 0.70 , 1.00 1.05 0.93 , 1.20

MicroCRP (mg/l) 0.8 0.3 , 1.5 2.3 0.8 , 3.9

Data are given as median with 25 percentile and 75 percentile.

*missing value for one subject

Normal we ight Overwe ight

25, 75 percentile 25, 75 percentile

Apo A1 = apolipoprotein A1; Apo B = apolipoprotein B; BMI = body mass index; CRP = C-reactive protein; Fat

= total body fat; Fat % = amount of body fat in %; HDL-C = high density lipoprotein cholesterol; LDL-C = low density lipoprotein cholesterol; Lean = lean body mass; TC = total cholesterol; TG = triglycerides; VAT = visceral adipose tissue

Tabell 6: Baseline characteristics

(33)

23

4.2 Fasting serum concentrations of circulating inflammatory markers in normal weight and overweight subjects

Differences in fasting levels of inflammation markers between the normal weight and the overweight group are presented in table 7. The overweight group had significantly higher fasting levels of TNF-R1 than the normal weight group (p=0.006) (table 7). No differences in fasting levels of ICAM-1, VCAM-1, E-selectin and TNF-R2 were detected between the groups. Fasting concentrations of hsCRP was significantly higher in the overweight groups than in the normal weight group (p=0.028) (table 7). Amount of visceral adipose tissue correlated positively with baseline levels of E-selectin (r=0.404, p=0.024) and TNF-R1 (r=0.4, p=0.026).

Table 7: Fasting serum concentrations of circulating inflammatory markers.

Median Median

ICAM-1 (ng/ml) 118.0 106.5 , 136.7 126.7 117.3 , 138.4 0.25

VCAM-1 (ng/ml) 403.9 343.4 , 501.2 405.4 347.7 , 456.5 0.68

E-selectin (ng/ml) 20.4 14.4 , 25.0 27.8 16.1 , 36.5 0.15

TNF-R1 (pg/ml) 919.9 825.6 , 1013.1 1003.4 972.5 , 1129.1 0.006

TNF-R2 (pg/ml) 1784.3 1604.3 , 2064.4 1828.6 1687.4 , 2119.7 0.65

MicroCRP (mg/l) 0.4 0.4 , 1.8 1.7 0.7 , 4.2 0.028

¹Mann-Whithney U test

CRP = C-reactive protein; ICAM-1 = intracellular adhesion molecule 1; TNF-R1 = tumor necrosis factor alpha receptor 1; TNF-R2 = tumor necrosis factor alpha receptor 2

p¹ between

groups 25, 75 percentiles 25, 75 percentiles

Overwe ight n=12 Normal we ight n=19

(34)

24

Table 8: Postprandial changes in serum concentrations of inflammation markers.

Median Median Median Median

ICAM-1 (ng/ml)

Butter 114 110 , 138 118 113 , 136 0.08 129 109 , 135 128 122 , 151 0.08 0.59

Cheese 123 103 , 134 119 102 , 138 0.78 131 114 , 150 141 115 , 148 0.24 0.41

Whipped cream 118 103 , 142 124 110 , 138 0.90 128 121 , 130 130 122 , 146 0.21 0.16

Sour cream 114 103 , 137 121 114 , 141 0.007 130 119 , 146 131 122 , 137 0.94 0.16

VCAM-1 (ng/ml)

Butter 410 362 , 517 416 394 , 509 0.11 386 341 , 435 410 359 , 461 0.14 0.95

Cheese 404 336 , 512 395 343 , 506 0.31 394 355 , 487 412 372 , 510 0.43 0.27

Whipped cream 398 347 , 515 424 359 , 550 0.17 404 340 , 442 390 351 , 437 0.88 0.41

Sour cream 396 347 , 515 390 356 , 543 0.18 394 347 , 462 404 335 , 439 0.70 0.27

E-selectin (ng/ml)

Butter 28.4 20.1 , 31.6 27.6 19.9 , 30.8 0.69 35.5 21.3 , 48.0 35.7 21.6 , 48.8 0.53 0.56

Cheese 29.5 20.8 , 32.3 28.1 18.4 , 32.7 0.09 37.1 21.3 , 46.4 36.5 22.4 , 46.7 0.58 0.18

Whipped cream 25.7 21.1 , 33.0 27.7 18.9 , 32.4 0.30 36.3 21.6 , 45.9 38.6 22.0 , 49.0 0.023 0.25

Sour cream 26.6 17.7 , 35.2 28.0 18.9 , 34.6 0.21 37.3 22.7 , 52.4 36.5 24.0 , 54.7 0.35 1.00

TNF-R1 (pg/ml)

Butter 933 827 , 1015 953 810 , 1101 0.16 1063 1048 , 1158 1021 937 , 1132 0.050 0.012

Cheese 890 833 , 984 926 796 , 1021 0.52 1032 909 , 1130 982 916 , 1109 0.12 0.44

Whipped cream 921 797 , 1034 876 773 , 1002 0.26 1016 920 , 1146 1010 940 , 1058 0.64 0.80

Sour cream 931 816 , 996 915 779 , 974 0.45 1034 948 , 1099 996 938 , 1139 0.64 0.62

TNF-R2 (pg/ml)

Butter 1775 1676 , 2094 1776 1610 , 2060 0.72 0.014§ 1747 1580 , 2036 1685 1568 , 2118 0.12 0.24

Cheese 1816 1558 , 2009 1668 1364 , 1965 0.006 1796 1626 , 2236 1719 1546 , 2130 0.07 0.27

Whipped cream 1769 1543 , 2152 1697 1465 , 2009 0.053 1987 1656 , 2090 1785 1615 , 2000 0.07 0.51

Sour cream 1829 1415 , 2177 1774 1458 , 2056 0.20 0.044¶ 1760 1552 , 2348 1790 1572 , 2226 0.14 0.51

MicroCRP (mg/l)

Butter 0.50 0.30 , 1.80 0.60 0.30 , 1.60 0.75 1.10 0.63 , 3.65 1.10 0.68 , 3.58 0.75 0.77

Cheese 0.50 0.30 , 0.80 0.50 0.30 , 0.80 0.66 1.55 0.60 , 3.43 1.70 0.63 , 3.45 0.12 0.29

Whipped cream 0.50 0.30 , 1.60 0.50 0.30 , 1.60 0.50 1.45 0.55 , 4.45 1.65 0.55 , 4.05 0.93 0.98

Sour cream 0.40 0.20 , 1.80 0.40 0.20 , 1.60 0.33 1.30 0.50 , 4.48 1.30 0.50 , 4.20 0.89 0.86

¹Change 0h-6h tested with Wilcoxon Signed Ranks test

²Differences between meals tested with Friedman's ANOVA

³Different postprandial response between normal weight and overweight group tested with Mann-Whithey U test

§Change 0h-6h between butter and cheese tested with Wilcoxon Signed Ranks test after significant Friedman's ANOVA

¶Change 0h-6h between sour cream and cheese tested with Wilcoxon Signed Ranks test after significant Friedman's ANOVA

CRP = C-reactive protein; ICAM-1 = intracellular adhesion molecule 1; TNF-R1 = tumor necrosis factor alpha receptor 1; TNF-R2 = tumor necrosis factor alpha receptor 2

Baseline (0h) 6h

25, 75 percentiles 25, 75 percentiles

6h Normal we ight = 19 Baseline (0h)

25, 75 percentiles 25, 75 percentiles

p¹ between time points

0.51 0.26

p² between

meals

p³ between

groups p²

between meals

0.28

0.15

0.23

0.51

0.53

0.62

0.80

0.30

0.42 p¹

between time points Ove rwe ight = 12

(35)

25

4.3 Postprandial changes in adhesion molecules

Serum levels of ICAM-1 increased significantly in the normal weight group 6 hours after ingestion of the sour cream meal (p=0.007) (table 8, figure 9). Neither intake of butter, cheese nor whipped cream significantly altered the serum levels 6 hours after intake of the meals in the normal weight group. Serum levels of ICAM-1 did not change significantly from baseline to 6 hours after ingestion of any of the four test meals in the overweight group (table 8, figure 9).

Serum levels of VCAM-1 did not significantly differ from baseline to 6 hours after ingestion of any of the four test meals in either the normal or the overweight group (table 8, figure 10).

Figure 9: Serum concentrations of ICAM-1 at 0h and 6h. Data is presented as median with IQR.

Figure 10: Serum concentrations of VCAM-1 at 0h and 6h. Data is presented as median with IQR.

(36)

26

Serum levels of E-selectin did not change significantly from baseline to 6 hours after

ingestion of any of the four test meals in the normal weight group (table 8, figure 11). Serum levels of E-selectin increased significantly in the overweight groups 6 hours after ingestion of the whipped cream meal (p=0.023) (table 8, figure 11). Neither intake of butter, cheese nor sour cream affected the serum levels in the overweight group.

4.4 Postprandial changes in TNF-R1 and TNF-R2

No significant postprandial changes in serum levels of TNF-R1 were observed in the normal weight group 6 hours after ingestion of any of the four test meals (table 8, figure 12). In the overweight group, a close to significant decrease in serum levels of TNF-R1 was observed 6 hours after ingestion of the butter meal (p=0.05) (table 8, figure 12). Neither intake of cheese, whipped cream nor sour cream significantly altered the serum levels in the overweight group (table 8, figure 12).

Figure 11: Serum concentrations of E-selectin at 0h and 6h. Data is presented as median with IQR.

(37)

27

Figure 12: Serum concentrations of TNF-R1 at 0h and 6h. Data is presented as median with IQR.

Serum levels of TNF-R2 decreased significantly in the normal weight group 6 hours after ingestion of the cheese meal (p=0.006) (table 8, figure 13). A close to significant decrease in serum levels of TNF-R2 was observed 6 hours after intake of the whipped cream meal (p=0.053). A significant greater decrease in TNF-R2 was observed 6 hours after the cheese meal than 6 hours after the butter (p=0.014) and sour cream (p=0.044) meal in the normal weight group (table 8, figure 13).

4.5 Postprandial changes in hsCRP

Concentrations of CRP did not differ from baseline to 6 hours after ingestion of any of the four test meals in either the normal or the overweight group (table 8, figure 14).

Figure 13: Serum concentrations of TNF-R2 at 0h and 6h. Data is presented as median with IQR.

(38)

28

4.6 Differences in postprandial response between the normal weight and the overweight group

The postprandial response of ICAM-1, VCAM-1, E-selectin, TNF-R2 and hsCRP did not differ between the normal weight and the overweight group (table 8). However, the serum concentration of TNF-R1 decreased significantly more from baseline to 6 hours in the

overweight group compared to the normal weight group after the butter meal (p=0.012) (table 8, figure 15).

Figur 15: Differences in postprandial change in serum levels of TNF-R1 after each of the test meals between the normal weight and the overweight group. Data is presented as median with IQR.

Figure 14: Serum concentrations of hsCRP at 0h and 6h. Data is presented as median with IQR.

(39)

29

4.7 Correlations between postprandial response in inflammatory markers and triglycerides

A significant negative correlation was observed between the increase in triglycerides 2 hours and the decrease in TNF-R1 6 hours after the ingestion of the butter meal in the two groups combined (r= -0.731, p=0.01). The same correlation was observed in the overweight group separately (r= -0.622, p=0.031).

(40)

30

5 Discussion

5.1 Discussion of the method and design

This study was designed to study postprandial effects of dairy meals on TG. Thus, the number of subjects was based on expected changes in postprandial serum TG, since this was the primary endpoint of the study. Based on observations from previous studies, 18 subjects were considered sufficient to detect changes in TG postprandially (75-77). Inflammatory markers were defined as secondary endpoints in the present study. The number of participants may have contributed to the few postprandial changes of inflammatory markers observed in the study since there was a tendency to postprandial changes in inflammatory markers after several of the meals. The likelihood that we have included less participants than needed to achieve sufficient strength of the study makes it likely that we have observed a type 2 error.

The possibility of making type 2 errors is bigger when conducting analyses on such a small sample. A result of this might be that we have failed to detect associations that actually exist.

Thus, the close to significant associations could possibly have been significant if more participants had completed the intervention. On the other hand, the variations in the response of inflammatory markers after each meal were large in both normal weight subjects and overweight subjects, and this may reflect the fact that some of the dairy products does not affect these inflammatory markers in one way or another 6 hours after a meal. Other markers of inflammation have previously been found to change after high-fat meals, even in smaller study samples than ours. Myhrstad et al. observed a postprandial increase in serum

concentration of IL-6 in addition to an increase in mRNA IL-8 level in PBMCs 6 hours after a high-fat meal containing linseed and cod oil, respectively. The results in serum IL-6 were obtained from 14 subjects, while the results in mRNA IL-8 were obtained from only 10 subjects (78). Thus, there is a possibility that the biomarkers analyzed in the present study are not the best markers to determine postprandial inflammation. However, this study is part of an ongoing project, and other inflammation markers, among these IL-6 and TNF-α, will be examined later on. In addition, samples taken 2 and 4 hours postprandially are available for analysis later on, but since expected most changes in inflammatory markers after 6 hours based on previous studies we decided to analyze the samples at 6 hours after intake in the present study (55).

(41)

31 The use of cross-over design of this study has several strengths. Much data is collected using only few participants. The risk of confounders is minimized as all participants serve as their own control person. A challenge with the design of the study was however the extent of the intervention. As the participants had to spend a whole day away from work and other

obligations, several participants withdrew from the study after the first intervention. Of the 52 subjects that were included after the screening and randomized to a meal sequence, 21

subjects withdrew before the last visit.

All participants ate the same meals. The meals were identical except for the dairy products, and the observed effects can therefore be dedicated to the different constituents in the dairy product. A possible limitation of the design of the intervention could be the lack of adjustment of each meal to match the energy expenditure of each participant. By doing this we could have ensured that the relative fat load had been the same for all contestants. All meals contained between 600-700kcal and for some individuals, this amount of energy could make up a larger part of the usual intake of nutrients than for others. However, to determine a person’s exact energy and nutrient intake is difficult and provides much uncertainty. Thus, even though calculations had been made for determining the intake of each individual, new sources of error would probably occur. In addition, a possible limitation of the intervention was the lack of a control meal. As we hypothesized that the SFA in a meal would lead to an inflammatory response, the inflammatory response after a meal low in total fat or SFA might have provided us with important information about the impact of SFA.

The amount of fat in each serving was approximately 45 grams, which is lower than the amount of fat provided in other studies of high-fat meals. However, we considered that this amount was sufficient to obtain a postprandial TG response. The butter meal contained 52 grams of butter, which equals four and a half serving packages. The cheese meal contained more than 100 grams of cheese. We could have given larger amounts of fat in order to use the same as in other high-fat meals, but it could have been difficult for the participants to ingest an even larger amount of dairy in one single meal. It can be discussed whether the results found in studies with much higher amount of fat are relevant for determining the impact of dairy in the diet to a normal population, as it is not necessarily realistic that people would ingest that amount of fat on a regular basis.

Postprandial inflammation-