BSc Thesis

Does the Delivery Matrix of Dairy Fat (Full Fat Cheddar vs Reduced Fat Cheddar vs Butter) Effect Body Composition in

Overweight Older Adults Living in Ireland?

Student Name: Roisin McGann

Student ID: 12388981

Project supervisors: Dr. Eileen Gibney

Dr. Emma Feeney

Date submitted 25 February 2016

Submitted in completion of my fourth year thesis for the BSc Human nutrition degree,

to the Institute of Food and Health, University College Dublin,

February 2016

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Abstract:Obesity is an emerging public health problem both in Ireland and worldwide. This epidemic

has been associated with many chronic health outcomes including metabolic syndrome.

Increased intakes of saturated fat has been linked to increased LDL cholesterol and reduced

vascular flow, both of which are associated with cardiovascular disease, which may progress

to metabolic syndrome. However, this concept may have slightly different effects depending

on the delivery matrix of the food. Fermented dairy products (i.e. cheese) have recently been

identified for their cholesterol-lowering properties unlike their non-fermented counterparts

(i.e. butter) and there is evidence to suggest that metabolic health is not negatively affected

following high levels of consumption. This could be due to the high calcium content of such

dairy products resulting in higher excretion of faecal fat. This study aimed to investigate the

impact of dairy fat consumption in three different matrices on body composition as a marker

of metabolic health. Twenty-three overweight Irish adults over the age of 50 were

randomised to one of three dietary interventions where 42g of dairy fat available in different

matrices was administered daily for a 6-week period in three different matrices (A) 120g full-

fat cheddar (32% fat); (B) 120g reduced-fat cheddar (22% fat) and 21g of butter; (C) 49g

butter plus calcium caseinate powder plus a calcium supplement. Differences in nutrient

intake were observed in study groups A and B, where saturated fat and protein intakes

increased respectively. No differences were observed in body composition for either of the

cheese groups, noting its neutral effect on metabolic health, however a modest decrease in

body mass index was noted following the butter diet. This is an ongoing study and further

research will examine whether changes in blood cholesterol deviate depending on their

delivery matrix in order to gain a broader insight into the alterations in metabolic health.

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Table of contents

Abstract:.....................................................................................................................................2

Chapter 1: Introduction:.............................................................................................................4

1.1 Obesity..............................................................................................................................4

1.2 Metabolic Syndrome........................................................................................................4

1.2.1 Abdominal fat.........................................................................................................5

1.2.2 Cholesterol..............................................................................................................5

1.2.3 Blood pressure........................................................................................................6

1.3 Diet quality.......................................................................................................................6

1.4 Objective........................................................................................................................9

Chapter 2: Methodology..........................................................................................................13

2.1 Study Design...............................................................................................................13

2.1.1 Recruitment methods............................................................................................14

2.1.2 Intervention...........................................................................................................14

2.1.3 Subjects.................................................................................................................15

2.2 Analytic procedures.....................................................................................................17

2.2.1 Anthropometry......................................................................................................17

2.2.2 Blood pressure......................................................................................................17

2.2.3 Dietary Intake.......................................................................................................17

2.3 Statistical procedures...................................................................................................18

Chapter 3: Results....................................................................................................................19

3.1 Demographics.................................................................................................................19

3.2 An analysis of nutrient intake before and after the intervention period.........................21

3.2 An analysis of body composition before and after the intervention period................23

Chapter 4: Discussion..............................................................................................................25

Chapter 5: Conclusions and future research.............................................................................28

Appendices...............................................................................................................................33

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Chapter 1: Introduction:

1.1 ObesityObesity is a major public health problem and with global rates having doubled since 1980, it

could be stated that the issue has reached epidemic proportions (1). In Ireland, the prevalence

of obesity has increased enormously since 1990 (2), with most recent statistics reporting rates

at 26.2% for men and 24.2% for females (3). In the United States, recent statistics have

revealed that 40% and 30% of men and women are overweight and 35% and 37% of men and

women respectively are obese (4). In the United Kingdom, rates of obesity have increased

from 15% to 25% between 1993 and 2013 (5). Data also shows, that this issue is not solely

confined to developed countries, as increasing urbanisation of developing countries has led to

a rise in the prevalence of obesity. Factors that cause this include improved economics,

increased availability of food, decreased physical activity levels and the consumption of a

“Western” style diet (6). In Ireland, the estimated cost of obesity is a substantial €1.13 billion

per year (7).

This emerging issue is defined as a state of positive energy balance, where energy intake

chronically exceeds energy expenditure (8). Increased food intake and lack of PA are the two

underlying factors of positive energy balance, which could result in obesity at a later stage

(8). Body mass index (BMI) is an anthropometric measurement used to define the relative

weight for height of the individual. As it is significantly correlated with total body fat this

marker can accurately assess overweight and obesity and as well as that can be used for

monitoring alterations in body weight (9). The World Health Organisation (WHO) have

categorised adult weight as ‘underweight, normal weight and overweight’ according to a BMI

of ≤ 18.5, 18.5-24.9 and ≥ 25kg/m2 respectively (10).

1.2 Metabolic SyndromeSince the late 20th century, obesity has been associated with elevated risk factors for

hypertension and dyslipidemia, giving rise to cardiovascular diseases including ischemic

heart disease and stroke (11). The aforementioned metabolic abnormalities have the potential

to collate and give rise to a fatal condition known as “metabolic syndrome” (MetS) (12). This

occurs as a result of poor metabolic health and is defined by five major risk factors including,

abdominal weight gain, increased serum triglyceride (TAG), increased LDL-cholesterol

concentrations, reduced serum HDL-cholesterol concentrations as well as raised blood

pressure (12). In order to be diagnosed with MetS, three of the aforementioned conditions

must be present (13).

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1.2.1 Abdominal fatUnlike subcutaneous fat, which is stored underneath the skin, visceral fat is stored within the

abdominal cavity and is highly metabolically active. As a result, increased abdominal

adiposity has been thought to affect hormonal function as well as demonstrate metabolic

abnormalities (14). Waist circumference (WC) is correlated with level of abdominal fat and

therefore is the anthropometric measurement often used to infer visceral fat content (9). The

National Cholesterol Education Program Adult treatment panel (NCEP-ATP) have

standardized cut-off points in defining the presence of MetS as ≥ 102cm and 88cm for men

and women respectively (13).

1.2.2 CholesterolAs it is insoluble in blood, cholesterol requires special proteins known as “Apo proteins” in

order to be transferred in the serum. When bound to cholesterol these proteins are referred to

as lipoproteins. In defining MetS, important lipoproteins include chylomicrons (CM), LDL

cholesterol (LDL-C) and HDL cholesterol (HDL-C); each of which vary according to size,

protein and fat content (15). CMs are the largest and lightest lipoproteins and have the highest

TAG content while HDL-C is the smallest of the three lipoproteins and has the lowest TAG

content. CMs function by transporting dietary lipids through the blood stream (15). In the

event of circulatory LDL-C accumulation, a fatty substance known as plaque develops

resulting in atherosclerosis. The presence of plaque results in the formation of blood clots

cutting off blood supply to the heart causing heart attacks. A clot could disrupt blood flow to

the brain increasing the likelihood of a stroke (15). HDL-C returns intracellular cholesterol to

the liver for removal from the body. This form of cholesterol is considered “beneficial”.

Unlike LDL-C, HDL-C could protect against heart disease by aiding the removal of excess

cholesterol from atherosclerotic plaques slowing their growth and development (15). Table

2.1 outlines the healthy serum cholesterol recommended levels as stated by the Irish Heart

Foundation.

Table 2.1, the recommended lipoprotein levels for healthy serum cholesterol (15).Total cholesterol ≤5mmol/LLDL cholesterol ≤3mmol/LHDL cholesterol ≥1mmol/LTAG ≤1mmol/Lmmol/L, millimole per litre; LDL, low density lipoprotein, HDL, high density lipoprotein; TAG, triglyceride

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1.2.3 Blood pressureBlood Pressure is measured using both systolic and diastolic readings. Systolic blood

pressure (SBP) measures the pressure in the arteries as the heart beats, while diastolic blood

pressure (DBP) measures the pressure in the arteries between beats (16). Blood pressure cut-

off points for defining MetS according to the NCEP-ATP is ≥130 and ≥85mmHg for SBP

and DBP respectively (13).

1.3 Diet qualityIn nutritional epidemiology dietary quality is an umbrella term used to evaluate dietary habits

in particular populations as well as an index to predict various health outcomes and disease

development (17). Healthy dietary patterns such as those high in fruit and vegetables are less

energy dense and provide consumers with adequate nutrition whilst diets high in refined

grains and added fats are energy dense and nutritionally poor (18).

Dietary fat is a concentrated source of energy, providing 9kcal/g (38kJ/g) compared to both

carbohydrate and protein which provide only 4kcal/g (17kJ/g) (19). Despite its energy

density, dietary fat has characteristics which are beneficial to the consumer. For example, it is

known to provide essential fatty acids to enhance digestion, absorption and utilisation of fat-

soluble vitamins in the body (15). Therefore, it is essential that some fat be consumed as part

of a healthy balanced diet. The Food Safety Authority of Ireland (FSAI) recommend that

older adults have a total fat intake of 20% and 35% total dietary energy (TDE) for men and

women respectively (20). Total fat is divided into both saturated and unsaturated fatty acids.

Following high levels of consumption, saturated fatty acids (SFA) have been shown to

increase serum LDL-C concentrations and affect vascular flow. This effect is thought to be

due to increased levels of fatty deposits or plaques in the arterial layer initiating arterial

clotting and contribute to increased cardiovascular disease (CVD) incidence, which could

further result in MetS (21). However, in recent years it has been noted that SFAs could have

deviating effects depending on the delivery matrix in which they are consumed (22). This

phenomenon, otherwise known as ‘the food matrix’, has been previously defined by the

United States Department of Agriculture (USDA) as the molecular relationship between

nutrient and non-nutrient components of foods, i.e. the number of chemical bonds they have

with one another (22). Therefore, this hypothesis supports the theory that the other nutrients

present such as protein and carbohydrate work collectively with the SFAs to exert their

positive effects on metabolic health in humans (22).

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De Oliveira Otto et al. (22) has supported this theory by suggesting a possible association that

the health effects of foods containing SFAs may vary depending on the matrix in which the

food is available (i.e. cheese or meat). Given that both meat and dairy products have a similar

SFAs content, it would be expected that both would have similar effects upon consumption.

However, study findings suggested that SFAs delivered in the matrix of meat sources were

associated with increased risk of CVD while SFAs delivered in the matrix of dairy products

were not. Therefore, this prospective cohort study confirmed the importance of the food

source, whether it is cheese or meat, regarding its contribution to progression of metabolic

disorders (22).

Dairy fat, such as that found in dairy products is rich in both SFAs as well as cholesterol and

as a result has historically been reported to increase serum cholesterol (21) and all-cause

mortality from cardiac disorders (23). However, this concept remains controversial. Some

observational evidence has indicated that diets high in full-fat dairy products could contribute

to both obesity and cardio-metabolic disorders (24) while randomized controlled trials (RCT)

have provided opposing evidence outlining the positive metabolic effects of dairy intake in

subjects diagnosed with MetS (i.e. reduced abdominal obesity) following the consumption

both full-fat and reduced-fat dairy products (25). This discrepancy could relate back to the

delivery matrix of the various dairy products. Fermented dairy products (i.e. cheese, milk and

yogurt) have been shown to lower blood cholesterol which is not the case for than their non-

fermented counterparts (i.e. butter and cream) (26-28) and could therefore be protective

against CVD (12). High intakes of fermented dairy products (i.e. cheese, milk and yogurt)

have been associated with reduced markers of arterial stiffness, a protective factor of CVD, in

comparison to their non-fermented counterparts (i.e. butter and cream), (22). This was further

examined in a study by German et al. (2009) (29), who compared the effects of consuming

fermented dairy products on CVD development, concluding that this particular form of dairy

is not consistently associated with CVD incidence despite its high SFA composition. As a

result, it was concluded that the previous recommendation to reduce dairy consumption

should be reconsidered.

Cheese is an example of a nutritious fermented dairy product, high in important dietary

nutrients including calcium, phosphorus, zinc, vitamin A, riboflavin and vitamin B12, making

it an important contributor to a healthy balanced diet. Cheese is also widely recommended as

a good protein source for the aging population. Kris-Etherton et al. (1997) (21) has

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previously proposed that a moderate increased intake of protein could protect against

sarcopenia, a condition which is defined by chronic age-related muscle loss in older adults.

Paddon-Jones et al. (2008) (30) added to this theory in suggesting a decline in muscle protein

synthesis in elderly subjects who consume <20g of protein per meal. Furthermore, the

contribution of cheese to calcium and vitamin D levels is also important among the elderly

population. While adequate calcium intake could help slow age-related bone loss resulting in

osteoporosis and bone fractures (31), vitamin D is also required in elderly populations as it is

needed for the absorption of calcium. As vitamin D is most commonly obtained from the

action of sunlight on the skin, people who are housebound, living in Northern hemispheres or

in institutions may be at risk of deficiency (32). However, cheese is also high in saturated fat,

including the long chain SFAs such as myristic, palmitic and stearic acid (C: 14; C: 16:

C18:0), as well as sodium. The high content of these nutrients in dairy products are generally

viewed as aspects of concern for some people providing a negative reputation for these

nutritious foods.

Some observational studies have shown an inverse association between increased intakes of

fermented dairy products in the matrix of cheese and metabolic risk factors. These include

elevated HDL-C concentrations (33); decreased serum cholesterol and TAG concentrations;

and when compared to low intakes, was inversely associated with both increased body weight

(34), stroke and acute myocardial infarction (AMI) incidence (35). Although, certain case

control studies (36-40) have argued that increased cheese consumption increases the risk of

metabolic abnormalities, these have methodological limitations including their observational

study design making it impossible to establish causality.

A number of recent RCTs have demonstrated positive or neutral metabolic effects following

cheese consumption. For example, Nilsen et al. (2015) (41) recently established that elevated

consumption of dairy fat from Norvegia and Gamalost cheese had neutral cardiovascular

effects in participants with MetS in comparison to the controlled diet where a low intakes of

cheese were advised. Results indicated that in normocholesterolemic participants elevated

consumption of both cheeses had no effect on both serum cholesterol as well as incidence of

MetS. Similar findings were obtained by Thorning et al. (2015) (42) who explored the effect

of SFAs in the cheese matrix on metabolic markers of health in 14 overweight, post-

menopausal Danish women. While no changes were observed in body weight of the

participants, it was noted in this population that consuming twice the average Danish adult

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intake of cheese (36 %TDE) increased HDL-C concentrations and did not cause detrimental

changes in blood lipid concentrations. Findings also confirmed that cheese consumption

could increase faecal fat excretion, thus preventing dietary fat absorption. Other intervention

studies have provided evidence showing high intakes of cheese to have a neutral effect on

serum lipids and blood pressure (43) and cholesterol (44).

There are an array of mechanisms to support the theory that cheese may affect our metabolic

health in a positive way including; the inhibition of fat absorption due to the high content of

calcium in the matrix of cheese (26); the formation of complexes between the natural fat and

protein present in the colloidal form of natural cheese (28) as well as the presence of

bioactive peptides in cheese which could reduce cholesterol and microflora growth (44).

Moreover, unlike cheese, the milk fat globule membrane (MFGM) is disrupted in butter. As a

result, plasma cholesterol levels would likely to be raised after chronic consumption due to

the impairment of the lipoprotein profile when the MFGM is disrupted (45). In addition, the

positive metabolic effects of fermented dairy products including cheese could be due to their

ability to increase the prevalence of human gut microbiota in the colon of the large intestine.

These specific type of bacteria function by fermenting indigestible carbohydrates which

confers numerous health benefits to the host by increasing the production of short chain fatty

acids (SCFA) which inhibits hepatic cholesterol synthesis and decrease circulating

cholesterol as a result. Furthermore, increased bacterial activity in the colonocytes could

result in enhanced bile acid deconjugation as cholesterol is a precursor for bile acid synthesis,

the primary pathway for cholesterol catabolism (46).

1.4 ObjectiveIntervention studies to date have provided considerable evidence to suggest that saturated fat

from different food sources may have deviating effects on the body depending on the ‘matrix’

in which they are available. Cheese is a significant source of dairy fat and upon consumption

has been shown to increase overall saturated fatty acid intake. However, there is considerable

evidence to suggest that cheese consumption is not related to adverse metabolic outcomes.

This study has therefore aimed to investigate the deviating effects of the delivery matrix of

dairy fat delivered as full-fat cheddar; low fat cheddar and butter; and butter using fat and

calcium as controlling agents. Analysis aimed to examine how each delivery matrix could

affect body composition in overweight but otherwise healthy Irish older adults (≥50 years).

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This particular population was chosen as there is little research carried out within this age

group. This is an on-going study and will later feed into further research, which hopes to

examine this effect in greater detail by looking at the overall blood lipid profiles and how

they might be affected given the matrix in which they are available.

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Table 1.2. Overview of the various observational studies that found associations between dairy consumption in the form of cheese and metabolic health

Author (year) ObjectiveStudy Design Population Measurements Main outcomes

Babio (2015) (39)The association between DP consumption and incidence of MetS

Prospective cohort study.

n=1868; 55-80 years; No MetS, presence of T2DM or 3 CVD risk factors. 2 intervention groups [Mediterranean diet of 1L/w EVOO (n=646) or 30g/d mixed nuts (n=642) or a LFD (n=580)]

DI: validated SQ-FFQ; Blood Lipids: FBS and biochemical analyses. [TC: esterase-oxidase-peroxidase, TAG: glycerol-phosphate oxidase-peroxidase, HDL-C: direct measurement, LDL-C: Friedewald formula]

LFDPs were associated with lower incidence of METS however high cheese intake was not

Sadeghi (2014) (33)

The association between cheese consumption and cardiovascular risk factors

Cross sectional study

n=1752 (men, n=782; women, n=970); 2 groups- cheese >7 times/week (age=37.84 years BMI=25kg/m2 & cheese <7times/week; Healthy; Iranian

Height and weight using standardized equipment BMI: WC: between lowest rib and the iliac. HC: at the greatest point of hip; DI: validated FFQ; Blood lipids: FBS, TC and TAG: enzymatic colorimetric methods. HDL-C: dextrin sulphate-magnesium chloride, LDL-C: Friedewald equation

High cheese intake was associated with higher HDL-C and lower cardiovascular and metabolic risk factors.

Hostmark (2011) (34)

The association between frequency of cheese intake and MetS

Case-Control;

n=17,500 (men, n=7,915; women, n=9,685); Oslo; born in 1970, 1960, 1955, 1940–1941, and 1924–1925; 30, 40, 45, 59 to 60, or 75-76 years

DI: validated questionnaire. Blood Lipids: Venous NFBS; BP: Dinamap; weight, height, and WHR: standard procedures outlined in the study protocol

Cheese consumption was inversely associated with adverse metabolic symptoms, i.e.BW

Beydoun (2008) (40)

The association between consumption of a variety of DP with obesity, central obesity, and MetS

Cross sectional study;

n=14,618; =>18y; Pregnant and lactating women excluded.

WC: tape measure; DI: 24-h DR; HDL-C: heparin-manganese precipitation method, if the sample volume was available, a direct immunoassay technique was used.

Cheese intake was associated with obesity, central obesity, and MetS.

Houston (2008) (36)

The association between the frequency of cheese intake and cardiovascular risk factors.

Cross sectional;

n= 10,872; 25-75y; US; Classified by consumption of cheese in servings/month (0–2, 3–8, 9–16, 17–29 and >=30).

BMI: WC: measured at the high point of the iliac crest. DI: interview-administered FFQ; Blood lipids: collected using standard methodology, LDL-C: Friedewald equation; BP: standards recommended by the AMA during the medical exam.

High cheese intake was associated with raised BMI, WC and a less favorable CVD risk profile in men but not in women.

Snijder (2007) (38)The association of DP in their separate forms, with components of MetS.

Cross sectional

n=2,064; Men and women; 50–75y; Equally divided into quartiles classified according to their mean dairy intake g/day [Q1=0; Q2=2.91; Q3=4.14; Q4= 5.57); Netherlands

BMI; WC: measured at the high point of the iliac crest; DI: SQ-FFQ; Blood Lipids: FBS and use of enzymatic techniques; BP: random-zero sphygmomanometer.

Cheese was associated with raised BMI

Kabagambe (2003) (37)

The association between total and individual SFAs and their food sources on MI. Case-control

n=993 (485 nonfatal MI & 508 controls); Adults; 57 years; Costa Rica

DI; validated FFQ; FA composition of widely used oils and margarines were analyzed by Gulch.

Cheese was associated with an increased risk of nonfatal MI

Tavani (2002) (35)The association between cheese consumption and AMI Case-control

n=985 (nonfatal AMI=07; control=478); Men (n=378) and women (n=129); 25-79y; Italy

DI: validated FFQ; Lifestyle information: Hospital interviews

Cheese consumption had a neutral effect on AMI,

Samuelson (2001) (47)

The association between specific FAs and its composition of SLE, to serum lipid, Apo lipoprotein and insulin concentrations Case control

n=93 (girls, n=51; boys, n=42); Adolescents (15 years) Sweden; 15y; Healthy

Height: standardized wall measuring-stick. Weight: digital scales; DI: 7-d food diary; Blood lipids: FBS measured by enzymatic methods.

Cheese consumption was inversely associated with elevated serum cholesterol and TAG levels.

Iso (1999) (48)

The association between calcium, potassium and magnesium in cheese and incidence of stroke.

Prospective cohort study

n=85,764; Women; 34-59y; 14 year follow-up; USA

DI: SQ-FFQ, Ascertainment of stroke: non-fatal stroke incidents reported by questionnaire and their medical records were then reviewed. Fatal stroke incidence were reported by close relatives and were documented by medical health records

Cheese consumption was inversely associated with stroke incidence

DP, dairy products; MetS, metabolic syndrome; n=number of participants; T2DM, Type two Diabetes Mellitus; CVD, Cardio vascular disease; EVOO, extra-virgin olive oil; LFD, low fat diet; DI, dietary intake; SQ-FFQ; semi quantitative food frequency questionnaire; LFDP, low fat dairy products; FBS, fasted blood sample; TC, total cholesterol; TAG, triglycerides; HDL-C, HDL-cholesterol, LDL-C, LDL-cholesterol, BMI; body mass index; WC, Waist circumference, HC, Hip circumference; BP, blood pressure; WHR, waist to hip ratio; DR, dietary record; SFA, saturated fatty acids; AMI, acute myocardial infarction; AHA, American heart association ;SLE, Serum lipid esters

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Table 1.3. Overview of the various intervention studies that found an effect between dairy consumption in the form of cheese and metabolic healthAuthor (year) Objective Population Intervention Measurements Main OutcomesNilsen (2015) (41) The effect of 2 types of

Norwegian cheeses (varying fat and calcium content) on MetS and serum cholesterol levels.

n=153 (50 Norwegian; 53, Gamalost; 50 control); Norway; 43.1 years; 25.7kg/m2, WC=83.1cm; SBP: features of MetS at baseline

RCT; 3 arms (1) 50g/d of Gamalost (a fat and salt free cheese) (2) 80g/d of Norvegia (27% fat cheese) and (3) control (limited cheese) Groups 1 and 2 maintained their habitual diet. The control group was asked to limit their intake of the intervention cheeses.

Weight: digital scales, height: stadiometer, BMI. WC: measuring tape midway between the iliac crest and the lowest rib margin; DI: validated FFQ; Blood Lipids: FBS using the Vacutainer system; BP: Microlife BP A200 sphygmomanometer.

Both cheeses diets lowered blood cholesterol effect compared to the control group of low cheese intake.

Thorning (2015) (42)

The effects of cheese matrix on blood lipids, lipoproteins and fecal excretion of fat & bile acids.

n=14; Women; 45–68y; 25-32kg/m2, TC = 4.5-7mmol/L; SBP <160 mm Hg; DBP <100mmHg; Overweight & postmenopausal

RCT. 3 experimental isocaloric, weight-maintenance diets. 2 diets, high in SFA in the matrix of cheese (96-120g) or meat (164g), control diet was high in carbohydrate. Each diet had a 2-week duration and was separated by washout periods >=2-weeks.

Height: stadiometer, FBW: Lindeltronic 800 scale. WC and HC: flexible novelistic tape. DI: bomb calorimeter; Blood Lipids: enzymatic colorimetric procedure. TC and TAG: enzymatic procedures. FBP: sphygmomanometer.

SFAs in the cheese matrix increased HDL-C concentrations compared to the control high CHO, low fat diet. Confirms that cheese increases fecal fat excretion. No change in BC

Schlienger (2014) (43)

The effect of SFAs on lipid parameters and BP with regards to different types of DP

n=158 (yogurt=76, 41 male & 35 female; cheese=82, 43 male & 39 female); 49y; 19-30 kg/m2; French; Normotensive

RCT; run-in period (3 weeks), treatment period (5 weeks). 2 groups, 125g of full-fat yoghurt or 30g of Camembert cheese twice a day

BMI, DI: validated FFQ; Blood lipids: plasma samples using standard enzyme based laboratory methods. Blood samples centrifuged and plasma samples kept frozen

Fermented cheeses (Camembert), could be consumed daily without affecting serum lipids or BP

Soerensen (2014) (49)

To investigate whether milk and cheese based diets with similar calcium contents increase blood lipids differently

N=15; men aged 18-50y, 20-28kg/m2; Copenhagen; weight stable; healthy

RCT-crossover trial; randomly assigned to 3 different 14-day intervention periods (diet high in semi-skimmed milk, semi-hard cows cheese and a low calcium control diet). Intervention periods separated by a 2-week washout period.

During each period urine (day 14) and feces (days 10-14) were collected; FBW and height were measured; BP was measured using an inflated cuff; FBS were drawn before and after the intervention

No difference in BC; compared with control diet increased milk and cheese intake attenuated SFA-induced increases in TC and LDL-C

Hjerpsted (2011) (44)

The effects of a high butter, high hard cheese and an ordinary diet on BP and FBL

N=49 (n=28 men, n=21 women); 55.5y; 25.3kg/m2; WC=90.9cm;

RCT; Two 6week interventions separated by a 2wk washout where habitual diet was consumed. Test diets substituted 13%TDE with fat. Experimental foods: 143g of cheese and 47g of butter.

DI: 3-d DR; Blood lipids: FBS. LDL and HDL-C were assessed by an enzymatic colorimetric procedure while TC and TAGs were assessed in serum by enzymatic procedures; BP: automatic BPM.

The cheese diet decreased TC, LDL-C and decreased HDL-C compared to the butter diet. Cheese has a neutral effect on cholesterol

Nestel (2005) (26) To determine whether dairy fat in cheese raises LDL-C similar to butter.

N=20; (14 men, 6 women); 56y; Mean BMI 27.7kg/m2; SBP=124mmHg; DBP: 76mmHg; Healthy, free living subjects

RCT; 2 intervention periods. 4-wks (butter and cheese). 2-week run in period of LFD and increased carbohydrate. 2-wk washout between interventions.

DI: validated 3-day FFQ; Blood lipids: FBS, Glucose: standard enzyme-based methods. BP automated means;

TC and LDL-C were higher after the butter diet meaning cheese fat has less tendency to raise cholesterol

Biong (2004) (27) The effects of Jarlsberg cheese on serum lipoproteins, hemostatic variables and hcy compared to butter

n=22; 31.5y; 27kg/m2; WHR ratio =0·8; Healthy; ordinary dietary habits

RCT; 3 dietary interventions, lasing 3-wks (1) cheese (2) butter & casein as calcium caseinate and (3) butter and egg white separated by a week of habitual DI.

BMI: weight (kg)/height (m) 2. FBW: DI: FFQ; Blood Lipids: FBS taken using the Vacutainer system; TC and TAGs: enzymic methods. HDL-C: measured directly by a detergent-containing method.

Cheese increased cholesterol less than butter, given the identical fat content.

Tholstrup (2004) (28)

The effects of milk, cheese and butter (identical energy, lactose & casein), on fasting and postprandial blood lipids and lipoproteins.

n= 14; Men; 23y; 22kg/m2; Healthy

RCT; 3-wk periods separated by 1 month of habitual diet. 1) 1.5L of FFM/10 MJ [54g of fat & 1779mg ca/10 MJ] 2) 64 g of butter/10 MJ [54g of fat & 10mg ca/MJ], 3) 205g of hard cheese [26% fat/10 MJ & 1989mg calcium/10 MJ]

DI: 7-d WFD; Blood lipids: FBS using venipuncture with minimum stasis,

Moderately lower LDL-C after cheese diet compared to butter diet.

n, number of participants; RCT, randomised control trials; BMI, body mass index; WC, waist circumference; HC, hip circumference; DI, dietary intake; FBS, fasted blood sample; TC, total cholesterol; LDL-C; LDL-cholesterol; HDL-C, HDL-cholesterol; TAG, triglyceride, MetS, metabolic syndrome; SFA, saturated fatty acid; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBW, fasted body weight; FBP, fasted blood pressure; BC, body compositio; CHO, carbohydrate; %TDE, percentage total daily energy; DR, dietary record; BPM, blood pressure monitor; FBL, fasting blood lipids; CVD, cardiovascular disease; LFD, low fat dairy; WHR, waist to hip ratio; NFBW, non-fasted body weight; WFD, weighed food diary; DP, dairy product; TF, total fat; MUFA, mono unsaturated fat, PUFA, polyunsaturated fat; hcy, homocysteine-

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Chapter 2: Methodology

2.1 Study DesignUsing data from the National Adult Nutrition Survey (NANS), the Food for Health Ireland

(FHI) Healthy Cheese study is a randomised controlled trial (RCT) that aims to examine the

impact of the food matrix on metabolic markers of health with respect to dairy fat intake. In

our study, volunteers were given the same amount of dairy fat (42g) in one of four ways as

illustrated in figure 3.1; full-fat cheddar (32% fat) reduced-fat cheddar (22% fat); butter using

fat and calcium as controlling factors (group C) and full-fat cheddar after a 6-week wash out

period (delayed- group D). All study arms were balanced for dairy fat, protein, lactose and

calcium (table 2.1). Due to time constraints, this analysis has focused on only three arms

(groups A, B and C), comparing anthropometric measurements and nutrient intake before and

after the 6-week intervention period. This study was carried out at the Department of Food

and Health, Science centre south, University College Dublin (UCD), Ireland. Ethical

approval was obtained from the UCD human research ethics committee (LS-15-44-Feeney-

Gibney). This study was also registered online as a trial; ISRCTN 86731958. Written

informed consent was obtained from all subjects.

Figure 3.1. Overview of the intervention groups assigned in this randomised controlled trial

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Table 3.1. Daily nutrient contribution for each 6-week intervention dietary intervention; balanced for protein, fat, lactose and calcium

Group Intervention Protein (g) Fat (g) Lactose Calcium (mg)A (n=8) 120g full-fat cheddar 31.2 40.8 0.12 828B (n-9) 120g reduced-fat cheddar & 21g butter 30.0 43.2 0.12 828C (n=6) 49g butter, 30g calcium caseinate powder & a

calcium supplement27.3 39.2 0 816.8

D (n=8) 120g (full fat) cheese (delayed intervention) 31.2 40.8 0.12 828n, number of participants; g, grams; mg, milligrams

2.1.1 Recruitment methodsVolunteers were recruited via email, poster advertisements and through locally held events

specific to older adults. Contacts details were taken from participants who expressed interest

in the study and were stored in a volunteer contact excel spread-sheet. A volunteer leaflet,

information and a screening questionnaire were sent to these participants. Details of those

who have been contacted, what information they have received and whether they wish to

participate were stored in the aforementioned spread-sheet. A screening questionnaire

(appendix 2) was sent to each person who expressed interest in participation via email or by

post. This was a general assessment of each participant’s health status and was self-reported

by each participant. Details were recorded into a spread-sheet.

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2.1.2 InterventionEligible participants, who consented to participate in the study, were randomly assigned to

one of the four groups; full-fat cheddar (group A) reduced-fat cheddar (group B); butter

(group C) and full-fat cheddar following a 6-week wash out period (delayed; group D).

Cheddar cheese was chosen as it is the most commonly consumed cheese in Ireland, as

indicated by the National Adult Nutrition Survey (50). Randomisation to each study arm was

conducted using the online randomisation tool “Research randomizer” (available at

www.randomizer.org) assigning a unique study code identifier to each volunteer. The delayed

group who removed cheese and all other dairy products from their diet for a 6-weeek period,

imitating non-cheese consumers, followed by a 6-week period where they were asked to

consume 120g of full-fat cheddar (32% fat)cheese identical to study group A. However due

to the restricted time period where this study was conducted it was not possible to analyse

this data and these volunteers were excluded. Following randomisation volunteers visited the

UCD Institute of Food and Health volunteer suite on two days six weeks apart. On each visit

day volunteers arrived fasted for at least 12 h overnight. and having completed a 3-day food

diary. Anthropometric measurements and fasting blood samples were taken on arrival,

however, due to time constraints of the present study it was not possible to analyse the fasted

blood samples. Detailed descriptions of analytic procedures (i.e. anthropometry, blood

pressure and dietary intake) are outlined in section 2.2.Volunteers were asked to maintain

their habitual diet during the intervention period and those randomised to groups A and B

were asked to consume the cheese in its natural form and not to place it under direct heat (i.e.

melt or cook the cheese). These instructions were given to each participant prior to

commencing the intervention (appendix 1).

2.1.3 SubjectsFour hundred and sixty-four subjects living in the island of Ireland expressed potential

interest in the study and provided their contact details. Following initial contact, 404

volunteers from the total sample of 464 were either ineligible for inclusion or no longer

interested in taking part. Exclusion criteria included those taking prescribed medication for

cholesterol-lowering purposes, and/or those following a specific diet to control or modify

their cholesterol, or anyone actively trying to lose weight. This left a total 60 overweight but

otherwise healthy subjects enrolled in the study. However, this is an ongoing study, and

therefore only those who had completed the intervention were considered in our analysis.

Therefore, it was necessary to exclude those randomised to the delayed intervention group

(group D), leaving 23 participants from study arms A, B and C included in our analysis.

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Exclusion criteria included those taking prescribed medication for cholesterol-lowering

purposes, and/or those following a specific diet to control or modify their cholesterol.

Additionally, those who were in the process of trying to lose weight were excluded from the

analysis. Compliance was monitored once every two weeks throughout the intervention

period via a telephone call. A log was also used where participants were instructed to record

if they did/did not consume the prescribed intervention food and furthermore, volunteers

following the cheese diet were instructed to keep the packaging and return it on completion

of the study. Details of the compliance form given to each participant are outlined in

appendix 3.

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Figure 3.2. Overview of the response rate obtained in the present study

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2.2 Analytic procedures

2.2.1 Anthropometry Anthropometric data including height, weight, percentage body-fat (%BF), body mass index

(BMI), waist circumference (WC) and hip circumference (HC) were measured in each

participant in the fasted state both at baseline and on completion of the 6-week intervention

period. Height, without shoes or socks, was measured to the nearest 0.1 cm using a portable

stadiometer (Leicester portable stadiometer). Body composition was measure using

bioelectrical impedance analysis (Tanita, BC-420 MA), calculating both BMI and %BF. WC

and HC were measured to the nearest 0.1cm using a non-stretch tape, in a standing position

with the participants both feet together and breathing normally. WC was measured at a level

point, midway between the lower rib margin and the iliac crest with the tape around the body

in the horizontal position. HC measurements were taken over the widest part of the buttocks.

Measurements were taken in duplicate and the average of the two was recorded.

2.2.2 Blood pressureBlood pressure (BP) was measured using the M6 Comfort BP monitor before and after the 6-

week intervention was complete. Measurements were taken in a sitting position with

participants’ feet being firmly rested on the floor. The BP monitor was applied to the upper

arm, centring the blue strip on the middle of the participant’s inner arm and the air tube

pointing down the inner arm towards their hand. The cuff was snugly wrapped around the

participants arm and was securely fastened with the Velcro tape. The lower edge of the cuff

was placed 1-2cm above the inner side of the elbow joint. Three consecutive measurements

were taken and the average of the second and third measurement was recorded.

Measurements were taken on the participants left arm with their hand facing upwards.

Participants were informed of their BP after the whole process was completed. In the event of

repeated measurements ≥160/100mmHg, such participants were advised to contact their GP.

2.2.3 Dietary IntakeDietary intake was monitored using a 3-day estimated food diary before and after the 6-week

intervention period. The food diary was completed on two weekdays and one weekend day.

Each volunteer was asked to record detailed information about the amount and types of all

foods, beverages and supplements consumed over the 3-day period noting the date, time,

location and cooking method (if applicable) of each meal consumed. Volunteers were also

asked to record any leftover food/ beverages and quantify the amount.

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The completed food diaries were quantified by a trained nutrition researcher with the

participant during each of the two visits, using a food atlas (51) to determine portion size. The

food diary data were entered into and analysed using the nutrition analysis software ‘Nuritics’

(www.nutritics.com - V.097 research edition). Nutritics allocated the nutritional value to food

items using the McCance & Widdowson 6th edition food and nutrient composition tables (52)

and the Irish Food Composition Database (53). The nutritional information of foods

unavailable in nutritics was added manually by research assistants (appendix 4).

2.3 Statistical proceduresStatistical tests were run on SPSS statistics (IBM, 2009). Means and standard deviations of

anthropometric measurements and dietary intake were gathered at baseline and post

intervention. These measurements were stratified both for gender and intervention group and

differences between both groups at baseline were assessed using a one-way ANOVA test of

association. Differences between participant genders across intervention group were assessed

using a chi-square test of association. Using a repeated measures ANOVA test of association,

baseline anthropometric measurements and nutrient intakes were compared before and after

the 6-week follow up. Differences between men and women as well as intervention group A,

B and C was further assessed using the aforementioned test.

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Chapter 3: Results

3.1 DemographicsThe mean nutrient intakes for the total population (n=23) at baseline are displayed below.

These results as stratified both by gender (table 3.1) as well as by intervention group; full-fat

cheddar (32% fat), reduced-fat cheddar (22% fat), and butter (table 3.2). Analysis showed

differences in % total energy from saturated fatty acid (SFA) intake where males consumed

higher amounts compared to females, however when stratified by the analysed intervention

groups, no differences were observed for nutrient intake. Baseline demographic

characteristics are displayed below stratified by gender (tables 3.3) as well as by intervention

group (3.4). The mean height, body weight and waist circumference (WC) of the men were

significantly greater than that of the women while female participants had a higher

percentage body fat (%BF) compared to males. However, age, body mass index (BMI) and

blood pressure was identical (table 3.3). When the total population was stratified by study

group differences in gender and diastolic blood pressure (DBP) were observed (table 3.4).

Table 3.1. Baseline nutrient intakes stratified by genderTotal (n=23) Female (n=12) Male (n=11) P-valueMean SD Mean SD Mean SD

Energy (KJ) 9515.14 2748.58 9345.96 3340.84 9684.33 2154.23 0.78Protein (%E) 16.10 4.83 16.10 5.39 16.10 4.47 1.00Fat (%E) 37.81 6.20 35.30 5.30 40.31 6.23 0.06SFA (%E) 14.54 2.37 13.26 1.74 15.82 2.25 0.01MUFA (%E) 13.56 3.56 12.32 3.44 14.80 3.36 0.10PUFA (%E) 6.30 1.88 6.49 2.34 6.11 1.36 0.65CHO (%E) 43.11 9.71 45.28 9.75 40.93 9.62 0.30Starch (g) 150.88 54.63 159.69 70.21 142.08 34.17 0.46Sugar (g) 93.29 43.40 94.05 41.35 92.53 47.38 0.94Added sugar (g) 52.22 33.59 54.50 25.39 49.93 41.39 0.76Dietary Fibre (g) 19.90 8.36 21.07 8.07 18.72 8.88 0.53Calcium (mg) 974.16 467.23 903.40 469.91 1044.93 476.03 0.49Vitamin D (ug) 4.17 2.87 4.15 3.44 4.18 2.34 0.98Cholesterol (mg) 313.38 125.28 299.71 139.89 327.05 113.93 0.62Magnesium (mg) 329.42 199.23 318.41 147.18 340.44 89.02 0.68Phosphorus (mg) 1462.83 579.14 1319.88 612.26 1605.78 533.42 0.26Zinc (mg) 10.60 5.06 9.78 5.55 11.42 4.65 0.46Sodium (mg) 2989.04 1237.73 2911.79 1665.14 3066.28 656.76 0.78Potassium (mg) 3171.26 1168.67 3019.08 1504.70 3321.45 743.69 0.55Vitamin A (ug) 1350.00 1394.87 1487.68 1620.06 1212.33 1191.45 0.66Riboflavin (mg) 1.78 0.74 1.63 0.77 1.93 0.71 0.35Vitamin B6 (mg) 2.22 0.83 2.22 1.07 2.22 0.53 1.00Vitamin B12 (ug) 5.77 3.01 5.05 2.65 6.49 3.29 0.27n, no. of people; SD, standard deviation; p-value this represents the differences in mean dietary intake in the population at baseline stratified by gender. The differences were assessed by one way ANOVA; KJ, kilojoule; %E, the percentage energy of nutrient intake; MUFA, monounsaturated fatty acid, PUFA, polyunsaturated fatty acid; g, gram; mg, milligram; ug, microgram

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Table 3.3. Demographic characteristics [mean (SD)] for all participants stratified by intervention groupTotal (n=23) Group A (n=8) Group B (n=9) Group C (n=6) p-value

Gender (n, female/male)

12/11 5/3 4/5 3/3 0.04

Age (years) 58.61 6.25 59.38 6.44 58.22 5.33 58.17 8.18 0.92Height (cm) 171.36 7.86 169.33 9.81 171.91 6.65 173.25 7.40 0.65Weight (kg) 79.65 14.00 81.93 19.53 77.61 7.76 79.68 14.70 0.83BMI (kg/m2) 26.97 3.25 28.17 3.72 26.27 2.28 26.42 3.91 0.45%BF 33.22 7.86 37.11 6.09 31.14 8.30 31.15 8.51 0.23WC(cm) 96.26 11.27 99.75 14.70 93.51 7.62 95.28 10.79 0.55SBP (mmHg) 132.00 19.50 130.81 16.34 141.06 21.65 130.81 16.34 0.15DBP (mmHg) 84.24 14.03 83.75 11.14 92.13 14.33 72.40 10.38 0.04n, number of participants; SD, standard deviation; p-value; this represents the level of significance between groups. This was obtained using a chi-squared test for gender and a one way ANOVA test for anthropometric measurements; cm, centimetres; kg, kilograms; BMI, Body mass index; %BF, percentage body fat; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; mmHg, milligrams of mercury.

Table 3.4. Baseline nutrient intakes stratified by intervention groupTotal (n=23) Group A (n=8) Group B (n=9) Group C (n=6) p-value

Mean SD Mean SD Mean SD Mean SDEnergy (KJ) 9515.1 2748.6 8616.3 2365.4 9622.3 1542.8 10403.1 4371.4 0.52Protein (%E) 16.10 4.83 16.39 6.91 16.22 4.32 15.56 3.22 0.95Fat (%E) 37.81 6.20 40.31 8.68 34.93 4.24 15.56 3.20 0.19SFA (%E) 14.54 2.37 14.23 2.55 14.51 2.64 14.94 2.06 0.87MUFA (%E) 13.56 3.56 15.38 4.46 12.06 3.01 19.69 2.44 0.18PUFA (%E) 6.30 1.88 6.70 1.43 5.99 2.28 6.31 1.17 0.77CHO 43.11 9.71 40.83 11.48 44.33 9.68 43.93 8.79 0.77Sugar (g) 93.29 43.40 90.89 54.84 96.80 34.09 90.82 49.05 0.96Dietary Fibre (g) 19.90 8.36 18.25 2.85 20.60 8.86 20.77 12.36 0.83Calcium (mg) 974.16 467.23 671.60 301.59 1052.35 449.21 1209.89 528.21 0.09Vitamin D (ug) 4.17 2.87 5.01 3.84 3.82 2.87 3.70 1.49 0.66Cholesterol (mg) 313.38 125.28 325.50 103.35 288.36 143.76 336.77 134.31 0.75Sodium (mg) 2989.04 1237.73 2553.53 995.62 2898.98 712.37 3632.21 1912.15 0.30Riboflavin (mg) 1.78 0.74 1.45 0.71 2.01 0.67 1.84 0.70 0.34Vitamin A (ug) 1350.00 1394.87 962.29 9651.51 1934.95 1930.96 924.92 808.34 0.27Vitamin B12 (ug) 5.77 3.01 4.48 2.00 6.01 3.73 6.80 2.68 0.37n, no. of people; SD, standard deviation; p-value this represents the differences in mean dietary intake in the population at baseline stratified by gender. The differences were assessed by one way ANOVA; KJ, kilojoule; %E, the percentage energy of nutrient intake; MUFA, monounsaturated fatty acid, PUFA, polyunsaturated fatty acid; g, gram; mg, milligram; ug, microgram

3.2 An analysis of nutrient intake before and after the intervention periodMean nutrient intakes for each of the study groups before and after the 6-week intervention

period are outlined in table 3.5. The changes in mean energy intake (kJ) relative to the Mean

Table 3.2. Demographic characteristics for all participants stratified by genderTotal (n=23) Female (n=12) Male (n=11) p-value

Mean SD Mean SD Mean SDAge (years) 58.61 6.25 57.67 6.41 59.64 6.20 0.46Height (cm) 171.36 7.86 165.80 5.59 177.43 4.90 <0.0001Weight (kg) 79.65 14.00 71.79 10.78 88.23 12.17 0.002BMI (kg/m2) 26.97 3.25 26.01 2.76 28.01 3.54 0.14%BF 33.22 7.86 38.03 5.41 27.97 6.74 0.001WC (cm) 96.26 11.67 90.91 7.56 101.62 12.10 0.02SBP (mmHg)

132.00 19.50 126.14 17.77 138.45 20.15 0.15

DBP (mmHg)

84.24 14.03 79.68 12.88 89.25 12.13 0.12

Differences in mean anthropometric measurements of the population at baseline, stratified by gender. The was obtained using one way ANOVA tests of association; cm, centimetres; kg, kilograms; BMI, Body mass index; %BF, percentage body fat, WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; mmHg, milligrams of mercury.

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Daily Intake (MDI) each increased following the study period but varied between each of the

analysed study groups. A noticeably larger change between the full-fat cheddar group over

the reduced-fat cheddar group with the butter group having the least increase, however this

didn’t reach statistical significance noticeably larger change between group A over B with

group C having the least increase. Other large but insignificant variations between visits 1

and 2 for group A includes increased intakes of fat, calcium, vitamin D, cholesterol and

vitamin as well as decreased carbohydrate and sugar. Similar trends were observed for group

B aside from their notable decreased vitamin D, cholesterol and vitamin A intakes. Study

group C did not alter their dietary habits much following the intervention period other than

increase their intakes of vitamin A. Statistical associations were found between percentage

energy intake from SFAs and the full-fat cheddar intervention where intakes markedly

increased following the study period. Another statistically significant observation from this

analysis is the increased percentage energy intake from protein following the reduced-fat

cheddar intervention. No differences were found in nutrient intakes for those randomised to

group C. The differences were assessed after having adjusted for gender.

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Table 3.5. Differences in nutrient intakes for all participants before and after the 6 week intervention period, stratified by group

Group A P-value Group B p-value Group C p-valueMean SD Mean SD Mean SD

Energy (KJ)Visit 1 8616.27 2365.38 NS 9622.28 1542.82 NS 10403.12 4371.42 NSVisit 2 12238.87 5899.08 8423.54 2119.81 10435.94 1675.64

Protein (%E)Visit 1 16.39 6.91 NS 16.22 4.32 0.02 15.56 3.22 NSVisit 2 15.94 7.11 20.38 1.95 16.14 2.61

Fat (%E)Visit 1 40.31 8.68 NS 34.93 4.23 NS 39.20 4.05 NSVisit 2 51.98 16.11 39.20 4.05 41.52 10.52

SFA (%E)Visit 1 14.23 2.55 0.02 14.51 2.64 NS 14.94 2.06 NSVisit 2 18.60 2.04 17.93 3.87 16.77 3.45

MUFA (%E)Visit 1 15.38 4.46 NS 12.06 3.01 NS 13.69 2.44 NSVisit 2 16.01 10.39 10.35 1.20 13.12 4.50

PUFA (%E)Visit 1 6.70 1.43 NS 5.99 2.57 NS 6.31 1.17 NSVisit 2 9.95 8.71 5.17 1.83 5.93 2.68

CHO (%E)Visit 1 40.83 11.48 NS 44.33 9.68 NS 43.93 8.79 NSVisit 2 25.76 8.78 37.53 6.13 40.75 14.07

Sugar (g)Visit 1 90.89 54.84 NS 96.80 34.09 NS 90.82 49.05 NSVisit 2 63.38 23.35 73.34 32.42 87.09 24.67

DF (g)Visit 1 18.25 2.85 NS 20.60 8.86 NS 20.77 12.36 NSVisit 2 16.62 6.21 18.21 112.57 24.46 8.81

Calcium (mg)Visit 1 671.60 301.59 NS 1052.35 449.21 NS 1209.89 528.21 NSVisit 2 1248.88 173.67 18.21 112.57 1198.57 438.64

Vitamin D (ug)Visit 1 5.01 3.84 NS 3.82 2.87 NS 3.70 1.49 NSVisit 2 216.34 352.36 3.39 2.12 4.38 4.14

Cholesterol (mg)Visit 1 325.50 103.35 NS 288.36 143.76 NS 336.77 134.31 NSVisit 2 814.28 928.58 3.39 2.12 397.52 212.42

Sodium (mg)Visit 1 2553.53 995.62 NS 2898.98 712.37 NS 3632.21 1912.15 NSVisit 2 2848.37 1181.26 3636.52 1099.52 2866.56 866.59

Riboflavin (mg)Visit 1 1.45 0.71 NS 2.01 0.77 NS 1.84 0.70 NSVisit 2 1.19 0.57 1.17 0.51 1.58 0.51

Vitamin A (ug)Visit 1 962.28 960.72 NS 1934.95 1930.96 NS 924.92 808.34 NSVisit 2 18923.56 30159.63 1150.25 949.80 1605.38 1111.49

Vitamin B12

Visit 1 4.48 2.00 NS 6.09 3.73 NS 6.80 2.68 NSVisit 2 6.26 1.50 4.27 1.76 5.66 4.05

n, number of people; SD, standard deviation; P-value, this represents the differences in mean nutrient intakes obtained at visit 1 vs. visit 2 across intervention groups.. The differences were assessed using repeated measures tests of association adjusted for gender; NS, non-significant; KJ, kilojoule; %E, the percentage energy of nutrient intake; CHO, carbohydrate; SFA, saturated fatty acid; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid; AS, added sugar; DF, dietary fibre; Mg, magnesium; g, gram; mg, milligram; ug, microgram

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3.2 An analysis of body composition before and after the intervention periodRepeated measures analysis which examined the variations between measures of body

composition measurements were run both stratified by gender (see appendix 5) as well as

by intervention group (figures 3.1, 3.2, 3.3) in order to examine whether measurements

changed following the 6-week study period. The bar charts below highlight the differences

between mean BMI, %BF and WC measurements respectively before and after the study

period. Analysis showed only very slight differences in body composition. Statistical

associations were found between WC following intervention group C where it decreased in

completion of the 6-weeks. The differences in body composition were assessed after

having adjusted for gender.

Figure 4.1. Mean BMI for all participants at visits 1 and 2, stratified by group

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Figure 4.2. Mean percentage body fat for all participants at visits 1 and 2, stratified by group.

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Chapter 4: DiscussionThe present study examined for the first time the effects of the delivery matrix of dairy fat, a

concentrated source of saturated fatty acids (SFA), on body composition in overweight adults

(≥50 years) living in Ireland. The study provided 42g/d dairy fat in three different matrices;

full-fat cheddar (32% fat), reduced-fat cheddar (22% fat) and butter, each group having been

matched for each protein, fat, lactose and calcium. The key finding of this study was that

body composition, a marker of metabolic health, showed no substantial differences following

each of the cheese diets. This was in line with our hypothesis, as it confirmed that dairy fat

may have deviating metabolic effects depending on the matrix in which it is available.

Consistent with our hypothesis, some cheese intervention studies to date have shown a

neutral effect between cheese intake and body composition as well as on metabolic risk

factors (42, 43, 49) while others observed a decrease in body weight and positive metabolic

effects upon consumption (26-28, 41). Intriguingly, this effect is also observed even when as

much as 200g of cheese is consumed daily. In fact the dietary intervention improved the

participants overall blood lipid profile in comparison to the control diet where cheese intake

was restricted (28, 44). Historically, SFAs have been reported to have numerous adverse

health effects in that chronic over-consumption could increase body weight as well as serum

LDL cholesterol concentrations (21), factors which have been strongly linked to the

development of metabolic syndrome (MetS) (13). As a result of such negative implications,

Irish dietary guidelines have recommended reducing the intake of saturated fat to remain

below 10% E in order to reduce the progression of this life-threatening disorder (20). MetS is

a particularly important problem in an elderly population as its health effects are generally

more severe than that of other population groups. This could be due to their longer exposure

to environmental risk factors (i.e. dietary intake) (54). Furthermore, this population are at

greater risk of vitamin D deficiency affecting their ability to absorb calcium efficiently. As

calcium is very much related to lower body weight (55), imbalances in calcium metabolism

could contribute to a poor overall metabolic health. Thus, if a particular food were to

negatively affect metabolic health the outcome would be more evident in older populations

such as that used in our study. However, the results obtained in the current study deny this

generalisation that all SFAs exert negative metabolic effects following consumption but

rather corroborate the theory that different food sources of SFA could have deviating effects

depending on the matrix in which they are consumed. In line with other studies, this study

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confirms the possibility that there may be something special about the matrix in which cheese

is available, causing neutral or even positive metabolic effects rather than the expected

negative outcomes upon consumption.

Although our study looked individually at body composition as a marker of metabolic health,

similar human trials have additionally examined blood cholesterol and have provided further

evidence that this nutrient dense dairy product is not harmful to out metabolic health and

could even be consumed in large amounts if desired by the individual (41-43, 49). There are

biological mechanisms which support the potential health effects of cheese and its protective

effect against MetS. First, cheese is an important source of calcium which allows it to interact

with its SFAs to form calcium fatty acid soaps in milk products, increasing faecal fat

excretion and thus improve the HDL to LDL cholesterol ratio (56). Secondly, milk-derived

bioactive peptides in cheese have been suggested to play a protective role against MetS by

result of their possible antihypertensive properties (57) and ability to regulate central fat

accumulation (58).

Deviation in body composition was very small when visits 1 and 2 were compared. This was

noted across each of the study groups. As the diets contained similar amounts of dairy fat,

similar changes in body composition were expected. However, the fact that BMI had

significantly decreased for the butter group and not for the full-fat and reduced-fat cheese

groups was interesting given that each diet had been matched for fat, protein, lactose and

calcium. Although body composition was not the metabolic marker of health assessed,

previous research by Biong et al. (27) has identified an overall improved blood lipid profile

following Jarlsberg cheese consumption in comparison to butter and calcium caseinate of

equal fat content in healthy Norwegian adults. Thus, the study indicated an overall improved

state of metabolic health following the cheese diet but not for the butter diet. One of the

reasons to why this result was not confirmed by our study may be due to the increased fibre

intakes of the participants following the butter intervention in contrast to both cheese groups.

This might have been due to the discretional addition of the caseinate powder foods such as

fruit and vegetable juices, or even to a bowl of porridge, all of which offer more fibre then

would dairy products. Although our analysis has shown this result to be insignificant, it could

partly describe the improved body composition following the butter group when their first

and last visits were compared as the nutrient is not absorbed to the extent of other

macronutrients and offers less energy per gram (59). Due to the low energy provided by fibre,

body composition would expectably decrease resulting in a lower BMI (8). Ongoing research

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in this area aiming to examine changes in blood lipid profiles is therefore necessary in order

to confirm whether this delivery matrix of dairy fat had additional positive effects or whether

the noted decrease in body composition was irrelevant.

Analysis of nutrient intake before and after the intervention have shown increased energy

intakes from SFAs following the cheese intervention. This was expected as due to the large

amount of energy added as fat, which provides a greater amount of energy per gram of intake

compared to other macronutrients it is unsurprising that a diet higher in fat would contribute

to higher energy intakes (19). However, despite increases in energy and SFA intake in this

group, metabolic markers were left unaffected which has provided further reason to postulate

that the SFAs delivered in the matrix of cheese do not have adverse metabolic health effects.

The fact differences in energy intake before and after the intervention were insignificant in

our analysis can be justified by the low number of participants who underwent the

intervention. The increased energy from protein noted in the reduced-fat cheddar group

similarly was expected prior our analysis due to the high protein content of cheese (21, 30).

Unexpectedly the opposite effect was observed for those following full-fat cheddar diet of

similar protein content. This effect could have been simply due to subject variation in dietary

assessment (i.e. reporting bias) or furthermore, the small sample size of the study may have

added difficulty in obtaining an accurate indication of mean nutrient intake.

Strengths and limitationsOne major strengths of the current study is that it was the first to examine the effect by which

dairy fat is consumed on body composition in a sample of Irish adults (aged ≥50 years) with a

baseline BMI of ≥25kg/m2. The study used a controlled dietary intervention whereby each of

the analyzed groups was matched for dairy fat, protein lactose and calcium. This created a

strong experimental study design where the matrix effect of cheese was accurately examined

by comparing it to the consumption of its basic components (i.e. butter, calcium caseinate

powder and a calcium supplement) . However, the results obtained in the present thesis

should also be interpreted in the context of several limitations. First, it is undeniable that the

self-assessment of usual food intake (e.g. 24 h recall, food frequency questionnaires, diet

diaries, etc.) in free-living volunteers remains, a major difficulty and could lead to

misclassification of dairy consumption. Nevertheless, potential random measurement error,

which could have been caused by dietary changes during follow up, was minimised through

the calculation of mean nutrient intakes at the end of the dietary intervention. This ensured

that study participants did not drastically change their diet resulting in altered body

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composition. A second limitation of our study was the lack of response rate contributing to

the small sample size of this study must also be noted as a limiting factor as it allowed for

less statistical power in the analysis in comparison to what would be expected in studies with

larger populations.

Chapter 5: Conclusions and future researchExamination of the delivery matrix of dairy fat on body composition, in an elderly Irish

population following a 6-week intervention period has highlighted no substantial between the

two cheddar diets but decreased body mass index following the butter diet. The most

important conclusion drawn from our study was that elevated intakes of the full-fat cheddar

(32% fat) did not have a negative impact on body composition despite the participants

increased percentage energy coming from saturated fatty acids. Thus, it is reasonable to

postulate from are results that saturated fatty acids delivered in the matrix of cheese do not

affect this important marker of metabolic health even at high rates of consumption. These

results are in line with that of previous research and could likely provide information for how

food intake guidelines should be set in the future.

This is an on-going study and further research will later be carried out having made numerous

changes to that of the current study. First, it should include a larger sample size. This would

be of benefit in order to gain a broader understanding on the effects of elevated consumption

of cheddar cheese (120g) within a healthy, overweight elderly population in Ireland. Analysis

of blood lipid profiles will also be included in our research which should answer the question

to whether one or all of the assessed matrices had an overall serum cholesterol lowering

effect, or vice versa. This additional analytic procedure would be important in order to gain a

broader understanding on the effects of the delivery matrix of dairy (full-fat cheddar vs

reduced fat cheddar vs butter) on metabolic markers of health. Furthermore, our novel

research will analyse the results obtained from those participants undertaking the delayed

intervention period in order to gain a broader understanding on whether metabolic markers of

health may deviate in a non-cheese consumer over a large consumer of cheese.

Total word count: 6160

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26. Nestel PJ, Chronopulos A, Cehun M. Dairy fat in cheese raises LDL cholesterol less than that in butter in mildly hypercholesterolaemic subjects. European journal of clinical nutrition. 2005;59(9):1059-63.27. Biong AS, Muller H, Seljeflot I, Veierod MB, Pedersen JI. A comparison of the effects of cheese and butter on serum lipids, haemostatic variables and homocysteine. The British journal of nutrition. 2004;92(5):791-7.28. Tholstrup T, Hoy CE, Andersen LN, Christensen RD, Sandstrom B. Does fat in milk, butter and cheese affect blood lipids and cholesterol differently? Journal of the American College of Nutrition. 2004;23(2):169-76.29. German JB, Gibson RA, Krauss RM, Nestel P, Lamarche B, van Staveren WA, et al. A reappraisal of the impact of dairy foods and milk fat on cardiovascular disease risk. European journal of nutrition. 2009;48(4):191-203.30. Paddon-Jones D, Short KR, Campbell WW, Volpi E, Wolfe RR. Role of dietary protein in the sarcopenia of aging. Am J Clin Nutr. 2008;87(5):1562s-6s.31. Heaney RP, Gallagher JC, Johnston CC, Neer R, Parfitt AM, Whedon GD. Calcium nutrition and bone health in the elderly. The American journal of clinical nutrition. 1982;36(5 Suppl):986-1013.32. Tangpricha VP, EN. Chen, TC. Holick, MF. Vitamin D insufficiency among free-living healthy young adults. Lancet. 1989;2:1104-5.33. Sadeghi M, Khosravi-Boroujeni H, Sarrafzadegan N, Asgary S, Roohafza H, Gharipour M, et al. Cheese consumption in relation to cardiovascular risk factors among Iranian adults- IHHP Study. Nutrition research and practice. 2014;8(3):336-41.34. Hostmark AT, Tomten SE. The Oslo health study: cheese intake was negatively associated with the metabolic syndrome. J Am Coll Nutr. 2011;30(3):182-90.35. Tavani A, Gallus S, Negri E, La Vecchia C. Milk, dairy products, and coronary heart disease. Journal of epidemiology and community health. 2002;56(6):471-2.36. Houston DK, Driver KE, Bush AJ, Kritchevsky SB. The association between cheese consumption and cardiovascular risk factors among adults. Journal of human nutrition and dietetics : the official journal of the British Dietetic Association. 2008;21(2):129-40.37. Kabagambe EK, Baylin A, Siles X, Campos H. Individual saturated fatty acids and nonfatal acute myocardial infarction in Costa Rica. European journal of clinical nutrition. 2003;57(11):1447-57.38. Snijder MB, van der Heijden AA, van Dam RM, Stehouwer CD, Hiddink GJ, Nijpels G, et al. Is higher dairy consumption associated with lower body weight and fewer metabolic disturbances? The Hoorn Study. The American journal of clinical nutrition. 2007;85(4):989-95.39. Babio N, Becerra-Tomas N, Martinez-Gonzalez MA, Corella D, Estruch R, Ros E, et al. Consumption of Yogurt, Low-Fat Milk, and Other Low-Fat Dairy Products Is Associated with Lower Risk of Metabolic Syndrome Incidence in an Elderly Mediterranean Population. The Journal of nutrition. 2015;145(10):2308-16.40. Beydoun MA, Gary TL, Caballero BH, Lawrence RS, Cheskin LJ, Wang Y. Ethnic differences in dairy and related nutrient consumption among US adults and their association with obesity, central obesity, and the metabolic syndrome. The American journal of clinical nutrition. 2008;87(6):1914-25.41. Nilsen R, Hostmark AT, Haug A, Skeie S. Effect of a high intake of cheese on cholesterol and metabolic syndrome: results of a randomized trial. Food & nutrition research. 2015;59:27651.42. Thorning TK, Raziani F, Bendsen NT, Astrup A, Tholstrup T, Raben A. Diets with high-fat cheese, high-fat meat, or carbohydrate on cardiovascular risk markers in overweight postmenopausal women: a randomized crossover trial. The American journal of clinical nutrition. 2015;102(3):573-81.43. Schlienger JL, Paillard F, Lecerf JM, Romon M, Bonhomme C, Schmitt B, et al. Effect on blood lipids of two daily servings of Camembert cheese. An intervention trial in mildly hypercholesterolemic subjects. International journal of food sciences and nutrition. 2014;65(8):1013-8.44. Hjerpsted J, Leedo E, Tholstrup T. Cheese intake in large amounts lowers LDL-cholesterol concentrations compared with butter intake of equal fat content. Am J Clin Nutr. 2011;94(6):1479-84.

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45. Rosqvist F, Smedman A, Lindmark-Mansson H, Paulsson M, Petrus P, Straniero S, et al. Potential role of milk fat globule membrane in modulating plasma lipoproteins, gene expression, and cholesterol metabolism in humans: a randomized study. The American journal of clinical nutrition. 2015;102(1):20-30.46. St-Onge MP, Farnworth ER, Jones PJ. Consumption of fermented and nonfermented dairy products: effects on cholesterol concentrations and metabolism. The American journal of clinical nutrition. 2000;71(3):674-81.47. Samuelson G, Bratteby LE, Mohsen R, Vessby B. Dietary fat intake in healthy adolescents: inverse relationships between the estimated intake of saturated fatty acids and serum cholesterol. The British journal of nutrition. 2001;85(3):333-41.48. Iso H, Stampfer MJ, Manson JE, Rexrode K, Hennekens CH, Colditz GA, et al. Prospective study of calcium, potassium, and magnesium intake and risk of stroke in women. Stroke; a journal of cerebral circulation. 1999;30(9):1772-9.49. Soerensen KV, Thorning TK, Astrup A, Kristensen M, Lorenzen JK. Effect of dairy calcium from cheese and milk on fecal fat excretion, blood lipids, and appetite in young men. The American journal of clinical nutrition. 2014;99(5):984-91.50. Irish Universities Nutrition Alliance. National Adult Nutrition Survey: Summary report. 2011.51. Nelson M, Atkinson M, Meyer J. A photographic atlas of food portion sizes: MAFF publications London:; 1997.52. Food Standard Agency. McCance and Widdowson’s; The Composition of Foods. Sixth summary edition. Cambridge: Royal Socirty Chemistry; 2002.53. Black L, Ireland J, Møller A, Roe M, Walton J, Flynn A, et al. Development of an on-line Irish food composition database for nutrients. Journal of Food Composition and Analysis. 2011;24(7):1017-23.54. Ervin RB. Prevalence of metabolic syndrome among adults 20 years of age and over, by sex, age, race and ethnicity, and body mass index: United States. National health statistics reports. 2009;13:1-8.55. Davies KM, Heaney RP, Recker RR, Lappe JM, Barger-Lux MJ, Rafferty K, et al. Calcium intake and body weight 1. The Journal of Clinical Endocrinology & Metabolism. 2000;85(12):4635-8.56. Boon N, Hul G, Stegen J, Sluijsmans W, Valle C, Langin D, et al. An intervention study of the effects of calcium intake on faecal fat excretion, energy metabolism and adipose tissue mRNA expression of lipid-metabolism related proteins. International Journal of Obesity. 2007;31(11):1704-12.57. Ricci-Cabello I, Herrera MO, Artacho R. Possible role of milk-derived bioactive peptides in the treatment and prevention of metabolic syndrome. Nutrition reviews. 2012;70(4):241-55.58. Zemel MB, Richards J, Mathis S, Milstead A, Gebhardt L, Silva E. Dairy augmentation of total and central fat loss in obese subjects. International journal of obesity (2005). 2005;29(4):391-7.59. Opinion of the Scientific Panel on Dietetic Products, Nutrition and Allergies on dietary reference values for carbohydrates and dietary fibre [press release]. 2010.

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Acknowledgements

The author would like to acknowledge with thanks the support received by the Institute of

Food and Health in University College Dublin, the small sample of older adults who agreed

to participate in the study and to the “National Dairy Council” and “Food for Health Ireland”

for funding this study. Special thanks are given to; Dr. Eileen Gibney and Dr. Emma Feeney

who supervised the analyses conducted throughout the study period. The author would like to

acknowledge Zita Hamilton, a PhD student with the Institute of Food and Health, who was

also a great support over the time in which the thesis was undertaken.

The author’s responsibilities involved the data collection including taking anthropometric

measurements with the help of ZH as well as quantify the 3-day food diary completed by

each volunteer on each of their visits to intervention suite. The study author additionally

entered participants dietary data into nutritics and carried out the analysis conducted in SPSS.

With the assistance of EF the author designed a standard operating procedure document to

help describe the usage and the clarification of unsure terms for the novel nutrition software

‘nutritics’ as highlighted in supplementary materials (appendix 4).

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Appendices

Appendix 1

Instructions and Recipes for study group A: We need you to consume one packet of cheese every day as part of your normal dietary

routine, for the next 6 weeks.

Please limit all other dairy to no more than 50 ml milk / (2 oz.) per day – this is equivalent

to the amount of milk in two cups of tea/coffee.

If there is an occasion where you do not completely finish all of your provided samples in

the day, please ensure to make a note of this on the sheet provided.

Please keep your empty cheese packets and return them to us, along with the cool bag, at the

end of the 6 weeks.

Please note: It is crucial for our analysis that we know exactly how much of the cheese you

have eaten. In the event there are days where you miss or don’t finish an entire packet, please

be sure to note this in the compliance log.

Here are some ideas for ways to incorporate the cheese into your normal dietary routine:

Cheese

Please ensure that the cheese is never melted/heated

Breakfast/Snack:

On slices of bread/in a sandwich

On a piece of toast (NB placed on toast - not melted)

On crackers

On slices of apple/pear

Slices on their own alongside other breakfast items

Lunch/Dinner:

On bread/in a sandwich

Chopped up and sprinkled on salads

As part of a cheese plate with dips, olives, cured meats, crackers, vegetable sticks, etc.

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751752

Instructions and Recipes for study group B: We need you to consume one packet of cheese and 3 pats of spread every day as part of

your normal dietary routine, for the next 6 weeks.

Please limit all other dairy to no more than 50 ml milk / (2 oz.) per day – this is equivalent

to the amount of milk in two cups of tea/coffee.

If there is an occasion where you do not completely finish all of your provided samples in

the day, please ensure to make a note of this on the sheet provided.

Please hold on to your empty cheese packets and return them to us at the end of the 6 weeks.

Please note: It is crucial for our analysis that we know exactly how much of the cheese and

spread you have eaten. In the event there are days where you miss or don’t finish an entire

packet/pat, please be sure to note this in the compliance log.

Here are some ideas for ways to incorporate the cheese and spread in to your normal dietary

routine:

Cheese

Please ensure that the cheese is never melted/heated

Breakfast/Snack:

On slices of bread/in a sandwich

On a piece of toast (NB placed on toast - not melted)

On crackers

On slices of apple/pear

Slices on their own alongside other breakfast items

Lunch/Dinner:

On bread/in a sandwich

Chopped up and sprinkled on salads

As part of a cheese plate with dips, olives, cured meats, crackers, vegetable sticks, etc.

Dairy fat spread

Spread on crackers, toast, bread/sandwiches

Melted on vegetables, a baked potato, boiled potatoes

Mixed through mashed potato/sweet potato/vegetables

Melted on top of fish or meat with garlic, lemon and pepper

Mixed through scrambled eggs

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Instructions and Recipes for study group C: We need you to consume 7 pats of spread, 30g of protein powder (3 level scoops) and a

calcium tablet every day as part of your normal dietary routine, for the next 6 weeks.

Please limit all other dairy to no more than 50 ml milk / (2 oz.) per day – this is equivalent

to the amount of milk in two cups of tea/coffee.

If there is an occasion where you do not completely finish all of your provided samples in

the day, please ensure to make a note of this on the sheet provided.

Please note: It is crucial for our analysis that we know exactly how much of the spread and

protein you have eaten. In the event there are days where you miss or don’t finish an entire

pat/protein scoop or miss a calcium tablet, please be sure to note this in the compliance log.

Here are some ideas for ways to incorporate the spread and protein powder into your normal

dietary routine:

Dairy fat spread

Spread on crackers, toast, bread/sandwiches

Melted on vegetables, a baked potato, boiled potatoes

Mixed through mashed potato/sweet potato/vegetables

Melted on top of fish or meat with garlic, lemon and pepper

Mixed through scrambled eggs

Protein Powder

30g equates to 3 level scoops - consume all at once or in 3 separate meals

Mixed into fruit juice (use blender)

Mixed into fruit smoothie (use blender)

Mixed into porridge (after cooking)

Mixed into mashed potato/vegetables

Mixed into soups (after cooking - use blender to mix)

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Appendix 2

The health questionnaire sent to each participant prior taking part in the study

1. Do you consider yourself to be in good health? (Place an ‘X” in the appropriate box)

Yes NoIf “No”, please state the reason

2. Do you currently or have you ever suffered from any of the following? (Place an ‘X” in the appropriate box)Lactose intolerance? Yes No

Lactose allergy?Yes No

Any other form of food allergy?Yes NoIf yes to any of the above, please give details:

3. Do you suffer from any of the following metabolic diseases, conditions or chronic illnesses?

Diabetes? Yes No

Thyroid disease?Yes No

Cancer? Yes No

Cardiovascular disease?Yes No

Crohns disease?Yes No

Coeliac disease?Yes No

Irritable bowel syndrome?Yes No

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818

819820821

822823

824825

826

827828

829

830831

832

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834

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836

837

838

839

840

841

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Any other illness or disorder?Yes NoIf you answered yes to any of the above, please give details:

4. Are you currently taking any prescription medication for cholesterol reduction purposes? Yes NoIf you answered yes, please give details:

5. Are you currently taking any prescription medication for any reason?Yes NoIf you answered yes, please list:

6. Are you currently on a prescribed / therapeutic diet for any reason? (Examples include weight loss, cholesterol reduction, blood glucose control, etc.)

Yes NoIf you answered yes, please give details:

7. Please list any other medical information you think may be of relevance:

8. Do you smoke cigarettes?Yes No

9. Are you a vegetarian?

Yes No

The aim of this study is to examine the effects of dairy fat intake on metabolic health.

10. Are you willing to consume dairy fat in the form of cheese, or in the form of a control dairy fat, daily, for 6 weeks, as part of this study?

Yes No

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Appendix 3

Table 1, Compliance form for study group A How many slices of cheese did you eat? Any notes?

Week 1

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 2

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 3

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 4

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 5

Day 1

39

861

862

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 6

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 7

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

40

863

Table 2, Compliance form for study group BHow many slices of cheese did you eat? How many spreads did you eat? Any notes?

Week 1

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 2

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 3

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 4

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 5

Day 1

Day 2

Day 3

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Day 4

Day 5

Day 6

Day 7

Week 6

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 7

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

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Table 3, Compliance form for study group CHow many slices of cheese did you eat? How much protein

powder did you eat?

Did you take the

calcium tablet?

Any notes?

Week 1

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 2

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 3

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 4

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 5

Day 1

Day 2

43

866

Day 3

Day 4

Day 5

Day 6

Day 7

Week 6

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

Week 7

Day 1

Day 2

Day 3

Day 4

Day 5

Day 6

Day 7

44

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Appendix 4

Nutritics – Standard Operating Procedures

Click ‘New Client’ on the left.

Enter details: CS5, CS6, etc for the first name. Surname is CheeseStudy Enter weight, height, age as per the information entered in the online ‘Data Entry’ on

the CheeseStudy G-drive(Found in G-drive > Cheese Study Documents >Data Entry > Participant Information Forms

Client Group – Enter A, C, C or D according to assigned diet.

Next, on ‘Logs’ on right hand side, click / choose ‘create’.

In log box, type ‘baseline’ (or ‘mid-way log’ for the mid-way diet log, or ‘Post-log’ for the post-intervention diary)

   

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Clarification for unsure items:Study samplesGroup A/D: Full fat cheddar - Study Cheese (Group A/D)

Group B: Reduced fat cheddar - Study Cheese (Group B)Butter (7g per pat)

Group C: Casein powder - Study (Group C)Butter (7g per pat)Holland & Barrett Chewable Calcium

BeveragesBeer, if not specified choose Lager, standard.Coffee, choose infusion averageCappuccino, choose skimmed milkMilk, choose (semi) skimmed/whole, average Water: If not specified, choose tap.White wine: White wine, medium (for all)Red wine - Use: Red wineBreakfast tea - use: Tea, black, infusion, averageGreen tea - Tea, green, infusionSmithwick’s Blonde Ale - Use: Pale ale, bottledSherry - Use: Sherry, mediumElderflower cordial - Use: Lime juice cordial, concentrate

ConfectionaryDark chocolate - Use Chocolate, dark, 60-69% (Unless specified)

Bread/crackers:Choose brown/white bread, average (unless otherwise specified) Chia seed bread - choose Granary breadRye crisp bread/Ryvita - Crisp bread, rye, plainStafford’s Vienna roll - Use: White bread, slicedWholemeal bread roll - choose Brown roll, softWhite baguette - White bread, French stickFocaccia - Bread, focaccia, herb/garlic and coriander

GrainsSteamed/boiled white rice - use White rice, basmati, boiledRavioli - Pasta, egg, fresh, filled with meat, boiled in unsalted water

Calcium Tablet: 500 mg calcium tablet (Holland and Barrett chewable calcium)

Casein Powder: 30g calcium caseinate powder  (Casein powder - Study Group C)

Cereal: If soaked in boiling water overnight, choose the average weight for milk in cereal (135g)Coleslaw: If unspecified, choose coleslaw  with mayo, retail

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904905906907908909910911912913914915916917918919920921922923924925926927928929930931932933934935936937938939940

Crisps: If not specified choose premium cooked in sunflower oil

Salt/Pepper/SugarIf sugar is not specified choose white

Dairy (1ml milk/cream =1.03g)If study butter was used for frying, enter separately and and choose dry fryingIf cheese is ever not specified, choose cheddar, averageIf milk is ever not specified, choose Semi-skimmed milk, average.Dash of milk, one pre-packaged portionPhiladelphia: cream cheese, average, high fatBlue cheese - Cheese, stilton, blueMuller corner fruit - Use: Yoghurt, Muller fruit corner, all flavoursPlain yoghurt - Use: Yoghurt, whole milk, and plain

Eggs and egg productsScrambled eggs purchased in a café or restaurant is to be entered as eggs, scrambled with semi skimmed milk and fatLarge egg = 68g (average)Medium egg = 58g (average)

FishNo baked hake available, choose baked cod in this case (Hake grilled is available??)Smoked Coley - choose smoked Cod, smoked

Fruit: Choose weighed with skinGrapes:  ave. weight of one grape = 5g

FryingIf butter not available choose blended oilFor stir fried carrots, choose microwaved and add oil separately (tsp)

JuiceIf weight is unspecified, choose 160ml

Liquorice: choose all-sorts.

MeatBacon, if not specified choose bacon rashers, middle, grilledFor burgers, unless specified otherwise assume it was served in a bunMince, if not specified choose beef mince stewed.Sandwich ham, if not specified choose Ham, premiumSausages, if cooking method is not specified choose grilledSirloin steak fried in sunflower - use: Beef, sirloin steak, fried in corn oil, leanRoast beef - use: Beef, top-side, roasted, well-done, leanBlack Pudding - 25g per slice (average slice)Ribeye steak (no cooking method) - Ribeye Steak, Boneless, GrilledMuesli: If not specified, choose Muesli, no added sugar

Oil: If not specified, choose blended oil

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962963964965966967968969970971972973974975976977978979980981982983984985986987988989990991

PotatoesFor baby potatoes, choose new potatoesRoast potatoes, no oil specified - use: Potatoes, roasted in sunflower oilFor steamed potatoes, choose potatoes old microwaved flesh and skinIf not specified, boiled in unsalted waterMashed potatoes - use: Potatoes mashed with…(whatever fat usually used)

SaucesMayonnaise, if not specified choose retail, averageRed pepper hummus, no reduced fat, choose red pepper hummus retail for allTaco sauce, if not specified choose brown sauce, hotIf weight is unspecified, choose 2 tablespoons (for any sauce or dressing)Tomato ketchup brand not specified - choose Tomato ketchup, retail, average

Spreads:Butter, if unspecified choose 7g (standard pat)Margarine, if not specified choose margarine, butter substituteDairygold - margarine, butter substituteJam, if unspecified choose stone fruitApricot jam - Jam, fruit with no seedsPeanut butter, if unspecified choose smooth

Takeaways:Chips, fried in blended oil, takeawayGoujons, fried in blended oil (fish and chicken)

Tuna: If unspecified, choose canned in brine, drained

Vegetables: If steamed, choose microwavedWhen eating out, choose boiled in salted waterLettuce leaves: If not specified choose averageLeeks: If saturated add as raw and add oil separately Bell peppers: average weight = 220gGrilled mushrooms - use: Portobello mushrooms, grilledBrussel sprouts - Average weight of one sprout = 21gCauliflower cheese - semi skimmed milk

Desserts/confectionary:Ice cream, not specified- use: Ice cream, dairy, vanillaCream cake - Use: sponge cake, with dairy cream and jam

Weights:

Oil: If not specified, choose 1 tsp(salt) Pinch: 0.25gPotatoes: if not specified choose the demographic value1g salt=400 mg sodiumFor unspecified weights choose average portions1ml milk/cream =1.03g

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10351036103710381039104010411042

Entering new foods:

For micronutrients giving %RDA, use FSAI Irish recommendations for >50y (pg. 5)

Calcium: 800mgVitamin D: 10ugIron: 12mg (average between males and females)Vitamin C: 60mgVitamin A: 650ug (average of males and females)Vitamin B12: 1.4ugVitamin B-6: 15ug

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Appendix 5

Table 1. Differences in nutrient intakes for all participants before and after the 6 week intervention period, stratified by gender

Total population (n=23)

P-value Female (n=12) p-value

Male (n=11) p-value

Mean SD Mean SD Mean SDEnergy (KJ)

Visit 1 9515.1 2748.6 NS 9346.0 3340.8 NS 9684.3 21.5 NSVisit 2 10480.4 3918.9 10318.0 5160.5 10624.8 2713.8

Protein (%E)Visit 1 16.10 4.83 NS 16.10 5.39 0.03 16.10 4.47 NSVisit 2 17.32 4.80 16.56 5.81 17.99 3.93

Fat (%E)Visit 1 37.81 6.20 NS 35.30 5.30 NS 40.32 6.23 NSVisit 2 45.31 12.22 45.98 15.79 44.73 8.93

SFA (%E)Visit 1 14.54 2.37 NS 13.26 1.74 NS 15.82 2.25 NSVisit 2 17.76 3.07 18.44 3.64 17.15 2.52

MUFA (%E)Visit 1 13.56 3.56 NS 12.32 3.44 NS 14.80 3.36 NSVisit 2 17.76 3.07 12.79 8.83 13.81 4.80

PUFA (%E)Visit 1 6.30 1.88 NS 6.49 2.34 NS 6.11 1.36 NSVisit 2 7.13 5.61 7.38 7.03 6.90 4.43

CHO (%E)Visit 1 43.11 9.71 NS 45.28 9.75 NS 40.93 9.62 NSVisit 2 34.51 11.90 34.80 16.22 34.26 7.25

Sugar (g)Visit 1 93.29 43.30 0.04 94.05 41.35 0.03 92.53 47.38 NSVisit 2 74.68 27.01 71.22 27.47 77.75 27.86

DF (g)Visit 1 19.90 8.36 NS 21.07 8.07 0.001 18.73 8.88 NSVisit 2 19.85 9.08 19.28 8.24 20.36 10.25

Calcium (mg)Visit 1 974.16 467.23 NS 903.40 469.91 NS 1044.93 476.03 NSVisit 2 1019.39 442.74 1018.65 393.20 1020.04 506.77

Vitamin D (ug)Visit 1 4.17 2.87 NS 4.15 3.44 NS 4.18 2.34 NSVisit 2 78.90 223.05 109.98 297.62 51.27 142.04

Cholesterol (mg)Visit 1 313.38 125.28 NS 299.71 139.88 NS 327.05 113.93 NSVisit 2 522.23 582.81 570.92 800.13 478.96 338.63

Sodium (mg)Visit 1 2989.04 1237.73 NS 2911.79 1665.14 NS 3066.28 656.76 NSVisit 2 3086.61 1052.10 2599.27 789.07 3519.80 1104.71

Riboflavin (mg)Visit 1 1.78 0.74 NS 1.63 0.77 NS 1.93 0.71 NSVisit 2 1.32 0.54 1.13 0.54 1.49 0.51

Vitamin A (ug)Visit 1 1350.00 1394.87 NS 1212.33 1191.45 NS 1487.68 1620.06 NSVisit 2 7583.81 18958.42 9744.82 25540.63 5662.92 11799.95

Vitamin B12

Visit 1 5.77 3.01 NS 5.05 2.65 NS 6.49 3.29 NSVisit 2 5.46 2.70 5.46 3.11 5.46 2.47

n, number of people; SD, standard deviation; P-value, this represents the differences in mean nutrient intakes obtained at visit 1 vs. visit 2 across gender groups; NS, non-significant; KJ, kilojoule; %E, the percentage energy of nutrient intake; CHO, carbohydrate; SFA, saturated fatty acid; MUFA, monounsaturated fatty acid; PUFA, polyunsaturated fatty acid; AS, added sugar; DF, dietary fibre; Mg, magnesium; g, gram; mg, milligram; ug, microgram

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Table 2. Differences in anthropometric measurements before and after the 6 week intervention, stratified by genderTotal population

(n=23)P-value Female (n=12) p-value Male (n=11) p-value

Mean SD Mean SD Mean SDBMI (kg/m2)

Visit 1 26.97 3.25 NS 26.01 2.76 NS 28.01 3.54 NSVisit 2 25.84 3.24 26.05 2.34 27.70 3.73

%BFVisit 1 33.22 7.86 NS 38.03 5.41 0.03 27.97 6.74 NSVisit 2 23.11 7.42 36.95 5.16 26.83 5.75

WC (cm)Visit 1 96.26 11.27 0.01 90.91 7.56 NS 101.62 12.10 0.04Visit 2 95.70 11.60 90.46 8.11 100.93 12.52

SBP (mmHg)Visit 1 132.0 19.50 NS 126.14 17.77 NS 138.45 0.15 0.03Visit 2 127.0 19.82 120.0 18.15 134.0 19.69

DBP (mmHg)Visit 1 84.24 14.03 NS 12.88 79.68 NS 89.25 14.13 NSVisit 2 81.70 12.36 77.14 9.53 86.27 13.57

n, number of people; SD, standard deviation; p-value, this represents the differences between the mean anthropometric measurements obtained at visit 2 vs. visit 1 across gender groups. The differences were assessed using repeated measures tests of association and were adjusted for gender and baseline saturated fatty acid intake; cm, centimeters; kg, kilograms; BMI, Body mass index; %BF, percentage body fat, WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; mmHg, milligrams of mercury.

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