Genetic Variation Predicting Lactose Intolerance

(LCT -13910C>T), Dairy Intake, 25-Hydroxyvitamin D and

Risk of Cardiometabolic Disease

by

Ohood Alharbi

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Department of Nutritional Sciences

University of Toronto

© Copyright by Ohood Alharbi 2018

ii

Genetic Variation Predicting Lactose Intolerance (LCT -13910C>T),

Dairy Intake, 25-Hydroxyvitamin D and Risk of Cardiometabolic

Disease

Ohood Alharbi

Doctor of Philosophy

Department of Nutritional Sciences

University of Toronto

2018

Abstract

Background: The LCT-13910C>T variant is associated with lactose intolerance (LI) in >70 ethnic

populations. In Canada, the prevalence of the LCT -13910C>T variant is not known. Individuals

with LI might avoid dairy, which is a rich source of calcium and vitamin D. Dairy has been

associated with increased risk of cardiometabolic diseases, but findings have been equivocal.

Objectives: To determine the prevalence of LI risk genotypes in major ethnic groups living in

Canada and their association with 25(OH)D levels and biomarkers of cardiometabolic disease, and

to determine food predictors of calcium and vitamin D in different LCT genotypes.

Methods: A total of 1,495 participants from the Toronto Nutrigenomics and Health (TNH) study

were used for the present study. Fasting blood samples were obtained for genotyping, 25(OH)D,

biomarkers of cardiometabolic disease, and plasma proteomics. Dairy intake was assessed using a

196-item semi-quantitative food frequency questionnaire.

Results: Approximately 32% of Caucasians, 99% of East Asians, 74% of South Asians, and 59% of

those with other ethnicities had the CC genotype associated with LI. In Caucasians, compared to the

TT genotype, those with the CC genotype had lower dairy intake, and plasma 25(OH)D levels. The

CT and CC genotypes were associated with lower calcium intake and increased risk of suboptimal

iii

(

iv

Acknowledgments

First and foremost, I thank the almighty who gave me the strength, power and determination to

continue pursuing this degree. My great appreciation goes to the person who I’m dedicating this

thesis work toward, my mother, thank you! If it was not for her continuous encouragement,

reassurance, and support I would’ve not it made this far. Second, to my PhD research supervisor,

Dr. Ahmed El-Sohemy, Thank you very much! For everything.

I am very thankful for all my Advisory committee members for the time they have spent during the

Advisory committee meetings, the feedback/comments, and the continuous support. Dr. Anthony

Hanley, thank you very much for welcoming my questions, for the great epidemiological lessons,

and for always provoking critical thinking in me.

I am also very thankful for Dr. Mary L’abbe generous time, and guidance; there is always

something to learn, and a positive message that push forward, thank you. I am tremendously

thankful for Cecily Alexander’s, my Dietetic Independent-Practicum mentor, support. Her

mentorship has provided me with great life lessons in nutrition, communication, leadership,

management and beyond; all of which have shaped my professional skills during this PhD degree.

Cecily, I am forever grateful! I am extremely thankful for my Master’s degree supervisor, Dr.

Leonard Piche. I am speechless about the amount of time, advices, encouragement and unlimited

lessons Dr. Piche has been generous with. His guidance and continues support never got less after

graduation. His support during my Masters and PhD degree has indeed enlightened my path. I am

also very thankful for my undergraduate research project supervisor Dr. Adeel Chaudhary, who has

never stopped pushing me to work harder toward the end point I am pursuing today. Beatrice

Boucher, thank you for your continuous support inside and outside the class, indeed, discussing

future goals with you have been rewarding. Thank you Laura Pasut, for being my mentor, for your

countless support for my professional development, I am grateful. Dr. Elham Aljaali, thank you

very much for the unlimited support to pursue all the professional development possible potentials

during my PhD, I am grateful.

Many thanks to the Honorable, Edwin Holder, I am forever grateful for having you as a mentor, as

well as for your never-ending support to obtain the skills, qualifications, and experiences that can

sculpt me professionally; indeed, I am all gratitude.

v

I am grateful for all my friends who supported me throughout my PhD. Mamdouh Mowadh, who I

forever church his support, thank you! For staying up late regardless of the time difference in

Australia to give me your feedback, for being there when I applied, studied, had exams, defended,

and for all the times your tried to cheer me up whenever I had deadlines. I am so grateful to have

you throughout my academic journey as a colleague and friend. Liliana Bolio, Tess and Simon

Zanatta, during my graduate studies, you all provided me with the family support I am missing in

Canada, I am so grateful. Luke Johanston, thank you! For the support, tips, guidance, and for

encouraging me during the toughest times, you are an inspiration to me as a friend and in school.

Lisa Cianfrini thank you very much for the continuous support and encouragement that you’ve

always provided. For the chatters and advices and for never letting me struggle alone throughout all

my graduate studies. I am so grateful for believing in me, and so blessed to have you as a friend and

colleague! Tremendous thank you! Thanks to Shaker, Marta Alonso, Lora Sri Nofi, Eman Taibah

and Manal Zubair and many others who their supports never stopped even when they were

thousands of miles away.

My great and unlimited appreciation to my siblings; Mashael Alharbi, tremendous thank you!

having you as an older sister have a great impact on my professional journey. Applying to Medical

school and following you to Canada are just two of many. Your career accomplishments have

always inspired me to pursue and achieve more. Meshal, although it is only two years’ difference,

thanks for providing with the father figure and for teaching me confidence, independence, and

strength to use during my PhD, including your usual answer “So what, go and learn it!”, too simple!

Thanks to my brother and best friend Mohammed, Thank you for taking care of my diet when

school work was on the way, for making me laugh when I was too immerse in studying, and mostly

for supporting me here in Canada when times being away from mom were too hard, marvelous

Thank you!

Last but not least, my grandmother (in Canada) Hilary Alderson, I am so grateful for all the times

we spent together discussing my progress academically and professionally, for your life lessons that

helped to go through this journey, as well as for your constant supports and phone calls to ensure

that I am doing well. Indeed, my academic journey, wouldn’t be the same without your input, thank

you!

vi

Table of Contents

Acknowledgments …………………………………..………………………………….…. iv

Table of Contents …………………………………………………..………………….….. vi

List of Tables ……………………………………………………………………….…...… xi

List of Figures ……………………………………………………………………….……. xiii

Abbreviations ……………………………………………………………………….…….. xiv

Chapter 1: Introduction …………………………………………………………………… 1

Chapter 2: Background and Literature Review …………………………………………… 4

2.1 Lactose Intolerance (Lactase Non-Persistence) ……………………………………. 5

2.1.1 Pathophysiology of Lactose Intolerance ……………………..……………….. 10

2.1.2 Genetics and Epigenetics of Lactose Intolerance …………..………………… 11

2.1.3 Diagnosis of Lactose Intolerance …………..…………………………………. 15

2.2 Milk and Dairy Products ………………………………………………………...….. 16

2.2.1 Dairy and 25(OH)D in Lactose Intolerance …………………...………………. 19

2.2.2 Dairy Intake and Risk of Cardiometabolic Disease ……………...……………. 22

2.2.3 Lactose Fermentation by the Colonic Microbiota and Risk of Chronic

Diseases ……………………………………………………………………………... 24

2.3 Proteomics Approach in Nutrigenomics Studies of Cardiometabolic Diseases ….… 26

2.4 Mendelian Randomization ………………………………………………………….. 27

2.5 Summary of Literature Review …………………………………………………….. 29

2.6 Hypothesis and Objectives …………………………………………………………. 30

Chapter 3 Lactose intolerance (LCT-13910 C>T) genotype is associated with plasma

vii

25(OH)D concentrations in Caucasians: A Mendelian Randomization Study……………... 31

3.1 Abstract……………………………………………………………………………… 32

3.2 Introduction.…………………………………………………………………...…...... 34

3.3 Methods ……………..……………………………………………………………… 35

3.3.1 Study Design and Participants ……………………………………………….. 35

3.3.2 Ethics ………………………………………………………………………… 37

3.3.3 Biochemical and 25(OH)D Measurements ………………………………….. 37

3.3.4 Genotyping …………………………………………………………………... 37

3.3.5 Assessment of Dairy Consumption ………………………………………….. 37

3.3.6 Statistical Analysis …………………………………………………………… 38

3.4 Results …………………………………………………...……………….…………. 40

3.4.1 Participants’ Characteristics ………………………………………………….. 40

3.4.2 Vitamin D Levels …………………………………………………………….. 40

3.4.3 Dairy Consumption by LCT-13910 C>T Genotypes …………………………. 42

3.5 Discussion …………………………………………………………………….…….. 43

Chapter 4 Food Contributors to Calcium and Vitamin D Intake in Young Adults with the

LCT-13910>T Genotype …………………………………………………………………... 50

4.1 Abstract …………………………………………………………………….……….. 51

4.2 Introduction ……………………………………………………………...…….……. 53

4.3 Methods…………….……………………………………….………………............. 54

4.3.1 Study Design and Participants …………………………………………….…... 54

4.3.2 Ethics ……………………………………………………………………….…. 55

4.3.3 Genotyping …………………………………………………………………….. 55

viii

4.3.4 Dietary Assessment……………….……………………………………………. 56

4.3.5 Statistical Analysis …………………………………………………………….. 56

4.4 Results …………………………………………………………………….………… 58

4.4.1 Participants’ Characteristics ………………………………………………….. 58

4.4.2 Food Contributors to Calcium Intake by LCT-13910C>T Genotypes ……….. 58

4.4.3 Food Contributors to Vitamin D Intake by LCT-13910C>T Genotypes …….. 60

4.5 Discussion …………………………………………………………………….……. 65

Chapter 5 Association Between Dairy and the Plasma Proteome is Influenced by the LCT

Genotypes ……………………………………………………………………….…………. 68

5.1 Abstract ……………………………………………………………………….…….. 69

5.2 Introduction ….………………………………………………………………….…... 71

5.3 Methods……………...……………………………………………………………… 73

5.3.1 Study Design and Participants ………………………………………………... 73

5.3.2 Ethics ……………………………………………………………………….…. 73

5.3.3 Genotyping …………………………………………………………………… 73

5.3.4 Biomarkers of Cardiometabolic Disease and the Plasma Proteome ………….. 74

5.3.5 Assessment of Dairy Consumption …………………………………………… 74

5.3.6 Mendelian Randomization (Instrumental Variable) …………………………... 75

5.3.7 Statistical Analysis …………………………………………………………….. 75

5.4 Results ……………………………………………………………………….……… 77

5.4.1 Participants’ Characteristics …………………………………………………… 77

5.4.2 Biomarkers of the Cardiometabolic Health and the plasma proteome…………. 79

5.4.3 Dairy Intake and the Plasma Proteome by the LCT-13910 Genotypes………… 79

ix

5.5 Discussion …………………………………………………………………….……... 94

Chapter 6 Summary, Limitations, Future Directions, and Implication ……………………. 104

6.1 Summary …………………………………………………………………….………. 105

6.1.1 LCT-13910C>T, Intermediate Lactose Intolerance, and Dairy Intake…………. 107

6.1.2 Calcium and Vitamin D Intakes of LCT-13910C>T Genotypes and Risk of

Sub Optimal 25(OH)D………………………………………………………………... 108

6.1.3. The Influence of the Genetics of Lactose Intolerance on the Association

Between Dairy and Risk of Cardiometabolic Disease..………………………………. 109

6.2 Limitations …………………………………………………………………….…….. 111

6.2.1 Genetics and Diagnosis of Lactose Intolerance ………………………………... 111

6.2.2 Dairy Intake Assessment ……………………………………………………… 111

6.2.3 Plasma 25(OH)D………………………………………………….…………… 112

6.2.4 Lactose Intolerance, Dairy Intake, and Changes in the Plasma Proteome …… 113

6.2.5 Other Variables ………………………………………………………………… 113

6.3 Future Research ……………………………………………………………………... 114

6.4 Implications …………………………………………………………………….…… 117

6.4.1 Lactose Intolerance and Intermediate Lactose Intolerance……………………... 117

6.4.2 Risk of Suboptimal 25(OH)D and Lower Calcium Intake………....................... 117

6.4.3 Dairy Intake and Risk of Cardiometabolic Diseases …………………………... 118

6.4.4 Potential for Personalized Nutrition Recommendations for the Management of

Lactose Intolerance ………………………………………………………………….. 118

6.4.5 Public Health Implication ……………………………………………………... 119

6.4 Thesis Summary ……………………………………………………………………. 120

x

References …………………………………………………………………………………. 121

xi

List of Tables

Table 2.1: Terms used exchangeable with lactose intolerance ……………………………………. 5

Table 2.2: Types of lactose intolerance ………………………………………………………….... 6

Table 2.3: Frequency of common SNPs causative for lactase persistence (LP)…………………… 8

Table 2.4: Gene variants associated with lactose intolerance …………………………………… 14

Table 2.5: Diagnostic tests of lactose intolerance………………………………………………… 16

Table 2.6: Lactose content of dairy products……………………………………………………… 18

Table 3.1: Participants characteristics in Caucasians men and women aged 20-29 year living in

Canada………………………………………………………………………………………………

44

Table 3.2: LCT-13910 C>T genotypes and risk of suboptimal levels of plasma vitamin D in

Caucasian men and women aged 20-29 year living in Canada…………………………………….

45

Table 3.3: Dairy products intake in Caucasian men and women aged 20-29 years living in Canada

with the LCT-13910 C>T genotypes…………………………………………………………………

46

Table 4.1: Participants’ Characteristics………….…………………………………………………… 58

Table 4.2a: Foods and beverages contributing to calcium intake individuals with the LCT

genotypes………………………………………………………………………………………………

61

Table 4.2b: Calcium intake and food sources of calcium in individuals with the LCT

genotypes………………………………………………………………………………………………

62

Table 4.3a: Foods and beverages contributing to vitamin D intake among individuals with the LCT

genotypes………………………………………………………………………………………………

63

Table 4.3b: Vitamin D intake and food sources of vitamin D in individuals with the LCT

genotypes……………………………………………………………………………………………....

64

xii

Table 5.1: Subject characteristics…..……………………………………………………………........ 77

Table 5.2: LCT-13910 C>T genotypes, the plasma proteome, and biomarkers of cardiometabolic

disease…..……………………………………………………………..…………………………........

80

Table 5.3: Correlation between total dairy intake, and the plasma proteome as well as biomarkers of

cardiometabolic disease in individuals with different LCT genotypes………………………........

82

Table 5.4: Correlation between milk intake, and the plasma proteome as well as biomarkers of

cardiometabolic disease in individuals with different LCT genotypes………………………............

85

Table 5.5: Correlation between cheese intake, and the plasma proteome as well as biomarkers of

cardiometabolic disease in individuals with different LCT genotypes………………………............

88

Table 5.6: Correlation between yogurt intake, and the plasma proteome as well as biomarkers of

cardiometabolic disease in individuals with different LCT genotypes………………………............

91

xiii

List of Figures

Figure 2.1: Illustration of Lactase Activity Using the Lactose Intolerance (-13910C>T) Genotypes 10

Figure 2.2: Location of LI SNPs in the map of the LCT region and the MCM6 gene region ……… 12

Figure 2.3: Vitamin D Synthesis, Intake, and Activation …………………………………………... 21

Figure 2.4: Mendelian Randomization (Instrumental Variable Analysis) …………………………. 28

Figure 3.1: Prevalence of -13910 C>T genotypes in ethnically diverse men and women aged

20-29 year living in Canada …………………………………………………………………………

41

Figure 3.2: Plasma 25(OH)D levels across LCT-13910C>T genotypes in Caucasian men and

women aged 20-29 year living in Canada ……………………………………………………………

44

Figure 5.1: Application of Mendelian Randomization Approach using LCT-13910C>T genotypes

and Cardiometabolic disease …………………………………………………………………………

76

Figure 5.2: Dairy intake among individuals with the LCT-13910C>T genotypes ………………….. 78

Figure 5.3: Summary of plasma proteins correlated with dairy intake individuals with the CC

genotypes that predicts LI…………………………………………………………………………….

101

xiv

Abbreviations

ANOVA Analysis of variance

ANCOVA Analysis of Covariance

BMI Body Mass Index

CRP High-sensitivity C-reactive Protein

CHD Coronary Heart Disease

CAD Coronary Artery Diseases

CVD Cardiovascular diseases

D.E.S.I.R. Devenir Des Spondylarthopatheir Indifferenciees Recentes

FFQ Food Frequency Questionnaire

GESUS Danish General Suburban Population Study

GWAS Genome Wide Association Studies

HBT Hydrogen Breath Test

HDL High-density lipoprotein cholesterol

HOMA-Beta Homeostasis model of beta cell function

HOMA-IR Homeostasis model of insulin resistance

HPLC High performance liquid chromatography

IFG Impaired Fasting Glucose

IV Instrumental variable

LCT Lactase

LDL Low-density lipoprotein cholesterol

LI Lactose Intolerance

LNP Lactase Non-Persistence

xv

LP Lactase Persistence

LTT Lactose Tolerance Test

MCM6 Minichromosome Maintenance component 6

MetS Metabolic Syndrome

METs Metabolic equivalent hours per week

MR Mendelian randomization

MRM Multiple reaction monitoring

MI Myocardial Infarction

NHANES National Health and Nutrition Examination Survey

PREDIMED the PREvención con DIeta MEDiterránea

ppm Parts per million

RAS Renin-angiotensin System

SAS Statistical Analysis Systems

RAS Renin-angiotensin System

SNP Single Nucleotide Polymorphism

T2DM Type 2 Diabetes Mellitus

TNH Toronto Nutrigenomics and Health

TG Triglycerides

1

CHAPTER 1

INTRODUCTION

2

1.1 Introduction

The disaccharide lactose is the principal sugar in mammalian milk, accounting for 40% of the

total caloric intake during infancy [1]. More than 10,000 years ago all humans were unable to

digest lactose after weaning due to a physiological down regulation of the enzyme lactase

responsible for digesting lactose [2]. This down regulation of the enzyme lactase, presenting as

the lactose intolerance trait “LI”, is also known as Lactase Non-Persistence (LNP), Adult-type

Lactose Intolerance, and primary hypolactasia/lactose intolerance [2]. Human adaptation to

agriculture and dairy practices led to a strong positive selection that introduced multiple gene

variants that allowed human to express higher levels of the enzyme lactase into adulthood [3].

High lactase activity levels in adulthood (i.e., milk drinkers) is a recently introduced Mendelian

trait known as lactase persistence (LP) [4]. It is well known that this trait is a result of Gene-

Culture Coevolution, which is a part of the “Niche Construction” (i.e., extra-genetic inheritance).

Gene-Culture Coevolution involves the interaction between genetics, dairy, and cultural

behaviour (i.e., pastoralism and dairy practice) [5]. Recent evidence confirmed the association

between the prevalence of LP and the prevalence of pastoralism and dairying [5].

Nowadays, only 35% of the world population has the LP trait, and the prevalence varies

depending on age, ethnicity, and geographic location [6]. In Canada, the prevalence of gene

variants predicting lactose intolerance is still unknown. The LCT-13910C>T variant is the most

common gene variant associated with LI, and it has been shown to be associated with dairy

intake in European-derived populations [6]. Since individuals with LI often limit or avoid dairy

intake, the genetics of lactose intolerance has been of interest in understanding the effect of dairy

intake on health and disease risk [7]. Only 2 servings of vitamin D fortified dairy products per

day contribute more than 50% of vitamin D and calcium intake [8]. Therefore, individuals with

LI who might choose to avoid dairy, without compensating for equivalent amounts of calcium

3

and vitamin D are at higher risk of developing health conditions related to calcium and vitamin D

deficiency [9]. Furthermore, switching from cow’s milk to milk alternatives has been shown to

be associated with lower levels of plasma 25-hydroxivitamin D (25(OH)D) [10]. Using a

Mendelian randomization approach, LI genotypes can be used as an instrumental variable to

assess risks associated with dairy intake [11]. Various studies have examined risk of

cardiometabolic diseases and other chronic diseases using the LCT genotypes, yet the association

between dairy and these health conditions remains controversial.

4

CHAPTER 2

BACKGROUND AND LITERATURE REVIEW

5

Chapter 2

Background and Literature Review

2.1 Lactose Intolerance (Lactase non-persistence)

Lactose intolerance (LI) was first described by Hippocrates around 400 BC, and properly

understood and defined only in 1960s [12]. Lactose intolerance refers to a group of symptoms

(i.e., stomach discomfort, bloating, excessive flatulence, diarrhea and in some cases headache

and vomiting) associated with lactose maldigestion. The term “lactose intolerance”, however, is

used interchangeably with different terms associated with LI [13] (Table 2.1).

Table 2.1. Terms used exchangeable with lactose intolerance

Term Interpretation

Lactase Persistent

(LP)

The dominant genetic trait in adults with continuous ability to digest lactose

throughout adulthood

Lactase Non-Persistent

(LNP)

The natural decline in intestinal lactase to

6

Table 2.2. Types of lactose intolerance

Type Definition

Primary lactose

intolerance

An inherited trait characterized by a natural decline in the intestinal enzyme

lactase (i.e., LNP), and it is the most common type of LI.

Secondary lactose

intolerance

Loss in the enzyme lactase activity due to injury to the small intestine (i.e.,

chronic inflammation, infection, etc.)

Developmental lactose

deficiency

A condition that may occurs at birth in premature infants, and it lasts only

for a short period.

Congenital lactose

intolerance

An extreme rare condition that manifest at birth with very little or no lactase

enzyme activity. It is inherited with different genetic variants from the ones

that cause the LNP type

Adapted from [14]

affecting 65% of the world populations [4, 15]. In this type of LI, the enzyme lactase responsible

for digesting lactose, the main sugar in milk, is genetically programmed to decline after weaning

[16]. The intestinal enzyme lactase (LCT), also known as lactase-phlorizin hydrolase (LPH), is

responsible for the digestion of lactose and it has two main enzymatic activities: phlorizin

hydrolase activity (EC 3.2.1.62), and lactase (EC 3.2.1.108). The enzyme lactase is found at

lower levels in the end of the duodenum and it peaks in the jejunum of the small intestine [1].

Lactase activity is high during infancy (~90 µg protein/ml), and it naturally declines after

weaning to

7

self-reported having LI, as one in every six Canadians cannot tolerate milk [20]. However, the

prevalence of LI risk genotypes remains unknown in Canada.

In 35% of the world populations, genetic variants located in the MCM6 gene adjacent to the

lactase (LCT) gene were associated with high lactase activity during adulthood, history of

pastoralism, as well as the lactase persistence (LP) (i.e., milk drinkers) trait [4, 15]. The

prevalence of the LP variants is mainly driven by the “gene-culture coevolution”, which suggests

that LP most likely originated in populations where milk was used as a primary source of

nutrition [2, 21, 22]. Heterogeneity of LI /LP prevalence in Africa and East Asia supports that

the prevalence of LI is also influenced by immigration in some populations [23]. Individuals

with the LI genotype have lactase activity of

8

Table 2.3. Frequency of common SNPs causative for lactase persistence (LP)

Continent Country

Frequency (%) References

G-13907 T-13910 G-13915 C-14010 A-22018

Africa Algeria 0 17 - 33.3 0 0 _ [25, 26]

Africa Angola 0 0 0 1 - 6 _ [27]

Africa Tunisia _ 22 _ _ 26 [28]

Africa Cameroon 0 8 - 39 3 - 13.3 0 _ [4, 25, 29]

Africa Chad 0 2.3 0 0 _ [25]

Africa China 0 0 0 0 _ [25]

Africa Congo 0 0 0 0 _ [25]

Africa Ethiopia 22 - 29.7 0 - 1.3 13 – 18 0 - 8.3 _ [4, 25, 30, 31]

Africa Ghana 0 0 0 0 _ [32]

Africa Kenya 0 -11.8 0 0 - 50 0 - 100 _ [25, 30]

Africa Malawi 0 0 0 0 _ [29]

Africa Morocco 0 13.6 - 23 0 - 18 0 16 [26, 29, 33]

Africa Mozambique 0 0 0 0 _ [27]

Africa Namibia 0 0 0 0 _ [25]

Africa Nigeria 0 0 0 0 _ [25]

Africa Senegal 0 0 0 0 _ [4, 29]

Africa Somalia 0 3 1.6 0 1 [33, 34]

Africa South Africa 0 0 -22 0

4.5 -

14.3 _ [25] [32]

Africa Sudan 0 - 25 0 - 48 0 - 24.7 0 _ [4, 25, 29, 30, 33]

Africa Tanzania 0.0 0.0 0.0 0 - 62.5 _ [25, 30]

Asia/ Middle

East Saudi Arabia 0 0.4 - 1 52- 65 0 [4, 33, 35]

Asia/ Middle

East Kuwait 0 - 2 0 - 1 55 - 57 _ _ [36]

Asia/ Middle

East Oman _ 6 17 _ _

[37]

Asia/ Middle

East Yemen 0 0 15 0 _ [25]

Asia/ Middle

East Palestine 0 2.3 4.6 0 _ [25]

Asia/ Middle

East Jordan 0 0 - 5 5- 34.8 0 _ [4, 33]

Asia/ Middle

East Lebanon 0 4 0 0 _ [25]

Asia/ Middle

East Israel 0 0 - 2.6 0 - 13 0 [3, 4]

Asia/ Middle

East Iran 0 5 - 21 0 0 21 [34]

Asia/ Middle

East Turkey _ 3 _ _ _ [38]

Asia India _ 17 _ _ 24 [39]

Asia China _ 0 -10 _ _ _ [25, 40, 41]

Asia Japan _ 0.0 _ _ _ [40]

9

Asia Pakistan 0 0 - 41 0 0 0 - 43 [25, 33, 34, 40]

Asia India _ 0.7 - 21 _ _ _ [42]

Asia South Korea _ 0 _ _ _ [33]

Asia East Asia _ 0 _ _ 0 [40]

Eurasia Afghanistan _ 10 _ _ _ *

Eurasia Armenia _ 1 _ _ _ *

Eurasia Russia 0 7 - 28 0 0 _ [25, 33, 40]

Eurasia Siberia _ 6.0 _ _ _ [40]

Eurasia Bosnia _ 24.8 _ _ 24.5 [43]

Eurasia Uzbekistan _ 19 _ _ _ [44]

Europe Ukraine _ 21.7 _ _ _ *

Europe Germany _ 55.6 _ _ _ [38]

Europe Greece _ 9 - 13.4 _ _ 12 [38, 45]

Europe Hungary _ 62.0 _ _ _ [46]

Europe Italy 0 5- 55 0 6

7.8 -

26.9

[25, 34, 38, 40,

45]

Europe France _ 34- 66.7 _ _ 37 [33, 34, 38, 40]

Europe Finland _ 75 - 84 _ _ 55-62 [33, 34, 40]

Europe UK _ 73.4 _ _ _ [38]

Europe Netherlands _ 41 _ _ _ [47]

Europe Sweden 0 89 - 93 0.0 0 _ [40] [48]

Europe Basques _ 66 _ _ 64 [34]

Europe Czech Republic _ 27 - 76 _ _ _ [49]

North America U.S. (Utah) _ 74 _ _ 76 [34]

North America

U.S. (European

American) _ 77 _ _ 77.10 [40]

North America

U.S. (African

American) _ 9- 14 _ _ 9 -13 [34]

South America

Brazil (European

Brazilian) _ 0 - 29.5 _ _ 0.0 [40, 50]

South America

Brazil (Mixed

Brazilian) _ 0 - 17.5 _ _ 0.0 [40, 50]

South America Mexican _ 32 – 35 _ _ 32 [51, 52]

*According to Muclare [53] as cited by Ranciaro et al [25].

10

Figure 2.1. Illustration of lactase activity using the lactose intolerance (-13910C>T) genotypes

(Lactase activity values were reported from [17])

2.1.1 Pathophysiology of Lactose Intolerance

The monosaccharide lactose is broken down by the enzyme lactase to its two constituents,

glucose and galactose, for absorption by the intestinal microvilli into the bloodstream [1, 2]. In

the presence of low lactase activity, the undigested lactose passes the small intestine, and is then

fermented by the colonic microflora resulting in the production of short chain fatty acids

(SCFAs) and gaseous byproducts (i.e., hydrogen, carbon dioxide, and methane) [1, 6]. The

undigested lactose will increase osmolality, which in response will cause secretion of fluids and

electrolytes to maintain an osmotic equilibrium. This process leads to diarrhea, which is common

in individuals with LI following ingestion of lactose [54]. The fermentation byproducts also

11

cause abdominal pain, bloating, flatulence and borborygmi [6]. In rare cases, severe symptoms

such as skin rash, headache, and vomiting may develop after milk consumption [1]. The severity

of symptoms varies between individuals depending on the amount of lactose ingested at one

time, as well as gut motility [1]. Additional factors that affect gut motility include food

consistency, whether lactose is ingested alone or with food, food temperature, the quantity of

lactase expressed in the small intestine, and the quantity and type of colonic microflora [1].

2.1.2 Genetics and Epigenetics of Lactose Intolerance

In the 1970s, it became well recognized that LI is inherited in a recessive manner [19]. However,

mutations in the LCT gene responsible for encoding lactase were not consistent with the

manifestation of LI symptoms [2, 4, 18, 19, 55, 56]. Lactase is encoded by the LCT gene

(OMIM, 603202), which is 94.3 kb in length and located on the long arm (q) of chromosome 2 at

position 21. The LCT gene consists of 17 exons and is transcribed into a mature 6-kb transcript

[57]. However, except for some synonymous mutations, individuals with LI and LP have

identical coding sequences [58, 59]. Using linkage and allelic association studies, independently

evolved single nucleotide polymorphisms (SNP) located in a region with enhancer activity in the

adjacent minichromosome maintenance complex component 6 (MCM6) gene were found to be

associated with LI in different ethnic populations (Figure 2.2) [18, 25, 32, 34, 60-62].

In 2002, Enattah et al. [18] identified two SNPs, C/T-13910 and G/A-22018 in introns 13

and 9, respectively, 14 kb upstream of the LCT gene in nine extended Finnish families that

enabled Northern Europeans to maintain lactase activity as adults.

12

Figure 2.2. Location of LI SNPs in the map of the LCT region and the MCM6 gene region

Adapted from [60]

Even though G/A-22018 and C/T-13910 SNPs are in linkage disequilibrium (LD) in European

and European mixed populations [18, 50], in a few cases, G/A-22018 was shown to be

independently associated with LI in Finland [18], China [63], and Japanese Brazilians [64]. Also,

these two variants were absent in African and Middle Eastern populations. Multiple

polymorphisms of close proximity to the C/T-13910 variant were found to be responsible for LI

in populations where pastoralism of sheep and camel was common [29, 33, 60, 65]. Of these

variants, the T/G-13915 SNP was found to be common in Saudi Arabia, Jordan, Sudan, Ethiopia,

Yemen, Oman, and Kuwait [25, 33, 35, 65, 66], while C/G-14010 was common in Tanzania,

13

Kenya, South Africa, Angola, Mozambique, and Ethiopia, and C/G-13907 was common in

Sudan and Ethiopia [4, 31, 33, 60, 67]. Recently, three variants G/A-13838, -3906T/A, and -

13908 associated with LI were found to have originated independently in populations living in

Tibet [68].

Results from in vitro studies identified several transcription factor-binding sites for enhancer

elements located within the same region as the identified LCT SNPs [69]. These include Oct-1

and GATA-6 in the region from -13909 to 13934, HNF4α and Fox/HNF3α in the region -13857

to -13817, and Cdx-2 in the region -14022 to 14032 [69]. In vitro studies showed that the T-

13910 variant responsible for the LP trait enhances transcription in promoter reporter construct

assays in cell lines [69, 70]. The A-22018 LP variant showed a similar, but weaker, effect [70,

71]. In differentiated Caco-2 cells, the T-13910 region enhanced LCT promoter-driven reporter

gene expression by 40-fold [72]. Moreover, the transcription factor Oct-1 binds more strongly to

the T-13910 variant than to the C-13910 variant [69]. Other variants associated with the LP trait

(C-14010, G-13915 and G-13907) showed an enhancing activity that was similar to, but weaker

than, the one observed for T-13910 [33, 65]. The C-14010 variant enhanced the LCT promoter

[31], and had a stronger affinity for the transcription factor Oct-1 than the G-14010 variant [73].

Similar activity was observed for the G-13915 variant [74].

Within individuals, there is a variation in the expression of the LCT gene across tissues and cell

types and it naturally decreases throughout the lifespan [16, 18]. The down regulation of the

enzyme lactase, which is age dependent, is attributed to epigenetic modification at specific

regulatory elements located at the same region of the LCT and the MCM6 gene associated with

LI [16]. Variation in the cellular expression of LCT in the small intestine was associated with

DNA modification in MCM6 exon 13 – intron 13 region, and LCT gene intron 2 and exon 1

region [16, 75]. The MCM6 gene was not sensitive to environmental stimuli (i.e. milk), which

14

indicates that this region is not responsible for the evolution of the LP trait. On the other hand,

other regulatory elements located in the LCT gene, intron 2 and intron 8, were found to be

responsive to milk exposure enabling a partial recovery of the expression of the LCT gene in the

small intestine [75]. This suggests that the region responsible for LP evolution is located in the

LCT gene. It also confirms that the LP trait is a result of epigenetic changes due to an interaction

between diet (i.e., milk) and genetics (LCT gene and MCM6 gene). Peroxisome proliferator-

activated receptor gamma PPARγ, member of the nuclear receptor superfamily of ligand-

activated transcriptional factors, has been shown to upregulate LCT expression in Caco-2 cell

with the LI genotypes (CC-13910/GG-22018). Furthermore, LCT mRNA activity was induced in

Caco-2 cell with the LI genotype using the same PPARγ agonist. Lactase expression in

individuals with LI is controlled by epigenetic changes [76]. It appears that an evolutionary

constructive of epigenetic regulation preceded the evolution of the genetics of LP [75].

All the LCT identified SNPs are extended from different haplotypes (Table 2.3). The European

LP SNP C/T-13910 and the African-harbored LP SNP C/G-13907 shared the same haplotype

background (A) [4]. Other few rare variants associated with LI were also found to be extended

from the same haplotype background [4, 33].

Table 2.4. Gene variants associated with lactose intolerance

Substitution -

Position of SNP* LP Allele rs numbers

Haplotype (Hollox et

al. 2000 nomenclature) Geographic location

Evidence of

Function

C > G -13,907 G rs41525747 A Africa [33, 60]

C > T -13,910 T rs4988235 A

Europe, Middle East, some

parts of Asia and Africa [69, 70, 77]

T >- G -13,915 G rs41380347 C Saudi Arabia and Middle East [33, 60]

G > C -14,010 C rs145946881 B Africa [60, 73]

G > A -22,018 A rs182549 A East Asia, South Asia [71]

Modified from [4]* Ancestry allele > mutant allele, Position of SNPs in pb upstream of the LCT gene.

15

2.1.3 Diagnosis of Lactose Intolerance

The most accurate method available for diagnosing LI is the biopsy-based quick test (the gold

standard), which has a sensitivity and specificity of 95% and 100%, respectively [78] (Table

2.4). Lactase activity is measured using biopsies from the post-bulbar duodenum, where Lactase

activity of

16

variants is important to capture LI in mixed ethnicities. Recently, a reverse-hybridization strip

assay based on multiplex DNA amplification and ready-to-use membrane test strips that detect

common and rare LCT-polymorphic variants (C/G-13907, C/T-13910, T/C-13913, G/A-13914,

T/G-13915, G/A-22018) was validated by Tag et al [88] using 125 previously genotyped samples

from different ethnic backgrounds [60, 67]

Table 2.5. Diagnostic tests of lactose intolerance

Jejunal Biopsies

Lactose Tolerance Test

(LTT)

Breath Hydrogen

Test (HBT) Genetic Testing

Sensitivity 100% 75% 70% 100%

Specificity 95% 96% 90% 96%

Procedure

Endoscopy, Direct

Enzyme Test

25g lactose ingested after restricting lactose

from diet

Saliva sample after 30 minutes no

food only water allowed

Diagnosis

Low lactase activity

(7,500 years ago in Europe as a result of human

adaptation to dairy practice and consumption. Evidence of cheese consumption by herders has

been dated between 6,800 – 7,400 years ago [38, 89]. Also, remnants of milk fat were found on

an ~8,500 year old piece pottery in the Middle East Fertile Crescent [90]. This is consistent with

the notion that cattle herding and milk consumption began in the Middle east then migrated to

Europe where the most frequent LP variant arose [91].

17

All mammalian milk is rich in lactose (~ 40 – 75 g of lactose per liter), except the Pinnipeds.

One cup (250 ml) of homogenized milk contains ~ 12 – 18 g of lactose, and similar amounts are

found in sweetened condensed milk, chocolate milk, and goat milk (Table 2.5). However, very

minimal amounts of lactose can be found in some milk products such as sour cream (2g / 60 ml),

cottage cheese (3g / 125 ml), and frozen yogurt (4.5 - 8g /125 ml). It has been reported that

individuals with LI can tolerate up to 12 g of lactose per serving [14]. However, some

individuals with low lactase activity (LI) cannot tolerate even small amounts of lactose in their

diet [1]. Often those with LI choose to follow a dairy-free diet. A recent survey completed by

2,606 Canadians indicated that of those who self-reported LI, 4.7% eliminated all milk products

in their daily diet, 36% eliminated some milk products, 15% greatly reduced the amount of milk

products, and 13% slightly reduced the amount of milk used [20]. The LCT variant C/T-13910,

the most studied LI variant in the LCT gene, has been shown to be associated with dairy intake

in European derived populations [6, 92]. For instance, in recent cohorts, participants of European

ancestry with LP genotype T/T-13910 and T/C-13910 had a significantly higher intake of dairy

consumption than those with the C/C-13910 genotype [92-96]. However, there are no data

available regarding milk consumption and SNPs associated with LI in a Canadian population.

Dairy products are good source of nutrients of public health concern, calcium and vitamin D [9].

Therefore, a dairy-free diet has been linked to disorders associated with low vitamin D and

calcium levels.

18

Table 2.6. Lactose content in dairy products

Dairy Products Serving Lactose (g)

Sweetened, condensed milk 1 cup 20

Whole, 2%, 1%, skim milk, or chocolate milk 1 cup 12.5

Evaporated milk 1/2 cup 12

Plain yogurt 3/4 cup 12

Goat milk 1 cup 10.8

Buttermilk 1 cup 10

Ice milk, vanilla 1/2 cup 9

Kefir 3/4 cup 8

Ice cream, vanilla 1/2 cup 5 - 6

Yogurt with probiotics 3/4 4 - 9

Cottage cheese, creamed 1/2 cup 3

Sherbet, orange 1/2 cup 2

Cream cheese, swiss cheese 1.5 oz 1.5

Blue cheese, colby cheese 1.5 oz 1.2

Mozzarella cheese 1.5 oz 1.1

Gouda cheese 1.5 oz 0.9

Cheddar or processed cheese 1.5 oz 0.8

Half and half cream, light cream 1tbsp 0.6

Sour cream 1 tbsp 0.5

Dry curd cottage cheese 1/2 cup 0.5

Whipping cream 1 tbsp 0.4

Parmesan, grated 1 tbsp 0.2

Camembert or limburger cheese 1.5 oz 0.2

LactaidMT milk 1 cup 0.1

Butter 1 tbsp trace

Source: Manual of Dietetics, 6th Edition [97]

According to the Scientific Report of the 2015 Dietary Guideline Advisory Committee, low-fat

dairy is a part of a healthy dietary pattern that’s more than optimal in the United States [9].

Individuals with LI might choose to switch to plant-based alternatives such as soy beverages,

19

rice or almond milk, which are gaining in popularity in recent years. In Canada, only 12.5 % of

those who self- reported LI switched to lactose free milk-alternatives or used lactase pills. Of this

12.5%, only 28% used soy beverages and 92% consumed less than 1 serving per day [20]. In

Canada, it is mandated by law to fortify every 100 ml of fluid milk with 40 IU vitamin D, while

milk-alternative product fortification is optional [98]. School-aged children with LI who

switched from cow’s milk to plant based milk alternatives, had a high risk of having lower levels

of plasma 25(OH)D [10]. Due to the association between LI and dairy intake, this trait has been

of interest in examining risks associated with dairy free diet [7].

2.2.1 Dairy and 25(OH)D in Lactose Intolerance

Vitamin D is a fat-soluble vitamin and a prohormone that is mainly synthesized by the body

through exposure to sunlight (Figure 2.2). There are two major forms of the vitamin: D2

(ergocalciferol) and D3 (cholecalciferol). Vitamin D2 is either added artificially to food or by

irradiation of mushrooms. Vitamin D3 is synthesized in the skin by sunlight from 7-

dehydrocholesterol or consumed from meat sources, and it represent ~95% of vitamin D

absorbed by the human body [99]. Both forms are inactive, and both are activated by

hydroxylation reactions in the liver to form 25(OH)D, which undergoes a second reaction in the

kidney to form the biologically active hormone 1,25-hydroxyvitamin D (calcitriol). In the blood,

the precursor of 1,25-hydroxyvitamin D is 25(OH)D, which is bound to vitamin D binding

protein (DBP) in the circulation. Levels of 25(OH)D are used as a marker of plasma vitamin D

levels, since it is the best available indicator for the sum of ergocalciferol and cholecalciferol.

Sun exposure, which is the primary source for producing endogenous vitamin D, is minimal from

mid-October to mid-March in Canada [100]. Because of the limited sources of vitamin D [101],

it is required in Canada and the United states by law to fortify milk products with 40 IU of

20

vitamin D per 100 ml [102]. According to data obtained from the National Health and Nutrition

Examination Survey (NHANES) from the period 2003–2004 and 2005–2006, two servings of

dairy contribute 58% of vitamin D intake in individuals aged 2 years and older in the United

States [103]. Similar estimates are not available for the Canadian population.

Dark meat fish (i.e., salmon, rockfish, and tuna) and mushroom are the only natural dietary

sources of vitamin D [99]. Fortified milk products remain main sources of vitamin D, and a

dairy-free diet has been linked to lower intakes of this vitamin [104]. For instance, vitamin D

intake from milk products and fortified beverages was significantly lower among Canadians who

self-reported LI compared to those who did not (p

21

Figure 2.3. Vitamin D synthesis, intake, and activation

Source: Dietary Reference Intakes for Calcium and Vitamin D (2011) [99]

The association between vitamin D status and bone health is well recognized, but the link

between this vitamin and several chronic disease is still under investigation [106]. Besides

22

emerging evidence of an inverse association between 25(OH)D and cardiometabolic disease

risks [107], a few studies have reported no association [108]. Furthermore, 25(OH)D was found

to be inversely associated with the incidence of the metabolic syndrome (MetS) (OR = 0.63, 95%

CI 0.44–0.90) [109]. According to a recent study that used Mendelian randomization, an

increased risk of cardiovascular mortality for every 20 nmol/L lower plasma 25(OH)D

concentration (HR: 1.12, 95%CI (1.03–1.22) was observed in three cohort studies of 95,766

participants of Danish descent [110]. This inconsistency has been attributed to differences in

methodology, unaccounted confounding factors and/or genetic differences [111]. Therefore,

additional studies are needed that investigate the risk of cardiovascular disease with lower levels

of plasma 25(OH)D among those who avoid sources of vitamin D (e.g., milk) in their diet.

2.2.2 Dairy Intake and Risk of Cardiometabolic Disease

Dairy has been linked to cardiometabolic health, however, the nature of association has been

controversial [112-114]. Dairy products are good sources of carbohydrate, protein, vitamin A,

vitamin C, vitamin D, calcium, phosphorus, but also sources of saturated fat, which is linked to

increased cardiometabolic risk [115]. Recent findings, however, show an inverse association

between saturated fat from milk and cardiometabolic risk [116]. In Canada, milk fortification

with vitamin A, vitamin C, and vitamin D is a legal requirement under Health Canada’s Food

and Drug Act [102]. Thus, avoiding milk and dairy products, which might occur due to LI

symptoms, may place individuals at risk for low intakes of these nutrients [10], and possibly risk

of nutrient-related chronic diseases [14]. For instance, LI, a marker of lower dairy intake, was

found to be associated with impaired fasting glucose (IFG) and T2DM [92].

Cardiometabolic diseases are defined as a cluster of the following metabolic risks factors:

hypertension, dyslipidemia, central obesity, glycemic dysregulation, and cardiovascular disease

23

[117]. Cardiometabolic diseases are a leading cause of death worldwide [118]. Evidence of an

inverse association between dairy and cardiovascular disease [119, 120] and metabolic risk

factors has been reported in the last decade [119, 121-123]. Furthermore, pentadecanoic acid

(15:0), a fatty acid that is considered a biomarker of dairy intake, was inversely associated with

T2DM incidence in a 5-year follow-up study [124]. In another study, whole fat milk was

inversely associated with systolic and diastolic blood pressure, and triglycerides, but was

positively associated with HDL-cholesterol and fasting glucose [125]. However, the findings

from other studies were not in agreement [114]. The inconsistent findings may be due, in part, to

the heterogeneity of the methods used, complexity of dairy food (i.e., low-fat vs. high fat,

fermented dairy vs. non-fermented dairy, source of several potent nutrients, geographic location

and fortification with vitamin D, etc.), as well as the size and demographics (i.e., age, sex,

ethnicity) of the populations studied [11].

Because of the significant decrease in milk intake associated with LI genotypes, recent studies

explored the association between the LP and LI genotypes and the risk of metabolic and

cardiovascular diseases [7]. These studies used an approach known as Mendelian randomization

(Section 2.4), which allows the use of gene variants as a proxy for an environmental exposure to

examine the link between that exposure and an outcome of interest. Results from these studies

were similar to nutritional studies that used the traditional approach [11]. Some findings have

indicated an association between the LP genotype, milk drinking, and overweight and obesity,

higher BMI [95] [126], and increased MetS [92, 127, 128], CVD and mortality risk in women

[129]. While some studies have shown an inverse association between the LP phenotype and

metabolic diseases, majority of the studies showed no association between drinking milk and

high BMI, overweight/obesity, mortality and cardiovascular diseases [130, 131], T2DM [132],

and high blood pressure [126, 133]. Inconsistencies among studies linking LCT genotypes and

24

risk of cardiometabolic diseases are due to many factors including the complex nature of milk

and dairy products and differences in the methods and study design (i.e., grouping the CT and

TT genotypes in some studies as the LP phenotype and separating them in others). Another

potential explanation is neglecting the involvement of the gut microbiota in the fermentation of

lactose in individuals with LI. Because variants for LP are still emerging and have not been

studied fully, the lack of evidence of an association between these variants and cardiometabolic

risk suggests that further research is needed.

2.2.3 Lactose Fermentation by the Colonic Microbiota and Risk of Chronic Diseases

Lactose, mainly consumed in dairy products, may act as a selective prebiotic in individuals with

LI. In vivo, lactose has a prebiotic Index of 5.75 [134]. Prebiotic index (PI) is a quantitative

equation that helps in estimating the prebiotic fermentation in vitro, and it is based on the

changes of specific type of bacteria during fermentation (i.e., . Bifidobacterium, lactobacilli,

clostridia and bacteroides) [135]. Lactose fermentation in individuals with LI is associated with

the growth of Bifidobacterium and lactobacilli [13]. A recent randomized controlled trial (RCTs)

included participants with LI, who were prescribed a lactose-free diet and high purity short chain

galacto-oligosaccharides (GOS) for 36 days [136]. After 36 days, the participant started to

incorporate dairy (i.e., lactose) into their diet and excluded GOS for 30 days. GOS, which are

produced during lactase hydrolysis, are prebiotics and serve as a substrate for Bifidobacterium

and lactobacilli. Lactose fermenting bacterial species increased by day 36, in particular

Bifidobacterium spp which increased in 90% of the participants. Two other lactose fermenting

bacteria (i.e., Faecalibacterium spp, Lactobacillus spp) increased in density after the first 36

days. Only Roseburia spp increased besides the previously mentioned lactose fermenting

bacteria after dairy treatment [136]. That study used data on self-reported LI and similar data is

25

not available for the LCT genotypes. However, findings from a recent metagenome study showed

that the CC genotype of the -13910C>T variant is associated with an increase in the abundance

of Bifidobacterium spp [137]. Several benefits have been proposed for lactose fermentation by

the colonic bacteria [13, 138]. First, it is well understood that there is an increase in lactose

tolerance and a decrease in LI symptoms scores associated with changes in the gut microbiome

following a diet that contains lactose [136, 139]. Also, lactose fermentation has been shown to

decrease Bactericides and Clostridia spp. Both are bacterial species known for their harmful

effect on colonic cells including ammonia formation [140]. Therefore, another proposed benefit

for lactose fermentation by colonic bacteria is the reduction in ammonia production, which is

toxic to colonic cells [139]. Szilagyi and others have proposed that lactose fermentation in those

with the LI trait might influence association with chronic diseases [11, 141]. Due to the burden

of administrating lactose to individuals with LI [14], no study yet has examined the effect of

dairy consumption in individuals with LI on biomarkers of cardiometabolic diseases. Thus, it is

worth investigating whether or not lactose fermentation by the colonic bacteria in individuals

with LI is influencing associations observed between dairy and chronic diseases.

26

2.3 Proteomics Approach in Nutrigenomics Studies of Cardiometabolic Diseases

Proteomics is one of the high-throughput ‘omics’ technologies that scans a large, diverse

spectrum of proteins found in human plasma [96]. There are over 3,000 different proteins found

in human plasma, and only 20 proteins make up over 99% of the total protein mass in plasma

[142]. These proteins have a dynamic complex admixture, and are informative in the clinical

setting about the physiological status of the human body [142]. Changes in the plasma proteome

have been shown to be linked to different dietary patterns [143]. Furthermore, these changes

have been linked to cardiometabolic disease risks. However, only a few were confirmed to be

markers of CVD risk. For instance, increased levels of the plasma protein cardiac troponin-T is

considered when identifying cases of myocardial infarction [142]. Another example is the

apolipoprotein A1 plasma protein, which is considered a robust marker and possible risk factor

of CVD [144]. Using data from the Toronto Nutrigenomics and Health Study, several high-

abundant plasma proteins were identified as useful biomarkers of cardiometabolic disease and

dietary patterns [143]. These protein groups were identified as biomarkers of positive acute

phase proteins, negative acute phase proteins, and proteins implicated in coagulation or innate

immunity cascade [143]. Of these proteins many were associated with circulating concentrations

of ascorbic acid (vitamin C) [145], α-tocopherol (vitamin E) [146], and caffeine [147]. Some of

these proteins were also associated with circulating 25(OH)D[148]. While proteomics has been

used to examine the nutritional value and function of milk proteins [149], to our knowledge, no

study has yet examined the effects of dairy intake on the human plasma proteome.

27

2.4 Mendelian Randomization

Mendelian randomization (MR) is an approach used in genetic epidemiological studies to

examine long term exposure of an environmental stimuli on health and disease [150, 151]. The

MR approach is similar to traditional RCTs, but the MR approach reflects long term exposure

since the alleles are randomized before birth. Therefore, MR is believed to be free of biases that

affect randomization. MR is believed to provide more robust associations under the right

conditions. It also avoids multiple types of errors associated with the traditional epidemiological

studies, which includes reverse causation, reporting bias, and associative selection (Berkson’s

bias) [152]. There are three main conditions that must first be met to apply the MR approach

[153]. First, there must be a robust association between the genetic variant and the modifiable

exposure. Second, there must be no association present between the genetic variant and the

outcome of interest except through the exposure. Third, confounding factors that affect the

outcome or the exposure must not be related to the genetic variant. In this case the genetic

variant can act as an ‘Instrumental variable’ (IV) for the exposure of interest (Figure 2.3) [152,

153]. The LCT-13910C>T is robustly associated with dairy intake in European derived

populations, and have been used as a proxy for dairy intake in Mendelian Randomization studies

examining health risks associated with dairy consumption [7].

28

Figure 2.4. Mendelian Randomization (Instrumental Variable Analysis)

29

2.5 Summary of Literature Review

Several gene variants associated with LI arose independently in different populations were in

strong agreement with LI prevalence, and were present at varying frequencies in different

ethnocultural groups [15]. The LCT-13910C>T is the most common variant, associated with LI

in >70 diverse ethnic populations. While 65% of the world’s population experiences LI [15], the

prevalence of genotypes (gene variants) that are implicated in LI among different ethnocultural

groups in Canada has not been reported. Individuals with LI often develop unpleasant

gastrointestinal symptoms following dairy consumption, which could lead to the avoidance of

dairy products. Moreover, Canadian adults who self-reported LI either eliminated or greatly

reduced their daily dairy consumption, and were not consuming more than one serving per day of

milk alternatives (i.e., soy beverages) [20]. Moreover, a recent study reported that children who

consumed non-cow’s milk substitutes had significantly lower serum 25(OH)D concentrations

than children who consumed cow’s milk [10]. Fortified milk and dairy products are the main

sources of calcium and vitamin D in the Western diet. Regular dairy consumption, as well as

optimum levels of circulating vitamin D metabolites have been linked to a lower risk of

cardiovascular disease and diabetes [109, 125]. However, no previous study has examined the

association between LI gene variants, dairy intake, and cardiometabolic risk factors in Canada.

This project will determine the prevalence of LI genotypes among a multi-ethnic cohort of

Canadian young adults, and it will examine the relationship between LI genotypes, dairy

consumption, and biomarkers of cardiometabolic disease.

30

2.6 Hypothesis and Objectives

Genetic variation associated with lactose intolerance (LCT-13910C>T) is associated with lower

dairy intake, lower plasma 25(OH)D levels and elevated biomarkers of cardiometabolic disease.

Study Objectives:

1. Determine the prevalence of the LCT-13910C>T genotypes, and to examine the association

between LCT genotype, dairy consumption and 25(OH)D levels.

2. Determine food predictors of dietary calcium and dietary vitamin D using LI risk genotypes

3. Investigate the association between dairy intake and risk of cardiometabolic disease by:

A. Applying a Mendelian randomization approach to determine the association between

dairy consumption and 54-plasma proteins implicated in various chronic diseases.

B. Determining the association between dairy consumption on 54-plasma proteins in

young adults with different LCT genotypes.

31

CHAPTER 3

LACTOSE INTOLERANCE (LCT-13910C>T) GENOTYPE

IS ASSOCIATED WITH PLASMA 25(OH)D

CONCENTRATIONS IN CAUCASIANS: A MENDELIAN

RANDOMIZATION STUDY

Adapted from:

Alharbi O. and El-Sohemy A. (2017) Lactose intolerance (LCT-13910 C>T) genotype is

associated with plasma 25(OH)D concentrations in Caucasians: A Mendelian Randomization

Study. J Nutr. 7(6): 1063 - 69

32

Chapter 3: Lactose intolerance (LCT-13910 C>T) genotype is

associated with plasma 25(OH)D concentrations in Caucasians: A

Mendelian Randomization Study

3.1 ABSTRACT

Background: The LCT-13910C>T gene variant is associated with lactose intolerance (LI) in

different ethnic groups. Individuals with LI often avoid dairy, a major dietary source of vitamin

D in North America, which may lead to inadequate vitamin D intake. However, the association

between LCT genotype and 25(OH)-vitamin D concentrations are not clear.

Objective: The objective was to determine the prevalence of LI genotype in different ethnic

groups living in Canada and to determine whether LCT genotype is associated with 25(OH)D

plasma concentrations.

Methods: Blood samples were drawn from a total of 1,495 men and women aged 20-29 years

from the TNH study for genotyping and plasma 25(OH)D analysis. Dairy intake (milk, cheese,

yogurt and ice cream) was assessed using a 196-item food frequency questionnaire. The

prevalence of LCT-13910C>T genotypes was compared using χ2. Using a Mendelian

randomization (MR) approach, we examined the association between LCT genotypes and

25(OH)D levels.

Results: Approximately 32% of Caucasians, 99% of East Asians, 74% of South Asians, and

59% of those with other or mixed ethnicities had the CC genotype associated with LI. Compared

to those with the TT genotype, those with the CC genotype had lower total dairy intake (2.15 ±

0.09, vs 2.67 ± 0.12 servings/day, p=0.003), lower skim milk intake (0.20 ± 0.03 vs 0.46 ± 0.06

servings/day, p=0.0004), and lower plasma 25(OH)D (63 ± 1.9 vs 75.8 ± 2.4 nmol/L, p

33

The CT and CC genotypes were associated with a 50%, and a two-fold increased risk,

respectively, of suboptimal (

34

3.2 Introduction

Lactose intolerance (LI) refers to a group of gastrointestinal symptoms associated with dairy

consumption. Individuals with LI often avoid or eliminate dairy, which is a major dietary source

of vitamin D in North America [9]. In Canada and the U.S., milk must be fortified with 40 IU

vitamin D per 100ml [106]. According to data from NHANES, dairy products contribute ~ 60%

of the average daily vitamin D intake [8]. Individuals with LI may choose to substitute milk with

plant-based milk alternatives, which are lactose-free beverages [20]. However, the fortification

of plant-based milk alternatives with vitamin D is optional in certain countries, including Canada

[106]. A lactose-free diet has been associated with an increased risk of certain health conditions

related to low vitamin D intake [14]. Moreover, the consumption of lactose-free beverages in

pre-school aged children has been associated with a higher risk of suboptimal plasma 25(OH)D

[10]. Yet, the association between LI and 25(OH)D plasma levels is not clear [14, 105, 154].

The ability to consume milk without gastrointestinal symptoms in adulthood is a Mendelian

phenotype called lactase persistence (LP) [4]. The intestinal enzyme lactase is responsible for the

digestion of lactose, the main sugar found in milk [4]. In most individuals, lactase activity is

downregulated sometime after weaning due to an interaction between epigenetic and genetic

factors, which results in LI [16]. A single nucleotide polymorphism (rs4988235) located at

position -13910 upstream of the LCT gene has been shown to regulate lactase expression [18],

and has been associated with LI in >70 ethnically diverse populations [155]. The ancestral

genotype is associated with symptoms of LI [18], lower milk consumption [92, 95, 156], and

diminished lactase activity in European-derived populations [17, 18, 155]. The sucrase-to-lactase

(S:L) enzyme activity ratio in the CC genotype is consistent with the cut-off for LI [155]. The

CT or TT genotype has a S:L ratio consistent with the cut-off for lactase persistence (LP) [17,

35

155]. Yet, it is not clear if the CT genotype exhibits an intermediate phenotype [24, 157]. It has

been estimated that 65% of the world population has a genotype associated with LI [15]. In

Canada, ~16% of Canadians self-reported LI [20]; however, the prevalence of the genotype

predicting LI among the major ethnic groups living in Canada remains unknown.

Mendelian randomization (MR) is an approach that mimics randomized trials by assessing the

relationship between a lifetime exposure and an outcome using genetic variants (i.e.,

instrumental variable (IV)) that are robustly associated with the exposure of interest [152, 153,

158]. Since MR is based on Mendel’s law of independent allele assortments, this approach is less

likely to be affected by confounding or reverse causation [150, 151]. In the present study, we

explored the prevalence of the CC genotype associated with LI in an ethnically diverse

population of Canadian adults living in Toronto. We examined the association between LCT-

13910C>T genotype and dairy intake as well as plasma 25(OH)D levels. We also examined

whether LCT genotype was associated with suboptimal (

36

data and information on ethnicity were obtained using a general health and lifestyle

questionnaire. Participants also completed a physical activity questionnaire and recorded their

dietary intake using the 196-item Toronto-modified Willett Food Frequency Questionnaire

(FFQ). Fasting blood samples were obtained for genotyping for the rs4988235 polymorphism

(LCT-13910C>T) and 25(OH)D. Of the 1639 participants enrolled in the TNH study, we

excluded 11 participants with no blood values, and three participants with incomplete FFQ

records or physical activity questionnaire. We excluded 125 participants who were likely to be

under-reporters (3,500 kcal per day for females or >4,000

kcal per day for males) on the FFQ. We also excluded two participants who reported conditions

(i.e., coeliac, Crohn’s disease) that confound with primary LI. Outliers (n=3) identified when

examining dietary vitamin D were also excluded prior to the analysis.

For the first objective, 1,495 participants with complete FFQ data and data on the LCT -

13910C>T genotypes were included. Based on self-reported ancestry, participants were grouped

as Caucasian (n=720), East Asian (n=506), South Asian (n=160), and other (n=109); the latter

group consisting of those who reported ethnicities of Aboriginal Canadian, Afro-Caribbean, or

mixed ancestry, as previously described [143]. We included n=720 Caucasians in analyses that

examined dairy intake and plasma 25(OH)D levels between LCT-13910C>T genotypes. We

excluded the ‘other’ group because it provided an insufficient sample size, and excluded ‘East

Asians’ because only three individuals had the CT genotype, and none of the subjects had the TT

genotype. ‘South Asians’ were excluded because the mean of 25(OH)D levels was

37

3.3.2 Ethics

The study protocol was approved by the Research Ethics Board of the University of Toronto. It

also conforms to standards for the use of human subjects in research as outlined in the

Declaration of Helsinki. Written informed consent was obtained from all participants in the

study.

3.3.3 Biochemical and 25(OH)D Measurements

Participants provided 12-hr overnight fasting blood samples at a LifeLabs medical laboratory

(Toronto, Ont., Canada). Plasma samples were analyzed for 25(OH)D concentrations using

HPLC-MS/MS at the University Health Network Specialty Lab at Toronto General Hospital

(Toronto, Ont., Canada). Details of the analysis were published previously [159]. We compared

the means of 25(OH)D levels between LCT-13910C>T genotypes, and assessed the risk of

suboptimal 25(OH)D levels (T (rs4988235) using the

iPLEX Gold assay with mass spectrometry-based detection (Sequenom MassARRAY platform;

Sequenom Inc).

3.3.5 Assessment of Dairy Consumption

Dairy intake, in servings per day, was measured using Toronto-modified Willett food frequency

38

questionnaire (FFQ) [160, 161]. Participants were instructed to record their diet over the past

month, as well as the vitamin and mineral supplements they used. The FFQ was supplemented

with illustrations and prompts to decrease the burden on participants.

We compared the daily intake of total dairy, milk, cheeses, yogurts, ice cream, and butter

between genotypes in all Caucasians, as well as in men and in women separately. Intake of

dietary vitamin D (I/U) was also estimated and compared between genotypes in all groups.

3.3.6 Statistical Analysis

All statistical analyses were carried out using SAS version 9.4 (SAS Institute Inc. Cary, N.C.,

USA). To improve normality, we either 𝑙𝑜𝑔𝑒– or square root– transformed non-normally

distributed continuous variables prior to analysis. For ease of interpretation, we have reported

untransformed means with p values obtained from models using transformed variables. We used

Pearson’s Chi square test (χ2) to compare the prevalence of the CC, CT, and TT genotypes at

position -13910 between the ethno-cultural groups.

We used analysis of variance (ANOVA), and analysis of covariance (ANCOVA) to compare

participants’ characteristics between the three genotypes. Using Pearson’s Chi square test (χ2),

we compared the prevalence of decreased whole milk consumption in the past 10 years between

genotypes. Differences in the distribution of supplement (i.e. vitamin D-containing supplements

and multivitamins) use between genotypes were tested using 𝑥2 test. Using analysis of

covariance (ANCOVA), we examined the association between LCT-13910C>T genotypes and

dairy consumption. When a significant association was present between an IV and an exposure

of interest, the MR approach allows the use of the IV as a proxy to examine the relationship

39

between the exposure and an outcome. Therefore, when a significant association (P < 0.05) was

observed between the LCT variant and dairy intake, we applied the MR approach to determine

the association between dairy consumption and plasma 25(OH)D using LCT-13910C>T

genotypes as the IV. In each model, covariates were considered if variables were significantly

associated with the outcome measure, or altered the beta (slope) by ± 10%. All models were

stratified by sex, and were adjusted for multiple comparisons using the Tukey-Karmer procedure

to control for potential false positive findings; the α error was set at 0.05 [162]. Models

examining anthropometric measurements, as well as dairy intake were adjusted for age, sex, and

total energy intake. The model examining levels of plasma 25(OH)D was adjusted for age, sex,

waist circumference (WC), physical activity, supplements use, smoking, season of blood draw,

and oral contraceptive use among women.

Using multivariate logistic regression, we assessed the risk of suboptimal (T genotypes in all Caucasians, and in men and women.

According to the Endocrine Society [163] and the Canadian Osteoporosis Society [100], vitamin

D 75 nmol/L has been

associated with a lower risk of all cause-mortality [164], and decreased risk of vitamin D

associated chronic diseases [100, 163-166]. The adjusted models controlled for the same

covariates used for the 25(OH)D analysis.

40

3.4 Results

3.4.1 Participants’ characteristics

The CC genotype associated with LI was significantly different between ethnic groups (p <

0.0001) (Figure 1). In Caucasians, 32% had the CC genotype predictive of LI. However, almost

all East Asians, and > 50% of South Asians and those of mixed ethnicities had the CC genotype.

In Caucasians, the average height in individuals with the CC genotype (P = 0.03), or the CT

genotype (p = 0.01) was significantly lower than the average height of those with the TT

genotype. Furthermore, weight in kilograms was significantly lower in individuals with the CC

genotype (p = 0.02) compared to those with the TT genotype. Similar associations were

observed between -13910C>T genotypes’ height and weight in women only. Among women, the

CC genotype had significantly lower body mass index (p = 0.04) compared to the TT genotype.

Systolic blood pressure (SBP), diastolic blood pressure (DBP), as well as levels of daily physical

activity were similar across LCT-13910C>T genotypes. Study participants’ characteristics are

shown in (Table 3.1).

3.4.2 Vitamin D Levels

In the unadjusted model, plasma 25(OH)D was significantly lower in individuals with the CC

genotype compared to those with the CT or the TT genotypes (p < 0.0001). Similar results were

observed for men (p = 0.003) and women (p = 0.001). The association remained significant after

adjusting for several covariates (Figure 3.2). The difference in the adjusted means was

significant between the CC and the CT genotypes (p < 0.0001) as well as between the CC and

the TT genotypes (p < 0.0001).

41

Figure 3.1. Prevalence of -13910 C>T genotypes in ethnically diverse men and women aged 20-

29 year living in Canada1

1P-value was calculated using 𝑥2 tests to examine the differences in the distribution of -13910 C>T

genotypes between ethnic groups living in Canada

In adjusted models stratified by sex, the differences remained significant between the CC and the

CT genotype in women only (p = 0.001), and between the CC and the TT genotype in men (p =

0.002), and women (p = 0.0002).

In the logistic regression models (Table 3.2), the risk of suboptimal plasma 25(OH)D was 50%

greater in those with the CT genotype and a two-fold increased risk among those with the CC

genotype. In the adjusted models stratified by sex, a significant increased risk of suboptimal

plasma 25(OH)D was observed only in the CC genotype with a two-fold increased risk among

42

women, and three-fold increased risk among men.

Figure 3.2. Plasma 25(OH)D levels across LCT-13910C>T genotypes in Caucasians men and

women aged 20-29 year living in Canada1

1Values are crude means ± standard errors. P-values were obtained using analysis of covariance

(ANCOVA). Models were adjusted for age, sex, waist circumference (WC), physical activity,

supplements use, smoking, season of blood draw, and oral contraceptive useas appropriate. Adjustment

for multiple comparisons was performed with the Tukey-Kramer procedure. Labeled means without a

common letter differ, p < 0.05.

3.4.3 Dairy Consumption by LCT-13910 C>T Genotypes

Intake of milk and other dairy products by LCT-13910C>T genotype is shown in (Table 3).

Participants who had the CC genotype, or the CT genotype had a significantly lower number of

43

dairy servings per day compared to those who had the TT genotype. This effect was mainly

attributed to skim milk consumption, which was significantly lower in those with the CC

genotype compared to those with the CT or TT genotype. Total dairy intake was also

significantly lower in the CC genotype and the CT genotype compared to the TT genotypes in

men only. In women, total dairy intake was significantly lower only in the CC genotype

compared to the TT genotype (p = 0.048). Moreover, 48% of those with the CC genotype

decreased their whole milk intake in the past 10 years compared to 45% and 37% of those with

the CT and the TT genotypes (p = 0.03), respectively. Among women, total vitamin D and

dietary vitamin D intakes (IU/day) were lower in those with the CC genotype than those with the

CT genotype (p = 0.007) and (p = 0.02), respectively. The prevalence of vitamin D-containing

supplements use did not differ between genotypes.

3.5 Discussion

The CC genotype associated with LI was most common in East Asians and least common in

Caucasians. The LCT-13910C>T variant is associated with lower total dairy intake, skim milk

intake, as well as plasma 25(OH)D levels. The CC genotype was associated with a 2-fold

increased risk of insufficient and suboptimal 25(OH)D levels, which was due, in part, to a lower

intake of dairy. Those with the CT genotype had a 50% increased risk of suboptimal 25(OH)D

suggesting that those with the heterozygous genotype of the LCT gene exhibits an intermediate

phenotype and likely experience some signs of LI. We also found that those with either the CT or

CC genotype were shorter in height compared to those with the TT genotype.

44

Table 3.1. Participant characteristics in Caucasian men and women aged 20-29 year living in Canada with the LCT-13910 C>T genotypes1

1Data presented as crude means ± standard errors. P values were calculated ANOVA, or ANCOVA using 𝑙𝑜𝑔𝑒- or square root transformed values. Tukey-Kramer procedure was used to adjust for multiple comparisons. Labeled means in a row without a common letter differ, P < 0.05. 2Adjusted for age, sex, and total energy intake

Total (n=720)

P

Men (n=234)

P

Women (n=486)

P CC 229 (32%)

CT

290 (40%) TT

201 (28%) CC

82 (35%) CT

89 (38%) TT

63 (27%) CC

147 (30%) CT

201 (42%) TT

138 (29%)

Age (years) 23.1 ± 0.17 23.4 ± 0.15 23.2 ± 0.18 0.4 23 ± 0.28 23.9 ± 0.26 23.3 ± 0.34 0.08 23.2 ± 0.21 23.2 ± 0.18 23.2 ± 0.22 0.9

Height (cm)2 170 ± 0.60b 169 ± 0.52b 171 ± 0.59a 0.008 178 ± 0.68 178 ± 0.82 179 ± 0.93 0.7 165 ± 0.56b 165 ± 0.44a 167 ± 0.51a 0.002

Weight (kg)2 66.9 ± 0.85b 67.4 ± 73a,b 69.1 ± 0.93a 0.03 76.7 ± 1.24 75.1 ± 1.33 77 ± 1.62 0.3 61.4 ± 0.84b 64 ± 0.76a,b 65.5 ± 1.0a 0.004

BMI (kg/m2)2 23.1 ± 0.23 23.5 ± 0.21 23.5 ± 0.25 0.2 24 ± 0.35 23.8 ± 0.38 24.1 ± 0.43 0.5 22.5 ± 0.28b 23.4 ± 0.25a,b 23.3 ± 0.31a 0.03

Waist circumference (cm)2 75.6 ± 0.58 75.7 ± 0.50 76.3 ± 0.65 0.4 82.5 ± 0.79 82 ± 0.87 81.8 ± 1.13 0.2 71.8 ± 0.58b 73.4 ± 0.53a,b 73.7 ± 0.69a 0.05

Systolic blood pressure (mm Hg) 116 ± 0.79 116 ± 0.64 116 ± 0.80 0.8 127 ± 1.07 124 ± 1.02 125 ± 1.27 0.1 110 ± 0.70 112 ± 0.66 112 ± 0.82 0.08

Diastolic blood pressure (mm Hg) 69.5 ± 0.50 69.6 ± 0.48 69.7 ± 0.58 0.8 71.3 ± 0.82 70.8 ± 0.92 71.5 ± 1.02 0.8 68.5 ± 0.61 69.2 ± 0.61 68.8 ± 0.70 0.7

Physical activity (Met-h/wk) 8.4 ± 0.20 8.1 ± 0.18 8.3 ± 0.22 0.7 8.2 ± 0.38 8.2 ± 0.33 8.6 ± 0.41 0.7 8.5 ± 0.23 8.1 ± 0.22 8.1 ± 0.26 0.5

45

Table 3.2. LCT-13910 C>T genotypes and risk of suboptimal levels of plasma vitamin D in Caucasians men and women aged 20-29 year

living in Canada1

1All models were carried using multivariate logistic regression. Vitamin D levels of ≥ 30 -

46

Table 3.3. Dairy products intake in Caucasian men and women aged 20-29 years living in Canada with the LCT-13910 C>T genotypes1

Total (n=720)

P

Men (n=234)

P

Women (n=486)

P CC

229 (32%)

CT

290 (40%)

TT

201 (28%)

CC

82 (35%)

CT

89 (38%)

TT

63 (27%)

CC

147 (30%)

CT

201 (42%)

TT

138 (29%)

Total dairy intake (Servings/day)2 2.15 ± 0.09a 2.46 ± 0.09a 2.67 ± 0.12b 0.003 1.91 ± 0.14a 2.04 ± 0.15a 2.68 ± 0.21b 0.006 1.95 ± 1.40a 2.19 ± 1.42a,b 2.30 ± 1.47b 0.06

Fluid Milk (Servings/day)

Total milk (skim, whole milk) 0.23 ± 0.03a 0.36 ± 0.04b 0.48 ± 0.06b 0.004 0.24 ± 0.07 0.21 ± 0.05 0.46 ± 0.02 0.2 0.23 ± 0.04a 0.43 ± 0.05a, b 0.48 ± 0.07b 0.02

Skim milk 0.20 ± 0.03a 0.31 ± 0.04b 0.46 ± 0.06b 0.0004 0.16 ± 0.06 0.18 ± 0.05 0.44 ± 0.02 0.2 0.22 ± 0.04a 0.38 ± 0.05b 0.47 ± 0.07b 0.003

Whole milk 0.03 ± 0.01 0.05 ± 0.01 0.02 ± 0.01 0.5 0.07 ± 0.03 0.04 ± 0.01 0.02 ± 0.01 0.8 0.01 ± 0.00 0.05 ± 0.03 0.02 ± 0.01 0.1

Cheeses (Servings/day)

Total cheese 0.75 ± 0.04 0.76 ± 0.04 0.90 ± 0.06 0.05 0.72 ± 0.06a, b 0.63 ± 0.06a 0.91 ± 0.05b 0.01 0.76 ± 0.05 0.81 ± 0.05 0.90 ± 0.07 0.4

Cream cheese 0.11 ± 0.01a 0.09 ± 0.01b 0.09 ± 0.01a, b 0.02 0.10 ± 0.02 0.05 ± 0.01 0.07 ± 0.02 0.08 0.12 ± 0.02 0.10 ± 0.01 0.09 ± 0.01 0.1

Cottage cheese 0.05 ± 0.01 0.04 ± 0.01 0.04 ± 0.01 0.9 0.04 ± 0.01 0.04 ± 0.01 0.04 ± 0.02 0.9 0.05 ± 0.01 0.04 ± 0.01 0.04 ± 0.01 0.8

Other types of cheese 0.59 ± 0.04 0.63 ± 0.04 0.78 ± 0.05 0.2 0.58 ± 0.06 0.55 ± 0.06 0.80 ± 0.09 0.08 0.60 ± 0.05 0.67 ± 0.05 0.76 ± 0.07 0.4

Yogurts and Ice cream (Servings/day)

Total yogurt (plain, flavoured) 0.29 ± 0.03 0.30 ± 0.02 0.31 ± 0.03 0.9 0.21 ± 0.04 0.23 ± 0.04 0.23 ± 0.05