Caffeine Intake and the Plasma Proteome

By

Ouxi Tian

A thesis submitted in conformity with the requirements

for the degree of Master of Science (M.Sc.)

Graduate Department of Nutritional Sciences

University of Toronto

© Copyright by Ouxi Tian 2013

ii

Caffeine Intake and the Plasma Proteome

Ouxi Tian

Master of Science

Graduate Department of Nutritional Sciences

University of Toronto

2013

Abstract

Caffeine has been implicated in a number of health conditions, but the mechanisms

and physiological pathways affected are not all well understood. Genetic variation in the

caffeine metabolizing gene, CYP1A2 (rs762551), is associated with differences in caffeine

metabolism and disease risk. The objectives of this thesis were to determine the association

between caffeine intake and the plasma proteome and whether CYP1A2 genotype modifies any

associations. Subjects (n=1095) were from the Toronto Nutrigenomics and Healthy Study.

Initially, no significant associations were seen between caffeine intake and 54 high-abundance

plasma proteins. After stratification by CYP1A2 genotype, plasma gelsolin levels decreased

significantly with increasing caffeine intake among the slow metabolizers (A/C +C/C). These

results suggest that plasma gelsolin levels are affected by caffeine intake and this association is

only evident among slow metabolizers. Gelsolin may have a role in mediating some of the

biological effects of caffeine.

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Acknowledgments

First and foremost, I would like to thank my thesis supervisor, Dr. Ahmed El-Sohemy, for not

only giving me the opportunity to complete this project, but also for being a great mentor. Your

patience, empathy, and wisdom have made this journey a memorable and rewarding experience. I am

grateful of all the support and help you have given me.

I would also like to express my appreciation to Dr. Anthony Hanley and Dr. Richard Bazinet of my

Advisory Committee for their valuable encouragement, insightful comments, and challenging questions.

I would like to thank my loved ones, who have supported me throughout the entire process, by

keeping me spirited, and providing me with the comfort of home-cooked meals and occasional ice

cream! I will be grateful forever for your love.

To the El-Sohemy lab, I am deeply grateful for the support and guidance each and every one of

you has offered me. No matter whether it is a SAS code or a random question, you have never failed to

help me to find a solution. The success of my thesis would not have been possible without your help and

encouragement.

Thank you to the Nutritional Sciences Department as well, especially Dr. Pauline Darling, who

trusted in me and accepted me into the program.

Finally, I would like to thank all of the brothers and sisters from my Bible Study group for your

continued prayers, care, and support. I am grateful for God’s provision of joys, challenges, and grace for

growth.

“The Lord is my strength and my shield; my heart trusts in him, and he helps me. My heart leaps for joy,

and with my song I praise him.” (Psalm 28:7, NIV)

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Table of Contents

Chapter 1: Introduction and Literature Review ············································································· 9

1.1 Introduction ···························································································································· 11

1.1.1 Thesis Organization ······························································································ 11

1.2 Caffeine ··································································································································· 11

1.2.1 Physical and Chemical Properties of Caffeine ····················································· 11

1.2.2 Caffeine: Sources and Consumption ···································································· 12

1.2.3 Pharmacokinetics of Caffeine ·············································································· 17

1.2.3.1 Absorption and Distribution ····························································· 17

1.2.3.2 Caffeine Metabolism ········································································ 17

1.2.4 Cytochrome P450································································································· 19

1.2.4.1 CYP1A2 and Factors that Influence CYP1A2 Activity ························ 19

1.2.5 The CYP1A2 Gene································································································· 23

1.2.5.1 Genetic Variation in CYP1A2 ····························································· 23

1.3 Caffeine Pharmacology ··········································································································· 26

1.3.1 Caffeine Mechanism of Action – the Adenosine Receptor Antagonist ··············· 26

1.3.2 Physiological and Psychological Effects of Caffeine ············································ 27

1.4 Caffeine Intake and Associated Health Outcomes ································································· 30

1.4.1 Caffeine and Cardiovascular Diseases ································································· 30

1.4.1.1 Caffeine, CYP1A2, and Cardiovascular Diseases ······························· 32

1.4.2 Caffeine and Parkinson’s disease········································································· 33

1.4.2.1 Caffeine, CYP1A2, and Parkinson’s disease ······································ 34

1.4.3 Caffeine and Breast Cancer·················································································· 35

1.4.3.1 Caffeine, CYP1A2, and Breast Cancer ··············································· 35

1.4.4 Caffeine and Type 2 Diabetes ·············································································· 36

1.4.5 Caffeine and Health Summary ············································································· 37

1.5 Proteomics – an Approach towards Understanding the Influence of Diet on Health············ 37

1.5.1 Proteomics and Nutrition Research ····································································· 38

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1.5.2 Proteomics – the Search for Biomarkers ····························································· 39

1.5.3 Proteomics Techniques ························································································ 41

1.5.4 The Proteomics Panel ·························································································· 43

1.6 Hypothesis, Objectives, and Thesis Organization ··································································· 44

Chapter 2: Association between Caffeine Intake and the Plasma Proteome in Humans ··········· 45

2.1 Abstract ··································································································································· 45

2.2 Introduction ···························································································································· 46

2.3 Materials and Methods ··········································································································· 48

2.3.1 Study Population ·································································································· 48

2.3.2 Physical Activity and Anthropometric Measurements ········································ 49

2.3.3 Biochemical Measurements and Genotyping······················································ 49

2.3.4 Plasma Proteomics Measurements ····································································· 50

2.3.5 Statistical Analysis ································································································ 50

2.4 Results ····································································································································· 52

2.5 Discussion ································································································································ 59

Chapter 3: Association between Caffeine Intake and Plasma Gelsolin Concentrations in Other

Caffeine-Related Genes among Slow Metabolizers of Caffeine ··················································· 63

3.1 Abstract ··································································································································· 63

3.2 Introduction ···························································································································· 64

3.3 Materials and Methods ··········································································································· 66

3.3.1 Study Population ·································································································· 66

3.3.2 Biochemical Measurements and Genotyping······················································ 66

3.3.3 Plasma Proteomics Measurement – Gelsolin, Isoform 1 ···································· 66

3.3.4 Statistical Analysis ································································································ 66

3.4 Results ····································································································································· 67

3.5 Discussion ································································································································ 73

Chapter 4: Overview and General Discussion ·············································································· 79

4.1 Overview ································································································································· 79

4.2 Limitations ······························································································································· 81

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4.3 Future Directions ···················································································································· 83

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List of Tables

Table 1-1: Physical and Chemical Properties of Caffeine ····························································· 12

Table 1-2: Caffeine Content of Beverages, Foods and Medications ············································ 16

Table 1-3: Major Substrates, Inducers and Inhibitors of CYP1A2 ················································ 22

Table 1-4: Proteomics Panel – 54 high abundance plasma proteins ··········································· 43

Table 2-1: Subject Characteristics by Categories of Caffeine Consumption ································ 54

Table 2-2: Mean Plasma Protein Concentrations ········································································· 56

Table 3-1: Subject characteristics by categories of caffeine consumption for slow caffeine

metabolizers (A/C+C/C) ········································································································ 69

Table 3-2: Mean plasma gelsolin concentration by categories of caffeine consumption and

ethnocultural group amongst CYP1A2 slow metabolizers ··················································· 71

Table 3-3: Mean plasma gelsolin concentration by categories of caffeine consumption and

genotypes of caffeine-related genes amongst CYP1A2 slow metabolizers ························· 72

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List of Figures

Figure 1-1: SNPs within the CYP1A2 Gene ···················································································· 25

Figure 2-1: Plasma Gelsolin Concentrations among Slow Caffeine Metabolizers························ 58

1

Chapter 1 – Introduction and Literature Review

1.1 Introduction

Caffeine is one of the most widely consumed bioactive substance in the world [1].

Approximately 80% of the world’s population consume caffeine regularly and 90% of adults in

North America consume caffeine on a daily basis [2]. Like cocaine and amphetamine, caffeine

stimulates motor activation, increases arousal, and produces desirable as well as adverse

effects [3]. In addition to coffee and tea, caffeine is also commonly found in energy drinks, soft

drinks, and chocolate [4]. However, the most important source of caffeine globally comes from

coffee [5].

Because of caffeine’s widespread use and its variability in consumption behaviour, many

studies have sought to elucidate caffeine’s effects on health and disease risk. In particular, the

effect of caffeine on cardiovascular diseases (CVD), such as hypertension and myocardial

infarction (MI), have been very controversial [6]. Although the adverse effects of caffeine on

cardiovascular health have been emphasized, recent studies show that long-term caffeine

consumption is not associated with an increased risk of CVD events [7, 8]. Inconsistencies

between study results could be attributed to methodological issues including differences in

study design and a variety of confounding factors. For example, caffeine metabolism can be

influenced by genetics, gender, age, and smoking status [9].

Variation in the cytochrome P450 1A2 gene (CYP1A2), which codes for the enzyme

responsible for caffeine metabolism, has been associated with different CVD outcomes.

2

Consumption of 4 or more cups of coffee per day has been associated with a significantly

increased risk of MI in those with the CYP1A2 -163C allele (slow metabolizers) while subjects

with the CYP1A2 AA genotype (fast metabolizers) experienced a protective effect with

moderate coffee consumption [10]. This modifying effect of CYP1A2 (rs762551) was also

supported by a prospective study on hypertension [11]. In addition to CVD, genetic variation in

CYP1A2 has been shown to play a role in modulating the association between caffeine and

several other health outcomes including Parkinson’s disease [12], breast cancer[13], and

osteoporosis [14]. However, due to the complexity of caffeine metabolism and other

confounders, the results of these studies are inconsistent. Therefore, the influence of caffeine

on disease-associated pathways is still unclear and must be studied more comprehensively to

better understand the potential health effects of caffeine.

Recent advances in proteomics allows for the targeted-quantification of numerous high-

abundance plasma proteins. Many plasma proteins are useful indicators of health and disease

risk [15]. Therefore, examining the relationship between caffeine intake and plasma proteins

may help identify proteins that are influenced by caffeine. Since these proteins represent

molecules from many physiologically important pathways, they can also provide insight into

pathways affected by caffeine intake. A study on dietary patterns showed that individuals

consuming different diets also have different plasma proteomic profiles, which may influence

the risk of developing diseases later in life [16]. However, to date, there is no published data on

the association between caffeine consumption and the plasma proteome.

3

The purpose of this thesis was to determine whether caffeine intake is associated with

the human plasma proteome. Because individuals with different CYP1A2 genotype exhibit

differences in caffeine metabolism (and to account for residual confounding), another focus of

this thesis was to determine whether the rs762551 polymorphism in CYP1A2 modifies any

association between caffeine intake and plasma proteins. An association between CYP1A2,

caffeine intake and the plasma proteome would indicate that a genetic subset of individuals is

more influenced by the effects of caffeine. Proteins that are associated with caffeine

consumption would also provide further understanding into disease-related pathways linked to

caffeine intake.

1.1.1 Thesis Organization

This thesis consists of four chapters. Chapter 1 provides an overview of recent

literature on caffeine as well as the benefits of applying proteomics towards nutrition research.

Chapter 2 discusses the association between caffeine intake and the plasma proteome, and the

modifying effect of CYP1A2. Chapter 3 explores the association between caffeine intake, the

plasma proteome and other caffeine-related genes. Chapter 4 summarizes the research and

identifies limitations as well as directions for future research.

1.2 Caffeine

1.2.1 Physical and Chemical Properties of Caffeine

Caffeine (1, 3, 7-trimethylxanthine) is a colourless and odourless powder at room

temperature [17]. It is a naturally occurring alkaloid that can be extracted from coffee beans,

tea leaves, cocoa beans, cola nuts and other plants via decaffeination [18]. In addition, caffeine

4

can also be synthesized chemically through methylation of other methylxanthines

(theobromine or theophylline) and synthesis from dimethyl urea and malinic acid [18]. Caffeine

has a slightly bitter taste and has a chemical formula of C8H10N4O2 [17]. Other physical and

chemical properties of caffeine are outlined below in Table 1-1.

Table 1-1 Physical and Chemical Properties of Caffeine

Property Value

Molecular weight 194.2 g/mol 1

Melting point 236oC 1

Sublimation point 178oC 1

Specific gravity 1.2 1

Volatility 0.5% 1

Solubility (in water) 2.2% 1

Vapour pressure (at 178 oC) 760 mmHg 1

Vapour density 6.7 1

pH (1% solution) 6.9 1

Base dissociation constant 10.4 2

Median lethal dose 192 mg/kg (200 μM) 3

1 [17], 2 [19], 3 [20, 21]

1.2.2 Caffeine: Sources and Consumption

Caffeine is a naturally occurring substance that can be found in the fruit, leaves, and

seeds of a variety of plant species [22]. The world’s primary sources of dietary caffeine come

from coffee beans (Coffea Arabica and Coffea Robusta) and tea leaves (Camellia sinensis) [23].

5

Other common sources of caffeine include cacao bean (Theobroma cacao), kola nut (Cola

acuminate), guarana berries (Paullinia cupana), and yerba mate (Ilex paraguariensis) [23].

In North America, caffeine is most commonly consumed in the form of beverages such

as coffee (60%-75%) and tea (15%-30%) in the adult diet [20]. In comparison, caffeinated soft

drinks and chocolate contribute to the majority of caffeine intake in children [20]. With

caffeine’s increasing popularity, new caffeinated products such as energy drinks, sport drinks,

juices, waters, and snack items, have been developed over the past decade and have become

widely accepted by many individuals [22]. In addition to caffeinated foods and beverages,

caffeine can also be found in a variety of over-the-counter medications such as cold remedies

and analgesics as well as in dietary supplements [24]. Therefore, due to its widespread use and

availability, caffeine has often been referred to as the world’s most commonly used

psychoactive substance with intake levels exceeding that of alcohol and nicotine [21].

The amount of caffeine in foods and beverages is highly variable [20]. Overall, coffee

and energy drinks contain the most amount of caffeine on average while chocolate and other

cocoa confectionary products contain the least [25]. As shown in Table.1.2.2, there is also great

variability in caffeine concentration within the same type of product, especially for tea and

coffee. For example, the amount of caffeine in a cup of plain brewed coffee can range between

80-135 mg and the amount of caffeine in a cup of green tea can range between 9-36 mg (Table

1-2) [22]. This variability is often the result of plant variety, environmental growing conditions,

treatment, processing, storage, and preparation method [26]. Furthermore, caffeine

concentration can also vary by brand and purchase location [27-30]. A study analyzing the

6

caffeine content of coffees found that even between the same type of coffees purchased at the

same store, the amount of caffeine differed greatly depending on the time it was purchased

[31].

The pattern of caffeine consumption around the world also differs greatly between

populations. African countries such as Kenya and South Africa consume the lowest amount of

caffeine (50 mg/d), whereas adults from European countries such as Sweden and Finland may

reach an average daily intake of more than 400 mg caffeine [22, 23]. In the US and Canada, the

average intake of caffeine from all sources ranges from 210-238 mg/person per day, which falls

in the middle of the global caffeine intake spectrum [23]. The primary source of caffeine in

North America and many European countries is coffee, whereas tea is the beverage of choice in

the United Kingdom and some Asian countries [20]. Furthermore, the amount of daily caffeine

intake also varies by age. In Canada, adults consume about 2.4 mg/kg of caffeine daily, and

children consume 1.1 mg/kg of caffeine per day [32]. Based on extensive research on the health

effects of caffeine, Health Canada has also set out recommendations for caffeine consumption.

Specifically, a moderate intake of up to 400 mg of caffeine per day is not associated with

adverse effects in healthy adults; however, pregnant or nursing women, and children 13 years

of age and younger are recommended to not exceed 300 mg/d and 2.5 mg/kg of body weight,

respectively [20].

At the individual level, caffeine consumption patterns are also highly variable. Many

factors such as lifestyle, health, and social habits, have all been proposed to influence caffeine

consumption behaviours [33]. A study examining factors related to caffeine consumption found

7

that the time of day was an important predictor of caffeine intake. The arousing effects of

caffeine are often sought after by individuals at times when arousal is low such as during sleep

deprivation or post-prandial drowsiness [34]. In addition to environmental and social factors,

genetics also plays a role in both consumption behaviour and response to caffeine intake.

Specifically, a polymorphism in the adenosine A2A receptor (A2AR) gene (ADORA2A, 1976 T>C)

has been associated with caffeine-induced anxiety and habitual caffeine consumption [35].

Compared to carriers of the C allele, individuals with the TT genotype tend to consume lower

amounts of caffeine (<100 mg/d) and are more sensitive to caffeine-induced anxiety [35].

Furthermore, variation in the catechol-O-methyltransferease (COMT) gene that affects the

breakdown of epinephrine and norepinephrine, is found to be associated with an increase in

self-reported heart rate after caffeine consumption in those with the Met/Met genotype [36].

8

Table 1-2. Caffeine Content of Beverages, Foods and Medications1

Source Volume or Weight Caffeine Content

Range (mg)

Roasted and Ground Coffee Brewed 150ml 80 to 135 Drip 150ml 115 to 175 Espresso 45 to 60ml 100 Decaffeinated 150ml 3 to 4 Instant Coffee 150ml 65 to 100

Tea Tea, Brewed 150ml 40 to 120 Bagged 150ml 8 to 91 Loose Leaf 150ml 30 to 48 Green 150ml 9 to 36 Black 150ml 21 to 50 Yerba Mate, bagged 1 to 2g 65 to 130 Yerba Mate, loose 40 to 50g 260 Lipton, brisk 355ml 9 Nestea un/sweetened 355ml 26 Snapple, flavoured 355ml 32

Soft Drinks Coca Cola / Diet Coke 355ml 34 Pepsi-Cola / Diet Pepsi 355ml 37 Mountain Dew 355ml 55 Dr. Pepper 355ml 41 Barq’s Root Beer 355ml 22

Energy Drinks Full Throttle 473ml 144 Red Bull 245ml 77 Rockstar 240ml 160

Chocolate Baker’s baking chocolate,

semisweet 28g 13

Milk chocolate bar 28g 3 to 10 Dark chocolate bar 28g 20 Chocolate milk, store bought 240ml 8 Cocoa, hot chocolate from mix 150ml 4 to 6

Medications Excedrin Extra Strength 1 tablet 65 Midol Maximum Strength 1 tablet 60 NoDoz Maximum Strength 1 tablet 200

1Adapted from [19-22, 37, 38].

9

1.2.3 Pharmacokinetics of Caffeine

1.2.3.1. Absorption and Distribution

After ingestion, caffeine is rapidly and completely absorbed from the gastrointestinal

tract into the blood stream [39]. In healthy adults, the intake of a single dose of caffeine (4

mg/kg) is noted to reach peak plasma concentrations within 1-2 hours [40]. A concentration of

0.25 to 2 mg/l or approximately 1 to 10 µM is achieved after consuming a dose of 0.4 to

2.5 mg/kg caffeine, which is equivalent to consuming one cup of coffee [41]. The rate of gastric

emptying for caffeine can reach up to 90% within 3.5 hours after intake [42]. Depending on the

source of caffeine, the rate of absorption is also variable. For example, a study looking at the

rate of caffeine absorption found that caffeine’s pharmacological effects were experienced

fastest (within 5 to 10 minutes) when it was administered in the form of chewing gum [43].

Caffeine is also a lipophilic molecule that is able to pass through all biological

membranes and becomes readily distributed to all areas of the body [44]. After ingestion,

caffeine can be found in many bodily fluids such as plasma, cerebrospinal fluid, saliva, bile, and

breast milk [45]. Furthermore, the passage of caffeine through tissues is not limited by the

blood-brain barrier, and an equilibrium of caffeine can be continuously maintained between

the mother and her fetus [44, 46]. Therefore, pregnant women are advised to limit caffeine

intake as the fetus is readily exposed to caffeine and its potentially harmful effects [47].

1.2.3.2. Caffeine Metabolism

There are several routes of caffeine metabolism that take place primarily in the liver:

3-N-demethylation to paraxanthine (84%), 1-N-demethylation to theobromine (12%), and 7-N-

10

demethylation to theophylline (4%) [48]. Paraxanthine is the primary metabolite of caffeine and

shares a similar molecular structure and half-life with caffeine [49]. The level of paraxanthine in

the serum is less variable than caffeine throughout the day; however, its serum concentration is

not as sensitive as that of caffeine’s in response to recent intake [49, 50]. Theobromine

contributes to the greatest percentage of biologically active caffeine metabolites [51]. Similar to

caffeine, theobromine is rapidly absorbed into the body and has a serum half-life of 8-12 hours

[52]. The most important pharmacologic effects of this metabolite include stimulation of the

cardiovascular system, relaxation of smooth muscles and diuresis [53]. Theophylline, the third

major metabolite of caffeine also exhibits similar effects as caffeine in the body. However,

theophylline is more potent and has stronger toxicological effects than caffeine and

theobromine [54]. Furthermore, the half-life of theophylline is longer and more variable than

that of caffeine [54]. All three of the aforementioned caffeine metabolites undergo further

demethylations and oxidation to urates in the liver with about 3% remaining in the form of

caffeine in the urine [55, 56]. The cytochrome P450 1A2 (CYP1A2) enzyme is responsible for the

majority of caffeine metabolism in the liver, especially via the 3-N-demethylation pathway [48].

In addition to CYP1A2, other CYP isoforms such as CYP2E1, CYP2C8, CYP3A4, as well as N-

acetyltransferase and xanthine oxidase, are involved in the further breakdown of caffeine

metabolites as well [56, 57].

The rate of caffeine metabolism is affected by many factors. Lower doses of caffeine

intake (70-100 mg) show linear pharmacokinetics [41] while higher doses (250-500 mg) exhibit

significant delay in elimination half-life showing saturable metabolism [58]. The average half-

life of caffeine has been observed to be 2.5-5 hours [40]. However, the elimination of caffeine

11

shows large inter-individual variability and has been observed to have half-lives ranging

between 2.3-9.9 hours [39]. The large variability in caffeine half-life can be attributed to many

factors including age, sex, use of oral contraceptives, smoking, pregnancy, and health status

[59]. Newborn infants have the longest caffeine plasma half-life of 65-100 hours due to their

premature digestive and hepatic systems [60]. The rate of metabolism increases drastically with

the half-life of caffeine decreasing to 14 hours in infants between 3-5 months old [60]. At 6

months old, the metabolism of caffeine in infants becomes comparable to the rate of

metabolism in adults [60]. In addition to age, there is also evidence supporting gender

differences in caffeine metabolism. Females were shown to metabolize caffeine less efficiently

than males due to slower hepatic enzyme activity [61]. Female users of oral contraceptives also

have a caffeine half-life that almost doubles that of non-users [62]. In addition, the half-life of

caffeine has been noted to increase from 4 hours to 18 hours during the first and third

trimesters of pregnancy, respectively [44]. The rate of caffeine elimination, as observed by

Carillo and others, is two times faster in individuals smoking cigarette [63]. In contrast,

individuals with chronic liver disease and those who are long-term consumers of alcohol have

increased caffeine half-life [59, 64]. Many of the aforementioned factors are linked to variation

in CYP1A2 activity.

1.2.4 Cytochrome P450

1.2.4.1 CYP1A2 and Factors that Influence CYP1A2 Activity

CYP1A2 protein expression and activity have been shown to exhibit wide inter-individual

variation with more than 40-fold differences in liver CYP1A2 mRNA expression [65]. CYP1A2

12

belongs to a superfamily of heme-containing metalloenzymes that is involved in the

metabolism of a variety of exogenous and endogenous compounds [66]. While some CYP

enzymes are expressed in different tissues throughout the body, CYP1A2 is exclusively

expressed in the liver [66]. The active-site of CYP1A2 is narrow and planar. Therefore, only

planar molecules with polyaromatic rings, such as caffeine, can fit into the active-site of CYP1A2

[67]. In addition to caffeine, the substrates of CYP1A2 include a variety of drugs such as

clozapine [68] and olanzapine [69], and endogenous compounds such as estradiol [70] and

bilirubin [71]. CYP1A2 is also responsible for the bioactivation of several procarcinogens

including polycyclic aromatic hydrocarbons and aromatic heterocyclic amines [72, 73]. Table 1-

3 provides a list of the major substrates as well as inducers and inhibitors of CYP1A2.

CYP1A2 activity is influenced by numerous environmental factors that are associated

with differences in the rate of caffeine clearance [74]. A well-known potent inducer of CYP1A2

is cigarette smoke [75]. The polycyclic aromatic hydrocarbons present in tobacco smoke

activate the aryl hydrocarbon receptor [76]. Since the aryl hydrocarbon receptor is a

transcription factor that leads to the expression of the CYP1A2 gene, the rate of caffeine

metabolism is higher in smokers compared to non-smokers [76]. In contrast, exogenous female

sex steroids such as ethinyl estradiol and gestodene, which are used in oral contraceptives, are

known to inhibit and decrease CYP1A2 activity [77]. Therefore, users of oral contraceptives or

women with high levels of estrogen often experience delayed caffeine clearance [78].

Furthermore, many food-derived chemicals also modulate the expression and activity of CYP-

related enzymes [79]. Consumption of cruciferous vegetables such as broccoli and cauliflower,

have been shown to induce CYP1A2 activity while apiaceous vegetables such as carrots and

13

celery are known to inhibit CYP1A2 activity [80]. Intake of charcoal-grilled meat is also

associated with higher CYP1A2 activity in men [81]. In addition to dietary factors, heavy

exercise has been associated with enhanced hepatic drug metabolism as well [82]. A study

examining 30 days of vigorous exercise (8-11 hours/day) in male subjects noted a 70% increase

in CYP1A2 activity [83]. Ethnicity also plays a role in determining the efficiency of CYP1A2

activity and caffeine metabolism. Perera and colleagues observed that South Asian (Sri Lankan

and Indian) subjects have significantly lower CYP1A2 activity compared to European

participants [84]. Koreans, Chinese, and individuals of African descent also have slower CYP1A2

activity compared to Caucasians [85, 86]. Although CYP1A2 activity can be influenced by many

dietary and lifestyle factors, twin studies suggest that up to 70% of the observed variations in

CYP1A2 activity are due to genetics [87] .

14

Table 1-3: Major Substrates, Inducers and Inhibitors of CYP1A2

Substrates Inducers Inhibitors

17β-estradiol Broccoli Amiodarone

Caffeine Brussels sprout Cimetidine

Clozapine Charcoal-grilled meat Ciprofloxacin

Flutamide Insulin Fluroquinolones

Frovatriptan Methylcholanthrene Fluvoxamine

Lidocaine Modafinil Furafylline

Melatonin Nacfillin Interferon

Olanzapine Β-naphthoflavone Methoxsalen

Ropivacaine Omeprazole Mibefradil

Tacrine Tobacco smoke Ticlopidine

Theophylline

Tizanidine

Triamterene

Zolmitriptan

Aflatoxin B1

Aromatic and heterocyclic

amines

Adapted from: [74]

15

1.2.5 The CYP1A2 Gene

The CYP1A2 gene is part of the CYP1A cluster and is located on chromosome 15 at

15q24.1 [88]. CYP1A2 is the positive strand of the q-arm of chromosome 15 while CYP1A1 is the

negative strand [88]. These two genes are separated by a 23.3 kb spacer region while sharing a

common 5’-flanking region [88]. This spacer region is suggested to regulate the expression of

CYP1A2 and CYP1A1; however, the exact mechanism is unclear [89]. The CYP1A2 gene spans a

region of 7.8 kb, which includes seven exons and six introns [90]. Overall, the CYP1A2 gene only

produces one transcript, which translates into a protein of 516 amino acid residues (CYP1A2)

[88]. As shown in Figure 1.2.5.1, there are several single nucleotide polymorphisms within

CYP1A2, which are associated with altered CYP1A2 expression and activity.

1.2.5.1 Genetic Variation in CYP1A2

Since caffeine metabolism is mediated by CYP1A2 activity, variation in the gene coding

for this enzyme has been the focus of much research. As early as the 1960s, research has noted

variability in O-deethylation, which is a marker reaction for CYP1A2 [91]. More than 150 SNPs

have been identified for CYP1A2, and numerous studies involving multi-ethnic groups have

shown differences in minor allele distributions as well as common haplotype frequencies [74]. A

single AC nucleotide substitution within intron 1 at position -163 (rs762551) is one of the

most common CYP1A2 polymorphisms in Caucasians. The frequency of this polymorphism

ranges from 30% in the British population [92] to 70% in the Swedish population [86].

The -163A>C polymorphism in CYP1A2 has been associated with decreased enzyme

inducibility and activity among Caucasians [74]. A study examining this -163A>C polymorphism

16

reported that smokers who are carriers of the C allele metabolized caffeine at a rate that was

1.6-fold slower than individuals with the AA genotype [93]. Although this observation was

limited to smokers, later research on caffeine and the -163A>C polymorphism showed that

caffeine consumption alone is also an inducer of CYP1A2 in non-smokers [94]. Djordjevic and

colleagues demonstrated that heavy coffee consumption (>3 cups/day) is associated with

higher CYP1A2 inducibility in subjects homozygous for the A allele [94]. Therefore, genetic

variation in CYP1A2 is associated with the rate of caffeine metabolism and clearance. Carriers of

the C allele are ‘slow’ caffeine metabolizers [93] and tend to breakdown caffeine at a slower

rate. Individuals with the AA genotype are ‘fast’ caffeine metabolizers and can eliminate

caffeine from the body more rapidly than slow metabolizers [74].

In addition to modifying the rate of caffeine metabolism, CYP1A2 polymorphisms are

also associated with disease risk. Studies show that slow caffeine metabolizers have an

increased risk of developing myocardial infarction [10] and hypertension [11], whereas an

opposite effect was observed in fast caffeine metabolizers. A study by Guessous et al., [95]

found that non-smokers with the fast metabolizer genotype reported higher caffeine intake and

were 35% less likely to become hypertensive compared to non-smokers who are slow caffeine

metabolizers. Therefore, the capacity to break down caffeine may be a determinant in one’s

susceptibility to CVD events. However, the -163A>C polymorphism within CYP1A2 is associated

with caffeine metabolism and not caffeine consumption behaviour. A recent genome-wide

association studies (GWAS) suggested that the -163A>C variant is only mildly associated with

coffee consumption and that other variants within CYP1A2 may have a greater influence on

habitual caffeine intake [96]. A different polymorphism (rs6968865) in the CYP1A1-CYP1A2

17

gene region and two other polymorphisms in the upstream inducer gene of CYP1A1 and

CYP1A2 (the aryl-hydrocarbon receptor; AHR), were reported to be associated with habitual

caffeine intake in a Costa Rican population [97].

Figure 1.2.5.1 SNPs within the CYP1A2 Gene

Adapted from: [74].

18

1.3.1 Caffeine Pharmacology

1.3.1 Caffeine Mechanism of Action – the Adenosine Receptor Antagonist

The antagonistic effect of caffeine on the adenosine receptors (A1 and A2A) is the most

important pharmacodynamic property of caffeine. Adenosine is a purine nucleoside that can

increase or decrease cellular concentrations of cyclic AMP through its action on the adenosine

A1 and A2A receptors [59]. Adenosine has higher binding affinity for the A1 receptors that inhibit

adenyl cyclase, and lower binding affinity for the A2A receptors that stimulates adenyl cyclase

[59]. These receptors are present in many tissues throughout the body including the brain, the

respiratory, gastrointestinal, renal, and cardiovascular systems, as well as in adipose tissue [59].

Since adenosine is not produced and released from vesicles in response to the firing of neurons,

it does not meet the classical definition of a neurotransmitter [98]. However, similar to an

inhibitory neurotransmitter, adenosine acts to either reduce the activity of excitatory tissues

such as the brain and the heart, or induce vasodilation that increases the delivery of metabolic

substrates [99]. Furthermore, adenosine also elicits sedation and anticonvulsant activities

through reducing the spontaneous firing of neurons in different regions of the brain [100]. The

binding of adenosine to A2A receptors inhibits the release of many neurotransmitters including

acetylcholine, norepinephrine, dopamine, gamma amino butyric acid and serotonin [98]. The

structure of caffeine (double ring) is very similar to adenosine thereby making caffeine a non-

selective adenosine receptor antagonist [101, 102]. Under normal physiological conditions,

caffeine has high affinity for the A1 and A2a receptors, and competes with adenosine for binding

to these receptors [103]. Through the competitive inhibition of adenosine receptors, caffeine

19

effectively increases the release of many neurotransmitters, which affects neurotransmission in

different regions of the brain [48, 104]. For example, caffeine consumption increases the level

of circulating catecholamines (norepinepherine, epinephrine and domapine) and serotonin in

the brain [104]. Overall, caffeine’s effects on the body are the reversal of those normally

achieved by the binding of adenosine to its receptors [105]. Stimulatory effects such as

increased arousal and alertness are produced after consuming caffeinated foods and beverages

[105]. However, the extent of these effects varies greatly between individuals, and is partially

explained by genetic variation in the pathways of caffeine metabolism [35]. Polymorphisms in

the adenosine A1 and A2A receptors and dopamine D1 and D2 receptors have been associated

with self-reported anxiety [35, 106].

Other mechanisms of action, such as the mobilization of intracellular calcium to

enhance neurotransmission [107] and the inhibition of phosphodiesterase activity [108], have

been proposed for caffeine as well. However, studies show that the levels of caffeine intake

required to mobilize calcium and inhibit phosphodiesterase are extremely high and are toxic to

humans [107, 109, 110]. In contrast, the concentration of caffeine required to antagonize

adenosine receptors is much lower and can be achieved with normal dietary doses (10-100 µM)

[110]. Therefore, the competitive antagonism of adenosine receptors remains the most

plausible explanation for the physiological effects of caffeine.

1.3.2 Physiological and Psychological Effects of Caffeine

Caffeine produces subjective and behavioural effects that are similar to those

produced by classical psychostimulant drugs such as amphetamine and cocaine, which are

20

dopaminergically mediated [110]. Depending on the dosage and frequency of caffeine intake,

several physiological effects may occur. Positive experiences such as increased alertness,

concentration, arousal, and enhanced cognitive performance have been associated with low to

moderate (200-300 mg) caffeine consumption [111, 112]. A recent study demonstrated that

caffeine, as a central nervous system stimulant, was able to facilitate cognitive function through

a proofreading task [113]. A systemic review of 13 randomized trials in subjects with shift work

disorder or jet lag also observed that compared to placebo, those that consumed caffeine were

much better at reasoning, recall, orientating, and forming concepts [114]. Therefore, the

desirable physiological effects produced by caffeine can often lead to dependence and

reinforcing behaviours in those that consume it [115]. Indeed, there are studies providing

clinical evidence for caffeine dependence syndrome [116, 117]. In contrast, unpleasant effects

such as increased anxiety, nervousness, and tension are experienced by some caffeine

consumers as well [37].

Dose-response to caffeine varies greatly between individuals. Some caffeine consumers

may experience instant discomfort such as headaches or sleeplessness after only a single cup of

coffee [118]. On the contrary, through increased exposure, many other coffee consumers

continue to experience the pleasant effects of caffeine even after drinking several cups of

coffee [118]. Tolerance to the acute effects of caffeine can develop rapidly, and habitual

consumers experience the effects of caffeine much differently compared to those that consume

caffeine infrequently [91]. The difference in susceptibility to caffeine’s effects may occur at the

metabolic or at the drug-receptor level, which may be influenced by demographic and

environmental factors [91]. In particular, genetic predisposition has become an important

21

source of variability and a key topic of caffeine research in recent years [91, 118]. A study on

habitual caffeine consumption showed that genetic polymorphism in the adenosine A2A

receptor (ADORA2A) rather than CYP1A2 is associated with regular caffeine consumption [35].

Individuals who are carriers of the ADORA C allele were more likely to consume more caffeine

than those homozygous for the T allele [35]. Similarly, a study on the catechol-O-

methyltransferase gene (COMT) and caffeine consumption showed that self-reported increased

heart rate after consuming caffeine was associated with COMT genotype. The authors

suggested that individuals who have an impaired ability to breakdown catecholamines are more

likely to experience increased heart rate [36].

Caffeine withdrawal is another condition to be considered when examining caffeine’s

effect on the body. Withdrawal symptoms such as lethargy, depression and irritability, often

occur after12-24 hours of caffeine abstinence [119]. Flu-like symptoms such as nausea,

vomiting, and muscle stiffness are also associated with caffeine withdrawal in some individuals

[119]. Furthermore, the severity of withdrawal symptoms appears to be positively associated

with the amount of usual caffeine intake; however, a modest consumption of 100 mg/d has

been associated with withdrawal as well [119]. The prevention or relief of withdrawal

symptoms can be achieved with as little as 25 mg of caffeine [120]. Therefore, some have

argued that the positive effects associated with caffeine intake may be simply the reversal of

the adverse effects of caffeine withdrawal [121]. In contrast, others have also suggested it is

the combination of both reinforcing effects and the reversal of withdrawal that is responsible

for caffeine intake behaviours [34, 122].

22

In addition to the aforementioned effects, caffeine intake has also been associated with

diuresis, cerebrovascular constriction, increased lipolysis, increased respiratory rate and

bronchodilation, increased gastric secretion, and enhanced athletic performance [59, 110, 123].

Moreover, caffeine intake also adversely affects sleep quality and increases daytime sleepiness

[59, 124, 125]. Overall, sensitivity to caffeine dose and effects is highly variable between

individuals, which may be explained by differences in pharmacokinetics [56] and genetics [74].

1.4 Caffeine Intake and Associated Health Outcomes

As the most widely consumed stimulant in the world, caffeine’s influence over disease

risk has been studied extensively. Consumption of caffeinated beverages has been associated

with both beneficial and adverse health outcomes; however, results have been inconsistent.

Therefore, based on current data, there is insufficient evidence to either promote or discourage

the use of caffeine as part of one’s daily diet. Nevertheless, Health Canada recommends

limiting consumption to a daily intake of no more than 400 mg for healthy adults and no more

than 300 mg for pregnant or nursing women[126].These recommendations are based on early

studies linking caffeine intake to CVD [126]. For children less than 12 years old, the

recommended intake levels are based on a daily dose of 2.5 mg/kg of body weight (which was

shown to be safe in young adolescents) and the average body weight of children

[126].Behavioural effects such as hyperactivity and increased anxiety were also considered in

these recommendations [126].With a rise in the number of caffeinated products being

marketed to consumers [122], it is necessary to further investigate caffeine’s effects on the

body and its impact on disease-associated pathways. Since genetic variation in CYP1A2 is

23

known to modify the associations between caffeine intake and certain health outcomes, it is

also necessary to examine the influence of genetics on the complex relationship between

caffeine and disease risk. The following section is a summary of some of the most studied and

controversial health outcomes related to caffeine consumption.

1.4.1 Caffeine and Cardiovascular Diseases

Cardiovascular-related complications are among the leading causes of death around

the world [5]. The onset and pathogenesis of cardiovascular diseases (CVD) are complex and

multi-faceted. Health conditions such as high cholesterol, high homocysteine level,

atherosclerosis and arterial calcification are all categorized as risk factors for CVD [127]. As

suggested by the American Heart Association, even a modest modification to diet and lifestyle

can be beneficial for reducing cardiovascular risk [128]. Although many epidemiological studies

have examined the relationship between caffeine intake and CVD, results are still controversial

[129]. The most well-known effect of caffeine on the cardiovascular system is its ability to

induce arterial stiffness, which can increase blood pressure [5].

As the consumption of caffeinated foods and beverages continues to increase, any

possible relationship between caffeine intake and CVD may be significant towards the overall

burden of the disease. To date, results of epidemiological studies have demonstrated inverse

associations [130-133], positive associations [134-138], no association [139-143], U- and J-

shaped associations [144-146], as well as sex-specific associations [147-151] between caffeine

intake and CVD. For example, a recent dose-response, meta-analysis on habitual coffee

consumption and the risk of heart failure found that consuming 4 servings of coffee per day is

24

inversely associated with the risk of heart failure [152]. Another meta-analysis of 21 prospective

cohort studies conducted between 1966 and 2008 showed no differences in the relative risks of

developing coronary artery disease between different levels of long-term coffee consumption

[8]. Instead, long-term coffee consumption was associated with a significant decrease in

coronary artery disease in both men and women at 10-year follow-up [8]. Although the results

of this meta-analysis are in agreement with previous meta-analyses on prospective cohort

studies, conflicting findings are seen in case-control studies [153]. Case-control studies

examining the association between caffeine intake and CVD often show a significant increase in

CVD risk as caffeine consumption increases [153]. The inconsistencies between prospective

cohort and case-control studies may be due to the difference in the duration of time between

exposure and outcome. Studies show that caffeine consumption is more likely to elicit an acute

onset cardiovascular event [134, 154, 155], and does not contribute to the development of

chronic CVD risk factors such as diabetes [156, 157] or atherosclerosis [139, 151]. Therefore,

short-term case-control studies are more likely to capture the acute effects of caffeine leading

to a positive association between caffeine consumption and CVD development. In contrast, the

acute effects of caffeine are attenuated or less pronounced in prospective studies of long-term

coffee consumption. Happonen et al. [135] showed that the risk of acute coronary events

decreased in men consuming moderate amounts of coffee at 14 year follow-up compared to 5

year follow-up. In addition to differences in study design, other associated lifestyle factors such

as smoking and chronic alcohol abuse may also contribute to the overall risk for CVD. Kleemola

and colleagues noted that the increase in mortality from coronary heart disease in men who are

heavy coffee drinkers is mainly attributed to the effects of smoking and high serum cholesterol

25

levels [146]. Adjustments for these factors resulted in lower risk ratios of CVDs in heavy coffee

drinkers compared to non-drinkers [146]. Therefore, inadequate adjustments for confounding

factors may have also lead to discrepancies in studies on caffeine and CVD.

1.4.1.1 Caffeine, CYP1A2, and Cardiovascular Diseases

Genetics is another major contributing factor towards the variability seen in caffeine

consumption and CVD risk. The influence of genetic variation in CYP1A2 on coffee consumption

and the risk of myocardial infarction (MI) was studied by Cornelis et al. [10] in a Costa Rican

population. Results of this case-control study showed that subjects with the slow metabolizer

genotype were at an increased risk of developing MI as they consumed 2 or more cups of

coffee per day. In comparison, subjects that metabolize caffeine at a faster rate (fast

metabolizers) experienced a protective effect as they consumed coffee at a moderate level (1-3

cups/d)[10]. The authors suggested that the increased risk among slow metabolizers can be

attributed to caffeine alone as variation in CYP1A2 genotype has not been associated with any

potential lifestyle confounders, and caffeine is the sole substance in coffee that is metabolized

via CYP1A2 [10]. The results of this study were supported by another study examining the

modifying effect of CYP1A2 genotype on coffee consumption and the risk of hypertension [11].

Similarly, this prospective study showed that slow caffeine metabolizers were at an increased

risk of developing hypertension compared to fast metabolizers with increasing coffee

consumption [11]. The protective effect of moderate coffee consumption on CVD risk in the fast

metabolizers seen in both studies could be explained by the unmasking of beneficial

compounds such as antioxidants in coffee [158]. These two studies also suggest that variation in

26

CYP1A2 indicates that the associations are not likely due to residual confounding since any

unaccounted for exposures associated with caffeine intake should be similar between fast and

slow metabolizers. Therefore, it is important to stratify subjects by CYP1A2 genotype in studies

involving caffeine.

1.4.2 Caffeine and Parkinson’s disease

Although the effect of caffeine on CVD risk is debatable, caffeine consumption has

been associated with significant decreases in other chronic disease such as Parkinson’s disease

(PD). An inverse association between caffeine and PD has been observed in several

epidemiological studies [159-161]. PD is a neurodegenerative disorder resulting from the death

of dopaminergic neurons in the brain [162]. Current research on PD has put a strong emphasis

on identifying adenosine A2A receptor antagonists, such as caffeine, that may have the potential

to delay dopaminergic neurodegeneration [162]. A 30-year follow-up study by Ross and

colleagues showed that high caffeine intake was associated with a significant decrease in the

development of PD [159]. Specifically, the risk of PD in men who consumed 28 oz or more

coffee per day was five times lower than men who consumed no coffee after adjusting for age

and smoking status [159]. The results of that study were supported by several large prospective

studies, which all consistently showed an inverse association between caffeine intake and the

risk of PD [161, 163].

1.4.2.1 Caffeine, CYP1A2, and Parkinson’s disease

Similar to caffeine’s relationship with CVD, differences in metabolism due to genetic

variation in CYP1A2 have also been studied in regards to PD. Since slow caffeine metabolizers

27

experience an extended duration of caffeine exposure, the neuroprotective effects of caffeine

would also be expected to be more pronounced in slow caffeine metabolizers. To date, studies

have not shown any significant differences between slow and fast metabolizers on the

protective effects of caffeine on PD [164, 165]. However, the number of studies on caffeine,

CYP1A2, and PD is limited and adjustments for confounders are also lacking in these studies.

For instance, Popat et al. [164] did not adjust for smoking status, which is a known inducer of

CYP1A2 that can increase the efficiency of caffeine metabolism in certain individuals [93].

Therefore, further understanding of the pathways and mechanisms behind caffeine’s effect on

PD risk is warranted.

1.4.3 Caffeine and Breast Cancer

The association between caffeine and breast cancer is also unclear. Some studies show a

lower risk for breast cancer with coffee consumption [166-168], while others suggest no

association [169, 170]. For instance, a case-control study of 1,932 breast cancer cases and 1,985

controls reported that caffeinated-coffee consumption was associated with a 40% decrease in

breast cancer risk [166]. In contrast, another prospective cohort study from the Women’s

Health Study showed no association between breast cancer and coffee consumption [170].

However, because breast cancer is a multifactorial disease with complex etiology [171], many

potential confounders exist making studies on breast cancer and caffeine intake challenging.

The mechanism by which caffeine may affect breast carcinogenesis is also uncertain as caffeine

has been shown to stimulate and suppress the development of mammary tumors in animal

studies [172]. Furthermore, there are also no randomized controlled trials on caffeine

28

consumption and breast cancer as long-term caffeine consumption is difficult to measure and

the latency period for breast cancer is also variable [173, 174].

1.4.3.1 Caffeine, CYP1A2, and Breast Cancer

The modifying effect of CYP1A2 genotype on caffeine intake and health has also been

reflected in breast cancer development. A cross-sectional study of 195 women showed that

CYP1A2 activity is associated with several breast cancer risk factors including free estradiol

levels, blood lipids, sex hormone binding globulin levels, and body size [175]. Although this

study suggested that increased CYP1A2 activity is positively associated with many factors that

increase breast cancer risk, the level of CYP1A2 enzyme activity was measured instead of

stratifying subjects based on genotype [175]. To further understand the role of CYP1A2 in

breast cancer development, Kotsopoulus et al. [13] utilized a genetics approach and reported

that carriers of the BRCA1 mutation were 64% less likely to develop breast cancer if they have

the slow metabolizer genotype and consumed coffee regularly. However, no protective effect

was observed among fast metabolizers. Another study on breast volume and CYP1A2 genotype

also observed that moderate to high coffee consumption in women who are slow caffeine

metabolizers is associated with lower breast volume [176]. Previous studies have shown that

small breast size in lean women is associated with a decreased risk of breast cancer [177, 178].

Overall, results from the aforementioned studies have demonstrated the protective effect of

regular coffee consumption on breast cancer development, which is primarily restricted to slow

caffeine metabolizers [176]. In contrast, there are also studies showing no association between

CYP1A2 genotype, coffee consumption and breast cancer risk. For example, a recent case-

29

control study by Lowcock et al., [179] found that the rs762551 SNP in CYP1A2 did not modify

the association between coffee or caffeine intake and breast cancer risk. Therefore, the

relationship between caffeine and breast cancer remains uncertain and requires further

investigation.

1.4.4 Caffeine and Type 2 Diabetes

Another beneficial health effect associated with coffee consumption is the decreased

risk of type 2 diabetes [180]. A prospective study in Germany showed that in comparison to

drinking one cup of coffee per day, coffee consumption of more than four cups per day was

associated with a decreased risk of type 2 diabetes and did not increase the risk for CVD or total

cancer risk [181]. Results from a meta-analysis also showed that coffee consumption is

inversely associated with type 2 diabetes [180]. However, the observed decrease in diabetes

risk was similar for both caffeinated and decaffeinated coffee suggesting that constituents of

coffee other than caffeine, such as antioxidants and minerals, may be responsible for this effect

[182].

1.4.5 Caffeine and Health Summary

In summary, the pharmacological effects of caffeine have influences over multiple

systems in the body. The acute effects of caffeine may be harmful towards cardiovascular

health while long term consumption has been shown to be protective against PD and breast

cancer risk, which may be modulated by CYP1A2 genotype. Inconsistent results from the many

epidemiological studies of caffeine and disease risk demand further research into the

underlying mechanisms and pathways, which may help to clarify the relationships between

30

caffeine and disease outcome. The following section on proteomics describes one such

approach towards the understanding of biological processes related to caffeine.

1.5 Proteomics – an Approach towards Understanding the Influence of Diet on Health

Nutrition plays an influential role in the early stages of disease development, but the

underlying mechanisms and biological pathways are not well understood. The complexity of

diet and its bioactive components make identifying the roles and actions of food components in

the body a challenging task; yet traditional experimental methods are too limited in their ability

to study the interactions between diet and disease [183]. The emerging field of nutrigenomics,

which combines genetics techniques with ‘omics’ research (proteomics, metabolomics),

presents a significant advantage towards understanding the complex relationships between

diet and disease on the biochemical and molecular level [184]. Specifically, proteomics has

been used as a strategy for studying the expression of multiple proteins at a given time point

and characterizing the status of cells, tissues, and organs under specific biological conditions

[185]. This type of approach is appropriate for nutrition research as it examines the functional

product (protein) of gene expression rather than simply measuring the transcriptional status of

DNA via methods such as gene arrays [184]. Therefore, proteomics is also useful towards

evaluating the consequences of dietary interventions.

1.5.1 Proteomics and Nutrition Research

Protein expressions within an organism are often influenced by the interactions

between the genome and the environment such as diet [183]. Proteomics, as a quantitative

technique, is a preferred approach for determining the effects of such influences [183]. Plasma,

31

lymphocytes, and platelets, can all be easily extracted from the blood for analysis [186]. The

proteome is a full collection of proteins expressed by the human genome and the plasma has

been described as “the largest version of the human proteome in one sample” [187]. The

plasma contains more than 106 different molecules representing the products of approximately

30,000 genes [187]. Therefore, the human plasma provides an invaluable resource for the

identification of disease markers and pathways [188]. Biomarkers are important for health

research as they reflect the adequacy of nutrition, monitor the progression of disease, and

detect responses to dietary or medicinal interventions [183].A proteomics approach was also

used in a study examining the effects of fish oil, conjugated linoleic acid, and elaidic acid on

lipid and glucose metabolism [189]. Using an animal model, this study found that

supplementing dietary fatty acids decreased plasma cholesterol levels; however, the level of

insulin did not increase consistently across all three types of fatty acid supplementation [189].

This study also identified 65 cystolic and 8-membrane proteins that are significantly regulated

in pathways related to lipid and glucose metabolism as well as oxidative stress, which

demonstrates another advantage of using proteomics in nutrition research [189]. This study

showed that the use of proteomics along with biological standards was beneficial towards

gaining a deeper understanding of the mechanisms behind dietary fatty acid intake and lipid

metabolism.

1.5.2 Proteomics – the Search for Biomarkers

In addition to assessing dietary interventions, changes in plasma protein concentrations

are also reflective of health conditions and disease risk [184]. The composition or concentration

32

of plasma proteins is thought to differ under various physiological conditions such that most

abnormalities of the body are reflected by the plasma proteome [187]. Therefore, the

comparison of a panel of independent disease-related proteins is especially useful towards the

evaluation of disease risk and health response. Studies searching for biomarkers of CVD have

identified a wide array of proteins including C-reactive protein and homocysteine that are

closely associated with CVD risk and outcomes [187]. In addition to identifying traditional or

well-known biomarkers [190], proteomics is also a useful tool for the discovery of novel links

between less studied plasma proteins and various health conditions. For example, a cross-

sectional study on hormonal contraceptive use and the plasma proteome identified several

proteins that differed in concentration between users and non-users that have not been

examined in previous research [191]. These proteins belong to various physiological pathways

that may be involved in inflammation, coagulation and blood pressure [191]. Therefore, in

addition to the traditional effects of hormonal contraceptives on the cardiovascular system, this

study provided insight into novel pathophysiologic effects of hormonal contraceptive use [191].

Studies examining the association between dietary intakes of vitamin C [192] and vitamin E

[193] and disease risk have also identified plasma proteins from several different physiological

pathways that are associated with these vitamins. In addition to vitamin C’s anti-inflammatory

properties and its association with acute-phase proteins [194], vitamin C was found to be

inversely related to circulating levels of vitamin D binding protein [192]. Therefore, with the aid

of proteomics, vitamin C is suggested to have unknown roles in pathways related to vitamin D

physiology [192]. Furthermore, proteomics has also been used in cancer detection [195] and

the identification of new biomarkers related to cardiometabolic disease [187].

33

1.5.3 Proteomics Techniques

Since a comprehensive analysis of proteins in the body can help further elucidate the

biological roles of proteins and identify changes in physiological pathways triggered by food

components in the diet, it is important to understand the nature of techniques employed in

proteomics research [184]. Traditional methods such as two-dimensional (2D) gel

electrophoresis followed by mass spectrometry are widely used in proteomic studies [196].

These methods play an important role in the identification of proteins as they can provide a

physical separation of polypeptides, which gives information on the molecular weight, iso-

electric point, and other parameters that are characteristic of the protein [196]. However, 2D

gel electrophoresis is unable to capture the dynamic range of protein concentrations in the

human plasma [184]. Protein concentrations in the plasma can vary up to 10 orders of

magnitude in humans [184]. For example, the concentration of albumin in the plasma is ~5.5 x

1010 pg/ml while the concentration of interleukin-6, a cytokine involved in inflammation, is ~1-5

pg/ml [187, 197]. Thus, the sensitivity of traditional proteomic methods may be limited and

insufficient for measuring the effects of certain food components on the plasma proteome.

Newer technologies have been developed in recent years, which greatly advanced the

field of proteomics. These advancements have helped identify many clinically useful biomarkers

in a sensitive and specific manner. In particular, mass spectrometry-based multiple reaction

monitoring (MRM), is one such method that enables the targeted quantification and profiling of

several proteins in complex biological samples [198]. MRM involves a two-stage filtering

process starting with the ionization and fragmentation of all peptides in a given sample. After

34

peptide fragmentation, the second filtering process only allows for a small number of

sequence-specific fragment ions to be mass-analyzed instead of a full scan of all possible

peptide fragments as usually completed in traditional mass spectrometry methods [199, 200].

With the addition of stable isotope labeled peptides as synthetic internal standards, the

concentration of the protein of interest can be measured by comparing signals from exogenous

labeled and endogenous unlabeled fragments [199, 200]. Because the exogenous (synthetic

internal standards) and endogenous (protein of interest) fragments have the same

physiochemical properties, they will only differ by mass [199, 200]. Therefore, unlike traditional

mass spectrometry methods, MRM is highly selective and allows for the identification for

specific peptides or protein fragments of interest. This method can be used to determine the

absolute concentration of peptides across a dynamic range of up to 104 [199, 200].

1.5.4 The Proteomics Panel

In the current project, mass-spectrometry based MRM was used to measure the

concentration of 54 high-abundance plasma proteins representing molecules from various

physiological pathways (Table 1-4). Previous research showed that the concentration of these

proteins vary across ethnic groups and those with different dietary habits [16]. Therefore, it

was of interest to see whether caffeine intake and variation in CYP1A2 also influence the

concentration of these proteins.

35

Table 1-4: Proteomics Panel – 54 High Abundance Plasma Proteins

Hemostasis Lipid metabolism Amyloid diseases Innate immunity Transmembrane

transport Signal transduction Other

Plasminogen Apolipoprotein A-IV Apolipoprotein A-IV Complement factor B Transferrin Plasminogen Alpha-1B-

glycoprotein Coagulation factor XIIa HC Apolipoprotein C-III Plasminogen Complement factor H Apolipoprotein A-I Vitronectin Alpha-2-antiplasmin

Alpha-1-Anti-trypsin Vitamin D-binding

protein Serum amyloid P-

component ComplementC9 Albumin Angiotensinogen

Alpha-1-antichymotrypsin

Fibrinogen gamma chain Apolipoprotein A-II

precursor Prothrombin

Complement C4 beta chain

Ceruloplasmin Prothrombin Adiponectin

Fibrinogen beta chain Albumin Transthyretin L-selectin Complement C3 Afamin

Kininogen-1 Apolipoprotein E Apolipoprotein A-I Complement C3 Fibrinogen gamma chain Alpha-1-acid

glycoprotein 1

L-selectin Angiotensinogen Gelsolin, isoform 1 Fibronectin Alpha-2-HS-glycoprotein

Complement C1 inactivator Apolipoprotein A-I Fibrinogen alpha chain Alpha-2-macroglobulin Apolipoprotein A-IV

Apolipoprotein B-100 Apolipoprotein B-100 Fibrinogen beta chain Apolipoprotein C-I

Transferrin Kininogen-1 Apolipoprotein C-III

Clusterin Fibrinogen alpha chain Apolipoprotein D

Albumin Apolipoprotein L1

Prothrombin Zinc-alpha-2-glycoprotein

Antithrombin-III Beta-2-glycoprotein I

Apolipoprotein A-I Complement C4

gamma chain

Fibronectin Vitamin D-binding

protein Histidine-rich glycoprotein Fibrinopeptide A

Alpha-2-macroglobulin Heparin cofactor II

Alpha-2-antiplasmin Haptoglobin beta

chain

Fibrinogen alpha chain

Hemopexin Inter-alpha-trypsin

inhibitor HC Vitronectin

36

1.6 Hypothesis, Objectives, and Thesis Organization

The main goal of the research presented in the following chapters is to further

understand the effect of caffeine intake on biological pathways in the body. A proteomics

approach is used to identify proteins in the plasma that may be influenced by different levels of

caffeine intake. Since the rs762551 SNP in CYP1A2 is known to modify caffeine metabolism, it is

also of interest to determine whether slow and fast caffeine metabolizers exhibit differences in

plasma proteomic profiles with different levels of caffeine consumption. The results of this

research may help identify potential biomarkers in disease-related pathways that are

associated with caffeine intake. Overall, it is hypothesized that different levels of caffeine intake

are associated with differences in the plasma proteome, which may be modified by the

rs762551 SNP of CYP1A2.

Objective 1 (Chapter 2): To determine the association between caffeine intake and the plasma

proteome, and whether the CYP1A2 polymorphism (rs762551) modifies any associations.

Objective 2 (Chapter 3): To determine whether any proteins associated with caffeine intake

from Objective 1 are modified by polymorphisms in other caffeine-related genes.

37

Chapter 2: Association between caffeine intake and the plasma

proteome in humans

2.1 Abstract

Caffeine intake has been associated with both an increased and decreased risk of various health

conditions. However, many of the physiological pathways affected remain unclear. CYP1A2 is

the major enzyme that metabolizes caffeine and a single nucleotide polymorphism (rs762551)

affects the rate of caffeine metabolism. The main objective of this study was to determine the

association between caffeine intake and the plasma proteome and whether CYP1A2 genotype

modifies any association. Subjects (n=1095) aged 20-29 years from the Toronto Nutrigenomics

and Health Study completed a 196-item semi-quantitative food frequency questionnaire and

provided a fasting blood sample from which DNA and plasma were obtained for genotyping and

proteomics analysis. Fifty-four proteins were measured by mass spectrometry multiple reaction

monitoring (MS-MRM). Subjects were categorized into three groups according to habitual

caffeine intake (<100 mg/d, 100-200 mg/d, and >200 mg/d) and stratified by CYP1A2 genotype.

Among carriers of the C allele (slow metabolizers), plasma concentrations of gelsolin isoform 1

were significantly (P<0.005) lower among those in the highest category of caffeine intake

compared to those with the lowest level of intake. No differences in protein concentration

were observed for AA homozygotes (fast metabolizers). These findings show that caffeine

intake is associated with lower gelsolin levels only among slow caffeine metabolizers, and

suggest that gelsolin might mediate some of the biological effects of caffeine.

38

2.2 Introduction

Caffeine (1, 3, 7-trimethyxanthine) is the most popular stimulant used around the world

and is found in many commonly consumed beverages [1]. More than 80% of the world’s

population consumes caffeine regularly [2]. Caffeine consumption has multiple systemic effects

and has been associated with several diseases including those of the cardiovascular,

neuropsychiatric, and endocrine systems [180, 201, 202]. Although numerous studies have

examined the effects of caffeine on the risk of various diseases, the results have been

inconsistent [134, 145, 203, 204]. Genetic variation that influences caffeine metabolism may

modify the association between caffeine intake and disease risk [10, 11].

Cytochrome P450 1A2 (CYP1A2) is responsible for the majority of caffeine metabolism

in the liver [48]. This enzyme is encoded by the CYP1A2 gene on chromosome 15q24.1 and

plays an important role in the metabolism of drugs, caffeine, and environmental toxins [9].

Over 95% of caffeine is metabolized through demethylation by CYP1A2 in the liver [56]. CYP1A2

activity is highly variable between individuals and is affected by both environmental and genetic

factors [74]. A common AC single nucleotide polymorphism (rs762551) in the CYP1A2 gene is

associated with decreased enzyme inducibility and activity [74]. Carriers of the C allele can be

considered ‘slow’ caffeine metabolizers and tend to break down caffeine at a slower rate [93].

Individuals who are homozygous for the A allele are ‘fast’ caffeine metabolizers (13), and may

be less susceptible to any effects of caffeine compared to slow metabolizers. Cornelis et al.,

showed that slow metabolizers are at an increased risk of myocardial infarction with increased

coffee consumption while fast metabolizers experienced a protective effect with moderate

39

coffee consumption [10]. Consistent with these findings, CYP1A2 genotype was shown to

modify the association between coffee consumption and risk of hypertension with slow

metabolizers having an increased risk of developing hypertension and fast metabolizers having

a lower risk [11].

Although CYP1A2 variants may modify the association between caffeine and

cardiovascular disease risk, the underlying mechanisms and physiological pathways affected by

caffeine are not well understood. Recent advances in proteomics, such as the development of

mass spectrometry-based multiple reaction monitoring (MRM), allow for the targeted

quantification of numerous high-abundance plasma proteins across a wide range of

concentrations simultaneously [198]. Many of these high-abundance plasma proteins belong to

disease-associated pathways and could be useful biomarkers of disease [16, 191]. Novel

proteomics techniques also provide a more comprehensive view of the plasma proteome,

which may enhance the ability to predict disease risk and evaluate the effects of dietary

exposures [16]. For example, a cross-sectional study on dietary patterns and the plasma

proteome reported that different dietary habits and ethnocultural groups have distinct plasma

proteomic profiles [16]. Such differences in plasma protein concentrations may be predictive of

future cardiometabolic disease risk [16]. Another study on hormonal contraceptive use and the

plasma proteome also identified significant differences in protein concentrations between

users and non-users [191]. Although there are no published data on caffeine intake and the

plasma proteome, one study did show that salivary protein profiles differed significantly

between subjects with different sensitivities to the bitter taste of caffeine [205]. Therefore, it is

likely that individuals consuming different amounts of caffeine also show variations in plasma

40

protein concentrations. Since the rs762551 variant in CYP1A2 has been shown to modify the

association between caffeine and disease risk, it is possible that it also modifies the association

between caffeine intake and plasma proteomic profiles. Accounting for genetic variation in

caffeine metabolism also minimizes the likelihood of any association being due to residual

confounding [158]. This comparison of plasma proteomic profiles between individuals with

different CYP1A2 genotypes consuming different amounts of caffeine may help uncover other

physiological effects of caffeine and identify proteins that may be regulated by caffeine intake.

The objectives of this study were to determine whether different levels of caffeine

intake are associated with different concentrations of 54 high-abundance plasma proteins in an

ethnically diverse population of healthy young adults, and whether CYP1A2 genotype modifies

any associations observed.

2.3 Materials and Methods

2.3.1 Study Population

Subjects were participants of the Toronto Nutrigenomics and Health Study (TNH),

which is a cross-sectional examination of an ethnically diverse group of men and women aged

20-29 years (n=1,639). Subjects were recruited from the University of Toronto between the fall

of 2004 and the fall of 2010. All subjects completed a one-month, 196-item semi-quantitative

Toronto-modified Willet food frequency questionnaire (FFQ) and a general health and lifestyle

questionnaire (GHLQ), and provided a venous blood sample after a 12 hour fast. Women who

were pregnant or breastfeeding, and individuals unable to provide a blood sample were

excluded from the study. Based on self-reported ethnicity, subjects were categorized into 4

41

ethnocultural groups: Caucasians (n=778), East Asians (n=565), South Asians (n=175) and Other

(n=119), as described previously [16]. Written consent was obtained from all subjects, and the

study was approved by the Research Ethics Board at the University of Toronto.

2.3.2 Physical Activity and Anthropometric Measurements

Physical activity, as measured by questionnaire, was expressed as metabolic

equivalent task (MET) hours per week [16]. Anthropometric measures including height and

weight were performed with subjects dressed in light clothing and without shoes as described

previously [16]. Resting systolic blood pressure, diastolic blood pressure and heart rate were

measured as described elsewhere [36].

2.3.3 Biochemical Measurements and Genotyping

Blood samples were collected from subjects after a 12-hour overnight fast and

analyzed at LifeLabs medical laboratory services (Toronto, ON, Canada). Biomarkers of glycemic

control, lipid metabolism, and the systemic inflammatory marker C-reactive protein (CRP) were

measured on-site following standard procedures as described elsewhere [206]. At the time of

participation, subjects were screened for potential acute inflammatory conditions and if such a

condition was found, a two-week recovery period was allowed before drawing blood. The

homeostasis model of insulin resistance (HOMA-IR) and beta-cell function (HOMA-β) were

calculated from previously established formulas [207]. DNA was extracted from fasting whole

blood samples using standard procedures [206]. Genotyping for the candidate CYP1A2

rs762551 polymorphism was completed by real-time PCR using TaqMan® SNP Genotyping

Assays from Applied Biosystems (Foster City, CA) as described previously [36].

42

2.3.4 Plasma Proteomics Measurements

We measured 54 high-abundance plasma proteins with coefficients of variation <15%,

as described previously [16]. In brief, a mass spectrometry-based multiple reaction monitoring

(MRM) assay was designed to measure high-abundance plasma proteins belonging to major

biological pathways including hemostasis, innate immunity, lipid metabolism, amyloid disease,

transmembrane transport, and signal transduction [187, 198, 208, 209]. Protein measurements

were completed at the Genome British Columbia Proteomics Centre at the University of

Victoria (Victoria, BC, Canada).

2.3.5 Statistical Analysis

All statistical analyses were completed using SAS (version 9.2; SAS Institute Inc, Cary,

NC, USA). During the time of study, proteomics analysis was completed for 1,116 out of 1,639

subjects from the TNH study cohort. Current smokers were excluded from the study as

chemicals in cigarette smoke induce CYP1A2 activity and increase caffeine clearance [210].

Furthermore, we excluded 7 subjects because of possible over- (>3,500 kcal/day for women,

>4,000 kcal/day for men) or under-reporting of energy intake (<800 kcal/day), 9 subjects

because of missing genotype data, and 5 subjects because of missing anthropometric or

biochemical data, which left 1,095 subjects in the final study sample. These subjects were

categorized into three levels of caffeine intake (<100 mg/d, 100-200 mg/d, and >200 mg/d).

Subject characteristics were compared across the three levels of caffeine intake using χ2 tests

for categorical variables and analysis of variance (ANOVA) for continuous variables. The

distribution of continuous variables was assessed prior to analysis and when necessary, loge or

square-root transformations were used to improve normality. The Tukey-Kramer procedure

43

was used when examining the differences in means across the three intake groups for all

comparisons. All reported P-values are two sided and considered significant when P<0.05.

Biomarkers of glycemic control and lipid metabolism were also compared amongst the three

intake groups using ANOVA.

A general linear model was used to examine associations between 54 plasma proteins

from the proteomics panel and the three categories of caffeine consumption. P-values from

models using transformed variables are reported, and untransformed mean plasma protein

concentrations and standard errors are reported for ease of interpretation. Covariates included

in the adjusted model (Model 2) were age, sex, ethnocultural group, oral contraceptive use

among women, BMI and energy intake. P-values from both the unadjusted model (Model 1)

and the adjusted model (Model 2) are reported. Significance of differences in means across the

three caffeine intake categories for each protein was determined using the Tukey-Kramer

procedure. The Bonferroni correction for multiple testing was also applied with a significance

level of P<0.0009 (54 tests; α = 0.05; P-value=0.05/54 tests). The same analyses were repeated

and stratified by ethnocultural group.

Interactions were tested between caffeine intake and CYP1A2 genotype on plasma

protein concentrations. For proteins that showed a significant P-value for the interaction term,

the associations between caffeine and those proteins were examined further after stratifying

by CYP1A2 genotype (A/A vs. A/C+C/C).

44

2.4 Results

The study population was divided into 3 groups according to levels of caffeine

consumption measured by the FFQ. Six hundred sixty subjects were in the lowest caffeine

consumption category (<100 mg/d), while 243 subjects and 192 subjects were grouped in the

100-200 mg/d and >200 mg/d intake categories, respectively (Table 2-1). Overall, fasting serum

insulin was significantly higher in subjects consuming <100 mg/d compared to the other two

categories. BMI and energy intake were significantly higher in subjects consuming >200 mg/d of

caffeine compared to the two lower levels of caffeine consumption. Both HOMA-IR and HOMA-

β were significantly higher among subjects consuming <100 mg/d of caffeine when compared

to the other categories of intake.

Initially, none of the 54 plasma proteins analyzed were significantly different between

caffeine intake categories in both the unadjusted and adjusted models (Table 2-2). After testing

for interactions between caffeine intake and CYP1A2 genotype on plasma protein

concentrations, there was a significant interaction only with gelsolin (P=0.04). Therefore,

gelsolin concentrations were compared between caffeine consumption categories after

subjects were stratified by CYP1A2 (rs762551) genotype. After stratification, there were 502

fast metabolizers (A/A) and 593 slow metabolizers (A/C or C/C). Among slow metabolizers,

mean plasma concentrations of gelsolin, were significantly lower for those consuming >200

mg/d caffeine compared to those consuming <100 mg/d. This finding remained significant after

Tukey-Kramer adjustment in Model 1 and Model 2 adjusted for age, sex, ethnocultural group,

BMI, energy intake and oral contraceptive use among women (p<0.004 and p<0.003,

respectively) (Figure 2-1). Significant differences in mean plasma gelsolin concentrations were

45

only observed among slow metabolizers and not among fast metabolizers. The entire study

population was stratified by ethnocultural group but no significant differences in mean protein

concentrations were found for any of the 54 plasma proteins (results not shown).

46

Table 2-1: Subject characteristics by categories of caffeine consumption1.

Caffeine consumption categories

Total population

n=1,095 <100 mg/d

n=660 100-200 mg/d

n=243 >200 mg/d

n=192 P-value2

Age, years 22.7 ± 2.5 22.4 ± 2.3a3 22.9 ± 2.5b 23.4 ± 2.7b <0.0001 Sex

Male 324 (29) 221 (68) 60 (18) 43 (14) 0.002

Female 771 (71) 439 (57) 183 (24) 149 (19) Ethnocultural Group

Caucasian 514 (47) 273 (53) 123 (24) 118 (23) <0.0001 East Asian 385 (35) 263 (68) 75 (19) 47 (12)

South Asian 116 (11) 68 (58) 36 (31) 12 (10) Other 80 (7) 56 (70) 9 (11) 15 (19) Body mass index, kg/m2 22.7 ± 3.5 22.6 ± 3.4a 22.6 ± 3.6a 23.5 ± 3.6b 0.003

Waist circumference, cm 73.8 ± 8.9 73.5 ± 8.8 73.5 ± 9.0 75.3 ± 8.8 0.16 Physical activity, MET-hours/week 7.6 ± 3.1 7.5 ± 3.2 7.9 ± 3.1 7.7±2.8 0.23

Systolic blood pressure, mm Hg 113.7 ± 11.3 113.8 ± 11.4 112.8 ± 11.5 114.4 ± 10.8 0.51 Diastolic blood pressure, mm Hg 68.9 ± 8.0 68.8 ± 7.9 68.9 ± 8.1 69.5 ± 8.4 0.92 Oral contraceptive use (among women)

No 537(70) 304 (69) 130 (71) 103 (69) 0.90 Yes 234 (30) 135 (31) 53 (29) 46 (31)

Total serum cholesterol, mmol/L 4.2 ± 0.8 4.2 ± 0.7 4.3 ± 0.8 4.3 ± 0.8 0.10 Serum HDL cholesterol, mmol/L 1.6 ± 0.4 1.5 ± 0.4 1.6 ± 0.4 1.6 ± 0.4 0.10 Serum LDL cholesterol, mmol/L 2.2 ± 0.6 2.2 ± 0.6 2.3 ± 0.7 2.3 ± 0.6 0.22 Serum ratio of total to HDL cholesterol 2.8 ± 0.8 2.8 ± 0.7 2.8 ± 0.8 2.8 ± 0.7 0.98 Triacylglycerides, mmol/L 1.0 ± 0.5 1.0 ± 0.5 0.9 ± 0.4 1.0 ± 0.5 0.83 Free fatty acids, umol/L 489.7 ± 244.3 491.5 ± 244.4 491.8 ± 239.7 481.5 ± 250.5 0.91 Fasting glucose, mmol/L 4.8 ± 0.4 4.8 ± 0.4 4.8 ± 0.4 4.7 ± 0.4 0.30

47

Fasting serum insulin, pmol/L 47.7 ± 29.3 49.1 ± 30.1a 46.6 ± 29.2b 44. 9 ± 27.0b 0.02

HOMA-IR 1.4 ± 0.9 1.5 ± 1.0a 1.4 ± 0.9ab 1.3 ± 0.9b 0.03 HOMA-β 108.9 ± 71.3 110.8 ± 66.6a 105.9 ± 78.0b 107.7 ± 79.3ab 0.04 Serum C-reactive protein, mg/L 1.3 ± 2.7 1.3 ± 2.9 1.1 ± 1.9 1.5 ± 2.8 0.87

Energy, kcal/day 1952.4 ± 634 1880.1 ± 604a 2060.4 ± 682b 2074.0 ± 672b <0.0001

Alcohol, g/day 5.0 ± 8.3 3.6 ± 5.5a 6.6 ± 9.2b 7.8 ± 12.9b <0.0001 1All values are mean ± standard deviation or n (%). 2P-value calculated from χ2 analysis for categorical variables and ANOVA for normalized continuous variables. 3Means with different superscript letters are significantly different from one another (P<0.05) after Tukey-Kramer adjustment.

48

Table 2-2: Mean plasma protein concentration by categories of caffeine consumption1.

Caffeine consumption categories P-value2

Plasma proteins, µmol/L <100 mg/d 100-200 mg/d >200 mg/d Model 1 Model 2

Alpha-1B-glycoprotein 1.65 ± 0.02 1.71 ± 0.03 1.69 ± 0.04 0.23 0.62 Alpha-2-antiplasmin 1.92 ± 0.02 1.95 ± 0.03 1.94 ± 0.03 0.68 0.78 Alpha-2-macroglobulin 5.91 ± 0.07 5.91 ± 0.11 5.77 ± 0.13 0.45 0.85 Alpha-1-Anti-trypsin 11.14 ± 0.12 11.11 ± 0.19 11.08 ± 0.20 0.97 0.28 Alpha-1-antichymotrypsin 3.37 ± 0.03 3.33 ± 0.05 3.44 ± 0.06 0.35 0.25 Adiponectin 0.06 ± 0.00 0.07 ± 0.00 0.07 ± 0.00 0.08 0.75 Afamin 0.26 ± 0.00 0.26 ± 0.00 0.26 ± 0.01 0.78 0.56 Alpha-1-acid glycoprotein 1 1.76 ± 0.03 1.75 ± 0.04 1.79 ± 0.05 0.78 0.33

Angiotensinogen 0.95 ± 0.03 1.00 ± 0.05 1.02 ± 0.05 0.29 0.55 Alpha-2-HS-glycoprotein 8.87 ± 0.08 8.84 ± 0.14 8.73 ± 0.16 0.63 0.35

Albumin 966.5 ± 5.8 951.4 ± 10.7 952.8 ± 11.7 0.23 0.44 Apolipoprotein A-I 43.20 ± 0.39 44.76 ± 0.69 44.41 ± 0.73 0.09 0.32 Apolipoprotein A-II precursor 24.95 ± 0.22 25.58 ± 0.38 25.73 ± 0.44 0.16 0.43 Apolipoprotein A-IV 1.42 ± 0.02 1.46 ± 0.03 1.42 ± 0.03 0.68 0.88 Apolipoprotein B-100 0.79 ± 0.01 0.82 ± 0.02 0.80 ± 0.02 0.29 0.26 Apolipoprotein C-I 3.17 ± 0.03 3.26 ± 0.05 3.33 ± 0.07 0.09 0.67 Apolipoprotein C-III 2.33 ± 0.03 2.49 ± 0.05 2.49 ± 0.07 0.01 0.12 Apolipoprotein D 0.34 ± 0.00 0.35 ± 0.01 0.34 ± 0.01 0.60 0.43 Apolipoprotein E 0.50 ± 0.01 0.50 ± 0.01 0.50 ± 0.01 0.99 0.99 Apolipoprotein L1 0.42 ± 0.01 0.42 ± 0.01 0.42 ± 0.01 0.93 0.33

Antithrombin-III 3.57 ± 0.02 3.57 ± 0.05 3.57 ± 0.05 1.00 0.93 Zinc-alpha-2-glycoprotein 1.05 ± 0.02 1.07 ± 0.03 1.03 ± 0.03 0.78 0.56 Beta-2-glycoprotein I 2.77 ± 0.02 2.84 ± 0.05 2.82 ± 0.05 0.47 0.71 Complement C1 inactivator 4.74 ± 0.05 4.70 ± 0.09 4.60 ± 0.09 0.34 0.57 Complement C3 19.77 ± 0.19 19.64 ± 0.32 19.60 ± 0.35 0.75 0.06 Complement C4 beta chain 1.46 ± 0.02 1.43 ± 0.03 1.45 ± 0.04 0.90 0.83 Complement C4 gamma chain 1.59 ± 0.02 1.58 ± 0.04 1.61 ± 0.04 0.89 0.98 Complement C9 2.70 ± 0.03 2.70 ± 0.05 2.74 ± 0.06 0.77 0.53 Complement factor B 1.46 ± 0.02 1.44 ± 0.03 1.46 ± 0.03 0.70 0.26 Complement factor H 0.60 ± 0.01 0.60 ± 0.01 0.61 ± 0.01 0.73 0.31 Clusterin 1.52 ± 0.01 1.52 ± 0.02 1.54 ± 0.02 0.77 0.79

Ceruloplasmin 2.31 ± 0.04 2.35 ± 0.06 2.41 ± 0.07 0.20 0.90 Vitamin D-binding protein 2.83 ± 0.03 2.86 ± 0.05 2.87 ± 0.05 0.76 0.85 Coagulation factor XIIa HC 0.26 ± 0.00 0.27 ± 0.01 0.29 ± 0.01 0.01 0.80 Fibrinogen alpha chain 12.16 ± 0.26 11.86 ± 0.34 12.30 ± 0.34 0.41 0.46 Fibrinogen beta chain 9.62 ± 0.17 9.51 ± 0.24 9.78 ± 0.24 0.41 0.60 Fibrinogen gamma chain 9.64 ± 0.19 9.45 ± 0.27 9.53 ± 0.23 0.75 0.43 Fibronectin 0.63 ± 0.05 0.59 ± 0.06 0.63 ± 0.05 0.29 0.46 Fibrinopeptide A 7.10 ± 0.13 7.07 ± 0.17 7.25 ± 0.17 0.34 0.71

49

Gelsolin, isoform 1 1.23 ± 0.01 1.21 ± 0.02 1.17 ± 0.02 0.03 0.05

Heparin cofactor II 0.69 ± 0.01 0.71 ± 0.01 0.72 ± 0.02 0.18 0.96 Haptoglobin beta chain 10.79 ± 0.22 10.44 ± 0.34 10.68 ± 0.38 0.84 0.19 Hemopexin 10.10 ± 0.09 10.23 ± 0.14 10.23 ± 0.17 0.64 0.31 Histidine-rich glycoprotein 1.33 ± 0.02 1.32 ± 0.03 1.32 ± 0.03 0.93 0.88 Inter-alpha-trypsin inhibitor HC 0.63 ± 0.01 0.62 ± 0.01 0.62 ± 0.01 0.52 0.41 Kininogen-1 2.14 ± 0.02 2.19 ± 0.04 2.21 ± 0.04 0.24 0.90 L-selectin 0.07 ± 0.00 0.07 ± 0.00 0.07 ± 0.00 0.26 0.14 Plasminogen 1.23 ± 0.01 1.24 ± 0.02 1.25 ± 0.02 0.61 0.91 Prothrombin 0.58 ± 0.00 0.58 ± 0.01 0.58 ± 0.01 0.84 0.88 Plasma retinol-binding protein 0.91 ± 0.01 0.94 ± 0.02 0.95 ± 0.02 0.15 0.40 Serum amyloid P-component 0.45 ± 0.01 0.44 ± 0.01 0.45 ± 0.01 0.53 0.62

Transferrin 12.57 ± 0.12 12.57 ± 0.20 12.63 ± 0.24 0.98 0.46 Transthyretin 5.74 ± 0.05 5.77 ± 0.09 5.75 ± 0.09 0.96 0.61 Vitronectin 3.71 ± 0.04 3.77 ± 0.06 3.74 ± 0.06 0.63 0.56

1Data presented are unadjusted plasma protein means ± standard errors. 2P-values from ANCOVA with log or square-root transformed plasma protein concentrations where necessary. Model 1 P-values are unadjusted while Model 2 P-values are adjusted for age, sex, ethnocultural group, oral contraceptive use among women, body mass index and energy intake.

HC: heavy chain; HS: Heremans–Schmid.

50

Figure 2-1: Mean plasma concentration of gelsolin, isoform 1 by caffeine consumption levels in

fast (A/A) and slow metabolizers (A/C and C/C).

A/A A/C+C/C1.0

1.1

1.2

1.3<100 mg/d

100-200 mg/d

>200 mg/d

CYP1A2 (rs762551)

Ge

lso

lin

, Is

ofo

rm-1

(µ

mo

l/L

)

a

ab

b

Unadjusted means and standard errors are shown. P-values were calculated with ANCOVA

using the transformed gelsolin variable and adjusted for age, sex, ethnocultural group, body

mass index, energy intake, and oral contraceptive use among women. In carriers of the C allele,

mean plasma gelsolin concentration is significantly lower in subjects consuming >200 mg/d of

caffeine compared to those consuming <100 mg/d (P=0.003). No significant differences in mean

plasma gelsolin concentration were seen in subjects homozygous for the A allele (P=0.8).

51

2.5 Discussion

Using a plasma proteomics approach, we undertook this study to examine whether

different levels of caffeine consumption are associated with plasma protein concentrations and

whether genetic variation affecting caffeine metabolism modifies any associations between

caffeine intake and the plasma proteome. The concentrations of 54 plasma proteins involved in

various physiological pathways were measured simultaneously in an ethnically diverse

population of healthy young adults. Initially, we found no significant differences in mean

plasma protein concentrations across the three levels of caffeine intake. However, because

genetic variation in CYP1A2 is associated with differences in caffeine metabolism, we further

stratified our study population by the rs762551 SNP in CYP1A2. We found a significant caffeine

x CYP1A2 interaction on plasma gelsolin concentrations where increased caffeine consumption

was associated with decreased plasma gelsolin only in subjects with the slow metabolizer

genotype.

In the present study population, the amount of caffeine intake differed significantly

across ethnocultural groups. A higher percentage of Caucasian subjects consumed >200 mg/d

of caffeine compared to East Asians, South Asians and others. This consumption pattern is in

agreement with the global trend of caffeine intake. Data from Europe and North America show

that 90% of adults consume caffeine on a daily basis, with an average intake of 227 mg/d [122,

211]. Furthermore, 75% of dietary sources of caffeine come from drinking coffee in the North

American diet [20]. In contrast, the preferred caffeinated beverage in East Asian countries such

as China and Japan is tea, which has a lower caffeine content compared to coffee on a per cup

basis [212].

52

We found no significant associations between caffeine intake and any of the plasma

proteins when the entire study population was analyzed. In order to account for the biologically

effective ‘dose’ of caffeine and any residual confounding, we tested for the caffeine x CYP1A2

interaction, and further stratified our analysis by the rs762551 SNP in CYP1A2. After stratifying

by CYP1A2 genotype, no significant differences in mean plasma protein concentrations were

observed between different levels of caffeine consumption among the fast metabolizers.

However, gelsolin isoform 1 (gelsolin) concentrations decreased significantly with increasing

caffeine intake in the slow metabolizers. In slow metabolizers, caffeine is metabolized less

efficiently and remains in the body for a longer period of time [213]. Therefore, the sustained

effects of caffeine may have contributed to the observed differences in plasma gelsolin

concentration in subjects with the slow metabolizer genotype. This observation in the slow

caffeine metabolizers supports findings from previous studies that some of the cardiovascular

effects of caffeine are modified by CYP1A2 genotype [10, 11].

The association between caffeine intake and gelsolin concentrations may offer insight

into the biological pathways affected by caffeine intake. Gelsolin is a multifunctional actin-

binding protein expressed both extracellularly in the plasma (isoform 1) and intracellularly in

the cytoplasm (isoform 2) [214]. Plasma gelsolin is secreted by most cell types in the body with

a higher amount being produced by smooth, skeletal, and cardiac muscles [215]. This protein is

known to cleave and scavenge circulating actin filaments, which helps to counteract the

potentially harmful effects of actin release during cellular injury [216]. Therefore, plasma

gelsolin has been described as a “debris cleaner” that may limit inflammation and reduce blood

clotting by preventing the polymerization of actin filaments [217]. In addition to its established

53

role as an actin remodelling protein, gelsolin is also involved in numerous physiological

processes [216]. Altered expression of gelsolin is linked to diseases such as amyloidosis [218],

cancer [219], and rheumatoid arthritis [220]. Furthermore, low plasma gelsolin levels are

associated with increased inflammation and poor prognosis of acute illnesses [221]. Therefore,

plasma gelsolin is suggested to be a prognostic biomarker for various health outcomes [222].

Interestingly, plasma gelsolin levels have also been shown to decrease shortly after acute

myocardial infarction [223]. Suhler et al., observed that plasma gelsolin decreased almost by

half of its normal level in several disease states associated with acute cellular injuries such as

myocardial infarction and hepatic failure [223]. A study on gelsolin and various types of heart

disease also showed that gelsolin levels decreased significantly in patients with acute coronary

syndrome [224]. Therefore, the observed decrease in plasma gelsolin concentrations in subjects

consuming the highest amount of caffeine suggests that gelsolin may be involved in pathways

linking caffeine consumption to cardiovascular disease.

There are several limitations to the present study. The study population consisted of

young adults that are generally healthy and are at low risk of chronic disease. Therefore,

plasma proteomic profiles of these subjects may have fewer deviations from normal and

potential biomarkers of disease risk may not be as evident. Because the majority of our study

population were not heavy caffeine consumers, a smaller range was used when defining the

three caffeine consumption categories. Thus, the effect of heavier caffeine consumption (e.g.

>400 mg/d) on plasma protein concentrations may not have been captured by the current

population.

54

To our knowledge, this is the first study to examine the association between caffeine

intake and the plasma proteome. Plasma gelsolin concentrations were inversely associated with

caffeine consumption in carriers of the C allele (slow metabolizer genotype) for rs762551 in

CYP1A2. Our study suggests that plasma gelsolin levels are affected by varying amounts of

caffeine and this association was unmasked after stratifying by CYP1A2 genotype. These

findings also suggest that caffeine may have physiological effects that impact disease-related

pathways involving gelsolin such as cardiovascular diseases, in a genetic subset of the

population with slow caffeine metabolism.

55

Chapter 3: Association between caffeine intake and plasma gelsolin

concentrations in other caffeine-related genes among slow

metabolizers of caffeine.

3.1 Abstract

Consuming higher amounts of caffeine may decrease plasma gelsolin levels; however,

this association was only observed among slow metabolizers of caffeine. The objectives of this

study were to determine whether the association between caffeine intake and plasma gelsolin

levels is more pronounced in slow metabolizers with polymorphisms in the gelsolin gene or

seven other caffeine-related genes. Subjects were from the Toronto Nutrigenomics and Health

Study and only those with the slow metabolizer genotype (CYP1A2 A/C +C/C, n=593) were

analyzed. Subjects were categorized into three groups according to habitual caffeine intake

(<100 mg/d, 100-200 mg/d, and >200 mg/d) and stratified by polymorphisms in the gelsolin

(GSN), aryl-hydrocarbon receptor (AHR), adenosine A2A receptor (ADORA2A), 5-HT2A serotonin

receptor (HTR2A), 2-adrenoceptor (ADRB2), dopamine D2 receptor (DRD2), catechol-O-

methyltransferase (COMT), and CYP1A1/1A2 genes. Overall, gelsolin concentrations were

inversely associated with increased caffeine intake. These associations were significant in

subjects with the GSN GG, AHR CT, CYP1A1/1A2 GG, HTR2A CT, ADRB2 GG, and DRD2 CT

genotypes (P<0.05). These findings suggest that other caffeine-related genes modify the inverse

association between caffeine intake and gelsolin concentrations.

56

3.2 Introduction

In Chapter 2, we observed that initially, caffeine intake showed no significant

association with any of the 54 plasma proteins when the entire study population was examined

as a whole. However, when we stratified subjects by CYP1A2 genotype, plasma gelsolin

concentrations decreased significantly with increased caffeine intake in the slow metabolizers.

One possible explanation for this decrease in gelsolin level could be due to a delay in caffeine

breakdown in those with the slow metabolizer genotype where it is more likely to exert a

biological effect. Since decreased levels of plasma gelsolin have been observed in cohorts with

heart disease [93], it is important to further understand caffeine’s association with this protein.

Within the slow metabolizers, stratification by polymorphisms in the gelsolin gene (GSN) and

other caffeine-related genes may allow us to identify further subgroups that display an even

greater responsiveness of caffeine on plasma gelsolin levels. These exploratory analyses will aid

in the identification of a specific subgroup (based on genotype) in the population that may be

more influenced by caffeine intake.

Previous research shows that genetics play a significant role in modulating one’s

response to caffeine intake. A common A>C single nucleotide polymorphism within CYP1A2, the

gene that codes for the primary enzyme responsible for caffeine metabolism (cytochrome P450

1A2), results in lower enzyme inducibility and impaired caffeine metabolism [10]. Therefore,

those with lower enzyme inducibility are slow metabolizers and tend to experience the effects

of caffeine for a longer period of time compared to fast metabolizers [158]. This difference in

the rate of caffeine metabolism also has health implications (as described in Chapter 2). In

57

addition to CYP1A2, variations in several other caffeine-related genes have been shown to

modify the acute effects, withdrawal symptoms, consumption behaviour, and disease risks

related to caffeine consumption. For example, the rs6968865 SNP in the aryl-hydrocarbon

receptor (AHR) gene has been associated with increased caffeine consumption [225]. AHR is an

inducer of CYP1A1 and CYP1A2, which modifies the rate of caffeine metabolism [96, 226]. A

study suggested that the AHR-T variant may be associated with enhanced CYP1A1 and CYP1A2

activities thereby increasing caffeine clearance, which may allow individuals to consume more

caffeine [225]. Furthermore, sequence variants within the CYP1A1 and CYP1A2 gene regions

have also been associated with caffeine consumption behaviours [225]. Genetic variation in

dopamine (DRD2) and serotonin (HTR2A) receptors may modify the effects of caffeine on

health as well especially for cardiovascular disease risk. Variation in the DRD2 gene has been

associated with blood pressure regulation [227] while polymorphisms in HTR2A have been

implicated in MI and stroke [228].

As caffeine’s main mechanism of action is through the antagonism of adenosine

receptors, genetic variations in the gene coding for the A2A receptor (ADORA2A) have influences

over caffeine consumption behaviours as well. For example, in a Costa Rican population, it was

shown that subjects homozygous for the ADORA2A T allele tend to consume more caffeine than

those who had the CC genotype [35]. In addition to ADORA2, polymorphisms in the 2-

adrenoceptor (ADRB2) and catechol-O-methyltransferase (COMT) have also been associated

with caffeine consumption behaviours and cardiovascular disease risk [36, 228].

58

The current study aims to further understand the association between caffeine intake

and gelsolin concentrations by examining the influence of polymorphisms in the

aforementioned genes on plasma gelsolin levels in slow metabolizers. Only the slow

metabolizers are examined as the association between caffeine and gelsolin was only observed

among this group of subjects. We also hope to identify specific genotypes that may have

influenced the inverse association between caffeine intake and gelsolin concentrations

observed previously.

3.3 Materials and Methods:

3.3.1 Study Population

Refer to Chapter 2 (Pg 48)

3.3.2 Biochemical Measurements and Genotyping

Refer to Chapter 2 (Pg 49)

In addition to genotyping the rs762551 polymorphism within CYP1A2, the rs1560980 SNP of

GSN and the following SNPs from 7 other caffeine-related genes were also genotyped as

described in Chapter 2 (Pg 49): rs4410790 (AHR), rs2470893 (CYP1A1/1A2), rs5751876

(ADORA2A), rs6313 (HTR2A), rs1042713 (ADRB2), rs6277 (DRD2), and rs4680 (COMT).

3.3.3 Plasma Proteomics Measurement – Gelsolin, Isoform 1

Refer to Chapter 2 (Pg 50)

59

3.3.4 Statistical Analysis

All statistical analyses were conducted in SAS (version 9.2; SAS Institute Inc, Cary, NC,

USA). Only subjects with the slow metabolizer genotype (A/C + C/C) as determined in Chapter 2

were included in the current study (n=593). Subjects were stratified by GSN genotype and

plasma gelsolin concentrations were compared between the three levels of caffeine intake

using ANCOVA. To improve normality, gelsolin concentrations were square-root transformed

prior to analysis. However, untransformed mean plasma gelsolin concentrations and standard

errors are reported for ease of interpretation. The Tukey-Kramer procedure was used to

determine the significance of differences in means across caffeine intake levels for each GSN

genotype. P-values shown are two sided with a significance level at P<0.05. Covariates included

in the model were age, sex, ethnocultural group, oral contraceptive use among women, BMI

and energy intake. The same analysis was repeated and stratified by ethnocultural group and

AHR, CYP1A1/1A2, ADORA2A, HTR2A, ADRB2, DRD2 and COMT genotypes. The interactions

between caffeine and ethnocultural group and each of the aforementioned genes were also

tested.

3.4 Results

In slow metabolizers, mean plasma gelsolin concentrations were compared across

three levels of caffeine intake with stratification by GSN genotype and seven other SNPs.

Subject characteristics as outlined in Table 3-1 were consistent with what was observed in

Chapter 2. The majority of subjects consumed caffeine at the lowest level of intake (<100

mg/d). Energy intake increased significantly with increased caffeine intake (P<0.0002). In

60

general, plasma gelsolin concentrations decreased with increased caffeine consumption (Table

3-2, Table 3-3). When taking ethnicity into consideration, our results showed a significant

decrease in plasma gelsolin for Caucasian subjects only (Table 3-2); however, the interaction

between caffeine and ethnocultural group was not significant (P<0.4). The decreasing trend in

plasma gelsolin levels was also significant for the GSN GG, AHR CT, CYP1A1/1A2 GG, HTR2A CT,

ADRB2 GG, and DRD2 CT genotypes (P<0.05); however, no significant interactions were

observed for any of these genes in relation to caffeine on gelsolin concentrations (Table 3-3).

61

Table 3-1: Subject characteristics by categories of caffeine consumption for slow caffeine metabolizers (A/C+CC)1.

Caffeine consumption categories

Total population

n=593 <100 mg/d

n=357 100-200 mg/d

n=146 >200 mg/d

n=90 P-value2

Age, years 22.6 ± 2.5 22.4 ± 2.4a3 22.6 ± 2.4a 23.5 ± 2.6b 0.0006 Sex

Male 189 (32) 128 (68) 35 (18) 26 (14) 0.003

Female 404 (68) 229 (57) 111 (27) 64 (16) Ethnocultural Group

Caucasian 265 (45) 142 (53) 69 (26) 54 (20) 0.0006 East Asian 206 (35) 139 (68) 44 (21) 23 (11)

South Asian 73 (12) 39 (53) 27 (37) 7 (10) Other 49 (8) 37 (76) 6 (12) 6 (12) Body mass index, kg/m2 22.8 ± 3.4 22.7 ± 3.3 22.5 ± 3.7 23.3 ± 2.9 0.11

Waist circumference, cm 74 ± 9.0 74.1 ± 9.1 73.2 ± 9.0 74.9 ± 8.4 0.31 Physical activity, MET-hours/week 7.5 ± 3.0 7.2 ± 3.1 7.9 ± 3.0 7.7±2.8 0.12

Systolic blood pressure, mm Hg 114.0 ± 11.5 114.1 ± 11.6 112.2 ± 11.2 116.2 ± 11.3 0.08 Diastolic blood pressure, mm Hg 68.9 ± 7.9 68.8 ± 7.5 68.7 ± 8.5 69.6 ± 8.4 0.86 Oral contraceptive use (among women)

No 473 (80) 290 (61) 114 (24) 69 (15) 0.53 Yes 120 (20) 67 (56) 32 (27) 21 (17)

Total serum cholesterol, mmol/L 4.2 ± 0.8 4.2 ± 0.7 4.3 ± 0.9 4.3 ± 0.8 0.33 HDL cholesterol, mmol/L 1.6 ± 0.4 1.5 ± 0.4 1.6 ± 0.4 1.6 ± 0.5 0.08 LDL cholesterol, mmol/L 2.2 ± 0.6 2.2 ± 0.6 2.3 ± 0.7 2.3 ± 0.6 0.77

Total to HDL cholesterol ratio 2.8 ± 0.8 2.9 ± 0.7 2.8 ± 0.8 2.8 ± 0.8 0.64 Triglycerides, mmol/L 1.0 ± 0.6 1.0 ± 0.6 0.9 ± 0.5 1.0 ± 0.5 0.64 Free fatty acids, umol/L 494.4 ± 240.4 489.7 ± 241.5 506.9 ± 242.6 492.8 ± 234.4 0.72 Fasting glucose , mmol/L 4.8 ± 0.4 4.8 ± 0.3 4.8 ± 0.4 4.7 ± 0.4 0.23 HOMA-IR 1.4 ± 0.9 1.5 ± 0.9 1.4 ± 0.9 1.3 ± 0.8 0.05

62

HOMA-β 107.4 ± 71.6 110.0 ± 66.0a 99.1 ± 64.3b 110.6 ± 98.6ab 0.01

Fasting serum insulin, pmol/L 47.7 ± 29.2 49.0 ± 29.5a 45.2 ± 29.3b 44. 8 ± 27.5b 0.10 Serum C-reactive protein, mg/L 1.3 ± 2.8 1.3 ± 3.1 1.1 ± 1.9 1.5 ± 2.9 0.75

Energy, calories/day 1936.5 ± 655 1866.1 ± 617a 2006.3 ± 668b 2202.8 ± 742b 0.0002

Alcohol, g/day 4.9 ± 7.7 3.5 ± 5.5a 6.3 ± 9.3b 7.8 ± 10.6b 0.0008 1All values are mean ± standard deviation or n (%). 2P-value calculated from χ2 analysis for categorical variables and ANOVA for normalized continuous variables. 3Means with different superscript letters are significantly different from one another (P<0.05) after Tukey-Kramer adjustment.

63

Table 3-2: Mean plasma gelsolin concentration by categories of caffeine consumption and

ethnocultural group amongst CYP1A2 slow metabolizers.

Data presented are unadjusted plasma gelsolin concentrations ± standard errors in subjects with slow

metabolizer genotype (A/C + C/C).

1P-values from ANCOVA with square-root transformed plasma gelsolin concentration. P-values are

adjusted for age, sex, oral contraceptive use among women, body mass index and energy intake.

2Reference gelsolin concentrations in CYP1A2 slow metabolizers (AC + CC).

3Plasma gelsolin concentrations are in µmol/L.

Caffeine consumption categories Ethnocultural group

n <100 mg/d 100-200 mg/d >200 mg/d P-value1 P-interaction

Slow metabolizers 593 1.24 ± 0.023a 1.19 ± 0.03ab 1.14 ± 0.03b 0.005 0.04 rs7625512

Caucasian 265 1.27 ± 0.03a 1.18 ± 0.04ab 1.13 ± 0.04b 0.01 0.97 East Asian 206 1.21 ± 0.02 1.19 ± 0.05 1.12 ± 0.05 0.20 Others 122 1.27 ± 0.04 1.21 ± 0.06 1.20 ± 0.11 0.64

64

Table 3-3: Mean plasma gelsolin concentration by categories of caffeine consumption and

genotypes of caffeine-related genes among CYP1A2 slow metabolizers.

Data presented are unadjusted plasma gelsolin mean concentrations ± standard errors in subjects with

slow metabolizer genotype (A/C + C/C).

1P-values from ANCOVA with square-root transformed plasma gelsolin concentration. P-values are

adjusted for age, sex, ethnocultural group, oral contraceptive use among women, body mass index and

energy intake.

2Reference gelsolin concentrations in CYP1A2 slow metabolizers (AC + CC).

3Plasma gelsolin concentrations are in µmol/L.

All Slow Metabolizers

Caffeine consumption categories

<100 mg/d (n) 100-200 mg/d (n) >200 mg/d (n) P-value1 P-interaction

1.24 ± 0.02a (362) 1.19 ± 0.03ab (148) 1.14 ± 0.03b (83) 0.005 0.04 Gene and SNPs Genotype (n)

GSN GG (564) 1.26 ± 0.023a (344) 1.20 ± 0.03ab (141) 1.17 ± 0.03b (79) 0.01 0.26 rs1560980 CG/CC (28) 0.91 ± 0.12 (15) 0.95 ± 0.15 (7) 0.69 ± 0.12 (6) 0.87 AHR CC (160) 1.22 ± 0.03 (96) 1.18 ± 0.06 (42) 1.17 ± 0.07 (22) 0.35 0.51 rs4410790 CT (295) 1.26 ± 0.02a (180) 1.16 ± 0.04ab (71) 1.12 ± 0.04b (44) 0.006 TT (136) 1.23 ± 0.03 (82) 1.27± 0.05 (34) 1.15 ± 0.05 (20) 0.54 CYP1A1/1A2 AA/AG (92) 1.27 ± 0.06 (50) 1.13 ± 0.04 (25) 1.17 ± 0.08 (17) 0.08 0.53 rs2470893 GG (500) 1.24 ± 0.02a (305) 1.20 ± 0.03ab (123) 1.13 ± 0.03b (72) 0.02 ADORA2A CC (166) 1.21 ± 0.03 (110) 1.17 ± 0.03 (32) 1.14 ± 0.06 (24) 0.34 0.65 rs5751876 CT (309) 1.26 ± 0.02 (177) 1.21 ± 0.04 (83) 1.16 ± 0.04 (49) 0.06 TT (114) 1.26 ± 0.03 (66) 1.16 ± 0.04 (32) 1.09 ± 0.07 (16) 0.05 HTR2A CC (153) 1.25 ± 0.03 (86) 1.23 ± 0.07 (38) 1.16 ± 0.05 (29) 0.36 0.90 rs6313 CT (299) 1.22 ± 0.02a (179) 1.15 ± 0.03ab (78) 1.12 ± 0.05b (42) 0.03 TT (137) 1.27 ± 0.03 (91) 1.25 ± 0.04 (30) 1.15 ± 0.06 (16) 0.27 ADRB2 GG (195) 1.26 ± 0.03a (120) 1.13 ± 0.04ab (47) 1.09 ± 0.06b (28) 0.01 0.43 rs1042713 GA (259) 1.23± 0.02 (149) 1.23 ± 0.05 (67) 1.19 ± 0.05 (43) 0.71 AA (120) 1.22 ± 0.03 (77) 1.18 ± 0.04 (27) 1.08 ± 0.06 (16) 0.06 DRD2 CC (297) 1.20 ± 0.02 (194) 1.17 ± 0.04 (66) 1.19 ± 0.05 (37) 0.64 0.19 rs6277 CT (195) 1.28± 0.03a (108) 1.19 ± 0.04ab (51) 1.11 ± 0.05b (36) 0.02 TT (81) 1.29 ± 0.05 (45) 1.23 ± 0.09 (24) 1.14 ± 0.08 (12) 0.15 COMT GG (211) 1.23 ± 0.02 (130) 1.26 ± 0.05 (52) 1.12 ± 0.06 (29) 0.11 0.04 rs4680 GA (269) 1.25± 0.03 (159) 1.18 ± 0.04 (63) 1.11 ± 0.04 (47) 0.08 AA (111) 1.26 ± 0.04 (66) 1.09 ± 0.04 (32) 1.28 ± 0.09 (13) 0.14

65

3.5 Discussion:

Within slow metabolizers, plasma gelsolin concentrations were stratified by the

rs1560980 SNP in GSN. We expected the association between caffeine and gelsolin to be more

significant in this subgroup of subjects who were slow metabolizers as there was no influence

of caffeine on gelsolin concentrations among fast metabolizers. Subjects homozygous for the G

allele in the GSN gene continued to show decreased plasma gelsolin with increased caffeine

intake and a significant difference was seen between subjects consuming >200 mg/d of caffeine

and those consuming <100 mg/d. No significant differences in plasma gelsolin concentrations

were found in carriers of the C allele; however, this could be due to the low sample size (n=28)

in this subgroup. Although a similar trend in gelsolin concentrations was observed, this

difference in protein concentration between the three levels of caffeine intake was not more

significant than our previous analysis described in Chapter 2. We then stratified our analysis by

ethnocultural group and observed that gelsolin concentrations were inversely associated with

caffeine intake in Caucasians and East Asians, but the association was only significant among

Caucasians. In the present study, East Asians tended to have lower plasma concentrations of

gelsolin compared to Caucasians, which is consistent with results from a previous study on

hormonal contraceptive use and the plasma proteome [191]. Although it is possible that the

association between caffeine consumption and plasma gelsolin is modified by ethnicity, the

interaction between caffeine and ethnocultural group on gelsolin concentrations in the current

analysis was not significant.

66

In the current study, although there were no significant caffeine-gene interactions, we

were still interested in stratifying our subjects by genotype in order to identify the association

between caffeine and gelsolin within SNPs in the AHR, ADORA2A, HTR2A, ADRB2, DRD2, COMT

and CYP1A1-1A2 gene regions. A significant inverse association between caffeine intake and

gelsolin was seen in the heterozygotes (CT genotype) at rs4410790 within the AHR gene. The

aryl-hydrocarbon receptor encoded by AHR is involved in the metabolism of xenobiotics and

the transcriptional regulation of CYP1A1 and CYP1A2 [226, 229]. A genome-wide association

study on habitual caffeine consumption showed that the rs4410790 SNP in AHR is associated

with caffeine intake [96]. Furthermore, a study based on a Costa Rican population found that

heavy caffeine consumers are likely to be carriers of the C allele for the rs4410790 SNP in AHR

[97]. In the current study, although more subjects across all the genotypes seem to be

consuming caffeine at the lowest intake category, the study population and the range of

caffeine intake are very different from those studied in the Costa Rican population. These

differences may have resulted in discrepancies between current findings and those of the Costa

Rican study in regards to AHR’s association with caffeine intake.

In addition to CYP1A2, CYP1A1 also has a role in caffeine metabolism [56]. In the current

study, subjects homozygous for the G allele at rs2470893 within the CYP1A1-1A2 gene region

showed a significant inverse association between caffeine intake and plasma gelsolin

concentrations. Similar to AHR, polymorphisms within the CYP1A1-1A2 gene regions have been

associated with caffeine consumption in GWAS as well [96, 97]. CYP1A1 is co-regulated with

CYP1A2 and encodes the aromatic hydrocarbon hydroxylase enzyme, which is involved in the

metabolism of benzo(a)pyrene found in coffee [230, 231]. A study by Terry et al., suggested

67

that polymorphic variants in CYP1A1 may modify the association between caffeine and ovarian

cancer in women [231]. In the current study, plasma gelsolin concentrations decreased

significantly as caffeine intake increased in subjects homozygous for the G allele in the CYP1A1-

1A2 gene region. Since plasma gelsolin levels are associated with several types of cancer

including ovarian cancer [232], it may be of interest for future research to investigate whether

SNPs within the CYP1A1-1A2 region modify the association between caffeine intake and ovarian

cancer.

Subjects homozygous for the G allele in the ADRB2 gene demonstrated the greatest

range of decrease in gelsolin levels with increased caffeine intake. The 2-adrenoceptor

(ADRB2) is a G-protein coupled receptor that plays a role in mediating the actions of

catecholamines in different tissues [233, 234]. Several polymorphisms have been identified

within ADRB2 including the nonsynonymous Gly16Arg [235].This polymorphism involves a G to

A single nucleotide substitution at position 46 of the ADR2 gene (A46G), and has been

associated with altered receptor function [236]. Studies show that GG homozygotes had

reduced resting ventricular function and attenuated vasodilatation compared to individuals

homozygous for the A allele [237, 238]. Some epidemiological studies on blood pressure and

cardiovascular health also found significant associations between the A46G polymorphism of

ADRB2 and blood pressure regulation. A cross-sectional study by Castellano et al.,[239] noted

that in a subgroup of younger individuals (below 50 years old), GG homozygotes had higher

systolic blood pressure compared to AA homozygotes. More recently, a meta-analysis on

polymorphisms in ADRB2 and the risk of essential hypertension also showed an association

between A46G and blood pressure; however, this association was only significant in a mixed

68

African population [240]. Although there is no evidence or support for a relationship between

ADRB2 A46G and plasma gelsolin levels, it is still interesting to see that within the slow

metabolizers – those that are more prone to the cardiovascular effects caffeine, GG

homozygotes showed the greatest decrease in gelsolin concentrations with increased caffeine

consumption. This observation may have implications for cardiovascular risk in slow

metabolizers and future research is needed. It is also of interest to note that polymorphisms

within ADRB2 have been associated with increased systolic blood pressure in response to

caffeine intake as well [241].

Polymorphisms within HTR2A and DRD2 were also associated with a modest, but

significant decrease in plasma gelsolin concentrations with increased caffeine consumption. 5-

HT2A (encoded by HTR2A), is a serotonin receptor widely expressed throughout the brain [242].

Caffeine intake has been associated with the up-regulation of 5-HT2A in the brain [243, 244],

which increases serotonin release [245]. Therefore, serotonin receptors such as 5-HT2A, are

considered as possible downstream targets of caffeine [243, 244], and may explain caffeine’s

influence on mood and the cardiovascular system. Similar to the other genes mentioned earlier,

several polymorphisms have been identified within the HTR2A gene as well.The most studied

variant of HTR2A is a single nucleotide substitution from C to T (rs6313) at position 102 that

results in a synonymous polymorphism [246, 247]. In addition to its association with

schizophrenia, this T102C polymorphism has also been associated with CVD. For example,

individuals with the TT genotype have been found to have higher platelet aggregation [248,

249], and have been associated with the development of MI [250] as well as the metabolic

syndrome [251]. In contrast, another study looking at the genetic determinants of coffee

69

consumption and risk of MI reported that subjects with the combined -163C allele for CYP1A2

and CC genotype for HTR2A are at a higher risk for MI with increasing consumption of coffee

[228]. The authors suggested that individuals with the slow metabolizer genotype may be at

increased risk of CVD via mechanisms promoting blood pressure increase and the serotonergic

system [228]. However, in the current study, the significant decrease in gelsolin concentration

was only noted in CT heterozygotes and not in either homozygote. This observation is likely due

to the much higher number of subjects with the CT genotype in the study population.

The DRD2 gene codes for the dopamine D2 receptor, which is found in the brain, spinal

cord, and the systemic arteries [252]. D2 receptors co-localize and interact with A2A receptors

such that they can counteract the activation of adenylate cyclase by the A2A receptor [253-255].

Because of these interactions, adenosine A2A receptor antagonists such as caffeine can trigger

the activation of D2 receptors and lead to the potentiation of dopaminergic signalling [256,

257]. Therefore, the stimulatory effects of caffeine on the motor system are attributed to the

interactions between D2 and A2A receptors [258]. In addition, research also suggests that the D2

receptors are associated with the reinforcing effects of caffeine as dopamine is known for its

role in mood and reward [3]. A study on habitual caffeine consumption showed that the C957T

polymorphism (rs6277) in DRD2 is significantly associated with caffeine consumption in slow

metabolizers [228]. Therefore, the author suggested that the dopamine D2 receptor may be

required for producing a stronger reinforcing effect in this sub-group of individuals as slow

caffeine metabolizers experience prolonged exposure to caffeine [228]. In the current study,

there was also a higher frequency of individuals with the C allele who are also slow caffeine

metabolizers. This group of individuals showed a significant decrease in plasma gelsolin with

70

increased caffeine intake. Since gelsolin may play a role in CVD, the combination of a slow

metabolizer genotype and the DRD2 C allele may have implications for CVD risk. However,

there is no published data on the relationship between plasma gelsolin and DRD2. Further

investigation is required for understanding the effects of this polymorphism on gelsolin

concentrations and possibly CVD.

In summary, we observed that within the slow caffeine metabolizers, the overall

association between caffeine intake and plasma gelsolin concentrations remained consistent

across all variants within the seven genes examined. Although some associations were

significant while others were not, the range of difference in plasma gelsolin concentrations was

not more significant than what was observed in the entire population (Chapter 2) and no

significant caffeine x gene interactions were seen for any of the SNPs. These observations are

likely due to the low subject numbers for several genotypes. An uneven distribution of subjects

across the various genotypes presented a challenge for identifying specific subgroups that may

be more influenced by caffeine intake.

71

Chapter 4: Overview and General Discussion

4.1 Overview

The hypotheses of this thesis were that (i) different levels of caffeine intake are

associated with plasma protein concentrations and (ii) these associations may be modified by a

polymorphism in CYP1A2 (rs762551, A>C) or in other caffeine-related genes. The objectives

were to determine whether individuals consuming different amounts of caffeine exhibit

differences in plasma protein concentrations and whether CYP1A2 genotype modifies any of

the associations observed. It was also of interest to see whether polymorphisms in other

caffeine-related genes modify the association between caffeine intake and plasma proteins

(gelsolin) within slow caffeine metabolizers. The summary of the findings from each

experimental chapter is as follows:

Objective 1 (Chapter 2): To determine whether different levels of caffeine intake are associated

with the concentrations of 54 high-abundance plasma proteins and whether the rs762551

CYP1A2 variant modifies any of the associations observed.

Results: Initially, no significant associations were found between caffeine intake and the plasma

proteome. After stratification by CYP1A2 genotype, we saw that among slow caffeine

metabolizers, plasma gelsolin concentrations were significantly lower in the highest category of

caffeine intake. No differences in protein concentrations were observed for fast metabolizers.

72

Objective 2 (Chapter 3): To determine whether polymorphisms in GSN, AHR, CYP1A1/1A2,

ADORA2A, HTR2A, ADRB2, DRD2, and COMT modify the association between caffeine intake

and plasma gelsolin concentrations in slow caffeine metabolizers.

Results: A significant inverse association between caffeine intake and plasma gelsolin

concentrations were seen in slow metabolizers with the GSN GG, AHR CT, CYP1A1/1A2 GG,

HTR2A CT, ADRB2 GG and DRD2 CT genotypes.

The above findings suggest that genetic variation in CYP1A2 plays a role in modifying the

association between caffeine intake and the plasma proteome. Caffeine intake is inversely

associated with plasma gelsolin concentrations, but only among slow metabolizers. As caffeine

metabolism is delayed in slow metabolizers, it is expected that these individuals will be more

influenced by the effects of caffeine. Indeed, previous research showed that with increasing

coffee consumption, subjects with the slow metabolizer genotype were more at risk for

developing MI and hypertension compared to fast metabolizers [10, 11]. In the present study,

plasma gelsolin concentrations were inversely associated with caffeine consumption in slow

metabolizers. Gelsolin is a multi-functional protein and has been implicated in the development

of CVD [216]. Since caffeine intake is associated with cardiovascular outcomes, the observation

that slow metabolizers who consume higher amounts of caffeine have lower plasma gelsolin

concentrations suggests that gelsolin may be involved in pathways linking caffeine to CVD.

73

4.2 Limitations

There are several limitations to be considered when interpreting the results of this

research. First, our study population consisted of generally healthy young adults who are at

lower risk for developing chronic diseases. Their plasma proteomic profiles likely reflect a

healthy range of proteins. Therefore, potential biomarkers of disease risk may have not been

prominent enough for us to capture in the current study. In addition, all serum and plasma

measurements were taken at one point in time, which is unlikely to represent long-term status

of disease risk. Furthermore, other unaccounted factors such as the stage of menstrual cycle,

may also affect the level of certain plasma proteins in female subjects [16].

Our data collection was initiated in 2004 when fewer highly caffeinated products (such

as energy drinks) were available to consumers. As seen from the amount of caffeine

consumption, the majority of our study population consumed caffeine at the lowest level of

intake (<100 mg/d). Consequently, the range of caffeine consumption in our study was

narrower compared to previous studies and may not reflect the effect of heavier caffeine

consumption (>400 mg/d). The majority of our subjects are also self-selected university

students and may not be representative of the overall Canadian population. The smaller sample

of East Asians and South Asians in our study population may have also compromised the

ethnicity-specific analyses in this study by making comparisons between subjects from different

ethnocultural groups more difficult. Therefore, the generalisability of our results may be

limited.

74

The MRM assay used in the current study for quantifying plasma proteins was based on

the measurement of surrogate protein peptides with an assumption of 100% digestion

efficiency. The possibility of incomplete digestion still exists and may contribute to slight

inaccuracies in protein concentrations. However, MRM assays are generally considered very

reproducible, which is supported by its low intra- and interassay variability. Furthermore, to

minimize Type I error resulting from multiple comparisons between caffeine intake and 54

plasma proteins, a post-hoc Bonferroni correction was applied. None of the proteins showed

any significant differences even after CYP1A2 stratification when measured against the

Bonferroni P-value. However, the Bonferroni correction may be overly conservative and may

have inflated the probability of Type II errors [259]. Therefore, a less conservative approach

was taken and the results were interpreted at a significance level of P<0.05 (as obtained from a

Tukey’s post-hoc test).

The cross-sectional nature of the current study also prevents us from drawing any causal

relationship between caffeine intake and the plasma proteome. Caffeine is likely consumed on

a daily basis throughout life in habitual caffeine consumers; therefore, it is important to

examine the impact of long-term caffeine consumption on plasma protein concentrations. The

observed association between caffeine intake and plasma gelsolin concentrations in slow

metabolizers requires further confirmation through prospective and experimental studies.

When slow metabolizers were examined by looking at the association between caffeine

and gelsolin concentrations in other caffeine-related genes, the number of subjects was

unevenly distributed across the three genotypes within each gene. For example, the

75

CYP1A1/1A2 GG+GA and DRD2 TT genotypes had very low subject numbers. Small sample sizes

would have increased the risk of both Type I and Type II errors. Finally, despite adjustment for a

range of covariates in the statistical models and stratifying the study population by CYP1A2

genotype, the possibility of residual confounding still exists.

4.3 Future Directions

The current project has provided insight into the influence of caffeine on the plasma

proteome and supported the modifying effect of CYP1A2 on caffeine and health. The plasma

proteome is very dynamic and varies in response to external as well as intern influences that

occur during development, aging, health and disease [260]. However, despite controversial

findings on caffeine and certain health outcomes, no studies have examined caffeine’s

association with the plasma proteome. Therefore, our study provides a ‘first look’ at caffeine

and its potential relationship with a set of high-abundance plasma proteins, which future

research can be built upon. Taking into consideration some of the limitations of the current

research, it may be important for future studies to look at cohorts that consume caffeine at a

higher level. Since previous research has demonstrated a dose-response relationship between

caffeine and certain biological effects [58, 112], comparing plasma proteomics profiles of

heavier caffeine consumers (>400 mg/d) to those that consume little or no caffeine may be

more reflective of the influence of caffeine on plasma proteins. Alternatively, another approach

to confirm the current finding of an inverse association between caffeine intake and plasma

gelsolin concentrations could be to focus on subjects consuming caffeine at the >200 mg/d

76

category and to see whether plasma gelsolin concentrations continue to decrease with

increasing caffeine intake.

Gelsolin has been implicated in the development of various diseases including CVD

[216]. As caffeine consumption is also associated with CVD, future research is needed to

confirm whether gelsolin plays a role in pathways relating caffeine to CVD. It may be interesting

to compare plasma gelsolin concentrations in habitual caffeine consumers with CVD and those

without the disease. In addition to CVD, studies could look at other health outcomes or diseases

that are associated with both gelsolin and caffeine intake. It is also of importance to note that

studies on the cardiovascular effects of caffeine should always consider and stratify subjects by

CYP1A2 polymorphism to minimize residual confounding.

The exploratory analyses on caffeine-related genes were limited by low subject

numbers with certain genotypes. Larger sample sizes are required to provide more meaningful

results and to lower the chances of committing Type I and Type II errors. Future studies could

exam these caffeine-related genes and determine whether an interaction exists between them

and CYP1A2 genotype and the plasma proteome. Furthermore, studies should also confirm and

expand on the current findings in other cohorts that have a wider range of demographic

differences. A less homogeneous population would improve the generalisability of results and

provide more insight into caffeine’s influence over the plasma proteome in different ethnic

groups. In addition, the current study population is unique as it consists of mainly university

students and a higher proportion of females. Therefore, future studies could also examine the

77

effect of caffeine on plasma proteins in different age groups as the concentration of some

plasma proteins may change during development [261, 262].

In summary, findings from the present research have demonstrated that increased

caffeine intake is associated with decreased plasma gelsolin concentrations; however, this

effect was only unmasked in subjects with the slow metabolizers genotype for CYP1A2. Future

studies are needed to confirm the results of this research and further explore gelsolin’s role in

pathways common to both caffeine intake and disease development.

78

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