Genetic Variability in Caffeine Acute Effects and Withdrawal Symptoms

by

Joanne Margaret Brathwaite

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 Joanne Margaret Brathwaite 2011

Genetic Variability in Caffeine Acute Effects

and Withdrawal Symptoms

Joanne Margaret Brathwaite

Master of Science

Graduate Department of Nutritional Sciences

University of Toronto

2011

Abstract

The mechanisms underlying caffeine‟s acute effects and withdrawal symptoms are not entirely understood. The purpose was to determine whether the clusters of acute effects or withdrawal symptoms are associated with genetic polymorphisms in DARPP-32 and COMT, which mediate some of caffeine‟s physiological effects. Subjects (n=1135) were from the Toronto

Nutrigenomics and Healthy Study. Fourteen well-described acute effects of caffeine co-exist in six groups, while fourteen well-characterized withdrawal symptoms co-exist in three groups.

Neither the rs907094 C>T polymorphism in the PPP1R1B gene encoding DARPP-32, nor the

COMT Val158Met affected the odds of reporting any acute effects or withdrawal symptoms cluster. Among individuals consuming ≥ 200 mg/d of caffeine, Met/Met homozygotes were more likely to report the “increased heart rate” acute effects cluster. These results suggest that „slow‟

COMT activity, conferred by the Met allele, may explain part of the inter-individual variability in the risk for increased heart rate among heavy caffeine consumers.

ii Acknowledgments

As I reflect on this accomplishment, I am reminded that this Master of Science degree would not have been possible without the guidance and support of many individuals. I would like to thank my family and friends for their encouragement, love and support. Thank you to the members of the El-Sohemy lab, both past and present, for their advice, assistance and friendship. Thank you to the extended Nutritional

Sciences family for their friendship and support. Last, but not least, thank you to my supervisor, Dr.

Ahmed El-Sohemy, for giving me this unique opportunity, and for his mentorship and support every step of the way. I have truly benefited from this experience, and will treasure these lessons and friendships.

iii Table of Contents

Chapter One ...... 1 1.1 Introduction ...... 2 1.2 Physical and Chemical Properties of Caffeine ...... 3 1.3 Sources and Trends of Caffeine Consumption ...... 5 1.4 Pharmacokinetics of Caffeine ...... 9 1.4.1 Absorption and Distribution ...... 9 1.4.2 Metabolism and Excretion ...... 9 1.5 Pharmacodynamics and Mechanisms of Action of Caffeine ...... 11 1.5.1 Pharmacodynamics ...... 11 1.5.2 Mechanisms of Action ...... 13 1.6 Dopamine and cAMP-Regulated Phosphoprotein of 32kDa (DARPP-32) ...... 14 1.6.1 Protein Phosphatase-1, Regulatory (Inhibitor) Subunit 1B (PPP1R1B) Gene ...... 17 1.7 Catecholamine Metabolism ...... 18 1.8 Catechol-O-methyltransferase (COMT) ...... 18 1.8.1 Catechol-O-methyltransferase (COMT) Gene ...... 20 1.9 Caffeine Withdrawal, Dependence and Tolerance ...... 22 1.9.1 Caffeine Withdrawal Symptoms ...... 22 1.9.2 Dependence ...... 24 1.9.3 Tolerance...... 25 Chapter Two...... 28 2.1 Hypothesis, Objectives and Thesis Organization ...... 29 Chapter Three...... 30 3.1 Abstract ...... 31 3.2 Introduction ...... 32 3.3 Methods ...... 35 3.3.1 Subjects and Data Collection ...... 35 3.3.2 Caffeine and Energy Intake ...... 36 3.3.3 Caffeine Habits Questionnaire ...... 36 3.3.4 Genotyping ...... 37 3.3.5 Statistical Analysis ...... 38 3.3.6 Principal Components Analysis ...... 38

iv 3.3.7 Analysis of Genetic Association ...... 39 3.4 Results ...... 41 3.4.1 Principal Components Analysis ...... 41 3.4.2 PPP1R1B Association ...... 42 3.5 Discussion ...... 55 Chapter Four ...... 60 4.1 Abstract ...... 61 4.2 Introduction ...... 62 4.3 Methods ...... 64 4.3.1 Subjects and Data Collection ...... 64 4.3.2 Caffeine and Energy Intake ...... 64 4.3.3 Caffeine Habits Questionnaire ...... 64 4.3.4 Genotyping ...... 64 4.3.5 Statistical Analysis ...... 64 4.4 Results ...... 65 4.5 Discussion ...... 74 Chapter Five ...... 80 5.1 Synopsis ...... 81 5.2 Limitations ...... 83 5.3 Future Research ...... 85 References ...... 87

v List of Abbreviations

A1R, A2AR adenosine A1 or A2A receptor AdoMet S-adenosyl-L-methionine ADORA2A adenosine A2A receptor gene BMI body mass index BP blood pressure cAMP cyclic adenosine monophosphate Cdk5 cyclin-dependent kinase 5 c-fos FBJ murine osteosarcoma viral oncogene homolog CHD coronary heart disease CI confidence interval COMT catechol-O-methyltransferase CYP1A2 cytochrome P450 1A2 D1R, D2R dopamine D1 or D2 receptor DARPP-32 dopamine and cAMP-regulated phosphoprotein of 32kDA dbSNP NCBI SNP database DSM-IV-TR Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Text revision FFQ food frequency questionnaire GABA γ-aminobutyric acid GPi globus pallidus HWE Hardy-Weinberg Equilibrium ICD-10 International Classification of Disorders IEGs immediate early genes L-dopa L-3,4-dihydroxyphenylalanine, levodopa MAO monoamine oxidase MB-COMT membrane-bound COMT MET metabolic equivalent task unit MI myocardial infarction MSN medium spiny neurons NET norepinephrine transporter NGFI-A/B nerve growth factor-induced clones A or B OR odds ratio PCA principal component analysis PCR polymerase chain reaction PKA protein kinase A PP-1 protein phosphatase-1 PP-2A protein phosphatase-2A PPP1R1B protein phosphatase-1, regulatory (inhibitor) subunit 1B S-COMT soluble COMT SD standard deviation SNP single nucleotide polymorphism SNpr substantia nigra pars reticulate Thr34, Thr75 threonine 34 and threonine 75 of DARPP-32 UTR untranslated region

vi List of Tables

Table 1-1 Physical and Chemical Properties of Caffeine ...... 4 Table 1-2 Caffeine Content of Beverages, Foods and Medications ...... 8 Table 3-1 Subject Characteristics by PPP1R1B (rs907094) Genotype ...... 44 Table 3-2 Self-reported Acute Effects Within 12h of Consuming a Caffeine-Containing Beverage ...... 45 Table 3-3 Principal Components Factor Analysis Loadings of the Acute Effects of Caffeine andCronbach‟s α ...... 46 Table 3-4 Self-Reported Withdrawal Symptoms Within 48 Hours of Abstaining from Caffeine- Containing Beverages...... 48 Table 3-5 Principal Components Factor Analysis Loadings of Caffeine Withdrawal Symptoms and Cronbach‟s α ...... 49 Table 3-6 Frequency of the Clusters of Acute Effects of Caffeine Among PPP1R1B Genotypes and the OR (95% CI) of Reporting the Clusters...... 51 Table 3-7 Frequency of the Acute Effects "Gastrointestinal" Cluster Among PPP1R1B Genotypes Stratified by Sex and the OR (95% CI) of Reporting the Cluster...... 52 Table 3-8 Frequency of the Clusters of Caffeine Withdrawal Symptoms Among PPP1R1B Genotypes and the OR (95% CI) of Reporting the Clusters ...... 53 Table 3-9 Frequency of the Caffeine Withdrawal Symptoms Cluster of "Flu-like - Anxiousness" Among PPP1R1B Genotypes Stratified by Level of Caffeine Intake and the OR (95% CI) of Reporting the Cluster...... 54 Table 4-1 Subject Characteristics by COMT Val158Met Genotype ...... 67 Table 4-2 Frequency of the Clusters of Acute Effects of Caffeine Among COMT Genotypes and the OR (95% CI) of Reporting the Clusters ...... 68 Table 4-3 Frequency of the Acute Effects "Gastrointestinal" Cluster Among COMT Val158Met Genotypes Stratified by Level of Caffeine Intake and the OR (95% CI) of Reporting the Cluster ...... 69 Table 4-4 Frequency of the Acute Effects Cluster of "Increased Heart Rate" Among COMT Val158Met Genotypes Stratified by Level of Caffeine Intake and the OR (95% CI) of Reporting the Cluster ...... 70 Table 4-5 Frequency of the Clusters of Caffeine Withdrawal Symptoms Among COMT Genotypes and the OR (95% CI) of Reporting the Clusters...... 71 Table 4-6 Frequency of the Caffeine Withdrawal Symptoms Cluster of "Dysphoric mood" Among COMT Genotypes Stratified by Sex and the OR (95% CI) of Reporting the Cluster...... 72 vii Table 4-7 Frequency of the Caffeine Withdrawal Symptoms Cluster of "Dysphoric mood" Among COMT Val158Met Genotypes Stratified by Level of Caffeine Intake and the OR (95% CI) of Reporting the Cluster ...... 73

viii List of Figures

Figure 3-1 Frequency of Factors of Self-Reported Acute Effects within 12 Hours of Consuming One Caffeine-Containing Beverage ...... 47 Figure 3-2 Frequency of Factors of Self-Reported Withdrawal Symptoms within 48 Hours of Abstaining from Caffeine-Containing Beverages ...... 50

ix List of Appendices

Appendix I: Caffeine-Containing Beverage & Food FFQ Items ...... 104 Appendix II: Caffeine Habits Questionnaire ...... 107

x

1 Chapter One

Literature Review

1 2

1.1 INTRODUCTION

Caffeine is the most commonly consumed psychostimulant in the world [1]. Its widespread use is related in part to its desirable acute effects [2] and the desire to avoid its adverse withdrawal symptoms, which may develop following cessation of regular consumption [3]. Numerous physiological, behavioural and subjective acute effects of caffeine and caffeine withdrawal have been characterized [2, 4, 5]. However, the biological basis of these acute effects and withdrawal symptoms remains poorly understood. The specific neuronal or endocrine systems that underlie each acute effect or withdrawal symptom of caffeine have yet to be identified. The extent to which these systems mediate caffeine‟s acute effects and withdrawal symptoms is unknown.

Whether the acute effects and withdrawal symptoms of caffeine occur independently of one another or in clusters representative of common underlying mechanisms also remains unknown.

The type and severity of caffeine‟s acute effects and withdrawal symptoms vary between individuals [6, 7]. This diversity has been explained partly by different lifestyle, environmental, health-related and physiological factors. Twin studies [8-10] and genetic association [11] studies suggest that genetics are one of the major biological factors influencing caffeine response.

However, the specific genetic variants and mechanisms affecting caffeine response have yet to be identified. One possible candidate is the protein phosphastase-1, regulatory (inhibitor) subunit

1B (PPP1R1B) gene encoding dopamine and cAMP-regulated phosphoprotein of 32kDa

(DARPP-32), which is a signal modulator in dopaminergic neurons that mediate some of the physiological effects of caffeine [12]. Another candidate is the catechol-O-methyltransferase

(COMT) gene which encodes the COMT enzyme that inactivates catecholamines such as neurotransmitters (i.e. norepinephrine), which also mediate some of the physiological effects of caffeine [1].

3 The purpose of this thesis was to determine whether genetic polymorphisms encoding the

DARPP-32 phosphoprotein or COMT enzyme are associated with any of the acute effects or withdrawal symptoms clusters. Positive associations would suggest that the examined polymorphisms account for part of the inter-individual variability in the incidence of the acute effects or withdrawal symptoms of caffeine, and that DARPP-32 and COMT activity plays a role in producing the effects or symptoms that are associated with the polymorphisms.

1.2 PHYSICAL AND CHEMICAL PROPERTIES OF CAFFEINE

1, 3, 7-Trimethylxanthine is the systematic name for caffeine [13]. Pure caffeine can be obtained through a number of methods including decaffeination of coffee, black tea and green tea, methylation of other methylxanthines (theobromine or theophylline) and synthesis from dimethyl urea and malinic acid [14]. At room temperature, pure caffeine is odourless and bitter, in the form of a white powder or long, flexible crystals [14]. Physical and chemical properties of caffeine are described in Table 1-1.

4 Table 1-1. Physical and Chemical Properties of Caffeine

Property Value Molecular weight 194.2g/mol 1 Melting point 236°C 1 Sublimation point 178°C 1 Specific gravity 1.2 1 Volatility 0.5% 1 Solubility (in water) 2.2% 1 Vapour pressure (at 178°C) 760 mmHg 1 Vapour density 6.7 1 pH (1% solution) 6.9 1 Base dissociation constant 10.4 2 Estimated lethal dose 10g/d (1750mg/kg) 3

1 [13], 2 [15], 3 [16, 17] .

5

1.3 SOURCES AND TRENDS OF CAFFEINE CONSUMPTION

Caffeine is a mild central nervous system stimulant [1]. Caffeine occurs naturally in the seeds, leaves and/or fruits of more than 60 plant species across the world, including Coffea arabica/robusta (coffee beans), Camellia sinensis (tea leaves), Theobroma cacao (cacao bean),

Paullinia cupana (guarana berries), Cola acuminate (kola nut), and Ilex paraguariensis (yerba mate) [18]. Caffeine is a natural pesticide to certain insects [19] and its bitter taste is a deterrent to herbivores [20]. Coffee beans and tea leaves are the primary dietary source of caffeine, worldwide [18]. The main sources of dietary caffeine for Canadian adults are coffee (80.6%), tea

(12.3%) and soft drinks (5.9%) [21].

Caffeine can be extracted from the naturally occurring sources listed above, and is used as an additive for other foods and beverages. Examples include energy drinks and soft drinks, as well as bottled water, chewing gum, mints and candy, foods often consumed by children and adolescents [20, 22]. Caffeine serves as an analgesic adjuvant when added to pain relief medication [14], and can be added to dietary supplements due to its appetite suppressant effects and because it may stimulate energy expenditure [23-25].

Caffeine use exceeds that of alcohol and nicotine, prompting many to call it the world‟s most commonly used psychoactive drug [17]. More than 80% of the world‟s population regularly consumes a caffeinated product [26]. Average caffeine intake has been estimated to be 70 to 76 mg/d [1], or less than one cup of coffee, containing about 100 mg of caffeine.

The predominance of caffeine use is related to its widespread availability, social acceptance, reinforcing beneficial effects such as increased alertness [27], and the avoidance of withdrawal symptoms [3]. However, caffeine‟s reinforcing effects are biphasic; at doses exceeding 10 mg/kg, the effects are aversive and usually associated with dysphoric or toxic effects [1]. As a

6 result, most individuals who regularly consume caffeine adjust their intake to achieve plasma caffeine levels that maximize the desired effects, while avoiding the unpleasant ones [6].

Caffeine content can vary widely within the different types of caffeinated products with coffee and energy drinks containing the most caffeine, while chocolate or cocoa products contain the least (see Table 1-2). The caffeine content of coffee and tea depends on factors including plant variety, growing conditions, treatment, processing, storage and method of preparation [27]. Tea leaves have more caffeine than coffee beans on a weight basis, but the method of preparation results in up to one-third less caffeine in tea per cup [28]. Caffeine content of coffee can also vary by brand and purchase location [29-32].

The primary sources of dietary caffeine vary between populations by culture, geographic region and age group [1]. For North America and many European countries, coffee is the main source of caffeine, whereas tea is the primary source in the United Kingdom and in Asian countries [16,

18]. The quantity of caffeine regularly consumed also varies between populations. Adults from

Scandinavian countries, including Denmark and Finland have the highest average caffeine intake, consuming more than 300 mg/d, while individuals from the African countries of Kenya and South Africa consume among the lowest caffeine, approximately 50 mg/d [33]. North

America falls into the middle of the caffeine consumption spectrum, with Canada and the United

States consuming between 150 to 200 mg/d of caffeine per person [33]. Caffeine intake can also be influenced by concern for the safety of consumers. For healthy adults, Health Canada advises limiting daily caffeine intake to 400 mg, equivalent to four 8-ounce cups of coffee [16].

Differences in habitual consumption occur by age group, with adults consuming approximately 4 mg/kg, compared to 1.1 mg/kg for children and adolescents [18, 33]. In addition, adults tend to consume caffeine through coffee or tea sources, while adolescents consume it through soft drinks, energy drinks and coffee-type drinks [20, 22].

7 In addition to the population-based factors listed above, various individual reasons can influence habitual consumption. Personal factors such as lifestyle, personality, health conditions and caffeine tolerance shape an individual‟s choice to consume caffeine [18, 34]. Evidence from twin studies shows that personal genetic factors also account for some of the variability in caffeine consumption [10, 35, 36]. Twin studies have shown a greater similarity in caffeine consumption patterns of monozygotic twins, compared to dizygotic twins [35, 37]. A polymorphism in the adenosine A2A receptor (A2AR) gene (ADORA2A, 1976 T>C) has been linked to caffeine consumption [38]. Individuals with the TT genotype were more likely to limit their caffeine intake to less than 100 mg/d, as compared to carriers of the C allele.

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

Caffeine Content Source Volume or Weight 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 Adapted from [15-17, 27, 33, 39].

9

1.4 PHARMACOKINETICS OF CAFFEINE

1.4.1 ABSORPTION AND DISTRIBUTION

Caffeine is almost completely (99%) absorbed from the stomach and small intestine into the bloodstream approximately 45 minutes following oral ingestion [40-43] and reaches peak plasma concentration in less than 120 minutes [1, 44]. Caffeine doses of 5 to 8 mg/kg yield peak plasma concentrations of 8 to 10 mg/l [42, 45]. Consumption of one cup of coffee provides a dose of 0.4 to 0.25 mg/kg, which leads to a peak concentration of 0.25 to 2 mg/l, or 1 to 10 µM [42].

Caffeine is a lipophilic molecule, and, upon absorption, it passes freely through plasma membranes resulting in virtually equal distribution through body tissues, including blood, saliva, breast milk and semen [14, 46]. For this reason, caffeine concentration in serum can be ascertained through a noninvasive saliva sample [47-49]. Caffeine passes readily through the placenta [50], and high caffeine levels have been reported in premature infants born to mothers with high caffeine intake [51]. Caffeine crosses the blood-brain barrier via diffusion as well as saturable carrier-mediated transport [52]. Its unrestricted uptake into the brain may account for the rapid onset of psychological symptoms following caffeine consumption [53].

1.4.2 METABOLISM AND EXCRETION

Caffeine metabolism occurs in the liver. Metabolism of low doses of caffeine, 70 to 100 mg, exhibits linear pharmacokinetics [42], while higher doses, 250 to 500 mg, result in nonlinear rates, evidenced by prolonged rates of clearance [54]. Cytochrome P450 1A2 (CYP1A2) is responsible for 95% of caffeine metabolism [55, 56]. CYP1A2 converts approximately 80% of caffeine to paraxanthine (1,7-dimethylxanthine) via N3 demethylation, 11% to theobromine (3,7- dimethylxanthine) via N1 demethylation, and 4% to theophylline (1,3-dimethylxanthine) via N7

10 demethylation [57]. These metabolites then undergo N-monodimethylation reactions, producing the monomethylxanthines 1-methylxanthine, 3-methylxanthine and 7-methylxanthine, respectively, as well as hydroxylation reactions yielding the uric acid derivatives 1,3,7- trimethyluric acid, 1,3-dimethyluric acid, 1,7-dimethyluric acid, 3,7-dimethyluric acid, 1- methyluric acid, 3-methyluric acid and 7-methyluric acid [58]. Paraxanthine is also metabolized via an unknown intermediate to uracil derivatives. The ethanol-inducible CYP2E1 also converts caffeine to theophylline and theobromine [55].

Caffeine elimination half-life ranges from 2 to 12 hours, averaging 4 to 6 hours in adult humans

[53]. Overnight caffeine abstinence (10 to 14 hours) is generally sufficient for caffeine elimination in most individuals [56]. However, there have been individuals with large amounts of caffeine detected after 24 hours of abstinence [59, 60]. The variability in the rate of caffeine elimination may be due, in part, to differences in caffeine dosage [1, 54], as the metabolic enzymes are saturable at doses exceeding 10 mg/kg [61].

CYP1A2 inducibility is affected by several factors, including CYP1A2 -163 A>C genotype [62], age [63], sex [64, 65], oral contraceptive use [66-68], pregnancy [69], smoking status [70, 71], long-term alcohol use [72], and liver disease [73]. The CYP1A2 -163 A>C polymorphism

(rs762551) causes an A to C substitution at position -163. Carriers of the C allele exhibit a slower rate of caffeine metabolism compared to individuals homozygous for the A allele [62].

Up to 70% of CYP1A2 variability is due to -163 A>C genotype and other environmental factors explain the remaining portion [74]. For example, caffeine metabolism in females is 20 to 30% faster than in males [16]. Among females taking oral contraceptives, the half-life of caffeine is double that of females who do not use them [75]. Pregnancy increases the half-life of caffeine from 4 hours in the first trimester up to 15 hours during the third trimester [76-78]. Infants have increased caffeine half-life due to lower activity of cytochrome P450 [63] and immaturity of

11 some demethylation and acetylation pathways [79]. In full-term newborns, caffeine half-life is approximately 80 hours [80], while it can be greater than 100 hours in premature newborns [71].

After six months of age, caffeine half-life is almost equivalent to that of an adult [1]. Caffeine half-life is reduced 30 to 50 % in smokers [81], while long-term alcohol consumption and chronic liver disease increase its half-life [53, 73].

Approximately a dozen of caffeine‟s metabolites are excreted and detected in urine [82]. The major excreted metabolites include 3-methyluracil, 1-methyluracil, 1,7-dimethyluric acid and paraxanthine (1,7-dimethylxanthine) [83]. Only 1 to 2% of caffeine is excreted unmetabolized

[84].

1.5 PHARMACODYNAMICS AND MECHANISMS OF ACTION OF CAFFEINE

1.5.1 PHARMACODYNAMICS

Caffeine elicits a range of subjective, behavioural and physiological effects through stimulation of the central nervous system. These effects occur with varying severities, caused by a number of different factors. One of these factors is the dose-dependent response to caffeine. Low to moderate caffeine doses (less than 200 mg) produce the desirable, positive effects, such as enhanced mood or alertness [54], and enhanced cognition and processing [85]. Moderate doses

(200 to 300 mg) are associated with a heightened sense of well-being, arousal and energy [86,

87]. High caffeine doses (exceeding 400 mg) are associated with the adverse effects, including anxiety, nervousness, jitteriness, tremors and seizures, as well as insomnia [53, 86]. All of these dose-effects, however, can differ between individuals. Caffeine habituation can also influence response where infrequent or light caffeine consumers are more vulnerable to some adverse effects that regular or heavy consumers may not experience [88]. For instance, insomnia occurs

12 mainly in light caffeine users [89]. Among individuals diagnosed with anxiety or panic disorders, even low caffeine doses can precipitate an episode [90-92].

In some subjects, caffeine can have different hemodynamic effects. Studies have reported that dietary caffeine can decrease [93-95], increase [96, 97] or have no effect [98-102] on heart rate.

Infrequent caffeine consumers may experience a slight decrease in heart rate coupled with an increase in blood pressure [95]. These effects are minimal in individuals who regularly consume moderate amounts of caffeine [103], and tolerance often develops [104]. A response of reduced heart rate following caffeine intake is most likely due to a baroreflex-induced vagal stimulation secondary to an increase in blood pressure [105-109]. In the case of caffeine overdose, toxic levels of caffeine can cause hypotension and severe tachycardia [22].

In the body, caffeine consumption influences other physiological systems, including respiratory, muscular, gastrointestinal and renal systems. Caffeine causes an increase in respiratory rate and minute volume (tidal volume x respiratory rate) by sensitizing the medullary centre to CO2 [53,

110]. It relaxes bronchial smooth muscle thereby increasing vital lung capacity, yet stimulates skeletal muscle resulting in increased capacity for work [111]. Caffeine promotes gastric secretion of acid and pepsin [112, 113], and may cause gastroesophageal reflux by decreasing lower esophageal sphincter pressure [114], but evidence is discordant. Cohen and Booth found that lower esophageal sphincter pressure was increased with both caffeinated and decaffeinated coffee, but minimal effect of caffeine alone [115]. Caffeine stimulates diuresis by inhibiting sodium, chloride and water reabsorption into the renal tubule [53, 110]. Caffeine also appears to have adjunctive and analgesic properties [116].

In addition to caffeine dose, habituation, mental health, and other factors contribute to variability in the acute effects mentioned above. Physiological factors such as rate of gastric emptying and

13 absorption [117], in addition to difference in the metabolic half-life of caffeine [118] are associated with this variability. Body weight influences physiological caffeine concentration and therefore can affect the intensity and/or type of acute effects [14]. Responses to caffeine can also depend on personality type (introversion versus extroversion) [119-121]. While many of caffeine‟s effects are physiological, some are psychological and stem from the expected effects of caffeine. Results from a placebo-caffeine performance study showed that, for participants receiving placebo, the suggestion of “enhanced” or “impaired” performance affected their task completion results [122]. Twin studies suggest that genetics may explain some of the variability in acute effects [9, 123]. An A2AR polymorphism (ADORA2A, 1976 T>C) has been linked to caffeine-induced anxiety [11, 124]. The TT genotype was significantly associated with anxiogenic responses to 150 mg of caffeine when compared to placebo [124] and to the C allele carriers [11].

1.5.2 MECHANISMS OF ACTION

Several potential mechanisms of caffeine action at the cellular level have been hypothesized. The first is through competitive inhibition of cyclic nucleotide phosphodiesterases, which metabolize cyclic adenosine monophosphate (cAMP). This inhibition occurs because the structure of caffeine and cAMP are related [125]. The second is due to the direct release of intracellular calcium through action on ryanodine receptors [126]. However, these two mechanisms require millimolar concentrations of caffeine [127], which would be toxic to humans. A third mechanism is antagonism of adenosine receptors. This is the only mechanism where caffeine action is realized at physiological levels (i.e. at micromolar concentrations) achieved through dietary

sources [53, 128, 129]. Caffeine non-selectively antagonizes adenosine A1 and A2A receptors, which are located throughout the body in the brain and adipose tissue, as well as respiratory,

14 cardiovascular, gastrointestinal and renal systems. This antagonism is thought to be the most important primary mechanism of caffeine action [130].

1.5.2.1 ADENOSINE RECEPTOR SIGNALING

Adenosine is an inhibitory neuromodulator acting to reduce spontaneous firing of neurons in the brain, producing sedation [131]. Adenosine receptors are G-coupled proteins that activate adenylyl cyclase to produce cAMP [129]. Adenosine A1 receptors (A1R) are located throughout the brain, while adenosine A2A receptors (A2AR) are concentrated in dopaminergic neurons in the striatum [129]. These dopaminergic neurons in the striatum of the basal ganglia play a role in critical brain functions [132]. Upon binding to its receptors, adenosine acts presynaptically to inhibit the release of neurotransmitters acetylcholine, norepinephrine, dopamine, gamma amino butyric acid and serotonin. When caffeine binds to A2AR, it causes a reversal of adenosine‟s inhibitory action, leading to a release of serotonin and catecholamines (norepinephrine, epinephrine and dopamine) in the brain, and an increase in the level of circulating catecholamines [53]. The indirect release of catecholamines into the brain following A2AR antagonism is thought to be caffeine‟s secondary mechanism of action, as these neurotransmitters influence many different physiological functions [1].

1.6 DOPAMINE AND CAMP-REGULATED PHOSPHOPROTEIN OF 32KDA (DARPP-32)

Dopaminergic neurons in the striatum of the basal ganglia play a role in critical brain functions

[132]. In addition to dopamine, these neurons receive input through a number of different neurotransmitters, neuromodulators, other signalling molecules, as well as drugs (i.e. glutamate,

15 GABA, adenosine, Na+, cocaine, opiates) [133]. Disordered signaling through these dopaminergic pathways has been associated with neurologic and psychiatric disorders [134].

Dopamine- and cAMP-regulated phosphoprotein of 32kDa (DARPP-32; also known as protein phosphatase-1, regulatory (inhibitor) subunit 1B, PPP1R1B) is an important modulator of dopaminergic pathways, amplifying cAMP-dependent signalling in neurons [135]. DARPP-32 belongs to the protein phosphatase inhibitor 1 family [136] and does not have catalytic activity.

DARPP-32 is a bidirectional neuronal phosphoprotein that functions to inhibit either protein phosphatase-1 (PP-1) [137], or a protein kinase A (PKA) [138], depending on its own phosphorylation state.

DARPP-32 contains four phosphorylation sites: Thr34, Thr75, Ser97 (Ser102 in rats) and Ser137

[139]. When phosphorylated by PKA at Thr34 [phospho-Thr34-DARPP-32] [140], DARPP-32 inhibits PP-1 through a multi-site binding interaction [133]. PP-1 is a major phosphatase of the central nervous system whose target proteins ultimately affect NMDA receptors and various ion channels [133]. When phosphorylated by cyclin-dependent kinase 5 (Cdk5) at position Thr75

[phospho-Thr75-DARPP-32], DARPP-32 competitively inhibits PKA activity [138], which is critical for signal transduction in these neurons. Phosphorylation of DARPP-32 at either Thr34 or

Thr75 is mutually exclusive, and is the key component to DARPP-32 signaling [141].

Phosphorylation at Ser137 enhances phosphorylation at Thr34 [142, 143]. Primarily in neurons expressing dopamine D1 receptors (D1R), DARPP-32 phosphorylation at Ser97 controls the subcellular position of the phosphoprotein [144]. This change in localization is triggered through

D1R binding of a stimulant like cocaine, and through food reward reinforcement.

DARPP-32 is expressed in two different groups of striatal medium-sized spiny neurons (MSNs) that project into the substantia nigra pars reticulate (SNpr) and globus pallidus (GPi) [145], controlling motor function [12, 132, 146, 147]. Half of the MSNs innervate “directly” into the

16 SNpr and GPi, forming the striatonigral pathway, which results in increased motor activity. The remaining MSNs project “indirectly” and form the striatopallidal pathway, resulting in depressed motor function. The neurons of the striatonigral pathway co-express DARPP-32 and D1R, while the striatopallidal neurons co-express DARPP-32, dopamine D2 receptors (D2R)[148], as well as

A2AR [149]. A complex antagonistic relationship between D2R and A2AR controls cAMP/PKA production and DARPP-32 signaling [150].

Dopamine binding to the D2R reduces Thr34 phosphorylation, possibly through inhibition of adenylyl cyclase and suppression of PKA-dependent phosphorylation [151], or from increased concentration of intracellular calcium, leading to calcineurin-dependent dephosphorylation of

DARPP-32 [152]. Adenosine binding triggers cAMP formation, activating PKA, which phosphorylates DARPP-32 at Thr34 and leads to the inactivation of PP-1, in turn regulating the phosphorylation of different effector proteins [133]. PKA also activates PP-2A which dephosphorylates phospho-Thr75-DARPP-32. Binding of an adenosine A2A receptor antagonist, such as caffeine, reduces cAMP production and PKA activation, as well as PP-2A activity, resulting in a reduction of phospho-Thr34-DARPP-32 and a build-up of phospho-Thr75-

DARPP-32, respectively [153]. Higher levels of phospho-Thr75-DARPP-32 further inhibit PKA activity through a positive feedback mechanism [141]. Phosphorylation at Thr75 relieves inhibition of PP-1 that is now able to dephosphorylate target proteins [138]. In summary, adenosine binding results in phosphorylation at Thr34 and subsequent motor depression, while caffeine antagonism results in Thr75 phosphorylation and motor stimulation [153, 154].

Animal knockout models have shown that DARPP-32 activity is critical for the stimulatory effects of caffeine [153]. Additional models suggest that DARPP-32 is also involved in reward sensitization associated with caffeine [154]. It is plausible that DARPP-32 regulation could influence other physiological effects of caffeine, possibly through the phosphorylation and

17 dephosphorylation of effector proteins and the expression of immediate early genes (IEGs)

[147]. The caffeine content of just one cup of coffee is able to reduce the expression of several

IEGs, including c-fos, and nerve growth factor-induced clones (NGFI-A, NGFI-B) [155, 156].

These early responders regulate expression of effector genes, but their overall role in caffeine- induced signaling is not clear.

1.6.1 PROTEIN PHOSPHATASE-1, REGULATORY (INHIBITOR) SUBUNIT 1B (PPP1R1B) GENE

The human DARPP-32 protein is 204 amino acids in length and has high sequence homology with murine and bovine DARPP-32 [157]. The DARPP-32 sequence is highly conserved through the amino-terminus [137], which contains the protein‟s phosphorylation sites. DARPP-32 is encoded by the protein phosphatase-1, regulatory (inhibitor) subunit 1B protein (PPP1R1B) gene, located at chromosome 17q12. The PPP1R1B gene consists of 7 exons and is 44,983 kb in length. Eighty-eight polymorphisms have been reported within the National Centre for

Biotechnology Information (NCBI) single nucleotide polymorphism database (dbSNP), many of which reside in intronic or untranslated regions (UTR) of the gene and are very rare and have not been verified [158]. None of these single nucleotide polymorphisms (SNPs) are reported to alter the coding sequence of any of the four phosphorylation sites. Intronic polymorphisms in this gene have been associated with schizophrenia, nicotine dependence, anger personality trait and cognitive functioning [159-161]. One SNP, a C>T substitution in intron 5 (rs907094), showed the strongest associations with these outcomes. One haplotype containing the rs907094 T-allele conferred susceptibility to nicotine dependence in a Caucasian population [160], while in another study the T-allele was independently associated with greater anger scores [161]. It is unknown whether this polymorphism influences the variability in the acute effects or withdrawal symptoms of caffeine.

18

1.7 CATECHOLAMINE METABOLISM

In the brain and central nervous system, caffeine indirectly stimulates the release of norepinephrine [1]. Norepinephrine exerts its physiological effects by binding to various adrenergic receptors throughout the body. It is possible that these various observed effects may be diminished by removal of norepinephrine from circulation, and this occurs though several synergistic modalities. In the basal state, norepinephrine turnover and metabolism is controlled through neuronal reuptake mechanisms [162-164]. In basal conditions, the majority of catecholamine turnover is controlled through neuronal reuptake mechanisms [162-164], while enzymatic breakdown plays a minor yet necessary role. Once catecholamines are recaptured from the synapse or plasma, they are metabolized to inactive metabolites by the actions of two enzymes: Monoamine oxidase (MAO) in the cytoplasm of neurons, or catechol-O- methyltransferase (COMT) in non-neuronal cells.

1.8 CATECHOL-O-METHYLTRANSFERASE (COMT)

Discovered in the 1950s, COMT (EC 2.1.1.6) catalyzes the transfer of a methyl group from S- adenosyl-L-methionine (AdoMet) to a hydroxyl group on a catechol nucleus [165]. A catechol is one of a number of molecules containing a benzene-1,2-diol group. Physiological substrates of

COMT include L-dopa, the catecholamines (dopamine, norepinephrine and epinephrine), as well as hydroxylated catecholamine metabolites, catecholestrogens [166], ascorbic acid and dihydroxyindolic intermediates of melanin [167]. Dietary and pharmacological substances, including triphenols, substituted catechols and flavonoids, are also COMT substrates [167]. The

COMT enzyme is highly conserved, and has high homology among species including humans, monkeys, rats, mice and dogs [168]. In mammals, COMT activity is highest in the liver and

19 kidney [168]. COMT is also expressed in the blood cells, skin, heart, lungs, smooth and skeletal muscles, adipose tissue, intestinal tract, reproductive organs and various glands [168]. COMT activity is sexually dimorphic [169-171].

There are two isoforms of COMT with differential distribution throughout cells and the body. S-

COMT is found in the cytoplasm and nucleus of cells mainly in the periphery [168], and is believed to be primarily responsible for the elimination of active or toxic exogenous catechols, thereby acting as a detoxifying barrier between the blood and other tissues [163, 167]. MB-

COMT is tethered to intracellular membranes and is predominantly associated with neuronal and extraneuronal cells [168]. MB-COMT is thought to be mainly involved in the termination of dopaminergic and noradrenergic synaptic neurotransmission when there are physiologically low catecholamine concentrations [172]. MB-COMT appears to be the main enzyme when dopamine concentrations are below 10µM and norepinephrine concentrations are below 300 µM [172,

173].

S-COMT and MB-COMT have identical kinetic mechanisms, but recombinant human MB-

COMT (Km = 10 µM) has higher affinity for the catechol substrates than S-COMT (Km = 108

µm) [174]. The COMT enzyme is a single domain molecule in which eight α-helices are arranged around a central mixed β-sheet. The active site consists of an S-adenosyl-L-methionine

(AdoMet) binding domain, plus the catalytic site. The Lys144 residue acts a catalytic base, a

Mg2+ cofactor controls the octahedral coordination of the reaction through bonds to the Asp141,

Asp169 and Asn170 residues, as well as two hydroxyl groups on the catechol and a water molecule [175]. COMT-mediated O-methylation is a rate-limiting step for the inactivation of biologically actively substrates, marking them for further breakdown and elimination [176].

COMT is not easily induced or suppressed, but can be inhibited by polyphenols present in coffee

[177]. Chlorogenic acid, caffeic acid and caffeic acid phenethyl ester inhibit COMT O-

20 methylation by a mixture of competitive and non-competitive methods. Computational modeling showed that polyphenols bound the COMT active site with greater affinity than the catechol estrogen substrates [177].

COMT activity is a logical focus for treatment of Parkinson‟s disease, as COMT metabolizes the drug L-dopa, but findings are inconclusive [178]. Polymorphisms of COMT activity have been associated with a variety of psychological disorders including obsessive-compulsive disorder

[170], aggressive and antisocial impulsive schizophrenia [179, 180], and velo-cardio-facial syndrome which is characterized by bizarre behavior and often schizophrenia [181]. However, associations are weak or null for schizophrenia [169, 179, 182, 183], and findings are mixed for depression [184, 185].

A recent systematic review has summarized the literature examining the role of COMT activity and substance use or abuse, though findings are inconsistent [186]. The high activity form of

COMT has been associated with polysubstance users [187], while the low activity form of

COMT has been associated with late-onset alcoholism [188], and findings for nicotine are mixed

[189-191].

1.8.1 CATECHOL-O-METHYLTRANSFERASE (COMT) GENE

The COMT isoforms are encoded by a single COMT gene which is located at chromosome

22q11.2 in humans [192-194]. The COMT gene is 51,304 kb in length and consists of 6 exons, of which the first two are non-coding. COMT expression is controlled by distinct promoters located in exon 3 [193, 194]. A distal 5‟ promoter (P2) regulates synthesis of a 1.5kb mRNA, which can code for both proteins by means of a leaky scanning mechanism of translation initiation [195-

197]. Expression of the shorter 1.3kb transcript is regulated by the P1 promoter, which is located

21 between the S-COMT and MB-COMT ATG start codons, partly overlapping the MB-COMT coding sequence [196]. S-COMT contains 221 amino acids with a molecular mass of 24.4kDa and MB-COMT contains an additional 50 amino acids, with a total molecular weight of 30.0 kDa [196]. For MB-COMT, 20 of the extra amino acids at the N-terminus act as hydrophobic membrane anchors, while the remainder of the molecule is suspended on the cytoplasmic side of the cell [193, 194].

Five hundred fifteen polymorphisms have been reported in the COMT gene in the NCBI dbSNP

[198]. Many of these SNPs reside in intronic or untranslated regions (UTR) of the gene and are very rare and/or have not been verified [198]. Of the twenty-five SNPs in the coding region, eleven are non-synonymous mutations, resulting in an amino acid change in the COMT protein

[199]. COMT enzyme activity is genetically polymorphic in humans, resulting in a low, intermediate or high activity phenotype [200-202]. The lower activity enzyme phenotypes have increased thermolability caused by a valine to methionine substitution at amino acid position 158 in MB-COMT, and position 108 in S-COMT [203, 204]. This substitution is encoded by a guanine to adenine substitution at nucleotide position 721 [203]. This Val158/108Met (MB-

COMT/S-COMT) (rs4680) polymorphism is responsible for the majority of physiologically relevant differences in COMT activity [186] and affects both the MB-COMT and S-COMT forms equally [204]. The Val158/108Met has been associated with smoking cessation [191].

Computational modeling of COMT haplotypes containing the synonymous SNPs His62/12His and

Leu136/86Leu along with Val158/108Met have been observed to alter mRNA secondary structure and modify pain sensitivity thresholds [205]. Within critical regions, these SNPs can affect mRNA folding and stability, and unstable folding can mark the molecule for degradation [205].

As we are concerned with the metabolism of endogenous neurotransmitter substrates catalyzed primarily by MB-COMT, the Val158/108Met polymorphism can be abbreviated to Val158Met.

22

1.9 CAFFEINE WITHDRAWAL, DEPENDENCE AND TOLERANCE

1.9.1 CAFFEINE WITHDRAWAL SYMPTOMS

The caffeine withdrawal syndrome is characterized by symptoms arising up to 48 hours following a reduction in usual caffeine intake [206] or caffeine cessation [87, 207, 208]. These common symptoms peak between 20 and 48 hours [209] and may last up to one week [210, 211] or several months [212] following caffeine cessation. Symptoms can arise after ceasing to regularly consume as little as 100 mg per day [18], which is approximately one 8oz cup of coffee, and can occur following cessation after only 3 days of caffeine consumption [206].

Symptoms can be prevented or rapidly reversed following consumption of as little as 25 mg of caffeine [206].

In a systematic review, Juliano and Griffiths validated 13 of 49 withdrawal symptoms reported in

66 experimental and survey studies carried out between 1872 and 2004 [5]. The 13 validated symptoms are headache, fatigue, decreased energy/activeness, decreased alertness, drowsiness, decreased contentedness, depressed mood, difficulty concentrating, irritability, foggy/not clearheaded, flu-like symptoms, nausea/vomiting and muscle pain/stiffness [5]. The anxiety symptom was not validated in this review, but the American Psychiatric Association‟s

Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-

TR) proposes it as a potential symptom of the caffeine withdrawal syndrome [213].

Using principal components analysis (PCA), Ozsungur and colleagues found that all thirteen validated withdrawal symptoms, plus anxiety, grouped into three distinct clusters [214], suggesting that some symptoms co-exist. These clusters were termed fatigue, dysphoric mood and flu-like somatic [214]. The authors proposed that the clusters may represent the adenosinergic, adrenergic, dopaminergic or serotonergic pathways, since these pathways mediate

23 many of the physiological effects of caffeine [1, 214]. Additional research is needed to elucidate the specific roles these pathways play in the occurrence of caffeine withdrawal symptoms.

The dominant hypothesis is that the adenosinergic system underlies caffeine withdrawal. It is believed that habitual consumption of caffeine, which competitively antagonizes adenosine receptors [131], leads to receptor upregulation in the brain [212, 215-217]. This shifts cerebral adenosine A1 receptors to a high affinity state [218], and increases functional sensitivity to adenosine [212, 218, 219]. Since adenosine depresses neuronal firing and release of neurotransmitters, which themselves may mediate some of caffeine‟s acute effects [220], a hypersensitivity to adenosine during abstinence could mediate the withdrawal symptoms of caffeine [219, 221].

Caffeine withdrawal can impair daily functioning and cause clinically significant distress [5]; thus it is included as an official diagnosis in the World Health Organization‟s International

Classification of Disorders (ICD-10) [222] and a proposed research diagnosis in the DSM-IV-TR

[213]. There is evidence to suggest that an aversion to caffeine withdrawal symptoms actually contributes to the reinforcing properties of caffeine [3, 7, 211, 223].

The severity and type of caffeine withdrawal symptoms varies between individuals [5, 206].

Dose differences can explain some of the variability [206, 214, 224, 225], but there are other factors. Evidence from twin studies shows heritability estimates as high as 77% for experiencing withdrawal symptoms [35]. However, the exact genetic factors responsible for caffeine withdrawal risk have not been identified. As mentioned before, it is possible that genetic variation within the adenosinergic, adrenergic, dopaminergic and/or serotonergic systems plays a role in caffeine withdrawal, as these systems may mediate many of caffeine‟s physiological effects [1].

24

1.9.2 DEPENDENCE

The occurrence of the caffeine withdrawal syndrome, upon abstinence following habitual caffeine use, is evidence of a person‟s physical dependence on caffeine [226]. Physical caffeine dependence is separate from clinical caffeine dependence syndrome [227], and clinical dependence is not required for physical dependence [228]. Physical dependence is one proposed criterion for a diagnosis of clinical dependence [227].

Diagnosis of a clinical dependence syndrome requires satisfaction of at least three of the six

ICD-10 [222] or seven DSM-IV criteria for substance-related disorders [213, 229]. The seven

DSM-IV dependence criteria are: (i) tolerance; (ii) substance-specific withdrawal syndrome; (iii) substance is often taken in large amounts of over a longer period than intended; (iv) persistent desire or unsuccessful efforts to cut down or control use; (v) a great deal of time spent in activities necessary to obtain, use or recover from the effects of the substance; (vi) important social, occupational or recreational activities given up or reduced because of substance abuse; and (vii) use continues despite knowledge of a persistent or recurrent physical or psychological problem that is likely to have been caused or exacerbated by the substance [213]. The six ICD-10 criteria are similar to those of the DSM-IV, with DSM-IV criteria (v) and (vi) combined into one

[222, 229]. In ICD-10, clinical dependence on caffeine could be diagnosed within the “Mental and behavioral disorders due to use of other stimulants [i.e. not cocaine], including caffeine” block [230]. However, caffeine dependence is not yet included in the DSM-IV, due to a lack of clinical evidence [26, 230, 231].

Two studies provide preliminary clinical evidence of a caffeine dependence syndrome. The first study used volunteers who self-identified as having problems with caffeine. Through structured psychiatric interviews based on DSM-IV criteria for substance dependence, 16% of subjects were diagnosed with caffeine dependence after meeting three out of a subset of four diagnostic

25 criteria (i, ii, iv and vii), selected from the full list of seven criteria [227]. Three criteria (iii, v, vi) were excluded as they were somewhat inappropriate for a socially accepted, licit substance such as caffeine [227]. Hughes et al. conducted random phone interviews to assess how regular caffeine consumers endorsed the DSM-IV and ICD-10 criteria for substance-related disorders, as applied to themselves [230]. Thirty percent of caffeine consumers endorsed at least three of the

DSM-IV criteria, and were described as having caffeine dependence, while 16% endorsed the criteria for physiological dependence, having met the criteria of tolerance or withdrawal [230].

1.9.3 TOLERANCE

Tolerance to the acute physiological, behavioural and subjective effects of caffeine occurs with habitual caffeine use [221, 226, 232]. Tolerance refers to a gradual change in responsiveness to a substance, after a subject is repeatedly exposed to it [27]. Tolerance can, therefore, lead to consumption of greater amounts of the substance, in order to achieve the desired effects [20,

213]. An increase in caffeine use due to development of tolerance may lead to physical or physiological dependence [230].

Tolerance may develop toward some, but not all of caffeine‟s effects [104, 233]. Within a few days of regular consumption, tolerance develops to some physiological effects including heart rate, plasma epinephrine and norepinephrine levels, and renin activity [104], as well as diuresis

[234]. Tolerance may also develop toward subjective effects such as tension-anxiety, jitteriness, nervousness and activity/stimulation/energy [235]. Tolerance to the adverse subjective effects associated with high doses of caffeine may result in individuals consuming even higher doses

[27].

26 Tolerance is incomplete for some of caffeine‟s effects. Thus, habitual consumers may exhibit attenuated responses to caffeine compared to non-users, but still show a significant response compared to baseline [27]. This phenomenon is particularly evident with caffeine‟s effect on sleep [89]. In one sleep trial, a group of young adult males received 400 mg/d of caffeine for 7 days, followed by placebo for 2 days [236]. Compared to baseline, sleep efficiency of an early- stage caffeine day was reduced to 80%, while efficiency on a late-stage caffeine day saw a moderate reduction to 90% of baseline [236]. The proportion of individuals exhibiting elevation of peripheral blood pressure may also be affected by incomplete tolerance to caffeine [98, 237,

238].

Inter-individual differences in tolerance to caffeine‟s effects is often a concerning factor when considering blood pressure elevation. For example, Farag et al. were able to separate subjects into low and high tolerance groups based on their degree of hemodynamic response to caffeine

[239]. In the group of subjects with low caffeine tolerance, daily caffeine administration did not diminish the magnitude of blood pressure elevation caused by the caffeine challenge dose. The findings are in agreement with an earlier report by Lovallo et al., who showed no effect of a caffeine challenge dose in half of the subjects on caffeine maintenance [237]. It is possible that these inter-individual differences may be explained by genetic factors. Kendler et al. administered questionnaires to female twin pairs to assess caffeine use, as well as tolerance and withdrawal [35]. While the odds of reporting tolerance were greater among monozygotic twins than among dizygotic twins (4.2 versus 1.8, respectively), the overall prevalence of tolerance was approximately 15% for both groups [35].

The exact mechanism(s) underlying the development of tolerance to caffeine‟s effects remains to be elucidated [27]. Adaptive changes via regulation of adenosine receptors does not appear to influence tolerance [240, 241]. Instead, tolerance may be due to adaptive changes altering gene

27 transcription [1] or compensatory changes in the dopaminergic system following chronic adenosine receptor antagonism [242].

2 Chapter Two

Hypothesis, Objectives and Thesis Organization

28 29

2.1 HYPOTHESIS, OBJECTIVES AND THESIS ORGANIZATION

The hypotheses of this thesis were that (i) a polymorphism in PPP1R1B encoding DARPP-32 and (ii) a polymorphism in COMT may affect sensitivity to, and incidence of some of the acute effects and withdrawal symptom clusters. The objectives were to determine whether PPP1R1B

(rs907094, C>T) or COMT (rs4680, Val158Met) genotypes modify the likelihood of reporting any of the groups of acute effects or withdrawal symptoms. The chapter-specific objectives are as follows:

Objective 1 (Chapter Three): To determine whether PPP1R1B, rs907094 C>T genotype affects the likelihood of reporting any clusters of acute effects or withdrawal symptoms of caffeine.

Objective 2 (Chapter Four): To determine whether COMT, rs4680 (Val158Met) genotype affects the likelihood of reporting any clusters of acute effects or withdrawal symptoms of caffeine.

3 Chapter Three

Association between PPP1R1B rs907094 and the Acute

Effects and Withdrawal Symptoms of Caffeine

30 31

3.1 ABSTRACT

14 acute effects and 14 withdrawal symptoms co-exist in 6 and 3 clusters, respectively, which may represent common mechanisms of caffeine action and withdrawal. Some of these mechanisms may involve dopamine and cAMP regulated phosphoprotein of 32kDa (DARPP-

32), which has been shown to mediate some of the stimulatory effects of caffeine. The rs907094

C>T polymorphism in the PPP1R1B gene encoding DARPP-32 potentially affects mRNA stability. The objective was to determine whether the rs907094 C>T polymorphism was associated with the clusters of acute effects and withdrawal symptoms. Subjects were 20-29 year old females (n=801) and males (n=334) from the Toronto Nutrigenomics and Health Study.

Multiple logistic regression was used to assess the association between PPP1R1B rs907094 C>T and the acute effects and withdrawal symptoms clusters. No significant associations were observed between the polymorphism and the acute effects clusters. There was an association between the PPP1R1B polymorphism and the “Flu-like – Anxiousness” withdrawal symptoms cluster, where heterozygotes were significantly less likely to report this cluster compared to T/T homozygotes. These findings suggest that DARPP-32 may play a role in experiencing some of the caffeine withdrawal symptoms examined here.

32

3.2 INTRODUCTION

Gastrointestinal caffeine absorption is rapid and complete, with peak plasma concentrations reached within 30-40 minutes following ingestion [5, 243]. The half-life of caffeine in the blood ranges from 2.5 to 10 hours, depending on a variety of factors that affect the activity of the cytochrome P450 1A2 (CYP1A2) enzyme, the primary metabolizer of caffeine [244].

Caffeine can elicit several dose-dependent, reinforcing and adverse physiological, behavioural and subjective effects [5, 122, 245]. There are 14 well-characterized effects including headache

[3, 246], increased energy/activeness [247-249], increased alertness/attentiveness [247, 250,

251], elevated mood [247, 252, 253], increased heart rate [104, 213, 254, 255], anxiety/nervousness [255-257], panic attacks [91, 258], restlessness [213, 248], agitation [213,

259], tremors/jitters/shakiness [254, 260, 261], dizziness [262, 263], insomnia/impaired sleep

[264-266], upset stomach [3, 267] and laxative effect [268]. It is yet to be determined whether these acute effects occur independently or if they co-exist in clusters governed by common underlying mechanisms.

Caffeine withdrawal syndrome is characterized by symptoms that arise up to 48 hours after reducing usual caffeine intake [206] or abstaining from caffeine following regular use [5, 269].

Symptoms peak between 20 to 51 hours following abstinence [5] and can last for 2 to 9 days

[269]. There are fourteen well-described caffeine withdrawal symptoms including headache, tiredness/ fatigue, decreased energy/activeness, drowsiness/sleepiness, decreased contentedness/well-being, anxiety, depressed mood, difficulty concentrating, irritability, muzzy/foggy/not clearheaded, flu-like symptoms, nausea/vomiting and muscle pain/stiffness

[267]. These symptoms can impair daily functioning and cause clinically significant distress [5].

Caffeine withdrawal is thus an official diagnosis in the World Health Organization‟s

33 International Classification of Disorders (ICD-10) [222] and a proposed research diagnosis in the

American Psychiatric Association‟s Diagnostic and Statistical Manual of Mental Disorders,

Fourth Edition, Text Revision (DSM-IV-TR) [213]. Using principal components analysis

(PCA), Ozsungur and colleagues found that all fourteen withdrawal symptoms grouped into three distinct clusters [214], suggesting biological commonality, but the mechanisms that account for these clusters remain to be determined.

As the previous evidence suggests, it is possible that caffeine abstinence promotes withdrawal symptoms within clusters through common physiological mechanisms. Similarly, it is possible that caffeine elicits acute effects through shared mechanisms within each of the clusters. One potential mechanism could be the cAMP/PKA pathway of dopaminergic neurons, given that this pathway mediates some of the physiological effects of caffeine [12]. G-protein coupled adenosine A2A receptors activate a cAMP/PKA pathway associated with motor depression [12].

Dopamine and cAMP regulated phosphoprotein of 32kDa (DARPP-32; also known as protein phosphatase-1, regulatory (inhibitor) subunit 1B, PPP1R1B) is an important pathway modulator, amplifying cAMP-dependent signalling [135]. As a molecular switch, DARPP-32 activity is dependent on its phosphorylation state. Adenosine binding results in phosphorylation at Thr34 and subsequent motor depression, while caffeine antagonism results in Thr75 phosphorylation and motor stimulation [153, 154]. Animal knockout models have shown that DARPP-32 activity is critical for some of the stimulatory effects of caffeine [153]. Additional models suggest that

DARPP-32 is also involved in reward sensitization associated with caffeine [154]. It is possible, therefore, that DARPP-32 regulation could influence other physiological effects of caffeine, possibly through the phosphorylation and dephosphorylation of target proteins.

DARPP-32 is encoded by the protein phosphatase-1, regulatory (inhibitor) subunit 1B protein

(PPP1R1B) gene, which is located on chromosome 17q12. Polymorphisms in this gene have

34 been associated with schizophrenia, nicotine dependence, anger personality trait and cognitive functioning [159-161]. One single nucleotide polymorphism (SNP), a C>T substitution in intron

5 (rs907094), showed the strongest associations with these outcomes. One haplotype containing the rs907094 T-allele conferred susceptibility to nicotine dependence in a Caucasian population

[160], while in another study the T-allele was independently associated with greater anger scores

[161]. While the functional significance of this SNP is yet to be elucidated, these gene- association studies suggest it is a promising target governing DARPP-32 function, and may, therefore, play a role in mediating some of the effects of caffeine.

It is possible that the rs907094 T-allele may confer an increased risk of sensitivity to caffeine related effects, compared to the C-allele. This in turn could lead to increased reports of any caffeine acute effects clusters that DARPP-32 might mediate. Similarly, the T-allele could confer decreased sensitivity to any withdrawal symptoms clusters following caffeine abstinence that

DARPP-32 might mediate. Therefore, the objective of the present study was to determine whether PPP1R1B rs907094 C>T genotype affects the likelihood of reporting any clusters of acute effects or withdrawal symptoms of caffeine.

35

3.3 METHODS

3.3.1 SUBJECTS AND DATA COLLECTION

Subjects (n=1310) were selected from the Toronto Nutrigenomics and Health Study. This cross- sectional study is an ethnoculturally diverse examination of males and females aged 20-29 years recruited from the University of Toronto campus. Potential subjects were not included in the study if they did not speak English, were pregnant or breastfeeding, or were unable to provide a blood sample. Subjects were recruited between September 2004 and June 2009 through

University of Toronto campus postings, email bulletins, University newspaper advertisements and classroom announcements.

Anthropometric measurements were collected, including height and weight. Subjects completed a general health and lifestyle questionnaire reporting their sex, age, ethnocultural group, smoking status, oral contraceptive use and level of physical activity, which was measured in metabolic equivalent task (MET) units. 1 MET is equivalent to 1 kcal/kg bodyweight/hr energy, which is the average expenditure of an adult while sitting at rest [270]. Levels of physical activity were assessed by a questionnaire asking subjects to report the number of hours they spent engaging in various types of activity on a typical weekday and weekend day during the last month [271]. The choices included sleeping (0.9 METs), sitting or reclining (1.0 MET), light activity (2.4 METs), moderate activity (3.6 METs) and vigorous activity (7.5 METs). Subjects provided written informed consent and the research protocol was approved by the University of Toronto Research

Ethics Board.

For the present study, subjects were also excluded if they were current smokers (n=88) as caffeine metabolism is increased in smokers [70], or reported use of antidepressants (n=43) or

36 anti-anxiety medication (n=18) as psychiatric medications may alter perceptions of caffeine‟s effects [272]. After exclusions, 1135 subjects (801 female and 334 male) remained.

3.3.2 CAFFEINE AND ENERGY INTAKE

Caffeine consumption and energy intake over the previous month were calculated from the estimated intake of various foods and beverages, assessed using the Toronto-modified Willet food frequency questionnaire (FFQ), which is semi-quantitative and includes 196 items

(Appendix I). The FFQ items prompted subjects to choose the option from a list that best captured their intake quantity and frequency of given foods and beverages, which were in turn converted into average daily intake quantities. The FFQ assessed all major dietary sources of caffeine in North America. Subjects were also able to record energy drinks in response to the question “Are there any other caffeinated beverages not mentioned above that you usually drink at least once per week?”. The FFQ provided questions with pre-set options on the method by which subjects‟ homemade coffee was usually prepared and where they usually purchased coffee, to account for the different caffeine content of coffee across preparation methods and places purchased. Completed FFQs were analyzed by Harvard University and the values were merged into the study database.

3.3.3 CAFFEINE HABITS QUESTIONNAIRE

The caffeine habits questionnaire (Appendix II) assessed subjects‟ regular consumption, acute effects and withdrawal symptoms of caffeine. Subjects were asked, “Do you currently, or have you ever, consumed caffeine-containing beverages (e.g. coffee, tea, cola) regularly?”. Regular consumption was clarified as daily or several times per week. Subjects responded with “Yes, I

37 currently consume them regularly”, “Yes, I used to consume them regularly but do not anymore”, or “No, I have never regularly consumed them”. Subjects were then asked, “If yes, please indicate next to each of the following withdrawal symptoms the degree to which you experience(d) them up to 48 hours after ceasing to consume caffeine-containing beverages”.

Subjects were then asked “Do you experience any of the following effects up to 12 hours after consuming one caffeine-containing beverage (e.g. coffee, tea, cola)?” To each of the 14 withdrawal symptoms and 14 acute effects, subjects responded either “don‟t know”, “none”,

“mild”, “moderate”, or “severe”. Subjects who responded either “Yes, I used to consume them regularly but do not anymore” (n=118) or “No, I have never regularly consumed them”, (n=310) to the question “Do you currently, or have you ever, consumed caffeine-containing beverages regularly?”, were excluded, as well as subjects who did not answer this question (n=112), leaving 595 subjects for withdrawal symptom analysis. Subjects who no longer regularly consume may have inaccurately recalled past withdrawal symptoms if recollection required long- term memory, and subjects who never consumed were instructed to skip the withdrawal symptoms questions.

3.3.4 GENOTYPING

Genomic DNA was isolated from whole blood using the GenomicPrep™ Blood DNA Isolation kit (Amersham Pharmacia Biotech Inc., Piscataway, NJ). Genotyping of the PPP1R1B polymorphism (rs907094) was performed by real-time PCR using TaqMan® Allelic

Discrimination Assay (Assay ID: C___7452370_1_; Context sequence:

TGAGGGGCCTGTGACATGTGGATTA[A/G]CTGTGGGTCCTCCTTGAGTATACGA) from

Applied Biosystems (Foster City, CA, USA) according to manufacturer‟s directions. Polymerase chain reaction (PCR) conditions were 95°C for 10min, followed by 40 cycles of 95°C for 15sec

38 and 60°C for 1min. Allelic discrimination was completed using the ABI Prism 7000 Sequence

Detection System. To ensure assay validity and reproducibility, ten percent of the samples within each PCR run were replicated, and each run also contained four negative controls.

3.3.5 STATISTICAL ANALYSIS

All statistical analyses were performed using the Statistical Analysis Software program (SAS version 9.2, SAS Institute, Cary, NC, USA). Subject characteristics were assessed using mean ± standard deviation for continuous variables (age, energy intake, physical activity level, weight,

BMI, caffeine intake) and frequency and percentage for categorical variables (sex, ethnocultural group, CYP1A2 genotype, oral contraceptive use). Caffeine consumption by source was calculated from FFQ nutrient source reports provided by Harvard University.

The frequency of reporting degrees of intensity of acute effects within 12 hours of consuming a caffeinated beverage and withdrawal symptoms up to 48 hours after abstinence from caffeinated beverages was assessed. Subject responses were dichotomized as “No” (none) and “Yes” (mild, moderate, and severe) due to the low response rates in the moderate and severe categories, which may have been related to a low average level of habitual caffeine consumption in this population

(~120 mg/day).

3.3.6 PRINCIPAL COMPONENTS ANALYSIS

Principal components analysis (PCA) was performed to detect patterns of association among acute effects and withdrawal symptoms [273]. The number of factors underlying acute effects and withdrawal symptoms was determined using the scree test and the Kaiser criterion for eigenevalues (representing the amount of variance accounted for by each factor). According to

39 the scree test, eigenvalues are plotted in a scree plot. Only those factors whose eigenvalues lie above the “elbow” of the curve joining the plotted eigenvalues are retained. According to the

Kaiser criterion for eigenvalues, only factors with eigenvalues greater than 1 are retained as only these eigenvalues explain variance better than any single effect or symptom [274]. Varimax orthogonal rotation maximized each factor‟s squared loading variance, thereby simplifying factor structure. Orthogonal rotation improves the interpretation of factor loadings, rendering them equivalent to correlations between observed variables and components [275]. Effects or symptoms with loadings above 0.50 were deemed elements of a factor. Cronbach‟s α coefficients were calculated to determine the factors‟ internal consistency, representing the extent of correlation between elements of a factor [276]. The frequency of occurrence within the population of each factor of acute effects and withdrawal symptoms was assessed.

3.3.7 ANALYSIS OF GENETIC ASSOCIATION

All statistical analyses were performed using Statistical Analysis Software (SAS v9.2; SAS

Institute, Cary, NC, USA). Hardy-Weinberg Equilibrium (HWE) was assessed using chi-squared tests with 1 degree of freedom. p-values were two-sided and significant when less than or equal to 0.05. Testing for departure from HWE in genetic association studies is often used to detect errors or peculiarities in the data set [277]. These may arise due to sampling errors, genotyping errors, failure to detect rare alleles and inclusion of non-existent alleles.

Subject characteristics were assessed among genotypes using mean ± standard deviation for continuous variables (age, physical activity level, energy intake, BMI, caffeine intake, alcohol intake), and frequency and percentage for categorical variables (caffeine intake level, ethnocultural group, sex, CYP1A2 -163 A>C genotype and oral contraceptive use). Differences

40 in subject characteristics between genotypes were determined by one-way ANOVA using the

GLM procedure for continuous variables, and by chi-squared tests using the FREQ procedure for categorical variables.

Unconditional multivariate logistic regression with adjustment for covariates yielded odds ratios

(OR) and 95% confidence intervals (CI) representing the likelihood of experiencing each acute effects and withdrawal symptoms cluster among genotypes, using the majority homozygous genotype as the reference group. The following covariates were considered for inclusion in the regression models: sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake and oral contraceptive use in females. Non-normally distributed variables were loge-transformed (BMI, alcohol intake) for inclusion in the models. Considered covariates were included in the final models if they modified the -2 log (likelihood) ratio of the model.

For each cluster, covariate interactions with genotype were revealed by significant gene- covariate logistic regression interaction terms. We evaluated potential gene-covariate interactions by (1) determining the relationship between the covariate and the likelihood of reporting a given cluster using logistic regression and, (2) by comparing -2 log (likelihood) ratios from a model with gene and covariate main effects only, and from another that included their interaction term.

If the differences were significant at p<0.05, then stratified analyses were conducted for categorical variables. Caffeine intake level and CYP1A2 genotype were considered as potential confounders and tested through gene-variable interactions for each cluster. Significant interactions were then investigated through stratified analyses.

41

3.4 RESULTS

3.4.1 PRINCIPAL COMPONENTS ANALYSIS

Table 3-1 shows the subject characteristics. Of the total amount of caffeine consumed by subjects, 60.3% came from coffee, 30.8% from tea, 5.4% from cola and carbonated beverages,

1.5% from sweetened iced tea, 1.0% from chocolate, 0.7% from other beverages, including energy drinks and hot chocolate, and 0.3% came from confectionaries and other sources.

Table 3-2 shows the frequency of reporting the degrees of each acute effect of caffeine. For most of the acute effects, the majority of subjects reported an intensity of “none”, except for increased energy, increased alertness and increased heart rate. For most of the acute effects, a low percentage of subjects reported a “moderate” or “severe” intensity. Table 3-3 shows the factor loadings and Cronbach‟s α coefficients for the acute effects. Effects with factor loadings greater than 0.50 were included within a factor. Twelve of the 14 acute effects clustered into 4 factors.

The first factor was termed “Anxiousness” because it included agitation, anxiety/nervousness, restlessness, panic attacks and tremors/jitters/shakiness. The second factor was termed “Arousal” and included increased energy/activeness, increased alertness and elevated mood. The third factor was termed “Gastrointestinal” and included laxative effect and upset stomach. The fourth factor, termed “Headache – Dizziness”, consisting of the headache and dizziness effects. Two of the acute effects – “Insomnia/Impaired Sleep” and ”Increased Heart Rate” – did not load onto any of the four factors, and were considered to be 2 additional individual factors, for a total of six acute effect factors. The frequency of reporting the acute effects factors is shown in figure 3-1.

The most commonly reported factor is “Arousal” (77 %), followed by “Increased Heart Rate”

(44%), “Anxiousness” (43%), “Gastrointestinal” (40%), “Insomnia/Impaired Sleep” (36%) and

“Headache – Dizziness” (15%).

42 Table 3-4 shows the frequency of reporting the caffeine withdrawal symptoms. Except for the withdrawal symptoms of tiredness/fatigue, decreased energy/activeness, decreased alertness/attentiveness and drowsiness/sleepiness, the majority of subjects reported an intensity of “none”. A low percentage of subjects reported a “moderate” or “severe” intensity. Table 3-5 shows the factor loadings and Cronbach‟s α coefficients for the withdrawal symptoms. All 14 withdrawal symptoms clustered into 3 factors. Two symptoms, difficulty concentrating and foggy/not clearheaded, loaded onto 2 factors. The first factor, termed “Fatigue” included decreased alertness/attentiveness, tiredness/fatigue, decreased energy/activeness, drowsiness/sleepiness, difficulty concentrating, foggy/not clearheaded and headache. The second factor was termed “Dysphoric Mood” and included depressed mood, decreased contentedness/well-being, irritability, foggy/not clearheaded and difficulty concentrating. The third factor was termed “Flu-like – Anxiousness” and included muscle pain/stiffness, nausea/vomiting/upset stomach, anxiety/nervousness and flu-like symptoms. Figure 3.2 shows the frequency of the reported withdrawal symptom factors. The most commonly reported factor was “Fatigue” (72%) followed by “Dysphoric Mood” (52%) and “Flu-like Anxiousness” (19%).

3.4.2 PPP1R1B ASSOCIATION

Genotype frequencies for the PPP1R1B rs907094 C>T polymorphism were 18% for C/C, 42% for C/T and 40% for T/T and did not deviate from Hardy-Weinberg equilibrium. The minor

PPP1R1B „C‟ allele frequency was 39%. Table 3-1 shows the subject characteristics by

PPP1R1B rs907094 C>T genotype. The PPP1R1B polymorphism was also associated with differences in age and weight, but this was due to population admixture and differences in minor allele frequencies are no longer present when the data was adjusted for ethnocultural group.

43 Table 3-6 shows the unadjusted and adjusted ORs (95% CIs) for reporting the acute effects clusters by PPP1R1B genotype. PPP1R1B genotype did not influence the likelihood of reporting the acute effect clusters. Table 3-7 shows the stratified analyses for the “gastrointestinal” cluster associated with a significant gene-sex interaction (p<0.001). Among males, the heterozygous individuals were less likely to report the cluster compared to the T/T individuals, while there was no difference for the C/C individuals.

Table 3-8 shows the unadjusted and adjusted ORs (95%CIs) for reporting the withdrawal symptom clusters by PPP1R1B genotype. Individuals with the C/T genotype were significantly less likely to report the “flu-like anxiousness” cluster compared to the T-allele homozygotes.

There was no difference between the C/C and T/T individuals. Table 3-9 shows the stratified results for the “flu-like anxiousness” cluster associated with a significant gene-diet interaction

(p=0.04). Among individuals in the highest caffeine intake group (>200mg caffeine/d) heterozygotes were less likely to report this cluster compared to T/T individuals. Again, there was no difference between the C/C individuals and the T/T reference group.

44

Table 3-1. Subject Characteristics by PPP1R1B (rs907094) Genotype.

Characteristic C/C C/T T/T p (n=202) (n=475) (n=458)

Sex, n(%)

Female 141 (17.6) 341 (42.6) 319 (39.8) 0.75§

Male 61 (18.3) 134 (40.1) 139 (41.6)

Age, years 22.2 ± 2.3 22.5 ± 2.5 22.9 ± 2.4 0.0008† Ethnocultural group, n (%)

Caucasian 33 (6.4) 190 (37.1) 289 (56.5) <0.0001§

East Asian 137 (33.3) 192 (46.6) 83 (20.1)

South Asian 10 (8.1) 48 (38.7) 66 (53.2)

Other 22 (25.9) 43 (50.6) 20 (23.5)

Weight, kg 62.1 ± 13.5 63.0 ± 12.4 64.8 ± 12.6 0.02† BMI, kg/m² 22.6 ± 3.8 22.6 ± 3.4 22.9 ± 3.5 0.31† Energy intake, kcal/d 2039.4 ± 1072.3 2040.8 ± 868.2 2082.2 ± 820.5 0.74† Physical activity level, MET hrs/week 173.8 ± 74.7 186.6 ± 72.0 184.0 ± 76.1 0.12† Caffeine intake

mg/d 101.2 ± 110.7 112.3 ± 131.4 118.7 ± 119.9 0.25†

mg/kg body weight/d 1.7 ±1.8 1.8 ± 2.0 1.9 ±1.9 0.54†

CYP1A2 genotype, n (%)

A/A 76 (14.8) 221 (43.2) 215 (42.0) 0.06§

A/C + C/C 126 (20.2) 254 (40.8) 243 (39.0)

p-values for differences between genotypes were tested using †one-way ANOVA and §χ2 test. Subject characteristics are shown as mean ± SD for continuous variables and n (%) for categorical variables. Abbreviations: SD = standard deviation; BMI = body mass index; MET = metabolic equivalent task.

45

Table 3-2. Self-reported Acute Effects Within 12h of Consuming a Caffeine-Containing Beverage.

Acute Effect None Mild Moderate Severe "Don't know" n (%) Headache 873 (85.3) 86 (8.4) 18 (1.8) 4 (0.4) 42 (4.1) Increased energy/ 332 (32.5) 403 (39.4) 221 (21.6) 24 (2.4) 43 (4.2) Activeness Increased alertness 291 (28.5) 409 (40.0) 248 (24.2) 25 (2.4) 50 (4.9) Elevated mood 500 (48.9) 281 (27.5) 156 (15.3) 14 (1.4) 72 (7.0) Increased heart rate 472 (46.1) 251 (24.5) 105 (10.3) 22 (2.2) 173 (16.9) Anxiety/ Nervousness 737 (72.0) 151 (14.8) 63 (6.3) 12 (1.2) 60 (5.9) Panic attacks 924 (90.3) 38 (3.7) 15 (1.5) 1 (0.1) 45 (4.4) Restlessness 663 (64.8) 223 (21.8) 70 (6.8) 17 (1.7) 50 (4.9) Agitation 809 (79.1) 124 (12.1) 32 (3.1) 8 (0.8) 50 (4.9) Tremors/ Jitters/ Shakiness 793 (77.5) 125 (12.2) 56 (5.5) 15 (1.5) 34 (3.3) Dizziness 912 (89.2) 55 (5.4) 12 (1.2) 6 (0.6) 38 (3.7) Insomnia/ Impaired sleep 627 (61.3) 226 (22.1) 93 (9.1) 32 (3.1) 45 (4.4) Upset Stomach 759 (74.2) 158 (15.4) 53 (5.2) 11 (1.1) 42 (4.1) Laxative effect 672 (65.7) 201 (19.7) 74 (7.2) 13 (1.3) 63 (6.2)

46

Table 3-3. Principal Components Factor Analysis Loadings of the Acute Effects of Caffeine and Cronbach‟s α.

Acute Effect Factor1 Factor2 Factor3 Factor4 Anxiousness' Arousal' Gastrointestinal Headache - Dizziness' Agitation 0.78 0.08 0.16 0.02 Anxiety/ Nervousness 0.73 0.18 0.15 0.11 Restlessness 0.71 0.21 0.18 0.01 Panic attacks 0.67 -0.01 -0.15 0.18 Tremors/ Jitters/ Shakiness 0.59 0.14 0.15 0.24 Increased energy/ Activeness 0.10 0.85 0.09 0.05 Increased alertness 0.06 0.85 0.11 0.04 Elevated mood 0.18 0.72 -0.02 0.09 Laxative effect 0.13 0.16 0.79 -0.10 Upset Stomach 0.16 0.02 0.64 0.35 Dizziness 0.20 0.14 -0.08 0.75 Headache 0.09 0.03 0.19 0.72 Insomnia/ Impaired sleep 0.36 0.36 0.28 0.19 Increased heart rate 0.45 0.48 0.31 0.06 Chronbach's α coefficient 0.78 0.79 0.42 0.42

47

Figure 3-1. Frequency of Factors of Self-Reported Acute Effects Within 12 Hours of Consuming One Caffeine- Containing Beverage.

48

Table 3-4. Self-Reported Withdrawal Symptoms Within 48 Hours of Abstaining from Caffeine-Containing Beverages.

Withdrawal Symptom None Mild Moderate Severe "Don't know" n (%) Headache 366 (62.0) 112 (19.0) 63 (10.7) 18 (3.1) 31 (5.3) Tiredness/ Fatigue 229 (38.7) 192 (32.4) 116 (19.6) 27 (4.6) 28 (4.7) Decreased energy/ Activeness 264 (44.6) 185 (31.3) 93 (15.7) 19 (3.2) 31 (5.2) Decreased alertness/ Attentiveness 271 (45.9) 175 (29.7) 89 (15.1) 20 (3.4) 35 (5.9) Drowsiness/ Sleepiness 262 (44.3) 186 (31.5) 96 (16.2) 17 (2.9) 30 (5.1) Decreased contentedness/ Well-being 396 (66.9) 106 (17.9) 45 (7.6) 5 (0.8) 40 (6.8) Depressed mood 440 (74.5) 84 (14.2) 20 (3.4) 3 (0.5) 44 (7.5) Difficulty concentrating 352 (59.6) 134 (22.7) 53 (9.0) 8 (1.4) 44 (7.5) Irritability 398 (67.3) 96 (16.2) 48 (8.1) 10 (1.7) 39 (6.6) Foggy/ Not clearheaded 380 (64.2) 110 (18.6) 51 (8.6) 11 (1.9) 40 (6.8) Flu-like symptoms 525 (88.7) 25 (4.2) 7 (1.2) 1 (0.2) 34 (5.7) Nausea/ Vomiting/ Upset stomach 532 (89.9) 24 (4.1) 7 (1.2) 2 (0.3) 27 (4.6) Muscle pain/ Stiffness 535 (90.5) 18 (3.1) 6 (1.0) 1 (0.2) 31 (5.3) Anxiety/ Nervousness 491 (82.9) 48 (8.1) 20 (3.4) 2 (0.3) 31 (5.2)

49

Table 3-5. Principal Components Factor Analysis Loadings of Caffeine Withdrawal Symptoms and Cronbach‟s α.

Withdrawal Symptom Factor1 Factor2 Factor3 Fatigue Dysphoric Mood Flu-like – Anxiousness Decreased alertness/ Attentiveness 0.86 0.21 0.02 Tiredness/Fatigue 0.86 0.16 0.07 Decreased energy/ Activeness 0.82 0.22 0.07 Drowsiness/ Sleepiness 0.79 0.17 0.04 Difficulty concentrating 0.55 0.51 0.17 Foggy/ Not clearheaded 0.52 0.53 0.10 Headache 0.51 0.30 0.10 Depressed mood 0.19 0.81 0.17 Decreased contentedness/ Well-being 0.33 0.72 0.05 Irritability 0.30 0.65 0.25 Muscle pain/ Stiffness <0.01 0.13 0.82 Nausea/ Vomiting/ Upset stomach 0.09 -0.07 0.81 Anxiety/ Nervousness 0.02 0.45 0.59 Flu-like symptoms 0.14 0.27 0.52 Chronbach's α coefficient 0.89 0.83 0.66

50

Figure 3-2. Frequency of Factors of Self-Reported Withdrawal Symptoms Within 48 Hours of Abstaining from Caffeine-Containing Beverages.

Fatigue 72

Factor Dysphoric Mood 52

Flu-like - Anxiousness 19

0 20 40 60 80 100 Frequency (%)

51

Table 3-6. Frequency of the Clusters of Acute Effects of Caffeine Among PPP1R1B Genotypes and the OR (95% CI) of Reporting the Clusters.

Yes No OR (95% CI) OR (95% CI) Cluster / PPP1R1B genotype n (%) Unadjusted Adjusted1 Cluster 1 "Anxiousness" T/T 175 (43) 229 (57) 1.00 1.00 C/T 173 (42) 243 (58) 0.93 (0.71, 1.23) 0.83 (0.62, 1.11) C/C 87 (47) 97 (53) 1.17 (0.83, 1.67) 0.94 (0.64, 1.40) Cluster 2 "Arousal" T/T 302 (76) 98 (24) 1.00 1.00 C/T 321 (78) 93 (22) 1.12 (0.81, 1.55) 1.10 (0.78, 1.55) C/C 140 (77) 41 (23) 1.11 (0.73, 1.68) 1.09 (0.68, 1.74) Cluster 3 "Headache-Dizziness" T/T 60 (15) 342 (85) 1.00 1.00 C/T 62 (15) 355 (85) 1.00 (0.68, 1.46) 0.96 (0.64, 1.44) C/C 31 (17) 154 (83) 1.15 (0.72, 1.84) 1.06 (0.62, 1.80) Cluster 4 "Gastrointestinal" 2 T/T 174 (43) 227 (57) 1.00 1.00 C/T 163 (40) 246 (60) 0.86 (0.65, 1.14) 0.90 (0.67, 1.22) C/C 64 (35) 117 (65) 0.71 (0.50, 1.03) 0.85 (0.56, 1.28) Cluster 5 "Increased Heart Rate" T/T 147 (43) 195 (57) 1.00 1.00 C/T 154 (44) 194 (56) 1.05 (0.78, 1.42) 1.07 (0.78, 1.48) C/C 77 (48) 83 (52) 1.23 (0.84, 1.79) 1.29 (0.84, 1.98) Cluster 6 "Insomnia" T/T 141 (36) 250 (64) 1.00 1.00 C/T 133 (33) 274 (67) 0.86 (0.64, 1.15) 0.86 (0.63, 1.16) C/C 77 (43) 103 (57) 1.33 (0.93, 1.90) 1.32 (0.88, 1.98)

¹ Logistic regression model adjusted for sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake and oral contraceptive use in females. 2 Logistic regression model was also adjusted for mean fibre intake.

52

Table 3-7. Frequency of the Acute Effects "Gastrointestinal" Cluster Among PPP1R1B Genotypes Stratified by Sex and the OR (95% CI) of Reporting the Cluster.

Yes No OR (95% CI) OR (95% CI) Interaction p Sex / PPP1R1B genotype n (%) Unadjusted Adjusted1 Male T/T 40 (34) 78 (66) 1.00 1.00 <0.001 C/T 25 (22) 89 (78) 0.55 (0.31, 0.98) 0.51 (0.27, 0.96) C/C 16 (31) 36 (69) 0.87 (0.43, 1.75) 1.23 (0.54, 2.78) Female T/T 134 (47) 149 (53) 1.00 1.00 C/T 138 (47) 157 (53) 0.98 (0.71, 1.35) 1.04 (0.74, 1.47) C/C 48 (37) 81 (63) 0.66 (0.43, 1.01) 0.79 (0.49, 1.27)

¹ Logistic regression model adjusted for sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake, oral contraceptive use in females and mean fibre intake.

53

Table 3-8. Frequency of the Clusters of Caffeine Withdrawal Symptoms Among PPP1R1B genotypes and the OR (95% CI) of Reporting the Clusters.

Yes No OR (95% CI) OR (95% CI) Cluster / PPP1R1B genotype n (%) Unadjusted Adjusted1 Cluster 1 "Fatigue" T/T 186 (72) 74 (28) 1.00 1.00 C/T 169 (71) 70 (29) 0.96 (0.65, 1.42) 1.02 (0.66, 1.57) C/C 64 (78) 18 (22) 1.41 (0.79, 2.55) 1.66 (0.83, 3.31) Cluster 2 "Dysphoric mood" T/T 137 (53) 120 (47) 1.00 1.00 C/T 115 (48) 123 (52) 0.82 (0.58, 1.17) 0.81 (0.55, 1.19) C/C 48 (59) 34 (41) 1.24 (0.75, 2.05) 1.25 (0.69, 2.26) Cluster 3 "Flu-like - Anxiousness" T/T 50 (20) 204 (80) 1.00 1.00 C/T 37 (16) 201 (84) 0.75 (0.47, 1.20) 0.58 (0.35, 0.96) C/C 21 (26) 59 (74) 1.45 (0.81, 2.61) 0.88 (0.45, 1.72)

¹ Logistic regression model adjusted for sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake and oral contraceptive use in females.

54

Table 3-9. Frequency of the Caffeine Withdrawal Symptoms Cluster of "Flu-like - Anxiousness" Among PPP1R1B Genotypes Stratified by Level of Caffeine Intake and the OR (95% CI) of Reporting the Cluster.

Caffeine intake / Yes No OR (95% CI) OR (95% CI) Interaction p PPP1R1B genotype n (%) Unadjusted Adjusted1

< 100 mg/d

T/T 18 (19) 78 (81) 1.00 1.00 0.04

C/T 12 (13) 82 (87) 0.63 (0.29, 1.40) 0.51 (0.22, 1.20)

C/C 10 (32) 21 (68) 2.06 (0.83, 5.13) 1.59 (0.54, 4.68)

100 - 200 mg/d

T/T 12 (14) 73 (86) 1.00 1.00

C/T 15 (19) 66 (81) 1.38 (0.60, 3.17) 0.83 (0.31, 2.22)

C/C 4 (20) 16 (80) 1.52 (0.43, 5.33) 0.46 (0.10, 2.23)

> 200 mg/d

T/T 20 (27) 53 (73) 1.00 1.00

C/T 10 (16) 53 (84) 0.50 (0.21, 1.17) 0.32 (0.12, 0.86)

C/C 7 (24) 22 (76) 0.84 (0.31, 2.28) 0.49 (0.14, 1.64)

¹ Logistic regression model adjusted for sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake and oral contraceptive use in females.

55

3.5 DISCUSSION

The current study sought to determine whether PPP1R1B rs907094 C>T genotype affects the likelihood of reporting any clusters of acute effects or withdrawal symptoms of caffeine. Initial analyses determined whether 14 well-described acute effects of caffeine or 14 well-characterized caffeine withdrawal symptoms co-exist in clusters. Our findings show that 12 of the 14 acute effects co-exist in 4 groups, which we termed “anxiousness”, “arousal”, “gastrointestinal” and

“headache – dizziness” based on their qualitative interpretability. The 2 remaining effects

(insomnia/impaired sleep and increased heart rate) occurred independently, for a total of 6 acute effect factors. It is possible that the acute effects within a given factor arise through a common physiological mechanism and that each factor represents a distinct mechanism.

The most commonly reported factor was arousal, while headache – dizziness was the least reported. Arousal included the positive symptoms of increased energy/activeness, increased alertness and elevated mood. Arousal is commonly reported in the literature [225, 278-280], an observation that corresponds with our findings, as well as with caffeine‟s widespread use [16]. In agreement with our findings, headache and dizziness are not frequently reported acute effects [3,

246, 281]. The literature is inconsistent on the gastrointestinal effects of caffeinated coffee [282,

283], and it is possible that this cluster, reported by 40% of subjects in the present study, may be influenced by a number of other factors such as consumption of other bioactives and the health status of the individual [284-286].

There is much interest in the potential relationship between caffeine and cardiovascular health

[16, 17]. While several studies report little to no effect of dietary caffeine on measured heart rate

[98-100], one large Australian cross-sectional study linked self-reported palpitations to caffeine

56 intake [96]. In our study, increased heart rate was an independent effect, and 44% of subjects in our population perceived this effect after consuming a caffeinated beverage. It is possible that this phenomenon is due to anticipatory or expected effects of caffeine [108, 287], but more research is required to examine this possibility. To our knowledge, no systematic reviews or validations of reported acute effects of caffeine have been published. A systematic review or validation, similar to the comprehensive coverage of caffeine withdrawal symptoms [5, 267] would provide a solid starting point for further research on the acute effects of caffeine.

We found that all 14 caffeine withdrawal symptoms clustered into 3 factors, which we termed

“Fatigue”, “Dysphoric Mood” and “Flu-like – Anxiousness”. It is possible that a common physiological mechanism underlies symptoms within a factor, and that separate mechanisms influence each factor. Recently, Ozsungur and colleagues empirically derived caffeine withdrawal symptom factors from the same 14 withdrawal symptoms [214]. The results of the principal components factor analysis from the present study differ slightly from the findings of

Ozsungur et al. Ozsungur et al. found that the anxiety/nervousness symptom clustered strongly with the dysphoric mood symptoms (factor loading of 0.73) [214], while we observed it as part of the flu-like symptoms cluster (factor loading of 0.59). This difference may be explained by sample size, as Ozsungur et al. used a smaller number of subjects (n=495), all of whom were also included in the present study. Additionally, Ozsungur et al. excluded individuals who reported a history of mood disorders when conducting the dysphoric mood association analysis, after establishing the clusters [214]. In the present study, all individuals who reported medication history of mood disorders were excluded before the PCA, as this could have influenced the clustering. Caffeine withdrawal symptom clusters have been derived conceptually by Juliano and

Griffiths [5] and are nearly identical to the empirical derivation conducted herein. The authors

57 proposed five clusters: (1) headache, (2) fatigue or drowsiness, (3) dysphoric mood, depressed mood or irritability, (4) difficulty concentrating and (5) flu-like somatic symptoms, nausea, vomiting, or muscle pain/stiffness [5]. These conceptual clusters differ from our empirical clusters in three ways. In our study, difficulty concentrating clustered at the borderline of both fatigue (factor loading = 0.55) and dysphoric mood (factor loading n=0.51). In addition, headache clustered with fatigue, though barely meeting the cut off value (factor loading = 0.51).

For Juliano and Griffiths, anxiety/nervousness did not fulfill the validation criteria, and the authors suggest that increased anxiety may be explained by anticipation effects [5].

It is plausible that these acute effects and withdrawal symptom clusters derived both conceptually and empirically may represent several common mechanisms influencing caffeine signalling. These mechanisms may act through pathways that mediate caffeine signalling, including the adenosinergic and dopaminergic systems. One of these common underlying mechanisms may alter the cAMP/PKA indirect pathway, as it is known to mediate some of the physiological effects of caffeine [12]. As an initial attempt to explore whether DARPP-32 activity mediates any of the acute effects or withdrawal symptoms clusters of caffeine, we determined whether a common polymorphism (PPP1R1B rs907094, C>T) influences the likelihood of reporting the clusters.

PPP1R1B genotype did not influence the likelihood of reporting any of the six acute effects clusters. Stratification after a significant sex-gene interaction for the gastrointestinal cluster revealed that male heterozygotes had reduced odds of reporting either a laxative effect or upset stomach following caffeine intake. There was no difference among females, and since two-thirds of the sample is female, this null result may be more representative of the true effect. These models were adjusted for mean fibre intake.

58

PPP1R1B genotype was significantly associated with the flu-like anxiousness withdrawal symptom cluster. This cluster contained the symptoms of muscle pain/stiffness, nausea/ vomiting/ upset stomach, anxiety/ nervousness and flu-like symptoms. This association was driven by a strong gene-diet interaction. Among individuals consuming 200mg/d or greater of caffeine, the heterozygotes were only one-third as likely to report a combination of these symptoms. Compared to the T/T reference group, the OR point estimate for the C-allele homozygotes suggests a reduced occurrence of these symptoms, but the confidence interval includes unity. This lack of association for the C/C individuals may be due to the small sample size for this cluster.

Low doses of caffeine do not result in total blockage of the A2A receptors, necessitating a role of

DARPP-32 for sustaining or amplifying the caffeine signal [153]. In our population, a low level of receptor blockage can also be achieved when heavy caffeine consumers abstain and caffeine levels drop, relieving receptor antagonism and precipitating withdrawal symptoms. The onset of withdrawal symptoms would, therefore, be the result of a decrease in caffeine signalling. It is possible that the C-allele confers a protective effect, allowing the C-allele carriers to stave off distressful withdrawal symptoms arising from the decreasing levels of caffeine in the body.

Similar to our association, Reuter and colleagues found that individuals with the rare C/C genotype had significantly lower anger scores than C/T or T/T individuals, suggesting perhaps a greater tolerance to stressors [161], but the intronic position of this SNP makes it difficult to determine its mechanism of action. Haplotype analysis examining several SNPs in concert may provide greater insight into the role of intronic SNPs. As part of a 4-SNP haplotype, the rs907094 T-allele was positively associated with nicotine dependence [160], suggesting a need for T-carriers to seek nicotine as a coping mechanism. Within a common 7-SNP haplotype, the

59 rs907094 T-allele was associated with striatal structure and reactivity, as well as mRNA abundance [159] suggesting an impact of genetic variation on neuronal function and mRNA expression. Taken together, this evidence illustrates the potential effect of non-coding SNPs, and maintains interest in DARPP-32.

Lack of association between the clusters of acute effects of caffeine and PPP1R1B rs907094

C>T does not necessarily eliminate the possibility that DARPP-32 activity may underlie some of these acute effects. Future studies could continue to investigate the possible involvement of

DARPP-32 activity in producing acute effects of caffeine by examining the impact of other SNPs within the gene or its regulatory regions. This approach may be bolstered by haplotype analysis of the gene region, since SNPs may exist that work together to elicit these responses. Future research should also include the effector proteins altered by caffeine-mediated PP-1 activation.

The mechanisms of these downstream pathways need to be elucidated, and investigations of genetic variation in these components have the potential to improve our understanding of the pharmacodynamics of caffeine signalling.

In summary, we found that 14 well-described acute effects of caffeine co-exist in six groups: anxiousness, arousal, headache-dizziness, gastrointestinal, insomnia/impaired sleep and increased heart rate, while 14 well-characterized withdrawal symptoms co-exist in three groups: fatigue, dysphoric mood and flu-like - anxiousness. We also observed that PPP1R1B rs907094

C>T genotype modifies the odds of the caffeine withdrawal symptom of flu-like anxiousness and interacts with regular caffeine intake level to alter the likelihood of this symptom. These findings suggest that six mechanisms may underlie the acute effects of caffeine and three mechanisms may give rise to caffeine withdrawal symptoms. The findings also suggest that DARPP-32 plays a role in delaying the onset of selected withdrawal symptoms.

4 Chapter Four

Association between COMT Val158Met and the Acute

Effects and Withdrawal Symptoms of Caffeine

60 61

4.1 ABSTRACT

Fourteen acute effects and fourteen withdrawal symptoms of caffeine co-exist in six and three clusters, respectively, which may represent common mechanisms of caffeine action and withdrawal. Some of the physiological effects of caffeine may be modulated by catecholamine inactivation, which is catalyzed by the catechol-O-methyltransferase (COMT) enzyme. COMT activity is determined by a Val158Met polymorphism in the COMT gene, with the Val158 homozygotes having 3- to 4-fold greater activity than the Met158 homozygotes. The objective was to determine whether the COMT Val158Met is associated with the clusters of acute effects and withdrawal symptoms. Subjects were 20-29 year old females (n=801) and males (n=334) from the Toronto Nutrigenomics and Health Study. Multiple logistic regression was used to assess the association between COMT Val158Met and the clusters. We identified significant gene-diet interactions between COMT and caffeine intake levels for the acute effects cluster

“Increased Heart Rate” (p=0.001), as well as the withdrawal symptoms cluster “Dysphoric

Mood” (p=0.02). Among subjects consuming greater than 200 mg/d of caffeine, the adjusted OR

(95% CI) of Met/Met homozygotes reporting the “increased heart rate” cluster, compared to Val carriers, was 2.98 (1.04, 8.51). Within subjects consuming less than 100 mg/d of caffeine, the adjusted OR (95% CI) of heterozygotes reporting the “dysphoric mood” cluster, compared to

Val/Val homozygotes, was 2.18 (1.07, 4.45). These findings suggest that, depending on the level of habitual caffeine intake, the COMT Val158Met polymorphism may play a role in experiencing the acute effects and withdrawal symptoms of caffeine.

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4.2 INTRODUCTION

In Chapter 3, we observed that 12 of 14 well-described acute effects of caffeine co-exist in four clusters, defined as „Anxiousness‟, „Arousal‟, „Gastrointestinal‟ and „Headache – Dizziness‟. The remaining 2 effects of „Insomnia‟ and „Increased Heart Rate‟ occurred individually, for a total of

6 clusters of acute effects. We also observed that all 14 well-established caffeine withdrawal symptoms co-exist in three clusters, termed „Fatigue‟, „Dysphoric Mood‟ and „Flu-like

Anxiousness‟. It is possible that caffeine promotes acute effects within each cluster through a common physiological mechanism, and that there are six distinct mechanisms, each underlying a different cluster. Similarly, it is possible that caffeine abstinence elicits withdrawal symptoms through common mechanisms within each of the three clusters. These proposed mechanisms could mediate some of the physiological effects of caffeine through inactivation of catecholamine neurotransmitters [1].

Catecholamines such as dopamine, norepinephrine and epinephrine are neurotransmitters and hormones released as part of the body‟s response to stressors [176]. Catecholamine signalling occurs through binding to adrenergic receptors throughout the body, and leads to neuroendocrine changes resulting in mood and cardiovascular responses [176]. While caffeine potentiates the synthesis and release of catecholamines from the adrenal medulla [288, 289], studies on the effects of caffeine on catecholamine levels in plasma [105] and urine [98] have yielded inconsistent results. Similarly, attempts to measure caffeine-associated neuroendocrine or cardiovascular changes have failed to reach consensus [2, 98, 122, 290].

Catecholamine signalling is regulated through the action of reuptake mechanisms and catabolic enzymes. One such enzyme is catechol-O-methyltransferase (COMT), which inactivates catecholamines through O-methylation [165]. COMT is expressed in tissues throughout the

63 body, including the liver, brain, kidney, red blood cells [167] and heart [291]. To our knowledge, the role of COMT-mediated catecholamine inactivation following caffeine intake has not been experimentally or empirically established.

In humans, COMT exists as three enzymatic forms that lead to three levels of COMT activity: low, intermediate and high [200-202]. The high-activity form of the enzyme exhibits three-to- four fold greater activity than the low-activity form. This difference is controlled through a common SNP (rs4680) which encodes a guanine to adenine transition at codon 158, resulting in a valine to methionine substitution (Val158Met) [203]. The Met158 allele is associated with increased thermal instability and a reduced rate of catecholamine breakdown. This common polymorphism is a genomic marker for COMT activity in the catecholamine signalling pathway.

The role of catecholamine inactivation in the development of clusters of acute effects and withdrawal symptoms of caffeine can be assessed in a preliminary fashion by using this genomic marker of COMT activity.

It is possible that, relative to the Val158 allele, the Met158 allele may increase sensitivity to, and in turn incidence of, any acute effects clusters regulated by COMT activity. Related to its functional significance, it is possible that this polymorphism also alters sensitivity, and in turn incidence of specific caffeine withdrawal symptoms clusters modulated by COMT. The objective of this study, therefore, was to determine whether COMT Val158Met genotype affects the likelihood of reporting any clusters of acute effects or withdrawal symptoms of caffeine.

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4.3 METHODS

4.3.1 SUBJECTS AND DATA COLLECTION

Refer to Chapter Three, Section 3.3.1.

4.3.2 CAFFEINE AND ENERGY INTAKE

Refer to Chapter Three, Section 3.3.2.

4.3.3 CAFFEINE HABITS QUESTIONNAIRE

Refer to Chapter Three, Section 3.3.3.

4.3.4 GENOTYPING

Genotyping of the COMT Val158Met (rs4680) was performed by real-time PCR using a Custom

TaqMan® SNP Genotyping Assay (forward primer sequence CCCAGCGGATGGTGGATTT; reverse primer sequence AACGGGTCAGGCATGCA; reporter sequence for „A‟ allele

(methionine) CTTGTCCTTCATGCCAGC; reporter sequence for „G‟ allele (valine)

TTGTCCTTCACGCCAGC) from Applied Biosystems (Foster City, CA, USA), according to manufacturer‟s directions. Polymerase chain reaction conditions were 95°C for 10min, followed by 40 cycles of 95°C for 15sec and 60°C for 1min. Allelic discrimination was completed using the ABI Prism 7000 Sequence Detection System. To ensure assay validity and reproducibility, ten percent of the samples within each PCR run were replicated, and each run also contained four negative controls.

4.3.5 STATISTICAL ANALYSIS

Refer to Chapter Three, Section 3.3.5.

65

4.4 RESULTS

Genotype frequencies for the COMT Val158Met polymorphism were 37% for Val/Val, 46% for

Val/Met and 17% for Met/Met, and did not deviate from Hardy-Weinberg equilibrium. The minor COMT Val allele frequency was 40%. Subject characteristics by COMT genotype are shown in table 4-1. The different ethnocultural groups had significantly different COMT genotype frequencies (p<0.0001). The COMT polymorphism was also associated with differences in weight and BMI (p=0.0002, p=0.03, respectively), but these associations were due to population admixture and no longer present when the data were adjusted by ethnocultural group.

Table 4-2 shows the unadjusted and adjusted ORs (95% CIs) for reporting the acute effects clusters by COMT genotype. In the main effects models, COMT genotype did not influence the likelihood of reporting the acute effect clusters. As shown in table 4-3, there was a gene-diet interaction between caffeine intake and COMT genotype for the “gastrointestinal” cluster

(p=0.002). Among individuals consuming 100-200 mg/d of caffeine, the OR (95% CI) for reporting this cluster was 0.49 (0.24, 0.97) for the Val/Met genotype compared to the Val/Val genotype, while those with the Met/Met genotype were no more likely to report this cluster than the Val/Val genotype. There were no differences in the likelihood of reporting this cluster among the consumers of less than <100 mg/d or >200 mg/d of caffeine. Table 4-4 shows the results of another diet-gene interaction between caffeine intake and COMT genotype, for the “increased heart rate” cluster (p=0.001). Among those consuming >200 mg/d caffeine, the OR (95% CI) of reporting this cluster for the Met/Met genotype, compared to the Val/Val genotype, was 3.04

(1.16, 7.96) when unadjusted, and 2.98 (1.04, 8.51) after adjusting for covariates. There were no differences in the likelihood of reporting this cluster among the <100 mg/d or 100-200 mg/d consumption categories.

66 Table 4-5 shows the unadjusted and adjusted ORs (95% CIs) for reporting the withdrawal symptoms clusters by COMT genotypes. In the main effects models, COMT genotype did not influence the likelihood of reporting the acute effect clusters. As shown in tables 4-6 and 4-7, there were significant gene-sex (p=0.02) and gene-diet (p=0.02) interactions for the “dysphoric mood” cluster. Among females, the OR (95% CI) of Val/Met individuals reporting this cluster compared to Val/Val individuals was 1.65 (1.03, 2.64). However, among males there was no difference in the OR (95% CI) when comparing Val/Met individuals to Val/Val subjects (0.73

(0.28, 1.91)). Among the consumers of <100 mg/d of caffeine, Val/Met individuals were more likely to report the “dysphoric mood” cluster than their Val/Val counterparts, resulting in an OR

(95% CI) of 2.21 (1.08, 4.52). However, this was not the case for the other caffeine consumption groups. The ORs (95% CI) were non-significant for Val/Met compared to Val/Val for those consuming 100-200 mg/d of caffeine (0.91 (0.44, 1.88)) or >200 mg/d (1.57 (0.68, 3.64)).

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Table 4-1. Subject Characteristics by COMT Val158Met Genotype.

Characteristic Val/Val Val/Met Met/Met P (n=422) (n=518) (n=195)

Sex, n(%) Female 309 (38.6) 358 (44.7) 134 (16.7) 0.32 § Male 113 (33.8) 160 (47.9) 61 (18.3) Age, years 22.5 ± 2.3 22.6 ± 2.5 22.7 ± 2.5 0.60 † Ethnocultural group, n (%) Caucasian 122 (23.8) 264 (51.6) 126 (24.6) <0.0001 § East Asian 236 (57.28) 151 (36.7) 25 (6.1) South Asian 32 (25.8) 61 (49.2) 31 (25.0) Other 31 (36.5) 42 (49.4) 12 (14.1) Weight, kg 61.6 ± 12.1 64.5 ± 13.1 65.4 ± 13.5 0.0002 † BMI, kg/m² 22.4 ± 3.4 23.0 ± 3.5 22.9 ± 3.5 0.03 † Energy intake, kcal/d 2049.8 ± 886.7 2084.1 ± 903.4 2002.2 ± 857.3 0.54 † Physical activity level, MET hrs/week 179.5 ± 74.1 183.7 ± 75.1 190.0 ± 72.1 0.26 † Caffeine intake mg/d 103.9 ± 111.4 121.5 ± 133.5 109.8 ± 119.1 0.09 † mg/kg body weight /d 1.7 ± 1.8 1.9 ± 2.0 1.7 ± 1.8 0.20 † CYP1A2 genotype, n (%) A/A 194 (37.9) 233 (45.5) 85 (16.6) 0.86 § A/C + C/C 228 (36.6) 285 (45.8) 110 (17.7)

p-values for differences between genotypes were tested using †one-way ANOVA and §χ2 test. Subject characteristics are shown as mean ± SD for continuous variables and n (%) for categorical variables. Abbreviations: SD = standard deviation; BMI = body mass index; MET = metabolic equivalent task.

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Table 4-2. Frequency of the Clusters of Acute Effects of Caffeine Among COMT Genotypes and the OR (95% CI) of Reporting the Clusters.

Yes No OR (95% CI) OR (95% CI)

Cluster / COMT genotype n (%) Unadjusted Adjusted 1 Cluster 1 "Anxiousness" Val/Val 171 (45) 208 (55) 1.00 1.00 Val/Met 185 (41) 264 (59) 0.85 (0.65, 1.12) 0.98 (0.73, 1.31) Met/Met 79 (45) 97 (55) 0.99 (0.69, 1.42) 1.22 (0.83, 1.80) Cluster 2 "Arousal" Val/Val 301 (79) 78 (21) 1.00 1.00 Val/Met 348 (78) 99 (22) 0.91 (0.65, 1.27) 0.92 (0.65, 1.31) Met/Met 136 (78) 39 (22) 0.90 (0.59, 1.40) 0.91 (0.57, 1.44) Cluster 3 "Headache-Dizziness" Val/Val 63 (16) 319 (84) 1.00 1.00 Val/Met 67 (15) 380 (85) 0.89 (0.61, 1.30) 0.96 (0.65, 1.43) Met/Met 23 (13) 152 (87) 0.77 (0.46, 1.28) 0.84 (0.46, 1.46) Cluster 4 "Gastrointestinal" 2 Val/Val 132 (36) 237 (64) 1.00 1.00 Val/Met 159 (36) 278 (64) 1.03 (0.77, 1.37) 1.10 (0.81, 1.49) Met/Met 60 (35) 112 (65) 0.96 (0.66, 1.41) 1.06 (0.71, 1.60) Cluster 5 "Increased Heart Rate" Val/Val 98 (27) 262 (73) 1.00 1.00 Val/Met 135 (31) 298 (69) 1.21 (0.89, 1.65) 1.09 (0.78, 1.53) Met/Met 55 (33) 112 (67) 1.31 (0.88, 1.95) 1.08 (0.69, 1.67) Cluster 6 "Insomnia" Val/Val 80 (22) 289 (78) 1.00 1.00 Val/Met 97 (22) 341 (78) 1.03 (0.74, 1.44) 1.09 (0.76, 1.55) Met/Met 45 (26) 129 (74) 1.26 (0.83, 1.92) 1.30 (0.83, 2.06)

¹ Logistic regression model adjusted for sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake and oral contraceptive use in females. 2 Logistic regression model also adjusted for mean fibre intake.

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Table 4-3. Frequency of the Acute Effects "Gastrointestinal" Cluster Among COMT Val158Met Genotypes Stratified by Level of Caffeine Intake and the OR (95% CI) of Reporting the Cluster.

Caffeine intake / Yes No OR (95% CI) OR (95% CI) Interaction p COMT genotype n (%) Unadjusted Adjusted ¹ < 100 mg/d Val/Val 79 (34) 156 (66) 1.00 1.00 0.002 Val/Met 89 (35) 166 (65) 1.06 (0.73, 1.54) 1.12 (0.75, 1.67) Met/Met 42 (42) 57 (58) 1.45 (0.90, 2.36) 1.43 (0.84, 2.44) 100 - 200 mg/d Val/Val 41 (52) 38 (48) 1.00 1.00 Val/Met 40 (40) 61 (60) 0.61 (0.33, 1.10) 0.49 (0.24, 0.97) Met/Met 20 (44) 25 (56) 0.74 (0.35, 1.55) 0.60 (0.26, 1.39) > 200 mg/d Val/Val 23 (40) 35 (60) 1.00 1.00 Val/Met 50 (56) 39 (44) 1.95 (1.00, 3.82) 1.97 (0.95, 4.09) Met/Met 17 (57) 13 (43) 1.99 (0.81, 4.86) 2.01 (0.76, 5.31)

¹ Logistic regression model adjusted for sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake, oral contraceptive use in females and mean fibre intake.

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Table 4-4. Frequency of the Acute Effects Cluster of "Increased Heart Rate" Among COMT Val158Met Genotypes Stratified by Level of Caffeine Intake and the OR (95% CI) of Reporting the Cluster.

Caffeine intake / Yes No OR (95% CI) OR (95% CI) Interaction p COMT genotype n (%) Unadjusted Adjusted 1 < 100 mg/d Val/Val 82 (41) 116 (59) 1.00 1.00 0.001 Val/Met 93 (42) 126 (58) 1.04 (0.71, 1.54) 1.12 (0.74, 1.70) Met/Met 35 (44) 45 (56) 1.10 (0.65, 1.86) 1.21 (0.68, 2.15) 100 - 200 mg/d Val/Val 40 (56) 31 (44) 1.00 1.00 Val/Met 42 (47) 48 (53) 0.68 (0.36, 1.27) 0.61 (0.30, 1.22) Met/Met 14 (37) 24 (63) 0.45 (0.20, 1.02) 0.47 (0.19, 1.12) > 200 mg/d Val/Val 19 (36) 34 (64) 1.00 1.00 Val/Met 36 (49) 38 (51) 1.69 (0.82, 3.49) 1.43 (0.64, 3.20) Met/Met 17 (63) 10 (37) 3.04 (1.16, 7.96) 2.98 (1.04, 8.51)

¹ Logistic regression model adjusted for sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake and oral contraceptive use in females.

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Table 4-5. Frequency of the Clusters of Caffeine Withdrawal Symptoms Among COMT Genotypes and the OR (95% CI) of Reporting the Clusters.

Cluster / COMT genotype Yes No OR (95% CI) OR (95% CI) n (%) Unadjusted Adjusted 1 Cluster 1 "Fatigue" Val/Val 131 (67) 64 (33) 1.00 1.00 Val/Met 205 (75) 70 (25) 1.43 (0.96, 2.14) 1.49 (0.94, 2.37) Met/Met 83 (75) 28 (25) 1.45 (0.86, 2.44) 1.41 (0.78, 2.56) Cluster 2 "Dysphoric mood" Val/Val 93 (48) 101 (52) 1.00 1.00 Val/Met 151 (56) 121 (44) 1.36 (0.94, 1.96) 1.38 (0.92, 2.09) Met/Met 56 (50) 55 (50) 1.11 (0.69, 1.76) 1.08 (0.64, 1.83) Cluster 3 "Flu-like - Anxiousness" Val/Val 39 (20) 153 (80) 1.00 1.00 Val/Met 47 (17) 222 (83) 0.83 (0.52, 1.33) 1.07 (0.64, 1.78) Met/Met 22 (20) 89 (80) 0.97 (0.54, 1.74) 1.36 (0.71, 2.61)

¹ Logistic regression model adjusted for sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake and oral contraceptive use in females.

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Table 4-6. Frequency of the Caffeine Withdrawal Symptoms Cluster of "Dysphoric mood" Among COMT Genotypes Stratified by Sex and the OR (95% CI) of Reporting the Cluster.

Sex / COMT genotype Yes No OR (95% CI) OR (95% CI) Interaction p n (%) Unadjusted Adjusted 1 Male Val/Val 16 (41) 23 (59) 1.00 1.00 0.02 Val/Met 31 (39) 48 (61) 0.93 (0.43, 2.03) 0.73 (0.28, 1.91) Met/Met 15 (56) 12 (44) 1.80 (0.67, 4.84) 1.36 (0.42, 4.49) Female Val/Val 77 (50) 78 (50) 1.00 1.00 Val/Met 120 (62) 73 (38) 1.67 (1.09, 2.56) 1.65 (1.03, 2.64) Met/Met 41 (49) 43 (51) 0.97 (0.57, 1.64) 1.03 (0.57, 1.86)

¹ Logistic regression model adjusted for sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake and oral contraceptive use in females.

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Table 4-7. Frequency of the Caffeine Withdrawal Symptoms Cluster of "Dysphoric mood" Among COMT Val158Met Genotypes Stratified by Level of Caffeine Intake and the OR (95% CI) of Reporting the Cluster.

Caffeine intake / COMT Yes No OR (95% CI) OR (95% CI) Interaction p genotype n (%) Unadjusted Adjusted 1 < 100 mg/d Val/Val 26 (33) 52 (67) 1.00 1.00 0.02 Val/Met 45 (43) 59 (57) 1.53 (0.83, 2.81) 2.21 (1.08, 4.52) Met/Met 15 (36) 27 (64) 1.11 (0.51, 2.44) 1.35 (0.54, 3.38) 100 - 200 mg/d Val/Val 34 (52) 31 (48) 1.00 1.00 Val/Met 40 (49) 42 (51) 0.87 (0.45, 1.67) 0.91 (0.44, 1.88) Met/Met 24 (60) 16 (40) 1.37 (0.62, 3.04) 1.36 (0.57, 3.26) > 200 mg/d Val/Val 33 (65) 18 (35) 1.00 1.00 Val/Met 66 (77) 20 (23) 1.80 (0.84, 3.85) 1.57 (0.68, 3.64) Met/Met 17 (59) 12 (41) 0.77 (0.30, 1.97) 0.51 (0.18, 1.49)

¹ Logistic regression model adjusted for sex, age, ethnocultural group, physical activity level, BMI, alcohol intake, energy intake and oral contraceptive use in females.

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4.5 DISCUSSION

In Chapter 3, we observed that the 14 well-described acute effects of caffeine co-exist in six clusters, while the 14 well-established caffeine withdrawal symptoms co-exist in three clusters.

We hypothesized that caffeine elicits acute effects within a given cluster through a common physiological mechanism, and that there are six such mechanisms, each representing one cluster.

In a parallel hypothesis, the cessation of caffeine intake gives rise to withdrawal symptoms that group into three independent clusters, each of which may represent a single physiological mechanism. Some of these possible mechanisms may involve catecholamine/norepinephrine inactivation, wherein the COMT enzyme is a key component. We used a genomics approach to investigate whether COMT activity elicits any of the clusters of acute effects or withdrawal symptoms, by determining whether a functional polymorphism in COMT (Val158Met) altered the odds of reporting each of the clusters.

The COMT Val158Met polymorphism classified individuals as having either high (Val/Val), intermediate (Val/Met) or low (Met/Met) COMT activity. We hypothesized that relative to the

Val158 allele, the Met158 allele may increase sensitivity to and, in turn, incidence of any acute effects clusters regulated by catecholamines such as epinephrine. Related to its functional significance, it is possible that this polymorphism also alters sensitivity and, in turn, incidence of any caffeine withdrawal symptoms clusters modulated by epinephrine or other catecholamines.

The results from the present study showed that the COMT Val158Met polymorphism affected neither the odds of reporting any acute effects cluster nor withdrawal symptoms of caffeine in the main effects models. However, we identified significant gene-diet interactions between the

COMT polymorphism and caffeine intake level for two of the acute effects clusters and one withdrawal symptom cluster.

75 In the gastrointestinal tract, catecholamines bind to β-adrenergic receptors in the intestine, causing relaxation and a decrease in gastrointestinal motility [292, 293]. A genetic predisposition for reduced COMT activity (Met158-allele carriers) could affect gastrointestinal motility following a surge in catecholamine release caused by caffeine consumption, and therefore explain the symptoms observed by some individuals. We observed a gene-diet interaction for the

COMT polymorphism and the “gastrointestinal” cluster of acute effects. Within the moderate caffeine consumers (100 to 200 mg/d caffeine), heterozygous individuals were significantly less likely to report gastrointestinal acute effects than high activity Val158 homozygotes. This cluster includes the two individual effects of “upset stomach”, which is an adverse effect, and “laxative effect”, which may be a desired effect. We examined these two outcomes separately and determined that the positive association observed for the gastrointestinal cluster is influenced by the laxative effect.

Without consideration for genetic factors, the published evidence is varied on caffeine‟s gastrointestinal effects. Our finding that only moderate caffeine consumers had a lower incidence of gastrointestinal acute effects was in agreement with findings from a cross-sectional study examining the prevalence of upper gastrointestinal symptoms in Canadians [286]. The authors of that study reported that individuals with moderate (1 to 3 cups/d) coffee consumption had a decreased prevalence of chronic upper gastrointestinal symptoms, when compared to individuals who either abstain, or have high caffeine intake [286]. In one longitudinal study, women consuming less than one coffee per day were at reduced risk of constipation, while women at the high end of the consumption spectrum (>6 cups/d) were significantly more likely to report constipation [294]. This evidence suggests that moderate consumers reported fewer upper gastrointestinal symptoms compared to abstainers or those consuming high levels, but further research assessing the role of genetic variability is warranted.

76 While several studies report little to no effect of dietary caffeine on measured heart rate [17, 98-

100], one large Australian cross-sectional study linked subjective reports of palpitations to caffeine intake [96]. At 44% frequency, the “increased heart rate” cluster was the second most commonly reported cluster in this young population. The likelihood of reporting this cluster was influenced by a significant gene-diet interaction between COMT Val158Met and caffeine intake level. Only among the high caffeine intake category, those with slow COMT activity were three times as likely to report this cluster compared to individuals with the highest level of COMT activity. These findings are in agreement with one prospective follow-up study investigating how

COMT modifies the relationship between coffee and coronary heart disease (CHD) [295].

Happonen et al. found that among men homozygous for the 158Met-allele, which confers slow

COMT activity, heavy coffee drinkers had a 2-fold greater odds of acute coronary events [295].

High caffeine intake leads to increased synthesis and release of catecholamines into the circulation [296], and increased levels of excreted catecholamines can be detected in urine [295].

COMT expression in the cerebral cortex has been associated with blood pressure regulation in spontaneously hypertensive rats, compared to healthy rats [297]. Within mammalian heart tissue, intrinsic cardiac adrenergic cells capable of synthesizing and releasing norepinephrine and epinephrine have been identified [291]. The norepinephrine and epinephrine released in murine heart muscle cells affected the rate of beats within cell culture [291] and could reasonably affect heart rate in adults. Since COMT is also expressed in heart muscle [298], differences in COMT activity could locally alter heart rate through impaired catecholamine inactivation.

A recent meta-analysis investigating blood pressure response after habitual intake of coffee and caffeine found a non-significant increase in heart rate [97], but none of the randomized controlled trials therein considered the effect of genetics. Consideration of genetic variation could identify sub-groups who are particularly vulnerable. Cornelis et al. determined that a

77 polymorphism in the CYP1A2 gene encoding the cytochrome P450 1A2 (CYP1A2) enzyme modified the association between consumption of caffeinated coffee and risk of nonfatal myocardial infarction [299]. CYP1A2 is the primary metabolizer of caffeine [62], and among individuals with slow CYP1A2 activity, coffee was associated with increased risk of MI [299], since these individuals were genetically predisposed to slow caffeine metabolism. Within the present study, there was no effect of CYP1A2 genotype on the likelihood of reporting the increased heart rate cluster, or any of the other clusters. One important factor affecting the hemodynamic effects of caffeine in habitual consumers is the development of tolerance [104,

239, 300]. Even after accounting for genetic variation and other factors, inter-individual differences in tolerance may exist and could contribute to the inconsistencies among previous studies.

We identified a gene-sex interaction for the COMT polymorphism and “dysphoric mood” cluster of withdrawal symptoms. Dysphoric mood included the adverse withdrawal symptoms of

“depressed mood”, “decreased contentedness/well-being”, “irritability”, “foggy/not clearheaded”, and “difficulty concentrating”. Among females, heterozygous individuals showed greater odds of reporting this cluster, compared to 158Val homozygotes. Males showed no effect of COMT genotype, and there was no difference in the likelihood of reporting the dysphoric mood cluster. Differences in COMT expression or activity have previously been observed as being sex-specific. COMT activity was assayed in the brains of healthy Wistar rats consuming caffeine [301]. In males only, caffeine intake and stress resulted in increased anxiety-like behaviour [301]. This effect was not observed in females, and the authors hypothesized that these female rats may have been protected by the presence of female hormones, reducing the effects of caffeine or stress [301]. These effects were observed while the rats were in a caffeinated state, and there are no studies investigating COMT activity in the withdrawn state.

78 We also identified a gene-diet interaction, where heterozygotes within the low caffeine (<100 mg/d) group were twice more likely to report the dysphoric mood withdrawal cluster than the

158Val homozygotes. These findings in the low caffeine intake group are in contrast to previous work which has shown that the incidence of the dysphoric mood symptoms increases with increasing caffeine intake [214, 224]. Lack of association within our total sample, or the moderate (100-200 mg/d), or high (≥200 mg/d) caffeine intake subgroups, does not necessarily preclude the possible role of COMT in experiencing the mood effects associated with caffeine abstinence. All of the subjects included in this analysis were habitual caffeine consumers.

Habitual caffeine use produces physical dependence, and in some individuals, abrupt caffeine cessation will precipitate withdrawal symptoms.

Caffeine stimulates the release of catecholamines, which in turn are associated with arousal [1], and COMT catabolises these catecholamines [165]. However, there is little evidence of change in catecholamine levels associated with caffeine abstinence. Within non-habitual caffeine consumers, Robertson et al. administered one week of caffeine at 750 mg/d, followed by four days of placebo drinks. The authors reported no difference in urinary catecholamine levels after placebo, compared to the caffeinated state [104]. One limitation of that study is the use of subjects who were not habitual caffeine consumers. They may not have had sufficient exposure to develop dependence and then experience withdrawal following placebo. In a single-blind study, overnight caffeine abstinence was followed by either 300 mg of caffeine or placebo administration, and urine was collected over the next four hours to measure total excreted catecholamines [302]. Compared to the caffeinated state, placebo was associated with decreased levels of urinary epinephrine, which was interpreted as an effect of caffeine, rather than of caffeine withdrawal [302]. Without having measured urinary catecholamines at critical time points, i.e., immediately before and after administration of caffeine or placebo, the study was

79 unable to assess the effects of abstinence. Additionally, the investigators did not consider

CYP1A2 or COMT activity, which would have affected rates of caffeine and catecholamine metabolism, respectively.

Caffeine withdrawal syndrome is officially recognized in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) and International

Classification of Diseases (ICD-10) [213, 222], and caffeine abuse characteristics are similar to those of classical drugs of abuse and can affect mood [1]. However, previous studies on stimulant abuse do not include caffeine, and are inconclusive on the possible role of COMT. A recent meta-analysis reported an overall lack of association between the COMT Val158Met polymorphism and stimulant abuse [186]. Interestingly, for nicotine addiction, the “fast” Val/Val homozygotes experienced stronger negative cognitive effects [190], compared to “slow”

Met/Met individuals [303], during nicotine abstinence. One explanation for this observation is that the high activity Val158 allele could render individuals more susceptible to “seeking” behaviour when in the withdrawn state. An individual with the high activity COMT genotype would reach the “low” mood level faster, and may have a greater experience of withdrawal symptoms.

In summary, we observed that the COMT Val158Met genotype modifies the association between caffeine intake and the acute effect of increased heart rate. We also observed that COMT

Val158Met genotype interacts with both sex and caffeine intake to modify the odds of reporting the dysphoric mood withdrawal symptoms cluster. These findings suggest that COMT plays an important role in mediating caffeine‟s physiological effects, given that it may mediate signalling of the catecholamine system.

5 Chapter Five

Overall Discussion

80 81

5.1 SYNOPSIS

The hypotheses of this thesis were that (i) a polymorphism in PPP1R1B encoding DARPP-32 and (ii) a polymorphism in COMT may affect sensitivity to, and incidence of some of the acute effects and withdrawal symptom clusters of caffeine. The objectives were to determine whether

PPP1R1B (rs907094, C>T) or COMT (rs4680, Val158Met) genotypes modify the likelihood of reporting any of the groups of acute effects or withdrawal symptoms of caffeine. The chapter- specific objectives are as follows:

Objective 1 (Chapter Three): To determine whether PPP1R1B, rs907094 C>T genotype affects the likelihood of reporting any clusters of acute effects or withdrawal symptoms of caffeine.

Results: We found that 14 well-described acute effects of caffeine co-exist in six groups, while

14 well-characterized withdrawal symptoms co-exist in three groups. The findings suggest that six mechanisms may underlie the acute effects of caffeine and three mechanisms may underlie the caffeine withdrawal symptoms. We also observed that the rs907094 C>T polymorphism did not affect the odds of reporting any cluster of acute effects of caffeine in main effect models.

Among males, the C/T genotype was associated with lower odds of reporting the

“gastrointestinal” acute effects cluster. PPP1R1B C/T genotype was associated with decreased risk of reporting the “flu-like - anxiousness” caffeine withdrawal symptoms cluster compared to the TT genotype. Among ≥200 mg/d caffeine consumers, the C/T genotype showed a further reduced likelihood of reporting this cluster.

Objective 2 (Chapter Four): To determine whether COMT, rs4680 (Val158Met) genotype affects the likelihood of reporting any clusters of acute effects or withdrawal symptoms of caffeine.

82 Results: The COMT rs4680 Val158Met polymorphism did not influence the odds of reporting any cluster of acute effects of caffeine in main effect models. Among individuals consuming

100-200 mg/d of caffeine, the heterozygote genotype was associated with lower odds of reporting the gastrointestinal acute effects cluster. Among individuals consuming ≥ 200 mg/d caffeine, Met/Met homozygotes were at increased risk for reporting the “increased heart rate” acute effects cluster. The COMT polymorphism did not influence the odds of reporting any caffeine withdrawal symptoms clusters in main effect models. Among females, heterozygotes were more likely to report “dysphoric mood” symptoms. Among individuals consuming <100 mg/d of caffeine, heterozygotes were more likely to report this cluster.

The findings suggest that six mechanisms may underlie the acute effects of caffeine and three mechanisms may underlie the caffeine withdrawal symptoms. Such complex mechanisms, if they exist, may interact with pathways including the adrenergic, adenosinergic and dopaminergic systems, which mediate some of the physiological effects of caffeine. Lack of association between the acute effects clusters and PPP1R1B rs907094, C>T or the withdrawal symptoms clusters and COMT Val158Met (rs4680) does not necessarily eliminate the possibility that

DARPP-32 and COMT activities mediate some of these clusters. However, the results do suggest that DARPP-32 activity plays a mechanistic or signaling role in some aspects of caffeine withdrawal, and that polymorphisms examined herein may explain part of the inter-individual variability in risk for “flu-like – anxiousness” withdrawal symptoms. Additionally, the results suggest that COMT activity plays a signaling role in some aspects of caffeine acute effects, and that polymorphisms examined herein may explain part of the inter-individual variability in risk for increased heart rate acute effects.

83

5.2 LIMITATIONS

The caffeine habits questionnaire assessed the type and degree of acute effects experience within

12 hours of consuming “one caffeinated beverage”, and this may have been a limitation of the present study. The caffeine content of the beverage that subjects used as a reference would have varied between subjects because of differences in beverage type, size, and/or method of preparation. As the beverage was not standardized, this would have increased the variability of the acute effects data. This noise could have altered the identification of acute effects clusters and potentially masked associations between the clusters and genotype.

There are also potential limitations in the caffeine habits questionnaire when assessing the type and severity of caffeine withdrawal symptoms up to 48 hours after ceasing to consume caffeinated beverages. It is possible that within this duration, subjects unwittingly consumed caffeine from other sources. As little as 25mg, easily obtained from one tablet of caffeinated over-the-counter pain medication can be enough to prevent caffeine withdrawal symptoms [206].

This could have caused misclassification or underestimation in the susceptibility to some symptoms. However, it is unlikely that such misclassification errors would have confounded associations between caffeine withdrawal symptoms cluster and genotype because the errors would have been equally distributed across genotypes.

Another potential limitation to the questionnaire assessing the acute effects and withdrawal symptoms was that subjects could have misinterpreted or misattributed symptoms of other conditions they were experiencing. Symptoms of acute illness or premenstrual syndrome may have been misclassified as an effect of caffeine causing an overestimation of these symptoms.

Also, these questionnaires relied on memory, whose individual accuracy varies. However, it is unlikely that misclassification errors resulting from the aforementioned misinterpretation of

84 symptoms or inaccurate recollections confounded our results. These errors would have been equal across genotypes. Given the limitations outlined above, the most rigorous assessment of acute effects and withdrawal symptoms of caffeine would involve controlled caffeine dosing followed by administration of the questionnaires. Additionally, collection of blood or urine would add important information about the metabolism of caffeine or catecholamines, especially if collected over a range of time points.

For many of the acute effects or withdrawal symptoms, subjects reported that they did not experience any symptoms. Once the clusters were stratified into genotype groups, and then caffeine intake levels, some subgroups had low numbers. Such small subgroup sizes would have increased the risk of both Type I and Type II errors. Additionally, the multiple comparisons performed between the genotypes and clusters of acute effects and withdrawal symptoms may have increased the risk of Type I errors. However, this is unlikely since the results remained significant after applying a post-hoc Bonferroni correction.

Lastly, the acute effects and withdrawal symptoms clusters may not have represented common underlying physiological mechanisms of caffeine action and withdrawal as we hypothesized they did. It is possible that, with a larger sample size, or a different population the 14 acute effects and

14 withdrawal symptoms could have factored into different groups. While our sample of young adults aged 20-29 years may not have been representative of the adult population in age, this population might have reduced potential misclassification of susceptibility to acute effects and withdrawal symptoms of caffeine. Compared to older adults, individuals in this age range are likely to have fewer health conditions that could influence or complicate the identification of acute effects or withdrawal symptoms of caffeine.

85

5.3 FUTURE RESEARCH

Future research can build upon the current project‟s findings as well as limitations and examine related unexplored targets. Although the acute effects of caffeine have been well documented, there is yet to be a systematic review or meta-analysis published. Ideally, future studies assessing acute effects and withdrawal symptoms of caffeine would involve controlled caffeine dosing followed by questionnaires. The Profile of Mood States (POMS) questionnaire [304] could be used to assess subjects‟ mood before and after caffeine administration. The POMS questionnaire is sensitive to caffeine ingestion [93] and contains 65 adjectives capturing six dimensions of mood. This approach would standardize the exposure conditions and remove the potential for recall error. Collection of fluids, such as plasma or urine, would allow for unbiased measurements of caffeine and catecholamine metabolism within both the caffeinated and withdrawn states. These biomarkers could provide a wealth of data to correlate caffeine doses with reported subjective effects. Recruiting more subjects would increase the chances of having large subgroup sizes following stratification, and would strengthen our statistical power for secondary analyses. This would reduce the chances of committing Type I and Type II errors.

Future studies could continue to investigate the involvement of DARPP-32 and COMT activity in giving rise to clusters of acute effects or withdrawal symptoms by examining other genetic variants. Common haplotypes in both genes have been associated with either cognitive functioning, or mRNA expression and protein production [159, 186]. Within these haplotypes,

SNPs may work in concert to alter the function of the gene. Examining haplotypes in addition to single SNPs may provide more evidence for the role of these proteins in mediating the effects of caffeine.

86 Additional enzymatic targets can be found within the adrenergic signaling pathway, downstream of DARPP-32. DARPP-32 signaling results in changes to the phosphorylation patterns of effector proteins and the expression of immediate early genes (IEGs) [147]. The caffeine content of just one cup of coffee is able to reduce the expression of several IEGs, including c-fos, and nerve growth factor-induced clones (NGFI-A, NGFI-B) [155, 156]. These early responders regulate expression of effector genes, but their overall role in caffeine-induced signaling is not clear. As a regulator of catecholamine signaling, COMT acts in conjunction with the monoamine oxidase (MAO) enzyme, as well as the norepinephrine transporter (NET) to remove released catecholamines [167]. Genetic variation encoding these proteins could mediate caffeine‟s effects.

In summary, this research has provided further insight into the mechanisms by which caffeine consumption increases the risk of any acute effects or withdrawal symptoms of caffeine and identification of individuals who may be more susceptible to the increased heart rate acute effect, or the flu-like – anxiousness withdrawal symptom associated with caffeine consumption. This research incorporated genetic markers involved in caffeine signaling and has demonstrated the power of nutrigenomics in furthering our understanding of how diet and genetic variation interact to produce the various caffeine-related effects.

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104 Appendix I

Caffeine-Containing Beverage & Food FFQ Items

105

106

107 7 Appendix II

CAFFEINE HABITS QUESTIONNAIRE

Do you currently, or have you ever, consumed caffeine-containing beverages (e.g., coffee, tea, cola) regularly?

 No, I have never regularly consumed them (GO TO Q17)  Yes, I currently consume them regularly  Yes, I used to consume them regularly but do not anymore

If yes, please indicate next to each of the following withdrawal symptoms the degree to which you experience(d) them up to 48 hours after ceasing to consume caffeine-containing beverages.

SYMPTOM Don’t None Mild Moderate Severe know Headache Tiredness/fatigue Decreased energy/activeness Decreased alertness/attentiveness Drowsiness/sleepiness Decreased contentedness/well-being Depressed mood Difficulty concentrating Irritability Foggy/not clearheaded “Flu-like” symptoms Nausea/vomiting/upset stomach Muscle pain/stiffness Anxiety/nervousness

Do you experience any of the following effects up to 12 hours after consuming one caffeine-containing beverage (e.g., coffee, tea, cola)?

EFFECT Don’t None Mild Moderate Severe know Headache Increased energy/ activeness Increased alertness/attentiveness Elevated mood Increased heart rate Anxiety/nervousness Panic attacks Restlessness Agitation Tremors/ Jitters/ Shakiness Dizziness Insomnia/ Impaired sleep Upset stomach Laxative effect