Association of Three Biomarkers of Nicotine As Pharmacogenomic Indices of Cigarette Consumption in Military Populations DISSERTA

Association of Three Biomarkers of Nicotine as Pharmacogenomic Indices of Cigarette

Consumption in Military Populations

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

William Arthur Matcham

Graduate Program in Nursing

The Ohio State University

2014

Dissertation Committee:

Professor Karen L. Ahijevych, PhD, Advisor

Professor Donna L. McCarthy, PhD

Professor Kristine Browning, PhD

Professor Yvette Conley, PhD

William Arthur Matcham

2014

ABSTRACT

Tobacco-related diseases have reached epidemic proportions. There is no risk-free level of tobacco exposure. In the United States, tobacco use is the single largest preventable cause of death and disease in both men and women. Cigarette smoking alone accounts for approximately 443,000 deaths per year (one fifth of total US deaths) costing a staggering $193 billion per year in avoidable healthcare expenses and lost productivity.

Literature shows military populations have rates of tobacco use two to three times higher than the civilian population. Military personnel returning from deployment in conflict areas can exceed 50% smoking prevalence. Research shows that genetic factors account for 40-70% of variation in smoking initiation and 50-60% of variance in cessation success. In the U.S., tobacco is responsible for more deaths than alcohol, AIDS, car accidents, illegal drugs, murders and suicides combined.

This descriptive, cross-sectional study examined three of the biological markers used in tobacco research: the α4β2 brain nicotinic receptors (nAChR) that contribute to genetic risk for nicotine dependence, nicotine metabolite ratio (NMR) as a phenotypic marker for CYP2A6 activity, and bitter taste phenotype (BTP) to determine their impact on cigarette consumption in military populations. Sociodemographic and military variables were examined to determine if they impacted biomarker relationships. The availability of reliable biomarkers will facilitate development of personalized smoking cessation therapies for military personnel.

The first chapter reviewed the state of the science related to the nicotine metabolism in the human body, nicotine acetylcholine receptor in the brain and perception of bitter taste as they apply to nicotine and smoking research. An in-depth description of CYP2A6 genetics and phenotype measurement is presented including identification of gene variation, problems with standardizing genetic testing, naming conventions and classifications. The function of nicotine acetylcholine receptor is reviewed with a detailed description of the rs16969968 single nucleotide polymorphism used to characterize risk of nicotine dependence. Bitter taste phenotype is reviewed in the context of cigarette smoking.

The second chapter provided an overview of recruitment techniques used with military personnel. A timeline of recruitment activities was followed by a review of internal and external environmental influences that affected recruitment. An analysis of lessons learned is presented with a summary of strategies to overcome recruitment challenges which can be applied to broader populations than military personnel.

The third chapter presents the method and procedures of the study. Inadequate subject accrual resulted in only 15 of the expected 160 participants completing the study.

The results of the study were analyzed with biserial and Kendall’s tau coefficients but overall were not significant. The planned prediction modeling and interaction analysis could not be conducted due to low participant enrollment. Results did show some interesting relationships between military and sociodemographic variables. This study has provided valuable data to characterize the diverse individuals in the military and provides evidence for inclusion of this important group in future studies.

iii

To my wife Ann for inspiring me to dream

ACKNOWLEDGMENTS

I would like to express sincere gratitude to Dr. Karen Ahijevych for her unwavering support and mentorship in my academic pursuits and professional development as a scholar. I would like to thank Dr. Donna McCarthy, Dr. Kristine

Browning and Dr. Yvette Conley for their expertise and continued support throughout my doctoral education. I appreciate your time and energy to provide comments and edits on my documents and always being available for consultation.

I would also like to thank Dr. von Sadovszky for her support, guidance and mentorship as I transition into a faculty role. Special thanks to Dr. Christopher Bartlett for volunteering to represent the Graduate Faculty during my dissertation defense.

Most importantly I would like to thank my wife Ann for her endless patience and unwavering support.

This research was supported by a generous financial award from the Epsilon

Chapter of Sigma Theta Tau at the Ohio State University College of Nursing.

VITA

June 1989 ...... Elyria West High School

1998 ...... A.A.S Nursing, Lorain County Community

College

2003 ...... A.A.S Technical Studies – Electronic

Communication Technology / Digital

Instrumentation Systems, Excelsior College

2007 ...... B.S.N. Nursing, Capital University

2011 ...... Fellowship Summer Genetics Institute,

National Institutes of Health, National

Institute of Nursing Research

2012 ...... M.S. Nursing Science, The Ohio State

University

2012 ...... Graduate Interdisciplinary Specialization in

University and College Teaching, The Ohio

State University

1999 to 2000 ...... Registered Nurse, Perioperative Services,

University Hospitals of Cleveland

2000 to 2005 ...... Registered Nurse, Traveling Nurse

Perioperative Services, RN Network

2005 to 2009 ...... Registered Nurse, Intensive and Cardiac

Care Unit, Doctor’s Hospital West

2004 to 2012 ...... Registered Nurse, Disaster Medical

Assistance Team OH-5, National Disaster

Medical System, U.S. Department of Health

and Human Services

2008 to 2009 ...... Graduate Teaching Associate, College of

Nursing, The Ohio State University

2009 to Present ...... Graduate Administrative Associate, College

of Nursing Information Technology, The

Ohio State University

2011 to Present ...... Graduate Research Associate, College of

Nursing, The Ohio State University

Fields of Study

Major Field: Nursing

vii

Table of Contents

Abstract ...... ii

Acknowledgments...... v

Vita ...... vi

List of Tables ...... xiii

List of Figures ...... xv

Chapter 1: Three Biomarkers of Tobacco Use: State of the Science ...... 1

Introduction ...... 1

Nicotine Metabolism ...... 3

Nicotine ...... 3

Metabolism ...... 4

Nicotine Metabolism Genetics ...... 6

Genetic Grouping Nomenclature ...... 11

Phenotypic Measurement of Nicotine Metabolism ...... 15

NMR Phenotype Group Nomenclature ...... 17

NMR Phenotype Ethnic Variation ...... 18

Nicotine Dependence ...... 20

viii

Nicotine Acetylcholine Receptors ...... 21

Nicotine Receptor Genetic Variation ...... 22

RS16969968 Genetic Variant of α4β2α5 Receptors ...... 24

RS16969968 as proxy for Nicotine Dependence ...... 25

Bitter Taste Perception ...... 30

Bitter taste genetics ...... 31

Bitter Taste Phenotype (BTP)...... 32

Relationship of Bitter Taste to Smoking Literature ...... 32

Conclusion and Future Direction ...... 34

Chapter 2: Research Participant Recruitment of Military Cigarette Smokers: Lessons

Learned ...... 36

Introduction ...... 36

Purpose ...... 38

Methods ...... 38

Recruitment Timeline and IRB Amendments ...... 40

Lessons Learned ...... 45

Recruitment Issues Related to Internal Environment ...... 45

Inclusion and Exclusion Criteria ...... 45

Electronic Systems for Study Delivery ...... 46

Protocol Flexibility ...... 47

Sensitive Questions of Military History ...... 47

Recruitment language ...... 48

Website Hosting ...... 48

Participant Travel ...... 49

Recruitment Issues Related to the External Environment ...... 49

Smoking Legislation ...... 50

Federal Government Shut Down ...... 51

Access to Military Facilities ...... 52

Tobacco Alternative ...... 53

Summary of Strategies to Overcome Recruitment Challenges ...... 54

Chapter 3: Relationship of Three Biomarkers of Nicotine, Cigarette Consumption and

Military Experiences in a Sample of Military Smokers...... 56

Introduction ...... 56

Overview of Tobacco use in the Military...... 57

Three Biomarkers of Tobacco Use...... 60

Conceptual Framework ...... 62

Data Collection ...... 64

Recruitment ...... 64

Variables...... 65

Methods ...... 71

Design ...... 71

Sample ...... 72

Human Subject Protection ...... 72

Procedures ...... 73

Data Analysis ...... 74

Results ...... 74

Discussion ...... 79

References ...... 83

Appendix A: Pathways of Nicotine Metabolism in the Human Body ...... 106

Appendix B: CYP2A6 Allele Frequencies by Ethnicity Tables ...... 108

Appendix B Table Source Citations: ...... 119

Appendix C: Tables for Classification of CYP2A6 Alleles into Functional Metabolic

Groups ...... 122

Appendix C Table Source Citations ...... 128

Appendix D: Nicotine Metabolite Ratio (NMR) and CYP2A6 Genotype Groupings and

Nomenclature Tables ...... 130

Appendix D Table Source Citations ...... 135

Appendix E: Summary table of Phenotypes Associated with rs16969968 Risk Genotype in the Literature ...... 136

Appendix E Table Source Citations ...... 138

Appendix F: Research Recruitment Advertisement and Approximate Media Circulation

Table ...... 139

Appendix G: Glass & McAtee Stream of Causation Framework Adapted for Nicotine

Research ...... 142

Appendix H: Data and Intercorrelation of Variables Tables ...... 144

xii

LIST OF TABLES

Table 1. Major CYP2A6 Alleles Representing Genetic Changes ...... 8

Table 2. Summary of Eight Common CYP2A6 Alleles in Three Major Population Groups

...... 9

Table 3. Five CYP2A6 Alleles used to Define Chinese Metabolic Groups. Allele

Frequency in other Ethnic Groups ...... 14

Table 4. rs16969968 Allele frequencies by ethnicity from 1000 Genome project ...... 28

Table 5. Independent and Dependent Variables, Measures and Expected Outcomes ...... 66

Table 6. Sociodemographic and Military Variables Measured in Study ...... 69

Table 7. Socio-demographic Characteristics of Participants (N=15)...... 75

Table 8. Military Attributes of Participants (N=15)...... 76

Table 9. Cigarette use Attributes of Participants (N=15)...... 77

Table 10. Measured Biomarkers of Nicotine Use (N=15)...... 78

Table 11. CYP2A6 Allele Frequencies for Caucasians, Europeans and European Descent

...... 109

Table 12. CYP2A6 Allele Frequencies for African, African Descent, and Indians, ...... 112

Table 13. CYP2A6 Allele Frequencies for Asian, Chinese, Japanese, Korean, and Thai

...... 115

Table 14. CYP2A6 Allele Frequencies for Brazillians, Ecuadorians, Iranian, Sri Lanka, and Malays ...... 117

xiii

Table 15. CYP2A6 Normal/High/Extensive/Super Metabolizers Groups ...... 123

Table 16. CYP2A6 Intermediate/Moderate Metabolizer Groups ...... 124

Table 17. CYP2A6 Slow/Reduced Metabolizer Group ...... 125

Table 18. CYP2A6 Poor/Loss of Function Metabolizer Group ...... 126

Table 19. CYP2A6 Group Numbering System for Metabolizer Groups ...... 127

Table 20. Normal NMR Metabolism Grouping ...... 131

Table 21. Intermediate NMR Metabolism Grouping ...... 132

Table 22. Reduced NMR Metabolism Grouping ...... 133

Table 23. Slow NMR Metabolism Grouping ...... 134

Table 24. Summary Table of Phenotypes Associated with rs16969968 Risk Genotype in the Literature ...... 137

Table 25. Research Recruitment Advertisement and Approximate Media Circulation . 140

Table 26. Intercorrelation of Biomarkers of Nicotine, Relevant Sociodemographic, and

Military Variables ...... 145

xiv

LIST OF FIGURES

Figure 1. Nicotine Metabolism Pathways ...... 5

Figure 2. Primary Pathway of Nicotine Metabolism...... 6

Figure 3. CYP2A6 Variation and Allele Grouping ...... 7

Figure 4. Comparison of NMR Quartiles and Effect on Nicotine Metabolism ...... 15

Figure 5. Illustration of a Neural α4β2 Nicotinic Receptor ...... 22

Figure 6. Genetic variations in CHRNA gene ...... 23

Figure 7. Recruitment Timeline ...... 41

Figure 8. Pathways of Nicotine Metabolism in the Human Body...... 107

Figure 9. Stream of Causation Gene-Environment Interaction Framework ...... 143

CHAPTER 1: THREE BIOMARKERS OF TOBACCO USE: STATE OF THE SCIENCE

Introduction

Tobacco-related diseases have reached epidemic proportions. There is no risk-free level of tobacco exposure.1 In the United States, tobacco use is the single largest preventable cause of death and disease in both men and women. Cigarette smoking alone accounts for more than 440,000 deaths per year (one fifth of total US deaths) costing a staggering $96 billion per year in avoidable healthcare expenses and $97 billion in lost productivity.2 Research shows that genetic factors account for 40-75% of variation in smoking initiation, 70-80% of variation in smoking maintenance and about 50-60% of variance in cessation success.3-7 Research has identified risk for smoking initiation and nicotine addiction as inherited traits. Tobacco is projected to be responsible for 50% more deaths than HIV/AIDS8 and accounts for more U.S. deaths than alcohol, AIDS, car accidents, illegal drugs, murders and suicides combined.6

It is generally accepted that genetics affect smoking behavior; however, researchers do not agree upon how much of behavior variance is accounted for by genetic variation. A study by Boardman et al. using twin pairs showed only 35% of the variance in regular cigarette smoking is due to genetic influences, much less than the 70-80% shown by other researchers.9 In a related twin study, Swan et al. demonstrated that variance of smoking behaviors explained by biology can be significantly altered

depending on characteristics of the population. In his study, Swan and associates showed variation in plasma nicotine metabolites attributing to additive genetic influences dropped from 67.4% to 49.4% after taking into account covariates such as gender, oral contraceptive use, and age.10

Appreciable strides have been made over the last several decades to decrease smoking initiation and increase cessation success; however, smoking prevalence in the

U.S. population remains above the goal of 12% set in Healthy People 2020.11-12 The latest smoking prevalence report from the CDC reports that in 2012 the adult smoking prevalence in the U.S. dropped to 18.1% 13, but there still is great ethnic diversity with

Asians at 9.9% and American Indian/Alaskan Natives at 31.5% prevalence.2 Of current adult smokers, 68.8% reported wanting to quit, 54% have tried but approximately only

6% were successful without assistance.12 One study showed that concurrent uses of multiple treatment modalities can increase12-month abstinence rates to ~30%.14

Military populations have rates of smoking two to three times higher than the civilian age-adjusted all adult prevalence 15 with personnel returning from deployment in conflict areas exceeding 50% smoking prevalence.2, 16-19 A 2012 study of deployed Air

Force personnel showed that more than half (53%-63%) used tobacco at all stages of the deployment cycle and displayed a higher than garrison (non-deployed) use of smokeless tobacco.20 Cigarette smoking during military service is associated with lifelong increased cigarette consumption compared to those who have not served.19, 21

Personalized tobacco treatment options for this population may make tobacco cessation efforts more successful and reduce tobacco-related mortality and morbidity.

One strategy to help increase cessation success is to customize treatment to the individual. Biologic markers of nicotine use can be used to tailor psychosocial / psychoeducational recommendations and pharmacologic support to the individual’s smoking behavior.

This article will summarize the state of the science of three biomarkers used in nicotine and tobacco research that have the potential to advance the science informing tobacco cessation modalities. In the first section, I will provide a review of nicotine metabolism in the human body, followed by a discussion of genetic influences on nicotine metabolism and current biologic methods of measuring nicotine metabolism.

The second section will provide a review of the genetic risk of nicotine dependence related to polymorphisms in nicotine receptors in the brain, how changes in receptor morphology affect nicotine dependence and finally a genetic test currently being used to determine risk. In the third section, a review of the current literature on bitter taste as it relates to tobacco use is presented, as well as a discussion of how current taste-testing techniques contribute to smoking cessation success. In the final section of the paper on future directions, I will explore potential implications of using combinations of these three biomarkers in smoking cessation research to develop novel treatment modalities.

Nicotine Metabolism

Nicotine

Nicotine, the primary addictive constituent of burned tobacco, is a toxic alkaloid

(used as an insecticide) that is quickly absorbed through the lungs into the blood stream.

Nicotine produces biologic effects by binding to nicotinic receptors throughout the body,

but most highly concentrated in the brain.22-24 Inhaling tobacco smoke bypasses portal circulation and hepatic first pass metabolism which normally reduces bioavailability of ingested nicotine down to 30-40% of oral dose.25 Nicotine quickly circulates to the brain where it binds with nicotine receptors producing pleasurable feeling through activation of the dopamine reward system. Nicotine not bound to the receptors on the first pass is circulated through the liver where it is metabolized by the P450 cytochrome system.

Repeated dosing with nicotine maintains blood concentrations but over time can cause powerful physiologic and psychological addiction.

Metabolism

In humans, nicotine in the body is primarily (~80%) metabolized in the liver by the cytochrome P450 family 2, subfamily A, polypeptide 6 (CYP2a6) enzyme (coded for by a gene with the same name) through C-oxidation into the major proximate metabolite, cotinine (Cot) (see Figure 1).

The remaining nicotine is metabolized through several lesser pathways (including

UGT glucuronidation ~15% and FMO3 variation ~2%) into minor metabolites or cleared from the body as unchanged nicotine.8, 24, 26-28 See Figure 8 in Appendix A for a comprehensive diagram of all nicotine metabolism pathways and downstream metabolites.

Cotinine is further metabolized into trans-3`-hydroxycotinine (3HC) and several lesser metabolites. The metabolic pathway of Cot to 3HC, unlike the other downstream metabolites is controlled almost exclusively by the liver enzyme CYP2A6 (see Figure 2) with only a minor contribution by glucuronidation (UGT).

Figure 1. Nicotine Metabolism Pathways

From “Metabolism and Disposition Kinetics of Nicotine” by J. Hukkanen, P. Jacob and N. Benowitz, 2005, Pharmacological Reviews, 57, p.89.

Nicotine metabolism is a function of dose, route and elimination which all affect smoking behavior.29 As nicotine is inactivated, there is less circulating drug to bind with brain receptors, which produces withdrawal symptoms that trigger cravings for additional nicotine intake (smoking).8 Most habitual smokers continue to consume cigarettes in an attempt to mitigate withdrawal effects produced by nicotine being eliminated from the body through metabolism as opposed to receiving the pleasurable ‘buzz’ associated with early smoking.30-32 Individuals with faster metabolism must consume larger numbers of cigarettes to maintain nicotine levels and prevent symptoms of withdrawal.33-35

Figure 2. Primary Pathway of Nicotine Metabolism.

From “Metabolism and Disposition Kinetics of Nicotine” by J. Hukkanen, P. Jacob and N. Benowitz, 2005, Pharmacological Reviews, 57, p.89.

Nicotine Metabolism Genetics

The CYP2A6 gene on chromosome 19 at location 19q13.2 (gene id: 1548) codes for the production of the CYP2a6 liver enzyme that catalyzes the reaction to detoxify certain drugs, including nicotine, and is highly polymorphic.36, 37 In 2013, dbSNP Short

Genetic Variation database lists 356 single nucleotide polymorphisms (SNP) in the

CYP2A6 gene region consisting of synonymous, non-synonymous, missense, intron, donor, deletion, acceptor and frame shift errors, many of which are very rare with minor allele frequencies (MAF) as low as .0005.38 Of the 356 SNPs, 136 have been characterized as coding errors which result in functional changes to amino acid coding.39

To better understand the functional changes and to organize the CYP2a6 variants, the 136 SNPs have been grouped together into functional alleles that are compared to a reference strand designated as *1 that represents the sequence found in the majority of the population and is considered to represent ‘normal’ nicotine metabolism.36 At the time of writing, 83 CYP2A6 alleles had been identified, 38 of which are currently characterized in the literature as functional alleles and have been given designation of *1 - *38 (see

Figure 3), with many possessing minor variants such as *1A, *1B4, etc. (full list available online at http://www.cypalleles.ki.se/cyp2a6.htm).40, 41

Figure 3. CYP2A6 Variation and Allele Grouping

The alleles most predominantly used for nicotine metabolism classification are summarized in Table 1 and show the diversity of nucleotide and gene changes. These changes require complex genetic testing to detect in order to build each allele profile.

Multiple alleles are needed to determine the overall effect on an individual’s nicotine metabolism.

Unfortunately, most of the genome data available was derived from individuals of

European Descent (white) so the standards of ‘normal’ metabolism may not apply universally to all races especially since this gene is so variable.42 Research has shown that even individual’s in the ‘wild type’ group have significant variation in CYP2A6 activity.

Table 1. Major CYP2A6 Alleles Representing Genetic Changes

Gene Position and nucleotide Effect on Enzyme Allele Protein affected (SNP) changes Activity *1 CYP2A6.1 None Normal activity

*2 CYP2A6.2 51 G>A; 1799 T>A None

*4 (A-H) CYP2A6 Gene Deletion Decreased Activity

6558 T>C Gene Conversion in *7 CYP2A6.7 Decreased Activity 3` flanking region

-1013 A>G; -48 T>G; -1680 A>G; -1301 A>C; -1289 G>A; *9 (A-B) CYP2A6.1 Decreased Activity 1620 T>C; 1836 G>T; 6354 T>C; 6692 C>G 10 Amino Acid Substitution exons 1-2 of CYP2A7 origin, *12 (A-C) CYP2A6.12 Decreased Activity exon 3-9 of CYP2A6 origin plus various SNP changes

209 C>T; 1779 G>A; 4489 *17 CYP2A6.17 C>T; 5065 G>A; 5163 G>A Decreased Activity 5717 C>T; 5825 A>G

51 G>A; 2141_2142 deleteAA *20 CYP2A6.20 (Frameshift); 2296C>T; Decreased Activity 5684T>C; 6692C>G Note: All data summarized from data available at http://www.cypalleles.ki.se/cyp2a6.htm

The frequency distribution of the CYP2A6 alleles is remarkably different between ethnic groups.43 Table 2 demonstrates some of this diversity with a selection of eight of the common alleles as they appear in three different ethnic populations. For comprehensive details on allele distribution between ethnic groups, see Tables 16-19 in

Appendix B.

Table 2. Summary of Eight Common CYP2A6 Alleles in Three Major Population Groups

*1A *2 *4 *7 *9 *12 *17 *20 European 54 - 67% 1-3% 0 - 5% 0 - 1% 5 - 8% 2 - 3% 0% 0% African American 67% 0 – 1% 2% 0% 8 - 10% 1% 8 - 11% 2% No Asian 16 - 62% 0 - 1% 11 - 18% 2 - 13% 14 - 22% data 0% 0% Note: Values rounded to nearest whole number

There are also significant differences between allele frequencies within racial subgroups. In a study of Asian cancer patients, researchers found that the allele frequency for *4 deletions was much higher in Chinese (15%) than Japanese (2.2-3.2%) Malays

(7%) or Indians (2%) populations.44 Another investigator had similar findings with a *4A allele frequency of 7.1% of Ecuadorians but only 4% of Spaniards.45

This variation in allele distribution makes comparing data across ethnic/racial studies very difficult, especially when there is no standardized vernacular to define the functional classifications of nicotine metabolism being used in research. This is not unique to CYP2A6 or nicotine research. In a large study of eleven phase I drug metabolism genes (including CYP2A6), researchers found that there where large ethic differences among the genes and gene subfamilies with most of the diversity coming from low-frequency polymorphisms.46

Through functional genetic analysis, researchers have shown that the CYP2A6 gene encodes the enzymatic protein CYP2A6 which is primarily (~80%) responsible for first stage nicotine metabolism and almost exclusively responsible for metabolism of nicotine metabolites via the 3HC pathways. Genome wide association studies (GWAS) show that CYP2A6 variants are highly associated with smoking behaviors, negative

health outcomes (obesity and hypertension) and lung cancer.47, 47-51 Other research supports that CYP2A6 genetic variants may be used to predict smoking cessation success, especially with nicotine replacement therapy.3

One problem that exists with the use of *1 wild-type allele as the standard normal nomenclature is that in any given study, if a suspected gene variant being tested for is not found, then it is assumed that the individual is a wild-type. This caused misclassification through over estimation of wild-type in some populations, especially in studies that only test for a few allele variations. In a 2010 study by Mwenifumbo, et al., accurate genotyping and classification was further complicated because some variants such as

*1B4 and the *1L gene conversion (which would normally be grouped with *1 wild- type) confounds genotyping assays that use the standard 2A6R3 and 2A6R4 primers because the conversion is located where these primers are designed to anneal during the

PCR reaction, causing false negative results. The researchers compensated by designing new primers to specifically detect the variants, but acknowledge that due to the enormous variation in the CYP2A6 gene, there is no area in the 3`-flanking region that can be used with 100% confidence as a gene specific primer location.52 Other researchers have found variants such as *28 that may produce gain-of-function copy number variants producing greater than *1 ‘normal’ metabolism.53

As shown in Table 2 and Tables 16-19 in Appendix B, several alleles classified as

‘common’ and used to classify individuals into functional metabolism groups do not occur equally in all populations. This makes having a unified and universal classification and nomenclature system for CYP2A6 metabolism very difficult. In the literature, each

investigator grouped and classified individuals based on results produced in their population of interest. For example, Han et al. (2012) found *7 allele was common

(9.8%) in his Asian population despite the fact that it does not occur in African

Americans and less than one percent in Europeans. In a sample of Black African descent, researchers found new deletion alleles *4G and *4H with a combined allele frequency of

1.6% which have never been found in Caucasians.54

The science surrounding CYP2A6 allele variation is still evolving. At this point, there are many alleles that have not been tested in diverse ethnic groups and over 45 that have not been characterized in the literature. New variations of CYP2A6 continue to be discovered and their potential impact on the state of the science is yet unknown.

Genetic Grouping Nomenclature

Alleles represent variations in a gene and can be used to classify major differences in metabolism between groups of people. In the case of CYP2A6, the functional alleles allow classification of the genetically informed nicotine metabolic rate of an individual. The first delineation of normal vs. impaired nicotine metabolism were published by the researchers at the Addiction Research Foundation of Ontario in 1998 and contained only three alleles; the normal (wild-type) *1 allele and two null/inactive alleles, *2 and *3 but since then there has been no consensus on standard naming conventions.55 In this nomenclature and classification system it is assumed that the most common allele in the population (*1) is the normal, with 100% nicotine metabolism. The functional metabolic group designations have changed over the last two decades as more

research has provided expanded evidence of allele functions and genetic classification

(see Tables 11-15 in the Appendix C).

The assumption that *1 represents the ‘normal’ in a population may be accurate in populations with high frequency of *1 alleles such as Ghanaian (80.5%)45, or central

African Aka tribes (80.5%)56; however it may introduce error when an investigator classifies an individual in a sample that does not possess the *1 allele in a majority of the represented population. Research done by Soriano on *1 allele frequency shows Japanese only have a 16.4 - 20.3% frequency, Chinese 27.2% and Thai 32%, which is much lower than needed to consider this a ‘majority’ allele in the population45 possibly warranting a different approach to nicotine metabolism classification in these populations.

Inconsistency in genetic grouping nomenclature between investigators makes it difficult to compare study results across publications and does not provide a stable template for future investigations. In addition to naming inconsistencies, grouping classification differences also exist with some researchers utilizing two metabolism groups (normal/reduced)57, 58 while others use three groups or more

(super/normal/intermediate/slow).52, 59-61 Other researchers choose to divide participants using a four group system based on known functional effects: WTIA (wild type *1A homozygotes), WT1B (1 or more alleles with *1B increased activity), PHI (partial haploinsufficiency), and HI (Haploinsufficiency).62 Johnstone (2006) states that this method conserves statistical power while providing more descriptive group names.

A few researchers have adopted a numbered group nomenclature system using groups 1-4 to designate different levels of metabolic activity. While this approach

eliminates the various descriptive categories such as reduced/poor/slow, it introduced other challenges. In one study the investigator used “group1” to represent the 100% metabolic activity with 2-4 indicating decreased function with 4 being the lowest63 while another investigator used “group 4” to represent 100% activity and used 3-1 to represent decreasing activity with 1 being the lowest64 making comparison of data between studies very difficult.

Other research utilized a four grouping system of normal/intermediate/slow/poor based on the predicting pharmacokinetic properties from five significant genotyped alleles (*4, *5, *7, *9, *10) in a population of Chinese with all others classified as *1.

This grouping effectively make a quartile system with 100%, 75%, 50% and 25% residual function per group respectively48; however these alleles are scarce or untested

(see Table 3) in most other major population limiting usefulness to only some Asian populations. This same investigator later proposed to classify individuals based on three broad categories that concentrate on only a few of the functionally important CYP2A6 alleles. This uses a group of alleles that normally abolish enzymatic activity (*2, *4, *5 and *20), a group that shows reduced activity (*6, *7, *10, *11, *12, *17, *18, and *19) and a normal group that does not contain any of these variants (*1)47 This system would test for only 12 of the 38 alleles helping to avoid over-testing for variants with extremely low frequency.

Table 3. Five CYP2A6 Alleles used to Define Chinese Metabolic Groups. Allele Frequency in other Ethnic Groups

Allele *4 *5 *7 *9 *10 European 0% 0% 1% 5 - 8% 0% African 2% No Data 0% 8-10% No Data American

Black African 3% No Data No Data 7.2% No Data Asian 0 – 31% 0 – 1% 2 – 13% 14 – 22% 0 – 2% Note: Data summarized from Tables 16-19 in Appendix B. All values rounded to nearest whole number.

The current science shows that researchers in the field have not reached a consensus on the minimum number or type of alleles to detect in a study that would constitute a complete genetic picture of smoking behaviors especially in ethnically diverse populations. This is partly because new variants are being discovered, but mostly because non-SNP changes such as deletions and gene conversions provide major challenges to standard gene and allele-specific genotyping.42, 54 Lack of consensus leads to lack of standardization of methods, naming conventions, and difficulty in comparing results across studies.

In addition, limitations to determining nicotine metabolism rate using genotype include: expense, complicated procedure, does not account for gene-environment interactions or other biologic features that affect metabolism such as drugs, circulating blood volume, portal circulation rates, diet, and age. To further complicate application of this gene to smoking research, the gene has some variants that only exist in certain ethnic/racial populations or that are very rare, making genotyping very complex and expensive.

Phenotypic Measurement of Nicotine Metabolism

Since direct assay of numerous genotypes is difficult and expensive, especially with mixed ethnicity participants, a ratio of trans-3`-hydroxycotinine to its parent cotinine

(3HC/COT) can be used to calculate a nicotine metabolite ratio (NMR) to be utilized as a pharmacogenetically informed biomarker of CYP2A6 genetic variation.57-59 NMR is normally analyzed in quartiles with the highest quartile representing normal metabolism and lowest quartile represented slow nicotine metabolism (see Figure 4). In a study by Ho et al, individuals in the slow metabolizing group had lower risk of smoking dependence and higher quit rates (OR 1.85, P=.003).59

Figure 4. Comparison of NMR Quartiles and Effect on Nicotine Metabolism

NMR 4th Quartile ‘Normal’ comparison Normal Metabolism (highest value) group

rd NMR 3 Quartile Intermediate/Moderate Slightly reduced Metabolism cigarettes consumption NMR 2nd Quartile Significantly reduced NMR 1st Quartile cigarette consumption, Slow Metabolism (lowest value) lower dependence risk, higher quit rates

Normally a phenotype would be established by measuring the first proximal metabolite in the pathway of interest against the parent drug. In the case of tobacco research a ratio of cotinine to nicotine would be calculated as a phenotypic proxy for oral nicotine clearance which should reflect differences in CYP2A6 activity. Unfortunately two problems exist, nicotine has a very short half-life (2 hours) compared to cotinine (16

hours) which makes the measure very dependent upon time of last nicotine consumption65 and cotinine can be produced in small amounts even in the absences of

CYP2A625

The ratio of 3HC to its precursor cotinine provides a phenotypic measure of the rate of nicotine metabolism that is significantly correlated with daily smoking rates and

CYP2A6 genotypes.62, 66, 67 The stability of this measure is related to cotinine being metabolized to 3HC exclusively by CYP2A6 combined with the short (5-6 hours) half- life of 3HC. The longer half-life of cotinine creates a generation-limiting metabolic pathway to 3HC that is stable over time and effectively reflects the underlying CYP2a6 activity.25

First applied to tobacco cessation treatments in 2006, Lerman et al. showed this nicotine metabolite ratio could be used to predict the effectiveness of transdermal nicotine treatment. Later studies reinforced the robustness of the test as NMR is highly correlated with CYP2A6 metabolic activity and genotyping groups while accounting for environmental influences and is independent of time since last cigarette and time of day assessed.58, 59, 66-70

This phenotypic marker is highly correlated with CYP2A6 genotypes where higher values of NMR predict lower plasma nicotine levels reflecting faster nicotine metabolism.6, 66 St. Helen et al. conducted a reliability test of NMR in plasma and saliva at room temperature and found NMR was a good proxy measurement that was reproducible over time and stable for up to 14 days.71 Although NMR is highly correlated with CYP2A6 activity, it only accounts for approximately 69% of the variance in oral

nicotine clearance.25 Lerman et al. (2006) demonstrated that the odds of abstinence after completing a smoking cessation program with transdermal patches are reduced by 30% for each increasing pretreatment quartile of the metabolic ratio. In the same study, higher

NMR predicted lower nicotine concentrations and more severe cravings. Research by Ho et al. showed that individuals in the slow metabolizing group will have lower 3HC/COT values and display a lower risk of smoking dependence due to requiring less nicotine to maintain desired nicotine levels, leading to reduced nicotine consumption and higher quit rates.53, 58, 59, 72

NMR Phenotype Group Nomenclature

Similar to CYP2A6 genotype classification, no consensus has been reached on

NMR group nomenclature. Many investigators use statistical quartiles of their sample or median splits to assign characteristics. Usually the first through third quartile is labeled as reduced function and the fourth quartile as normal function. Lerman and associates use a similar method to examine efficacy of transdermal nicotine but the first quartile was designated as “normal metabolizer phenotype” (NM-P) and the second through fourth quartiles were combine into “reduced metabolizer phenotype” (RM-P)58

The quartile method works well with a sample but cannot always be generalized to the population. If skewed, the quartile data is relational only to the sample itself and cannot be generalized in the context of the population because a determination of ‘fast

CYP2A6 metabolism’ based on a 3HC/Cot quantification that falls in the fourth quartile of a skewed sample may only be classified as ‘moderate CYP2A6 metabolism’ in a different sample. Tables 20-24 in Appendix D present a comparison of various nicotine

metabolism ratio and CYP2a6 genotyping groupings with corresponding functional nomenclatures used in the literature. However, as shown in Tables 20-24, there is great variance between how each investigator divide their NMR data into the four categories.

When examining the two extremes on the table, ‘normal/quartile 4’ group has a quantitative NMR ranging from 0.28 to 1.2, while the ‘slow/quartile 1’ ranges from

0.11to 0.345 yielding overlap and eliminating between-group variance. In a study by

Lerman and associates, *2/*9 individuals were grouped with RM-P (reduced metabolism) despite the fact that this genotype presented with an average 3HC/Cot ratio of 0.65

(155% normal metabolism), much higher than their stated NM-P (normal) group.58

Many of the investigators did not clearly describe or support how they chose to divide their data into groups making the matter even more confusing for readers. Until a reliable standardized system for NMR classification is created (and agreed upon) there will continue to be debate about how to apply this phenotypic measure to nicotine research.

NMR Phenotype Ethnic Variation

Ethnic differences affect the NMR when used to compare groups across populations. Derby and associates showed that the mean NMR was significantly different between Whites (0.95; p<.05) and Japanese (0.50; p<0.05) and intermediate between

Whites and Hawaiians (0.68; p=0.06) making universal use of a pre-defined NMR scale very difficult in mixed ethnicity samples.73

In a study by Zhu et al. (2012) the researchers showed absolute levels of 3HC were also different across the type of nicotine product used. Cigarettes, commercial

smokeless tobacco and iqmik (a chewed mixture of Kentucky tobacco and ash from the fungus phellinun igniariust that grows on Birch trees) showed differences in quartile distribution of NMR classification by product.74

Additionally, the use of quartiles for NMR must be race-specific because the percentage of cotinine metabolized to trans-3`-hydroxycotinine via glucuronidation

(UGT) vs. CYP2a6 enzyme, while minor, varies between races. The quartiles cannot be directly compared without statistical adjustment for UGT metabolic effects.75 Further, about 50% of African Americans are CYP2A6 reduced metabolizers compared to only

20% in Caucasians70 which can shift the number of individuals in each NMR quartile and skew results.

While UGT metabolism of nicotine to cotinine is important when comparing samples of different racial background, research has shown that differences in percent of

UGT metabolism further down the pathway of 3HC to 3HC glucuronide (specifically due to UGT2B17 deletions and UGT2B10*2 genotypes) did not significantly alter NMR between groups.76

One advantage of NMR is that it is cost effective, non-invasive and the ratio allows for use of a continuous variable to characterize the phenotype instead of categorical CYP2A6 genotype groups which allows for raw data comparisons with other literature when categorical grouping and nomenclatures are not standardized.70

Most recently in the literature, the preferred method of measuring nicotine and metabolites to calculate the NMR is with either HPLC or LCMS. Miyazawa et al (2011) demonstrated a sensitive method to measure nicotine metabolism to cotinine using high

pressure liquid chromatography (HPLC) that can detect down to 0.05 μm for cotinine and

0.1 μm for nicotine. A second widely used method is liquid chromatography with tandem mass spectrometry (LCMS/MS) which utilized frozen blood plasma or saliva, and allows for very specific and accurate measurements of each metabolite (cotinine and 3HC) reported as ng/ml concentrations.71, 77

Another method to measure nicotine metabolite ratio not discussed in this paper is to quantify total nicotine metabolite excretion in the urine, known as urinary total nicotine equivalents (TNE). The calculation is done by summing four to six of the major nicotine metabolites (Cot+Cot-G+3HC+3HC-G, with or without Nic+Nic-g) in the urine then correcting for urine creatinine concentrations.70, 74, 78 This method involves the collection, refrigeration and analysis of an individual’s urine for a 24 hour period which creates a large participant burden and is impractical for routine large scale nicotine research, but can be very helpful in new method validation studies.

The current science demonstrates that, despite lack of consensus in naming conventions, the NMR is a cost-effective, non-invasive pharmacogentically informed phenotype that allows researchers to estimate underlying gene activity. The NMR also accounts for gene-gene and gene-environment interactions that cannot be measured with direct genotyping.

Nicotine Dependence

Inhaling smoke from burned cigarette tobacco allows pulmonary circulation to deliver high-dose nicotine directly to the brain where it readily crosses the blood/brain barrier and saturates neuronal nicotine receptors primarily in the thalamus,

interpeduncular nucleus and amygdala.8, 24, 79, 80 There are many different types and subtypes of neural nicotine receptors, but the α4β2 nicotine acetylcholine receptor

(nAChr) is the most common subtype in the brain with high affinity for competitive binding of nicotine over acetylcholine.81 Binding of nicotine to nAChrs containing α4 and

β2 subunits results in activation of ion channels (usually calcium) that activate dopamine release in the nigrostriatal area of the brain affecting dopamine release, producing pleasurable effects.26

Nicotine half-life in the body is approximately 2 hours resulting in rapid decrease of receptor saturation due to nicotine metabolism and excretion.24, 27, 79 Repeated dosing of nicotine through habitual tobacco smoking can lead to addiction and dependence through saturation and desensitization of the α4β2 receptors.80, 82 Interestingly, chronic use of nicotine causes reversible deactivation (desensitization) of α4β2 receptors to nicotine in an attempt to prevent excitotoxicity81 causing a dose-dependent up-regulation of high-affinity nicotine binding receptors in the brain83 and neuroadaptation leading to tolerance.82

Nicotine Acetylcholine Receptors

The nicotinic acetylcholine receptors (nAChR) occur throughout the body, in muscle, cilia and the brain and are comprised of five subunits bundled around a central ion channel (usually calcium or sodium). In mammals, subunits can be comprised of one of 9 α-subunits (α2-10) and 3 β-subunits (β 2-4).27, 82 Muscle-type nAChRs have poor binding affinity for nicotine and muscarinic-type nAChRs only respond to acetylcholine simulation.84 Neuronal α4β2 nAChRs will be discussed in this section and are the

predominant human brain receptor subtype with high affinity for nicotine, consisting of two α4 and three β2 subunits configured around a calcium ion channel as illustrated in

Figure 5.81

Figure 5. Illustration of a Neural α4β2 Nicotinic Receptor

From “Alcohol’s Actions on Neuronal Nicotinic Acetylcholine Receptors” by T. Davis and C. deFiebre, National Institute on Alcohol Abuse and Alcoholism available at http://pubs.niaaa.nih.gov/publication

Nicotine Receptor Genetic Variation

Formation of the nAChRs is controlled by several genes on Chromosome 15 including the CHRNA, and CHRNB genes. An important locus in this DNA area codes for the α5, α3, and β4 subunits and is commonly referred to as CHRNA5-A3-B4 which refers to a cluster of genes location on the strand, not the nicotinic receptor configuration.

There are many gene variations that can affect nicotine receptor binding affinity, but arguably the most significant is when genetic variation causes one of the three β2

subunits to be replaced with an α5 subtype (α4β2α5). The new amino acid sequence produced by the genetic variation causes slight changes in electrical charges and alters the stoichiometry of the receptor resulting in distinct pharmacological and biophysical profiles properties.84 The addition of the α5 to the nAChR complex significantly increases acetylcholine binding affinity while reducing sensitivity to activation by nicotine.84-87

The α5 subtype is genetically controlled by the CHRNA gene on Chromosome

15. This gene is highly variable and contains many types of genetic variations including frameshifts, missense, non-coding and synonymous errors, as illustrated in the 1000

Genomes browser of variation surrounding variant rs16969968 in Figure 6.88

Figure 6. Genetic variations in CHRNA gene

Rs16969968 marked in red. From “1000 Genome Browswer: A Deep Catalog of Human Genetic Variation: Release 14” by The European BioInformatics Institiute, 2013.

RS16969968 Genetic Variant of α4β2α5 Receptors

Literature demonstrates that one of the most functionally significant variations in the α5 subtypes is rs16969968, a non-synonymous, single nucleotide polymorphism

(SNP) in the q25.1 band of chromosome 15, gene CHRNA, position 78882925, that causes an amino acid change (D398N) in the protein product resulting in an α5 complex with decreased affinity for nicotine that requires higher levels of blood nicotine to permeate and activate the α4β2α5 receptors.

The rs16919968 minor risk allele "A" frequency varies widely between ethnic groups from 3% to 36% with an A/A (Risk) genotype possessing significantly higher risk for nicotine dependence and A/G (Intermediate) having only a slightly higher risk for nicotine dependence, compared to G/G (Normal) genotypes.88 Researchers have reported different allele frequencies for their studies, but the classification of ethnicity is usually based on racial/ethnic self-identity and self-report, not genetic ancestry.

The rs16969968 variant is functionally significant in tobacco research because increased nicotine demand needed to permeate the receptors increases an individual’s cigarette consumption (↑CPD) (p=4.49 x 10-8, OR 1.42) placing them at higher risk to develop nicotine dependence.86 Sherva et al. (2008) found that after adjustment for gender and race, rs16969968 was associated significantly with smoking status in

Caucasians (OR = 1.51, P = 0.01) and in Caucasians/African Americans in a combined sample (OR = 1.48, P = 0.01). While this is only one of many possible polymorphisms in the CHRN cluster, rs16969968 has been shown to effectively characterize functional changes in this gene cluster that can affect smoking behaviors.

RS16969968 as proxy for Nicotine Dependence

Saccone and colleagues conducted the first published GWAS specifically aimed at determining genetic associations with nicotine dependence. In their paper, from 300 candidate genes they found compelling biologic evidence that participants with the A/A genotype at rs16969968, a SNP in the CHRNA5 gene, were twice as likely to be nicotine dependent than G/G genotypes and that rs578776 was associated with nicotine dependence.89 Other SNPs in the CHRNA3 gene were also found such as rs1051730, but they were in strong linkage disequilibrium (LD) (r2 ≥0.99) with rs16969968 indicating that they are highly correlated, are considered good surrogates for one another and both represent the variability in the genomic region. This study used the Fagerström test for nicotine dependence (FTND), and cigarette consumption (CPD) as primary phenotypes and produced hundreds of potential genetic markers for investigation. Thorgeirsson conducted a GWAS using CPD in smokers and found that rs1051730 was associated with

CPD (p>5x10-8) but not rs16969968 despite the previously reported high LD.90 In a follow-up study, Saccone et al. analyzed 226 selected SNPs covering 16 CHRN genes through genotyping in participants of European descent and found significant associations between rs16969968 / rs578776 and nicotine dependence risk.91

In a related study, Bierut et al. found that rs16969968 alleles act recessively, were highly associated (p=.007) with habitual smoking (FTND >4) and that other variants showing association in this gene area (rs2036527, rs17486278, rs1051730, rs7487223) were highly correlated (r2>0.79) with rs16969968.92 In the same year, Grucza et al. also replicated results showing the rs16969968 ‘A’ allele increased risk for nicotine

dependence while decreasing risk for cocaine dependence.93 Sherva and associates’ study replicated results reinforcing rs16969968’s association with smoking behaviors but suggest significant association based on additive effect of the minor ‘A’ alleles instead of recessive effects, then expanded to show an association with subjective pleasurable experiences in smoking initiation.32 Beirut analyzed the reported additive effects of the rs16969968 risk allele and determined that compared to the G/G genotype, having one copy of ‘A’ gives individual’s 1.3 fold increased risk while two copies gives a 2-fold risk of developing nicotine dependence once they start smoking.94, 95 Further, on average, each ‘A’ risk allele adds 1 cigarette per day to self-reported consumption.96

In a replication of previous studies, Chen et al. used two independent Caucasian samples from the Virginia Adult Twin Study and demonstrated that rs16969968

(p=0.0068 and 0.0028) and rs1051730 (p=0.0237 and 0.0039) were significantly associated with scores from the FTND.97

Weiss and Baker conducted two related studies using three cohorts of long-term

European American smokers and found that the CHRNA5-A3-B4 locus was associated with nicotine dependence severity (p=2.0x10-5), measured by FTND, in subjects that began smoking before the age of 16 with the most noteworthy SNP identified at this locus of rs16969968.98 This was later replicated by Harz et al. showing rs16969968 association in early-onset smokers99, a finding disputed by Grucza who found that rs16969968 was only important in determining nicotine dependence in later onset smokers100 and Stephens who shows no correlation of rs16969968 with age of smoking initiation.101 A second Baker and Weiss study used a subsample of the same population

and showed that the CHRNA5-A3-B4 locus was associated with a broad range of non- age dependent phenotypes including tolerance, craving, loss of control, cigarette consumption, and severity of withdrawal symptoms.102 As shown on Table 24 in

Appendix E, these two studies provide the first evidence in support of a gene x environment (G x E) interaction in smoking behaviors and nicotine dependence.

Chen further explored gene x environment interactions by hypothesizing that parent monitoring as an environmental modifier to cigarette exposure not only changes chances of smoking initiation, but modifies risk of dependence for individuals with genetic susceptibility. His study showed an interaction between rs16969968 high risk

(A/A) genotype and low parent monitoring as significant risk (p=0.034) for nicotine dependence.103 Likewise, Johnson et al. demonstrated that environmental influences of peer smoking had a significant (p=0.0077) interaction with rs16969968 for increased risk of nicotine addiction.104

All of the above studies were Caucasian or European American samples. In 2009,

Saccone et al. determined that although occurring with different frequency (see Table 4), the rs16969968 risk allele was significantly associated with nicotine dependence in

Caucasians and African Americans. Further the investigators determined that rs578776, which has low correlation with rs16969968, was associated with nicotine dependence and lung cancer in Caucasians, but not African Americans which made it a poor choice for ethnically diverse studies.86 In a meta-analysis across 27 datasets of 32,587 smokers,

Chen and associates showed that of 11 tested variants, only rs16969968 was significantly

(p<0.001) associated with smoking in diverse ethnic populations of European, Asian and

African American descent.105

Table 4. rs16969968 Allele frequencies by ethnicity from 1000 Genome project

European African East Mixed All Ethnicity Asian American Combine “A” Allele 36% 3% 3% 23% 18%

“G” Allele 64% 97% 97% 77% 82% Note: From “1000 Genome Browswer: A Deep Catalog of Human Genetic Variation: Release 14” by The European BioInformatics Institiute, 2013.

In addition to affecting smoking behavior, variation in rs16969968 was associated with negative health consequences. Association with lung cancer is highly disputed in the literature with some studies supporting association86, 92, 106-112 and some refuting support.

Some studies show no association of rs16969968 with cancer113-116 citing that genetic variation is simply a proxy for increased exposure to tobacco carcinogens that increases overall cancer risk.

Hansen et al. showed that a four-SNP haplotype (rs11637635 C, rs17408276 T, rs16969968 G, rs578776 G) was associated with increased (P=0.002) lung cancer risk in

African Americans117 which is contrary to other studies that showed the G allele of rs16969968 as being protective against nicotine addiction and cancer. Timoeeva et al. produced similar results showing both rs16969968 and rs578776 were associated

(p<0.001) with lung cancer in a large cohort over eight European countries.108

There is some divergence in the literature that rs16969968 is not the best, nor only variant to use when examining nicotinic receptor gene influence on smoking phenotypes despite extensive study and support. Some researchers contend that despite high linkage disequilibrium (the non-random association of alleles at two or more loci, that descend from single, ancestral chromosomes) in the CHRNA5 locus, rs1501730, rs55853698 and rs55853698 have stronger associations with nicotine dependence than other variants

(including rs16969968).4, 90, 118

A considerable body of research has accumulated in the last few years that replicates the results of association between rs16969968 variant and nicotine dependence in general populations112, 119-127, in Canadian women128, adolescent smokers129, smoking severity in schizophrenia130, and CHRNA5 promoter variation.131 Xie et al. also showed that men with the rs16969968 risk alleles that experienced childhood adversity were significantly (p=0.0044) more likely to develop nicotine dependence.132

Ware et al. conducted a meta-analysis in 2011 looking at 71 published samples that provided support for association between rs16969968 and rs1051730 SNPs with heaviness of smoking. The analysis provided compelling evidence that both variants were strongly associated with cigarette consumption (P<0.001) although rs1051730 may have a slightly stronger signal despite having unknown functional relevance.87 Munafo et al. reported that after synthesis and meta-analysis of the literature, rs16969968 and 1501730 are in perfect linkage disequilibrium and interchangeable, therefore the complex should now be referenced as “rs1051730-rs16969968,” although this has not been uniformly adopted throughout the discipline.96 These meta-analyses results were bolstered by Wen

et al. who performed a bivariate Mann-Whitney simulation on 184 known nicotine related SNPs and only rs16969968 was significantly (p=1.06x10-3) associated with nicotine dependence.133

The scientific foundation for use and application of CHRNA5 genotypes and phenotypes has been well established. Future research should evaluate the effect of nicotine on brain activation as a function of rs16969968- rs1051730 complex using advanced neural imaging technologies, such as fMRI85, 127, to identify new pharmacologic therapies and translate this knowledge into the clinical arena. Wang et al. have already used these data to start exploring mRNA expression levels in African American populations to map if genetic variation could affect tumor growth and other biologic functions.134 A key feature in future use of rs16969968- rs1051730 will be to inspire clinical studies relating real-world cessation treatment option outcomes to biologic measures that will help future clinicians and researchers individualize treatment to improve overall cessation success.

Bitter Taste Perception

Differences in human ability to taste bitter was first discussed in the literature in

1932 when Arthur Fox, a chemist at DuPont, accidentally dispersed some phenylthiocarbamide (PTC) in the air causing co-workers to comment on how bitter it tasted.135 This lucky accident started an area of research exploring human flavor sensation and food preferences. Bitter taste is widely explored in the food and neurologic worlds. In the last two decades investigators have begun applying this taste science to nicotine and tobacco research in an attempt to distinguish if flavor sensation has an effect

on smoking habits. Following the forward advancing genomic revolution, this science has incorporated genetic predisposition to bitter taste as well as subjective taste sensation.

Wooding supports use of bitter taste genetic variety with evidence that balanced natural selection has maintained taster/non-taster variation in the human genome throughout molecular evolution of the species136

The multifactorial dimensionality reduction-pedigree disequilibrium test (MDR-

PDT) is a complex, non-parametric method to analyze pedigree data in an attempt to identify genetic polymorphisms associated with increased risk of multifactorial diseases.137, 138 Using a MDR-PDT, Lou and associates showed a significant interaction between two bitter tasting genes TAS2R38 and TAS2R16 in affecting nicotine dependence with a prediction accuracy of 0.556 (p=.002).139

While direct genotyping of TAS2R38 gene can provide good bitter taste information, it does not account for all the environmental variables and personal preferences that constitute complex taste preference.140 A commonly used proxy measure for bitter taste phenotypes (BTP) that is inexpensive, easy and non-invasive is evaluation for bitter taste phenotype using paper discs impregnated with specific solutions of PTC or

6-n-propylthiouracil (PROP), an odorless chemical similar to PTC.141

Bitter taste genetics

The ability to taste bitter is controlled by several gene variants in the TAS2R38 gene on chromosome 7 with three specific coding SNPs corresponding to both PTC and

PROP taste variation that represent approximately 60-85% of the variability in human bitter taste.142-146 Further, approximately 70% of the population and 50% of smokers have

the ability to taste bitter.147 Using the three SNPs on TAS2R38, researchers build haplotypes to characterize bitter taste potential.148 As described in the literature, taster phenotypes reflect a genetic PAV haplotype, non-taster reflect an AVI haplotype, with all other haplotypes considered medium/intermediate tasters.142-145, 148 Nicotine has a bitter flavor so it is logical to assume that tobacco use could be altered by bitter taste preference.

Bitter Taste Phenotype (BTP)

Bitter taste phenotype (BTP), as measured with 6-n-propylthiouracil (PROP) impregnated paper discs, is a non-invasive measurement of bitter taste that correlates well to taste receptor genotype.140-142, 149 Psychophysical phenotypes of bitter taste have been characterized in the literature as non-taster, medium/moderate taster and super taster of bitter140, 141, 145 based on genetic predisposition and taste experience.

Enoch et al., one of the first research teams to publish on the topic of bitter taste and nicotine addiction, found that there was a significantly larger portion (P=.003) of non-tasters of bitter (using PTC) who were smokers than bitter tasters suggesting the flavor of the nicotine may affect risk for nicotine addiction.147 Since this report, the literature in this area is controversial with multiple studies showing conflicting results.

Relationship of Bitter Taste to Smoking Literature

Relatively few studies have been conducted examining the relationship of bitter taste to smoking habits. Some researchers suggest that non-tasters of bitter smoke longer, have higher Fagerstrӧm nicotine dependence scores149 and may be more at risk for heavy smoking and development of nicotine addiction147 while other literature has reported

conflicting results about the relationship between BTP and cigarettes per day (CPD). In one study using PTC testing, investigators found tasters to have significantly higher

FTND dependence scores (6.4 vs. 5.8, p<.01) than non-tasters149; however, Tanaka found no significant relationships between TAS2R38 haplotypes and smoking prevalence or dependence, but showed that tasters tended (non-significantly) to smoke fewer cigarettes than non-tasters.150

Cannon et al. found that the intermediate haplotypes were significantly (P=.001) associated with lower smoking prevalence; however they did not find that genetic taster status (PAV) relative to non-taster status (AVI) reduced odds of being a smoker as they had hypothesized.151 Conversely, Keller et al. found that carriers of the taster (PAV) haplotype showed a significantly (P=.002) lower consumption in a German population.152

Conflicting with Cannon’s and Keller’s results, Mangold described finding a significant inverse relationship with smoking quantity (P=0.165) in tasters and a significant positive relationship (P=.0120) in non-taste status in African American smokers.142 This showed heightened sensitivity to bitter taste as a protective factor for nicotine dependence. A recent study by Ahijevych et al. exploring the association of BTP and smoking quantity

(CDP) suggests that BTP assessment with oral PROP is not associated with CPD in regular smokers.153 This research did show a significant correlation between race and

BTP with African American women tending to be more associated with higher taster categories with 33.3% being in the ‘super-taster’ category.153-155

Currently the effects of bitter taste preference on smoking status, consumption and habits are relatively poorly studied in the literature with controversial results. There

is strong evidence in support of the bitter taste genotype and phenotype as effective methods of determining genetic ability to taste bitter. Marginal support does show that bitter taste may be important to nicotine and tobacco research, indicating a need for further investigation and clarification on how bitter taste can be used to mitigate smoking initiation, assist in early identification of genetic risk for nicotine dependence and improve cessation success with individualized treatment options based on taste preference. There is also a gap in the literature that explores the relationship of bitter taste with other known smoking genotypes and phenotypes that could affect overall smoking behavior.

Conclusion and Future Direction

Each of the biomarkers described in this paper has a significant impact on some aspect of smoking, nicotine consumption or tobacco-related disease. Since each has an effect individually, it is logical to assume there may be combinations of gene-gene and gene-environment interactions. Scant literature was found that compares the associations of multiple biomarkers on the overall effect of smoking behaviors.

Future investigators that use multiple biomarkers will be able to capture more of the association between biology, smoking behavior and cessation treatments to improve the science and understanding of nicotine dependence for improved cessation therapy.

Individualized healthcare and personalized medicine will require the use of biomarker panels to fully capture the individual’s unique smoking phenotype. While the science of each biomarker is still being developed, it will be important in the future to understand how all the markers fit together to influence smoking behavior and health outcomes. This

type of multiple marker association study would build the scientific base needed to assist providers in providing cost-effective, time-efficient, individualized care to increase smoking cessation success and decrease the health burden of tobacco-related disease.

CHAPTER 2: RESEARCH PARTICIPANT RECRUITMENT OF MILITARY CIGARETTE SMOKERS: LESSONS LEARNED

Introduction

Military members are at higher risk than civilians for using tobacco products that can lead to nicotine addiction. Tobacco use in this vulnerable, high-risk population far exceeds civilian smoking prevalence of 18.1%13 with >50% of active duty personnel returning from Iraq and Afghanistan consuming cigarettes daily.2, 16-19 Even after leaving active service, approximately 36% of former military personnel reported being current smokers2 and 75% of veterans reported using tobacco at some point in their lives.18

Besides current cigarette use, Blake and associates showed that military recruits that smoke have 1.46 times greater risk for upper respiratory infections than nonsmokers, and are at higher risk for developing long-term smoking related disease.156 Recruitment of current and former military personnel for studies is necessary to understand the complex social and environmental aspects of military life that interact with service men and women’s behavior and personal characteristics to influence tobacco use.

Our study focused on recruitment of current, regular smokers who had served in any branch of the U.S. Military that were willing to complete an online survey and provide a blood sample for analysis. Research can only contribute to the science if there are sufficient data from an adequate sample.157 Although recruitment is considered the

most time intensive, laborious and difficult aspect of the research process, most researchers do not report their efforts in their publications.158 Therefore there is scant documentation on best strategies in the literature for recruitment and retention of current and former military personnel who smoke.

Several investigators in healthcare fields have reported lessons learned from recruitment in different populations. A physical therapy study reported that throughout

America, approximately 85% of clinical trials exceed time schedules due to poor participation with 60-80% never meeting recruitment goals and 30% of trial sites never recruiting a single participant.158 Nasser et al. reported that one third of the studies conducted at Oregon Health & Science University from 2005-2009 were terminated for low-enrollment (0-1 participant) which cost the institution over $1 million annually.157

Peters-Lawrence and associates were forced to terminate their patient-controlled anesthesia study early due to low enrollment and identified insufficient staff, competing protocols, and subject ineligibility as major recruitment barriers.159 Similarly, Blanton et al (2006) identified narrow time frames, strict eligibility criteria, lack of transportation, parking concerns, inadequate financial resources to compensate participants, and low self-referral rates as major barriers to successful recruitment of stroke patients for research. Kolanowski et al. (2013) found internal and external factors that affected the efficacy of their recruitment efforts for dementia patients which included overestimation of eligible population, overly strict inclusion criteria, unstable staff at recruitment locations, large time burdens, regulatory changes, and communication problems.160

Without adequate time, sufficient financial support, and dedicated staff, recruitment efforts can have poor outcomes.158

Purpose

The purpose of the paper is to report the recruitment outcomes of our study examining the association of three biomarkers of nicotine with cigarette consumption among current and former military personnel. We will 1) describe the recruitment methodology used, 2) examine recruitment issues related to internal and external environments and 3) discuss lessons learned from these findings and provide suggestions for improved recruitment efficiency in future smoking research and specifically research to better understand the high rates of smoking in military personnel.

Methods

This cross-sectional, descriptive, study was designed to examine the association of CYP2A6 metabolic activity, brain nicotine receptor variation, and the ability to taste bitter, and how these three biomarkers affect cigarette consumption. The goal of the study was to recruit 160 participants to achieve a statistical power of .86 when building predictive models during analysis. A sample of 18-55 year old, healthy men and women who were current regular smokers (>10 cigarettes per day for >1 year) that served in, or were currently serving in any branch of the US military (Active, Reserves, National

Guard, Reserve Officer Training Corp Cadets (ROTC), or Veteran) were recruited using flyers, social media and newspaper advertisements. Participants were required to have access to an internet-connected computer to complete the online screening and survey components. Volunteers were excluded if they had any significant physical or

psychological acute (e.g. pneumonia) or chronic illness (e.g. diabetes, seizure disorder, schizophrenia), or used daily prescription medication that inhibited or induced liver

CYP2A6 activity (e.g. Ketoconazole, Methoxsalen) or altered brain chemistry at nicotine receptor sites (e.g. Phenobarbital). Target recruitment to achieve statistical power was

160 participants. Detailed on Table 25 in Appendix F, in the 14 month recruitment window research advertisements reached more than 385,043 people, of which 82 contacted the researcher but only 15 completed the study. Approximately two-thirds of the advertising was conducted in military or veteran specific media outlets with the rest reaching general populations that possibly contain veterans.

Initial recruitment strategies included posting flyers in the following: Veteran’s

House near The Ohio State University (OSU) campus, Veteran’s lounge on campus,

Vets4Vets social media sites and emails through the OSU Veteran’s Affairs office and

Vets4Vets. ROTC participants were recruited with flyers in the ROTC building on campus and announcements at unit assembly. Additional flyers were posted around the

OSU campus and in local sporting goods and grocery stores in Columbus, Ohio. Potential participants had the option to contact the investigator by phone, email or proceed directly to an informational website at www.smokingresearch.us which linked to a 14 item online screening form. When an individual screened eligible, participants were asked to print and complete consent and HIPPA forms and return to the investigator via email or fax. At every stage in the process, the participant was offered an option to contact the investigator via phone or email for live interaction and assistance. Wipke-Tevis and associates have reported that HIPPA rules have increased the complexity of research

recruitment and decreased efficiency by not allowing researchers to directly contact potential participants.161

Once consent was received from a potential participant, a link to the secure online survey (79 items) was provided where it could be completed in the privacy of the participant’s home. After the survey was complete, the online system sent a notification to the investigator to contact the participant via email to schedule a 15-minute appointment for collection of biologic samples and distribution of $10 gift card payment.

The appointment was typically the first time the investigator and participant met in person.

Recruitment Timeline and IRB Amendments

Recruitment occurred over 14 months beginning January, 2013 and involved three amendments to the IRB to address recruitment barriers. A graphic timeline of recruiting efforts is presented in Figure 7. In a study by Brase and associates, students from higher tier Universities that were compensated for their participation were easier to recruit than regional universities or unpaid participants.162 Initial recruitment efforts targeted the 1600

ROTC and veteran students on the Ohio State University campus. In addition three in- person presentations were made to student veteran’s groups (Vet4Vets) and an electronic flyer posted to social media (Facebook, Craig’s list, Twitter, Websites). This effort yielded one participant from the ROTC and one from Vets4Vets.

In month 3, recruitment was expanded to include the city of Columbus, engagement with community veteran’s groups and purchased advertising space in local newpapers that serve the Ohio State University campus and surrounding neighborhoods.

Advertisements were placed in the campus electronic newsletters: OSU Today (2 days),

OSU Weekly (one week), Buckeye News Net (one week) and the Lantern newspaper (four weeks).

Figure 7. Recruitment Timeline

These resources reached approximately 115,500 people, not all of whom were cigarette smokers or associated with the military. Personalized letters were sent to 78

Veterans of Foreign Wars (VFW) posts, 78 American Legion (AL) posts and 3 Amvets

(AV) posts in the central Ohio area which introduced the research and requested assistance with flyer distribution and recruitment. Twenty three posts distributed the flyers and ten agreed to allow researchers to conduct in-person presentations to their membership.

The expansion of recruitment area, newspaper, social media and letter campaign yielded great interest and support, but only a single eligible participant enrolled.

Interestingly, five people completed the screening and online surveys with false

information out of curiosity about the study, then notified the investigator that they were either non-smokers or not affiliated with the military.

In month 8 of the recruiting process, with only three participants enrolled in the study, the IRB approved the expansion of recruitment area to the entire state of Ohio and surrounding states, allowed for a choice of $10 gift cards from several options as payment, and approved incorporating the initial consent form into the online survey to reduce participant burden of printing and sending forms to the investigators. This amendment also allowed participants to designate their own unique ID to code survey results and samples instead of having the system assign random codes, giving the individual more control over data security choices. The State of Ohio Department of the

American Legion Headquarters public affairs officer graciously posted notices of this research on their webpage, Facebook page, and in the state-wide Ohio Legion Newspaper that is distributed to 95,818 members on a quarterly basis. In addition, personalized letters were sent to ten AL and VFW post commanders in the Cleveland metropolitan area and social media advertising was expanded to cover the entire state. Several VFW post activities such as Friday night dinners and celebrations were attended by the investigator. While several people responded to the request for participants, none were eligible due to advanced age, non-smoking status or lack of military affiliation.

In month 10 of the recruiting process, with only three participants enrolled in the study, a final amendment was approved by the IRB which increased the honorarium to

$20 and introduced the use of Study Search and Research Match services for participant recruitment. In an article describing recruitment for a behavioral intervention, Velott and

associates discussed using a mixture of active and passive recruitment techniques to successfully reach recruitment goals.163 Study Search was a passive recruitment method that allowed potential participants to search for basic information about the research study. Research Match on the other hand, was a secure volunteer registry funded by the

National Institutes of Health (NIH), which actively connects people who want to participate in research with investigators recruiting participants. Research Match contained 909 volunteers between the age of 18-55 who smoked cigarettes and lived within 250 miles of the Ohio State University campus. Veteran status was not available in the dataset. Ashburn and associates found that even though individuals enroll in specialty registries (such as Parkinson’s disease), participation in research through registries is very low at 13%.164 Similarly, Frobell and associates reported that due to low participation rates, a priori sample size calculations need to be multiplied by at least 5.5 to provide an accurate estimate of how many people need to be screened to reach adequate enrollment numbers.165

There is no universal table to assist researchers in determining the level of risk and compensation for research participants, but Brigham and Woman’s Hospital has published a matrix with general guidelines. On this matrix, “blood draw for research purposes from healthy volunteer subjects” suggests a $5-$25 payment.166 Brase et al. report a significant increase in performance of sampling techniques with increased level of payment for participation.162

Participation could have been improved with higher honorariums, especially given that the participants had a multi-stage process to navigate and biologic sampling

that included a blood draw. Simplification of the process to accomplish all research goals in a single time point could have also increased participation by reducing participant burden. The initial $10 payment was based on financial concerns for paying 160 participants on a limited budget. Increasing payments to $20 generated more inquiries and enrollments. Future studies need to set reasonable payment levels based on the anticipated time burden, effort and motivation level of the participant.158 In addition, lag time between initial contact and enrollment should be minimized and the process should be streamlined.167

In summary, the interest and participation rate increased significantly in the last 4 months of recruiting with the use of Research Match and an increase of payment to $20.

Utilization of Research Match resulted in 48 responses and enrollment of nine participants in four months, over double the amount enrolled in the first 10 months combined. The overall 14 month recruitment efforts resulted in an 18.3 % enrollment rate of individuals contacting researcher for additional study information. Of the 385,043+ potential participants on Table 25 in Appendix F, 82 contacted the researcher for more information, 14 were ineligible, 3 withdrew, 50 did not respond to follow-up emails or phone calls and 15 were enrolled. Ineligible individuals either did not meet the inclusion criteria or provided false information and later redacted their application. Three individuals withdrew from the study after completing the survey, two related to fear of needles and a third who worked out of state and could not meet in person.

Lessons Learned

Current and former military were recruited for this study from the community using a wide variety of advertising techniques. This section will address recruitment issues related to internal and external environmental factors that affected recruitment efficacy. A discussion of lessons learned and suggestions to improve recruitment efforts in future studies will be offered.

Recruitment Issues Related to Internal Environment

Online recruitment systems have been used successfully to recruit participants into tobacco related studies168, 169 Chang et al. noted that the most important reason cited by people for participation in research was a benefit to him/herself.170

Inclusion and Exclusion Criteria

The study utilized very broad inclusion criteria to maximize subject recruitment but may have affected participation. The factors of the criteria will be reviewed in this section. First, any current smoker ever associated with the military (current or former) in any branch with any type of service (active, reserve, guard, ROTC, veteran, cadet) were eligible to participate. In addition, the only exclusion criteria were age, health and medication related.

The exclusion criteria limited participation to over 18 year old, as this is the minimum age to join the U.S. Military and a maximum age of 55 because only approximately 1% of the military is over the age of 50171 and research has demonstrated physiologic changes that affect metabolic process begin to manifest around the age of 60 and can affect phenotypic measures of metabolism.172 The use of genetic analysis to

determine metabolic category would eliminate the age limitation since genetics do not change over time; however, there is currently no consensus in the literature on what

CYP2A6 alleles should be included to gain an accurate genetic metabolic risk profile and the procedure would need to account for racial genetic diversity making the process cumbersome and expensive.

Health status of potential participants excluded only individuals with chronic conditions or medications that affected metabolism or brain chemistry. A list of seven medications shown in the literature to induce/inhibit nicotine metabolism or block nicotine receptors were utilized in screening.22, 173-175 These exclusions were necessary to ensure accurate data collection and not artificially altered results. Participation may have been increased through elimination of the exclusion criteria, but scientific accuracy would have been reduced and results may not be generalizable.

Participation could have been increased by including former smokers or individuals who would like to quit, however the nicotine metabolite ratio (NMR) used as a phenotypic measure of genetic nicotine metabolism quantifies nicotine metabolites in the blood at time of collection and would be ineffective with non-smokers. If a genetic test was used in place of the NMR, then a larger participant pool could be utilized for recruitment.

Electronic Systems for Study Delivery

Our study involved an overly complex electronic system for recruitment that was very efficient, but potentially perceived as impersonal, which failed to link personal benefit and importance to the individual participant.176, 177 Recruitment graphics,

language and distribution methods were modified multiple times throughout the recruitment period. Merging the consent process into the IRB approved digital survey reduced the burden on those who participated, but did not affect recruitment success.

Increased participant payment and utilization of Research Match showed a marked increase in volunteer inquiries and successful participant enrollments. However, since both changes were implemented at the same time, it is difficult to determine which technique was most successful.

Protocol Flexibility

The initial recruitment protocols could have been designed with more flexibility to encourage enrollment. A major barrier to participation was the blood draw. The need for participants to meet with the investigator for biologic sample collection could have been precluded with the use of non-invasive genomic sample collection (buccal swab or saliva sample) that is stable at room temperature and can be transported via standard mail. This would have reduced the burden on participants and increased the geographic reach of recruitment efforts, but may be more costly than DNA extraction from blood.

Sensitive Questions of Military History

Although this study regarding biomarkers and cigarette smoking appears relatively benign on the surface, the topics may present ethical and methodological problems due to the sensitive or threatening nature of some questions. Many military personnel start or continue to smoke as a coping mechanism for stress, depression or boredom.19 The questionnaire asked participants about smoking habits throughout the

deployment cycle as well as any duty in conflict areas, which could have elicited negative emotional responses despite being warned this information would be collected.178, 179

Recruitment language

The investigator failed to establish a strong recruiting theme that conveyed why the research was important to individual participants.176 Investigator relied upon patriotism and altruistic motivation for participation and underestimated the barriers to recruitment of this population. In a clinical trial for heart failure, Chang and associates showed that the most common reason for study participation was benefit to the participant.170 Flyers and email recruitment were impersonal, making them easy to ignore and delete. In-person conversations would have been flexible, personalized, memorable, and would have exerted social pressure to motivate participation. Collaboration with existing clinical studies would provide additional services or treatments that benefit the participant and improve health outcomes while encouraging participation.167 Offering low cost benefits such as smoking cessation education, blood pressure checks or cancer screening could help increase visibility of the study and provide a personal benefit to the individual. Collaboration with non-healthcare related agencies such as meals on wheels, veteran stand-downs, barber shops, and food banks could provide additional benefit to participants and help encourage participation.

Website Hosting

An additional issue with the recruitment material was the use of a non-OSU website and email address on the flyers to host the informational webpage and screening forms. The decision to purchase hosting space on a non-university server

(www.smokingresearch.us) was out of necessity because there were no domain services or on-campus hosting service available to student investigators. This approach was ineffective as many of the potential participants felt the use of a non-OSU domain implied the research was industry-sponsored and not academic in nature. While the email addresses were updated to reflect academic affiliation in the week ten IRB amendment, hosting space was still unavailable.

Participant Travel

The original study proposal only recruited from the OSU campus which eliminated the need for travel. When the recruitment area was increased, travel became a major barrier to recruitment. Several eligible participants declined to participate due to travel concerns. Chang and associates found that distance between participant’s residence and study site was the most significant (P<.001) factor in declination of participation.170

Similarly, Blanton reported transportation was the most non-medically related recruitment concern of participants.158 The recruiting material was revised to include wording that the investigator would meet the participants at a mutually-convenient location for sample collection. Consideration should be made in future research to ensure adequate funding for either participant travel or investigator travel to facilitate research participation.

Recruitment Issues Related to the External Environment

There are many external factors that affect study recruitment that cannot be anticipated or controlled by researchers. These macro-environmental factors influence the world in which the research is being conducted and involve understanding the broader

societal influences at work. Recruitment for this study was affected by smoking legislation, federal government shutdowns, introduction of e-cigarettes, and lack of access to recruit at military sponsored community events, VA facilities and military bases.

Smoking Legislation

In 2006, coordinated public health efforts helped voters approve State Ohio legislation banning smoking in public places180 and raised awareness about smoking behaviors that have negative health impacts on society and encouraged smoking cessation. On January 1, 2014, the Ohio State University campus moved from being a non-smoking health science campus to a tobacco-free all-campus181 in an attempt to increase the health and wellbeing of the public. Many major corporations in Ohio, such as

Cleveland Clinic, Hollywood Casino, and the American Lung Association, have policies of not hiring smokers182, 183 making some workers unwilling to be associated with smoking studies for fear of losing their jobs. In theory these changes may have decreased the number of available smokers to volunteer for research (due to increased cessation programs). While national smoking prevalence is trending steadily down from 20.9% in

2005 to 18.1% in 201213, in the state of Ohio, the adult cigarette smoking prevalence is variable from 27.9% in 2004184 to a low of 20.1% in 2010185, then rebounding to 25.1% in 2011.186

The Ohio Department of Health recently released a collection of smoke-free reports showing a decreasing trend of negative health outcomes but did not have enough data to definitively determine if primary cigarette smoking prevalence has been

significantly impacted by the legislative change.187 In support of the Ohio Smoking Ban legislation, the Columbus posts of the American Legion and Veterans of Foreign Wars collectively voted to make their facilities non-smoking for the health of the membership, despite the exemption for private clubs.188

Federal Government Shut Down

During the conduct of this study, the US Government shut down from October 1 –

16, 2013 after Congress failed to enact funding for fiscal year 2014 resulting in the furlough of over 850,000 employees, including 98% of the National Science Foundation

(NSF), three quarter of the Nation Institutes of Health (NIH) and two thirds of the Centers for Disease Control (CDC), prevented hundreds of patients from enrolling in NIH sponsored research, and put most Federal government support for research on hold.189 At the time of writing, no federal agency had received full Fiscal Year 2014 annual appropriations which could lead to future budget cuts that would have long-term effects on research support.

The shutdown closed most local resources utilized by veterans190, delayed veteran’s disability payments, and stalled the processing of Veteran’s Administration

(VA) claims that were already six months behind.189 Approximately 120,000 civilian

Federal workforce jobs were eliminated189 including stakeholders at the Defense Supply

Center Columbus (DSCC) that were supporters of this research. Personnel who were concerned for their continued employment and family wellbeing were not motivated or incentivized to participate in research. The financial crisis greatly reduced services and funding for military veterans and reservists.

Stand Downs are collaborative efforts to provide services to homeless Veterans providing services such as food, shelter, clothing, and health screenings.191 The homeless

Stand Down in Columbus Ohio scheduled for October 5, 2013 was indefinitely delayed due to the government shutdown. Permission to recruit at the next Stand Down conducted in a northern Ohio city in early 2014 was denied by the executive board who stated that conducting research would be predatory and not support the intent of the event (C.

Anthony, personal communication, January 23, 2014).

Access to Military Facilities

Instability in military command structures present problems for investigators planning research that involves military participants. Command personnel are rotated to new positions approximately every two years. Local support was obtained for this study during the planning stage, however, when the recruitment phase began, new personnel were in command positions and decided to deny access to National Guard and Reserve units after consultation with military legal advisors. Similarly, the VA has extremely strict rules to protect the privacy and rights of the veterans it serves192, 193 and despite having a high priority for smoking research and cessation18, the VA will only permit investigators that are employees or sponsored by employees to conduct research in their facilities.194 Chlan et al. reported major barriers to recruitment site access were time consuming processes of: securing credentialing, paying fees and establishing relationships with institutional leaders.195

A Navy corpsman at DSCC agreed to post flyers in the center and allow investigator access to deploying troops, but was transferred to a new post before

arrangements could be completed. As an alternative, recruitment specialists in the CCTS, facilitated recruitment flyer distribution in the DSCC warehouses and veteran counseling office. Primary access for the investigator was denied due to security restrictions and the need for sponsored authority to enter the DSCC compound.

Two main problems existed with utilizing active duty bases for recruitment. First, in order to gain physical access to the base, one must have authority to enter and proper identification neither of which the investigator possessed. Second, the Department of

Defense (DOD) requires additional compliance activities, documentation and subject protections in addition to branch-specific IRB requirements and protocols196 which can take several years to obtain. Recruitment of active duty personnel from military bases was not a feasible option for this study.

Tobacco Alternative

The electronic cigarette is a popular new device that allows individuals to ‘smoke’ in public spaces despite smoking bans and related legislation.197 The use of electronic cigarettes or other equivalents cannot be easily equated to traditional cigarette consumption since there are many types of cartridges that can be used in e-cigarettes.

Traditional cigarettes deliver approximately 1-1.4 mg of nicotine per cigarette to smokers, depending on depth of inhalation and other smoking topography habits.198 E- cigarette liquid can contain between 0 mg for non-nicotine liquid to 48 mg/ml nicotine for XXX-High density liquid which is dose titrated by the user.199 The inclusion criteria for this study were individuals that regularly smoked cigarettes (>10/day), to ensure adequate amounts of nicotine were present in the blood for quantification of nicotine

metabolite ratios. Users of electronic cigarettes were ineligible to participate in the study.

As the use of e-cigarettes increased in the pool of potential volunteers, the number of individuals eligible to participate in the study decreased. Future studies should consider this mode of nicotine delivery and build inclusion criteria to allow inclusion if applicable to the study.

Summary of Strategies to Overcome Recruitment Challenges

Recruitment is one of the most difficult steps in research and many researchers underestimate the challenges of recruitment.158, 167 Recruitment planning should start early and account for both internal (burden, payment, travel, recruitment language) and external barriers (venue access, emerging alternatives, policy changes and institutional collaborations) to research participation.167

Based on the investigator’s experience in conducting this study, the following strategies are recommended to enhance future recruitment in the military population.

 Do not assume military personnel and veterans have patriotism or altruistic motivation to participate in research.  Research will be more successful with time investments to gain access to sites and gain trust of institutional champions that are willing to participate in the research and ensure adequate access to target population.167, 195  Collaboration with established smoking research centers or cessation treatment programs could provide added personal benefit to participants, encourage participation and reduce participation travel and expense burdens with combined study visits.  Use multimodal recruitment services such as Research Match and Study Search for cost effective access to prescreened populations that have expressed interest in participating in research.

 Utilize effective recruitment language scripts that links biologic predisposition with health outcomes that are important to the target population.  Ensure adequate financial resources are planned for the recruitment phase to provide multiple sources of advertising, necessary electronic resources (such as hosting services), appropriate participant payments, sample collection and travel expenses for both participant and investigator.  Offer non-invasive technology for biologic sample collection that is stable at room temperature and can be mailed to participants for home collection.

Both current and former military personnel smoke at significantly higher rates than civilians2 and should continue to be recruited for participation in smoking studies.

Improving participation of military smokers in research is an important step towards providing individualized smoking cessation treatments to improve the health of the fighting forces and reduce long-term tobacco related disease. Smoking study recruitment techniques should be flexible and customized to meet the needs of the military population to ensure adequate access to target populations.

CHAPTER 3: RELATIONSHIP OF THREE BIOMARKERS OF NICOTINE, CIGARETTE CONSUMPTION AND MILITARY EXPERIENCES IN A SAMPLE OF MILITARY SMOKERS

Introduction

In the United States, military populations had rates of tobacco use as high as 60% in deployed troops15-17, 19, 200, over three times higher than the civilian adult smoking prevalence of 18.1%.2, 13 Current nicotine dependence treatment recommendations acknowledge well documented psychosocial aspects of smoking behavior such as ethnicity201, gender202, 203, income204, and education79 as being important to cessation success. In addition, smoking cessation programs have been shown to be more effective if tailored to the individual through use of combination treatment modalities.30

This study examined the relationship of three biomarkers of nicotine in a sample of military smokers. The biological markers examined were: the α4β2 brain nicotinic receptors (nAChR) that contribute to genetic risk for nicotine dependence, nicotine metabolite ratio (NMR) as a pharmacogenetically informed phenotypic marker of

CYP2A6 activity, and bitter taste phenotype (BTP) to provide additional valuable information about how biology affects smoking behaviors, measured as cigarettes per day (CPD).

The primary aims of this study were:

1) To determine relationships between three biological markers of tobacco use in

military smokers.

2) To determine if variations within the three biomarkers affect cigarette

consumption in military smokers.

3) To determine if the biomarker relationships were affected by relevant

sociodemographic and military variables.

This study focused on informing personalized healthcare by connecting biology with environmental and military experiences to inform future cessation treatment selection. This study also highlights the military as a high risk population with unique social and physical attributes that may not fit into traditional smoking cessation treatment protocols. Implementing personalized smoking cessation programs could reduce tobacco use in US service members and improve long-term health of military troops which would increase operational readiness.

The first section of the paper provides an overview of tobacco use in military populations and highlights the importance of tobacco research in this population. The second section presents a short description of the three biomarkers and contributing variables used in this study followed by a review of the conceptual framework and research methods. Finally, study results, study limitations, and future directions for tobacco research in military populations are presented.

Overview of Tobacco use in the Military

Service in the US Military was a risk factor for smoking, even for individuals with no history of smoking initiation before entering service.18 This was contrary to

general population data which suggested that a lack of smoking initiation before age 18 was a protective factor and reduced likelihood of tobacco use later in life. Military smokers engaged in regular exercise regimens and other health maintaining activities usually not seen in similar civilian cigarette smokers.205 Individuals who chose to serve in the United States armed forces shared characteristics such as, leadership, self-sacrifice, courage and integrity. Military training, operational risk, daily stress and unconventional lifestyle during service combined to produce a very distinct, unique subgroup in the

American population.206

US Military personnel in garrison (non-deployed) have cigarette smoking rates over 30% while troops returning from deployment in conflict areas, such as Iraq and

Afghanistan, can exceed 50% cigarette smoking prevalence.15-17, 19, 200 This increased prevalence may have been related to exposure to complex environments with unique combinations of physical, emotional and psychological factors that interact with biology and behavior. This is a special, high-risk smoking population whose smoking behaviors have not been well characterized. Exploration of this population is important because cigarette smoking during military service was associated with lifelong increased cigarette consumption and subsequent increased tobacco-related disease prevalence.19, 21 A more immediate impact, community acquired pneumonia risk was 2.93 times higher in young soldiers that were highly dependent on nicotine compared to non-users, reducing operational readiness, and increasing military healthcare burden.207 Female Navy recruits that smoked had higher rates of hospitalization and longer stays than non-smokers.208

Other studies showed that smoking abstinence in pilots during 12 hour shifts resulted in

deterioration of cognitive tasks related to job performance35, and Navy female recruits who were daily smokers when entering the military had poorer career outcomes, lower pay grade achievement and more less-than-honorable discharges than Navy females who never smoked.209-211

High rates of military smoking continued to be a problem despite the fact that in

1987 the Department of Defense banned all tobacco use at training commands to prevent the use of 'smoke breaks' as a reward/punishment to encourage obedience.18 This change in culture produced a cadre of personnel that were smoke free after several months of initial entry (boot camp) and military job skill training; however, significant numbers returned to, and increased smoking use after this period of forced smoking cessation.

Examples of specific environmental factors unique to the military include smoking as a

‘right of service’, or a means of socializing to relieve stress and boredom that may contribute to the high rate of smoking relapse.16, 200 Subsidized tobacco prices in commissaries and tobacco delivery to troops in conflict zones may also contribute to significant smoking relapse.212-215 Seventy five percent of veterans reported using tobacco at some point in their life prompting the Veteran’s Health Administration (VHA) to create a goal of creating a healthier fighting force through increased tobacco quit rates to reduce long-term burden of tobacco-related disease treatment.18 This aligns with the

Healthy People 2020 genomics objectives and tobacco use goals concerning the increased need for genetic tests and family history to guide clinical and public health interventions to improve tobacco cessation.12

Three Biomarkers of Tobacco Use

Three biological markers that could be included in new screening and treatment guidelines are genetic variations in the α4β2 brain nicotinic receptors, CYP2A6 metabolism of nicotine and bitter taste phenotype that affect tobacco use and dependence.

Nicotinic acetylcholine receptors (nAChR) occur throughout the body in muscle, cilia and in the brain and are comprised of five subunits (α2-10 or β 2-4) bundled around a central ion channel (usually calcium on neurons). The α4β2 nAChRs (consisting of two

α4 and three β2 subunits) were the predominant human brain receptor subtype with high affinity for nicotine.80 When one of the three β2 subunits is replaced with an α5 subunit yielding α4β2α5, the stoichiometry of the receptor is altered due to slight changes in electrical charges. While highly polymorphic, the variation that most significantly effected nicotine dependence was the non-synonymous, single nucleotide polymorphism

(SNP) rs16969968 on chromosome 15 which caused an amino acid change (D398N) of aspartic acid (G allele) to asparagine (A allele) in the α4β2α5 nicotinic receptors

(nAChR) protein product.92, 94 The rs16919968 minor allele ‘A’ frequency varies widely between ethnic groups from 3% in Asian and Africans to 36% in Europeans.88

Individuals with a single risk allele (A/G) have been shown to have slightly increased risk for nicotine dependence while individual’s with two risk alleles (A/A) result in significantly higher risk compared to normal (G/G). Higher risk results from reduced receptor permeation and higher levels of blood nicotine being required to modulate the release of neurotransmitters.85, 91

Nicotine circulating in the blood not bound to receptors is metabolized through C- oxidation into cotinine (Cot), then trans-3`-hydroxycotinine (3HC), mainly by a liver enzyme CYP2a6. The CYP2a6 gene on chromosome 19 codes for the production and synthesis of the CYP2a6 liver enzyme and is highly polymorphic with some variants only existing in certain ethnic/racial populations making genotyping very complex and expensive.42, 216 Since direct assay of numerous genotypes is difficult, especially with mixed ethnicity participants, nicotine metabolite ratio (NMR) of 3HC/COT was utilized as a pharmacogenetically informed biomarker of CYP2A6 genetic variation.53, 59 NMR was analyzed in quartiles with the highest quartile representing normal metabolism and lowest quartile represented slow nicotine metabolism. In a study by Ho et al, individuals in the slow metabolizing group had lower risk of smoking dependence and higher quit rates (OR 1.85, P=.003).59

Nicotine from cigarettes enters the body through the mouth and has a bitter flavor.

The ability to taste bitter is controlled by several gene variants in the TAS2R38 gene on chromosome 7 which has been found to represent approximately 85% of the variability in human bitter taste with 70% of the population and 50% of smokers having the ability to taste bitter.142, 147 Measurement of bitter taste phenotype (BTP) with 6-n-propylthiouracil

(PROP) filter paper discs is a noninvasive measurement of bitter taste perception that correlates well to taste receptor genotype.149 Psychophysical phenotypes of bitter taste have been characterized in the literature as non-taster, moderate taster and super taster of bitter. Super tasters are more adverse to bitter taste which may affect daily cigarette consumption.

An individual’s smoking behavior is complex. Elements influencing behavior include physical and psychological nicotine dependence, nicotine metabolic pathways, genetic and environmental factors and personal preference, therefore cigarette consumption data were collected via self-report of average number of cigarettes smoked per day.

Conceptual Framework

Gene-environment interaction theories stem from the writings of Plato and

Descartes who first questioned the relative contributions of biological inheritance and environmental factors on human development. Philosophers referred to these phenomena as an argument (or discussion) about human's possessing innate knowledge (nature) vs. the mind being sculpted through wisdom (nurture).217 In biology, the debate over the relative importance of an individual’s innate qualities as compared to experiential learning gained from the environment has existed for centuries and been given many names, such as nativism vs. behaviorism, nature vs. nurture, gene environment interaction, heredity and environment, and genetic determinism vs. environmentalism.

Many classical scientist such as Mendel, Morgan, and Darwin examined heredity vs environment through careful observation, however when Watson and Crick discovered the structure of DNA in 1953, science was provided with a valuable tool to help understand the underlying nature of biologic inheritance that drives observed behavior.218

The June 12, 1957 Surgeon General Leroy Burney’s official declaration of tobacco use as a causative agent of lung cancer development followed by the 1964

Surgeon General’s report on smoking and health encouraged scientists to examine the

causative nature of cigarette smoking.219 Many of the theories used by nursing science to investigate smoking cessation originated in psychology and addressed psychosocial aspects of smoking (nurture) but did not address the biological (nature) link of genetics and nicotine dependence.220 Genetic science provided new discovery and an in-depth understanding of why the differences in smoking behaviors occur which opened new avenues of research and therapy utilizing biological and genetic information to expand the body of knowledge pertaining to nicotine addiction, dependence and cessation.221

This study utilized a modified version of the stream of causation framework to explore the gene-environment interactions related to smoking in military populations using an axis of nested hierarchies running perpendicular to life stream as introduced by

Glass & McAtee.2006 In Figure 9 of Appendix G, this framework illustrates how factors below the water line of the stream (biology/genetics) can affect the surface flow

(behaviors) without being seen while other observable factors exist in the environment above the stream.222 Both the above and below factors affect the outcome of behavior independently and in combination over time. This framework facilitates understanding complex gene-environment interactions producing unique patterns of behavior in each individual which would require personalized treatment options.

The investigator hypothesized three main outcomes from this study. First, that the three biomarkers of nicotine would be related with metabolism being positively associated with the rs16969968 risk allele ‘A’ and negatively associated with bitter taste perception. Second, that the three biomarkers will be positively associated with cigarette consumption with increased nicotine dependence risk (as specified for each biomarker)

resulting in increased consumption, and thirdly that each additional variable will significantly impact the biomarker’s relationship to cigarette consumption.

Data Collection

Demographic, smoking history and military experience data for this study were collected via electronic survey utilizing Checkbox software from healthy male and female adults between the ages of 18 and 55 from the military community who regularly smoked cigarettes. Military community was defined as any individual who is currently serving or has previously served in any branch of the US Military as enlisted or commissioned officer or cadet (active duty, National Guard, Reserves, ROTC or veteran).

Biomarker data was collected from blood samples and a non-invasive test for bitter taste perception performed by the investigator.

Recruitment

The original study aims were to characterize the relationship of genetic risk of nicotine dependence r/t polymorphisms in brain receptors (rs16969968), nicotine metabolism phenotype utilizing NMR, and bitter taste phenotype (BTP) in combinations to determine the most effective predictive model of CPD in a sample of current and former military personnel. Once the best model to predict effect on CPD was determined, covariates would be added to examine their effect on CPD. A sample size of 160 participants was needed for an estimated .86 statistical power.

Recruitment for this study over 14 months did not achieve the anticipated 160 participants needed to achieve statistical power or build predictive models. Recruitment consisted of physical and electronic distribution of flyers, electronic newsletters,

newspaper advertisement and subscription to Research Match recruitment databases to identify potential participants within 250 miles of the Ohio State University campus. See

Table 25 in Appendix F for details about recruitment media and time frame of advertising. Recruitment literature was distributed to approximately 385,043 individuals of which 82 responded, however, 50 (61.0%) did not respond to follow-up communication, 14 (17.1%) did not meet eligibility requirements, 3 (3.7%) withdrew and

15 (18.3%) completed the study.

Variables

This section will operationally define each dependent and independent variable, and discuss how it was measured for analysis (see Table 5). Demographic and psychosocial variables were collected by self-report with online survey software.

Biologic data were obtained from blood samples collected by the investigator and processed either in the College of Nursing laboratory or through a contracted national reference lab.

Genetic risk for nicotine dependence (rs16969968) was assayed in the OSU

College of Nursing research facilities. The investigator collected approximately 5ml of peripheral venous blood into a vacuum tube coated with 7.2mg of K2 EDTA (Beckton &

Dickson, Franklin Lakes, New Jersey) and gently mixed. The blood was immediately cooled on ice until stored at -80◦ C. Repeated freeze-thaw cycles were avoided. DNA was purified from the frozen whole blood samples using a standard laboratory procedure for 5

Prime Ready PCR DNA Column kits (5 Prime, Hilden, Germany).

Table 5. Independent and Dependent Variables, Measures and Expected Outcomes

Unit of Expected Variable Measure Type Measure Output nAChr Risk for Nominal Allele ‘A’ or ‘G’ Independent variation Nicotine to build risk Variable (rs16969968) Dependence genotype Nicotine Nicotine Continuous Ratio of Independent Metabolite Metabolism rate metabolites as Variable Ratio (NMR) numbers

Bitter Taste Perceived bitter Continuous 0 – 100 mm taste Independent Phenotype taste level intensity Variable (BTP)

Cigarettes per Cigarette Continuous 0 - ∞ cigarettes Dependent day (CPD) consumption Variable

DNA concentration was determined with a NanoDrop 2000 micro-volume spectrophotometer (Thermo Scientific, Waltham, MA). Samples were diluted to 20ng/ul to produce standardized DNA mass of 20 ng per sample well. Amplification of target

SNP rs16969968 was conducted using a wet DNA prep method utilizing commercially prepared, custom Taqman assay ID# C_26000428_20 (Applied Biosystems, Carlsbad,

Ca) in a Bio-Rad CFX-96 thermal cycler (Bio-Rad, Hercules, Ca) with a program of 10 minute hold at 95°C followed by 50 cycles of 15 second denature at 92°C and 1 minute anneal at 60°C to optimize product amplification. Determination of presence of allele ‘A’ or ‘G’ at position rs16969968 was determined via Bio-Rad allelic discrimination software and verified visually with relative fluorescence unit (RFU) graph analysis by the investigator (Bio-Rad). Data was converted into categories of A/A = increased risk, A/G= slightly increased risk and G/G= normal risk.

The NMR (3HC/COT ratio) was determined by quantification of cotinine and trans-3`-hydroxycotinine by a contracted reference laboratory using a CLIA standard protocol involving liquid chromatography – tandem mass spectrometry from frozen blood serum samples as detailed in their literature223, 224, then calculating the ratio of 3HC:Cot and dividing into quartiles. Samples were collected by the investigator in 5ml red top serum separator vacuum tube. The sample was stored at room temperature for 30-60 minutes to promote clotting then centrifuged at 1500 x g (2250 rpm) for 10 minutes to obtain plasma. The supernatant was pipetted into transport tubes provided by the reference lab, labeled, and frozen in the -80◦ C freezer until batched and shipped. The reference lab conducted the analysis and reported nicotine and cotinine levels in ng/ml with expected results between 30-50 ng/ml for nicotine and 200-800 ng/ml for cotinine.224

Reference lab error resulted in a lack of 3HC quantitation and the samples were expended in the process so the investigator was unable to calculate the NMR or determine the nicotine metabolism rate for these samples. Cotinine, the proximate metabolite of nicotine with a half-life of approximately 18 hours, was obtained and has been significantly correlated with cigarette consumption in the literature.63 Since cotinine is only 80% metabolized through the CYP2A6 pathway, it is not an accurate proxy measurement for genetically controlled nicotine metabolism. However, due to the long half-life, cotinine does provide information about nicotine exposure over the period of several days and is commonly used to distinguish smokers from non-smokers.22-24, 221, 225

Cotinine will be used as an independent variable in the analysis in place of NMR, but not as a replacement proxy measurement for nicotine metabolism.

Bitter Taste Phenotype (BTP) was determined by the oral 6-n-propylthiouracil

(PROP) filter disc procedure when participants met with the investigator for blood collection. Two 1.5 cm filter paper discs were used; the first with 1.0 mol/l NaCl solution, the second impregnated with a 50 mmol/l PROP solution that dissolved in saliva when placed on the center of the tongue. Participants rated the intensity of the taste of each paper disk by placing a mark on a 100mm semi-logarithmically labeled magnitude scale marked “barely detectable to the strongest imaginable” (Figure 7). The distance, in mm, from 0 to the participant mark was measured and recorded. The resulting data was used to categorize participants into one of three phenotype groups: non-tasters with intensity rating of 15 mm or less, moderate tasters from 16 mm through 67 mm, and super tasters above 67 mm.

Cigarettes per day (CPD) were assessed through self-report by participants of average number of cigarettes smoked per day.226 These data were collected as a continuous variable.

Relevant sociodemographic and military variables can significantly contribute to smoking behaviors. See Table 6 for variables that were important in military populations and collected for inclusion in analysis.

Age was collected as age in whole years through self-report on the survey.

Individuals must be 18 years old to serve in the U.S. Military. Genetics do not change over time so veterans who have been separated from service for many years will maintain

similar genetic smoking risk profiles as they had when in service. Over 99.6% of military personnel are between the ages of 18 and 55.171 Recruitment was less than 56 years of age to capture as many service members possible while limiting the possibility of age- related alterations to biomarkers such as reduced c-oxidation metabolism, reduced taste perception and slowing of other metabolic processes, such as blood circulation, liver function, and disease formation change with age.24

Table 6. Sociodemographic and Military Variables Measured in Study

Variable Unit of Measure Expected Outcome Age Continuous Age in whole years Gender Nominal Male/Female Education Ordinal Education Category Race Nominal Racial Category Rank Ordinal Rank Category Branch of Service Nominal Branch Category Deployment Experience Continuous Length in Months Combat Experience Nominal Yes/No Smoke in Boot Camp Nominal Yes/No Smoke before Entry Nominal Yes/No Self-Rate Addiction Continuous 0-100

Participants self-identified as either male or female. Women tend to clear nicotine approximately 13% faster than men with use of oral hormonal contraceptives accelerating this clearance to 28% faster. Pregnancy can further increase nicotine clearance to 60% faster than males.24 Other research has shown significantly higher NMR in women than men.62 Earlier studies identified that Navy women had 2.5 times the smoking rate of

civilians210, 227, and 30.4% of women recruits in the Air force were current smokers with

6.6% reporting a history of smokeless tobacco use186-187, 209-210, 228-229

Education level was self-identified from selecting a category ranging from high school only, high school plus military training, years of college leading to a degree or highest degree held. Research has demonstrated smoking rates are higher in populations of lower educational achievement.230, 231 The military hierarchical structure contained a distinct separation between enlisted and officer ranks. In general, officers are more educated because they are required to attain the minimum of a bachelor’s degree before being commissioned and a master’s degree for advanced rank while enlisted personnel only require a high school diploma (or GED) for their entire military career.

Race was self-identified using standard Department of Defense racial categories.

Significant differences exist in the genetically controlled metabolism of nicotine by racial origin. Some variants of the CYP2a6 gene such as *4G, *4H, *1B4 and *1L only exist in very small racial subgroups of African descent. Genetic variants can alter expression of

CYP2a6, altering metabolic activity, affecting cigarette smoking behaviors.29, 52, 54, 59, 216,

232

Current or last rank held was obtained through self-report in the online survey.

Military rank is used as a proxy for socioeconomic status (SES). Rank is a complex mix of salary, education, social status and responsibility level. The higher the rank held, the more educated one is and the better the pay and benefits received. Literature shows that smoking rates are higher in lower SES groups who also have less successful quit attempts, reduced social support for quitting and stronger addiction to tobacco.204

Military branch was self-identified from a list of Army, Navy, Air Force, Marines, and Coast Guard in the online survey. Military branch is important to predicting smoking behavior. A 2005 Department of Defense survey on cigarette use reported differences in branch-specific smoking prevalence. The Army showed the highest rate of cigarette smoking at 38.2% followed by Marines (36.3%), Navy (32.4%), and Air force (23.3%).18

Deployment experience was collected as self-report of the number of months spent deployed during military career. Service in the military and deployment to combat zones alters the risk of tobacco use for soldiers. Data obtained from military personnel returning from Iraq and Afghanistan showed smoking prevalence between 50-60% in deployed personnel (compared to 18.1% in civilians) with an additional 12.2% reporting concurrent use of smokeless tobacco.16, 233

Participants were asked to describe the degree to which they felt addicted to cigarettes by using a self-rated addiction scale of 0-100 with zero indicating no perceived addiction at all and 100 indicating extreme addiction.

Methods

Design

A one group, cross-sectional descriptive design was implemented to address the aims of this project. Smoking history, demographic, and psychosocial data were collected via an online survey. Biologic samples were collected via a one-time two vial blood draw

(5ml and 5ml) performed by the investigator to assess selected biomarkers.

The primary aims of this study were to determine relationships between three biological markers of nicotine in military smokers, if variations within the three

biomarkers affect cigarette consumption in military smokers and if the biomarker relationships were affected by relevant sociodemographic and military variables.

Sample

A sample of 18-55 year old, healthy regular smokers who were current or former military of any branch or service that resided within 250 miles of the Ohio State

University campus were recruited. Participants were screened for medication use, age and smoking behaviors. Inclusion criteria included healthy men and women who were current regular smokers (>10 cigarettes per day for >1 year) and had served in, or are currently serving in any branch of the US military (Active, Reserves, ROTC, Guard). Participants were required to have access to an internet-connected computer to complete the survey component. Exclusion criteria included any significant physical or psychological acute

(e.g. pneumonia) or chronic illness (e.g. diabetes, seizure disorder, schizophrenia) illness, or use of daily prescription medication that inhibited or induced liver CYP2a6 activity

(ex. Ketoconazole, Methoxsalen) or alter brain chemistry at nicotine receptor sites

(Phenobarbital).

Human Subject Protection

Strategies to ensure voluntary participation included electronic recruitment and screening to allow interested individuals to complete the screening and survey from their homes without pressure or scrutiny of the researcher. Additionally, the survey was coded with a unique ID created by the participant to help protect their identity. No information from the research was shared with military organizations or the veteran’s affairs office.

To help protect privacy, online screening and survey were conducted via the Checkbox®

Survey Software (Checkbox Survey Solutions, Watertown, Ma) on secure servers installed directly on the college's infrastructure, behind several firewalls, giving the investigator total control over the survey environment.

Potential risks to participants were assessed and included the possibility of minor bleeding or bruising from blood sample collection site, or discomfort with questions about military experiences. While there were no direct benefits to participants, the knowledge gained from this research could inform improvements to individualize smoking cessation treatment, which could benefit this group of cigarette smokers in the future since the studied biologic functions can affect smoking cessation success. This was important in military personnel because they are at much higher risk to start smoking, to smoke heavier, and have a more difficult time quitting than others.18

Procedures

Recruitment efforts directed interested volunteers to the study website

(www.smokingresearch.us) which contained a brief overview of the study and detailed steps to participate. Interested volunteers completed the short survey regarding inclusion and exclusion criteria, which was scored automatically. If the participant met inclusion criteria, they were provided with a link to the online survey that collected information about demographics, smoking behaviors and military experience with a consent document as the initial content. When the survey was completed, participants were prompted to contact the investigator to schedule an appointment for bitter taste perception using oral PROP method and collection of 2 vials of peripheral blood (5ml and 5ml) for genetic SNP analysis of rs16969968 and quantification of 3HC/Cot metabolite ratio.

Smoking status was verified through exhaled CO measurement to ensure active smoking since the 3HC/Cot test requires nicotine and metabolites to be in the blood stream at time of sample collection.

Data Analysis

Data were screened for normality, outliers and homogeneity. Descriptive statistics were used to summarize the sample characteristics and distribution of each variable. To determine the association between the three biomarkers of nicotine (Aim 1), a Pearson product-moment correlation coefficient (point biserial) was used to examine the dependence between interval and nominal variables.234 Aims 2 and 3 examined the relationship between the three biomarkers and cigarette consumption and if the relevant variables affected these relationships by calculating either a Person product-moment correlation coefficient (point biserial), which measures dependence between two variables that include interval and nominal measures, or a Kendall’s tau statistic to examine the degree of association or dependence between interval/interval and interval/nominal variables.234 Kendall’s tau is a non-parametric test that does not require a normal distribution and works well for small samples.

Results

Participants completed the online survey in an average of 13.4 minutes. As shown in Table 7, the sample mostly consisted of non-degree holding employed Caucasian males with an annual income over $45,000 and a variety of occupations. Eighty percent of participants were married or divorced with an average age of 43.5 years.

Table 7. Socio-demographic Characteristics of Participants (N=15).

Variable n % M SD Range Age 43.5 8.5 27-55 Gender Male 11 73.4 Female 4 26.7 Race/Ethnicity Caucasian 13 86.7 African American 2 13.3 Marital Status Single, never married 1 6.7 Live with partner 2 13.3 Married 6 40.0 Divorced 6 40.0 Employment Full-time 9 60.0 Part-time 2 13.3 Unemployed 2 13.3 Disabled 2 13.3 Education HS/GED 1 6.7 Tech training/some 9 60 College Bachelor’s degree 2 13.3 Graduate degree 3 20 Annual Income level < $5000 1 6.7 $5001 – 24,999 2 13.3 $25,000 - 44,999 3 20 > $45,000 9 60

All participants were members of at least one branch of the military with Air force and Coast Guard not represented in the sample. Table 8 shows that most were enlisted

(73.4%), served on Active duty (73.3%) at some point in their military careers and

86.7% were deployed for a career average of fourteen months, however this experience ranged widely between two and fifty two months. Of these deployments, 53.3% were to conflict areas with 13.3% of those in conflict areas being involved in direct combat.

Table 8. Military Attributes of Participants (N=15).

Variable n % M SD Range Military branch Army 6 40 Marines 3 20 Navy 6 40 Military service component Active Duty 7 46.7 Reserves 4 26.7 Active + Reserves 1 6.7 Active + Guard 1 6.7 Active + Guard + Reserves 2 13.3 Rank Enlisted 11 73.4 Officer 2 13.3 Not answered 2 13.3 Deployment Experience (Months) 13 86.7 14.0 17.0 0 - 52

Participants’ smoking behaviors in Table 9 are consistent with military tobacco use previously reported in the literature. Most smoked greater than 10 years (M=16.7), smoked before entering the military (60%) and had high self-rated levels of addiction

(M=78.7). On average, participants initiated smoking at an early age (M=16.5) and all that smoked during military service had a history of smoking before entering.

Interestingly, forty six percent reported smoking during initial entry training (boot camp) despite the Department of Defense ban on tobacco use in training commands.18

Table 10 shows a majority of participants possess the normal G/G genotype

(73.3%) for rs16969968, and are moderate or super tasters of bitter (66.6%). Lab error resulted in no 3HC measurements to calculate the NMR, however cotinine levels were provided. Unfortunately, interpretation of cotinine results is time and dose dependent, information not collected in this study.

Table 9. Cigarette use Attributes of Participants (N=15).

Variable n % M SD Range Age of smoking initiation 16.5 5.1 14 -24 # years regular smoker 16.7 10.8 3 - 40 Cigarettes smoked per day 19.1 7.0 5 - 35 Rate level of addiction 0-100 78.7 30.4 0 - 100 Smoked before joining Military Yes 9 60 No 4 26.7 Smoked while in Military Yes 7 46.7 No 8 53.3 Smoked during initial training Yes 7 46.7 No 6 40.0 n/a (Officers) 2 13.3

Analysis of variable intercorrelations on Table 26 in Appendix H failed to support the first hypothesis stating that the three biomarkers would be related in the fact that no relationships were found. The significant relationship (t=.47, p<.05) between cigarette consumption and cotinine level was expected since cotinine can reflect nicotine dose.

Findings do not support the second hypothesis that cigarette consumption would be positively associated with increased risk identified by the three biomarkers. The third hypothesis that select variables would modify the biomarker’s impact on cigarette consumption could not be adequately analyzed due to a lack of an established relationship between the biomarkers and consumption.

Table 10. Measured Biomarkers of Nicotine Use (N=15).

Variable n % M SD Range Exhaled CO (ppm) 22.9 7.7 15 - 43 Serum Nicotine (ng/ml) 13.4 4.6 6.3 - 22 Serum Cotinine (ng/ml) 264.7 84.5 164 - 453 Serum 3HC (ng/ml) ------Bitter Taste Phenotype (BTP) Non-taster (15mm or less) 5 33.3 Moderate-taster (15mm– 67mm) 8 53.3 Super-taster (above 67mm) 2 13.3 SNP rs16969968 genotype Homozygous ‘A’ allele 1 6.7 Homozygous ‘G’ allele 11 73.3 Heterozygous ‘A/G’ 3 20.0 Nicotine Metabolite Ratio (3HC/Cot) ------Note. Double dash (--) indicates data not obtained due to external lab error.

While not significant overall, the data does show interesting relationships between some of the military variables that could be used in future studies. Not surprisingly, cigarette consumption was inversely proportional with education (r=-.51, p<.05) showing more educated individuals consumed fewer cigarettes as supported in the literature.

Although rank alone was unrelated to consumption, this could be a function of military rank as a proxy for social economic status (SES) since officers are required to have a minimum of a bachelor degree, receive better pay, more benefits and function as role models for enlisted and junior officers which may exert social pressure to control cigarette consumption. This is contradicted by the fact that level of education was directly related (r=.67, p<.05) to smoking status before entering military service which would indicate early smokers were more educated; however, this may be another phenomenon of rank since officers attend college and enter military service later in life than enlisted personnel, offering a greater chance of smoking initiation before entry.

Bitter taste perception was inversely related to (r=-.58, p<.05) participants who smoked during initial entry training, possibly indicating that people who had lower perceptions of bitter taste smoked despite tobacco bans during this period.

Other interesting relationships included a greater length of deployment for higher ranking individuals (t=.58, p<.05). Inverse relationships existed between rank and both service in a combat zone (t=-.70, p<.01) and length of deployment with combat experience (r=-.65, p<.05). While interesting, conclusions from these relationships cannot be made without further context (such as length of service, military job, or number of deployments).

Discussion

The data of this study provided limited information from which to make conclusions. The original aims could not be adequately addressed due to a lack of data to calculate the nicotine metabolite ratio (NMR), one of the main biomarkers of the study.

With the small sample size, the planned study utilizing regression analysis and predictive modeling could not be accomplished to determine how the main variable interactions would be affected by addition of military and sociodemographic variables. The investigator chose to analyze the available data as a study to help identify strategies to improve participation and data collection for future studies.

To maximize the small sample size, the bitter taste perception variable was entered in analysis as both three and two categories (with moderate and super tasters combine into a single ‘taste’ group). However no significant relationships were identified with either categorization. Further, rs16969968 was also analyzed as both a three and a

two category variable since only one participant was homozygous for the A allele.

Participants with one or two risk (A) alleles were grouped together (n=4) with no significant relationships identified between the risk allele and either cotinine or bitter taste.

While the results of this study were not significant overall, the process provided the investigator with many important learned lessons. Despite the small sample size, the lack of laboratory data significantly limited the ability to accomplish the goals of a study.

Reliable lab services must be obtained and verified before specimens are sent for processing. In the next study the investigator will send control samples to the laboratory to ensure the quantitative testing can be completed as contracted and in a reasonable amount of time. In addition, some of each sample will be retained for reanalysis or analysis elsewhere if the primary lab is unable to perform the tests.

Future collaborations with laboratories and researchers that specialize in genetic nicotine research could enhance study design and ensure accurate sample processing. In addition for this study, blood sample collection amount was minimized to meet IRB requirements and reduce participant burden, however the small collection size resulted in only enough serum for a single analysis. Future studies will provide enough collected volume for redundant analysis to ensure laboratory error does not expend the entire sample resulting in data loss.

Primary sample recruitment was very time consuming and difficult. As detailed in the second chapter, the investigator failed to adequately engage the target population and convey how the study would personally benefit the participant. Future research will

utilize collaborations with treatment teams and military organizations to provide tangible benefits to the participants while collecting research data.

This study has provided valuable data to help characterize a population of diverse people. Several descriptive characterizations will be presented to help visualize the population and support inclusion of military smokers in future studies. The single participant that was homozygous for the rs16969968 risk allele ‘A’ with moderate bitter taste perception is described. This participant has the high risk of nicotine dependence due to the presence of two ‘A’ risk alleles (A/A) and a moderate perception of bitter taste.

The thirty plus year old, single, Caucasian female served as a lower ranking enlisted individual in the US Navy Reserves. She had a high school education and some military technical training, worked full-time earning $30-35,000 per year. She started smoking at age 14 and is somewhat interested in quitting despite being very addicted with a score of

100 out of 100 on the self-rated addiction scale. She smoked before entry into the military, during initial entry training and while serving with no deployment experience.

This woman smoked 18 cigarettes per day with two unsuccessful quit attempts in the past year.

In comparison, an older married Caucasian male served as a high ranking officer in the Army, started smoking at age 18, smoked through college and continued smoking while in the military. This participant has moderate bitter taste perception and is homozygous normal (G/G) for genetic nicotine receptor dependence risk. Deployed for fifty-two months (twenty three months in a conflict area) with direct combat experience, he smoked before deployment, increased consumption during deployment and tried to

reduce upon return. He earned a master’s degree and worked as a teacher making

>$50,000 a year. Currently smoking 15 cigarettes per day he is very interested in quitting and rates himself as not addicted (0 out of 100) on the self-rating scale but has had two unsuccessful quit attempts in the last year.

While all participants in this study are current smokers, they also possess diverse military experience and unique traits that influence smoking behaviors and dependence.

Both of the examples present individuals that have been unsuccessful with smoking cessation but possess very different social and physiologic factors. The military population is very diverse and should be included in future nicotine research to examine the relationship of three commonly used biomarkers in nicotine and tobacco research.

These data can be used by practitioners to build evidence-based, clinically relevant individualized smoking cessation therapies that will help reduce tobacco–related healthcare burden and improve the readiness and well-being of the US military fighting force.

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APPENDIX A: PATHWAYS OF NICOTINE METABOLISM IN THE HUMAN BODY

106

Figure 8. Pathways of Nicotine Metabolism in the Human Body.

From “Metabolism and Disposition Kinetics of Nicotine” by J. Hukkanen, P. Jacob and N. Benowitz, 2005, Pharacological Reviews, 57, p.89.

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APPENDIX B: CYP2A6 ALLELE FREQUENCIES BY ETHNICITY TABLES

108

Table 11. CYP2A6 Allele Frequencies for Caucasians, Europeans and European Descent

CYP2A6 Alleles European Caucasian Descent French Spaniards Finish Swedish Turkish *1 96.5% (25) 96.0% (25) 53.6% (29) *1A 66.5% (32) 57.3% (22) 67% (28) 64.0% (10) 64.9% (28) *1A (51A) 19.0% (2) 1.8% (29) *1x2A 0.39% (2) 0.7% (32) 0.7% (28) 1.2% (28)

*1B 33.3% (29) 30.0%(32) 27.6% (22) 32% (10) *1B1 33.5% (28) 30.9% (28) *1B1-17 28.3% (20) *1B4 0.6% (20) *1F 1.8% (22) *1G 1.2% (22) *1H 7.70% (2) *1L 0.6% (20)

2.2% (29) 3% (14) 3.06% (2) *2 1.1-3% (32) 2.2% (27) 2.0% (15) 1.2% (22) 3.0% (25) 3.0% (25) *3 0.7% (29) 1% (18) 4.0% (17) 0.9% (2) *4 0.13% (15) 1.2% (27) .05-4.9% (32)

Table continues

109

Table 11 Continued

CYP2A6 Alleles European Caucasian Descent French Spaniards Finish Swedish Turkish 3.0% (22) *4A 4.0% (28) 0.0% (8) 4.0% (10) 4.0% (28) *4D 0.0% (22) *4A, *4D-F 0.0% (20) *4C *5 0.2% (32)

*6 0% (18) *7 1.0% (32) 0.0% (6)

*8 0.0% (32) 5.8% (29) *9 6.1% (2) 5.2% (32) 7.9% (22) 5% (18) 7% (18) 8.0% (15) *9A 7.1% (27) 7.1% (28) 6.4% (28) *10 0.00% (32) *11 2.5% (29) *12 2% (14) 2.3% (2) 2.1% (15) *12A 2.0% (27) *14 4.13% (2) *15 *16

*17 0.09% (2) 0.0% (5) *18 *18A 1.1% (6) *18B 1.1% (6) *19 0.0% (6)

Table Continues

110

Table 11 Continued

CYP2A6 Alleles European Caucasian Descent French Spaniards Finish Swedish Turkish

*20 0.0% (2) 0.0% (6) *21 0.92% (2) *23 0.01% (2) 0.06% (2) *24 0.0% (1) *25 0.0% (2) *26 0.0% (2) *27 0.0% (2) *28 0.10% (2) *31 0.0% (2)

*35 0.06% (2) 0.0% (1) 0.0% (1) *36 0.06% (2) *37 0.06% (2) *38 0.05% (2) Note: Numbers in parenthesis indicate source citation. A list of table source citation is available at the end of Appendix B.

111

Table 12. CYP2A6 Allele Frequencies for African, African Descent, and Indians,

CYP2A6 Black Aka Alleles African Canadian African (Central Indians American African Descent Ghanaian African) (India) 67.36% *1 (16)

*1A 52.01% 66.5% (22) 80.5% (28) (24) *1A (51A) 22.0% (2)

*1x2A 0.92% (2) 0.0% (24)

*1B 18.2% (12) 18.3% (12) 13.89% 39.37% 11.2% (22) 19.8% (19) (16) (24)

*1B1 11.9% (28) *1B1-17 19.7% (20) 15.3% (20) *1B4 0.0% (20) 0.5% (20) *1F 0.0% (22)

*1G 13.3% (22)

*1H 11.39% (2) *1L 9.2% (20) 6.8% (20) 0.9% (12) 0.28% (2) *2 0.3% (32) 0.0% (22) 0.4% (12) 0.29% (24) *3

*4 1.9% (12) 2.4% (2) 2.7% (12) 1.44% (24) 0.6% (8) *4A 0.5% (22) 1.9% (28) *4D 0.5% (22) *4A, *4D-F 1.3% (20) 2.7% (20)

*4C

*5 0.86% (24) *6 *7 0% (7) 0.0% (24)

*8 0.86% (24)

Table continues

112

Table 12 Continued

CYP2A6 Black Aka Alleles African Canadian African (Central Indians American African Descent Ghanaian African) (India) 9.6% (12) *9 7.68% (2) 7.2% (12) 8.0% (22) 7.2% (19) 5.7% (7) 7.64% (16)

*9A 5.7% (28) *10 0.0% (24) *11

*12 0.4% (12) 0.0% (12) 0.6% (2) 0.0% (19) *12A *14 0.42% (2) 0.9% (19) *15 0.0%(19) *16 0.0% (19) 7.3% (12) *17 7.3% (19) 8.0% (12) 9.4% (5) 11.11% 10.5% (2) 10.5% (23) (16) *18 *18A 0.0% (7) *18B 0.0% (7) *19 0.0% (7) 1.5% (12) 1.1% (12) *20 1.62% (2) 1.1% (19) 1.6% (7) 1.7% (23) 0% (16) *21 0.16% (2) 0.7% (19) 1.1% (12) 2.0% (12) *23 1.55% (2) 2.0% (19) 0.7% (12) *24 2.25% (2) 1.3% (12) 0.7% (1) 1.4% (1) 1.3% (19) *25 0.9% (12) 0.5% (12) 0.14% (2) 0.5% (19) *26 0.7% (12) 0.7% (12) 0.35% (2) 0.7% (19) *27 0.7% (12) 0.2% (12) 0.42% (2) 0.0% (19) 2.4% (12) 0.9% (12) *28 2.1% (2) 0.9% (19) *31 1.4% (2) 2.9% (12) *35 2.25% (2) 2.5% (12) 2.9% (1) 2.5% (1)

Table Continues

113

Table 12 Continued

*36 2.25% (2) *37 2.25% (2) *38 0.0% (2) Note: Numbers in parenthesis indicate source citation. A list of table source citation is available at the end of Appendix B.

114

Table 13. CYP2A6 Allele Frequencies for Asian, Chinese, Japanese, Korean, and Thai

CYP2A6 Alleles Asian Chinese Japanese Korean Thai *1 84.9% (25) 34.30% (24) 40-42.0% *1A 43.2% (32) (32) 27.2% (28) 16.4-20.3% 62% (4) (28) 32% (26) *1A (51A) 1.45% (24) 0.0% (21) *1x2A 0.4% (32) 0.0% (32) 0.4% (28) 0.0% (28) 0.2% (21) 44.48% (24) 27.7% (21) *1B 40.6% (32) 38 – 41% (32) 42% (4) 25.6% (9) 37.1% (21) 27% (26) *1B1 34.5-51.3% (28) 27% (28) *1B1-17 31.6% (20) *1B4 0.0% (20) *1F *1G *1H *1L 2.0% (20) 0.0% (25) *2 0.0% (24) 0.0% (21) 0.7% (32) 0.0% (32) 0.0% (21)

*3 0.0% (21) 0.0% (21) 4.94% (24) 7% (18) 20.1% (21) *4 8.5% (13) 17% (18) 11-18% (17) 0.5-4.9% (32) 20-31% (32) 11% (18) 14% (26) *4A 22.3% (8) 11% (21) 6.7 - 15.1% (28) 24.4% (28) 11.0% (8) *4D 1.0% (32) *4A, *4D-F 15.3% (20) *4C 13% (4) 0.5% (21) *5 1.16% (24) 0.0% (21) 1.2% (13) 0.0% (32) 0.5% (11) *6 0.0% (21) 0.4% (32) 0.0% (21)

Table Continues

115

Table 13 Continued

CYP2A6 Alleles Asian Chinese Japanese Korean Thai 6.98% (24) 6.5% (21) 3.6% (21) *7 3% (18) 11% (18) 4% (18) 6.3% (13) 6.3% (32) 9.8% (11) 2.2% (32) 13.0% (6) 10.0% (6) 5% (26) 1.4% (21) *8 1.45% (24) 2.2% (21) 3.5% (32) 1.6% (32) 1.4-2.2% (11) 16% (18) *9 13.5% (13) 15.7% 20% (18) (32) 20.7% (9) 22% (18) 20% (26)

*9A 15.6% (28) 20.3% (28) 1.74% (24) *10 2.4% (13) 1.1% (21) 0.4% (32) 1.6% (32) 0.5% (21) 2% (26) *11 0.5% (21) 0.7% (21)

*12 *12A *14 *15 *16 *17 0.0% (5) 0.0% (5) *18 0.5% (11) *18A 0.0% (6) 0.5% (6) *18B 0.0% (6) 0.0% (6) *19 0.5% (6) 1.0% (6) *20 0.0% (6) 0.0% (6) *21 *23 *24 0.0% (1) 0.0% (1) 0.0% (1) *25 *26

*27 *28 *31 *35 0.5 -0.8% (1) 0.5% (1) 0.8% (1) 0.5-0.8% (11) 0.6% (1) *36 0.3% (1) *37 0.3% (1) *38 Note: Numbers in parenthesis indicate source citation. A list of table source citation is available at the end of Appendix B.

116

Table 14. CYP2A6 Allele Frequencies for Brazillians, Ecuadorians, Iranian, Sri Lanka, and Malays

CYP2A6 Alleles Brazilians Ecuadorian Iranian Sri Lanka Malays *1 58.9-62.8% *1A 62.3% (31) 61.7% (28) (30) 27.04% (24) *1A (51A) *1x2A 0.5% (28) 0.37% (24) 31.5-34.4% *1B 29.9% (31) (30) 46.67% (24) *1B1 31.2% (28) *1B1-17 *1B4 *1F *1G *1H *1L *2 1.7% (31) 2.20% (3) 0.0% (24) *3 *4 0.5% (31) 0.95% (3) 7.04% (24) *4A 7.1% (28) *4D *4A, *4D-F *4C 2.8-9.6% (30) *5 0.93% (24) *6 *7 4.26% (24) *8 5.0% (24) *9 5.7% (31) 12.44% (3) *9A 10.3% (28) *10 4.26% (24) *11 *12 1.34% (3) *12A *14 *15 *16 *17 *18 *18A *18B *19 *20 *21 *23

Table Continues

117

Table 14 Continued CYP2A6 Alleles Brazilians Ecuadorian Iranian Sri Lanka Malays *24 *25 *26 *27 *28 *31 *35 *36 *37 *38 Note: Numbers in parenthesis indicate source citation. A list of table source citation is available at the end of Appendix B.

118

Appendix B Table Source Citations:

1. Al Koudsi N, Ahluwalia JS, Shih-Ku L, Sellers EM, Tyndale RF, Al Koudsi N. A novel CYP2A6 allele (CYP2A6*35) resulting in an amino-acid substitution (Asn438Tyr) is associated with lower CYP2A6 activity in vivo. Pharmacogenomics Journal. 2009 08/01;9(4):274-82. 2. Bloom, Joseph, Hinrichs, Anthony L., Wang, Jen C., von Weymarn, Linda B., Kharasch, Evan D., Bierut, Laura J., Goate, Alison, Murphy,Sharon E.,. The contribution of common CYP2A6 alleles to variation in nicotine metabolism among European–Americans. Pharmacogenetics and Genomics Pharmacogenetics and Genomics. 2011;21(7):403-16. 3. Emamghoreishi M, Bokaee HR, Keshavarz M, Ghaderi A,Tyndale RF,. CYP2A6 allele frequencies in an iranian population. Archives of Iranian medicine. 2008;11(6):613-7. 4. Fang W.-J., Mou H.-B., Zheng Y.-L., Zhao P., Mao C.-Y., Peng L., Xu N., Huang M.-Z.,Jin D.-Z.,. Characteristic CYP2A6 genetic polymorphisms detected by TA cloning-based sequencing in chinese digestive system cancer patients with S-1 based chemotherapy. Oncol.Rep.Oncology Reports. 2012;27(5):1606-10. 5. Fukami T, Nakajima M, Yoshida R, Tsuchiya Y, Fujiki Y, Katoh M, et al. A novel polymorphism of human CYP2A6 gene CYP2A6*17 has an amino acid substitution (V365M) that decreases enzymatic activity in vitro and in vivo. Clin Pharmacol Ther. 2004 Dec;76(6):519-27. 6. Fukami T, Nakajima M, Higashi E, Yamanaka H, Sakai H, McLeod HL, et al. Characterization of novel CYP2A6 polymorphic alleles (CYP2A6*18 and CYP2A6*19) that affect enzymatic activity. Drug Metab Dispos. 2005 Aug;33(8):1202-10. 7. Fukami T, Nakajima M, Higashi E, Yamanaka H, McLeod HL, Yokoi T. A novel CYP2A6*20 allele found in african-american population produces a truncated protein lacking enzymatic activity. Biochem Pharmacol. 2005 Sep 1;70(5):801-8. 8. Fukami T, Nakajima M, Sakai H, McLeod HL, Yokoi T. CYP2A7 polymorphic alleles confound the genotyping of CYP2A6*4A allele. Pharmacogenomics J. 2006 Nov-Dec;6(6):401-12. 9. Fujieda M, Yamazaki H, Saito T, Kiyotani K, Gyamfi MA, Sakurai M, Dosaka-Akita H, Sawamura Y, Yokota J, Kunitoh H,Kamataki T,. Evaluation of CYP2A6 genetic polymorphisms as determinants of smoking behavior and tobacco-related lung cancer risk in male japanese smokers. Carcinogenesis. 2004;25(12):2451-8. 10. Gambier N, Batt A, Marie B, Pfister M, Siest G, Visvikis-Siest S. Association of CYP2A6*1B genetic variant with the amount of smoking in french adults from the stanislas cohort. The Pharmacogenetics Journal. 2005;5:271-5. 11. Han S, Choi S, Chun YJ, Yun CH, Lee CH, Shin HJ, Na HS, Chung MW,Kim D,. Functional characterization of allelic variants of polymorphic human cytochrome P450 2A6 (CYP2A6*5, *7, *8, *18, *19, and *35). Biol Pharm Bull. 2012;35(3):394-9. 12. Ho MK, Mwenifumbo JC, Al Koudsi N, Okuyemi KS, Ahluwalia JS, Benowitz NL, et al. Association of nicotine metabolite ratio and CYP2A6 genotype with smoking cessation treatment in african- american light smokers. Clin Pharmacol Ther. 2009 Jun;85(6):635-43. 13. Liu T, David SP, Tyndale RF, Wang H, Zhou Q, Ding P, et al. Associations of CYP2A6 genotype with smoking behaviors in southern china. Addiction. 2011;106(5):985-94. Table Source Citations Continues

119

Appendix B Table Source Citations Continued

14. Malaiyandi V, Sellers EM, Tyndale RF. Implications of CYP2A6 genetic variation for smoking behaviors and nicotine dependence. Clin Pharmacol Ther. 2005 03/01;77(3):145-58. 15. Malaiyandi V, Lerman C, Benowitz NL, Jepson C, Patterson F, Tyndale RF. Impact of CYP2A6 genotype on pretreatment smoking behaviour and nicotine levels from and usage of nicotine replacement therapy. Mol Psychiatry. 2006 Apr;11(4):400-9. 16. Mann H. The highly polymorphic human cytochrome P450 (CYP) 2A6 gene: Examining diversity and nicotine metabolism in a central african population. 2013. 17. Mizutani T . PM frequencies of major CYPs in asians and caucasians. Drug Metab Rev. 2003;35(2-3). 18. Muliaty D, Yusuf I, Setiabudy R, Wanandi S. CYP2A6 gene polymorphisms impact to nicotine metabolism. Medical Journal of Indonesian. 2010;19(1):46-51. 19. Mwenifumbo JC, Al Koudsi N, Ho MK, Zhou Q, Hoffmann EB, Sellers EM, et al. Novel and established CYP2A6 alleles impair in vivo nicotine metabolism in a population of black african descent. Hum Mutat. 2008 05/01;29(5):679-88. 20. Mwenifumbo JC, Zhou Q, Benowitz NL, Sellers EM, Tyndale RF. New CYP2A6 gene deletion and conversion variants in a population of black african descent. Pharmacogenomics. 2010 Feb;11(2):189- 98. 21. Nakajima M, Kuroiwa Y, Yokoi T. Interindividual differences in nicotine metabolism and genetic polymorphisms of human CYP2A6. Drug Metab Rev. 2002 Nov;34(4):865-77. 22. Nakajima M, Yoshida R, Fukami T, McLeod HL, Yokoi T. Novel human CYP2A6 alleles confound gene deletion analysis. FEBS Lett. 2004 Jul 2;569(1-3):75-81. 23. Nakajima M, Fukami T, Yamanaka H, Higashi E, Sakai H, Yoshida R, et al. Comprehensive evaluation of variability in nicotine metabolism and CYP2A6 polymorphic alleles in four ethnic populations. Clin Pharmacol Ther. 2006 Sep;80(3):282-97. 24. Nurfadhlina M, Foong K, Teh LK, Tan SC, Mohd Zaki S, Ismail R. CYP2A6 polymorphisms in malays, chinese and indians. Xenobiotica. 2006 Aug;36(8):684-92. 25. Oscarson, Mikael, McLellan, Roman A, Gullstén, Harriet, Yue, Qun-Ying, Lang, Matti A, Luisa Bernal, Maria, Sinues, Blanca, Hirvonen, Ari, Raunio, Hannu, Pelkonen, Olavi,Ingelman-Sundberg, Magnus,. Characterisation and PCR-based detection of a CYP2A6 gene deletion found at a high frequency in a chinese population. FEBS Letters FEBS Letters. 1999;448(1):105-10. 26. Peamkrasatam S, Sriwatanakul K, Kiyotani K, Fujieda M, Yamazaki H, Kamataki T,Yoovathaworn K,. In vivo evaluation of coumarin and nicotine as probe drugs to predict the metabolic capacity of CYP2A6 due to genetic polymorphism in thais. Drug metabolism and pharmacokinetics. 2006;21(6):475-84. 27. Schoedel KA, Hoffmann EB, Rao Y, Sellers EM, Tyndale RF. Ethnic variation in CYP2A6 and association of genetically slow nicotine metabolism and smoking in adult caucasians. Pharmacogenetics. 2004 09;14(9):615-26. 28. Soriano A, Vicente J, Carcas C, Gonzalez-Andrade F, Arenaz I, Martinez-Jarreta B, et al. Differences between spaniards and ecuadorians in CYP2A6 allele frequencies: Comparison with other populations. Fundam Clin Pharmacol. 2011;25(5):627-32. Table Source Citations Continues

120

Appendix B Table Source Citations Continued

29. Swan GE, Lessov-Schlaggar CN, Bergen AW, He Y, Tyndale RF, Benowitz NL. Genetic and environmental influences on the ratio of 3'hydroxycotinine to cotinine in plasma and urine. Pharmacogenet Genomics. 2009 May;19(5):388-98. 30. Topcu Z, Chiba I, Fujieda M, Shibata T, Ariyoshi N, Yamazaki H, Sevgican F, Muthumala M, Kobayashi H,Kamataki T,. CYP2A6 gene deletion reduces oral cancer risk in betel quid chewers in sri lanka. Carcinogenesis. 2002;23(4):595-8. 31. Vasconcelos GM, Struchiner CJ,Suarez-Kurtz G,. CYP2A6 genetic polymorphisms and correlation with smoking status in brazilians. The pharmacogenomics journal. 2005;5(1):42-8. 32. Xu, Chun, Goodz, Shari, Sellers, Edward M., Tyndale, Rachel F. CYP2A6 genetic variation and potential consequences. Advanced Drug Delivery Reviews Advanced Drug Delivery Reviews. 2002;54(10):1245-56.

121

APPENDIX C: TABLES FOR CLASSIFICATION OF CYP2A6 ALLELES INTO FUNCTIONAL METABOLIC GROUPS

122

Table 15. CYP2A6 Normal/High/Extensive/Super Metabolizers Groups

Phenotype Various genotypes Allele Nomenclatures (% Sample Ethnicity Citation observed nicotine metabolism)

Super Metabolism (20- (1) *1B 30% higher than Caucasian only *1/*1B, *1B/*1B

‘normal’)

*1 Normal/ High (100%) Mixed Ethnicity *1/*1 (2-6)

Black African *1, *14 Normal (100%) *1/*1, *1/*14, *14/*14 (1) Descent Present *1 Normal (100%) Caucasian *1/*1 (7)

Anyone without a *4, *5, *1 Normal (100%) Chinese (8, 9) *7, *9 or *10 allele

Anyone with *1B or without *2, *4, *9, *12, *1 Normal (100%) African American (10, 11) *17, *20, *23, *24, *25, *26, *27, *28, or *35

*1A WT1A (wild type) Mixed Ethnicity *1A homozygotes (12)

Extensive Metabolizer *1A/*1A, *1A/*1B, *1A, *1B Thai (13) (EM) (% undefined) *1B/*1B

One or more alleles with *1B WT1B (% undefined) Mixed Ethnicity potential increased (12) activity *1B

Extensive Metabolizer (% Aka (Central *1A/*1A, *1A/*1B, *1A, *1B (14) undefined) African) *1B/*1B

Note: Table Source Citations at end of Appendix C

123

Table 16. CYP2A6 Intermediate/Moderate Metabolizer Groups

Phenotype Nomenclatures Sample Various genotypes Allele Citation (% nicotine Ethnicity observed metabolism)

Combination of Moderate/ *1 and either *9 decreased (70-80% Mixed Ethnicity *1/*9, *1/*12 (2-6) or *12 normal)

Combination of Moderate/ African *1 and either *9 decreased (50-75% *1/*9, *1/*12 (10) American or *12 normal)

Intermediate Only one 'decrease of *1, *9 Metabolizer (%75 Chinese (8, 9) function' allele (*1/*9) normal)

Intermediate (75% *9A, *12A Caucasian *1/*9A, *1/*12A (7) normal)

Combination of Intermediate (40- African *1 and *4, *17 or *1/*4, *1/*17, *1/*20 (11) 60% normal) American *20

Intermediate /Decrease-of- Black African *9, *21, *25 (1) function (64% Descent normal)

PHI (Partial One partially inactive *9, *12 Haploinsuficiency) Mixed Ethnicity (12) allele *9, *12 (% undefined)

Intermediate *1A/*9, *1A/*17, *1B and one of Aka (Central Metabolizer (% *1A/*20, *1B/*9, (14) *9, *17, or *20, African) undefined) *1B/*17, *1B/*20

Note: Table Source Citations at end of Appendix C

124

Table 17. CYP2A6 Slow/Reduced Metabolizer Group

Phenotype Sample Various genotypes Allele Nomenclatures (% Citation Ethnicity observed nicotine metabolism)

Reduced function (~70% *35 African Descent *1/*35, *9/*35 (15) activity)

*2, *4, *1/*2, *2/*2, *1/*4, Slow (50% or less activity *9A, Caucasian *4/*4, *9A/*9A, (7) normal) *12A *12A/*12A, *9A/*12A

either one 'loss of *1, *4, Slow Metabolizer (50% of function' or two Chinese (8, 9) *9 normal) 'decreased function' alleles (*1/*4, *9/*9)

*9 and Slow/Low/Decreased Mixed Ethnicity *9/*9, *9/*12, *12/*12 (2-6) *12 Function (40% of normal)

*9/*9, *9/17, *9/*20, *9, *17, Slow Metabolizer (% Aka (Central *17/*17, *17/*20, (14) *20 undefined) African) *20/*20

*2, *4, Reduced Enzymatic European At least one of *2, *4, *9, and (6) Function Ancestor *9, *12 present *12

*2, *4, 1 or 2 copies of: *2, *9, *10, Reduced Function (% Alaskan Native *4, *9, *10, *12, *17 (16) *12, *17 function undefined) and *35 and *35 Alleles other Reduced metabolizer (% Any variant not *1/*1 (4) than undefined) *1/*1 Note: Table Source Citations at end of Appendix C

125

Table 18. CYP2A6 Poor/Loss of Function Metabolizer Group

Phenotype Various Nomenclatures Allele Sample Ethnicity genotypes Citation (% nicotine observed metabolism) Poor metabolizers *25, *26, *1/*25, *1/*26, (40-60% of African American (10) *27 and *35 *1/*27, *1/*35 normal) *17, *20, *23, *24, Slow/Loss-of- Black African (1) *26, *27, function (40%) Descent *28 One ‘loss of function’ + *4, *5, *7, Poor Metabolizer decreased Chinese (8, 9) *9, *10 (25% function) function allele OR two 'decreased function' alleles *2, *4, *7, *10, *12, Complete loss of Any combination *17, *20, function (0% Mixed Ethnicity (2-6) of the alleles *23-26, and activity of normal) *35 *1A/*4C, *1B/*4C, *4C/*4C, *1A/*7, Poor Metabolizers *4C, *7, *8, *1A/*8, *1A/*9, (PM) (% function Thai (13) *9, *10 *1A/*10, *1B/*7, undefined) *1B/*8, *1B/*10, *4C/*9, *7/*9, *7/*10 1 completely inactive allele or 2 *2, *4, *9, Haploinsufficiency Mixed Ethnicity partially inactive (12) *12 (HI) (% undefined) alleles *2, *4, *9, *12 One ‘loss of *2, *4, *17, function’ + *20, *23, decreased *24, *25, Poor Metabolizers African American (11) function allele OR *26, *27, two 'decreased *35 function' alleles Note: Table Source Citations at end of Appendix C

126

Table 19. CYP2A6 Group Numbering System for Metabolizer Groups

Phenotype Nomenclatures (% Sample Various genotypes Allele Citation nicotine Ethnicity observed metabolism)

“Group 4” (100% *1 Caucasian *1/*1 (17) activity)

*1, *9A, “Group 3” (60-70% Caucasian *1/9, *1/*12A (17) *12A activity)

*2, *4, *1/*2, *1/*4, “Group 2” (40-50% *9A, Caucasian *9A/*9A, (17) activity) *12A *9A/*12A

*2, *4, Group 1 (<40% Caucasian *2/*12, *4/*7 (17) *7, *12 activity)

“Group 1” (100% or *1 Japanese *1/*1 (18, 19) ‘normal’)

*1, *4, “Group 2” (53.9- *1/*4, *1/*7, *1/*9, *7, *9, 72.2% activity of Japanese (18, 19) *1/*10 *10 normal)

*4/*7, *4/*9, “Group 3” (11.7- *4/*10, *7/*7, *4, *7, 35.8% activity of Japanese *7/*9, *7/*10, (18, 19) *9, *10 normal) *9/*9, *9*10, *10/*10 “Group 4” (Non- *4 functional Deletion Japanese *4/*4 (18, 19) allele) Note: Table Source Citations at end of Appendix C

127

Appendix C Table Source Citations

1. Mwenifumbo JC, Lessov-Schlaggar C, Zhou Q, Krasnow RE, Swan GE, Benowitz NL, et al. Identification of novel CYP2A6*1B variants: The CYP2A6*1B allele is associated with faster in vivo nicotine metabolism. Clinical Pharmacology & Therapeutics. 2008 01/01;83(1):115-21. 2. Tang DW, Hello B, Mroziewicz M, Fellows LK, Tyndale RF, Dagher A. Genetic variation in CYP2A6 predicts neural reactivity to smoking cues as measured using fMRI. NeuroImage NeuroImage. 2012;60(4):2136-43. 3. Benowitz NL, Swan GE, Jacob P,3rd, Lessov-Schlaggar CN, Tyndale RF. CYP2A6 genotype and the metabolism and disposition kinetics of nicotine. Clin Pharmacol Ther. 2006 Nov;80(5):457-67. 4. Lerman C, Jepson C, Wileyto EP, Patterson F, Schnoll R, Mroziewicz M, Benowitz N,Tyndale RF,. Genetic variation in nicotine metabolism predicts the efficacy of extended-duration transdermal nicotine therapy. Clin Pharmacol Ther. 2010;87(5):553-7. 5. Pianezza ML, Sellers EM,Tyndale RF,. Nicotine metabolism defect reduces smoking. Nature. 1998;393(6687):750. 6. Wassenaar CA, Dong Q, Wei Q, Amos CI, Spitz MR, Tyndale RF. Relationship between CYP2A6 and CHRNA5-CHRNA3-CHRNB4 variation and smoking behaviors and lung cancer risk. JNCI Journal of the National Cancer Institute JNCI Journal of the National Cancer Institute. 2011;103(17):1342-6. 7. Malaiyandi V, Lerman C, Benowitz NL, Jepson C, Patterson F, Tyndale RF. Impact of CYP2A6 genotype on pretreatment smoking behaviour and nicotine levels from and usage of nicotine replacement therapy. Mol Psychiatry. 2006 Apr;11(4):400-9. 8. Liu T, Tyndale RF, David SP, Wang H, Yu XQ, Chen W, et al. Association between daily cigarette consumption and hypertension moderated by CYP2A6 genotypes in chinese male current smokers. J Hum Hypertens. 2013;27(1):24-30. 9. Liu T, David SP, Tyndale RF, Wang H, Zhou Q, Ding P, et al. Associations of CYP2A6 genotype with smoking behaviors in southern china. Addiction. 2011;106(5):985-94. 10. Ho MK, Mwenifumbo JC, Al Koudsi N, Okuyemi KS, Ahluwalia JS, Benowitz NL, et al. Association of nicotine metabolite ratio and CYP2A6 genotype with smoking cessation treatment in african- american light smokers. Clin Pharmacol Ther. 2009 Jun;85(6):635-43. 11. Ho MK, Faseru B, Choi WS, Nollen NL, Mayo MS, Thomas JL, et al. Utility and relationships of biomarkers of smoking in african-american light smokers. Cancer Epidemiol Biomarkers Prev. 2009 Dec;18(12):3426-34. 12. Johnstone E, Benowitz N, Cargill A, Jacob R, Hinks L, Day I, et al. Determinants of the rate of nicotine metabolism and effects on smoking behavior. Clin Pharmacol Ther. 2006 Oct;80(4):319-30. 13. Apinan, Roongnapa, Tassaneeyakul, Wichittra, Mahavorasirikul, Wiratchanee, Satarug, Soisangwang, Kajanawart, Sirimas, Vannaprasaht, Suda, Ruenweerayut, Ronnatrai,Na-Bangchang, Kesara,. The influence of CYP2A6 polymorphisms and cadmium on nicotine metabolism in thai population. ENVTOX Environmental Toxicology and Pharmacology. 2009;28(3):420-4. 14. Mann H. The highly polymorphic human cytochrome P450 (CYP) 2A6 gene: Examining diversity and nicotine metabolism in a central african population. 2013. Table Source Citations Continue

128

Appendix C Table Source Citations Continued

15. Al Koudsi N, Ahluwalia JS, Shih-Ku L, Sellers EM, Tyndale RF, Al Koudsi N. A novel CYP2A6 allele (CYP2A6*35) resulting in an amino-acid substitution (Asn438Tyr) is associated with lower CYP2A6 activity in vivo. Pharmacogenomics Journal. 2009 08/01;9(4):274-82. 16. Zhu AZ, Binnington MJ, Renner CC, Lanier AP, Hatsukami DK, Stepanov I, Watson CH, Sosnoff CS, Benowitz NL,Tyndale RF,. Alaska native smokers and smokeless tobacco users with slower CYP2A6 activity have lower tobacco consumption, lower tobacco-specific nitrosamine exposure and lower tobacco-specific nitrosamine bioactivation. Carcinogenesis. 2013;34(1):93-101. 17. Malaiyandi V, Goodz SD, Sellers EM,Tyndale RF,. CYP2A6 genotype, phenotype, and the use of nicotine metabolites as biomarkers during ad libitum smoking. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2006;15(10):1812-9. 18. Nagano T, Shimizu M, Kiyotani K, Kamataki T, Takano R, Murayama N, Shono F,Yamazaki H,. Biomonitoring of urinary cotinine concentrations associated with plasma levels of nicotine metabolites after daily cigarette smoking in a male japanese population. International journal of environmental research and public health. 2010;7(7):2953-64. 19. Fujieda M, Yamazaki H, Saito T, Kiyotani K, Gyamfi MA, Sakurai M, Dosaka-Akita H, Sawamura Y, Yokota J, Kunitoh H,Kamataki T,. Evaluation of CYP2A6 genetic polymorphisms as determinants of smoking behavior and tobacco-related lung cancer risk in male Japanese smokers. Carcinogenesis. 2004;25(12):2451-8.

129

APPENDIX D: NICOTINE METABOLITE RATIO (NMR) AND CYP2A6 GENOTYPE GROUPINGS AND NOMENCLATURE TABLES

130

Table 20. Normal NMR Metabolism Grouping

NMR NMR Strata CYP2aA6 Grouping Sample Demographic Source Mean (SD) (Method) Name

0.26 (0.01) Normal 100% Mixed Ethnicity (1)

0.28 (0.18) Normal Normal 100% Black African Descent (2)

0.29 (0.02) Increased >100% Mixed Ethnicity (1)

0.42 (0.19) Normal Normal 100% Caucasian (3)

Normal Metabolizer 0.43 (0.04) Normal Black African Descent (4) (NM)

.45 (.22) Normal Normal Caucasian (5)

.595 (.330) Normal Alaskan Natives smokers (6)

Alaskan Natives smokeless .633(.319) Normal (6) tobacco user

0.668 (0.21) 4th Quartile 96% Caucasian (7)

Faster (higher .685 (.296) Alaskan Natives smokers (6) median split)

Faster (higher Alaskan Natives smokeless .700 (.275) (6) median split) tobacco user

Alaskan Natives Iqmik .704 (.447) Normal (6) smokeless tobacco user

Faster (higher Alaskan Natives Iqmik .843 (.367) (6) median split) smokeless tobacco user

0.9 4th Quartile Mixed Ethnicity (8)

Normal (100% 1.20 (.63) African Canadians (9) activity) Normal (100% 1.21 (0.80) African American (9) activity) Note: Table Source Citations located at end of Appendix D.

131

Table 21. Intermediate NMR Metabolism Grouping

NMR NMR Strata CYP2aA6 Grouping Sample Demographic Source Mean (SD) (Method) Name

0.18 (0.09) Intermediate Intermediate (64%) Black African Descent (2)

Intermediate 0.32 (0.07) Intermediate Black African Descent (4) Metabolizer (IM)

.37 (.25) Intermediate Intermediate Caucasian (5)

0.41 3rd Quartile Mixed Ethnicity (8)

0.410 (0.04) 3rd Quartile 96% Caucasian (7)

Note: Table Source Citations located at end of Appendix D.

132

Table 22. Reduced NMR Metabolism Grouping

NMR NMR Strata CYP2aA6 Grouping Sample Demographic Source Mean (SD) (Method) Name

0.08 (0.14) Reduced (7% activity) African Canadians (9)

0.16 (0.02) Decreased Mixed Ethnicity (1)

Reduced (22% 0.27 (0.09) African American (9) activity)

0.29 2nd Quartile Mixed Ethnicity (8)

Alaskan Natives smokeless .293 (.135) Reduced (6) tobacco user

0.313 (0.03) 2nd Quartile 96% Caucasian (7)

.316 (.175) Reduced Alaskan Natives smokers (6)

Reduced (39% 0.47 (0) African Canadians (9) activity) Alaskan Natives Iqmik .459 (.207) Reduced (6) smokeless tobacco user Reduced (42% 0.51 (0) African American (9) activity)

Reduced (62% 0.75 (0.26) African American (9) activity)

Reduced (62% 0.78 (0.51) African American (9) activity)

Reduced (70% 0.84 (.50) African Canadians (9) activity)

Note: Table Source Citations located at end of Appendix D.

133

Table 23. Slow NMR Metabolism Grouping

NMR NMR Strata CYP2aA6 Grouping Sample Demographic Source Mean (SD) (Method) Name

0.11 (0.09) Slow Slow (40%) Black African Descent (2)

0.17 1st Quartile Mixed Ethnicity (8)

0.192 (0.20) 1st Quartile 96% Caucasian (7)

Slow Metabolizer 0.22 (0.02) Slow Black African Descent (4) (SM)

.23 (.17) Slow Slow Caucasian (5)

Slower (lower Alaskan Natives smokeless .256(.092) (6) median split) tobacco user

Slower (lower .263 (.105) Alaskan Natives smokers (6) median split)

Slower (lower Alaskan Natives Iqmik .345 (.144) (6) median split) smokeless tobacco user

Note: Table Source Citations located at end of Appendix D.

134

Appendix D Table Source Citations

1. Swan GE, Lessov-Schlaggar C. Tobacco addiction and pharmacogenetics of nicotine metabolism. J Neurogenet. 2009 01/01;23(3):262-71. 2. Mwenifumbo JC, Al Koudsi N, Ho MK, Zhou Q, Hoffmann EB, Sellers EM, et al. Novel and established CYP2A6 alleles impair in vivo nicotine metabolism in a population of black african descent. Hum Mutat. 2008 05/01;29(5):679-88. 3. Lerman C, Jepson C, Wileyto EP, Patterson F, Schnoll R, Mroziewicz M, Benowitz N,Tyndale RF,. Genetic variation in nicotine metabolism predicts the efficacy of extended-duration transdermal nicotine therapy. Clin Pharmacol Ther. 2010;87(5):553-7. 4. Ho MK, Mwenifumbo JC, Al Koudsi N, Okuyemi KS, Ahluwalia JS, Benowitz NL, et al. Association of nicotine metabolite ratio and CYP2A6 genotype with smoking cessation treatment in african-american light smokers. Clin Pharmacol Ther. 2009 Jun;85(6):635-43. 5. Malaiyandi V, Lerman C, Benowitz NL, Jepson C, Patterson F, Tyndale RF. Impact of CYP2A6 genotype on pretreatment smoking behaviour and nicotine levels from and usage of nicotine replacement therapy. Mol Psychiatry. 2006 Apr;11(4):400-9. 6. Zhu AZ, Binnington MJ, Renner CC, Lanier AP, Hatsukami DK, Stepanov I, Watson CH, Sosnoff CS, Benowitz NL,Tyndale RF,. Alaska native smokers and smokeless tobacco users with slower CYP2A6 activity have lower tobacco consumption, lower tobacco-specific nitrosamine exposure and lower tobacco-specific nitrosamine bioactivation. Carcinogenesis. 2013;34(1):93-101. 7. Strasser AA, Benowitz NL, Pinto AG, Tang KZ, Hecht SS, Carmella SG, et al. Nicotine metabolite ratio predicts smoking topography and carcinogen biomarker level. Cancer Epidemiol Biomarkers Prev. 2011 Feb;20(2):234-8. 8. Lerman C, Tyndale R, Patterson F, Wileyto EP, Shields PG, Pinto A, et al. Nicotine metabolite ratio predicts efficacy of transdermal nicotine for smoking cessation. Clin Pharmacol Ther. 2006 Jun;79(6):600-8. 9. Al Koudsi N, Ahluwalia JS, Shih-Ku L, Sellers EM, Tyndale RF, Al Koudsi N. A novel CYP2A6 allele (CYP2A6*35) resulting in an amino-acid substitution (Asn438Tyr) is associated with lower CYP2A6 activity in vivo. Pharmacogenomics Journal. 2009 08/01;9(4):274-82.

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APPENDIX E: SUMMARY TABLE OF PHENOTYPES ASSOCIATED WITH RS16969968 RISK GENOTYPE IN THE LITERATURE

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Table 24. Summary Table of Phenotypes Associated with rs16969968 Risk Genotype in the Literature

Year Phenotype P-value Ethnicity Investigator Citation 2007 Nicotine Dependence P=6.4x10-4 European Descent Saccone 1 (FTND>4) 2008 Habitual Smoking P=0.007 European Descent Bierut 2 2008 Cigarettes per day P<0.001 Non-Hispanic White Spitz 3 2008 Pleasurable “buzz” P=0.01 Caucasians Sherva 4 2008 Protective cocaine P=0.0045 European American Grucza 5 dependence 2008 Nicotine Addiction (FTND P=2.0x10-5 3 samples of European Weiss 6 >5) severity American 2009 Nicotine Dependence P=4.49x10-8 European Descent Saccone 7 (FTND>4) +African American 2009 Nicotine Dependence P=0.0068 and 2 samples of European Chen 8 (FTND>2) 0.0028 Americans 2009 Tolerance, Craving, loss of Significant 2 cohorts of European Baker 9 control, Withdrawal severity Americans 2009 Low Parent Monitoring = P=0.034 European Descent Chen 10 Higher nicotine dependence 2009 Nicotine Dependence P=2.84x10-5 2 cohorts of European Saccone 11 (FTND>4) Ancestry 2010 Peer smoking = higher P=0.0077 European American Johnson 12 nicotine dependence 2011 Heaviness of Smoking Significant Canadian Women Conlon 13 2011 Smoking Severity P=0.001 Schizophrenic Hong 14 smokers 2012 Heavy Compulsive smoking P=2.6x10-7 European Descent Chen 15 (FTND>3), cravings and CDP 2012 Childhood adversity = P=0.0044 European Descent Xie 16 higher Nicotine Dependence Note: Table Source citations available at the end of Appendix E.

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Appendix E Table Source Citations

1. Saccone SF, Hinrichs AL, Chase GA, Konvicka K, Madden PAF, Breslau N, et al. Cholinergic nicotinic receptor genes implicated in a nicotine dependence association study targeting 348 candidate genes with 3713 SNPs. Hum Mol Genet. 2007 01/01;16(1):36-49. 2. Bierut LJ, Stitzel JA, Wang JC, Hinrichs AL, Grucza RA, Xiaoling X, et al. Variants in nicotinic receptors and risk for nicotine dependence. Am J Psychiatry. 2008 09/01. 3. Spitz MR, Amos CI, Dong Q, Lin J, Wu X. The CHRNA5-A3 region on chromosome 15q24-25.1 is a risk factor both for nicotine dependence and for lung cancer. J Natl Cancer Inst. 2008 11/05;100(21):1552-6. 4. Sherva R, Wilhelmsen K, Pomerleau CS, Chasse SA, Rice JP, Snedecor SM, et al. Association of a single nucleotide polymorphism in neuronal acetylcholine receptor subunit alpha 5 (CHRNA5) with smoking status and with ‘pleasurable buzz' during early experimentation with smoking. Addiction. 2008 09/01;103(9):1544-52. 5. Grucza RA, Wang JC, Stitzel JA, Hinrichs AL, Saccone SF, Saccone NL, et al. A risk allele for nicotine dependence in CHRNA5 is a protective allele for cocaine dependence. Biol Psychiatry. 2008 12/01;64(11):922-9. 6. Weiss RB, Baker TB, Cannon DS, von Niederhausern A, Dunn DM, Matsunami N, et al. A candidate gene approach identifies the CHRNA5-A3-B4 region as a risk factor for age-dependent nicotine addiction. PLoS genetics. 2008;4(7):e1000125. 7. Saccone NL, Wang JC, Breslau N, Johnson EO, Hatsukami D, Saccone SF, et al. The CHRNA5- CHRNA3-CHRNB4 nicotinic receptor subunit gene cluster affects risk for nicotine dependence in african-americans and in european-americans. Cancer Res. 2009 09/01;69(17):6848-56. 8. Chen L, Johnson EO, Breslau N, Hatsukami D, Saccone NL, Grucza RA, et al. Interplay of genetic risk factors and parent monitoring in risk for nicotine dependence. Addiction. 2009 10/01;104(10):1731-40. 9. Saccone NL, Saccone SF, Hinrichs AL, Stitzel JA, Duan W, Pergadia ML, et al. Multiple distinct risk loci for nicotine dependence identified by dense coverage of the complete family of nicotinic receptor subunit (CHRN) genes. Am J Med Genet. 2009 06/05;150B(4):453-66. 10. Johnson EO, Chen LS, Breslau N, Hatsukami D, Robbins T, Saccone NL, Grucza RA,Bierut LJ,. Peer smoking and the nicotinic receptor genes: An examination of genetic and environmental risks for nicotine dependence. Addiction. 2010;105(11):2014-22. 11. Conlon, M. S., Bewick,M.A.,. Single nucleotide polymorphisms in CHRNA5 rs16969968, CHRNA3 rs578776, and LOC123688 rs8034191 are associated with heaviness of smoking in women in northeastern ontario, canada. Nicotine & Tobacco Research Nicotine & Tobacco Research. 2011;13(11):1076-83. 12. Hong LE, Yang X, Wonodi I, Hodgkinson CA, Goldman D, Stine OC, et al. A CHRNA5 allele related to nicotine addiction and schizophrenia. Genes Brain Behav. 2011 Jul;10(5):530-5. 13. Chen LS, Baker TB, Piper ME, Breslau N, Cannon DS, Doheny KF, Gogarten SM, Johnson EO, Saccone NL, Wang JC, Weiss RB, Goate AM,Bierut LJ,. Interplay of genetic risk factors (CHRNA5- CHRNA3-CHRNB4) and cessation treatments in smoking cessation success. Am J Psychiatry. 2012;169(7):735-42. 14. Xie, Pingxing, Kranzler, Henry R, Zhang, Huiping, Oslin, David, Anton, Raymond F, Farrer, Lindsay A,Gelernter, Joel,. Childhood adversity increases risk for nicotine dependence and interacts with α5 nicotinic acetylcholine receptor genotype specifically in males. Neuropsychopharmacology. 2012;37(3):669-76.

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APPENDIX F: RESEARCH RECRUITMENT ADVERTISEMENT AND APPROXIMATE MEDIA CIRCULATION TABLE

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Table 25. Research Recruitment Advertisement and Approximate Media Circulation

Enrolled Length of Approximate Number of & Source Type of Media Advertisement Circulation Respondents Completed Campus, Flyers Reserves and Advertisement Flyers 14 months community 4 4 Ohio Legion 1 Ad in News Newspaper Quarterly paper 95,818 Veterans 8 0 Columbus American Letter - 78 Posts Legion Facebook 10 months ~10000 0 0 VFW Columbus Letters - 78 Posts Posts Facebook 10 months ~8,000 0 0 Columbus American In-person Legions Presentations 7 posts ~150 2 0 American Legion Post Printed Newsletters Newsletters 12 posts ~6000 10 0 Craig's list Social Media 3 month General Public 0 0 Buckeye Net Electronic 1 Week - Aug 60,000 News Newsletter 25, 2013 Undergraduates 5 0 Electronic 39,000 Faculty OSU Today Newsletter Daily - 9/4/2013 /Staff 0 0 Electronic Daily - 39,000 OSU Today Newsletter 9/19/2013 Faculty/Staff 0 0 16,500 Electronic Week of Graduate/Prof OSU Weekly Newsletter 9/8/2013 students 0 0 Student 4 weeks (20 ads The Lantern Newspaper total) 15,000/day 0 0 CCTS 900 visits - 32 Facebook Social Media 4 months shares 0 0 OSU Research Electronic 4 months 250 51,125 in Match Database mile radius database 48 9 OSU Study Electronic 1,000 unique Search Database 4 months visitors/month 0 0 Veteran's Affairs on Email Campus Recruitment Email Flyers Twice 1,600 3 0 ROTC Recruitment Building Flyers 14 months 500 1 1 Ohio VFW Website Website 1 month 73,438 0 0 Rickenbacker Air National 1,000 Guard Base Flyers 2 Months Reservists/Guard 0 0

Table Continues 140

Table 25 Continued

Enrolled Length of Approximate Number of & Source Type of Media Advertisement Circulation Respondents Completed 8,000 DOD Defense civilian 630 Supply Center Contractors Columbus 82 military (DSCC) Flyers 4 months personnel 0 0 Flyers, Presentation, Vets4Vets Social Media 6 months 500 + veterans 1 1 Ohio American Legion Website Website 4 months General Public 0 0 Totals 385,043+ 82 15

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APPENDIX G: GLASS & MCATEE STREAM OF CAUSATION FRAMEWORK ADAPTED FOR NICOTINE RESEARCH

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Figure 9. Stream of Causation Gene-Environment Interaction Framework

Framework modified to examine gene-environment interactions that impact cigarette consumption in military cigarette smokers. Adapted from “Behavioral Sciences at the Crossroads in Public Health: Extending Horizons, Envisioning the Future” by T. Glass and M. McAtee, 2006, Social Sciences & Medicine, 62, 7, p.1653. 143

APPENDIX H: DATA AND INTERCORRELATION OF VARIABLES TABLES

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Table 26. Intercorrelation of Biomarkers of Nicotine, Relevant Sociodemographic, and Military Variables

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 1. Bitter Taste -- Phenotype 2. Rs16969968 allele .08 -- 3. Cotinine level .02 .21 -- 4. Cigarettes per Day -.12 .21 .47* -- 5. Gender .19 -.16 -.19 .11 -- 6. Age -.14 .24 .19 .22 -.20 -- 14 7. Education .10 -.01 .00 -.51* -.32 .30 --

8. Military Branch -.17 .18 -.08 -.16 .29 -.11 -.13 -- 9. Rank -.22 .25 .27 -.05 -.36 .00 .35 -.12 -- 10. Length of -.01 .17 -.06 -.33 -.55 .28 .40 -.16 .58* -- Deployment 11. Combat -.40 -.06 .35 .40 .33 -.31 -.32 .27 -.70** -.65* -- Experience 12. Smoke in Boot -.58* .18 -.19 -.43 -.28 .52 .40 .10 .47 .50 -.76** -- Camp 13. Smoke Before .03 -.19 -.31 -.65* -.29 .48 .67* .10 .23 .48 -.41 .56 -- entry into Military Note: * p<.05, **p<.01. Italicized boldface values are Kendall Tau-b coefficients for intercorrelation of ordinal/ordinal, ordinal/interval and interval/interval variables. Regular text values are Pearson Product-Moment Coefficients (biserial) for intercorrelation of ordinal/nominal and nominal/interval variables.

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