Marijuana, Methamphetamine, and Oxycodone: a Multilevel Approach to Understanding Drug Effects

Marijuana, Methamphetamine, and Oxycodone: A multilevel approach to understanding drug effects

Diana R. Keith

Submitted in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

In the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

2014

Diana R. Keith

ABSTRACT

Marijuana, Methamphetamine, and Oxycodone: A multilevel approach to understanding drug effects

Diana R. Keith

Drug use and abuse remains an important public health problem in the United States. In particular, there has been considerable recent concern regarding the illicit use of marijuana, methamphetamine, and prescription pain relievers. However, several important gaps remain in our knowledge of these drugs. The current studies aim to address three of these gaps. Further, the present studies utilized a translational, multilevel approach in order to better understand substance use as a whole. First, although there have been a number of studies examining marijuana use in college students, there is a lack of information regarding the consequences of marijuana and alcohol co-use, as well as the relationship between marijuana use and mental health and stress. Study 1 examined the relationship between frequency of marijuana use and other substance use, binge drinking, negative consequences associated with drinking, mental health problems, and stress. Results show that students who reported more frequent marijuana use were more likely to use all other substances, binge drink, and have drinking-related encounters with the police. Frequency of use was also related to diagnosis and/or treatment for major depression and substance use disorders. On the other hand, any marijuana use was associated with greater likelihood of experiencing a number of negative consequences from drinking, and diagnosis or treatment for anxiety disorders. Marijuana was not related to stress.

This data contributes to the field by indicating that marijuana use is indeed related to mental health among this population. This has important implications for university administrators and health professionals. Study 2 was the first empirical investigation of the acute effects of

marijuana during simulated night shift work in marijuana users. Night shift workers are particularly susceptible to performance impairments and often use drugs in order to manage their sleep-wake cycles. The results indicated that smoked marijuana attenuated performance and mood disruptions during simulated night shift work. This data furthers the database by indicating that marijuana has cognitive-enhancing effects under certain conditions. Additional research will be needed in order to determine whether these effects were caused by circadian modulation of the stimulant-related effects of marijuana, or by residual effects on improved sleep. Another important gap addressed by the present studies is the dearth of empirical information regarding the effects of methamphetamine when combined with oxycodone. Study 3 addressed this gap by beginning a systematic investigation of this drug combination utilizing a pre-clinical model with a wide range of cognitive and behavioral measures. Results indicated that the drug combination produced stronger effects than either drug alone, although this was observed in a limited number of measures. These data importantly provide the first empirical determination of the effects of the methamphetamine-oxycodone combination.

TABLE OF CONTENTS

List of Figures vi

List of Tables vii

Chapter 1: General Introduction

1.1 Marijuana 1

1.1.1 Illicit marijuana use in the United States 2

1.1.2 Acute effects of marijuana 4

1.1.3 Factors influencing the acute effects of marijuana 4

1.2 Methamphetamine

1.2.1 Illicit methamphetamine use in the United States 6

1.2.2 Acute effects of methamphetamine 7

1.3 Non-medical use of pain relievers

1.3.1 Illicit pain reliever use in the United States 8

1.3.2 Acute effects of oxycodone 9

1.4 Poly-drug abuse of amphetamines and opioids 9

1.5 Scientific and public health significance 11

Chapter 2: Cannabis Use Among Undergraduates: Mental Health and Substance Use

Correlates

2.1 Introduction 12

2.2 Methods

2.2.1 Sample 14

2.2.2 Measures

2.2.2.1 Demographics 15

2.2.2.2 Marijuana and other substance use 15

2.2.2.3 Binge drinking and negative consequences from drinking 16

2.2.2.4 Menta l health 16

2.2.2.5 Stress 16

2.2.3 Data Anal ysis 17

2.3 Results

2.3.1 Demographics 17

2.3.2 Marijuana and other substance use 18

2.3.3 Binge drinking and negative consequences from drinking 19

2.3.4 Mental health 19

2.3.5 Stress 20

2.4 Discussion 20

Tables 26

Chapter 3: Smoked Marijuana Improves Performance and Mood During Simulated Night

Shift Work in Marijuana users

3.1 Introduction 32

3.2 Methods

3.2.1 Participants 34

3.2.2 Laboratory 35

3.2.3 Pre-Study Training 36

3.2.4 Design 36

3.2.5 Procedure 37

3.2.6 Subjective Effects and Cognitive/Psychomotor Battery 38

3.2.7 Sleep monitoring 40

3.2.8 Drug 41

3.2.9 Data Anal ysis 42

3.3 Results

3.3.1 Effects of Shift Condition

3.3.1.1 Cognitive Performance 43

3.3.1.2 Subjective-effects ratings 45

3.3.1.3 Sleep 45

3.3.1.4 Cigarette smoking 45

3.3.2 Effects of Marijuana

3.3.1.1 Cognitive Performance 47

3.3.1.2 Subjective-effects ratings 47

3.3.1.3 Sleep 48

3.3.1.4 Cigarette smoking 48

3.4 Discussion 50

Tables 55

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Chapter 4: Poly-drug abuse: rewarding and behavioral effects of methamphetamine and oxycodone alone and in combination

4.1 Introduction 59

4.2 Methods

4.2.1 Animals and housing 61

4.2.2 Preparation of drugs 61

4.2.3 Experimental groups 62

4.2.4 Measures

4.2.4.1 Conditioned place preference 62

4.2.4.2 Open Field 63

4.2.4.3 Novel object recognition 64

4.2.5 Statistical Analyses 65

4.3 Results

4.3.1 Conditioned place preference 66

4.3.2 Locomotor activity and sensitization 67

4.3.3 Open Field 67

4.3.4 Novel Object Recognition 69

4.4 Discussion 71

4.4.1 Reward 72

4.4.2 Anxiety 73

4.4.3 Short-term memory

4.4.3.1 Acute effects 75

4.4.3.2 Long-term effects 75

4.4.4 Limitations 76

4.4.5 Conclusions 77

Table 78

Chapter 5: General Discussion

5.1 Marijuana, substance use and misuse, and mental health among undergraduates 79

5.2 The acute effects of marijuana on cognitive performance during night shift work 80

5.3 Methamphetamine, oxycodone, and their combination 80

5.4 A translational approach to substance abuse and dependence 81

References 83

LIST OF FIGURES

Figure 3.1. Mean subjective-effect ratings as a function of dose and day 44 Figure 3.2. Mean performance effects as a function of dose and day 46 Figure 3.3. Mean sleep effects as a function of dose and day 49

Figure 4.1. Conditioned Place Preference: Day 1, CPP over time, and sensitization 68 Figure 4.2. Open Field: time in center, velocity, and distance travelled 70 Figure 4.3. Novel Object Recognition: acute and long-term effects 71

LIST OF TABLES

Table 2.1. Demographics associated with marijuana use among undergraduates 26 Table 2.2 Marijuana use and any past 30 day other substance use among undergraduates 27 Table 2.3. Marijuana use and binge drinking 28 Table 2.4. Marijuana use and negative consequences from drinking 29 Table 2.5. Marijuana use and past 12 month diagnosis and/or treatment for mental health 30 disorders Table 2.6. Marijuana use and stress 31

Table 3.1. Study design 55 Table 3.2. Effects of shift condition and smoked marijuana on subjective effects on Day 56 1 Table 3.3. Effects of shift condition and smoked marijuana on psychomotor 57 performance and subjective effects on Day 2 Table 3.4. Effects of shift condition and smoked marijuana on psychomotor 58 performance and subjective effects on Day 3

Table 4.1. Study design and representative dosing schedule for MA-alone 78

vii

ACKNOWLEDGEMENTS

I would like to thank the great many people who have contributed to this work and encouraged, supported, and guided me along the way. First, I would like to thank my committee members: Carl Hart, Rae Silver, Peter Balsam, James Curley, and Michael McNeil for taking the time to participate in this project.

I especially want to thank Carl Hart, Rae Silver, and Renee Goodwin. Carl- I've always been so inspired by your goal of changing how the world thinks about drugs. It takes such strength to fight against the status quo and you are one of the bravest and most fearless people I've ever met. I might be just one person, but you've had a profound effect on me. You've taught me so much over my years here, but most importantly you’ve taught me how to think critically and question the world. You've been a huge influence and helped me in so many ways. I will never forget that and I will always be grateful to you. Rae- I have been so lucky to have you as a second mentor. I can't thank you enough for all your assistance. Your unwavering support and dedication to your students has always been apparent. You pushed me when I needed to be pushed and gave me extra support when I was lost. I am also incredibly inspired by your approach to science. You aren't afraid to pursue new ideas and cutting edge research. There's a sense of wonder and discovery in your lab that is so fundamental to transformative science. I only hope that I can create the same magic in my own lab someday. I'd also like to give special thanks to Renee Goodwin. Renee- It's truly amazing how much time you have dedicated to helping me these past few months, even though I am not your student. You've really been a guiding light for me through the epidemiological work. Thank you so much for your help! Your contributions have been invaluable and I really don't think I could have done it without you.

I'd also like to thank everyone else who has directly contributed to this work: 1) Joseph LeSauter for being a constant source of advice and support. It was really a pleasure to work with you! 2) Malini Riddle, the primary research assistant in the animal work, who will do great things one day 3) Peter Balsam, who was always so amazing and supportive when I asked for advice about design, analyses, etc., 4) the co-authors and contributors to the human work: Erik Gunderson, Margaret Haney, Richard Foltin, Drs. John Mariani and Jeanne Manubay and technical assistance by Brooke Roe, Susan Loftus, Diana Paksarian, Jana Colley, Michael Rubin, and Liah Barnett and 5) the co-authors and contributors to the epidemiological work: Michael P. McNeil and Farah Taha. I'd also like to thank Matthew Kirkpatrick for his constant friendship and advice. You are the very prototype of leading by example.

Finally, I would like to thank all of my friends and family for being so supportive and loving. Graduate school has so many ups and downs and it means so much to me that you've always been there for me. I really couldn't have done it without you!

viii

DEDICATION

I would like to dedicate this work in loving memory of my grandmother, Marie Baggett, who passed away on Feb. 18, 2014. Her inspiring life, hard work, unconditional love, and sage advice have truly helped shape the person I've become. I love you so much.

Chapter 1: General Introduction

Illicit drug use in the United States has been slowly increasing for the past 15 years. In

1999, 6.3 percent of Americans aged 12 years and older reported illicit drug use in the past 30 days (referred to as “current use”: SAMSHA 2002). By 2012, this number had risen to 9.2 percent (SAMSHA 2013a). In particular, there has been considerable recent concern regarding the illicit use of marijuana, methamphetamine, and prescription pain relievers.

1.1 Marijuana

Federally, marijuana is classified as a Schedule I drug, making it one of the most restricted drugs in the country. Schedule I drugs are said to have no currently accepted medical use, high potential for abuse, and lack accepted safety for use even under medical supervision.

Thus, under federal law, it is illegal to manufacture, distribute, dispense, or possess marijuana.

Despite the federal classification, 20 states and the District of Columbia have enacted laws protecting the medical use of marijuana. A growing number of other states are considering measures that will also legalize medical marijuana. Further, 16 states have made possession of marijuana a civil violation rather than a criminal one. Finally, Colorado and Washington have legalized marijuana for recreational use. Some have predicted that recent legislative move will increase marijuana availability and use. And, as a result, concern has been raised about marijuana-related negative consequences, including performance disruptions, addiction (defined by the DSM V as meeting two or more criteria for substance use disorder) and mental illness.

One of the goals of this project is to investigate some of these issues.

1.1.1 Illicit marijuana use in the United States

Marijuana is the most commonly used illicit drug in the world. In the U.S., approximately

18.9 million (7.2 percent of the population) are current illicit marijuana users (SAMSHA 2013a).

Between 2007 and 2012, the rate of past 30 day marijuana use increased from 5.8 to 7.3 percent; daily or almost daily use increased from 2.1% to 2.9% of the population (SAMSHA 2013a).

Marijuana also represents the illicit drug with the largest number of persons classified with past year abuse or dependence (4.3 million or 1.7% of the population; SAMSHA 2013a). In 2011, approximately 333,000 Americans sought treatment for their marijuana use, accounting for 18 percent of all treatment admissions (SAMSHA 2013b). Marijuana was the third most commonly reported primary substance at treatment admission, after alcohol (39%), and opiates (25%;

SAMSHA 2013b). Data from the Drug Abuse Warning Network (DAWN) show that there were approximately 455,000 emergency visits related to marijuana either alone or in combination with other illicit drugs, representing 36 percent of all illicit drug-related emergency department visits

(SAMSHA 2013c). It is important to note that high rates of marijuana-related mentions in the emergency department data are not surprising given that it is the most widely used illicit drug.

Also, one should be mindful that most emergency department drug mentions are due to multiple drugs rather than a single drug. Nonetheless, the sheer number of current marijuana users suggests that there will be some negative consequences that warrant public health attention.

One such concern is marijuana use by young people. Indeed, marijuana use- as well as use of other recreational drugs- is most common among those between the ages of 18-25 years.

The number of young people using marijuana has increased in the past several years. For example, in 2008, the percentage of young adults reporting current marijuana use was 16.6; by

2012, the number was 18.7 percent (SAMSHA 2013a).

While many young people may not experience any negative effects from marijuana use, some studies estimate that 1 in 11 people who ever use marijuana will become dependent

Young adults also show the highest rates of drug abuse and dependence. In 2007, 21.1 percent of persons aged 18 to 25 were classified as needing treatment for alcohol or illicit substance use (SAMSHA 2009). Only 7 percent of those received treatment (SAMSHA 2009).

Most young adults in need of treatment did not recognize that they had drug-related problems.

Further, drug use by young adults has also been associated with a number of mental health problems. It is important to note that the relationship between mental health and substance use is bidirectional; substance use may intensify pre-existing mental health problems, and mental health problems may worsen substance abuse. The complex interaction between the two makes the association difficult to study.

Taken together, this data indicates that young adults are particularly vulnerable population to marijuana-related negative consequences. The transition from adolescence to adulthood is a key life change that brings increased risk for drug use and mental health problems.

To date, there has been little research examining the relationship between marijuana use and mental health in college students, a population that is more likely to engage in marijuana use

(past year use: college: 34.9 percent, non-college: 32.7 percent) and, as a result, might be at great risk for mental health problems. Therefore, the first aim of this project was to investigate the substance use- and mental health- correlates of marijuana use in college students. This study

utilized a convenience sample of undergraduates enrolled at a university in the Northeast.

Participants completed an online survey assessing drug use behaviors, mental health, and demographics. Specifically, we examined the relationships between marijuana use and other illicit drug use, binge drinking, negative consequences from drinking, mental health problems and perceived stress. This data will inform the database regarding marijuana use and mental health among college students.

1.1.2 Acute effects of marijuana

While the above data will provide important information about mental health correlates and reported marijuana use, they do not provide a measure of marijuana’s direct (or acute) effects. Study 2 will investigate the acute effects of marijuana. It is well known that marijuana increases heart rate and euphoria, with peak effects occurring within 10 minutes after smoking and all effects diminishing within 2 hours (Hart et al. 2001; 2005). The drug also has been shown to produce negative effects on cognitive functioning, but the extent of these effects are dependent upon a variety factors, including the marijuana use history of the user.

1.1.3 Factors influencing the acute effects of marijuana

A critical factor that modulates marijuana-related effects is marijuana use history. For example, during intoxication, infrequent users have been shown to exhibit slower reaction time and disruptions in short-term memory and inhibitory control (e.g., Fant et al. 1998; Curran et al.

2002; D’Souza et al. 2004). The performance of frequent marijuana smokers, on the other hand, is less affected during intoxication (e.g., Ward et al. 1997; Haney et al. 1999; Hart et al. 2001,

2010; Vadhan et al. 2007; Ramaekers et al. 2009). Moreover, there is even evidence suggesting

that marijuana functions as a nootropic (i.e., cognitive-enhancer) for some frequent smokers

(e.g., Weil et al. 1968; Schafer & Brown 1991).

Environment factors, such as time-of-day, are also known to powerfully influence drug effects, although there are virtually no data assessing the influence of time-of-day on marijuana- related effects. Keith et al. (2013) assessed the influence of time-of-day on methamphetamine- related effects in mice and found that the drug produce greater intake and motor activity effects when administered during the early sleep period versus the late sleep period. These data show drug effects are altered as a function of time of day.

Rotating shift work, particularly night-shift work, requires individuals to alter their normal sleep-wake cycles and perform during times when their circadian rhythms make them least alert (for a review, see Arendt 2010). Hence, shift-workers have higher rates of workplace- related injuries (Dembe et al. 2008), performance decrements (Fido and Ghali 2008) and reduced productivity (Akerstedt 1998). These workers may be more susceptible to drug misuse due to shift workers seeking strategies, including pharmacological ones, to offset shift work-related disruptions. There are no data investigating marijuana-associated effects on cognitive performance and mood during shift work. Thus, the second aim was to examine the effects of smoked marijuana on the daytime and night-time performance during shift work in current marijuana smokers.

One difficulty in studying the effects of drugs in humans is creating a balance between a controlled laboratory environment and an experience that mimics the “real world.” Human laboratory studies must control as many potentially confounding variables as much as possible, while creating an environment that does not make participants uncomfortable. To this end, Hart and colleagues have developed a controlled laboratory model of shift work utilizing the

Residential Laboratory at the New York Psychiatric Institute (NYSPI). This laboratory models a

4-bedroom apartment and is located within the hospital, allowing for continuous observation of behavior. Each participant resides in a private bedroom, equipped with a bed, desk, Macintosh computer system, refrigerator, microwave, toaster, food preparation area, and a barcode scanner for food requesting and reporting. The laboratory also includes a common area, in which participants were free to engage in social and recreational activities, such as watching videotaped films, playing video games and board games, reading, and exercising. In Study 2, participants lived in this laboratory for three weeks while shifting between working 3 days on a day shift

(0830-1730h) and 3 days on a night shift (0030-0930h), with shift condition switching several times during the study. This method allows for a naturalistic setting that mimics night-shift work conditions experienced in the natural ecology. Study 2 examined the effect of marijuana on cognition and mood during simulated shift work. We hypothesized that marijuana would exacerbate night-shift related decrements in performance but improve mood.

1.2 Methamphetamine

1.2.1 Illicit methamphetamine use in the United States

Methamphetamine use has also generated a considerable amount of concern, despite comparatively low levels of use. In 2012, the number of past 30 day methamphetamine users was approximately 440,000, or 0.2 percent of the population (SAMSHA 2013a). In 2011, approximately 102,000 people sought treatment for methamphetamine dependence (SAMSHA

2013b). Further, DAWN data indicates that approximately 160,000 or 12.8 percent of all illicit

drug-related emergency visits in 2011 were related to methamphetamine alone or in combination with other drugs.

Longitudinal data indicates that methamphetamine use has been declining in recent years.

Between 2006 and 2012, the number of current methamphetamine users decreased by almost half

(731,000 to 440,000; SAMSHA 2013a). Between 2004 and 2012, the number of past year initiates of methamphetamine similarly decreased by more than 50 percent (318,000 to 133,000;

SAMSHA 2013a). Nonetheless, methamphetamine use and production is still perceived as extensive.

It is important to note that when compared to marijuana users, a greater percentage of methamphetamine users experience drug-related problems, despite overall lower levels of use.

Indeed, there are a number of potential negative consequences associated with methamphetamine abuse and dependence. For example, large doses of the drug can cause paranoia that mimics psychosis (Grelotti et al. 2010) and hypertensive crisis leading to stroke (Ho et al. 2009).

However, to date, there is still a paucity of research on the consequences of this drug.

1.2.2 Acute effects of methamphetamine

Briefly, it is well established that intranasal methamphetamine increases cardiovascular measures such as heart rate and blood pressure, with peak effects occurring approximately 10 minutes after administration and lasting for 4 hours (Hart et al. 2008). Methamphetamine produces prototypical stimulant effects such as increased ratings of “high” and “stimulated,” and decreased ratings of “tired” and “hungry.” Similar to cardiovascular measures, these effects peak within 5 to 10 minutes and last for 4 hours. Methamphetamine also improves reaction time and

functioning in a few cognitive domains, such as vigilance and visuospatial processing. Decreased food intake and sleep are also commonly observed following methamphetamine administration

(Comer et al. 2001). Study 3 will investigate the direct (or acute) effects of methamphetamine when combined with a narcotic pain-reliever.

1.3 Nonmedical use of pain relievers

1.3.1 Illicit pain reliever use in the United States

In addition to marijuana and methamphetamine, there has been considerable concern regarding the nonmedical use of pain relievers. Second only to marijuana, in 2012 there were 4.9 million current users of nonmedical pain relievers (SAMSHA 2013a). Approximately 187,000 people sought treatment for non-heroin opiate dependence, a full 10% of all treatment admissions in 2011 (SAMSHA 2013b). In particular, there has been concern regarding the use of oxycodone. This drug is used to manage moderate to severe pain over a period of 24-hours with a controlled release formula. The drug is often abused by crushing the pill and snorting or injecting the drug to bypass the controlled-release mechanism. In 2010, a new, abuse-deterrent formulation of oxycodone was approved by the FDA in 2010. The new formulation makes it difficult for users to crush, cut, or dissolve the pill in order to release greater amounts of oxycodone. In 2013, the FDA withdrew approval for the original formulation of oxycodone, effectively barring the medication. Despite these attempts to deter abuse, the number of new nonmedical oxycodone users has remained relatively stable since 2004, at approximately

500,000 new users per year (SAMSHA 2013a). However, there was a slight decrease in new users 2012, with 372,000 initiating use (SAMSHA 2013a). Nonetheless, oxycodone products

remain a substantial public health concern. Oxycodone accounted for 151,000 emergency room visits in 2012, almost half of all visits related to narcotic pain relievers (SAMSHA 2103c). It is important to note that the vast majority of opioid overdoses occur when the drugs are taken in combination with other sedatives and alcohol. These drugs have additive effects in decreasing respiration and can be fatal when taken together. As narcotic pain relievers are second only to marijuana in illicit use, there has also been substantial recent concern regarding other popular drug combinations. However, less is known about the effects of combining pain relievers with other types of drugs.

1.3.2 Acute effects of oxycodone

Oral oxycodone (10mg) decreases respiration and pupil size (Zacny, Paice, and Coalson

2012). The drug also increases positive subjective effects such as “feel drug effect,” and “like drug.” Some negative subjective effects such as “skin-itching” are also increased. Oxycodone does not disrupt cognitive performance unless larger doses (20 and 30 mg) of the drug are used

(Zacny and Gutierrez 2003; Zacny, Paice, and Coalson 2012).

1.4 Poly-drug abuse of amphetamines and opioids

Poly-drug abuse is widespread among illicit drug users. For example, in 2009, 67 percent of all stimulant-related emergency room visits involved multiple drugs (SAMSHA 2012). Of particular concern is the combination of methamphetamine and opiates. Recent data indicate that

MA-pain reliever combinations accounted for 17.4 percent of all MA-related emergency department episodes (SAMHSA 2010).

Despite the significance of poly-drug abuse, the vast majority of human laboratory studies on substance abuse are aimed at understanding single drug effects. This is partly due to the difficulty and danger in conducting human studies evaluating novel drugs and drug combinations. In this regard, preclinical research is a valuable tool for determining the biological and behavioral effects of drugs of abuse, especially when the behavioral effects are largely uncharacterized.

It is hypothesized that humans use drug combinations in order to: 1) enhance the effects of both drugs, 2) produce a unique profile of effects, 3) allow users to self-medicate, ex. to aid sleep, or 4) reduce the unwanted side-effects of one or both drugs. However, to our knowledge their have no prior studies investigating the effects of the methamphetamine-oxycodone combination. Thus, the third aim was to investigate the rewarding and behavioral effects of the methamphetamine-oxycodone combination in a preclinical model, with particular focus on whether the combination increases positive and decreases negative effects compared to each drug alone. In study 3, we investigated the individual and combined effects of one dose of methamphetamine (1 mg/kg, I.P.) and two doses of oxycodone (0.5 and 1 mg/kg, I.P.) on reward assessed by conditioned place preference, locomotor activity, sensitization, anxiety assessed by the open field test, and short-term memory, assessed by novel object recognition in mice. We hypothesized that the drug combination would produce greater reward and reduced anxiety-like behavior when compared to either drug alone. The results from this study will provide for the first time a scientific database about the consequences of this popular stimulant-opioid combination.

1.5 Scientific and Public Health Significance

Overall, these studies were conducted to further develop the database on the correlates and acute effects of marijuana, methamphetamine, and prescription pain relievers. The current studies aim to address three gaps in the literature; namely 1) to further elucidate the relationship between mental health and marijuana use in college students, 2) to investigate the acute effects of marijuana in occasional users that are subjected to rapidly changing shift work, and 3) to begin the systematic investigation of the uncharacterized methamphetamine-oxycodone combination in a preclinical model. Further, the present studies utilized a translational, multilevel approach in order to better understand substance use as a whole.

Chapter 2: Cannabis Use Among Undergraduates: Mental Health and Substance Use

Correlates

2.1 Introduction

Marijuana is the most commonly used illicit drug in the United States (US) (SAMSHA

2013a). Data indicate that while cigarette smoking continues to decline, the prevalence of marijuana use has been steadily increasing in the past decade among adolescents and young adults (Gledhill-Hoyt et al. 2000; Johnston 2013). For example, past 30-day marijuana use among young adults aged 18-25 increased from 16.6 percent in 2006 to 18.7 percent in 2012

(SAMSHA 2013).

While many young people may not experience any negative effects from marijuana use, some studies estimate that 1 in 11 people who ever use marijuana will become dependent

(Anthony 1994). Persistent, daily smoking of marijuana impairs lung function (Tashkin 2001) and withdrawal is associated with a number of deleterious effects such as disrupted sleep, decreased appetite, and negative mood states such as irritability, depression, and anger (Budney et al. 2007). As legalization and decriminalization of marijuana becomes more widespread, it will be important to monitor marijuana use in young adulthood as this is a critical period for the development of drug use problems.

Despite the need for research on marijuana use among young persons, a number of key areas are not well understood. First, marijuana use has commonly been found to be associated with the use of other substances among young people (Bell et al. 1997; Gledhill-Hoyt et al. 2000;

Degenhardt et al. 2001; Wagner and Anthony 2002; Mohler-Kuo et al. 2003). For example, one study that sampled undergraduates from 140 universities across the U.S. found that 98.5 to 99% of current marijuana users also reported current use of other substances (Mohler-Kuo et al.

2003). In particular, concurrent use of marijuana, cigarettes, and binge drinking is common among undergraduates (Schorling et al. 1994; Wechsler et al. 1995; Bell et al. 1997; Gledhill-

Hoyt et al. 2000; Jones et al. 2001; Mohler-Kuo et al. 2003). However, only a handful of studies have examined the interaction between marijuana use, binge drinking, and alcohol or drug- related negative consequences among college students (Shillington and Clapp 2001; Shillington and Clapp 2006; Rhodes et al. 2008). Shillington and Clapp 2006 found that undergraduates who used both marijuana and alcohol in the past year reported higher rates of substance-related negative consequences when compared to students who had only used alcohol. However, this study measured any past year marijuana or alcohol use; therefore it is unknown whether this relationship differs depending on frequency of use.

Secondly, the extent to which marijuana is related to mental health, and particularly depression and anxiety among young people remains unclear. Data from studies on the general population have shown that depression and substance abuse commonly co-occur (Chen et al.

2002). In young adults, the cumulative probability of a major depressive episode is 8% for those who report using marijuana less than 5 times in their life, compared to approximately 15% for those who used marijuana greater than 6 times in their life (Chen et al. 2002). Among college students, studies examining the relationship between marijuana and mental health have provided conflicting information (Buckner et al. 2010; Kouri et al. 1995; Dumas et al. 2002; Cranford et al. 2009). One study reported that students who used marijuana daily were indistinguishable from occasional users on a range of mental health measures, including prevalence of DSM Axis I and II disorders (Kouri et al. 1995). Conversely, another study reported that marijuana use was associated with poorer academic functioning and a wide range of mental health symptoms in

college students (Buckner et al.2010). Thus, the relationship between marijuana use, mental health, and academic functioning among undergraduates remains unclear.

Third, the relationship between stress and marijuana use remains largely uncharacterized.

Studies have consistently found that college students report stress relief as the most important reason for marijuana use (Simons et al. 1998; Simons et al. 2005; CASA 2007). Further, a few studies have indicated that coping motives predict cannabis use or cannabis use problems

(Simons et al. 1998; Simons et al. 2005). However, to our knowledge, the relationship between perceived stress and frequency of marijuana use among college students has not been investigated.

The current study aims to begin to fill this gap by addressing four aims: 1) To investigate the relationship between marijuana use and use of other illicit substances; 2) To examine the relationship between marijuana use and alcohol consumption, binge drinking and perceived negative consequences from drinking; 3) To investigate the relationship between marijuana use and mental health problems; 4) To examine the relationship between perceived stress and marijuana use among undergraduates at a small Northeastern university.

2.2 Methods

2.2.1 Sample

Data was collected during the Spring 2009 using the American Health Association- National

College Health Assessment (ACHA-NCHA) (ACHA 2009). The ACHA-NCHA is a national survey that consists of approximately 300 variables assessing student health, health-related behaviors, access to health information, and health-related outcomes. It has been evaluated extensively for reliability and validity in U.S. college students. This study uses data from only

the drug use, mental health, and stress variables from the full instrument. The present study included data from 1776 undergraduate students from a highly competitive private institution in the northeast US. Participants had the option to skip questions, which produced some variation in the sample size according to question. All enrolled undergraduate students in the primary undergraduate academic school were invited to participate in the study (N=5859) and received email invitations via their campus email account. Up to three reminders over the course of a three week period were sent to non-responders. A total of 1841 surveys were received (31.4%).

Participants had the option to be entered into a drawing for gift certificates from a travel provider or the bookstore upon completion of the survey. Data from graduate students were excluded from the analysis.

2.2.2 Measures

2.2.2.1 Demographics

Participants reported demographic information including age, gender, year in school, race/ethnicity, participation in Greek life, and approximate GPA.

2.2.2.2 Marijuana and other substance use

Participants were asked “Within the last 30 days, on how many days did you use:” marijuana, as well as cigarettes, alcohol, cocaine, methamphetamine, other amphetamines, and hookah.

Response choices ranged from 1: never used, 2: have used, but not in the past 30 days, 3: 1-2 days, 4: 3-5 days, 5: 6-9 days, 6: 10-19 days, 7: 20-29 days, to 8: used daily.

2.2.2.3 Binge drinking and negative consequences of drinking

Participants were asked “Over the last two weeks, how many times have you had 5 or more drinks in one sitting?” Response choices ranges from 1:N/A don’t drink, 2:none, 3:1 time, 4:2 times, 5: 3 times, 6: 4 times, 7: 5 times, 8: 6 times, 9: 7 times, 10: 8 times, 11: 9 times, 12:10 times. Participants were also asked, “Within the last 12 months, have you experienced any of the following as a consequence of your drinking: did something you later regretted, forgot where you were or what you did, got in trouble with the police, had unprotected sex, physically injured yourself, and physically injured another person.” Response choices ranged from 1 to 3, or 1: N/A don’t drink, 2: no, 3: yes.

2.2.2.4 Mental health

Participants were asked, “Within the past 12 months, have you been diagnosed or treated by a professional for depression (or anxiety/substance use disorder)?” Response choices ranged from

1 to 6, or 1: no, 2:diagnosed but not treated, 3:diagnosed and treated with medication,

4:diagnosed and treated with psychotherapy, 5:diagnosed and treated with medication and psychotherapy, or 6:other treatment .

2.2.2.5 Stress

Levels of stress during the past 12 months were assessed with a single question, “Within the last

12 months, how would you rate the overall level of stress you have experienced?” Responses ranged from 1 to 5, or 1: no stress , 2: less than average stress, 3: average stress, 4: greater than average stress, 5: tremendous stress.

2.2.3 Data Analysis

First, comparisons on demographic characteristics among students with different levels of marijuana use were made using chi-square tests with significance set at p<0.05. Next, logistic regression analyses were used to examine the relationships between marijuana use and other drug use, binge drinking, negative consequences of drinking, mental health problems, and perceived stress. Results estimating the strength of associations are reported using odds ratios with 95% confidence intervals. Odds ratios were adjusted to account for potential confounding by gender, race, year in school, and fraternity/sorority membership (i.e., all significant demographic factors

2.3 Results

Marijuana use in the past month was reported by 23.8% (323/1357) of undergraduates in this sample. Ten percent (10.2%) reported using marijuana 1-2 days in the past month, 7.1% reported using 3-9 days, and 6.6% reported using >10 days.

2.3.1 Demographics

Current marijuana use was significantly more common among males, white students, seniors, and participants of Greek life (Table 1). These demographic factors were also significantly associated with frequency of use. Female students were more likely to report no use or light use

(1-2 days) while males more commonly reported regular use (>10 days; p=.002). Sixty-four percent (64%) of the students who reported use of >10 days were white. Black and Asian/Pacific

Islander students exhibited low frequencies of marijuana use. Sophomore students more frequently reported light use (1-2 days), while seniors were more likely to report regular use (3-9

days or >10 days, p=.029). Seventeen percent (17.8%) of students who used marijuana >10 days reported belonging to a fraternity or sorority, compared to 7.8% of those who did not use marijuana (p=.0001). There were no significant associations between marijuana use and age or approximate GPA.

2.3.2 Marijuana and other substance use

Current marijuana users, compared to non-users, were significantly more likely to report current use of all other substances (Table 2). Frequent marijuana users were also more likely to use all other substances when compared to less frequent users and non-users. There was a strong association between any marijuana use and alcohol (p=.001). Almost 100% of students who reported any marijuana use also reported alcohol use. The strongest association was observed for cocaine. Compared to those who reported no past 30-day use, those who used marijuana 1-2 days had 6.9 higher odds (95% CI= 4.0 to 11.9) of also reporting current use of cocaine, those who used 3-9 days had 13.7 higher odds (95% CI= 7.8 to 23.9), and those who used >10 days had

25.8 higher odds (95% CI= 15.0 to 44.2). Further, of the students who reported >10 days of marijuana use, a large majority (>80%) also reported current use of cigarettes, hookah, and alcohol, almost half (46.7%) reported using cocaine, and 30.3% reported using amphetamines.

Compared to those who reported no past 30-day use, these students had 14.7 higher odds of cigarette use (95% CI= 8.1 to 26.6), 16.8 higher odds of alcohol use (95% CI= 2.3 to 123.0),

25.8 higher odds of cocaine use (95% CI= 15.0 to 44.2), 7.6 higher odds of amphetamine use

(95% CI= 2.9 to 20.2), and 22.3 higher odds of current hookah use (95% CI= 9.6 to 51.8).

2.3.3 Binge drinking and negative consequences of drinking

Frequency of marijuana use was significantly associated with frequency of binge drinking (Table

3). For example, thirty six percent (36%) of students who used marijuana regularly (>10 days) also reported binge drinking on at least three occasions in the past 2 weeks, compared to 23% of occasional users (1-2 days) and 6% of students who did not use marijuana. Seventy three percent

(73.2%) of students who did not use marijuana also did not binge drink. A number of negative consequences related to drinking were also significantly associated with marijuana use, even when controlling for binge drinking (Table 4). Specifically, students who reported any current use of marijuana were also more likely to report regretting something they did while intoxicated, forgetting what they did, and having unprotected sex. Frequency of marijuana use was associated with drinking-related trouble with the police. Specifically, when compared to non-users, students who used marijuana 3-9 days in the past month were 4.0 times more likely (95% CI= 1.3 to 12.4) to have an encounter with the police, and those who used >10 days were 7.6 times more likely

(95% CI= 2.8 to 20.9). Marijuana use was not associated with injuring self or others after adjusting for demographic factors.

2.3.4 Mental Health

Marijuana use was significantly associated with major depressive disorder, anxiety disorders, and substance use disorders (Table 5). Specifically, major depression was only significantly associated with >10 days marijuana use in the past month (AOR: 1.9; 95% CI= 1.0 to 3.6). In contrast, anxiety was significantly associated with any marijuana use, with no effect of frequency of use.

2.3.5 Stress

There were no significant associations between the degree of perceived stress and marijuana use

(Table 6).

2.4 Discussion

Results of this survey suggest that students who use marijuana are more likely to use alcohol and other illicit substances, binge drink and report negative consequences from drinking, when compared to students who did not use marijuana. Increased frequency of marijuana use was associated with depression, anxiety, and substance-use disorders. Stress was not related to marijuana use. Current marijuana use was reported by 23.9% of undergraduates in this sample, which is substantially higher than the national average for undergraduates of 15.1% (ACHA

2009).

The present results indicate that any marijuana use was associated with increased rates of alcohol use, while frequency of marijuana use was associated with increased rates of all other drugs use. This data is consistent with previous reports in high school seniors and college students (Resnicow et al. 1999). In particular, frequent marijuana users (>10 days) reported using many substances at very high rates. Almost all of these students (>80%) also smoked cigarettes and hookah and nearly half (46.7%) reported current cocaine use. Thirty percent (30%) also reported current amphetamine use.

There was a dose-response relationship between marijuana and binge drinking, such that regular users engaged in binge drinking more often than occasional users, who in turn binged more than non-users. Seventy-three percent (73%) of students who did not use marijuana in the

past month also reported that they did not engage in binge drinking. This data is generally consistent with previous reports indicating that college students who reported any current use of marijuana were 6.8 times more likely (95% CI 6.11-7.62) to engage in binge drinking (Mohler-

Kuo et al. 2003).

Any current marijuana use was associated with higher odds of experiencing several negative consequences related to drinking; namely students regretting and forgetting what they did while intoxicated and having unprotected sex. Frequency of marijuana use was associated with drinking-related encounters with the police. Specifically, when compared to non-users, students who used marijuana >10 days in the past month were 7.6 times more likely to have an encounter with the police. In general, this data is consistent with previous reports indicating that college students who reported any past-year alcohol and marijuana use were more likely to experience negative consequences from substance use than students who only used alcohol

(Shillington and Clapp 2001; Shillington and Clapp 2006; Rhodes et al. 2008). The present data builds on this work by indicating that frequency of use also mediates alcohol-related negative consequences, even when controlling for binge drinking. We speculate that poly-drug abuse, and in particular concurrent marijuana and alcohol use may be an important risk factor for experiencing negative consequences associated with drinking.

Frequency of marijuana use was significantly associated with major depression, anxiety, and substance use disorders, although to different extents. Depression was significantly associated with regular marijuana use (>10 days), while any marijuana use was associated with anxiety. The relationship between marijuana use and depression and anxiety were strongly modulated by gender.

There have been few prior studies examining the relationship between mental health and marijuana use among college students, with some conflicting results. For example, Cranford et al. 2009 found no association between current marijuana use and symptoms of depression, panic, or generalized anxiety disorders in students at a large Midwestern university, even when examining the role of gender (Cranford et al. 2009). The authors suggested that low base rates of marijuana use (12.9%) may have obscured the relationship between marijuana and mental health problems in more frequent users. On the other hand, Buckner et al. 2010 found that when compared to non-users, students who used marijuana at least once in the past month reported more symptoms of a variety of mental health problems, including anxiety, depression, hostility, interpersonal sensitivity, paranoia, and psychoticism, although these authors did not investigate the effect of gender (Buckner, Ecker et al. 2010). Further, frequent users did not experience more mental health problems than infrequent users. The authors concluded that any past 30 day marijuana use was associated with a wide range of mental health problems in college students.

The current results differ from both Cranford et al. 2009 and Buckner et al. 2010.

Students who smoked marijuana were more likely to report diagnosis and/or treatment for depression, anxiety, and substance use disorders. Further, the current results indicate a dose- response for depression. The disparity between the present study and previous studies may be due in part to methodological differences. Both Cranford et al. 2009 and Buckner et al. 2010 used self-report measures of mental health symptomology in the past month. Conversely, the present study examined reports of past 12-month diagnosis and/or treatment for mental health problems by a professional. Since only a small minority of those with depression, anxiety, and substance use disorders seek treatment, the current data suggests that the association between marijuana use and mental health in college students may be strongest in those with more severe

mental health problems, and specifically in treatment-seeking subsamples. It is possible that these students are more motivated to cope with their mental health problems in both adaptive

(treatment) and non-adaptive ways (drug use).

Further, the present results are the first data indicating that marijuana use is related to diagnosis and/or treatment for anxiety among college students. This is in contrast to previous research demonstrating that marijuana use is unrelated to anxiety disorders or anxiety symptomology among undergraduates (Kouri et al. 1995; Dumas et al. 2002; CASA 2007), with the exception of some social anxiety symptoms (Buckner et al. 2006; Buckner et al. 2007;

Buckner et al. 2008). The present methodology did not ask students to specify the type of anxiety disorder. However, the strength of the association suggests that the current results are not simply driven by a subset of students with social anxiety disorder.

It is important to note that many of the mental health measures associated with marijuana use were significantly modulated by gender. This data is consistent with a growing literature indicating sex differences in the prevalence of depression and anxiety disorders (for review, see

(Piccinelli and Wilkinson 2000; Bekker and van Mens-Verhulst 2007)). However, many studies investigating marijuana use and mental health among college students have not investigated the role of gender (Kouri et al. 1995; Dumas et al. 2002; CASA 2007). While not an aim of the current study, additional research will be needed in order to tease apart the effects of gender in the relationship between marijuana use and mental health.

There were no significant associations between marijuana use and perceived stress. This result is surprising given the well-established relationship between stress and substance use

(Hyman and Sinha 2009). However, the low variability in levels of perceived stress may have contributed to the observed results. Over 50% of students in the current sample reported

experiencing greater than average or tremendous stress in the past 12 months. Therefore, it is possible that while most students experienced high levels of stress, only a small subset of students used substances in order to cope with stress

The current results should be interpreted within the context of several limitations. First, the sample was comprised of students enrolled in a single university located in the Northeast.

Therefore, the results cannot be generalized to other groups. The current method also utilized a convenience sample with a 30% response rate. Further, the current study did not control for non- response bias. Therefore, students completing the survey may not be representative of typical students at the university. Finally, any conclusions or causal inferences from the current data must be tempered due to the cross-sectional nature of the study. Without longitudinal data, we cannot infer the temporal order of substance use and mental health problems. It is possible that students who used marijuana become anxious and depressed as a pharmacological or psychological consequence of their drug use. It is also possible that anxious and depressed students used substances for purposes of self-medication.

The current findings suggest that marijuana use among this sample of undergraduates is greater than national averages for college students. Frequency of use was associated with increased substance use, depression, anxiety, and substance- related problems. Perceived stress was generally high among all students, but was not related to marijuana use. Together, this data suggests that marijuana use and substance and mental health problems are strongly associated in undergraduates. Of particular concern are those students who used marijuana >10 days per month. These students typically engage in a number of substance use behaviors; almost all smoke cigarettes and hookah, almost half report current cocaine use, and they binge drink at high rates, and are more likely to experience negative consequences from their drinking. Additional

research will be needed to identify specific issues associated with gender, substance use, and mental health in college students. Further, outreach efforts need to be made in order to teach young adults how to use substances responsibly, recognize substance-related problems, and access treatment options if necessary.

Table 1: Demographics associated with marijuana use among undergraduates

Frequency of None 1-2 Days 3-9 Days >10 Days p-value marijuana use in the (1036) (139) (96) (90) past 30 days (n) (n) (n) (n) % % % % Age 19.8 19.9 19.9 20.2 .16 M (SD) (1.5) (1.3) (1.4) (1.3) Gender .001 Female (595) (84) (51) (39)

57.4% 60.4% 53.1% 43.3% Male (441) (55) (45) (50)

42.6% 39.7% 46.9% 55.6% Transgender (0) (0) (0) (1)

0.0% 0.0% 0.0% 1.1% Race <.0001 White (438) (74) (53) (57)

43.6% 54.0% 56.4% 64.0% Black (71) (3) (4) (4)

7.0% 2.2% 4.2% 4.5% Hispanic (103) (18) (15) (9)

10.3% 13.1% 16.0% 10.1% Asian or Pacific Islander (308) (26) (9) (10)

30.7% 19.0% 9.6% 11.2% American Indian, (9) (1) (0) (1)

Alaskan, Native HI 0.9% 0.7% 0.0% 1.1% Multiracial (75) (15) (13) (8)

7.5% 10.9% 13.8% 9.0% Undergraduate Year .02 in School 1 (322) (33) (29) (17)

31.0% 23.7% 30.2% 18.9% 2 (261) (49) (20) (20) 25.1% 35.3% 20.8% 22.2% 3 (258) (29) (22) (24) 24.9% 20.9% 22.9% 26.7% 4 (184) (26) (24) (28) 17.7% 18.7% 25.0% 31.1% 5+ (13) (2) (1) (1) 1.3% 1.4% 1.0% 1.1% Fraternity/Sorority (80) (16) (20) (16) <.0001 7.8% 11.5% 20.8% 17.8% Approximate GPA .158 A (576) (71) (55) (36) 55.6% 51.4% 57.3% 40.0% B (408) (63) (40) (48) 39.4% 45.7% 41.7% 53.3% C (40) (4) (1) (6) 3.9% 2.9% 1.0% 6.7% D/F (2) (0) (0) (0) 0.2% 0.0% 0.0% 0.0% 26

Table 2: Marijuana use and any past 30 day other substance use among undergraduates Frequency of None 1 – 2 days 3 – 9 days > 10 days marijuana use in (1034) (138) (96) (89) past 30 days (n) (n) (n) (n) % % % %

Cigarettes (123) (85) (74) (75) 11.9% 61.2% 77.9% 83.3% OR 1.0 4.6 10.3 15.6 95% CI (reference group) 3.2 – 6.6 6.2 – 16.9 8.7 – 27.9 Adjusted OR - 4.6 9.8 14.7 95% CI 3.2 - 6.7 5.9 - 16.4 8.1 - 26.6

Alcohol (745) (138) (96) (88) 72.1% 100.0% 100.0% 98.9% OR 1.0 - - 21.7 95% CI (reference group) 3.0 – 156.5 Adjusted OR - - - 16.8 95% CI 2.3 – 123.0

Cocaine (1) (24) (29) (42) 0.1% 17.3% 30.2% 46.7% OR 1.0 6.7 13.9 28.7 95% CI (reference group) 3.9 – 11.4 8.2 – 23.6 17.2 – 48.0 Adjusted OR - 6.9 13.7 25.8 95% CI 4.0 – 11.9 7.8 – 23.9 15.0 – 44.2

Amphetamines (9) (15) (15) (27) 0.9% 10.9% 15.6% 30.3% OR 1.0 5.9 6.4 8.2 95% CI (reference group) 2.4 – 14.1 2.4 – 16.9 3.3 – 20.7 Adjusted OR - 5.7 5.6 7.6 95% CI 2.3 – 14.0 2.0 – 15.4 2.9 – 20.2

Hookah (210) (102) (79) (83) 20.3% 73.4% 83.2% 92.2% OR 1.0 4.9 8.7 24.4 95% CI (reference group) 3.2 – 7.2 5.0 – 15.0 10.6 – 56.2 Adjusted OR - 4.8 8.4 22.3 95% CI 3.2 – 7.1 4.8 – 14.6 9.6 – 51.8

Table 3: Marijuana use and binge drinking None 1 – 2 days 3 – 9 days > 10 days (1149) (138) (96) (84) Frequency of marijuana use in the past 30 days (n) (n) (n) (n) % % % %

Number of times had 5 or more drinks in one sitting during the past 2 weeks None (769) (56) (25) (23) 59.5% 66.9% 40.6% 26.0% 27.4% 1-2 times (283) (50) (47) (30) 27.9% 24.6% 36.2% 49.0% 35.7% 3-4 times (80) (21) (14) (20) 9.2% 7.0% 15.2% 14.6% 23.8% 5+ times (17) (11) (10) (11) 3.3% 1.5% 8.0% 10.4% 13.1%

Frequent binge drinking (3+ (97) (32) (24) (31) times in past 2 weeks) 8.4% 23.2% 25% 36.9%

OR 1.0 3.3 3.6 6.3 95% CI (reference group) 2.1 – 5.1 2.2 – 6.0 3.9 – 10.4 Adjusted OR - 3.7 3.3 5.5 95% CI 2.3 – 5.9 1.9 – 5.6 3.3 – 9.1

Table 4: Marijuana use and negative consequences from drinking Frequency of marijuana use in past None 1 – 2 days 3 – 9 days > 10 days 30 days (714) (137) (94) (86)

(n) (n) (n) (n) % % % % Did something you (167) (72) (47) (47) later regretted 23.4% 52.6% 50.0% 54.7% 32.3% OR 1.0 2.5 2.3 2.8 95% CI (reference group) 1.7 – 3.6 1.5 – 3.5 1.8 – 4.4 Adjusted OR - 2.0 1.5 2.0 95% CI 1.4 – 3.0 1.0 – 2.3 1.2 – 3.2

Forgot what you did (123) (59) (48) (38) or who you were with 17.3% 43.7% 51.1% 44.7% 26.1% OR 1.0 2.6 3.4 2.7 95% CI (reference group) 1.8 – 3.7 2.2 – 5.3 1.7 – 4.3 Adjusted OR - 1.9 2.0 1.8 95% CI 1.3 – 2.9 1.3 – 3.2 1.1 – 2.9

Had a run-in with police (6) (4) (5) (8) 2.3% 0.8% 2.9% 5.3% 9.4% OR 1.0 3.0 5.6 10.8 95% CI (reference group) 1.0 – 9.7 1.9 – 16.6 4.1 – 27.1 Adjusted OR - 2.4 4.0 7.6 95% CI 0.7 – 7.9 1.3 – 12.4 2.8 – 20.9

Had unprotected sex (29) (27) (16) (17) 8.7% 4.1% 20.0% 17.0% 19.8% OR 1.0 3.2 2.6 3.2 95% CI (reference group) 2.0 – 5.2 1.5 – 4.7 1.8 – 5.7 Adjusted OR - 2.8 1.8 2.5 95% CI 1.7 – 4.6 1.0 – 3.4 1.3 – 4.6

Injured self (42) (24) (20) (15) 9.8% 5.9% 17.6% 21.3% 17.4% OR 1.0 2.0 2.5 2.0 95% CI (reference group) 1.2 – 3.2 1.5 – 4.3 1.1 – 3.6 Adjusted OR - 1.4 1.5 1.2 95% CI 0.8 – 2.3 0.9 – 2.7 0.7 – 2.3

Injured others (5) (4) (3) (4) 1.6% 0.7% 2.9% 3.2% 4.7% OR 1.0 2.4 2.6 3.9 95% CI (reference group) 0.8 – 7.4 0.7 – 9.2 1.3 – 12.1 Adjusted OR - 1.7 1.4 2.1 95% CI 0.5 – 5.5 0.4 – 5.3 0.5 – 8.0

Table 5: Marijuana use and past 12 month diagnosis and/or treatment for mental health disorders Frequency of None 1 – 2 days 3 – 9 days > 10 days marijuana use in (1063) (142) (97) (90) past 30 days (n) % (n) (n) (n) % % %

Major Depressive (74) (16) (13) (14) Disorder 7.1% 11.3% 13.5% 15.6%

OR 1.0 1.3 1.7 2.0 95% CI (reference group) .7 – 2.2 .9 – 3.1 1.1 – 3.6 Adjusted OR - 1.2 1.6 1.9 95% CI .6 – 2.1 .9 – 3.1 1.0 – 3.6

Anxiety Disorder (72) (23) (19) (16) (unspecified) 6.9% 16.4% 19.8% 18.0%

OR 1.0 2.0 2.5 2.4 95% CI (reference group) 1.2 – 3.3 1.4 – 4.3 1.3 – 4.2 Adjusted OR - 1.9 2.3 2.2 95% CI 1.2 – 3.2 1.3 – 4.1 1.2 – 4.1

Substance Use (4) (2) (4) (3) Disorder 0.4% 1.4% 4.2% 3.4%

OR 1.0 2.1 6.4 5.1 95% CI (reference group) .5 – 9.7 2.0 – 20.8 1.3 – 19.0 Adjusted OR - 2.4 7.8 5.2 95% CI .5 – 12.4 2.1 – 28.7 1.3 – 21.6

Table 6: Marijuana use and stress

Frequency of marijuana None 1 – 2 days 3 – 9 days > 10 days use in past 30 days (1453) (139) (95) (89)

(n) \(n) \(n) (n) % % % % Stress None or (128) (10) (5) (8) less than 8.8% 7.2% 5.3% 10.0% average 8.5% Average (545) (52) (39) (28) 37.4% 37.5% 37.4% 41.1% 31.1% Greater than (780) (77) (51) (53) average or 53.7% 55.4% 53.4% 59.6% tremendous 54.1%

Greater than average or (780) (77) (51) (53) tremendous 53.7% 55.4% 53.4% 59.6% 54.1% OR 1.0 1.1 1.0 1.3 95% CI (reference group) .8 – 1.5 .7 – 1.5 .8 – 2.0 Adjusted OR - 1.0 1.0 1.3 95% CI .7 – 1.5 .6 – 1.5 .8 – 2.0

Chapter 3: Smoked Marijuana Improves Performance and Mood During Simulated Night

Shift Work in Marijuana users

3.1 Introduction

Drug abuse, if unchecked, could lead to a possible loss to business of more than $200 billion per year due to increased absenteeism, accidents, and medical costs (Slavit et al. 2009).

Certain employees are more susceptible to drug use than others. For example, individuals who work nonstandard schedules, such as rotating or night shifts, are almost twice as likely to use illicit drugs than those working standard day shift schedules (SAMSHA 2008; Frone 2006). This issue becomes more concerning when one considers the fact that shift-workers account for approximately 20% of all workers in developed countries (Eastman et al. 1995). Rotating shift work, particularly night-shift work, requires individuals to alter their normal sleep-wake cycles and perform during times when their circadian rhythms make them least alert (for a review, see

Arendt 2010). Hence, shift-workers have higher rates of workplace-related injuries (Dembe et al.

2008), performance decrements (Fido and Ghali 2008) and reduced productivity (Akerstedt

1998).

Many shift workers use stimulants, sedatives or both in an effort to offset shift work- related disruptions. Low to moderate doses of stimulants (e.g., amphetamine, caffeine, modafinil) have been reported to be effective countermeasures for mood and performance decrements caused by sleep deprivation and fatigue (e.g., Hart et al. 2003b, 2006; Walsh et al.

1990), while sedatives have been demonstrated to improve sleep quality (e.g., Porcu et al. 1997;

Hart et al. 2003a, 2005). On the other hand, certain types of drug use by shift workers might exacerbate diminished performance, mood alterations, and sleep disturbances associated with

shift work. Cannabis [∆9-tetrahydrocannabinol ( ∆9-THC)-containing products including marijuana and hashish], the most widely used illicit drug in the world (UNODC 2010), is of particular concern. In the United States, 18 states and Washington D.C. have passed ballot initiatives allowing patients to use marijuana on the advice of their physicians. In addition, marijuana is the most commonly detected illicit drug in the workplace, accounting for approximately 50% of all positive urine toxicology screens (Quest Diagnostics 2010). Of course, because it may take 1 to 10 weeks for marijuana to be fully cleared from the system (Ellis et al.

1985; Huestis et al. 1996), a urine toxicology screen that is positive for marijuana metabolites does not provide information about time of use, nor an individual’s possible level of intoxication or her/his ability to perform workplace-related operations. The sheer number of marijuana users combined with data showing that 2 million Americans (i.e., 1.6% of the workforce) report being intoxicated on the drug while at work at some point (Frone 2006), however, suggest the importance of understanding the potential contributions of marijuana use to workplace productivity and safety.

Data from laboratory studies assessing the acute effects of smoked marijuana in experienced users show that the drug produces paradoxical effects; it enhances ratings of alertness and stimulation (e.g., Haney et al. 1999; Hart et al. 2001, 2002) but facilitates sleep

(e.g., Vandrey et al. 2011). The drug’s effect on performance depends upon the marijuana use history of research participants. That is, infrequent users tend to display marked disruptions during intoxication (e.g., Fant et al. 1998; Curran et al. 2002; D’Souza et al. 2004), whereas frequent users show little performance alterations (e.g., Hart et al. 2001, 2010; Vadhan et al.

2007; Ramaekers et al. 2009; Schwope et al. 2012). One consistent disruptive effect observed in frequent smokers, however, is that marijuana increases the amount of time participants need to

complete cognitive tasks (e.g. Hart 2001). This finding could have important implications in real world settings, such as in the workplace, where rapid decisions may be required.

There is little information about the effects of marijuana on performance or other measures (e.g., mood, sleep) of individuals subjected to work conditions that mimic those in the natural ecology. Thus, the purpose of this study was to examine the effects of smoked marijuana on the daytime and nighttime performance during shift work in current marijuana smokers. We developed a controlled laboratory model of shift work in order to more closely model the real world work environment. Participants live in a residential laboratory and work 3 days on a day shift (0830-1730h) and 3 days on a night shift (0030-0930h), with shift condition switching several times during the study. This within-participants model facilitates the examination of abrupt work shift change-related alterations in human behavior and the interactive effects of drugs on these alterations. We hypothesized that marijuana would exacerbate night-shift-related performance decrements but improve mood.

3.2 Methods

3.2.1 Participants

Ten healthy research participants (mean age [±SD] 27.2 ± 5.6) completed this 23-day residential study: three were female (two Black, one Hispanic) and seven were male (three

Black, one Hispanic, one Native American, and two White). They were solicited via word-of- mouth referral and newspaper advertisement in New York City. Participants’ formal education ranged from 10 to 16 years (mean 13.2) and all reported previous experience working irregular shift schedules. Participants reported smoking marijuana an average of 3.5 ± 0.5 days/week and

1.2 ± 0.4 marijuana cigarettes/smoking occasion. Nine reported occasional use of alcohol (1–6 drinks/week), and eight smoked tobacco cigarettes (1–20 cigarettes/day). Participants reported that they used other drugs infrequently: three had used methylenedioxymethamphetamine

(MDMA) less than a total of five times, three used illicit prescription opioids less than a total of three times, two used psychedelic mushrooms twice, one used cocaine once, and one reported using d-lysergic acid diethylamide (LSD) once. Urine toxicology analyses during the screening process confirmed the absence of illicit drug use other than marijuana (all participants tested positive for 9-THC only). All volunteers passed comprehensive medical and psychological evaluations and were within normal weight ranges according to the 1983 Metropolitan Life

Insurance Company height/weight table (mean body mass index: 25.3 ± 4.0).

Volunteers were informed that the purpose of the study was to evaluate the effects of marijuana on cognitive performance and mood of shift workers. Each participant signed a consent form approved by the New York State Psychiatric Institute’s Institutional Review Board and was fully informed about experimental and drug conditions at the end of the study.

Participants were compensated at a rate of $70 per day.

3.2.2 Laboratory

Three groups of 3-4 individuals stayed in a residential laboratory in the New York State

Psychiatric Institute (Foltin et al. 1996; Hart et al. 2003a). Each participant resided in a private bedroom, equipped with a bed, desk, Macintosh computer system, refrigerator, microwave, toaster, food preparation area, and a barcode scanner (Worthington Data Solutions, Santa Cruz,

CA) for food requesting and reporting. The laboratory also included a common area, in which

participants were free to engage in social and recreational activities, such as watching videotaped films, playing video games and board games, reading, and exercising. Food and tobacco cigarettes were available ad libitum during waking hours. To enable continuous observation of behavior, cameras and microphones are located in the common social area and in bedrooms, but not in bathrooms, showers, or private dressing areas. Communication between participants and research staff was kept to a minimum and primarily accomplished via computers in each participant’s bedroom.

3.2.3 Pre-Study Training

Prior to beginning the study, participants completed 2 sessions of training (3-4 hours per session), during which they were familiarized with laboratory and study procedures and trained on the computerized tasks that would be used in the study. By the end of the second session, none of the participants' performance on any of the tasks varied by more than 10 percent. The purpose of training is so that the tasks are well-learned prior to study participation to minimize the effect of learning on any task performance during the study. On a separate day, they smoked

3 puffs from a single marijuana cigarette (3.56% ∆9-tetrahydrocannabinol [THC]) to provide them with experience with the study drug and to monitor any potential unusual reactions. No unusual responses were observed.

3.2.4 Design

Table 1 shows that the study consisted of six 3-day blocks of sessions and all participants

experienced six dose/shift combinations: three puffs of placebo + day and night shift, 1.9% ∆9-

THC concentration + day and night shift, and 3.56% ∆9-THC concentration + day and night shift. During the night shift, participants were awakened at 0015, performed computerized tasks

(described below) from 0030 to 0930, and went to bed at 1600; during the day shift, they were awakened at 0815, performed computerized tasks from 0830 to 1730, and went to bed at 2400.

Shifts alternated 3 times during the study, and shift conditions were separated by an "off" day, during which participants were not on a work schedule but were required to go to bed 8.25 hrs prior to the next shift as they had done during other days. Two groups of participants (N = 7) began on the night shift and one group (N = 3) began on the day shift. Placebo or marijuana

(1.9% or 3.56% ∆9-THC) was smoked once per day, one hour after waking (0915 on the day shift and 0115 on the night shift). Days 8 and 16 were "drug washout" days during which participants smoked placebo marijuana before being switched to another drug condition. In order to minimize potential confounding effects, presentation of ∆9-THC concentrations were systematically varied between groups of 3-4 participants, although dose order was the same for all subjects in a cohort.

3.2.5 Procedure

Participants moved into the residential laboratory the day before the study began to habituate to inpatient living conditions. On each study day, participants first completed baseline cognitive/psychomotor tasks, a 44-item subjective-effects visual analog questionnaire, and a visual analog sleep questionnaire (described below). Then, they were weighed (but were not informed of their weight) and given time to eat breakfast. Following breakfast, eight 30-min

computerized task batteries, composed of the subjective-effects questionnaire and cognitive/psychomotor tasks, were completed. Participants were given a 15-min break between each task battery. From 1000 (0200) to 1245 (0445), participants completed 4 task batteries and, after a 1.5-hr lunch break period, they completed 4 additional task batteries from 1415 (0615) to

1700 (0900). Beginning at 1700 (0900), participants had access to activities available in the social area. Two films were shown daily, beginning at 1800 (1000) and 2100 (1300). Lights were turned out at 2400 (1600) for an 8.25-hr sleep period.

3.2.6 Subjective-effects and Cognitive/Psychomotor Battery

The 30 min computerized task batteries consisted of a visual analog questionnaire and cognitive/psychomotor tasks. The visual analog questionnaire is a 3 min task comprised of a series of 100-mm lines labeled "not at all" at one end and "extremely" at the other end (Hart et al., 2006). The lines were labeled with adjectives describing a mood (e.g., “I feel…” “alert,”

“depressed,” “social”), a drug effect (e.g., “I feel…” “stimulated,” “a good drug effect,” “a bad drug effect”), or a physical symptom (“I feel nauseous,” “I have a headache,” “My heart is beating faster than usual”). In addition, 15 min after smoking the marijuana cigarette, participants completed a 2 min drug-effect questionnaire (DEQ) during which they rated “good effects” and “bad effects” on a five-point scale: 0=“not at all” and 4=“very much.” They also rated how “strong” the drug effect was as well as their desire “to take the drug again.” Lastly, participants rated how much they liked the drug effect on a nine-point scale: -4 =“disliked very much,” 0 =“feel neutral, or feel no drug effect,” and 4 =“liked very much.”

The computerized psychomotor task battery was selected because it had been used in multiple other studies assessing the effects of drugs on performance. This facilitates the comparison of data collected in the current study with previous findings. The battery consisted of five tasks: 1) the Digit Recall Task; 2) the digit-symbol substitution task (DSST); 3) the divided attention task (DAT); 4) the rapid information task (RIT); and 5) the Repeated Acquisition Task

(RA task).

During the 2 min Digit Recall task, an 8-digit number was displayed for 3 sec on the computer screen. Participants were instructed to enter the number correctly while it was on the screen and again after it had disappeared from the screen. They were also told that they would be asked to reproduce and recognize the number near the end of the battery. This task was designed to assess changes in immediate and delayed recall (see Hart et al., 2001).

The DSST is a 3-min task (McLeod et al., 1982) that consisted of nine random three-row

´ three-column squares (one square blackened/row) displayed across the top of the computer screen. Each array was associated with a number (1-9). A randomly generated number appeared at the bottom of the screen, indicating which of the arrays should be reproduced on the nine-key keypad attached to the computer. Participants were instructed to reproduce as many patterns as possible by entering the patterns associated with the randomly generated numbers. This task was designed to assess changes in visuospatial processing.

The DAT is a 5-min task that combines concurrent pursuit-tracking and vigilance tasks

(Miller et al., 1988). Participants tracked a moving circle on the video screen using the mouse, and also had to signal when a small black square appeared at any of the four corners of the

screen. Accurate tracking of the moving stimulus increased its speed proportionately. This task was designed to assess changes in vigilance and inhibitory control.

During the 7 min RIT, a series of digits was presented at the rate of 100 digits per min, and subjects were instructed to press a response button as quickly as possible whenever they detected sequences of three consecutive odd or three consecutive even digits (Wesnes and

Warburton, 1983). A point was earned for each correct “hit” and a point was deducted for each

“miss” or “false alarm.” Participants were instructed to earn as many points as possible during the task. This task was designed to assess changes in sustained concentration and inhibitory control.

At the start of the 3-min RA task, participants were instructed to learn a 10- response sequence of button presses. A position counter incremented by one each time a correct button was pressed, and remained unchanged after an incorrect response. The points counter increased by one each time the 10-response sequence was correctly completed. The sequence remained the same throughout the task, but a new random sequence was generated for each subsequent task battery. Participants were instructed to earn as many points during the task as possible by pressing the buttons in the correct sequence. This task was designed to assess changes in learning and memory (see Kelly et al., 1993).

3.2.7 Sleep Monitoring

Objective sleep was measured using the portable Nightcap® sleep systems, which consisted of a headband worn by participants while they slept (Ajilore et al., 1995; Cantero et al.,

2002). A miniature electrode attached to the eyelid measured eye movement but did not disrupt

sleep, and a body movement sensor in the headband detected and recorded movements. The system provided measures of total sleep time, sleep onset latency, rapid eye movement latency, non-rapid eye movement sleep time, and sleep efficiency (total sleep time as a percentage of time in bed) and has been reported to correspond well to traditional polysomnography and subjective reports of sleep quality (Ajilore et al., 1995). In addition to wearing the portable sleep monitor, participants completed a visual analog sleep questionnaire each morning. This questionnaire consisted of a series of 100 mm lines labeled "not at all" at one end and "extremely" at the other end. The lines were labeled: "I slept well last night," "I woke up early this morning," "I fell asleep easily last night," "I feel clear-headed this morning," "I woke up often last night," "I am satisfied with my sleep last night," and a fill-in question in which participants were asked to estimate the number of hours they thought they slept the previous night (Haney et al., 2001).

3.2.8 Drug

One hour after waking each morning, participants were given one 1-gm marijuana cigarette (0, 1.9, or 3.56% ∆9-THC: provided by the National Institute on Drug Abuse). These concentrations of ∆9-THC were selected because performance impairments at similar doses have been well-established in this laboratory and others (Azorlosa et al. 1992; Chait and Perry 1994;

Ward et al. 1997; Hart et al. 2001, 2002 ). Cigarettes were smoked using a cued-smoking procedure, which produces ∆9-THC concentration-dependent changes in heart rate and subjective-effect ratings (see Foltin et al. 1987; Hart et al. 2001). Colored lights (mounted on the ceiling of the social/recreational area) signaled ‘light the cigarette’ (30 s), ‘get ready’ (5 s),

‘inhale’ (5 s), ‘hold smoke in lungs’ (10 s), and ‘exhale.’ Participants smoked three puffs in this

manner, with a 40-s interval between each puff. Cigarettes were tightly rolled at both ends and were smoked through a hollow plastic cigarette holder so that the contents were not visible.

Twenty-four hours prior to administration, cigarettes were removed from a freezer, where they were stored in an airtight container, and humidified at room temperature.

3.2.9 Data Analysis

Data from off and drug washout days (days 4, 8, 12, 16, 20) were not included in the analyses. The area under the curve (AUC) for the subjective effects visual analog questionnaire and the cognitive/psychomotor tasks was determined using the trapezoidal method (Tallarida and

Murray 1981). Peak subjective-effect and cognitive psychomotor data were analyzed similarly.

For the sake of brevity, data for the AUC analyses are discussed primarily because significant drug effects were similar for peak and AUC analyses.

Data were analyzed using 3-factor repeated-measures analyses of variance (ANOVA): the first factor was ∆9-THC concentration (placebo, 1.9, 3.56%), the second factor was shift condition (day, night), and the third factor was day within condition (1, 2, 3). For all analyses,

ANOVAs provided the error terms needed to calculate planned comparisons that were designed to answer two questions: (1) are cognitive/psychomotor task performance and subjective-effects ratings disrupted during the night shift, and (2) does marijuana alter night shift-related disruptions? To evaluate night shift-related disruptions, each day of placebo was compared to the corresponding night of placebo (e.g., the first day of placebo during the day shift vs. the first night of placebo during the night shift). To evaluate the effects of marijuana on night shift- related disruptions, each night of each drug condition was compared to a corresponding night of

another drug condition (e.g., the first night of 1.9% ∆9-THC during the night shift vs. the first night of placebo during the night shift). Marijuana-related effects during day-shift work were evaluated similarly. Huynh-Feldt corrections were used when appropriate and p-values < 0.05 were considered statistically significant.

3.3 Results

3.3.1 Effects of Shift Condition

3.3.1.1 Cognitive Performance

The upper panels of Figure 1 illustrate how selected performance varied between the day and night shift when participants received placebo. Planned comparisons revealed that the number of digits entered in the Digit Recall Task and the number of hits on the RIT were significantly reduced during all three nights that participants worked on the night shift compared to the corresponding days on the day shift ( p < 0.006). In addition, the number of completed trials on the repeated acquisition task was decreased on the first and third nights of the night shift

(p < 0.03). As shown in Tables 3-4, other performance measures (e.g., DAT: speed and hit latency; RIT: misses) were also altered significantly as a function of shift condition.

Figure 1. Upper panel: AUC values for total trials (RA), total number of hits (RIT), and total number of digits entered as a function of shift condition and day within condition. §Significant difference between the day and night shift conditions for that day following placebo administration ( p<0.05). Bottom panel: AUC values for total trials (RA), total number of hits (RIT), and total number of digits entered as a function of ∆9-THC concentration and day of the night-shift condition. *Significant difference between placebo and ∆9-THC concentration for that day ( p<0.05). #Significant difference between 1.9 and 3.56% ∆9-THC for that day ( p<0.05). Error bars represent 1 SEM. Overlapping error bars were omitted for clarity.

3.3.1.2 Subjective-effects ratings

The upper panels of Figure 2 show how selected subjective-effect ratings varied between the day and night shifts when participants received placebo. Ratings of “Self-Confident” were significantly decreased during all three nights of the night shift compared to the corresponding day shift days ( p < 0.05). Conversely, ratings of “Tired” and “Miserable” were significantly increased during two of the three nights of the night shift ( p < 0.04). Tables 2-4 show that several other subjective-effect ratings (e.g., “Unmotivated” and “Talkative”) were altered significantly as a function of shift condition.

3.3.1.3 Sleep

The upper panels of Figure 3 display how objective and subjective measures of sleep varied between the day and night shifts when participants received placebo. Total sleep time, as measured by the Nightcap®, was decreased across all three days that participants worked on the night shift, but this effect was statistically significant only on the first and third day of the night shift ( p < 0.004). Similarly, subjective estimates of total sleep time and ratings of sleep satisfaction were significantly decreased on the first day of the night shift ( p < 0.006).

3.3.1.4 Cigarette Smoking

There was no significant effect of shift on the number of cigarettes smoked by participants (F 1,30 =-0.75, p=0.41; day shift= 6.37±.87; night shift=6.07±.90).

Figure 2. Upper panel: AUC values for visual analog scale ratings of 'Self-Confident,' 'Tired,' and 'Miserable' as a function of shift condition and day within condition. §Significant difference between the day and night shift conditions for that day following placebo administration (p<0.05). Bottom panel: AUC values for visual analog scale ratings of 'Self-Confident,' 'Tired,' and 'Miserable' as a function of ∆9-THC concentration and day of the night-shift condition. *Significant difference between placebo and ∆9-THC concentration for that day ( p<0.05). Error bars represent one SEM. Overlapping error bars were omitted for clarity.

3.3.2 Effects of Marijuana

3.3.2.1 Cognitive Performance

The bottom panels of Figure 1 show how marijuana affected performance during the night-shift condition. (The night-shift data in the upper panels are the same data shown under the placebo condition in the bottom panels.) The 3.56% ∆9-THC concentration improved performance on the repeated acquisition task (increased the number trials) and RIT (increased the number of hits) on at least two of the three nights relative to placebo ( p < 0.03). Both active concentrations increased the number of digits entered during the Digit Recall Task on at least two of the three nights of the night shift ( p < 0.003). The lower concentration also significantly increased the number of hits on the RIT on the third night ( p < 0.02). Other significant performance effects produced by marijuana during the night shift are shown in Tables 2-4.

In contrast to the beneficial effects marijuana produced during night shift work, it had limited effects on performance during the day shift. The 1.9% ∆9-THC concentration significantly decreased the number of hits on the RIT on all three days of day shift work ( p <

0.04), increased the number of misses on the RIT on the first two days ( p < 0.05), and decreased the maximum speed on the DAT on the first and third day ( p < 0.05).

3.3.2.2 Subjective-effects ratings

The bottom panels of Figure 2 display how marijuana affected subjective-effect ratings during the night-shift condition. Both active concentrations significantly increased ratings of

“Self-Confident” on all three nights ( p < 0.03). The 3.56% ∆9-THC concentration decreased subjective ratings of “Tired” on the first night ( p < 0.05) and both concentrations significantly decreased these ratings on the third night ( p < 0.002). In addition, the larger concentration

increased ratings of “Stimulated” on all three nights ( p < 0.05), while both concentrations decreased ratings of “Miserable” on at least two of the three nights ( p < 0.04). As shown in Table

2-4, other subjective-effects ratings were also altered significantly during night shift work.

Notably, both concentrations significantly increased ratings of “Good drug effect” and “High” on all three nights ( p < 0.0001) and increased ratings of “Forgetful” on at least two nights of the night shift ( p < 0.05).

During the day shift condition, both active concentrations of ∆9-THC significantly increased ratings of “Good drug effect,” and “High” on all three days ( p < 0.001). Both concentrations also significantly increased ratings of “Stimulated” on all three days of the day shift ( p < 0.03).

3.3.2.3 Sleep

The bottom panels of Figure 3 display the effects of marijuana on sleep measures during the night shift condition. The 3.56% ∆9-THC concentration significantly increased total sleep time on the first day, as measured by the Nightcap® (p < 0.03); this concentration also increased estimations of total sleep time and subjective ratings of sleep satisfaction on the first day ( p <

0.05). During the day shift condition, there were no significant effects of ∆9-THC on objective or subjective measures of total sleep time or sleep satisfaction.

3.3.2.4 Cigarette Smoking

There was a significant effect of ∆9-THC concentration on the number of cigarettes smoked by participants (F 2, 30 =-4.153, p=0.033). This effect was driven by participants smoking

an average of 1 less cigarette when they received 3.56% ∆9-THC (placebo= 6.85±0.64; 1.9% ∆9-

THC =6.56±0.69; 3.56% ∆9-THC= 5.23±0.61).

Figure 3. Upper panel: Total sleep time from the previous evening as measured by the Nightcap® and selected mean subjective effects from the sleep questionnaire as a function of shift condition and day within condition. §Significant difference between the day- and night-shift conditions for that day following placebo administration ( p<0.05). Bottom panel: Total sleep time from the previous evening as measured by the Nightcap® and selected mean subjective effects from the sleep questionnaire as a function of ∆9-THC concentration and day of the night-shift condition. *Significant difference between placebo and ∆9-THC concentration for that day ( p<0.05). #Significant difference between 1.9 and 3.56% ∆9-THC for that day ( p<0.05). Error bars represent 1 SEM. Overlapping error bars were omitted for clarity.

3.4 Discussion

Psychomotor performance and subjective-effect ratings were altered during the night shift compared to the day shift: performance and a few subjective ratings were decreased (e.g., “Self-

Confident”), whereas other ratings were increased (e.g., “Tired”). These results are consistent with data from previous investigations on the effects of rotating work schedules on human performance and mood under controlled laboratory conditions (e.g., Reid and Dawson

2001; Sharkey et al. 2001; Hart et al. 2003a, b). Contrary to our prediction, smoked marijuana did not exacerbate night shift-related disruptions. Instead, the drug mainly attenuated night shift- related disruptions in regular marijuana smokers.

As expected, when participants worked on the night shift and received placebo, their performance was disrupted in several domains. The most pronounced alterations were noted on tasks that measured attention and vigilance. Specifically, participants performed significantly worse on the DAT (measures of speed and hit latency) and the RIT (measures of hits and misses) on all 3 nights of the night shift. In addition, inhibitory control disruptions, as measured by two separate tasks (i.e., DAT: false alarms and RIT: false alarms), were observed on 2 of the 3 nights. These findings replicate data collected under similar conditions (Reid and Dawson

2001; Sharkey et al. 2001; Hart et al. 2003a, b, 2005, 2006) and confirm that individuals who are required to make abrupt work shift schedule changes are prone to performance decrements. The study design used here is similar to irregular or rapidly rotating shift schedules in the natural ecology (Sallinen and Keckland 2010), and the current data are also congruent with recent findings from a study conducted in a more naturalistic setting. Chang et al. (2011) compared the performance of nurses working 2, 3, or 4 consecutive nights and found more perceptual and motor ability disruptions among nurses who worked two consecutive nights compared with those

who worked four consecutive nights. The results indicate that performance disruptions are more pronounced during the first few days of the night shift. Taken together, the data suggest that an acclimation period greater than 24 hours might be necessary in order to offset shift schedule change-related disruptions.

One possible explanation for the performance decrements observed during the night shift is that the abrupt change in work schedule produced sleep disruptions that affected next-day performance. Indeed, objective and subjective sleep measures partially support for this view.

Data from the Nightcap® showed that total sleep time was reduced by 2.5 hrs on the first night of the night shift. These data replicate previous findings indicating that night shift work is associated with 2-4 hrs of reduced sleep (Torsvall et al. 1989; Åkerstedt 1995; Niu et al. 2011).

Furthermore, participants reported reduced sleep quality on the first night of the night shift: e.g., their estimates of total sleep time and ratings of sleep satisfaction were significantly lower. Mood was also disrupted during the night shift. Participants reported feeling less “Self-Confident” on all three nights they worked on the night shift and endorsed items indicating that they were tired, miserable, and unmotivated on at least two nights. These data are consistent with previous findings when participants were abruptly placed on a night shift work schedule (Hart et al.

2003a, b).

It is noteworthy that performance disruptions persisted throughout the three nights that participants worked on the night shift, while measures of daytime sleep improved by the second day. One potential reason for these data is short-term circadian misalignment. Working on the night shift forces the body to function during the circadian trough of alertness, potentially compounding problems caused by sleep deprivation. Thus, the enduring performance disruptions may reflect a slow adjustment of the circadian rhythm and/or an accumulation of sleep debt that

prevents optimal performance. Indeed, previous research has indicated that the speed of circadian adjustment is approximately 1 hr per day, meaning that it would require at least 1 week for a diurnal cycle to switch to a nocturnal circadian cycle (Wever 1980; Hastings 1998). While we did not assess circadian phase, the current data show that daytime sleep improved over the course of three nights of night-shift work. Therefore, we speculate that a longer acclimation period to night shift conditions would also improve performance.

We predicted that smoking marijuana would exacerbate performance disruptions during the night shift. This hypothesis was not supported. The drug produced limited effects on performance on the first night of the night shift and attenuated decrements on subsequent nights.

On the second night, marijuana attenuated performance disruptions on four measures. Both active 9-THC concentrations reduced disruptions in inhibitory control (e.g., RIT: false alarms) and recall (e.g., Digit task: total entered) and only the larger concentration lessened disruptions in sustained attention (e.g., RIT: hits and misses). On the third night, marijuana also attenuated shift-related performance decrements: both 9-THC concentrations lessened disruptions in domains measuring reaction time (e.g., DAT: hit latency), vigilance/sustained attention (e.g.,

DAT: misses and maximum speed; RIT: hits), and recall (e.g., Digit: total entered).

To our knowledge the current study is the first investigation of marijuana-related effects during simulated shift work, making it difficult to compare our results to earlier research in this area. Previously, we reported that stimulant medications such as methamphetamine and modafinil attenuated many night shift related performance decrements (Hart et al. 2003b, 2006).

It is possible that the stimulant-like properties of marijuana played a role in decreasing night shift-related performance decrements observed in the current study. When participants smoked cigarettes containing the larger ∆9-THC concentration, they reported feeling markedly more

stimulated and less tired on the majority of nights. Thus, we speculate that the stimulant-like effects of marijuana may have counteracted night shift related decrements. Another possible explanation for the current results is that smoking marijuana prior to the work period aided in overall sleep recovery during the night shift. The larger concentration increased both objective and subjective measures of total sleep time, and improved sleep quality as measured by ratings of

“Satisfied Sleep.” In general, marijuana-related effects on sleep are congruent with previous data investigating the effects of the sleep medication zolpidem on next-day performance of individuals subjected to abrupt shift schedule changes (Hart et al. 2003a, 2005). Together, these observations suggest that marijuana cigarettes containing low to moderate ∆9-THC concentrations offset some disruptions caused by work shift schedule changes.

This interpretation should be considered in the context of several important caveats. The current study did not assess a broad range of ∆9-THC concentrations. It is likely that increasing

∆9-THC concentrations or the number of joints smoked would have exacerbated night shift- related disruptions. Similarly, the current study participants were experienced marijuana users who smoked multiple times each week (i.e, about three times per week). It is well documented that frequent marijuana users show fewer behavioral signs of disruption during intoxication than infrequent users. Thus, the generality of the present findings might be limited, as a different pattern of results might have been obtained if infrequent users were studied. Relatedly, it is possible that because study participants were regular marijuana smokers, they may have experienced marijuana withdrawal symptoms during placebo conditions and such symptoms could have influenced study findings. This seems less likely because withdrawal symptoms are typically observed only in heavy marijuana smokers, individuals who smoke multiple marijuana

cigarettes on a daily (or near daily) basis (Haney 2005). Heavy marijuana smokers were excluded from this study in order to avoid this potential confound.

In conclusion, these data demonstrate that performance, sleep, and mood are disrupted during night shift work when participants are subjected to abrupt shift changes. The data further show that marijuana cigarettes containing low to moderate ∆9-THC concentrations can decrease some night shift-related performance and mood disruptions. Because these are the first data describing these effects, future studies are needed before broad conclusions are drawn.

Table 1 . Study Design

Study day Shift condition Drug Condition ( ∆9-THC%)

1-3 Day 0

*4 Off Off

5-7 Night 1.9

*8 Night 0

9-11 Night 3.56

*12 Off Off

13-15 Day 3.56

*16 Day 0

17-19 Day 1.9

*20 Off Off

21-23 Night 0

Note. *Indicates days that were not included in data analyses; Off = off days Shift condition order was varied across participants ∆9-THC order was counterbalanced across participants Dosing times: 0915 during the day shift and 0115 during the night shift, i.e., 1-hr after waking

Table 2. Effects of Shift Condition and Smoked Marijuana on Subjective Effects on Day 1

Conditions Pbo Day Pbo Night 1.9% Δ9-THC Day 1.9% Δ9-THC Night 3.56% Δ9-THC Day 3.56% Δ9-THC Night Measure Mean Mean F-value Mean F-value Mean F-value Mean F-value Mean F-value (SEM) (SEM) (SEM) (SEM) (SEM) (SEM) Subjective Effects Good Drug Effect 47.30 39.00 0.08 253.20 47.51* 198.20 28.40* 308.30 76.34* 287.20 69.04* (20.44) (25.63) (64.35) (26.74) (63.97) (65.52) Unmotivated 128.20 213.50 6.71 § 122.90 2.17 136.20 5.51* 140.60 0.62 127.70 6.79* (42.10) (66.02) (55.18) (50.78) (58.35) (38.03) Social 478.00 372.80 11.91 § 506.50 0.87 379.60 0.05 478.20 0.04 504.10 18.56* † (53.90) (48.31) (43.05) (37.72) (50.34) (52.78) Talkative 436.40 334.20 9.28 § 424.00 0.14 325.80 0.06 414.80 0.41 445.10 10.92* † (55.81) (46.87) (57.43) (48.29) (52.26) (62.30) Stimulated 164.20 169.30 0.09 253.70 26.37* 199.70 3.04 249.60 24.00* 253.10 23.11* † (63.63) (60.49) (57.68) (54.14) (56.37) (61.80) Forgetful 79.80 50.70 1.43 104.70 1.04 104.70 4.93* 142.60 6.64* 161.60 20.69* 56 (35.66) (14.07) (33.21) (28.02) (40.46) (48.76) Data are represented as AUC values df= 1,40 Pbo = placebo § p < 0.05, significant difference between the placebo day and placebo night conditions * p < 0.05, significant difference from the placebo condition † p < 0.05, significant difference from the 1.9% night condition

Table 3. Effects of shift condition and smoked marijuana on psychomotor performance and subjective effects on Day 2

Conditions Pbo Day Pbo Night 1.9% Δ9-THC Day 1.9% Δ9-THC Night 3.56% Δ9-THC Day 3.56% Δ9-THC Night Measure Mean Mean F-value Mean F-value Mean F-value Mean F-value Mean F-value (SEM) (SEM) (SEM) (SEM) (SEM) (SEM) Performance Effects RIT: no. of misses 243.10 355.60 12.16 § 342.60 9.51* 320.70 1.17 239.50 0.12 269.40 7.14* (46.11) (96.41) (67.07) (39.42) (50.17) (56.50) RIT: false alarms 322.10 448.80 13.52 § 418.90 7.89* 372.10 4.95* 354.20 0.87 357.90 6.96* (59.60) (79.57) (70.10) (53.96) (64.92) (62.44)

Subjective Effects Good Drug Effect 1.90 19.20 0.34 209.80 48.44* 177.70 28.15* 315.30 110.07* 224.70 47.33* (1.51) (13.21) (52.28) (35.10) (69.84) (34.64) 57 Talkative 355.80 439.80 6.63 § 446.40 7.29* 354.60 6.45* 410.10 2.62 439.80 0.00 † (67.34) (52.78) (59.37) (56.63) (62.85) (51.92) Stimulated 147.70 183.70 4.27 § 219.90 17.16* 231.30 7.46* 227.00 20.70* 223.50 5.21 (63.01) (63.78) (61.20) (66.82) (53.32) (59.99) Forgetful 107.30 52.80 5.00 § 137.80 1.57 92.00 2.59 107.70 0.27 182.60 28.35* † (59.22) (32.36) (53.26) (36.66) (27.68) (58.05) Can’t Concentrate 95.50 89.80 0.02 100.30 0.16 196.30 7.88* 209.00 8.95* 214.40 10.78* (37.85) (40.44) (33.44) (72.43) (68.65) (71.61) Data are represented as AUC values df= 1,40 Pbo = placebo § p < 0.05, significant difference between the placebo day and placebo night conditions * p < 0.05, significant difference from the placebo condition † p < 0.05, significant difference from the 1.9% night condition

Table 4. Effects of shift condition and smoked marijuana on psychomotor performance and subjective effects on Day 3 Conditions Pbo Day Pbo Night 1.9% Δ9-THC Day 1.9% Δ9-THC Night 3.56% Δ9-THC Day 3.56% Δ9-THC Night Measure Mean Mean F- Mean F-value Mean F-value Mean F-value Mean F-value (SEM) (SEM) value (SEM) (SEM) (SEM) (SEM) Performance Effects RIT: no. of misses 248.90 360.30 11.93 § 268.90 0.38 283.20 5.71* 235.20 0.18 320.80 1.50 (58.82) (82.59) (40.49) (47.83) (46.45) (75.54) Digit: Imm. Recall 47.50 44.50 5.64 § 46.90 0.23 46.40 2.26 45.00 3.92* 47.80 6.83* (1.40) (1.80) (1.45) (1.67) (2.17) (1.01) Digit: Del. Recall 4.00 3.50 2.09 4.30 0.75 3.80 0.75 3.70 0.75 4.40 6.77* (0.21) (0.27) (0.30) (0.20) (0.33) (0.22) DAT: no. of misses 0.40 3.00 18.78 § 0.90 0.70 0.30 20.25* 0.70 0.25 1.20 9.00* (0.22) (1.27) (0.35) (0.15) (0.30) (0.47) DAT: speed 61.10 52.40 26.54 § 57.60 4.30* 58.60 13.48* 58.80 1.86 58.40 12.62* (0.80) (2.93) (2.11) (1.31) (1.59) (1.96) DAT: hit latency 6666.30 8996.40 24.49 § 6953.10 0.37 6563.30 26.70* 6855.40 0.16 7724.90 7.29* † (338.72) (842.68) (519.15) (350.13) (310.48) (534.15) 58 DAT: false alarms 7.80 19.00 12.37 § 13.90 3.67 11.40 5.70* 13.40 3.09 14.40 2.09 (1.13) (4.13) (3.34) (2.48) (3.80) (3.22) DSST: attempted 599.00 580.00 2.24 613.00 1.21 634.70 18.54* 587.40 0.83 584.70 0.14 † (37.43) (36.87) (36.94) (19.19) (38.43) (36.03) DSST: no. correct 582.30 559.60 3.32 584.90 0.04 610.40 16.62* 565.50 1.18 561.80 0.03 † (35.73) (34.91) (36.40) (17.59) (36.48) (34.01)

Subjective Effects Good Drug Effect 2.90 18.30 0.27 199.10 43.14* 205.40 39.23* 243.30 64.77* 240.90 55.53* (1.80) (8.71) (57.71) (41.04) (44.87) (38.66) Unmotivated 93.70 209.60 12.38 § 95.60 0.03 94.70 12.17* 149.00 2.82 176.20 1.03 † (30.58) (68.37) (40.54) (39.14) (55.27) (66.32) Stimulated 146.90 190.00 6.11 § 222.20 18.66* 219.00 2.77 189.90 6.09* 223.90 3.78* (62.55) (67.08) (60.27) (63.08) (56.36) (59.69) Forgetful 123.40 69.40 4.91 § 129.80 0.07 132.10 6.62* 172.90 4.12 196.70 27.26* (58.17) (42.14) (53.04) (52.61) (48.92) (60.33) † Data are represented as AUC values df= 1,40 Pbo = placebo § p < 0.05, significant difference between the placebo day and placebo night conditions * p < 0.05, significant difference from the placebo condition † p < 0.05, significant difference from the 1.9% night condition

Chapter 4: Poly-drug abuse: rewarding and behavioral effects of methamphetamine, oxycodone, and their combination

4.1 Introduction

Abuse of drug combinations has increased dramatically in recent years. Between 2009 and 2011, emergency department visits associated with the misuse of multiple drugs increased

116 percent. Abuse of drug combinations represents the majority of all drug-related visits to the emergency room (SAMSHA 2013). Further, two thirds (66.4%) of patients seeking substance abuse treatment report polydrug abuse (SAMSHA 2013). Despite the popularity of drug combinations, there is a paucity of research examining the unique issues associated with taking multiple drugs simultaneously. A lack of understanding about the consequences of drug combinations may obscure our view of substance abuse and hamper treatment efforts.

One popular speculation put forth to account for polydrug abuse is that drug interactions enhance the positive and decrease the negative effects of the combined drugs (Hunt et al. 2009).

Data examining the cocaine-heroin “speedball” combination, the most frequently studied combination in animals, generally support this view. Several studies have found that combinations of low doses of cocaine and heroin are more reinforcing than either of the drugs alone (Rowlett and Woolverton 1997; Ranaldi and Munn 1998; David et al. 2001; Rowlett et al.

2005; Martin et al. 2006; Rowlett et al. 2007). Fewer studies have examined the effects of this drug combination on locomotor activity or other behavioral and cognitive measures in rodents

(David et al. 2001; Leri et al. 2003; Leri et al. 2003; Cruz et al. 2011). This is a glaring gap in our knowledge because these measures may be of particular importance for understanding the consequences of polydrug abuse.

To our knowledge, there have been no prior animal or human studies documenting the consequences of the methamphetamine/oxycodone combination. This is somewhat surprising because methamphetamine is the most widely abused amphetamine (SAMSHA 2011) and its abuse has generated a considerable amount of concern. In addition, over the past decade the misuse of prescription pain relievers, especially oxycodone, has dramatically increased (Crane

2003). In fact, 17% of all methamphetamine-related emergency room visits also involve oxycodone (SAMSHA 2010).

Preclinical research is an important tool for determining the biological and behavioral effects of drugs of abuse. This is especially true in the case of novel drugs and drug combinations where behavioral effects are largely uncharacterized, making the danger and difficulty of human research unwarranted. However, limited communication between preclinical and clinical researchers often results in the use of experimental measures that do not overlap and drug doses that do not translate well. A goal of the present experiment was to examine the effects of low doses of the MA/ oxy combination in measures that could be easily compared to data from a human study on the same topic. To this end, the current experiment included a range of measures aimed to characterize the subjective and cognitive effects of low doses in a preclinical model.

The primary goal of the present experiment was to systematically investigate the behavioral and locomotor effects of the methamphetamine-oxycodone combination. A second goal was to evaluate whether this combination enhances positive and decreases negative effects compared to each drug alone. To address these questions, we first used a conditioned place preference paradigm to compare the effects of methamphetamine, oxycodone, and their combination on reward. Next we examined state-anxiety using an open field paradigm. Finally,

short-term memory was assessed during acute intoxication and one week after the last drug administration.

4.2 Methods

4.2.1 Animals and housing

Subjects were adult male C57BL/6N mice 7-9 weeks of age (Jackson Laboratory, Bar Harbor,

ME). Mice were housed individually in transparent polycarbonate cages (27 × 16.5× 12 cm) and placed in sound attenuating, ventilated chambers (Phenome Technologies Inc. Lincolnshire, IL).

The room was maintained at 23 ± 2 0C and 72% humidity. Standard mouse chow (Purina, St.

Louis, MO) and water were available ad libitum . Animals were adapted to a 12:12 light:dark cycle (200 lux), with lights off at zeitgeber time 1200 (ZT 12) and on at ZT 0000 (ZT 0) for 14-

16 days before the start of the experiment. All behavioral experiments were conducted between

ZT 2 and 8 during lights on. Animals were cared for in accordance with the Columbia University

Institutional Animal Care and Use Committee and Animal Welfare regulations.

4.2.2 Preparation of drugs

Stock solutions of methamphetamine (MA) hydrochloride and oxycodone (oxy) hydrochloride

(Sigma-Aldrich Inc., St. Louis, MO) were prepared (saline vehicle) so as to keep injection volumes consistent among groups as follows: oxy1 = 0.0625 mg/ml, oxy2 = 0.125 mg/ml , MA- alone =0.125 mg/ml, MA/oxy1 combination - MA=0.125 mg/ml, oxy1= 0.0625 mg/ml,

MA/oxy2 combination - MA=0.125 mg/ml each of and oxy2=0.125 mg/ml.

4.2.3 Experimental groups

Animals were assigned to one of six experimental groups (N=8/grp). 1) 1 mg/kg MA (MA- alone); 2) 0.5 mg/kg oxy (oxy1); 3) 1 mg/kg oxy (oxy2); 4) 1 mg/kg MA / 0.5 mg/kg oxy

(MA/oxy1); 5) 1 mg/kg each of MA and oxy (MA/oxy2), and 6) saline. Behavioral tests

(conditioned place preference (CPP), open field (OF), and novel object recognition (NOR)) were conducted over a three-week period ( Table 1 ).

4.2.4 Measures

4.2.4.1 Conditioned Place Preference

A 3-compartment conditioned place preference (CPP) apparatus (MED-CPP-MSAT; Med

Associates, Vt., USA) was used. Each chamber consists of three distinct compartments separated by automatic metal guillotine doors. One compartment has black walls with a stainless steel rod floor, and the other has white walls with a stainless steel mesh floor. The central compartment is a neutral grey with a smooth PVC removable floor. A small amount of corncob bedding was placed in the trays underneath the floors in both compartments. To give an olfactory cue, a tea bag (caffeine-free Chamomile Tea, Trader Joe’s) was placed in the corncob bedding in the white compartment. During the experiments, the CPP apparatus was kept in an isolated quiet testing room. Photo-beam signals from the chambers were relayed to a desktop PC running MED-PC for

Windows (Med Associates, Vt., USA).

Briefly, the CPP paradigm consisted of pre-conditioning (days 1-2), conditioning (days 3-

8), and post-conditioning phases (days 9-12). During all phases of CPP, mice were brought to the testing room 45 minutes prior to injection in order to habituate. For the pre-conditioning phase

(days 1-2), mice were weighed, injected with saline, placed in the central grey compartment and

allowed 30 minutes of free exploration of all three chambers of the apparatus. The apparatus was cleaned between each mouse using 70% ethyl alcohol. The time spent by the mouse in each compartment was recorded as the extent of baseline preference. An unbiased design was used to randomly assign animals to a drug-paired side of the apparatus. Drug conditioning spanned over

6 days (days 3–8). Mice received a single injection of drug or saline before being restricted to the corresponding side of the apparatus for 30 minutes, with half of the animals receiving drug on the first day and half receiving saline. Drug and saline were injected on alternating days for a total of 3 pairings each. Post-conditioning spanned over four days (Days 9-12) during which animals received a saline injection and were placed in the central grey compartment and allowed

30 minutes of free exploration of all three chambers. The difference in the percent of time spent in the drug paired vs. the saline paired compartment between post-conditioning and pre- conditioning represented the degree of conditioning induced by the drugs. Amount of activity in each compartment, defined by photobeam breaks, was also recorded during pre-conditioning, conditioning, and post-conditioning phases.

4.2.4.2 Open Field

The open field arena (43.2 cm x 43.2 cm x 30.5 cm) consists of a white plastic base with clear plexiglass walls (ENV-515; Med Associates, Vt., USA). The walls were covered with white paper. The arena was kept in a quiet isolated testing room. Illumination was provided by the room’s overhead lighting and was of equal intensity in all parts of the arena (~100 lux). Data was recorded on a video camcorder (Canon DC210) affixed to a tripod above the testing arena.

Behavior was automatically scored using the TopScan 2.0 program (Clever Sys Inc, Reston,

VA).

The open field (OF) test consisted of a single 10 minute test on day 13. Mice were transported to the test room in their home cages 45 minutes prior to testing in order to habituate.

Mice were then injected with drug and returned to the home cage for 30 minutes. Mice were then placed in the center of the arena and allowed to explore the apparatus for 10 minutes. The experimenter was not present in the room during testing. The arena was cleaned between tests using 70 % ethyl alcohol. The TopScan program was used to delineate areas within the arena; the center was defined as the inner 50% of the arena. The following behaviors were scored: time in center (s), time outside of center (s), distance in arena (mm), and velocity (mm/s).

4.2.4.3 Novel Object Recognition

The novel object recognition (NOR) paradigm was conducted according to the method of Denny et al. 2011. The test was performed in the same arena (ENV-515; Med Associates, Vt., USA) used for the open field test. Illumination and data collection were also identical to that used for the open field test. The NOR task was performed twice: on day 14 following acute injection of drug and on day 21 following injection of saline.

Each NOR test used a set of three objects, which were available in duplicate. The first set of objects included 1) a blue plastic box (10 cm x 8.5 cm x 3cm); 2) a clear plastic funnel

(diameter 7 cm, maximal height 13 cm); and 3) a grey cylindrical piece of pipe (diameter 5 cm, height 7.5 cm). The second set of objects included 1) a white and yellow pipette- tip box (12 cm x 8.5 cm x 4cm); 2) a clear 100 mL pyrex bottle with orange screw cap (diameter 5 cm, height 10

cm); and 3) a white plastic Coplin staining jar with a round base/top (diameter 5.5cm) and a square body (3.5 cm x 3.5 cm, height 10 cm). The objects elicited equal levels of exploration in pilot experiments (data not shown). All animals were tested with both sets of objects. Half of the animals were tested with the first set of objects on day 14 following acute drug injection, and half on day 21 following saline injection.

The NOR procedure involved a total of five 5-minute exposures, with 3 minute intervals between each exposure (see Denny et al. 2012). First, mice were transported to the room in their home cages 45 minutes prior to testing in order to habituate. They were then weighed, injected with drug (day 14) or saline (day 21) and immediately placed in the center of the arena for the first exposure. The first four exposures served as habituation/learning sessions where two objects were placed symmetrically on either side of the arena (~ 7cm from wall). During the 5 th exposure, one of the two familiar objects was replaced by a novel object (test session occurred at

~ 29 min after injection). During the 3 minute inter-trial intervals, animals were returned to their home cage located in the testing room and the arena and objects were cleaned with 70% ethyl alcohol. The experimenter was not present in the room during testing. The novel object, the replaced familiar object, and the location of the novel object were counterbalanced across mice.

The following behaviors were scored: sniffing within 1cm of object (s), distance in arena (mm), and velocity (mm/s).

4.2.5 Statistical Analysis

Data was analyzed using a 2x3 ANOVA in SAS for 1) CPP, 2) time in center for OF, 3) velocity in OF, 4) distance travelled in OF, 5) acute NOR, and 6) long-term NOR. Independent variables

included methamphetamine dose (0, 1 mg/kg) and oxycodone dose (0, 0.5, or 1 mg/kg). The interaction term was used to assess the effects of the drug combination. Linear mixed models were used to investigate 1) sensitization during CPP by comparing activity in the first and third drug exposure, 2) the persistence of conditioned place preference during 4 days of post- conditioning tests and 3) acute vs. long-term memory effects within animals.

4.3 Results

4.3.1 Conditioned place preference

Significant place preference was observed in all drug conditions when compared to saline ( Fig.

1a; largest p value = 0.005). Place preference was significantly influenced by oxycodone dose

(F 2,42 =7.31, p=.002) and a significant interaction between methamphetamine and oxycodone

(F 2,42 =4.87, p=.014). MA-alone produced a small but significant increase in place preference and oxycodone alone produced a dose-dependent response. The drug combination at both doses induced a place preference that was greater than MA-alone but less than oxy1 and oxy2 alone.

However, these differences were not significant. When comparing the persistence of place preference over 4 days of post-conditioning tests (Fig. 1b) , there were main effects of oxycodone dose, day, and a significant interaction between oxycodone and methamphetamine (smallest main effect, F3,111 =3.44, p=.019). On day 2 of post-conditioning, all groups showed significantly elevated place preference compared to saline (MA: t111 =-2.12, p=.036; oxy1: t 111 =-2.94, p=.004;

MA/oxy1: t 111 =-2.37, p=.019; oxy2: t 111 =-3.81, p=.0002; MA/oxy2: t 111 =-2.86, p=.005). The place preference response on day 2 was similar to day 1, with both doses of the drug combination showing an intermediate response when compared to MA-, oxy1, and oxy2 alone. On day 3 only

the oxy2 group showed significant place preference (t111 =-3.75, p=.0003) and by day 4, place preference was not observed in any drug condition.

4.3.2 Locomotor activity and Sensitization

Locomotor activity was measured during the conditioning phase of the CPP procedure.

Activity was significantly increased by MA but not oxycodone. Both doses of the drug combination produced an increase in activity at levels similar to MA-alone. To assess whether sensitization occurred, we compared the activity levels from the first and third drug exposure

(Fig. 1c ). Analysis revealed a significant main effect of methamphetamine and a significant interaction between methamphetamine and exposure number (smallest main effect, F 1,39 =15.98, p=.0003). Animals that received MA either alone or in combination showed an increase in activity from the first to the third drug exposure (MA-alone: t 39 =-2.2, p=.034; MA/oxy1: t 39 =-

2.55, p=.015; MA/oxy2: t 39 =-2.02, p=.05). Sensitization was not observed in the oxy1 or oxy2 groups.

4.3.3 Open field

State anxiety was quantified by the amount of time spent in the center of the open field arena, with greater time in the center indicating less anxiety. Analysis revealed a significant main effect of methamphetamine and oxycodone (smallest main effect, F2,46 =3.57, p=.037), such that animals that received active drug spent less time in the center compared to those that received saline. MAoxy1 produced significantly greater anxiety-like behavior than oxy1 but not MA-

alone. In contrast, MAoxy2 produced significantly greater anxiety-like behavior than either drug alone.

Figure 1. Bar histograms (A) display conditioned place preference on the first day of post- conditioning tests. * indicates significant difference from saline. The line graph (B) shows conditioned place preference over all four days of post-conditioning tests for each drug condition. All drug conditions exhibited significant place preference during the first two days of post conditioning tests. By the fourth day, no animals exhibited place preference. Error bars were removed for clarity. The line graph (C) displays sensitization data by showing the change in drug-induced locomotor activity from drug exposure 1 and 3. Animals that received MA alone or in combination exhibited significant sensitization. Letters indicate significant differences at p<.05.

Locomotor activity in the open field was measured by velocity and distance travelled.

Velocity was significantly increased by administration of active drug (smallest main effect,

F2,46 =28.28, p=.0001). Oxy1, oxy2, and MA-alone produced similar increases in velocity. Both drug combinations produced significantly greater velocity than either drug alone. . All drug conditions alsoproduced a dose-dependent increases in distance travelled (smallest main effect,

F2, 46 =25.15, p<.0001). Similar to the velocity data, both drug combinations produced significantly greater activity than each drug alone (largest p value for MA/oxy1 comparison, p=.003; MA/oxy2, p=.0001). Further, when comparing distance travelled in the center vs. periphery, distance in the center decreased with drug dose even as total distance increased

(Fig2c ).

4.3.4 Novel Object Recognition

Short-term memory was assessed during acute intoxication and one week later. There were no significant main effects of acute drug administration on the percent of time spent exploring the novel object (smallest main effect, F 2,45 =2.22, p=.12). However, similar to the activity data from open field, distance travelled was significantly affected by methamphetamine, oxycodone, and a significant interaction between the two (smallest main effect, F 2,46 =7.93, p=.001) .

There were also no significant main effects of drug treatment on memory one week later

(smallest main effect, F 2,44 =2.18, p=.13). Mixed model analysis indicated that there were no significant main effects of test (acute vs. one week later) or drug treatment on novel object recognition (smallest main effect, F 2,37 =2.0, p=.15).

Figure 2. Bar histograms display anxiety-like behavior (A), velocity (B), and distance travelled (C) of mice in the open field paradigm. Letters indicate significant differences. All post hoc tests are p < .05.

Figure 3. Bar histograms display the acute (A) and long-term (B) effects of drug administration on short-term memory measured by the novel object recognition paradigm. There were no significant acute or long-term effects of drug administration on this measure.

4.4 Discussion

The present results indicate that the reward value of MA and oxycodone were not altered when the drugs were administered together. Further, MA sensitization was not altered by the addition of oxycodone. There were no acute or long-term effects of either drug or their combination on short-term-memory. However, both doses of the drug combination produced significantly greater anxiety-like behavior compared to either drug alone. Similarly, both drug combinations significantly increased locomotor activity, measured by velocity and distance travelled, when compared to each drug alone. Importantly, these data provide the first empirical determination of the behavioral effects of the MA and oxy combination. The current data replicates and extends previous studies examining amphetamine –opioid combinations by

studying a new, popular stimulant-opioid combination. (Masukawa et al. 1993; Rowlett and

Woolverton 1997; Ranaldi and Munn 1998; David et al. 2001; Rowlett et al. 2005; Martin et al.

2006; Pereira et al. 2006; Rowlett et al. 2007; Lan et al. 2009).

4.4.1 Reward

As expected, both MA and oxycodone, when administered alone, produced significant increases in the ratio of time spent on the drug-paired side, interpreted as demonstrating drug- induced reward. This is consistent with a sizable literature showing that amphetamines and opioids reliably produce conditioned-place preference in rodents (Wongwitdecha and Marsden

1995; Suzuki et al. 1996; Tokuyama et al. 1996; Der-Avakian et al. 2007; Liu et al. 2009). To our surprise, however, the preference induced by MA was not altered by the addition of oxycodone. Our working hypothesis was that one reason for the popularity of stimulant-opioid combinations is because the combination enhances the rewarding effects compared to either drug alone. It is possible that the present range of MA-oxycodone doses examined were too narrow to observe enhancement of the rewarding effects of the drug combination. Other findings support this interpretation (Mello et al. 1995; Rowlett and Woolverton 1997; Duvauchelle et al. 1998;

Gaiardi et al. 1998; Negus et al. 1998; Rowlett et al. 1998; Ward et al. 2005; Lan et al. 2009).

For example, a report by Lan et al. 2009 compared place preference across 10 conditions that included three low doses of MA, morphine, and their combination. All drug conditions exhibited place preference, but a significant enhancement of CPP compared to each drug alone was observed at only one dose of the drug combination (MA: 0.75 mg/kg, morphine: 5 mg/kg).

Therefore, it is possible that an effect would have been observed if a broader range of doses were

examined. Future studies should assess wider dose range and also investigate drug self- administration to directly assess drug taking behavior.

Mice that received methamphetamine exhibited sensitization; however, the addition of oxycodone did not alter the effect. Further, oxycodone alone did not induce sensitization at either of the doses tested. This data is congruent with previous studies utilizing similar doses of methamphetamine- (Jing et al. 2014) and oxycodone-alone (Niikura et al. 2013).

4.4.2 Anxiety

We predicted that MA would increase anxiety-like behavior and that these effects would be offset when the drug was given in combination with oxycodone. This prediction was not borne out. While MA increased anxiety-like behavior, the drug combination produced sub- additive effects.

The current results are consistent with previous data indicating that MA is anxiogenic in open field and elevated plus maze measures of anxiety in rodents (Lapin 1993; Cancela et al.

2001; Hayase et al. 2005). These results are also consistent with a few human studies demonstrating that a stress test or administration of stress hormones mildly increases the dysphoric effects of amphetamines while producing no alteration in positive subjective effects and dampening some stimulant-like effects (Wachtel et al. 2001; Wachtel and de Wit 2001;

Harris et al. 2003; Soderpalm et al. 2003; Hearn et al. 2004). For example, Harris et al. 2003 administered 50 mg hydrocortisone to MA-experienced subjects approximately 1.5 hours prior to a 0.5 mg/kg IV infusion of MA. Participants reported significantly greater ratings of “bad drug

effect” than those that received placebo hydrocortisone, while ratings of “high” and “good drug effect” were unaltered (Harris et al. 2003).

Oxycodone was also moderately anxiogenic in the open field test. Specifically, the higher dose of oxycodone increased anxiety-like behavior. To our knowledge, this is this first data investigating the effects of oxycodone on anxiety-like behavior. At first glance, the current results are surprising given the multitude of studies demonstrating that similar opioids, such as morphine, are anxiolytic (Rex et al. 1998; Koks et al. 1999; Shin et al. 2003; Glover and Davis

2008; Rezayof et al. 2009). However, important pharmacological differences between oxycodone and other opiates may account for the observed anxiogenic effects. Unlike morphine, oxycodone acts on the kappa-opioid receptor in addition to the mu-opioid receptor, and it is postulated that oxycodone exerts some analgesic effects through this mechanism (Ross and

Smith 1997; Nielsen et al. 2007). Accumulating evidence suggests that kappa-opioid receptors are intrinsically involved in regulating the response to stress (for review, see Van't Veer and

Carlezon 2013). In humans, selective kappa-opioid agonists produce negative mood states including dysphoria and anxiety (Pfeiffer et al. 1986). Further, a number of animal studies have indicated that kappa-opioid antagonists reduce anxiety-like behavior caused by exposure to stress

(McLaughlin et al. 2003; Knoll et al. 2007; Land et al. 2008; Wiley et al. 2009; Carr et al. 2010;

Van't Veer et al. 2012). Indeed, kappa-opioid antagonists are currently undergoing Phase II clinical trials for the treatment of depression and anxiety (Harrison 2013). Taken together, this data suggests that rather than reducing the negative effects of the combined drugs, the MA/ oxycodone combination may instead increase susceptibility to negative affect following stressful situations.

4.4.3 Short-term memory

4.4.3.1 Acute effects

MA alone or in combination did not alter memory performance under the current conditions. We made no predictions about cognitive performance but were cognizant of the fact that drug-taking behavior is not only influenced by drug-related subjective effects but also by drug-induced performance effects. For example, data from laboratory studies of humans show that methamphetamine self-administration is enhanced under conditions that engender poor performance (Kirkpatrick et al. 2009). In addition, amphetamine misuse has dramatically increased among college students over the past decade (McCabe et al. 2006), presumably because of its performance-enhancing effects. However, the lack of observed performance effects on cognition is consistent with human data indicating that low doses of MA produces null effects or subtle increases in measures of memory (Weitzner 1965; Hurst et al. 1969; Rapoport et al. 1980; Kennedy et al. 1990; Soetens et al. 1993; Soetens et al. 1995; Mattay et al. 2000;

Breitenstein et al. 2004; Whiting et al. 2007; Hart et al. 2008; Whiting et al. 2008; Zeeuws et al.

2010; Kirkpatrick et al. 2012). This data is also consistent with human studies indicating that low doses of oxycodone produce null effects or subtle decreases in measures of memory (Zacny and

Gutierrez 2003; Friswell et al. 2008; Cherrier et al. 2009; Schoedel et al. 2010).

4.4.3.2 Long-term effects

We also measured memory performance 7 days after the last drug injection. There were no long-term effects of MA, oxycodone, or the drug combination on short-term memory. The current study adds to a handful of experiments that have measured the long-term effects of non-

neurotoxic doses of MA on novel object recognition (Belcher et al. 2006; Belcher et al. 2008;

Lee et al. 2011). These studies have produced mixed results, with some indicating null effects of

MA and others showing impairment. For example, Belcher et al. 2006 utilized a sensitizing regimen of 3mg/kg MA injections every 2 days for a total of 10 injections. Animals that received

MA showed significant memory disruptions one week after the last drug injection. This data is incongruent with the present results, which also utilized a sensitizing dose of MA. However, in

2008, Belcher et al. found that a 13-day escalating-dose plus binge MA regimen did not produce memory impairment (Belcher et al. 2008). Further studies will be needed to verify the cognitive effects of non-neurotoxic doses of MA in animal models.

4.4.4 Limitations

The present results should be interpreted within the context of several limitations. First, a single dose of methamphetamine was used in the current study. Future studies should utilize a wider range of MA and oxycodone doses. Second, we observed that approximately half of the animals that received the largest MA/ oxycodone drug combination did not interact with either object in the short-term memory task (Fig 3b). This may indicate that the larger dose of the drug combination produced a very strong effect that may have interfered with other behavioral measures as well. The current doses were chosen because they are near the threshold for inducing CPP. CPP has been induced with MA doses of 5 mg/kg and oxycodone up to 5 mg/kg

(Tokuyama, Takashi, and Kaneto 1996; Suzuki, Kishimoto, and Misawa 1996, Wongwitdecha and Marsdem 1995; Liu et al. 2009; Der-Avakian et al. 2007). We propose that future studies examining drug combinations use a broader range of low doses due to the stronger effects

produced by combining drugs. Finally, the observed increases in locomotor activity in the open- field paradigm may have confounded the measure of anxiety-like behavior. However, we propose that this is unlikely because MA and both doses of oxycodone exhibited similar levels of increased activity, but differed in their effects on measures of anxiety-like behavior.

4.4.5 Conclusions

The current study importantly contributed the first empirical determination of the behavioral effects of the MA and oxycodone combination. Many behavioral effects of MA were unaltered by the addition of oxycodone. Specifically, CPP and sensitization were not increased by the drug combination, indicating that there is not an increased abuse liability. Locomotor activity and stress-induced anxiety-like behavior showed a sub-additive drug interaction. Further, there were no acute or long-term effects of the drug combination on short-term memory. Taken together, this data provides some evidence suggesting that the MA/ oxycodone drug combination produced a stronger effect than either drug alone, although in a limited number of measures.

Table 1: Study design and representative dosing schedule for MA-alone

Week Monday Tuesday Wednesday Thursday Friday Saturday Sunday

Pre-Cond Pre-Cond 1 MA Sal MA Sal MA 1 2

Post-Cond Post-Cond Post-Cond Post-Cond NOR 2 Sal OF 1 2 3 4 acute NOR 3 long- term

MA= Methamphetamine (1 mg/kg) Sal= Saline Pre-Cond= Pre-Conditioning day OF= Open Field test Post-Cond=Post-Conditioning day NOR= Novel Object recognition test

Chapter 5: General Discussion

For the past 15 years, there has been considerable concern regarding the illicit use of marijuana, methamphetamine, and prescription pain relievers. However, several important gaps remain in our knowledge of these drugs. The current studies aim to address three of these gaps.

5.1 Marijuana, substance use and misuse, and mental health among undergraduates

Young adults aged 18-25 represent the largest drug-using population in the United States.

This age range also coincides with an important transition from adolescence to adulthood, with young people experiencing greater freedoms and responsibilities. Unfortunately, this time in life also coincides with the onset of many mental health disorders, making this a particularly vulnerable population. However, there are considerable gaps in our understanding of the relationship between marijuana use, mental health, and stress among young people. Study 1 examined the relationship between frequency of marijuana use and other substance use, binge drinking, negative consequences associated with drinking, mental health problems, and stress.

The results indicated that frequency of marijuana use was related to substance use, misuse, and depression and anxiety. Surprisingly, marijuana use was not related to stress. This data contributes to the field by indicating that marijuana use is indeed related to mental health within this population. This has important implications for university administrators and health professionals. Those wishing to prevent or treat substance use on campus should target efforts on students that present with mental health problems. Further, this is the first empirical data indicating that stress was not related to marijuana use. This is surprising, given that many young people report that they use drugs in order to help them cope with stress. It is possible that students commonly report drug use as stress-related coping because it is considered a socially acceptable reason for drug use. It is also possible that only a minority of students use marijuana

in order to cope, and that these students are at the greatest risk for substance abuse and mental health problems.

5.2 The acute effects of marijuana on cognitive performance during night shift work

Previous data indicates that several factors related to the person and environment in which a drug is taken can influence the acute effects. For example, regular marijuana smokers show widespread tolerance to the cognitive effects of marijuana. Further, data has indicated that the time of day of administration is an important modulator of drug effects. However, there have been few studies examining these effects in humans. Study 2 investigated the acute effects of marijuana during simulated night shift work in marijuana users. Results showed that smoked marijuana attenuated performance and mood during simulated night shift work. This data was surprising given that marijuana impairs performance during the day in well-rested individuals.

This data furthers the database by indicating that marijuana has cognitive-enhancing effects under certain conditions. Additional research will be needed in order to determine whether these effects were caused by circadian modulation of the stimulant-related effects of marijuana, or by residual effects on improved sleep.

5.3 Methamphetamine, oxycodone, and their combination

Finally, another important gap addressed by the present studies is the dearth of empirical information regarding the effects of methamphetamine when used in combination with oxycodone. Study 3 addressed this gap by beginning a systematic investigation of this combination utilizing a pre-clinical model with a wide range of cognitive and behavioral measures. Results indicated that the drug combination produced stronger effects than either drug

alone, although this was observed in a limited number of measures. As stimulant-opioid combinations are very popular among users, these data importantly provide the first empirical evidence of the effects of the methamphetamine-oxycodone combination.

5.4 A translational approach to substance abuse and dependence

The present studies utilized a translational, multilevel approach in order to better understand substance use as a whole. A challenge in the scientific community, and particularly in research regarding substance use, is the limited communication between scientists using different approaches. In particular, there is a lack of scientific discussion between pre-clinical and clinical researchers. This often results in the use of experimental measures that do not overlap and drug doses that do not translate well. For example, the utilization of neurotoxic doses in animal studies of drug use have negatively skewed our understanding of drug effects. In this paradigm, drug-naïve animals are repeatedly administered suprathreshold doses of MA designed to induce overdose. Under these conditions, it would be surprising if the resulting brain damage did not have serious repercussions on cognitive function and behavior. While this may be a useful method of brain perturbation, it is problematic to extrapolate these results to human drug dependence. First, humans typically use slowly escalating doses, which has been shown to provide neural protection to the toxic effects of high dose (Riddle et al. 2002; Thomas et al.

2005; O’Neil et al. 2006; Danacea et al. 2007). Secondly, accumulating evidence in humans suggests that long-term MA dependence does not cause severe brain damage resulting in cognitive deficits, but rather only subtly affects cognition in the long term (for review, see Hart et al. 2012). However, the pervasive view that MA is severely neurotoxic has been partially informed by animal studies utilizing neurotoxic dosing regimens. In some cases, phenomenon

observed in rodent models that have no observable human equivalency have spawned elaborate theories used to explain human drug use.

Taken together, this has important implications for future studies. First, it is of paramount importance that the results of animal studies are not over-extrapolated and do not overshadow or influence the interpretation of results from human studies. Future studies using animal models should also be carefully designed to mimic the human condition as closely as possible.

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