(FADS) Genetic Variants and Dietary Polyunsaturated Fatty Acid Intake: Associations with Negative Affect

Fatty Acid Desaturase (FADS) Genetic Variants and Dietary Polyunsaturated Fatty Acid Intake: Associations with Negative Affect

Dissertation

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

Liisa Hantsoo, M.A.

Graduate Program in Psychology

The Ohio State University

2012

Dissertation Committee:

Janice K. Kiecolt-Glaser, Ph.D., Advisor

Charles Emery, Ph.D.

Ruchika Prakash, Ph.D.

Copyrighted by

Liisa Hantsoo

2012

Abstract

Background: Depressive symptomatology has been associated with polyunsaturated fatty acid (PUFA) levels in diet and tissue. However, results have been mixed, and studies have failed to account for genetic factors that may influence such associations. Tissue PUFA levels are strongly influenced by elongase and desaturase activity, which are governed by the fatty acid desaturase (FADS) genes. Inefficient desaturase activity has been associated with depression. Further, FADS genotypes have been associated with neuropsychological phenotypes such as postpartum depression.

Given these associations, the study of relationships among dietary PUFA intake, FADS genotype, and mood symptoms is warranted. Aims: The present study investigated associations among dietary intake of PUFAs, the rs174575 polymorphism of the fatty acid desaturase (FADS2) gene, and psychological outcomes. The primary aim was to determine if FADS2 genotype interacts with omega-3 (n-3) and omega-6 (n-6) PUFA dietary intake to influence associations with depressive symptoms, anxiety symptoms, anger, hostility, neuroticism, or optimism. Methods: A sample of 172 female undergraduate students provided genetic material from buccal cells, and completed a food frequency questionnaire and self-report measures including the Center for

Epidemiological Studies Depression Scale, PROMIS Anxiety and Anger Scales, Cook

Medley Hostility Scale, NEO Neuroticism Scale, and LOT-R Optimism Scale. Results:

The main finding was that higher n-3 intake was associated with greater hostility. ii

However, n-3 intake was not related to depressive symptoms, anxiety, anger, neuroticism, or optimism, nor was n-6 intake. Further, genotype did not interact with PUFA intake for any psychological outcomes. Conclusion: This study provided no evidence that the rs174575 fatty acid desaturase polymorphism influences associations between PUFA intake and mood.

iii

Dedication

Dedicated to the mentors, friends, and loved ones who supported me along the way.

Acknowledgments

I would like to thank my advisor, Dr. Janice Kiecolt-Glaser, for her feedback and guidance. She encouraged my curiosity and supported my pursuit of this research project.

I would also like to thank my co-advisor, Dr. Charles Emery, for his helpful feedback and support during my time in graduate school. I also appreciate the guidance of Dr. Ruchika

Prakash, who has been a valuable member of my dissertation committee. A special thanks to my fellow lab members, Jean Philippe Gouin and postdoctoral fellows

Jeannette Bennett and Christopher Fagundes, for providing feedback on statistical analyses and manuscript drafts. Finally, this project would not have been possible without the expert technical guidance provided by Min Chen and Bryon Laskowski of Dr. Ronald

Glaser’s lab. Their patience and guidance, as I worked on the wetlab portion of my dissertation, was invaluable. I would also like to acknowledge the funding that I received to support this project; the American Psychological Association Division 38 Health

Psychology Graduate Student Research Award, and the Ohio State University Critical

Difference for Women Grant. The generous support of these agencies allowed this project to come to fruition, and I appreciate their support.

Vita

1979...... Born, Silver Spring, Maryland

2001...... B.A. Neuroscience, The Johns Hopkins

University

2005...... M.L.A. Individualized, University of

Pennsylvania

2007 …...... University Fellow, The Ohio State

University

2008...... Graduate Teaching Associate, Department

of Psychology, The Ohio State University

2010...... M.A. Clinical Psychology, The Ohio State

University

Publications

Kiecolt-Glaser, J.K., Gouin, J.P., Hantsoo, L. (2009). Close relationships, inflammation,

and health. Neurosci. Biobehav. Rev, 35(1), 33-38.

Wozniak, J., Mick, E., Waxmonsky, J., Kotarski, M., Hantsoo, L., Biederman, J. (2009).

Comparison of open-label, 8-week trials of olanzapine monotherapy and

topiramate augmentation of olanzapine for the treatment of pediatric bipolar

disorder. J Child Adolesc Psychopharmacol,19(5), 539-45.

Hanisch, L. H., Palmer S. C., Marcus, S. C., Hantsoo, L., Vaughn, D. J., & Coyne, J. C.

(2009). Comparison of objective and patient-reported hot flash measures in men

with prostate cancer. Journal of Supportive Oncology, 7(4), 131-135.

Bennett, I.M., Palmer, S.C., Nicholson, J., Hantsoo, L., Rinaldi, J., Coyne, J. (2009).

"One end has nothing to do with the other": Patient Attitudes Regarding Help

Seeking Intention for Depression in Gynecologic and Obstetric Settings. Archives

of Women's Mental Health, 12, 301–308.

Gouin, J.P., Hantsoo, L., Kiecolt-Glaser, J.K. (2008). Immune Dysregulation and Chronic

Stress Among Older Adults: A Review. Neuroimmunomodulation, 15(4-6), 251-

59.

Hanisch, L., Hantsoo, L., Freeman, E., Sullivan, G., Coyne, J. (2008). Hot Flashes and

Panic Attacks: A Comparison of Symptomatology, Neurobiology, Treatment, and

a Role for Cognition. Psychological Bulletin, 134(2), 247-69.

Kenen, R.H., Shapiro, P.J., Hantsoo, L., Friedman, S., Coyne, J. (2007). Women with

vii

BRCA1 or BRCA2 mutations renegotiating a post-prophylactic mastectomy

identity: self-image and self-disclosure. Journal of Genetic Counseling, 16(6),

789-98.

Wozniak, J., Biederman, J., Mick, E., Waxmonsky, J., Hantsoo, L., Best, C., Cluette-

Brown, J. & Laposata, M. (2007). Omega-3 fatty acid monotherapy for pediatric

bipolar disorder: A prospective open label trial. European

Neuropsychopharmacology, 17(6-7), 440-7.

Liang, J., Etheridge, A., Hantsoo, L., Rubinstein, A., Nowak, S., Halpern M.E. (2001).

Asymmetric Nodal Signaling in the Zebrafish Diencephalon Positions the Pineal

Organ. Development, 127, 5101-5112.

Fields of Study

Major Field: Psychology

viii

Table of Contents

Abstract……………………...……………………………………………….……………ii

Dedication………………...……...……………………………………………………….iv

Acknowledgments…………………...………………………………………..……..…….v

Vita…………………………………...………………………..………………………….vi

List of Tables………………………………………………………………………..…….x

List of Figures…………………………………………………………...………...……xxii

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

Chapter 2: Methodology………………………………………………………...……….43

Chapter 3: Results………………………………………………………...……………...62

Chapter 4: Discussion…………………………………………………………...…….…78

References…………………………………………………………...……………….…103

Appendix A: Tables and Figures…………………………………………………….…124

Appendix B: Recruitment Posting…………………………………………….……..…258

Appendix C: Measures……………………………………………..………………...…259

Appendix D: Abbreviations………………………………………………………….…277

List of Tables

Table 1. Sociodemographic Characteristics of Participants...... 124

Table 2. rs174575 Genotypes …………………………………………...……………..125

Table 3. Self-Reported Diet and Health Characteristics...... 126

Table 4. Nutritional Data from Food Frequency Questionnaire (Block FFQ)……..…..127

Table 5. Psychological Outcome Data...... 128

Table 6. Psychological Outcome Data: Cutoff Scores...... 128

Table 7. Between Group Differences by Race………………………………….………129

Table 8. Between Group Differences by Academic Year…………………………..…..130

Table 9. Between Group Differences by Genotype...... 131

Table 10. Between Group Differences by Genotype...... 131

Table 11. Between Group Differences in CES-D Score……………………….……….132

Table 12. Between Group Differences in PROMIS Anxiety SF Score…………..….…133

Table 13. Between Group Differences in PROMIS Anger SF Score…..………………133

Table 14. Between Group Differences in Cook Medley Hostility Scale Score….……..134

Table 15. Between Group Differences in NEO Neuroticism Scale Score……..……….134

Table 16. Between Group Differences in LOT-R Scale Score……………………...….135

Table 17. Bivariate Correlation Table……………………….……………………...….136

Table 18. Regression Table for Hypothesis 1 with CES-D Score as Outcome……...... 137

Table 19. Regression Table for Hypothesis 1 with PROMIS Anxiety SF Score as

Outcome………………………….……………………………………………..138

Table 20. Regression Table for Hypothesis 1 with PROMIS Anger SF Score as

Outcome……………..………………………………………………………….139

Table 21. Regression Table for Hypothesis 1 with Cook Medley Hostility Score as

Outcome…………..………………………………………………………….…140

Table 22. Regression Table for Hypothesis 1 with NEO Neuroticism Score as

Outcome……………….…………………………………………………….….141

Table 23. Regression Table for Hypothesis 1 with LOT-R Score as Outcome………...142

Table 24. Regression Table for Hypothesis 2 with CES-D Score as Outcome……...... 143

Table 25. Regression Table for Hypothesis 2 with PROMIS Anxiety SF Score as

Outcome……………………………………………………………………...…144

Table 26. Regression Table for Hypothesis 2 with PROMIS Anger SF Score as

Outcome……..……………………………………………….…………………145

Table 27. Regression Table for Hypothesis 2 with Cook Medley Hostility Score as

Outcome………...……………………………………………...……………….146

Table 28. Regression Table for Hypothesis 2 with NEO Neuroticism Score as

Outcome………...………………………………………………………………147

Table 29. Regression Table for Hypothesis 2 with LOT-R Score as Outcome…..…….148

Table 30. Regression Table for Hypothesis 3 with CES-D Score as Outcome………...149

Table 31. Regression Table for Hypothesis 3 with PROMIS Anxiety SF Score as

Outcome………………………………………………………………………...150

Table 32. Regression Table for Hypothesis 3 with PROMIS Anger SF Score as

Outcome………………………………………………………………………...151

Table 33. Regression Table for Hypothesis 3 with Cook Medley Hostility Score as

Outcome………………………………………………………………………...152

Table 34. Regression Table for Hypothesis 3 with NEO Neuroticism Score as

Outcome………………………………………………………………………...153

Table 35. Regression Table for Hypothesis 3 with LOT-R Score as Outcome………...154

Table 36. Regression Table for Hypothesis 3 with CES-D Score as Outcome………...155

Table 37. Regression Table for Hypothesis 3 with PROMIS Anxiety SF Score as

Outcome………………………………………………………………………...156

Table 38. Regression Table for Hypothesis 3 with PROMIS Anger SF Score as

Outcome………………………………………………………………………...157

Table 39. Regression Table for Hypothesis 3 with Cook Medley Hostility Score as

Outcome………………………………………………………………………...158

Table 40. Regression Table for Hypothesis 3 with NEO Neuroticism Score as

Outcome………………………………………………………………………...159

Table 41. Regression Table for Hypothesis 3 with LOT-R Score as Outcome………...160

Table 42. Regression Table for Exploratory Analysis 1 with CES-D Score as

Outcome………………………………………………………………………...161

xii

Table 43. Regression Table for Exploratory Analysis 1 with PROMIS Anxiety SF Score

as Outcome……………………………………………………………………...162

Table 44. Regression Table for Exploratory Analysis 1 with PROMIS Anger SF Score as

Outcome………………………………………………………………………...163

Table 45. Regression Table for Exploratory Analysis 1 with Cook Medley Hostility Score

as Outcome……………………………………………………………………...164

Table 46. Regression Table for Exploratory Analysis 1 with NEO Neuroticism Score as

Outcome………………………………………………………………………...165

Table 47. Regression Table for Exploratory Analysis 1 with LOT-R Score as

Outcome………………………………………………………………………...166

Table 48. Regression Table for Exploratory Analysis 2, Hypothesis 1 with CES-D Score

as Outcome…………………………………………………..…………….…...167

Table 49. Regression Table for Exploratory Analysis 2, Hypothesis 1 with PROMIS

Anxiety SF Score as Outcome……………………………………………..…...168

Table 50. Regression Table for Exploratory Analysis 2, Hypothesis 1 with PROMIS

Anger SF Score as Outcome…..……………………………………….…..…...169

Table 51. Regression Table for Exploratory Analysis 2, Hypothesis 1 with Cook Medley

Hostility Score as Outcome…………………………..………………………...170

Table 52. Regression Table for Exploratory Analysis 2, Hypothesis 1 with NEO

Neuroticism Score as Outcome………………………………………………...171

Table 53. Regression Table for Exploratory Analysis 2, Hypothesis 1 with LOT-R Score

as Outcome…………………………………………………………….….…...172

xiii

Table 54. Regression Table for Exploratory Analysis 2, Hypothesis 2 with CES-D Score

as Outcome……………………………………………………………………...173

Table 55. Regression Table for Exploratory Analysis 2, Hypothesis 2 with PROMIS

Anxiety SF Score as Outcome……………………………………………..…...174

Table 56. Regression Table for Exploratory Analysis 2, Hypothesis 2 with PROMIS

Anger SF Score as Outcome…..…………………………………….…….…...175

Table 57. Regression Table for Exploratory Analysis 2, Hypothesis 2 with Cook Medley

Hostility Score as Outcome…………………………..………………………...176

Table 58. Regression Table for Exploratory Analysis 2, Hypothesis 2 with NEO

Neuroticism Score as Outcome…………………………………………….…...177

Table 59. Regression Table for Exploratory Analysis 2, Hypothesis 2 with LOT-R Score

as Outcome…………………………………………………………….……....178

Table 60. Regression Table for Exploratory Analysis 2, Hypothesis 3 with CES-D Score

as Outcome………………………………………………………………..…...179

Table 61. Regression Table for Exploratory Analysis 2, Hypothesis 3 with PROMIS

Anxiety SF Score as Outcome………………………………………….……...180

Table 62. Regression Table for Exploratory Analysis 2, Hypothesis 3 with PROMIS

Anger SF Score as Outcome…..……………………………………..………...181

Table 63. Regression Table for Exploratory Analysis 2, Hypothesis 3 with Cook Medley

Hostility Score as Outcome…………………………..………………………...182

Table 64. Regression Table for Exploratory Analysis 2, Hypothesis 3 with NEO

Neuroticism Score as Outcome………………………………………………...183

xiv

Table 65. Regression Table for Exploratory Analysis 2, Hypothesis 3 with LOT-R Score

as Outcome……………………………………………………………………...184

Table 66. Regression Table for Exploratory Analysis 3, Hypothesis 1 with CES-D Score

as Outcome……………………………………………………………………...185

Table 67. Regression Table for Exploratory Analysis 3, Hypothesis 1 with PROMIS

Anxiety SF Score as Outcome……………………………………..…………...186

Table 68. Regression Table for Exploratory Analysis 3, Hypothesis 1 with PROMIS

Anger SF Score as Outcome…..…………………………………….……..…...187

Table 69. Regression Table for Exploratory Analysis 3, Hypothesis 1 with Cook Medley

Hostility Score as Outcome…………………………..………………………...188

Table 70. Regression Table for Exploratory Analysis 3, Hypothesis 1 with NEO

Neuroticism Score as Outcome……………………………………….………...189

Table 71. Regression Table for Exploratory Analysis 3, Hypothesis 1 with LOT-R Score

as Outcome……………………………………………………………………...190

Table 72. Regression Table for Exploratory Analysis 3, Hypothesis 2 with CES-D Score

as Outcome……………………………………………………………………...191

Table 73. Regression Table for Exploratory Analysis 3, Hypothesis 2 with PROMIS

Anxiety SF Score as Outcome………………………………………..………...192

Table 74. Regression Table for Exploratory Analysis 3, Hypothesis 2 with PROMIS

Anger SF Score as Outcome…..……………………………………...………...193

Table 75. Regression Table for Exploratory Analysis 3, Hypothesis 2 with Cook Medley

Hostility Score as Outcome…………………………..………………………...194

Table 76. Regression Table for Exploratory Analysis 3, Hypothesis 2 with NEO

Neuroticism Score as Outcome…………………………………………….…...195

Table 77. Regression Table for Exploratory Analysis 3, Hypothesis 2 with LOT-R Score

as Outcome……………………………………………………………………...196

Table 78. Regression Table for Exploratory Analysis 3, Hypothesis 3 with CES-D Score

as Outcome……………………………………………………………………...197

Table 79. Regression Table for Exploratory Analysis 3, Hypothesis 3 with PROMIS

Anxiety SF Score as Outcome…………………………………………..……...198

Table 80. Regression Table for Exploratory Analysis 3, Hypothesis 3 with PROMIS

Anger SF Score as Outcome…..………………………………..…….………...199

Table 81. Regression Table for Exploratory Analysis 3, Hypothesis 3 with Cook Medley

Hostility Score as Outcome…………………………..……………...………....200

Table 82. Regression Table for Exploratory Analysis 3, Hypothesis 3 with NEO

Neuroticism Score as Outcome………………………………………………....201

Table 83. Regression Table for Exploratory Analysis 3, Hypothesis 3 with LOT-R Score

as Outcome……………………………………………………………..……….202

Table 84. Regression Table for Exploratory Analysis 4, Hypothesis 1 with CES-D Score

as Outcome……………………………………………………………………...203

Table 85. Regression Table for Exploratory Analysis 4, Hypothesis 1 with PROMIS

Anxiety SF Score as Outcome………………………………………..………...204

Table 86. Regression Table for Exploratory Analysis 4, Hypothesis 1 with PROMIS

Anger SF Score as Outcome…..…………………………………….…..……...205

xvi

Table 87. Regression Table for Exploratory Analysis 4, Hypothesis 1 with Cook Medley

Hostility Score as Outcome…………………………..………………………...206

Table 88. Regression Table for Exploratory Analysis 4, Hypothesis 1 with NEO

Neuroticism Score as Outcome…………………………………………….…...207

Table 89. Regression Table for Exploratory Analysis 4, Hypothesis 1 with LOT-R Score

as Outcome……………………………………………………………………...208

Table 90. Regression Table for Exploratory Analysis 4, Hypothesis 2 with CES-D Score

as Outcome…………………………………………….………………………..209

Table 91. Regression Table for Exploratory Analysis 4, Hypothesis 2 with PROMIS

Anxiety SF Score as Outcome………………………………………………….210

Table 92. Regression Table for Exploratory Analysis 4, Hypothesis 2 with PROMIS

Anger SF Score as Outcome…..………………………………………………..211

Table 93. Regression Table for Exploratory Analysis 4, Hypothesis 2 with Cook Medley

Hostility Score as Outcome…………………………..………………………...212

Table 94. Regression Table for Exploratory Analysis 4, Hypothesis 2 with NEO

Neuroticism Score as Outcome……………………….………………………...213

Table 95. Regression Table for Exploratory Analysis 4, Hypothesis 2 with LOT-R Score

as Outcome……………………………………..…………………………..…...214

Table 96. Regression Table for Exploratory Analysis 4, Hypothesis 3 with CES-D Score

as Outcome……………………………………………………………………...215

Table 97. Regression Table for Exploratory Analysis 4, Hypothesis 3 with PROMIS

Anxiety SF Score as Outcome…………………………………………..……...216

xvii

Table 98. Regression Table for Exploratory Analysis 4, Hypothesis 3 with PROMIS

Anger SF Score as Outcome…..………………………………………...……...217

Table 99. Regression Table for Exploratory Analysis 4, Hypothesis 3 with Cook Medley

Hostility Score as Outcome…………………………..………………………...218

Table 100. Regression Table for Exploratory Analysis 4, Hypothesis 3 with NEO

Neuroticism Score as Outcome……………………………………….………...219

Table 101. Regression Table for Exploratory Analysis 4, Hypothesis 3 with LOT-R Score

as Outcome……………………………………………………………………...220

Table 102. Regression Table for Exploratory Analysis 5, Hypothesis 1 with CES-D Score

as Outcome……………………………………………………………………...221

Table 103. Regression Table for Exploratory Analysis 5, Hypothesis 1 with PROMIS

Anxiety SF Score as Outcome……………………………………..…………...222

Table 104. Regression Table for Exploratory Analysis 5, Hypothesis 1 with PROMIS

Anger SF Score as Outcome…..……………………………………...………...223

Table 105. Regression Table for Exploratory Analysis 5, Hypothesis 1 with Cook Medley

Hostility Score as Outcome…………………………………………..………...224

Table 106. Regression Table for Exploratory Analysis 5, Hypothesis 1 with NEO

Neuroticism Score as Outcome……………………………………….………...225

Table 107. Regression Table for Exploratory Analysis 5, Hypothesis 1 with LOT-R Score

as Outcome……………………………………………………………………...226

Table 108. Regression Table for Exploratory Analysis 5, Hypothesis 2 with CES-D Score

as Outcome……………………………………………………………………...227

xviii

Table 109. Regression Table for Exploratory Analysis 5, Hypothesis 2 with PROMIS

Anxiety SF Score as Outcome………………………………..………………...228

Table 110. Regression Table for Exploratory Analysis 5, Hypothesis 2 with PROMIS

Anger SF Score as Outcome…..……………………………………...………...229

Table 111. Regression Table for Exploratory Analysis 5, Hypothesis 2 with Cook Medley

Hostility Score as Outcome…………………………..………………………...230

Table 112. Regression Table for Exploratory Analysis 5, Hypothesis 2 with NEO

Neuroticism Score as Outcome…………………………………………….…...231

Table 113. Regression Table for Exploratory Analysis 5, Hypothesis 2 with LOT-R Score

as Outcome……………………………………………………………………...232

Table 114. Regression Table for Exploratory Analysis 5, Hypothesis 3 with CES-D Score

as Outcome……………………………………………………………………...233

Table 115. Regression Table for Exploratory Analysis 5, Hypothesis 3 with PROMIS

Anxiety SF Score as Outcome……………………………………..…………...234

Table 116. Regression Table for Exploratory Analysis 5, Hypothesis 3 with PROMIS

Anger SF Score as Outcome…..……………………………………...………...235

Table 117. Regression Table for Exploratory Analysis 5, Hypothesis 3 with Cook Medley

Hostility Score as Outcome…………………………..………………………...236

Table 118. Regression Table for Exploratory Analysis 5, Hypothesis 3 with NEO

Neuroticism Score as Outcome………………………………………….……...237

Table 119. Regression Table for Exploratory Analysis 5, Hypothesis 3 with LOT-R Score

as Outcome……………………………………………………………………...238

xix

Table 120. Regression Table for Exploratory Analysis 6, Hypothesis 1 with Cook Medley

Cynicism Subscale as Outcome…………………………………………..…...239

Table 121. Regression Table for Exploratory Analysis 6, Hypothesis 1 with Cook Medley

Hostile Attribution Subscale Score as Outcome………………………...……...240

Table 122. Regression Table for Exploratory Analysis 6, Hypothesis 1 with Cook Medley

Hostile Affect Subscale Score as Outcome…..………………………………...241

Table 123. Regression Table for Exploratory Analysis 6, Hypothesis 1 with Cook Medley

Aggressive Responding Subscale Score as Outcome……………………...…...242

Table 124. Regression Table for Exploratory Analysis 6, Hypothesis 1 with Cook Medley

Social Avoidance Subscale Score as Outcome………………………….……...243

Table 125. Regression Table for Exploratory Analysis 6, Hypothesis 2 with Cook Medley

Cynicism Subscale as Outcome………………………………………..…..…...244

Table 126. Regression Table for Exploratory Analysis 6, Hypothesis 2 with Cook Medley

Hostile Attribution Subscale Score as Outcome………………………..….…...245

Table 127. Regression Table for Exploratory Analysis 6, Hypothesis 2 with Cook Medley

Hostile Affect Subscale Score as Outcome…..………………………………...246

Table 128. Regression Table for Exploratory Analysis 6, Hypothesis 2 with Cook Medley

Aggressive Responding Subscale Score as Outcome………………...………...247

Table 129. Regression Table for Exploratory Analysis 6, Hypothesis 2 with Cook Medley

Social Avoidance Subscale Score as Outcome……………………….………...248

Table 130. Regression Table for Exploratory Analysis 6, Hypothesis 3 with Cook Medley

Cynicism Subscale as Outcome……………………………………..……..…...249

Table 131. Regression Table for Exploratory Analysis 6, Hypothesis 3 with Cook Medley

Hostile Attribution Subscale Score as Outcome…………………...…………...250

Table 132. Regression Table for Exploratory Analysis 6, Hypothesis 3 with Cook Medley

Hostile Affect Subscale Score as Outcome…..………………………………...251

Table 133. Regression Table for Exploratory Analysis 6, Hypothesis 3 with Cook Medley

Aggressive Responding Subscale Score as Outcome……………………...…...252

Table 134. Regression Table for Exploratory Analysis 6, Hypothesis 3 with Cook Medley

Social Avoidance Subscale Score as Outcome………………………….……...253

Table 135. Regression Table for Exploratory Analysis 7, Hypothesis 1 with Cook Medley

75th Percentile Probability as Outcome………………………………….....…...254

Table 136. Regression Table for Exploratory Analysis 7, Hypothesis 2 with Cook Medley

75th Percentile Probability as Outcome………………………………….……...255

Table 137. Regression Table for Exploratory Analysis 7, Hypothesis 3 with Cook Medley

75th Percentile Probability as Outcome…..…………………………...………...256

xxi

List of Figures

Figure 1. Proposed genotype by dietary n-3 interaction for depressive symptoms...... 257

Figure 2. Proposed genotype by dietary n-6 interaction for depressive symptoms...... 257

xxii

Chapter 1: Introduction

Depressive symptomatology has been associated with polyunsaturated fatty acid

(PUFA) levels in both diet and tissue (Appleton, Rogers, & Ness, 2008; Conklin,

Manuck, et al., 2007; Lin, Huang, & Su, 2010a). However, results have been mixed, and studies have failed to account for genetic factors that may influence such associations.

Tissue PUFA levels are only partially dependent on dietary intake of fatty acid precursors, and are strongly influenced by elongase and desaturase activity. The fatty acid desaturase (FADS) genes govern elongase and desaturase activity (Lattka, Illig, Heinrich,

& Koletzko, 2009a; Malerba et al., 2008; Rzehak et al., 2009; Schaeffer et al., 2006).

Remarkably, depressed individuals show evidence of inefficient desaturase activity

(Assies et al., 2010; Maes et al., 1999).

FADS genotypes have been associated with neuropsychological phenotypes such as postpartum depression, attention deficit hyperactivity disorder (ADHD) and IQ

(Brookes, Chen, Xu, Taylor, & Asherson, 2006; Caspi et al., 2007; Xie & Innis, 2008).

As omega-3 (n-3) and omega-6 (n-6) dietary precursors compete for the same desaturase enzymes, inefficient desaturase activity may exert varying effects depending on n-3 and n-6 levels in the diet. If diet is high in n-6 precursors, inefficient desaturase may prevent accumulation of n-6 product arachidonic acid (AA), which is associated with inflammation and depression (Conklin, Manuck, et al., 2007; Maes, Christophe, 1

Bosmans, Lin, & Neels, 2000; Pischon et al., 2003). In this manner, dietary intake may interact with genetic factors to influence disease outcome (Calder, 2006). Given these associations, examining relationships among dietary PUFA intake, FADS genotype, and mood symptoms is warranted.

The present research study investigated associations among dietary intake of

PUFAs, the rs174575 polymorphism of the fatty acid desaturase (FADS2) gene, and psychological outcomes. The primary aim was to determine if FADS2 genotype interacted with omega-3 (n-3) and omega-6 (n-6) PUFA dietary intake to influence associations with depressive symptoms, anxiety symptoms, anger, hostility, neuroticism, or optimism.

Polyunsaturated Fatty Acids

Polyunsaturated fatty acids (PUFAs) are aliphatic compounds containing two or more double bonds within a hydrocarbon chain, capped by a carboxyl group. PUFAs form an important component of the mammalian cell membrane’s phospholipid bilayer, influencing membrane fluidity and the behavior of membrane-bound receptors. The human body can synthesize a number of fatty acids, but cannot synthesize omega-3 nor omega-6 fatty acids.

Instead, these PUFAs must be obtained from the diet. The precursor components of these essential fatty acids are alpha-linolenic acid (ALA) for the omega-3 pathway and linoleic acid (LA) for the omega-6 pathway.

Biosynthesis of PUFAs.

Once ingested from dietary sources, the precursor essential fatty acids LA (n-6) and

ALA (n-3) are converted to long-chain PUFAs such as AA, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), by a desaturation and elongation process. In desaturation, a desaturase enzyme introduces a double bond into the fatty acid chain via removal of two hydrogen atoms. In elongation, an elongase enzyme inserts an ethyl group, lengthening the fatty acid chain. This process continues, lengthening the fatty acid chain from 18 carbons

(e.g. ALA) to 20 carbons (e.g. EPA) or more. The n-6 and n-3 pathways utilize and compete for the same desaturases and elongases (Sprecher, 2002).

Desaturation is the rate-limiting step in this cascade, controlled by desaturase enzyme activity. There are four desaturase enzymes in humans (Δ9desaturase,

Δ6desaturase, Δ5 desaturase, and Δ4 desaturase), which exert their effects at different steps of the desaturation-elongation cascade. Δ6desaturase and Δ5 desaturase are the rate- limiting enzymes in the conversion of the n-6 precursor LA and the n-3 precursor ALA into their long-chain products, n-6 AA and n-3 EPA and DHA (Nakamura & Nara, 2004;

Sprecher, 2002). Δ6desaturase acts first, converting LA to gamma-linolenic acid in the n-

6 pathway, and converting ALA to stearidonic acid in the n-3 pathway. After an elongation step, Δ5 desaturase catalyzes the formation of AA in the n-6 pathway and EPA in the n-3 pathway. At this point, AA and EPA are either converted into eicosanoids, or continue in the desaturation-elongation process, being converted to long-chain PUFAs such as docosahexaenoic acid. Δ5 desaturase and Δ6desaturase are found throughout the human body, but are in highest concentration in the brain, heart, and liver (Cho,

Nakamura, & Clarke, 1999; Nakamura & Nara, 2004).

In women, the conversion of ALA to EPA and DHA may be more efficient than in men, possibly due to estrogen levels (Burdge & Wootton, 2002). Women taking hormonal contraceptives containing 17-ethynylestradiol showed threefold greater DHA production than women not taking such contraceptives (Burdge & Wootton, 2002), and

17-ethynylestradiol administration in male to female transsexual individuals resulted in a

0.4% increase in DHA (Giltay, Gooren, Toorians, Katan, & Zock, 2004). However, the precise mechanism is not known (Koletzko, Lattka, Zeilinger, Illig, & Steer, 2011).

Biological Functions of PUFAs.

PUFAs have important influences on cell membrane structure and function, as well as inflammation. Membrane fluidity affects cells in tissues throughout the body, including the nervous system. n-6 PUFAs are straight-chained and stiff; in the cell membrane, they pack tightly together and hinder membrane fluidity, which may impair signal conductance in neurons (Lapillonne, Clarke, & Heird, 2003). However, n-3 PUFAs have a curvy molecular configuration, such that when they are contained in a neuronal cell membrane, gaps occur between adjacent molecules. These gaps increase cell membrane fluidity, which allows membrane-spanning ion channels to more easily undergo conformational change, thus enhancing cell-cell communication (Singer &

Nicolson, 1972). According to the membrane tension theory, relative concentrations of n-

3 and n-6 PUFAs in the neuronal cell membrane alter the tension it exerts on ion channels, thus influencing conformational change and conductance (Leaf, Xiao, & Kang,

2002). Numerous experimental studies support the conjecture that PUFA composition

4 affects a cell membrane’s structure and fluidity. In vitro, DHA increases cell membrane permeability (Ehringer, Belcher, Wassall, & Stillwell, 1990). Indeed, within the lipid bilayer, DHA in particular imparts cell membranes with the fluidity needed for proper functioning during axonal and synaptic growth (Hashimoto, Hossain, Shimada, & Shido,

2006; Suzuki, Park, Tamura, & Ando, 1998). In animals, supplementation with fish oil increased cell membrane fluidity (Tappia, Ladha, Clark, & Grimble, 1997). In humans, brain water proton transverse relaxation measurement reflected that n-3 PUFA supplementation increases neuronal membrane fluidity (Hirashima et al., 2004).

PUFA concentration in the neuronal cell membrane also influences activity of neurotransmitters including the monoamines and anandamide (Artmann et al., 2008;

Heron, Shinitzky, Hershkowitz, & Samuel, 1980; Innis, 2007). PUFAs in the neuronal membrane regulate serotonin (5-HT) reuptake and release (Block & Edwards, 1987) and regulate the rate-limiting enzyme in 5-HT synthesis, tryptophan hydroxylase (Crane &

Greenwood, 1987; Mullen & Martin, 1992) . PUFA deficiency is associated with increased monoamine oxidase (MAO) activity, indicating downregulation of 5-HT activity (Delion et al., 1997).

While AA-derived eicosanoids increase production of the proinflammatory cytokines interleukin 1 (IL-1), tumor necrosis factor alpha (TNF-α), and interleukin 6

(IL-6), the n-3 PUFAs downregulate production of these inflammation-promoting eicosanoids (Maes et al., 2000; Pischon et al., 2003). Specifically, AA in lymphocyte and monocyte membranes is converted to eicosanoids, reactive oxygen radicals, and proinflammatory adhesion molecules. Conversely, n-3 PUFAs such as EPA and DHA

5 are principally anti-inflammatory. EPA is a precursor of anti-inflammatory eicosanoids, including 3-series prostaglandins and 5-series leukotrienes (Yaqoob, 2003). At the cellular level, n-3 PUFAs regulate transcription factors such as nuclear factor kappa B

(NF-kB) involved in expression of proinflammatory genes (Deckelbaum, Worgall, &

Seo, 2006; Zhao, Joshi-Barve, Barve, & Chen, 2004), decrease synthesis of proinflammatory cytokines such as TNF-α (Caughey, Mantzioris, Gibson, Cleland, &

James, 1996; Zhao et al., 2004), and diminish expression of adhesion molecules at the cell surface which influence leukocyte accumulation at the site of inflammation (Caughey et al., 1996; Kim, Schmee, & Thomas, 1990).

PUFAs and Health

PUFAs serve myriad biological functions in the human body, and thus influence a host of diseases (Simopoulos, 2008). Indeed, PUFA composition of plasma or tissue has been associated with a variety of disorders, including cardiovascular disease (Albert et al., 2005; Baylin & Campos, 2004; Hu et al., 1999; Kark, Kaufmann, Binka, Goldberger,

& Berry, 2003; Psota, Gebauer, & Kris-Etherton, 2006), skin conditions (Calder, 2006), diabetes (Hodge et al., 2007; Laaksonen et al., 2002; L. Wang et al., 2003), and mood disorders (Tanskanen et al., 2001; Wozniak et al., 2007). Many of these conditions are related to either to cell membrane fluidity or inflammation.

Concentrations of AA, EPA, and DHA in the cell membrane affect membrane fluidity. In the nervous system, this may affect signal transduction, receptor activity, or neurotransmitter metabolism (Artmann et al., 2008; Chalon, 2006; Innis, 2007), which

6 may contribute to psychiatric disorders. As described, PUFAs also influence function of serotonin (5-HT), a neurotransmitter implicated in the pathogenesis of depression.

PUFAs are transported to the brain for incorporation into neuronal cell membranes. It was originally posited that precursors LA and ALA are transported across the blood-brain barrier, then converted to long-chain PUFAs in the brain (Bourre et al., 1992). However, evidence suggests that long-chain PUFAs are synthesized elsewhere in the body, then transported by the blood through the choroid plexus (Bourre et al., 1997). Thus, serum concentrations of PUFAs are likely to reflect levels of PUFAs in the brain (Maes et al.,

1999).

As PUFAs are eicosanoid precursors, molecules which are key in inflammatory processes, PUFAs may influence development and progression of cardiovascular disease

(Baylin & Campos, 2004; Kark et al., 2003; Renaud & Lanzmann-Petithory, 2001), or dermatological conditions such as eczema or atopic dermatitis (Duchen & Bjorksten,

2001). Increased serum levels of n-3 PUFAs (or, relatedly, low n-6 : n-3 ratios) are associated with lower proinflammatory cytokine production (Bouwens et al., 2009;

Kelley, Siegel, Fedor, Adkins, & Mackey, 2009; Kiecolt-Glaser et al., 2007; Maes et al.,

2000; Micallef, Munro, & Garg, 2009; Nelson & Hickey, 2004). In a large scale study, low plasma n-3 PUFA levels were associated with higher proinflammatory IL-6 and

TNF-α levels, and lower anti-inflammatory transforming growth factor beta (TGF-β) and

IL-10 levels, in a dose-dependent manner (Ferrucci et al., 2006) . The ATTICA study, which examined diet and health in over 3000 individuals, found that intake of over 150 grams of fish per week was associated with lower levels of CRP, IL-6, TNF- α, and

7 amyloid α (Zampelas et al., 2005). The Atherosclerosis Risk in Communities (ARIC) study found that dietary intake of n-3 PUFAs intake was negatively associated with fibrinogen, blood clotting factor VIII, and Von Willebrand factor (VWF) in both

Caucasians and African Americans, but had a positive association with the anticoagulant protein C in Caucasians only (Shahar et al., 1993)

Supplementing the diet with PUFAs also has beneficial health effects. In a study examining cardioprotective effects of PUFAs, 3 grams of ALA daily from flaxseed oil capsules produced a 60% increase in plasma EPA as well as increases in DPA, compared to an olive oil placebo group in a predominantly African-American sample with chronic illness. This demonstrated conversion of ALA to EPA and DPA in a minority population

(Harper, Edwards, DeFilippis, & Jacobson, 2006). In a sample of healthy volunteers in

Mexico, daily supplementation with 3 g/day of salmon oil was associated with reduced cholesterol and triglycerides (Carvajal & Angulo, 1997). Similarly beneficial impact of n-

3 supplementation on lipid profile was found in a population in India (Khandelwal et al.,

2009).

PUFAs in the Diet

In the American diet, the average intake of n-6 to n-3 fatty acids is 16:1, considerably higher than the estimated ancestral ratio of 4:1 (Eaton & Konner, 1985;

Simopoulos, 1999). This shift in ratio is likely due to a decrease in n-3 intake, combined with an increase in use of omega-6-rich vegetable oils. LA, of the n-6 family, is the most commonly consumed PUFA in the U.S. and is found in corn oil, safflower oil, sunflower

8 oil, and animal fat, as well as products made from these oils, such as margarines, spreads, or toppings. Soybean oil, while a source of both n-3 and n-6 PUFAs, contains more n-6 than n-3 in a ratio of 7:1. Soybean oil accounts for 75% of oil use in the U.S. (Raper,

Cronin, & Exler, 1992). Further, these n-6-rich oils are used in production of fast food items and convenience foods, consumption of which has increased drastically since the early 1900s (Raper & Marston, 1986). This increase in omega-6s in the diet has correlated with an increase in diseases linked to inflammation such as cardiovascular disease and depression.

Omega-3 fatty acids are found mainly in fish oils, leafy vegetables, and some seeds and nuts, such as flaxseeds and walnuts (Sijben & Calder, 2007). Research by the

U.S. Department of Agriculture reveals that the primary source of n-3 fat in the American diet is fish, accounting for 90% of EPA and 75% of DHA intake. In fact, annual per capita fish consumption in the United States increased from 12.1 pounds per capita in the

1930s to 18.6 pounds per capita per year in 1985, a point of peak consumption. This increase in fish consumption is likely due to increases in consumption of lean fish and shellfish (Raper et al., 1992). Despite increased per capita fish intake, per capita annual

EPA intake has decreased from the 1930s, which likely reflects a decrease in consumption of canned salmon, sardines, and cured fish. Conversely, DHA levels have risen per capita since the 1930s; canned tuna is a rich source of DHA, which may have contributed to this increase (Raper et al., 1992). Poultry is another important source of n-

3 PUFAs, particularly DHA. Poultry consumption has increased in recent decades, spurring an increase in the amount of DHA per capita consumption (Raper et al., 1992).

The n-3 PUFA precursor ALA is provided by plant sources, such as canola oil, walnuts, cottonseed based margarines, and beans. Average ALA consumption has increased from 1.5 g/day in the 1930s to 2.8 g/day in the mid-1980s (Raper et al., 1992).

The Institute of Medicine of the National Academy of Sciences released Dietary

Reference Intakes for Adequate Intakes for ALA in 2002, recommending 1.6 g/day for men and 1.1 g/day for women (2002).

The American Dietetic Association released evidence-based nutrition practice guidelines emphasizing the importance of n-3 fatty acids from plant and marine sources in a healthy diet (ADA, 2005). Recommendations from international bodies such as the

UK Scientific Advisory Committee on Nutrition and the World Health Organization, based on evidence from epidemiological studies and clinical trials, recommend 0.4 to 1 g/day combined EPA and DHA (Lichtenstein, 2006; WHO, 2003). However, NHANES reports that the average American n-3 intake is far below recommended ranges, with EPA at 0.04 g/day and DHA at 0.07 g/day, while n-6 intake is elevated at 15.9 g/day (Ervin,

Wright, Wang, & Kennedy-Stephenson, 2004). Indeed, the typical diet is skewed toward n-6 PUFAS, such as AA, found in meat, eggs, and offal. AA intake in Western diets averages 50-500 mg/day (Calder, 2008; Lagarde, 2008; Sala-Vila, Miles, & Calder,

2008). Research on populations in the Midwest indicates that that typical consumption of n-3 fatty acids is low due to infrequent fish consumption (Lewis, Widga, Buck, &

Frederick, 1995). A recent study showed that in a sample of young adults aged 20-30 in the Midwest, average intake of omega-3 PUFAs was 1.55, n-6 was 20.9, EPA was 0.065

10 and DHA was 0.165, and had an n-6:n-3 ratio similar to the 15:1 ratio typical of

American diets (McDaniel, Ahijevych, & Belury, 2010).

Geographical location and ethnicity may influence PUFA intake. In Black women living in South Africa, the breast milk of urban-dwelling women had higher levels of

ALA and eicosadienoic acid compared to rural women, whose diet was less Westernized, low in animal protein and fat and high in carbohydrate and fiber (van der Westhuyzen,

Chetty, & Atkinson, 1988). In addition to differences by geographical region in PUFA intake, there are also slight differences by racial group. The Atherosclerosis Risk in

Communities (ARIC) Study found that in Caucasian Americans, mean dietary intake of n-6 PUFAs was 7.99 grams per day (g/day) and mean n-3 PUFA intake was 0.22 g/day.

In African Americans, mean dietary intake of n-6 PUFAs was 7.05 grams per day (g/day) and mean n-3 PUFA intake was 0.32 g/day (Volcik, Nettleton, Ballantyne, &

Boerwinkle, 2008). In the large-scale CARDIA study, dietary intake of LA, ALA, AA,

EPA, and DHA was higher in African-American women than in Caucasian women when assessed by a food frequency questionnaire. The n-6: n-3 PUFA ratio was higher in

African-American women than in Caucasian women. Further, intake of fish rich in n-3 fatty acids was higher in African American subjects than Caucasian subjects (Iribarren et al., 2004).

Americans who consume a 2000 kilocalorie daily diet should take in 2178 mg per day of n-3 fatty acids, to attain 50% n-3s in tissue long-chain fatty acid composition

(Hibbeln, Nieminen, Blasbalg, Riggs, & Lands, 2006). Given that n-3 and n-6 PUFAs compete for the same metabolic pathways, this could be reduced to ~22 mg per day of n-

3 fatty acids if the amount of n-6 fatty acids, particularly LA, were reduced to less than

2% of daily caloric intake (Hibbeln et al., 2006). However, in the typical American diet, n-6-rich soybean oil delivers 20% of daily caloric intake, with 9% of calories from LA

(Gerrior & Bente, 2002).

In typical Western diets, intake of essential fatty acid precursors is 20-fold greater than their downstream counterparts. For instance, LA intake is 20 times greater than AA intake, and ALA intake is 20 times greater than EPA and DHA intake (Agriculture,

1997). These precursors are metabolized by desaturation and chain elongation into long- chain PUFAs. Thus, dietary LA provides a source of AA, and, similarly, ALA provides

EPA and DHA for incorporation into tissues. However, these processes may be less efficient than direct consumption of long-chain PUFAs such as AA, EPA or DHA

(Horrobin, 2001). For instance, intake of 50 grams of canola oil per day, which is converted to EPA and DHA (from ALA), is equivalent to one serving of oily fish per week (Welch et al., 2006b).

Dietary Intake of PUFAs: Correlations with Tissue Composition

Dietary PUFA deficiencies can profoundly alter the composition of tissue membranes, including those in the brain (Bourre et al., 1992). Various tissues respond differently to dietary changes; plasma levels reflect the past 1-2 weeks of diet, red blood cell membrane reflects the past 1-2 months, and adipose tissue reflects the past several years (Arab, 2003; Katan, Deslypere, vanBirgelen, Penders, & Zegwaard, 1997) . Serum and plasma phospholipid PUFA levels are particularly sensitive to dietary changes, while

12 erythrocyte membrane phospholipid PUFA levels reflect that of circulating lipoproteins, indicating long-term results of PUFA metabolism (Lattka, Illig, Heinrich, & Koletzko,

2009b).

As intake of both fatty acid precursors and fatty acids themselves can influence tissue PUFA levels, it is important to assess both intake of precursor fatty acids (e.g. linoleic and alpha-linolenic) and long-chain PUFAs (e.g. EPA and DHA, AA) of the n-3 and n-6 series. Such estimation can be performed with a detailed dietary questionnaire, or biomarkers such as blood or tissue PUFA levels. Dietary questionnaires, such as food frequency questionnaires and 24 hour food recalls, have been studied extensively to determine correlations between reported dietary intake and tissue PUFA levels in a number of ethnic groups. A large study in France found that percentage AA, LA, EPA, and DHA in plasma were significantly correlated with reported dietary intakes; highest correlations were for EPA and DHA (Astorg et al., 2009). In a sample of Norwegian men, n-3 PUFAs in adipose tissue and serum accurately reflected dietary intake of these fatty acids (Andersen et al., 1999). In a study of American men examining correlations between dietary PUFA intake (reported by FFQ) and PUFA tissue levels (adipose tissue and erythrocyte membrane), the correlation for EPA and DHA in Caucasians was higher than the correlation in African Americans, but this was not statistically significant

(Godley et al., 1996). The Multi-Ethnic Study of Atherosclerosis (MESA) study found that dietary nonfried fish intake, measured by a food frequency questionnaire, positively correlated with plasma EPA and DHA levels across Caucasian, Chinese American,

African American, and Hispanic participants (Chung et al., 2008).

Dietary intake does not always mirror tissue levels of PUFAs. In one sample, while dietary intake of LA, AA, EPA and DHA correlated with their respective plasma levels, ALA was not correlated with percentage ALA in plasma lipids (Astorg et al.,

2008). However, this study used a food frequency questionnaire based on the past 24 hours, which may have provided an attenuated measure of typical PUFA intake. Isotope- labeled ALA feeding studies indicates a large range of ALA-to-EPA conversion across subjects, from 0.2 to 21% (Burdge, 2004). In one study, consumption of fish and fish oil supplements explained only 20-25% of the plasma phospholipid composition (Welch et al., 2006b). Other studies have shown high concentrations of n-3 PUFAs in individuals who did not consume fish (Andersen, Solvoll, & Drevon, 1996; Welch et al., 2006a).

This underscores the fact that it is not absolute dietary intake, but how the body processes fatty acids in the diet, e.g. by desaturases and elongases, which influences tissue PUFA levels (Malerba et al., 2008; Simopoulos, 2010).

FADS Genes

The desaturation process is modulated by Δ5 and Δ6desaturase, which are coded for by the fatty acid desaturase genes, FADS1 and FADS2, respectively. These genes were characterized in the human genome in 2000, mapping to chromosome 11q12-13.1

(Marquardt, Stohr, White, & Weber, 2000). FADS1 and FADS2 are clustered in a segment on chromosome 11. FADS1 spans a 17.2 kb region, FADS2 spans a 39.1 kb region. The two genes have similar intron and exon organization, are tightly linked (Schaeffer et al., 2006), and exhibit a high level of sequence homology (Marquardt et al., 2000). The FADS genes are

14 in high linkage disequilibrium, indicating that they are closely associated such that particular haplotypes are preserved (Lattka et al., 2010).

Given the importance of the Δ5 and Δ6desaturase in biosynthesis of long-chain

PUFAs from dietary precursors, the FADS genes encoding these enzymes became ideal candidates to study genetic influences on desaturase activity and tissue PUFA levels (Lattka et al., 2009b). High precursor and low product levels indicate inefficient desaturase activity, which may be due to low transcription levels or low conversion rate by desaturase (Lattka et al.; Martinelli et al., 2008).

FADS Genotype and PUFA Composition in Tissues.

FADS genotype accounts for upwards of 28% of the variability observed in tissue fatty acid levels (Lattka et al., 2010; Schaeffer et al., 2006). An initial analysis of single nucleotide polymorphisms (SNPs) within the FADS1 and FADS2 genes found strong associations between FADS SNPs and serum phospholipid PUFA levels (Schaeffer et al.,

2006). Minor allele carriers of these SNPs had higher levels of ALA, LA, eicosadienoic acid, dihomo-gamma-linolenic acid (DGLA), and lower levels of EPA, docosapentaenoic acid

(DPA), gamma-linolenic acid (GLA), AA, and adrenic acid (Schaeffer et al., 2006). These results reveal that minor allele carriers have an accumulation of desaturase precursors, and a deficit of desaturase products, indicating low desaturase activity.

Associations among FADS SNPs and PUFA levels were replicated in studies encompassing a number of ethnic groups, with tissues including plasma, erythrocyte membrane, and breast milk phospholipids. One replication examined 13 FADS SNPs, and

15 found results similar to those of Schaeffer (Schaeffer et al., 2006) in serum phospholipids and erythrocyte membranes. That is, minor allele carriers had higher serum levels of precursor substances LA and ALA, but lower levels of AA product (Malerba et al., 2008). Another replication found similar associations for plasma and erythrocyte membrane PUFA levels in minor allele carriers (Rzehak et al., 2009). In the plasma phospholipids and erythrocytes of women carrying FADS minor alleles, precursor n-6 LA levels were high, while product n-6

AA levels were low. This was mirrored in n-3 precursors and products, wherein minor allele carriers showed higher precursor ALA levels and lower n-3 product levels, including EPA and DHA. Minor allele carriers also showed lower levels of n-3 and n-6 desaturation products in breast milk (Lattka et al., 2011), including rs174575 minor allele carriers (Xie &

Innis, 2008). The imbalance in product: precursor ratios indicates inefficient desaturase activity in FADS minor allele carriers. Most recently, the Invecchiare in Chianti

(InCHIANTI) study, a large scale genome-wide association study in 1075 subjects, demonstrated strong associations among FADS polymorphisms and plasma levels of AA, eicosadienoic acid (EDA), and EPA, with minor allele carriers showing lower levels (Tanaka et al., 2009). Similar results reflecting inefficient desaturase activity in minor allele carriers have been demonstrated in an Asian population (Kwak et al., 2011) and a geographically isolated population of European descent in the United States (Tangier Island, Virginia)

(Mathias et al., 2010). In young adults between 20 and 29 years, FADS minor allele carriers also showed lower desaturase activity compared to major allele homozygotes (Merino et al.,

2011).

While there is a clear relationship between FADS SNPS and tissue levels of PUFAs, studying the desaturase activity associated with these polymorphisms in humans is difficult.

Desaturase activity is typically measured using measurement of rate of conversion of a radiolabeled PUFA precursor to its product (Harnack, Andersen, & Somoza, 2009); ethical concerns prohibit this in humans (Simopoulos, 2010). Instead, researchers approximate

FADS activity by measuring product-to-precursor ratio, wherein high precursor to product ratio indicates inefficient desaturase activity (Bokor et al., 2010; Martinelli et al., 2008).

FADS Genes and Psychological Outcomes.

Postmortem brain tissue studies reveal that individuals with major depression, bipolar disorder, or schizophrenia had higher levels of FADS2 mRNA in the prefrontal cortex compared to healthy controls (McNamara et al., 2009; McNamara & Liu, 2011). Further, a genetic microarray study found downregulation of FADS1 gene activity in postmortem prefrontal cortex tissue of men with MDD who committed suicide (Lalovic, Klempan,

Sequeira, Luheshi, & Turecki, 2010).

Interactions Between FADS Genotype and Dietary PUFA Intake.

Paradoxically, high desaturase activity can exacerbate either a proinflammatory (n-6 skewed) or anti-inflammatory (n-3 skewed) profile, dependent upon diet. In populations eating a typical Western diet, characterized by high levels of n-6 PUFAs, high desaturase activity promotes heightened levels of the n-6 product AA, which itself is a precursor for proinflammatory eicosanoids. However, individuals who consume high levels of n-3 PUFAs

17 and have high desaturase activity would favor an anti-inflammatory eicosanoid profile

(Simopoulos, 2010). Thus, FADS major allele carriers, who appear to have high desaturase activity, may be at heightened risk for n-6:n-3 imbalance if they consume a diet high in n-6

PUFAs. However, if they consume a diet high in n-3s, they may be at lower risk for chronic diseases associated with altered polyunsaturated fatty acid metabolism or n-6:n-3 ratio, such as coronary heart disease (CHD) or depression. In a population feeding on an n-6-rich

Western diet, the detrimental proinflammatory effects are likely to predominate (Martinelli et al., 2008; Simopoulos, 2006).

Interactions Among FADS Genotype, Dietary PUFA, and Disease Phenotypes.

Given the strong associations between FADS polymorphisms and PUFA tissue levels,

FADS1 and FADS2 SNPs are likely candidates in diseases involving inflammation or altered n-6:n-3 ratios (Simopoulos, 2010). For instance, the prevalence of inflammation-based disorders such as allergic rhinitis and eczema is lower in carriers of FADS SNPs associated with lower AA concentrations (Glaser, Heinrich, & Koletzko, 2010; Schaeffer et al., 2006).

FADS SNPs have also been associated with psychiatric disorders, such as postpartum depression (Xie & Innis, 2008) and ADHD (Brookes et al., 2006).

Numerous studies have demonstrated interactions between genotype and diet on disease risk. Carriers of a rare variant of the human 5-lipoxygenase gene (5-LO) had increased risk for atherosclerosis, but this was dependent on diet: the rare variant in combination with high AA dietary intake produced high risk of atherosclerosis, while the same variant in combination with a high EPA or DHA intake actually blunted atherosclerosis

18 risk (Dwyer et al., 2004). Similarly, women with the AA genotype of the 5-lipogenase- activating protein gene (ALOX5AP) who were in the top quartile of LA consumption showed higher breast cancer risk, but in women with lower LA consumption, genotype was not associated with breast cancer risk (J. Wang, John, & Ingles, 2008). In men with prostate cancer who carried the rs4648310 variant of the cyclooxygenase-2 (COX-2) gene, high n-3

PUFA intake was associated with decreased risk of aggressive cancer; men with low n-3

PUFA intake and this genetic variant had the most aggressive tumors, compared to men with the same variant and high n-3 PUFA intake (Fradet, Cheng, Casey, & Wittel, 2009). Another polymorphism in the COX-2 gene, rs5275, showed a strong inverse association between the rare variant and prostate cancer, but only in men with high fatty fish intake (Hedelin et al.,

2007). FADS genes show similar interactions. The FADS SNP rs174546 major allele (C) was associated with higher high density lipoprotein (HDL) cholesterol in individuals with a high dietary intake of n-6 PUFAs, but not in individuals with low n-6 intake (Lu et al.).

FADS Polymorphism rs174575.

The rs174575 SNP occurs in a highly linked region of the FADS gene which ranges from rs174537 to rs509360 (Tanaka et al., 2009). SNPs in this region show strong evidence for association, with a particularly strong association among rs174575, rs174553, rs174583, and rs99780, which occur in a highly preserved single linkage disequilibrium block

(Marquardt et al., 2000; Nakamura & Nara, 2004). This region contains a haplotype block associated with plasma and erythrocyte PUFA levels (Rzehak et al., 2009; Schaeffer et al.,

2006). That is, the rs174575 major (C) allele is in linkage with the FADS1 and FADS2 major

19 alleles associated with more efficient PUFA processing (Simopoulos, 2010), and duplicates information contained in its neighboring SNPs (Molto-Puigmarti et al., 2010).

Association studies have linked rs174575 to PUFA concentrations in plasma, erythrocytes, and breast milk, as well as neuropsychological phenotypes such as postpartum depression and IQ, making it an ideal candidate for examining psychiatric outcomes linked to

PUFA levels, such as depression. Minor allele homozygotes (GG) for rs174575 showed lower AA, EPA, and DHA in breast milk, and higher n-6 precursor-to-product ratio in plasma and erythrocytes, indicating less efficient conversion of precursor PUFAs to long- chain products than major allele carriers (Xie & Innis, 2008). In addition to lower AA phospholipid concentrations, minor allele (G) carriers also showed lower rates of depression both during and after pregnancy, compared to major allele homozygotes (CC) who showed a high rate of postpartum depression (Xie & Innis, 2009). This supports the correlation between low n-6 PUFA levels (or, high n-3 PUFA levels) and low rates of depression. In this case, the inefficient desaturase activity in FADS minor allele carriers was protective, preventing accumulation of proinflammatory AA and perhaps guarding against depression, although diet was not assessed in this study.

In addition to association studies, some studies have explored gene-diet interactions involving rs174575, with outcomes such as breast milk composition and IQ. In a sample of

309 women who underwent genotyping of the rs174575 SNP, food frequency questionnaires were used to assess fish and fish oil intake, examining the interaction between rs174575 and diet on breast milk composition (Molto-Puigmarti et al., 2010). In minor allele homozygotes

(GG), DHA levels were lower in plasma phospholipids and breast milk. However, in major

20 allele carriers, (GC, CC), increasing dietary fish / fish oil intake increased DHA proportions in breast milk. In minor allele homozygotes (GG), low DHA levels were not boosted by increased dietary fish / fish oil intake. This reflects that in major allele carriers, who are efficient at converting n-3 and n-6 precursors to products, a high level of n-3 in the diet is beneficial, resulting in enhanced EPA and DHA levels in tissue. However, in minor allele carriers, who have less efficient desaturase activity, enhanced n-3 in the diet does not necessarily translate to enhanced n-3 levels in tissue.

In a large scale study by Caspi and colleagues, carriers of the rs174575 major (C) allele whose diet included PUFA-rich breast milk showed a 6 to 7 point advantage in IQ over

C-carriers who did not consume breast milk (Caspi et al., 2007). In minor allele homozygotes

(GG), IQ was not influenced by breast milk intake, indicating the low-efficiency desaturase minor allele individuals, there was no advantage to consuming breast milk, but no disadvantage from not consuming it. A similar study found that GG children who consumed breast milk had no advantage compared to breastmilk fed infants of other genotypes, while

GG children who consumed formula had lower IQs than children of other genotypes who consumed formula (Steer, Smith, Emmett, Hibbeln, & Golding). Indeed, another study found that breastfed children’s IQs were not influenced by genotype, while GG fed formula as infants had lower IQs than CG or CC infants fed formula milk (Steer, Davey Smith, Emmett,

Hibbeln, & Golding, 2010). These studies did not account for PUFA composition of breast milk or formulas consumed, which may explain the discrepancy in results. There were also differences between studies in length of breastfeeding, or breastfeeding followed by formula feeding. Others propose that these results merely reflect a differential likelihood of

21 breastfeeding as a function of parental education, and not an interaction effect between rs174575 and breastfeeding on IQ (Martin et al., 2011).

PUFAs and Mood

Depression.

The PUFA hypothesis of depression posits important relationships between PUFA intake and depression (Lin et al., 2010a). Large-scale epidemiological studies across many nations indicate an inverse relationship between annual fish consumption and major depression prevalence. In populations with high fish (i.e. n-3 PUFA) consumption, depression rates tend to be lower when adjusting for other factors (Hibbeln & Salem, 1998;

Tanskanen et al., 2001). There are three main lines of evidence for the relationship between

PUFAs and depression: observational or epidemiological studies in which correlations between usual dietary intake of PUFAs and depressive symptoms or syndrome are studied, clinical studies in which this relationship is examined in individuals diagnosed with depression, and randomized controlled trials in which subjects are provided omega-3 supplements (open-label trials) or placebo (placebo controlled trials) taken over the course of weeks or months.

Depressive symptoms are quite prevalent in the general population, and often lead to functional impairment and increased risk for developing major depression (Cuijpers,

1998; Kessler, Zhao, Blazer, & Swartz, 1997). A study in older adults found a strong negative association between fish intake measured by a food frequency questionnaire and depressive symptoms measured by a self-report depression scale (Bountziouka et al.,

2009). Results showed that one portion of fish consumption per week was associated with a 0.58 times lower risk of scoring above the clinical threshold for depressive symptomatology. In a large sample of Finnish adults, those who consumed fish less frequently had a higher likelihood of having depressive symptoms (Tanskanen et al.,

2001). A prospective study of the Northern Finland 1966 Birth Cohort looked for associations between Hopkins Symptom Checklist-25 depression subscale or doctor- diagnosed lifetime depression and fish consumption during the previous 6 months

(Timonen et al., 2004). Results indicated that among females, those who consumed fish rarely had 2.5-fold increased risk of depression compared with regular fish eaters. Also, the risk of suicidal ideation increased by 1.5-fold in rare female fish eaters compared to regular female fish eaters.

While a number of dietary association studies have focused on European populations, particularly in Scandinavia (Tanskanen et al., 2001; Timonen et al., 2004), several studies have examined associations between PUFA intake and depressive symptoms in more diverse populations. In a sample of older Hispanic American adults, depressed individuals had lower consumption of n-3-rich foods, compared to nondepressed individuals (Fitten et al., 2008). In a case-control study of Chinese individuals, suicide attempters had low erythrocyte EPA levels compared to controls

(Huan et al., 2004). In a population of healthy Australian adults, a traditional diet including fish was associated with a lower likelihood of depression and anxiety, compared to a Western diet characterized by high amounts of fried foods (Jacka et al.,

2010).

However, there are mixed findings. One study found that neither the proportion of

EPA nor that of DHA was directly correlated with the mental component of a quality of life scale, the Short Form 36 Health Survey (SF-36). However, there was a significant positive trend in mental well-being across quintiles of the ratio of EPA:AA (Crowe, Skeaff, Green, &

Gray, 2007). Further work showed that fish consumption was positively associated with higher self-reported mental health status on the SF-36 (Silvers & Scott, 2002). Comparing those who ate fish versus those who never ate fish, fish consumers had an 8.2 point advantage for the SF-36 mental health scale and a 7.5 point advantage for the mental component score. However, another study found no significant associations between the mental component score of the SF-36 and PUFA plasma concentrations, nor with fish consumption, but did find an association between fish consumption and the physical component score after correcting for PUFA levels (Schiepers, de Groot, Jolles, & van Boxtel,

2010). In fact, a recent review shows mixed results in studies examining associations between fish intake and depressive symptoms, with about half of the studies included showing an inverse relationship between fish intake and depressive symptoms, and half showing no significant relationship (Murakami & Sasaki, 2010).

In addition to examining correlations between dietary n-3 PUFAs and mood in nonclinical populations, tissue levels of PUFAs have also been correlated with mood in these populations. In healthy community-dwelling adults with no Axis I psychopathology, higher

AA serum levels and AA:EPA ratio, adjusted for age, gender, and race, were associated with greater depressive symptomatology as measured by the Beck Depression Inventory (Conklin,

Manuck, et al., 2007). Higher plasma levels of DHA, but not other n-3 or n-6 PUFAS, were

24 associated with lower BDI scores in a sample of healthy adults (Appleton, Gunnell, et al.,

2008). A recent meta-analysis found that compared to control subjects, subjects diagnosed with depression had low levels of EPA, DHA, and total n-3 PUFAs in serum and erythrocyte membranes (Lin et al., 2010a). Individuals experiencing depressive symptoms who fell above the clinical threshold of depression on the CES-D showed a negative correlation between

DHA and CES-D score (Schiepers, de Groot, Jolles, & van Boxtel, 2009). Among moderately to severely depressed individuals, there was a positive correlation between AA:

EPA ratio and severity of depressive symptoms as measured by the Hamilton Depression

Rating Scale (Adams, Lawson, Sanigorski, & Sinclair, 1996).

In a study comparing healthy controls with depressed subjects, erythrocyte membrane n-3 PUFA levels were low in the depressed subjects compared to healthy controls. Further, severity of depression, measured by Beck Depression Inventory score, correlated negatively with erythrocyte membrane n-3 PUFA levels and with dietary intake of n-3 PUFAs

(Edwards, Peet, Shay, & Horrobin, 1998). In fact, compared to controls, individuals diagnosed with major depression had significantly lower adrenic acid, GLA, DPA, EPA, and higher AA: EPA ratios (Maes et al., 1999). While levels of precursors (ALA, LA) were normal in depressed subjects, the elongation and desaturation products were low, suggesting, according to Maes, a desaturase defect.

In a case-control study, plasma and erythrocyte levels of long-chain (chain length of

20 or greater carbon atoms) saturated fatty acids, monounsaturated fatty acids and PUFAs were lower in depressed subjects than in controls (Assies et al., 2010). Further, concentrations of short chain fatty acids were significantly higher in these depressed subjects,

25 indicating inefficient desaturation and elongation of fatty acids in depressed subjects. Indeed, estimated enzymatic activity, based on ratios of certain PUFAs, suggested altered desaturase and elongase activity in depressed subjects. The authors conclude that fatty acid status of individuals with MDD differs from that of healthy controls, and may be due to differences in diet and/or changes in synthesizing enzyme activities in the elongase-desaturase pathway

(Assies et al., 2010).

Postmortem studies reveal similar n-3 deficiencies in brain tissue of depressed individuals. In postmortem frontal cortex tissue of individuals with major depressive disorder

(MDD), DHA levels were reduced 22% compared to controls (McNamara et al., 2007).

Individuals with MDD or bipolar disorder had higher AA: DHA ratios in postmortem anterior cingulate cortex (ACC) tissue, compared to healthy controls (Conklin et al., 2010).

This relationship appeared to be influenced by age, but only in the mood disordered subjects.

There was a negative correlation between age and ACC tissue EPA and ALA levels in mood disordered subjects, but not in healthy controls.

Clinical trials of n-3 supplementation also provide evidence for relationships between n-3s and positive mood, and n-6s and negative mood. In healthy adults, n-3 supplementation increased positive mood, and decreased negative mood states including anger, anxiety, and depression (Fontani et al., 2005a). In depressed individuals, n-3 PUFA supplementation provided significant improvement in depression in three of four randomized clinical trials

(Hallahan & Garland, 2005). A meta-analysis found that n-3 PUFA supplementation significantly improved depressive symptoms in subjects with mood disorders, with a moderate effect size (Lin & Su, 2007). Indeed, it seems that n-3 supplementation may exert

26 its strongest effect in individuals with major depressive disorder, while having less robust effects in healthy individuals. A review of randomized controlled trials analyzed 35 n-3 trials, and suggested that, compared to placebo, n-3 PUFAs have a beneficial effect on depressed mood (Appleton, Rogers, & Ness, 2010). Intriguingly, larger effects were found in trials with participants who had more severe depressive symptoms. There were also more robust effects in individuals diagnosed with a depressive disorder, versus those who merely exhibited some depressive symptoms.

Experimental feeding studies in animals suggest that such reduction in depressive symptoms may be mechanistically due to increased brain-derived neurotrophic factor

(BDNF) and dendritic arborization, which are positively associated with n-3 intake (Calderon

& Kim, 2004; Kawakita, Hashimoto, & Shido, 2006). Conversely, feeding female cynomolgus monkeys an atherogenic diet that mirrored a typical Western diet (equivalent to human consumption of 500 mg cholesterol per day), 42.4% of calories as fat, and n-6:n-3 ratio of 25:1) over 27 months resulted in 42% of the monkeys developing depressive behavior. Among the most depressed monkeys, circulating n-6 PUFAs, particularly LA, were higher than n-6 levels in nondepressed monkeys (Chilton et al., 2011). Dietary depletion studies in rats, in which n-3 PUFAs are eliminated from the diet, result in reduced monoaminergic function (Delion, Chalon, Guilloteau, Besnard, & Durand, 1996); (Zimmer et al., 2002) and behavioral indices of depression and aggression (DeMar et al., 2006).

Additionally, rats fed a low n-3 diet exhibited increased IL-6, TNF-alpha and CRP production, as well as upregulated n-6 fatty acid biosynthesis, increased membrane AA

27 composition, and increased regional brain 5-HT turnover, consistent with findings in depressed humans (McNamara et al., 2009).

However, not all studies have reliably linked PUFA levels, either endogenous or supplemented, with depressive symptoms (Appleton, Gunnell, et al., 2008; Doornbos et al.,

2009). Two meta-analyses failed to find a significant antidepressant effect of n-3 supplementation on mood (Appleton et al., 2006; Rogers et al., 2008). Given these inconsistent results, and the strong associations between FADS polymorphisms and levels of

PUFAs, SNPs in the FADS1 and FADS2 gene region are likely candidates in diseases involving alterations in concentrations of omega-3 or omega-6 fatty acids, such as depression.

Depression in Women.

Depression prevalence in women is nearly twofold that of men (Paykel, Brugha,

& Fryers, 2005; Rorsman et al., 1990). The National Comorbidity Survey found that based on DSM criteria, women had a 21% lifetime prevalence of a major depressive episode, while men had below 13% prevalence (Blazer, Kessler, McGonagle, & Swartz,

1994). Indeed, a replication of the National Comorbidity Survey showed a 1.7 odds ratio of DSM-defined lifetime history of depression in women, compared to 1.0 for men

(Kessler et al., 2003). In young adults aged 15-24 years, the prevalence of past year major depression is 16% in females and 9% in males, and prevalence of past month major depression is 8% in females and 3.8% in males (Kessler & Walters, 1998). Women may be particularly sensitive to the effects of n-3 levels. A large longitudinal study of 3317 young adults found that those in the highest quintiles of EPA, DHA, and EPA plus DHA

28 intake over 10 years had fewer depressive symptoms at year 10 as measured by the CES-

D, and that these associations were more pronounced in women (Colangelo, He,

Whooley, Daviglus, & Liu, 2009). Indeed, while two studies found overall inverse associations between fish consumption or n-3 PUFA intake and risk of depressive symptoms or psychiatric illness, these effects were driven by a strong effect in women

(Sanchez-Villegas et al., 2007; Tanskanen et al., 2001). A birth cohort study found that individuals who consumed fish rarely had 2.5-fold increased risk of depression and 1.5- fold increased risk of suicidal ideation, but only in women (Timonen et al., 2004). In postmortem frontal cortex tissue of individuals with major depressive disorder (MDD), women with MDD had a significantly greater deficit in tissue DHA concentration than male subjects with MDD. Women showed a 32% DHA deficit compared to controls, while men showed only a 16% DHA deficit (McNamara et al., 2007). In a test of gene- diet interaction, Moltó-Puigmartí et al (2010) found that breast milk DHA proportions were lower in female rs174575 minor allele carriers than in major allele homozygotes.

Further, DHA proportions in breast milk increased with fish and fish-oil intake only in major allele carriers. These results suggest that women with the minor allele of rs174575 may have limited capacity to incorporate PUFAs. Indeed, dietary factors may interact with genotype and gender (Ordovas, 2007). For instance, the Framingham Heart Study found that higher PUFA intake benefited carriers of the APOA1 A allele in women only

(Ordovas et al., 2002).

Neuroticism.

The generally inverse association between negative emotion and n-3 PUFA status also occurs at the trait level. Neuroticism is a personality trait associated with negative affectivity and risk for depression (Khan, Jacobson, Gardner, Prescott, & Kendler, 2005;

Krueger, 1999). Neuroticism is defined by a tendency to experience negative emotions, and typically individuals high in neuroticism perceive the world as threatening and distressing

(Jacobs et al., 2006). Neuroticism is characterized by ineffective coping mechanisms and strong negative emotions in reaction to stress, which may lead to the development of depression. Neuroticism was positively associated with AA and AA:EPA ratio in serum of healthy controls, while low neuroticism was associated with high serum EPA levels. Further analyses revealed all six of the Neuroticism subscale facets covaried positively with the

AA:EPA ratio. In this study, PUFA serum levels explained 4.2% to 9.6% of the variance in neuroticism (Conklin, Manuck, et al., 2007). This held true in a similar study in which DHA and EPA covaried inversely with NEO-Neuroticism scores (Conklin, Harris, et al., 2007). In sum, research suggests an inverse association between n-3 PUFAs and neuroticism, and a positive association between n-6 PUFAs and neuroticism, a trait measure of negative affectivity.

Anxiety.

Although the literature on PUFAs and anxiety is not as extensive as that for depression, associations between anxiety and dietary PUFA intake as well as tissue PUFA levels have been demonstrated. Individuals with social anxiety show higher n-6 PUFA and lower n-3 PUFA levels in erythrocytes than controls. In fact, severity of social anxiety

30 symptoms was positively correlated with n-6 levels and negatively correlated with n-3 levels

(Green et al., 2006). Randomized controlled trials have shown anxiety reduction via n-3

PUFA dietary supplementation. In a group of substance abusers who received n-3 supplementation over 3 months, anxiety scores decreased compared to individuals receiving a placebo pill. Larger decrements in anxiety symptomatology were associated with an increase in EPA in tissue, but were not significantly associated with an increase in DHA tissue levels (Buydens-Branchey, Branchey, & Hibbeln, 2008). One study demonstrated self- reported reductions in anxiety in a sample of young adults suffering from test-taking anxiety after a 3-week course of mixed ALA and LA, compared to placebo (Yehuda, Rabinovitz, &

Mostofsky, 2005). In addition to reduced test anxiety, symptom reduction occurred for poor appetite, low mood, concentration, fatigue, academic organization, and sleep difficulty.

Interestingly, a number of these symptoms are also characteristic of depression. Another study found that healthy adults taking n-3 PUFAs for 35 days showed reduced anxiety on the

Profile of Mood States scale (POMS), and that this correlated negatively with AA:EPA ratio

(Fontani et al., 2005b). Overall, findings indicate an inverse association between n-3 PUFAs and anxiety, and a positive association between n-6 PUFAs and anxiety.

Hostility and Anger.

Hostility refers to a traitlike negative orientation toward interpersonal interactions, characterized by enmity, denigration, and ill will (Smith, 1992). The main facets of hostility include cynicism and mistrust of others (T. Q. Miller, Smith, Turner, Guijarro, & Hallet,

1996). Hostility may be manifested in behaviors such as direct challenge, indirect challenge,

31 or withdrawal (Haney et al., 1996). Anger, in contrast, is a state characteristic encompassing a constellation of behavioral, cognitive, and physiological responses. Despite these differences, there are associations among hostility, aggression, and anger (Ramirez &

Andreu, 2006). Further, both hostility and anger have been linked to PUFA tissue levels and intake.

PUFA tissue levels correlate with hostility. In unmedicated individuals with schizophrenia, erythrocyte EPA and DHA, and the ratio of EPA:AA, was negatively correlated with a hostility score after adjustment for age and sex. AA was positively correlated with hostility scores (Watari, Hamazaki, Hirata, Hamazaki, & Okubo, 2010). In a sample of men with antisocial personality disorder, low plasma level DHA was associated with greater hostility (Virkkunen, Horrobin, Jenkins, & Manku, 1987).

Dietary association studies also show correlations between PUFA intake and hostility.

The CARDIA study, a longitudinal study of over 3500 young adults in the U.S., found that higher dietary intake of n-3 PUFAs, measured by a food frequency questionnaire, was correlated with lower hostility levels, while AA intake was positively, albeit weakly, associated with higher hostility as assessed by the Cook–Medley Scale (Iribarren et al.,

2004). Participants who reduced levels of n-6 in their diet over the course of five years showed decreased levels of aggressive hostility and depressive symptoms, as measured by the Hopkins Symptom Checklist. Aggressive hostility is characterized by angry outbursts and urges to injure others, and this decreased with decreasing dietary n-6 over time. While this study did not assess blood or tissue levels of PUFAs, the decreases in aggressive hostility and depressive symptoms were associated with reductions in cholesterol (Weidner, 1992).

These findings are also reflected in randomized controlled trials. In young adults, supplementation of DHA with fish oil capsules reduced hostility during high stress periods by 3%, while unsupplemented controls showed a 25% increase in hostility levels during high stress periods (Hamazaki, Itomura, Sawazaki, & Nagao, 2000). A study in older Thai adults by the same researchers revealed similar relationships. Those who received DHA supplementation for 2 months showed decreased aggression toward others when subjected to a stressor, compared to those receiving placebo pills. Interestingly, this held true only in urban white-collar workers in the sample, and was not significant among rural dwellers in the sample (Hamazaki et al., 2002). In a sample of women diagnosed with borderline personality disorder, EPA supplementation for 2 months was associated with decreases in aggression, hostility, and severity of depressive symptoms as assessed with a clinician- administered scale (Zanarini & Frankenburg, 2003).

Similar results are found for anger. Compared to those receiving placebo, young adults taking n-3 PUFAs for about 1 month showed reduced anger in the Profile of Mood

States test (POMS), which correlated negatively with AA:EPA ratio (Fontani et al., 2005b).

In a sample of substance abusers, those who received n-3 supplementation over a 3 month period showed decreases in POMS anger scores. This decrease in anger correlated with decreased n-6 in tissue. Magnitude of reduction in anger score was also associated with magnitude of increase in n-3 PUFA tissue levels, particularly DHA and DPA, over the 3 month supplementation period. This suggests that there was no ceiling effect in this study, that is, all subjects had low n-3 PUFA levels at baseline, and only those who showed large changes in tissue n-3 levels also showed reductions in anger (Buydens-Branchey et al.,

2008). Again, research suggests an inverse association between n-3 PUFAs and the negatively-valenced psychological outcomes of anger and hostility, and a positive association between n-6 PUFAs and anger and hostility.

Optimism.

While depression, anxiety, hostility, anger, and neuroticism are forms of negative affectivity, optimism is a form of positive affectivity. Optimism is a traitlike tendency to expect positive outcomes (Schweizer, Beck-Seyffer, & Schneider, 1999), and is associated with effective coping mechanisms in times of stress (Billingsley, Waehler, & Hardin, 1993;

Catanzaro, Wasch, Kirsch, & Mearns, 2000) as well as psychological well-being (Scheier,

Carver, & Bridges, 1994) and physical health (Peterson, Seligman, & Vaillant, 1988;

Raikkonen, Matthews, Flory, Owens, & Gump, 1999).

In a sample of young adults, fish intake as assessed by the FFQ was positively correlated with optimism, measured by the Life Orientation Test-Revised (LOT-R). This relationship was only significant in women, and not in men (Kelloniemi, Ek, & Laitinen,

2005). In a cross-sectional sample of older adults, individuals within the highest tertile of n-3 intake as measured by the FFQ had high dispositional optimism on the 4Q scale (consisting of 4 items: “I still expect much from life,” “I do not look forward to the years to come,” “My days seem to pass by slowly,” “I am still full of plans”). However, this relationship did not hold true for optimism assessed by the LOT-R. Also, while EPA and DHA intake were associated with optimism, fish intake itself was not related to dispositional optimism (van de

Rest et al., 2009). A similar longitudinal study of older adults examined relationship

34 between diet and dispositional optimism, assessed using the same 4-item questionnaire. Total

PUFA intake from fish was not associated with dispositional optimism, but consuming ≥400 mg of combined EPA and DHA per day from any source was associated with higher dispositional optimism, compared to individuals consuming less than 400 mg/day, after controlling for sociodemographic factors (Giltay, Geleijnse, Zitman, Buijsse, & Kromhout,

2007). Thus, for optimism, a positively-valenced psychological outcome, research suggests a positive association with n-3 PUFA levels.

Biological Mechanisms Linking PUFAs and Mood

There are two major biological pathways hypothesized to underlie the relationship between PUFAs and mood: neuronal cell membrane dysfunction, and inflammation. Each may provide a unique contribution to the depressive phenotype; neuronal membrane dysfunction may affect mood and cognitive symptoms of depression, while inflammation may underlie physical symptoms of depression (Su, 2008).

Neuronal Cell Membrane, Neurotransmitter Function, & Depression.

As described, alterations in PUFA levels in cell membranes have profound influences on neuronal function (Farooqui, Hirashima, & Horrocks, 1992). The n-6: n-3 imbalance in depression may indicate changes in the signal transduction process, harkened by changes in cell membrane composition (Horrobin, 2001). Particularly salient for mood disorders such as depression is monoamine function. In piglets, n-3 deficient diets are associated with lower brain levels of serotonin (Owens & Innis, 1999). Serotonin and norepinephrine metabolism

35 and transmission are also affected by neuronal membrane PUFA levels, and may contribute to the mood and cognitive aspects of depression (Lin, Huang, & Su, 2010b). Deficits in DHA are associated with dysfunction in neuronal membrane stability, and neurotransmission of serotonin, norepinephrine, and dopamine (Chalon, 2006; Horrobin & Bennett, 1999; Su,

Huang, Chiu, & Shen, 2003). Chronic n-3 deficiency increases 5-HT2 receptor density and decreases dopamine receptor 2 (D2) density in the frontal cortex (Berg, Maayani, & Clarke,

1996; Chalon et al., 1998; Chalon, Vancassel, Zimmer, Guilloteau, & Durand, 2001; Delion et al., 1994). In an animal model of depression, rats showed increased concentration of AA in brain tissue, ranging from 21-31% (Green, Herman, & Yadid, 2005). Conversely, high plasma n-3 PUFA levels are associated with high cerebrospinal fluid (CSF) concentrations of the 5-HT metabolite 5-hydroxyindoleacetic acid (5-HIAA) in healthy subjects (Hibbeln et al.,

1998), and PUFA supplementation increased cerebrospinal fluid 5-HIAA concentrations

(Nizzo et al., 1978). As low levels of 5-HIAA are associated with hostility and depression, this suggests a possible biological mechanism for the link between n-3 PUFAs and depressive symptoms or hostility (Horrobin, 2001).

Inflammation, PUFAS, and Depression.

The cytokine hypothesis of depression posits that inflammatory molecules mediate development of depressive symptoms (Dantzer, O'Connor, Freund, Johnson, & Kelley, 2008;

Loftis, Huckans, & Morasco, 2009; A. H. Miller, Maletic, & Raison, 2009). Key evidence for the cytokine hypothesis of depression is sickness behavior, characterized by symptoms such as fatigue, psychomotor slowing, insomnia, musculoskeletal pain, and poor concentration in

36 humans and animals when infected, or when administered prostaglandins or proinflammatory cytokines such as interferon alpha (Capuron & Miller, 2004; Dieperink, Willenbring, & Ho,

2000; Konsman, Parnet, & Dantzer, 2002; Raison et al., 2005). Somatic symptoms of depression in humans include changes in sleep and appetite, low energy, fatigue, inability to concentrate, loss of interest in the surroundings, and somatic symptoms such as muscle or joint aches, indicating that there may be a connection between inflammation and depression.

Bacterial endotoxin lipopolysaccharide administration in humans increases inflammation, accompanied by a rise in negative mood symptoms, which has been used as a model of depression (DellaGioia & Hannestad, 2010). Administration of fish oil attenuates this response, halving TNF-α production (Pluess et al., 2007). In mice, dietary n-3 supplementation attenuated bacterial endotoxin-induced sickness behaviors (Watanabe,

Kanada, Takenaka, & Hamazaki, 2004). In rats, interleukin 1 beta (IL-1β) induced sickness behavior was attenuated by a diet enriched with EPA but not by a diet rich in n-6s (Song,

Leonard, & Horrobin, 2004). Accordingly, at the molecular level, EPA and DHA suppress

IL-1β transcription, but AA does not attenuate the transcription of this proinflammatory molecule.

Eicosanoids (prostaglandins, prostacyclins, thromboxanes and leukotrienes) are signaling molecules that regulate inflammation, and are derived from PUFAs. AA is the precursor for the prostaglandin 2 series, thromboxanes, and the leukotriene 4 series. These

AA-derived eicosanoids increase the production of proinflammatory TNF-α, IL-1, and IL-6

(Ferrucci et al., 2006) and mediate symptoms of cytokine-induced sickness behavior

(Dantzer, 2005; Mahony & Tisdale, 1989; Milton, 1989). Dietary supplementation with

PUFAs alters endogenous metabolism of prostaglandins. Enrichment of the diet with AA increases glucocorticoid and prostaglandin E2 (PGE2) secretion, and also increases anxiety behavior in rodents (Song, Li, Leonard, & Horrobin, 2003). EPA is the precursor for the prostaglandin 3 series and the leukotriene 4 series. EPA counteracts the proinflammatory effects of AA, reducing PGE2 synthesis or its activation by IL-1β (James, Gibson, &

Cleland, 2000; Song et al., 2004).

In sum, a high dietary n-6: n-3 ratio skews cytokine production toward a proinflammatory profile, and increased proinflammatory cytokines may potentiate depressive symptoms. In tissue, low plasma n-3 PUFA levels have been associated with higher proinflammatory IL-6 and TNF-α levels, and lower anti-inflammatory TGF-β and

IL-10 levels (Ferrucci et al., 2006). In humans, low levels of the n-3 PUFA docosapentaenoic acid in erythrocyte membranes is associated with high levels of IL-6, a proinflammatory cytokine (Yao, Sistilli, & van Kammen, 2003). However, dietary supplementation with n-3 PUFAs such as ALA reduces serum levels of IL-6, CRP, TNF-

α, IL-1β, and serum amyloid A (Caughey et al., 1996; Rallidis et al., 2003). As n-6: n-3 levels and depressive symptoms work synergistically to enhance proinflammatory cytokine production (Kiecolt-Glaser et al., 2007), enhanced proinflammatory cytokine production may increase risk of inflammation-related disorders in individuals with depressive symptoms who consume an n-6 heavy diet (Kiecolt-Glaser, 2010).

To conclude, depressive symptomatology is associated with PUFA status in both nonclinical and clinical populations. In community-dwelling adults, higher serum AA

38 levels and higher AA:EPA ratio were associated with greater depressive symptomatology on the BDI (Conklin, Manuck, et al., 2007), while plasma levels of the n-3 DHA were inversely associated with BDI scores in a sample of healthy adults (Appleton, Gunnell, et al., 2008). Meta-analysis reveals that depressed individuals have low levels of EPA,

DHA, and total n-3 PUFAs in serum and erythrocyte membranes (Lin & Su, 2007).

Further, depressed individuals showed normal precursor n-3s and n-6s PUFAs, but elongation and desaturation products were low, suggesting a desaturase defect (Maes et al., 1999). Indeed, a case-control study found that plasma and erythrocyte concentrations of short chain fatty acids were significantly higher, while levels of long-chain PUFAs were lower in depressed subjects compared to controls, suggesting inefficient desaturase activity in depressed individuals (Assies et al., 2010). Thus, depressed individuals show both low n-3 products and high PUFA precursors. The differences in fatty acid status of individuals with depression may be due to differences in diet, altered enzyme activity in the elongase-desaturase pathway, or a combination of these.

Indeed, associations between depressive symptoms and dietary PUFA intake have been mixed, indicating the influence of some other factor, such as desaturase activity. The

FADS genotype accounts for upwards of 28% of the variability observed in tissue fatty acid levels (Lattka et al., 2009b; Schaeffer et al., 2006). Carriers of the minor alleles of a number of FADS SNPs had higher levels of precursor PUFAs ALA and LA, and lower levels of product PUFAs EPA, DPA, GLA, AA, and adrenic acid in serum phospholipids and erythrocyte membranes (Malerba et al., 2008; Rzehak et al., 2009; Schaeffer et al., 2006;

Tanaka et al., 2009). These findings indicate that minor allele carriers have an accumulation

39 of desaturase precursors, and a shortage of desaturase products, suggesting low desaturase activity. Depending on diet, low desaturase activity in FADS minor allele carriers may be protective. If diet is high in n-6 precursors, inefficient desaturase may prevent accumulation of AA, which is associated with depression (Conklin, Manuck, et al., 2007). In a sample of women, rs174575 minor allele (G) carriers showed lower AA phospholipid concentrations and lower rates of depression compared to major allele homozygotes (CC) (Xie & Innis,

2009).

The considerable variability in desaturase-mediated PUFA conversion rates between individuals (Emken, Adlof, & Gulley, 1994) affects product-to-precursor ratio, which may in turn influence disease phenotypes. In a study of coronary artery disease, patients within the highest tertile of AA:LA (product-to-precursor ratio) had double the high-sensitivity C- reactive protein (hs-CRP) concentrations of patients in the lowest tertile, indicating that a

20% difference in desaturase activity accounted for a 100% increase in the inflammatory marker hs-CRP (Martinelli et al., 2008). Indeed, in a number of disease phenotypes, dietary intake interacts with genetic factors to influence disease outcome (Calder, 2006; Simopoulos,

2008). For instance, in celiac disease, dietary gluten appears to interact with genetic susceptibility to influence disease occurrence, with research suggesting that SNPs of the human leukocyte antigen (HLA) class II genes are necessary but not sufficient for the phenotypic manifestation of celiac disease (Liu, Rewers, & Eisenbarth, 2005). In terms of

PUFA intake, the FADS SNP rs174546 major allele (C) was associated with higher HDL cholesterol in individuals with a high dietary intake of n-6 PUFAs, but not in individuals with

40 low n-6 intake (Lu et al., 2010). Such findings urge further study of interaction between genetic and nutritional factors in disease etiology.

The Present Study

The relationship between FADS genotype and tissue PUFA levels indicates that it is not only diet, but genetic factors, that influence tissue levels of PUFAs. This establishes a clear biological connection between genotype and physiology. A key component in examining genetic interactions is first establishing a clear biological mechanism connecting the genotype of interest and phenotype (Moffitt, 2006). This study aims to assess the relationships among dietary PUFA, genetic factors, and depressive symptoms.

Hypotheses

(1) It was expected that dietary n-3 PUFA intake would be inversely associated with depressive symptomatology, anxiety, anger, hostility, and neuroticism, and positively associated with optimism. (2) Comparing rs174575 major allele homozygotes to minor allele carriers, I predicted that individuals who have FADS2 polymorphisms previously associated with lower plasma levels of EPA and DHA (that is, minor allele carriers) would have higher levels of depressive symptomatology, anxiety, anger, hostility, and neuroticism, and lower optimism scores than major allele homozygotes. (3)

I hypothesized that the association between n-3 PUFA intake and negatively valenced psychological outcomes (depressive symptoms, anxiety, anger, hostility, and neuroticism) would be negative in major allele homozygotes. The association between n-6 PUFA

41 intake and negatively valenced psychological outcomes (depressive symptoms, anxiety, anger, hostility, and neuroticism) would be positive in major allele homozygotes. (Please see Appendix A for a figure illustrating the proposed interactions.)

Chapter 2: Methodology

Participants

Participants were female Ohio State University undergraduate students enrolled in an introductory psychology course, who were recruited from the Research Experience

Program (REP). Participants were recruited via the REP website (http://rep.psy.ohio- state.edu/), where a brief description of the study was posted (Appendix B). Inclusion criteria were female gender and age of at least 18 years. Participants from the REP program do not receive financial compensation, but participation in REP counts for approximately 10% of a student’s total grade in the Introductory Psychology 100 course.

Protocol

Study Visit Overview

Study sessions occurred twice per week, with enrollment of up to 22 participants per session. Each subject participated in a single study visit, lasting approximately 90 minutes. After providing informed consent, participants completed a series of paper and pencil measures (Appendix C), described below. After one hour had passed since the study visit began, or when the subject completed the questionnaires, whichever occurred last, subjects provided a buccal cell sample via mouthwash rinse (described in further

43 detail below). The buccal cell sample provided DNA for genotyping (described in further detail below).

Measures

Self-Report Questionnaires.

Background Information.

Background questions collected data on age, ethnicity, and education level. As medications such as antidepressants may influence depressive or anxious symptomatology, medication use was also assessed by self report. To briefly assess

PUFA supplement use, subjects were asked about their intake of fish oil and flax oil supplements. To assess history of depression, subjects responded yes or no to the question, “Have you ever been diagnosed with depression, and/or treated for depression with medication or therapy?”

Center for Epidemiological Studies Depression Scale (CES-D).

The Center for Epidemiological Studies Depression Scale (CES-D) is a brief 20- item measure that assesses current depressive symptoms. Participants rated the frequency of symptoms occurring in the past week, such as depressed mood, feelings of helplessness and hopelessness, worthlessness, and somatic symptoms such as low energy, changes in sleep, and shifts in appetite. Possible scores on the CES-D range from 0 to 60, with a higher score indicating more depressive symptomatology. The CES-D has been used widely in research studies as a measure of depressive symptomatology, particularly

44 in studies on PUFAs and depression (Murakami & Sasaki, 2010). The CES-D is also well established in young adults. In a sample of college students, the CES-D demonstrated satisfactory levels of specificity and positive predictive value for current, past year, and lifetime depressive disorder consistent with the clinician-administered Diagnostic

Interview Schedule-IV (Shean & Baldwin, 2008b). The CES-D has acceptable test-retest reliability and excellent construct validity (Radloff, 1977). The test-retest reliability is

0.54 over a 4-week interval. The CES-D has good discriminative validity, accurately distinguishing depressed from non-depressed participants in community and clinical samples (Radloff, 1977). Research-based population norms provide CES-D cutoffs for varying levels of depression, such that outcomes can be dichotomous or categorical.

Epidemiological research provides severity cutoff scores for nondepressed (0–15), mild

(16–20), and moderate (21–30) severity. These cutoffs have been validated for identifying clinically significant levels of depression in both community and clinical populations (Prescott et al., 1998; Shean & Baldwin, 2008a; Weissman, Sholomskas,

Pottenger, Prusoff, & Locke, 1977).

In this sample, total CES-D scores were calculated, and then categorized according to the nondepressed, mild, and moderately depressed cutoffs. To ensure a range of depressive symptomatology within the sample, after the first 80 subjects I assessed the number of subjects scoring 16 or above (mild cutoff) on the CES-D. As the percentage of subjects scoring above this clinical cutoff was greater than 42%, no efforts were needed to increase the number of subjects with depressive symptomatology via

45 prescreening. The subject pool was continually monitored to ensure adequate numbers of subjects scoring above and below the CES-D clinical cutoff.

PROMIS Questionnaires.

The Patient-Reported Outcomes Measurement Information System (PROMIS) is an NIH initiative to create and validate publicly accessible scales to measure key symptoms for a range of health and mental health constructs. Based on item response theory, PROMIS has created and validated a number of emotional distress scales, which assess important aspects of negative affect including depression, anxiety, and anger

(Reeve et al., 2007). Scale construction was based on compiling items from already well- validated instruments. These items were then subjected to item response theory (IRT) analyses to identify items tapping a particular domain (Fries, Bruce, & Cella, 2005). The most concise response sets were compiled, and Short Form instruments were created based on these. Short form scoring tables allow conversion of raw score into a standardized T-score. T-Score distributions are set so that T = 50 represents the mean within the US population, with a standard deviation of 10 points. A high score represents more of the construct being measured. For negatively worded constructs such as anxiety, a score of 60 represents one standard deviation worse than average (www.nihpromis.org).

In this sample, the total raw score was calculated for each subject, then converted into a

T-score, with a mean of T = 50.

PROMIS Anxiety Short Form: The PROMIS Anxiety item bank included fear

(e.g., fearfulness, feelings of panic), anxious misery (e.g., worry, dread), hyperarousal

(e.g., tension, nervousness, restlessness), and somatic symptoms related to arousal (e.g., racing or pounding heart, dizziness). The items on the PROMIS Anxiety Short Form assessed seven items over the past 7 days. These items included feeling fearful, anxious, worried, nervous, uneasy, tense, and having difficulty concentrating. The PROMIS

Anxiety-SF is composed of 7 items, each with five response options (i.e., Never, Rarely,

Sometimes, Often, and Always). Thus, the possible raw scores range from 7 (7 items times 1 point) and the maximum 35 (7 items times 5 points) (www.nihpromis.org).

PROMIS Anger Short Form: The PROMIS Anger item bank included angry mood

(e.g., irritability, frustration), negative social cognitions (e.g., interpersonal sensitivity, envy, disagreeableness), verbal aggression, and efforts to control anger. Anger is characterized by a hostile attitude, and may be evidenced by verbal or nonverbal interpersonal antagonism. The PROMIS Anger Short Form assessed eight items over the past 7 days. These items include feeling irritated, grouchy, or like one is “ready to explode.” As the PROMIS Anger-SF is composed of 8 items, each with five response options (i.e., Never, Rarely, Sometimes, Often, and Always), the possible raw scores can range from 8 (8 items times 1 point) to the maximum 40 (8 items times 5 points).

(www.nihpromis.org).

Cook-Medley Hostility Scale.

The Cook-Medley Hostility Scale is derived from the Hostility scale of the

Minnesota Multiphasic Personality Inventory (Hathaway, 1940), a well-established instrument for assessing trait characteristics. The Cook-Medley scale was initially developed in a sample of teachers, and reflects differences in ability to get along with others; low scorers described themselves as hostile, and their students as lazy, insincere, or untrustworthy (Cook & Medley, 1954). Based on a principal components analysis,

Costa and McCrae found two major components within the scale, paranoid alienation and cynical distrust (P. T. Costa, Jr., Zonderman, McCrae, & Williams, 1986). In college students, cynical distrust emerged as a principal factor (Houston, Smith, & Cates, 1989).

Studies examining convergent validity confirm that high scorers tend to have high levels of cynicism and mistrust (Greenglass & Julkunen, 1991). The scale has good discriminant validity, and is able to distinguish hostility from trait depression or anxiety. This scale was used in a large-scale longitudinal study, the CARDIA study, examining associations between dietary PUFA and hostility (Iribarren et al., 2004). In the current sample, the total score was calculated for each subject, as in the CARDIA study.

NEO Neuroticism Scale.

Neuroticism assessment is based on facets of negative emotionality, and is often measured with the NEO Five Factor Inventory (NEO-FFI) which evaluates anxiety, angry hostility, depression, impulsiveness, self-consciousness, and vulnerability. The NEO-FFI is a well-established measure, often used in research to assess the “big five” personality traits. Neuroticism is one of five domains assessed by the NEO-FFI, and includes 12

48 items. Items are scored on a 5-point Likert scale. A higher score indicates greater level of neuroticism. Normative samples suggest that scores of 0-16 indicate low neuroticism, 17-

25 indicates moderate or average neuroticism, and over 25 indicates high neuroticism (P.

T. Costa & McCrae, 1992). Facets within the neuroticism domain include anxiety, angry hostility, depression, self-consciousness, impulsivity, and vulnerability. The NEO-PI-R has high internal consistency and satisfactory retest reliability (r = .75–0.83 over 3 months) (P. T. Costa & McCrae, 1992). In this sample, the total NEO Neuroticism score was calculated for each subject.

Life Orientation Test (LOT-R).

The Life Orientation Test (LOT-R) is a 10-item self-report measure of optimism, comprised of six scored items plus four “filler” items. The LOT-R is a short version, based on the original LOT scale (Scheier et al., 1994). It was developed to assess individual differences in generalized optimism versus pessimism, and assesses differences in expectancies about future outcomes. Positive expectations for future events are associated with optimism and negative expectations are associated with pessimism.

Responses are coded on a 5-point Likert scale ranging from “strongly disagree” (0) to

“strongly agree” (4). Responses to scored items are coded such that high values imply optimism, and studies often classify those in the upper quartile (18–24 points) as optimists and those in the lower quartile (0–12 points) as pessimists (Raikkonen et al.,

1999).

The LOT-R has been found to have a high correlation (r=0.95) with the results from the original LOT (Scheier et al., 1994). Its coefficient alpha is 0.78, indicating good internal consistency (Scheier et al., 1994). It has also demonstrated good discriminative validity for neuroticism (Scheier et al., 1994; Smith, Pope, Rhodewalt, & Poulton, 1989) and depression (Achat, Kawachi, Spiro, DeMolles, & Sparrow, 2000). Factor analysis confirms that dispositional optimism as measured by the LOT-R is bidimensional, with an optimism and a pessimism factor. These 2 dimensions were demonstrated to be independent across gender and age groups (Herzberg, Glaesmer, & Hoyer, 2006). The

LOT-R has been used extensively in health psychology research (Trivedi et al., 2009) as well as research in college populations (Chang & Bridewell, 1998). It has also been used in examining associations between PUFAs and optimism (Kelloniemi et al., 2005). In this study, the LOT-R score was calculated for each subject, then categorized by quartile.

Block Food Frequency Questionnaire (FFQ).

To provide an objective measure of overall nutrient intake, participants completed the Block Food Frequency Questionnaire, 2005 edition (Nutritionquest). Meta-analysis reveals that compared to weighted food records and 24 hour recalls, FFQs showed high correlation with subcutaneous fat levels. Importantly, FFQs closely mirror plasma levels of long chain fatty acids (McNaughton, Hughes, & Marks, 2007; Welch et al., 2006b). In food recalls, n-3 intake is often underreported, by as much as 45% (Briefel, Sempos,

McDowell, Chien, & Alaimo, 1997) and research shows that a range of 54 days for men and 71 days for women is required to get within 10% accuracy of n-3 intake (Basiotis,

Welsh, Cronin, Kelsay, & Mertz, 1987). The Block FFQ assesses nutrient intake over the past year, ensuring that PUFA intake is adequately captured.

The Block FFQ contains questions about the type, frequency, and quantity of foods, beverages, and vitamins consumed typically in the past year. Frequency of intake was measured with a Likert scale ranging from never to daily. Subjects estimated serving sizes using photographs of standard portion sizes, provided with the FFQ. The FFQ also contains background questions on age, weight, height, and ethnicity; BMI can be calculated from weight and height (Cazzola, Rondanelli, Russo-Volpe, Ferrari, &

Cestaro, 2004). FFQs engender low participant burden, and are quick to administer

(Zulkifli & Yu, 1992). The Block FFQ takes about thirty to forty minutes to complete

(www.nutritionquest.com). Based on intake frequency and serving size, software allowed calculation of dietary intake of energy and macro- and micronutrients, including LA,

ALA, AA, EPA, and DHA. NutritionQuest (Berkeley, California) performed nutrient intake analysis. Nutrient intakes were calculated from the Nutrition Data Systems for

Research (NDSR) database, which includes the USDA nutrition database as well as consumer, manufacturer, and science review board nutrition databases.

Buccal Cell Collection.

Buccal cells were collected by mouthwash rinse, a noninvasive procedure that provides better DNA yields than similar techniques such as the cytobrush (Garcia-Closas et al., 2001). Participants were instructed to abstain from brushing teeth, flossing, smoking, chewing gum, drinking, or eating for 1 hour before sample collection.

Participants received a 50 mL polypropylene conical centrifuge tube containing 10 mL of mouthwash, and given instructions on the buccal cell collection procedure. Participants rinsed the mouth with mouthwash for 60 seconds, while rubbing their tongue on the oral mucosa and teeth. Participants then spat the sample back into the conical tube. A timer was visible for participants during the mouthwash procedure. Samples were stored in the original collection tube at 4°C for 24 to 48 hours. Samples were then centrifuged and

DNA extraction was performed.

Processing and Analysis of Genetic Material

DNA Extraction & Repurification

DNA was extracted from buccal cells with components from the Gentra Puregene

Buccal Cell Kit (Qiagen) using laboratory facilities at the Ohio State University Institute for Behavioral Medicine Research (IBMR). Briefly, pelleted buccal cells were lysed with a buffered sodium lauryl sulfate solution, and cell membrane lipids were removed. A protease enzyme was used to digest protein contaminants that were later precipitated out of the solution. Ribonuclease was added to remove RNA contaminants. Isopropanol and ethanol washes precipitated DNA out of solution. This isolated DNA was resuspended in a buffer solution, and stored at -80 degrees Celsius. After DNA extraction, each DNA sample was measured for concentration and purity using a spectrophotometer. The ratio of the absorbance at 260 and 280nm (A260/280) was used to determine purity. For samples with a 260/280 reading below 1.6, indicating possible residual proteins or organic compounds, a repurification protocol was performed according to the Gentra

Puregene Buccal Cell Kit. In brief, DNA samples were processed a second time with the reagents listed above, in a similar protocol.

DNA Quantification

While spectrophotometric measurement provides a rough index of sample concentration, it may be skewed by residual RNA or protein contaminants. Thus, a more precise technique, using a fluorescent nucleic acid stain, was used as a secondary measure of DNA concentration. The PicoGreen dsDNA Quantitation Reagent is a fluorescent dye that allows the detection and quantitation of DNA concentrations as low as 25 pg/mL.

To quantify DNA concentrations using the PicoGreen technique, the following protocol was employed. The PicoGreen reagent (concentrated dye in anhydrous dimethylsulfoxide (DMSO)) was diluted 1:200 in a solution of 10 mM tris(hydroxymethyl)aminomethane hydrochloride (Tris-HCl), 1 mM ethylenediaminetetraacetic (EDTA) (TE buffer). This working solution was protected from light in a foil-wrapped tube, as the PicoGreen reagent is susceptible to photodegradation. To prepare solutions for a standard curve, serial dilutions of a lambda

DNA standard were made, creating 1000 ng/mL, 100 ng/mL, 10 ng/mL, 1 ng/mL, and 0 ng/mL standards. Finally, serial dilutions were performed on the DNA samples. Samples were diluted in TE solution through serial dilution, reaching a final 1000-fold dilution.

These samples were plated, along with the DNA standards, at 100 uL per well. PicoGreen was then introduced to each well in the microplate, at 100 uL per well. This step was

53 performed in low-light conditions, to protect the PicoGreen reagent from photodegradation. The samples were measured in a fluorescence microplate reader.

Concentrations attained with PicoGreen were compared to the concentration measurements from the spectrophotometer, to ensure that results were consistent.

SNP Genotyping with Real-Time PCR

The FADS2 gene was genotyped for the allelic polymorphism rs174575, using a functionally tested SNP genotyping assay from Applied Biosystems. Based on DNA concentration from the PicoGreen quantitation, the mass balance equation was used to calculate serial dilutions for each of the 168 DNA samples. Each DNA sample was diluted with nuclease-free water to a final concentration of 4 ng/uL. With all DNA samples at a uniform concentration, 20 ng were taken from each sample for genotyping.

Purified DNA from each sample was aliquoted in duplicate on a 96 well optical reaction plate. Genotyping in duplicate allows the experimenter to verify identical genotype between two samples for each participant, which reduces the likelihood of incorrectly genotyping a sample. In addition to up to 46 duplicate samples, the plate contained a no- template control in duplicate (nuclease-free water), and a positive control in duplicate

(sample with a known rs174575 genotype). The DNA samples and no template control were all plated at 5 ul per well.

To prepare the genotyping reaction mix, TaqMan® Universal PCR Master Mix

No AmpErase® uracil-N-glycosylase (UNG) was mixed with the Genotyping Assay components. The Genotyping Assay contains the primer and probe; this includes forward

54 and reverse primers to amplify the polymorphic region of interest, and two TaqMan® minor groove binder (MGB) probes. Each probe contains a fluorescent dye attached to its

5’ end, and at the 3’ end has a minor groove binder and a nonfluorescent quencher. One probe is labeled with fluorescent VIC dye, which detects the allele 1 sequence (C). The other probe is labeled with fluorescent FAM dye, and detects the allele 2 sequence (G).

As the primer/probe mix is photosensitive, the reaction mix was kept in a foil-wrapped tube until use. The reaction mix was then plated at 15 ul per well, in low light conditions.

This resulted in a final reaction volume of 20 ul per well. The plate was then sealed, vortexed, and centrifuged.

The prepared plate was placed in an Applied Biosystems 7300 Real-Time PCR

System. An allelic discrimination plate read document was prepared using Applied

Biosystems Sequence Detection System software, assigning labels to each well and specifying conditions for cycling. During the first step, TaqMan probe anneals to its complementary sequence, between the primer binding sites. DNA polymerase from the

TaqMan Universal PCR Master Mix, No AmpErase UNG, amplifies the target DNA via these sequence-specific primers. TaqMan probes provide a fluorescent signal with the amplification of each allele. Probes that are hybridized to the target sequence are then cleaved. Cleavage separates the reporter dye from the quencher dye, resulting in increased fluorescence by the reporter dye. The TaqMan probe, which is labeled with a fluorescent dye, has a high fluorescence polarization value when hybridized, but a low value after cleavage by Taqman polymerase. Thus, an increase in fluorescence occurs when probes that have hybridized to the complementary sequence are cleaved. In this

55 way, the fluorescence signal generated by polymerase chain reaction (PCR) amplification indicates which alleles are present in each sample; an increase in VIC fluorescence indicates presence of only allele 1 (CC), an increase in FAM fluorescence indicates presence of only allele 2 (GG), and an increase in both dye types indicates a heterozygote.

The cycling conditions were executed according to manufacturer’s instructions for annealing, extension, and denaturing cycles. The products were genotyped using

Applied Biosystems Sequence Detection System software at the Ohio State Institute for

Behavioral Medicine Research. The Sequence Detection System software uses the fluorescence measurements made during the plate read to plot fluorescence values based on the signals from each well; plotted fluorescence readings indicate which alleles are present in each sample. All steps of DNA extraction, repurification, quantification, and genotyping were performed by the investigator.

Statistical Analyses

Sample Size

To date, no study has examined associations among the rs174575 genotype, dietary

PUFA intake, and mood symptoms. However, previous studies examined associations between rs174575 and mood in women (Xie & Innis, 2009), and associations between dietary PUFA intake and symptoms of depression, anxiety, and anger or hostility. Also, studies examined interactions among rs174575, PUFA intake, and IQ (Caspi et al., 2007).

Thus, to determine minimum sample sizes necessary to detect these effects of interest, I looked to data from published literature documenting such effects.

A study examining associations between n-3 intake and mental health in healthy community dwelling adults found an effect size of d = 0.6 to 0.7 for two outcome measures of mental health, a moderate-to-large effect (Silvers & Scott, 2002). For psychological outcomes associated with FADS genotype, rs174575 genotype was associated with postpartum depression with the minor allele providing 0.103 additional risk (Xie & Innis,

2009), and the FADS2 SNP rs498793 C allele was associated with a 1.6 odds ratio (CI: 1.15-

2.23) for ADHD in a case-control study (Brookes et al., 2006). Caspi’s (2007) study on the interaction between rs174575, diet, and the neuropsychological outcome IQ showed a moderate effect size (d = 0.487). Effect sizes of at least d = 0.25 are considered clinically meaningful (Cohen, 1998). Given these findings, I estimated that effect sizes would fall in the moderate range. Using a more conservative small to moderate effect size, ƒ2 = 0.085, a minimum sample of 160 subjects was required for power of 80%, alpha of 0.05, to detect statistically significant interactions between the variables of interest. This regression model included two covariates, as well as main effects and interactions of genotype and diet.

An additional factor for which one must account in genetic research is minor allele frequency. By definition, the minor allele is that which is less frequent in the population. If a minor allele is extremely rare, it may be difficult to obtain a large enough sample size to assure adequate numbers of minor allele carriers in each group. For rs174575, the minor (G) allele frequency (q) is relatively high at 0.28

(appliedbiosystems.com). The FADS2 alleles conform to Hardy-Weinberg equilibrium

(Schaeffer et al., 2006), and studies on rs174575 typically contain 45%-47% minor allele carriers, as in studies by Caspi (2007) and Molto-Puigmarti (2010). In this study design, according to Hardy-Weinberg equilibrium, 160 subjects would provide approximately 87 major allele homozygotes (CC) and 73 minor allele carriers (CG, GG). In the final sample of 168, there were 95 (56.5%) major allele homozygotes (CC) and 73 (43.5%) minor allele carriers (CG, GG).

Statistical Models.

The analyses used to assess the main hypotheses were General Linear Models

(GLM). Psychological outcomes for all hypotheses included depressive symptomatology

(CES-D score), anxiety symptomatology (PROMIS Anxiety Short Form score), anger

(PROMIS Anger Short Form score), hostility (Cook-Medley scale score), neuroticism

(NEO Neuroticism scale score), and optimism (LOT Short Form score) as continuous variables. If many subjects were taking a particular medication, such as oral contraceptives, I examined relationships between medication use and the dependent variables.

Hypothesis 1 proposed that n-3 PUFA intake would be inversely associated with depressive symptomatology, anxiety, anger, hostility, and neuroticism, and positively associated with optimism. To test this hypothesis, I regressed the independent predictor dietary n-3 PUFA intake, with antidepressant use, daily kilocalorie intake, and BMI as covariates, onto each outcome measure (CES-D score, PROMIS Anxiety score, PROMIS

Anger score, Cook-Medley Hostility score, NEO Neuroticism score, or LOT Optimism score).

Hypothesis 2 examined associations between FADS genotype and psychological outcomes. To test this hypothesis, I regressed the independent predictor genotype

(categorized as major allele homozygote or minor allele carrier) with antidepressant use, daily kilocalorie intake, and BMI as covariates, onto each outcome measure (CES-D score, PROMIS Anxiety score, PROMIS Anger score, Cook-Medley Hostility score,

NEO Neuroticism score, or LOT Optimism score).

Hypothesis 3 posited an interaction between dietary PUFA intake and FADS genotype for psychological outcomes. I employed hierarchical multiple regression to examine interaction between n-3 PUFA intake and n-6 PUFA intake, respectively, and genotype (categorized as major allele homozygote or minor allele carrier) for each dependent variable (CES-D score, PROMIS Anxiety score, PROMIS Anger score, Cook-

Medley Hostility score, NEO Neuroticism score, LOT Optimism score). For n-3 PUFA intake, in the first step I entered antidepressant use, daily calorie intake and BMI as covariates. In the second step, I regressed each dependent variable individually on dietary n-3 intake and genotype, and in the third step I entered the two-way interaction between dietary n-3 intake and genotype. Identical analyses were carried out for n-6 dietary intake. I classified genotype as a bivariate variable (CC vs. G-carrier).

Covariates included in each model included BMI, daily kilocalorie intake, and antidepressant use. Covariates were selected a priori, based on previous literature, which is considered a conservative approach (Raab, Day, & Sales, 2000). BMI is often included

59 as a covariate in studies examining the relationship between PUFA intake and affective outcomes, as BMI may be confounded with PUFA intake (Crowe et al., 2007; Timonen et al., 2004; Schiepers, de Groot, Jolles, & van Boxtel, 2010; van de Rest et al., 2009;

Giltay, Geleijnse, Zitman, Buijsse, & Kromhout, 2007; Colangelo et al., 2009; Murakami

& Sasaki, 2010). BMI may also influence metabolic processing of dietary PUFAs, providing biological rationale for its frequent use as a covariate (Ramel, Parra, Martinez,

Kiely, & Thorsdottir, 2009; Zhou, Kubow, Dewailly, Julien, & Egeland, 2009).

It is also possible that energy intake influences the relationship between n-3

PUFA status and psychological outcome measures. Energy intake is commonly used as a covariate examining the relationship between PUFA intake and affective outcomes (van de Rest et al., 2009; Iribarren et al., 2004; Colangelo et al., 2009). In fact, one study initially found an inverse relationship between n-3 intake and depressive symptoms in adolescents, but this relationship was no longer significant after controlling for energy intake and other lifestyle factors (Oddy et al., 2011). This emphasizes that significant associations between n-3 intake and depressive symptoms may be influenced by collinearity with dietary or lifestyle factors, and suggests the need to control for these factors.

Finally, antidepressant medication use was chosen a priori as a covariate, as antidepressant use has been associated with variances in PUFA intake (Colangelo et al.,

2009; Lucas et al., 2011). As antidepressant effectiveness may be enhanced by n-3 PUFA intake, it may be difficult to parse out whether PUFA intake has an effect independent of antidepressant medication in antidepressant users (Sontrop & Campbell, 2006). Thus,

60 because participants taking antidepressants may represent a subgroup with greater severity of depressive symptoms, posthoc analyses were performed after removing participants using antidepressants, for each of the main hypotheses.

Covariates may also be selected post-hoc when factors are correlated with an outcome variable (Raab et al., 2000). Statistically non-significant differences between groups may produce confounding effects, particularly in small samples, if an independent variable has a strong relationship with the confounder (Szklo et al., 2000). Thus, before statistical analyses for main hypotheses were performed, Chi Square and analysis of variance tested for differences between groups for main variables, e.g. differences in

PUFA intake (such as differences in mean PUFA intake by racial groups), differences in genotype (such as differences in genotype frequency by racial group), or differences in depressive symptoms (e.g. differences in mean CES-D score by racial groups, differences in mean CES-D score by academic quarter or week during academic quarter). As oral contraceptives were a commonly used medication within the sample, I also examined relationships between oral contraceptive use and the dependent variables. These results are summarized below as “Group Differences.” A two-sided .05 alpha level was used for all analyses. Analyses were executed using SPSS 19.

Chapter 3: Results

Between November 2, 2010 and March 3, 2011, 172 individuals participated in the study. Of the 172 participants who provided informed consent, 1 participant had missing genetic data (no buccal cells were recovered), 2 participants had low DNA yields, and 1 participant had partially missing questionnaire data. This produced a final sample of 168 participants. Among the final sample of 168 participants, 4 declined to provide height and/or weight information, so BMI could not be calculated. This resulted in 164 subjects with complete data.

Sociodemographic Characteristics

Data on age, race and education level are included in Table 1. The mean age of the sample was 18.93 years (S.D. 1.47), with a range of 18 – 29 years. The majority of the sample were first-year undergraduates (69.6%), followed by second-year undergraduates (18.5%), third-year undergraduates (8.3%), fourth-year undergraduates

(2.4%) and other or nontraditional students (1.2%). Racial composition of the sample was largely Caucasian (76.8%); African-American women comprised 10.1% of the sample, followed by Asian American women (7.1%), Hispanic or Latina (2.4%), Indian

(1.2%), multi- or biracial (1.2%), and Native American or Native Hawaiian (0.6%). As there were few participants per group for Hispanic or Latina, Indian, multi- or biracial,

62 and Native American or Native Hawaiian, these participants were grouped together for analyses, so that race was classified among 4 groups.

Genetic Data

Genotype information is summarized in Table 2. Of 168 participants, 95 (56.5%) were major allele homozygotes (CC), while 73 (43.5%) were minor allele carriers (CG,

GG). Within the minor allele carriers, 65 (38.7% of total sample) were genotype CG, and

8 (4.8% of total sample) were genotype GG. A Chi-square test ( 2 =∑(O−E)2/E) confirmed that genotype frequencies conformed to Hardy-Weinberg equilibrium. That is, the observed frequencies of CC, CG, and GG participants did not differ from the expected Hardy-Weinberg frequencies ( 2(1, N = 168) = 0.55, p = 0.45).

Diet, Health, and Use of Vitamins & Medications

Table 3 presents self-reported health information, background dietary information, and vitamin and medication use. A small percentage of the sample reported a vegetarian diet (n = 12; 7.1%), and one participant reported a vegan diet (0.6%).

Multivitamin use was frequent, reported by 43 participants (25.6%). Many participants also reported hormonal contraceptive use (n = 65, 38.7%). Psychiatric medication use included antidepressants (n = 12, 7.1%), ADHD medication (n = 5, 3%), benzodiazepines

(n = 3, 1.8%), beta-blockers (n = 1, 0.6%), and mood stabilizers (n = 1, 0.6%). Other medication use included antibiotics (n = 12, 7.1%), over the counter analgesics or allergy medications (n = 10, 6%), prescription acid reflux medications (n = 4, 2.4%), prescription

63 asthma medications (n = 3, 1.8%), insulin (n = 3, 1.8%), other prescription medications

(e.g. tacrolimus, spironolactone, indocin, metoprolol) (n = 7, 4.2%) and other supplements or vitamins (e.g. vitamin B, vitamin D, calcium) (n = 20, 11.9%). Six participants (3.6%) reported fish oil supplement use, and 2 participants (1.2%) reported flaxseed supplement use. Nearly one-fifth of the sample (n = 31, 18.5%) reported a history of diagnosis or treatment for depression.

Food Frequency Questionnaire (FFQ) Data

Body Mass Index (BMI), Basic Nutritional & Dietary Intake.

Of the sample, 4 declined to provide weight and/or height information, resulting in 164 participants with complete data including BMI. Based on self reported weight and height, the mean BMI was 23.05 (S.D. 4.65, minimum 16.73, maximum 45.84). Table 4 summarizes mean daily dietary intake of a number of nutrients. Mean daily kilocalorie

(kcal) intake was 1818.76 (S.D. 947.83, minimum 474.35, maximum 6310.37. Mean daily fat intake was 67.35 g (S.D. 40.62, minimum 9.2, maximum 287.94), with mean daily saturated fat intake at 21.61 g (S.D. 13.47, minimum 2.48, maximum 96.85) and mean daily trans-fat intake at 2.44 g (S.D. 1.68, minimum 0.32, maximum 12.36).

On average, participants received 32.52% of their daily calories from fat, 14.64% from protein, 51.3% from carbohydrate, 16.3% from sweets, and 5.23% from alcohol.

Participants reported, on average, consuming 5.32 daily servings of grains, 3.2 of fats, oils, and sweets, 2.23 of vegetables, 1.85 of meat, 1.41 of fruit, 1.38 of dairy, 0.55 of whole grains, 0.31 of low n-3 fish, and 0.12 of high n-3 fish.

Fatty Acid Intake.

Intake of specific n-3 and n-6 PUFAs are summarized in Table 4. Mean daily

MUFA intake was 25.81 g (S.D. 12.72, minimum 3.4, maximum 59.41), and mean daily

PUFA intake was 14.61 g (S.D. 9.06, minimum 2.2, maximum 59.41). Mean n-3 daily intake from diet was 1.36 g (S.D. 0.96, minimum 0.17, maximum 6.01), mean n-3 daily intake from supplements was 0.025 g (S.D. 0.09, minimum 0, maximum 0.5), and combined total n-3 daily intake was 1.38 g (S.D. 0.96, minimum 0.18, maximum 6.01).

Mean daily n-6 intake from diet was 12.46 g (S.D. 7.73, minimum 2.03, maximum

53.01), mean daily n-6 intake from supplements was 0.005 g (S.D. 0.018, minimum 0, maximum 0.1), and combined total n-6 daily intake was 12.46 g (S.D. 7.73, minimum

2.03, maximum 53.01). The mean n-6:n-3 ratio was 9.42 (S.D. 1.8, minimum 5.47, maximum 17.09). Dietary intake did not differ significantly between vegetarians and nonvegetarians (Table 4).

As described, while organizations such as the World Health Organization recommend 0.4 to 1 g/day combined EPA and DHA (Lichtenstein, 2006; WHO, 2003), the average American n-3 intake is far below this, with combined EPA and at 0.11 g/day, versus 15.9 g/day of n-6 (Ervin et al., 2004). In the U.S. Midwest, where the present study occurred, n-3 consumption tends to be low due to infrequent fish consumption

(Lewis et al., 1995). In a sample of young adults aged 20-30 in the Midwest, similar to the demographic of the present study, average n-3 intake was 1.55 g/day and average n-6 intake was 20.9 g/day, with an n-6:n-3 ratio similar to the 15:1 ratio typical of American

65 diets (McDaniel et al., 2010). In the present study, the mean n-6:n-3 ratio was slightly lower than this, at 9.42. In fact, the present study showed lower n-3 intake (1.38 g/day) as well as lower n-6 intake (12.46 g /day) as compared to the aforementioned study.

Questionnaire Data

Tables 5 and 6 present data for the CES-D, PROMIS Anxiety Short Form,

PROMIS Anger Short Form, NEO Neuroticism Scale, LOT-R, and Cook-Medley

Hostility Scale, summarized below.

Center for Epidemiological Studies Depression Scale (CES-D).

The CES-D consisted of 20 items and was highly reliable instrument (α = 0.909) in this sample. The mean CES-D score was 15.05 (S.D. 10.41, minimum 0, maximum

52), with 67 participants (39.9%) scoring at or above the clinical cutoff of 16, indicating mild depressive symptoms. While 101 participants (60.1%) scored in the nondepressed

(0–15) range, 28 (16.7%) scored as mildly depressed (16–20), 23 (13.7%) scored as moderately depressed (21–30), and 16 (9.5%) scored as severely depressed (above 30).

The mean CES-D score was similar to the mean scores of other undergraduate samples

(M = 17.9, M = 22.5) (Gjerde, Block, & Block, 1988; Regestein et al., 2010). In fact, in a recent study of a undergraduates with demographic qualities similar to that of the present sample, 48% of students scored above 15 (Regestein et al., 2010).

PROMIS Anxiety Short Form.

The PROMIS Anxiety-SF Scale showed excellent reliability (7 items, α = 0.904) in this sample. The mean PROMIS Anxiety-SF raw score was 16.64 (S.D. 5.58, minimum 7, maximum 31), and the mean T score was 55.46 (S.D. 7.75, minimum 36.3, maximum 31). Based on population means, 112 participants (66.7%) fell into the average range. Forty-four participants (26.2%) were 1 S.D. below average, and 7 were 2

S.D. below average, indicating more anxiety than average. Five participants (3%) were 1

S.D. above average, indicating less anxiety than average.

PROMIS Anger Short Form.

The PROMIS Anxiety-SF also showed excellent reliability (8 items, α = 0.916) in this sample. The mean PROMIS Anger-SF raw score was 17.84 (S.D. 6.2, minimum 8, maximum 40), and the mean T score was 52.19 (S.D. 68.9, minimum 32.4, maximum

85.2). Based on population means, 124 participants (73.8%) fell into the average range.

Thirty-one participants (18.5%) were 1 S.D. below average, 3 (1.8%) were 2 S.D. below average, and 1 (0.6%) was 3 S.D. below average, all indicating more anger than average.

Nine participants (5.4%) were 1 S.D. above average, indicating less anger than average.

NEO Neuroticism Scale.

The NEO Neuroticism Scale consisted of 12 items, and demonstrated good reliability (α = 0.872). The mean NEO Neuroticism score was 21.25 (S.D. 8.91, minimum 2, maximum 42). Based on cutoff scores from normative samples, the mean

67 score in this sample falls in the moderate or average range of neuroticism (P. T. Costa &

McCrae, 1992).

Life Orientation Test (LOT-R).

The 10-item LOT-R Scale was a reliable measure (10 items, α = 0.806) in this sample. The mean LOT-R score was 14.61 (S.D. 5.28, minimum 2, maximum 24). This is slightly higher than the mean LOT-R score (8.9) in a sample of American Midwestern adolescents (Raikkonen & Matthews, 2008). Studies often classify those in the upper quartile (18–24 points) as optimists and those in the lower quartile (0–12 points) as pessimists (Raikkonen et al., 1999). Based on these cutoffs, 57 participants (33.9%) scored as average, 57 (33.9%) scored as pessimists, and 54 (32.1%) scored as optimists.

Cook-Medley Hostility.

The Cook-Medley Hostility Scale demonstrated good reliability (50 items, α =

0.765) in this sample. The mean Cook-Medley Hostility score was 18.62 (S.D. 6.11, minimum 4, maximum 32). This is within the established normal range for adult samples

(Swenson, Pearson, & Osbourne, 1973). Importantly, this is similar to means in the

CARDIA study, a demographically similar sample, in which the overall mean Cook-

Medley Hostility score was 17.03 (15.95 for women) (Iribarren et al., 2004). The

CARDIA study operationalized high hostility as scoring in the 75th percentile or above on the Cook-Medley Hostility scale. According to that criterion, 45 participants met or exceeded the threshold of high hostility, a score of 23 or above, in the current sample.

Group Differences

Statistics for group differences, including p values, are summarized in Tables 7-

16. Before statistical analyses for main hypotheses were performed, Chi Square and analysis of variance were used to determine differences between groups for main variables. These analyses were performed to ensure there were no extraneous factors

(such as week in quarter, racial differences, etc.) influencing the main outcome variables.

There were no statistically significant differences among racial groups (Table 7), academic year groups (Table 8), or genotype groups (Tables 9-10) on factors including

BMI, self-reported history of depression, antidepressant use, fish oil or flaxseed oil use, daily n-3 intake, or daily n-6 intake. There was a difference among racial groups on average daily kilocalorie (kcal) intake (Table 7), such that mean daily kilocaloric intake of African American females was 2562, compared to 1990 for Asians, 1784 for others, and 1707 for Caucasians. Further, African American participants consumed an average of 2.6 g n-3 PUFAs per day, compared to 1.4 g for Asians, 1.23 g for Caucasians, and

1.09 g for other racial groups. Also, the sample showed a trend toward difference in genotype frequency by race, driven by absence of minor allele carriers among Asians in the group.

There were no differences in CES-D score (Table 11), PROMIS Anxiety SF score

(Table 12), PROMIS Anger SF score (Table 13), Cook Medley Hostility Scale score

(Table 14), NEO Neuroticism Scale score (Table 15), or LOT-R (Table 16) score by

69 genotype, race, year in school, exam weeks, week in quarter, vegetarian or vegan diet, fish oil or flaxseed oil supplement use, oral contraceptive use, or ADHD medication use.

There was a significant difference in PROMIS Anxiety SF scores between antidepressant users and nonusers (Table 12). The mean PROMIS Anxiety score in antidepressant users was 60.58, and 55.07 in those not using antidepressants. PROMIS

Anger SF score showed a significant positive correlation with daily kilocalorie intake

(Table 13). There was also a significant difference in PROMIS Anger SF score based on year in school (Table 13); freshmen and sophomores had higher scores than senior or transfer / nontraditional students. Transfer and nontraditional students had lower Anger

SF scores than all other academic years, except seniors. Antidepressant users had a mean

NEO Neuroticism score of 26.75, compared to 20.83 in non-users (Table 15).

Vegetarians had a mean NEO Neuroticism score of 27.67, compared to 20.76 in nonvegetarians (Table 15). Finally, there was a significant positive correlation between

LOT-R Scale score and BMI (Table 16). Also, antidepressant users had a mean LOT-R score of 11.33, compared to 14.86 in non-users, indicating that antidepressant users were less optimistic than non-users (Table 16).

The most commonly used medications in this sample were oral contraceptives

(38.6%), antidepressants (7.1%) and stimulants or ADHD medications (2.9%). Thus, correlations between these 3 medications and the outcome variables were examined. As described above, there were no differences between oral contraceptive users and non- users for any of the outcome variables. There were differences in antidepressant users versus non-users. As described, antidepressant users were more anxious, had greater

70 neuroticism, and were less optimistic than non-users. Finally, there were no differences for any of the outcome variables in ADHD medication users versus non-users. These results are summarized in Tables 11-16.

Additionally, a bivariate correlation table is included for variables and covariates of interest (Table 17). Among the covariates BMI, daily kilocalorie intake, and antidepressant use, none correlated significantly with each other. Total n-3 and total n-6 were significantly positively correlated with each other, as well as with daily kilocalorie intake. Further, higher intake of n-3, n-6, and calories were significantly associated with greater anger and hostility. Higher BMI was significantly associated with lower optimism, as described above. Finally, all of the dependent measures of negative emotionality were significantly and positively associated with each other.

Analyses of Primary Hypotheses

Statistics for analyses of primary hypotheses are summarized in Tables 17-40.

Hypothesis 1: Association Between n-3 PUFA Intake and Psychological Outcomes

Hypothesis 1 proposed that dietary n-3 PUFA intake would be inversely associated with depressive symptomatology, anxiety, anger, hostility, and neuroticism, and positively associated with optimism. For depressive symptomatology as measured by the CES-D, there was not a significant relationship between total n-3 intake (combined dietary and supplements) and CES-D score, when controlling for antidepressant use, daily kilocalorie intake, and BMI (Table 18). There were similarly no significant relationships between n-3 intake and PROMIS Anxiety SF Score (Table 19), PROMIS

Anger SF score (Table 20), NEO Neuroticism score (Table 22), nor LOT-R score (Table

23). However, there was a significant relationship between n-3 intake and Cook Medley

Hostility Scale score, such that higher n-3 intake was associated with greater hostility

(Table 21).

Hypothesis 2: Association Between FADS Genotype and Psychological Outcomes

Hypothesis 2 proposed that in comparing rs174575 major allele homozygotes to minor allele carriers, individuals with FADS2 polymorphisms previously associated with lower plasma levels of EPA and DHA (that is, minor allele carriers) would have higher levels of depressive symptomatology, anxiety, anger, hostility, and neuroticism, and lower optimism scores than major allele homozygotes. Analyses controlled for antidepressant use, daily kilocalorie intake, and BMI. FADS genotype was not significantly related to CES-D score (Table 24), PROMIS Anxiety SF Score (Table 25),

PROMIS Anger SF score (Table 26), Cook Medley Hostility Scale score (Table 27),

NEO Neuroticism score (Table 28), nor LOT-R score (Table 29).

Hypothesis 3: Interaction of FADS Genotype and PUFA Intake on Psychological

Outcomes

Hypothesis 3 proposed that the association between n-3 PUFA intake and negatively valenced psychological outcomes (depressive symptoms, anxiety, anger, hostility, and neuroticism) would be negative in major allele homozygotes. The association between n-6 PUFA intake and negatively valenced psychological outcomes

(depressive symptoms, anxiety, anger, hostility, and neuroticism) would be positive in major allele homozygotes. Analyses controlled for antidepressant use, daily kilocalorie intake, and BMI. There were no significant interactions between genotype and n-3 intake for CES-D score (Table 30), PROMIS Anxiety SF Score (Table 31), PROMIS Anger SF score (Table 32), Cook-Medley Hostility Scale score (Table 33), nor NEO Neuroticism score (Table 34). There was a significant interaction between n-3 intake and genotype for

LOT-R score (Table 35). However, simple slope tests using Hayes and Matthes’ probing interactions procedure (Hayes & Matthes, 2009) revealed that the slopes were not significantly different than zero.

Similarly, genotype and n-6 intake did not interact to influence CES-D score

(Table 36), PROMIS Anxiety SF Score (Table 36), PROMIS Anger SF score (Table 38),

Cook-Medley Hostility Scale score (Table 39), or NEO Neuroticism score (Table 40).

There was a trend toward significance for the interaction between genotype and n-6 intake for LOT-R score (Table 41), however, Hayes and Matthes’ simple slope test revealed that the slopes were not significantly different than zero.

Exploratory Analyses

In the absence of significant findings in the main hypotheses, I performed additional analyses. These analyses 1) examined correlations between n-6 intake and psychological outcomes, 2) tested main hypotheses excluding vegetarians from the sample, 3) tested main hypotheses excluding antidepressant users from the sample, 4)

73 tested main hypotheses only in participants scoring 16 or greater on the CES-D, 5) tested main hypotheses only in those self-reporting a history of depression, 6) tested main hypotheses using the Cook Medley Hostility Scale subscales, and 7) tested main hypotheses for those scoring above or below the Cook Medley cutoff score for high hostility. These results are summarized in Tables 41 – 137.

Exploratory Analysis 1: Association Between n-6 PUFA Intake and Psychological

Outcomes

As some studies have shown positive correlations between n-6 intake and negative affect, total n-6 intake was regressed onto each psychological outcome, controlling for antidepressant use, daily kilocalorie intake, and BMI. There were no significant relationships between total n-6 intake and CES-D score (Table 42), PROMIS

Anxiety SF Score (Table 43), PROMIS Anger SF score (Table 44), Cook-Medley

Hostility Scale score (Table 45), or NEO Neuroticism score (Table 46), or LOT-R Score

(Table 47).

Exploratory Analysis 2: Main Hypotheses Excluding Vegetarians

As vegetarians may represent a group with different nutritional intake than nonvegetarians, tests of the main hypotheses were repeated, excluding vegetarians.

Before these analyses were run, differences in nutritional intake between vegetarians and nonvegetarians were examined. Vegetarians were no different than nonvegetarians in intake of daily kilocalories, protein, fat, carbohydrates, cholesterol, saturated fats, or

PUFAs including LA, AA, ALA, stearidonic acid, EPA, DPA, DH, total n-3, total n-6, or n6:n3 ratio (Table 4).

When the main hypotheses were repeated excluding vegetarians, there were no significant relationships between total n-3 intake and CES-D score (Table 48), PROMIS

Anxiety SF Score (Table 49), PROMIS Anger SF score (Table 50), NEO Neuroticism score (Table 52), or LOT-R Score (Table 53). There was a significant relationship between n-3 intake and Cook Medley Hostility Scale score, such that higher n-3 intake was associated with greater hostility (Table 51), similar to the results of the original analyses. Also similar to the original analyses, there was a significant interaction between n-3 intake and genotype for LOT-R score (Table 65), but simple slope tests again revealed that the slopes were not significantly different than zero.

Exploratory Analysis 3: Main Hypotheses Excluding Antidepressant Users

Antidepressant users may represent a subgroup of the sample with more pronounced negative emotionality. Indeed, in this sample antidepressant users were more anxious, had greater neuroticism, and were less optimistic than non-users. Thus, the main hypotheses were tested again excluding antidepressant users from the sample. Among antidepressant users, there were no significant relationships between total n-3 intake and

CES-D score (Table 66), PROMIS Anxiety SF Score (Table 67), PROMIS Anger SF score (Table 68), NEO Neuroticism score (Table 70), or LOT-R Score (Table 71).

Greater n-3 intake was again associated with greater hostility (Table 69), and there was a

75 significant interaction between n-3 intake and genotype for LOT-R score (Table 83), which proved to be nonsignificant based on simple slope tests.

Exploratory Analysis 4: Main Hypotheses in Participants Scoring At CES-D Mild Cutoff

Validated severity cutoff scores for the CES-D establish that a score of 16 or above indicates at least mild depressive symptomatology. The main hypotheses were tested again in individuals scoring at or above 16 on the CES-D. According to these analyses, in these more depressed participants, higher n-3 intake was associated with lower levels of optimism (Table 89). Further, among this depressed subsample, genotype was significantly correlated with hostility, such that minor allele carriers had greater hostility (Table 93). Finally, within this subsample there was a significant interaction between n-3 intake and genotype for CES-D score (Table 96). However, the Hayes

Matthes procedure indicated that the slopes were not significantly different than zero.

Within this subsample, there were no significant relationships between total n-3 intake and CES-D score (Table 84), PROMIS Anxiety SF Score (Table 85), PROMIS Anger SF score (Table 86), Cook Medley Hostility Score (Table 87), or NEO Neuroticism score

(Table 88).

Exploratory Analysis 5: Main Hypotheses in Participants Reporting a History of

Depression

Thirty-one participants self-reported a history of depression, based on a single

Yes / No questionnaire item. The main hypotheses were repeated in this subsample of

76 individuals, who reported having been diagnosed with or treated for a depressive disorder in their lifetime. This analysis yielded no significant main effects or interactions (Tables

102 – 119).

Exploratory Analysis 6: Cook Medley Hostility Scale Subscales

This set of analyses tested main effects of n-3 intake and genotype, and interactions, on the five Cook Medley Hostility Scale subscales proposed by Barefoot et al. (1989). These subscales are Hostile Attributions, Cynicism, Hostile Affect,

Aggressive Responding, and Social Avoidance (Barefoot, Dodge, Peterson, Dahlstrom, &

Williams, 1989). The interaction between genotype and PUFA was also tested. These analyses revealed that three of the Cook Medley Hostility subscales were positively associated with n-3 intake: Cynicism, Hostile Attribution, and Hostile Affect (Tables 120

- 124).

Exploratory Analysis 7: Cook Medley Hostility Scale 75th Percentile Cutoff

The CARDIA study used a Cook Medley Hostility score in the seventy-fifth percentile or greater to indicate high hostility. According to the CARDIA criterion, logistic regression was used to test main effects of PUFA intake, genotype, and their interaction on likelihood of scoring in the seventy-fifth percentile or higher for hostility

In this sample, higher n-3 intake was associated with a significantly greater likelihood of being in the high hostility group (Table 135).

Chapter 4: Discussion

The aims of this study were to examine interactions among PUFA intake, FADS genotype, and affective outcomes. The main finding was a significant relationship between n-3 intake and Cook Medley Hostility Scale score, such that higher n-3 intake was associated with greater hostility. However, interactions between PUFA intake and genotype were not significant for any psychological outcome measures. Exploratory analyses revealed that among mildly to severely depressed participants, higher n-3 intake was associated with lower levels of optimism. Further, within this subsample, genotype was significantly correlated with hostility, such that low-efficiency desaturase minor allele carriers had greater hostility.

Primary Hypotheses

Relationship Between n-3 Intake and Cook Medley Hostility Scale

Regression analyses revealed that higher n-3 intake was associated with greater hostility. These results are surprising, as previous research found that higher n-3 was associated with lower levels of negatively valenced outcomes such as anger and hostility.

A large study of young adults in the U.S. (CARDIA) found a negative association between dietary n-3 DHA and hostility, assessed by the Cook–Medley Hostility Scale

(Iribarren et al., 2004). The CARDIA study used the same assessment measure (food frequency questionnaire) and outcome measure (Cook Medley Hostility Scale) as the present study, but produced results in the opposite direction.

While these two studies used similar methodologies, there are a number of key differences that may explain the discrepant results. The CARDIA study included a sample of over 3500 American young adults, aged 18-30, from a number of major metropolitan areas. The overall mean Cook-Medley hostility score was 17.03, and 15.95 for women; this score was divided into quartiles for analyses, such that highest quartile of hostility was associated with low fish intake and low DHA intake. In the present study, overall mean Cook-Medley hostility score was 18.62, slightly higher than the CARDIA mean for women. The mean BMI for women in the present sample was 23.02, similar to that of the CARDIA women (26.2). Mean daily intakes were 1818.76 kcal (much lower than CARDIA’s 2369.5 for women), 1.36 g n-3 PUFAs (much lower than CARDIA’s

6.69 g for women), 12.45 g n-6 PUFAs (also much lower than CARDIA’s 65 g for women). Interestingly, the CARDIA study did not include n-3 supplements in total dietary intake, while the present study did. However, the mean n-6: n-3 ratio in the present study was 9.42, similar to CARDIA’s 10.05 for women. In sum, participants in the present study had similar BMIs, Cook-Medley Hostility scores, and n-6: n-3 ratios, but had much lower n-3 and n-6 intake compared to participants in the CARDIA study.

To explain the association between n-3 and hostility in the present study, it is possible that a limited range of n-3 intake in this sample influenced findings. In fact, compared to previous research in a sample with similar demographic characteristics, the

79 present study showed lower n-3 intake and lower n-6 intake (McDaniel et al., 2010). It is possible that there is a floor effect, in which participants’ intake of n-3 PUFAs was too low or too limited in range to show an effect, or limited ability to detect differences.

Indeed, one study found that only participants with low n-3 PUFA levels at baseline who showed large changes in tissue n-3 levels during a supplementation period also showed reductions in anger (Buydens-Branchey et al., 2008). Thus, perhaps it is change in n-3 levels that shows a more robust effect.

Further, substrate composition of total n-3 and total n-6 in the diet may be a critical factor to consider. In this sample, n-3 and n-6 intake were both skewed toward short-chain precursor PUFAs. While the mean n-3 intake was 1.38 g, nearly all of this

(1.29 g, or 93.5%) was ALA, while only 0.02 g was EPA and 0.04 g was DHA. Thus, participants were only taking in 0.06 g of combined EPA and DHA per day. While this

ALA consumption is in line with the 1.1 g/day for women recommended in the Dietary

Reference Intakes for Adequate Intakes published by the National Academy of Sciences

Institute of Medicine (2002), the EPA and DHA intake is scant compared to the World

Health Organization recommended intake of 0.4 to 1 g/day combined EPA and DHA

(2003).

As EPA and DHA appear to be the key factors in associations between PUFA and mood, it is possible that high n-3 intake with low EPA and DHA intake in this sample is driving the association between n-3 and hostility. That is, perhaps high ALA intake itself is not beneficial in terms of psychological outcomes such as hostility. In one study, 3 grams of ALA daily from flaxseed oil capsules produced a 60% increase in plasma EPA

(Harper et al., 2006). The present sample, ingested less than half of that amount of ALA, and ingested very little EPA and DHA. This suggests that plasma and tissue levels of

EPA and DHA in this sample may be quite low. Also, the n-3 precursor ALA competes with the n-6 precursor LA for the same desaturase-elongase pathway. Thus, if large amounts of LA are being ingested relative to ALA, LA will outcompete ALA in this biosynthetic pathway. This results in greater production of AA and even less production of EPA and DHA. In this sample, mean total daily n-6 intake was 12.46 g, with 12.37 g

(99.2%) of this in the form of LA. Thus, on average, participants in this study took in

12.37 g of n-6 precursor compared to 1.29 g of n-3 precursor. This indicates that of their

PUFA precursor intake, 90.6% was in the form of n-6 precursor.

To attain 50% EPA and DHA in tissue, it is suggested that individuals consuming a 2000 kilocalorie daily diet should take in around 2 g per day of long-chain n-3 fatty acids (Hibbeln et al., 2006). Calculations suggest that if the amount of n-6 fatty acids, particularly LA, were reduced to less than 2% of daily caloric intake (i.e. 40 kilocalories in a 2000 kilocalorie diet, or about 4.5 grams of n-6 at 9 kilocalories per gram of fat), required n-3 intake could be reduced to ~22 mg per day of n-3 fatty acids (Hibbeln et al.,

2006). In this sample, LA intake averaged 12.37 g, about three times higher than the amount suggested to prevent its outcompetition of ALA in the desaturase-elongase pathway. Thus, it is likely that not only was LA outcompeting ALA and producing greater amounts of AA relative to EPA or DHA, but the raw intake of EPA and DHA was low, a sum effect of diminished EPA and DHA in tissue.

Physical activity level may also be a factor of interest in considering the association between n-3 intake and hostility. The CARDIA study assessed habitual physical activity, including leisure activity, sports activity, and work activity. This composite activity level variable was included in the CARDIA regression model. The present study did not account for physical activity. It is possible that a confound such as high activity level, perhaps in competitive sports, accounted for both high hostility and high energy intake, including n-3 PUFAs. This may be a factor in extreme cases, such as exercise dependence, in which excessive exercise has been linked with greater hostility

(Grandi, Clementi, Guidi, Benassi, & Tossani, 2011). Acute changes in hostility may also occur after athletic competition (Chiodo et al., 2011). However, most research suggests that regular exercise reduces hostility. Long-term exercise intervention studies showed decreases in hostility (Lavie & Milani, 2005, 2006) and exercise has shown acute reductions in anger or hostility as well (Kennedy & Newton, 1997).

Exercise may influence PUFA metabolism itself. In rodents, exercise enhanced the effect of dietary DHA supplementation in increasing hippocampal levels of the synaptic membrane-bound protein syntaxin-3 (Chytrova, Ying, & Gomez-Pinilla, 2010).

DHA diet supplementation plus exercise resulted in greater increases in hippocampal

BDNF than DHA alone, and was associated with improved maze learning in rats (Wu,

Ying, & Gomez-Pinilla, 2008). In CARDIA, total energy intake was positively correlated with hostility, but this was not explained by confounding with physical activity. In the present study, total energy intake (daily kilocalories) was included as a covariate, but this

82 covariate was not significant in the model, indicating some other factor driving the positive association between n-3 intake and hostility.

Finally, while the Cronbach’s alpha was accceptable for the Cook-Medley

Hostility Scale in this sample, poor internal consistency could explain the association between greater n-3 intake and greater hostility. With a Cronbach’s alpha of α = 0.765, it is possible that the scale was not accurately assessing hostility in this sample. In fact, all of the dependent measures of negative emotionality were significantly and positively associated with each other, suggesting that the measures may all be assessing a similar construct.

No Relationships Between n-3 Intake and CES-D, PROMIS Anxiety SF, PROMIS Anger

SF, NEO Neuroticism, or LOT-R Score

Regression analyses were not significant for n-3 intake and a number of psychological outcomes. First, n-3 intake was not related to depressive symptoms, based on

CES-D score. A number of studies have shown negative associations between fish intake, a proxy for n-3 intake, and depressive symptoms. For instance, in Australian adults, a diet high in fish was associated with a lower likelihood of depression and anxiety, versus a diet high in n-6 rich fried foods (Jacka et al., 2010). In Finnish adults, those who consumed fish infrequently had a higher likelihood of having depressive symptoms (Tanskanen et al., 2001).

In Finnish females, those who consumed fish infrequently had a 2.5-fold increased risk of depression compared with frequent fish eaters, based on Hopkins Symptom Checklist-25 depression subscale and doctor-diagnosed lifetime depression (Timonen et al., 2004). Older

Hispanic American adults who were depressed had lower consumption of n-3-rich foods, compared to nondepressed individuals (Fitten et al., 2008). And a large longitudinal study of young adults found that those in the highest quintiles of EPA, DHA, and EPA plus DHA intake over 10 years had lower CES-D scores, particularly in women (Colangelo et al., 2009).

However, there may be confounding factors associated with fish consumption that explain these results. For instance, depressive symptoms are often associated with decreases in self-care or health behaviors, including eating a healthy diet (Appleton, Gunnell, et al.,

2008). Further, fish intake is a rough proxy for n-3 intake, and may be counterbalanced by high levels of n-6 in the diet. In fact, about half of the studies in a meta-analysis showed no significant relationship between fish intake and depressive symptoms (Murakami & Sasaki,

2010).

Physiological levels of n-3 also correlate with depressive symptoms. Higher plasma levels of DHA were associated with lower BDI scores in a sample of healthy adults

(Appleton, Gunnell, et al., 2008). A meta-analysis found that individuals diagnosed with depression had low levels of EPA, DHA, and total n-3 PUFAs in serum and erythrocyte membranes (Lin et al., 2010a). Individuals experiencing depressive symptoms who fell above the clinical threshold of depression on the CES-D showed a negative correlation between

DHA and CES-D score (Schiepers et al., 2009). In depressed participants, severity of depression, measured by Beck Depression Inventory score, correlated negatively with erythrocyte membrane n-3 PUFA levels and with dietary intake of n-3 PUFAs (Edwards et al., 1998). Postmortem studies reveal similar n-3 deficiencies in brain tissue of depressed individuals (McNamara et al., 2007).

Supplementation trials also corroborate the negative association between n-3s and depressed mood. In healthy adults, n-3 supplementation increased positive mood (Fontani et al., 2005a), and in depressed individuals, n-3 PUFA supplementation provided significant improvement in depression in three of four randomized clinical trials (Hallahan & Garland,

2005). A meta-analysis found that n-3 PUFA supplementation significantly improved depressive symptoms in participants with mood disorders, with a moderate effect size (Lin &

Su, 2007). Indeed, an important caveat is that depression severity may modify the effect of n-3 PUFAs on depressed mood, as described previously (Appleton et al., 2010). Such results suggest that n-3 PUFAs may be beneficial only for individuals with severe depression, not for individuals with milder depression or for nondepressed individuals. Therefore, it is possible that there was not a strong relationship between n-3 intake and psychological outcomes in this sample due to the relative psychological health of the participants. In individuals with more severe symptomatology or clinical diagnoses, a relationship may have been evident.

This study differed from the aforementioned studies in several ways. First, physiological levels of n-3 were not measured. While many studies examine associations between blood or tissue levels of n-3 and mood, the present study did not do so. The present study also did not employ n-3 supplementation, which has shown effects in some studies, particularly for clinically depressed individuals. In addition, a number of previous studies used clinically depressed populations, while the present study used a sample of participants with a range of CES-D scores. It is possible that associations between n-3 intake and depressive symptoms are most profound in clinically depressed individuals.

In this sample, n-3 intake and PROMIS Anxiety SF score were not significantly related. Most studies examining associations between n-3 and anxiety are randomized controlled trials of n-3 supplementation. These trials have shown anxiety reduction via n-

3 PUFA dietary supplementation in substance abusers (Buydens-Branchey et al., 2008), young adults suffering from test-taking anxiety (Yehuda et al., 2005), and healthy adults

(Fontani et al., 2005b). Research on anxiety and n-3 intake is not as extensive as that for depression and n-3 intake, and the fact that most of the anxiety research focuses on supplementation makes it difficult to interpret the results of the present study, as it did not use supplementation.

Similarly, regression analyses did not show a significant relationship between n-3 intake and PROMIS Anger SF score. However, as described above, higher hostility was associated with higher n-3 intake. While anger is a state characteristic, hostility refers to a traitlike negative orientation toward interpersonal interactions (Smith, 1992). Thus, while there was a relationship between n-3 intake and the trait hostility, there was no relationship between n-3 and state anger. Studies on anger and n-3 are also, largely, randomized controlled trials. Young adults taking n-3 PUFAs for one month showed reduced anger in the Profile of Mood States test (POMS) (Fontani et al., 2005b), and in substance abusers, those who received n-3 supplementation over a 3 month period showed decreases in POMS anger scores (Buydens-Branchey et al., 2008). Therefore, it may be that supplementation over time produces decreases in state anger, but not trait hostility. In fact, the PROMIS Anger SF scale assessed the past week, while the Cook

Medley Hostility Scale assessed general characteristics, i.e. whether the statement is

“usually” true for that individual.

Further, n-3 intake was not associated with neuroticism, a measure of traitlike negative affectivity, in this sample. In previous studies, low neuroticism was associated with high serum EPA levels, and PUFA serum levels explained 4.2% to 9.6% of the variance in neuroticism. (Conklin, Manuck, et al., 2007). While there is relatively little research on neuroticism and n-3 intake, previous studies would suggest a relationship between greater n-3 intake and lower neuroticism.

Finally, optimism and n-3 intake were not significantly related in the present sample.

Previous research in young adults shows a positive association between fish intake and optimism, particularly in women (Kelloniemi et al., 2005). In older adults, individuals within the highest tertile of n-3 intake had high optimism on the 4-item 4Q scale, but not on the more exhaustive LOT-R (van de Rest et al., 2009). Total PUFA intake from fish was not associated with dispositional optimism, but consuming ≥400 mg of combined EPA and DHA per day from any source was associated with higher dispositional optimism after controlling for sociodemographic factors (Giltay et al., 2007). While the present study did not find an association between optimism and n-3 intake, there was an interaction between n-3 intake and genotype for optimism.

Interaction Between Genotype and n-3 Intake on LOT-R Score

There was a significant interaction between n-3 intake and genotype for LOT-R score, such that the association between optimism and n-3 intake was negative in minor

87 allele carriers and positive in major allele homozygotes. However, simple slope tests revealed that the slopes were not significantly different than zero. Although the simple slopes were not different, the direction of this interaction makes sense in the context of the proposed hypotheses. Hypothesis 3 proposed that the association between n-3 PUFA intake and negatively valenced psychological outcomes would be negative in major allele homozygotes; correspondingly, the association between n-3 PUFA intake and positively valenced psychological outcomes should be positive in major allele homozygotes. Major allele homozygotes are characterized by more efficient desaturase activity, implying that the more n-3s they consume, the more efficiently these n-3s will be incorporated into tissue. Conversely, minor allele carriers exhibit inefficient desaturase activity, implying that there may be a cap to how much n-3 is beneficial as their desaturase pathway may only process limited n-3 amounts.

No Significant Interaction Between Genotype and n-3 Intake or n-6 Intake and CES-D,

PROMIS Anxiety SF, PROMIS Anger SF, Cook Medley Hostility Scale, or NEO

Neuroticism Score

The present study did not find a significant interaction between n-3 intake or n-6 intake and rs174575 genotype in relation to any of the psychological outcomes. Only a handful of previous studies have explored gene-diet interactions involving rs174575. In postpartum women, increasing dietary fish intake in efficient-desaturase major allele carriers increased DHA proportions in breast milk; dietary fish intake did not boost DHA levels in the less efficient minor allele homozygotes (Molto-Puigmarti et al., 2010). However, the

88 outcome was a biological marker, that is, DHA in breast milk. The present study examined psychological outcomes, as opposed to biological ones, which may not demonstrate as robust of an effect.

One gene-diet interaction study that did examine a psychological outcome found that rs174575 major allele carriers with a diet high in PUFA-rich breast milk as infants showed a 6 to 7 point advantage in IQ, versus major allele carriers who did not consume breast milk (Caspi et al., 2007). In minor allele homozygotes, IQ was not influenced by breast milk intake; for low-efficiency desaturase individuals, there was no advantage to consuming breast milk, but no disadvantage from not consuming it. The present study differs from the Caspi in a number of ways. First, the present study examined affective outcomes, as opposed to IQ. While IQ is relatively stable, individual reports of mood can vary considerably over time. Perhaps repeated measures of mood would have provided different results. Next, the Caspi study examined PUFA intake during infancy, while the present study examined PUFA intake during adulthood. It may be that PUFA intake during infancy or childhood exerts a more robust effect, as brain development, influenced by PUFA intake, is still occurring (Gibson, Muhlhausler, & Makrides, 2011). Perhaps in adulthood, fatty acid intake is less critical in comparison.

Exploratory Analyses

No Significant Relationship Between n-6 Intake and Psychological Outcomes

Previous studies suggest that n-6 PUFAs are positively correlated with a number of negatively valenced psychological outcomes. In healthy community-dwelling adults

89 with no Axis I psychopathology, higher AA serum levels and AA:EPA ratio, adjusted for age, gender, and race, were associated with greater depressive symptomatology as measured by the Beck Depression Inventory (Conklin, Manuck, et al., 2007). Individuals with major depression had significantly higher AA:EPA ratios than healthy controls

(Maes et al., 1999). Higher AA tissue levels were associated with higher hostility levels

(Watari et al., 2010). Also, higher serum AA and AA:EPA in healthy adults was associated with greater neuroticism (Conklin, Manuck, et al., 2007). In terms of anxiety, individuals with social anxiety had higher n-6 erythrocyte levels compared to controls.

Further, there was a significant relationship between greater social anxiety severity and higher n-6 levels (Green et al., 2006).

However, in the present study, relationships between n-6 intake and psychological outcomes were nonsignificant. This could be because many of the aforementioned studies employed tissue measurement of PUFAs, while the present study relied on dietary self report. Also, the mean n-6 intake in the present study was 12.45 g daily, much lower than CARDIA’s 65 g for women, which may have affected results. While the mean n-6: n-3 ratio in the present study was 9.42, similar to CARDIA’s 10.05 for women, this was lower than the average American ratio of 16 (Eaton & Konner, 1985; Simopoulos, 1999).

Thus, perhaps there was an insufficient range of n-6 intake in this sample.

No Significant Relationship Between PUFA Intake or Genotype and Psychological

Outcomes in Non-Vegetarians

When vegetarians were excluded from this sample, results remained largely the same. There were no significant relationships between total n-3 intake and any of the negatively valenced psychological outcomes, save for hostility. Higher n-3 intake was associated with greater hostility, as in the original analyses. Given that vegetarians did not have statistically significant differences in nutritional intake compared to nonvegetarians in this sample, these results make sense. If vegetarians had differed from nonvegetarians in PUFA intake, particularly in the short-chain PUFAs LA and ALA, it is possible that this could have influenced outcomes. Certainly, vegetarians do not consume fish (a key source of the n-3 long-chain PUFAs EPA and DHA) nor poultry or offal

(which provide primarily n-6 long-chain PUFA AA) (Raper et al., 1992). In fact, vegetarians only consume plant sources of PUFAs, which are the short-chain ALA (in leafy vegetables, beans, seeds, and nuts) (Sijben & Calder, 2007) and LA (found in corn oil, safflower oil, sunflower oil, and soybean oil) (Raper et al., 1992).

However, in this sample, neither vegetarians nor nonvegetarians consumed large amounts of fish, and did not differ significantly in long-chain PUFA consumption.

Therefore, differences in dietary intake of EPA and DHA was not a confound in this sample. This is consistent with research showing that typical consumption of long-chain n-3 fatty acids is low in Midwestern samples due to infrequent fish consumption (Lewis et al., 1995). Interestingly, dietary differences in PUFA consumption may not produce clinically significant differences due to the plasticity of PUFA metabolism. In a large sample of adults in the EPIC (European Prospective Investigation into Cancer and

Nutrition) study, dietary diaries revealed that vegetarians consumed only 57-80% of the

91 n-3s that fish eaters did. However, differences in plasma PUFA status between vegetarians and nonvegetarians were small. In fact, vegetarians had greater product : precursor ratios than fish eaters, suggesting that vegetarians may have upregulated ALA conversion to compensate for low dietary intake of long-chain PUFAs (Sontrop &

Campbell, 2006).

No Significant Relationship Between PUFA Intake or Genotype and Psychological

Outcomes in Non-Users of Antidepressants

In this sample, antidepressant users were more anxious, had greater neuroticism, and were less optimistic than non-users. However, when antidepressant users were excluded from this sample, results for main hypotheses were again similar to the original results. Greater n-3 intake was again associated with greater hostility, but not with other psychological outcome measures. While greater anxiety and neuroticism in participants taking antidepressants suggested that they comprised a subgroup of the sample with greater negative emotionality, this did not influence results. Intriguingly, previous studies indicate that high long-chain n-3 PUFA intake may enhance antidepressant efficacy

(Sontrop & Campbell, 2006). However, in this sample, long-chain n-3 PUFA intake was low in antidepressant users and non-users, indicating that antidepressant medication effects were likely not influenced by n-3 intake.

Significant Relationship Between Genotype and Hostility in Participants with Mild-to-

Severe Depressive Symptomatology

As a CES-D score of 16 or above indicates mild depressive symptomatology, relationships among genotype, PUFA intake, and psychological outcomes were examined in individuals scoring at or above 16 on the CES-D. In these more depressed participants, higher n-3 intake was associated with lower levels of optimism. Conversely, high n-6 intake was associated with higher levels of optimism. This is the reverse of the typical pattern, which demonstrates high n-3 associated with greater optimism and high n-6 associated with greater negative emotionality. As in the full sample, in this subsample total n-3, total n-6, and high kilocalorie intake were all positively correlated with each other. Again, higher intake of n-3, n-6, and kilocalories were significantly associated with greater anger and hostility, and higher BMI was significantly associated with lower optimism. Thus, correlations among these factors may have influenced the relationship between high n-3 intake and low optimism.

Further, among this subsample with greater depressive symptomatology, genotype was significantly correlated with hostility, such that low-efficiency desaturase minor allele carriers had greater hostility. This fits with Hypothesis 2, which predicted that minor allele carriers, who have FADS2 polymorphisms previously associated with lower plasma levels of EPA and DHA, would have higher levels of negative emotionality than major allele homozygotes. However, plasma PUFA levels were not measured in this study, so it is not conclusive whether minor allele carriers’ higher hostility levels were also associated with low plasma EPA and DHA. Further, this is a small subsample

(n=67), so results should be interpreted cautiously. Also, multiple unplanned posthoc

93 tests introduce familywise error, further urging cautious interpretation of this result

(Rosenberg, Che, & Chen, 2006)

No Significant Relationship Between PUFA Intake or Genotype and Psychological

Outcomes in Participants Reporting a History of Depression

Among the 31 participants who self-reported a history of depression, there were no significant associations between PUFA intake and psychological outcomes, nor were there associations between genotype and psychological outcomes. However, this subsample is quite small, and thus power was drastically reduced. Further, this was a single-item self report measure. It is possible that a less subjectively biased clinician- assessed history of depression would produce different results.

Association Between Cook Medley Hostility Subscales and n-3 Intake

Within the Cook Medley Hostility Scale, three subscales were positively associated with n-3 intake: Cynicism, Hostile Attribution, and Hostile Affect. These subscales were among five established by Barefoot et al (1989), based on rational analysis of item content. Cynicism reflects negative beliefs about others, namely, interpreting others' behavior as deceitful or selfish. Hostile Attribution indicates a tendency to interpret others’ behavior as hostile or harmful, reflecting feelings of suspicion, paranoia, and fear of threat to the self, and is associated with reactive aggression. Finally, Hostile Affect refers to negative emotions associated with social relationships, including anger and impatience when interacting with others.

In this sample, high n-3 intake was associated with Cynicism, Hostile

Attributions, and Hostile Affect, or negative beliefs and emotions toward other people.

This contradicts previous research, in which greater dietary n-3 was associated with lower hostility (Iribarren et al., 2004; Weidner, 1992).

Association Between Cook Medley High Hostility Cutoff and n-3 Intake

The CARDIA study used a cutoff score of seventy-fifth percentile or greater on the Cook Medley Hostility Scale to indicate high hostility. In the CARDIA sample, higher dietary intake of n-3 PUFAs was associated with lower likelihood being in the high hostility group (Iribarren et al., 2004). However, in this sample, higher n-3 intake was associated with a significantly greater likelihood of being in the high hostility group.

Again, this result opposes findings in other studies, possible reasons for which are described above.

Conclusion

While the results of the present study did not support the proposed hypotheses, there are a number of factors that should be considered for future work. First, it is possible that other polymorphisms, not just the rs174575 SNP, moderate the relationship between n-3 intake and mood. For instance, polymorphisms for long chain fatty acid CoA ligase, an enzyme implicated in incorporation of PUFA into the cell membrane, may be a target of future research. It is also possible that intake of nutrients that modify n-3 metabolism confound the apparent associations between n-3 intake and mood outcomes (Appleton,

Gunnell, et al., 2008). A recent article has suggested that gene-environment interactions may impart genetic plasticity, rather than genetic vulnerability (Belsky et al., 2009). Thus, a particular genotype may confer both a vulnerability to environmental deprivation, as well as enhanced function in a favorable environment. Further research is needed in this area, to examine both possible detrimental effects of n-6 in the diet and beneficial effects of n-3 in the diet, based on FADS genotype.

Limitations

There are a number of limitations to be acknowledged in the present study. A major limitation was that blood or tissue PUFA levels were not measured, as this was cost prohibitive. Thus, if there were an interaction between dietary PUFA intake and genotype on plasma or tissue levels of PUFAs, it would not be discerned in the present study. Ideally, red blood cell PUFA levels would be measured, to circumvent possible bias in dietary self-report. Direct measurement of red blood cell PUFAs would also allow determination of product to precursor ratios, so that desaturase efficiency could be approximated for each genotype group. Previous work shows that compared to controls, individuals diagnosed with major depression had significantly higher precursor to product ratios (Maes et al., 1999). In a case-control study, plasma and erythrocyte levels of

PUFAs indicated inefficient desaturase activity in depressed participants compared to controls (Assies et al., 2010).

Plasma and erythrocyte n-6 precursor-to-product ratios were higher in rs174575 minor allele homozygotes in previous research, indicating less efficient conversion of

96 precursor PUFAs to long-chain products than in major allele carriers (Xie & Innis, 2008).

Minor allele homozygotes also had lower rates of depression during and after pregnancy, compared to major allele homozygotes (Xie & Innis, 2009). However, these studies were in pregnant or nursing women, who are in a physiologically different state than the women in the present study. Another study revealed that in major allele carriers, increasing dietary fish or fish oil intake increased DHA proportions in breast milk, but not in minor allele homozygotes (Molto-Puigmarti et al., 2010). Again, this study differs from the present study as its participants were nursing women. Research shows that the conversion of ALA to EPA and DHA is more efficient in those with higher estrogen levels (Burdge & Wootton, 2002). Thus, in nursing women, desaturase efficiency may be potentiated, impacting tissue levels and mood, while in women who are not pregnant or nursing, such as in this sample, this effect may be minimized.

This research also relied on self-report measures for psychological outcomes, as opposed to clinician-rated symptoms or diagnoses. Measures such as the CES-D have been used extensively in research settings and show good reliability with clinician-rated outcomes, but such measures do have flaws. For instance, self-reported current depressive symptomatology may be influenced by factors such as academic stress. To account for this, I assessed for differences in depressive symptomatology by week in academic quarter, and by examination weeks (based on the Psychology 100 examination schedule). There were no differences in CES-D score based on week in quarter or exam weeks. However, this did not account for other examinations (e.g. courses other than

Psychology 100), or for stressors in participants’ personal lives that may have influenced symptoms of depression, anxiety, hostility, or anger.

Another limitation was sample size. This study’s sample size was similar to those of other groups studying interactions between rs174575, PUFA status, and psychological outcomes (Brookes et al., 2006; Xie & Innis, 2009). However, few studies have examined interactions among rs174575, PUFA intake, and psychological outcomes. Of these studies, a significant gene by environment interaction was found in a study with a sample greater than two thousand subjects (Caspi et al., 2007).

In fact, some argue that sample sizes for gene by environment interaction studies ought to be quite large (Munafo, Durrant, Lewis, & Flint, 2009). In gene by environment interaction studies, the power to detect a small interaction effect is relatively low, for any gene frequency. However, with stronger interaction effects, power increases dramatically with gene frequency. One model suggests that statistical power peaks as minor allele frequency approaches 0.30 (Foppa & Spiegelman, 1997). For gene frequencies less than

0.30 and moderate interaction effects, power becomes sensitive to gene frequency, such that misspecification of the gene frequency can result in incorrect power estimation. Yet for minor allele frequencies above 0.30, the aforementioned model predicts that misspecification of allele frequency will not affect power or sample size calculations. For these reasons, it is best to estimate genotype distributions before conducting a gene- environment interaction study, as was done in the present study. In the present study, the rs174575 minor allele frequency is 0.28 (appliedbiosystems.com), and studies on rs174575 typically contain 45%-47% minor allele carriers (Caspi et al., 2007; Molto-

Puigmarti et al., 2010). Thus, power should not have been influenced by allele frequency in this case. Nonetheless, a larger sample would increase power, and would also allow analysis in a triallelic manner, providing more precise characterization.

Case-control studies, which allow comparison of healthy controls to affected cases, also provide greater power in gene by environment interaction studies. If the number of cases is fixed, adding control subjects to the sample greatly increases power

(Foppa & Spiegelman, 1997). Thus, repeating this study using a case-control design (e.g. comparing depressed versus nondepressed individuals, or anxious versus non-anxious individuals) would be ideal.

Another complexity in gene by environment interaction studies is an assumption of independence. That is, assuming that one’s genetic status is independent from one’s environmental exposure. This independence assumption is not always correct, and may vary, for instance, by ethnicity or cultural factors (Foppa & Spiegelman, 1997; Yang &

Khoury, 1997). Such gene-environment correlations are a critical factor to consider in gene by environment interaction studies, yet may be difficult to control. Ethnicity in particular may influence gene-environment correlations (Moffitt, 2006). While previous research shows that FADS allele frequencies are similar in most ethnic groups, diet may vary widely between cultural or ethnic groups. Alternatively, unmeasured genetic characteristics or environmental exposures (e.g. exercise, as described above) may have confounded results in this study.

Also, precision with which genotype and environmental exposure is measured is a key factor in gene-environment interaction studies (Moffitt, 2006). To ensure that

99 genotype and environmental exposure were measured accurately, several measures were taken. To minimize errors in genotyping, all samples were run in duplicate to confirm that both samples per subject yielded the same genotype. And while dietary intake of

PUFAs was based on self report, the Block FFQ was chosen with careful consideration for its ability to accurately capture n-3 and n-6 intake. To enhance accuracy in dietary self-report, subjects were provided serving size guides, with photographic depictions of various portion sizes. Thus, careful efforts were made to ensure accuracy in measuring both genotype and environmental exposure.

Finally, the present study’s sample represented a relatively narrow demographic.

The participants were mostly young, college-educated women free of major psychiatric disorders. This limits generalizability of results. For instance, future research should include men. In the present study, a female sample was selected due to the higher prevalence of depressive symptoms in women, more efficient conversion of short chain to long chain PUFAs in women, and that gene x diet interaction studies on which this study was based used female samples. While conversion of ALA to EPA and DHA may be more efficient in women, this is likely due to estrogen levels (Burdge & Wootton,

2002). Thus, a male sample would circumvent possible influences of hormonal fluctuation on PUFA metabolism. In addition to including men in the sample, a wider range of ages and socioeconomic status levels would have allowed generalizability of results to a wider population. Outcomes such as depression may vary by socioeconomic status (Paczkowski & Galea, 2010) and age (Fiske, Wetherell, & Gatz, 2009; Nandi,

Beard, & Galea, 2009).

100

While this sample appeared to be relatively free of psychopathology, a small subsample reported using antidepressant medications. It is possible that controlling for antidepressant use in the original analyses biased the results. To ensure that this was not the case, the hypotheses were retested, excluding antidepressant users. Results remained unchanged. In fact, it is possible that repeating this study in a sample of clinically depressed individuals, or a case-control study with healthy controls and depressed individuals, would have revealed associations between n-3 or n-6 intake and mood that is only evident in more depressed populations.

Also, the participants’ consumption of PUFAs was rather homogenous, providing a narrow range of n-3 and n-6 intake. While n-3 and n-6 intake was similar in vegetarians and non-vegetarians in this study, future research might examine n-3 PUFA and gene interactions in vegetarians, who consume only n-3 precursors, not products. Thus, tissue long chain product PUFA levels in vegetarians are more influenced by desaturase efficiency than in omnivores, who may attain high levels of EPA and DHA directly from the diet.

Another shortcoming of the study is that factors such as exercise and memory were not assessed. As described above, exercise may be associated with certain psychological characteristics, or influence PUFA metabolism itself. Additionally, when relying on self-report measures, accurate memory is critical. This is especially true for measures such as the Block FFQ, which requires subjects to recall their typical intake of various foods over the past year. While efforts were made to promote accuracy in dietary

101 self-report by providing photographic depictions of portion sizes, errors in recall or underreporting may occur (Hebert et al., 2002; Hebert et al., 2001).

Finally, the study was correlational in nature, with no control or experimental groups. Thus, it cannot provide evidence for any mechanism underlying associations among dietary PUFA intake, FADS2 genotype, and psychological outcomes. To examine mechanism, future work might include longitudinal research, such as examining treatment response to n-3 supplementation in depressed individuals based on genotype. In samples of healthy young adults, n-3 dietary supplementation reduced hostility during high stress periods (Hamazaki et al., 2000), and in another sample reduced anger in the

Profile of Mood States test (Fontani et al., 2005b). Conversely, reducing levels of n-6 in the diet over several years resulted in decreased levels of aggressive hostility (Weidner,

1992). If supplementation studies such as these could be replicated to include FADS genotype, we would be able to determine whether these effects are moderated by genotype.

102

References

Achat, H., Kawachi, I., Spiro, A., 3rd, DeMolles, D. A., & Sparrow, D. (2000). Optimism and depression as predictors of physical and mental health functioning: The normative aging study. Ann Behav Med, 22(2), 127-130. ADA. (2005). Evidence analysis library. The disorders of lipid metabolism evidence- based nutrition practice guideline. Adams, P. B., Lawson, S., Sanigorski, A., & Sinclair, A. J. (1996). Arachidonic acid to eicosapentaenoic acid ratio in blood correlates positively with clinical symptoms of depression. Lipids, 31, S157-S161. Agriculture, T. M. o. (1997). Dietary intake of iodine and fatty acids. London. Albert, C. M., Oh, K., Whang, W., Manson, J. E., Chae, C. U., Stampfer, M. J., et al. (2005). Dietary alpha-linolenic acid intake and risk of sudden cardiac death and coronary heart disease. Circulation, 112(21), 3232-3238. Andersen, L. F., Solvoll, K., & Drevon, C. A. (1996). Very-long-chain n-3 fatty acids as biomarkers for intake of fish and n-3 fatty acid concentrates. Am J Clin Nutr, 64(3), 305-311. Andersen, L. F., Solvoll, K., Johansson, L. R. K., Salminen, I., Aro, A., & Drevon, C. A. (1999). Evaluation of a food frequency questionnaire with weighed records, fatty acids, and alpha-tocopherol in adipose tissue and serum. American Journal of Epidemiology, 150(1), 75-87. Appleton, K. M., Gunnell, D., Peters, T. J., Ness, A. R., Kessler, D., & Rogers, P. J. (2008). No clear evidence of an association between plasma concentrations of n-3 long-chain polyunsaturated fatty acids and depressed mood in a non-clinical population. Prostaglandins Leukotrienes and Essential Fatty Acids, 78(6), 337- 342. Appleton, K. M., Hayward, R. C., Gunnell, D., Peters, T. J., Rogers, P. J., Kessler, D., et al. (2006). Effects of n-3 long-chain polyunsaturated fatty acids on depressed mood: Systematic review of published trials. American Journal of Clinical Nutrition, 84(6), 1308-1316. Appleton, K. M., Rogers, P. J., & Ness, A. R. (2008). Is there a role for n-3 long-chain polyunsaturated fatty acids in the regulation of mood and behaviour? A review of the evidence to date from epidemiological studies, clinical studies and intervention trials. Nutr Res Rev, 21(1), 13-41. Appleton, K. M., Rogers, P. J., & Ness, A. R. (2010). Updated systematic review and meta-analysis of the effects of n-3 long-chain polyunsaturated fatty acids on depressed mood. Am J Clin Nutr, 91(3), 757-770. 103

Arab, L. (2003). Biomarkers of fat and fatty acid intake. Journal of Nutrition, 133(3), 925S-932S. Artmann, A., Petersen, G., Hellgren, L. I., Boberg, J., Skonberg, C., Nellemann, C., et al. (2008). Influence of dietary fatty acids on endocannabinoid and n- acylethanolamine levels in rat brain, liver and small intestine. Biochimica Et Biophysica Acta-Molecular and Cell Biology of Lipids, 1781(4), 200-212. Assies, J., Pouwer, F., Lok, A., Mocking, R. J. T., Bockting, C. L. H., Visser, I., et al. (2010). Plasma and erythrocyte fatty acid patterns in patients with recurrent depression: A matched case-control study. Plos One, 5(5). Astorg, P., Bertrais, S., Alessandri, J. M., Guesnet, P., Kesse-Guyot, E., Linard, A., et al. (2009). Long-chain n-3 fatty acid levels in baseline serum phospholipids do not predict later occurrence of depressive episodes: A nested case-control study within a cohort of middle-aged french men and women. Prostaglandins Leukotrienes and Essential Fatty Acids, 81(4), 265-271. Astorg, P., Bertrais, S., Laporte, F., Arnault, N., Estaquio, C., Galan, P., et al. (2008). Plasma n-6 and n-3 polyunsaturated fatty acids as biomarkers of their dietary intakes: A cross-sectional study within a cohort of middle-aged french men and women. European Journal of Clinical Nutrition, 62(10), 1155-1161. Barefoot, J. C., Dodge, K. A., Peterson, B. L., Dahlstrom, W. G., & Williams, R. B., Jr. (1989). The cook-medley hostility scale: Item content and ability to predict survival. Psychosom Med, 51(1), 46-57. Basiotis, P. P., Welsh, S. O., Cronin, F. J., Kelsay, J. L., & Mertz, W. (1987). Number of days of food intake records required to estimate individual and group nutrient intakes with defined confidence. J Nutr, 117(9), 1638-1641. Baylin, A., & Campos, H. (2004). Arachidonic acid in adipose tissue and nonfatal acute myocardial infarction. Circulation, 110(17), 3605. Belsky, J., Jonassaint, C., Pluess, M., Stanton, M., Brummett, B., & Williams, R. (2009). Vulnerability genes or plasticity genes? Mol Psychiatry, 14(8), 746-754. Berg, K. A., Maayani, S., & Clarke, W. P. (1996). 5-hydroxytryptamine(2c) receptor activation inhibits 5-hydroxytryptamine(1b)-like receptor function via arachidonic acid metabolism. Molecular Pharmacology, 50(4), 1017-1023. Billingsley, K. D., Waehler, C. A., & Hardin, S. I. (1993). Stability of optimism and choice of coping strategy. Percept Mot Skills, 76(1), 91-97. Blazer, D. G., Kessler, R. C., McGonagle, K. A., & Swartz, M. S. (1994). The prevalence and distribution of major depression in a national community sample - the national comorbidity survey. American Journal of Psychiatry, 151(7), 979-986. Block, E. R., & Edwards, D. (1987). Effect of plasma-membrane fluidity on serotonin transport by endothelial-cells. American Journal of Physiology, 253(5), C672- C678. Bokor, S., Dumont, J., Spinneker, A., Gonzalez-Gross, M., Nova, E., Widhalm, K., et al. (2010). Single nucleotide polymorphisms in the FADS gene cluster are associated with delta-5 and delta-6 desaturase activities estimated by serum fatty acid ratios. Journal of Lipid Research, 51(8), 2325-2333.

104

Bountziouka, V., Polychronopoulos, E., Zeimbekis, A., Papavenetiou, E., Ladoukaki, E., Papairakleous, N., et al. (2009). Long-term fish intake is associated with less severe depressive symptoms among elderly men and women the medis (mediterranean islands elderly) epidemiological study. Journal of Aging and Health, 21(6), 864-880. Bourre, J. M., Bonneil, M., Chaudiere, J., Clement, M., Dumont, O., Durand, G., et al. (1992). Structural and functional importance of dietary polyunsaturated fatty- acids in the nervous-system. In N. G. Bazan, M. G. Murphy & G. Taffano (Eds.), Neurobiology of essential fatty acids (Vol. 318, pp. 211-229). Bourre, J. M., Dinh, L., Boithias, C., Dumont, O., Piciotti, M., & Cunnane, S. (1997). Possible role of the choroid plexus in the supply of brain tissue with polyunsaturated fatty acids. Neuroscience Letters, 224(1), 1-4. Bouwens, M., van de Rest, O., Dellschaft, N., Bromhaar, M. G., de Groot, L., Geleijnse, J. M., et al. (2009). Fish-oil supplementation induces antiinflammatory gene expression profiles in human blood mononuclear cells. American Journal of Clinical Nutrition, 90(2), 415-424. Briefel, R. R., Sempos, C. T., McDowell, M. A., Chien, S., & Alaimo, K. (1997). Dietary methods research in the third national health and nutrition examination survey: Underreporting of energy intake. Am J Clin Nutr, 65(4 Suppl), 1203S-1209S. Brookes, K. J., Chen, W., Xu, X. H., Taylor, E., & Asherson, P. (2006). Association of fatty acid desaturase genes with attention-deficit/hyperactivity disorder. Biological Psychiatry, 60(10), 1053-1061. Burdge, G. C. (2004). Alpha-linolenic acid metabolism in men and women: Nutritional and biological implications. Current Opinion in Clinical Nutrition and Metabolic Care, 7(2), 137-144. Burdge, G. C., & Wootton, S. A. (2002). Conversion of alpha-linolenic acid to eicosapentaenoic, docosapentaenoic and docosahexaenoic acids in young women. Br J Nutr, 88(4), 411-420. Buydens-Branchey, L., Branchey, M., & Hibbeln, J. R. (2008). Associations between increases in plasma n-3 polyunsaturated fatty acids following supplementation and decreases in anger and anxiety in substance abusers. Prog Neuropsychopharmacol Biol Psychiatry, 32(2), 568-575. Calder, P. C. (2006). Abnormal fatty acid profiles occur in atopic dermatitis but what do they mean? Clinical and Experimental Allergy, 36(2), 138-141. Calder, P. C. (2008). The relationship between the fatty acid composition of immune cells and their function. Prostaglandins Leukotrienes and Essential Fatty Acids, 79(3-5), 101-108. Calderon, F., & Kim, H. Y. (2004). Docosahexaenoic acid promotes neurite growth in hippocampal neurons. J Neurochem, 90(4), 979-988. Capuron, L., & Miller, A. H. (2004). Cytokines and psychopathology: Lessons from interferon-alpha. Biological Psychiatry, 56(11), 819-824. Carvajal, O., & Angulo, O. (1997). Effect of n-3 polyunsaturated fatty acids on the lipidic profile of healthy mexican volunteers. Salud Publica Mex, 39(3), 221-224.

105

Caspi, A., Williams, B., Kim-Cohen, J., Craig, I. W., Milne, B. J., Poulton, R., et al. (2007). Moderation of breastfeeding effects on the iq by genetic variation in fatty acid metabolism. PNAS, 104(47), 18860-18865. Catanzaro, S. J., Wasch, H. H., Kirsch, I., & Mearns, J. (2000). Coping-related expectancies and dispositions as prospective predictors of coping responses and symptoms. J Pers, 68(4), 757-788. Caughey, G. E., Mantzioris, E., Gibson, R. A., Cleland, L. G., & James, M. J. (1996). The effect on human tumor necrosis factor alpha and interleukin 1 beta production of diets enriched in n-3 fatty acids from vegetable oil or fish oil. American Journal of Clinical Nutrition, 63(1), 116-122. Cazzola, R., Rondanelli, M., Russo-Volpe, S., Ferrari, E., & Cestaro, B. (2004). Decreased membrane fluidity and altered susceptibility to peroxidation and lipid composition in overweight and obese female erythrocytes. J Lipid Res, 45(10), 1846-1851. Chalon, S. (2006). Omega-3 fatty acids and monoamine neurotransmission. Prostaglandins Leukotrienes and Essential Fatty Acids, 75(4-5), 259-269. Chalon, S., Delion-Vancassel, S., Belzung, C., Guilloteau, D., Leguisquet, A. M., Besnard, J. C., et al. (1998). Dietary fish oil affects monoaminergic neurotransmission and behavior in rats. Journal of Nutrition, 128(12), 2512-2519. Chalon, S., Vancassel, S., Zimmer, L., Guilloteau, D., & Durand, G. (2001). Polyunsaturated fatty acids and cerebral function: Focus on monoaminergic neurotransmission. Lipids, 36(9), 937-944. Chang, E. C., & Bridewell, W. B. (1998). Irrational beliefs, optimism, pessimism, and psychological distress: A preliminary examination of differential effects in a college population. J Clin Psychol, 54(2), 137-142. Chilton, F. H., Lee, T. C., Willard, S. L., Ivester, P., Sergeant, S., Register, T. C., et al. (2011). Depression and altered serum lipids in cynomolgus monkeys consuming a western diet. Physiol Behav. Chiodo, S., Tessitore, A., Cortis, C., Cibelli, G., Lupo, C., Ammendolia, A., et al. (2011). Stress-related hormonal and psychological changes to official youth taekwondo competitions. Scand J Med Sci Sports, 21(1), 111-119. Cho, H. P., Nakamura, M. T., & Clarke, S. D. (1999). Cloning, expression, and nutritional regulation of the mammalian delta-6 desaturase. Journal of Biological Chemistry, 274(1), 471-477. Chung, H., Nettleton, J. A., Lemaitre, R. N., Barr, R. G., Tsai, M. Y., Tracy, R. P., et al. (2008). Frequency and type of seafood consumed influence plasma (n-3) fatty acid concentrations. J Nutr, 138(12), 2422-2427. Chytrova, G., Ying, Z., & Gomez-Pinilla, F. (2010). Exercise contributes to the effects of dha dietary supplementation by acting on membrane-related synaptic systems. Brain Res, 1341, 32-40. Cohen, J. (1998). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Earlbaum Associates.

106

Colangelo, L. A., He, K., Whooley, M. A., Daviglus, M. L., & Liu, K. (2009). Higher dietary intake of long-chain omega-3 polyunsaturated fatty acids is inversely associated with depressive symptoms in women. Nutrition, 25(10), 1011-1019. Conklin, S. M., Harris, J. I., Manuck, S. B., Yao, J. K., Hibbeln, J. R., & Muldoon, M. F. (2007). Serum omega-3 fatty acids are associated with variation in mood, personality and behavior in hypercholesterolemic community volunteers. Psychiatry Research, 152(1), 1-10. Conklin, S. M., Manuck, S. B., Yao, J. K., Flory, J. D., Hibbeln, J. R., & Muldoon, M. F. (2007). High omega-6 and low omega-3 fatty acids are associated with depressive symptoms and neuroticism. Psychosomatic Medicine, 69(9), 932-934. Conklin, S. M., Runyan, C. A., Leonard, S., Reddy, R. D., Muldoon, M. F., & Yao, J. K. (2010). Age-related changes of n-3 and n-6 polyunsaturated fatty acids in the anterior cingulate cortex of individuals with major depressive disorder. Prostaglandins Leukot Essent Fatty Acids, 82(2-3), 111-119. Cook, W. W., & Medley, D. M. (1954). Proposed hostility and pharisaic-virtue scales for the mmpi. J Applied Psychology, 38, 414-418. Costa, P. T., Jr., Zonderman, A. B., McCrae, R. R., & Williams, R. B., Jr. (1986). Cynicism and paranoid alienation in the cook and medley ho scale. Psychosom Med, 48(3-4), 283-285. Costa, P. T., & McCrae, R. R. (1992). Revised neo personality inventory (neo pi-r) and neo five-factor inventory (neo-ffi) professional manual. . Odessa, FL: Psychological Assessment Resources, Inc. Crane, S. B., & Greenwood, C. E. (1987). Dietary-fat source influences neuronal mitochondrial monoamine-oxidase activity and macronutrient selection in rats. Pharmacology Biochemistry and Behavior, 27(1), 1-6. Crowe, F. L., Skeaff, C. M., Green, T. J., & Gray, A. R. (2007). Serum phospholipid n-3 long-chain polyunsaturated fatty acids and physical and mental health in a population-based survey of new zealand adolescents and adults. American Journal of Clinical Nutrition, 86(5), 1278-1285. Cuijpers, P. (1998). Prevention of depression in chronic general medical disorders: A pilot study. Psychological Reports, 82(3), 735-738. Dantzer, R. (2005). Somatization: A psychoneuroimmune perspective. Psychoneuroendocrinology, 30(10), 947-952. Dantzer, R., O'Connor, J. C., Freund, G. G., Johnson, R. W., & Kelley, K. W. (2008). From inflammation to sickness and depression: When the immune system subjugates the brain. Nature Reviews Neuroscience, 9(1), 46-57. Deckelbaum, R. J., Worgall, T. S., & Seo, T. (2006). N-3 fatty acids and gene expression. American Journal of Clinical Nutrition, 83(6), 1520S-1525S. Delion, S., Chalon, S., Guilloteau, D., Besnard, J. C., & Durand, G. (1996). Alpha- linolenic acid dietary deficiency alters age-related changes of dopaminergic and serotoninergic neurotransmission in the rat frontal cortex. J Neurochem, 66(4), 1582-1591. Delion, S., Chalon, S., Guilloteau, D., Lejeune, B., Besnard, J. C., & Durand, G. (1997). Age-related changes in phospholipid fatty acid composition and monoaminergic 107

neurotransmission in the hippocampus of rats fed a balanced or an n-3 polyunsaturated fatty acid-deficient diet. Journal of Lipid Research, 38(4), 680- 689. Delion, S., Chalon, S., Herault, J., Guilloteau, D., Besnard, J. C., & Durand, G. (1994). Chronic dietary alpha-linolenic acid deficiency alters dopaminergic and serotoninergic neurotransmission in rats. Journal of Nutrition, 124(12), 2466- 2476. DellaGioia, N., & Hannestad, J. (2010). A critical review of human endotoxin administration as an experimental paradigm of depression. Neuroscience and Biobehavioral Reviews, 34(1), 130-143. DeMar, J. C., Jr., Ma, K., Bell, J. M., Igarashi, M., Greenstein, D., & Rapoport, S. I. (2006). One generation of n-3 polyunsaturated fatty acid deprivation increases depression and aggression test scores in rats. J Lipid Res, 47(1), 172-180. Dieperink, E., Willenbring, M., & Ho, S. B. (2000). Neuropsychiatric symptoms associated with hepatitis c and interferon alpha: A review. American Journal of Psychiatry, 157(6), 867-876. Doornbos, B., van Goor, S. A., Dijck-Brouwer, D. A. J., Schaafsma, A., Korf, J., & Muskiet, F. A. J. (2009). Supplementation of a low dose of dha or dha plus aa does not prevent peripartum depressive symptoms in a small population based sample. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 33(1), 49-52. Duchen, K., & Bjorksten, B. (2001). Polyunsaturated n-3 fatty acids and the development of atopic disease. Lipids, 36(9), 1033-1042. Dwyer, J. H., Allayee, H., Dwyer, K. M., Fan, J., Wu, H. Y., Mar, R., et al. (2004). Arachidonate 5-lipoxygenase promoter genotype, dietary arachidonic acid, and atherosclerosis. New England Journal of Medicine, 350(1), 29-37. Eaton, S. B., & Konner, M. (1985). Paleolithic nutrition - a consideration of its nature and current implications. New England Journal of Medicine, 312(5), 283-289. Edwards, R., Peet, M., Shay, J., & Horrobin, D. (1998). Omega-3 polyunsaturated fatty acid levels in the diet and in red blood cell membranes of depressed patients. Journal of Affective Disorders, 48(2-3), 149-155. Ehringer, W., Belcher, D., Wassall, S. R., & Stillwell, W. (1990). A comparison of the effects of linolenic (18-3-omega-3) and docosahexaenoic (22-6-omega-3) acids on phospholipid-bilayers. Chemistry and Physics of Lipids, 54(2), 79-88. Emken, E. A., Adlof, R. O., & Gulley, R. M. (1994). Dietary linoleic-acid influences desaturation and acylation of deuterium-labeled linoleic and linolenic acids in young-adult males. Biochimica Et Biophysica Acta-Lipids and Lipid Metabolism, 1213(3), 277-288. Ervin, R. B., Wright, J. D., Wang, C. Y., & Kennedy-Stephenson, J. (2004). Dietary intake of fats and fatty acids for the united states population: 1999-2000. Adv Data(348), 1-6. Farooqui, A. A., Hirashima, Y., & Horrocks, L. A. (1992). Brain phospholipases and their role in signal transduction. In N. G. Bazan, M. G. Murphy & G. Taffano (Eds.), Neurobiology of essential fatty acids (Vol. 318, pp. 11-25). 108

Ferrucci, L., Cherubini, A., Bandinelli, S., Bartali, B., Corsi, A., Lauretani, F., et al. (2006). Relationship of plasma polyunsaturated fatty acids to circulating inflammatory markers. Journal of Clinical Endocrinology & Metabolism, 91(2), 439-446. Fiske, A., Wetherell, J. L., & Gatz, M. (2009). Depression in older adults. Annu Rev Clin Psychol, 5, 363-389. Fitten, L. J., Ortiz, F., Fairbanks, L., Rosenthal, M., Cole, G. N., Nourhashemi, F., et al. (2008). Depression, diabetes and metabolic-nutritional factors in elderly hispanics. J Nutr Health Aging, 12(9), 634-640. Fontani, G., Corradeschi, F., Felici, A., Alfatti, F., Migliorini, S., & Lodi, L. (2005a). Cognitive and physiological effects of omega-3 polyunsaturated fatty acid supplementation in healthy subjects. Eur J Clin Invest, 35(11), 691-699. Fontani, G., Corradeschi, F., Felici, A., Alfatti, F., Migliorini, S., & Lodi, L. (2005b). Cognitive and physiological effects of omega-3 polyunsaturated fatty acid supplementation in healthy subjects. Eur J Clin Investigation, 35(11), 691-699. Foppa, I., & Spiegelman, D. (1997). Power and sample size calculations for case-control studies of gene-environment interactions with a polytomous exposure variable. Am J Epidemiol, 146(7), 596-604. Fradet, V., Cheng, I., Casey, G., & Wittel, J. S. (2009). Dietary omega-3 fatty acids, cyclooxygenase-2 genetic variation, and aggressive prostate cancer risk. Clinical Cancer Research, 15(7), 2559-2566. Fries, J. F., Bruce, B., & Cella, D. (2005). The promise of promis: Using item response theory to improve assessment of patient-reported outcomes. Clin Exp Rheumatol, 23(5 Suppl 39), S53-57. Garcia-Closas, M., Egan, K. M., Abruzzo, J., Newcomb, P. A., Titus-Ernstoff, L., Franklin, T., et al. (2001). Collection of genomic DNA from adults in epidemiological studies by buccal cytobrush and mouthwash. Cancer Epidemiol Biomarkers Prev, 10(6), 687-696. Gerrior, S. A., & Bente, L. (2002). Nutrient content of the u.S. Food supply, 1909-1999: A summary report. Washington, D.C.: U.S. Department of Agriculture, Center for Nutrition Policy and Promotion. Gibson, R. A., Muhlhausler, B., & Makrides, M. (2011). Conversion of linoleic acid and alpha-linolenic acid to long-chain polyunsaturated fatty acids (lcpufas), with a focus on pregnancy, lactation and the first 2 years of life. Matern Child Nutr, 7 Suppl 2, 17-26. Giltay, E. J., Geleijnse, J. M., Zitman, F. G., Buijsse, B., & Kromhout, D. (2007). Lifestyle and dietary correlates of dispositional optimism in men: The zutphen elderly study. J Psychosom Res, 63(5), 483-490. Giltay, E. J., Gooren, L. J., Toorians, A. W., Katan, M. B., & Zock, P. L. (2004). Docosahexaenoic acid concentrations are higher in women than in men because of estrogenic effects. Am J Clin Nutr, 80(5), 1167-1174. Gjerde, F., Block, J., & Block, J. H. (1988). Depressive symptoms and personality during late adolescence: Gender differences in the externalization-internalization of symptom expression. J Abnorm Psychol, 97(4), 475-486. 109

Glaser, C., Heinrich, J., & Koletzko, B. (2010). Role of FADS1 and FADS2 polymorphisms in polyunsaturated fatty acid metabolism. Metabolism-Clinical and Experimental, 59(7), 993-999. Godley, P. A., Campbell, M. K., Miller, C., Gallagher, P., Martinson, F. E., Mohler, J. L., et al. (1996). Correlation between biomarkers of omega-3 fatty acid consumption and questionnaire data in african american and caucasian united states males with and without prostatic carcinoma. Cancer Epidemiol Biomarkers Prev, 5(2), 115- 119. Grandi, S., Clementi, C., Guidi, J., Benassi, M., & Tossani, E. (2011). Personality characteristics and psychological distress associated with primary exercise dependence: An exploratory study. Psychiatry Res. Green, P., Herman, I. G., & Yadid, G. (2005). Increased arachidonic acid concentration in the brain of flinders sensitive line rats, an animal model of depression. Journal of Lipid Research, 46(6), 1093-1096. Green, P., Hermesh, H., Monselise, A., Marom, S., Presburger, G., & Weizman, A. (2006). Red cell membrane omega-3 fatty acids are decreased in nondepressed patients with social anxiety disorder. European Neuropsychopharmacology, 16(2), 107-113. Greenglass, E. R., & Julkunen, J. (1991). Cook-medley hostility, anger, and the type a behavior pattern in finland. Psychol Rep, 68(3 Pt 2), 1059-1066. Hallahan, B., & Garland, M. R. (2005). Essential fatty acids and mental health. British Journal of Psychiatry, 186, 275-277. Hamazaki, T., Itomura, M., Sawazaki, S., & Nagao, Y. (2000). Anti-stress effects of dha. [Proceedings Paper]. Biofactors, 13(1-4), 41-45. Hamazaki, T., Thienprasert, A., Kheovichai, K., Samuhaseneetoo, S., Nagasawa, T., & Watanabe, S. (2002). The effect of docosahexaenoic acid on aggression in elderly thai subjects--a placebo-controlled double-blind study. Nutr Neurosci, 5(1), 37- 41. Haney, T. L., Maynard, K. E., Houseworth, S. J., Scherwitz, L. W., Williams, R. B., & Barefoot, J. C. (1996). Interpersonal hostility assessment technique: Description and validation against the criterion of coronary artery disease. J Pers Assess, 66(2), 386-401. Harnack, K., Andersen, G., & Somoza, V. (2009). Quantitation of alpha-linolenic acid elongation to eicosapentaenoic and docosahexaenoic acid as affected by the ratio of n6/n3 fatty acids. Nutrition & Metabolism, 6. Harper, C. R., Edwards, M. J., DeFilippis, A. P., & Jacobson, T. A. (2006). Flaxseed oil increases the plasma concentrations of cardioprotective (n-3) fatty acids in humans. J Nutr, 136(1), 83-87. Hashimoto, M., Hossain, S., Shimada, T., & Shido, O. (2006). Docosahexaenoic acid- induced protective effect against impaired learning in amyloid beta-infused rats is associated with increased synaptosomal membrane fluidity. Clin Exp Pharmacol Physiol, 33(10), 934-939. Hathaway, S. R., McKinley, J. C. . (1940). The mmpi manual. New York: Psychological Corporation. 110

Hayes, A. F., & Matthes, J. (2009). Computational procedures for probing interactions in ols and logistic regression: Spss and sas implementations. Behav Res Methods, 41(3), 924-936. Hebert, J. R., Ebbeling, C. B., Matthews, C. E., Hurley, T. G., Ma, Y., Druker, S., et al. (2002). Systematic errors in middle-aged women's estimates of energy intake: Comparing three self-report measures to total energy expenditure from doubly labeled water. Ann Epidemiol, 12(8), 577-586. Hebert, J. R., Peterson, K. E., Hurley, T. G., Stoddard, A. M., Cohen, N., Field, A. E., et al. (2001). The effect of social desirability trait on self-reported dietary measures among multi-ethnic female health center employees. Ann Epidemiol, 11(6), 417- 427. Hedelin, M., Chang, E. T., Wiklund, F., Bellocco, R., Klint, A., Adolfsson, J., et al. (2007). Association of frequent consumption of fatty fish with prostate cancer risk is modified by cox-2 polymorphism. International Journal of Cancer, 120(2), 398-405. Heron, D. S., Shinitzky, M., Hershkowitz, M., & Samuel, D. (1980). Lipid fluidity markedly modulates the binding of serotonin to mouse-brain membranes. Proceedings of the National Academy of Sciences of the United States of America- Biological Sciences, 77(12), 7463-7467. Herzberg, P. Y., Glaesmer, H., & Hoyer, J. (2006). Separating optimism and pessimism: A robust psychometric analysis of the revised life orientation test (lot-r). Psychol Assess, 18(4), 433-438. Hibbeln, J. R., Linnoila, M., Umhau, J. C., Rawlings, R., George, D. T., & Salem, N. (1998). Essential fatty acids predict metabolites of serotonin and dopamine in cerebrospinal fluid among healthy control subjects, and early- and late-onset alcoholics. Biological Psychiatry, 44(4), 235-242. Hibbeln, J. R., Nieminen, L. R. G., Blasbalg, T. L., Riggs, J. A., & Lands, W. E. M. (2006). Healthy intakes of n-3 and n-6 fatty acids: Estimations considering worldwide diversity. American Journal of Clinical Nutrition, 83(6), 1483S- 1493S. Hibbeln, J. R., & Salem, N. (1998). Fish consumption may predict a lower prevalence of major depression: A cross-national analysis. In A. P. Simopoulos (Ed.), Return of omega-3 fatty acids into the food supply - i. Land-based animal food products and their health effects (Vol. 83, pp. 226-226). Hirashima, F., Parow, A. M., Stoll, A. L., Demopulos, C. M., Damico, K. E., Rohan, M. L., et al. (2004). Omega-3 fatty acid treatment and t-2 whole brain relaxation times in bipolar disorder. American Journal of Psychiatry, 161(10), 1922-1924. Hodge, A. M., English, D. R., O'Dea, K., Sinclair, A. J., Makrides, M., Gibson, R. A., et al. (2007). Plasma phospholipid and dietary fatty acids as predictors of type 2 diabetes: Interpreting the role of linoleic acid. American Journal of Clinical Nutrition, 86(1), 189-197. Horrobin, D. F. (2001). Phospholipid metabolism and depression: The possible roles of phospholipase a(2) and coenzyme a-independent transacylase. Human Psychopharmacology-Clinical and Experimental, 16(1), 45-52. 111

Horrobin, D. F., & Bennett, C. N. (1999). Depression and bipolar disorder: Relationships to impaired fatty acid and phospholipid metabolism and to diabetes, cardiovascular disease, immunological abnormalities, cancer, ageing and osteoporosis - possible candidate genes. Prostaglandins Leukotrienes and Essential Fatty Acids, 60(4), 217-234. Houston, B. K., Smith, M. A., & Cates, D. S. (1989). Hostility patterns and cardiovascular reactivity to stress. Psychophysiology, 26(3), 337-342. Hu, F. B., Stampfer, M. J., Manson, J. E., Ascherio, A., Colditz, G. A., Speizer, F. E., et al. (1999). Dietary saturated fats and their food sources in relation to the risk of coronary heart disease in women. American Journal of Clinical Nutrition, 70(6), 1001-1008. Huan, M., Hamazaki, K., Sun, Y., Itomura, M., Liu, H., Kang, W., et al. (2004). Suicide attempt and n-3 fatty acid levels in red blood cells: A case control study in china. Biol Psychiatry, 56(7), 490-496. Innis, S. M. (2007). Dietary (n-3) fatty acids and brain development. Journal of Nutrition, 137(4), 855-859. Iribarren, C., Markovitz, J. H., Jacobs, D. R., Schreiner, P. J., Daviglus, M., & Hibbeln, J. R. (2004). Dietary intake of n-3, n-6 fatty acids and fish: Relationship with hostility in young adults - the cardia study. European Journal of Clinical Nutrition, 58(1), 24-31. Jacka, F. N., Pasco, J. A., Mykletun, A., Williams, L. J., Hodge, A. M., O'Reilly, S. L., et al. (2010). Association of western and traditional diets with depression and anxiety in women. Am J Psychiatry, 167(3), 305-311. Jacobs, N., Kenis, G., Peeters, F., Derom, C., Vlietinck, R., & van Os, J. (2006). Stress- related negative affectivity and genetically altered serotonin transporter function: Evidence of synergism in shaping risk of depression. Arch Gen Psychiatry, 63(9), 989-996. James, M. J., Gibson, R. A., & Cleland, L. G. (2000). Dietary polyunsaturated fatty acids and inflammatory mediator production. American Journal of Clinical Nutrition, 71(1), 343S-348S. Kark, J. D., Kaufmann, N. A., Binka, F., Goldberger, N., & Berry, E. M. (2003). Adipose tissue n-6 fatty acids and acute myocardial infarction in a population consuming a diet high in polyunsaturated fatty acids. American Journal of Clinical Nutrition, 77(4), 796-802. Katan, M. B., Deslypere, J. P., vanBirgelen, A., Penders, M., & Zegwaard, M. (1997). Kinetics of the incorporation of dietary fatty acids into serum cholesteryl esters, erythrocyte membranes, and adipose tissue: An 18-month controlled study. Journal of Lipid Research, 38(10), 2012-2022. Kawakita, E., Hashimoto, M., & Shido, O. (2006). Docosahexaenoic acid promotes neurogenesis in vitro and in vivo. Neuroscience, 139(3), 991-997. Kelley, D. S., Siegel, D., Fedor, D. M., Adkins, Y., & Mackey, B. E. (2009). Dha supplementation decreases serum c-reactive protein and other markers of inflammation in hypertriglyceridemic men. Journal of Nutrition, 139(3), 495-501.

112

Kelloniemi, H., Ek, E., & Laitinen, J. (2005). Optimism, dietary habits, body mass index and smoking among young finnish adults. Appetite, 45(2), 169-176. Kennedy, M. M., & Newton, M. (1997). Effect of exercise intensity on mood in step aerobics. J Sports Med Phys Fitness, 37(3), 200-204. Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., et al. (2003). The epidemiology of major depressive disorder - results from the national comorbidity survey replication (ncs-r). Jama-Journal of the American Medical Association, 289(23), 3095-3105. Kessler, R. C., & Walters, E. E. (1998). Epidemiology of dsm-iii-r major depression and minor depression among adolescents and young adults in the national comorbidity survey. Depress Anxiety, 7(1), 3-14. Kessler, R. C., Zhao, S. Y., Blazer, D. G., & Swartz, M. (1997). Prevalence, correlates, and course of minor depression and major depression in the national comorbidity survey. Journal of Affective Disorders, 45(1-2), 19-30. Khan, A. A., Jacobson, K. C., Gardner, C. O., Prescott, C. A., & Kendler, K. S. (2005). Personality and comorbidity of common psychiatric disorders. Br J Psychiatry, 186, 190-196. Khandelwal, S., Demonty, I., Jeemon, P., Lakshmy, R., Mukherjee, R., Gupta, R., et al. (2009). Independent and interactive effects of plant sterols and fish oil n-3 long- chain polyunsaturated fatty acids on the plasma lipid profile of mildly hyperlipidaemic indian adults. Br J Nutr, 102(5), 722-732. Kiecolt-Glaser, J. K. (2010). Stress, food, and inflammation: Psychoneuroimmunology and nutrition at the cutting edge. Psychosomatic Medicine, 72(4), 365-369. Kiecolt-Glaser, J. K., Belury, M. A., Porter, K., Beversdorf, D. Q., Lemeshow, S., & Glaser, R. (2007). Depressive symptoms, omega-6 : Omega-3 fatty acids, and inflammation in older adults. Psychosomatic Medicine, 69(3), 217-224. Kim, D. N., Schmee, J., & Thomas, W. A. (1990). Dietary fish oil added to a hyperlipidemic diet for swine results in reduction in the excessive number of monocytes attached to arterial endothelium. Atherosclerosis, 81(3), 209-216. Koletzko, B., Lattka, E., Zeilinger, S., Illig, T., & Steer, C. (2011). Genetic variants of the fatty acid desaturase gene cluster predict amounts of red blood cell docosahexaenoic and other polyunsaturated fatty acids in pregnant women: Findings from the avon longitudinal study of parents and children. Am J Clin Nutr, 93(1), 211-219. Konsman, J. P., Parnet, P., & Dantzer, R. (2002). Cytokine-induced sickness behaviour: Mechanisms and implications. Trends in Neurosciences, 25(3), 154-159. Krueger, R. F. (1999). Personality traits in late adolescence predict mental disorders in early adulthood: A prospective-epidemiological study. J Pers, 67(1), 39-65. Kwak, J. H., Paik, J. K., Kim, O. Y., Jang, Y., Lee, S. H., Ordovas, J. M., et al. (2011). FADS gene polymorphisms in koreans: Association with omega6 polyunsaturated fatty acids in serum phospholipids, lipid peroxides, and coronary artery disease. Atherosclerosis, 214(1), 94-100. Laaksonen, D. E., Lakka, T. A., Lakka, H. M., Nyyssonen, K., Rissanen, T., Niskanen, L. K., et al. (2002). Serum fatty acid composition predicts development of impaired 113

fasting glycaemia and diabetes in middle-aged men. Diabetic Medicine, 19(6), 456-464. Lagarde, M. (2008). Docosahexaenoic acid: Nutrient and precursor of bioactive lipids. European Journal of Lipid Science and Technology, 110(8), 673-678. Lalovic, A., Klempan, T., Sequeira, A., Luheshi, G., & Turecki, G. (2010). Altered expression of lipid metabolism and immune response genes in the frontal cortex of suicide completers. J Affect Disord, 120(1-3), 24-31. Lapillonne, A., Clarke, S. D., & Heird, W. C. (2003). Plausible mechanisms for effects of long-chain polyunsaturated fatty acids on growth. Journal of Pediatrics, 143(4), S9-S16. Lattka, E., Eggers, S., Moeller, G., Heim, K., Weber, M., Mehta, D., et al. (2010). A common FADS2 promoter polymorphism increases promoter activity & facilitates binding of transcription factor elk1. Journal of Lipid Research, 51(1), 182-191. Lattka, E., Illig, T., Heinrich, J., & Koletzko, B. (2009a). Do FADS genotypes enhance our knowledge about fatty acid related phenotypes? Clinical Nutrition, 29(3), 277-287. Lattka, E., Illig, T., Heinrich, J., & Koletzko, B. (2009b). FADS gene cluster polymorphisms: Important modulators of fatty acid levels and their impact on atopic diseases. Journal of Nutrigenetics and Nutrigenomics, 2(3), 119-128. Lattka, E., Rzehak, P., Szabo, E., Jakobik, V., Weck, M., Weyermann, M., et al. (2011). Genetic variants in the FADS gene cluster are associated with arachidonic acid concentrations of human breast milk at 1.5 and 6 mo postpartum and influence the course of milk dodecanoic, tetracosenoic, and trans-9-octadecenoic acid concentrations over the duration of lactation. Am J Clin Nutr, 93(2), 382-391. Lavie, C. J., & Milani, R. V. (2005). Prevalence of hostility in young coronary artery disease patients and effects of cardiac rehabilitation and exercise training. Mayo Clin Proc, 80(3), 335-342. Lavie, C. J., & Milani, R. V. (2006). Adverse psychological and coronary risk profiles in young patients with coronary artery disease and benefits of formal cardiac rehabilitation. Arch Intern Med, 166(17), 1878-1883. Leaf, A., Xiao, Y. F., & Kang, J. X. (2002). Interactions of n-3 fatty acids with ion channels in excitable tissues. Prostaglandins Leukotrienes and Essential Fatty Acids, 67(2-3), 113-120. Lewis, N. M., Widga, A. C., Buck, J. S., & Frederick, A. M. (1995). Omega-3-fatty-acids in diets of low-income pregnant-women. Faseb Journal, 9(3), A186-A186. Lichtenstein, A. H. (2006). Dietary fat, carbohydrate, and protein: Effects on plasma lipoprotein patterns. Journal of Lipid Research, 47(8), 1661-1667. Lin, P. Y., Huang, S. Y., & Su, K. P. (2010a). A meta-analytic review of polyunsaturated fatty acid compositions in patients with depression. Biological Psychiatry, 68(2), 140-147. Lin, P. Y., Huang, S. Y., & Su, K. P. (2010b). A meta-analytic review of polyunsaturated fatty acid compositions in patients with depression. Biol Psychiatry, 68(2), 140- 147.

114

Lin, P. Y., & Su, K. P. (2007). A meta-analytic review of double-blind, placebo- controlled trials of antidepressant efficacy of omega-3 fatty acids. Journal of Clinical Psychiatry, 68(7), 1056-1061. Liu, E., Rewers, M., & Eisenbarth, G. S. (2005). Genetic testing: Who should do the testing and what is the role of genetic testing in the setting of celiac disease? Gastroenterology, 128(4 Suppl 1), S33-37. Loftis, J. M., Huckans, M., & Morasco, B. J. (2009). Neuroimmune mechanisms of cytokine-induced depression: Current theories and novel treatment strategies. Neurobiology of Disease, 37(3), 519-533. Lu, Y. C., Feskens, E. J. M., Dolle, M. E. T., Imholz, S., Verschuren, W. M. M., Muller, M., et al. (2010). Dietary n-3 and n-6 polyunsaturated fatty acid intake interacts with FADS1 genetic variation to affect total and hdl-cholesterol concentrations in the doetinchem cohort study. American Journal of Clinical Nutrition, 92(1), 258- 265. Lucas, M., Mirzaei, F., O'Reilly, E. J., Pan, A., Willett, W. C., Kawachi, I., et al. (2011). Dietary intake of n-3 and n-6 fatty acids and the risk of clinical depression in women: A 10-y prospective follow-up study. Am J Clin Nutr, 93(6), 1337-1343. Maes, M., Christophe, A., Bosmans, E., Lin, A. H., & Neels, H. (2000). In humans, serum polyunsaturated fatty acid levels predict the response of proinflammatory cytokines to psychologic stress. Biological Psychiatry, 47(10), 910-920. Maes, M., Christophe, A., Delanghe, J., Altamura, C., Neels, H., & Meltzer, H. Y. (1999). Lowered omega 3 polyunsaturated fatty acids in serum phospholipids and cholesteryl esters of depressed patients. Psychiatry Research, 85(3), 275-291. Mahony, S. M., & Tisdale, M. J. (1989). Role of prostaglandins in tumor necrosis factor induced weight-loss. British Journal of Cancer, 60(1), 51-55. Malerba, G., Schaeffer, L., Xumerle, L., Klopp, N., Trabetti, E., Biscuola, M., et al. (2008). Snps of the FADS gene cluster are associated with polyunsaturated fatty acids in a cohort of patients with cardiovascular disease. Lipids, 43(4), 289-299. Marquardt, A., Stohr, H., White, K., & Weber, B. H. F. (2000). Cdna cloning, genomic structure, and chromosomal localization of three members of the human fatty acid desaturase family. Genomics, 66(2), 175-183. Martin, N. W., Benyamin, B., Hansell, N. K., Montgomery, G. W., Martin, N. G., Wright, M. J., et al. (2011). Cognitive function in adolescence: Testing for interactions between breast-feeding and FADS2 polymorphisms. J Am Acad Child Adolesc Psychiatry, 50(1), 55-62 e54. Martinelli, N., Girelli, D., Malerba, G., Guarini, P., Illig, T., Trabetti, E., et al. (2008). FADS genotypes and desaturase activity estimated by the ratio of arachidonic acid to linoleic acid are associated with inflammation and coronary artery disease. American Journal of Clinical Nutrition, 88(4), 941-949. Mathias, R. A., Vergara, C., Gao, L., Rafaels, N., Hand, T., Campbell, M., et al. (2010). FADS genetic variants and omega-6 polyunsaturated fatty acid metabolism in a homogeneous island population. J Lipid Res, 51(9), 2766-2774. McDaniel, J. C., Ahijevych, K., & Belury, M. (2010). Effect of n-3 oral supplements on the n-6/n-3 ratio in young adults. West J Nurs Res, 32(1), 64-80. 115

McNamara, R. K., Able, J., Liu, Y., Jandacek, R., Rider, T., Tso, P., et al. (2009). Omega-3 fatty acid deficiency during perinatal development increases serotonin turnover in the prefrontal cortex and decreases midbrain tryptophan hydroxylase- 2 expression in adult female rats: Dissociation from estrogenic effects. J Psychiatr Res, 43(6), 656-663. McNamara, R. K., Hahn, C. G., Jandacek, R., Rider, T., Tso, P., Stanford, K. E., et al. (2007). Selective deficits in the omega-3 fatty acid docosahexaenoic acid in the postmortem orbitofrontal cortex of patients with major depressive disorder. Biological Psychiatry, 62(1), 17-24. McNamara, R. K., & Liu, Y. (2011). Reduced expression of fatty acid biosynthesis genes in the prefrontal cortex of patients with major depressive disorder. J Affect Disord, 129(1-3), 359-363. McNaughton, S. A., Hughes, M. C., & Marks, G. C. (2007). Validation of a ffq to estimate the intake of pufa using plasma phospholipid fatty acids and weighed foods records. Br J Nutr, 97(3), 561-568. Medicine, I. o. (2002). Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids. from http://iom.edu/Reports/2002/Dietary-Reference-Intakes-for-Energy- Carbohydrate-Fiber-Fat-Fatty-Acids-Cholesterol-Protein-and-Amino-Acids.aspx Merino, D. M., Johnston, H., Clarke, S., Roke, K., Nielsen, D., Badawi, A., et al. (2011). Polymorphisms in FADS1 and FADS2 alter desaturase activity in young caucasian and asian adults. Mol Genet Metab. Micallef, M. A., Munro, I. A., & Garg, M. L. (2009). An inverse relationship between plasma n-3 fatty acids and c-reactive protein in healthy individuals. European Journal of Clinical Nutrition, 63(9), 1154-1156. Miller, A. H., Maletic, V., & Raison, C. L. (2009). Inflammation and its discontents: The role of cytokines in the pathophysiology of major depression. Biological Psychiatry, 65(9), 732-741. Miller, T. Q., Smith, T. W., Turner, C. W., Guijarro, M. L., & Hallet, A. J. (1996). A meta-analytic review of research on hostility and physical health. Psychol Bull, 119(2), 322-348. Milton, A. S. (1989). Thermoregulatory actions of eicosanoids in the central-nervous- system with particular regard to the pathogenesis of fever. In A. I. Barkai & N. G. Bazan (Eds.), Arachidonic acid metabolism in the nervous system - physiological and pathological significance (Vol. 559, pp. 392-410). Moffitt, T. E., Caspi, A., Rutter, M. (2006). Measured gene-environment interactions in psychopathology: Concepts, research strategies, and implications for research, intervention, and public understanding of genetics. Perspectives on Psychological Science, 1(1), 5-27. Molto-Puigmarti, C., Plat, J., Mensink, R. P., Muller, A., Jansen, E., Zeegers, M. P., et al. (2010). FADS1 FADS2 gene variants modify the association between fish intake and the docosahexaenoic acid proportions in human milk. American Journal of Clinical Nutrition, 91(5), 1368-1376.

116

Mullen, B. J., & Martin, R. J. (1992). The effect of dietary-fat on diet selection may involve central serotonin. American Journal of Physiology, 263(3), R559-R563. Munafo, M. R., Durrant, C., Lewis, G., & Flint, J. (2009). Gene x environment interactions at the serotonin transporter locus. Biol Psychiatry, 65(3), 211-219. Murakami, K., & Sasaki, S. (2010). Dietary intake and depressive symptoms: A systematic review of observational studies. Molecular Nutrition & Food Research, 54(4), 471-488. Nakamura, M. T., & Nara, T. Y. (2004). Structure, function, and dietary regulation of delta 6, delta 5, and delta 9 desaturases. Annual Review of Nutrition, 24, 345-376. Nandi, A., Beard, J. R., & Galea, S. (2009). Epidemiologic heterogeneity of common mood and anxiety disorders over the lifecourse in the general population: A systematic review. BMC Psychiatry, 9, 31. Nelson, T. L., & Hickey, M. S. (2004). Acute changes in dietary omega-3 fatty acid intake lowers soluble interleukin-6 receptor in healthy adult normal weight and overweight males. Cytokine, 26(5), 195-201. Nizzo, M. C., Tegos, S., Gallamini, A., Toffano, G., Polleri, A., & Massarotti, M. (1978). Brain cortex phospholipids liposomes effects on csf hva, 5-hiaa and on prolactin and somatotropin secretion in man. Journal of Neural Transmission, 43(2), 93- 102. Nutritionquest, B. F. F. Q. (Berkeley, CA). Oddy, W. H., Hickling, S., Smith, M. A., O'Sullivan, T. A., Robinson, M., de Klerk, N. H., et al. (2011). Dietary intake of omega-3 fatty acids and risk of depressive symptoms in adolescents. Depress Anxiety, 28(7), 582-588. Ordovas, J. M. (2007). Gender, a significant factor in the cross talk between genes, environment, and health. Gender Medicine, 4, S111-S122. Ordovas, J. M., Corella, D., Cupples, L. A., Demissie, S., Kelleher, A., Coltell, O., et al. (2002). Polyunsaturated fatty acids modulate the effects of the apoa1 g-a polymorphism on hdl-cholesterol concentrations in a sex-specific manner: The framingham study. Am J Clin Nutr, 75(1), 38-46. Owens, S. D., & Innis, S. M. (1999). Docosahexaenoic and arachidonic acid prevent a decrease in dopaminergic and serotoninergic neurotransmitters in frontal cortex caused by a linoleic and alpha-linolenic acid deficient diet in formula-fed piglets. Journal of Nutrition, 129(11), 2088-2093. Paczkowski, M. M., & Galea, S. (2010). Sociodemographic characteristics of the neighborhood and depressive symptoms. Curr Opin Psychiatry, 23(4), 337-341. Paykel, E. S., Brugha, T., & Fryers, T. (2005). Size and burden of depressive disorders in europe. Eur Neuropsychopharmacol, 15(4), 411-423. Peterson, C., Seligman, M. E., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five-year longitudinal study. J Pers Soc Psychol, 55(1), 23-27. Pischon, T., Hankinson, S. E., Hotamisligil, G. S., Rifai, N., Willett, W. C., & Rimm, E. B. (2003). Habitual dietary intake of n-3 and n-6 fatty acids in relation to inflammatory markers among us men and women. Circulation, 108(2), 155-160.

117

Pluess, T. T., Hayoz, D., Berger, M. M., Tappy, L., Revelly, J. P., Michaeli, B., et al. (2007). Intravenous fish oil blunts the physiological response to endotoxin in healthy subjects. Intensive Care Medicine, 33(5), 789-797. Prescott, C. A., McArdle, J. J., Hishinuma, E. S., Johnson, R. C., Miyamoto, R. H., Andrade, N. N., et al. (1998). Prediction of major depression and dysthymia from ces-d scores among ethnic minority adolescents. J Am Acad Child Adolesc Psychiatry, 37(5), 495-503. Psota, T. L., Gebauer, S. K., & Kris-Etherton, P. (2006). Dietary omega-3 fatty acid intake and cardiovascular risk. American Journal of Cardiology, 98(4A), 3I-18I. Raab, G. M., Day, S., & Sales, J. (2000). How to select covariates to include in the analysis of a clinical trial. Control Clin Trials, 21(4), 330-342. Radloff, L. S. (1977). The ces-d scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385-401. Raikkonen, K., & Matthews, K. A. (2008). Do dispositional pessimism and optimism predict ambulatory blood pressure during school days and nights in adolescents? J Pers, 76(3), 605-630. Raikkonen, K., Matthews, K. A., Flory, J. D., Owens, J. F., & Gump, B. B. (1999). Effects of optimism, pessimism, and trait anxiety on ambulatory blood pressure and mood during everyday life. J Pers Soc Psychol, 76(1), 104-113. Raison, C. L., Borisov, A. S., Broadwell, S. D., Capuron, L., Woolwine, B. J., Jacobson, I. M., et al. (2005). Depression during pegylated interferon-alpha plus ribavirin therapy: Prevalence and prediction. Journal of Clinical Psychiatry, 66(1), 41-48. Rallidis, L. S., Paschos, G., Liakos, G. K., Velissaridou, A. H., Anastasiadis, G., & Zampelas, A. (2003). Dietary alpha-linolenic acid decreases c-reactive protein, serum amyloid a and interleukin-6 in dyslipidaemic patients. Atherosclerosis, 167(2), 237-242. Ramel, A., Parra, D., Martinez, J. A., Kiely, M., & Thorsdottir, I. (2009). Effects of seafood consumption and weight loss on fasting leptin and ghrelin concentrations in overweight and obese european young adults. Eur J Nutr, 48(2), 107-114. Ramirez, J. M., & Andreu, J. M. (2006). Aggression, and some related psychological constructs (anger, hostility, and impulsivity); some comments from a research project. Neurosci Biobehav Rev, 30(3), 276-291. Raper, N. R., Cronin, F. J., & Exler, J. (1992). Omega-3-fatty-acid content of the united- states food-supply. Journal of the American College of Nutrition, 11(3), 304-308. Raper, N. R., & Marston, R. M. (1986). Levels and sources of fat in the u.S. Food supply. Prog Clin Biol Res, 222, 127-152. Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., et al. (2007). Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the patient-reported outcomes measurement information system (promis). Med Care, 45(5 Suppl 1), S22-31. Regestein, Q., Natarajan, V., Pavlova, M., Kawasaki, S., Gleason, R., & Koff, E. (2010). Sleep debt and depression in female college students. Psychiatry Res, 176(1), 34- 39.

118

Renaud, S. C., & Lanzmann-Petithory, D. (2001). Alpha-linolenic acid in the prevention of cardiovascular diseases. In T. Hamazaki & H. Okuyama (Eds.), Fatty acids and lipids-new findings (Vol. 88, pp. 79-85). Rodriguez, S., Gaunt, T. R., & Day, I. N. (2009). Hardy-weinberg equilibrium testing of biological ascertainment for mendelian randomization studies. Am J Epidemiol, 169(4), 505-514. Rogers, P. J., Appleton, K. M., Kessler, D., Peters, T. J., Gunnell, D., Hayward, R. C., et al. (2008). No effect of n-3 long-chain polyunsaturated fatty acid (epa and dha) supplementation on depressed mood and cognitive function: A randomised controlled trial. British Journal of Nutrition, 99(2), 421-431. Rorsman, B., Grasbeck, A., Hagnell, O., Lanke, J., Ohman, R., Ojesjo, L., et al. (1990). A prospective study of first-incidence depression. The lundby study, 1957-72. Br J Psychiatry, 156, 336-342. Rosenberg, P. S., Che, A., & Chen, B. E. (2006). Multiple hypothesis testing strategies for genetic case-control association studies. Stat Med, 25(18), 3134-3149. Rzehak, P., Heinrich, J., Klopp, N., Schaeffer, L., Hoff, S., Wolfram, G., et al. (2009). Evidence for an association between genetic variants of the fatty acid desaturase 1 fatty acid desaturase 2 (FADS1 FADS2) gene cluster and the fatty acid composition of erythrocyte membranes. British Journal of Nutrition, 101(1), 20- 26. Sala-Vila, A., Miles, E. A., & Calder, P. C. (2008). Fatty acid composition abnormalities in atopic disease: Evidence explored and role in the disease process examined. Clinical and Experimental Allergy, 38(9), 1432-1450. Sanchez-Villegas, A., Henriquez, P., Figueiras, A., Ortuno, F., Lahortiga, F., & Martinez- Gonzalez, M. A. (2007). Long chain omega-3 fatty acids intake, fish consumption and mental disorders in the sun cohort study. Eur J Nutr, 46(6), 337-346. Schaeffer, L., Gohlke, H., Muller, M., Heid, I. M., Palmer, L. J., Kompauer, I., et al. (2006). Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids. Human Molecular Genetics, 15(11), 1745-1756. Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): A reevaluation of the life orientation test. J Pers Soc Psychol, 67(6), 1063-1078. Schiepers, O. J. G., de Groot, R. H. M., Jolles, J., & van Boxtel, M. P. J. (2009). Plasma phospholipid fatty acid status and depressive symptoms: Association only present in the clinical range. Journal of Affective Disorders, 118(1-3), 209-214. Schiepers, O. J. G., de Groot, R. H. M., Jolles, J., & van Boxtel, M. P. J. (2010). Fish consumption, not fatty acid status, is related to quality of life in a healthy population. Prostaglandins Leukotrienes and Essential Fatty Acids, 83(1), 31-35. Schweizer, K., Beck-Seyffer, A., & Schneider, R. (1999). Cognitive bias of optimism and its influence on psychological well-being. Psychol Rep, 84(2), 627-636. Shahar, E., Folsom, A. R., Wu, K. K., Dennis, B. H., Shimakawa, T., Conlan, M. G., et al. (1993). Associations of fish intake and dietary n-3 polyunsaturated fatty acids

119

with a hypocoagulable profile. The atherosclerosis risk in communities (aric) study. Arterioscler Thromb, 13(8), 1205-1212. Shean, G., & Baldwin, G. (2008a). Sensitivity and specificity of depression questionnaires in a college-age sample. J Genet Psychol, 169(3), 281-288. Shean, G., & Baldwin, G. (2008b). Sensitivity and specificity of depression questionnaires in a college-age sample. Journal of Genetic Psychology, 169(3), 281-288. Sijben, J. W. C., & Calder, P. C. (2007). Differential immunomodulation with long-chain n-3 pufa in health and chronic disease. Proceedings of the Nutrition Society, 66(2), 237-259. Silvers, K. M., & Scott, K. M. (2002). Fish consumption and self-reported physical and mental health status. Public Health Nutrition, 5(3), 427-431. Simopoulos, A. P. (1999). Evolutionary aspects of omega-3 fatty acids in the food supply. Prostaglandins Leukotrienes and Essential Fatty Acids, 60(5-6), 421-429. Simopoulos, A. P. (2006). Evolutionary aspects of diet, the omega-6/omega-3 ratio and genetic variation: Nutritional implications for chronic diseases. Biomedicine & Pharmacotherapy, 60(9), 502-507. Simopoulos, A. P. (2008). The importance of the omega-6/omega-3 fatty acid ratio in cardiovascular disease and other chronic diseases. Experimental Biology and Medicine, 233(6), 674-688. Simopoulos, A. P. (2010). Genetic variants in the metabolism of omega-6 and omega-3 fatty acids: Their role in the determination of nutritional requirements and chronic disease risk. Experimental Biology and Medicine, 235(7), 785-795. Singer, S. J., & Nicolson, G. L. (1972). Fluid mosaic model of structure of cell- membranes. Science, 175(4023), 720-&. Smith, T. W. (1992). Hostility and health: Current status of a psychosomatic hypothesis. Health Psychol, 11(3), 139-150. Smith, T. W., Pope, M. K., Rhodewalt, F., & Poulton, J. L. (1989). Optimism, neuroticism, coping, and symptom reports: An alternative interpretation of the life orientation test. J Pers Soc Psychol, 56(4), 640-648. Song, C., Leonard, B. E., & Horrobin, D. F. (2004). Dietary ethyl-eicosapentaenoic acid but not soybean oil reverses central interleukin-1-induced changes in behavior, corticosterone and immune response in rats. Stress-the International Journal on the Biology of Stress, 7(1), 43-54. Song, C., Li, X. W., Leonard, B. E., & Horrobin, D. F. (2003). Effects of dietary n-3 or n- 6 fatty acids on interleukin-1 beta-induced anxiety, stress, and inflammatory responses in rats. Journal of Lipid Research, 44(10), 1984-1991. Sontrop, J., & Campbell, M. K. (2006). Omega-3 polyunsaturated fatty acids and depression: A review of the evidence and a methodological critique. Prev Med, 42(1), 4-13. Sprecher, H. (2002). The roles of anabolic and catabolic reactions in the synthesis and recycling of polyunsaturated fatty acids. Prostaglandins Leukotrienes and Essential Fatty Acids, 67(2-3), 79-83.

120

Steer, C. D., Davey Smith, G., Emmett, P. M., Hibbeln, J. R., & Golding, J. (2010). FADS2 polymorphisms modify the effect of breastfeeding on child iq. Plos One, 5(7), e11570. Steer, C. D., Smith, G. D., Emmett, P. M., Hibbeln, J. R., & Golding, J. (2010). FADS2 polymorphisms modify the effect of breastfeeding on child iq. Plos One, 5(7). Su, K. P. (2008). Mind-body interface: The role of n-3 fatty acids in psychoneuroimmunology, somatic presentation, and medical illness comorbidity of depression. Asia Pacific Journal of Clinical Nutrition, 17, 151-157. Su, K. P., Huang, S. Y., Chiu, C. C., & Shen, W. W. (2003). Omega-3 fatty acids in major depressive disorder - a preliminary double-blind, placebo-controlled trial. European Neuropsychopharmacology, 13(4), 267-271. Suzuki, H., Park, S. J., Tamura, M., & Ando, S. (1998). Effect of the long-term feeding of dietary lipids on the learning ability, fatty acid composition of brain stem phospholipids and synaptic membrane fluidity in adult mice: A comparison of sardine oil diet with palm oil diet. Mech Ageing Dev, 101(1-2), 119-128. Swenson, W. M., Pearson, J. S., & Osbourne, D. (1973). An mmpi source book: Basic item, scale, and pattern data on 50,000 medical patients. Minneapolis: University of Minnesota Press. Szklo, M., Chambless, L. E., Folsom, A. R., Gotto, A., Jr., Nieto, F. J., Patsch, W., et al. (2000). Trends in plasma cholesterol levels in the atherosclerosis risk in communities (aric) study. Prev Med, 30(3), 252-259. Tanaka, T., Shen, J., Abecasis, G. R., Kisialiou, A., Ordovas, J. M., Guralnik, J. M., et al. (2009). Genome-wide association study of plasma polyunsaturated fatty acids in the inchianti study. Plos Genetics, 5(1). Tanskanen, A., Hibbeln, J. R., Hintikka, J., Haatainen, K., Honkalampi, K., & Viinamaki, H. (2001). Fish consumption, depression, and suicidality in a general population. Archives of General Psychiatry, 58(5), 512-513. Tappia, P. S., Ladha, S., Clark, D. C., & Grimble, R. F. (1997). The influence of membrane fluidity, tnf receptor binding, camp production and gtpase activity on macrophage cytokine production in rats fed a variety of fat diets. Molecular and Cellular Biochemistry, 166(1-2), 135-143. Timonen, M., Horrobin, D., Jokelainen, J., Laitinen, J., Herva, A., & Rasanen, P. (2004). Fish consumption and depression: The northern finland 1966 birth cohort study. Journal of Affective Disorders, 82(3), 447-452. Trivedi, R. B., Blumenthal, J. A., O'Connor, C., Adams, K., Hinderliter, A., Dupree, C., et al. (2009). Coping styles in heart failure patients with depressive symptoms. J Psychosom Res, 67(4), 339-346. van de Rest, O., Spiro, A., Krall-Kaye, E., Geleijnse, J. M., de Groot, L., & Tucker, K. L. (2009). Intakes of (n-3) fatty acids and fatty fish are not associated with cognitive performance and 6-year cognitive change in men participating in the veterans affairs normative aging study. Journal of Nutrition, 139(12), 2329-2336. van der Westhuyzen, J., Chetty, N., & Atkinson, P. M. (1988). Fatty acid composition of human milk from south african black mothers consuming a traditional maize diet. Eur J Clin Nutr, 42(3), 213-220. 121

Virkkunen, M. E., Horrobin, D. F., Jenkins, D. K., & Manku, M. S. (1987). Plasma phospholipid essential fatty acids and prostaglandins in alcoholic, habitually violent, and impulsive offenders. Biol Psychiatry, 22(9), 1087-1096. Volcik, K. A., Nettleton, J. A., Ballantyne, C. M., & Boerwinkle, E. (2008). Peroxisome proliferator-activated receptor [alpha] genetic variation interacts with n-6 and long-chain n-3 fatty acid intake to affect total cholesterol and ldl-cholesterol concentrations in the atherosclerosis risk in communities study. Am J Clin Nutr, 87(6), 1926-1931. Wang, J., John, E. M., & Ingles, S. A. (2008). 5-lipoxygenase and 5-lipoxygenase- activating protein gene polymorphisms, dietary linoleic acid, and risk for breast cancer. Cancer Epidemiology Biomarkers & Prevention, 17(10), 2748-2754. Wang, L., Folsom, A. R., Zheng, Z. J., Pankow, J. S., Eckfeldt, J. H., & Investigators, A. S. (2003). Plasma fatty acid composition and incidence of diabetes in middle-aged adults: The atherosclerosis risk in communities (aric) study. American Journal of Clinical Nutrition, 78(1), 91-98. Watanabe, S., Kanada, S., Takenaka, M., & Hamazaki, T. (2004). Dietary n-3 fatty acids selectively attenuate lps-induced behavioral depression in mice. Physiol Behav, 81(4), 605-613. Watari, M., Hamazaki, K., Hirata, T., Hamazaki, T., & Okubo, Y. (2010). Hostility of drug-free patients with schizophrenia and n-3 polyunsaturated fatty acid levels in red blood cells. Psychiatry Res, 177(1-2), 22-26. Weidner, G., Connor, S.L., Hollis, J.F., Connor, W.E. (1992). Improvements in hostility and depression in relation to dietary change and cholesterol lowering. The family heart study. Ann Intern Med, 117(10), 820-823. Weissman, M. M., Sholomskas, D., Pottenger, M., Prusoff, B. A., & Locke, B. Z. (1977). Assessing depressive symptoms in five psychiatric populations: A validation study. Am J Epidemiol, 106(3), 203-214. Welch, A. A., Bingham, S. A., Ive, J., Friesen, M. D., Wareham, N. J., Riboli, E., et al. (2006a). Dietary fish intake and plasma phospholipid n-3 polyunsaturated fatty acid concentrations in men and women in the european prospective investigation into cancer-norfolk united kingdom cohort. Am J Clin Nutr, 84(6), 1330-1339. Welch, A. A., Bingham, S. A., Ive, J., Friesen, M. D., Wareham, N. J., Riboli, E., et al. (2006b). Dietary fish intake and plasma phospholipid n-3 polyunsaturated fatty acid concentrations in men and women in the european prospective investigation into cancer-norfolk united kingdom cohort. American Journal of Clinical Nutrition, 84(6), 1330-1339. WHO. (2003). Diet, nutrition, and the prevention of chronic diseases. Geneva. Wozniak, J., Biederman, J., Mick, E., Waxmonsky, J., Hantsoo, L., Best, C., et al. (2007). Omega-3 fatty acid monotherapy for pediatric bipolar disorder: A prospective open-label trial. European Neuropsychopharmacology, 17(6-7), 440-447. Wu, A., Ying, Z., & Gomez-Pinilla, F. (2008). Docosahexaenoic acid dietary supplementation enhances the effects of exercise on synaptic plasticity and cognition. Neuroscience, 155(3), 751-759.

122

Xie, L., & Innis, S. M. (2008). Genetic variants of the FADS1 FADS2 gene cluster are associated with altered (n-6) and (n-3) essential fatty acids in plasma and erythrocyte phospholipids in women during pregnancy and in breast milk during lactation. Journal of Nutrition, 138(11), 2222-2228. Xie, L., & Innis, S. M. (2009). Association of fatty acid desaturase gene polymorphisms with blood lipid essential fatty acids and perinatal depression among canadian women: A pilot study. Journal of Nutrigenetics and Nutrigenomics, 2(4-5), 243- 250. Yang, Q., & Khoury, M. J. (1997). Evolving methods in genetic epidemiology. Iii. Gene- environment interaction in epidemiologic research. Epidemiol Rev, 19(1), 33-43. Yao, J. K., Sistilli, C. G., & van Kammen, D. P. (2003). Membrane polyunsaturated fatty acids and csf cytokines in patients with schizophrenia. Prostaglandins Leukotrienes and Essential Fatty Acids, 69(6), 429-436. Yaqoob, P. (2003). Fatty acids as gatekeepers of immune cell regulation. Trends in Immunology, 24(12), 639-645. Yehuda, S., Rabinovitz, S., & Mostofsky, D. I. (2005). Mixture of essential fatty acids lowers test anxiety. Nutritional Neuroscience, 8(4), 265-267. Zampelas, A., Panagiotakos, D. B., Pitsavos, C., Das, U. N., Chrysohoou, C., Skoumas, Y., et al. (2005). Fish consumption among healthy adults is associated with decreased levels of inflammatory markers related to cardiovascular disease - the attica study. Journal of the American College of Cardiology, 46(1), 120-124. Zanarini, M. C., & Frankenburg, F. R. (2003). Omega-3 fatty acid treatment of women with borderline personality disorder: A double-blind, placebo-controlled pilot study. Am J Psychiatry, 160(1), 167-169. Zhao, Y., Joshi-Barve, S., Barve, S., & Chen, L. H. (2004). Eicosapentaenoic acid prevents lps-induced tnf-alpha expression by preventing nf-kappa b activation. Journal of the American College of Nutrition, 23(1), 71-78. Zhou, Y. E., Kubow, S., Dewailly, E., Julien, P., & Egeland, G. M. (2009). Decreased activity of desaturase 5 in association with obesity and insulin resistance aggravates declining long-chain n-3 fatty acid status in cree undergoing dietary transition. Br J Nutr, 102(6), 888-894. Zimmer, L., Vancassel, S., Cantagrel, S., Breton, P., Delamanche, S., Guilloteau, D., et al. (2002). The dopamine mesocorticolimbic pathway is affected by deficiency in n-3 polyunsaturated fatty acids. Am J Clin Nutr, 75(4), 662-667. Zulkifli, S. N., & Yu, S. M. (1992). The food frequency method for dietary assessment. J Am Diet Assoc, 92(6), 681-685.

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Appendix A: Tables and Figures

Table 1.

Sociodemographic Characteristics of Participants

Sociodemographic Characteristic n

Age (years) (Mean, S.D.) 18.93 (1.47) BMI (Mean, S.D.) 23.05 (4.65) Race Caucasian 129 African-American 17 Asian 12 Hispanic / Latin American 4 Indian 2 Biracial 2 Native American 1 Native Hawaiian 1 Academic Year Freshman 117 Sophomore 31 Junior 14 Senior 4 Other 2

Table 1. Sociodemographic characteristics of study participants. Number of participants is reported for each category, except age and BMI, for which mean and standard deviation is reported. BMI is calculated from self-reported information on the Block FFQ.

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Table 2. rs174575 Genotypes

Genotype n Observed n Expected CC 95 96.76 CG 65 61.47 GG 8 9.76

Table 3. FADS SNP rs174575 genotypes of participants (n = 168). CC: major allele homozygote, CG: heterozygote, GG: minor allele homozygote. N expected is based on Hardy-Weinberg Equilibrium (Rodriguez, Gaunt, & Day, 2009). The observed frequencies of CC, CG, and GG participants did not differ from the expected Hardy-Weinberg frequencies ( 2(1, N = 168) = 0.55, p = 0.45), confirming that the sample was in Hardy-Weinberg equilibrium.

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Table 3.

Self-Reported Diet and Health Characteristics

Characteristic n Diet Vegetarian 12 Vegan 1 Supplement and Vitamin Use Fish Oil 6 Flaxseed Oil 2 Multivitamin 43 Other Vitamin / Supplement 20 Medication Use Hormonal Contraceptive 65 Antidepressant 12 Stimulant / ADHD Medication 5 Benzodiazepine 3 Beta Blocker 1 Mood Stabilizer 1 Antibiotic 12 Asthma Medication 3 Ulcer or GERD Medication 4 OTC Pain / Allergy Medication 10 Insulin 3 Other Medication 7 Depression History Report history of depression 31

Table 2. Special diet, supplement, vitamin and medication use, and self-reported depression history in participants (n = 168). Number of participants endorsing the characteristic is reported for each.

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Table 4.

Nutritional Data from Food Frequency Questionnaire (Block FFQ)

Nutritional Information Mean (S.D.) Daily Intake Overall Sample Non-Vegetarians Vegetarians Kilocalories 1818.76 (884.04) 1832.57 (945.71) 1639.23(999.3) Protein (g) 66.75 (34.21) 67.59 (36.56) 55.88 (38.87) Fat (g) 67.35 (36.95) 68.73 (40.73) 49.30 (35.91) Carbohydrate (g) 230.87 (113.5) 230.56 (118.58) 234.83 (128.54) Sugars (g) 111.52 (62.79) 111.06 (65.17) 117.35 (65.03) Cholesterol (mg) 199.1 (127.46) 205.91 (131.55) 110.50 (117.25) Trans Fat (g) 2.44 (1.51) 2.49 (1.68) 1.74 (1.71) Saturated Fat (g) 21.62 (13.48) 22.20 (13.58) 14.03 (9.44) Monounsaturated Fat (g) 25.81 (15.72) 26.32 (15.73) 19.14 (14.55) Polyunsaturated Fat (g) 14.61 (9.06) 14.78 (9.03) 12.36 (9.56) Daily n-3 Intake (g) Dietary n-3 1.36 (0.96) 1.37 (0.96) 1.09 (0.92) Supplement n-3 0.025 (0.093) 0.02 (0.09) 0.01 (0.04) Total n-3 1.38 (0.97) 1.40 (0.96) 1.10 (0.91) ALA 1.29 (0.91) 1.31 (0.92) 0.99 (0.69) Stearidonic Acid 0.003 (0.004) 0.002 (0.003) 0.00 (0.01) EPA 0.02 (0.04) 0.02 (0.034) 0.03 (0.11) DPA 0.009 (0.013) 0.009 (0.009) 0.01 (0.03) DHA 0.04 (0.05) 0.04 (0.03) 0.05 (0.15) Daily n-6 Intake (g) Dietary n-6 12.45 (7.73) 12.65 (7.74) 9.89 (7.3) Supplement n-6 0.005 (0.02) 0.005 (0.02) 0.00 (0.00) Total n-6 12.46 (7.73) 12.66 (7.75) 9.89 (7.3) LA 12.37 (7.68) 12.56 (7.70) 9.85 (7.27) AA 0.088 (0.06) 0.09 (0.06) 0.03 (0.04)

Table 4. Nutritional data from Block FFQ. There were no statistically significant differences between non-vegetarians and vegetarians for dietary intake of any nutrients.

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Table 5.

Psychological Outcome Data

Measure Mean (S.D.) Minimum Maximum CES-D 15.05 (10.409) 0 52 PROMIS Anxiety SF T-Score 55.46 (7.75) 36.30 74.30 PROMIS Anger SF T-Score 52.19 (8.95) 32.40 85.20 Cook Medley Hostility Score 18.62 (6.11) 4 32 NEO Neuroticism Score 21.25 (8.92) 2 42 LOT-R Score 14.61 (5.28) 2 24

Table 5. Mean, standard deviation, minimum, and maximum for psychological outcome measures.

Table 6.

Psychological Outcome Data: Cutoff Scores

Measure n CES-D No depression (0-15) 101 Mild depression (16-10) 28 Moderate Depression (21-30) 23 Severe Depression (>30) 16 LOT-R Pessimist (0-12) 57 Average (13-17) 57 Optimist (18-24) 54

Table 6. Cutoff scores for psychological outcome measures with empirically established cut points.

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Table 7.

Between Group Differences by Race

Source Type III SS df F p Depression History 0.500 7 0.461 0.862 Antidepressant Use 0.581 7 0.083 0.275 BMI 160.338 7 1.061 0.392 Daily Kilocalories 14085977.5 7 2.368 0.025* Daily n-3 Intake 30.695 7 5.612 <0.00* Daily n-6 Intake 1433.23 7 3.834 0.001*

Race Mean BMI Mean Kcal Mean n-3 Mean n-6 White 23.2825 1707.5470 1.2314 11.4560 Black 23.6738 2562.0453 2.6080 20.6624 Asian 20.3858 1990.0283 1.4973 13.0288 Native Hawaiian 29.0500 2766.8900 1.7200 17.6900 Native American 19.7400 732.9900 .3790 3.8700 Hispanic/Latina 21.6575 2021.8450 1.2490 12.6050

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Table 8.

Between Group Differences by Academic Year

Source Type III SS df F p Depression History 0.248 4 0.404 0.805 Antidepressant Use 0,036 4 0.131 0.971 BMI 160.338 7 1.061 0.392 Daily Kilocalories 1.946 1 0.089 0.765 Daily n-3 Intake 1.486 4 0.393 0.814 Daily n-6 Intake 103.686 4 0.428 0.788

Academic Year Mean BMI Mean Kcal Mean n-3 Mean n-6 Freshman 22.7781 1776.3671 1.3814 12.3511 Sophomore 21.9681 1975.2077 1.4033 13.1371 Junior 29.1692 1750.5693 1.3409 12.1255 Senior 20.1900 1781.5050 1.0278 9.3300 Other 21.1150 2426.1550 2.0700 17.3250

130

Table 9.

Between Group Differences by Genotype

2 df p Depression History 0.377 1 0.554 Race 13.723 7 0.056 Academic Year 2.812 4 0.590

Table 10.

Between Group Differences by Genotype

Source Type III SS df F p BMI 18.451 1 0.851 0.358 Daily Kilocalories 419884.312 1 0.466 0.496 Daily n-3 Intake 0.266 1 0.284 0.595 Daily n-6 Intake 17.105 1 0.285 0.594

Genotype Mean BMI Mean Kcal Mean n-3 Mean n-6 CC 22.7587 1862.5898 1.4167 12.7443 GG & GC 23.4369 1761.7351 1.3365 12.1006

131

Table 11. Between Group Differences in CES-D Score

Source Type III SS df F p Genotype 1.157 1 0.011 0.918 Race 138.870 3 0.423 0.737 Academic Year 674.381 4 1.578 0.183 Exam Week 4.222 1 0.039 0.844 Week in Quarter 940.282 7 1.253 0.277 Vegetarian Diet 48.960 1 0.450 0.503 Vegan Diet 4.242 1 0.039 0.844 Fish Oil Supplement Use 26.240 1 0.241 0.624 Flaxseed Oil Supplement Use 86.934 1 0.801 0.372 Hormonal Contraceptive Use 0.512 1 0.005 0.945 Antidepressant Use 176.576 1 1.636 0.203 ADHD Medication Use 3.361 1 0.033 0.856 BMI 20.018 1 0.196 0.658 Daily Kilocalorie Intake 39.208 1 0.360 0.549

132

Table 12. Between Group Differences in PROMIS Anxiety SF Score

Source Type III SS df F p Genotype 77.814 1 1.297 0.256 Race 26.874 3 0.147 0.932 Academic Year 259.372 4 1.081 0.368 Exam Week 0.006 1 0.000 0.992 Week in Quarter 619.481 7 1.503 0.170 Vegetarian Diet 164.122 1 2.759 0.099 Vegan Diet 0.708 1 0.012 0.914 Fish Oil Supplement Use 11.999 1 0.199 0.656 Flaxseed Oil Supplement Use 35.898 1 0.596 0.441 Hormonal Contraceptive Use 31.776 1 0.527 0.469 Antidepressant Use 339.039 1 5.803 0.017* ADHD Medication Use 80.282 1 1.316 0.253 BMI 74.925 1 1.228 0.270 Daily Kilocalorie Intake 4.780 1 0.079 0.779

Table 13. Between Group Differences in PROMIS Anger SF Score

Source Type III SS df F p Genotype 2.475 1 0.031 0.861 Race 297.599 3 1.245 0.295 Academic Year 991.949 4 3.266 0.013* Exam Week 13.033 1 0.162 0.688 Week in Quarter 495.029 7 0.879 0.525 Vegetarian Diet 20.462 1 0.254 0.615 Vegan Diet 94.491 1 1.182 0.279 Fish Oil Supplement Use 103.324 1 1.293 0.257 Flaxseed Oil Supplement Use 3.465 1 0.043 0.836 Hormonal Contraceptive Use 60.845 1 0.759 0.385 Antidepressant Use 6.034 1 0.075 0.785 ADHD Medication Use 4.833 1 0.059 0.808 BMI 161.188 1 1.991 0.160 Daily Kilocalorie Intake 522.983 1 6.758 0.01*

133

Table 14. Between Group Differences in Cook Medley Hostility Scale Score

Source Type III SS df F p Genotype 53.080 1 1.426 0.234 Race 90.573 3 0.806 0.492 Academic Year 190.597 4 1.286 0.278 Exam Week 0.988 1 0.026 0.871 Week in Quarter 271.327 7 1.041 0.405 Vegetarian Diet 19.055 1 0.509 0.477 Vegan Diet 29.128 1 0.780 0.379 Fish Oil Supplement Use 18.286 1 0.489 0.486 Flaxseed Oil Supplement Use 5.306 1 0.141 0.707 Hormonal Contraceptive Use 54.871 1 1.475 0.226 Antidepressant Use 107.260 1 2.907 0.090 ADHD Medication Use 90.815 1 2.423 0.122 BMI 85.399 1 2.276 0.133 Daily Kilocalorie Intake 104.171 1 2.822 0.095

Table 15. Between Group Differences in NEO Neuroticism Scale Score

Source Type III SS df F p Genotype 21.441 1 0.268 0.605 Race 19.281 3 0.08 0.971 Academic Year 272.885 4 0.855 0.492 Exam Week 7.955 1 0.100 0.753 Week in Quarter 546.480 7 0.981 0.447 Vegetarian Diet 532.090 1 6.930 0.009 Vegan Diet 22.698 1 0.284 0.595 Fish Oil Supplement Use 95.451 1 1.202 0.275 Flaxseed Oil Supplement Use 45.669 1 0.573 0.450 Hormonal Contraceptive Use 4.744 1 0.059 0.808 Antidepressant Use 390.923 1 5.036 0.026 ADHD Medication Use 36.651 1 0.456 0.500 BMI 120.545 1 1.511 0.221 Daily Kilocalorie Intake 8.037 1 0.101 0.752

134

Table 16. Between Group Differences in LOT-R Scale Score

Source Type III SS df F p Genotype 74.139 1 2.685 0.103 Race 38.028 3 0.450 0.718 Academic Year 183.058 4 1.667 0.160 Exam Week 16.890 1 0.604 0.438 Week in Quarter 65.824 7 0.328 0.941 Vegetarian Diet 13.546 1 0.484 0.488 Vegan Diet 1.952 1 0.070 0.792 Fish Oil Supplement Use 7.627 1 0.272 0.603 Flaxseed Oil Supplement Use 8.987 1 0.321 0.572 Hormonal Contraceptive Use 33.365 1 1.198 0.275 Antidepressant Use 138.507 1 5.087 0.025* ADHD Medication Use 2.074 1 0.073 0.788 BMI 140.730 1 5.083 0.026* Daily Kilocalorie Intake 3.949 1 0.141 0.708

135

Table 17. Bivariate Correlation Table

Anti- CES- Anger CMH LOT-

Genotype N3 N6 Kilocalorie BMI Depressant D Anxiety Neuroticism R Genotype Correlation 1 -.041 -.041 -.053 .072 -.057 -.008 -.088 .014 .040 .092 -.126 p .595 .594 .496 .358 .466 .918 .256 .861 .605 .234 .103 N3 Correlation -.041 1 .948** .868** .013 -.063 .139 .023 .228** .010 .206** .033 p .595 .000 .000 .872 .419 .072 .763 .003 .901 .007 .675 N6 Correlation -.041 .948** 1 .937** .007 -.088 .064 -.012 .189* -.020 .153* .027 p .594 .000 .000 .931 .254 .408 .876 .014 .798 .047 .724 Dietary Correlation -.053 .868** .937** 1 -.03 -.098 .047 -.022 .198* -.025 .129 .029 Kilocalories p .496 .000 .000 .696 .205 .549 .779 .010 .752 .095 .708 BMI Correlation .072 .013 .007 -.031 1 .023 .035 .087 .110 .096 -.118 -.17* p .358 .872 .931 .696 .765 .658 .270 .160 .221 .133 .026 Anti- Correlation -.057 -.063 -.088 -.098 .023 1 .099 .184* .021 .172* .131 -.17* Depressant p .466 .419 .254 .205 .765 .203 .017 .785 .026 .090 .025 CES-D Correlation -.008 .139 .064 .047 .035 .099 1 .539** .491** .627** .320** -.38** Total p .918 .072 .408 .549 .658 .203 .000 .000 .000 .000 .000 Anxiety Correlation -.088 .023 -.012 -.022 .087 .184* .539** 1 .554** .676** .299** -.38** T Score p .256 .763 .876 .779 .270 .017 .000 .000 .000 .000 .000 Anger Correlation .014 .228** .189* .198* .110 .021 .491** .554** 1 .474** .394** -.33** T Score p .861 .003 .014 .010 .160 .785 .000 .000 .000 .000 .000 NEO Correlation .040 .010 -.020 -.025 .096 .172* .627** .676** .474** 1 .432** -.66** Neuroticism p .605 .901 .798 .752 .221 .026 .000 .000 .000 .000 .000 CMH Correlation .092 .206** .153* .129 -.12 .131 .320** .299** .394** .432** 1 -.43** Total p .234 .007 .047 .095 .133 .090 .000 .000 .000 .000 .000 LOT-R Correlation -.126 .033 .027 .029 -.17* -.172* -.38** -.387** -.33** -.666** -.43** 1 Total p .103 .675 .724 .708 .026 .025 .000 .000 .000 .000 .000 * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

136

Table 18.

Regression Table for Hypothesis 1 with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.014 BMI 0.072 0.170 0.421 0.675 Antidepressant 4.343 3.041 1.428 0.155 Dietary Kilocalories 0.000 0.001 0.368 0.713

Step 2 0.010 BMI 0.053 0.170 0.311 0.756 Antidepressant 4.142 3.039 1.363 0.175 Dietary Kilocalories -0.002 0.002 -0.971 0.333 Total n3 2.373 1.836 1.293 0.198

* indicates statistical significance.

137

Table 19.

Regression Table for Hypothesis 1 with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.042 BMI 0.138 0.130 1.060 0.291 Antidepressant 5.511 2.326 2.369 0.019* Dietary Kilocalories 0.00 0.001 -.137 0.891

Step 2 0.008 BMI 0.125 0.130 0.962 0.337 Antidepressant 5.376 2.327 2.310 0.022* Dietary Kilocalories -0.001 0.001 -1.065 0.288 Total n3 1.592 1.406 1.132 0.259

* indicates statistical significance.

138

Table 20.

Regression Table for Hypothesis 1 with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Predictor S.E. t p ∆R2 Step 1 0.056 BMI .224 .149 1.506 0.134 Antidepressant 1.333 2.666 0.500 0.618 Dietary Kilocalories .002 .001 2.721 0.007*

Step 2 0.017 BMI .203 .149 1.366 0.174 Antidepressant 1.100 2.653 0.415 0.679 Dietary Kilocalories .000 .002 -0.237 0.813 Total n3 2.745 1.603 1.713 0.089

* indicates statistical significance.

139

Table 21.

Regression Table for Hypothesis 1 with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.050 BMI -0.155 0.102 -1.518 0.131 Antidepressant 3.481 1.822 1.910 0.058 Dietary Kilocalories 0.001 0.001 1.731 0.085

Step 2 0.048 BMI -0.179 0.100 -1.792 0.075 Antidepressant 3.214 1.783 1.803 0.073 Dietary Kilocalories -0.002 0.001 -1.748 0.082 Total n3 3.136 1.077 2.911 0.004*

* indicates statistical significance.

140

Table 22.

Regression Table for Hypothesis 1 with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.040 BMI 0.176 0.149 1.181 0.239 Antidepressant 5.933 2.665 2.226 0.027* Dietary Kilocalories 0.000 0.001 -0.216 0.829

Step 2 0.004 BMI 0.166 0.150 1.109 0.269 Antidepressant 5.824 2.671 2.180 0.031* Dietary Kilocalories -0.001 0.002 -0.804 0.423 Total n3 1.284 1.614 0.796 0.427

* indicates statistical significance.

141

Table 23.

Regression Table for Hypothesis 1 with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.060 BMI -.0195 0.088 -2.216 0.028* Antidepressant -3.451 1.570 -2.198 0.029* Dietary Kilocalories 0.00 0.000 0.198 0.843

Step 2 0.001 BMI -0.198 0.088 -2.235 0.027* Antidepressant -3.483 1.577 -2.209 0.029* Dietary Kilocalories 0.000 0.001 -0.255 0.799 Total n3 0.374 0.952 0.392 0.695

* indicates statistical significance.

142

Table 24.

Regression Table for Hypothesis 2 with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.014 BMI 0.072 0.170 0.421 0.675 Antidepressant 4.343 3.041 1.428 0.155 Dietary Kilocalories 0.000 0.001 0.368 0.713

Step 2 0.00 BMI 0.075 0.171 0.436 0.664 Antidepressant 4.296 3.056 1.406 0.162 Dietary Kilocalories 0.000 0.001 0.346 0.730 Genotype -0.397 1.611 -0.246 0.806

* indicates statistical significance.

143

Table 25.

Regression Table for Hypothesis 2 with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.042 BMI 0.138 0.130 1.060 0.291 Antidepressant 5.511 2.326 2.369 0.019* Dietary Kilocalories 0.000 0.001 -0.137 0.891

Step 2 0.007 BMI 0.148 0.130 1.135 0.258 Antidepressant 5.353 2.330 2.298 0.023* Dietary Kilocalories 0.000 0.001 -0.227 0.821 Genotype -1.334 1.228 -1.086 0.279

* indicates statistical significance.

144

Table 26.

Regression Table for Hypothesis 2 with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.056 BMI 0.224 0.149 1.506 0.134 Antidepressant 1.333 2.666 0.500 0.618 Dietary Kilocalories 0.002 0.001 2.721 0.007*

Step 2 0.001 BMI 0.220 0.150 1.467 0.144 Antidepressant 1.406 2.678 0.525 0.600 Dietary Kilocalories 0.002 0.001 2.741 0.007* Genotype 0.616 1.411 0.436 0.663

* indicates statistical significance.

145

Table 27.

Regression Table for Hypothesis 2 with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.05 BMI -0.155 0.102 -1.518 0.131 Antidepressant 3.481 1.822 1.910 0.058 Dietary Kilocalories 0.001 0.001 1.731 0.085

Step 2 0.012 BMI -0.165 0.102 -1.624 0.106 Antidepressant 3.646 1.819 2.004 0.047* Dietary Kilocalories 0.001 0.001 1.852 0.066 Genotype 1.396 0.959 1.456 0.147

* indicates statistical significance.

146

Table 28.

Regression Table for Hypothesis 2 with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.04 BMI 0.176 0.149 1.181 0.239 Antidepressant 5.933 2.665 2.226 0.027* Dietary Kilocalories 0.000 0.001 -0.216 0.829

Step 2 0.002 BMI 0.169 0.150 1.131 0.260 Antidepressant 6.036 2.675 2.256 0.025* Dietary Kilocalories 0.000 0.001 -0.163 0.871 Genotype 0.873 1.410 0.619 0.537

* indicates statistical significance.

147

Table 29.

Regression Table for Hypothesis 2 with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.06 BMI -0.195 0.088 -2.216 0.028* Antidepressant -3.451 1.570 -2.198 0.029* Dietary Kilocalories 0.000 0.000 0.198 0.843

Step 2 0.013 BMI -0.185 0.088 -2.111 0.036* Antidepressant -3.600 1.567 -2.297 0.023* Dietary Kilocalories 0.000 0.000 0.073 0.942 Genotype -1.251 0.826 -1.515 0.132

* indicates statistical significance.

148

Table 30.

Regression Table for Hypothesis 3 with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.014 BMI 0.072 0.170 0.421 0.675 Antidepressant 4.343 3.041 1.428 0.155 Dietary Kilocalories 0.000 0.001 0.368 0.713

Step 2 0.010 BMI 0.055 0.171 0.324 0.746 Antidepressant 4.106 3.054 1.345 0.181 Dietary Kilocalories -0.002 0.002 -0.968 0.334 Total n3 2.358 1.843 1.280 0.203 Genotype -0.309 1.609 -0.192 0.848

Step 3 0.000 BMI 0.054 0.172 0.314 0.754 Antidepressant 4.097 3.064 1.337 0.183 Dietary Kilocalories -0.002 0.002 -0.974 0.332 Total n3 2.301 1.891 1.217 0.226 Genotype -0.673 3.009 -0.224 0.823 n3 x Genotype 0.281 1.956 0.144 0.886

* indicates statistical significance.

149

Table 31.

Regression Table for Hypothesis 3 with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.042 BMI 0.138 0.130 1.060 0.291 Antidepressant 5.511 2.326 2.369 0.019* Dietary Kilocalories 0.000 0.001 -0.137 0.891

Step 2 0.014 BMI 0.136 0.131 1.037 0.301 Antidepressant 5.230 2.331 2.244 0.026* Dietary Kilocalories -0.001 0.001 -1.067 0.288 Total n3 1.529 1.407 1.087 0.279 Genotype -1.277 1.228 -1.040 0.300

Step 3 0.000 BMI 0.136 0.131 1.035 0.302 Antidepressant 5.232 2.339 2.237 0.027* Dietary Kilocalories -0.001 0.001 -1.056 0.292 Total n3 1.543 1.444 1.069 0.287 Genotype -1.186 2.297 -0.517 0.606 n3 x Genotype -0.070 1.494 -0.047 0.963

* indicates statistical significance.

150

Table 32.

Regression Table for Hypothesis 3 with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.056 BMI 0.224 0.149 1.506 0.134 Antidepressant 1.333 2.666 0.500 0.618 Dietary Kilocalories 0.002 0.001 2.721 0.007*

Step 2 0.019 BMI 0.197 0.149 1.320 0.189 Antidepressant 1.183 2.664 0.444 0.658 Dietary Kilocalories 0.000 0.002 -0.236 0.814 Total n3 2.781 1.608 1.730 0.086 Genotype 0.720 1.404 0.513 0.609

Step 3 0.002 BMI 0.192 0.150 1.282 0.202 Antidepressant 1.151 2.670 0.431 0.667 Dietary Kilocalories 0.000 0.002 -0.280 0.780 Total n3 2.581 1.648 1.566 0.119 Genotype -0.55 2.622 -0.212 0.833 n3 x Genotype 0.982 1.705 0.576 0.565

* indicates statistical significance.

151

Table 33.

Regression Table for Hypothesis 3 with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.050 BMI -0.15 0.102 -1.518 0.131 Antidepressant 3.481 1.822 1.910 0.058 Dietary Kilocalories 0.001 0.001 1.731 0.085

Step 2 0.063 BMI -0.19 0.100 -1.917 0.057 Antidepressant 3.388 1.777 1.906 0.058 Dietary Kilocalories 0.00 0.001 -1.754 0.081 Total n3 3.210 1.073 2.992 0.003* Genotype 1.515 0.937 1.618 0.108

Step 3 0.005 BMI -0.02 0.100 -1.965 0.051 Antidepressant 3.355 1.779 1.886 0.061 Dietary Kilocalories 0.00 0.001 -1.819 0.071 Total n3 3.001 1.098 2.733 0.007* Genotype 0.185 1.747 0.106 0.916 n3 x Genotype 1.025 1.136 0.903 0.368

* indicates statistical significance.

152

Table 34.

Regression Table for Hypothesis 3 with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.040 BMI 0.176 0.149 1.181 0.239 Antidepressant 5.933 2.665 2.226 0.027* Dietary Kilocalories 0.000 0.001 -0.216 0.829

Step 2 0.006 BMI 0.159 0.150 1.055 0.293 Antidepressant 5.929 2.681 2.211 0.028* Dietary Kilocalories 0.000 0.002 -0.802 0.424 Total n3 1.329 1.618 0.821 0.413 Genotype 0.923 1.413 0.653 0.515

Step 3 0.013 BMI 0.146 0.150 0.972 0.332 Antidepressant 5.849 2.672 2.189 0.030* Dietary Kilocalories 0.000 0.002 -0.917 0.361 Total n3 0.823 1.649 0.499 0.619 Genotype 0.000 2.624 -0.877 0.382 n3 x Genotype 2.484 1.706 1.456 0.147

* indicates statistical significance.

153

Table 35.

Regression Table for Hypothesis 3 with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.060 BMI -0.19 0.088 -2.216 0.028* Antidepressant 0.000 1.570 -2.198 0.029* Dietary Kilocalories 0.000 0.000 0.198 0.843

Step 2 0.014 BMI -0.18 0.088 -2.125 0.035* Antidepressant 0.000 1.573 -2.304 0.023* Dietary Kilocalories 0.000 0.001 -.257 0.797 Total n3 0.313 0.950 0.330 0.742 Genotype 0.000 0.829 -1.495 0.137

Step 3 0.024 BMI -0.17 0.088 -2.025 0.045* Antidepressant 0.000 1.558 -2.284 0.024* Dietary Kilocalories 0.000 0.001 -0.099 0.922 Total n3 0.727 0.962 0.756 0.451 Genotype 1.397 1.530 0.913 0.363 n3 x Genotype -2.03 0.995 -2.042 0.043*

* indicates statistical significance.

154

Table 36.

Regression Table for Hypothesis 3 with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.014 BMI 0.072 0.170 0.421 0.675 Antidepressant 4.343 3.041 1.428 0.155 Dietary Kilocalories 0.000 0.001 0.368 0.713

Step 2 0.010 BMI 0.079 0.173 0.458 0.648 Antidepressant 4.301 3.065 1.403 0.163 Dietary Kilocalories 0.001 0.003 0.338 0.736 Total n6 -0.075 0.318 -0.237 0.813 Genotype -0.408 1.616 -0.252 0.801

Step 3 0.000 BMI 0.078 0.173 0.450 0.653 Antidepressant 4.310 3.076 1.401 0.163 Dietary Kilocalories 0.001 0.003 0.340 0.734 Total n6 -0.086 0.329 -0.261 0.794 Genotype -0.762 3.156 -0.241 0.810 n6 x Genotype 0.030 0.228 0.131 0.896

* indicates statistical significance.

155

Table 37.

Regression Table for Hypothesis 3 with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.042 BMI 0.138 0.130 1.060 0.291 Antidepressant 5.511 2.326 2.369 0.019* Dietary Kilocalories 0.000 0.001 -0.137 0.891

Step 2 0.008 BMI 0.143 0.132 1.088 0.278 Antidepressant 5.348 2.336 2.289 0.023* Dietary Kilocalories -0.001 0.002 -0.399 0.691 Total n6 0.083 0.243 0.343 0.732 Genotype -1.322 1.232 -1.073 0.285

Step 3 0.000 BMI 0.145 0.132 1.095 0.275 Antidepressant 5.335 2.344 2.277 0.024* Dietary Kilocalories -0.001 0.002 -0.403 0.687 Total n6 0.098 0.251 0.390 0.697 Genotype -0.823 2.405 -0.342 0.733 n6 x Genotype -0.042 0.173 -0.242 0.809

* indicates statistical significance.

156

Table 38.

Regression Table for Hypothesis 3 with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.056 BMI 0.224 0.149 1.506 0.134 Antidepressant 1.333 2.666 0.500 0.618 Dietary Kilocalories 0.002 0.001 2.721 0.007*

Step 2 0.001 BMI 0.216 0.151 1.429 0.155 Antidepressant 1.403 2.686 0.522 0.602 Dietary Kilocalories 0.002 0.002 0.701 0.484 Total n6 0.062 0.279 0.223 0.824 Genotype 0.625 1.416 0.441 0.660

Step 3 0.000 BMI 0.216 0.152 1.421 0.157 Antidepressant 1.406 2.695 0.522 0.603 Dietary Kilocalories 0.002 0.002 0.700 0.485 Total n6 0.058 0.288 0.202 0.840 Genotype 0.490 2.765 0.177 0.860 n6 x Genotype 0.011 0.199 0.057 0.955

* indicates statistical significance.

157

Table 39.

Regression Table for Hypothesis 3 with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.050 BMI -0.15 0.102 -1.518 0.131 Antidepressant 3.481 1.822 1.910 0.058 Dietary Kilocalories 0.001 0.001 1.731 0.085

Step 2 0.024 BMI -0.18 0.102 -1.771 0.078 Antidepressant 3.630 1.814 2.001 0.047* Dietary Kilocalories 0.000 0.002 -0.698 0.486 Total n6 0.263 0.188 1.397 0.164 Genotype 1.434 0.956 1.499 0.136

Step 3 0.001 BMI -0.18 0.102 -1.785 0.076 Antidepressant 3.648 1.819 2.006 0.047* Dietary Kilocalories 0.000 0.002 -0.685 0.494 Total n6 0.242 0.195 1.243 0.216 Genotype 0.709 1.867 0.380 0.705 n6 x Genotype 0.061 0.135 0.453 0.651

* indicates statistical significance.

158

Table 40.

Regression Table for Hypothesis 3 with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.040 BMI 0.176 0.149 1.181 0.239 Antidepressant 5.933 2.665 2.226 0.027* Dietary Kilocalories 0.000 0.001 -0.216 0.829

Step 2 0.003 BMI 0.165 0.151 1.093 0.276 Antidepressant 6.032 2.683 2.248 0.026* Dietary Kilocalories 0.000 0.002 -0.297 0.767 Total n6 0.072 0.279 0.258 0.797 Genotype 0.884 1.415 0.625 0.533

Step 3 0.011 BMI 0.156 0.151 1.037 0.302 Antidepressant 6.111 2.677 2.282 0.024* Dietary Kilocalories 0.000 0.002 -0.267 0.790 Total n6 -0.02 0.286 -0.071 0.943 Genotype 0.000 2.748 -0.817 0.415 n6 x Genotype 0.263 0.198 1.327 0.186

* indicates statistical significance.

159

Table 41.

Regression Table for Hypothesis 3 with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.060 BMI -0.19 0.088 -2.216 0.028* Antidepressant -3.45 1.570 -2.198 0.029* Dietary Kilocalories 0.000 0.000 0.198 0.843

Step 2 0.014 BMI -0.18 0.088 -2.109 0.037* Antidepressant -3.60 1.572 -2.291 0.023* Dietary Kilocalories 0.000 0.001 -0.123 0.902 Total n6 0.026 0.163 0.156 0.876 Genotype -1.25 0.829 -1.505 0.134

Step 3 0.021 BMI -0.17 0.088 -2.040 0.043* Antidepressant -3.66 1.559 -2.352 0.020* Dietary Kilocalories 0.000 0.001 -0.169 0.866 Total n6 0.103 0.167 0.619 0.537 Genotype 1.391 1.600 0.869 0.386 n6 x Genotype -0.22 0.115 -1.922 0.056

* indicates statistical significance.

160

Table 42.

Regression Table for Exploratory Hypothesis: n-6 Intake with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.014 BMI 0.072 0.170 0.421 0.675 Antidepressant 4.343 3.041 1.428 0.155 Dietary Kilocalories 0.000 0.001 0.368 0.713

Step 2 0.010 BMI 0.076 0.172 0.442 0.659 Antidepressant 4.349 3.050 1.426 0.156 Dietary Kilocalories 0.001 0.003 0.340 0.735 Total n6 -0.073 0.317 -0.231 0.818

* indicates statistical significance.

161

Table 43.

Regression Table Exploratory Hypothesis: n-6 Intake with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.042 BMI 0.138 0.130 1.060 0.291 Antidepressant 5.511 2.326 2.369 0.019* Dietary Kilocalories 0.000 0.001 -0.137 0.891

Step 2 0.001 BMI 0.133 0.131 1.011 0.314 Antidepressant 5.504 2.333 2.360 0.020* Dietary Kilocalories -0.001 0.002 -0.397 0.692 Total n6 0.091 0.243 0.373 0.710

* indicates statistical significance.

162

Table 44.

Regression Table Exploratory Hypothesis: n-6 Intake with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Predictor S.E. t p ∆R2 Step 1 0.056 BMI 0.224 0.149 1.506 0.134 Antidepressant 1.333 2.666 0.500 0.618 Dietary Kilocalories 0.002 0.001 2.721 0.007*

Step 2 0.000 BMI 0.221 0.150 1.470 0.144 Antidepressant 1.329 2.674 0.497 0.620 Dietary Kilocalories 0.002 0.002 0.703 0.483 Total n6 0.059 0.278 0.211 0.833

* indicates statistical significance.

163

Table 45.

Regression Table Exploratory Hypothesis: n-6 Intake with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.050 BMI -0.155 0.102 -1.518 0.131 Antidepressant 3.481 1.822 1.910 0.058 Dietary Kilocalories 0.001 0.001 1.731 0.085

Step 2 0.011 BMI -0.170 0.102 -1.658 0.099 Antidepressant 3.461 1.817 1.904 0.059 Dietary Kilocalories -0.001 0.002 -0.696 0.487 Total n6 0.255 0.189 1.350 0.179

* indicates statistical significance.

164

Table 46.

Regression Table for Exploratory Hypothesis: n-6 Intake with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.040 BMI 0.176 0.149 1.181 0.239 Antidepressant 5.933 2.665 2.226 0.027* Dietary Kilocalories 0.000 0.001 -0.216 0.829

Step 2 0.000 BMI 0.172 0.150 1.145 0.254 Antidepressant 5.927 2.673 2.218 0.028* Dietary Kilocalories -0.001 0.002 -0.298 0.766 Total n6 0.067 0.278 0.240 0.810

* indicates statistical significance.

165

Table 47.

Regression Table for Exploratory Hypothesis: n-6 Intake with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.060 BMI -0.195 0.088 -2.216 0.028* Antidepressant -3.451 1.570 -2.198 0.029* Dietary Kilocalories 0.000 0.000 0.198 0.843

Step 2 0.000 BMI -0.196 0.089 -2.218 0.028* Antidepressant -3.454 1.575 -2.193 0.030* Dietary Kilocalories 0.000 0.001 -0.121 0.903 Total n6 0.033 0.164 0.198 0.843

* indicates statistical significance.

166

Table 48.

Regression Table for Exploratory Analysis 2: Hypothesis 1 Excluding Vegetarians with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.014 BMI 0.072 0.170 0.421 0.675 Antidepressant 4.343 3.041 1.428 0.155 Dietary Kilocalories 0.000 0.001 0.368 0.713

Step 2 0.010 BMI 0.053 0.170 0.311 0.756 Antidepressant 4.142 3.039 1.363 0.175 Dietary Kilocalories -0.002 0.002 -0.971 0.333 Total n3 2.373 1.836 1.293 0.198

* indicates statistical significance.

167

Table 49.

Regression Table for Exploratory Analysis 2: Hypothesis 1 Excluding Vegetarians with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.042 BMI 0.138 0.130 1.060 0.291 Antidepressant 5.511 2.326 2.369 0.019* Dietary Kilocalories 0.000 0.001 -0.137 0.891

Step 2 0.008 BMI 0.125 0.130 0.962 0.337 Antidepressant 5.376 2.327 2.310 0.022* Dietary Kilocalories -0.001 0.001 -1.065 0.288 Total n3 1.592 1.406 1.132 0.259

* indicates statistical significance.

168

Table 50.

Regression Table for Exploratory Analysis 2: Hypothesis 1 Excluding Vegetarians with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Predictor S.E. t p ∆R2 Step 1 0.056 BMI 0.224 0.149 1.506 0.134 Antidepressant 1.333 2.666 0.500 0.618 Dietary Kilocalories 0.002 0.001 2.721 0.007*

Step 2 0.017 BMI 0.203 0.149 1.366 0.174 Antidepressant 1.100 2.653 0.415 0.679 Dietary Kilocalories 0.000 0.002 -0.237 0.813 Total n3 2.745 1.603 1.713 0.089

* indicates statistical significance.

169

Table 51.

Regression Table for Exploratory Analysis 2: Hypothesis 1 Excluding Vegetarians with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.050 BMI -0.155 0.102 -1.518 0.131 Antidepressant 3.481 1.822 1.910 0.058 Dietary Kilocalories 0.001 0.001 1.731 0.085

Step 2 0.048 BMI -0.179 0.100 -1.792 0.075 Antidepressant 3.214 1.783 1.803 0.073 Dietary Kilocalories -0.002 0.001 -1.748 0.082 Total n3 3.136 1.077 2.911 0.004*

* indicates statistical significance.

170

Table 52.

Regression Table for Exploratory Analysis 2: Hypothesis 1 Excluding Vegetarians with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.040 BMI 0.176 0.149 1.181 0.239 Antidepressant 5.933 2.665 2.226 0.027* Dietary Kilocalories 0.000 0.001 -0.216 0.829

Step 2 0.004 BMI 0.166 0.150 1.109 0.269 Antidepressant 5.824 2.671 2.180 0.031* Dietary Kilocalories -0.001 0.002 -0.804 0.423 Total n3 1.284 1.614 0.796 0.427

* indicates statistical significance.

171

Table 53.

Regression Table for Exploratory Analysis 2: Hypothesis 1 Excluding Vegetarians with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.060 BMI -.0195 0.088 -2.216 0.028* Antidepressant -3.451 1.570 -2.198 0.029* Dietary Kilocalories 0.00 0.000 0.198 0.843

Step 2 0.001 BMI -0.198 0.088 -2.235 0.027* Antidepressant -3.483 1.577 -2.209 0.029* Dietary Kilocalories 0.000 0.001 -0.255 0.799 Total n3 0.374 0.952 0.392 0.695

* indicates statistical significance.

172

Table 54.

Regression Table for Exploratory Analysis 2: Hypothesis 2 Excluding Vegetarians with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.014 BMI 0.072 0.170 0.421 0.675 Antidepressant 4.343 3.041 1.428 0.155 Dietary Kilocalories 0.000 0.001 0.368 0.713

Step 2 0.00 BMI 0.075 0.171 0.436 0.664 Antidepressant 4.296 3.056 1.406 0.162 Dietary Kilocalories 0.000 0.001 0.346 0.730 Genotype -0.397 1.611 -0.246 0.806

* indicates statistical significance.

173

Table 55.

Regression Table for Exploratory Analysis 2: Hypothesis 2 Excluding Vegetarians with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.042 BMI 0.138 0.130 1.060 0.291 Antidepressant 5.511 2.326 2.369 0.019* Dietary Kilocalories 0.000 0.001 -0.137 0.891

Step 2 0.007 BMI 0.148 0.130 1.135 0.258 Antidepressant 5.353 2.330 2.298 0.023* Dietary Kilocalories 0.000 0.001 -0.227 0.821 Genotype -1.334 1.228 -1.086 0.279

* indicates statistical significance.

174

Table 56.

Regression Table for Exploratory Analysis 2: Hypothesis 2 Excluding Vegetarians with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.056 BMI 0.224 0.149 1.506 0.134 Antidepressant 1.333 2.666 0.500 0.618 Dietary Kilocalories 0.002 0.001 2.721 0.007*

Step 2 0.001 BMI 0.220 0.150 1.467 0.144 Antidepressant 1.406 2.678 0.525 0.600 Dietary Kilocalories 0.002 0.001 2.741 0.007* Genotype 0.616 1.411 0.436 0.663

* indicates statistical significance.

175

Table 57.

Regression Table for Exploratory Analysis 2: Hypothesis 2 Excluding Vegetarians with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.05 BMI -0.155 0.102 -1.518 0.131 Antidepressant 3.481 1.822 1.910 0.058 Dietary Kilocalories 0.001 0.001 1.731 0.085

Step 2 0.012 BMI -0.165 0.102 -1.624 0.106 Antidepressant 3.646 1.819 2.004 0.047* Dietary Kilocalories 0.001 0.001 1.852 0.066 Genotype 1.396 0.959 1.456 0.147

* indicates statistical significance.

176

Table 58.

Regression Table for Exploratory Analysis 2: Hypothesis 2 Excluding Vegetarians with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.04 BMI 0.176 0.149 1.181 0.239 Antidepressant 5.933 2.665 2.226 0.027* Dietary Kilocalories 0.000 0.001 -0.216 0.829

Step 2 0.002 BMI 0.169 0.150 1.131 0.260 Antidepressant 6.036 2.675 2.256 0.025* Dietary Kilocalories 0.000 0.001 -0.163 0.871 Genotype 0.873 1.410 0.619 0.537

* indicates statistical significance.

177

Table 59.

Regression Table for Exploratory Analysis 2: Hypothesis 2 Excluding Vegetarians with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.06 BMI -0.195 0.088 -2.216 0.028* Antidepressant -3.451 1.570 -2.198 0.029* Dietary Kilocalories 0.000 0.000 0.198 0.843

Step 2 0.013 BMI -0.185 0.088 -2.111 0.036* Antidepressant -3.600 1.567 -2.297 0.023* Dietary Kilocalories 0.000 0.000 0.073 0.942 Genotype -1.251 0.826 -1.515 0.132

* indicates statistical significance.

178

Table 60.

Regression Table for Exploratory Analysis 2: Hypothesis 3 Excluding Vegetarians with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.014 BMI 0.072 0.170 0.421 0.675 Antidepressant 4.343 3.041 1.428 0.155 Dietary Kilocalories 0.000 0.001 0.368 0.713

Step 2 0.010 BMI 0.055 0.171 0.324 0.746 Antidepressant 4.106 3.054 1.345 0.181 Dietary Kilocalories -0.002 0.002 -0.968 0.334 Total n3 2.358 1.843 1.280 0.203 Genotype -0.309 1.609 -0.192 0.848

* indicates statistical significance.

179

Table 61.

Regression Table for Exploratory Analysis 2: Hypothesis 3 Excluding Vegetarians with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.042 BMI 0.138 0.130 1.060 0.291 Antidepressant 5.511 2.326 2.369 0.019* Dietary Kilocalories 0.000 0.001 -0.137 0.891

Step 2 0.014 BMI 0.136 0.131 1.037 0.301 Antidepressant 5.230 2.331 2.244 0.026* Dietary Kilocalories -0.001 0.001 -1.067 0.288 Total n3 1.529 1.407 1.087 0.279 Genotype -1.277 1.228 -1.040 0.300

* indicates statistical significance.

180

Table 62.

Regression Table for Exploratory Analysis 2: Hypothesis 3 Excluding Vegetarians with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.056 BMI 0.224 0.149 1.506 0.134 Antidepressant 1.333 2.666 0.500 0.618 Dietary Kilocalories 0.002 0.001 2.721 0.007*

Step 2 0.019 BMI 0.197 0.149 1.320 0.189 Antidepressant 1.183 2.664 0.444 0.658 Dietary Kilocalories 0.000 0.002 -0.236 0.814 Total n3 2.781 1.608 1.730 0.086 Genotype 0.720 1.404 0.513 0.609

* indicates statistical significance.

181

Table 63.

Regression Table for Exploratory Analysis 2: Hypothesis 3 Excluding Vegetarians with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.050 BMI -0.15 0.102 -1.518 0.131 Antidepressant 3.481 1.822 1.910 0.058 Dietary Kilocalories 0.001 0.001 1.731 0.085

Step 2 0.063 BMI -0.19 0.100 -1.917 0.057 Antidepressant 3.388 1.777 1.906 0.058 Dietary Kilocalories 0.00 0.001 -1.754 0.081 Total n3 3.210 1.073 2.992 0.003* Genotype 1.515 0.937 1.618 0.108

* indicates statistical significance.

182

Table 64.

Regression Table for Exploratory Analysis 2: Hypothesis 3 Excluding Vegetarians with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.040 BMI 0.176 0.149 1.181 0.239 Antidepressant 5.933 2.665 2.226 0.027* Dietary Kilocalories 0.000 0.001 -0.216 0.829

Step 2 0.006 BMI 0.159 0.150 1.055 0.293 Antidepressant 5.929 2.681 2.211 0.028* Dietary Kilocalories 0.000 0.002 -0.802 0.424 Total n3 1.329 1.618 0.821 0.413 Genotype 0.923 1.413 0.653 0.515

* indicates statistical significance.

183

Table 65.

Regression Table for Exploratory Analysis 2: Hypothesis 3 Excluding Vegetarians with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.060 BMI -0.19 0.088 -2.216 0.028* Antidepressant 0.000 1.570 -2.198 0.029* Dietary Kilocalories 0.000 0.000 0.198 0.843

Step 2 0.014 BMI -0.18 0.088 -2.125 0.035* Antidepressant 0.000 1.573 -2.304 0.023* Dietary Kilocalories 0.000 0.001 -.257 0.797 Total n3 0.313 0.950 0.330 0.742 Genotype 0.000 0.829 -1.495 0.137

* indicates statistical significance.

184

Table 66.

Regression Table for Exploratory Analysis 3: Hypothesis 1 Excluding Antidepressant Users with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.002 BMI 0.052 0.183 0.284 0.777 Dietary Kilocalories 0.000 0.001 0.390 0.697

Step 2 0.011 BMI 0.032 0.183 0.176 0.860 Dietary Kilocalories -0.002 0.002 -0.970 0.334 Total n3 2.475 1.917 1.291 0.199

* indicates statistical significance.

185

Table 67.

Regression Table for Exploratory Analysis 3: Hypothesis 1 Excluding Antidepressant Users with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.006 BMI 0.137 0.140 0.975 0.331 Dietary Kilocalories 0.000 0.001 -0.057 0.954

Step 2 0.006 BMI 0.126 0.141 0.892 0.374 Dietary Kilocalories -0.001 0.001 -0.882 0.379 Total n3 1.416 1.471 0.962 0.338

* indicates statistical significance.

186

Table 68.

Regression Table for Exploratory Analysis 3: Hypothesis 1 Excluding Antidepressant Users with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Predictor S.E. t p ∆R2 Step 1 0.064 BMI 0.262 0.159 1.646 0.102 Dietary Kilocalories 0.002 0.001 2.766 0.006*

Step 2 0.018 BMI .239 .158 1.510 0.133 Dietary Kilocalories .000 .002 -0.240 0.810 Total n3 2.813 1.656 1.699 0.091

* indicates statistical significance.

187

Table 69.

Regression Table for Exploratory Analysis 3: Hypothesis 1 Excluding Antidepressant Users with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.031 BMI -0.155 0.109 -1.414 0.159 Dietary Kilocalories 0.001 0.001 1.626 0.106

Step 2 0.054 BMI -0.181 0.107 -1.690 0.093 Dietary Kilocalories -0.002 0.001 -1.865 0.064 Total n3 3.305 1.120 2.952 0.004*

* indicates statistical significance.

188

Table 70.

Regression Table for Exploratory Analysis 3: Hypothesis 1 Excluding Antidepressant Users with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.007 BMI 0.162 0.159 1.021 0.309 Dietary Kilocalories 0.000 0.001 -0.241 0.810

Step 2 0.003 BMI 0.153 0.160 0.960 0.338 Dietary Kilocalories -0.001 0.002 -0.704 0.483 Total n3 1.114 1.670 0.667 0.506

* indicates statistical significance.

189

Table 71.

Regression Table for Exploratory Analysis 3: Hypothesis 1 Excluding Antidepressant Users with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.025 BMI -.0183 0.093 -1.956 0.052 Dietary Kilocalories 0.00 0.000 .059 0.953

Step 2 0.000 BMI -0.184 0.094 -1.962 0.052 Dietary Kilocalories 0.000 0.001 -0.173 0.863 Total n3 0.221 0.983 0.225 0.822

* indicates statistical significance.

190

Table 72.

Regression Table for Exploratory Analysis 3: Hypothesis 2 Excluding Antidepressant Users with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.002 BMI 0.052 0.183 0.284 0.777 Dietary Kilocalories 0.000 0.001 0.390 0.697

Step 2 0.00 BMI 0.053 0.184 0.290 0.772 Dietary Kilocalories 0.000 0.001 0.379 0.705 Genotype -0.197 1.679 -0.117 0.907

* indicates statistical significance.

191

Table 73.

Regression Table for Exploratory Analysis 3: Hypothesis 2 Excluding Antidepressant Users with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.006 BMI 0.137 0.140 0.975 0.331 Dietary Kilocalories 0.000 0.001 -0.057 0.954

Step 2 0.005 BMI 0.148 0.141 1.035 1.035 Dietary Kilocalories 0.000 0.001 -0.117 -0.117 Genotype -1.336 1.282 -0.887 -0.887

* indicates statistical significance.

192

Table 74.

Regression Table for Exploratory Analysis 3: Hypothesis 2 Excluding Antidepressant Users with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.064 BMI 0.262 0.159 1.646 0.102 Dietary Kilocalories 0.002 0.001 2.766 0.006*

Step 2 0.003 BMI 0.254 0.160 1.589 0.114 Dietary Kilocalories 0.002 0.001 2.803 0.006* Genotype 1.034 1.454 0.712 0.478

* indicates statistical significance.

193

Table 75.

Regression Table for Exploratory Analysis 3: Hypothesis 2 Excluding Antidepressant Users with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.031 BMI -0.155 0.109 -1.414 0.159 Dietary Kilocalories 0.001 0.001 1.626 0.106

Step 2 0.013 BMI -0.166 0.109 -1.518 0.131 Dietary Kilocalories 0.001 0.001 1.727 0.086 Genotype 1.439 0.996 1.444 0.151

* indicates statistical significance.

194

Table 76.

Regression Table for Exploratory Analysis 3: Hypothesis 2 Excluding Antidepressant Users with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.007 BMI 0.162 0.159 1.021 0.309 Dietary Kilocalories 0.000 0.001 -0.241 0.810

Step 2 0.002 BMI 0.156 0.160 0.977 0.330 Dietary Kilocalories 0.000 0.001 -0.203 0.840 Genotype 0.799 1.455 0.549 0.584

* indicates statistical significance.

195

Table 77.

Regression Table for Exploratory Analysis 3: Hypothesis 2 Excluding Antidepressant Users with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.025 BMI -0.183 0.093 -1.956 0.052 Dietary Kilocalories 0.000 0.000 0.059 0.953

Step 2 0.016 BMI -.0172 0.093 -1.850 0.066 Dietary Kilocalories 0.000 0.000 -.048 0.962 Genotype -1.338 0.849 -1.577 0.117

* indicates statistical significance.

196

Table 78.

Regression Table for Exploratory Analysis 3: Hypothesis 3 Excluding Antidepressant Users with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 BMI .052 .183 .284 .777 .052 Dietary Kilocalories .000 .001 .390 .697 .000

Step 2 BMI .032 .185 .176 .861 .032 Dietary Kilocalories -.002 .002 -.966 .336 -.002 Total n3 2.473 1.930 1.281 .202 2.473 Genotype -.020 1.681 -.012 .991 -.020

Step 3 .030 .185 .164 .870 .030 BMI -.002 .002 -.979 .329 -.002 Dietary Kilocalories 2.400 1.968 1.219 .225 2.400 Total n3 -.576 3.143 -.183 .855 -.576 Genotype .427 2.037 .210 .834 .427 n3 x Genotype .030 .185 .164 .870 .030

* indicates statistical significance.

197

Table 79.

Regression Table for Exploratory Analysis 3: Hypothesis 3 Excluding Antidepressant Users with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.006 BMI .137 .140 .975 .331 Dietary Kilocalories .000 .001 -.057 .954

Step 2 0.011 BMI .134 .141 .951 .343 Dietary Kilocalories -.001 .001 -.846 .399 Total n3 1.317 1.478 .891 .374 Genotype -1.042 1.287 -.810 .420

Step 3 0.000 BMI .135 .142 .950 .344 Dietary Kilocalories -.001 .001 -.831 .407 Total n3 1.338 1.507 .888 .376 Genotype -.882 2.407 -.366 .715 n3 x Genotype -.123 1.560 -.079 .937

* indicates statistical significance.

198

Table 80.

Regression Table for Exploratory Analysis 3: Hypothesis 3 Excluding Antidepressant Users with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.064 BMI .262 .159 1.646 .102 Dietary Kilocalories .002 .001 2.766 .006*

Step 2 0.022 BMI .229 .159 1.438 .153 Dietary Kilocalories .000 .002 -.276 .783 Total n3 2.931 1.663 1.762 .080 Genotype 1.245 1.448 .859 .391

Step 3 0.004 BMI .222 .160 1.393 .166 Dietary Kilocalories -.001 .002 -.351 .726 Total n3 2.702 1.693 1.597 .113 Genotype -.491 2.703 -.182 .856 n3 x Genotype 1.333 1.752 .761 .448

* indicates statistical significance.

199

Table 81.

Regression Table for Exploratory Analysis 3: Hypothesis 3 Excluding Antidepressant Users with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.031 BMI -.155 .109 -1.414 .159 Dietary Kilocalories .001 .001 1.626 .106

Step 2 0.072 BMI -.195 .107 -1.831 .069 Dietary Kilocalories -.002 .001 -1.949 .053 Total n3 3.465 1.116 3.105 .002* Genotype 1.687 .972 1.737 .085

Step 3 0.004 BMI -.200 .107 -1.869 .064 Dietary Kilocalories -.002 .001 -2.019 .045* Total n3 3.302 1.135 2.908 .004* Genotype .449 1.813 .248 .805 n3 x Genotype .951 1.175 .809 .420

* indicates statistical significance.

200

Table 82.

Regression Table for Exploratory Analysis 3: Hypothesis 3 Excluding Antidepressant Users with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.007 BMI .162 .159 1.021 .309 Dietary Kilocalories .000 .001 -.241 .810

Step 2 0.005 BMI .146 .161 .909 .365 Dietary Kilocalories -.001 .002 -.727 .468 Total n3 1.198 1.679 .713 .477 Genotype .885 1.462 .605 .546

Step 3 0.013 BMI .134 .160 .835 .405 Dietary Kilocalories -.001 .002 -.866 .388 Total n3 .775 1.701 .456 .649 Genotype -2.322 2.716 -.855 .394 n3 x Genotype 2.463 1.760 1.399 .164

* indicates statistical significance.

201

Table 83.

Regression Table for Exploratory Analysis 3: Hypothesis 3 Excluding Antidepressant Users with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.025 BMI -.183 .093 -1.956 .052 Dietary Kilocalories .000 .000 .059 .953

Step 2 0.016 BMI -.173 .094 -1.845 .067 Dietary Kilocalories .000 .001 -.108 .914 Total n3 .095 .981 .097 .923 Genotype -1.331 .854 -1.558 .121

Step 3 0.032 BMI -.162 .093 -1.747 .083 Dietary Kilocalories .000 .001 .116 .907 Total n3 .487 .984 .495 .622 Genotype 1.642 1.571 1.045 .298 n3 x Genotype -2.283 1.018 -2.242 .026*

* indicates statistical significance.

202

Table 84.

Regression Table for Exploratory Analysis 4: Hypothesis 1 in Participants Scoring at or above 16 on the CES-D with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.009 BMI -0.157 0.220 -0.713 0.479 Antidepressant -0.241 3.334 -0.072 0.943 Dietary Kilocalories 0.000 0.001 -0.097 0.923

Step 2 0.000 BMI -0.154 0.223 -0.690 0.493 Antidepressant -0.229 3.364 -0.068 0.946 Dietary Kilocalories 0.000 0.003 0.044 0.965 Total n3 -0.242 2.452 -0.099 0.922

* indicates statistical significance.

203

Table 85.

Regression Table for Exploratory Analysis 4: Hypothesis 1 In Participants Scoring at or above 16 on the CES-D with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.061 BMI .078 .174 .450 .654 Antidepressant 3.118 2.644 1.180 .243 Dietary Kilocalories -.001 .001 -1.217 .228

Step 2 0.001 BMI .083 .177 .468 .642 Antidepressant 3.139 2.667 1.177 .244 Dietary Kilocalories -.001 .002 -.370 .713 Total n3 -.400 1.944 -.206 .838

* indicates statistical significance.

204

Table 86.

Regression Table for Exploratory Analysis 4: Hypothesis 1 In Participants Scoring at or above 16 on the CES-D with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Predictor S.E. t p ∆R2 Step 1 0.094 BMI .318 .201 1.582 .119 Antidepressant -.562 3.046 -.185 .854 Dietary Kilocalories .002 .001 2.163 .035*

Step 2 0.050 BMI .273 .198 1.378 .173 Antidepressant -.770 2.987 -.258 .797 Dietary Kilocalories -.001 .002 -.644 .522 Total n3 4.053 2.177 1.862 .068

* indicates statistical significance.

205

Table 87.

Regression Table for Exploratory Analysis 4: Hypothesis 1 In Participants Scoring at or above 16 on the CES-D with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.057 BMI -.160 .141 -1.141 .258 Antidepressant 3.176 2.132 1.490 .141 Dietary Kilocalories .000 .001 .589 .558

Step 2 0.032 BMI -.185 .140 -1.317 .193 Antidepressant 3.061 2.114 1.448 .153 Dietary Kilocalories -.002 .002 -1.014 .315 Total n3 2.230 1.541 1.447 .153

* indicates statistical significance.

206

Table 88.

Regression Table for Exploratory Analysis 4: Hypothesis 1 In Participants Scoring at or above 16 on the CES-D with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.035 BMI .044 .163 .270 .788 Antidepressant 2.910 2.470 1.178 .244 Dietary Kilocalories .000 .001 -.576 .567

Step 2 0.002 BMI .051 .165 .310 .758 Antidepressant 2.943 2.490 1.182 .242 Dietary Kilocalories .000 .002 .056 .956 Total n3 -.649 1.815 -.357 .722

* indicates statistical significance.

207

Table 89.

Regression Table for Exploratory Analysis 4: Hypothesis 1 In Participants Scoring at or above 16 on the CES-D with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.095 BMI -.279 .125 -2.240 .029* Antidepressant -1.734 1.891 -.917 .363 Dietary Kilocalories .000 .001 -.412 .682

Step 2 0.104 BMI -.319 .119 -2.676 .010* Antidepressant -1.920 1.796 -1.069 .289 Dietary Kilocalories -.004 .001 -2.657 .010* Total n3 3.620 1.309 2.765 .008*

* indicates statistical significance.

208

Table 90.

Regression Table for Exploratory Analysis 4: Hypothesis 2 In Participants Scoring at or above 16 on the CES-D with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.009 BMI -.157 .220 -.713 .479 Antidepressant -.241 3.334 -.072 .943 Dietary Kilocalories .000 .001 -.097 .923

Step 2 0.000 BMI -.157 .225 -.699 .487 Antidepressant -.238 3.393 -.070 .944 Dietary Kilocalories .000 .001 -.097 .923 Genotype .016 2.283 .007 .994

* indicates statistical significance.

209

Table 91.

Regression Table for Exploratory Analysis 4: Hypothesis 2 In Participants Scoring at or above 16 on the CES-D with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.061 BMI .078 .174 .450 .654 Antidepressant 3.118 2.644 1.180 .243 Dietary Kilocalories -.001 .001 -1.217 .228

Step 2 0.028 BMI .116 .176 .662 .511 Antidepressant 2.637 2.650 .995 .324 Dietary Kilocalories -.001 .001 -1.194 .237 Genotype -2.394 1.784 -1.342 .185

* indicates statistical significance.

210

Table 92.

Regression Table for Exploratory Analysis 4: Hypothesis 2 In Participants Scoring at or above 16 on the CES-D with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.094 BMI .318 .201 1.582 .119 Antidepressant -.562 3.046 -.185 .854 Dietary Kilocalories .002 .001 2.163 .035*

Step 2 0.035 BMI .269 .201 1.335 .187 Antidepressant .066 3.040 .022 .983 Dietary Kilocalories .002 .001 2.151 .036* Genotype 3.128 2.046 1.529 .132

* indicates statistical significance.

211

Table 93.

Regression Table for Exploratory Analysis 4: Hypothesis 2 In Participants Scoring at or above 16 on the CES-D with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.057 BMI -.160 .141 -1.141 .258 Antidepressant 3.176 2.132 1.490 .141 Dietary Kilocalories .000 .001 .589 .558

Step 2 0.097 BMI -.217 .136 -1.595 .116 Antidepressant 3.898 2.055 1.897 .063 Dietary Kilocalories .000 .001 .558 .579 Genotype 3.596 1.383 2.600 .012*

* indicates statistical significance.

212

Table 94.

Regression Table for Exploratory Analysis 4: Hypothesis 2 In Participants Scoring at or above 16 on the CES-D with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.035 BMI .044 .163 .270 .788 Antidepressant 2.910 2.470 1.178 .244 Dietary Kilocalories .000 .001 -.576 .567

Step 2 0.015 BMI .019 .165 .113 .910 Antidepressant 3.234 2.495 1.296 .200 Dietary Kilocalories -.001 .001 -.597 .553 Genotype 1.614 1.679 .961 .340

* indicates statistical significance.

213

Table 95.

Regression Table for Exploratory Analysis 4: Hypothesis 2 In Participants Scoring at or above 16 on the CES-D with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.095 BMI -.279 .125 -2.240 .029* Antidepressant -1.734 1.891 -.917 .363 Dietary Kilocalories .000 .001 -.412 .682

Step 2 0.024 BMI -.254 .126 -2.018 .048* Antidepressant -2.062 1.899 -1.086 .282 Dietary Kilocalories .000 .001 -.386 .701 Genotype -1.630 1.278 -1.276 .207

* indicates statistical significance.

214

Table 96.

Regression Table for Exploratory Analysis 4: Hypothesis 3 In Participants Scoring at or above 16 on the CES-D with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.009 BMI -.157 .220 -.713 .479 Antidepressant -.241 3.334 -.072 .943 Dietary Kilocalories .000 .001 -.097 .923

Step 2 0.000 BMI -.154 .228 -.677 .501 Antidepressant -.225 3.424 -.066 .948 Dietary Kilocalories .000 .003 .043 .966 Total n3 -.243 2.473 -.098 .922 Genotype .018 2.303 .008 .994

Step 3 0.076 BMI -.147 .221 -.666 .508 Antidepressant .133 3.323 .040 .968 Dietary Kilocalories .001 .003 .368 .714 Total n3 .666 2.433 .274 .785 Genotype 7.431 4.070 1.826 .073 n3 x Genotype -5.192 2.384 -2.178 .034*

* indicates statistical significance.

215

Table 97.

Regression Table for Exploratory Analysis 4: Hypothesis 3 In Participants Scoring at or above 16 on the CES-D with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.061 BMI .078 .174 .450 .654 Antidepressant 3.118 2.644 1.180 .243 Dietary Kilocalories -.001 .001 -1.217 .228

Step 2 0.028 BMI .120 .178 .674 .503 Antidepressant 2.657 2.674 .994 .325 Dietary Kilocalories -.001 .002 -.373 .711 Total n3 -.369 1.932 -.191 .849 Genotype -2.390 1.799 -1.329 .189

Step 3 0.003 BMI .121 .179 .676 .502 Antidepressant 2.716 2.696 1.007 .318 Dietary Kilocalories -.001 .002 -.300 .765 Total n3 -.219 1.975 -.111 .912 Genotype -1.161 3.302 -.352 .726 n3 x Genotype -.861 1.934 -.445 .658

* indicates statistical significance.

216

Table 98.

Regression Table for Exploratory Analysis 4: Hypothesis 3 In Participants Scoring at or above 16 on the CES-D with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.094 BMI .318 .201 1.582 .119 Antidepressant -.562 3.046 -.185 .854 Dietary Kilocalories .002 .001 2.163 .035*

Step 2 0.084 BMI .225 .199 1.135 .261 Antidepressant -.149 2.980 -.050 .960 Dietary Kilocalories -.001 .002 -.652 .517 Total n3 4.014 2.153 1.865 .067 Genotype 3.084 2.004 1.539 .129

Step 3 0.001 BMI .225 .200 1.122 .267 Antidepressant -.189 3.008 -.063 .950 Dietary Kilocalories -.002 .002 -.679 .500 Total n3 3.912 2.203 1.776 .081 Genotype 2.253 3.684 .611 .543 n3 x Genotype .582 2.158 .270 .788

* indicates statistical significance.

217

Table 99.

Regression Table for Exploratory Analysis 4: Hypothesis 3 In Participants Scoring at or above 16 on the CES-D with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.057 BMI -.160 .141 -1.141 .258 Antidepressant 3.176 2.132 1.490 .141 Dietary Kilocalories .000 .001 .589 .558

Step 2 0.128 BMI -.241 .136 -1.774 .081 Antidepressant 3.781 2.036 1.857 .068 Dietary Kilocalories -.002 .002 -1.063 .292 Total n3 2.185 1.470 1.486 .143 Genotype 3.572 1.369 2.609 .012*

Step 3 0.002 BMI -.241 .137 -1.765 .083 Antidepressant 3.746 2.054 1.824 .073 Dietary Kilocalories -.002 .002 -1.094 .278 Total n3 2.096 1.504 1.394 .169 Genotype 2.847 2.516 1.132 .263 n3 x Genotype .508 1.473 .345 .731

* indicates statistical significance.

218

Table 100.

Regression Table for Exploratory Analysis 4: Hypothesis 3 In Participants Scoring at or above 16 on the CES-D with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.035 BMI .044 .163 .270 .788 Antidepressant 2.910 2.470 1.178 .244 Dietary Kilocalories .000 .001 -.576 .567

Step 2 0.017 BMI .026 .168 .155 .878 Antidepressant 3.270 2.515 1.300 .199 Dietary Kilocalories .000 .002 .056 .955 Total n3 -.669 1.817 -.368 .714 Genotype 1.621 1.692 .958 .342

Step 3 0.018 BMI .023 .167 .139 .890 Antidepressant 3.138 2.516 1.247 .217 Dietary Kilocalories .000 .002 -.102 .919 Total n3 -1.005 1.842 -.545 .588 Genotype -1.115 3.081 -.362 .719 n3 x Genotype 1.916 1.805 1.062 .293

* indicates statistical significance.

219

Table 101.

Regression Table for Exploratory Analysis 4: Hypothesis 3 In Participants Scoring at or above 16 on the CES-D with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.095 BMI -.279 .125 -2.240 .029* Antidepressant -1.734 1.891 -.917 .363 Dietary Kilocalories .000 .001 -.412 .682

Step 2 0.129 BMI -.293 .120 -2.446 .017* Antidepressant -2.257 1.799 -1.255 .215 Dietary Kilocalories -.004 .001 -2.678 .010* Total n3 3.641 1.299 2.802 .007* Genotype -1.670 1.210 -1.381 .173

Step 3 0.005 BMI -.292 .121 -2.423 .019* Antidepressant -2.204 1.811 -1.217 .229 Dietary Kilocalories -.004 .001 -2.545 .014* Total n3 3.776 1.326 2.847 .006* Genotype -.571 2.218 -.257 .798 n3 x Genotype -.770 1.299 -.593 .556

* indicates statistical significance.

220

Table 102.

Regression Table for Exploratory Analysis 5: Hypothesis 1 in Participants Reporting a History of Depression with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.036 BMI -.332 .352 -.943 .354 Antidepressant 1.747 4.667 .374 .711 Dietary Kilocalories 0.000 .003 .025 .980

Step 2 0.052 BMI -.236 .358 -.657 .517 Antidepressant 2.560 4.680 .547 .589 Dietary Kilocalories .005 .005 .978 .337 Total n3 -7.261 6.095 -1.191 .245

* indicates statistical significance.

221

Table 103.

Regression Table for Exploratory Analysis 5: Hypothesis 1 In Participants Reporting a History of Depression with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.062 BMI .005 .245 .021 .983 Antidepressant 3.931 3.253 1.208 .238 Dietary Kilocalories -.001 .002 -.417 .680

Step 2 0.028 BMI .056 .253 .220 .828 Antidepressant 4.356 3.302 1.319 .199 Dietary Kilocalories .002 .004 .469 .643 Total n3 -3.798 4.300 -.883 .386

* indicates statistical significance.

222

Table 104.

Regression Table for Exploratory Analysis 5: Hypothesis 1 In Participants Reporting a History of Depression with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Predictor S.E. t p ∆R2 Step 1 0.044 BMI -.096 .249 -.384 .704 Antidepressant 2.543 3.309 .768 .449 Dietary Kilocalories .002 .002 .749 .461

Step 2 0.00 BMI -.093 .261 -.354 .726 Antidepressant 2.571 3.411 .754 .458 Dietary Kilocalories .002 .004 .478 .636 Total n3 -.253 4.443 -.057 .955

* indicates statistical significance.

223

Table 105.

Regression Table for Exploratory Analysis 5: Hypothesis 1 In Participants Reporting a History of Depression with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.215 BMI -.340 .156 -2.174 .039* Antidepressant 3.614 2.073 1.743 .093 Dietary Kilocalories 0.000 .001 .055 .957

Step 2 0.023 BMI -.372 .161 -2.306 .030* Antidepressant 3.345 2.105 1.589 .125 Dietary Kilocalories -.002 .002 -.676 .505 Total n3 2.403 2.742 .877 .389

* indicates statistical significance.

224

Table 106.

Regression Table for Exploratory Analysis 5: Hypothesis 1 In Participants Reporting a History of Depression with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.090 BMI -.375 .241 -1.552 .133 Antidepressant 1.718 3.202 .537 .596 Dietary Kilocalories 0.000 .002 .012 .990

Step 2 0.017 BMI -.336 .250 -1.341 .192 Antidepressant 2.047 3.270 .626 .537 Dietary Kilocalories .002 .004 .563 .578 Total n3 -2.931 4.259 -.688 .498

* indicates statistical significance.

225

Table 107.

Regression Table for Exploratory Analysis 5: Hypothesis 1 In Participants Reporting a History of Depression with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.035 BMI -.024 .166 -.146 .885 Antidepressant -1.389 2.204 -.630 .534 Dietary Kilocalories .001 .002 .679 .503

Step 2 0.133 BMI -.097 .162 -.601 .553 Antidepressant -2.003 2.110 -.949 .352 Dietary Kilocalories -.003 .002 -1.191 .245 Total n3 5.485 2.748 1.996 .057*

* indicates statistical significance.

226

Table 108.

Regression Table for Exploratory Analysis 5: Hypothesis 2 In Participants Reporting a History of Depression with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.036 BMI -.332 .352 -.943 .354 Antidepressant 1.747 4.667 .374 .711 Dietary Kilocalories 0.000 .003 .025 .980

Step 2 0.005 BMI -.312 .362 -.861 .397 Antidepressant 2.087 4.847 .431 .670 Dietary Kilocalories 0.000 .003 -.018 .986 Genotype 1.577 4.521 .349 .730

* indicates statistical significance.

227

Table 109.

Regression Table for Exploratory Analysis 5: Hypothesis 2 In Participants Reporting a History of Depression with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.062 BMI .005 .245 .021 .983 Antidepressant 3.931 3.253 1.208 .238 Dietary Kilocalories -.001 .002 -.417 .680

Step 2 0.105 BMI -.061 .239 -.256 .800 Antidepressant 2.792 3.192 .875 .390 Dietary Kilocalories .000 .002 -.216 .831 Genotype -5.276 2.977 -1.772 .089

* indicates statistical significance.

228

Table 110.

Regression Table for Exploratory Analysis 5: Hypothesis 2 In Participants Reporting a History of Depression with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.044 BMI -.096 .249 -.384 .704 Antidepressant 2.543 3.309 .768 .449 Dietary Kilocalories .002 .002 .749 .461

Step 2 0.000 BMI -.098 .258 -.379 .708 Antidepressant 2.511 3.445 .729 .473 Dietary Kilocalories .002 .002 .734 .470 Genotype -.145 3.213 -.045 .964

* indicates statistical significance.

229

Table 111.

Regression Table for Exploratory Analysis 5: Hypothesis 2 In Participants Reporting a History of Depression with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.215 BMI -.340 .156 -2.174 .039* Antidepressant 3.614 2.073 1.743 .093 Dietary Kilocalories 0.000 .001 .055 .957

Step 2 0.028 BMI -.316 .158 -1.994 .057 Antidepressant 4.023 2.120 1.898 .069 Dietary Kilocalories 0.000 .001 -.062 .951 Genotype 1.895 1.977 .958 .347

* indicates statistical significance.

230

Table 112.

Regression Table for Exploratory Analysis 5: Hypothesis 2 In Participants Reporting a History of Depression with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.090 BMI -.375 .241 -1.552 .133 Antidepressant 1.718 3.202 .537 .596 Dietary Kilocalories 0.000 .002 .012 .990

Step 2 0.001 BMI -.383 .249 -1.536 .137 Antidepressant 1.583 3.331 .475 .639 Dietary Kilocalories 0.000 .002 .036 .971 Genotype -.627 3.107 -.202 .842

* indicates statistical significance.

231

Table 113.

Regression Table for Exploratory Analysis 5: Hypothesis 2 In Participants Reporting a History of Depression with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.035 BMI -.024 .166 -.146 .885 Antidepressant -1.389 2.204 -.630 .534 Dietary Kilocalories .001 .002 .679 .503

Step 2 0.001 BMI -.020 .171 -.118 .907 Antidepressant -1.318 2.294 -.575 .571 Dietary Kilocalories .001 .002 .642 .526 Genotype .325 2.139 .152 .880

* indicates statistical significance.

232

Table 114.

Regression Table for Exploratory Analysis 5: Hypothesis 3 In Participants Reporting a History of Depression with CES-D Score as Outcome

CES-D Score Predictor S.E. t p ∆R2 Step 1 0.036 BMI -.332 .352 -.943 .354 Antidepressant 1.747 4.667 .374 .711 Dietary Kilocalories 0.000 .003 .025 .980

Step 2 0.077 BMI -.162 .372 -.436 .667 Antidepressant 3.609 4.881 .739 .467 Dietary Kilocalories .006 .006 1.127 .271 Total n3 -9.136 6.547 -1.395 .176 Genotype 3.884 4.736 .820 .420

Step 3 0.008 BMI -.202 .388 -.520 .608 Antidepressant 3.433 4.979 .690 .497 Dietary Kilocalories .006 .006 1.058 .301 Total n3 -6.849 8.320 -.823 .419 Genotype 7.755 9.724 .798 .433 n3 x Genotype -3.559 7.766 -.458 .651

* indicates statistical significance.

233

Table 115.

Regression Table for Exploratory Analysis 5: Hypothesis 3 In Participants Reporting a History of Depression with PROMIS Anxiety SF Score as Outcome

PROMIS Anxiety SF Score Predictor S.E. t p ∆R2 Step 1 0.062 BMI .005 .245 .021 .983 Antidepressant 3.931 3.253 1.208 .238 Dietary Kilocalories -.001 .002 -.417 .680

Step 2 0.108 BMI -.038 .254 -.148 .884 Antidepressant 3.029 3.335 .908 .373 Dietary Kilocalories .001 .004 .137 .892 Total n3 -1.424 4.474 -.318 .753 Genotype -4.917 3.236 -1.520 .142

Step 3 0.001 BMI -.030 .266 -.112 .912 Antidepressant 3.063 3.416 .897 .379 Dietary Kilocalories .001 .004 .147 .885 Total n3 -1.869 5.709 -.327 .746 Genotype -5.670 6.672 -.850 .404 n3 x Genotype .693 5.329 .130 .898

* indicates statistical significance.

234

Table 116.

Regression Table for Exploratory Analysis 5: Hypothesis 3 In Participants Reporting a History of Depression with PROMIS Anger SF Score as Outcome

PROMIS Anger SF Score Predictor S.E. t p ∆R2 Step 1 0.044 BMI -.096 .249 -.384 .704 Antidepressant 2.543 3.309 .768 .449 Dietary Kilocalories .002 .002 .749 .461

Step 2 0.00 BMI -.094 .275 -.343 .734 Antidepressant 2.546 3.607 .706 .487 Dietary Kilocalories .002 .004 .452 .656 Total n3 -.209 4.839 -.043 .966 Genotype -.092 3.500 -.026 .979

Step 3 0.130 BMI -.208 .268 -.776 .446 Antidepressant 2.043 3.436 .595 .558 Dietary Kilocalories .001 .004 .284 .779 Total n3 6.342 5.742 1.105 .281 Genotype 10.99 6.710 1.639 .115 n3 x Genotype 0.000 5.359 -1.902 .070

* indicates statistical significance.

235

Table 117.

Regression Table for Exploratory Analysis 5: Hypothesis 3 In Participants Reporting a History of Depression with Cook Medley Hostility Score as Outcome

Cook Medley Hostility Score Predictor S.E. t p ∆R2 Step 1 0.215 BMI -.340 .156 -2.174 .039* Antidepressant 3.614 2.073 1.743 .093 Dietary Kilocalories 0.000 .001 .055 .957

Step 2 0.038 BMI -.344 .168 -2.049 .052 Antidepressant 3.741 2.205 1.697 .103 Dietary Kilocalories -.001 .003 -.503 .620 Total n3 1.696 2.957 .573 .572 Genotype 1.467 2.139 .686 .499

Step 3 0.097 BMI -.411 .164 -2.507 .020* Antidepressant 3.441 2.108 1.632 .116 Dietary Kilocalories -.002 .002 -.709 .486 Total n3 5.597 3.523 1.589 .126 Genotype 8.072 4.117 1.960 .062 n3 x Genotype -6.072 3.288 -1.847 .078

* indicates statistical significance.

236

Table 118.

Regression Table for Exploratory Analysis 5: Hypothesis 3 In Participants Reporting a History of Depression with NEO Neuroticism Score as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.090 BMI -.375 .241 -1.552 .133 Antidepressant 1.718 3.202 .537 .596 Dietary Kilocalories 0.000 .002 .012 .990

Step 2 0.017 BMI -.333 .263 -1.266 .218 Antidepressant 2.081 3.458 .602 .553 Dietary Kilocalories .002 .004 .547 .589 Total n3 -2.993 4.638 -.645 .525 Genotype .128 3.355 .038 .970

Step 3 0.000 BMI -.337 .276 -1.223 .234 Antidepressant 2.064 3.543 .583 .566 Dietary Kilocalories .002 .004 .527 .604 Total n3 -2.765 5.920 -.467 .645 Genotype .514 6.919 .074 .941 n3 x Genotype -.355 5.526 -.064 .949

* indicates statistical significance.

237

Table 119.

Regression Table for Exploratory Analysis 5: Hypothesis 3 In Participants Reporting a History of Depression with LOT-R Score as Outcome

LOT-R Score Predictor S.E. t p ∆R2 Step 1 0.035 BMI -.024 .166 -.146 .885 Antidepressant -1.389 2.204 -.630 .534 Dietary Kilocalories .001 .002 .679 .503

Step 2 0.145 BMI -.120 .169 -.711 .484 Antidepressant -2.329 2.217 -1.050 .304 Dietary Kilocalories -.003 .003 -1.269 .217 Total n3 6.067 2.974 2.040 .052 Genotype -1.207 2.151 -.561 .580

Step 3 0.000 BMI -.122 .177 -.690 .497 Antidepressant -2.337 2.272 -1.029 .314 Dietary Kilocalories -.003 .003 -1.241 .227 Total n3 6.182 3.796 1.628 .117 Genotype -1.011 4.437 -.228 .822 n3 x Genotype -.179 3.544 -.051 .960

* indicates statistical significance.

238

Table 120.

Regression Table for Exploratory Analysis 6: Hypothesis 1 with Cook Medley Hostility Cynicism Subscale as Outcome

Cook Medley Cynicism Predictor S.E. t p ∆R2 Step 1 0.032 BMI -.044 .033 -1.327 .186 Antidepressant .564 .596 .946 .346 Dietary Kilocalories .000 .000 1.660 .099

Step 2 0.041 BMI -.052 .033 -1.571 .118 Antidepressant .484 .586 .825 .410 Dietary Kilocalories -.001 .000 -1.570 .118 Total n3 .944 .354 2.668 .008*

* indicates statistical significance.

239

Table 121.

Regression Table for Exploratory Analysis 6: Hypothesis 1 with Cook Medley Hostility Hostile Attribution Subscale as Outcome

Cook Medley Hostile Attribution Predictor S.E. t p ∆R2 Step 1 0.018 BMI -.037 .037 -.993 .322 Antidepressant .774 .663 1.167 .245 Dietary Kilocalories .000 .000 .892 .374

Step 2 0.046 BMI -.045 .036 -1.246 .215 Antidepressant .680 .650 1.047 .297 Dietary Kilocalories -.001 .000 -2.060 .041* Total n3 1.103 .393 2.810 .006

* indicates statistical significance.

240

Table 122.

Regression Table for Exploratory Analysis 6: Hypothesis 1 with Cook Medley Hostility Hostile Affect Score as Outcome

Cook Medley Hostile Affect Predictor S.E. t p ∆R2 Step 1 0.020 BMI -.009 .018 -.511 .610 Antidepressant .355 .330 1.074 .285 Dietary Kilocalories .000 .000 1.436 .153

Step 2 0.024 BMI -.013 .018 -.682 .496 Antidepressant .321 .328 .980 .329 Dietary Kilocalories .000 .000 -1.099 .274 Total n3 .397 .198 2.008 .046*

* indicates statistical significance.

241

Table 123.

Regression Table for Exploratory Analysis 6: Hypothesis 1 with Cook Medley Hostility Aggressive Responding Score as Outcome

Cook Medley Aggressive Responding Predictor S.E. t p ∆R2 Step 1 0.044 BMI -.024 .030 -.804 .422 Antidepressant 1.226 .544 2.252 .026* Dietary Kilocalories .000 .000 1.501 .135

Step 2 0.012 BMI -.028 .030 -.925 .356 Antidepressant 1.186 .543 2.182 .031* Dietary Kilocalories .000 .000 -.570 .569 Total n3 .473 .328 1.440 .152

* indicates statistical significance.

242

Table 124.

Regression Table for Exploratory Analysis 6: Hypothesis 1 with Cook Medley Hostility Social Avoidance as Outcome

NEO Neuroticism Score Predictor S.E. t p ∆R2 Step 1 0.008 BMI -.003 .017 -.158 .875 Antidepressant .133 .312 .427 .670 Dietary Kilocalories 0.00 .000 -.977 .330

Step 2 0.008 BMI -.004 .017 -.251 .802 Antidepressant .115 .312 .369 .712 Dietary Kilocalories .000 .000 -1.444 .151 Total n3 .210 .189 1.117 .266

* indicates statistical significance.

243

Table 125.

Regression Table for Exploratory Analysis 6: Hypothesis 2 with Cook Medley Hostility Cynicism Subscale as Outcome

Cook Medley Cynicism Predictor S.E. t p ∆R2 Step 1 0.032 BMI -.044 .033 -1.327 .186 Antidepressant .564 .596 .946 .346 Dietary Kilocalories .000 .000 1.660 .099

Step 2 0.002 BMI -.045 .033 -1.358 .177 Antidepressant .583 .599 .974 .332 Dietary Kilocalories .000 .000 1.693 .092 Genotype .163 .316 .517 .606

* indicates statistical significance.

244

Table 126.

Regression Table for Exploratory Analysis 6: Hypothesis 2 with Cook Medley Hostility Hostile Attribution Subscale as Outcome

Cook Medley Hostile Attribution Predictor S.E. t p ∆R2 Step 1 0.018 BMI -.037 .037 -.993 .322 Antidepressant .774 .663 1.167 .245 Dietary Kilocalories .000 .000 .892 .374

Step 2 0.019 BMI -.042 .037 -1.125 .262 Antidepressant .847 .660 1.285 .201 Dietary Kilocalories .000 .000 1.043 .299 Genotype .621 .348 1.786 .076

* indicates statistical significance.

245

Table 127.

Regression Table for Exploratory Analysis 6: Hypothesis 2 with Cook Medley Hostility Hostile Affect Subscale as Outcome

Cook Medley Hostile Affect Predictor S.E. t p ∆R2 Step 1 0.020 BMI -.009 .018 -.511 .610 Antidepressant .355 .330 1.074 .285 Dietary Kilocalories .000 .000 1.436 .153

Step 2 0.000 BMI -.010 .019 -.524 .601 Antidepressant .359 .332 1.082 .281 Dietary Kilocalories .000 .000 1.446 .150 Genotype .039 .175 .222 .824

* indicates statistical significance.

246

Table 128.

Regression Table for Exploratory Analysis 6: Hypothesis 2 with Cook Medley Hostility Aggressive Responding Score as Outcome

Cook Medley Aggressive Responding Predictor S.E. t p ∆R2 Step 1 0.044 BMI -.024 .030 -.804 .422 Antidepressant 1.226 .544 2.252 .026* Dietary Kilocalories .000 .000 1.501 .135

Step 2 0.009 BMI -.027 .030 -.890 .375 Antidepressant 1.267 .545 2.326 .021* Dietary Kilocalories .000 .000 1.599 .112 Genotype .348 .287 1.210 .228

* indicates statistical significance.

247

Table 129.

Regression Table for Exploratory Analysis 6: Hypothesis 2 with Cook Medley Hostility Social Avoidance Score as Outcome

Cook Medley Social Avoidance Predictor S.E. t p ∆R2 Step 1 0.008 BMI -.003 .017 -.158 .875 Antidepressant .133 .312 .427 .670 Dietary Kilocalories 0.00 .000 -.977 .330

Step 2 0.019 BMI -.005 .017 -.285 .776 Antidepressant .167 .311 .539 .590 Dietary Kilocalories 0.00 .000 -.833 .406 Genotype .289 .164 1.768 .079

* indicates statistical significance.

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Table 130.

Regression Table for Exploratory Analysis 6: Hypothesis 3 with Cook Medley Hostility Cynicism Score as Outcome

Cook Medley Hostility Subscale Predictor S.E. t p ∆R2 Step 1 0.032 BMI -.044 .033 -1.327 .186 Antidepressant .564 .596 .946 .346 Dietary Kilocalories .000 .000 1.660 .099

Step 2 0.044 BMI -.053 .033 -1.612 .109 Antidepressant .506 .588 .861 .391 Dietary Kilocalories -.001 .000 -1.566 .119 Total n3 .954 .355 2.688 .008* Genotype .199 .310 .641 .522

Step 3 0.006 BMI -.051 .033 -1.551 .123 Antidepressant .518 .588 .881 .379 Dietary Kilocalories -.001 .000 -1.483 .140 Total n3 1.030 .363 2.838 .005* Genotype .685 .577 1.187 .237 n3 x Genotype -.375 .376 -.999 .320

* indicates statistical significance.

249

Table 131.

Regression Table for Exploratory Analysis 6: Hypothesis 3 with Cook Medley Hostility Hostile Attribution Score as Outcome

Cook Medley Hostile Attribution Subscale Predictor S.E. t p ∆R2 Step 1 0.018 BMI -.037 .037 -.993 .322 Antidepressant .774 .663 1.167 .245 Dietary Kilocalories .000 .000 .892 .374

Step 2 0.068 BMI -.051 .036 -1.399 .164 Antidepressant .756 .645 1.172 .243 Dietary Kilocalories -.001 .000 -2.075 .040* Total n3 1.135 .389 2.915 .004* Genotype .663 .340 1.950 .053

Step 3 0.012 BMI -.054 .036 -1.485 .140 Antidepressant .737 .643 1.146 .254 Dietary Kilocalories -.001 .000 -2.189 .030* Total n3 1.015 .397 2.557 .012* Genotype -.103 .632 -.163 .871 n3 x Genotype .590 .411 1.437 .153

* indicates statistical significance.

250

Table 132.

Regression Table for Exploratory Analysis 6: Hypothesis 3 with Cook Medley Hostility Hostile Affect Score as Outcome

Cook Medley Hostile Affect Subscale Predictor S.E. t p ∆R2 Step 1 0.020 BMI -.009 .018 -.511 .610 Antidepressant .355 .330 1.074 .285 Dietary Kilocalories .000 .000 1.436 .153

Step 2 0.025 BMI -.013 .018 -.702 .484 Antidepressant .327 .329 .994 .322 Dietary Kilocalories .000 .000 -1.095 .275 Total n3 .400 .199 2.014 .046* Genotype .054 .173 .310 .757

Step 3 0.010 BMI -.013 .019 -.723 .471 Antidepressant .324 .330 .982 .328 Dietary Kilocalories .000 .000 -1.122 .263 Total n3 .382 .204 1.874 .063 Genotype -.062 .324 -.191 .849 n3 x Genotype .089 .211 .423 .673

* indicates statistical significance.

251

Table 133.

Regression Table for Exploratory Analysis 6: Hypothesis 3 with Cook Medley Hostility Aggressive Responding Score as Outcome

Cook Medley Aggressive Responding Subscale Predictor S.E. t p ∆R2 Step 1 0.044 BMI -.024 .030 -.804 .422 Antidepressant 1.226 .544 2.252 .026* Dietary Kilocalories .000 .000 1.501 .135

Step 2 0.022 BMI -.031 .030 -1.020 .309 Antidepressant 1.228 .543 2.260 .025* Dietary Kilocalories .000 .000 -.570 .569 Total n3 .491 .328 1.496 .137 Genotype .366 .286 1.278 .203

Step 3 0.007 BMI -.033 .031 -1.084 .280 Antidepressant 1.215 .543 2.238 .027* Dietary Kilocalories .000 .000 -.656 .513 Total n3 .412 .335 1.229 .221 Genotype -.136 .533 -.256 .799 n3 x Genotype .387 .347 1.116 .266

* indicates statistical significance.

252

Table 134.

Regression Table for Exploratory Analysis 6: Hypothesis 3 with Cook Medley Hostility Social Avoidance Score as Outcome

Cook Medley Social Avoidance Subscale Predictor S.E. t p ∆R2 Step 1 0.008 BMI -.003 .017 -.158 .875 Antidepressant .133 .312 .427 .670 Dietary Kilocalories 0.000 .000 -.977 .330

Step 2 0.024 BMI -.007 .017 -.377 .707 Antidepressant .166 .311 .533 .595 Dietary Kilocalories .000 .000 -1.093 .276 Total n3 .028 .032 .865 .389 Genotype .293 .164 1.790 .075

Step 3 0.008 BMI -.007 .017 -.427 .670 Antidepressant .174 .311 .559 .577 Dietary Kilocalories .000 .000 -1.067 .288 Total n3 .019 .033 .565 .573 Genotype -.017 .319 -.053 .958 n3 x Genotype .026 .023 1.134 .258

* indicates statistical significance.

253

Table 135.

Logistic Regression Table for Exploratory Analysis 7: Hypothesis 1 with Cook Medley Hostility 75th Percentile Likelihood as Outcome

Cook Medley 75th Percentile Predictor S.E. Wald 2 p Exp(B) BMI -.053 .043 1.516 .218 .948 Dietary Kilocalories -.001 .000 2.524 .112 .999 Antidepressant -1.093 .626 3.050 .081 .335 Total n3 .912 .408 4.989 .026* 2.489

* indicates statistical significance.

254

Table 136.

Logistic Regression Table for Exploratory Analysis 7: Hypothesis 2 with Cook Medley Hostility 75th Percentile Likelihood as Outcome

Cook Medley 75th Percentile Predictor S.E. Wald 2 p Exp(B) BMI -.051 .044 1.349 .246 .950 Dietary Kilocalories .000 .000 1.027 .311 1.000 Antidepressant 1.218 .623 3.822 .051 3.382 Genotype -.469 .364 1.659 .198 .626

* indicates statistical significance.

255

Table 137.

Logistic Regression Table for Exploratory Analysis 7: Hypothesis 3 with Cook Medley Hostility 75th Percentile Likelihood as Outcome

Cook Medley 75th Percentile Predictor S.E. Wald 2 p Exp(B) BMI -.066 .046 2.094 .148 .936 Dietary Kilocalories -.001 .000 2.946 .086 .999 Antidepressant 1.175 .645 3.312 .069 3.237 Total n3 .825 .418 3.885 .049* 2.281 Genotype .394 .700 .317 .573 1.483 n3 x Genotype .674 .432 2.433 .119 1.962

* indicates statistical significance.

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Figure 1.



Major Allele Homozygote Depressive Symptoms DepressiveSymptoms Minor Allele

Carrier Dietary n-3 Intake 

Figure 1. Proposed genotype by dietary n-3 interaction for depressive symptoms.

Figure 2.



Major Allele Homozygote Depressive Symptoms DepressiveSymptoms Minor Allele

Carrier Dietary n-6 Intake 

Figure 2. Proposed genotype by dietary n-6 interaction for depressive symptoms.

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Appendix B: REP Recruitment Posting

(Appears on the “List of Experiments” page on the website http://rep.psy.ohio-state.edu/) Title Dietary Fatty Acids, Genetic Factors, and Mood Hours 2 Place OSU Psychology Building Requirements 18 years of age or older. Female. Prerequisites none No eating, drinking, mints / hard candy, chewing Restrictions gum, brushing teeth, or flossing during the experiment. Liisa Hantsoo, M.A. and Janice Kiecolt-Glaser, Researcher Ph.D. Researcher E-mail [email protected] Description: **You must be at least 18 years of age. You must be female.** The purpose of this study is to explore the relationships between diet, genetic factors, and mood. You will be asked to complete pencil and paper questionnaires. You will also provide a cheek cell sample by swishing mouthwash for one minute while rubbing the insides of your cheeks against your teeth. The experiment itself will take about 1.5 hours. When you are scheduled to participate, please come to the REP Waiting Area in the Psychology Bldg * on time, and the experimenter will come and meet you. Please bring a pencil AND pen with you to the study visit.

You will be asked about vitamin, supplement, and medication use, including dosage / amount. Please make note of any vitamins, supplements, or medications you take so that you are able to accurately report them during the study visit.

Please note that during the session, you must not eat (including mints or hard candies), chew gum, drink, or brush/floss your teeth, as these will interfere with the cheek cell collection procedure. * The Psychology Building is located at 1835 Neil Ave. It is building #144 on the campus map. It is behind Lazenby Hall, which is building #041 on the campus map. View building location online at: http://www.osu.edu/map/building.php?building=144

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Appendix C: Measures

General Demographics and Health Information Questionnaire Participant: _ _ _ _ Today’s Date: _ _ / _ _ / _ _

1. Please list your current age: _ _ years 2. Please indicate your year in school: _ Freshman _ Sophomore _ Junior _ Senior _ Other (please specify): ______

3. Which one of the groups below would you say best represents your race? _ White _ Black or African American _ Asian _ Native Hawaiian or Other Pacific Islander _ American Indian, Alaska Native _ Other (please specify): ______Don't know/Not sure 4. Are you vegetarian? _Yes _ No

5. Are you vegan? _Yes _ No

6. Do you take fish oil supplements? _Yes _ No (If Y, list below.)

7. Do you take flax seed oil supplements? _Yes _ No (If Y, list below.)

8. In the box below, please list all medications AND vitamins or supplements you are currently taking. Medications can be prescription medications or over-the-counter. Include the NAME / BRAND NAME, DOSE / AMOUNT and FREQUENCY (how often) you take the medications, vitamins, or supplements. If the medication, vitamin, or supplement does not have a particular dose, please put "NA". Medication / Supplement / Vitamin Name Dose / Amount Frequency

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260

Center for Epidemiologic Studies Depression Scale (CES-D) Questionnaire

Please mark the response that best describes how you felt or behaved DURING THE PAST WEEK.

Less 1-2 3-4 5-7 than days days days 1 day 1. I was bothered by things that usually don't bother me. 2. I did not feel like eating; my appetite was poor. 3. I felt that I could not shake off the blues even with help from my family or friends. 4. I felt that I was just as good as other people. 5. I had trouble keeping my mind on what I was doing. 6. I felt depressed. 7. I felt that everything I did was an effort. 8. I felt hopeful about the future. 9. I thought my life had been a failure. 10. I felt fearful. 11. My sleep was restless. 12. I was happy. 13. I talked less than usual. 14. I felt lonely. 15. People were unfriendly. 16. I enjoyed life. 17. I had crying spells. 18. I felt sad. 19. I felt that other people disliked me. 20. I could not get going.

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PROMIS Anxiety Short Form Questionnaire

Please respond to each item by marking 1 box per row. In the past 7 days…

Never Rarely Some- Often Alway times s 1) I felt fearful 2) I felt anxious 3) I felt worried 4) I found it hard to focus on anything other than my anxiety 5) I felt nervous 6) I felt uneasy 7) I felt tense

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PROMIS Anger Short Form Questionnaire

Please respond to each item by marking 1 box per row. In the past 7 days…

Never Rarely Some- Often Alway times s 1) I was irritated more than people knew 2) I made myself angry about something just thinking about it 3) I felt angry 4) I felt like I was ready to explode 5) I stayed angry for hours 6) I felt angrier than I should 7) I was grouchy

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NEO FFI 12 Item Questionnaire

Please indicate the extent to which you agree or disagree with each of the statements below.

Strongl Disagre Neutral Agree Strongl y e y Disagre Agree e 1) I am not a worrier 2) I often feel inferior to others 3) When I’m under a great deal of stress, sometimes I feel like I’m going to pieces 4) I rarely feel lonely or blue 5) I often feel tense and jittery 6) Sometimes I feel completely worthless 7) I rarely feel hurt or anxious 8) I often get angry at the way people treat me 9) Too often, when things go wrong, I get discouraged and feel like giving up 10) I am seldom sad or depressed 11) I often feel helpless and want someone else to solve my problems 12) At times I have been so ashamed I just want to hide

Adapted and reproduced by special permission of the publisher, Psychological Assessment Resources Inc., 16204 North Florida Avenue, Lutz, FL 33549, from the NEO Five Factor Inventory by Paul T. Costa Jr., Ph.D. and Robert T. McCrae, Ph.D., Copyright 1978, 1985, 1989, 1991, 2003 by PAR Inc. Further reproduction is prohibited without permission of PAR Inc.

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LOT-R Questionnaire

Please be as honest and accurate as you can throughout. Try not to let your response to one statement influence your responses to other statements. There are no "correct" or "incorrect" answers. Answer according to your own feelings, rather than how you think "most people" would answer.

I I I I agree I agree disagree disagree neither a little a lot a lot a little agree nor disagree 1) In uncertain times, I usually expect the best. 2) It's easy for me to relax. 3) If something can go wrong for me, it will. 4) I'm always optimistic about my future. 5) I enjoy my friends a lot.] 6) It's important for me to keep busy.] 7) I hardly ever expect things to go my way. 8) I don't get upset too easily.] 9) I rarely count on good things happening to me. 10) Overall, I expect more good things to happen to me than bad.

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Cook-Medley Hostility Questionnaire

Indicate if this is usually true or usually false for you by circling T or F.

False True 1) When I take a new job, I like to be tipped off on who should be gotten next to 2) When someone does me a wrong, I feel I should pay him back if I can, just for the principle of the thing 3) I prefer to pass by school friends, or people I know but have not seen for a long time, unless they speak to me first 4) I have often had to take orders from someone who did now know as much as I did 5) I think a great many people exaggerate their misfortunes in order to gain the sympathy of others 6) It takes a lot of argument to convince most people of the truth 7) I think most people would like to get ahead 8) Someone has it in for me 9) Most people are honest chielfly through fear of being caught 10) Most people will use somewhat unfair means to gain profit or an advantage rather than to lose it 11) I commonly wonder what hidden reason another person may have for doing something nice to me 12) It makes me impatient to have people ask me advice or otherwise interrupt me when I am working on something important 13) I feel that I have often been punished without cause

14) I am against giving money to beggars

15) Some of my family have habits that bother and annoy me very much 16) My relatives are nearly all in sympathy with me

17) My way of doing things is apt to be misunderstood by others

18) I don’t blame anyone for trying to grab everything he can get in this world 19) No one cares very much what happens to you

20) I can be friendly with people who do things which I consider wrong 21) It is safer to trust nobody

22) I do not blame a person for taking advantage of someone who lays himself open to it

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23) I have often felt that strangers were looking at me critically

24) Most people make friends because friends are likely to be useful to them 25) I am sure I am being talked about

26) I am not likely to speak to people until they speak to me

27) Most people inwardly dislike putting themselves out to other people 28) I tend to be on my guard with people who are somewhat more friendly than I expected 29) People often disappoint me

30) I like to keep people guessing at what I’m going to do next

31) I frequently ask people for advice

32) I have often met people who were supposed to be experts but were no better than I 33) It makes me feel like a failure when I hear of the success of someone I know well 34) I have sometimes stayed away from another person because I feared doing or saying something that I might regret afterwards 35) I would certainly enjoy beating a crook at his own game

36) People generally demand more respect for their own rights than they are willing to allow for others 37) I have at times had to be rough with people who were rude or annoying 38) I am quite often not in on the gossip and talk of the group I belong to 39) There are certain people whom I dislike so much that I am inwardly pleased when they are catching it for something they have done 40) I am often inclined to go out of my way to win a point with someone who has opposed me 41) I am often said to be hotheaded

42) The man who had most to do with me when I was a child (such as my father, stepfather, etc.) was very strict with me 43) I have often found people jealous of my good ideas, just because they had not thought of them first 44) When a man is with a woman he is usually thinking about things related to her sex 45) I do not try to cover up my own opinions as a rule

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46) I have frequently worked under people who seem to have things arranged so that they get credit for good work but are able to pass off their mistakes onto those under them 47) I strongly defend my own opinions as a rule

48) People can pretty easily change me even tough I thought that my mind was already made up on a participant 49) Sometimes I am sure that other people can tell what I am thinking

50) A large number of people are guilty of bad sexual conduct

268

Block Food Frequency Questionnaire

269

270

271

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274

275

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Appendix D: Abbreviations

5-HIAA 5-hydroxyindoleacetic acid

5-HT serotonin

5-LO 5-lipoxygenase

AA arachidonic acid

ADHD attention deficit hyperactivity disorder

ALA alpha-linolenic acid

ALOX5AP 5-lipogenase-activating protein gene

CES-D Center for Epidemiological Studies Depression Scale

CHD coronary heart disease

COX-2 cyclooxygenase-2

CSF cerebrospinal fluid

D2 dopamine receptor 2

DGLA dihomo-gamma-linolenic acid

DHA docosahexaenoic acid

DMSO dimethylsulfoxide

DPA docosapentaenoic acid

EDTA ethylenediaminetetraacetic

EPA eicosapentaenoic acid

277

FADS fatty acid desaturase

GLA gamma-linolenic acid

HDL high density lipoprotein

HLA human leukocyte antigen hs-CRP high-sensitivity C-reactive protein

IL-1 interleukin 1

IL-1β interleukin 1 beta

IL-6 interleukin 6

LA linoleic acid

LOT-R Life Orientation Test-Revised

NDSR Nutrition Data Systems for Research

NEO-FFI NEO Five Factor Inventory

NF-kB nuclear factor kappa B n-3 omega-3 n-6 omega-6

PCR polymerase chain reaction

POMS Profile of Mood States test

PROMIS Patient-Reported Outcomes Measurement Information System

PUFAs polyunsaturated fatty acids

SNP single nucleotide polymorphism

TGF-β transforming growth factor beta

TNF-α tumor necrosis factor alpha

278

Tris-HCl tris(hydroxymethyl)aminomethane hydrochloride

UNG uracil-N-glycosylase

279