Neurological Modulation of Sleep: Mechanisms and Function of Sleep Health [1 ed.] 0128166584, 9780128166581

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NEUROLOGICAL MODULATION OF SLEEP

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NEUROLOGICAL MODULATION OF SLEEP MECHANISMS AND FUNCTION OF SLEEP HEALTH Edited by

Ronald Ross Watson Mel and Enid Zuckerman College of Public Health and School of Medicine Arizona Health Sciences Center University of Arizona Tucson, AZ, United States

Victor R. Preedy Department of Nutrition and Dietetics; Department of Clinical Biochemistry; Director of the Genomics Centre, King’s College, London United Kingdom

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/ permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-816658-1 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Nikki Levy Acquisition Editor: Joslyn Chaiprasert-Paguio Editorial Project Manager: Timothy Bennett Production Project Manager: Paul Prasad Chandramohan Cover Designer: Christian Bilbow Typeset by TNQ Technologies

Contents Contributors Preface

4. Obesity, Inflammation, and OSA: Exercise as Therapy

xi xv

´ VIA CAMPOS CORGOSINHO, ANA RAIMUNDA DA ˆ MASO, FLA

FREDERICO MORAES CARDOSO MARQUES, DAVID THIVEL, ´ LIO DE MELLO TATIANE DOS SANTOS ANDRADE, AND MARCO TU

Section A Introduction and background of sleep disruption 1. Physiological and Cultural Perspectives of Sleep Disorders’ Impacts in Middle East Countries DELDAR MORAD ABDULAH

Introduction Prevalence of Sleep Disorders in Middle East Countries Impacts of Sleep Disorders Interventional Initiatives Sleep Clinics and Sleep Medicine Education Research References

3 3 7 11 12 13 13

5. Genetics of Sleep and Sleep Disorders

49

Molecular Mechanisms of Sleep Genetics of Sleep Disorders Genetics of Circadian Rhythm Disorders Insomnia Hypersomnia Parasomnia NREM Parasomnias REM Parasomnias Conclusion References

AKIYOSHI SHIMURA

19 20 21 21 21

40 42 44

49 50 50 51 51 52 52 52 52 52

6. Sleep, Genetics, and Human Health MENGYU FAN AND LU QI

3. Circadian Changes in Gut Peptide Levels and Obesity

Introduction Sleep and Human Diseases The Genetics of Sleep Gene-Sleep Interaction on Human Diseases Future Direction and Conclusion Remarks References

MEGAN J. DAILEY AND MEGAN M. MAHONEY

Introduction Molecular Control of Circadian Rhythms in the Brain and Gut Gut Peptides: Connecting SleepeWake Disruption With Metabolic Dysregulation Circadian Changes in Gut Peptide Levels: Entrained by Food Mechanisms of Food Entrainment-Induced Gut Peptide Rhythms Circadian Changes in Gut Peptide Levels: Independent of Food Sleep, Palatable Foods, Gut Peptides, and Obesity: What Is the Link? References

35 36 37 37 39

DENIZ KIRAC, TEOMAN AKCAY, AND KORKUT ULUCAN

2. Chronotype and Performance in Students Chronotype Performance and Chronotype Daytime Sleepiness, Sleep Quality, and Life Habits in Adolescents Summary/Conclusion References

Introduction Obesity as a Main Risk Factor for Obstructive Sleep Apnea The Vicious Cycle Between Obesity and OSA Inflammation: A Common Link Between Obesity and OSA OSA and Obesity-Related Disorders Effects of Exercises and Nutrition on the Treatment of Obesity and OSA Conclusion and Future Directions References

25 25

55 55 58 60 62 63

7. Obesity Hypoventilation Syndrome

27

AMANDA J. PIPER

27

Obesity Hypoventilation Syndrome Mechanisms Underlying the Development of OHS Clinical Consequences of OHS Treatment Modalities and Outcomes Conclusion References

29 30 31 31

v

67 67 71 71 75 75

vi

Contents

8. Sleep Satisfaction, SleepeWake Pattern, and Aging ARCADY A. PUTILOV

Introduction Associations Between SleepeWake Pattern, Sleep Satisfaction, and Age Neurophysiological Underpinning of the Age-Modulating Link Between SleepeWake Pattern and Sleep Satisfaction References

79 80 82 85

9. Etiopathogenesis of Circadian SleepeWake Rhythm Disorders ¨ SUN MAYDA DOMAC FU ¸

Introduction Conclusion References

87 90 90

Section B 10. Modulation and Consequences of Sleep Duration in Child Obesity MARCUS VINICIUS NASCIMENTO-FERREIRA, AUGUSTO CE´SAR

FERREIRA DE MORAES, FRANCISCO LEONARDO TORRES-LEAL, AND ´ CLITO BARBOSA CARVALHO HERA

95 96 99 99

11. Physiopathology of Narcolepsy and Other Central Hypersomnias ¨ SUN MAYDA DOMAC FU ¸

Introduction Narcolepsy Narcolepsy Type 1 Narcolepsy Type 2 Idiopathic Hypersomnia KleineeLevin Syndrome Infection Conclusion References

103 103 103 105 105 106 106 107 107

12. Lateral Hypothalamic Control of Sleep in the Context of Cancer ´ REZ JEREMY C. BORNIGER AND NATALIE NEVA

Introduction Sleep Disruption in Patients With Cancer and Cancer Survivors Arousal Circuitry

111 112 114

114 115 118 119 120

13. Sleep Dysfunction and Intestinal Dysbiosis TEJAS V. JOSHI AND PARTH J. PAREKH

Introduction Sleep Physiology Sleep Dysfunction and Its Link to Intestinal Dysbiosis Conclusion References

125 125 126 128 128

14. Sleep Restriction and Circadian Misalignment: Their Implications in Obesity ´ N-RUGERIO, CAMBRAS TRINITAT, AND MARI´A FERNANDA ZERO

MARIA IZQUIERDO-PULIDO

Introduction Sleep Restriction and Circadian Misalignment Relations Among Sleep Restriction, Circadian Misalignment and Diet Mechanisms Linking Sleep Restriction and Circadian Misalignment With Obesity Beneficial Effect of Sleep Hygiene on Obesity Conclusions References

Adverse effects of sleep disruption

Introduction Sleep Duration in Child Obesity Conclusion References

Lateral Hypothalamic Control of Sleep/Wake States Integration of Systemic Signals in the Lateral Hypothalamus Cancer Deregulates Systemic Factors That Influence Hypothalamic Function Conclusion and Future Directions References

131 133 136 137 140 140 141

15. Exercise, Sleep, and Type 1 Diabetes PETER G. JACOBS AND RAVI REDDY

Introduction Background on Diabetes and Management Approaches Type 1 Diabetes and Sleep Quality Impact of Exercise on Sleep in Type 1 Diabetes Impact of Sleep on Glycemic Control Mechanisms for Sleep and Exercise Impact on Type 1 Diabetes Integrating Sleep and Exercise Metrics Into Type 1 Diabetes Management References

145 146 148 148 149 151 153 154

16. Hypocretin (Orexin): What It Does and How It Links With Narcolepsy and Food Choices MARIA FERNANDA SOARES NAUFEL, GISELLE DE MARTIN TRUZZI, AND FERNANDO MORGADINHO SANTOS COELHO

Hypocretin Narcolepsy Type 1 as a Model of Hypocretin Deficiency Hypocretin, Narcolepsy, and Comorbidities Hypocretin, Obesity, Food Preference, and Narcolepsy References

159 161 162 164 165

Contents

17. Neuropathic Pain and Sleep Quality ESRA DOGRU HUZMELI, SENEM URFALI, AND OZDEN GOKCEK

Introduction Neuropathic Pain Mechanisms Pathophysiology of Sleep Disorders Nonpharmacologic Interventions of Sleep Disorders Cognitive Behavior Therapy Conclusion References

169 169 170 172 172 173 173

18. Sleep Quality in Neurodegenerative Diseases ESRA OKUYUCU, BORAN URFALI, AND MURAT GUNTEL

Introduction and Neurosurgical Aspects Sleep Quality in Neurodegenerative Diseases Evaluation of Sleep Quality in Neurodegenerative Diseases Treatment of Sleep Disorders in Neurodegenerative Diseases Parkinson’s Disease Alzheimer’s Disease References

175 177 177 179 179 180 180

19. The Effects of Short Sleep Duration and Deprivation on Gustation and Olfaction: Implications for Dietary Intake CHIA-LUN YANG AND ROBIN M. TUCKER

Introduction Effects of Short Sleep Duration on Taste Function and Perception Effects of Short Sleep Duration on Olfactory Function and Perception Future Directions Conclusions References

183 183 185 186 187 187

Dietary components and sleep 20. High-fat diet Influences on Sleep Regulation: Lessons From Animal Studies MARCO LUPPI

193 195

21. Chronotypes, Eating Habits, and Food Preferences: An Intervention Proposal for Obesity Treatment ˜ OZ AND JOAQUI´N S. GALINDO MUN ´ NDEZ MORANTE JUAN JOSE´ HERNA

Chronobiology, Chronotype, and Eating Habits Influence of Chronotype on Obesity Development

197 199

200 201 202 203

22. The Influence of Diet on Sleep FARIS M. ZURAIKAT AND MARIE-PIERRE ST-ONGE

Introduction Relation Between Diet and Sleep Diet as a Modifier of Sleep: Intervention Studies The Impact of Specific Foods on Sleep: Randomized Trials Conclusions References

205 205 208 209 213 214

23. Impact of Sleep Restriction on Food Intake and Food Choice AVELINO A. DE LEON AND ERIN C. HANLON

Introduction Epidemiological Findings and Meta-Analyses Sleep, Appetite, Hunger, and Food Intake Potential Mechanisms of Increased Hunger and Appetite Controlled in Laboratory Studies on Hunger and Appetite Sleep Deficiency and Energy Expenditure Sleep, Appetite, and Reward Systems The Endocannabinoid System Sleep Quality and Food Intake Sleep Extension and Food Intake Summary References

217 218 218 219 220 221 222 223 224 225 226 226

24. Moderate Sleep Restriction and Body Composition XUEWEN WANG

Introduction Cross-sectional Observational Studies Prospective Observational Studies Interventional Studies: Short-term Studies Interventional Studies: Longer-term Studies Conclusion References

Section C

Introduction References

Influence of Chronotype on Eating Habits Influence of Chronotype on Food Preferences Chronotype-Adjusted Diet: Suitable for Obesity Treatment? References

vii

229 229 230 231 231 233 233

25. Sleep, Stress, and Vitamin D ARNAUD METLAINE

Introduction Sleep: A Serious Health Problem Sleep, Stress, and Metabolism and Hypothalamice PituitaryeAdrenal Axis Sleep Burnout and Metabolism Sleep and Vitamin D Conclusions References

235 236 236 237 237 240 241

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Contents

Anatomical Changes Which can Affect Sleep and Sleep Disorders Summary and Future Directions References

Section D Caffeine, alcohol and sleep 26. The Role of Tea in Sleep Improvement and Cancer Prevention JENNIFER CHINOMSO EHIRI

Introduction Cancer and Tea Gamma-Aminobutyric Acid Tea and Sleep Green Tea and Sleep Chamomile Tea and Sleep Other Teas and Sleep The Amino Acid L-Theanine in Tea and Sleep Summary References

245 245 246 246 247 251 251 252 252

27. Sleep Health and Alcohol Use

31. Sleep and Use of Green Tea With Lowered Caffeine: Decreased Early Morning Awakening and Increased Slow-Wave Sleep KEIKO UNNO AND YORIYUKI NAKAMURA

Introduction Preparation of LCGT and the Interaction of Caffeine With Other Tea Components Clinical Studies of LCGT in Elderly and Middle-Aged Participants Effect of LCGT Intake on Sleep Effect of LCGT on sAA and Sleep Discussion References

MARY BETH MILLER, LINDSEY FREEMAN, ASHLEY F. CURTIS,

255 255 256 257 257 259 260 261 261

28. Caffeine, Sleep, and Antioxidant Status OLAKUNLE J. ONAOLAPO AND ADEJOKE Y. ONAOLAPO

Introduction Caffeine: Antioxidant or Prooxidant? Sleep Physiology Sleep: Oxidant or Antioxidant? Caffeine and Sleep Caffeine, Sleep Deprivation, and Brain Behavior Conclusion References

265 265 269 269 270 270 271 272

29. Alcohol Intake, Sleep Deprivation, and Neuromuscular Fatigue RODRIGO RODRIGUES

Introduction Conclusion References

275 281 281

30. Sleep Disorders and Gestational Diabetes Mellitus

285

308 308 309 311 311 312

Neuromodulation of sleep 32. Neurological Modulations of Sleep: Mechanisms and Function in Sleep Health NICOLE P. BOWLES AND ANDREW W. MCHILL

Overview Sleep, Energy Metabolism, Hunger Central Regulation of Energy Homeostasis Peripheral Regulation of Energy Homeostasis Summary References

317 317 318 319 321 321

33. Surgery: A Focus on the Hypoglossal Nerve for Sleep Apnea PETER M. BAPTISTA JARDIN AND GUILLERMO PLAZA

Introduction Hypoglossal Nerve Implant Descriptions Indications of Hypoglossal Nerve Stimulation Published Data on Hypoglossal Nerve Stimulation Therapy With the Inspire Hypoglossal Nerve Stimulation Results in Subgroups of Patients After Hypoglossal Nerve Stimulation Operative Technique of Inspire Hypoglossal Nerve Stimulation System Activation and Titration After Hypoglossal Nerve Stimulation Titration With a Polysomnography Conclusions References

325 326 329 330 331 333 334 335 335 336

34. Sleep and Sleep Disorders in Rett Syndrome

NARICHA CHIRAKALWASAN AND SIRIMON REUTRAKUL

Introduction Physiological and Hormonal Changes During Pregnancy and Effects on Sleep and Sleep Disorders

307

Section E

JEFF BOISSONEAULT, AND CHRISTINA S. MCCRAE

Introduction Alcohol Effects on Sleep Physiology Alcohol Effects on Sleep Behavior Alcohol Effects on Subjective Sleep Experience Effect of Sleep Health on Alcohol Use Clinical Implications Future Directions Conclusion References

286 302 302

285 286

AMEE A. PATEL AND DANIEL G. GLAZE

Clinical Vignette Introduction

339 339

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Contents

Sleep and Rett Syndrome Sleep Characteristics Polysomnogram Findings Respiratory Pattern during Sleep Evaluation of Sleep Disorders Treatment of Sleep Disorders Future Directions Clinical Pearls References

339 340 340 341 342 343 344 344 345

35. Sleep Problems and Disorders Associations With Eating Behavior and Obesity ´ NIO MACEDO ˜ O SOARES AND ANTO MARIA JOA

Introduction Sleep Problems and Eating Disturbances/Obesity The Association Between Eating Behavior Disturbances and Sleep Problems Weight, Body Mass Index, Obesity, and Sleep Problems Eating Behavior Disturbances and Sleep Problems: Potential Psychological Mechanisms of Association Discussion References

347 347 349 352 354 355 358

Vitamins, amino acids and sleep 37. Chrononutritional Modulation of Sleep and the Circadian Clock by Amino Acids SHINOBU YASUO

Introduction Bioregulatory Functions of Amino Acids Modulation of Stress- and Sleep-Related Behaviors by Amino Acids Screening of Amino Acids that Modulate the Circadian Clock L-Serine as an Enhancer of Light-Induced Circadian Resetting L-Ornithine as a Modulator of Sleep, Stress, and the Circadian Clock Glycine as a Modulator of Sleep Conclusions and Perspectives References

375 375 376 376 377 378 379 379 380

38. Linking Vitamin D and Sleep KOSTAS ARCHONTOGEORGIS, EVANGELIA NENA AND PASCHALIS

36. Sleep and Addictions: Linking Sleep Regulation With the Genesis of Addictive Behavior

STEIROPOULOS

ILANA S. HAIRSTON

Sleep Patterns Mechanisms of Addiction Linking Sleep and Sleep Difficulty with Addiction Pathways Sleep, Plasticity, and AddictionsdTying It all Together Concluding Remarks References

Section F

361 363 365 367 368 368

Vitamin D and Sleep Vitamin D and Sleep Disorders Discussion References

385 386 394 395

Index

401

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Contributors Deldar Morad Abdulah Adult Nursing Department, College of Nursing, University of Duhok, Duhok, Kurdistan, Iraq

Esra Dogru Huzmeli Hatay Mustafa Kemal University, School of Physical Therapy and Rehabilitation, Antakya, Hatay, Turkey

Teoman Akcay Medical Park Hospital, Department of Pediatric Endocrinology, Istanbul, Turkey

Jennifer Chinomso Ehiri Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, United States

Tatiane dos Santos Andrade Universidade Federal de Goia´s, Faculdade de Nutric¸a˜o, Goiaˆnia, Goia´s, Brazil

Mengyu Fan Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China

Kostas Archontogeorgis MSc Programme in Sleep Medicine, Medical School, Democritus University of Thrace, Alexandroupolis, Greece

Augusto Ce´sar Ferreira De Moraes Youth/Child cArdiovascular Risk and Environmental (YCARE) Research Group, School of Medicine, University of Sao Paulo, Sa˜o Paulo, Sa˜o Paulo, Brazil; Department of Epidemiology School of Public Health, University of Sao Paulo, Sa˜o Paulo, Sa˜o Paulo, Brazil

Peter M. Baptista Jardin Department of Otorhinolaryngology, Clı´nica Universidad Navarra, Pamplona, Navarra, Spain Jeff Boissoneault Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, United States

Lindsey Freeman Department of Psychological Sciences, University of Missouri, Columbia, MO, United States

Jeremy C. Borniger Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States

Joaquı´n S. Galindo Mun˜oz National Health Service, University Hospital “Miguel Servet”, Zaragoza, Spain Daniel G. Glaze Pediatrics and Neurology, The Children’s Sleep Center, Baylor College of Medicine/Texas Children’s Hospital, Houston, TX, United States; The Blue Bird Circle Rett Center, Baylor College of Medicine/Texas Children’s Hospital, Houston, TX, United States

Nicole P. Bowles Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States Hera´clito Barbosa Carvalho Youth/Child cArdiovascular Risk and Environmental (YCARE) Research Group, School of Medicine, University of Sao Paulo, Sa˜o Paulo, Sa˜o Paulo, Brazil

Ozden Gokcek Hatay Mustafa Kemal University, School of Physical Therapy and Rehabilitation, Antakya, Hatay, Turkey

Naricha Chirakalwasan Division of Pulmonary and Critical Care Medicine, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand; Excellence Center for Sleep Disorders, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand

Murat Guntel Department of Neurology, Tayfur Ata So¨kmen Faculty of Medicine, Hatay Mustafa Kemal University, Antakya, Hatay, Turkey Ilana S. Hairston Department of Psychology, Tel Hai Academic College, Kiryat Shemona, Israel

Fla´via Campos Corgosinho Universidade Federal de Goia´s, Faculdade de Nutric¸a˜o, Goiaˆnia, Goia´s, Brazil

Erin C. Hanlon The University of Chicago, Department of Medicine, Section of Adult and Pediatric Endocrinology, Diabetes, and Metabolism, Chicago, IL, United States

Ashley F. Curtis Department of Psychiatry, University of Missouri, Columbia, MO, United States

Juan Jose´ Herna´ndez Morante Faculty of Nursing, Catholic University of Murcia, Murcia, Spain

Megan J. Dailey Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Maria Izquierdo-Pulido Department of Nutrition, Food Science and Gastronomy, School of Pharmacy and Food Science, University of Barcelona, Barcelona, Spain; INSAUB, Nutrition and Food Safety Research Institute, Barcelona, Spain; CIBER Physiopathology of Obesity and Nutrition (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain

Ana Raimunda Daˆmaso Universidade Federal de Sa˜o Paulo, Department of Nutrition, Sa˜o Paulo, Sa˜o Paulo, Brazil Avelino A. De Leon The University of Chicago, Committee on Molecular Metabolism and Nutrition, Chicago, IL, United States

Peter G. Jacobs Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, United States; Artificial Intelligence for Medical Systems (AIMS) Lab, Oregon Health & Science University, Portland, OR, United States

Marco Tu´lio de Mello Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

xi

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Contributors

Tejas V. Joshi Department of Internal Medicine, Louisiana State University, New Orleans, VA, United States Deniz Kirac Yeditepe University, Faculty of Medicine, Department of Medical Biology, Istanbul, Turkey Marco Luppi Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy Anto´nio Macedo Department of Psychological Medicine, Faculty of Medicine, University of Coimbra, Rua Larga, Coimbra, Portugal Megan M. Mahoney Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Department of Comparative Biosciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States Frederico Moraes Cardoso Marques Universidade Federal de Goia´s, Programa de po´s-graduac¸a˜o em Cieˆncia da Sau´de, Goiaˆnia, Goia´s, Brazil Giselle de Martin Truzzi Departamento de Psicobiologia, Universidade Federal de Sa˜o Paulo, Brazil Fu¨sun Mayda Domac¸ University of Health Sciences, Erenko¨y Mental Health and Neurological Diseases Training and Research Hospital, Neurology Department and Sleep _ Medicine Center, Istanbul, Turkey Christina S. McCrae Department of Psychiatry, University of Missouri, Columbia, MO, United States

Adejoke Y. Onaolapo Behavioural Neuroscience and Neurobiology Unit, Department of Anatomy, Faculty of Basic Medical Sciences, Ladoke Akintola University of Technology, Ogbomoso, Oyo, Nigeria Parth J. Parekh Department of Internal Medicine, Digestive and Liver Disease Specialists, Division of Gastroenterology, Eastern Virginia Medical School, Norfolk, VA, United States Amee A. Patel Pediatric Pulmonology/Children’s Sleep Center, Baylor College of Medicine/Texas Children’s Hospital, Houston, TX, United States Amanda J. Piper Department of Respiratory and Sleep Medicine, Royal Prince Alfred Hospital, Camperdown, NSW, Australia; Sleep and Circadian Group, Woolcock Institute of Medical Research, Glebe, NSW, Australia; Central Clinical School, Faculty of Medicine and Health Science, University of Sydney, Sydney, NSW, Australia Guillermo Plaza Department of Otorhinolaryngology, Hospital Universitario de Fuenlabrada, Madrid, Spain; Department of Otorhinolaryngology, Hospital Sanitas La Zarzuela, Madrid, Spain Arcady A. Putilov Laboratory of Sleep/Wake Neurobiology, The Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia

Andrew W. McHill Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, United States

Lu Qi Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States; Department of Nutrition, Harvard School of Public Health, Boston, MA, United States

Arnaud Metlaine Hotel-Dieu Sleep Center- Paris (APHP), Paris Descartes University, Paris, France

Ravi Reddy Oregon Health & Science University, Portland, OR, United States

Mary Beth Miller Department of Psychiatry, University of Missouri, Columbia, MO, United States

Sirimon Reutrakul Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Illinois College of Medicine at Chicago, Chicago, IL, United States

Yoriyuki Nakamura Tea Science Center, University of Shizuoka, Suruga-ku, Shizuoka, Japan Marcus Vinicius Nascimento-Ferreira Youth/Child cArdiovascular Risk and Environmental (YCARE) Research Group, School of Medicine, University of Sao Paulo, Sa˜o Paulo, Sa˜o Paulo, Brazil; Instituto de Ensino Superior Sul do Maranha˜o (IESMA/UNISULMA), Imperatriz, Maranha˜o, Brazil Evangelia Nena Laboratory of Hygiene and Environmental Protection, Medical School, Democritus University of Thrace, Alexandroupolis, Greece Natalie Neva´rez Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States Esra Okuyucu Department of Neurology, Tayfur Ata So¨kmen Faculty of Medicine, Hatay Mustafa Kemal University, Antakya, Hatay, Turkey Olakunle J. Onaolapo Behavioural Neuroscience and Neuropharmacology Unit, Department of Pharmacology, Faculty of Basic Medical Sciences, Ladoke Akintola University of Technology, Osogbo, Osun, Nigeria

Rodrigo Rodrigues Laborato´rio de Fisiologia do Exercı´cio e Avaliac¸a˜o Fı´sica, Centro Integrado de Sau´de, Centro Universita´rio da Serra Gau´cha, Caxias do Sul, Rio Grande do Sul, Brazil Fernando Morgadinho Santos Coelho Departamento de Neurologia e Psicobiologia, Universidade Federal de Sa˜o Paulo, Brazil Akiyoshi Shimura Department of Psychiatry, Tokyo Medical University, Tokyo, Japan Maria Joa˜o Soares Department of Psychological Medicine, Faculty of Medicine, University of Coimbra, Rua Larga, Coimbra, Portugal Maria Fernanda Soares Naufel Departamento de Neurologia, Universidade Federal de Sa˜o Paulo, Brazil Marie-Pierre St-Onge Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States; Sleep Center of Excellence, Columbia University Irving Medical Center, New York, NY, United States; New York Obesity Nutrition Research Center, Columbia University Irving Medical Center, New York, NY, United States; Institute of Human Nutrition, Columbia University Irving Medical Center, New York, NY, United States

Contributors

xiii

Paschalis Steiropoulos MSc Programme in Sleep Medicine, Medical School, Democritus University of Thrace, Alexandroupolis, Greece

Senem Urfali Department of Anesthesiology and Reanimation, Tayfur Ata So¨kmen Faculty of Medicine, Hatay Mustafa Kemal University, Antakya, Hatay, Turkey

David Thivel Clermont Auvergne University (UFRSTAPS), Impasse Ame´lie Murat, Clermont-Ferrand, France

Xuewen Wang Department of Exercise Science, University of South Carolina, Columbia, SC, United States

Francisco Leonardo Torres-Leal Metabolic Diseases, Exercise and Nutrition (DOMEN) Research Group, Federal University of Piaui´, Teresina, Piauı´, Brazil

Chia-Lun Yang Michigan State University, Department of Food Science and Human Nutrition, East Lansing, MI, United States

Cambras Trinitat Department of Biochemistry and Physiology, School of Pharmacy and Food Science, University of Barcelona, Barcelona, Spain

Shinobu Yasuo Laboratory of Regulation in Metabolism and Behavior, Faculty of Agriculture, Kyushu University, Nishiku, Fukuoka, Japan

Robin M. Tucker Michigan State University, Department of Food Science and Human Nutrition, East Lansing, MI, United States

Marı´a Fernanda Zero´n-Rugerio Department of Nutrition, Food Science and Gastronomy, School of Pharmacy and Food Science, University of Barcelona, Barcelona, Spain; INSA-UB, Nutrition and Food Safety Research Institute, Barcelona, Spain

Korkut Ulucan Marmara University, Faculty of Dentistry, Department of Medical Biology and Genetics, Istanbul, Turkey Keiko Unno School of Pharmaceutical Sciences, University of Shizuoka, Suruga-ku, Shizuoka, Japan Boran Urfali Department of Neurosurgery, Tayfur Ata So¨kmen Faculty of Medicine, Hatay Mustafa Kemal University, Antakya, Hatay, Turkey

Faris M. Zuraikat Department of Medicine, Columbia University Irving Medical Center, New York, NY, United States; Sleep Center of Excellence, Columbia University Irving Medical Center, New York, NY, United States; New York Obesity Nutrition Research Center, Columbia University Irving Medical Center, New York, NY, United States

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Preface

INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION Adequate sleeping is essential for psychological well-being and cognitive processes. Sleep disturbances can have serious negative effects on a person’s ability, function, and overall well-being. An example was prepared as a chapter relative to people living in the Middle East. Then chronotype and performance in students were reviewed. There is a tendency of the internal body clock phenotype called chronotype to affect sleep pattern, daytime performance variance, and daytime sleepiness due to sleep disturbance. Dailey found that a significant contributing factor to circadianinduced alterations in energy homeostasis is due to changes in the levels or rhythms of gut peptides. Obesity and obstructive sleep apnea had become important public health problems. Therefore, Campos Corgosinho’s chapter proposed exercise as a therapy for consequences of sleep disruption with its obesity. An excellent strategy to combat obesity would be the reduction of body weight through the implementation of healthy lifestyle together with sleep hygiene and improvement of inflammatory state. Piper reviewed obesity hypoventilation syndrome as a serious medical disorder, which is frequently overlooked in the differential diagnoses of patients presenting to hospital with respiratory, metabolic, and cardiovascular complaints. Once identified, the condition is most commonly treated using positive pressure therapy to manage sleep-disordered breathing. Genetics of sleep and its disorders as summarized by Kirac are critical components. Generation sequencing makes it possible to detect new mutations that may associate with sleep mechanism and sleep disorders. Fan expands this idea with the latest findings from the epidemiological studies of the relationships of sleep deficiency and sleep disorders with human diseases, the genetics of sleep behaviors, and the geneesleep interaction on health outcomes. Epidemiological evidence continues to support the conclusion that sleep problems have an important and substantial negative effect on human health. Putilov summarized sleep research consistently reveals that sleep satisfaction does not accurately reflect the age-associated worsening of subjective and objective indicators of night sleep quality.

ADVERSE EFFECTS OF SLEEP DISRUPTION Modulation and consequences of sleep duration in child obesity was reviewed by Nascimento-Ferreira. Short sleep duration increased childhood obesity while long sleep duration impaired whole-body energy metabolism and obesity. Encouraging sleep recommendations can be effective effort to prevent and control child obesity. Domaҫ’s chapter proposes that hypocretin deficiency, genetic factors, autoimmunity, and infections were hypothesized for the physiopathology of central hypersomnias. Borniger reviews poor sleep as a strong predictor of subsequent mortality in cancer patients. The underlying mechanisms mediating cancer-associated changes in sleep are unknown. Discussed are recent evidence supporting a cross-talk among tumors in the periphery, the nervous, endocrine, metabolic, and immune systems leading to sleep and systemic disruption. Special emphasis is given to the lateral hypothalamus, which contains many neural populations that couple sleep to metabolic state, immune status, and the environment. Then Joshi discussed emerging data which demonstrated that sleep dysfunction promotes inflammation and propagates disease states marked by an inflammatory component. The circadian rhythm and proper sleep hygiene play a crucial role in maintaining the integrity of the intestinal barrier and microbial homeostasis. Zeron-Rugerio summarizes that sleep is tightly coupled to the circadian system, sleep timing, and circadian misalignment affecting glucose metabolism, which is disrupted after a night of short sleep duration. The relationship between sleep and obesity is bidirectional, and sleep is a part of weight-loss intervention programs. Jacobs then notes a complex relationship between sleep, exercise, and glucose control in people with type 1 diabetes. The authors explore the relationship of sleep, exercise, and type 1 diabetes and provide ideas on how future automated glucose management systems may better integrate exercise and sleep metrics. Naufel describes hypocretin (orexin) via what it does and how it links with narcolepsy and food choices. Patients with type 1 narcolepsy have less satiety with regard to food choices and snack

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Preface

ingestion. Studies are necessary to understand the mechanisms why hypocretin deficiency drives higher prevalence of obesity and other comorbities. Huzmeli finds that in patients with neuropathic pain, sleep quality can be affected by various factors such as associated diseases, clinical symptoms, and psychological disorders which may cause difficulties in the treatment of the disease. There is a positive relationship between pain sensitivity and intensity of sleeplessness. Thus there is a decrease in pain tolerance based in patients with neuropathic pain and sleep problems. Okuyucu’s chapter finds that in neurodegenerative diseases, the most frequently observed sleep disorders are insomnia, rapid eye movement, sleep behavior disorder, periodic leg movements in sleep, restless legs syndrome, central or obstructive sleep apnea, excessive daytime sleepiness, and nocturnal stridor. These sleep disorders are frequently observed with great potential of negatively affecting the quality of life. Yang summarized shorter sleep duration, either through curtailment or deprivation, acutely and, typically, negatively influences chemosensory function and perception. This chapter helps to more fully understand how sleep affects the chemical senses and how these alterations may perturb dietary intake.

DIETARY COMPONENTS AND SLEEP Luppi reviews work with animals, allowing greater range of study variables. The interaction between a high-fat diet and sleep regulation takes place against the common background of metabolism, at a hypothalamic level. When they are compared to normal fed controls, a lower daily amount of wakefulness mirrored by a higher amount of non-REM sleep that is also more fragmented and of a worse quality is seen. Mun˜oz proposes aligning eating rhythms to biological rhythms may improve metabolic control and decrease obesity. St-Onge summarizes epidemiological studies suggest a bidirectional relation between sleep and overall dietary patterns, whereby diets rich in fiber, whole grains, and fruits and vegetables, are associated with longer sleep duration, better sleep quality, and fewer insomnia symptoms. This chapter evaluates the evidence linking diet as a moderator of sleep outcomes. In the combination of epidemiological and intervention findings, a pattern emerges connecting healthy diets comprised of high intakes of serotonin- and melatonin-rich fruits, high-fiber foods, and zinc-rich seafood along with low fat intake, leading to the promotion of healthier sleep. De Leon’s chapter reviews the epidemiological evidence and laboratory findings that support a role for sleep duration in mediating feeding behavior, as well as explores the potential mechanisms by which sleep duration affects food intake and food choice, thus contributing to the increased prevalence of obesity. Wang studies the existing evidence supporting sleep restriction, even mild to moderate ( 50 kg/m2) develop OHS while others remain normocapnic. Nevertheless, the prevalence of OHS does increase as BMI increases.7 Data from animal and human studies have established that the development of awake hypercapnia in those with morbid obesity arises from a complex interaction of circumstances including altered pulmonary mechanics, changes in respiratory drive, sleep-disordered breathing, and neurohormonal factors. While these circumstances are generally present in anyone with obesity, the development of awake hypoventilation occurs when the normal compensatory mechanisms designed to maintain ventilation in the face of the increased loads from obesity fail.8

Neurological Modulation of Sleep https://doi.org/10.1016/B978-0-12-816658-1.00007-7

Obesity and Lung Function Individuals with OHS exhibit a more central pattern of obesity, reflected in higher neck and waist:hip ratios.9 This excess adipose tissue around the thorax and abdomen restricts lung volumes, with vital capacity (VC), functional residual capacity (FRC), and expiratory reserve volume (ERV) all lower in OHS compared to eucapnic obese individuals. Breathing at low lung volumes has a number of physiological consequences, including reduced chest wall compliance and increased airway resistance, both of which will contribute to the

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© 2020 Elsevier Inc. All rights reserved.

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FIGURE 7.1 Illustration of the impairments associated with obesity hypoventilation syndrome (OHS), long with the disabilities and handicaps arising from this disorder. While OHS is thought of as a respiratory disorder, it is associated with significant cardiovascular and metabolic abnormalities. Hs-CRP, high sensitivity C-reactive protein; RANTES, regulated on activation, normal T cell expressed and secreted. (From Borel et al., Respirology. 2012 with permission).

markedly increased WOB seen in OHS compared to eucapnic obesity.10 Low lung volumes also promote small airway closure and air trapping, imposing an additional load on breathing.11 Even when awake and sitting upright, OHS individuals have increased upper airway resistance,12 adding further to increased WOB. The increased mechanical loads on the respiratory system are substantially greater in those with OHS compared to eucapnic obese individuals even at similar levels of BMI.10,12 At the same time, the capacity to meet this increased load is reduced. Although reports of respiratory muscle strength and endurance in obesity have varied, most reports have confirmed inspiratory muscle impairment in the presence of severe obesity or OHS.13,14 In a mouse model of obesity, a long-term high-fat diet resulted in progressive adipose tissue expansion and collagen deposition within the diaphragm, affecting its motion in vivo and contraction properties ex vivo.15 While the morphology of the diaphragm in OHS has not been systematically evaluated, autopsy findings in a man with OHS found marked infiltration of the respiratory musculature by fat which could have contributed to respiratory muscle dysfunction.16 Any impairment in respiratory muscle performance is likely to be exacerbated in the supine position, as this places the inspiratory muscles at a mechanical disadvantage either from the development of intrinsic positive end expiratory pressure (PEEP)11 or from overstretching of the diaphragm from the upward pressure of the abdominal mass.17

To reduce the high WOB, individuals with morbid obesity alter their respiratory pattern to one characterized by a higher breathing frequency and lower tidal volume.18 This pattern is more marked in those with OHS.19 Although this may assist in reducing the oxygen cost of breathing while maintaining high overall minute ventilation, such a pattern also increases dead space ventilation and eventually becomes disadvantageous as it worsens gas exchange, favoring a rise in CO2. Small airway closure arising from reduced lung volumes also serves to worsen ventilation-perfusion matching, resulting in more pronounced hypoxemia in OHS compared to eucapnic obesity.

Respiratory Drive Obese individuals have higher basal oxygen consumption and CO2 production20 in addition to markedly increased WOB compared to normal weight controls.17,18 In order to maintain eucapnia despite increased mechanical loads on the respiratory system, resting ventilation must increase, and this is achieved by an increase in central respiratory drive.11,18 Individuals with OHS, however, do not augment their drive to compensate for the increased load,21 and as a consequence minute ventilation is lower than required to maintain eucapnia. Ventilatory responsiveness to hypoxia and hypercapnia are also reduced in OHS compared to eucapnic individuals with or without

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

MECHANISMS UNDERLYING THE DEVELOPMENT OF OHS

obesity or OSA,22e24 further promoting CO2 retention. A more blunted ventilatory responsiveness to CO2 is associated with more severe hypoventilation during rapid eye movement (REM) sleep.22 This reduction in chemoresponsiveness appears to be acquired, most likely from sleep-disordered breathing, since normalization of nocturnal breathing with positive airway pressure (PAP) therapy produces improvements in ventilatory responsiveness to both carbon dioxide and oxygen even when BMI and lung function remain unchanged.22e25

Sleep-Disordered Breathing Although sleep hypoventilation alone can be present in OHS,25,48 around 70%e80% of OHS patients also have significant upper airway obstruction,26 with no clinical or anthropometric differences between the two groups27 (Fig. 7.2). However, the development of awake hypercapnia in patients with OSA is the exception rather than the rule and even among the morbidly obese sleep apneic only 25%e30% will be hypercapnic.28,29 This suggests that other permissive factors must exist for the development of awake hypercapnia in obese individuals with OSA. Differences in the pattern of ventilation between hypercapnic patients and those with eucapnia following obstructed nocturnal breathing has been observed, giving rise to a model which explains the process by which abnormal breathing during sleep eventually produces daytime hypercapnia.30 During apneic or hypopneic periods, a small accumulation of CO2 occurs as ventilation is reduced or absent. Eucapnic individuals compensate for this by augmenting ventilation during the subsequent interapnea period, whereas hypercapnic patients demonstrate reduced ventilation for a given CO2 load during this period.31 At the same time, the interapnea period is shorter in hypercapnic individuals, reducing the time available to unload CO2.32 These two features permit an acute rise in CO2 overnight not seen in subjects able to maintain eucapnia.33 The overnight increase in CO2 will be buffered by increased bicarbonate, but this in turn will blunt ventilatory responsiveness to CO2.34 Usually, these changes are very small over a single night, and both CO2 and bicarbonate can be normalized during wakefulness over the next day. However, with further blunting of the ventilatory response to CO2 or inadequate excretion of bicarbonate by the renal system, this balance will be upset permitting daytime hypercapnia to emerge.35 Increased awake bicarbonate levels are a very sensitive, but not specific, predictor of OHS,28,29 and correlate with more blunted CO2 responsiveness.36 A recent in vitro study found that both intermittent and sustained hypercapnia accelerated adipogenesis in subcutaneous and visceral preadipocytes.37 The authors

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postulated a positive feedback mechanism whereby the presence of hypoventilation and hypercapnia would increase adipogenesis and consequently weight gain. This increased fat mass around the chest wall and abdomen would further worsen respiratory system mechanics while the deposition of fat in the neck region would promote further obstructed breathing. In this way, more sleep-disordered breathing and hypoventilation would promote further weight gain which in turn worsens sleep-disordered breathing in this population. Subjects with OHS also experience more significant degrees of sleep hypoxemia than those with eucapnic OSA.7,38 Several studies have shown sleep time spent with SpO2 < 90% is a strong predictor of daytime hypercapnia.7,9 Sustained hypoxia could potentially interfere with neurotransmitters directly involved in respiratory control, or may delay arousal from sleep in the face of resistive loading,39 thereby extending periods of abnormal breathing permitting greater CO2 retention during sleep.

Metabolic and Neurohormonal Influences on Ventilation Overlaying this interplay between altered pulmonary function, reduced respiratory drive, and sleepdisordered breathing are the metabolic consequences of obesity which could also influence breathing. Leptin, a protein designed to regulate appetite and energy expenditure, has been proposed as a potential contributor to the development and progression of hypoventilation. In humans, serum leptin levels are elevated in both obesity and OSA and are suggested to be a compensatory mechanism to stimulate breathing in the presence of the increased ventilatory load created by obesity.40 Fasting serum leptin levels are higher in OHS patients compared to eucapnic obese individuals.40 Hyperleptinemia has been shown to be associated with a reduction in both respiratory drive and ventilatory responsiveness to CO2 in a group of very obese individuals,41 while in another study, hypercapnic ventilatory response was significantly lower in hypercapnic patients compared with those who were eucapnic despite similar levels of serum leptin.42 These data suggest that leptin acts to augment respiratory drive in obese individuals in order to maintain eucapnia. Hence, the stimulatory effects of leptin appear to be attenuated in OHS, likely from leptin resistance associated with reduced leptin permeability of the bloodebrain barrier.43 Consequently, despite high serum levels of serum leptin, a deficiency of leptin in the CNS would promote the development of awake hypoventilation by altering respiratory drive. Leptin may also be involved in maintaining neuromuscular control of the upper airway muscles during sleep

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FIGURE 7.2 Patterns of sleep disordered breathing in obesity hypoventilation syndrome (OHS). Panel A: the first two channels depict the overnight sleep stages (Hypno) and oxygen saturation (SpO2) in a patient with repetitive obstructive events throughout sleep. During REM sleep, these obstructive events are superimposed on hypoventilation, resulting in profound desaturation. The three lower channels illustrate the breathing pattern during a 2 min epoch of REM sleep. Despite continued respiratory efforts (thoracic band) there is no airflow (flow), signifying obstructive events. Despite several recovery breaths between events, SpO2 fails to return to baseline levels during this period. Obstructive apnea is present in the majority of patients with OHS. Panel B: In this patient, the most obvious periods of abnormality are confined to REM sleep only. The lower three channels demonstrate nonapneic breathing but a sustained low saturation, typical of sleep hypoventilation. This type of sleep disordered breathing is seen in around 10% of individuals with OHS.

through increased respiratory drive.44 Resistance to leptin could reduce this drive, promoting pharyngeal collapse and contributing to the high incidence of apnea and hypopnea seen in patients with OHS. In a study of leptin-resistant diet-induced obese mice, bypass of the bloodebrain barrier through the use of intranasal leptin significantly reduced obstructed breathing during sleep

resulting in a reduction in the number of oxygen desaturation events in REM sleep and increasing minute ventilation during flow limited breathing.45 Sleep-related hypoventilation was also attenuated through an increase in minute ventilation during nonflow limited breathing. Whether intranasal leptin delivery in humans will provide similar responses remains to be investigated.

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

TREATMENT MODALITIES AND OUTCOMES

In OHS, the hormone Insulin-like growth factor-1 (IGF-1) is significantly lower compared to obese controls, and inversely associated with PaCO2 and positively associated with VC, two significant features of OHS.46 IGF-1 has also been have been associated with ventilatory responsiveness to CO2 in other disorders.47 This highlights the potential for various metabolic and hormonal factors to promote or modify compensatory mechanisms designed to maintain eucapnia in the presence of excessive weight.

CLINICAL CONSEQUENCES OF OHS Patients with OHS demonstrate significant cardiovascular morbidity, with higher prevalence rates of hypertension, congestive heart failure, pulmonary hypertension, and cor pulmonale than weight matched eucapnic individuals,1,2,48,49 as well as a higher likelihood of exhibiting three or more these comorbid conditions.49 The excess cardiovascular burden seen in OHS is not particularly surprising given the clinical characteristics of these individuals: markedly increased mechanical load on the respiratory system resulting in a high WOB10; high circulating leptin levels40; a significant hypoxic burden7; and moderate to severe OSA.7,28 All the aforementioned factors are associated with systemic inflammation and increased cytokine production and are more markedly abnormal in patients with OHS.1 This, along with more severe endothelial dysfunction1 and higher triglyceride levels46 compared to eucapnic obesity, places the OHS patient at significant risk of developing cardiovascular comorbidities. These morbidities are present several years prior to a diagnosis of OHS being made,4 and despite frequent contact with the health-care system, the presence of hypoventilation and its consequences are often overlooked.2,50 At least a third of patients with OHS are likely present with acute hypercapnic respiratory failure,3,51,52 and even then the diagnosis may not be made or the institution of appropriate therapy undertaken.5,50 A late diagnosis can result in the development of significant secondary consequences, with poorer clinical outcomes even when intervention is commenced.3,50

TREATMENT MODALITIES AND OUTCOMES Pharmacotherapy Improvements in ventilatory responsiveness to CO2, resting ventilation, and daytime blood gases have been reported following the use of respiratory stimulant

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medications including medroxyprogesterone53 and acetazolamide.36 However, these have been short-term observational studies, and there is a paucity of data regarding the safety and longer term benefits of using medication to improve respiratory drive. Currently, pharmacotherapy is not recommended as the primary therapy in OHS.54

Positive Airway Pressure Therapy PAP to manage sleep disordered breathing can be delivered either as continuous (CPAP) or bilevel (BPAP) therapy. With CPAP therapy, a single level of pressure is delivered throughout the respiratory cycle, splinting the upper airway open. In addition, FRC is increased which assists in reducing the WOB by offsetting intrinsic PEEP,11 while improving hypoxemia through prevention of small airway closure and better ventilation-perfusion matching. Bilevel PAP delivers two levels of pressure: a higher pressure during inspiration (IPAP) with the aim of increasing tidal volumes while reducing inspiratory effort, and a lower pressure during expiration (EPAP) which provides the same benefits as CPAP. Furthermore, the trigger to inspiratory support may be entirely patient-generated (Spontaneous (S) mode), entirely machine-generated (Timed (T) mode) or a combination of the two (Spontaneous-time (ST) mode). Volume targeted pressure support (VTPS) is a more recent approach to home ventilation whereby the device is set to automatically adjust the level of pressure support delivered in order to provide a guaranteed preset tidal volume or minute ventilation, usually in the range of 8e10 mL/kg ideal body weight for OHS.55e57 A further advancement in ventilator technology has been the addition of autotitrating EPAP which can be added to either fixed level or volume-targeted pressure support modes. Continuous Positive Airway Pressure Since obstructive events are common in the majority of patients with OHS, titration of PAP usually starts with CPAP, with the aim of eliminating obstruction and increasing nocturnal SpO2 >90%. If sleep hypoxemia or hypopneas persist, or if nocturnal CO2 remains high the patient is switched to BPAP. With this approach around 50%e80% of stable OHS patients presenting to a sleep laboratory can be managed with CPAP therapy.38,58 In many centers, the decision that CPAP has “failed” is based on the initial titration night. However, several studies have demonstrated a progressive improvement in nocturnal gas exchange following the commencement of CPAP can occur.59,60 In a randomized study comparing CPAP to BPAP, no between-group differences in improvement in daytime arterial blood

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gases, resolution of daytime sleepiness, or therapy compliance therapy were found.59 In those allocated to CPAP, the median (range) sleep time spent with SpO2 23% and a fall in hemoglobin compared to baseline were both associated with better prognosis.88 Whether there is a threshold value for awake CO2 below which better clinical outcomes are achieved is not known. However, the requirement for ongoing oxygen therapy is an independent predictor of higher mortality52 and should flag the individual in whom more careful monitoring and follow-up is required. Weight Loss and Lifestyle Modifications Weight loss can ameliorate many of the breathing abnormalities associated with OHS by addressing all three major mechanisms contributing to the disorder: improving pulmonary function,89 reducing the severity of upper airway obstruction,90 and increasing respiratory drive.14 Although weight loss may be achieved through diet and lifestyle modification, maintaining this longer term is difficult. The addition of a multidisciplinary bespoke weight loss program in patients managed with BPAP was shown to achieve more significant weight loss than BPAP alone after 3 months.91 Significant improvements in blood pressure, exercise capacity, and quality of life were also seen. However, by 12 months these effects were lost, in part due to difficulties retaining patients in such a program. Bariatric surgery generally provides a more rapid and long-term solution to weight loss.92 Although there are a number of surgical techniques available, gastric bypass and sleep gastrectomy are more effective in achieving weight less than laparoscopic adjustable gastric banding

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

REFERENCES

(LAGB).93 Nevertheless, OSA may only be partially corrected even with significant weight loss90 and consequently patients should be reviewed to determine if and when PAP therapy for sleep-disordered breathing can be removed. Weight loss also addresses the metabolic and inflammatory aspects of obesity. Although BPAP improves survival compared to no treatment,94,95 cardiovascular morbidity related to extreme obesity persists. There is currently no evidence that BPAP significantly alters inflammatory, metabolic, or cardiovascular markers despite significant improvements in sleep architecture and gas exchange.81 Furthermore, the persistence of high inflammatory markers88 or cardiovascular abnormalities3 despite noninvasive ventilation has been associated with a higher risk of death. Weight loss should be advocated for all individuals with OHS as part of a holistic approach to managing the respiratory and cardiovascular consequences of this disorder.96 Although PAP may improve physical activity levels and promote weight loss to some degree,56 these changes are small, and more formal exercise and activity programs need to be implemented. Increased physical activity is needed not only for weight maintenance but also to improve metabolic profile, reduce visceral fat accumulation, and diminish cardiovascular risk.96 Dyspnea can pose a significant deterrent to exercise, and adjunctive techniques such as BPAP-assisted exercise training may need to be implemented. Although the addition of BPAP to cycle exercise training in a small group of obese CPAP-treated patients with OSA did not produce further improvements in functional capacity, it significantly improved blood pressure and waist circumference compared with cycle training alone.97 Motivation, pain, and exercise fear avoidance beliefs may be significant barriers to participation in formal exercise programs and reinforcement of sedentary behaviors in this population.98,99

CONCLUSION OHS is a serious medical disorder, which is frequently overlooked in the differential diagnoses of patients presenting to hospital with respiratory, metabolic, and cardiovascular complaints. Once identified, the condition is most commonly treated using positive pressure therapy to manage sleep disordered breathing. However, this approach only addresses one aspect of the disorder, and while it is usually effective in reversing daytime respiratory failure and improving respiratory drive, the inflammatory and microvascular abnormalities associated with the obesity aspects of the disorder remain. There is a growing awareness of the need to provide a more multifaceted management approach for

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these individuals, including weight loss and lifestyle modification, to reduce the ongoing cardiovascular risk they face even when sleep-disordered breathing has been well controlled.

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49.

50.

apnea. Intern Med. 1997;36:454e460. https://doi.org/10.2169/ internalmedicine.36.454. Goldring RM, Turino GM, Heinemann HO. Respiratory-renal adjustments in chronic hypercapnia in man. Extracellular bicarbonate concentration and the regulation of ventilation. Am J Med. 1971;51: 772e784. https://doi.org/10.1016/0002-9343(71)90305-6. Norman RG, Goldring RM, Clain JM, et al. Transition from acute to chronic hypercapnia in patients with periodic breathing: predictions from a computer model. J Appl Physiol. 2006;100:1733e1741. https://doi.org/10.1152/jappphysiol.00502.2005. Raurich JM, Rialp G, Ibanez J, Llompart-Pou JA, Ayestaran I. Hypercapnic respiratory failure in obesity-hypoventilation syndrome: CO2 response and acetazolamide treatment effects. Respir Care. 2010;55:1442e1448. Kikuchi R, Tsuji T, Watanabe O, et al. Hypercapnia accelerates adipogenesis: a novel role of high CO2 in exacerbating obesity. Am J Respir Cell Mol Biol. 2017;57:570e580. https://doi.org/10.1165/ rcmb.2016-0278OC. Banerjee D, Yee BJ, Piper AJ, Zwillich CW, Grunstein RR. Obesity hypoventilation syndrome: hypoxemia during continuous positive airway pressure. Chest. 2007;131:1678e1684. https://doi.org/ 10.1378/chest.06-2447. Hlavac MC, Catcheside PG, McDonald R, Eckert DJ, Windler S, McEvoy RD. Hypoxia impairs the arousal response to external resistive loading and airway occlusion during sleep. Sleep. 2006; 29:624e631. Phipps PR, Starritt E, Caterson I, Grunstein RR. Association of serum leptin with hypoventilation in human obesity. Thorax. 2002;57:75e76. https://doi.org/10.1136/thorax.57.1.75. Campo A, Fruhbeck G, Zulueta JJ, et al. Respiratory drive and hypercapnic response in obese patients. Eur Respir J. 2007;30:223e231. https://doi.org/10.1183/09031936.00115006. Makinodan K, Yoshikawa M, Fukuoka A, et al. Effect of serum leptin levels on hypercapnic ventilatory response in obstructive sleep apnea. Respiration. 2008;75:257e264. https://doi.org/10.1159/ 000112471. Schwartz MW, Peskind E, Raskind M, Boyko EJ, Porte Jr D. Cerebrospinal fluid leptin levels: relationship to plasma levels and to adiposity in humans. Nat Med. 1996;2:589e593. Polotsky M, Elsayed-Ahmed AS, Pichard L, et al. Effects of leptin and obesity on the upper airway function. J Appl Physiol. 2012; 112:1637e1643. https://doi.org/10.1152/japplphysiol.01222.2011. Berger S, Pho H, Fleury-Curado T, et al. Intranasal leptin relieves sleep disordered breathing in mice with diet induced obesity. Am J Respir Crit Care Med. 2019;199:773e783. https://doi.org/ 10.1164/rccm.201805-0879OC. Monneret D, Borel JC, Pepin JL, et al. Pleiotropic role of IGF-I in obesity hypoventilation syndrome. Growth Horm IGF Res. 2010;20: 127e133. https://doi.org/10.1016/j.ghir.2009.11.004. Lindgren AC, Hellstrom LG, Ritzen EM, Milerad J. Growth hormone treatment increases CO(2) response, ventilation and central inspiratory drive in children with Prader-Willi syndrome. Eur J Pediatr. 1999;158:936e940. https://doi.org/10.1007/ s004310051246. Kessler R, Chaouat A, Schinkewitch P, et al. The obesityhypoventilation syndrome revisited: a prospective study of 34 consecutive cases. Chest. 2001;120:369e376. https://doi.org/ 10.1378/chest.120.2.369. Lacedonia D, Carpagnano GE, Patricelli G, et al. Prevalence of comorbidities in patients with obstructive sleep apnea syndrome, overlap syndrome and obesity hypoventilation syndrome. Clin Respir J. 2018;12:1905e1911. https://doi.org/10.1111/crj.12754. Marik PE, Desai H. Characteristics of patients with the “malignant obesity hypoventilation syndrome” admitted to an ICU. J Intens Care Med. 2013;28:124e130. https://doi.org/10.1177/ 0885066612444261.

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

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91. Mandal S, Suh ES, Harding R, et al. Nutrition and exercise rehabilitation in obesity hypoventilation syndrome (NERO): a pilot randomised controlled trial. Thorax. 2018;73:62e69. https://doi.org/ 10.1136/thoraxjnl-2016-209826. 92. Gloy VL, Briel M, Bhatt DL, et al. Bariatric surgery versus nonsurgical treatment for obesity: a systematic review and metaanalysis of randomised controlled trials. BMJ. 2013;347:f5934. https://doi.org/10.1136/bmj.f5934. 93. Carlin AM, Zeni TM, English WJ, , et alMichigan Bariatric Surgery Collaborative. The comparative effectiveness of sleeve gastrectomy, gastric bypass, and adjustable gastric banding procedures for the treatment of morbid obesity. Ann Surg. 2013;257:791e797. https://doi.org/10.1097/SLA.0b013e3182879ded. 94. Pepin J-L, Borel J-C, Janssens J-P. Obesity hypoventilation syndrome: an underdiagnosed and undertreated condition. Am J Respir Crit Care Med. 2012;186:1205e1207. https://doi.org/ 10.1164/rccm.201210-1922ED. 95. Perez de Llano LA, Golpe R, Ortiz Piquer M, et al. Short-term and long-term effects of nasal intermittent positive pressure ventilation in patients with obesity-hypoventilation syndrome. Chest. 2005; 128:587e594. https://doi.org/10.1378/chest.128.2.587. 96. Borel J-C, Borel A-L, Monneret D, Tamisier R, Levy P, Pepin J-L. Obesity hypoventilation syndrome: from sleep-disordered breathing to systemic comorbidities and the need to offer combined treatment strategies. Respirology. 2012;17:601e610. https://doi.org/ 10.1111/j.1440-1843.2011.02106.x. 97. Vivodtzev I, Tamisier R, Croteau M, et al. Ventilatory support or respiratory muscle training as adjuncts to exercise in obese CPAP-treated patients with obstructive sleep apnoea: a randomised controlled trial. Thorax. 2018. https://doi.org/10.1136/thoraxjnl-2017-211152 (in press). 98. Jordan KE, Ali M, Shneerson JM. Attitudes of patients towards a hospital-based rehabilitation service for obesity hypoventilation syndrome. Thorax. 2009;64. https://doi.org/10.1136/ thx.2009.120808, 1007. 99. Wingo BC, Ard JD, Desmond RA, Evans RA, Roy J, Baskin M. Body mass index and chronic health conditions as predictors of exercise fear-avoidance beliefs. J Res Obes. 2013. https://doi.org/10.5171/ 2013.793181.

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

C H A P T E R

8 Sleep Satisfaction, SleepeWake Pattern, and Aging Arcady A. Putilov Laboratory of Sleep/Wake Neurobiology, The Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia hypothesis, Zilli et al.10 compared two groups of healthy people aged between 65 and 99 and between 19 and 28 years. They were not different in level of sleep satisfaction despite clear perception of worsening of night sleep characteristics demonstrated by people from the former group. The conclusion was made that, when these people rate their sleep satisfaction, they can rely more on the perceived freshness after awakening rather than on high frequency of nighttime awakenings that they also can clearly perceive.10 In other words, these people heed the perceived freshness after awakening not paying much attention to frequent nighttime awaken˚ kerstedt et al.11 showed that ings.10 More recently, A the values of the polysomnographic sleep characteristics for good sleep in older women (>51.5 years) were similar to the values for poor sleep in the younger women ( 4.0), well (SSS between 3.6 and 4.0), and not so well (SSS  3.5). To test significance of relationship between sleep satisfaction and sleepewake pattern in the whole sample and in subsamples of participants with different ages and SSS, we performed regression and correlation analyses (Table 8.1), three- and two-way MANOVAs (Table 8.2), and factor analysis (Table 8.3). Main effects and interactions between factors “Age” and “SSS” (Table 8.2, central column) are illustrated in Figs. 8.1 and 8.2. Significance level was fixed at P ¼ .05. The results presented in Tables 8.1 and 8.2 and in Figs. 8.1 and 8.2 suggest that the self-reported characteristics of sleepewake pattern exhibited significant changes across ages and that any of these changes was in expected direction. Particularly, age of study participants correlated with advance shift of times for going to bed and awakening, morning earliness, shortening of sleep duration, and reduction of nighttime sleep ability. SSS did not correlate significantly with age (Table 8.1). A set of characteristics of sleepewake pattern that correlated with SSS differed from the set of correlates of age. Some of the correlations and predictors revealed in analyses of the whole sample were intuitively expected, e.g., the correlations suggesting associations of higher SSS with longer nighttime sleep (Table 8.1). However, higher SSS was additionally associated with both morning earliness and late awakening (Table 8.1). Results of analyses performed separately on three age subsamples provided an explanation for these contradictive relationships. In the group of youngest participants (25 years) the significant correlates were, as expected, lateness, lateness traits, and longer sleep duration whereas in the group of oldest participants (46e67 years) higher SSS was, also as expected, associated with morning earliness and earlier time for going to bed (Table 8.1). If the strongest predictor of this score in the group of youngest participants was late awakening, such a predictor in the groups of older participants (26 years) was morning earliness. These results were further confirmed by results of three-way MANOVA of the whole dataset. They yielded significant interaction between factors “Age” and “SSS” for times for going to bed and awakening (Table 8.2). As can be seen in Fig. 8.1, sleep satisfaction was linked to agetypical characteristics of sleepewake pattern, such as long sleep duration and lateness in young adults and earliness in older adults. The results of MANOVAs and factor analysis additionally revealed significant relationship between sleep

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

TABLE 8.1 Correlations of Age and Sleep Satisfaction Score (SSS) With Other Variables. Correlation of

Age (For All and Three SSS Groups)

SSS (For All and Three Age Groups)

Grouping

SSS

£3.5

3.6e4.0

>4.0

Age

£25

26e45

>45

Group Size, N

178

52

57

69

178

88

54

36

CORRELATION WITH Gender#

0.069

0.086

0.083

0.186

0.026

0.101

0.006

0.003

Age

e

e

e

e

0.002

0.187

0.073

0.263

SSS

0.002

0.063

0.218

0.13

e

e

e

e

Go to bed

0.241***

0.031

0.399**

0.430***

0.012

0.246*

0.055

0.375*

Sleep latency

0.077

0.044

0.188

0.210

0.091

0.157

0.140

0.166

Awakening

0.280***

0.061

0.438**

0.522***

0.149**

0.404***

0.168

0.232

0.163**

0.048

0.213

0.366**

0.208***

0.354**

0.264

0.265

Napping score

0.003

0.145

0.017

0.132

0.090

0.180

0.172

0.146

KSS at 9:00

0.082

0.125

0.077

0.219

0.125*

0.136

0.141

0.241

W

0.071

0.009

0.035

0.149

0.088

0.177

0.215

0.037

V

0.012

0.041

0.095

0.072

0.132*

0.197

0.257

0.070

F

0.126

0.042

0.318*

0.237*

0.018

0.105

0.217

0.138

S

0.229***

0.281*

0.332*

0.504***

0.092

0.159

0.222

0.186

E

0.120

0.008

0.224

0.119

0.068

0.303**

0.097

0.167

M

0.172**

0.037

0.415**

0.353**

0.141**

0.018

0.327*

0.524**

Total sleep time #

Notes: Total sleep time: Difference between time of Awakening and time of sleep onset (calculated by adding Sleep latency to time to Go to bed); Napping score: 0 ¼ none, 1 ¼ once, and 2 ¼ more than once a week; KSS at 9:00: Self-reported score on the Karolinska Sleepiness Scale at 9:00 after sleepless night; #A Kendall’s tau, otherwise a Pearson coefficient of correlation. Level of significance for correlation coefficient: *** (P < .001), ** (P < .01), * (P < .05).

TABLE 8.2 F-ratios From Three- and Two-Way MANOVAs With Factors Age and/or SSS. MANOVA Factor

Three-Way Age

Two-Way Age 3 SSS

Age

SSS

3.3***

2.6**

3.7***

4.1***

SSS

MULTIVARIATE TEST (ROY’S LARGEST ROOT CRITERION) F-ratio

3.8***

F-RATIO FOR BETWEEN-GROUPS EFFECTS Age

e

e

e

e

0.2

SSS

e

e

e

0.3

e

Go to bed

9.6***

0.7

2.6*

10.6***

0.1

Sleep latency

0.7

2.1

1.0

0.9

1.9

Awakening

13.2***

1.0

2.7*

14.7***

2.2

Total sleep time

2.9

4.2*

0.5

3.7*

7.5**

Napping score

2.0

0.4

0.4

1.9

1.1

KSS at 9:00

0.9

2.7

0.1

1.1

3.8*

W

0.6

4.4*

0.3

0.7

6.4**

V

1.7

3.6*

0.4

1.6

4.1*

F

3.0

0.5

1.9

2.5

0.1

S

4.6*

2.8

1.1

4.9**

2.5

E

3.8*

0.4

1.4

4.5*

0.1

M

6.9**

9.2***

1.9

7.7**

5.8**

Notes: Gender was the third or second between-subjects factor in three- and two-way MANOVAs, respectively. Level of significance for F-ratio: *** (P < .001), ** (P < .01), * (P < .05). See also notes to Table 8.1.

82

8. SLEEP SATISFACTION, SLEEP-WAKE PATTERN, AND AGING

TABLE 8.3

Loadings on Six Principal Components and Three Rotated Factors.

Analysis PCs or RFs Eigenvalue Cumulative %

Principal Components (PCs) 1

2 3.12

17.9

3 2.11

30.4

4

5

1.74 41.5

Rotated Factors (RFs)

1.54 52.7

6 1.19

63.4

MDE 1.00

2.68

71.3

WDV 2.24

FDS 2.04

17.9

32.8

46.4

LOADING ON 6 NONROTATED PRINCIPAL COMPONENTS AND 3 VARIMAX ROTATED FACTORS Gender

0.173

0.261

0.309

0.023

0.142

0.646

0.035

0.088

0.429

Age

L0.593

0.188

0.005

0.016

0.334

0.128

L0.524

0.068

0.327

SSS

0.034

0.549

0.104

0.365

0.409

0.191

0.203

0.500

0.154

Go to bed

0.697

0.028

0.310

0.330

0.080

0.344

0.741

0.174

0.049

0.249

0.312

0.544

0.268

0.172

0.455

0.015

0.045

L0.673

Awakening

0.787

0.143

0.465

0.116

0.233

0.047

0.923

0.07

Total sleep time

0.415

0.243

0.224

L0.645

0.240

0.281

0.512

0.106

0.089

Napping score

0.198

0.234

0.425

0.433

0.529

0.053

0.114

0.389

0.331

KSS at 9:00

0.425

0.399

0.001

0.071

0.304

0.186

0.261

L0.521

0.014

W

0.138

0.690

0.112

0.518

0.090

0.075

0.021

0.638

0.316

V

0.470

0.606

0.268

0.101

0.176

0.057

0.112

0.787

0.166

F

0.497

0.029

L0.553

0.117

0.374

0.139

0.121

0.305

0.668

S

0.447

0.465

0.470

0.092

0.372

0.082

0.220

0.121

0.758

E

0.221

0.403

0.401

0.542

0.051

0.116

0.481

0.368

0.070

M

0.662

0.304

0.158

0.093

0.215

0.181

0.560

0.491

0.022

Sleep latency

0.008

Notes: M þ E, W þ V, and F þ S: Only three largest factors (“Lateness”, “Wakeability”, and “Sleepability”) were extracted, rotated, and interpreted as corresponding to three pairs of questionnaire scales, respectively. The highest of loadings (either 0.5 < or >0.5) are printed in Bold. See also notes to Table 8.1.

satisfaction and wakeability characteristics of sleepe wake pattern (Table 8.3 and Fig. 8.2). For instance, high SSS was sorted into “Wakeability” (second) factor that also included a low level of sleepiness after sleepless night and higher scores on anytime and daytime wakeability scales (Table 8.3). Such results suggest that only relationship of higher sleep satisfaction with better wakeability (second factor) persisted across the life span whereas higher sleepability (third factor) and earlier or later phase of wakeesleep cycle (first factor) were important contributors to sleep satisfaction only in separate age groups (Fig. 8.2).

NEUROPHYSIOLOGICAL UNDERPINNING OF THE AGEMODULATING LINK BETWEEN SLEEPeWAKE PATTERN AND SLEEP SATISFACTION These results on association between sleep satisfaction, sleepewake pattern, and age supported and ˚ kerstedt extended the findings reported by Zilli et al.,10 A

et al.,11 and other authors. It seems that the self-assessed characteristics of the sleepewake pattern exhibited notable shifts already on the age interval from early to late adulthood. Moreover, those characteristics of this pattern that can be linked to quality of night sleep (e.g., score on S scale) also exhibited significant change. However, despite these changes sleep satisfaction did not decline in older study participants as compared to younger participants. It remained adjusted to what is considered to be the age-specific pattern of the sleepe wake cycle. Thus, the results suggested that (1) similarly to night sleep characteristics, a set of characteristics of sleepewake pattern is significantly linked to sleep satisfaction, (2) despite profound difference between ages in these characteristics, the link remains significant across the life span, and (3) sleep satisfaction is higher when the characteristics of sleepewake pattern are typical for this age. Elderly people were proposed to be able to adjust their expectations about sleep to the changes they accept as age-related.9 The present results indicate that such an adjustment is also notable in groups of people of younger ages (till 67 years), and that it persists across

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

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NEUROPHYSIOLOGICAL UNDERPINNING OF THE AGE-MODULATING

Clock hour ± SEM

(A)

Go to bed

24

22

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≤25

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>45

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(B)

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Hours ± SEM

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Total sleep 8

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(D) Score ± SEM

10

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M (Morning Lateness)

5 0 -5

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≤25

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26-45

>45

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26-45

>45

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FIGURE 8.1 Self-assessments of sleepewake times and lateness in groups with different age and SSS. Estimated marginal means
Standard Error of Mean (SEM, vertical lines) for participants subdivided into nine groups (three ranges of age x three rages of SSS) calculated in three-way MANOVA (Table 8.2, left).

the interval of ages from early to late adulthood. Can the sleep aging process underlie this ability in spite of biological rather than psychological nature of this process? In the following discussion I am arguing for possibility that age-associated changes in sleepewake regulating processes can determine people’s ability to adjust their perception of “sleep goodness” to typical for their age sleepewake pattern. Namely, I think that the effect of aging on relative strengths of the antagonistic wake and sleep drives can determine the ability of older people to adjust expectations about their night sleep quality to age-specific sleepewake pattern. According to the two-process conceptualization of the sleepewake regulating mechanisms, the sleep drive

(i.e., the sleep-promoting process) arises from combination of two major processes, homeostatic and circadian, and the strength of this drive is indicated by amplitude of slow-wave activity and percentage of slow-wave sleep.25 Factor analysis of data of longitudinal intraindi˚ kerstedt with covidual studies reported by A workers26,27 suggested that the items designed for subjective evaluation of “sleep goodness” were sorted into two major factors. The first factor was represented by subjective sleep quality, calmness of sleep, ease of falling asleep, number of awakenings, sleep latency, etc. This set of items predicted “better” sleep with longer sleep duration, lower number of awakenings, higher amount and percentage of slow wave sleep, etc., and

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

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8. SLEEP SATISFACTION, SLEEP-WAKE PATTERN, AND AGING

Score ± SEM

(A) 4

KSS (Sleepiness)

5 6 7 8

Age:

≤25

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>45

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≤25

SSS ≤3.5

Score ± SEM

(B)

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Wakeability) 6

0

-6

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Score ± SEM

(C)

V (Da

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Wakeability)

6

0

-6

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Score ± SEM

(D)

S(

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Sleepability)

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0

-6

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≤25

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26-45

>45

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≤25

SSS=3.6-4.0

26-45

>45

SSS>4.0

FIGURE 8.2 Self-assessments of wakeability and sleepability in groups with different age and SSS. See notes to Fig. 8.1.

such a sleep was observed after prolonged previous wakefulness and in close proximity to the minimum of rectal temperature rhythm. The second factor was represented by ease of awakening and feeling refreshed after sleep. Its relationship with objective measures of night sleep quality and circadian phase was found to oppose the relationship shown by the first factor.26,27 One can suggest that contribution of the items loading at the second factor to general sleep satisfaction can increase due to age-associated weakening of the sleep drive in combination with advance of phase of the sleepewake cycle and other circadian rhythms. Indeed, the idea that older ages are characterized by reduction of the homeostatic sleep drive was corroborated by numerous experimental findings. Particularly, the experimental research

suggested that the reduction of slow-wave activity and slow-wave sleep is the most obvious age-related modification of the sleep electroencephalographic spectrum.28,29 Since such reduction appears to be already present in middle-aged adults,30 the age-associated dumping of amplitude of slow-wave activity and decrease of percentage of slow-wave sleep signify the earliest phase of the process of sleep aging.4 However, neither reduction of the sleep drive nor advance of the sleepewake cycle can explain why those older healthy people who report good night sleep fully ignore the clearly recognized signs of worsening of their night sleep quality. The answer can be found in the theoretic framework of slightly different from the two-process model

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

REFERENCES

conceptualization of sleepewake regulation known as the opponent process model.31,32 Based on lesion studies involving squirrel monkeys, Edgar et al.31 conceptualized the sleepewake regulation as an interaction between the competing drives for sleep and wake (i.e., the sleep- and wake-promoting processes). These two drives oppose each other and interact to regulate the daily cycle of sleep and wakefulness in an optimal manner. For instance, the circadian alerting process can oppose the sleep-promoting process in the species of diurnal primates during the subjective day.31 Similar interaction of opponent processes was proposed by Dijk and Czeisler32 to explain the maintenance of sleep across the whole night in humans. We earlier showed that a weakening of the sleep drive in older participants of nap and sleep deprivation studies was associated with the electroencephalographic changes pointing at possibility of disinhibition of their wake drive.33e35 Such relative strengthening of this drive can bring some advantages to healthy older people living in our postindustrial 24-hour societies. For instance, they may better tolerate sleep deprivation compared to younger people.36,37 Another example is the age-associated changes in maximal sleep capacity revealed in the experimental studies of sleep duration in the absence of social and circadian constraints. When younger adults have an opportunity to extend time in bed for several nights in a row, their sleep tends to lengthen to approximately 9 h, whereas significant shortening of such maximal sleep capacity to approximately 7.5 h was found in older people.38 The present results indicate that sleep satisfaction remained positively linked to the wakeability characteristics of the sleepewake cycle on the whole interval of ages from early to late adulthood. Therefore, the expected age-associated decline of sleepability characteristics of the sleepewake cycle shown by participants with high SSS was compensated by an increase of all their wakeability characteristics. Particularly, scores on anytime and daytime wakeability scales were elevated. Besides, score on morning lateness scale and KSS score at 9:00 after sleepless night were reduced. It seems that ability of adjustment of the perception of good night sleep to the typical for this age sleep-wake pattern persists in middle-aged adults due to the age-associated strengthening of their wake drive relative to their drive for sleep. Unimportance of the perceived signs of deterioration of night sleep quality (i.e., due to the weakening of the drive for sleep) can be explained by the appearance of feeling of full refreshment after night sleep and easiness of awakening in people of this and older ages (i.e., due to the strengthening of the opposing drive for wake). In sum, the feeling of good night sleep is not declining with advancing of age. There exists a significant link between sleep satisfaction and typical for this age features

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of sleepewake pattern. Sleep satisfaction can remain adjusted to the age-typical sleepewake pattern due to certain underlying neurophysiological changes across the life span, such as the change in relative strengths of the opposing drives for sleep and wake.

Acknowledgments The author was supported by a grant from the Russian Foundation for Basic Research (grant number19-013-00424). Conflict of Interest No potential conflict of interest was reported by the author.

References 1. Driscoll HC, Serody L, Patrick S, et al. Sleeping well, aging well: a descriptive and cross-sectional study of sleep in ‘successful agers’ 75 and older. Am J Geriatr Psychiatry. 2008;16:74e82. 2. Vitiello MV, Larsen LH, Moe KE. Age-related sleep change: gender and estrogen effects on the subjective-objective sleep quality relationships of healthy, noncomplaining older men and women. J Psychosom Res. 2004;56:503e510. 3. Foley DJ, Monjan AA, Brown SL, Simonsick EM, Wallace RB, Blazer DG. Sleep complaints among elderly persons: an epidemiologic study of three communities. Sleep. 1995;18:425e432. 4. Van Cauter E, Leproult R, Plat L. Age-related changes in slow wave sleep and REM sleep and relationship with growth hormone and cortisol levels in healthy men. J Am Med Assoc. 2000;284:861e868. 5. Zepelin H, McDonald CS, Zammit GK. Effects of age on auditory awakening thresholds. J Gerontol. 1984;39:581e586. 6. Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV. Metaanalysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep. 2004;27:1255e1273. 7. Foley D, Ancoli-Israel S, Britz P, Walsh J. Sleep disturbances and chronic disease in older adults: results of the 2003 National Sleep Foundation Sleep in America Survey. J Psychosom Res. 2004;56: 497e502. 8. Ohayon MM, Zulley J, Guilleminault C, Smirne S, Priest RG. How age and daytime activities are related to insomnia in the general population: consequences for older people. J Am Geriatr Soc. 2001;49:360e366. 9. Buysse DJ, Reynolds 3rd CF, Monk TH, Hock CC, Yeager AM, Kupfer DJ. Quantification of subjective sleep quality in healthy elderly men and women using the Pittsburgh Sleep Quality Index (PSQI). Sleep. 1991;14:331e338. 10. Zilli I, Ficca G, Salzarulo P. Factors involved in sleep satisfaction in the elderly. Sleep Med. 2009;10:233e239. ˚ kerstedt T, Schwarz J, Gruber G, Lindberg E, Theorell-Haglo¨w J. 11. A The relation between polysomnography and subjective sleep and its dependence on age e poor sleep may become good sleep. J Sleep Res. 2016;25:565e570. 12. Duffy JF, Dijk DJ, Klerman EB, Czeisler CA. Later endogenous circadian temperature nadir relative to an earlier wake time in older people. Am J Physiol Regul Integr Comp Physiol. 1998;275: R1478eR1487. 13. Horne JA, Ostberg O. Individual differences in human circadian rhythms. Biol Psychol. 1977;5:179e190. ¨ stberg O. A self-assessment questionnaire to deter14. Horne JA, O mine morningness-eveningness in human circadian rhythms. Int J Chronobiol. 1976;4:97e110. 15. Roenneberg T, Kuehnle T, Pramstaller PP, et al. A marker for the end of adolescence. Curr Biol. 2004;14:R1038eR1039.

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16. Putilov AA, Donskaya OG, Budkevich EV, Budkevich RO. Reliability and external validity of the six scales of 72-item sleepwake pattern assessment questionnaire (SWPAQ). Biol Rhythm Res. 2017;48:275e285. 17. Putilov AA, Donskaya OG, Verevkin EG. How many diurnal types are there? A search for two further “bird species”. Pers Indiv Differ. 2015;72:12e15. 18. Putilov AA, Donskaya OG, Verevkin EG. Can we feel like being neither alert nor sleepy? The electroencephalographic signature of this subjective sub-state of wake state yields an accurate measure of objective sleepiness level. Int J Psychophysiol. 2019;135:33e43. 19. Putilov AA. Age-related changes in the association of sleep satisfaction with sleep quality and sleepewake pattern. Sleep Biol Rhythm. 2018;16:169e175. 20. Putilov AA. Introduction of the tetra-circumplex criterion for comparison of the actual and theoretical structures of the sleep-wake adaptability. Biol Rhythm Res. 2007;38:65e84. 21. Putilov AA. Geometry of Individual Variation in Personality and SleepWake Adaptability. New York: Nova Science Pub Inc.; 2010. 22. Putilov AA. Association of the circadian phase with two morningness-eveningness scales of an enlarged version of the sleep-wake pattern assessment questionnaire. Arbeitswissbetriebl. Praxis. 2000;17:317e322. 23. Danilenko KV, Putilov AA, Terman A, Wirz-Justice A. Prediction of circadian phase and period using different chronotype questionnaires. In: Society for Research on Biological Rhythms, Ninth Meeting, Whistler Resort, Whistler, British Columbia, USA, June 24e26, 2004, Program and Abstracts. 2004:122. ˚ kerstedt T, Gillberg M. Subjective and objective sleepiness in the 24. A active individual. Int J Neurosci. 1990;52:29e37. 25. Daan S, Beersma DGM, Borbe´ly AA. Timing of human sleep: recovery process gated by a circadian pacemaker. Am J Physiol Regul Integr Comp Physiol. 1984;246:R161eR178. ˚ kerstedt T, Hume K, Minors D, Waterhouse J. Good sleep e its 26. A timing and physiological characteristics. J Sleep Res. 1997;6:221e229. ˚ kerstedt T. Objective components of individual 27. Kecklund G, A differences in subjective sleep quality. J Sleep Res. 1997;6:217e220.

28. Cajochen C, Mu¨nch M, Knoblauch V, Blatter K, Wirz-Justice A. Age-related changes in the circadian and homeostatic regulation of human sleep. Chronobiol Int. 2006;23:461e474. 29. Chinoy ED, Frey DJ, Kaslovsky DN, Meyer FG, Wright Jr KP. Agerelated changes in slow wave activity rise time and NREM sleep EEG with and without zolpidem in healthy young and older adults. Sleep Med. 2014;15:1037e1045. 30. Lafortune M, Gagnon JF, Latreille V, et al. Reduced slow-wave rebound during daytime recovery sleep in middle-aged subjects. PLoS One. 2012;7:e43224. 31. Edgar DM, Dement WC, Fuller CA. Effect of SCN lesions on sleep in squirrel monkeys: evidence for opponent processes in sleepwake regulation. J Neurosci. 1993;13:1065e1079. 32. Dijk DJ, Czeisler CA. Contribution of the circadian pacemaker and the sleep homeostat to sleep propensity, sleep structure, electroencephalographic slow waves, and sleep spindle activity in humans. J Neurosci. 1995;15:3526e3538. 33. Putilov AA. Principal component scoring of the resting EEG spectrum provides further evidence for age-associated disinhibition of the wake drive. Healthy Aging Res. 2015;4:1e9. 34. Putilov AA, Donskaya OG. Evidence for age-associated disinhibition of the wake drive provided by scoring principal components of the resting EEG spectrum in sleep provoking conditions. Chronobiol Int. 2016;33:995e1008. 35. Putilov AA, Mu¨nch MY, Cajochen C. Principal component structuring of the non-REM sleep EEG spectrum in older adults yields age-related changes in the sleep and wake drives. Curr Aging Sci. 2013;6:280e293. 36. Duffy JF, Willson HJ, Wang W, Czeisler CA. Healthy older adults better tolerate sleep deprivation than young adults. J Am Geriatr Soc. 2009;57:1245e1251. 37. Landolt HP, Re´tey JV, Adam M. Reduced neurobehavioral impairment from sleep deprivation in older adults: contribution of adenosinergic mechanisms. Front Neurol. 2012;3:62. 38. Klerman EB, Dijk DJ. Age-related reduction in the maximal capacity for sleep e implications for insomnia. Curr Biol. 2008;18: 1118e1123.

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

C H A P T E R

9 Etiopathogenesis of Circadian SleepeWake Rhythm Disorders Fu¨sun Mayda Domac¸ University of Health Sciences, Erenko¨y Mental Health and Neurological Diseases Training and Research Hospital, _ Neurology Department and Sleep Medicine Center, Istanbul, Turkey

Sleep and wake circadian rhythm is regulated by the oscillator neurons of the suprachiasmatic nucleus.5 Suprachiasmatic nuclei (SCN) are a pair of nucleus, located in the anterior hypothalamus.6 Being a circadian pacemaker and regulating the sleepewake cycle, SCN also regulates body temperature, autonomic nervous system, and secretion of hormones like melatonin and cortisol.6e10 Circadian rhythm is affected by various environmental factors by means of “time givers” or Zeitgebers. The main and potent Zeitgeber is external light, and due to darkness and lightness, the rhythm works synchronously and periodically.2,6,11 The other Zeitgebers are meal time, physical and social activities, and exercise.6 As the external light passes through the eye, it is perceived by photosensitive retinal ganglion cells and then projected to SCN via retinohypothalamic tractus.12 SCN regulates circadian rhythm via several tractii that carry input to SCN and output from SCN.13 The daily rhythmic information passes from SCN to the other endogenous circadian central and peripheral clocks, thereby coordinates the behavioral process and all functions of the systems of the organism.14e16 SCN regulates melatonin secretion from the pineal gland and DLMO begins 2e3 h before sleep time.2 Core body temperature (CBT) min takes place 2 h before waking up. These two parameters have important influences on sleepewake rhythm.17,18 Circadian sleepewake rhythm disorders are classified into seven groups according to the third revision of The International Classification of Sleep Disorders (ICSD-3), updated in 2014 (Table 9.1). Etiopathogenesis of each disorder will be discussed separately.

Abbreviations AASM American Academy of Sleep Medicine ASWPD Advanced sleepewake phase disorder Bmal1 Brain and muscle Arnt-like protein-1 CBTmin Minimum core body temperature Clock gene Circadian locomotor output cycles kaput gene CRSWD Circadian rhythm sleepewake disorder Cry 1 Cryptochrome 1 gen Cry 2 Cryptochrome 2 gen DLMO Dim light melatonin onset DSWPD Delayed sleepewake phase disorder ICSD-3 International Classification of Sleep Disorders, Third Edition ipRGCs Intrinsically photosensitive retinal ganglion cells ISWD Irregular sleepewake disorder JLD Jet lag disorder N24SWD None24-hour sleepewake disorder hPer1 Period homolog 1 gen hPer2 Period homolog 2 gen hPer3 Period homolog 3 gen SCN Suprachiasmatic nucleus SWD Shift work disorder

INTRODUCTION Circadian rhythm, an endogenous rhythm of the organism, is the main regulator of the sleepewake cycle.1 This endogenous rhythm represents the changes of physiological, biological, and behavioral processes during a day (approximately 24.2 h of period); it is composed by the biological circadian clock of the organism.2 This endogenous rhythm is regulated genetically via clock (hPer, Bmal1, Clock) and cryptochrome genes.3,4 Neurological Modulation of Sleep https://doi.org/10.1016/B978-0-12-816658-1.00009-0

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© 2020 Elsevier Inc. All rights reserved.

88 TABLE 9.1 1. 2. 3. 4. 5. 6. 7.

9. ETIOPATHOGENESIS OF CIRCADIAN RHYTHM DISORDERS

Circadian SleepeWake Rhythm Disorders.19

Delayed sleepewake phase disorder Advanced sleepewake phase disorder Irregular sleepewake disorder None24-hour sleepewake rhythm disorder Shift work type disorder Jet lag disorder Circadian sleepewake disorder not otherwise specified

Delayed SleepeWake Phase Disorder Delayed sleepewake phase disorder (DSWPD) is a recurrent incapability to sleep at a desirable or socially appropriate night time and also wake up at a desired or proper time in the morning.6,20e22 The main complaints are insomnia and difficulty to wake up in the morning that result in a wane in academic or professional productivities.6,14,22 It is mainly seen in adolescents and young adults.20 The prevalence has a wide range of 0.3%e16%, and there is no gender difference.21,23,24 Every human being has its own circadian rhythm period length also called as “tau” which is reset periodically at every approximately 24 h. Individuals with a longer period length (>24 h) are candidates for delays in bedtime while in ones with a shorter period length less than 24 h tend to sleep earlier.25 In DSWPD, longer circadian rhythm length (tau) was suspected to be a factor for the development of the disease.26,27 Diminished external light stimulus exposure and sensitivity at mornings or increased sensitivity or exposure to light at nights are suspected as an environmental risk factor.14,28 Behavioral factors like voluntary exposure to light before bedtime or sleep deprivation may also have a role.14,29 In patients with DSWPD, melatonin suppression was detected due to exposure to light at evenings asserting increased sensitivity to light stimulus in DSWPD.30 After head trauma, DSWPD developed in some patients with a possibility of a damage to SCN or related tractii resulting an abnormality in melatonin secretion.31 In familial DSWPD, autosomal dominance or multifactorial dominance of inheritance has been estimated.32 A mutation in CRY1 gene and polymorphisms in hPer3 were detected in genetic analyses which were related with the pathogenesis.33,34

Advanced SleepeWake Phase Disorder Advanced sleepewake phase disorder (ASWPD) is a recurrent incapability to sleep earlier than desired bedtime and also wake up at a socially appropriate time in the morning.6,35 Total sleep time and quality of

sleep are in normal ranges.36 The patients tend to sleep at the evening (6e9 p.m.) and wake up at 2e5 a.m. at early morning.14,19,37 They complain of insomnia with incapability to sustain sleep or inability to keep daytime alertness if they try to sleep at a later time while still getting up very early.25 The prevalence is 0.25%e7.13% and is mainly seen in male gender and older adults.24,26,30 Diminished external light stimulus exposure and sensitivity at evenings or increased sensitivity or exposure to light at mornings are suspected causes as environmental risk factors.14 Other zeitgebers like activity, exercise, and meal time are also in an asynchronization with biological circadian rhythm.25,38 Short circadian rhythm length (tau) was also another reason as it was detected in a familial ASWD.39 Some authors have found a relation with the season of the individual’s birth and circadian rhythm. They have concluded that duration of exposure to light stimuli during pregnancy and infancy had effects on the development and regulation on the circadian rhythm. Hence, being born at spring or summer, individuals tend to sleep at a later time than the ones born at autumn or winter. They have also found a tendency in males.40,41 It has also been hypothesized that gestational birth week and birth weight may have effects on the circadian rhythm. As preterm or low-weighted infants were hospitalized in intensive care units and were not exposed to normal light and darkness, in their adulthood they tended to sleep and wake up earlier and also have a short total sleep time.42e44 In ASWPD, genetic factors have an important role. Some studies have determined an autosomal-dominant inheritance in the family members with ASWD.39,45,46 Genetic studies have shown hypophosphorylation due to a mutation in the casein kinase 1 epsilon site of hPER2 gene, mutations in the casein kinase 1 delta sites of hPER1, hPER2, and hPER3 genes that result in a shorter tau.47e49

Irregular SleepeWake Type Disorder The circadian rhythm is irregular with minimum three separate and 2e4 h lasting sleep periods during day and night.6,14,22 Though interrupted, total sleep duration is in normal ranges (ICSD3). It is mainly encountered in neurodevelopmental, neurodegenerative, and psychiatric disorders.26,50e52 Though the exact prevalence is not well known, it is high at older adults.53,54 Gender difference is not established.26 As advancing age is an important factor, accompanying medical comorbidities due to older age increases the possibility of development.55e57 Life style changes, decrease in physical mobility, cognitive functions, and social relations reduce exposure to external light stimuli

I. INTRODUCTION AND BACKGROUND OF SLEEP DISRUPTION

INTRODUCTION

and the other zeitgebers resulting in asychronization with internal circadian rhythm.26,56 Pituitary tumors and head trauma also may be a reason for irregular sleepewake disorder (ISWD).31,52 Any mechanism for degeneration of the neurons in SCN or the related tractii affects the melatonin secretion leading to disruption in the lightedark cycle.31,50,52,56

None24-Hour SleepeWake Rhythm Disorder In this type of circadian sleepewake rhythm disorder, there is an asynchronization between the intrinsic circadian rhythm and the 24-hour day. Sleep and wake cycle delays 1 to 2 h every day and the patients have a bit longer circadian period than 24 h a day.14,58 Patients complain of insomnia and/or difficulty in alertness in daytime (ICSD).14,22 Sleep time inconsistency and decrease in social interactions may also lead to mood disorders.14,59 The prevalence of none24-hour sleepewake rhythm disorder (N24SWD) in blind individuals is 50%. Sighted individuals rarely develop the disorder with a male dominance.59,60 As the main Zeitgeber of circadian rhythm is external light stimuli, in some blind individuals, the cause of the disorder is attributed to the destruction of either ipRGCs or the retinohypothalamic tractus and thus resulting in an inaccessibility of light input from ipRGCs to the SCN.6,60 In some people, the reason of blindness is due to the rod and cone impairment with normal ipRGCs and thus permits conveying light stimulus to brain14,61,62; not all of the blind people with an inability of perception of light develop this type of circadian rhythm sleepewake disorder (CRSWD), as some of blind individuals have normal melatonin secretion and core body temperature in synchronization with daily circadian rhythm. However some of them may be in synchronization with the other Zeitgebers like daily time tables for exercise, meal, or social activities.63e65 Though it is rare, N24SWD is also seen in individuals with normal eyesight.6e59 Patients with delayed sleepe wake disorder has more probability to develop N24SWD.66 Dim light melatonin secretion onset and CBT properties are also similar with delayed sleepe wake disorder.67,68 Decreased sensitivity or exposure at inconvenient time to external light stimuli is another risk factor.14 There is a tendency to fall asleep at a later time, and intrinsic circadian rhythm is longer in patients with N24SWD than the healthy subjects.22,67,69 Though the exact mechanism is unclear, head trauma may trigger to develop N24SWD.69e71 A damage to SCN or tractus is suggested to disrupt the secretion of melatonin.69 Melatonin levels of the patients had decreased and were in asynchronization with CBT resulting with an abnormal circadian rhythm.72,73

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Shift Work Type Disorder Shift work may vary as a fixed or a rotating time table and may begin in early morning, evening, or night.74 Shift work enables to sleep and wake up at the acquired biological time. The main complaint is the sleepiness during the work time though alertness is needed and the other is insomnia. Some patients have both of the symptoms while the remaining complains of only insomnia.14,75 The complaints last for at least 3 months, and they are in association with work time table.19 Various medical (e.g., metabolic syndrome, obesity), cognitive (e.g., inattention, executive function failure), affective (e.g., depression), social (bad relations in friendship and marriage), and economic problems (e.g., bad work performance and increased risk for accidents) may accompany to sleep problems as well.58,76e78 The prevalence of shift work disorder is 5%e10% and is mainly seen in night shift or rotating shift workers.6,79 The prevalence increases in some professions like seen in nurses and firefighters.76,80 The risk increases by increasing age, and women are more prone to develop the disorder.7 The pathogenesis of this type of circadian sleep disorder is not well established.14 In genotype analysis, a long tandem repeat was observed on PER3 gene which was mostly evident in the patients that complain both of sleepiness and insomnia.81,82 As some of the workers develop the disease and the remaining do not, a difference in the sleep physiology was suspected. Total sleep duration is shorter in permanent night workers than the normal individuals.83,84 They also have uneasiness to continue sleep the day after the shift time, cause during the day, circadian sleep cycle urges to be alert.85 A rotating shift time table has less effect on the total sleep time unless the rotation is fast.86 A slow and clockwise rotating shift is better endured.87 An irregularity in sleep time and desynchronization between homeostatic and circadian rhythm and misalignment of sleep time exist in patients with sleepiness and with/without insomnia.88 But in the group with insomnia alone, the symptoms were thought to be due to preexisting insomnia and also were suspected to be exacerbated by the night shift work.82

Jet Lag Disorder Jet lag is a transient but repeatable disorder that comes to existence due to air travel crossing at least two time zones rapidly while the endogenous circadian rhythm cannot accommodate itself.6,14,89e91 A few days after the travel, in the new time zone, the travelers complain of difficulty to fall asleep at night, difficulty to maintain alertness during day, fatigue and accompanying cognitive, physical, and behavioral symptoms

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like decrease in executive functions, decline in mood, changes in appetite, and constipation.6,92e94 The prevalence of jet lag disorder (JLD) is not well established. There is no gender difference (American Academy of Sleep Medicine [AASM]). It is more seen in young adults than older adults.89,95 As the flight duration increases and more time zones are crossed, circadian misalignment is more prevalent. During long-lasting flights changes in the external light stimuli-dark cycle and a dysregulation in the beginning of melatonin secretion at dim light arise, an asynchrony between the local time and the biological clock is precipitated and all these induce to develop JLD.94,96,97 The severity and the existence of these symptoms are also related to the direction of air flight. Travel from west to east is less tolerable as to shorten the day instead of lengthening has more undesirable effects on circadian cycle.91,97 It has also been reported that traveling from north to south may also cause jet lag due to the length of the day independent of crossing several time zones.98 The quantity of sleep during the flight and the travel quality may have effects on JLD development as well as the properties of endogenous rhythm of the individuals.93,99

CONCLUSION Endogenous circadian rhythm disorders have different facilitators either environmental or genetic. Abnormalities in circadian rhythm lead to not only sleep and wake disorders but also various physiological, behavioral, and social problems. Appropriate diagnosis of these disorders will increase the possibilities for both treatment and also prevention.

References 1. Manthena P, Zee PC. Neurobiology of circadian rhythm sleep disorders. Curr Neurol Neurosci Rep. 2006;6(2):163e168. 2. Golombek DA, Rosenstein RE. Physiology of circadian entrainment. Physiol Rev. 2010;90(3):1063e1102. 3. Lowrey PL, Takahashi JS. Mammalian circadian biology: elucidating genome-wide levels of temporal organization. Annu Rev Genom Hum Genet. 2004;5:407e441. 4. Takahashi JS, Hong HK, Ko CH, McDearmon EL. The genetics of mammalian circadian order and disorder: implications for physiology and disease. Nat Rev Genet. 2008;9(10):764e775. 5. Kalsbeek A, Yi CX, Cailotto C, la Fleur SE, Fliers E, Buijs RM. Mammalian clock output mechanisms. Essays Biochem. 2011;49(1): 137e151. 6. Zee PC, Atarian H, Videnovic A. Circadian rhythm abnormalities. Continuum. 2013;19(1):132e147. 7. Sack RL, Auckley DA, Auger RR, et al. Circadian rhythm disorders: part I, basic principles, shift work and jet lag disorders. Sleep. 2007; 30(11):1460e1483. 8. Buijs RM, Scheer FA, Kreier F, et al. Organization of circadian functions: interaction with the body. Prog Brain Res. 2006;153:341e360.

9. Buijs RM, Escobar C, Swaab DF. The circadian system and the balance of the autonomic nervous system. Handb Clin Neurol. 2013;117: 173e191. 10. Serin Y, Acar Tek N. Effect of circadian rhythm on metabolic processes and the regulation of energy balance. Ann Nutr Metab. 2019;74(4):322e330. 11. Czeisler CA. The effect of light on the human circadian pacemaker. Ciba Found Symp. 1995;183:254e302. 12. Sadun AA, Schaechter JD, Smith LE. A retinohypothalamic pathway in man: light mediation of circadian rhythms. Brain Res. 1984;302(2):371e377. 13. Patton AP, Hastings MH. The suprachiasmatic nucleus. Curr Biol. 2018;28(15):816e822. 14. Abbott SM, Reid KJ, Zee PC. Circadian rhythm sleep-wake disorder. Psychiatr Clin N Am. 2015;38:805e823. 15. Zee PC, Manthena P. The brain’s master circadian clock: implications and opportunities for therapy of sleep disorders. Sleep Med Rev. 2007;11(1):59e70. 16. Buijs FN, Leo´n-Mercado L, Guzma´n-Ruiz M, Guerrero-Vargas NN, Romo-Nava F, Buijs RM. The circadian system: a regulatory feedback network of periphery and brain. Physiology. 2016;31(3): 170e181. 17. Duffy JF, Dijk DJ, Klerman EB, Czeisler CA. Later endogenous circadian temperature nadir relative to an earlier wake time in older people. Am J Physiol. 1998;275:1478e1487. 18. Fahey CD, Zee PC. Circadian rhythm sleep disorder and phototherapy. Psychiatr Clin N Am. 2006;29:989e1007. 19. American Academy of Sleep Medicine. International Classification of Sleep Disorders. 3rd ed. Darien; IL: American Academy of Sleep medicine; 2014. 20. Weitzman ED, Czeisler CA, Coleman RM, et al. Delayed sleep phase syndrome. A chronobiological disorder with sleep-onset insomnia. Arch Gen Psychiatr. 1981;38:737e746. 21. Wyatt JK. Delayed sleep phase syndrome: pathophysiology and treatment options. Sleep. 2004;27(6):1195e1203. 22. Pavlova M. Circadian rhythm sleep-wake disorders. Continuum. 2017;23(4, Sleep Neurology):1051e1063. 23. Schrader H, Bovim G, Sand T. The prevalence of delayed and advanced sleep phase syndromes. J Sleep Res. 1993;2(1):51e55. 24. Ando K, Kripke DF, Ancoli-Israel S. Delayed and advanced sleep phase symptoms. Isr J Psychiatry Relat Sci. 2002;39(1):11e18. 25. Sharkey KM. Advanced Sleep-Wake Phase Disorder; April 2019. www. uptodate.com. 26. Sack RL, Auckley D, Auger RR, et al. Circadian rhythm sleep disorders: part II, advanced sleep phase disorder, delayed sleep phase disorder, free-running disorder, and irregular sleep-wake rhythm an American Academy of Sleep Medicine review. Sleep. 2007; 30(11):1484e1501. 27. Micic G, de Bruyn A, Lovato N, et al. The endogenous circadian temperature period length (tau) in delayed sleep phase disorder compared to good sleepers. J Sleep Res. 2013;22(6):617e624. 28. Aoki H, Ozeki Y, Yamada N. Hypersensitivity of melatonin suppression in response to light in patients with delayed sleep phase syndrome. Chronobiol Int. 2001;18(2):263e271. 29. Crowley SJ, Van Reen E, LeBourgeois MK, et al. A longitudinal assessment of sleep timing, circadian phase, and phase angle of entrainment across human adolescence. PLoS One. 2014;9(11): e112199. 30. Paine SJ, Fink J, Gander PH, Warman GR. Identifying advanced and delayed sleep phase disorders in the general population: a national survey of New Zealand adults. Chronobiol Int. 2014;31(5): 627e636. 31. Ayalon L, Borokin K, Dishon L, et al. Circadian rhythm sleep disorders following mild brain injury. Neurology. 2007;68(14): 1136e1140.

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88. Kalmbach DA, Anderson JR, Drake CL. The impact of stress on sleep: pathogenic sleep reactivity as a vulnerability to insomnia and circadian disorders. J Sleep Res. 2018;27(6):e12710. 89. Waterhouse J, Edwards B, Nevill A, et al. Identifying some determinants of “jet lag” and its symptoms: a study of athletes and other travellers. Br J Sports Med. 2002;36(1):54e60. 90. Reilly T, Waterhouse J, Edwards B. Some chronobiological and physiological problems associated with long-distance journeys. Trav Med Infect Dis. 2009;7(2):88e101. 91. Cingi C, Emre IE, Muluk NB. Jetlag related sleep problems and their management: a review. Trav Med Infect Dis. 2018;24:59e64. 92. Cornelson BM. Overcoming jet lag. Can Fam Physician. 1985;31: 2105e2106. 93. Waterhouse J, Reilly T, Atkinson G, edwards b. Jet lag :trends and coping strategies. Lancet. 2007;369:1117. 94. Srinivasan V, Spence DW, Pandi-Perumal SR, Trakht I, Cardinali DP. Jet lag: therapeutic use of melatonin and possible application of melatonin analogs. Trav Med Infect Dis. 2008; 6(1e2):17e28. 95. Ariznavarreta C, Cardinali DP, Villanu´a MA, et al. Circadian rhythms in airline pilots submitted to long-haul transmeridian flights. Aviat Space Environ Med. 2002;73(5):445e455. 96. Bjorvatn B, Pallesen S. A practical approach to circadian rhythm sleep disorders. Sleep Med Rev. 2009;13(1):47e60. 97. Ambesh P, Shetty V, Ambesh S, Gupta SS, Kamholz S, Wolf L. Jet lag: heuristics and therapeutics. J Fam Med Prim Care. 2018;7(3): 507e510. 98. Diekman CO, Bose A. Reentrainment of the circadian pacemaker during jet lag: east-west asymmetry and the effects of northsouth travel. J Theor Biol. 2018;437:261e285. 99. Weingarten JA, Collop NA. Air travel: effects of sleep deprivation and jet lag. Chest. 2013;144(4):1394e1401.

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Adverse effects of sleep disruption

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C H A P T E R

10 Modulation and Consequences of Sleep Duration in Child Obesity Marcus Vinicius Nascimento-Ferreira1,2, Augusto Ce´sar Ferreira De Moraes1,3, Francisco Leonardo Torres-Leal4, Hera´clito Barbosa Carvalho1 1

Youth/Child cArdiovascular Risk and Environmental (YCARE) Research Group, School of Medicine, University of Sao Paulo, Sa˜o Paulo, Sa˜o Paulo, Brazil; 2Instituto de Ensino Superior Sul do Maranha˜o (IESMA/UNISULMA), Imperatriz, Maranha˜o, Brazil; 3Department of Epidemiology School of Public Health, University of Sao Paulo, Sa˜o Paulo, Sa˜o Paulo, Brazil; 4Metabolic Diseases, Exercise and Nutrition (DOMEN) Research Group, Federal University of Piaui, Teresina, Piauı´, Brazil

Abbreviations

16.6% to 31.0% in adolescents (12e19 years).6 Overweight and obesity are defined as abnormal or excessive fat accumulation that may impair health.5,7,8 Body mass index (BMI) is a simple index of weight-for-height that is commonly used to classify overweight and obesity.5,9 It is defined as a person’s weight in kilograms divided by the square of his height in meters (kg/m2).5 Scientists consider sleep as a modifiable risk factor for health status, with particular relevance to hypertension and metabolic disorders.10,11 In this sense, sufficient sleep (time) duration is an important behavior for combating pediatric obesity.12 Sleep duration is derived based on the number of hours slept per night.13 In the United States, national data have shown a decline in sleep duration among newborns to 10-year-olds over the past 50 years by 1.5e2 h which may be attributable to changes in lifestyle, such as waking up early for school and late night activities.14,15 Sleep disturbance, characterized by disruptions in quantity, quality, or timing of sleep, frequently occur in children. In Europe, a comprehensive study found a median of daily sleep of 10 h, with normal weight children sleeping on average 20 min per day more than overweight children.16 Data on sleep duration have been published for different age groups from several countries,16 showing that sleep duration has been related with health outcomes in the pediatric population.17 However, there is

AMPK AMP-activated protein kinase BMI Body Mass Index NREM NoneRapid Eye Movement REM Rapid Eye Movement

INTRODUCTION Childhood obesity is one of the most serious public health challenges of the 21st century.1 The worldwide epidemic of overweight and obesity among children (aged between 2 and 10 years) is of great concern, as childhood overweight and obesity track into adulthood.2,3 It has been reported that obese children are approximately seven times more likely to become obese adults compared to normal-weight children.4 Although obesity is preventable, its prevalence has increased at an alarming rate. Data from the World Health Organization indicated that the worldwide obesity has nearly tripled since 1975.5 Surprisingly, 41 million children under the age of 5 years were overweight or obese in 2016, and over 340 million children and adolescents aged 5e19 years were overweight or obese in 2016.5 In South America, for example, overweight and obesity prevalence ranges from 18.9% to 37.7% in school-aged children (5e11 years) and from

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no consensus about potential mechanisms (or remain incompletely understood) of how sleep duration can confer risk for childhood obesity.12,13 In this perspective, the following sections present: (1) a robust body of evidence about the modulation and consequences of sleep duration in child obesity; (2) new theories and hypotheses for the relation of sleep duration and obesity in pediatric population; and (3) sleep duration recommendations for the pediatric population. In this chapter, we discuss the role of sleep duration in child obesity. However, sleep is a dynamic multidimensional construct beyond sleep duration, including sleep disturbances and patterns. An elucidative study about distinct sleep dimensions and pediatric obesity was published by Jarrin, McGrath, and Drake.13 An extensively systematic review about sleep duration measurement was published by Nascimento-Ferreira et al.17 In addition, feasible epidemiological measurement and criteria to assess obesity (e.g., BMI) in children are available elsewhere.18

SLEEP DURATION IN CHILD OBESITY Obesity Physiopathology Obesity is an exaggeration of normal adiposity and plays an important role on the pathophysiological process of the high blood pressure and hypertension, clearly involving a chronic inflammatory state.19 Obesity is a major contributor to the metabolic dysfunction involving lipid and glucose, but on a broader scale, it influences organ dysfunction involving cardiac, liver, intestinal, pulmonary, endocrine, and reproductive functions. Inflammatory, insulin-resistant, hypertensive, and thrombotic-promoting adipokines, which are atherogenic, are counterbalanced by anti-inflammatory and antiatherogenic adipocyte hormones such as adiponectin, visfatin, and acylation-stimulating protein, whereas certain actions of leptin and resistin are proatherogenic.20 Obesity contributes to immune dysfunction from the impact of its inflammatory adipokine secretion and is a significant risk factor for many cancers, including hepatocellular, esophageal, and colon20 because of the accelerating effects that obesity has on the worsening of metabolic syndrome and cancer.20 Obesity induces high blood pressure and consequently hypertension.9 The anatomical location (obesity vs. abdominal obesity) may be the answer about functional differences between visceral and subcutaneous adipocytes.19 Briefly, obesity is associated with primary sodium retention, insulin resistance, and inflammation, which may promote an altered profile of vascular function and, consequently, hypertension.21 Abdominal obesity is related to elevated leptin levels and hyperinsulinemia as

well as stimulation of activity in the sympathetic nervous system. In abdominal obesity, there is overstimulation of the activity in the renineangiotensinealdosterone system and elevated renal sodium reabsorption, namely salt sensitivity, which is correlated with high blood pressure.22

Sleep Physiology Far from a simple absence of wakefulness, sleep is an active, regulated, and metabolically distinct state, essential for health and well-being.23 Assessment of sleep/ wake states can be made by behavioral observation, physiological monitoring, or a combination of the two.23 Behaviorally, sleep is characterized by loss of consciousness and by relative immobility in a recumbent posture with the eyes closed.23 Sleep itself is not a homogenous process.23 Sleep physiology is composed of two major states (rapid eye movement, REM; nonerapid eye movement, NREM) and a cyclical alternating pattern or architecture.13 Switches between NREM and REM sleep appear to be controlled by reciprocal inhibition between monoaminergic neurons and a specific subset of cholinergic neurons within the brainstem.24 These “REM-on” cholinergic neurons exhibit reciprocal inhibitory connections to noradrenergic (locus ceruleus) and serotonergic (raphe) neurons.25 When REM sleep is triggered, REM-on cholinergic neurons become maximally active, while noradrenergic and serotonergic neurons become virtually silent.23 The switching between activity and inhibition of these neurons results in a characteristic cycling between NREM and REM during the sleep period.23 REM is characterized by an increase in heart rate, blood pressure, and respiration level compared to NREM sleep.26 During NREM sleep, there is reduced tonus of large skeletal muscles that progresses to complete or near-complete atonia with a transition to REM sleep.23 Throughout sleep, there is a relative sparing of activity among respiratory pump muscles.23 Visual, olfactory, auditory, somatosensory, and even nociceptive sensory responses are all diminished but not eliminated during sleep.27 Furthermore, many sensory responses exhibit differing characteristics during NREM versus REM sleep.23 NREM sleep is subdivided into four stages: stages 1 and 2 (light sleep) and stages 3 and 4 (deep or slow-wave sleep).13 Slow-wave sleep is characterized by increased parasympathetic and decreased sympathetic activation (i.e., reduced brain activity, heart rate, cardiac output, breathing, and blood pressure compared to wake and REM sleep) and coincides with the most prominent changes in the endocrine system (i.e., stimulating and inhibiting hormone secretion).26 Greater time spent in slow-wave sleep is considered to

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be more restorative than other sleep stages given its predominant parasympathetic drive.13,26 The presence of REM sleep during the active time period may impair energy metabolism and promote obesity.28 Studies demonstrated that elevated body weight was associated with decreased nocturnal total sleep and REM sleep in humans.29 In contrast, weight loss in humans significantly increased the percentage of nighttime REM.30 Together these studies indicate that normal amounts of REM sleep at night may be crucial for normal body weight maintenance and that the occurrence of more REM sleep during the day may increase the risk for higher obesity.28

Sleep Modulation and Obesity in Childhood Currently it is strongly believed that sleep plays an important role in energy homeostasis, and also that sleep disturbances increase risk for obesity.31 Recent epidemiological and animal studies supported these earlier observations and showed a crucial interplay between sleep and body weight regulation.30 The above topic shows an association between body weight and sleep fundamentals (pattern or architecture).28 Moreover, it is currently accepted that this association is modulated by clock genes (e.g., CLOCK, BMAL1, RORa, REV-ERBa) of circadian rhythm.32,33 Our planet revolves around its axis causing light and dark cycles of 24 h. Organisms on our planet evolved to predict these cycles by developing an endogenous circadian (circa: about and dies: day) clock, which is synchronized to external time cues.34 The central circadian clock is located in the suprachiasmatic nuclei of the brain anterior hypothalamus.34 The suprachiasmatic nuclei clock is composed of multiple, single-cell oscillators synchronized to generate circadian rhythms.35 The endogenous period of the suprachiasmatic nuclei oscillation is approximately, but not exactly, 24 h.35 Therefore, it requires resetting each day to the external lightedark cycle to prevent drifting out of phase.34 Light is a strong synchronizer for the brain clock, perceived by the retina and transmitted via the retinohypothalamic tract to the suprachiasmatic nuclei.36 Similar clocks are found in peripheral tissues, such as the liver, intestine, and muscle, represented in Fig. 10.1.34 The suprachiasmatic nuclei transmit the information to peripheral oscillators to prevent the dampening of circadian rhythms via neuronal connections or circulating factors.34 In turn, suprachiasmatic nuclei rhythms can be altered by neuronal and endocrine inputs.37 The circadian clock is a cellular mechanism of gene transcription, translation, and posttranslational modifications.38 Thus, clock genes are considered important regulators of both clock function and metabolism34,39;

FIGURE 10.1 Effects of the suprachiasmatic (from brain anterior hypothalamus) on peripheral clocks. Adapted from Froy O. Circadian rhythms and obesity in mammals. ISRN Obes. 2012;2012:437198. (Images extracted from Google Pictures.)

among these, the CLOCK, BMAL1, REV-ERB and ROR families, PER2, CRY, PPAR are extensively studied, mainly in experimental models.34,40 Briefly, the core molecular clock consists of several transcription/translation feedback loops, including posttranscriptional regulation, that oscillate with an approximately 24hour periodicity,40 as mentioned above. CLOCK and BMAL1 heterodimerize to drive rhythmic expression of downstream target genes (REV-ERB, ROR, PPAR, DBP, PGC1a), which in turn regulate diverse metabolic processes, including glucose metabolism, lipid homeostasis, and thermogenesis.40 Many of these clock target genes in turn reciprocally regulate the clock in response to changes in nutrient status (e.g., PPRE, RORE) via cellular nutrient sensors [NADþ, NAMPT, SIRT1, AMP-activated protein kinase (AMPK)], generating a complex network of interlocking feedback loops that fine-tune the clock and coordinate metabolic processes with the daily cycles of sleep/wakefulness and fasting/feeding.40 Recently, REV-ERBa emerges as a potential key clock gene linking sleep duration and obesity in pediatric population.33 Studies on genetic variations have shown that REV-ERBa is associated with obesity in adults41,42 and youths,43 as well as with sleep duration. Additionally, it has been shown that REV-ERBa could modulate obesity in a sex-specific manner.33,41 The REV-ERBa gene could favor a visceral accumulation of fat,44 especially in males,41 showing a different role of adipogenesis among males and females. In a study with animal models, scientists showed the possible mechanisms of

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REV-ERBa influencing the clock function and mediating the interplay between circadian rhythms and metabolism.44 A principal mechanism could be the hepatic phosphatidylcholine regulated by the circadian clock through a BMAL1-REV-ERBa axis, which suggests that an intact circadian timing system is key for the temporal coordination of phospholipid metabolism.45 Likewise, lack of REV-ERBa may lead to deficits in engaging in waking behaviors.46 Similarly, REV-ERBa could thus act as a sensor of the metabolic imbalance imposed at the neuronal level by periods of extended wakefulness, which is in keeping with the current proposal that clock genes not only set time of day, but in the cerebral cortex can also be used to keep track of and respond to time spent awake.47 Another animal model study showed that the absence of normal function in the PER1 and PER2 clock genes seemed to protect male mice from metabolic reprogramming, suggesting that the circadian timing system has a role in regulating the physiological effects of sleep disruption.48

Biological Mechanisms Linking Sleep Duration and Obesity and Their Consequences Sleep duration has been related with health outcomes in the pediatric population.17 The literature suggests that sleep restriction is an important risk factor for poor health outcomes, including obesity.49e52 Additionally, excessive sleep duration may also be indicated as an associated factor for obesity.53,54 Thus, similar to other health outcomes, obesity seems to have a bimodal or U-shaped relationship with sleep duration (Fig. 10.2).55

Short-Duration Sleepers An important question raised by the researchers is whether sleep duration contributes directly to the mechanisms of (unhealthy) weight gain or reflects the presence of other relevant risk factors and pathways of reverse causation.56 Systematic reviews have confirmed that shorter sleep duration is associated with higher obesity (assessed by BMI) in children and adolescents.12,57,58 However, there is no consensus about the mechanisms,12,52 as well as the mechanisms underlying the relationship between sleeping and obesity remain incompletely understood.10,12 Previous research suggested that the association between shorter sleep and higher obesity is due to an effect on adiposity. Traditionally, studies have found that sleep deprivation can affect food choices, leading to a reduced intake of vegetables and fruits and an increased intake of energy-dense foods, such as fast food or sugarsweetened beverages.56,59 Thus, sleep loss may lead to weight gain by increases in caloric intake as inadequate sleep is related to increased ghrelin levels and decreased leptin levels, which can stimulate appetite and intake of excessive food.60 The literature also supports that sleep duration could be a contributing factor to obesity in children (but not vice versa), with increased television viewing identified as a potential mechanism linking sleep duration and obesity in short-duration sleepers.14,53 Additionally, recent findings suggest that shorter sleep duration is also associated with higher fat-free mass, an effect that is independent of the effect of sleep on fat mass, suggesting that the relationship between sleep and obesity is also determined by an effect on muscle.59,61 The mechanisms for the association between sleep and fat-free mass are unclear though maturation might play a part.59 In this sense, association between sleep duration and obesity is related not only to an effect on adiposity but also to an effect on other components of body composition.59,61

Obesity

Long-Duration Sleepers Short-duration sleepers

Long-duration sleepers

Sleep duration

FIGURE 10.2 obesity.55

U-shaped relationship between sleep duration and

Although associations between short sleep duration in early childhood and obesity are consistently found, less is known about long sleep duration in pediatric obesity.54 Further, there is little understanding regarding the mechanisms of association. The timing of eating, dietary intake, obesogenic eating behaviors, and changes in appetite-regulating hormones have been identified as possible mechanisms for sleepeobesity associations.54 Thus, cross-sectional studies have reported a higher prevalence of obesity among adults reporting habitually excessive sleep duration (typically defined as 9 h).54,62,63 Similar findings were also supported by longitudinal studies.64,65 In this sense, multiple

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TABLE 10.1

Sleep Duration Recommendations for Children.

Age

Hours of sleep per day 0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

2 years 3-5 years 6-10 years Not recommended May be appropriated

Recommended Adapted from National Sleep Foundation. How Much Sleep Do Babies and Kids Need? https://sleepfoundation.org/excessivesleepiness/content/how-much-sleep-do-babies-andkids-need; 2011 and National Sleep Foundation. How Much Sleep Do We Really Need? https://sleepfoundation.org/how-sleep-works/how-much-sleep-do-we-really-need; 2011.

pathways could mediate the association between long sleep duration and the risk of obesity.54 In a recent systematic review, scientists indicated that prolonged sleep duration can be due to impaired nocturnal sleep quality in adults.54 A potential explanation is grounded in possibility that compensatory increases in sleep duration are more likely to occur in those who chronically experience symptoms of insomnia and sleep-disordered breathing.54 Hence, it could be hypothesized that prolonged time in bed is an attempt of long-duration sleepers to cope with and compensate for poor sleep quality.54 Recent findings in children showed that bedtime may be more predictive of dietary obesity risk factors, whereas sleep duration may be more predictive of obesity.66 These findings suggest that health providers should consider both bedtime and sleep duration for reducing obesity risk in children.66 These recent results in children can corroborate with previous hypothesis in the adult population. In addition, other mechanisms linking long sleep duration and obesity are related to dietary habits and patterns. Studies suggest that long-duration sleepers consume a lower proportion of 24-h energy intake from primary meals, thus receiving a higher proportion from snacks,67 eat less dietary fiber, and have a reduced tendency for eating during conventional eating hours.67,68

Sleep Duration Recommendations Sleep duration reflects factors such as biological and developmental sleep needs as well as contextual or lifestyle demands (e.g., school start times, extracurricular activities).13 However, for a healthy lifestyle, the sleep duration recommendations for children are

presented in Table 10.1.69,70 Additionally, synchronizing physical activity and sedentary behavior interventions to the sleep recommendations might maximize the health-promoting benefits to prevent and treat metabolic pediatric obesity.71

CONCLUSION Far from a simple absence of wakefulness, sleep is an active and metabolically distinct state, essential for health and well-being potentially regulated by REVERBa clock gene. Short sleep duration is a significant risk for childhood obesity; whereas, long sleep duration is suggested to impair whole-body energy metabolism and consequently increase the risk of obesity. In this sense, obesity there seems to be a U-shaped relationship with sleep duration. Encouraging sleep recommendations can be effective effort to prevent and control child obesity.

References 1. Sahoo K, Sahoo B, Choudhury AK, Sofi NY, Kumar R, Bhadoria AS. Childhood obesity: causes and consequences. J Family Med Prim Care. 2015;4(2):187e192. 2. Lakshman R, Elks CE, Ong KK. Childhood obesity. Circulation. 2012;126(14):1770e1779. 3. World Health Orgazination (WHO). Growth Reference Data for 5e19 years; 2007. http://www.who.int/growthref/en/. 4. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med. 1997;337(13):869e873. 5. World Health Organization (WHO). Obesity and Overweight; 2018. https://www.who.int/news-room/fact-sheets/detail/obesityand-overweight.

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6. Rivera J, de Cossı´o TG, Pedraza LS, Aburto TC, Sa´nchez TG, Martorell R. Childhood and adolescent overweight and obesity in Latin America: a systematic review. Lancet Diabetes Endocrinol. 2014;2(4):321e332. 7. Wojnar J, Brower KJ, Dopp R, et al. Sleep and body mass index in depressed children and healthy controls. Sleep Med. 2010;11(3): 295e301. 8. Espan˜a-Romero V, Mitchell JA, Dowda M, O’Neill JR, Pate RR. Objectively measured sedentary time, physical activity and markers of body fat in preschool children. Pediatr Exerc Sci. 2013; 25(1):154e163. 9. Nascimento-Ferreira MV, De Moraes AC, Rendo-Urteaga T, et al. Cross-sectional, school-based study of 14-19 year olds showed that raised blood pressure was associated with obesity and abdominal obesity. Acta Paediatr. 2017;106(3):489e496. 10. Tobaldini E, Fiorelli EM, Solbiati M, Costantino G, Nobili L, Montano N. Short sleep duration and cardiometabolic risk: from pathophysiology to clinical evidence. Nat Rev Cardiol. 2018;16(4): 213e224. 11. Li H, Ren Y, Wu Y, Zhao X. Correlation between sleep duration and hypertension: a dose-response meta-analysis. J Hum Hypertens. 2019;33(3):218e228. 12. Li L, Zhang S, Huang Y, Chen K. Sleep duration and obesity in children: a systematic review and meta-analysis of prospective cohort studies. J Paediatr Child Health. 2017;53(4):378e385. 13. Jarrin DC, McGrath JJ, Drake CL. Beyond sleep duration: distinct sleep dimensions are associated with obesity in children and adolescents. Int J Obes. 2013;37(4):552e558. 14. Magee C, Caputi P, Iverson D. Lack of sleep could increase obesity in children and too much television could be partly to blame. Acta Paediatr. 2014;103(1):e27ee31. 15. Wang J, Adab P, Liu W, et al. Prevalence of adiposity and its association with sleep duration, quality, and timing among 9-12-yearold children in Guangzhou, China. J Epidemiol. 2017;27(11): 531e537. 16. Hense S, Pohlabeln H, De Henauw S, et al. Sleep duration and overweight in European children: is the association modified by geographic region? Sleep. 2011;34(7):885e890. 17. Nascimento-Ferreira MV, Collese TS, de Moraes AC, RendoUrteaga T, Moreno LA, Carvalho HB. Validity and reliability of sleep time questionnaires in children and adolescents: a systematic review and meta-analysis. Sleep Med Rev. 2015;30:85e96. 18. Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes. 2012;7(4):284e294. 19. Forkert E, Rendo-Urteaga T, Nascimento-Ferreira MV, de Moraes AC, Moreno L, Carvalho H. Abdominal obesity and cardiometabolic risk in children and adolescents, are we aware of their relevance? Nutrire. 2016;41(15):1e9. 20. Redinger RN. The pathophysiology of obesity and its clinical manifestations. Gastroenterol Hepatol. 2007;3(11):856e863. 21. Kotsis V, Stabouli S, Papakatsika S, Rizos Z, Parati G. Mechanisms of obesity-induced hypertension. Hypertens Res. 2010;33(5): 386e393. 22. Landsberg L, Aronne LJ, Beilin LJ, et al. Obesity-related hypertension: pathogenesis, cardiovascular risk, and treatment: a position paper of the Obesity Society and the American Society of Hypertension. J Clin Hypertens. 2013;15(1):14e33. 23. Carley DW, Farabi SS. Physiology of sleep. Diabetes Spectr. 2016; 29(1):5e9. 24. Hobson JA, McCarley RW, Wyzinski PW. Sleep cycle oscillation: reciprocal discharge by two brainstem neuronal groups. Science. 1975;189(4196):55e58. 25. Dunmyre JR, Mashour GA, Booth V. Coupled flip-flop model for REM sleep regulation in the rat. PLoS One. 2014;9(4):e94481.

26. van Eekelen A, Varkevisser M, Kerkhof G. Cardiac autonomic activity during human sleep: analysis of sleep stages and sleep cycles. Biol Rhythm Res. 2003;34:493e502. 27. Fontanini A, Katz DB. Behavioral states, network states, and sensory response variability. J Neurophysiol. 2008;100(3):1160e1168. 28. Mavanji V, Billington CJ, Kotz CM, Teske JA. Sleep and obesity: a focus on animal models. Neurosci Biobehav Rev. 2012;36(3): 1015e1029. 29. Liu X, Forbes EE, Ryan ND, Rofey D, Hannon TS, Dahl RE. Rapid eye movement sleep in relation to overweight in children and adolescents. Arch Gen Psychiatry. 2008;65(8):924e932. 30. Kalra M, Mannaa M, Fitz K, et al. Effect of surgical weight loss on sleep architecture in adolescents with severe obesity. Obes Surg. 2008;18(6):675e679. 31. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999e2008. J Am Med Assoc. 2010;303(3):235e241. 32. Sookoian S, Gemma C, Gianotti TF, Burguen˜o A, Castan˜o G, Pirola CJ. Genetic variants of clock transcription factor are associated with individual susceptibility to obesity. Am J Clin Nutr. 2008;87(6):1606e1615. 33. Nascimento Ferreira MV, Goumidi L, Carvalho HB, et al. Associations between REV-ERBa, sleep duration and body mass index in European adolescents. Sleep Med. 2018;46:56e60. 34. Froy O. Circadian rhythms and obesity in mammals. ISRN Obes. 2012;2012:437198. 35. Reppert SM, Weaver DR. Coordination of circadian timing in mammals. Nature. 2002;418(6901):935e941. 36. Quintero JE, Kuhlman SJ, McMahon DG. The biological clock nucleus: a multiphasic oscillator network regulated by light. J Neurosci. 2003;23(22):8070e8076. 37. Saeb-Parsy K, Lombardelli S, Khan FZ, McDowall K, Au-Yong IT, Dyball RE. Neural connections of hypothalamic neuroendocrine nuclei in the rat. J Neuroendocrinol. 2000;12(7):635e648. 38. Kang JI, Park CI, Namkoong K, Kim SJ. Associations between polymorphisms in the NR1D1 gene encoding for nuclear receptor REV-ERBa and circadian typologies. Chronobiol Int. 2015;32(4): 568e572. 39. Solt LA, Kojetin DJ, Burris TP. The REV-ERBs and RORs: molecular links between circadian rhythms and lipid homeostasis. Future Med Chem. 2011;3(5):623e638. 40. Huang W, Ramsey KM, Marcheva B, Bass J. Circadian rhythms, sleep, and metabolism. J Clin Invest. 2011;121(6):2133e2141. 41. Ruano EG, Canivell S, Vieira E. REV-ERB ALPHA polymorphism is associated with obesity in the Spanish obese male population. PLoS One. 2014;9(8):e104065. 42. Garaulet M, Smith CE, Gomez-Abella´n P, et al. REV-ERB-ALPHA circadian gene variant associates with obesity in two independent populations: Mediterranean and North American. Mol Nutr Food Res. 2014;58(4):821e829. 43. Goumidi L, Grechez A, Dumont J, et al. Impact of REV-ERB alpha gene polymorphisms on obesity phenotypes in adult and adolescent samples. Int J Obes. 2013;37(5):666e672. 44. Bugge A, Feng D, Everett LJ, et al. Rev-erba and Rev-erbb coordinately protect the circadian clock and normal metabolic function. Genes Dev. 2012;26(7):657e667. 45. Gre´chez-Cassiau A, Feillet C, Gue´rin S, Delaunay F. The hepatic circadian clock regulates the choline kinase a gene through the BMAL1-REV-ERBa axis. Chronobiol Int. 2015;32(6):774e784. 46. Mang GM, La Spada F, Emmenegger Y, et al. Altered sleep homeostasis in Rev-erba knockout mice. Sleep. 2016;39(3):589e601. 47. Franken P. A role for clock genes in sleep homeostasis. Curr Opin Neurobiol. 2013;23(5):864e872. 48. Husse J, Hintze SC, Eichele G, Lehnert H, Oster H. Circadian clock genes Per1 and Per2 regulate the response of metabolism-

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60. Bayon V, Leger D, Gomez-Merino D, Vecchierini MF, Chennaoui M. Sleep debt and obesity. Ann Med. 2014;46(5):264e272. 61. Scha¨fer AA, Domingues MR, Dahly DL, et al. Sleep duration trajectories and body composition in adolescents: prospective birth cohort study. PLoS One. 2016;11(3):e0152348. 62. Bjorvatn B, Sagen IM, Øyane N, et al. The association between sleep duration, body mass index and metabolic measures in the Hordaland Health Study. J Sleep Res. 2007;16(1):66e76. 63. Taheri S, Lin L, Austin D, Young T, Mignot E. Short sleep duration is associated with reduced leptin, elevated ghrelin, and increased body mass index. PLoS Med. 2004;1(3):e62. 64. Chaput JP, Despre´s JP, Bouchard C, Tremblay A. The association between sleep duration and weight gain in adults: a 6-year prospective study from the Quebec Family Study. Sleep. 2008;31(4): 517e523. 65. Theorell-Haglo¨w J, Berglund L, Berne C, Lindberg E. Both habitual short sleepers and long sleepers are at greater risk of obesity: a population-based 10-year follow-up in women. Sleep Med. 2014; 15(10):1204e1211. 66. Spaeth A, Hawley N, Raynor H, et al. Sleep, energy balance, and meal timing in schoolaged children. Sleep Med. 2019. 67. Kant AK, Graubard BI. Association of self-reported sleep duration with eating behaviors of American adults: NHANES 2005e2010. Am J Clin Nutr. 2014;100(3):938e947. 68. Kim S, DeRoo LA, Sandler DP. Eating patterns and nutritional characteristics associated with sleep duration. Public Health Nutr. 2011;14(5):889e895. 69. National Sleep Foundation. How Much Sleep Do Babies and Kids Need?; 2011. https://sleepfoundation.org/excessivesleepiness/ content/how-much-sleep-do-babies-and-kids-need. 70. National Sleep Foundation. How Much Sleep Do We Really Need?; 2011. https://sleepfoundation.org/how-sleep-works/how-muchsleep-do-we-really-need. 71. Nascimento-Ferreira N, Rendo-Urteaga T, De Moraes A, Moreno L, Carvalho H. Abdominal obesity in children: the role of physical activity, sedentary behavior, and sleep time. In: Watson RR, ed. Nutrition in the Prevention and Treatment of Abdominal Obesity. 2nd ed. London, UK: Academic Press (Elsevier); 2018: 82e91.

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C H A P T E R

11 Physiopathology of Narcolepsy and Other Central Hypersomnias Fu¨sun Mayda Domac¸ University of Health Sciences, Erenko¨y Mental Health and Neurological Diseases Training and Research Hospital, _ Neurology Department and Sleep Medicine Center, Istanbul, Turkey

Abbreviations AASM American Academy of Sleep Medicine BMAL-1 Brain and muscle aryl hydrocarbon receptor nuclear translocator-like protein-1 HcrtR1 Hypocretin receptor 1 HcrtR2 Hypocretin receptor 2 HLA Human leukocyte antigen ICSD-2 The International Classification of Sleep Disorders, second revision ICSD-3 The International Classification of Sleep Disorders, third revision IH Idiopathic hypersomnia KLS KleineeLevin syndrome LMOD Leiomodin MOG Myelin oligodendrocyte glycoprotein MSLT Multiple sleep latency test NT1 Narcolepsy type 1 NT2 Narcolepsy type 2 OX1R Orexin receptor 1 OX2R Orexin receptor 2 PER Period circadian protein REM Rapid eye movement SNP Single-nucleotide polymorphism SOREM Sleep onset rapid eye movement SPECT Single-photon emission computed tomography Trib2 Tribbles homolog 2

substance, hypersomnia associated with a psychiatric disorder. The remainings are insufficient sleep syndrome, isolated symptoms, and normal variants.1 In this chapter physiopathology of central hypersomnias will be discussed separately.

NARCOLEPSY Narcolepsy, a chronic and lifelong disease, was first described by Westphal in 1877 but it was named by Ge´linea´u in 1880.2,3 After the third revision of ICSD types of narcolepsy are classified as type 1 and type 2. This latest revision, though basically parallel to ICSD-2, has some changes in nomenclature and diagnosis of narcolepsy.4 Formerly named narcolepsy with cataplexy is recently named as NT1 while narcolepsy without cataplexy is recently named as NT2. Absence of orexin (hypocretin) is newly accepted the main difference in the diagnosis of the type of narcolepsy. The other important point is the acceptance of sleep onset rapid eye movement period (SOREM) recorded on the preceding nocturnal polysomnography test may replace one of the SOREMs in multiple sleep latency test (MSLT) to maintain the necessity of 2 SOREMs for the diagnosis of narcolepsy.1

INTRODUCTION Hypersomnias are one of the group of sleep disorders with the main clinical complaint of excessive daytime sleepiness. The third revision of The International Classification of Sleep Disorders (ICSD-3), updated in 2014, has classified central hypersomnias as narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), idiopathic hypersomnia (IH), and KleineeLevin syndrome (KLS). Secondary hypersomnias are as follows: hypersomnia due to a medical disorder, hypersomnia due to a medication or Neurological Modulation of Sleep https://doi.org/10.1016/B978-0-12-816658-1.00011-9

NARCOLEPSY TYPE 1 Like the other types of hypersomnias, the main symptom is intolerable sleepiness during the day called as excessive daytime sleepiness which is regardless of any other sleep disorder and amount of sleep the night before.5 Cataplexy is the characteristic symptom of type 1 which is described as a sudden loss of tone at voluntary muscles as a response of strong emotion like

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11. PHYSIOPATHOLOGY OF CENTRAL HYPERSOMNIAS

laughter, crying, or anger. Cataplexy formerly gave the name of this type of narcolepsy.6 Sleep paralysis is another symptom which is described as a reversible inability to move the parts of the body while falling asleep or awakening.7 Dreamlike experiences at sleep onset named as hypnagogic hallucinations and hypnopompic hallucinations upon awakening are the other symptoms of NT1. All of these symptoms are thought to be due to the intrusion of REM sleep into wakefulness.8,9 NT1 has an incidence of 0.7e1.37 per 100,000 personyears10e13 and a prevalence of 25e56 per 100,000 people.10e14 The age of onset of NT1 makes two peaks. One of them is at adolescence and around 15 years of age and the other smaller peak is around 35 years of age.15 Genetic predisposition and a positive family history effect the age at onset and symptoms appear earlier than those without a family history. Diagnostic delay may be longer in narcoleptic patients except the ones with an early age at onset and cataplexia as the first symptom.16 Narcolepsy seems to be more common in males but the diagnostic delay was found to be longer in females.10,15,16 During the last decade studies about the physiopathology of narcolepsy have increased and involved genetic, immunological, and environmental factors.17

Hypocretin (orexin) Deficiency Hypocretins are neuroexcitatory peptides and are produced by the neurons in the lateral hypothalamus. Hypocretin 1 (also called orexin-A) and hypocretin 2 (also called orexin-B) bind to and activate two Gprotein-coupled receptors.18,19 Hypocretin receptor 1 (HcrtR1), also known as orexin receptor 1 (OX1R), is expressed in deep layers of the cerebral cortex, hypothalamus, and locus ceruleus, whereas HcrtR2 (also known as OX2R) is expressed in neocortex, septum, and raphe nuclei.20,21 The afferent and efferent projections of hypocretin neurons are suggested to play a role in multiple hypothalamic functions like regulation of sleep and wakefulness, emotional processing, and reward associated behaviors.21,22 NT1 mainly develops by irreversible loss of more than 90% of hypocretin-producing neurons in the hypothalamus, and the main narcoleptic symptoms as excessive daytime sleepiness, hypnagogic hallucinations, sleep paralysis, and cataplexia are assumed to be due to hypocretin deficiency.23,24 The neuronal loss seems to be specific for hypocretin-producing neurons as the number of melanin-concentrating hormone neurons is found in normal counts.25 Symptoms seem to be the more severe as the hypocretin levels in cerebrospinal fluid (CSF) are the less.26 The interval between the loss

of hypocretin-producing neurons and the debut of the symptoms is still unclear.24

Genetic Factors DQB1*0602 haplotype is present in more than 90% of NT1 patients and was found to increase the development of the disease in several studies.27e31 The concordance rate of NT1 is about 20%e30% in monozygotic twins and 1%e2% in first-degree relatives.32e34 Thus Miyagawa et al. have suggested that NT1 is not a simple genetic disease and some other risk factors like environment may play role at the development.2 The disease was detected in some families but the suspected genes are not fully identified. Hor et al. found c.398C>G mutation on myelin oligodendrocyte glycoprotein (MOG) gene in multiplex families.35 In a genomewide linkage analysis study a linkage in 5.15 Mb region of Chromosome 21 of the affected family numbers was reported36 while another study suspected the possible region for familial NT1 on Chromosome 6.37

Autoimmunity As there was a global pandemic risk for A(H1N1) influenza virus infection in 2009, in several European countries, the United States, and Canada A(H1N1) pandemic vaccines were authorized. About a year after the immunization period a case series of teenagers with diagnosis of narcolepsy in association with AS03adjuvanted A(H1N1) vaccines were reported in Sweden followed by some other Northern European countries.12,36,38,39 No association was found between narcolepsy and other forms of A(H1N1) vaccines such as MF-59-adjuvanted, a different AS03-adjuvanted A(H1N1) or nonadjuvanted pandemic vaccine.40,41 Nucleoprotein A was found in higher concentrations in AS03-adjuvanted A(H1N1) vaccine approved by European Medicine Agency than the other H1N1 vaccines. The risk of narcolepsy was high in individuals with DQB1*0602 who were immunized with this form of pandemic vaccine.42,43 Thus it was hypothesized that a cross-reactivity between influenza nucleoprotein A and human 2OX2R was the possible underlying mechanism and also vaccine nucleoprotein A content triggered an autoimmune response and development of narcolepsy.43 In another study, after 2 years of vaccination the incidence of narcolepsy was not found to be statistically different from the unvaccinated cohort and thus it was thought that the initiation of narcolepsy symptoms was regardless of vaccination after 2 years.38 On the contrary, Huang et al. reported that instead of H1N1 vaccines, H1N1 itself plays a role in triggering narcolepsy.41

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IDIOPATHIC HYPERSOMNIA

Loss of hypocretin-producing neurons is thought to be within the context of autoimmunity as human leukocyte antigen (HLA) DQB1*0602 is well known to be in close relation with the disease.27,28,31,35,44 Laterre et al. have reported the presence of autoreactive hypocretin-specific CD4þ T cells and hypocretin-specific CD8þ T cells in serum and CSF of narcoleptic patients which establishes the autoimmune role in narcolepsy.45 Pedersen et al. have recently suggested that the occurrence of autoreactive hypocretin-specific CD8þ Tcells in HLA DQB1*0602 positive patients is more substantial.46 In another study, the presence of polymorphism in the T-cell receptor alpha gene locus mainly at rs1154155 was detected at patients with NT1.47 Though mainly cellular mechanism is suspected to hold a place, the role of humoral mechanism was also investigated in some studies. High levels of Trib2-specific antibody were detected in the sera of narcoleptic patients compared to controls and the levels were found to be in correlation with the short initiation of the disease.48e50 Maria Pia Giannoccaro et al. searched hypocretin receptor 2 antibodies in the sera of NT1 patients and only in the sera of 2 of 50 patients antibodies were detected but in low titers and they concluded that antibodies are rare in patients unless a history of vaccination.51

NARCOLEPSY TYPE 2 The difference from NT1 is the absence of cataplexy. MSLT criteria are also same as NT1.27 The exact prevalence of NT2 is unknown but is estimated as 0.02%.10 The physiopathology is not clear and some hypotheses are suggested as follows.

Hypocretin Deficiency Reduction of hypocretin in CSF was observed in 10%e24% of patients with NT2. The patients with low levels of hypocretin had HLA DQB1*06:02 positivity.29,52 Thannickal et al. detected a 33% loss in hypocretin producing neurons while the number of melaninconcentrating neurons were normal.25,53 Thus hypocretin deficiency is suspected to play a role in the pathophysiology of NT2.53

Autoimmunity HLA DRB1*1501-DQB1*0602 haplotype was determined in some of the patients with NT2. Migayawa et al. have examined HLA DQB1 alleles both in NT1 and NT2 patient groups. They have observed no difference of distribution of DQB1*06:02 positivity in both of the groups and they have suggested that NT1 and

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DQB1*06:02 positive NT2 may share the same pathophysiologic mechanism.31

IDIOPATHIC HYPERSOMNIA IH, first defined by Bedrich Roth, is diagnosed afterward the exclusion of the other sleep disorders with excessive daytime sleepiness.54e56 In the second revision of The International Classification of Sleep Disorders (ICSD-2), American Academy of Sleep Medicine (AASM) has defined 2 forms of IH based on the nocturnal sleep duration as IH with long sleep time (>10 h) and IH without long sleep time (6e10 h).4 But in the third revision these criteria have changed and sleep duration was not taken into account. MSLT criteria differ from narcolepsy; SOREM is seen less than 2 with a sleep latency shorter than 8 min.1 Patients cannot wake up easily either in the morning or after a daytime nap and the difficulty in maintaining full alertness is described as sleep drunkenness which is thought to be a result of chronic sleep needfulness while in another study was suspected to be due to the increase of sleep spindle index.9,54,57,58 Despite narcolepsy sleep fragmentation is rare in nocturnal sleep while daytime naps do not refresh the patients and sleep drunkenness continues.59 IH is a rare sleep disorder. The prevalence of IH is 0.005%e0.3% but due to lack of epidemiological studies and misdiagnosis of the disease the real prevalence is unclear.14,60 Though the symptoms mainly begin in adolescence or young adulthood there may be a delay for years until the diagnosis of the disease and the symptom initiation.61 No gender difference is reported.62 It may be either a lifelong disease or may improve in 14%e25% of the patients.59,63 Though there are several hypotheses, the physiopathology of IH is still not clearly defined.

Hypocretin Deficiency In IH hypocretin deficiency has not been reported.9,29,59 Histamine is an important neurotransmitter in providing wakefulness. Kanbayashi et al. have found low levels of histamin in CSF of patients both in IH and narcolepsy without hypocretin deficiency.59 In H1 receptor gene knockout mice hypocretin was not detected, hence the effect of hypocretin 1 was suggested to be mediated by histaminergic system.64

Genetic Factors In familial history of IH patients excessive day time sleepiness is observed in 34%e38% of family

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members.56,59 Clock genes are suspected to be in relation with IH. Lippert et al. have investigated clock genes in dermal fibroblasts of patients with IH and reported that the expression of clock genes (BMAL1, PER1, and PER2) was lesser than the control group.65 HLA DQB1*0602 was not in association with IH.66,67

periods.23,77e80 Environmental factors were suggested to result in transient alteration of hypothalamic function and inhibition of the neurons that secrete hypocretin during these episodes.79,81 Whereas, in some reports no decline in CSF was indicated.82e84

Genetic Factors SleepeWake Rhythm Disturbance Decrease in slow-wave sleep was thought to be due to an abnormality in homeostatic rhythm. Sforza et al. investigated the amount of slow-wave sleep and the homeostatic rhythm in patients with IH. They found decrease in the amount of slow-wave sleep but no involvement in homeostatic rhythm.68

KLEINEeLEVIN SYNDROME KLS is a rare sleep disorder with symptoms of relapsing remitting episodes of sleepiness in association with hyperphagia; hypersexuality; behavioral abnormalities like apathy, irritability, or aggression; cognitive abnormalities like unreality or confusion during these episodes.1,69,70 KLS is frequent in males and usually presents at adolescence.71,72 The main and indispensable complaint is recurrent hypersomnia. The sleep duration is about 12e18 h and ends spontaneously. All of the episodes may not come into being with the same associated symptoms. The symptoms may differ from episode to episode all of which vanish at the end of the period. Episodes of duration may change from a few days to several weeks that end instantly or confidentially.70 Between these episodes the patients have a history of normal sleep, appetite, and behaviors.72 The etiology of KLS is still unclear. Single-photon emission computed tomography (SPECT) studies affirmed hypoperfusion in orbitofrontal, dorsomedial prefrontal and anterior cingulate parts of frontal lobe, temporoparietal junction, hypothalamus and thalamus either in symptomatic episodes or asymptomatic intervals, and frontotemporal or temporal hypoperfusion in interictal period or after the remission and the symptoms are thought to be due to the involvement of these areas.73e77 There are different hypotheses about the physiopathology like hypocretin deficiency, genetic factors, autoimmunity, and infections.

Hypocretin Deficiency The role of hypocretin in KLS is conflicting. Some studies found lower CSF levels of hypocretin in hypersomnolence periods than the asymptomatic

There is a lack of comprehensive genetic studies and no special gene has been identified up-to-date. Al Sherif et al. investigated KLS patients with whole-genome single-nucleotide polymorphism (SNP) genotyping and exome sequencing and they found a heterozygous missense variant in leiomodin 3 (LMOD3). As LMOD proteins are structural proteins the authors have suggested KLS as a structural and neurodevelopmental disease.85 Though there are not large case series of familial KLS, 800- to 4000-fold elevated risk of KLS in first-degree family members suggested a genetic basis.86,87 Nguyen et al. reported the incidence of KLS as 4% in multiplex families and 8% in familial cases.87 In several reports KLS was diagnosed in monozygotic twins, siblings either females or males, siblings and their parents or grandparents.82,88e92 Environmental factors shared by the family members were thought to be a trigger in the development of familial KLS.86

Autoimmunity Though KLS was suspected to be in relation with autoimmunity, HLA subtypes were investigated in some studies the association with specific HLA alleles is controversial. Some studies found increase in DQB1*0201 allele while others have reported that DRB1*0301-DQB1*0201 and DRB1*0701-DQB1*0202 alleles were frequent in KLS patients, there was no relationship with DQB1*02 allele.81,86,93

INFECTION As the first episode seem to appear mostly whether in winter or autumn and some patients complain of flu-like symptoms before the episodes, KLS has been thought to be a result of infection.94 Hence the symptoms of KLS periods seem to be due to the involvement of specific brain regions, a recurrent localized encephalitis of these regions thought to be responsible of KLS symptoms.95 Several viral and bacterial agents that mainly result in upper airway infections like EpsteineBarr virus, influenza A and B, varicella zoster virus, and Streptococcus were suspected to be a factor for disease development.94,96e98 As there are several types of

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REFERENCES

infectious agents and the titers were not evaluated in most of the patients the exact effect of infection can be overruled in KLS.99

CONCLUSION Several mechanisms like hypocretin deficiency, genetic factors, autoimmunity, and infections were hypothesized for the physiopathology of central hypersomnias but still the exact reasons for disease come into being are unclear. Further studies for the recognition of the underlying etiology will also bring new insight for the development of new treatment strategies.

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64. Huang ZL, Qu WM, Li WD, et al. Arousal effect of orexin A depends on activation of the histaminergic system. Proc Natl Acad Sci USA. 2001;98(17):9965e9970. 65. Lippert J, Halfter H, Heidbreder A, et al. Altered dynamics in the circadian oscillation of clock genes in dermal fibroblasts of patients suffering from idiopathic hypersomnia. PLoS One. 2014;9(1):85255. 66. Heier MS, Evsiukova T, Vilming S, Gjerstad MD, Schrader H, Gautvik K. CSF hypocretin-1 levels and clinical profiles in narcolepsy and idiopathic CNS hypersomnia in Norway. Sleep. 2007; 30(8):969e973. 67. Trotti LM. Idiopathic hypersomnia. Sleep Med Clin. 2017;12(3): 331e344. 68. Sforza E, Gaudreau H, Petit D, Montplaisir J. Homeostatic sleep regulation in patients with idiopathic hypersomnia. Clin Neurophysiol. 2000;111(2):277e282. 69. Billard M, Jaussent I, Dauvilliers Y, Besset A. Recurrent hypersomnia: a review of 399 cases. Sleep Med Rev. 2011;15:247e257. 70. Billiard M. Kleine-Levin syndrome. Sleep Med Clin. 2012;7:303e312. 71. Arnulf I, Lin L, Gadoth N, et al. Kleine-Levin syndrome: a systematic study of 108 patients. Ann Neurol. 2008;63(4):482e493. 72. Lavault S, Golmard JL, Groos E, et al. Kleine-Levin syndrome in 120 patients: differential diagnosis and long episodes. Ann Neurol. 2015;77(3):529e540. 73. Landtblom AM, Dige N, Schwerdt K, Sa¨fstro¨m P, Grane´rus G. A case of Kleine-Levin syndrome examined with SPECT and neuropsychological testing. Acta Neurol Scand. 2002;105(4):318e321. 74. Landtblom AM, Dige N, Schwerdt K, Sa¨fstro¨m P, Grane´rus G. Short-term memory dysfunction in Kleine-Levin syndrome. Acta Neurol Scand. 2003;108(5):363e367. 75. Vigren P, Tisell A, Engstro¨m M, et al. Low thalamic NAAconcentration corresponds to strong neural activation in working memory in Kleine-Levin syndrome. PLoS One. 2013;8(2):e56279. 76. Kas A, Lavault S, Habert MO, Arnulf I. Feeling unreal: a functional imaging study in patients with Kleine-Levin syndrome. Brain. 2014;137:2077e2087. 77. Engstro¨m M, Latini F, Landtblom A-M. Neuroimaging in the Kleine-Levin syndrome. Curr Neurol Neurosci Rep. 2018;18(9):58. 78. Podesta´ C, Ferreras M, Mozzi M, Bassetti C, Dauvilliers Y, Billiard M. Kleine-Levin syndrome in a 14-year-old girl: CSF hypocretin-1 measurements. Sleep Med. 2006;7(8):649e651. 79. Lopez R, Barateau L, Chenini S, Dauvilliers Y. Preliminary results on CSF biomarkers for hypothalamic dysfunction in Kleine-Levin syndrome. Sleep Med. 2015;16(1):194e196. 80. Wang JY, Han F, Dong SX, et al. Cerebrospinal fluid orexin a levels and autonomic function in Kleine-Levin syndrome. Sleep. 2016; 39(4):855e860. 81. Dauvilliers Y, Mayer G, Lecendreux M, et al. Kleine-Levin syndrome: an autoimmune hypothesis based on clinical and genetic analyses. Neurology. 2002;59(11):1739e1745. 82. Katz JD, Ropper AH. Familial Kleine-Levin syndrome: two siblings with unusually long hypersomnic spells. Arch Neurol. 2002;59(12): 1959e1961. 83. Bourgin P, Zeitzer JM, Mignot E. CSF hypocretin-1 assessment in sleep and neurological disorders. Lancet Neurol. 2008;7(7):649e662. 84. Knudsen S, Jennum PJ, Alving J, Sheikh SP, Gammeltoft S. Validation of the ICSD-2 criteria for CSF hypocretin-1 measurements in the diagnosis of narcolepsy in the Danish population. Sleep. 2010; 33(2):169e176. 85. Al Shareef SM, Basit S, Li S, et al. Kleine-Levin syndrome is associated with LMOD3 variants. J Sleep Res. 2018:e12718. 86. Arnulf I, Rico TJ, Mignot E. Diagnosis, disease course, and management of patients with Kleine-Levin syndrome. Lancet Neurol. 2012;11(10):918e928. 87. Nguyen QT, Groos E, Leclair-Visonneau L, et al. Familial KleineLevin syndrome: a specific entity? Sleep. 2016;39(8):1535e1542.

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C H A P T E R

12 Lateral Hypothalamic Control of Sleep in the Context of Cancer Jeremy C. Borniger*, Natalie Neva´rez* Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States

Abbreviations AgRP Agouti-related protein BF Basal forebrain CNS Central nervous system DA Dopamine DREADDs Designer Receptors Exclusively Activated by Designer Drugs DRN Dorsal raphe nuclei EEG Electroencephalogram EMG Electromyogram GABA Gamma-aminobutyric acid Gi hM4Di DREADD (inhibition) Gq hM3Dq DREADD (activation) Hcrt Hypocretin/orexin LC Locus coeruleus LDT/PPT Laterodorsal/pedunculopontine tegmental nuclei LepRb Long-form leptin receptor LH Lateral hypothalamus LPS Lipopolysaccharides MAPK Mitogen-activated protein kinase MCH Melanin concentrating hormone MPN Medial preoptic nuclei NE Norepinephrine NREM Nonrapid eye movement sleep NTS Neurotensin POMC Proopiomelanocortin REM Rapid eye movement sleep SNpc Substantia nigra pars compacta TMN Tuberomammillary nuclei VLPO Ventrolateral preoptic area vPAG Ventral periaqueductal gray VTA Ventral tegmental area

INTRODUCTION Cancer is a systemic disease, where the tumor interacts with the host to both evade immune

destruction and meet its own metabolic demand. This interaction contributes to cancer-associated systemic problems that are highly prevalent across cancer types, including sleep/circadian disruption, fatigue, inflammation, metabolic dysregulation, and changes in mood and appetite.1e5 Tumors secrete proinflammatory factors and metabolic “waste” that can influence the activity of distal organs such as the liver and brain.6e10 This cross-talk facilitates tumor progression and aberrantly alters systemic physiology and behavior. Reciprocally, the host can influence tumor growth and metastasis via endocrine, immune, and as we are beginning to understand, neural pathways. Sleep impairments correlate with mortality in cancer patients across various cancer types and treatment regimens. These problems persist even when controlling for risk factors such as age, cortisol concentrations, depression, hormone receptor status, and metastasis.1,2,9 Chronically short or fragmented sleep can further impair the immune system to exacerbate tumor growth.11 The lateral hypothalamus (LH) is a key brain structure linking changes in peripheral physiology to arousal. Containing a diverse and heterogeneous set of neural populations, the LH monitors changes in energy balance, immune status, and endocrine signals to tune arousal (and other behaviors) accordingly. Indeed, many of the signals monitored by the LH are disrupted in cancer. Here, we review the role of the LH in sleep regulation within the context of cancer. Specific focus is given to wake-stabilizing hypocretin/orexin (Hcrt)-expressing neurons, which play an important and nonredundant role in arousal. Additionally, we discuss other neural populations in

* These authors contributed equally.

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this region, how they interact, and how they uniquely respond to peripheral signals that are altered in response to tumors growing in the periphery.

SLEEP DISRUPTION IN PATIENTS WITH CANCER AND CANCER SURVIVORS Disturbed sleep and/or circadian rhythms in both behavior and physiology are common in cancer patients. Indeed, 35%e80% of cancer patients report disrupted or poor sleep,12,13 as compared to 29%

e32% of the general population.14 There are many factors that may contribute to these problems, making it hard to understand the mechanisms at play. These include the cancer itself, the stress or stigma surrounding a cancer diagnosis, the patient population (e.g., differential age/sex/ethnicity), different treatment regimens (e.g., chemotherapy, immunotherapy, and/ or radiotherapy), or additional lifestyle factors.15 These symptoms are common across a variety of cancer types, with lung and breast cancer patients making up the majority of the population experiencing these comorbidities.1,16e18 Adding further complexity to the

FIGURE 12.1 Schematic illustration of cancerebodyebrain interactions through nervous, endocrine, metabolic, and immune pathways. The host response to the tumor results in systemic inflammatory signaling that deregulates the function of distal organs including the liver, gut, and adipose tissue. Subsequent changes in organ function alter whole-body energy balance, immunity, hormonal profiles, and neural signaling. The brain senses these changes, largely through neurons located in the lateral hypothalamus. In response, these neurons alter their activity leading to disrupted sleep. Additionally, they send neural and humoral signals back out to the periphery in an attempt to restore homeostasis. Fragmented and disturbed sleep further impairs these circuits, causing additional damage and facilitating cancer progression. CNS, central nervous system; GABA, gammaaminobutyric acid; Hcrt, hypocretin/orexin; LepRb, long-form leptin receptor; LH, lateral hypothalamus; MCH, Melanin concentrating hormone. II. ADVERSE EFFECTS OF SLEEP DISRUPTION

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BOX 12.1

D e t e r m i n a t i o n o f s l e e p ew a k e s t a t e s i n h u m a n s a n d m i c e There are three primary ways that sleep disturbances in cancer patients have been investigated: (1) actigraphy monitoring, (2) subjective questionnaires (e.g., the Pittsburgh Sleep Quality Index; PSQI), and (3) polysomnography; the most common being a combination of (1) and (2). Polysomnography is the “gold standard” in the diagnosis of sleepewake disorders in humans.39,39a,40 It is a comprehensive recording of biophysical changes that occur during sleep, comprising the electroencephalogram (EEG), electrooculogram (EOG), muscle activity (EMG), and heart rate (ECG), along with measures of respiration and blood oxygenation levels (peripheral pulse oximetry) (Fig. 12.2).

(9e12 Hz), beta (12e30 Hz), delta (i.e., slow waves; 0.5 e4 Hz), low (30e60 Hz) and high (60e100 Hz) gamma, and theta (5e9 Hz). Synchronization (i.e., high-voltage, low frequency) of the cortical EEG during NREM sleep (delta-rich) depends on a corticothalamocortical loop, which is modulated by local oscillators and is distally controlled by subcortical systems.44 During NREM sleep, delta waves dominate the EEG, and EMG activity (postural tone) is low or absent. Delta activity during NREM sleep is directly related to the duration of prior waking, providing an index of sleep drive. During wakefulness, EMG activity is high reflecting muscle activity,

FIGURE 12.2 EEG/EMG waveforms depicting typical brain/muscle activity during wakefulness, NREM sleep, and REM sleep a mouse. Wakefulness is dominated by mixed frequencies in the EEG and highamplitude fast EMG activity; NREM sleep is dominated by delta waves in the EEG (0.5e4 Hz) with little or no EMG activity; and REM sleep shows primarily theta (6e9 Hz) frequencies in the EEG, with no EMG activity. By analyzing the EEG/EMG frequencies, an experimenter can objectively determine sleep/wake states in rodents and humans. EEG, electroencephalogram; EMG, electromyography; NREM, nonrapid eye movement; REM, rapid eye movement.

The EEG reflects large-scale changes in electrical activity in the cerebral cortex, as the firing rate of cortical neurons steadily declines during NREM sleep in comparison to REM sleep and wakefulness.41e43 The EEG reflects the aggregate firing of cortical neuronal ensembles and can be parsed into conventional bandwidths that describe cortical firing rates at the approximate frequencies: alpha

and the EEG shows task-dependent spectral properties, with low theta frequencies building over time indicating growing sleep propensity.45 REM sleep, also known as paradoxical sleep, is characterized by a wake-like (high theta) EEG spectral composition, but (paradoxically) low or absent EMG activity.

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problem, a “chicken-or-the-egg” phenomenon has emerged where poor sleep is associated with increased cancer risk and progression, and cancer and/or cancer treatments further promote sleep disruption.1,2,7,19 Because of these factors, it has been challenging to pin down cause and effect. This lack of knowledge prevents targeted therapies from being developed and results in significant impairment in the quality of life and life span of patients with cancer and survivors. Cancer treatment(s) (e.g., cytotoxic chemotherapy) may play an additional role (independent of the cancer itself) in promoting sleep disturbance. A total 75% e96% of cancer patients are reported to suffer from chemotherapy-induced fatigue,20e25 and fatigue and sleep disturbances are two of the most commonly recognized complaints associated with chemotherapy.26e30 Despite these observations, the mechanisms by which cancer treatments alter arousal states are undefined. A leading hypothesis posits that chemotherapy-induced immune activation promotes sleep disruption. Indeed, many chemotherapeutic regimens initiate or exacerbate inflammation,31e36 and chemo-induced p38 MAPK signaling (a major inflammatory signaling pathway) is suspected to lead to downstream cytokine production and treatment-related problems, including disrupted sleep.37,38 In the subsequent sections, we first discuss how the brain controls sleepewake states, with special focus on lateral hypothalamic circuits. Cancer and/or cancer treatments deregulate systemic factors that these neurons are sensitive to (e.g., inflammatory and metabolic signals), altering their activity to promote sleep disturbances.

AROUSAL CIRCUITRY Broadly, the central nervous system (CNS) can be conceptualized as containing arousal-promoting and sleep-promoting structures. Their activity is finely tuned by fast (glutamate and GABA) and slow (neuromodulator) synaptic signaling, as well as humoral and sensory cues arriving from the periphery and environment. As we will discuss below, the hypothalamus plays a central role in integrating and relaying information from the CNS and the periphery to produce adaptive arousal responses via its widespread connections throughout the brain. The hypothalamus contains distinct sets of neurons promoting sleep and those that induce and stabilize wakefulness. For a description of basic sleep/ wake states, see Box 12.1. Wake supporting structures include the tuberomammillary nucleus (TMN) and the LH which contain histaminergic and Hcrt neurons, respectively. Reciprocally, melanin concentrating

hormone (MCH) containing cells within the LH dually promote sleep and are most active during rapid eye movement sleep (REM).46e48 They serve supporting functions through their projections to the brain stem, hypothalamus, and basal forebrain (BF). Additional sleep promoting neural populations in the rostral hypothalamus are g-aminobutyric acid (GABA)- and galanin-containing neurons within the medial preoptic area (MPN) and ventrolateral preoptic area (VLPO).41,49 Outside of the hypothalamus, the BF comprises another main wake-promoting structure. The BF can be subdivided into the diagonal band, the substantia innominata, and the medial septum, each containing distinct sets of neurons promoting wakefulness or sleep (GABAergic, cholinergic, and others).50 Moving caudally, the midbrain and brain stem hold many wake promoting neural populations including noradrenergic neurons in the locus coeruleus (LC), dopaminergic neurons in the ventral tegmental area (VTA), serotonin neurons in the dorsal raphe nucleus (DRN), dopaminergic neurons in the substantia nigra pars compacta (SNpc), noradrenergic, dopaminergic, and GABAergic neurons in the ventral periaqueductal gray (vPAG), and cholinergic neurons in the laterodorsal and pedunculopontine tegmental nuclei (LDT/ PPT).51e54 A full discussion of all these circuits is beyond the scope of this chapter, therefore, we will focus on the LH and its role in arousal control.

LATERAL HYPOTHALAMIC CONTROL OF SLEEP/WAKE STATES The LH plays many important roles within this complex arousal network. Besides its spatial location, the LH distinguishes itself by the heterogeneity of cell types and broad projections through which it regulates many homeostatic functions including arousal.55 A key neural population specific to this region are major players in LH functiondcells containing the excitatory neuromodulators Hcrt-1 and Hcrt-2 (also known as orexin-A and orexin-B; Hcrt).56,57 Additionally, MCH cells in the LH also contribute to the coordination of NREM and REM sleep while GABAergic projections into the LH from the VLPO and MPN promote sleep primarily through inhibition of Hcrt neurons.49,54,58,59 Arousal relevant neural populations within the LH include Hcrt cells, MCH cells, glutamatergic and GABAergic cells, and cells that express the long form leptin receptor LepRb.57,60 The sensitivity of these cells to peripheral signaling is a double-edged sword, where the same mechanisms that allow these cells to regulate homeostasis also permit them to disrupt systems across the body when influenced by cancer and cancer-induced systemic factors.

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Hypocretin Neurons Hcrt neurons comprise a restricted cell population spanning the tuberal hypothalamus, most densely concentrated in the LH. Loss of Hcrt neurons, Hcrt peptides, or their receptors results in narcolepsy with cataplexy, a disorder characterized by unstable arousal state boundaries where REM and non-REM sleep can co-occur with states of wakefulness.61e67 This highlights Hcrt’s central role in arousal stability. Hcrt cell degeneration results in fragmented wakefulness, although the total amount of sleep and wakefulness remains unchanged.68 This neural population increases firing rates and plays a central role in “active” wakefulness.69 They decrease firing during nonrapid eye movement sleep (NREM; also known as slowwave sleep [SWS]), with little or no activity during REM sleep.70e73 The first in vivo use of optogenetics demonstrated that these neurons are essential for transitions between sleep and wakefulness; stimulation of these neurons had an awakening effect in mice while their continued inhibition induced NREM sleep.74,75 Using DREADDs (designer receptors exclusively activated by designer drugs), Sasaki and colleagues confirmed these findings, demonstrating that the DREADD ligand clozapine-N-oxide (CNO) promotes wakefulness in mice with Hcrt neurons expressing the excitatory (Gq) DREADD receptor, while CNO decreased wakefulness and promoted NREM sleep in mice with Hcrt neurons expressing the inhibitory DREADD receptors.76 Further research has confirmed these findings using methods spanning pharmacology, chemogenetics, circuit mapping manipulation, and transgenic strategies. Importantly, Hcrt neurons are sensitive to signals arriving from the periphery, acting to integrate these inputs to dictate arousal states and behavioral action, as we discuss below.

Melanin Concentrating Hormone Neurons Comingled with Hcrt neurons in the LH are cells containing the peptide MCH which shows largely reciprocal activity profiles to Hcrt neurons.48,70,77 MCH neurons are silent during wakefulness, increase firing during non-REM sleep, and are most active during REM sleep.47,70 MCH knockout mice show a reduction in NREM sleep and increase in wakefulness.78 MCH-containing cells are sensitive to signals arriving from the periphery (e.g., glucose), as we discuss in subsequent sections, which give them a broader role in the regulation of energy balance and feeding behavior. As there is evidence of inhibitory feedback between Hcrt and MCH neurons in vitro,79 this cross-talk may serve to support appropriate

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coordination of sleep/wake transitions in addition to integration of systemic factors.

GABAergic Neurons GABAergic neurons from the VLPO and MPN project to Hcrt neurons within the LH and are active during NREM and REM sleep, suggesting that they play a critical role in inhibiting Hcrt-mediated excitability and thus, in suppressing arousal.54,80 Indeed, activation of POA GABAergic neurons inhibit Hcrt neurons in slice culture preparations and result in NREM sleep in vivo.58 Notably, GABAergic activation is not only important for the induction of sleep but also for the stability of sleep states as well. Indeed, deleting GABA-B receptors from Hcrt neurons in the LH results in sleep fragmentation (i.e., short sleep bouts) suggesting that GABAergic signaling at Hcrt neurons is essential for the consolidation of sleep episodes.81 GABAergic neurons within the LH (Hcrt and MCHnegative) itself also show arousal state dependent changes in neural activity82 and these cells seem to be important for arousal from NREM, but not REM sleep.83

Leptin-Receptor Expressing Neurons Leptin is an adipokine hormone, produced primarily by white adipocytes, and acts as a humoral signal of satiety. Adipose tissue mass correlates with circulating concentrations of leptin, and reduced sensitivity to leptin (i.e., leptin resistance) promotes obesity. Hypothalamic cells containing the long-form leptin receptor (LepRb) play a key role in the regulation of energy balance by the brain. They themselves are a heterogeneous population, where w60% of them also coexpress neurotensin, while only 30% of neurotensin (NTS) neurons express LepRb.84 Further, w95% of double-positive neurotensin/LepRb neurons also express galanin, while only 20%e44% of galanin neurons express LepRb.57,85 One pathway through which these neurons influence energy balance is via their inhibitory input onto neighboring Hcrt neurons.86 NTS þ neurons also inhibit Hcrt neurons to adjust arousal state, as in the case with sickness-induced lethargy.87

INTEGRATION OF SYSTEMIC SIGNALS IN THE LATERAL HYPOTHALAMUS As we discussed in the previous section, the LH is positioned as an integrator, constantly monitoring important

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inputs relaying energy balance, immune status, and stress (among others). Hcrt neurons are sensitive to a variety of signals arriving from the periphery, including leptin, ghrelin, dietary amino acids (AAs), glucose, and changes in extracellular pH and CO2. MCH neurons, which operate in an antagonistic fashion to that of intermingled Hcrt neurons, are also responsive to many of these inputs. Because these (and other) LH neurons have widespread projections throughout the brain and downstream autonomic output nuclei (Fig. 12.1), disruption of their activity can have widespread effects across the brain and body.53,88e90 Subsequent changes in behavior and peripheral physiology have significant effects on cancer growth and progression.

Signals of Energy Balance and Metabolic State Hormones and metabolites influence the activity of lateral hypothalamic neurons, which then integrate these signals (through unknown mechanisms) and modulate their excitability or firing rates to adjust behavioral state accordingly.57,91e93 Leptin, an adipokine hormone (primarily secreted by white adipocytes), is a humoral signal of satiety.94 Hcrt neurons express receptors for and are inhibited by leptin either directly95,96 or through neighboring neurons that express the longform leptin receptor (LepRb).86 Decreases in leptin concentrations during fasting enhances the activity of Hcrt neurons.84,97 Reciprocally, leptin administration can block cFos (immediate early gene) expression in Hcrt neurons even when they are stimulated optogenetically. It further blocks Hcrt-induced hypothalamice pituitaryeadrenal (HPA) axis activation coincident with induction of phosphorylated (activated) signal transducer and activator of transcription 3 (pSTAT3), a downstream component in the leptin signaling pathway.86 Additionally, leptin suppresses Hcrt neural activity via local neurotensin-expressing neurons in the LH via GABA-independent mechanisms.98 MCH neurons directly express the leptin receptor99 and are similarly inhibited by leptin. Broadly, leptin seems to inhibit hypothalamic excitatory neurons (e.g., MCH, Hcrt, POMC, NPY) while activating inhibitory neurons (e.g., neurotensin, NTS).100 Ghrelin, a gut hormone upregulated during extended periods of fasting, essentially operates in opposition to leptin, powerfully promoting feeding behavior.101 Ghrelin exists in an “inactive” (des-acyl) and “active” (acyl-) form. Ghrelin-O-acyltransferase (GOAT) is the enzyme that allows ghrelin to bind and activate its receptor (GHS-R). Hcrt neurons express these receptors, and central administration of acyl-ghrelin induces cFos immunoreactivity in Hcrt neurons.102e104 Additionally, ghrelin-induced feeding can be blocked via inhibition of Hcrt signaling.105,106

Independent of peripheral ghrelin concentrations, ghrelin signaling from the ventral hippocampus to LH Hcrt neurons promotes hyperphagia, providing an additional top-down circuit that regulates feeding behavior.107 A proximate hypothesis explaining these findings is that during periods of fasting, elevations in circulating ghrelin activate Hcrt neurons to promote arousal and foodseeking behavior to restore metabolic homeostasis. MCH neurons, on the other hand, do not seem to be directly sensitive to ghrelin,102 ghrelin inhibition, or modulators of glucose homeostasis (e.g., insulin and 2deoxy-D-glucose).108 Significant focus has been on how LH neurons detect and regulate glucose metabolism.96,109,110 Elevations in extracellular glucose inhibit Hcrt neurons via a tandem-pore potassium channel,111 and insulininduced hypoglycemia reciprocally enhances Hcrt neural activity.112 These data position Hcrt neurons as energy/nutrient sensors, integrating signals of energy balance to alter whole-organism metabolism. This is accomplished via physiological effects in the periphery or through behavioral adjustment of the organism. For example, disinhibition of LH Hcrt neurons increases glucose production in the liver (putatively through an autonomic pathway),109 and it has been suggested that hypoglycemia promotes arousal and food-seeking via the Hcrt system.113 A more recent study demonstrated that Hcrt neurons are rapidly inactivated upon eating, even when the food is “calorie-free”.114 This suggests that the physical act of eating, rather than the nutritional value of the food, contributes to Hcrt activity in relation to food-seeking and metabolism. MCH neurons are also sensitive to extracellular glucose concentrations and, in contrast to Hcrt neurons, are excited by elevations in glucose.110 Glucose excites MCH neurons via potassium channels sensitive to changes in intracellular ATP (KATP) and are negatively regulated by UCP2 (a mitochondrial protein that reduces ATP). Glucose-mediated activation of MCH neurons results in a reduction in blood glucose concentrations, highlighting a major role for these neurons in glucose homeostasis.115 In addition to the factors mentioned above, dietary AAs further modulate Hcrt neural activity.116 Nutritionally relevant mixtures of AAs stimulate Hcrt neurons, as shown through slice patch-clamp and cFos mapping studies.116 This excitatory property of AAs requires inhibition of KATP channels as well as activation of system-A AA transporters. Interestingly, nonessential AAs seem to be more stimulatory than essential AAs. Additionally, AAs buffer the effect of glucose on Hcrt neurons (discussed previously), suggesting that these cells sense macronutrient balance rather than net energy value in the extracellular space. There is limited evidence for the effects of AAs on MCH neural activity, and this is an area that requires further investigation.

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Acid and carbon dioxide (CO2) in the extracellular space are fundamental signals regulating arousal and breathing. Initial anatomical research noted that Hcrt may play a role in the regulation of breathing51 which was confirmed via selective injection of Hcrt into brain stem breathing control centers, which express Hcrt receptors.117 Additional research demonstrated that Hcrt neurons alter their activity in response to carbon dioxide and changes in pH118,119 slice electrophysiology, elevations of bath CO2, and reductions (acidification) of extracellular pH enhanced Hcrt neural activity. As Hcrt neurons were made to express green fluorescent protein (GFP) in these experiments, their responses to changes in pH and CO2 could be compared to neighboring non-Hcrt neurons (GFP-negative). For these cells, pH and CO2 either had no effect on firing rates or these cells showed the opposite response (increased firing in response to alkalization), suggesting that detection of these breathing-relevant stimuli was specific to Hcrt neurons in the LH.119 To understand whether these phenotypes were only present in vitro, an additional study demonstrated that inhaled CO2 also activated Hcrt neurons (although it is unclear whether this effect was direct).118 Other circuits sensitive to CO2 include parabrachial nucleus calcitonin gene-related peptideexpressing neurons,120 which likely influence the activity of LH neurons through direct and indirect synaptic connections.121 Dorsal raphe 5-HTþ neurons further mediate CO2-induced arousal independent from breathing rate.122 These actions may be governed by inputs arriving from Hcrt neurons in the LH, but this has yet to be demonstrated. Similarly, a prominent role for MCH neurons in sensing and responding to changes in pH and acid has not been reported.123

Stress and Glucocorticoids The feeling of stress reflects a state of hyperarousal. It is unsurprising that stress engages Hcrt neurons, which then exert modulatory properties on the HPA (stress) axis.124 The corticotropin releasing factor (CRF) peptidergic system directly innervates and depolarizes Hcrt neurons, an effect that can be blocked via CRF-R1 antagonism.125 It was hypothesized that Hcrt neurons integrate stressful stimuli (via the CRF system) to adjust arousal in the face of impending danger. All forms of stress, however, do not equally influence the Hcrt system. Indeed, Hcrt may be engaged in response to stress specifically when arousal requires increased attention to environmental cues.126 There is reciprocal feedback between the HPA axis and the Hcrt system, where activation of Hcrt neurons results in subsequent elevations in circulating glucocorticoids, which further aid in promoting arousal.86 Interactions between glucocorticoids and

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MCH neurons are less well defined. Adrenalectomy in combination with fasting enhances the expression of hypothalamic MCH; however, refeeding does not attenuate this change in neuropeptide expression.127 Additionally, immobilization stress, which powerfully activates Hcrt neurons and the HPA axis, has no apparent effect on adjacent and intermingled MCH neurons.128 These data suggest that MCH circuitry plays a smaller role in responding to and regulating the stress response than that of Hcrt.

Immunity and Injury “Sickness behavior,” an adaptive suite of behaviors occurring in response to sickness and injury, is characterized by reduced general arousal, acute increases and chronic reductions in sleep (with fragmentation), and reduced locomotor activity.129 This response is primarily driven by the adaptive release of molecular messages from the immune system (i.e., cytokines) interacting with the brain upon immune challenge or injury. Using the immediate early gene cFos as a proxy for neural activation, Grossberg and colleagues demonstrated that peripheral immune stimulation (intraperitoneal lipopolysaccharides; LPS) inhibited Hcrt neural activity coincident with reduced locomotor activity (i.e., “lethargy”).87 Subsequent central administration of Hcrt prevented LPS-induced lethargy, providing evidence for Hcrt’s causal role in inflammation-induced “lethargy.” In a cytotoxic chemotherapy-induced model of fatigue using doxorubicin, cyclophosphamide, and 5fluorouracil, drug administration resulted in a similar reduction in Hcrt cFos expression coincident with hypothalamic inflammation and reduced locomotor activity.130 Coadministration of central Hcrt abrogated this chemotherapy-induced “fatigue.” A similar cytotoxic chemotherapy administration protocol severely disrupted sleep in tandem with hypothalamic inflammation, suggesting that in addition to fatigue, sleep disruption occurs in response to acute cytotoxic chemotherapy, putatively through the Hcrt system.19 Although there is some evidence that Hcrt neurons express specific cytokine receptors,131 direct actions (e.g., electrophysiological) of specific cytokines on these neurons is lacking. Upstream NTS-expressing neurons, which send inhibitory input to Hcrt neurons, are excited by inflammatory cytokines, providing an indirect method by which inflammatory signaling results in reductions in Hcrt neural activity.87 In humans and mouse models of traumatic brain injury (TBI; which produces a massive inflammatory response), injury is associated with significant reductions in a number of hypothalamic cell populations responsible for regulating sleep and wakefulness, including Hcrt and MCH neurons.132e134

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TBI may inhibit the beneficial effects of an enriched environment on sleep/wake physiology. For example, in a mouse model of juvenile mild TBI (mTBI), an enriched environment both increased the number of MCH neurons and the amount of REM sleep, a phenotype that was prevented if mice received an mTBI during early life.135 Significant more work is needed to unravel the interactions among the immune system, sleep/wake circuitry, and behavior.

CANCER DEREGULATES SYSTEMIC FACTORS THAT INFLUENCE HYPOTHALAMIC FUNCTION Cancer interacts with its host to survive by evading the immune system and ensuring adequate energy availability. A number of factors are produced and secreted by cancer cells, including inflammatory molecules and metabolic “waste,” which contribute systemic changes in immunity and energy balance.9,10 Indeed, these factors alter the function of organs locally, or at a distal site, including the liver and brain.6e8,136 Our knowledge of the “host response” and how it can be harnessed to eradicate cancer is still in its infancy. As discussed in previous sections, hypothalamic neurons receive and integrate signals arriving from the periphery to adjust physiology and behavior accordingly through neural, endocrine, metabolic, and immune (NEMI) pathways. How cancer alters these normal homeostatic pathways is poorly understood. Therefore, viewing cancer as a systemic disease will likely lead to novel insights into multisystem communication pathways that can be manipulated to stem tumor growth. Recently, a role for lateral hypothalamic Hcrt neurons has been demonstrated to underlie sleep and metabolic abnormalities in a mouse model of nonmetastatic breast cancer, using approaches from neuroscience, immunology, endocrinology, and cancer biology.7 In this study, the authors examined sleep and whole-body metabolic changes in nonetumor-bearing mice (Balb/ C) and those harboring syngeneic tumors (nonmetastatic 67NR mammary cancer cells) over the course of cancer progression (w25 days). Peripheral inflammation developed during tumor growth, largely driven by the cytokine interleukin-6 (IL-6). This was associated with a shift toward hepatic gluconeogenesis (hyperglycemia and upregulation of g6pc, ldha expression) over glycolysis (reduction in gck expression), impaired insulin signaling (hyperinsulinemia and reduced pAkt expression), and sleep disruption. Cancer- or cancer treatmenterelated inflammation is thought to promote sleep problems (in part) via the actions of inflammatory cytokines like IL-6.137 IL-6 blockade (using monoclonal antibodies against IL-6), however, was unable to

attenuate metabolic or sleep problems in mice with tumors, suggesting that these phenotypes develop independent from the inflammatory milieu. Examining other metabolic factors, the authors observed that tumor-bearing mice had reduced concentrations of the adipokine hormone leptin and were hypersensitive to the orexigenic hormone ghrelin. As Hcrt neurons are sensitive to these peripheral metabolic signals (as discussed above), and they are powerful regulators of wakefulness, the researchers hypothesized that their activity would be affected by mammary cancer. Indeed, tumors promoted aberrant activity within Hcrt neurons, and inhibition of Hcrt signaling (via oral administration of almorexant, an Hcrt dual receptor antagonist) attenuated tumor-induced changes in hepatic glucose metabolism and sleep. In order for Hcrt neurons to alter systemic glucose metabolism, a signal must reach the liver from the brain. A well-described route through which this could occur is via the sympathetic nervous system (SNS).109 Ablating sympathetic postganglionic noradrenergic nerve terminals through administration of 6-hydroxydopamine (6-OHDA) rescued tumor-induced metabolic abnormalities, supporting the idea that Hcrt neurons modulate peripheral glucose concentrations via the SNS.7 Significantly more research is required to unravel the complex signaling network linking different types of cancer in the periphery to this and other major neural populations. However, these findings suggest that repurposing drugs targeting this system (e.g., Suvorexant [Belsomra]) may be a useful strategy for improving sleep and metabolic health in patients with cancer. A rich history of research has detailed a role for leptin in breast cancer development and progression.138 Leptin and its receptor are overexpressed in w75% of all mammary tumors (especially high-grade tumors), while these are not expressed in neighboring normal mammary epithelial cells.139e141 Leptin is regulated by other factors frequently disturbed in cancer, including insulin, TNF-alpha, and glucocorticoids.142 Additionally, leptinnotch signaling may render cancer cells resistant to the effects of chemotherapeutic treatments like 5fluorouracil.143 Whether tumor-derived leptin acts in the brain to influence systemic physiology and behavior remains to be determined. As demonstrated above, tumor-induced changes in adipokine secretion can have far-reaching effects on distinct neural populations within the CNS. Several recent studies have begun to untangle the complex relationships among cancer, immunity, and energy balance. For example, using mouse models of pancreatic ductal adenocarcinoma (PDA) and colorectal cancer, Fearon and colleagues demonstrated that tumorderived IL-6 distally suppressed hepatic ketogenesis. This metabolic stress caused elevations in circulating

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glucocorticoids, which then acted to suppress antitumor immunity.144 Stress-induced activation of the HPA axis likely requires signaling from the periphery to the brain through an unknown mechanism (e.g., IL-6). As we discussed in prior sections, Hcrt neurons are sensitive to glucocorticoid signaling and act in a positive feedback loop to engage the stress response. Indeed, optogenetic activation of Hcrt neurons strongly promotes peripheral glucocorticoid secretion, an effect that can be attenuated by leptin administration and engagement of LepRb signaling in the LH.86 Further, chronic stress has been repeatedly demonstrated to enhance cancer progression and metastatic spread, putatively via activation of the HPA axis and adrenergic signaling.145e147 Recently, causal evidence has accumulated for the role glucocorticoids play in promoting breast cancer metastasis, in part via activation of the downstream kinase ROR1.148 How Hcrt neural activation links to sleep disruption, stress, and cancer metastasis is largely unknown. Another example detailing how tumor-induced changes in systemic physiology influence distal organs was described by Sassone-Corsi and colleagues.6 In a mouse model of lung adenocarcinoma, they observed drastic alterations in systemic energy balance and circadian rhythms in metabolic and inflammatory factors within the liver. The authors conclude that lung adenocarcinoma-induced IL-6 is critical for this deregulation to occur. Specifically, JAK-STAT3 signaling induced by tumor-derived IL-6 promotes critical changes in hepatic gene expression. SOCS3, a negative regulator of IL-6 signaling, also binds insulin receptors (IRS1 and 2) and marks them for ubiquitinmediated degradation.149 This results in hyperglycemia and significant changes in energy balance. We repeated these findings (as discussed above) in a different cancer model (67NR, 4T1, 4T07 model of breast cancer) and extended these connections to changes in central neural activity.7 Studies like these are beginning to shed light on how the host response to the tumor can alter major physiological pathways and behavior that impair patient quality of life and facilitate cancer growth.

CONCLUSION AND FUTURE DIRECTIONS In the preceding sections, we highlighted several neural populations that both receive input from and influence peripheral factors relevant to cancer. This forms a bidirectional pathway from the periphery to the CNS, where messages from the NEMI systems become disrupted in the context of cancer. The LH is especially suited to integrate these inputs to maintain homeostasis, a process that becomes severely altered in cancer. For example, aberrant inputs result in disrupted and

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fragmented sleep in part via changes in lateral hypothalamic Hcrt neural activity.7 We are just beginning to understand the complex relationships among the brain, body, and cancer. Significant advancements have been made recently in (1) unraveling the cellular heterogeneity of the LH and (2) linking lateral hypothalamic neural activity with distinct changes in peripheral physiology. Mickelson and colleagues conducted the first single-cell transcriptomic profile of the LH, revealing novel gene expression profiles to classify 15 GABAergic and 15 glutamatergic neuron types.150 They identified transcripts that further delineate Hcrt and MCH neurons. Specifically, primary Hcrt þ neurons coexpressed Rfx4 (88.6%), Nptx2 (99.5%) and Pcsk1 (91.3%), in addition to Scg2 (100%) and Slc2a13 (98.4%). Several groups have posited that Hcrt neurons show functional heterogeneity based on their transcriptomic profiles or projection patterns. The authors found that additional classification of Hcrtþ neurons revealed two poorly defined subclusters, best distinguished by sex-specific genes, Ddx3y (subcluster 1) and Xist (subcluster 2), and an immediate-early gene, Fos (subcluster 1), among others. These data suggest a role for sexual dimorphism or neural activity in distinguishing subtypes of Hcrt neurons, which are two aspects that have not been systematically investigated. For MCH neurons, the authors identified Zic1 as a novel marker of this neural population. Additionally, MCH neurons could be classified into two subclusters. The first expresses Cartpt, Tacr3 and Nptx1, along with Lypd1, Parm1 and Amigo2, among others. In contrast, the other subcluster was better defined by the absence of Cartpt, with moderately selective expression of Scg2 and Nrxn3. These findings will prove invaluable in future work aiming to manipulate distinct subsets of lateral hypothalamic neurons with tight spatial and genetically defined control. Understanding how tumors and tumor-associated changes in physiology alter the transcriptomic profiles of neurons in the LH will provide additional insight into how the brain responds to cancer. Another recent study has made progress in understanding the link between chronic sleep disruption and peripheral disease. McAlpine and colleagues demonstrated that chronic sleep fragmentation causes degradation of lateral hypothalamic Hcrt neurons. This was shown to influence peripheral immunity via actions on preneutrophils in the bone marrow that express Hcrt receptor 1. These cells, when disinhibited by sleep fragmentation, promote aberrant development and egress of myeloid-derived cells (e.g., monocytes) from the bone marrow. Their actions in blood vessels then result in the development of atherosclerosis.151 This study was the first to identify Hcrt receptors on peripheral immune cells, causally linking Hcrt and myelopoiesis, and

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demonstrate a mechanism by which changes in lateral hypothalamic neural activity could cause major disturbances in peripheral physiology relevant to disease. It would be unsurprising if a similar mechanism were at play in the context of cancer. This is an exciting area that is just starting to gain traction, as interdisciplinary teams are coming together and applying cutting-edge technology to address these previously intractable problems.

Acknowledgments We thank Dr. Luis de Lecea for insightful comments and edits during the preparation of this manuscript. This chapter was made possible thanks to funding from an NIH BRAIN Initiative F32MH115431 (J.C.B).

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