Comorbidity: Symptoms, Conditions, Behavior and Treatments [1st ed. 2020] 978-3-030-32544-2, 978-3-030-32545-9

Table of contents :
Front Matter ....Pages i-xv
Comorbidity: What Is It and Why Is It Important? (Rhonda Brown, Einar Thorsteinsson)....Pages 1-22
Models of Comorbidity (Rhonda Brown, Einar Thorsteinsson)....Pages 23-41
Overweight/Obesity and Concurrent Disorders, Symptoms, Behaviour, and Body Temperature (Rhonda Brown, Yasmine Umar)....Pages 43-77
Overview of the Comorbidity Between Medical Illnesses and Overweight/Obesity (Christopher J. Nolan)....Pages 79-114
Comorbid Eating Disorders (C. Laird Birmingham)....Pages 115-138
Comorbid Psychiatric Illnesses (Einar Thorsteinsson, Rhonda Brown)....Pages 139-178
Arousal States, Symptoms, Behaviour, Sleep and Body Temperature (Rhonda Brown, Einar Thorsteinsson)....Pages 179-219
Design, Statistical and Methodological Considerations: Comorbidity (Einar Thorsteinsson, Rhonda Brown)....Pages 221-239
Typing It All Together (Rhonda Brown, Einar Thorsteinsson)....Pages 241-274

Citation preview

Comorbidity Symptoms, Conditions, Behavior and Treatments

Edited by Rhonda Brown Einar Thorsteinsson

Comorbidity

Rhonda Brown · Einar Thorsteinsson Editors

Comorbidity Symptoms, Conditions, Behavior and Treatments

Editors Rhonda Brown Research School of Psychology Australian National University Canberra, ACT, Australia

Einar Thorsteinsson School of Psychology University of New England Armidale, NSW, Australia

ISBN 978-3-030-32544-2 ISBN 978-3-030-32545-9  (eBook) https://doi.org/10.1007/978-3-030-32545-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover illustration: Gregory Davies/Stockimo/Alamy Stock Photo This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Collectively, as co-authors, we have extensive clinical and research experience related to the various clinical disorders, symptoms, behaviour and biology covered in the book. Furthermore, as research collaborators, we can provide a unique perspective on the likely evolution and nature of disease comorbidity, which integrates biological, medical and psychological perspectives. The book was written with an academic audience in mind, although other interested individuals may appreciate the exploration of possible mechanisms underpinning disease comorbidity. To be clear, this is not a self-help book that reflects upon the way in which people should live a better life or which reflects upon the way that we as individuals live our own lives. The stimulus for the book was research conducted by Laird Birmingham, Rhonda Brown and others, related to low body temperature and infection in anorexia nervosa patients, which later gave rise to discussions around the possible role played by body temperature in mediating some of the adverse health outcomes related to overweight/ obesity. However, more broadly, the co-authors have worked collectively, in several different research groups, to answer the following questions related to disease comorbidity: What is causing the comorbidity v

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between different medical and psychological conditions? What role (if any) is played by the shared (or overlapping) medical and psychological ­symptoms? Or is a common factor more likely to cause the co-occurrences? Finally, why is a similar profile of risk factors detected for a range of different but frequently comorbid illnesses and conditions? As argued in this book, there is a crucial need to more fully integrate a broader range of comorbid illnesses and conditions, and their often overlapping risk factors, into the same disease models; to arrive at a more complex real-world understanding of comorbid illness causation. If such a clinical model could be developed, it might be used to test complex hypotheses related to the evolution and nature of disease comorbidity as well as evaluate potential new therapies. Finally, as co-authors, we wish to thank the various researchers and clinicians we have worked with over many years, who each have contributed to the evolution of the thoughts that are collectively advanced in this book. Canberra, Australia Armidale, Australia

Rhonda Brown Einar Thorsteinsson

Contents

1 Comorbidity: What Is It and Why Is It Important? 1 Rhonda Brown and Einar Thorsteinsson 1.1 What Is Comorbidity? 1 1.2 Why Is Comorbidity Important? 4 1.3 What Is the Cost of Comorbidity? 8 16 References 2 Models of Comorbidity 23 Rhonda Brown and Einar Thorsteinsson 2.1 Computational and Clinical Models of Concurrent Symptom Development 23 2.2 Sleep, Body Temperature, and Circadian Rhythm Function 30 References 36

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3 Overweight/Obesity and Concurrent Disorders, Symptoms, Behaviour, and Body Temperature 43 Rhonda Brown and Yasmine Umar 3.1 Overweight/Obesity and Comorbid Disorders 43 3.2 Overweight/Obesity, Sleep Disorders, and Impaired Sleep 47 3.3 Overweight/Obesity, Disordered Eating, and Sleep 51 3.4 Overweight/Obesity, Disordered Eating, Sleep, and Body Temperature 58 References 64 4 Overview of the Comorbidity Between Medical Illnesses and Overweight/Obesity 79 Christopher J. Nolan 4.1 Medical Illnesses and Overweight/Obesity 80 4.2 Overweight/Obesity Comorbidities and Causal Linkages 91 4.3 Lessening the Burden of Comorbid Illnesses in Overweight and Obese Individuals 97 References 98 5 Comorbid Eating Disorders 115 C. Laird Birmingham 5.1 Anorexia Nervosa and Bulimia Nervosa 115 5.2 Eating Disorders and Medical Comorbidities 118 5.3 Eating Disorders and Anxiety and Mood Disorders 119 5.4 Can Comorbid Psychiatric Disorders Prevent Recovery from Eating Disorders? 121 5.5 Anorexia Nervosa, Body Temperature, Hyperactivity, and Clinical Outcomes 122 5.6 Body Warming to Treat Anorexia Nervosa, Hyperactivity, and Exercise Addiction 124 5.7 Other Medical Treatments for Anorexia Nervosa 126 References 129

Contents     ix

6 Comorbid Psychiatric Illnesses 139 Einar Thorsteinsson and Rhonda Brown 6.1 Comorbidity Between Anxiety and Depressive Disorder 139 6.2 Relationships Between Stress, Depression, Anxiety, and Impaired Sleep 143 6.3 Risk and Protective Factors for Mental Ill-Health 145 6.4 Causal Models for the Development of Depression and Anxiety 149 References 161 7 Arousal States, Symptoms, Behaviour, Sleep and Body Temperature 179 Rhonda Brown and Einar Thorsteinsson 7.1 Arousal States and Elevated Body Temperature 179 7.2 Symptoms and Elevated Body Temperature 181 7.3 Exercise, Sleep, Affective Distress, Overweight/Obesity and Body Temperature 191 7.4 Behaviour Linked to Impaired Sleep and Elevated Body Temperature 197 References 202 8 Design, Statistical and Methodological Considerations: Comorbidity 221 Einar Thorsteinsson and Rhonda Brown 8.1 Methodological Approaches 221 8.2 Statistical Approaches 228 8.3 Overlapping Risk and Protective Factors 232 8.4 Other Research and Data-Handling Approaches 234 8.5 Summary 236 References 237

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9 Typing It All Together 241 Rhonda Brown and Einar Thorsteinsson 9.1 What Causes Comorbidity? 241 9.2 Comorbidity—Where to from Here? 248 9.3 Possible Existing, Repurposed, and Novel Treatments for Comorbid Illness 255 References 264

Notes on Contributors

C. Laird Birmingham, M.D. is a Specialist in Internal Medicine, Epidemiologist and Biostatistician and a Professor of Psychiatry at the University of British Columbia, where he was previously Professor of Medicine. He was Leader of the BC Eating Disorders Epidemiology Project in the Centre for Health Evaluation and Outcome Sciences until 2008 and then Medical Director of the Woodstone Residential Treatment Centre for Eating Disorders until December 2013. He is a Member of the Brain Research Centre at UBC and Senior Associate Clinician Scientist at the Children and Family Research Institute. He has more than 40 years of experience in eating disorder research and treatment and has 280 publications including 131 refereed articles, 23 invited chapters and 9 books. Dr. Birmingham’s research has focused on nutrition and the brain, the effect of ambient temperature on anorexia nervosa and the medical management of eating disorders. He is focused now on LORETA imaging and neurofeedback of patients with disorder. Rhonda Brown  started her career as a lab-based researcher, developing an animal model for immune-mediated polyneuropathies during her Ph.D. and exploring the overlap between neurochemical, neuroendocrine and immune responses to stress and infective illness, including xi

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bacterial translocation (i.e. leaky gut), during her post-doctoral fellowship. She works as an Associate Professor in the Research School of Psychology, Australian National University. She teaches health psychology and her research examines predictive relationships between stress, affective distress (e.g. anxiety, depression), sleep, fatigue, other symptoms, and illness outcomes in patients (e.g. cancer, overweight/obesity, sleep apnoea, multiple sclerosis) and community-well individuals. She also collaborates with other researchers to examine work-stress, burnout, communication performance and empathy in medical staff and medical and psychology students as well as immune function, fever response and infection in patients with anorexia nervosa. Over the past 20-years, she has worked extensively with each of the co-authors of this book. Christopher J. Nolan is a clinician scientist and policy advisor in the field of diabetes and metabolic diseases. He recently stepped down as Director of Diabetes Services (2011–2018) and Director of Endocrinology (2016–2018) for ACT Health to take up a new position as Associate Dean of Research for the Medical School at the Australian National University. He is currently a Board Member of the Australian Diabetes Society (2018–) and an Associate Editor for Diabetologia (2016–). He directs an active diabetes research laboratory focusing on islet beta-cell failure in type 1 and 2 diabetes and the role of insulin hypersecretion in metabolic syndrome and related conditions. He is a lead investigator for the ANU Grand Challenges Project, Our Health in Our Hands, which includes research into improving the care of people with type 1 diabetes using a personalised medicine approach. Einar Thorsteinsson  works as Associate Professor at the University of New England, Australia. He worked on his Ph.D., the effects of social support on changes in cortisol and cardiovascular reactivity in response to stressful situations, at La Trobe University in Melbourne. He was awarded a Ph.D. in 1999 and then worked at La Trobe University in a fire fighting decision-making lab for two years before he moved back to focus on health psychology at the University of New England where he has built national and international research collaborations covering areas such as stress, social support, depression, anxiety, adolescent coping and health, and psychological well-being.

Notes on Contributors     xiii

Yasmine Umar is a Doctoral Candidate at the Australian National University, extensively researching the predictors of disrupted sleep, obesity and affective distress in the general Australian population. She has also explored the relationships between stress, infection symptoms and chronic fatigue. She currently practises as a clinical psychologist, specialising in youth oncology.

List of Figures

Fig. 2.1 Symptoms, states, and behaviour that can increase nocturnal body temperature, and if practiced at night, thereby potentially interfere with sleep onset Fig. 2.2 Original caption reads: “Diagrammatic representation of normally entrained endogenous rhythms of core body temperature (solid curve), plasma melatonin (dotted curve), and objective sleep propensity (dashed curve) placed in the context of the 24-h clock time and normal sleep period (shaded area).” Figure is from Lack et al. [25] Fig. 2.3 Original caption reads: “Fitted Fourier curves to the control group and insomniac group mean 24-h temperature data in the constant routine relative to subjects’ usual sleep onset times (vertical solid line). The usual mean lights out times (LOT) for each group are indicated as vertical dashed lines. The estimated mean wake maintenance zone (WMZ) for each group is indicated as shaded area.” Figure is from Morris et al. [30]

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1 Comorbidity: What Is It and Why Is It Important? Rhonda Brown and Einar Thorsteinsson

1.1

What Is Comorbidity?

Comorbidity refers to any distinct clinical entity that coexists with or occurs during the clinical course of another illness or condition [1]. In other words, it refers to the co-occurrence of two or more distinct illnesses, disorders or conditions in a single individual. As a result of the comorbidity, some disorders tend to occur together more often than they occur alone. For example, anxiety, depressed mood and impaired sleep often co-occur, and in this instance, the co-occurrence appears to be the rule rather than the exception [2]. In this book, the term co-occurrence is used to refer to the coexistence of multiple symptoms (or clinical signs), whereas comorbidity specifically R. Brown (B) Australian National University, Canberra, ACT, Australia e-mail: [email protected] E. Thorsteinsson University of New England, Armidale, NSW, Australia e-mail: [email protected] © The Author(s) 2020 R. Brown and E. Thorsteinsson (eds.), Comorbidity, https://doi.org/10.1007/978-3-030-32545-9_1

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refers to the coexistence of multiple illnesses, disorders or conditions. For simplicity, the terms illness, disease, disorder and condition will be used interchangeably, as appropriate to the medical or psychological literatures referenced in each chapter. It is not possible to provide a comprehensive analysis of all comorbid disorders and concurrent symptoms in this book. Nonetheless, the book represents a significant step forward in its coverage of a broad range of concurrent disorders including overweight/obesity, diabetes mellitus type-II, cardiovascular disease, sleep-disordered breathing, impaired sleep/insomnia, disordered eating (e.g. binge-eating disorder), anxiety, depression, fatigue, anorexia nervosa and bulimia nervosa. In contrast, prior published books on the topic have tended to examine a limited number of comorbidities, including that between anxiety and depression [3–10], depression and other disorders [3], comorbidity with rheumatic disease [11], epilepsy [12], hypertension [9] and lifetime (or non-concurrent) comorbidity [4]. However, Sartorius and colleagues [13] have comprehensively detailed the clinical challenges of managing medical illnesses (e.g. cardiovascular disease, cancer, infectious disease) that tend to co-occur with mental and behavioural disorders, including substance abuse, eating disorders and anxiety; they covered the clinical management of the comorbidities. In this book, a focus of attention is the comorbidity between overweight/obesity (or proxy measures of it, e.g. high body mass index [BMI] or weight gain) and impaired sleep/insomnia, which is increasingly observed in clinical practice, but as yet is not fully understood. Specifically, overweight/obese individuals tend to take longer to fall asleep (i.e. longer sleep onset latency) [14], sleep for a shorter time [15, 16], and have poorer sleep quality [17], relative to non-obese controls (or lower BMI). However, little else is known about this common comorbidity, although the sleep problems do typically resolve once the person loses weight [18]. In Chapter 3, this comorbidity will be discussed in greater detail as will the links between the phenomena and certain behaviour, which may play a causal role in contributing to the disorders. In Chapter 2, existing theories that seek to explain the presence and/or development of comorbid symptoms and disorders will be discussed.

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Additionally, in Chapter 4, comorbidity between overweight/obesity and diabetes mellitus type-II, sleep-disordered breathing (e.g. obstructive sleep apnoea [OSA]) and affective distress (e.g. anxiety, depression) will be discussed. In Chapter 6, the concurrence between anxiety, depression, insomnia/impaired sleep, fatigue, gut pathology and gut symptoms will be discussed. In Chapter 5, comorbidity between eating disorders (e.g. anorexia nervosa, bulimia nervosa) and sleep problems, anxiety, depression, gut problems and hyperactivity will be examined. In Chapter 7, symptoms/conditions (e.g. chronic pain, fatigue) that frequently co-occur with impaired sleep, psychopathology, and other co-occurring conditions will be briefly discussed, as will the potential role played by unhelpful behaviour, including sleep-disrupting behaviour. Statistically, disease comorbidity is typically evidenced by high coprevalence estimates between the different diagnoses; symptom concurrence is evidenced by moderate to high correlations between two or more composite measures (e.g. total construct scores), using validated questionnaires [19]. Consistent with this approach, the book chapters will provide detailed research evidence illustrating the degree of concurrence between the aforementioned disorders and symptoms, as appropriate to the specific chapter. Further, where possible, the emphasis will be on presenting meta-analytic and prospective longitudinal study results, rather than cross-sectional correlational results. That is to say, our current conception of causality typically requires that the cause of an event must precede its’ onset in time. Only longitudinal (and experimental) study results can fulfil that criterion, to a greater or lesser degree. However, appreciating the nature of the temporal relationship between two separate phenomena tells us little about the mechanism/s that underpin the relationship. As detailed in Chapter 2, there are few available theories to help guide the research on disease comorbidity, and as a result, we currently know little about the true nature of the phenomenon. Furthermore, a number of statistical and methodological (e.g. measurement) problems complicate our understanding of comorbidity, for example, by potentially inflating the extent of the observed relationship between the different phenomena. These methodological and statistical problems will be discussed in more detail in Chapter 8.

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Finally, in Chapter 9, we will tie the threads together from the various chapters and reflect upon the most likely mechanism/s underpinning the development of comorbidity between the aforementioned disorders. In particular, we will discuss the likely role played by circadian rhythm dysfunction in the development of the disorders, along with the role played by sleep-disrupting behaviour and biological processes (e.g. elevated nocturnal body temperature). Finally, we will explore a broad range of novel, existing and repurposed therapy approaches that could show utility in treating the comorbid conditions.

1.2

Why Is Comorbidity Important?

In the twenty-first century, the tendency of patients to develop multiple disorders or conditions, rather than a single medical or psychological problem, is relatively high. For example, in a large study of 198,670 Spanish patients aged over 14 years [20], 42% had at least one chronic condition, and the prevalence estimate for comorbidity was one-quarter (24.5%) although the prevalence was higher in women (28.1%) than in men (19.4%), and it increased with advancing age until 69 years, when it stabilised. Of the 26 chronic health conditions surveyed, three distinct comorbidity burden patterns were detected, including high comorbidity (pattern B), intermediate comorbidity (patterns A and D) and low comorbidity (pattern C). Pattern B conditions included ischemic heart disease, congestive heart failure, cerebrovascular diseases and chronic renal failure, mostly in older patients (>70 years). Pattern A conditions included cardiac arrhythmias, hypertension (with/without complications), diabetes (with/without complications) and hyperlipidaemia, mostly in older patients. Pattern D included 14 conditions, for example, obesity, osteoporosis, dementia, and cancer, whereas pattern C included asthma, thyroid disease, anxiety, depression and schizophrenia, mostly in younger (5 chronic or acute illnesses, of whom 81% were 5 million US military veterans, of whom 850,000 were depressed, high baseline depression was linked to a 17% increased risk of all-cause mortality and specific increases in mortality due to heart disease, respiratory

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illness, cerebrovascular disease, accidents, diabetes, nephritis, influenza, Alzheimer’s disease, septicaemia, suicide, Parkinson’s disease and hypertension [65]. Thus, it is evident that comorbidity between a mental health and physical health condition is linked to a shorter life expectancy. Specific details of the mechanisms that may underpin the elevated mortality risk in patients with comorbid illnesses will be discussed in greater detail in the relevant book chapters. Comorbidity is also typically linked to substantial disability. Disability can limit a person’s ability to function adequately in their current environment, either mentally or physically [67]; this can adversely impact upon multiple different aspects of their daily life. For example, a person with diabetic retinopathy may develop permanent vision impairment and this may interfere with their ability to perform daily domestic duties (e.g. cooking), care for children and/or do paid work, unless accommodations can be made in the workplace. Specifically, disabilities such as mobility and cognitive impairment often result in a change in the patients’ work status, including withdrawal from work, reducing work hours and/or changing the type of work performed, for example, as seen in patients with multiple sclerosis [68]. Furthermore, disease comorbidity can limit a person’s capacity for self-care [32], suggesting that patients with multiple comorbid disorders may struggle to manage the different illnesses and deal with the extent of their disabilities. According to 2012 figures, about 4.2 million Australians were affected by a disability, and compounding this issue, the unemployment rate was 9.4% among disabled individuals, compared to 4.9% in those who were not disabled [69]. Thus, disability is common in the community, although it is unclear just how much of this is related to comorbid disease burden. In particular, comorbidity between mental and physical health conditions has been shown to more than double the odds that a person will suffer from severe disability [70]. Several large mental health surveys have shown that when mental health conditions are compared to physical health conditions, the mental health conditions are more likely to predict severe disability burden; specifically, the odds of experiencing a severe disability were greater (more than additive) in patients with a mental and physical health condition/s [70]. Further, comorbidity between psychological

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disorders has consistently been shown to be related to poorer prognosis and greater therapeutic demands from patients [71]. Taken together, the results suggest that patients with comorbid health problems are more likely to experience disability, poorer prognosis, and have more complex health needs than patients with a single disorder, especially if they have a concurrent medical and psychological problem/s. Importantly, disability is described as a societal phenomenon inasmuch as it is typically defined in terms of the extent to which it impairs a person’s engagement with activities, including work, and the extent and nature of their social interactions [72]. Thus, the effects of disability will tend to extend beyond the individual to include their family unit, friends and social network, and its effects may change over time. For example, a patient may withdraw from their social network while they are unwell, but if their health improves, they may need to rebuild the network and/or make new friends or contacts. Alternately, a spouse may need to take over as the primary breadwinner or give up work to care for the patient, resulting in a loss of family income, financial stress and tension related to changes in family roles and responsibilities and the broader redefinition of social roles. Thus, it is apparent that comorbid disease burden has important implications for the broader community, including the possibility that the affected individual will prematurely withdraw from or reduce their engagement with important aspects of their world. For example, a person may need to withdraw from work and/or their social network due to disability, and additionally, they may become stigmatised because of the illness or the disability. Stigma involves labelling an individual or group therefore setting them apart from others. In nations such as Australia, stigmatised groups typically include people who are disabled, Indigenous, LGBTQI, unemployed, homeless, poor, asylum seekers and those with mental health conditions. Stigma is known to potentially lead to health inequality [73]; the more a person (or population) is stigmatised, the more likely they are to suffer disadvantage, although it has not been examined in regard to comorbidity. Nevertheless, a review examining the mental health of Australian homeless youth has suggested that homelessness is linked to increased suicidal behaviour, the presence of psychiatric disorders and psychological distress [74].

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Thus, it is apparent that comorbidity is associated with considerable personal costs to individual patients, including the physical and psychological impacts of the conditions. For example, they will likely experience multiple physical and/or psychological symptoms; need to undertake multiple different therapies, each of which may need to be taken at a different time of day or under different conditions (e.g. before meals); need to attend multiple different therapy appointments at different locations, at considerable personal cost; and need to work with multiple different health practitioners to co-manage their conditions. As a result, the burden of comorbidity and its treatment is likely to interfere with a patient’s ability to lead a normal life, derive a sense of personal control and psychological well-being and maintain important social relationships and social roles (e.g. parent, worker). Furthermore, certain aspects of the illnesses (e.g. severity, disability, prognosis, treatment) may increase the likelihood that they will experience stress, affective distress (e.g. anxiety, depression), grief and possibly other comorbid conditions (e.g. sleep disorder). In particular, patients with several different-but-related conditions may spend many hours seeing various healthcare professionals, each of whom will separately evaluate and treat the medical and psychological problems, in an uncoordinated way. For example, a patient with comorbid overweight/obesity, binge-eating disorder, diabetes mellitus type-II, OSA and depressed mood may variously be managed by an obesity clinic, endocrinologist, sleep apnoea clinic and clinical psychologist; they may separately participate in weight loss and exercise programs and be prescribed diabetes medication, continuous positive airway pressure (CPAP) therapy and cognitive behavioural therapy. As mentioned above, this uncoordinated approach to patient care can represent a substantial burden to individual patients and their families; for example, it may interfere with their capacity to earn a living or fulfil important social roles as well adding to the financial costs of caring for the patient. Finally, as mentioned in Sect. 1.2, most healthcare services are currently ill-equipped to treat patients with multiple comorbid conditions [75]. In most clinical settings, patients tend to be prescribed a single treatment plan for each separate condition, rather than a single coordinated therapy plan for individual patients. Each treatment plans will typically be administered by a different medical team or allied health professional;

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there is often little communication between the staff, and between healthcare workers and patients [76]. Similarly, in some poor countries, there is a lack of healthcare providers who have expertise in managing common comorbid illnesses (e.g. HIV/AIDS and tuberculosis) using an integrated treatment plan which takes into account the interactions between the different diseases [77]. Furthermore, where clinical practice guidelines exist for the treatment of comorbidities, they have typically been found to be less than adequate and may potentially increase the burden to patients [78]. Thus, it is clear that patients with substantial comorbid illness burden require an integrated therapy approach, which separately (and together) addresses each medical and psychological condition. For example, a patient could receive multiple co-therapies together or sequential therapies that are co-managed in individual patients, although few comorbidity therapy protocols currently exist. Nevertheless, multidisciplinary treatment approaches will likely have utility in optimally managing a person’s comorbid illnesses, as they tend to permit the provision of coordinated evidencebased therapy, using co-therapy or sequential therapy protocols. Multidisciplinary care typically involves patients attending a central location to see a number of medical and/or allied health staff involved in their care. Clinicians can communicate with each other about the precise sequencing of the prescribed evidence-based care and the management of the related problems (e.g. therapy side effects, affective distress). For example, a breast cancer patient may undergo surgery, chemotherapy and/or radiotherapy, as sequenced by the treatment team, using established sequential therapy protocols that minimise side effects and maximise the clinical response to therapy. Her psychological condition can also be managed in the same clinic by allied health staff in coordination with the medical team. Unfortunately, multidisciplinary care approaches have, for the most part, not been utilised in the treatment of comorbid illnesses, except in diabetes patients, who are sometimes managed in the multidisciplinary care setting; in which case, diabetes and comorbid conditions (e.g. depressed mood) can be concurrently treated, as discussed in Chapter 4.

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27. Starfield B, Lemke KW, Bernhardt T, Foldes SS, Forrest CB, Weiner JP. Comorbidity: Implications for the importance of primary care in ‘case’ management. The Annals of Family Medicine. 2003;1(1):8–14. https://doi.org/ 10.1370/afm.1. 28. Ferrari AJ, Charlson FJ, Norman RE, et al. Burden of depressive disorders by country, sex, age, and y: Findings from the Global Burden of Disease Study 2010. PLOS Medicine. 2013;10(11):e1001547. https://doi.org/10. 1371/journal.pmed.1001547. 29. Whiteford HA, Degenhardt L, Rehm J, et al. Global burden of disease attributable to mental and substance use disorders: Findings from the Global Burden of Disease Study 2010. The Lancet. 2013;382(9904):1575–1586. https://doi.org/10.1016/s0140-6736(13)61611-6. 30. Naylor C, Parsonage M, McDaid D, Knapp M, Fossey M, Galea A. Longterm conditions and mental health: The cost of co-morbidities. London, UK: The King’s Fund; 2012. 31. Australian Bureau of Statistics. Disability, ageing and carers, Australia: Summary of findings, 2012. Canberra, Australia: Author; 2013. 4430.0. 32. White CA. Cognitive behavioral principles in managing chronic disease. Western Journal of Medicine. 2001;175(5):338–342. 33. Brown RF, Valpiani EM, Tennant CC, et al. Longitudinal assessment of anxiety, depression and fatigue in people with multiple sclerosis. Psychology & Psychotherapy: Theory, Research & Practice. 2009;82:41–56. https://doi. org/10.1348/147608308x345614. 34. Jansson-Fröjmark M, Lindblom K. A bidirectional relationship between anxiety and depression, and insomnia? A prospective study in the general population. Journal of Psychosomatic Research. 2008;64(4):443–449. https://doi. org/10.1016/j.jpsychores.2007.10.016. 35. Valpiani EM, Brown RF, Thorsteinsson EB, Hine DW. Poor sleep quality mediates between depression to fatigue in a university student sample. Psychology and Education. 2011;48(1&2):59–71. 36. Sartorious N. Comorbidity of mental and physical diseases: A main challenge for medicine of the 21st century. Shanghai Archives of Psychiatry. 2013;25(2):68–69. https://doi.org/10.3969/j.issn.1002-0829.2013.02. 002. 37. Cuijpers P, Berking M, Andersson G, Quigley L, Kleiboer A, Dobson KS. A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments. The Canadian Journal of Psychiatry. 2013;58(7):376–385. https://doi.org/10.1177/070674371305800702.

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38. Malouff JM, Thorsteinsson EB, Rooke SE, Bhullar N, Schutte NS. Efficacy of cognitive behavioral therapy for chronic fatigue syndrome: A metaanalysis. Clinical Psychology Review. 2008;28(5):736–745. https://doi.org/ 10.1016/j.cpr.2007.10.004. 39. Okajima I, Komada Y, Inoue Y. A meta-analysis on the treatment effectiveness of cognitive behavioral therapy for primary insomnia. Sleep and Biological Rhythms. 2011;9(1):24–34. https://doi.org/10.1111/j.1479-8425.2010. 00481.x. 40. Linardon J, Wade TD, de la Piedad Garcia X, Brennan L. The efficacy of cognitive-behavioral therapy for eating disorders: A systematic review and meta-analysis. Journal of Consulting and Clinical Psychology. 2017;85(11):1080–1094. https://doi.org/10.1037/ccp0000245. 41. Buscemi N, Vandermeer B, Friesen C, et al. The efficacy and safety of drug treatments for chronic insomnia in adults: A meta-analysis of RCTs. Journal of General Internal Medicine. 2007;22(9):1335. https://doi.org/10.1007/ s11606-007-0251-z. 42. Zammit GK. Antidepressants and insomnia. Primary Psychiatry. 2008;15(5):61–69. 43. Everitt H, Baldwin DS, Stuart B, et al. Antidepressants for insomnia in adults. Cochrane Database of Systematic Reviews. 2018(5). https://doi.org/ 10.1002/14651858.cd010753.pub2. 44. McDaid D, Park A-L. Counting all the costs: The economic impact of comorbidity. In: Sartorius N, Holt RIG, Maj M, eds. Comorbidity of mental and physical disorders. Key issues mental health. Vol. 179. Basel, Switzerland: Karger; 2015:23–32. 45. Cortaredona S, Ventelou B. The extra cost of comorbidity: Multiple illnesses and the economic burden of non-communicable diseases. BMC Medicine. 2017;15(1):216. https://doi.org/10.1186/s12916-017-0978-2. 46. The Royal Australian & New Zealand College of Psychiatrists. The economic cost of serious mental illness and comorbidities in Australia and New Zealand. Melbourne, Australia: Royal Australian and New Zealand College of Psychiatrists (RANZCP); 2016. 47. Vigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. The Lancet Psychiatry. 2016;3(2):171–178. https://doi.org/10.1016/ s2215-0366(15)00505-2. 48. Sayers SL, Hanrahan N, Kutney A, Clarke SP, Reis BF, Riegel B. Psychiatric comorbidity and greater hospitalization risk, longer length of stay, and higher hospitalization costs in older adults with heart failure. Journal of the American

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Geriatrics Society. 2007;55(10):1585–1591. https://doi.org/10.1111/j.15325415.2007.01368.x. Hillman DR, Lack LC. Public health implications of sleep loss: The community burden. MJA. 2013;199(8):S7–S10. https://doi.org/10.5694/mja13. 10620. Rosekind MR, Gregory KB, Mallis MM, Brandt SL, Seal B, Lerner D. The cost of poor sleep: Workplace productivity loss and associated costs. Journal of Occupational and Environmental Medicine. 2010;52(1):91–98. https://doi. org/10.1097/jom.0b013e3181c78c30. Deloitte Access Economics. Re-awakening Australia. The economic cost of sleep disorders in Australia, 2010. Sleep Health Foundation; October 2011. Zochil ML, Thorsteinsson EB. Exploring poor sleep, mental health, and help-seeking intention in university students. Australian Journal of Psychology. 2018;70(1):41–47. https://doi.org/10.1111/ajpy.12160. Morales-Espinoza EM, Kostov B, Salami DC, et al. Complexity, comorbidity, and health care costs associated with chronic widespread pain in primary care. Pain. 2016;157(4):818–826. https://doi.org/10.1097/j.pain. 0000000000000440. Hippisley-Cox J, Fielding K, Pringle M. Depression as a risk factor for ischaemic heart disease in men: Population based case-control study. BMJ. 1998;316(7146):1714–1719. https://doi.org/10.1136/bmj. 316.7146.1714. Pi-Sunyer X. The medical risks of obesity. Postgraduate Medicine. 2009;121(6):21–33. https://doi.org/10.3810/pgm.2009.11.2074. Katon WJ, Rutter C, Simon G, et al. The association of comorbid depression with mortality in patients with type 2 diabetes. Diabetes Care. 2005;28(11):2668–2672. https://doi.org/10.2337/diacare.28.11.2668. Lin EH, Heckbert SR, Rutter CM, et al. Depression and increased mortality in diabetes: Unexpected causes of death. The Annals of Family Medicine. 2009;7(5):414–421. https://doi.org/10.1370/afm.998. Gallicchio L, Kalesan B. Sleep duration and mortality: A systematic review and meta-analysis. Journal of Sleep Research. 2009;18(2):148–158. https:// doi.org/10.1111/j.1365-2869.2008.00732.x. Wang X, Ouyang Y, Wang Z, Zhao G, Liu L, Bi Y. Obstructive sleep apnea and risk of cardiovascular disease and all-cause mortality: A metaanalysis of prospective cohort studies. International Journal of Cardiology. 2013;169(3):207–214. https://doi.org/10.1016/j.ijcard.2013.08.088.

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60. Fonseca MI, Pereira T, Caseiro P. Death and disability in patients with sleep apnea—A meta-analysis. Brazilian Archives of Cardiology. 2015;104(1):58– 66. https://doi.org/10.5935/abc.20140172. 61. Arcelus J, Mitchell AJ, Wales J, Nielsen S. Mortality rates in patients with anorexia nervosa and other eating disorders: A meta-analysis of 36 studies. Archives of General Psychiatry. 2011;68(7):724–731. https://doi.org/10. 1001/archgenpsychiatry.2011.74. 62. Ejaz SM, Khawaja IS, Bhatia S, Hurwitz TD. Obstructive sleep apnea and depression: A review. Innovations in Clinical Neuroscience. 2011;8(8):17–25. 63. Crump C, Winkleby MA, Sundquist K, Sundquist J. Comorbidities and mortality in persons with schizophrenia: A Swedish national cohort study. American Journal of Psychiatry. 2013;170(3):324–333. https://doi.org/10. 1176/appi.ajp.2012.12050599. 64. Saha S, Chant D, McGrath J. A systematic review of mortality in schizophrenia: Is the differential mortality gap worsening over time? Archives of General Psychiatry. 2007;64(10):1123–1131. https://doi.org/10.1001/archpsyc. 64.10.1123. 65. Zivin K, Yosef M, Miller EM, et al. Associations between depression and all-cause and cause-specific risk of death: A retrospective cohort study in the Veterans Health Administration. Journal of Psychosomatic Research. 2015;78(4):324–331. https://doi.org/10.1016/j.jpsychores.2015.01.014. 66. Pederson JL, Warkentin LM, Majumdar SR, McAlister FA. Depressive symptoms are associated with higher rates of readmission or mortality after medical hospitalization: A systematic review and meta-analysis. Journal of Hospital Medicine. 2016;11(5):373–380. https://doi.org/10.1002/jhm.2547. 67. World Health Organization. International classification of functioning, disability and health: ICF. Geneva, Switzerland: World Health Organization; 2001. 68. Honan CA, Brown RF, Hine DW, et al. The multiple sclerosis work difficulties questionnaire. Multiple Sclerosis Journal. 2012;18(6):871–880. https:// doi.org/10.1177/1352458511431724. 69. Australian Human Rights Commission. Willing to work: National inquiry into employment discrimination against older Australians and Australians with disability; 2015. https://www.humanrights.gov.au/sites/default/files/ document/publication/discussion-paper-disability.pdf. Accessed 3 November 2015.

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70. Scott K, Von Korff M, Alonso J, et al. Mental–physical co-morbidity and its relationship with disability: Results from the World Mental Health Surveys. Psychological Medicine. 2009;39(1):33–43. https://doi.org/10.1017/ s0033291708003188. 71. Schoevers RA, Deeg D, Van Tilburg W, Beekman A. Depression and generalized anxiety disorder: Co-occurrence and longitudinal patterns in elderly patients. The American Journal of Geriatric Psychiatry. 2005;13(1):31–39. https://doi.org/10.1097/00019442-200501000-00006. 72. Kurawa SS. The impact of disability on self and society: An agenda for research on rehabilitation of disabled in Nigeria. Procedia-Social and Behavioral Sciences. 2010;5:1804–1810. https://doi.org/10.1016/j.sbspro.2010. 07.368. 73. Hatzenbuehler ML, Phelan JC, Link BG. Stigma as a fundamental cause of population health inequalities. American Journal of Public Health. 2013;103(5):813–821. https://doi.org/10.2105/ajph.2012.301069. 74. Kamieniecki GW. Prevalence of psychological distress and psychiatric disorders among homeless youth in Australia: A comparative review. Australian and New Zealand Journal of Psychiatry. 2001;35(3):352–358. 75. Williams A. Patients with comorbidities: Perceptions of acute care services. Journal of Advanced Nursing. 2004;46(1):13–22. https://doi.org/10.1111/j. 1365-2648.2003.02961.x. 76. van der Aa MJ, van den Broeke JR, Stronks K, Plochg T. Patients with multimorbidity and their experiences with the healthcare process: A scoping review. Journal of Comorbidity. 2017;7(1):11–21. 77. Marais BJ, Lönnroth K, Lawn SD, et al. Tuberculosis comorbidity with communicable and non-communicable diseases: Integrating health services and control efforts. The Lancet Infectious Diseases. 2013;13(5):436–448. https:// doi.org/10.1016/s1473-3099(13)70015-x. 78. Boyd CM, Darer J, Boult C, Fried LP, Boult L, Wu AW. Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: Implications for pay for performance. JAMA. 2005;294(6):716– 724. https://doi.org/10.1001/jama.294.6.716.

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2.1

Computational and Clinical Models of Concurrent Symptom Development

Several authors have suggested that unravelling the causes of comorbidity ranks among the top clinical priorities [1, 2]. However, most existing theories of disease comorbidity provide only a broad overview of the manner in which comorbidity is likely to arise. Logically, there are several broad hypotheses that can be advanced to explain the existence of comorbidities, including that: (i) a causal relationship exists between the coexisting disorders; (b) a common factor(s) increases the likelihood that both disorders will occur; and/or (c) the relationship is spurious [3]. However, the broad overarching theories have, for the most part, not been tested empirically. R. Brown (B) Australian National University, Canberra, ACT, Australia e-mail: [email protected] E. Thorsteinsson University of New England, Armidale, NSW, Australia e-mail: [email protected] © The Author(s) 2020 R. Brown and E. Thorsteinsson (eds.), Comorbidity, https://doi.org/10.1007/978-3-030-32545-9_2

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Further, few comorbidity theories are comprehensive yet specific enough to help guide the exploration of relationships between specific comorbid disorders and the likely role played by intervening factors. As a result, we know little about the extent to which comorbidity is relevant to our understanding of single disorders and conditions; the mechanisms which likely underpin the development of comorbidities; and the nature of the interactions between specific risk factors and comorbid disorders. Nevertheless, in some instances, there is clarity as to the likely temporal evolution of the clinical co-occurrences (e.g. mood disorders and insomnia) [4]. However, bidirectional relationships are ubiquitous in the scientific literature, and they have been documented in regards to most of the disorders and symptoms covered in this book. For example, anxiety and depression can predict the later onset and/or worsening of impaired sleep, but, additionally, sleep problems can predict the later onset or worsening of anxiety and depressed mood [4]. Crucially, we do not yet fully understand the nature of these so-called bidirectional relationships. In particular, noting the existence of a bidirectional relationship between anxiety and depression over time tells us nothing about how the disorders are functionally related to each other, including whether (or not) they are causally related. One possibility, as discussed in Chapter 6, is that impaired sleep and low mood are causally related to each other; such that sleep disturbance can impair a person’s mood and low mood can worsen sleep, but it is unclear exactly how this occurs and whether the mechanism/s is the same (or different) in each case. In contrast, it might be the case that the relationship is spurious. For instance, anxiety and depression symptoms tend to overlap with each other (e.g. impaired sleep is characteristic of both states) [5]; and the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) criteria for the disorders includes a similar range of symptoms (e.g. impaired sleep, gut problems) [5]. Further, the scales and clinical interviews that examine these states tend to ask about a similar range of somatic and affective symptoms. Thus, the observed relationship between anxiety and depression may at least in part be a by-product of these overlapping symptoms and/or the double-counting of the symptoms, although this is unlikely to fully explain the observed relationships [6, 7]. Finally, it is possible that a common factor (e.g. stressful life-events) can explain the tendency of people

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to experience comorbid anxiety and depression; for example, stress may result in rumination at night which interferes with sleep and, in turn, this may contribute to anxiety and/or depressed mood [8]. However, it is not exactly clear how anxiety and depression comorbidity develops, despite the plethora of research on the topic, and partly this is due to the lack of complex real-world models of anxiety and depression co-causation. Outside the context of anxiety and depression comorbidity, even less is known about the roots of comorbidity, even in the case of disorders that are highly comorbid with each other (e.g. obesity, insomnia). Relatively few studies in the scientific literature have specifically sought to address this comorbidity, and few clinical theories have been developed to explain the phenomenon. Thus, there is a crucial need to develop a theoretical model/s that can parsimoniously explain the presence and development of the comorbidities. Nevertheless, some specific (but limited) theories of comorbidity do exist which seek to explain the mechanisms by which specific disorders can co-occur. For example, anxious and/or depressive rumination [9] and cognitive and somatic arousal (i.e. stress-diathesis model of insomnia) [10] are posited to interfere with the onset of sleep in some people (e.g. insomniacs), and, over time, this may contribute to shorter sleep duration and cumulative tiredness, if the hyper-arousal or rumination persists over time. Additionally, late night-eating has been posited to interfere with sleep onset and shorten sleep duration, and as a result, people with night-eating syndrome are likely to develop sleep problems and be overweight/obese, relative to unaffected individuals [11, 12]. However, as discussed in Chapter 3, it is unclear exactly how overweight/obese people will develop comorbid sleep problems, for example whether the weight or sleep problem develops first and the exact mechanism/s by which the late night-eating impacts on sleep and weight. As detailed in Chapters 3, 4, and 7, overweight/obese people are at an increased risk of developing a range of other comorbid disorders and concurrent symptoms including stress, anxiety, depression, binge-eating disorder, diabetes mellitus type-II, and sleep-disordered breathing, relative to unaffected individuals, which might also contribute to morbidity and mortality in affected individuals. Thus, it is apparent that overweight/obesity is highly comorbid with many different disorders, although we know little

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about the specific mechanisms underpinning some of the comorbidities. As a result, if an overly specific mechanism (e.g. night-eating syndrome diagnosis) is examined for its utility in explaining the sleep and weight problems in overweight/obese individuals, we may learn little about the extent to which other concurrences (e.g. depression) or related risk factors (e.g. physical inactivity) will interact together to contribute to the problems. Clearly, there is a crucial need to employ a more holistic theoretical approach to the problem of disease comorbidity, which specifies the likely roles played by multiple different intervening variables. Currently, there are only two theories that have sought to explain the presence and development of comorbidity in regards to multiple disorders and symptoms. The first is a computational model that seeks to explain the co-occurrence of multiple psychiatric symptoms. Schmittmann and colleagues [13] have used a network approach to conceptualise psychological attributes (e.g. anxiety) as ‘networks of directly related observables’; that is, a single construct (e.g. anxiety) might be conceptualised as a network of related variables (e.g. somatic arousal, cognitive arousal, fear cognitions), which overlaps with other constructs (e.g. somatic arousal, depression). In another paper, the authors [1] sought to examine the network’s structure and its dynamics in an effort to replicate the empirical evidence of psychiatric symptom comorbidity, using simulation methods to evaluate the plausibility of the network’s properties. This work has resulted in the network model of psychiatric symptoms that was derived from the analysis of overlapping symptoms in DSM-4. In their computational model, individual DSM-listed symptoms were represented as individual nodes. In the analysis, nearly one-half of the entire DSM-4 network of psychiatric symptoms (N = 208 symptoms, across 69 disorders) was shown to be connected in the so-called giant component. In the computational models, the distances between the nodes for major depressive episode (MD) and generalised anxiety disorder (GAD) were reported to be consistent with the published comorbidity rates for the disorders, and model simulations of MD and GAD reproduced the published population statistics (e.g. prevalence statistics) for the two disorders [1].

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Regarding potential mechanisms, Borsboom and colleagues [1] found that many of the highly overlapping psychiatric symptoms in their DSM4 analysis were linked to basic homeostatic brain functions, including eating, sleeping, sex and mood regulation. In particular, the four most highly connected psychiatric symptoms in the analysis were insomnia, psychomotor-agitation, psychomotor-retardation, and depression, which were collectively linked to 60–71 other DSM-4 symptoms and 29–35% of the symptoms in the giant component, for GAD and MD, respectively. Thus, impaired sleep/insomnia, changes in activity levels, and depression were the most highly connected symptom groups, which were highly correlated with each other, other symptom groups (e.g. anxiety), and changes in behaviour (e.g. eating, sex). These computational results are generally consistent with the observation that homeostatic processes are involved in mediating at least some of the comorbid relationships, for example between impaired sleep and fatigue, both of which are symptoms of depression [14]. On the basis of the results, Borsboom and colleagues [1, 15] posited that the most highly connected symptoms may be ‘causally-coupled variables’ that play a crucial role in maintaining the structure of the psychiatric symptom network. That is, activating a single node (e.g. anxiety) in the network may indirectly contribute to the activation of other nodes (e.g. depression) to cause other symptom group(s). For example, anxiety may propagate via the network structure to contribute to insomnia, which, in turn, may contribute to depressed mood, via anxious and/or depressive rumination, which delays sleep onset and shortens sleep, and over time, this may contribute to tiredness and depressed mood [9]. Thus, the shared symptoms of anxiety and depression (e.g. impaired sleep, fatigue) may ‘function as bridge symptoms that transfer symptom activation from one network to the other, like a virus may spread from one community to another via people who are in contact with both’ (p. 9) [13]. Taken together, the results suggest that the computational model has substantial utility in explaining the tendency of people to develop comorbid anxiety and depression, either concurrently or sequentially, potentially via a number of different mechanisms. Several other aspects of the model are also appealing: (1) there may be several or more potential causal pathways contributing to a particular disorder; (2) the symptom networks may

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be different in different individuals and they may tend to change over time; (3) this might result in different comorbidity pathways in different individuals or in the same person at different times; and/or (4) multiple different causation events may contribute to comorbid illnesses in some individuals [1, 13, 15]. That is to say, there may be different possible paths to comorbidity, and the pathways occurring in particular individuals may depend upon the characteristics of the person (e.g. life experiences, coping methods) and their specific life situation. As detailed above, so-called causally coupled variables or causal chains have already been posited to underpin the common co-occurrence of anxiety and depression [16] and fatigue and depressed mood [17], via impaired sleep and rumination [8]; for more discussion, see Chapter 6. Borsboom and colleagues [1] also posited that comorbidity between depressive and anxiety disorders will likely arise from causal chains of directly related symptoms (e.g. impaired sleep). Consistent with this assertion, Kim and Ahn [18] have shown that clinical psychologists tend to interpret their clients’ symptom patterns in terms of the particular causal networks in their clinical practice. However, most ‘causal chains’ that have been described in the literature are singular in their focus. For example, they typically investigate a single specific mechanism (e.g. impaired sleep leading to depression via rumination) but exclude other clinically relevant factors or comorbidities (e.g. emotional eating, overweight/obesity), which are also known to be linked to impaired sleep [19] and affective distress (e.g. depression) [20, 21]. Thus, again, there is a crucial need to employ a more holistic theoretical approach to the problem of disease comorbidity, which incorporates multiple highly concurrent symptoms, disorders, and behaviour together, so that a range of coexisting phenomena and potential intervening factors can be considered together. To address the aforementioned issues, the authors of this book have recently developed a new clinical theory of concurrent symptom development [22] which seeks to explain the mechanisms by which comorbidity is likely to arise in people with concurrent overweight/obesity, impaired sleep, eating disorders, affective distress (e.g. anxiety, depression), and other disorders (e.g. diabetes mellitus type-II). A specific biological mechanism— dysregulation of nocturnal body temperature (BT) and the circadian rhythm

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of BT —is proposed to explain comorbidities between the aforementioned disorders, and throughout the book, we examine the model for its utility in explaining the co-occurrence of highly connected conditions. A detailed analysis of the relevant literatures pertaining to the relationships between the disorders, symptoms, and behaviour, and perturbations in BT will be provided, as appropriate, in the following chapters. In short, the clinical theory posits that when certain symptoms (e.g. anxiety) or sleep-disrupting behaviour (e.g. stress-related eating) are experienced at night, this can result in an elevated nocturnal BT that is sufficient to interrupt sleep, occurring via a phase shift in the circadian rhythm of BT. Furthermore, once a person’s sleep is disrupted, they may tend to engage in similar (or different) sleep-disrupting behaviour (e.g. nighteating, electronic device use) while they wait for sleep to come, and over time, this may worsen their sleep and contribute to new disorders (e.g. overweight/obesity) [23]. In this way, a person’s late night-eating may contribute to a new disorder (e.g. insomnia, obesity) and/or the worsening or perpetuation of existing behaviour (e.g. night-eating) via the delayed onset of sleep at night, and as detailed in Chapter 3, the relationship appears to be indirect, complex, and bidirectional. That is, we propose that a bio-psycho-behavioural mechanism—an elevated nocturnal BT that interferes with sleep onset —can parsimoniously explain the presence and development of highly concurrent disorders including insomnia/impaired sleep and overweight/obesity, via the practice of sleep-disrupting behaviour (e.g. late night-eating) at night.That is to say, sleep-disrupting behaviour may be a behavioural mechanism by which disease comorbidity occurs, because of its tendency to raise a person’s BT [22], and the premise is examined in greater detail in Sect. 2.2. Simply put, we propose that impaired sleep may play a pivotal and bidirectional role in contributing to the causation, worsening, and/or perpetuation of other disorders, symptoms, and behaviour, as well as being the consequence of other disorders, symptoms, and behaviour. A pictorial representation of our model is presented in Fig. 2.1. Detailed empirical evidence will be provided to support this assertion throughout the book, as appropriate to the specific disorders, symptoms, and behaviour discussed in each chapter. However, it is worthwhile briefly

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Fig. 2.1 Symptoms, states, and behaviour that can increase nocturnal body temperature, and if practiced at night, thereby potentially interfere with sleep onset

reiterating that the study results of Borsboom and colleague’s [1] network simulations showed that the most highly connected of all the psychiatric symptoms were insomnia, psychomotor-agitation, psychomotorretardation, and depression. It is appreciated that all four of these symptom groups are substantially correlated with elevated BT, especially high nocturnal BT, as indeed are most of the concurrent symptoms, disorders, and behaviour discussed in this book, including overweight/obesity, nighteating, and other sleep-disrupting behaviour.

2.2

Sleep, Body Temperature, and Circadian Rhythm Function

We posit that a complex bio-psycho-behavioural mechanism that results in an elevated nocturnal BT which interferes with sleep onset can explain the high degree of comorbidity that exists between the aforementioned disorders, symptoms, and behaviour, as detailed in Fig. 2.1. To more fully explain this premise, a detailed explanation of the precise relationships that

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exist between sleep onset, sleep offset, and BT in clinical and non-clinical human samples is provided below. First, changes in core BT are thought to signal the onset and offset of sleep [24, 25], as shown in Fig. 2.2 [25]. In particular, before a person can fall asleep, they typically need to cool down; specifically, sleep onset is putatively triggered by a precipitous fall in core BT that occurs in the early evening. In people who live a conventional lifestyle, core BT tends to stabilise between 2:00 PM to 8:00 PM; then it precipitously declines (on average 41 minutes before elderly people report wanting to go to bed) [26]; it reaches a minimum at approximately 4:00–5:00 AM, after the mid-point of sleep; and then it starts to increase before the end of sleep, reaching its peak in the late afternoon [25–29]. As a result, most sleep tends to occur in a rather narrow temporal window from 6 hours before the temperature nadir and 2 hours after it (≈10 PM–7 AM) [25]. Thus, sleep typically begins when the rate of BT drop and body heat loss is maximal, that is, when BT is falling most precipitously, a process

Fig. 2.2 Original caption reads: “Diagrammatic representation of normally entrained endogenous rhythms of core body temperature (solid curve), plasma melatonin (dotted curve), and objective sleep propensity (dashed curve) placed in the context of the 24-h clock time and normal sleep period (shaded area).” Figure is from Lack et al. [25]

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that is reported to take about 60 minutes in a naturalistic setting. On the basis of this research, it is asserted that BT and the circadian rhythm of core BT play a key role in regulating the sleep-wake cycle, such that a sharp decrease in nocturnal BT is thought to signal an approaching sleep episode and an increase in BT heralds the end of a persons’ sleep [28, 29]. Nevertheless, sleep initiation is also linked to an increase in melatonin secretion and an increase in sleep propensity (e.g. sleepiness) [24, 25]. Figure 2.2 illustrates the typical daily changes in melatonin secretion, BT, and sleep propensity over 24 hours; the optimal time for a person to sleep (i.e. 11 PM–7 AM); and the periods of wakefulness when it is typically difficult to fall asleep (i.e. wake maintenance zone) [25]. The model indicates that good sleepers will tend to go to bed several hours after the wake maintenance zone has ended, which is likely to facilitate a shorter sleep onset latency [30]. In contrast, sleep tends to be disrupted when the expected nocturnal decline in BT is uncoordinated with the time the person tries to fall asleep. For example, high ambient temperature (e.g. hot summer’s night) is known to interfere with the onset of sleep by increasing a person’s BT [31]. Thus, changes in the circadian rhythm of core BT have been linked to dysregulation of the sleep-wake cycle, especially a difficulty in falling asleep [32]. Key circadian rhythm changes including core BT are known to be regulated by the human circadian clock or ‘pacemaker’ that is located in the suprachiasmatic nucleus of the anterior hypothalamus (SCN). It is thought that ambient light acts as the primary timekeeper for the human circadian clock; such that the retina sends impulses to the SCN when incident light falls upon it [33, 34]. Clinically, people with sleep problems are reported to show higher core BT across the sleep period, relative to good sleepers [35]. Similarly, patients with sleep-onset insomnia are reported to experience a delayed circadian rhythm of core BT that occurs about 2.5 hours later than in good sleepers, as assessed by polysomnography and rectal temperatures over 26 hours [30]. As a result, if a person with insomnia tries to fall asleep early in the night, they may be unable to do so if they are still in the wake maintenance zone of the sleep-wake cycle. However, if they went to bed 2–3 hours later, they may have less trouble sleeping, but this may be impractical if there is a need for them to wake up early for work or study. Thus, as shown in Fig. 2.3 [30], people with insomnia (or impaired sleep) tend to experience

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Fig. 2.3 Original caption reads: “Fitted Fourier curves to the control group and insomniac group mean 24-h temperature data in the constant routine relative to subjects’ usual sleep onset times (vertical solid line). The usual mean lights out times (LOT) for each group are indicated as vertical dashed lines. The estimated mean wake maintenance zone (WMZ) for each group is indicated as shaded area.” Figure is from Morris et al. [30]

a smaller and later decline in their BT, indicating that a phase-shift (i.e. to the right) has likely occurred in the circadian rhythm of core BT [30]. As a result, they will tend to experience a relatively elevated nocturnal (i.e. night-time) BT or will lack the normal drop in BT that occurs at night, and this can interfere with the onset of sleep [24–26, 28]. As detailed in Chapter 7, myriad factors have been shown to potentially impact on sleep quality including environmental factors such as high or low ambient temperature [36, 37] and night-time exposure to bright lights, which can increase alertness and interfere with sleep [38]. Ambient heating sources (e.g. electric blankets) can also disrupt sleep via an increase in nocturnal BT [39]. For example, high blanket temperature has been shown to result in less total sleep time (i.e. more wakefulness), more Stage 1 sleep, more sleep stage changes, and less sleep efficiency in one study [39]. Other studies have reported a reduced amount of Stage 1 sleep and rapid eye movement (REM) sleep [40] and more frequent and longer awakenings, more shifting between sleep stages, less REM and Stages 3 and 4 non-REM (NREM) sleep, and the delayed onset of Stages 3 and 4 sleep, even in healthy individuals [41]. Normally, a person’s BT decreases

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to about 34.5–36 °C at night, whereas electric blankets tend to keep BT at a relatively constant 37 °C, and elevated heart rate is linked to the elevated BT. Thus, an electric blanket can promote sleep in low ambient temperature conditions and may exert modest effects on sleep architecture, but it can lead to significant overheating, thereby promoting wakefulness and potentially reducing sleep efficiency [39, 40]. Similarly, higher ambient temperature (e.g. hot summer’s night) has been reported to increase BT [42] and interfere with sleep. For example, raising ambient temperature from 13 to 25 °C is linked to shorter REM cycle length, but not an alteration in REM duration, REM period, and REM sleep latency [43], suggesting that warmer nocturnal ambient temperature can interfere with sleep. However, the greatest disruptions in slow-wave and REM sleep are thought to be linked to cold ambient temperature [36]. For example, Haskell and colleagues [37] found that colder temperatures impacted adversely on sleep in men, as assessed via polysomnography. However, the men wore only shorts to bed and they did not use a blanket, whereas most people sleep with at least some form of bedding. Thus, it is evident that extremes in cold and heat can interfere with sleep, whereas moderate ambient temperatures can facilitate sleep, although sleepers typically manage their nocturnal BT by using more or less blankets or other bedding, as appropriate. Several physiological processes that are entrained to the circadian rhythm also promote sleep in humans, including a change in melatonin secretion [33]. Melatonin is an endogenous sleep-promoting hormone that is secreted by the pineal gland; along with BT, it is thought to assist in initiating and maintaining sleep during the night such as suggested through its importance in treating sleep disorders [44]. As Fig. 2.2 shows, melatonin secretion begins to increase in the evening, when a person may start to feel a little drowsy, and it reaches a maximum during the night before it starts to wane before the person wakes up. However, its secretion is inhibited by exposure to light [45], at which time a message is sent to the SCN to restrict its secretion [45, 46], which in turn reduces sleepiness. Clinically, melatonin preparations have been used to treat sleep problems, and a meta-analysis [47] showed that there is at least some clinical evidence indicating that the preparations can reduce sleep onset latency and

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improve sleep efficiency, suggesting that it may have some clinical utility in treating sleep problems. Additionally, sleep onset is thought to be triggered by external cues, called zeitgebers (i.e. social ‘time-givers’), which assist animals and humans to maintain a 24-hour behavioural schedule, consistent with the body’s physiological processes. Zeitgebers include habitual behaviour such as brushing one’s teeth, setting the alarm clock, and putting on pyjamas. Thus, the presence of salient conditioned cues (i.e. social zeitgeber theory) [48], such as the sun/dawn, low light at night, falling ambient temperature at night, and social time-givers are thought to collectively promote a good night’s sleep and potentially normalise the circadian rhythm of sleep and core BT [46, 48, 49]. As a result, people who live a conventional lifestyle and keep to a regular schedule tend to show better circadian rhythm functioning than those who do not [50, 51]. Thus, it is clear that sleep, especially the onset of sleep, involves a complicated set of circadian neuroregulatory dynamics that include changes in BT, melatonin secretion, and a complex array of associated behaviours [52]. In summary, a precipitous drop in BT, increased melatonin secretion, and the presence of zeitgebers are thought to collectively trigger the subjective sense that it is time to sleep. Responding promptly to these internal and external signals is likely to assist people in falling asleep faster and experiencing a more restful sleep. That is, if a person lives a conventional lifestyle, keeps to a regular schedule, and respectfully observes good sleephygiene practices, including habitual sleep-preparation behaviour, they are likely to experience good quality sleep and optimal circadian rhythm functioning. However, if they do not keep to a regular schedule and/or they engage in sleep-disrupting behaviour at night, they may find it difficult to fall asleep at night, and this latter premise will be examined in more detail in Chapters 3 and 7. Specifically, in Chapter 3, behaviour that is linked to overweight/obesity, including a sedentary lifestyle, physical inactivity, nighteating, and binge-eating, will be explored in regards to impaired sleep in overweight/obese individuals. That is, overweight/obese people tend to practice a range of unhelpful behaviour at night that can potentially interfere with sleep, and once impaired, their sleep problem may adversely impact upon their physical and/or mental health, including an increase

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in weight. As detailed in Chapters 3 and 7, all of the aforementioned behaviour can potentially increase a person’s BT; thus, if they are practiced at night, they will tend to increase nocturnal BT thereby potentially interfering with the capacity to fall asleep. Finally, as discussed in Chapter 9, the clinical model presented in this chapter posits that a complex bio-psycho-behavioural mechanism is likely to underpin the development of disease comorbidity. Simply put, the model suggests that a range of states, symptoms, and sleep-disrupting behaviour may interact with each other to result in circadian rhythm dysfunction, as indexed by a relative increase in BT at night, which may increase the risk of comorbid disorder development. As detailed in Chapter 9, we have principally focused on perturbations in BT (rather than melatonin secretion or other biological mechanism/s) as it provides a readily measurable index of circadian rhythm function and a parsimonious explanation of the comorbidities detailed in the book.

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3 Overweight/Obesity and Concurrent Disorders, Symptoms, Behaviour, and Body Temperature Rhonda Brown and Yasmine Umar

3.1

Overweight/Obesity and Comorbid Disorders

According to the World Health Organization (WHO), a person is overweight when their body mass index (BMI) is equal to or greater than 25 kg/m2 , whereas obesity is present when BMI is equal to or greater than 30 kg/m2 . Overweight/obesity occurs when a person’s energy intake persistently exceeds their energy expenditure, resulting in a positive energy balance [1]. Prevalence estimates for overweight/obesity appear to be increasing in developed and developing nations. In Australia, projected prevalence estimates suggest that by 2025 only one-third of the adult population will be of normal weight [2]. R. Brown (B) Australian National University, Canberra, ACT, Australia e-mail: [email protected] Y. Umar Australian National University, Canberra, ACT, Australia e-mail: [email protected] © The Author(s) 2020 R. Brown and E. Thorsteinsson (eds.), Comorbidity, https://doi.org/10.1007/978-3-030-32545-9_3

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It is well known that obesity is associated with an increased risk of comorbidity including various chronic illnesses, conditions, and signs such as high serum cholesterol, hypertension, diabetes mellitus type-II, asthma, arthritis [3], heart disease [4], and binge eating disorder (BED) [5]. The evidence pertaining to comorbid medical disorders is discussed in greater detail in Chapter 4. Additionally, obesity and weight gain are reported to co-occur with sleep problems, including obstructive sleep apnoea (OSA) and insomnia [6, 7], as discussed in Sect. 3.2. Further, obesity is linked to an increased risk of depressive disorders and more severe depression symptoms. For example, two meta-analyses by Rooke and Thorsteinsson [8] showed that a small significant correlation exists between weight gain and depression, which was stronger in women than in men, and overweight/obesity predicted later depression levels in prospective longitudinal studies. In the first meta-analysis, based on 31,189 participant responses, the weighted average correlation between depression (risk factor) and weight gain (outcome) was small but significant (r = .08), but the effect size was larger in females (r = .12) and in studies predicting obesity (r = .17). In the second metaanalysis, weight gain was evaluated as a risk factor for depression, based on 24,120 participant responses. The weighted average correlation between baseline overweight/obesity and later high depression levels was small (r = .05) but significant; although no moderators of the relationship were detected. Taken together, the results suggest that a bi-directional relationship exists between depression and obesity, with small effect sizes in both directions. However, depression more reliably preceded weight gain in females and obese individuals, suggesting that depression was a stronger driver towards weight gain in females and obese people than obesity was a driver towards later depression. Interestingly, in the analyses, the study [9] with the strongest link between depression and weight gain (r = .39) included female teenagers and an objective measure of weight as the criterion variable; whereas the study [10] with the smallest correlation between depression and weight gain (r = −.08) included male teenagers and a selfreport measure of weight gain. In contrast, the study [11] with the largest correlation (r = .20) between weight and depression included males and females and a self-report measure of weight; whereas the study [12] with

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the lowest (r = −.13) correlation included females and an objective measure of weight, suggesting that sample characteristics and the measures of weight can affect the study results. Similarly, a reciprocal relationship has been reported between obesity and depression, in a review of longitudinal studies [13]; indicating that baseline obesity was a risk factor for later depression onset (OR = 1.55) and being overweight increased the risk of later obesity (OR = 1.27), but baseline depression was not a risk factor for being overweight, although it was a risk factor for developing obesity (OR = 1.58). Finally, a bidirectional relationship was documented between obesity and depression in adolescents using longitudinal data from 13 studies [14]. Specifically, an increased risk of depression predicted obesity (RR = 1.70), and an increased risk (RR = 1.40) of obesity predicted later depression. Irrespective of the differences in correlational strength, the results suggest that depression is intimately linked to weight gain and overweight/obesity; and emotional eating, which is related to overweight/obesity, is posited to underpin the weight gain in depressed people. For example, obese people are reported to be more emotionally reactive and more likely to overeat when distressed (i.e. emotional eating), relative to normal-weight individuals, in laboratory and naturalistic settings. However, in the latter case, it appears only to apply to snacks and not meals [15]; although other mechanisms have been suggested to underpin the observed relationship. For example, impaired sleep is often reported by overweight/obese individuals, and it may potentially contribute to low mood, as discussed in Sect. 3.2. Obesity is also linked to an increased risk of anxiety disorders and more severe anxiety symptoms. For example, in a systematic review of 16 studies (including two prospective studies), the cross-sectional evidence suggested that a positive relationship existed between obesity and anxiety disorders, but the results of the prospective studies were mixed. The pooled OR from the cross-sectional studies was 1.4, indicating there was a 40% increased risk of an anxiety disorder in obese individuals, and a positive relationship existed in women and men [16]. Similarly, in a meta-analysis of 25 studies, Amiri and Behnezhad [17] found that the frequency of anxiety in obese individuals had a pooled OR of 1.30, whereas in overweight people, the OR was 1.10; suggesting that the propensity of obese people to experience

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anxiety is greater than in overweight people. Further, in a large US study (2002 National Health Interview Survey), anxiety and depression predicted later sleep problems (adjusted RR = 5.64) and obesity (adjusted RR = 1.15) [18, 19]. Taken together, the results suggest that anxiety, depression, impaired sleep, and overweight/obesity are interrelated and bidirectionally related phenomena. However, in another study, the degree of adiposity was shown to be unrelated to anxiety symptoms in a young adult Nigerian sample with mean normal weight, and the proportion of them with high anxiety was similar irrespective of their BMI status [20], suggesting that anxiety may be unrelated to high BMI in normal-weight individuals. Finally, a broad range of behavioural risk factors have been reported for obesity, including a sedentary lifestyle, physical inactivity, poor diet (e.g. high fat, sugar, processed food intake), dietary restraint, binge-eating, and maladaptive compensatory behaviour (e.g. purging), in addition to depression [9, 21]. Disordered eating practices are discussed in greater detail in Sect. 3.3 and physical inactivity and sedentarism are discussed in Chapter 7. Several unmodifiable (or difficult to modify) risk factors are also linked to the increased risk of overweight/obesity, including demographics (e.g. age, gender) and environmental risk factors (e.g. low median household income) [22]. However, these factors lie outside the scope of this book inasmuch as the primary focus here is on risk factors that are amenable to change, including behaviour, symptoms, and states, which might be targeted in clinical practice to improve health. With reference to dietary practices, according to the Australian Bureau of Statistics, the average Australian diet currently consists of a large proportion of ‘discretionary’ foods, that is, food we like eat but do not really need, which is typically high in sugar, salt, alcohol, and fat [23] and low in fibre and other essentials. Given this diet profile, it should not come as a surprise that Australia is currently experiencing an obesity epidemic, with a minority of Australian adults now classified in the ‘normal’ weight range [24]. In morbidly obese people, the situation is so serious that surgical intervention is suggested as the only option to effectively treat the disorder and improve patients’ long-term weight and quality of life [25]. However, in addition to poor diet, poor gut health, especially a change in gut microbiota, has been suggested to contribute to overweight/obesity via a change in the hormones produced by gut microbiota, which can

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regulate appetite [26, 27]. The potential role played by poor gut health in contributing to ill-health, including disease comorbidity, is discussed in greater detail in Chapter 6.

3.2

Overweight/Obesity, Sleep Disorders, and Impaired Sleep

About 20–35% of the Australian population are reported to experience impaired sleep at any one time [28], which is similar to the results obtained in US samples (30–35%) [29] and Japanese adults (26.4% of men, 31.1% of women) [30]. Similarly, sleep problems (e.g. inadequate sleep on school nights, struggling to fall asleep, daytime dysfunction, fatigue, tired during the day) are commonly reported by children and adolescents [31], with approximately 27% of students reportedly at risk of developing at least one sleep disorder, in a university student sample [32]. Impaired sleep is also common in older adults (e.g. 30% of females and 14% of males aged ≥65-years) [33], and medical illness and psychosocial factors (e.g. depressed mood) are suggested to increase the risk of impaired sleep in this population [34, 35]. Taken together, the results of recent populationbased studies suggest that one-quarter to one-third of the population will experience sleep impairment at any one time, which may adversely impact upon daytime functioning and induce significant symptoms. However, only a small proportion of people are ever diagnosed with a specific sleep disorder [28], suggesting that a large proportion of the community’s sleep problems are never diagnosed. The most common sleep disorders are insomnia (4.2% of adults) [36], obstructive sleep apnoea (OSA; 4% of adult males and 2% of females) [37], and restless leg syndrome (RLS; 2.7–7%) [38], in Australian samples; with similar insomnia figures reported in German adults (4% met criteria for insomnia) [39], but higher in French adults (9% with severe insomnia) [7]. In contrast, snoring is much more commonly reported, occurring in up to 42% of men and 31% of women in the USA [40]. Thus, a small proportion, perhaps only one-third of the people who experience sleep difficulties, will be diagnosed with a sleep disorder.

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Insomnia is characterised by the inability to fall asleep or stay asleep despite adequate attempts. Patients with insomnia typically experience trouble initiating sleep, waking during the night or early morning, and they may be unable to fall asleep after waking up. Affected individuals often report daytime dysfunction, including an impact on social life and academic or occupational functioning. In addition, they may experience irritability, reduced concentration, and reduced productivity at work. For a diagnosis of acute insomnia (Insomnia Disorder) to be made, the symptoms must occur at least three times a week for at least 3-months, whereas chronic insomnia is diagnosed when the person experiences two or more episodes of insomnia in a single year [41, 42]. The aetiology of insomnia is reported to be multifactorial [43]. It often occurs in the context of ill-health and comorbid illness including medical conditions (e.g. chronic pain) [44] and psychological disorders (e.g. depressed mood) [41], and it can be triggered by stressful life situations (e.g. work stress) or the person’s response to them [45, 46]. For example, people with insomnia tend to report higher perceived stress than good sleepers and they feel less control over the stressful situations in their lives, which may exacerbate the insomnia [43, 47]. Several theoretical models have been advanced to explain the manner in which insomnia is likely to develop and be maintained [43], including the role played by cognitive and somatic hyper-arousal [48]. OSA is characterised by periods of shallow or difficult breathing that can interfere with sleep quality [49]; that is, it is a form of sleep-disordered breathing (SDB) [50]. Clinically, SDB falls on a spectrum from snoring to sleep disorders, including OSA [50]. OSA can lead to medical complications, most commonly, cardiovascular conditions, including hypertension [50]; and it is associated with an increased risk of mortality and reduced quality of life. High comorbidity also exists between OSA and several medical disorders (e.g. diabetes mellitus type-II, overweight/obesity), making it a costly condition to manage in clinical practice [49, 50]. Older age, obesity, large neck size, and behavioural factors (e.g. alcohol intake) are known to be risk factors for the development of OSA, as well as a family history of the disorder [50]. It is a common disorder in patients who are undergoing bariatric surgery with the majority (71%) of them meeting criteria for an OSA diagnosis [51].

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RLS is characterised by uncomfortable sensations in the thighs and calves that can prevent the initiation and/or maintenance of sleep. The sensations are typically coupled with the perceived need to frequently move the affected limb(s). Symptoms typically occur when the patient is trying to relax or rest [52], and they are not reduced until the person moves their legs (e.g. walking). Although the urge to move the limbs can persist during the day, it appears to worsen at night [53]. RLS symptoms can mimic those of other conditions including stress, anxiety, and diabetes; thus, great care needs to be taken to correctly distinguish it from other disorders [52]. Little is known about the aetiology of RLS [52], although the condition is suggested to have a genetic basis [54] and the risk of developing the condition is known to increase with advancing age and it is more common in women [38] and people with heart disease [35]. Other rarer sleep disorders have been described in the scientific literature, including sleepwalking (i.e. somnambulism), which along with other motor behaviour (e.g. sitting up, fumbling around), typically occurs during Stages 3–4 NREM sleep, and it can adversely impact upon sleep quality [55]. However, there is insufficient space here to discuss them in detail, although, as discussed in Sect. 3.3, night eating syndrome is known to be characterised by impaired sleep [56], and its incidence is higher in overweight/obese people than in normal-weight people [57]. In summary, the three most common sleep disorders have a tendency to co-exist with other medical conditions, including overweight/obesity, diabetes mellitus type-II, heart disease, and affective distress. For example, large datasets have shown that obesity is highly comorbid with OSA [6] and insomnia [40], and more weight (and visceral fat) are risk factors for the development of OSA [58, 59] and insomnia. For instance, in a 10year prospective study, insomnia prevalence estimates were shown to be predicted by obesity and a number of lifestyle factors, including physical inactivity and alcohol dependence; all of which can increase the risk of developing insomnia in men [60]. Overweight/obesity (or proxy measures of it; e.g. high BMI) is linked to specific changes in sleep; for example, obese people tend to report significantly shorter sleep duration and greater subjective sleep disturbance than non-obese people. Additionally, waist circumference is reported to be inversely correlated with sleep duration; that is, the greater the

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waist circumference the shorter the sleep duration [61, 62]. Furthermore, shorter sleep duration is known to be a risk factor for later obesity [63, 64]. Patel and colleagues [63] found that people who slept less than 5hours/night were 3.7 times more likely to be obese than those who slept 7–8 hours/night. However, a review by Patel and Hu [65] shows that researchers have reported that a U-shaped relationship exists between sleep duration and BMI. Nevertheless, it is evident that a bidirectional relationship exists between impaired sleep and overweight/obesity; that is, impaired sleep increases the risk of overweight/obesity, and weight disorders also increase the risk of impaired sleep. A similar relationship has been observed between sleep and weight in children and adolescents. In the Australian Health and Fitness Survey, sleep duration was found to be negatively related to BMI and waist circumference in boys (but not girls), in a large sample of 7–15 year olds. In particular, boys who slept 5–8 hours/night were 3.1 times more likely to be overweight than those who slept ≥10 hours/night [66]. However, in another Australian study, shorter sleep duration was related to a greater obesity risk in both female and male children aged 5–15 years and a stronger correlation was detected in younger children [67]. Similarly, Chinese 3–4 year olds who slept 9–9.4 hours/night were 4.8 times more likely to be obese than those who slept >11hours/night [68]. Thus, a significant relationship exists between shorter sleep duration and greater obesity risk in children and adults, as shown in meta-analyses [65, 69]. However, many of these results were derived from cross-sectional studies, although several large longitudinal studies have detected a significant relationship between obesity and impaired sleep. In a 3-year retrospective longitudinal study of 21,469 adults, people who slept ≤5 hours/night were more likely to gain weight and become obese than those who slept ≥7 hours/night, after controlling for age, gender, and clinically relevant factors (e.g. alcohol intake, exercise frequency, hypertension, dyslipidaemia, diabetes, cerebral infarction, myocardial infarction) [70]. However, in another study of 35,247 adults, shorter (65 year olds) and weekend eating, but it was unrelated to high BMI/obesity, gender, ethnicity or season. Similarly, Sevincer and colleagues [87] showed that 9.5% of 200 college students screened positive for NES, and high NEQ score was correlated with impulsively and anxiety/depression symptoms, but not gender, nutritional factors or academic factors. Additionally, Borges and colleagues [88] found that 15.0% of a university student sample had NEQ scores ≥25, indicative of a possible NES diagnosis, and high NEQ score was correlated with stress, anxiety, and depression levels, but not gender, nutritional status, work, and academic factors. Thus, the results suggest that late night-eating is commonly practised in non-clinical populations, with about one-third of people reporting nighteating behaviour and 10–15% meeting criteria for NES, although this may be an overestimate as most of the above studies focused on younger adults. In addition, night-eating is related to poor sleep quality in community samples [84, 89], especially the occurrence of nocturnal ingestion (i.e. waking at night to eat), but not evening hyperphagia or morning anorexia [90]. As detailed above, this late night-eating behaviour appears to be consistently linked to the experience of high stress and affective distress, but perhaps not overweight/obesity or proxy measures of it. However, in contrast, in a large Danish study (MONICA cohort study) [91], late nighteating in obese middle-aged women (n = 12) was found to be related to weight gain over 6-years (average of 5.2 kg), whereas obese middleaged women who did not night-eat (n = 84) added only 0.9 kg over the same time period; but the relationship was not observed in women in other weight categories (n = 1050) or men, although there was a trend in the same direction (i.e. night-eating preceded weight gain). Thus, it is likely that late night-eating can contribute to significant weight gain in overweight individuals, at least in women, and more so in middle-aged rather than younger adults, but not necessarily in normal or low weight individuals. However, few prospective longitudinal studies have evaluated the relationship between night-eating and impaired sleep. Aronoff and colleagues [56] assessed the risk factors for NES in 110 obese metabolic clinic patients

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over 4 years, showing that 51% of them met criteria for NES, including a difficulty in initiating and maintaining sleep. NES diagnosis was correlated with male gender, high BMI and severe obesity, suggesting that it tended to be comorbid with overweight/obesity and sleep problems. Affected individuals were likely to view their eating behaviour as problematic if they were young, had a history of a psychiatric disorder (e.g. major depressive disorder or eating disorder), or if they had high BMI; but not on the basis of the severity of the NES symptoms, including impaired sleep [81]. Taken together, the results suggest that the existence of comorbidities, medical (e.g. obesity) and/or psychological (e.g. depression, eating disorder diagnosis) was most likely to prompt the perception that the night-eating was problematic to NES patients. In summary, the aforementioned research findings illustrate the potential adverse effects of late night-eating on sleep and weight, in clinical (e.g. NES) and non-clinical samples (e.g. university students). However, it is less clear whether the late night-eating precedes the occurrence of impaired sleep and overweight/obesity or if the reverse is (also) true, although it is suspected that the night-eating habits will tend to develop first [85]. However, few studies have examined whether a functional relationship exists between sleep and weight problems in NES patients. Several studies examined circadian rhythm functioning in NES patients to determine if its dysfunction can explain the NES—sleep relationship [92]. For example, Goel and colleagues [93] showed that NES patients had a delayed circadian rhythm for total caloric, fat, and carbohydrate intake, a phase-delay in leptin and insulin secretion, and a phase-delay in melatonin secretion in women with NES, relative to those without NES, assessed over three nights. On the basis of the results, NES was attributed to the dysregulation of patients’ peripheral (e.g. stomach) and/or central (e.g. suprachiasmatic nucleus) circadian timing systems. This mechanism, dysregulation of the circadian rhythm, including a phase-shift in the nocturnal rhythm of core body temperature (BT), is examined in Sect. 3.4 for its utility in explaining the relationship between night-eating, impaired sleep, and overweight/obesity. Similar to NES, sleep-related eating disorder (SRED) is characterised by recurrent episodes of eating after awakening at night [80, 81]. However, in contrast to NES, the consumption of food in affected individuals is

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believed to be involuntary, that is, patients may only be partly awake and unaware of their actions [80]. In particular, people with SRED tend to consume high-calorie low-preparation foods (e.g. ice cream, peanut butter); and potentially, the food preparation that occurs during the awakening can put them at risk of injury (e.g. hot drink spill) [80, 94]. Similar to NES, SRED is linked to the development of obesity and significant weight gain over time [80], especially if the individual is unable to control their food consumption. Finally, Binge-Eating Disorder (BED) is known to be linked to weight gain, obesity, and impaired sleep. BED is characterised by recurrent episodes of binge-eating that are not followed by compensatory behaviour, as is the case with bulimia nervosa [42]. The binge-eating episodes tend to involve eating past the point of feeling full, eating more than is normal, and experiencing shame after the binge-eating episode. A BED diagnosis is given when the symptoms are present at least once/week for ≥3 months. Lifetime prevalence estimates for BED in the US population are reported to be 3.5% and 2.0%, in women and men, respectively [95]. However, BED is more common in overweight/obese individuals. For example, 7.5% of obese adults [96] and 1% of obese children and adolescents meet criteria for BED [97]; which is double the prevalence estimate in the general population. Additionally, a BED diagnosis is associated with high BMI [95, 98], more severe obesity, and an earlier onset of the obesity [98, 99]. Longitudinally, overweight/obesity increases a person’s risk of later developing BED [100]. Taken together, the results indicate that BED is more common in overweight/obese individuals especially people with an earlier onset of obesity or more severe obesity. Furthermore, obesity is a risk factor for later binge-eating [5] and obese women who binge-eat are at greater risk of developing anxiety and/or depression [101]. Risk factors for BED include childhood obesity, negative comments about one’s shape and weight, childhood stressful life events, and a parental diagnosis of depression, suggesting that overweight/obesity, stress, and affective distress may together contribute to the development of BED [5]. However, it is also likely that BED contributes to weight gain and overweight/obesity over time. Mechanistically, dietary restraint, which is often practised by overweight/obese people, is thought to contribute to bingeeating and greater caloric intake in some individuals, which, in turn, may

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lead to weight gain [100]. However, other factors have been proposed to explain the relationship between BED and overweight/obesity, including the potential impact of binge-eating on sleep [102]. In a population-based twin study, binge-eating obese women were shown to have more severe insomnia than non-binge-eating obese women [101]. Similarly, women with obesity or BED experienced less sleep efficiency and more awakenings at night, relative to non-bingeing normal-weight women, but the groups did not differ on other sleep parameters, assessed using actigraphy and self-report questionnaires over 1 week [102]. Thus, similar to night-eating, binge-eating is linked to impaired sleep, including insufficient sleep, poor sleep quality, daytime sleepiness, and disrupted sleep, even after controlling for the effects of age, depression, obesity, and cohabitation status [103]. However, only a few cross-sectional studies have examined binge-eating in regard to sleep so the results require verification using a longitudinal study design. Nevertheless, it is apparent that late night-eating [84] and binge-eating [102] likely both impair sleep in at least in some individuals; in particular, impaired sleep is a hallmark symptom of NES and one of the diagnostic criteria for this condition [72]. Furthermore, it is noteworthy that binge-eating is typically practised in the evening and/or at night [104], thus, binge-eating may impair sleep in a similar manner to that which is occasioned by night-eating, although it is unclear exactly what the mechanism/s is in either case. Nevertheless, binge-eating and night-eating are both reported to be linked to the presence of stress and affective distress [105, 106]. For example, binge-eating is often preceded by stressful life events, and/or low mood and binge-eaters tend to report lower mood than in non-bingeeaters [107]. Similarly, night-eating is related to high stress and affective distress in college students [106]. In addition, night-eaters are reported to have higher plasma cortisol levels, but similar levels of ghrelin, subjective stress, and hunger ratings relative to non-night-eaters, after exposure to a laboratory stressor (i.e. Cold Pressor Test) [108]. Furthermore, Jääskeläinen and colleagues [109] showed that female adolescent high-stress eaters were more likely to consume sugar-free soft drinks, chocolate, and sweets and be obese than non-stressed eaters, whereas male adolescent stress eaters were more likely to binge-eat. Thus, binge-eating and night-eating are both

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known to be linked to impaired sleep, including insomnia, and a bidirectional relationship has been observed between the disordered eating and overweight/obesity, weight gain, and stress/affective distress. It is possible that stress/affective distress may act to disinhibit the disordered eating, which if practised at night, may tend to interfere with sleep, although it is unclear by which mechanism/s. However, further complicating this picture is the fact that night-eating and binge-eating tend to co-occur with each other, and there is substantial comorbidity between NES and BED. For example, some people meet criteria for both NES and BED [79], and more generally, the two behaviour tend to overlap significantly in the same individuals. Importantly, the clinical overlap between NES and BED, and the statistical overlap (r = .4) between night-eating and binge-eating behaviour, may tend to confound the investigation of linkages between weight, obesity, sleep, and eating behaviour [89, 104] especially as both disorders (NES and BED) are more common in obese individuals than in normal-weight individuals. Furthermore, there is substantial overlap in the symptoms of each disorder; for example, BED and NES are both characterised by eating large quantities of food late at night [104], thus, some researchers have questioned whether the two conditions are really separable clinical entities. Nevertheless, other researchers assert that the two disorders are clinically dissociable. For example, obese patients with either BED or NES who underwent biliopancreatic diversion surgery were shown to have different clinical trajectories. Patients with BED were reported to stop binge-eating after the surgery and they no longer binge-ate 3 years later, whereas the NES patients continued to night-eat 3-years after the surgery [104]. Thus, it is likely that the two disorders are independent clinical entities, which tend to often co-occur [110, 111], although due to the propensity of each behaviour to occur at night, it is likely that the same (or similar) mechanisms underpin their putative effects on sleep. Finally, as detailed in Sects. 3.1–3.3 and Chapter 7, at least some of the risk factors for impaired sleep, insomnia and overweight/obesity are similar, including each of the disorders themselves as well as disordered eating, affective distress, and physical inactivity. This pattern of overlapping risk factors for each of the different-but-related conditions is repeated throughout this book. That is to say, the disorders, symptoms, and states

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covered in this book are not only highly concurrent with each other; but a similar range of risk factors tends to exist for each of the comorbid conditions. That said, it is not exactly clear what this pattern of overlapping risk factors really means. It might indicate that a similar array of risk factors can contribute to the development of multiple different comorbid conditions. However, myriad different potentially bidirectional linkages may exist between the various risk factors and the disorders in different individuals. Alternately, the same risk factors may exert differential effects on the different disorders, due to interactions between the various risk factors and protective factors. For example, night-eating may contribute to impaired sleep over time, but it may only contribute to overweight/obesity if the eating is more akin to binge-eating, in terms of the quantity of food that is consumed at night. Alternately, it is possible that night-eating tends to occur in the context of stress/affective distress, whereas binge-eating is more strongly linked to perceptions of body image and feelings of affective distress. However, irrespective of the cause/s, there is a pressing need to better understand the complex dynamics that likely underpin the relationship between sleep problems, disordered eating, and overweight/obesity.

3.4

Overweight/Obesity, Disordered Eating, Sleep, and Body Temperature

As detailed above, overweight/obesity is highly concurrent with disordered eating (e.g. binge-eating and night-eating) and impaired sleep, but the precise mechanism/s underpinning the relationship are unclear. However, it is evident that obese people are more likely to eat late at night than normal-weight individuals [57, 89], thus, late night-eating may at least partly explain the observed relationship between overweight/obesity and impaired sleep in affected individuals. Late night-eating (and bingeeating) are known to be linked to impaired sleep (e.g. longer sleep onset latency) [84, 101] and weight gain in obese people [91, 100], and clinically, obese people are at an increased risk of a BED diagnosis [112] and/or NES diagnosis [113]. A similar profile of sleep deficits is also evident in overweight/obese people, binge-eaters, and night-eaters, and

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impaired sleep (e.g. short sleep duration) is reported to be associated with overweight/obesity, night-eating, and binge-eating [18, 69] including a longer sleep onset latency [84]. However, the sleep problems appear to resolve after the person has lost weight, suggesting that they are functionally related to the person’s weight [114]. Thus, it is possible that the sleep problems experienced by overweight/obese people are at least in part due to the indirect effects of late night-eating and/or binge-eating, but it is less clear exactly how this might occur. Yeh and Brown [89] evaluated the relationship between nighteating, binge-eating, sleep quality, and BMI in a community-derived sample. The results showed that binge-eating partly mediated the relationship between poor sleep quality to high BMI, whereas night-eating mediated the relationship between high BMI to poor sleep quality; although the results were only cross-sectional. Nevertheless, the results suggest that latenight eating may impair a person’s ability to fall asleep and while they wait for sleep to come, if they engage in binge-eating, then this might lead to weight gain over time, although other interpretations are possible, as detailed below. Several biological mechanisms have been proposed to explain the relationship between disordered eating, overweight/obesity, and impaired sleep, including an elevated BT. For example, perturbations in BT have been shown to be linked to overweight/obesity, although the results tend to be contradictory. Some studies show that overweight/obesity (or proxy measures of it) is positively correlated with BT; for instance, high BMI was associated with high tympanic temperature [115]. Similarly, in another study, mean BT was 36.1 ± 0.4, 36.4 ± 0.4, and 36.3 ± 0.4 °C in men, premenopausal, and postmenopausal women, respectively; and BT was positively correlated with BMI, waist, waist-to-hip ratio, body area, resting heart rate, glucose and insulin levels, in men and postmenopausal women, but in premenopausal women, BT was positively correlated only with resting heart rate and insulin and the factors were no longer significant after multivariable adjustment [115]. Further, Obermeyer and colleagues [116] showed, based on 243,506 temperature measurements obtained from 35,488 patients who had not received an infection diagnosis and were not prescribed antibiotics, that the mean BT was 36.6°C, although it was lower in older people and higher in women. High BT was also

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linked to higher BMI and cancer diagnosis, whereas low BT was detected in patients with hypothyroidism. Interestingly, the unexplained variation in BT predicted later mortality risk; specifically, an increase of 0.15°C was linked to an 8.4% increase in mortality risk 1 year later. Taken together, the results suggest that overweight/obesity is associated with higher BT. However, in several studies, overweight/obesity (or proxy measures of it) was shown to be negatively correlated with BT; for example, high body mass was correlated with low oral temperature [117], whereas high BMI was correlated with low tympanic temperature in men [118]. In contrast, core BT was similar in overweight/obese adults vs. normal-weight adults, using wireless temperature-sensing capsules to continuously monitor core BT [119]. Nevertheless, a larger study of 18,630 adults aged 20–98 years showed that obesity was correlated with high mean BT in every age group, after controlling for sex, BMI, and white cell count. BT tended to decrease with advancing age (0.3°F lower in oldest vs. youngest) and it was slightly higher in females than in males [120]. Taken together, the results for BT and overweight/obesity are somewhat contradictory, but in most studies, and in the largest studies, obesity (or proxy measures of it) was linked to higher BT. Furthermore, it is noteworthy that people with low BT tend to live longer than those with an elevated BT, as shown in the Baltimore Longitudinal Study of Aging Men [121]. So what might explain the conflicting results for BT and overweight/obesity? As suggested by Bastardot et al. [115], the disparate results may be due to differences in the study samples (e.g. gender, menopausal status, autonomic arousal, and/or hormonal secretion). However, the most compelling explanation is related to eating behaviour, in particular, latenight eating [72]. Quite simply, eating is thermogenic; that is, it increases BT regardless of the time of day that the food is consumed, whether it is during the day or night. For example, normal-weight unrestrained eaters showed an elevated liver temperature (0.8–1.5°C) from the start of a meal to 60–90 minutes later, whereas overweight and normal-weight restrained eaters showed a lack of a change in their post-lunch BT [122]. This result suggests that eating naturally leads to the production of body heat, although the rate and pattern of the food intake (e.g. restrained eating) may affect the person’s post-meal thermogenesis. Taken together, the results suggest that BT increases in anticipation of eating, remains elevated

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during a meal, and then declines an hour or so after eating, but this may be different in overweight/obese individuals or those who tend to restrict their eating. In this regard, the Islamic practice of abstaining from food and drink (i.e. fasting) during daylight hours, and consuming food in the evening during Ramadan, has provided a unique opportunity to examine the potential effects of late night-eating on BT and sleep. For example, BaHammam and colleagues [123] showed that during Ramadan, bedtime and wakeup times were delayed and there was a significant reduction in total sleep time (which slightly decreased from the first to third weeks of fasting) in Muslims but not non-Muslims, but there was no increase in daytime sleepiness. In a smaller study, BaHammam [124] showed that fasting during the first three-weeks of Ramadan in healthy Muslim medical students was related to the delayed onset of bedtime, a significant delay in wakeup time, greater daytime sleepiness, and a (non-significant) shortening of sleep, relative to baseline. Similarly, BaHammam and colleagues [125] reported a significant reduction in the proportion of REM sleep during fasting, but there was no significant change in daytime sleepiness, sleep onset latency, NREM sleep percentage, arousal index or sleep efficiency; whereas Roky and colleagues [126] showed that fasting was related to longer sleep onset latency and shorter total sleep time by 40 minutes, at the beginning and end of Ramadan as well as a decrease in slow-wave sleep and REM sleep duration. Mechanistically, Roky and colleagues [127] showed that sleep onset latency and an increase in subjective daytime sleepiness were associated with an increase in rectal temperature, suggesting that night-time food intake and its thermogenic effects are sufficient to impair a person’s sleep. Similarly, BaHammam and colleagues [124, 128] detected a delay in the peak of energy expenditure during the first two-weeks of fasting and a delay in the acrophase of skin temperature, indicating that a phase-shift had likely occurred in the circadian rhythm of BT. Taken together, the results suggest that daytime fasting and consuming food late in the evening may lead to a change in the circadian rhythm of BT thereby potentially interfering with sleep onset. Thus, if a person eats late at night, their BT will likely increase, and in turn, this may interfere with the normal evening decline in BT which

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is thought to cue the onset of sleep [129, 130], as discussed in detail in Sect. 2.2. Thus, disordered eating (e.g. night-eating) may interfere with the ability to fall sleep by increasing a person’s nocturnal BT, which potentially results in a phase-shift in the sleep-wake cycle, thereby interfering with sleep onset [128] and the circadian rhythm of core BT. In support of this assertion, adherents typically show a delay in the acrophase of skin temperature during Ramadan, indicating that a phase-shift has likely occurred in the circadian rhythm of core BT [128], and the high BT is linked to a longer sleep onset latency [127]. However, few studies have examined late night-eating in non-fasting individuals. Only one study [131] has shown that food intake late at night can lead to an increase in mental alertness at bedtime. Nonetheless, several studies have examined the relationship between specific types of food intake and sleep. For example, eating spicy food in the evening was shown to impact upon sleep by increasing sleep onset, reducing slow-wave sleep and Stage 2 sleep, and increasing total awake time [132]. Similarly, in a review, a low-fibre, high-fat, and sugar diet was reported to be associated with less restorative sleep and more sleep arousals [133], whereas fastfood intake partly mediated the relationship between later bedtime and higher BMI [134]. Taken together, the results suggest that certain types of food (e.g. high in fat, sugar, and spices) may be most likely to interfere with a person’s sleep, although the mechanism/s by which this occurs is unknown, and the effects may vary depending on how accustomed the person is to certain types of foods (e.g. spicy food). In sharp contrast, caloric restriction (CR) has consistently been shown to be linked to lower BT. For example, a recent randomised controlled trial of 25% CR in 150 people who dieted for ≥6-years showed that their core BT was lower than those who were not on the diet, by about 0.4°F after 6 months of CR therapy [135]. Similarly, in another study, mean 24hour, daytime, and night-time core BT were lower in people on CR diets, relative to those who were on a western diet [136]. Thus, it is apparent that eating can result in an increase in BT, whereas prolonged CR reliably reduces BT [135, 136]. Nevertheless, several other mechanisms have been proposed to explain the relationship between disordered eating, overweight/obesity, and impaired sleep. For example, some researchers posit that shorter sleep

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duration over time tends to promote a positive energy balance, which in turn, may lead to significant weight gain [137]. Alternately, a longer sleep onset latency and shorter sleep duration may provide additional time during which a person can consume food at night, thus, contributing to weight gain over time [137, 138]. However, other researchers have suggested that neuroendocrine and metabolic pathways are involved in mediating the effects of binge-eating on sleep and weight or vice versa. For example, a change in leptin and ghrelin secretion after sleep deprivation has been suggested to indirectly lead to binge-eating by promoting food intake [103, 139], as detailed below. Finally, impaired sleep is suggested to result in dysregulation of eating regulation hormones, which in turn, may lead to weight gain [137]. Sleep deprivation can alter leptin and ghrelin secretion; hormones that assist in the initiation and restriction of food intake [139]. Leptin is released from adipocytes to restrict hunger and produce satiation [140, 141], whereas ghrelin, a peptide secreted from the stomach, increases appetite [142], opposing the effects of leptin. As a result, during periods of positive energy balance, ghrelin levels decrease in the body, whereas the levels tend to increase after fasting [143]. In contrast, appetite is reported to increase in people who experience long-term sleep deprivation via a change in leptin and ghrelin secretion. In particular, shorter sleep duration (≤ 7.7hours/night) is linked to a decrease in leptin and an increase in ghrelin secretion, which results in an overall increase in appetite [144]. As little as two consecutive nights of sleep restriction (i.e. 4-hours total sleep loss) has been reported to be linked to an 18% decrease in leptin and a 28% increase in ghrelin during the day, which corresponded to a 24% increase in hunger and a 23% increase in appetite. Specifically, participants reported an increase in their specific appetite (33–45%) for high-calorie carbohydrate-containing foods such as sweets, salty food, and starchy food, whereas their appetite for fruits, vegetables, and high-protein food did not change after sleep restriction [139]. Taken together, the results suggest that a change in metabolic and neuroendocrine hormones may at least partly explain the comorbidity between overweight/obesity, disordered eating, and impaired sleep.

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In summary, we examined the premise that overweight/obesity, insomnia/impaired sleep, and disordered eating (e.g. night-eating) are functionally linked to an elevated nocturnal BT. Taken together, the results suggest that if a person engages in disordered eating at night, this may lead to a phase-shift in the circadian rhythm of core BT, which potentially interferes with the onset of sleep. Furthermore, once a person’s sleep has become disturbed, they may tend to engage in this sleep-disrupting behaviour more often, while they wait for sleep to come, which may result in additional weight gain over time [138]. In this way, disturbed sleep may contribute to the development of a new condition (e.g. overweight/obesity) and the worsening or perpetuation of existing behaviour (e.g. night eating). Additionally, impaired sleep may be the result of this behaviour, which is commonly practised by overweight/obese individuals. Thus, it is possible that an elevated nocturnal BT that interferes with sleep onset can underpin the development and/or perpetuation of concurrent symptoms and disorders, including insomnia/impaired sleep, overweight/obesity, and disordered eating. That is to say, sleep problems may play a pivotal and bidirectional role in contributing to the causation, worsening and/or perpetuation of other symptoms, disorders, and behaviour, via an increase in nocturnal BT, as well as potentially being caused by the same bio-behavioural mechanism/s, see Figs. 2.1–2.3 in Chapter 2.

References 1. WHO. Obesity and overweight. 2018. https://www.who.int/en/newsroom/fact-sheets/detail/obesity-and-overweight. Accessed 10 April 2018. 2. Walls HL, Magliano DJ, Stevenson CE, et al. Projected progression of the prevalence of obesity in Australia. Obesity. 2012;20(4):872–878. https:// doi.org/10.1038/oby.2010.338. 3. Mokdad AH, Ford ES, Bowman BA, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. The Journal of the American Medical Association. 2003;289(1):76–79. https://doi.org/10.1001/jama. 289.1.76.

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4 Overview of the Comorbidity Between Medical Illnesses and Overweight/Obesity Christopher J. Nolan

The Global Burden of Disease (GBD) 2015 Obesity Collaborators reported that, in 2015, a total of 107.7 million (5%) children and 603.7 million (12%) adults were obese in data assembled from 195 countries, with a doubling since 1980 in many countries [1]. Furthermore, among adults in 2015, overweight and obesity contributed to 4.0 million (7.1% of total) deaths from any cause and 120 million disability-adjusted life years (4.9% of total) globally [1]. Of major concern was the observation of rapid increases in obesity prevalence in young people, particularly within middle-income countries such as China, Brazil and Indonesia [1, 2]. A younger onset of obesity is likely to be followed by earlier onset of obesity-associated chronic diseases, such as type 2 diabetes (T2D), cardiovascular disease, renal disease, musculoskeletal disorders and some cancers [1–3]. The rising prevalence of obesity in women of child-bearing age is also adversely affecting pregnancy outcomes with transgenerational health C. J. Nolan (B) Australian National University, Canberra, ACT, Australia e-mail: [email protected]

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consequences [4, 5]. The 2018 Position Statement of the Obesity Society states a case that obesity is a disease in its own right and that, in addition to its effect to elevate the risk of premature mortality, it increases the risk for the development of more than 200 comorbid chronic diseases [6]. Furthermore, obesity can adversely impact all systems of the body and all stages of life (from in utero to old age).

4.1

Medical Illnesses and Overweight/Obesity

Overweight and obesity are conditions in which there is excessive accumulation of adipose tissue. The body mass index (BMI) (calculated as height [m]/weight [kg]2 ) is used to classify the weight of adults into underweight (7% (OR

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= 2.2), and diabetes complications (OR = 2.6) [57]. Furthermore, diabetes mellitus type-II is associated with increased risk of depression (OR = 2.0) irrespective of sex [58], and it is also associated with increased risk of anxiety [59]. See further discussions in Chapters 3–5. Physical activity may help to protect against the negative effects of stress on mental health. For example, findings from a randomised controlled trial suggest that exercise can improve wellbeing and reduce the experience of stress, distress, and burnout [60]. Further, a large meta-analysis has shown that exercise can have large effects in reducing symptoms of depression (1.11 standardised mean difference [SMD] units) [61], anxiety (SMD = 0.58) [62], and sleep problems (SMD = 0.50) [63]. In sharp contrast, a sedentary lifestyle is reported to adversely impact on health, including sleep [64], and it is weakly correlated with job strain [65] and bidirectionally associated with depression severity [66]. This suggests that physical inactivity may contribute to impaired sleep and stress/affective distress, as well as low mood and impaired sleep contributing to physical inactivity. In chronic illness patients, medical illness factors [67] are known to be risk factors for mental ill-health. This has been indicated in relation to numerous medical health conditions, including physical disability, symptom severity (e.g. pain) [67, 68], disfigurement, loss of vigour, and poor prognosis illness [69, 70]. Additionally, depression and anxiety have been shown to predict later pain and pain-related disability, but not the reverse [71]. Disability can limit a person’s ability to function adequately, either mentally or physically or both, in their current environment [72], which may adversely impact on various aspects of their lives including social interaction and social roles [73]. Compared with physical health conditions, mental health conditions can increase the odds of severe disability burden to a greater extent [74], and the odds of experiencing severe disability are more than additive when patients have both mental and physical health conditions [74]. Furthermore, the effects of disability tend to extend beyond the individual to the family unit and broader community; hence, the effects of disability can be extensive. For example, disabled patients are more likely to have greater support requirements than non-disabled patients (e.g. need for a carer), and the disability may interfere with their

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ability to work, potentially causing financial stress, which could further worsen the patient’s mental health. Electronic device use is reported to be a risk factor for mental ill-health. For example, Internet and social media use can bring a person in contact with online bullies or ‘trolls’, or it can provide a platform to spend too much money on online shopping or gambling. Problematic (or excessive) Internet usage is generally linked to negative mental health outcomes, and in therapy, it is often regarded as a form of addictive behaviour, although it is not recognised as such in the DSM-5 [5]. Specifically, a study of 449 participants (16–71 years of age) showed that problematic Internet use was linked to greater emotion-focused and avoidance coping in adults and greater rumination and less self-care in adolescents [75]. Similarly, a meta-analysis of 23 independent samples reported a relationship between problematic Facebook use and distress (r = .34) and wellbeing (r = −.22) [76]. However, another meta-analysis [77], based on 67 independent samples, showed the relationship between time spent on social network sites and mental health indicators (e.g. wellbeing, life satisfaction, depression, and loneliness) to be generally low (r < |.12|), suggesting that time spent on social networking sites may not sufficiently capture problematic Internet behaviour. Similarly, a recent 12-month follow-up pilot study surveying adolescents reported reduced depression levels in relation to instant messaging and social network use [78]. Taken together, the results suggest that Internet usage is not synonymous with mental ill-health, and the mixed results suggest that researchers have much yet to learn about the effects of Internet use on mental health. Other types of electronic devices (e.g. electronic readers, computer use) are examined in more detail in Chapter 7, in regard to mental health, sleep, and physical health. Coping strategy use is another known risk factor (or protective factor) for mental ill-health, depending on whether the strategy is adaptive (e.g. seeking out social support) [79] or maladaptive (e.g. ‘acting out’ involving drug/alcohol use) [80, 81]. Thus, adaptive coping facilitates good mental health while maladaptive coping may contribute to mental ill-health [80, 82, 83]. For example, in a sample of 510 Australian adolescents, greater social support (i.e. social support satisfaction and number of supports) was associated with reduced impact of stress on depression, whereas maladaptive coping (i.e. rumination, acting out including drug use) increased

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the potential impact of stress on depression [84]. Similarly, a study of 128 boys and girls (aged 13–19 years) in Botswana suggested the importance of using distraction and seeking out social support to reduce the potential impact of negative life-event stress on distress (e.g. perceived stress, sleep problems) [85]. Therefore, it is important for clinicians to ascertain if patients are using maladaptive coping, especially drug and alcohol use, as this may contribute to affective distress (e.g. anxiety, depression) relative to people who do not use illicit drugs or alcohol as a coping device [20]. It is also important to ascertain a patient’s level of social support as this may also have implications for their mental health. Finally, in regard to mental ill-health, it is noteworthy that the risk factors for affective distress (e.g. anxiety, depression) are similar to the risk factors for insomnia/impaired sleep (Chapters 2 and 7) and overweight/obesity (Chapter 3), and affective distress is also a risk factor for the latter two conditions, and vice versa. That is, the risk factors and protective factors for the conditions overlap considerably with each other. Therefore, as detailed in Chapters 7 and 9, it is possible that (i) a single risk factor could contribute to more than a single condition (e.g. excessive electronic device use leading to overweight/obesity and impaired sleep, in part via physical inactivity), and (ii) two or more risk factors could interact together to augment or attenuate the expected effects on mental health. However, to the authors’ knowledge, few studies have examined the possible contributions of these interacting risk factors to clinical outcomes.

6.4

Causal Models for the Development of Depression and Anxiety

Myriad different factors have been posited to contribute to the development of depressive illness, including stressful life-events, depression vulnerability (e.g. maladaptive coping strategy use, high-trait anxiety), lack of social support, impaired sleep, viral infections, chemical imbalance, physical inactivity, cytokine activity, genetics, inadequate diet (e.g. lack of Omega-3, too much sugar, lack of vitamin D3 ), gut dysfunction, and circadian rhythm dysfunction, and in many cases, the same factors are suggested to underpin the development of anxiety disorders. In particular, so

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many theories of depression causation exist that it is difficult to reconcile how all of them could provide a parsimonious explanation of the processes underpinning the disorder. Further, anxiety, depression, and fatigue are often difficult to separate in longitudinal predictive studies; that is, depression is a strong predictor of anxiety and fatigue, and depression is strongly predicted by anxiety and fatigue [86], suggesting that anxiety, depression, and fatigue might also be causally related. Adding further complication, in clinical practice, anxiety and depression may manifest only as the greater severity of somatic complaints, including fatigue (see Chapter 8). Thus, given the degree of overlap between the conditions (e.g. co-occurrence of symptoms) and the likely complexity of affective disorder causation [87], it is not surprising that the causal hypotheses for the three disorders also tend to overlap. However, as articulated by Borsboom and colleagues [88, 89] and Brown and colleagues [90], many different possible pathways leading to comorbidity are likely to exist, and the pathways occurring in a particular person may depend on personal characteristics (e.g. life experiences, coping methods, presence of infection) and the specific life situation. Detailed below is a brief summary of some of the dominant theories related to the causation of affective distress, including psychosocial and biological theories. Due to the extensive nature of the relevant literature, the focus here will be on potential causes of depressed mood rather than anxiety, fatigue, or other disorders.

6.4.1 Psychological Models Life-event stress reliably precedes the onset of MDD and is thought to be causally related to it [91]. The Camberwell Collaborative Depression Study found that stressors were 2.5 times more frequent in depressed patients than in controls, and in community samples, 80% of the depression cases were preceded by major life-events. Thus, life stress was frequently linked to depression diagnosis but most people did not become depressed after experiencing a major stressful life-event. This result likely reflects that some people have a particular vulnerability to develop depression, as detailed in

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the next section. In cases where individuals do develop depression, interpersonal loss experiences (e.g. bereavement, separation), chronic relationship stress (e.g. marital arguments), and dependent stressors (i.e. stressors to which a person might have contributed such as job loss due to unexplained absences) most consistently precede depression onset. Additionally, chronic stressors (e.g. chronic illness, marital discord) including social disadvantage (e.g. poverty) are stronger predictors of depression severity than acute stressors [92]. However, it is not clear exactly how stress contributes to depression, although several factors including social withdrawal, ruminative thinking, maladaptive coping, impaired sleep, and stress-induced immunosuppression leading to infective illness have all been proposed to mediate the relationship between stress and depressed mood. Only some people who experience a major stressful life-event will go on to develop depression, suggesting that depression vulnerability plays a role in the development of depression. However, there are myriad different potential vulnerabilities that could predispose a person to develop depression, as detailed in the psychological literature, including maladaptive coping strategy use (e.g. avoidant coping), inherited personality traits/disorders (e.g. borderline personality disorder, dysthymia), developmental factors (e.g. in utero stress), social modelling (e.g. child learns to respond excessively to stress from parent), and genetic or other biological factors [93]. Thus, it is unclear exactly what the vulnerability might entail. Nevertheless, it is noteworthy that a strong predictor of depressed mood is a prior history of a mental health condition and/or a family history of one [94– 96]. The relationship may indicate that there is a genetic or biological basis for the disorder, but it is equally plausible the family members shared the same disadvantaged social environment, were exposed to the same stressors, and/or that they responded to stressful situations in a similar manner. In contrast, the Tripartite Mode [97, 98] proposes that the comorbidity between anxiety and depression may be partly explained by a common negative affect which is shared by anxiety and depression. However, a review [97] of the model suggests that anxiety is clinically dissociable from depressed mood, given that anxiety is principally linked to hyper-arousal whereas depression is linked to low-positive affect. Such an assertion is

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supported by the results of a 3-month follow-up study, in which highdepression levels were predicted by negative affect, whereas low depression was predicted by positive affect with resilience mediating the effects to some degree [99]. Thus, it is possible that anxiety and depression have a common causation, but it is likely that separate processes may underpin the expression of these affective symptoms. The provision of adequate social support and satisfaction with support is known to predict better mental health outcomes (e.g. better wellbeing, fewer affective symptoms), whereas inadequate support (or the perception of inadequate support) predicts worse mental health (e.g. depression severity) [100]. This is paralleled by results indicating that better physical health (including better medical outcomes in medical patients) is linked to adequate social support, whereas worse physical health is linked to inadequate social support (i.e. loneliness and social isolation) [101], or at least the perception of it. The literature is mostly comprised of cross-sectional research studies; thus, it is unclear whether inadequate support adversely impacts on health, if health adversely impacts on social supports, or if both are likely to be true. Nevertheless, there are several theories of social support (e.g. stress-buffering hypothesis) [102] which broadly suggest that the provision of social support can protect an individual from experiencing distress. Similarly, social isolation is reported to increase a person’s morbidity rate [101] although research on the topic is limited. It and feeling lonely are strongly related to depressed mood [103], though loneliness may be a better predictor than social support, where loneliness irrespective of social support levels predicts levels of depression [103], but more studies are needed to enable modelling any bidirectional relationships that might exist with depression predicting lack of social support and loneliness. It is not difficult to appreciate that feeling lonely will tend to worsen a person’s thinking and mood, and that they may not feel much like socialising when they are feeling depressed. Interestingly, as detailed in Chapter 7, social exclusion has been linked to feeling colder [104] and a drop in body temperature that is associated with feeling cold [105], whereas enhancing a person’s perception of warmth increases social proximity and feelings of closeness [106]. Thus, social isolation and an actual or perceived lack of support may contribute to the development of depressed mood.

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In a similar vein, Martin Seligman has proposed that the current global depression epidemic is partly fuelled by socio-cultural changes that have occurred in modern society. For example, he suggests that our culture of individualism, the self-esteem movement, and the collapse of communal society structures have resulted in a lack of connectedness, fewer roles/responsibilities, social isolation, and lack of life meaning, which has likely increased the risk of developing a mental health problem. His recent books (e.g. Learned Optimism) [107] have focused on learning how to become more optimistic and achieving authentic happiness and flourishing, in part, by focusing on the acquisition of character strengths (e.g. persistence, courage) that can help to buffer against feelings of distress that could lead to depressed mood [108, 109].

6.4.2 Biological Models Stress-related immune suppression, which increases the risk of an infection, is posited to underpin the development of depression. Psychological stress typically results in stress hormone release (e.g. cortisol) that can potentially suppress immune system functioning, thereby potentially increasing a person’s susceptibility to infection [110–113], which may then result in depressed mood. Depression is also linked to immune suppression and an increased risk of infection, especially pneumonia and upper respiratory tract infection [114]. Findings from a meta-analysis of 38 depressionimmune studies [115] suggest that clinical depression is also reliably linked to impaired immune functioning, with the strongest associations found in older people and inpatients. Depressed people are also more likely to develop an infection and stay unwell for longer [116]. In older individuals, the number of depressive symptoms was positively associated with plasma Interleukin-6 levels contributing to increased inflammation and inflammation-related health problems [117]. Furthermore, viral and bacterial infections are known to precede or co-occur with depressed mood, and depression is associated with the subjective feeling of infection, fatigue, gastrointestinal upset, autonomic arousal [118], and more frequent infections [116], including specific viral [119–121] and bacterial infections [118], relative to healthy controls. Thus, an infection or its effects (e.g.

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cytokine response to infection) may contribute to depressed mood. However, a recent meta-analysis [122] showed that statistically significant associations only existed between depression and Borna disease virus, herpes simplex virus-1, varicella zoster virus, Epstein–Barr virus, and Chlamydophila trachomatis. Thus, there is preliminary evidence suggesting that depressed mood could be induced by viral and other infections. The mechanisms by which this condition might develop are examined below, including the effects of fever/elevated body temperature (BT), brain inflammation, and cytokine secretion. An elevated BT and circadian rhythm dysfunction have been posited to underpin the development of depressed mood. For example, positive affect is related to the normal circadian rhythm of core BT [90], whereas its dysregulation is linked to affective disorders including depressed mood [90]. For example, several studies have shown that BT (including nocturnal BT) is higher in depressed individuals, relative to healthy controls [90], which results in a flatter and delayed circadian rhythm of core BT and a phase delay in the sleep-wake cycle, as well as an early morning increase in BT, which is linked to early morning wakening [90]. Additionally, several hallmark features of depressed mood are suggestive of circadian rhythm dysfunction, including elevated nocturnal BT, phase advance in the core BT rhythm (e.g. peak cortisol and melatonin levels occur later at night), early morning wakening which is preceded by an early increase in BT, diurnal changes in mood, circannual rhythm of mood, and changes in sleep architecture (e.g. short REM latency) [90]. As detailed in Chapter 2, when a person’s BT fails to rapidly fall in the evening, because of processes that oppose it (e.g. elevated BT related to depressed mood), this could potentially result in impaired sleep, specifically the inability to fall asleep. However, as detailed below, the elevated BT in depressed individuals might alternatively be explained by other biological processes. Rausch and colleagues [123] suggest that the BT changes in depressed individuals provide support for the inflammatory model of depression, which posits that depressed mood is caused by activation of the inflammatory response system. For example, an elevated BT could indicate a fever response in depressed people, which may be due to infection. Consistent with this theory, antidepressants (e.g. selective serotonin reuptake inhibitors [SSRIs]) have been shown to exert anti-inflammatory effects;

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that is, reducing the release of nitric oxide and pro-inflammatory cytokines (e.g. interferon-γ, tumour necrosis factor-α [TNF-α], and interleukin-6 [IL-6]), all of which are detected in the sera and brains of depressed individuals [124, 125]. Furthermore, anti-inflammatory compounds such as aspirin have been shown to reduce depressive symptoms, either taken alone, taken for their anti-platelet effects or to reduce the risk of myocardial infarction [126] or in combination with SSRIs, relative to SSRIs alone [127] However, there are other possible explanations of the elevated BT and abnormal cytokine secretion in depressed people. As mentioned in Chapters 7 and 9, cytokines are also released in response to exercise [128], physical stress [129], cold [130], and heat stimulation [131, 132], and the cytokine responses to fever/infection are largely indistinguishable from those secreted in response to overheating/heat stroke, stress/autonomic arousal, and exercise/physical activity [132–134]. Thus, there are processes other than infection, such as autonomic arousal and overheating (or an elevated BT), which could alternatively explain the BT changes in depressed individuals. In related research, prolonged stress and depression have been shown to be related to brain changes (e.g. reduced cerebral blood flow, reduced cerebral metabolism, structural abnormalities). In particular, using fMRI, depression was shown to be linked to reduced cerebral blood flow [135], and using positron emission tomography, cerebral metabolism was shown to be reduced in the prefrontal cortex and hippocampus [136]. Paralleling the changes, post-mortem brain size in depressed people has been shown to be reduced (i.e. atrophy) [137] and there is reduced connectivity and/or atrophy in the hippocampus, anterior cingulate, orbital cortex, and dorsolateral and subgenual prefrontal cortex in depressed patients [137–139]. However, the processes underpinning these brain changes are not exactly clear, although it is likely that the brain atrophy in MDD patients occurs secondary to brain inflammation [140–142]. This interpretation is consistent with prior research showing that patients receiving cytokine therapy can develop depressive symptoms [143, 144], experimentally induced inflammation can promote mood deterioration in healthy subjects [145], elevated inflammatory markers predict poorer response to antidepressant therapy, and depressed patients who are non-responsive to antidepressant therapy show persistently elevated inflammation [146].

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Similar to the inflammatory model of depression, the cytokine theory of depression posits that depression may be induced by inflammation that occurs secondary to cytokine secretion [118]. Cytokines are proteins that are secreted by most immune cells to enable communication between them; not unlike neurotransmitters and neuromodulators in the nervous system, such that cytokines can bind to specific receptor sites on the external surface of immune cells, thus aiding in immune system defence against invading pathogens [147, 148]. In recent years, cytokines have been linked to various illnesses including rheumatoid arthritis [149], inflammatory bowel disease [150], cardiovascular disease [151], diabetes [152], and also mental health conditions such as psychosis [153] and obsessive compulsive disorder [154]. Specifically, the cytokine theory of depression posits that the inflammatory response system can be activated via external (e.g. infection) or internal stressors (e.g. cortisol, psychosocial stressors), to result in the production of pro-inflammatory cytokines and oxidative processes (e.g. nitric oxide, super oxide) that directly damage proteins, membranes, and genes, ultimately resulting in depression [118]. Consistent with the theory, is the abovementioned research showing that antidepressants (e.g. SSRIs) have anti-inflammatory effects [124, 125, 155, 156], and antiinflammatory compounds (e.g. aspirin) can improve depressive symptoms [126, 127]. Alternatively, it could be possible that the changes in brain function are a reflection of circadian rhythm dysfunction, although so far the hypothesis has not been examined. Finally, perturbations in the gut –brain axis are suggested to underpin the development of depression. The GBA facilitates bidirectional communication between the nervous system (e.g. via the vagus nerve), neurotransmitters produced in the gut (e.g. serotonin, gamma-aminobutyric acid [GABA]), and the immune system [157], and it is linked to about 1 kg of bacteria in the digestive tract [158]. For example, patients with MDD showed elevated antibodies to infection (IgM and IgA responses to lipopolysaccharide [LPS]), which is a toxin in the cell wall of gramnegative bacteria (e.g. Salmonella and Escherichia coli species). Patients also experienced frequent subjective feelings of infection, fatigue, GIT symptoms, and autonomic arousal [118]. Taken together, the results suggest that leaky gut (i.e. physical disruption or increased permeability of the intestinal tract) which gives rise to bacterial translocation (i.e. escape

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of indigenous bacterial flora from GIT to sterile extra-intestinal sites; e.g. liver, mesenteric lymph nodes, bloodstream) may explain the phenomenon. Simply put, bacterial translocation from the gut is known to result in the systemic release of pro-inflammatory cytokines, which could provide an ongoing source of cytokine secretion to fuel the depressed person’s brain inflammation [118]. Stress has been shown to be linked to intestinal mucosal dysfunction (e.g. gut bacterial infection) and extreme stressors (e.g. burns injury, surgery) are reported to induce bacterial translocation [159, 160]. Thus, the translocation of intestinal bacteria (and associated toxins) could provide a mechanism by which the aforementioned brain inflammation occurs [161], and this could be linked to the presence of inflammatory bowel disease or irritable bowel syndrome [162]. However, results of two animal studies suggest that ‘stress-induced bacterial translocation’ is unlikely to explain the relationship between gut dysfunction and depression. Ando and colleagues [163] showed that short-term restraint in mice induced the translocation of indigenous gram-positive bacteria only in a small proportion of mice, but translocation of gram-negative bacteria did not occur, although it did induce short-lived increases in plasma corticosterone and brain amine metabolism. In contrast, bacterial translocation occurred more slowly and it persisted after HPA axis and neurochemical responses had dissipated. However, when mice were infected with Salmonella typhimurium, spontaneous translocation did occur, and plasma cortisone, interleukin-6, and brain catecholamine and indoleamine metabolism were elevated. Similarly, Dunn and colleagues [164] showed that restraint stress did not reliably induce bacterial translocation, except in the presence of gut bacterial infection, whereas normal neurochemical responses to restraint occurred in germ-free rats. Taken together, the results suggest that bacterial translocation is linked to activation of the HPA axis and brain amine metabolism, similar to the effects of restraint stress, but the latter effects cannot be explained by the presence of bacterial translocation. Finally, in regard to gut–brain axis dysfunction, medically unexplained GIT symptoms are known to be linked to anxiety and depression. If a person has one or more GIT symptoms, they are at twice the risk of

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experiencing MDD and three-times more likely to experience panic disorder or agoraphobia; and the risk of psychopathology increases with more GIT symptoms, relative to their prevalence in the general population (e.g. 25–40%) [165]. Anxiety is most frequently linked to GIT symptoms, especially nausea, heartburn, constipation, upper dysmotility, and diarrhoea [166, 167]. Similarly, impaired sleep is common in patients with gastrointestinal problems, as is anxiety and/or depressed mood, and the latter states are thought to disrupt sleep which leads to further gastrointestinal symptoms [168]. Gut dysfunction (e.g. lack of microbial diversity, gut inflammation, leaky gut) is also thought to be linked to schizophrenia and bipolar disorder [169–171], whereas the presence of certain pathogenic gut bacteria and gut symptoms are linked to ASD [172]. In contrast, probiotics may moderate the effects of stress on the gut, possibly via a reduction in stress-reactivity and reduced HPA axis and SNS functioning, to lead to an improvement in depression [173]. A systematic review [174] of 10 RCTs (clinical and non-clinical) suggests that there is preliminary support for probiotics reducing anxiety and depression symptoms [174], similar to the results of an RCT study [175] in which probiotics administered to 40 patients with MDD over 8-weeks led to reduced depression levels (Hedges’ g = 0.73) [175]. However, the use of probiotics has not been shown to improve schizophrenia outcomes [176]. Finally, a poor diet has been shown to be linked to depressed mood and it is thought to be causally related to it, whereas a better diet (e.g. Mediterranean diet) may improve depression levels [177], although few studies have examined diet in regard to depression [178]. Thus, it appears that gut health and diet may also contribute to mental health, although this is a relatively new area of research enquiry.

6.4.3 Integrated Models of Depression Causation Myriad different socio-psychological and biological factors are posited to contribute to depressive disorder development, including stressors, depression vulnerability (e.g. high-trait anxiety), lack of social support, impaired sleep, viral infections, chemical imbalance, physical inactivity, cytokine

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activity, genetics, inadequate diet, gut dysfunction, circadian rhythm dysfunction, and comorbidity (e.g. with anxiety and/or fatigue). Supporting theories, for the most part, articulate that singular processes underpin the development of depression. That is, few if any of the theories properly address the ‘elephant in the room’—the common presence of psychological and somatic comorbidities. Nonetheless, recent biological theories (e.g. cytokine theory of depression) have sought to integrate psychological and biological factors together into the same model. So what should we make of all the disparate theories and their supporting empirical research? Is there a way forward? Obviously, there is a need to incorporate existing depression theories into more complex real-world models of depression causation. Wittenborn and colleagues [87] recently sought to do just this by mapping out the multiple feedback loops that likely exist between different psychosocial risk factors and biological processes; but the model is disconcerting in its complexity, and it is unclear where the strongest linkages exist among the various pathways. However, there is hope for a ‘grand unified theory’ of depression causation that links together the likely psychological and biological antecedents, maps out their likely interactions, and predicts the major causal pathways in depression causation. That is to say, a depression model is needed that can meaningfully link together, in a functional way, the various risk factors, interactions between them, and depression outcomes and also takes comorbidity into account. As detailed in Chapters 2 and 9, only two models have meaningfully sought to do this. The utility of the two models, in integrating together the various singular depression theories, will therefore be briefly examined. First, the network model of comorbid symptom development [88, 89, 179] broadly posits that there may be many different causal pathways to comorbidity, including the development of specific disorders (e.g. depression) in different individuals and in the same individual over time; or alternatively, multiple different causation events could contribute to comorbid illness in some individuals. Nevertheless, the model asserts that the shared symptoms of the comorbid disorders are most likely to be causally linked to the development of comorbidity. Similarly, our clinical theory of concurrent symptom development asserts that there may be multiple potential pathways to comorbidity, and the shared symptoms of the disorders will most

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likely contribute to comorbidity. Both models also assert that particular symptom groups (e.g. impaired sleep, physical inactivity) are most likely to contribute to comorbidity via homeostatic pathways (e.g. hypothalamic– pituitary–adrenal axis, sympathetic nervous system) that are regulated by human circadian clocks. As discussed in Sect. 6.2, an elevated nocturnal BT and the disruption of the circadian rhythm of core BT are linked to an increased risk of affective disorders, including bipolar disorder [123, 180, 181], whereas positive affect is linked to a normal circadian rhythm of core BT [182]. Additionally, many of the hallmark features of depressed mood can potentially be parsimoniously explained by circadian rhythm dysfunction, especially a phase advance in the circadian rhythm of core BT and the sleep-wake cycle. Thus, there is broad support for the assertions of the two models. However, can the two models reconcile the various singular theories of depression causation? Our model clearly accommodates many of the singular theories inasmuch as it asserts that a complex bio-psycho-behavioural mechanism underpins the development of comorbidity, including depression causation. It also asserts that impaired sleep plays a pivotal and bidirectional role in contributing to the causation, worsening, and/or perpetuation of some comorbid conditions, via an increase in nocturnal BT. In this respect, the model may have some utility in providing a framework for structuring the various depression risk factors and theories in a systematic way; that is, linking psychological risk factors together with physical symptoms, sleepdisrupting behaviour, and homeostatic pathways (e.g. SNS, HPA axis), circadian rhythm of core BT, and the sleep-wake cycle. More broadly, the clinical model could potentially accommodate theories that are related to stress, psychological vulnerability, infection, brain inflammation, circadian rhythm dysfunction, and gut–brain dysfunction, via changes in BT and circadian rhythm function, as evidenced in this chapter and in Chapter 7, aside from taking into account physical and psychological comorbidities. To a similar extent, the network model of comorbid symptom development can accommodate the likelihood of multiple causal pathways. Finally, the two theories have implications for the treatment of depressed mood. First, depression may be co-treated by treating a person’s comorbid sleep problem (as detailed in Chapter 9). Second, removing impediments to normal circadian rhythm functioning may improve depression (and

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other related) outcomes. Third, therapies that are known to normalise circadian rhythm functioning might have utility in treating depression. For example, bright light therapy is effective in treating seasonal affective disorder [183] using light that is brighter than normal lighting, and which can alter melatonin secretion, BT, and circadian rhythm functioning [184]. Bright light that is administered in the early morning can suppress melatonin and advance the circadian rhythm such that the nadir of BT occurs earlier in day; whereas if it is used in the evening, it will delay the circadian rhythm such that the nadir of BT occurs later in the day [185]. Nonetheless, both of the comorbidity theories can also accommodate other models of depression causation, which might have specific implications for therapy.

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7 Arousal States, Symptoms, Behaviour, Sleep and Body Temperature Rhonda Brown and Einar Thorsteinsson

7.1

Arousal States and Elevated Body Temperature

In this section, we explore the extent of the co-occurrence between arousal states (e.g. stress, anxiety), certain symptoms (e.g. fatigue, chronic pain) and changes in BT. Depression comorbidity is discussed in regard to BT in Chapter 6. Autonomic arousal states (e.g. psychological stress) have been shown to be functionally related to an elevated BT via the production of body heat, which occurs as a natural by-product of the autonomic arousal [1]. For example, examination stress and anxiety were linked to an elevated BT, prior to (i.e. anticipatory stress) and during an exam (e.g. increase in R. Brown (B) Australian National University, Canberra, ACT, Australia e-mail: [email protected] E. Thorsteinsson University of New England, Armidale, NSW, Australia e-mail: [email protected] © The Author(s) 2020 R. Brown and E. Thorsteinsson (eds.), Comorbidity, https://doi.org/10.1007/978-3-030-32545-9_7

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mean oral temperature of 0.6 °C 10–15 minutes prior to the exam) [2]. Similarly, young boxers showed an elevated BT (1.06 °F) before competing in a boxing contest, relative to mean BT values obtained prior to training, which was 1.44 °F higher than mean at-home BT values [3]. Medical residents have also shown higher axillary BT 5–10 minutes before an annual exam, relative to a calm situation 2–3 weeks later (37.0 vs. 36.4 °C) in all 22 participants, in a room with ambient temperature set at 23 °C. This elevation in BT was mirrored by an elevation in heart rate (90.6 vs. 67.9 beats/min) and diastolic (72.2 vs. 67.6 mmHg) and systolic blood pressures (120.4 vs. 104.1 mmHg) [4]. Taken together, the results indicate that an elevated BT (by about 0.6 °C) is functionally linked to autonomic arousal states and an elevated heart rate and blood pressure [5], although few studies have examined stress/affective distress in regard to BT. However, it is apparent that any state, emotional or otherwise, that is linked to an increase in autonomic arousal (e.g. rapid heart rate, butterflies in stomach, muscle tension) will tend to have thermogenic effects. For example, as detailed in Sect. 7.2, physical activity is linked to autonomic arousal (e.g. elevated heart rate) and an elevated BT. Thus, the perceptions of stress/anxiety will result in activation of the physiological processes that underpin autonomic arousal (e.g. sympathetic nervous system activation) [6], which, in turn, leads to increased energy expenditure to result in the production of body heat, translating into an elevated BT [1]. As discussed in detail in Chapter 2, an elevated nocturnal BT can potentially interrupt a person’s sleep. In contrast, social exclusion has been shown to be related to the perception of low ambient temperature as well as the experience of stress/affective distress. For example, Zhong and Leonardelli [7] showed that people who recalled a social exclusion experience tended to provide lower estimates of room temperature than those who recalled a social inclusion experience. Similarly, participants who were socially excluded from an online virtual interaction task reported a greater desire for warm food and drinks, relative to those who were socially included. Somewhat differently, IJzerman and Semin [8] showed that warmer conditions (i.e. warm vs. cold beverage or room) induced greater social proximity in the participants than colder ambient conditions. Finally, IJzerman and colleagues [9] found that

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social exclusion (from an online ball-tossing game) reduced the participant’s finger temperature, whereas the negative affect induced by the social exclusion was alleviated by holding a cup of warm tea. Taken together, this interesting research suggests that the experience of coldness is integral to the experience of social exclusion. However, it is unclear exactly how this biopsychosocial phenomenon is best explained, especially as social exclusion is typically linked to stress/affective distress, which in turn is linked to an elevated BT. Nonetheless, the perception of cold in these studies was mirrored by a real fall in peripheral BT [9], suggesting that a drop in skin temperature (but perhaps not core BT) underpins the phenomenon.

7.2

Symptoms and Elevated Body Temperature

Fatigue (i.e. pervasive sense of tiredness or lack of energy that is not related exclusively to exertion) [10] is reported to be linked to an elevated BT. Fatigue commonly coexists with depression, impaired sleep and overweight/obesity. For example, in multiple sclerosis patients, fatigue severity is correlated with physical disability (r = .3) and depression (r = .4), but only depression independently predicted fatigue, not age or disease or treatment factors [11]. Similarly, Brown et al. [12] found that depression strongly predicted fatigue severity 3 months later in multiple sclerosis patients, and fatigue also predicted later depression. Further, in a prospective population-based study, Addington and colleagues [13] showed that a history of dysphoric episode and the number of somatization symptoms strongly predicted new-onset fatigue and recurrent/chronic fatigue over 13 years, whereas a history of unexplained fatigue at baseline and follow-up was linked to the greater risk of new depression onset (Major Depressive Disorder), relative to patients who did not have fatigue. However, fatigue has received relatively little attention as an independent symptom of impaired sleep or in relation to impaired sleep, and it is frequently confused with tiredness/sleepiness [14]. However, Lichstein and colleagues [14] showed that fatigue was related to sleep disorders, especially insomnia, in 206 people with severe fatigue. Fatigue severity at 12 months was predicted by low per cent sleep efficiency but not daytime

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sleepiness, suggesting that daytime sleepiness and fatigue are independent phenomena. However, in another study, a change in insomnia severity predicted fatigue changes 1 year later, after 3.5 weeks of inpatient psychological therapy; but when anxiety and depression changes were included in the model, they (but not sleep) predicted the fatigue [15]. The results may indicate that affective distress (e.g. anxiety, depression) and impaired sleep together contribute to fatigue, although affective distress may make the greater contribution. This assertion is corroborated by recent results showing that non-depressive rumination mediated the relationships between stress, anxiety and depression to poor sleep quality, and poor sleep quality mediated the relationship between rumination to fatigue [16]; although the results were only cross-sectional. Nevertheless, the results suggest that rumination may at least partly explain the tendency of stress/affective distress to impair sleep, and together with poor sleep, they may at least partly explain the fatigue in some individuals. However, as discussed in Chapter 6, a bidirectional relationship is not always evident between low mood and sleep; for example, depressed mood in adolescent students predicted less total sleep time on school nights and longer sleep onset latency on weekends 1 year later, but impaired sleep did not predict later depressed mood [17]. As detailed below, these inconsistent study results may be at least partly due to the overlap in (or comorbidity between) depressed mood, fatigue and impaired sleep. Fatigue is also known to be linked to overweight/obesity and proxy measures of it; for example, it is predicted by higher BMI [18]. In a study of 103 truck drivers, of whom one-half (53.4%) were obese, the obese drivers were 1.22–1.69 times more likely to be rated as fatigued, using two fatigue measures, and 1.99 times more likely to be fatigued while involved in an at-fault safety-critical incident, relative to non-obese drivers [18]. Thus, obesity is linked to fatigue, sleep problems and reduced satisfaction with general health functioning and vitality [19], although it is noteworthy that a diagnosis of severe obesity (BMI ≥ 40 kg/m2 ) can actually preclude a patient from receiving a fatiguing illness (e.g. chronic fatigue syndrome) diagnosis [20]. Taken together, the results suggest that fatigue is rarely experienced on its own; rather, it is likely to be concurrent with other symptoms and disorders, including impaired sleep/insomnia, depression, overweight/obesity

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and chronic illness diagnoses [21–23]. Bidirectional relationships have also been documented between depression and fatigue, and fatigue and impaired sleep, but it is unclear exactly how the symptoms (or disorders) are related to each other. That is to say, in practice, it is often difficult to disentangle anxiety, depression, fatigue and impaired sleep in longitudinal predictive studies, as they often strongly and bidirectionally predict each other; their symptoms overlap to some degree; and further complicating this situation, anxiety, depression and impaired sleep can manifest as the greater severity of somatic complaints, including fatigue, as detailed in Chapter 6 and this chapter. Clinically, fatigue is the most frequently reported symptom by multiple sclerosis (MS) patients (53–87%) [23–26] which is typically persistent and moderate to severe in intensity [27]. It is also one of the most common symptoms reported by primary care patients (e.g. 25% with some degree of fatigue) [28]. Further, moderate-to-severe fatigue is typically present in chronic fatigue syndrome (CFS; Centre for Disease Control and Prevention [CDC], 2012) patients [29]. CFS is a relatively rare condition (e.g. prevalence estimates = 37.1 cases/100,000 people) [30] that is characterised by severe chronic fatigue that lasts at least 6 months and which is not significantly relieved by rest. It is a highly debilitating condition that can negatively impact upon daily living, employment and quality of life in affected individuals [31]. For a CFS diagnosis to be made, the fatigue must be accompanied by other symptoms including unrefreshing sleep, joint and muscle pain, tender lymph nodes and/or cognitive or mood disruption [29]. Clients with depressed mood [28] and anxiety [32] also often report significant fatigue, as do patients with medical conditions such as cancer and diabetes mellitus type-II [33, 34]. Finally, fatigue is reported by a significant proportion of otherwise healthy adults (e.g. 17– 22%) [35–40] including community samples [37, 40], healthy working adults [38, 39] and university students [35, 36], although the fatigue is typically transient and mild to moderate in intensity [35, 36]. Similarly, about two-thirds of adolescents report experiencing morning fatigue that can impair their waking more than once/week, which peaks in the transition to high school [41]; in this case, the fatigue is generally attributed to an imbalance between educational, social, other (e.g. sports) demands and physiological debt due to rapid growth and sexual development.

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As detailed below, many different factors have been advanced to explain the cause/s of fatigue in the scientific literature: It is variously explained by infection, post-viral syndrome, cytokines, medical illness, impaired sleep, stress (e.g. perceived stress, life event stressors, work stress) and certain cognitions and behaviour (e.g. all-or-nothing behaviour, catastrophizing, somatic preoccupation, neuroticism, fear-avoidance, escapeavoidance) and genetic factors. For example, in a Norwegian twin study (746 monozygotic & 770 dizygotic twins, aged 50–65 years), the heritability of fatigue was found to be moderate (.53), but it had both genetic and environmental risk factors. In particular, shared anxiety/depression symptoms were reported to be genetically related to fatigue (r = .73), whereas individual-specific environmental correlations with fatigue were only moderately correlated (r = .33) [42]. Stress/affective distress (i.e. perceived stress, negative life events, negative affect) at the onset of infection has been shown to be related to fatigue development in the acute phase of parvovirus B19 infection in primary care patients; it predicted chronic fatigue and arthritis 1–3 years later and CFS/myalgic encephalomyelitis (CFS/ME) caseness 1–3 years later. Taken together, the results suggest that the stress/affective distress–fatigue relationship may at least partly reflect the effects of intercurrent infection [43, 44]. For example, stress/distress at the onset of infection may be an early unrecognised symptom of the infection (e.g. irritability). However, it is also appreciated that psychological stress results in stress hormone (e.g. cortisol) release that can suppress immune system functioning, thereby, potentially increasing a person’s susceptibility to infection [45–47], which may then induce the fatigue. That is, fatigue may result from the effects of stress-induced immune suppression that increases the risk of infection, which then causes fatigue. Consistent with this assertion, acute fatigue is known to be a common manifestation of certain viral infections such as Epstein–Barr virus (EBV) infection. For example, a Norwegian study [48] examined 195 adolescents and young adults (12–20-year-olds) at baseline and 6 months after EBV infection. At 6 months, 91 of 195 (46.7%) participants had chronic fatigue using the Chalder fatigue questionnaire, 27 (13.8%) had chronic fatigue based on the Fukuda definition, and 20 (10.3%) had it based on the Canadian definition. Fatigue severity 6 months later was predicted by

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prior negative life events, high plasma C-reactive protein levels, clinical symptoms, functional impairment and negative emotions [48]. Thus, viral infection is likely to contribute to the development of chronic fatigue, but it may be perpetuated by other factors; for example, as detailed below, it might be perpetuated by infection-related cytokine release, physical deconditioning and/or sleep disturbance. Symptoms of infection (e.g. fever, gastrointestinal [GIT] symptoms) are also observed to precede or co-occur with fatigue onset in CFS patients [49–52] which likely indicates that an infection has caused the fatigue [53], possibly via an abnormal host response to infection [44]. Similarly, cytokines (i.e. immune transmitters), including the expression of tumour necrosis factor-α mRNA, have been shown to be elevated in MS patients with significant fatigue (Fatigue Severity Score > 4), relative to non-fatigued MS patients. Results suggest that inflammatory mediators such as cytokines, which are known to be activated in response to infection, may be involved in causing MS-related fatigue [54]. However, in contrast, Hickie et al. [55]. found that only the initial symptoms of infection (i.e. EBV, Q fever or Ross River virus)—not the infection itself or the host immune response to it —predicted later CFS onset in their study. CFS caseness was predicted by the severity of the acute infective illness, especially fatigue, but not demographics, psychological symptoms or serology. A prolonged fatiguing illness (i.e. disabling fatigue, musculoskeletal pain, neurocognitive difficulties, mood disturbance) was evident in 12% of the 253 patients at 6 months, of whom most met diagnostic criteria for CFS, although cytokine assays were not performed in this study. Taken together, the results of this study suggest that severe infection symptoms are most pertinent to the experience of chronic fatigue rather than serological (or possibly other) immune evidence of infection. Interestingly, gastrointestinal (GIT) [51, 56], glandular fever-like [53, 57], and cardiac symptoms and signs [58, 59] have all been reported to reliably precede or co-occur with fatigue in CFS patients. Similarly, glandular fever symptoms predicted fatigue in infectious mononucleosis patients [57]; generalised symptoms (e.g. chills) were related to fatigue risk in dengue fever patients [60]; GIT symptoms were related to worse fatigue in university students [61]; and flu-like illness and gastroenteritis were related

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to fatigue onset in healthy working adults [62]. Taken together, the results suggest that infections with certain features—that is, flu-like/generalised, glandular fever-like and/or GIT symptoms—may be especially likely to cause fatigue in clinical and non-clinical populations. However, there is substantial evidence that a combination of biological (e.g. infection), physical (e.g. physical de-conditioning) and psychological (e.g. stress) factors may contribute to the development of fatigue, including sleep disturbance, which in turn is a symptom of fatiguing illness and stress/affective distress [32, 63–66]. This assertion is consistent with prior study results showing that high perceived stress [67–71] and stressful life events [12, 35, 68] are related to worse fatigue in MS patients and university students, respectively; work-related stress predicts fatigue severity in healthy working adults [38, 69, 70]. Thus, acute and chronic stress has been shown to be linked to fatigue [68]. Additionally, the assertion is consistent with results showing that fatigue is related to stressful life events, lower social support satisfaction, poor sleep quality and the use of sleep medications [16, 35]; poor sleep quality mediates the stress–fatigue relationship in people with undiagnosed fatigue; and the relationship was additionally mediated by low social support in females [35]. Finally, the assertion is consistent with results showing that the key risk factors for fatigue in a sample of 2494 Indian women included low BMI, socioeconomic hardship (e.g. feeling hunger in past 3 months, less education, family debt), domestic violence from the husband and poor mental health [72]. Taken together, the results suggest that many different factors may predispose an individual to experience fatigue (e.g. work stress, impaired sleep), and these and other factors (e.g. viral infection, physical inactivity) may also precipitate and/or perpetuate the fatigue [35, 38, 55, 57, 71]. Fatigue is known to be integrally linked to an elevated BT; for example, fatigue tends to precede or be concurrent with infection symptoms that include fever, as well as post-exertional malaise, pharyngitis, myalgia, adenopathy (i.e. swollen and painful lymph nodes), GIT symptoms, sleep problems, cognitive impairment [49, 58, 73] and depression [74], in medical patients [75] and CFS/ME patients [51, 73]. Fever is an adaptive compensatory mechanism that activates the immune system, decreases bacterial and viral growth rates and improves host survival in response to

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infection; in the case of mild-to-moderate systemic infection, it can accelerate recovery from the infection. It (or an elevated BT) can be induced by neuroendocrine, neurological, immunological and behavioural mechanisms, and in some cases, the fever is correlated with the severity of the infective illness [76]. Furthermore, even in the non-clinical setting, hyperthermia that was induced in healthy cyclists who wore water-perfused jackets heated to 40.1–40.2 °C was correlated with high heart rate and feelings of fatigue/exhaustion [77], suggesting that autonomic arousal and an elevated BT may at least partly explain the presence of post-exertional fatigue. Mechanistically, cytokines (i.e. small protein immune transmitters) that are secreted in response to infection can directly induce infection symptoms, including fatigue [78, 79]. However, cytokines are not exclusively secreted in response to infection; they are also released in response to other thermogenic stimuli, including exercise [80], physical stress [81], cold [82] and heat stimulation [83–85]. Furthermore, cytokine responses that are mounted in response to infection are reported to be largely indistinguishable from those that are activated in response to heatstroke/overheating, stress/autonomic arousal and exercise/physical activity [84–86]. In contrast, core temperature ‘clamping’ (e.g. immersion to mid-chest in a cold 18 °C water bath) has been shown to abolish these plasma cytokine and stress hormone (e.g. cortisol) responses [83]. In simple language, the stress hormone and cytokine responses to hyperthermia are similar, whatever their proximal cause; whether it is an emotional stressor, thermal stressor, physical activity or infection [86]. However, in febrile patients, the hypothalamic thermal balance point is reset to a higher level such that peripheral and central BT is sensed as cold temperature signals by the thermoregulatory circuitry. In contrast, in heatstroke/hyperthermia, the BT is elevated without a corresponding elevation in the thermal balance point. Thus, in the case of fever, and to meet the new balance point, heat loss is inhibited via skin vasoconstriction, potentially leading to chills, piloerection and behavioural adaptations (e.g. seeking a warmer environment); heat gain mechanisms are activated, potentially leading to rigours. Thus, fever is often characterised by chills, rigours, elevated BT and after the infection, later sweating and a fall in BT [76,

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87]. Nonetheless, as detailed above, the immune response to hypothermia is largely similar whatever its cause, and a persistently elevated BT can switch on both stress hormone and cytokine pathways. Therefore, in addition to the putative effects of BT on sleep, it is possible that the activation of these inflammatory pathways via an increase in BT, whatever its cause, could directly contribute to ill-health. The potential role played by stress hormones and cytokines in contributing to depressed mood is discussed in detail in Chapter 6, and their potential role in contributing to the development of diabetes in overweight/obese individuals is discussed in Chapter 4. Finally, chronic pain is related to perturbations in BT and it is highly comorbid with sleep problems, anxiety, depressed mood and obesity. For instance, people who frequently experience pain tend to report sleep difficulties [88, 89]; for example, in a community-based study of Australian adults, pain most strongly predicted impaired sleep, then anxiety, age, somatic health, and annual low household income (i.e. proxy measure of financial stress) [90]. In particular, arthritis and rheumatic pain are reported to especially disrupt a person’s sleep—contributing to longer sleep onset latency, poorer sleep quality and fragmented sleep [91, 92]—with similar results obtained in chronic pain outpatients. However, pre-sleep cognitive arousal, rather than pain severity, was shown to best predict poor sleep quality [93], suggesting that the extent to which pain causes hyper-arousal impacts the most on sleep, not pain severity. Longitudinal studies have consistently shown a significant predictive relationship between pain and impaired sleep; for example, a significant relationship was detected between pain severity at night and shorter sleep and more awakenings and nightmares in 28 burns patients, but not all of them experienced impaired sleep every day, suggesting that effective pain management at night can help to maintain a patient’s sleep [94]. Similarly, Nicassio and Wallston [95] showed that greater baseline pain intensity predicted worse sleep disturbance 2 years later in 242 patients with rheumatoid arthritis, after controlling for demographics, depression and functional impairment, but impaired sleep did not predict later pain severity. Thus, it is unclear whether a reciprocal relationship exists between impaired sleep and pain, although several studies have indicated that sleep deprivation, especially a change in slow-wave sleep, can adversely impact

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upon pain sensitivity [88, 96], suggesting that a bidirectional relationship does exist between impaired sleep and pain. Paradoxically, pain medications (e.g. morphine) are also reported to impair sleep quality [97, 98], potentially leading to a difficulty in distinguishing between pain-induced and pain medication-induced sleep deficits. Nevertheless, the aforementioned studies suggest that the experience of pain, especially at night, tends to impair sleep, including a difficulty in falling asleep and poor sleep quality. Furthermore, in the case that a person’s slow-wave sleep is compromised, then this may worsen the perception of pain; if an opioid pain medication is used, this may also tend to impair sleep. Chronic pain is also highly comorbid with anxiety and depressed mood. Two large population-based surveys have examined comorbidity between chronic pain and DSM-IV mood and anxiety disorders. Demyttenaere and colleagues [99] detected a pooled OR of 2.3 at 21 months, between comorbid neck and/or back pain and major depressive disorder, relative to neck/back pain alone and the pooled OR for any anxiety disorder was 2.2, even after adjusting for age, sex, race/ethnicity and education, in a crossnational survey of 85,088 community-dwelling adults across 18 countries. Similarly, Von Korff and colleagues [100] found that the 12-month coprevalence estimates for comorbid chronic pain and any mood disorder was 17.5%; with major depression being the most common comorbid mood disorder. Specifically, the pooled OR for the major depressive disorder was 2.5, and it is was 2.3 for any anxiety disorder, in a sample of 5692 community-dwelling US residents. Thus, it is evident that people with chronic pain have at least twice the risk of developing an anxiety or depressive disorder, even in the non-clinical setting; in turn, negative affect may contribute to greater pain sensitivity, potentially via autonomic hyper-reactivity, hypervigilance to the pain or another mechanism (e.g. worry, avoidant behaviour) [101]. As might be expected, concurrence between pain and affective symptoms is even higher in chronic pain patients. For example, 82% of 728 patients who awaited treatment in the Canadian multi-disciplinary STOPPAIN project endorsed depression symptoms, and 56% of them reported moderate-to-extremely severe depression and 34.6% reported suicidal ideation [102]. Thus, anxiety and depressive disorders are common in

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chronic pain patients in the clinical setting. However, as morphine use has been shown to be linked to stress/affective distress [103, 104], it may not be the case that opiates can fully reduce a person’s pain-related distress. Chronic pain is also frequently comorbid with overweight/obesity (e.g. arthritis, low back pain) [105]. For example, in a US population study of 3637 adults, people classified as Class I obesity (BMI = 30–34.9) were 1.76 times more likely to report severe pain; people with Class II obesity (BMI = 35–39.9) were 1.89 times more likely to report severe pain; and those with Class III obesity (BMI ≥ 40) were 2.30 times more likely to experience severe pain, relative to normal-weight and underweight individuals—with similar trends for moderate-to-severe pain [106]. However, fewer longitudinal studies have been conducted, but they tend to show that comorbid obesity is common in chronic pain conditions and that pain complaints are commonly reported by obese individuals. Several potential mechanisms have been advanced to explain the comorbidity including mechanical/structural factors, chemical mediators, metabolic abnormalities, depression, sleep and lifestyle, but the relationship is likely to be multifactorial. Nonetheless, weight loss is an important aspect of pain rehabilitation in obese chronic pain patients; that is, the pain is likely to be reduced secondary to a person’s weight loss, although few studies have confirmed the assertion [107–109]. However, few studies have explicitly examined pain in regard to BT, other than in the cold pressor test (e.g. 1-minute immersion of hand in ice water) [110], which is linked to an increase in heart rate, blood pressure and SNS activation. Nevertheless, exposure to a cool ambient environment (15 °C ± 0.5 °C) can decrease skin temperature, which was linked to reduced thermal pain perception, relative to neutral (25 °C ± 0.5 °C) and warmer environments (35 °C ± 0.5 °C), in a study of 10 healthy volunteers, suggesting that human pain perception may be dependent upon ambient temperature [111]. In contrast, fibromyalgia syndrome (FM), which is characterised by widespread pain, is linked to low BT, and it is exacerbated by cold and stress and temporarily relieved by warmth. FM patients also often report non-restorative sleep, fatigue, cold intolerance and neuroendocrine abnormalities, including an elevated heart rate, low metabolic rate and low skin temperature and vasoconstriction in the skin overlying the tender joints. Additionally, there is an elevated risk

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of obesity, insulin resistance and hyperlipidaemia in the patients [112]. Further complicating this picture, a dose-dependent relationship has been observed between BT and opioid use; for example, a low morphine dose increases BT in rats, whereas a higher dose reduces BT [113]. However, due to the paucity of relevant studies, it is unclear exactly how the experience of chronic pain and its treatment is related to BT. Nevertheless, as detailed above, autonomic arousal states (e.g. stress, anxiety) and fatigue are known to be linked to an elevated BT, and as described in Chapter 2, an elevated nocturnal BT can interfere with sleep onset. Thus, it is possible that the thermogenic effects of stress, anxiety, fatigue and possibly chronic pain, can at least partly explain the concurrence that exists between these symptoms and impaired sleep, possibly via an increase in nocturnal BT. However, additionally, immune-mediated pathways may be activated in response to thermogenic stimulation, as detailed above, in regard to fatigue.

7.3

Exercise, Sleep, Affective Distress, Overweight/Obesity and Body Temperature

Volitional physical activity is known to improve sleep [114] and well-being, and it is linked to the experience of reduced stress and affective distress (e.g. depression) [115]. Specifically, in healthy adults, as activity levels increase, sleep onset latency tends to shorten and slow-wave sleep and total sleep time increases [116]. Regular exercise (i.e. ≥ once/week) is also correlated with fewer sleep complaints and less risk of developing a sleep disorder [117]; adults who meet the US Centers for Disease Control and Prevention (CDC) physical activity guidelines (i.e. 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity physical activity or a combination of both) are less likely to experience daytime sleepiness, relative to those who do not meet the recommended activity guidelines [118]. Randomised controlled trials (RCTs) have previously examined the benefits of physical activity on sleep in insomniacs. For example, a small

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16-week RCT incorporating aerobic exercise and sleep hygiene training showed that aerobic exercise led to greater improvements in sleep onset latency, sleep duration and sleep efficiency, relative to sleep hygiene training alone, in inactive older adults with insomnia, and the therapy resulted in less daytime sleepiness and depression than at baseline [119]. A recent Cochrane review has examined the effectiveness of physical activity programmes in treating insomnia in older adults [114], but only one trial met the study’s inclusion criteria, which showed that physical activity reduced sleep onset latency and improved sleep quality, sleep duration and quality of life in the participants. However, few studies have examined physical activity and sleep in the sleep laboratory setting. As part of the sleep heart health programme, the impact of vigorous exercise on sleep-disordered breathing was evaluated in 275 participants [120], using polysomnographic data and self-report questionnaires. At least 3 hours of vigorous exercise/week was shown to be related to a significant reduction in the risk of sleep-breathing symptoms, whereas a weaker relationship was detected between moderate-intensity exercise and insomnia prevalence. Taken together, the results suggest that even a few hours of regular moderate-to-high intensity exercise each week can improve sleep in the clinical and community setting. Nevertheless, the effectiveness of physical activity in treating sleep problems is likely to be dependent upon the specific type of sleep problem, severity of the sleep symptoms and the type of physical activity engaged in [114]. In particular, low-intensity exercise (e.g. tai chi) is not likely to improve a person’s sleep relative to higher intensity activities [121]. However, the timing of the physical activity can be critical in determining whether it improves or hinders sleep. For example, vigorous exercise undertaken late at night, especially just prior to bedtime, can lead to substantial pre-sleep autonomic arousal, which may potentially interfere with sleep onset. For this reason, late-night exercise is considered to represent poor sleep hygiene [122], whereas physical activity that is performed during the day, especially if it is regular, is expected to result in good quality sleep. In sharp contrast, a sedentary lifestyle (e.g. physical inactivity, desk job, watching TV) has been reported to adversely impact on health, including sleep [123]. Physical inactivity is weakly correlated with high job strain

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[124], and it is bidirectionally related to depression severity [125], suggesting that physical inactivity tends to co-occur with symptoms of stress and affective distress. On the other hand, physical activity has been shown to be linked to better mental health; for example, in a large cross-sectional study of 1,237,194 people aged 18 years or older (US CDC Behavioural Risk Factors Surveillance System survey) [126] matched on BMI, demographics, self-reported physical health and prior depression diagnosis, the people who exercised reported an average of 1.49 (43.2%) fewer days of poor mental health over the past month, relative to those who did not exercise. All exercise types were found to be related to lower mental health burden, especially team sports, cycling and aerobic and gym exercises, as were exercise durations of ≥45-minutes and frequencies of 3–5 times/week. Taken together, the results suggest that regular exercise can assist a person to fall asleep faster, sleep for a longer time, and may protect them from developing a sleep problem, whereas physical inactivity (or a sedentary lifestyle) tends to interfere with sleep. Several theories have been advanced to explain the way in which exercise impacts on sleep and mood: it may lead to beneficial physiological changes in homeostatic sleep regulation [127], including heat loss via bodily sweating; as a result, this may lower BT [128], which assists in sleep initiation, although the assertion requires testing in an empirical study. Alternately, it is theorised that exercise can improve sleep indirectly via its positive effects on mood and affective symptoms [129, 130]. For example, results from the British Birth Cohort Study (1958–2008) showed that less physical activity predicted later worse depression symptoms at all ages, whereas greater activity was related to a 19% reduced risk of later depression; depression in early adulthood predicted less physical activity in later life. Taken together, the results indicate that the activity–depression relationship is bidirectional, likely reflecting that physical activity can prevent the onset of mental health problems, whereas affective distress may hinder a person’s later engagement with activity [125]. Similarly, a recent review of the utility of physical activity programmes in improving mental health showed that exercise significantly reduced depression symptoms [130], and thus, it is likely that exercise can improve a person’s mood as well as their sleep.

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Mechanistically, volitional physical activity/exercise causes a transient increase in BT [131], which is paralleled by an increase in heart rate [132], energy expenditure and the production of body heat. This activityrelated increase in BT tends to be maximal when the activity occurs at around the trough of the circadian rhythm (4–6 a.m.), and it is smallest at the peak of the resting circadian rhythm of core BT (4–5 p.m.) [133]. Even non-volitional physical activity is linked to an increase in BT; for example, psychomotor changes in Alzheimer’s disease patients were related to a change in BT, especially psychomotor agitation (i.e. high nocturnal activity, later peak in activity), which was related to a phase-shift in the circadian rhythm of core BT (later core BT peak) and higher core BT, relative to healthy controls [134]. Furthermore, spontaneous physical activity (SPA, i.e. unplanned biologically driven behaviour such as fidgeting, foottapping, restless behaviour and activities of daily living; e.g. talking and the maintenance of posture) can transiently increase a person’s BT [135, 136]. Thus, volitional and non-volitional physical activity transiently increases BT; if the activity occurs just prior to bedtime, it may tend to interfere with sleep onset, by interfering with the normal evening decline in BT [137]. Interestingly, the positive effects of exercise on mood are thought to be at least partly mediated by the transient increase in core BT during exercise, which could potentially reset the circadian rhythm of BT [138]. However, paradoxically, a sedentary lifestyle is also linked to an elevated BT (e.g. increase in scrotal temperature in male sedentary workers) [139]. Taken together, the results suggest that a nonlinear relationship is likely to exist between physical activity and BT, such that physical activity and physical inactivity can both increase a person’s BT. However, it is not clear whether the elevated BT in sedentary individuals is transient (like the effects of physical activity) or if the change in BT tends to persist over time. Physical inactivity is also known to be related to high BMI [140] and higher overweight/obesity risk in non-obese individuals [141]. In contrast, physical activity can assist people to control their weight [142], and it facilitates long-term weight loss in overweight people [143], including in combination with dietary restriction [144]. A recent systematic review showed that long-term weight loss was related to higher physical activity levels, but the relationship was dependent upon the participants’

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adherence to the activity regimen [145]. That is, volitional physical activity programmes are an effective agent that promotes weight loss in some overweight/obese people [145], but its’ effects will tend to be suboptimal if the activity is of low intensity or the participants fail to properly adhere to the activity programme [146]. Importantly, most (>75%) of a person’s daily energy expenditure is linked to basal metabolism, food intake (i.e. diet-induced thermogenesis) and cold exposure (i.e. non-shivering thermogenesis), rather than physical activity [147]. In total, physical activity is comprised of obligatory physical activity (i.e. survival-oriented activities), voluntary physical activity (e.g. exercise) and SPA. Together, they are the strongest environmental determinants of total body and central abdominal fat mass. However, our obligate activity needs are largely absent in modern life and so, in practice, voluntary exercise/physical activity tends to exert little effect upon our total energy expenditure over time [147]. In contrast, most of the variability in daily energy expenditure (above baseline and independent of body size) is due to variability in SPA. SPA is linked to the expenditure of about 100–800 kcal/day [148, 149], and it contributes about 10% to the variability in BMI, similar to the proportion that is contributed by basal metabolic rate (12%) [150, 151]. Of particular relevance here, SPA has been shown to be inversely correlated with body weight and weight gain [152], such that overweight/obese individuals tend to show a dramatic decrement in SPA (i.e. 2 hours less/day) relative to lean individuals [153], who tend to be more active, restless and spend less time sitting or lying in bed, thereby burning more calories [150]. That is to say, SPA can contribute significantly to daily energy expenditure in lean people, but it appears to be greatly deficient in overweight/obese people. Physiologically, non-exercise activity thermogenesis (NEAT) is the energy that is expended during SPA. NEAT is thought to protect lean individuals from gaining weight during a caloric challenge by increasing activity levels after the overfeeding and decreasing them with underfeeding [149, 151]. NEAT is elevated in conjunction with high-intensity training [154], resistance training [155] and active work (e.g. agricultural and construction work), relative to sedentary office work [156]. In contrast, NEAT is markedly lower in overweight/obese individuals relative to lean or normalweight individuals [157], suggesting that a deficit in SPA (and NEAT) in

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overweight/obese people can contribute to weight gain over time. As discussed in Chapter 9, interventions that are capable of increasing SPA and NEAT may represent possible new targets for weight loss therapy [149], but so far, the premise has yet to be tested empirically. Finally, prolonged sitting has recently been shown to predict an elevated mortality risk. In a study of 222,497 adults enrolled in the Australian 45 and Up Study, sitting time was found to be positively related to all-cause mortality, after adjusting for sex, age, education, urban/rural residence, physical activity, BMI, smoking, self-rated health and disability. All-cause mortality hazard ratios ranged from 1.02 (4–8 hours sitting/day) to 1.15 (8–11 hours sitting/day) to 1.40 (≥11 hours sitting/day), and they were consistent across genders, age groups, BMI categories, physical activity levels and healthy participants vs. people with pre-existing cardiovascular disease or diabetes mellitus [158]. Taken together, the results suggest that prolonged sitting is a significant risk factor for all-cause mortality, although the mechanism underpinning this relationship has so far not been determined. Nonetheless, a small study of 11 healthy men has shown that rectal temperature, which is similar to core BT, was highest in people while they were standing (37.47 °C), slightly lower when they were sitting (37.26 °C) and lowest when they were supine (36.87 °C), whereas skin temperature was highest when supine (34.04 °C) and lower when either sitting or standing (33.49 vs. 33.48 °C), respectively [159]. In another study, normal sitting was associated with reduced blood flow volume and temperature in the lower extremities [160]. Taken together, the results indicate that different body locations provide a slightly different estimate of peripheral BT, which is typically lower than a person’s core BT [161], whereas a change in posture can induce a change in BT, with higher BT detected in people who are standing or sitting. In summary, in the scientific literature, physical inactivity and a sedentary lifestyle are linked to impaired sleep, affective distress and overweight/obesity, whereas physical activity can improve sleep, mood and weight. Furthermore, a bidirectional relationship has been detected between physical inactivity, impaired sleep, affective distress and overweight/obesity, as detailed above and in Chapter 3. However, it is not exactly clear how the different phenomena are related to each other, other

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than being temporally related. Nevertheless, an elevated BT (linked to physical inactivity) may parsimoniously explain the onset of sleep problems (e.g. phase-shift in the sleep-wake cycle) in sedentary individuals; over time, impaired sleep may contribute to low mood and weight gain, as detailed above, in the next section, and in Chapters 3 and 6.

7.4

Behaviour Linked to Impaired Sleep and Elevated Body Temperature

Many different sleep-disrupting behaviours have been examined in regard to sleep, and in most cases, the behaviour is also linked to an elevated BT. For example, in Chapter 3, we saw that late-night eating can interfere with sleep onset. It also tends to coexist with stress, affective distress and overweight/obesity, and it is associated with an elevated nocturnal BT. Similarly, many of the sleep-disrupting behaviour discussed in this section can interfere with sleep, and they are risk factors for other chronic conditions, including overweight/obesity, anxiety/depression and some physical health problems. For example, electronic device use (e.g. smartphones) is reported to adversely impact upon sleep, possibly via the effects of bright light exposure and/or increased nocturnal BT. A recent poll of 1508 Americans showed that 90% of them had used a device within 1 hour of bedtime, with older people (aged > 30 years) tending to favour TV watching, whereas younger people (1 °C increase in scrotal temperature in men, regardless of the leg position or use of a lap pad [175]. Thus, electronic device use, including watching television [176] and laptop computer use [177], is also associated with an elevated BT. As discussed in detail in Chapter 2, if a person practises sleep-disrupting behaviour at night which is linked to an elevated BT (or reduced melatonin secretion), it may potentially interfere with the onset of sleep. Substance use has been shown to interfere with sleep onset and to exert thermoregulatory effects, including a change in the circadian rhythm of core BT and altered sleep patterns [178]. For example, alcohol intake leads to an elevated BT, especially night-time alcohol use, which causes mild hyperthermia (+0.36 °C) and reduces the circadian amplitude of core BT by 43% in humans [179]. In contrast, a dose-dependent relationship has been shown to exist between morphine use and BT; for example, in a study of rats, low-dose morphine elevated BT, whereas a high-dose morphine reduced BT [113]. Thus, the nocturnal use of certain medicinal or nonmedicinal agents may potentially interfere with the onset of sleep, via an increase in BT and/or an alteration in the sleep-wake cycle. Antidepressant therapies including antidepressant drugs [180] and bright light therapy have been shown to lower nocturnal BT (e.g. in seasonal affective disorder patients) [181], whereas bright light therapy normalises the circadian rhythm of core BT in anorexia and bulimia nervosa patients [182]. However, the chronic administration of selective serotonin reuptake

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inhibitors (SSRIs) can lead to an overall increase in BT [183], whereas electroconvulsive therapy increases or decreases BT [184], and amphetamines consistently elevate it [185]. Thus, it is possible that the chronic administration of SSRIs can impair sleep (e.g. reduced slow-wave sleep) [178] via an increase in BT, although to the author’s knowledge, such a premise has yet to be examined. Shift work (e.g. night shift, rotating shift) can impair sleep in shift workers, relative to daytime workers [186, 187], especially if they try to fall asleep during the day at a time when their body is promoting wakefulness [188, 189]. Shift work is also linked to high stress and affective distress [190], including greater work stress and role overload [191]. Similarly, young (but not older) male shift workers showed higher cortisol levels than day workers [192], and they reported higher anxiety levels [193]. Further, a recent systematic review showed there is preliminary evidence that shift work can contribute to weight gain over time [194]. Additionally, shift workers are reported to show a smaller BT amplitude relative to dayshift staff [195, 196], and the amplitude was smaller the more night-shifts the workers completed over an eight-day period [196]. Finally, night shift workers are reported to experience a delayed BT acrophase (by 4-hours), relative to day-shift workers, and the changes are linked to sleep disruption and poor tolerance to shift work [195]. Thus, the nature of night shift work, which typically involves sleeping during the day and working at night, appears to conflict with the sleep-wake cycle and the circadian rhythm of core BT. Circadian rhythm dysfunction is the mechanism by which shift work is likely to interfere with a worker’s sleep, although some adaptation may occur in long-term night shift workers. In summary, a variety of sleep-disrupting behaviours are known to interfere with sleep, and in many cases, they are also risk factors for anxiety/depression and overweight/obesity, and they can potentially increase a person’s BT. As discussed in detail in Chapter 2, if a person practises sleep-disrupting behaviour at night which is linked to an elevated BT (or reduced melatonin secretion), it may potentially interfere with the onset of sleep. However, it is certainly appreciated that detecting a relationship/s between certain states, symptoms, disorders and behaviour is not a demonstration of causality, even if the factors are longitudinally related to each other. That is to say, just because two conditions are highly comorbid with

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each other or concurrent with affective distress or a particular behaviour, it does not imply that the distress or the behaviour was responsible for the comorbidities. Furthermore, just because a phenomenon is consistently linked to an elevated nocturnal BT, this does not necessarily mean that an elevated BT has caused the observed relationship. However, in many cases, there is substantial evidence showing that the aforementioned states, symptoms, disorders and behaviour are linked to an alteration in circadian rhythm functioning in affected individuals. Such results do tend to suggest that the phenomena may, at least in part, be caused by circadian rhythm dysfunction. There is insufficient space in this section to detail the methodological and statistical limitations that accrue to correlational research, in particular, its limited utility in helping us to fully appreciate the nature of the linkages between comorbid conditions and concurrent states, symptoms and behaviour, although such a detailed discussion is provided in Chapters 2 and 8. Finally, irrespective of these methodological and statistical considerations, we have provided considerable evidence in this chapter which shows that autonomic arousal (or affective states; e.g. stress, anxiety), symptoms (e.g. fatigue, chronic pain), sleep-disrupting behaviour (e.g. physical inactivity, electronic device use, TV watching, shift work) and medications or non-medicinal substances are linked to impaired sleep and, in many cases, also affective distress and overweight/obesity. In many cases, the phenomena are also linked to an elevated BT, and in some cases, a high nocturnal BT, although there is a lack of specific research pertaining to nocturnal BT, and the relationship between BT and chronic pain. As discussed in detail in Chapter 2, a relative hyperthermia at night is known to potentially interfere with sleep onset, possible via a phase-shift in the sleep-wake cycle. However, an elevated BT can also lead to activation of the inflammatory response system (e.g. cytokine secretion), which may represent another possible mechanism by which the aforementioned states, symptoms, disorders and behaviour can develop.

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8 Design, Statistical and Methodological Considerations: Comorbidity Einar Thorsteinsson and Rhonda Brown

8.1

Methodological Approaches

In this section, several methodological issues related to the measurement of disorders and symptoms will be discussed in detail. In the scientific literature, disease comorbidity is typically evidenced by high co-prevalence estimates between the different diagnoses, whereas symptom concurrence is evidenced by moderate to high correlations between two or more composite score measures (e.g. total construct scores), using validated questionnaires [1]. Study methodology issues related to the comorbidity between different disorders will be dealt with first and then symptom concurrence will be examined. To illustrate the extent of overlap between disorders, we will briefly revisit the diagnostic criteria for generalised anxiety disorder E. Thorsteinsson (B) University of New England, Armidale, NSW, Australia e-mail: [email protected] R. Brown Australian National University, Canberra, ACT, Australia e-mail: [email protected] © The Author(s) 2020 R. Brown and E. Thorsteinsson (eds.), Comorbidity, https://doi.org/10.1007/978-3-030-32545-9_8

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(GAD), major depressive disorder (MDD), insomnia disorder (ID), and chronic fatigue syndrome (CFS). A GAD diagnosis is based on excessive anxieties related to various life issues, which lasts more than 6 months, and with symptoms that include feeling on edge, irritability, difficulty concentrating, tiredness/fatigue and sleep problems [2]. MDD is characterised by a loss of interest/pleasure in all activities lasting for at least 2 weeks, which is associated with five or more other symptoms including somatic symptoms (e.g. changes in appetite, sleep, fatigue), neuropsychological changes (e.g. difficulty thinking, concentrating, making decisions), and affective/cognitive changes (e.g. feelings of worthlessness/guilt, suicidal ideation) [2]. ID can be diagnosed in people who experience prolonged impairment of sleep quality, including difficultly falling asleep and/or staying asleep, and feelings of fatigue, irritability, and tiredness [2]. In contrast, CFS is diagnosed on the basis of chronic fatigue that is associated with impaired sleep, muscle or joint pain, enlarged lymph nodes, and/or impaired concentration [3, 4]. Thus, it is evident that there is considerable overlap between the different disorders, including the occurrence of sleep problems, fatigue, and impaired concentration. To separately illustrate the range of methodological issues pertaining to the measurement of overlapping symptoms, the discussion will be limited to phenomenological and statistical overlap between anxiety, depression, fatigue, and highly concurrent symptoms. As detailed above, anxiety and depressive disorders are especially highly comorbid and they also share a number of symptoms in common, including impaired sleep, fatigue, and gastrointestinal (GIT; e.g. change in appetite) symptoms; and some symptoms of fatiguing illness also overlap with anxiety and depression symptoms [2]. As discussed in the next paragraph, in some cases, there are clear published protocols for the removal of symptom overlap when assessing the symptoms together. For example, protocols that outline which somatic items (e.g. sleep changes) should be removed from the depression measure to reduce the extent of artefactual overlap between depression and fatigue, which occurs as a result of double counting the symptoms as belonging to both depression and fatigue. Such an approach will tend to reduce the extent of the over-inflation of the observed relationship between depression and fatigue, which will permit a clearer understanding of the nature of the relationship, as discussed below.

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8.1.1 Removal of Overlapping Scale Items A correlation between two concepts can be inflated if the two concepts are measured using scales that contain similar (i.e. shared) items, including shared items on depression and fatigue scales. For example, the Depression, Anxiety, Stress Scales (DASS) contains a depression item ‘I was unable to become enthusiastic about anything’ which is similar to the Fatigue Severity Scale (FSS) item ‘My motivation is lower when I am fatigued’. Thus, if the DASS and FSS are used in the same study, at the least, it might be necessary to examine the inter-scale item correlations between the scale scores. Additionally, to reduce the risk of inflated correlations between the related concepts, the shared items could be removed from each (or one) scale to generate new scales, with new total scores, to reduce the extent of potential over-inflation of the correlation coefficients for the total scores. Similarly, it is apparent that when sleep problems, depression symptomology, and fatigue are examined together in the same study, the strength of the association between the measures could be overestimated [5]. As mentioned above, in some instances, there are clear protocols for the removal of shared items; for example, the Beck Depression Inventory-II [6] has a protocol for removing somatic depression symptoms so that they are not counted twice in studies examining depression and co-occurring fatigue and impaired sleep. That is to say, researchers can calculate a measure of ‘cognitive’ depression minus the somatic symptoms of depressed mood, which can then be related to somatic symptoms such as fatigue. Such accommodations are especially important when examining mental health symptoms in patients with chronic illness (e.g. multiple sclerosis). However, as detailed below, few other published protocols exist for the removal of the shared symptoms of the disorders (e.g. impaired sleep and anxiety). Similarly, maladaptive coping and behavioural measures can tend to assess different elements of the same behaviour, potentially resulting in an overinflated correlation between them. As an example, one adolescent coping scale called the Measure of Adolescent Coping Strategies [7, 8] includes a maladaptive sub-scale (acting out) which assesses drug use. It asks adolescents to rate each statement using 4-point scales from 1 (I did not use) to 4 (I used almost all the time) in regard to statements such as:

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‘I tried to make myself feel better by drinking alcohol, smoking, using drugs or medications’ [7, 8]. Thus, if a researcher examines ‘acting out’ in regard to behavioural drug use, the association between it and drug use will likely be inflated. However, in this case, there is no clear protocol for the removal of the overlapping items. As a consequence, a researcher will need to examine the statistical effects of the overlap and potentially control for it, or alternately make other accommodations (e.g. item removal), if necessary. Thus, there are scale-based methods that can assist researchers in reducing the extent of artefactual overlap between different ‘symptom groups’, but only for some symptoms/disorders (e.g. depression and fatigue). Therefore, there is a clear need to develop other evidence-based protocols for the removal of overlap between symptoms (e.g. impaired sleep and fatigue) that are commonly assessed together. However, alternately, there could be a theoretical reason not to make adjustments for the extent of the overlap, as any adjustment runs the risk of affecting the reliability and validity of the scales being used and potentially underestimates the extent of the relationship, which may have implications for examining possible common causal pathways. For example, if impaired sleep is causally linked to fatigue via tiredness, then removing the overlap between the measurement of sleep and fatigue, may obscure the assessment of the putative causal relationship. However, at the least, there is a need to more clearly operationalise the different disorders and their symptoms, especially in regard to their shared symptoms. If the reader wants to read more about some of these approaches in greater detail, there is a plethora of appropriate available books on the topic [9–11]. In the case that a published (item removal) protocol is not available, it might be tempting for a researcher to remove several overlapping items, especially in the case that there are a large number of questionnaires administered to the participants. For example, it might be tempting to remove items from an anxiety scale if they overlap with items in the fatigue scale. However, this may be problematic as it will be impossible to re-include the items later in the study, which may result in the inability to: (a) compare the fatigue and depression/anxiety scores to results from other studies; (b) assess the validity and reliability of the scales; and (c) merge your dataset with another dataset. After all, if you make sure to include all the items

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to begin with, you can always remove them from your calculations and analyses at any time, but you cannot add the items back. An additional issue in presenting similarly worded items in different questionnaires is that the participants may easily tire of the repetition. Thus, a short statement at the start of the questionnaire might need to explain to them that there is a degree of repetition in the questions, but to answer all of the items to the best of their ability. As a side note, if a researcher is presenting an online questionnaire using a good ‘questionnaire delivery system’, then it may be useful to consider randomising the order of scales. However, that said, to reduce the risk of the participants dropping out of the study, it may be best to place the distressing items towards the end of the questionnaire battery. Furthermore, researchers may like to put key measures (e.g. dependent variables) in the early part of the questionnaire, in case the participants drop out halfway through thus, leaving the researchers with some useful information. Thus, a strong argument could be mounted to present the full version of the original scales (i.e. with all items included) to study participants, which would enable the researcher to examine the scale scores (e.g. depression) with and without the different items removed (i.e. raw vs. raw-itemsremoved). The inclusion of the full-scale measures should prevent any post-sampling regrets, for example, if the researcher wished to calculate the total depression score to compare with the normed values for depression on that scale, which would be impossible if all the scale items had not been included. Finally, an alternate approach could be used to examine the relationship between different ‘symptom groups’ (e.g. anxiety, depression) [1]. Specifically, as each symptom group is comprised of a subset of symptoms, each could be conceptualised as separate but related sub-entities [1]. Such an assertion was made by Schmittmann and colleagues [12], who have conceptualised psychological attributes (e.g. anxiety) as ‘networks of directly related observables’. That is, a single construct (e.g. anxiety) could be conceptualised as a network of related variables that includes somatic arousal, cognitive arousal, and fear cognitions, which overlap with other constructs (e.g. depression) to some degree. That is to say, rather than being a single cohesive symptom group, anxiety might be better conceptualised as a clinical phenomenon that is comprised of somatic arousal, cognitive arousal,

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and fear cognitions, whereas depression could be comprised of negative affect, negative cognitions, and generalised (e.g. fatigue) and organ-specific (e.g. GIT) somatic symptoms. Structural equation modelling might be an especially useful approach in testing such a complex model, comprised of different constructs, that each has separate (but overlapping) subsets of symptoms. For example, a dependent variable (anxiety) could be comprised of a latent variable that was comprised of somatic arousal, cognitive arousal, and fear cognitions. Such an analytic approach has obvious utility—it could permit the modelling of different risk factors in regard to the separate aspects of each construct, with the expectation that there will be different risk factor–outcome relationships for different aspects of the construct. For example, ruminative thinking may more strongly predict anxiety-related fear cognitions than anxiety-related somatic arousal. However, to the author’s knowledge, no empirical studies have used such an analytic approach to model potential causal relationships between variables. Nevertheless, in recent years, the potentially separable aspects of the anxiety construct have been assessed in regard to anxiety disorder causation; for example, anxiety-sensitivity (i.e. fear of anxiety symptoms) and intolerance of uncertainty have been examined as separate (but overlapping) cognitive entities that are integral to (but separable from) state anxiety and the presence of anxiety disorders.

8.1.2 Longitudinal and Experimental Studies In this book, we have sought to present, where possible, meta-analytic results of prospective longitudinal studies, or if not, results from large prospective longitudinal studies. That is, we have provided evidence obtained from the pooled results derived from multiple studies (i.e. metaanalysis), or at the least, large studies that have examined the temporal relationship (over time) between the risk factors and later presence of the symptoms/disorders. As stated in Chapter 1, our current conception of causality typically requires that the cause of an event must precede its onset in time; thus, only longitudinal (and experimental) study results can fulfil that criterion, to a greater or lesser degree.

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At the least, it is necessary to look beyond correlational (cross-sectional) study results in an attempt to disentangle possible causal pathways that underpin the development of the disorders and symptoms. A longitudinal study design is also ideal for examining models that incorporate statistical mediation [10] and moderation analyses, which enables a researcher to use independent variables measured at Time 1 to predict mediators measured at Time 2 that, in turn, predict the dependent variable/s at Time 3. If the dependent variables are measured at Times 1–3, they can be used as covariates in the analyses; that is, their effects can be statistically controlled. Such an analysis would greatly assist a researcher in testing the potential mechanism/s that underpins a disorder or symptom, and understanding how the relationship evolves over time. As detailed in the next section, the approach would facilitate the testing of individual risk factors (or composite risk factors) as potential mechanisms over time, and also the likely indirect linkages between them and the symptoms (or disorders) over time. Nonetheless, some of the drawbacks of longitudinal studies include that the dropout rate is typically high, especially for online survey-based studies, especially in the case that sufficient time is required to pass to enable a significant change in the variables of interest. Without a significant change in the dependent variable over time, there will be insufficient changerelated variance that can be properly examined and predicted in the risk factor analyses. However, in contrast, this issue needs to be reconciled with the likely temporal effect of the risk factor, for example, whether it is short acting or is prolonged in its effects. This and other statistical approaches will be discussed in greater detail below. Experimental studies (e.g. randomised controlled trials; RCT) are required to properly establish causation. However, longitudinal and correlational studies do contribute to the scientific literature. In the first instance, they can be used to map out the extent of a relationship between two variables, as a prelude to conducting an experimental study. Additionally, study designs including longitudinal field studies which attempt to capture data that is reflective of ‘real life’ (rather than which is captured in an experimental lab environment) are also required, to interrogate the likely ‘causal pathways’ that have been established in experimental studies. For example, mindfulness-based therapies (e.g. mindfulness-meditation)

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have consistently been shown to improve mood in people with anxiety and/or depression symptoms. However, given the complexity of the therapy (e.g. various exercises tap awareness of bodily sensations, emotions and thinking), it has been difficult to pinpoint the exact mechanism/s that underpins the improvement. For example, some researchers suggest that the clinical improvement seen in clients on the therapy is due to their greater focus and reflection upon their ruminative thinking [13], whereas others suggest it is due to a greater focus on peripheral bodily sensations (e.g. in the case of progressive muscle relaxation), and various other mechanisms have been suggested [14]. Thus, mindfulness-meditation has obvious utility in improving mental health in at least some individuals, but it is unclear exactly how it works. In this case, a longitudinal study, which compares a number of competing potential mechanisms, would have obvious utility in helping to pinpoint the most likely mechanisms underpinning the observed improvement as a result of the therapy.

8.2

Statistical Approaches

The most important aspect of any statistical analysis is to be clear and transparent in documenting the statistical approach that has been taken. Thus, in a dataset care must be taken to document any manipulation of the variables (e.g. data transformation) in the syntax, variable labels, and value labels. Furthermore, when writing up the findings, researchers should make sure to clearly explain the statistical approach that was used in the method section of the paper, and if possible, provide open access to the dataset, so that readers can run different analyses using a different approach for comparison. Statistically, in analysing the relationship between two or more symptoms/states, it is typical to use one or more of the following analyses, including Pearson’s correlation coefficients (or Spearman’s rho); partial correlations; multiple regression analyses, including hierarchical multiple regression analyses; and structural equation modelling or path analysis.

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In regression analysis, residual scores capture the difference between the sample’s mean or best fitting regression line, as an example, and the individuals’ scores, thus capturing what has been left ‘unexplained’ by the regression line. These residual scores can be saved and used in further statistical analysis. For example, a study of depression and fatigue could run a regression analysis with depression as the predictor and fatigue as the outcome variable; and saving the residual score for fatigue would permit the researchers to create a fatigue variable, in which the variance explained by depression has been partialled out (removed). However, it could be argued that fatigue without any overlapping depression symptoms is not really fatigue at all, but a sub-component of fatigue. Similarly, as discussed above, partialling out the effects of fatigue on depression can give rise to a pure measure of ‘cognitive depression’, but this is only a subcomponent of depressed mood. As a result, any analyses conducted on the sub-components of depression and fatigue may not be replicated in future studies, which use the full versions of the depression and fatigue scales. Thus, it may be impossible to compare the study results with findings from previous studies that have used the complete scales. Similarly, a researcher might employ hierarchical regression analysis or partial correlations to enable the researcher to control for one variable and to get the correlation between the remaining variables. Thus, a researcher might be interested in the correlation between depression and anxiety, whilst controlling for fatigue, and this will work similarly to use residuals in regression analysis. However, instead of removing (or controlling for) the shared somatic symptoms of depression and fatigue, an alternate statistical approach could be to combine the depression and fatigue scores together into a latent variable, which, for example, might be used to test a model examining the effects of stressful life events on sleep quality. That is to say, if depression and fatigue were somewhat peripheral to the main research question (i.e. does stress predict impaired sleep?), then their scores could be combined and used as a covariate in the analysis. For example, the researcher may want to combine depression and fatigue using structural equation modelling (SEM) to create a latent variable that combines the two measures into a single latent construct (comprised of depression and fatigue) that captures

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the effects of depression and fatigue on health. The latent variable could then be used in the SEM to test the relationship between stress and sleep. Finally, longitudinal (and cross-sectional) study designs can enable a researcher to model multiple different symptoms and risk factors together (e.g. multiple structural equation models). This can be especially useful when the phenomenon under investigation, comorbidity, is most likely to be described in terms of (potentially) circular causal pathways, including the presence of bidirectional relationships and dysfunction in several biological systems. For example, in Chapter 3, we examined the premise that a night-eating-related increase in body temperature can parsimoniously explain the impaired sleep observed in people who eat late at night. As detailed below, the modelling of multiple different symptoms/disorders and risk factors together could be used to determine whether late-night eating precedes a change in body temperature or vice versa, and it might be used to test competing theories (e.g. impaired sleep was instead the result of the lighting that was used to prepare the night-time meal). That is, the models could test whether a light-induced change in melatonin secretion is most likely to explain the impaired sleep or a change in body temperature. Standard time-series modelling techniques can be employed to examine whether cause, X, correlates more strongly with an effect, Y, at a later time than Y correlates with X following on from Y. Such an approach would permit a determination of whether a change in body temperature followed late-night eating or vice versa. If late-night eating preceded the increase in body temperature more often than elevated body temperature preceded late-night snacking, this would suggest that late-night eating (or its causal antecedent/s) may have caused the elevated body temperature. In this example, there is also the question of latency: if late-night snacking affects body temperature how long does it take for the effect to take place and how long and strong will the effect tend to be? Standard time-series analytical techniques exist for ascertaining this lag and duration, and the more fine-grained the time-series data, the more accurate the estimates of lag and duration will be. However, as mentioned in the above section, it can be difficult to establish the optimal time interval between the various assessment points, in a longitudinal study design. For example, is 3-months sufficient follow-up

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time to allow changes to occurred in one or more of the outcome variables? Furthermore, the time it takes for a substantial change to occur in depression may be more (or less) than the time that is required for a significant change to occur in sleep. In the case that the two sets of symptoms have a different temporal evolution, problems can potentially arise in the study analyses. For example, if only a small change in sleep quality occurs over 3 months, then there will be little variance that might be predicted by late-night eating. Similarly, in an RCT (experimental) study, it is necessary to establish the optimal magnitude and duration of the therapy/intervention, as well as the magnitude and duration of any expected changes, to determine when it is best to observe the follow-up effects. For example, in an RCT that seeks to minimise late-night eating, the optimal components and duration of the therapy will need to be established, in regard to pinpointing the optimal effects on sleep. That said, this does not prove that causality exists, as it is always possible that a third variable, Z, is causally linked to both X and Y in such a way as to produce the cross-lagged differences in correlations. Therefore, it is still useful to employ ‘real life’ longitudinal field studies to identify potential causal relationships and then later test them in experimental studies to establish causality. Furthermore, a predictor variable (e.g. nighteating) may also affect other aspects of the data series being modelled (e.g. sleep over time); for example, night-eating may alter melatonin secretion and/or change body temperature. However, such an assessment may affect the autocorrelation structure of the body temperature series thus, potentially requiring a more sophisticated modelling and assessment approach. Autocorrelation (or serial correlation) is effectively a type of Pearson’s correlation coefficient that can be used to capture repeating patterns such as the circadian rhythm of body temperature. That is, for example, the circadian rhythm of body temperature (or the sleep–wake cycle) may require a more complex analytic approach, including the use of cosinor analysis, in which an algorithm that describes the cycle is examined [15]. Finally, in regard to the assessment of body temperature, a variety of covariates may need to be taken into account in the analyses, more so than if modelling other less complicated concepts. Specifically, and at the least, there is a need to consider controlling for various factors that are known to alter body temperature such as: ambient temperature [16], alcohol intake

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[17], age [18], caloric restriction [19, 20], drug use (e.g. mood stabiliser) [21, 22], caffeine use [23], and menopausal status [24].

8.3

Overlapping Risk and Protective Factors

As detailed above, overlapping (or shared) symptoms can result in measurement problems in studies that examine two or more comorbid disorders or two or more concurrent symptoms. However, additionally, many of the disorders and conditions mentioned in this book also share overlapping risk (and protective) factors, which may further complicate the analytic approach. Such factors may therefore need to be considered when designing the study, as discussed in detail in Chapter 9. As an example, using adaptive coping skills (e.g. seeking social support) may tend to buffer against the extent to which a stressor results in anxiety; thus, it might be regarded as a protective factor for anxiety, whereas maladaptive coping (e.g. escape-avoidance) tends to be regarded as a risk factor for anxiety disorder development. Furthermore, seeking out social support may also protect against the development of depression and sleep problems, possibly via a reduction in anxiety, but also by other possible mechanisms. Thus, for example, a statistical model may need to address the likely causal pathways between social support coping and anxiety, depression and sleep, and also possible interactions between them. Somewhat differently, as discussed in Chapter 7, electronic device use is a known risk factor for and likely precipitant of sleep problems (e.g. insomnia), mood disorders (e.g. depressed mood), weight gain, and overweight/obesity, although it is unclear whether similar (or different) mechanisms are involved in each case and if the disorders are also causally related to each other. However, irrespective of the mechanism/s involved in each case, there is a clear need to better integrate these overlapping risk factors together in more complex real-world models of disease causation. Furthermore, as some symptoms and disorders (e.g. anxiety) are also risk factors for other disorders (e.g. depression), there is a need to concurrently model the overlapping risk factors in regard to multiple related disorders. That is to say, if a risk factor is only considered in regard to a single disorder,

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then its real-world effects on overall health will be underestimated. In contrast, the putative effects of protective factors (e.g. seeking out social support) may in the same way be underestimated. Such a situation is likely to have clinical implications: for example, some risk factors (e.g. nighteating, impaired sleep) may be excluded or overlooked (in place of other better known risk factors such as binge-eating) in an intervention to treat overweight/obesity, despite the known relationship between night-eating, binge-eating, impaired sleep, and obesity status and weight gain over time [4, 25], note Chapter 3, Sect. 3.2. That is, night-eating and sleep hygiene practices could be included to good effect in the intervention to treat overweight/obesity, along with a focus on binge-eating, but this tends not to occur in reality, due to the aforementioned tendency of researchers to examine single disorders and limited risk factors. In regard to this example, the state of affairs is likely due to the strong focus on binge-eating behaviour in the clinical obesity literatures. However, more broadly, it is possible that a variety of different risk and protective factors will tend to interact with each other, to augment or attenuate any effects on different aspects of health. As detailed in Chapter 3, it is already appreciated that binge-eating tends to coexist with night-eating, impaired sleep, affective distress, and overweight/obesity, and each of the risk factors, including the symptoms, is in turn known to be related to each other. Thus, it is clearly time that researchers seek to model the extent of the real-world complexity that exists between known risk factors, protective factors, and related symptoms and disorders. As detailed in Chapter 9, most prior risk analysis studies have computed the odds ratios (or hazard ratios) individually for each separate risk factor, for each condition of interest; but they have rarely been computed for highly linked clusters of risk factors in regard to multiple comorbid conditions. Additionally, few prior longitudinal risk studies have evaluated a large pool of potential risk factors to determine which of them provides the most coherent explanation of the disorder/symptom. As a result, we know little about the way in which different (or related) risk factors interact with each other to contribute to two or more comorbid conditions. Thus, it is clear that in the future, we need to conduct complex risk analyses on clusters of linked risk factors and multiple conditions, rather than on single risk factors and single conditions.

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However, it is unclear what is the most appropriate analytic approach to use when a researcher wishes to test a complex real-world model, such as the one described. Nonetheless, the interactions among the different risk and protective factors and multiple outcomes are perhaps best incorporated into theoretical models that are tested using moderation type analyses [10] and SEM [11, 26], and also, causal models could be captured using mediation type analyses [10] and SEM [11, 26]. In all cases, a researcher will first need to interpret the findings carefully and consider exactly how they want to present the findings, including the reporting of odds ratio (OR), adjusted odds ratio (AOR) when other overlapping variables (e.g. confounding) are adjusted for, R 2 , or other indices.

8.4

Other Research and Data-Handling Approaches

Several other relevant issues are briefly described here, but not in any detail. First, given the recent advent of big data, many researchers can now access large datasets that include multiple related measures that are repeated over time, potentially including risk factor, symptom/disorder, and biological process data. Given our current ‘embarrassment of riches’, it might be useful to consider a ‘data mining’ approach to explore novel potential causal pathways, or novel ways of viewing the linkages between the different risk factors and outcome measures. Machine learning algorithms or artificial intelligence approaches could potentially be used to explore the data in this way. Such an approach will certainly be facilitated by the extensive access we researchers currently have to open-access datasets. Aside from using the datasets to conduct systematic analyses (e.g. meta-analysis), they could alternately be used to test a particular complex real-world model (of interacting risk factors and symptoms/disorders) in multiple datasets or in computer simulations of the data. However, as detailed in Chapter 2, few prior studies have sought to use such an analytic approach. Second, in regard to experimental trials (e.g. clinical RCTs), in most jurisdictions, it is first necessary to preregister the trial (e.g. Australian

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New Zealand Clinical Trials Registry; http://www.anzctr.org.au/). Similarly, CONSORT (Consolidated Standards of Reporting Trials, http:// www.consort-statement.org/) provides a checklist and a flow diagram template for reporting on the progress of randomised controlled trials. The checklist focuses on reporting the way in which the trial is designed, analysed, and interpreted. The registration process is required to ensure (and regulate) the quality of the research trial, minimise any harm to the participants involved in the trial, and provide oversight of possible ethical implications of the trial. Such a high degree of oversight of clinical RCTs has certainly led to an improvement in the quality of the research and it has ensured that any relevant patient considerations and ethical issues are properly addressed at the beginning. Unfortunately, for the most part, the same frameworks do not yet exist to assist researchers (and their participants) in optimising the design of non-experimental studies and the analysis and reporting of the study results. Nonetheless, a recently developed website AsPredicted (https:// aspredicted.org/) enables researchers to preregister their hypotheses, thus preventing them from changing the hypotheses to fit the particular study’s findings. Similarly, some journals are now encouraging researchers to submit results-free research articles (e.g. BMC Psychology) as part of a pilot programme, with the expectation that the results will be reported downstream. Such a submission process means that the journal editors and reviewers can evaluate the manuscript, blinded to the study results, at least in the initial stages of the review process. It is possible that this type of submission process may encourage journal reviewers to focus more on the quality of the research, rather than on the study results. Potentially, they could also offer editorial advice about the study design, if the feedback was provided in a timely manner. However, more broadly, such an approach has obvious merits in terms of providing better oversight of the methods that are used in research studies; and potentially, it could even maximise the research study’s potential. For example, a journal editor might suggest to a researcher that they include an additional explanatory variable in the study or choose a standardised measure of a construct, instead of (or in addition to) a novel measure, so that the results can be more easily compared to the results of other studies. Finally, de-identified datasets are

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increasingly being shared by researchers and research students in the public arena via websites such as figshare (https://figshare.com/). However, such an approach has yet to be standardised. Thus, in truth, there is a long way to go before we can fully understand the phenomenon of disease comorbidity, because at present, we have yet to put in place optimal frameworks and processes that might facilitate a fuller understanding of the phenomenon. As detailed in this chapter, such an understanding will only be achieved if we can better link the work of different researchers together in a more meaningful way, in particular, by improving access to more datasets that have been obtained from wellconducted studies.

8.5

Summary

As detailed in this chapter, sophisticated methodological and statistical considerations (and approaches) are required in the examination of comorbidity between different disorders, and concurrence between different symptoms, especially if examining the utility of complex causal models. With this in mind, researchers may need to make difficult decisions about the best way to deal with overlapping items on different symptom scales, which were designed without reference to different-but-related constructs (e.g. depression and fatigue). Further, this decision-making will need to be concurrent with any decision-making about preferred statistical approach as the wrong study methodology decision could preclude or limit the statistical approaches that might be available to the researchers. Furthermore, it is likely that the different mechanisms underpinning the comorbidity will require different methodological and/or statistical approaches. Of course, this decision-making will become even more complex if temporal (e.g. follow-up interval) or other considerations (e.g. duration of effect of the risk factor, interaction between risk factors) need to be addressed. Thus, practically speaking, it is difficult to optimally assess the potential linkages that likely exist between different comorbid symptoms/disorders and their risk factors, especially if one has to take into consideration all of the aforementioned methodological and statistical issues. In particular, it is difficult to optimally measure and evaluate the linkages that likely

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exist between the different phenomena, especially if one or more of them are causally linked to each other, as is asserted in this book, and also by Borsboom and colleagues [1]. Thus, aside from utilising (or developing) protocols for the removal of overlap between the measures of different-butrelated constructs, it might also be necessary to use an alternate statistical modelling approach, to optimise the data analysis process.

References 1. Borsboom D, Cramer AOJ, Schmittmann VD, Epskamp S, Waldorp LJ. The small world of psychopathology. PLoS One. 2011;6(11):e27407. https://doi. org/10.1371/journal.pone.0027407. 2. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (DSM-5). 5th ed. Washington, DC: American Psychiatric Association; 2013. 3. Mayo Clinic. Chronic fatigue syndrome. 2018; https://www.mayoclinic. org/diseases-conditions/chronic-fatigue-syndrome/symptoms-causes/syc20360490. Accessed 31 May 2019. 4. Centre for Disease Control and Prevention [CDC]. Chronic fatigue syndrome. 2012; http://www.cdc.gov/cfs/. Accessed 31 May 2019. 5. Demyttenaere K, De Fruyt J, Stahl SM. The many faces of fatigue in major depressive disorder. The International Journal of Neuropsychopharmacology. 2005;8(1):93–105. https://doi.org/10.1017/s1461145704004729. 6. Aikens JE, Reinecke MA, Pliskin NH, et al. Assessing depressive symptoms in multiple sclerosis: Is it necessary to omit items from the original Beck Depression Inventory? Journal of Behavioral Medicine. 1999;22(2):127–142. https://doi.org/10.1023/a:1018731415172. 7. Sveinbjornsdottir S, Thorsteinsson EB. Psychometric properties of the Measure of Adolescent Coping Strategies (MACS). Psychology. 2014;5(2):142– 147. https://doi.org/10.4236/psych.2014.52022. 8. Sveinbjornsdottir S, Thorsteinsson EB, Lingam GI. Model fit and comparisons for the Measure of Adolescent Coping Strategies (MACS): Fiji, Iceland, and Australia. Journal of Pacific Rim Psychology. 2017;11:e12. https://doi.org/ 10.1017/prp.2017.20.

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20. Soare A, Cangemi R, Omodei D, Holloszy JO, Fontana L. Long-term calorie restriction, but not endurance exercise, lowers core body temperature in humans. Aging. 2011;3(4):374–379. 21. Kwok JSS, Chan TYK. Recurrent heat-related illnesses during antipsychotic treatment. Annals of Pharmacotherapy. 2005;39(11):1940–1942. https://doi. org/10.1345/aph.1g130. 22. Mellerup ET, Widding A, Wildschiødtz G, Rafaelsen OJ. Lithium effect on temperature rhythm in psychiatric patients. Acta Pharmacologica et Toxicologica. 1978;42(2):125–129. https://doi.org/10.1111/j.1600-0773.1978. tb02179.x. 23. Goldstein ER, Ziegenfuss T, Kalman D, et al. International society of sports nutrition position stand: Caffeine and performance. Journal of the International Society of Sports Nutrition. 2010;7(5). https://doi.org/10.1186/15502783-7-5. 24. Molnar GW. Body temperatures during menopausal hot flashes. Journal of Applied Physiology. 1975;38(3):499–503. 25. Carmelli D, Swan GE, Bliwise DL. Relationship of 30-year changes in obesity to sleep-disordered breathing in the western collaborative group study. Obesity Research. 2000;8(9):632–637. https://doi.org/10.1038/oby.2000.81. 26. Byrne BM. Structural equation modelling with AMOS: Basic concepts, applications, and programming. 2nd ed. New York, NY: Routledge; 2010.

9 Typing It All Together Rhonda Brown and Einar Thorsteinsson

9.1

What Causes Comorbidity?

Comorbidity is common, affecting one-third or more of the global population; and recent co-prevalence estimates suggest that its presence is increasing [1, 2]. It is associated with substantial chronic illness burdendisability, high mortality, and high ongoing costs to the individual and the community, reflecting its substantial impact within and beyond the health care system [3]. In particular, a considerable degree of comorbidity exists between overweight/obesity, sleep problems (e.g. insomnia, sleep-disordered breathing), eating disorders (e.g. binge-eating disorder), metabolic syndrome, and related disorders including diabetes mellitus type-II, and in turn, R. Brown (B) Australian National University, Canberra, ACT, Australia e-mail: [email protected] E. Thorsteinsson University of New England, Armidale, NSW, Australia e-mail: [email protected] © The Author(s) 2020 R. Brown and E. Thorsteinsson (eds.), Comorbidity, https://doi.org/10.1007/978-3-030-32545-9_9

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they tend to coexist with autonomic arousal states (e.g. stress, anxiety, depression) and other symptoms (e.g. fatigue, chronic pain). Each of these symptoms and conditions is highly prevalent in the twenty-first century, as detailed throughout this book. As a result of the high prevalence estimates for each disorder, and their high co-prevalence estimates, most of the conditions occur together more often than they occur alone. For example, anxiety, depression, and insomnia/impaired sleep frequently coexist and the concurrence between them is observed to be the rule rather than the exception [4]. However, as researchers and clinicians, we have tended to focus on single clinical entities in isolation from other highly concurrent phenomena. More broadly, we have tended to focus on the differences between the different disorders, rather than on the similarities between them; reflective of our collective reductionist (rather than holistic) approach. Such a state of affairs is reminiscent of the earlier discourse related to sickness behaviour, as first described by Hart [5] and explored further by Dantzer and colleagues [6, 7]. Sickness behaviour includes fever, reduced food-motivated behaviour, behavioural depression (e.g. reduced spontaneous locomotion, social exploration, grooming, etc.), hypersomnia, and hyperalgesia, which are collectively, the non-specific manifestations of infective illness. These behavioural changes are known to be induced by the release of proinflammatory cytokines (i.e. immune system transmitters), which are released in response to infection. Sickness behaviour are adaptive inasmuch as their prevention can result in greater mortality; for example, bacterially infected animals that are force fed [8] or forced to exercise on a running wheel [9] are more likely to die; as are animals forced to exercise on a running wheel after dextran sodium sulphate administration, which is linked to colitis and pro-inflammatory cytokine release [10]. That is, preventing sickness behaviour can increase the risk of mortality, whereas their presence can increase the chances of surviving the infection; and in this regard, the behaviour are viewed as a highly organised behavioural strategy for surviving bacterial and other infections [5]. Furthermore, by focusing on the similarities between different infections, that is, on the non-specific (or shared) manifestations of the infections, researchers have improved our

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understanding of the complex bio-behavioural host responses that occur in response to infective illness. We make a similar assertion in this book: that there is need to focus on the similarities (or shared aspects) of the different comorbid disorders, as well as on the differences between them. In particular, there is a need to focus on the considerable overlap in the symptoms of the comorbid disorders as well as on their (often) shared risk factors. As discussed below, the assertion is consistent with the network model of psychiatric symptoms proposed by Borsboom and colleagues [11, 12], which posits that the shared symptoms of the comorbid disorders are most likely to contribute to concurrent symptom development. Therefore, there is likely to be much that can be gained from focusing on the shared symptoms of the disorders, rather than on their differences. In the scientific literature, empirical research has shown that longitudinal bidirectional relationships typically exist between the aforementioned disorders and symptoms. For example, anxiety and depression predict the later onset and/or worsening of sleep impairment, but, additionally, sleep problems can predict the later onset and/or worsening of anxiety and depressed mood [13]. However, we otherwise know very little about the true nature of comorbidity, despite (or because of ) the documentation of these bidirectional relationships. That is to say, it is difficult to comprehend exactly how depression can contribute to the development of fatigue, and at the same time, fatigue contributes to depressed mood. Further, the observation of bidirectional relationships tells us nothing about how the disorders are related to each other, including whether (or not) they are causally or functionally related. Additionally, it is unclear whether the cause/s of the comorbidities are the same (or different) as that of single disorders; or if complex interactions tend to occur between the various risk factors and comorbid conditions, resulting in more complex mechanism/s. Further complicating this picture is the considerable degree of symptom overlap between the different disorders. For example, anxiety disorders and depressed mood are both characterised by gut problems, changes in appetite, and impaired sleep [14]. Instruments that measure the symptoms and disorders also tend to focus on a similar range of somatic and affective symptoms (e.g. sleep problems, appetite change); potentially resulting in an overestimation of the strength of the clinical relationship

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between anxiety and depressed mood. Nevertheless, there are published scoring protocols for the removal of somatic items that overlap with other symptoms/disorders (e.g. fatigue), as discussed in more detail in Chapters 2 and 8. As detailed in Chapter 2, there are currently few available theories with any demonstrated utility in guiding empirical comorbidity research. In particular, there are few clinical theories with any utility in explaining the way in which different-but-related disorders and symptoms coexist; and the mechanisms by which they likely develop. Until recently, only a broad theoretical approach had been promulgated to explain the way in which comorbidity might arise, suggesting that either: (a) a causal relationship exists between the comorbid disorders; (b) a common factor(s) increases the likelihood that both disorders will occur; and/or, (c) the relationship is spurious or artefactual [15]. Unfortunately, for the most part, this broad overarching theoretical approach has not been tested empirically; and although some comorbidity models do exist, they typically seek to explain only a small number of clinical phenomena (e.g. overweight/obesity and insomnia), as discussed in the foregoing chapters. Only two theories have so far advanced a mechanism/s by which comorbidity might arise; that applies to multiple comorbid conditions, and which can give rise to specific and easily testable hypotheses. The first theory is a computational model that seeks to explain comorbid psychiatric symptom development, as discussed in detail in Chapter 2. Borsboom and colleagues [12, 16] posited that highly connected symptoms were most likely to play a key role in maintaining the structure of the psychiatric symptom network; such that activating a single node (e.g. anxiety) in the network indirectly contributes to the activation of other nodes (e.g. depression), thereby, contributing to concurrent symptom development. For example, anxiety may propagate via the network structure to cause insomnia, which, in turn, leads to depressed mood; via anxious and/or depressive rumination, which delays sleep onset and potentially shortens sleep, which over time, leads to tiredness, fatigue, and depressed mood [17]. Thus, Schmittmann et al. (2011: p. 9) assert that it is the shared symptoms of anxiety and depression (e.g. impaired sleep, fatigue) which ‘function as bridge symptoms that transfer symptom activation from one network

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to the other, like a virus may spread from one community to another via people who are in contact with both’. In simple language, it is the shared symptoms of the disorders that are most likely to lead to comorbidity. Borsboom et al. [12] also reported that many of the most highly overlapping psychiatric symptoms (e.g. insomnia, psychomotor-agitation, psychomotor-retardation, depression) were linked to basic homeostatic brain functions, including eating, sleeping, sex, and mood regulation. Thus, this model has considerable utility in delineating the ‘symptom groups’ that are most likely to play a key role in contributing to disease comorbidity; as well as the biological pathways that are likely to mediate the linkages (i.e. homeostatic pathways; e.g. hypothalamic-pituitary-adrenal [HPA] axis, sympathetic nervous system [SNS]) [18]. However, this model does not tend to specify the behavioural mechanism/s (e.g. binge-eating, electronic device use, etc.) that are most likely to contribute to disease comorbidity. Therefore, there is a need for a clinical theory that seeks to disentangle the likely complex causal relationships that exist between the different medical illnesses, psychological disorders, and various risk factors; to inform the more effective treatment of comorbid conditions. In this book, we advanced a clinical theory which posits that an elevated nocturnal body temperature (BT) that interrupts sleep onset may contribute to the development of comorbidity. A plethora of research evidence has been provided in the book to support this assertion, and collectively, it suggests that comorbidity, and each of the disorders separately, are likely to be functionally related to an elevated BT. Furthermore, and more broadly, the evidence presented in this book suggests that circadian rhythm dysfunction is present in patients with some of the aforementioned disorders, symptoms, and arousal states, which may play a causal role in precipitating the psychological and medical comorbidities. However, as discussed at the end of this section, in practice, it is difficult to disentangle the effects of BT from those of other circadian rhythm functions (e.g. melatonin secretion), as they are all so highly interrelated. Another assertion of our clinical theory is that sleep-disrupting behaviour may contribute to comorbidity by increasing the risk that one or more of the aforementioned symptoms, disorders, and/or states develops. For example, if a person engages in sleep-disrupting behaviour (e.g. bingeeating) at night, this may result in an elevated nocturnal BT, as discussed

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in Chapter 3. Similarly, if a person experiences certain symptoms/states (e.g. anxiety, pain) at night, this may lead to autonomic arousal and greater energy expenditure; which in turn will lead to an increase in nocturnal BT, as discussed in Chapter 7. Put simply, arousal states are known to be functionally related to an elevated BT via the production of body heat; which occurs as a by-product of the autonomic arousal [19]. Furthermore, as detailed in Chapter 2, if a person experiences an elevated nocturnal BT, this can interfere with the natural precipitous decline in BT that occurs in the evening, and which is thought to cue the onset of sleep; potentially delaying sleep onset and resulting in a phase-shift in the circadian rhythm of core BT [20, 21]. That is to say, an elevated nocturnal BT is likely to be functionally linked to impaired sleep. Finally, the model asserts that as bidirectional relationships tend to exist between the various phenomena, this may further increase the risk of comorbidity. For example, if a person’s sleep is disturbed by sleep-disrupting behaviour, while they wait for sleep to come, they may tend to engage in unhelpful behaviour (e.g. anxious rumination, night-eating) more often and/or they may develop new symptoms or disorders (e.g. affective distress). Thus, collectively, the theory asserts that a complex bio-psychobehavioural mechanism is likely to underpin the development of disease comorbidity. For example, if a highly stressed person ‘comfort eats’ late at night to help them cope with the stress; this may lead to an elevated nocturnal BT; which in turn may interfere with sleep onset; and if their distress and late night-eating persist over time, this may lead to insomnia. Furthermore, once the person is chronically sleep disturbed, they may tend to experience a worsening or perpetuation of existing problems (e.g. nighteating, distress) and/or develop new conditions (e.g. overweight/obesity), which in turn will contribute to other conditions (e.g. metabolic syndrome); possibly via an elevated nocturnal BT, as discussed in Chapters 3, 4, and 7. Put simply, our model asserts that sleep disturbance plays a pivotal and bidirectional role in contributing to the causation, worsening, and/or perpetuation of a broad range of comorbid conditions, via an increase in nocturnal BT. The model is illustrated pictorially in Fig. 2.1 in Chapter 2. Our clinical theory is consistent with the aforementioned computer network model of comorbid symptom development [12, 16], in several respects. First, both models posit that shared symptoms of the disorders

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are most likely to contribute to the development of comorbidity. Second, the models posit that a similar range of symptom groups (e.g. impaired sleep, physical inactivity/hyperactivity) are implicated in the causation of comorbidity. Third, the models suggest that specific biological pathways are likely to mediate these linkages (i.e. homeostatic pathways; e.g. hypothalamic-pituitary-adrenal axis, sympathetic nervous system) [18]. Fourth, the models accommodate the likelihood that myriad different pathways to comorbidity can exist, in different individuals or in the same individual over time. Consistent with the assertion, our clinical model posits that any disorder, symptom/state, or behaviour that is linked to an increase in nocturnal BT, if it is practiced at night, can potentially result in comorbidity in affected individuals. Thus, the clinical theory corroborates the broad assertions of the computational model inasmuch as both models assert that highly shared symptoms will tend to contribute to comorbidity, especially phenomena that are linked to homeostatic brain functioning (e.g. sleep, activity levels); which are regulated by the human circadian clock/s, as discussed in Chapters 3 and 7. Nevertheless, other circadian rhythm functions may be implicated, aside from perturbations in core BT, including changes in melatonin secretion and other hormones; although to some degree, it is arbitrary as to which of the highly interrelated circadian rhythm element/s are regarded as primary or proximal in this case. For example, an elevated BT is the natural consequence of all energy expenditure, including that which is related to hormonal secretion. That is to say, all energy expenditure results in the production of body heat [19]; whether it is the energy related to hormonal production, storage or secretion or other biological processes. In this regard, BT is an appropriate proxy measure of circadian rhythm functioning that can be used to illustrate the possible effects of circadian rhythm dysfunction in contributing to comorbidity. BT also provides a readily measurable clinical index of circadian rhythm function that might be used to examine the possible effects of circadian rhythm dysfunction on comorbidity. Nonetheless, bright lights and altered nocturnal melatonin secretion can provide an alternate explanation for some of the observed relationships in this book (e.g. electronic device use and impaired sleep). However, it provides a less than parsimonious explanation of the effects of hyper-arousal and other sleep-disrupting behaviour (e.g. night-eating)

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on sleep, if the state or activity does not reliably occur in the presence of light. Additionally, it cannot readily explain the effectiveness of so-called thermal therapies in correcting impaired sleep, as detailed in Sect. 9.3.

9.2

Comorbidity—Where to from Here?

The large scientific literature pertaining to circadian rhythm function and healthy sleep practices broadly asserts that people who live a conventional lifestyle and keep to a regular schedule will have better circadian rhythm functioning than those who do not [22, 23]. In particular, respectful observance of salient conditioned cues (e.g. light, ambient temperature changes); regular daytime activity; sitting down to a timely dinner; and good sleep hygiene practices are all thought to promote a good night’s sleep [18, 24–26]; and to maintain the circadian rhythm of sleep and core BT [27]. Thus, it is clear that sleep, especially the onset of sleep, involves a complicated set of circadian neuro-regulatory dynamics that includes perturbations in BT, melatonin secretion, and a complex array of associated behaviour [28]. Responding promptly to these internal and external signals can assist people to fall asleep faster and experience a more restful sleep [20, 21, 29]. That is, living a conventional lifestyle, keeping to a regular schedule, and respectfully observing healthy sleep hygiene practices, including habitual sleep-preparation behaviour, will likely lead to good quality sleep. In contrast, if a person fails to keep to a regular schedule and/or engages in sleep-disrupting behaviour at night, this may make it difficult to fall asleep, as discussed in Chapters 3 and 7. Furthermore, an assertion made in this book is that departures from a conventional lifestyle (e.g. healthy sleep practices) may facilitate the development of comorbid illness. Put simply, sleep-disrupting behaviour may do more to harm a person’s health than simply disrupting their sleep [30]. It may also activate a cascade of biological and behavioural processes that ultimately results in circadian rhythm dysfunction and ill health [30]. In a recent review, Zimmet and colleagues [31] explored circadian rhythm dysfunction (i.e. Circadian Syndrome) as a potential mechanism contributing to metabolic syndrome, a cluster of cardio-metabolic risk factors and comorbidities that increase the risk of cardiovascular disease and

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diabetes mellitus type-II. Further, they posited that conditions that are often comorbid with metabolic syndrome, including sleep disturbance, depression, steatohepatitis, and cognitive dysfunction, might also occur by this mechanism; and if so, ‘circadian medicine’ could be used to treat the patients, by altering the timing of light exposure, exercise, food consumption, sleep, and/or medication use. Additionally, it is known that hyperthermia is linked to the activation of the immune system. For example, aside from infection, cytokines (i.e. immune transmitters) are released in response to exercise [32], physical stress [33], cold [34], and heat stimulation [35, 36]. Importantly, the cytokine response to infection is largely indistinguishable from that which is caused by heatstroke/overheating, stress/autonomic arousal, and exercise/physical activity [36, 37]. In contrast, core temperature ‘clamping’ (e.g. immersion to mid-chest in cold 18 °C water bath) can abolish these plasma cytokine and stress hormone (e.g. cortisol) changes [35]. In simple language, an elevated BT switches on the immune system; and immune responses to hyperthermia are similar whatever their cause, whether it was caused by infection, autonomic arousal (e.g. stress/affective distress), and/or behaviour (e.g. exercise). However, cooling down is thought to shut these biological processes off, at least in the case of exercise [37]. More broadly, pro-inflammatory cytokines have been implicated in the causation of many different chronic illnesses in the medical literature. In some cases (e.g. multiple sclerosis), the disorder is thought to be proximally induced by infection [38, 39], and (older) cytokine therapies have been shown to effectively treat the condition [40]. Other chronic noncommunicable diseases have also been shown to be linked to abnormal cytokine secretion, including cardiovascular disease, diabetes, inflammatory bowel disease, rheumatoid arthritis, neurodegenerative disease, and conditions involving the lung, liver, pancreas, kidney, gastrointestinal tract, and reproductive systems (for review, see Chen and colleagues [41]), including age-related illnesses [42]. In simple language, findings in the large cytokine literature suggest that immune system activation underpins the development of many chronic illnesses, especially if the activation is excessive or continues on for too long. However, it is not clear exactly what factors are likely to switch on the immune system in patients with the disorders: Infection might be responsible for switching on the immune

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system, but it might also be switched on by other stimuli (e.g. thermogenic stimuli). That is to say, it is possible that hyperthermia or circadian rhythm dysfunction-related cytokine secretion contributes to the development of single disorders, and possibly also illness comorbidity. Thus, it is likely that several related biological processes may underpin the development of comorbidity, aside from infection, including the effects of: (a) elevated BT (or delayed melatonin secretion) in delaying sleep onset and interfering with sleep quality to potentially result in circadian rhythm dysfunction; (b) activation of the immune system and the release of pro-inflammatory cytokines, which occurs secondary to an elevated BT; and/or, (c) cytokine secretion that occurs secondary to bacterial translocation (i.e. leaky gut). In the latter case, bacterial translocation has been posited to play a key role in mediating the effects of anorexia nervosa [43] and depression [44], as discussed in Chapters 5 and 6. Thus, it is likely that a single biological mechanism does not underpin disease comorbidity; rather, multiple (related) mechanisms may potentially be operational in the same individual and collectively contribute to the phenomenon. Therefore, it is necessary to examine the potential role played by these related mechanisms to determine if one or more of them can explain the presence of comorbid illness. Aside from biological processes, sleep-disrupting behaviour may indirectly contribute to comorbidity, including physical inactivity, a sedentary lifestyle, night-eating, binge-eating, electronic device use, alcohol/substance use, etc. In Chapter 7, sleep-disrupting behaviour were examined in regards to sleep and several related conditions, including overweight/obesity and affective distress. Taken together, the results suggested that sleep-disrupting behaviour (e.g. night-eating, sedentary lifestyle) may provide a behavioural pathway by which disease comorbidity can develop; including the comorbidity between impaired sleep/insomnia, overweight/obesity, affective distress, and other health problems (e.g. diabetes mellitus type-II). That is to say, sleep-disrupting behaviour may do more than harm a person’s sleep; the behaviour and related sleep problem may contribute to other concurrent health problems (e.g. weight gain, low mood).

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For example, an article currently under review by this author (RFB) and her Ph.D. student (Y. Umar) examined a large pool of demographics, symptoms/states (e.g. anxiety, depression), disorders (e.g. overweight/obesity), and sleep-disrupting behaviour (e.g. sedentary lifestyle, electronic device use, night-eating, binge-eating, substance use, etc.) as predictors of sleep quality over time, in a large community-derived sample. Results showed that the strongest predictor of poor sleep quality at 3months was late night-eating. Poor sleep quality also predicted late nighteating at 3-months; night-eating was the strongest statistical mediator of the stress-sleep relationship over time; and it was the only mediator of the BMI-sleep relationship over time. Taken together, the results suggest that of all the sleep-disrupting behaviour examined, night-eating parsimoniously explained the impaired sleep in community-well individuals. Additionally, the results suggest that night-eating increased the risk of impaired sleep and weight gain over time; although this is not to say that other sleep-disrupting behaviour were not relevant (or significant in the analyses). One interpretation of these longitudinal bidirectional and mediational results is that a so-called vicious cycle can arise in some individuals; such that night-eating interferes with the onset of sleep, and while the person waits for sleep to come, they tend to eat more food, which over time, contributes to weight gain. Thus, a single risk factor may contribute to two or more conditions, and it might also possibly explain any concurrent affective distress (e.g. depression). However, it is alternately possible that other related risk factors (e.g. electronic device use, alcohol use) interacted with late night-eating to contribute to comorbidity, but such an assertion remains to be tested empirically. As detailed in Chapter 3, eating tends to be synchronised to limit nocturnal food ingestion in people who observe a normal circadian rhythm [45]. It is unclear exactly how food intake and sleep cycles become desynchronised, but it is likely that the timing of food intake interferes with sleep onset via an increase in nocturnal BT [20] and/or a change in eatingrelated hormones [46]. Night-eating can also disrupt the sleep-wake cycle and circadian rhythm functioning, by moving a person from a conventional diurnal (daytime) rhythm to a delayed (evening) rhythm; and as a

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consequence, it may lead to metabolic dysfunction [47] and possibly diabetes mellitus type-II. Therefore, in total, the effects of late night-eating may be similar to that exerted by sleep problems. However, it is also possible that late night-eating and impaired sleep aggravate each other, that is, they may interact with each other to worsen comorbidities (e.g. overweight/obesity); suggesting that the phenomena should be considered together in future studies [48], along with diabetes mellitus type-II. Thus, an additional focus of attention in this book is the considerable overlap that exists between the risk factors for different-but-related conditions; for example, night-eating, binge-eating, electronic device use, and physical inactivity are all risk factors for overweight/obesity and also insomnia/impaired sleep, as detailed in Chapters 3 and 7. As discussed in Chapter 2, it is possible that these overlapping (or shared) risk factors or the interactions between them play a key role in contributing to comorbidity; although the assertion has rarely been tested. In fact, most prior risk analysis studies have only computed the odds ratios (or hazard ratios) for individual risk factors, separately for each condition of interest. In contrast, odds ratios have rarely been computed for highly linked clusters of risk factors in relation to single or comorbid disorders. Further, few longitudinal risk studies have evaluated a large pool of potential risk factors to determine which of them are most likely to be pertinent to the disorder/s. As a consequence, it is often unclear which risk factors are likely to be the strongest drivers towards disease development. Moreover, we know little about the manner in which different (but related) risk factors interact with each other to contribute to comorbid conditions. Thus, future studies should endeavour to conduct complex risk analyses on linked clusters of risk factors and multiple conditions, psychological and medical, rather than on single risk factors and single conditions. As discussed in Chapter 7, sleep-disrupting behaviour often occur in the context of stress/affective distress, including stress-related night-eating, binge-eating, substance use (e.g. alcohol use), and electronic device use. In particular, school-aged children and university students often report high stress, affective distress (e.g. anxiety, depression), impaired sleep, and fatigue [49], and they often engage in stress-related eating. For example, in a recent study, female high stress eaters were more likely to consume sugar-free soft drinks, chocolate, and sweets, and be obese than

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non-stressed eaters, whereas male adolescent stress eaters were more likely to binge-eat [49]. Results suggest that, in addition to disrupting sleep, stress-related eating can contribute to overweight/obesity. Thus, it is possible that stress/affective distress and stress-related behaviour can combine to contribute to sleep problems and other comorbidities (e.g. overweight/obesity). Furthermore, it is possible that stress/affective distress, disordered eating, overweight/obesity, and impaired sleep are functionally related to each other, as suggested in Chapters 3 and 7. Finally, and more broadly, the research presented in this book has suggested that if we observe the daily rhythms of the natural world (e.g. ambient light & temperature) and our own nocturnal behaviour and body clocks, we can provide ourselves with the best opportunity for good quality sleep and the maintenance of good physical and mental health; including potentially, the avoidance of comorbidities. In contrast, if we engage in sleep-disrupting behaviour, this will tend to interfere with sleep; potentially resulting in the development of one or more medical and/or psychological disorders. Given the ready availability of electricity in the modern world, there are myriad different activities that we can engage in at night that could potentially disrupt our sleep. So is there any evidence that our sleep has changed in modern times? Is our sleep different to the way we slept in antiquity? The sleep historian Ekirch [50] has speculated that the structure and organisation of sleep have substantially changed in modern times; in particular, he suggests that we have moved away from polyphasic sleep. For example, in antiquity, we likely slept for several hours early in the night, woke up at around midnight to perform certain activities (e.g. urinate, stoke the fire), then returned to sleep until morning; paralleling the lack of available light and colder ambient temperature at night. This assertion is consistent with the results of several recent human studies showing that sleep does tend to become polyphasic when it is manipulated in certain ways; for example, when the photoperiod (i.e. length of exposure to light) is restricted from 16 hours to 10 hours. In this case, participants’ sleep tends to occur in two discrete periods with a 1–3-hour gap between them, and melatonin is secreted for a longer time during the night [51]. Taken together, the results suggest that shortening the photoperiod (such as occurs on a long

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winter’s night) may temporarily change the circadian rhythm of melatonin secretion, potentially changing the structure of sleep. However, a recent evaluation of preindustrial communities has shown that members do not tend to exhibit sleep patterns that are significantly different from those of people living in modern societies. For instance, Yetish and colleagues [52] examined the sleep patterns of people living in Tanzanian, Namibian, and Bolivian communities, and noted that on average, their sleep duration ranged from 5.7–7.1 hours; sleep onset occurred an average of 3-hours after sunset; and sleep duration was approximately 1-hour longer in the wintertime relative to the summertime. In contrast, Samson and colleagues [53, 54] reported that sleep patterns tended to be heterogeneous across cultures; for example, Hadza hunter-gatherers tended to sleep during the night and took an afternoon nap; Malagasy villagers slept in two phases during the night, separated by a short period of wakefulness, and they took afternoon naps; whereas preindustrial Western Europeans likely engaged in polyphasic sleep with a short period of wakefulness between the two sleep episodes at night. Thus, it is apparent that sleep patterns tend to be shaped by local conditions (e.g. ambient temperature, length of photoperiod) and the manner in which a person lives (e.g. nocturnal access to electricity/lights, cultural practices). Specifically, if the photoperiod is short and/or ambient conditions are cold, then there may be a tendency towards polyphasic sleep; whereas if the photoperiod is long and/or the nights are warm, this will likely result in shorter episodes of monophasic sleep. However, there appears to be little evidence suggesting that average sleep duration has decreased substantially in modern times, and this is probably the case for people who experience good quality sleep. However, currently, about one-third or more of the population report experiencing some degree of impaired sleep, including delayed sleep onset and shorter sleep duration, and this does likely represent a significant departure from the sleep practices of old.

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Possible Existing, Repurposed, and Novel Treatments for Comorbid Illness

Unravelling the causes of comorbidity currently ranks among the top priorities in clinical practice [12, 55]. However, there are currently few protocols and clinical practice guidelines that exist to assist clinicians in treating comorbid conditions in a coordinated way. Instead, the guidelines and protocols tend to focus on single disorders and they generally fail to take comorbidities into account. This has resulted in the comorbid disorders being treated as if they are isolated clinical entities, with each condition managed separately, often by different clinicians. Thus, there is a clear need to develop new clinical practice guidelines that do take comorbidity into account; especially in patients with highly prevalent and highly comorbid disorders. At the least, there is a need to routinely and systematically investigate the possible presence of comorbidities in individual medical patients and psychology clients. Additionally, if evidence of a comorbidity is detected, at the least, the disorder should be treated contemporaneously with the other disorder and/or the patient should be referred to an appropriate health or allied health professional for treatment. As a result, existing medical and psychological therapies have not generally been designed (and have not been tested) for their utility in treating multiple comorbid conditions; and few novel therapies appear to be on the horizon that might be used to effectively treat the disorders. As a consequence, there are few available therapies with any demonstrated utility in effectively treating more than a single disorder in a single patient, either concurrently or using a sequential therapy approach. Thus, there is a clear need to go back to first principles to explore the potential utility of existing and novel therapies. Accordingly, three broad treatment approaches will be explored in detail in this section. First, some therapy approaches are already known to effectively treat multiplebut-related conditions, including Cognitive Behavioural Therapy (CBT). Second, it might be possible to change, alter, repurpose and/or tailor existing therapies to treat the comorbidities, by developing new co-therapy or

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sequential therapy protocols or by tailoring therapy protocols to the individual needs of patients. Third, our clinical theory will be used to explore a range of possible new (or repurposed) approaches to therapy; for example, the use of therapies that can correct circadian rhythm dysfunction. First, several therapy approaches are known to effectively manage multiple-related-conditions, including Cognitive Behavioural Therapy (CBT). CBT has been shown to effectively treat depressed mood, fatigue, insomnia/sleep problems, and eating disorders; and there are disorderspecific CBT protocols for the treatment of depression [56], fatigue [57], insomnia (CBT-I) [58], and eating disorders (CBT-E) [59]. However, the CBT protocols do not generally provide any specific detail about the way in which the comorbid conditions should be managed together; for instance, if the treatments should be concurrent or sequential in a single individual. For instance, in a client with an eating disorder (e.g. binge-eating disorder), overweight/obesity, and insomnia, it is unclear whether the eating disorder, weight problem or sleep problem should be targeted first; or if the conditions should be co-managed together. If such a co-therapy protocol did exist, it could assist clinicians in optimally co-managing a client’s maladaptive behaviour (e.g. binge-eating) and their weight and sleep problems. For example, CBT-I principles could be integrated with CBT to address the propensity of overweight clients to engage in disordered eating, which interferes with sleep and contributes to substantial weight gain; thus, treating the person’s eating, weight, and sleep problems together. A coordinated therapy approach might interrupt the vicious cycle between a client’s late night-eating, weight gain, and impaired sleep, thereby, potentially resulting in the dovetailing of the therapy response. That is, improvement in one health problem may result in additional improvements in other health problems. For instance, if a client’s sleep problem is treated early, they will not be awake late at night to engage in dysfunctional thinking or eating (e.g. depressive rumination, binge-eating) which could interfere with their sleep or lead to weight gain over time; and as a result, they may lose additional weight over time [60]. In other words, the client’s comorbidity burden may improve by living a conventional lifestyle (e.g. observing the appropriate times for eating and sleeping) and appreciating that when

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they compromise their sleep this can lead to physical and mental health problems. However, only a few CBT co-therapy protocols have been developed and tested for their efficacy; for instance, in treating comorbid insomnia, depressed mood, and fatigue, using CBT-Insomnia (CBT-I). For example, in a recent meta-analytic study [61], individual face-to-face CBT-I that incorporated sleep restriction therapy was shown to effectively treat sleep and depressive symptoms in patients with insomnia, but not the fatigue. Similarly, Wu et al. [62]. showed that CBT-I effectively treated clients’ insomnia symptoms, normalised sleep parameters, and assisted in managing the coexisting psychiatric conditions, including depression. Thus, at least in the case of CBT-I, which is typically used to treat insomnia, it might also be able to effectively treat comorbid depressed mood, but not fatigue. However, there is still a lot of work to be done to more fully integrate these variants of CBT together into complex co-therapy (or sequential therapy) protocols which might be used to more effectively treat comorbid conditions. However, there may be at least several potential pitfalls in using a cotherapy or sequential therapy approach. For example, the concurrent use of several co-therapies could result in unanticipated drug (or other therapy) interactions, which could lead to significant therapeutic side effects at a lower than expected dose and/or offset other clinical improvements in individual patients. For instance, antidepressants have been examined for their utility in concurrently treating insomnia and depressed mood, but there is a lack of clarity as to whether the drug actually improves or worsen sleep [63, 64]. Further, co-therapy-related problems and the more general potential to do harm has yet to be explored in any detail in the psychological literature, although we do know much more about the nature of specific drug interactions and the need to avoid the concurrent use of certain medications in some patients. A second broad therapy approach might be to change, alter, or repurpose existing therapies to treat a number of comorbid conditions, including the tailoring of existing therapies. For example, a possibility suggested by Obermeyer et al. [65] is that individualised ‘precision temperatures’ could be used to tailor the decisions about testing and therapy to a patient’s specific physiology; that is, the therapeutic approach might be different

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depending on the patient’s BT (i.e. euthermic, hypothermic or hyperthermic). For instance, an elevated BT is used as a prognostic indicator for acute stroke. In a study of 725 stroke patients admitted to an acute stroke unit within 6-hours of stroke onset, the mean BT was typically normal upon admission, but it increased in major stroke patients 4–6 hours after stroke onset. An elevated BT 10–12 hours after stroke onset was linked to a poor clinical outcome; although, in mild to moderate stroke patients, there was no significant rise in BT. In particular, severe infarcts and intracerebral haemorrhage led to an increase in BT, whereas initial BT > 37.5 °C was unrelated to stroke severity or stroke outcome [66]. Similar results have been reported in a small study, showing that peak hyperthermia occurred 1.5–2 days after stroke onset. The researchers suggested that the results reflected the need to consider therapeutic hypothermia in the treatment of severe stroke patients [67], suggesting that the tailoring of the therapy to the thermal characteristics of patients might potentially be useful. Alternately, existing therapy protocols could be changed or repurposed to treat the comorbidities, for example, by developing and testing cotherapy or sequential therapy protocols, using existing medical and psychological treatments. Such an approach—involving the coordinated use of co-therapies and/or sequential therapies in the multidisciplinary setting—could be considered to be the optimal management of comorbid illness patients. In some countries, patients with comorbid overweight/obesity and diabetes mellitus type-II already have their conditions co-managed in the hospital or general practice (GP) setting. For example, in Australia and other countries, GP management plans (e.g. Australian Medicare #s 721, 723) are provided to chronic illness patients with complex illness presentations; and the GP typically coordinates the care team to co-manage the patient’s illnesses. Further, early intervention group-based approaches could be used to manage the weight of diabetes patients in this context, and such an approach is suggested to be cost effective [68]. However, mental health services have remained largely separated from physical health services, making it difficult to provide optimal care to patients with a physical and mental health problem/s. Thus, a more collaborative approach is required to co-manage the patients with the comorbidities, in particular, patients with comorbid physical and mental health problems.

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More broadly, there is a need to evaluate the cost effectiveness of alternate organisational models of care (e.g. multidisciplinary care clinics) to encourage a more collaborative approach in detecting and managing disease comorbidity [3]. However, as indicated above, multidisciplinary care approaches are currently, for the most part, not utilised in the treatment of comorbid conditions. Nevertheless, it is clear that patients with a significant comorbid illness burden require an integrated therapy approach, which separately (and together) addresses patient’s medical and psychological conditions. As mentioned in Chapter 1, multidisciplinary therapy approaches are likely to have considerable utility in managing these comorbid illnesses as the care provided in them tends to be coordinated; evidence-based; utilises co-therapy and/or sequential therapy protocols; and, clinicians typically communicate with each other about the precise sequencing of the prescribed care and the management of related problems (e.g. depressed mood). A third approach in exploring the potential of certain therapies to treat comorbidity is to examine predictions derived from theory. We therefore used our clinical model of concurrent symptom development to make predictions about the potential utility of new (or existing) therapy approaches. First, if as asserted, impaired sleep plays a pivotal and bidirectional role in facilitating disease comorbidity, it would appear prudent to co-manage a patient’s impaired sleep at the same time as managing their other comorbidities, using an evidence-based therapy approach such as CBT-I. Treating a patient’s comorbid sleep problem at the same time as treating their other disorder/s (e.g. obesity) might result in additional clinical improvements in the condition/s. However, it might also be necessary to address the presence of sleep-disrupting behaviour (e.g. binge-eating), which aside from disrupting sleep, may also contribute to the development of comorbidities (e.g. impaired sleep, obesity). Second, if as asserted, circadian rhythm dysfunction underpins the development of comorbidity then therapies that can correct this dysfunction might be clinically useful. For example, bright light therapy is known to reduce nocturnal core BT (e.g. in seasonal affective disorder patients) [69], and it can normalise the circadian rhythm of core BT in anorexia nervosa and bulimia nervosa patients [70]. Together, the results suggest that bright light therapy may be useful in treating affective disorders as well as certain

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eating disorders. Additionally, a recent review by Zimmet et al. in 2019 [31] posited that circadian rhythm dysfunction (i.e. Circadian Syndrome) is likely to contribute to metabolic syndrome and related comorbidities; which in turn can increase the risk of cardiovascular disease and diabetes mellitus type-II. On this basis, they suggested that ‘circadian medicine’ approaches might be useful in treating the disorders, by altering the timing of light exposure, exercise, food consumption, sleep, and/or medication use. As detailed below, ‘thermal exercise’ approaches can also normalise a person’s circadian rhythm function to treat sleep problems (e.g. insomnia). However, additional work is required to examine whether the therapy approach is useful in treating other comorbid illnesses. One final assertion of our clinical theory is that comorbidity may be linked to an elevated BT. It is therefore possible that therapies that can reduce BT (or correct the circadian rhythm of core BT) might be useful in treating comorbidities. Several therapies are thought to result in clinical improvement via a correction in BT, especially an elevated nocturnal BT. For example, sleep restriction therapy has been shown to reduce BT [71], as has antidepressant use [72] and bright light therapy [69]. However, the chronic administration of SSRIs can lead to an increase (rather than a decrease) in core BT [73], whereas electroconvulsive therapy increases or decreases it [74], and amphetamines consistently elevate it [75]. In contrast, prolonged caloric restriction is known to reduce a person’s BT [76, 77]. Further, aside from their antipyretic effects, anti-inflammatory agents such as aspirin have been shown to reduce depressive symptoms, either taken alone, taken for their anti-platelet effects or to reduce the risk of myocardial infarction [78] or in combination with SSRIs, relative to SSRIs alone [79]. Antidepressants (e.g. SSRIs) are also known to have anti-inflammatory effects [80–83]. However, few studies have examined the effects of any treatments on human BT, and mostly only in patients with uncomplicated clinical trajectories, rather than complex comorbid illnesses. Nevertheless, the existing studies suggest that normalising a person’s BT and/or circadian rhythm of core BT may result in the clinical improvement that is seen with some therapies, although the proposition has rarely been examined in the scientific literature. Interestingly, sleep restriction therapy, a type of psychological therapy was shown to reduce patients’ BT during sleep, without leading to a change

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in plasma cortisol concentration. On the basis of the results, the authors posited that the effects of the therapy were due to a change in BT rather than by reducing perceived stress or stress hormone release [71].That is, the therapy reduced BT and improved sleep, without altering the experience of perceived stress or HPA-axis-related stress hormone release. However, the therapy likely led to a reduction in autonomic arousal via reduced SNS activity, which in turn led to reduced BT during sleep, although the latter premise was not examined in the study. Irrespective of the biological mechanism involved, the results suggest that a psychological therapy on its own is sufficient to reduce a person’s BT, possibly via a reduction in autonomic arousal; which is a novel way of viewing the effects of the psychological therapy. Thermal exercise has also been shown to effectively treat sleep problems, including the use of cooling suits and cold baths on the one hand, and hot baths, footbaths, suits/vests, and ambient heating on the other. Specifically, body cooling strategies have been shown to correct the sleep problems experienced by insomniacs and to normalise the circadian rhythm of core BT [26, 84–86]. For example, body-cooling strategies (e.g. cold thermosuits) can assist insomniacs to sleep better (i.e. reduced night-time and early morning awakening, more Stages 3 and 4 sleep), with the best clinical effects seen in insomniacs and the elderly [26]. However, paradoxically, body heating strategies have been shown to also normalise sleep [87]. Specifically, heated thermo-suits can increase slow-wave sleep and reduce the tendency to wake at night, especially in insomniacs and the elderly [87]. Even taking a hot bath in the evening [e.g. 40–41 °C for 30-min, up to thorax] can increase slow-wave sleep and quicken the perceived onset of sleep in female insomniacs [85]; with similar results obtained in another study (i.e. hot bath [40–40.5 °C] over ≥2 consecutive nights at 1.5–2 hours before bedtime), in which older female insomniacs showed better sleep continuity and longer slow-wave sleep in the early part of sleep [88]. So how can both cold and heat stimulation improve sleep? Quite simply, applying heat (e.g. warm [38–39 °C] bath for 1-hour) initially results in hyper thermia (e.g. 1 °C increase in BT) [86]; but after the person gets out of the bath, their BT rapidly declines [23, 86]. Importantly, it is the rapid decline in skin temperature (induced by cold bathing or stepping out of a hot bath) that is thought to accentuate the natural evening decline in BT;

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when the stimulus is applied about 2-hours before planned sleep [20, 26]. That is to say, simply taking a hot or cold bath (or shower or footbath) about 2-hours before planned sleep may assist a person in reducing their nocturnal BT sufficiently to effectively treat their sleep problem, even in the case of insomnia [85, 86]. However, ‘thermal exercise’ has yet to be examined in regards to other comorbidities, including anxiety, depressed mood, and overweight/obesity. Nevertheless, exercise has been suggested to improve a person’s mood at least in part due to a transient increase in BT [89]. Additionally, it is suggested that body-cooling strategies should be explored for their potential utility in facilitating weight loss in overweight/obese individuals. Hansen, Gilman, Odland [90] observed that first-world individuals tend to live and work in temperature-controlled environments that approach core BT (i.e. thermal-neutral point); and they pondered whether the relative temperature equivalence might contribute to obesity via reduced physical activity, especially a reduction in spontaneous physical activity (SPA). As described in Sect. 7.2, SPA involves unplanned, unconscious, biologically driven behaviour that includes fidgeting, foottapping, restless behaviour, and activities-of-daily-living such as walking, talking, and the maintenance of posture; which is related to the expenditure of 100–800 kcal/day [91]. SPA and its associated energy expenditure, non-exercise activity thermogenesis (NEAT), are thought to protect lean people from putting on weight after a large meal by increasing their activity levels and decreasing them after underfeeding. However, pertinently, a dramatic reduction in SPA is observed in obese individuals (i.e. about 2 hours less/day), relative to lean individuals; and low SPA is known to be a risk factor for weight gain and obesity [92–94]. Thus, SPA contributes significantly to daily energy expenditure in lean individuals, although the unconscious activity is clearly deficient in overweight/obese individuals. The World Health Organisation and International Obesity Task Force have suggested that strategies that can promote NEAT via an increase in SPA should be examined for the potential in facilitating weight loss in overweight/obese people. However, in practice, there are only several ways of increasing a person’s SPA: losing weight, restricting calories, increasing volitional physical activity and/or exposure to cold [95–97]. The first three strategies likely represent the very pathway/s via which the person

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gained weight in the first place (i.e. high caloric intake, weight gain, and a sedentary lifestyle); thus, there may be little to be gained by trying to increase SPA in this way. However, body-cooling strategies (e.g. cooling vests) might be useful in increasing SPA and NEAT in overweight people, as an adjunct to involvement in a formal exercise program. Unfortunately, the therapeutic potential of SPA has so far been overlooked, despite the significant potential of this biological process to be manipulated by ambient factors (e.g. cold ambient temperature) [98]. However, there appears to be some utility in exploring the effects of body-cooling strategies on weight in overweight/obese individuals to determine if weight loss can be facilitated by an increase in SPA and NEAT; perhaps as an adjunct to involvement in a formal exercise program, which might also increase the person’s SPA. In contrast, and as detailed in Chapter 5, body-warming strategies are already being used to treat eating disorder patients, and in part, the therapy is thought to result in clinical improvement via the reduced propensity towards hyperactivity. That is, anorexia nervosa patients are typically hypothermic, and as a result, they may compensate by increasing SPA and NEAT; which transiently increases their BT, helping them to feel warm, but it also reduces the potential for weight gain [99]. Several RCTs have recently evaluated body-warming strategies as a stand-alone therapy or a component of a therapy package (e.g. mandometer, ambient room warming, restricted physical activity) in eating disorder patients. In the latter case, the approach was shown to effectively reduce activity levels, induce remission (75% of patients), and reduce the relapse rate at 5-years in eating disorder patients [100]. However, in a small study, Birmingham and colleagues [101] found that the therapy (i.e. heating vest set to medium heat for 3 hours/day for 21 days) was not linked to weight recovery in anorexia nervosa patients, relative to control patients who wore an unheated vest, but it was linked to lower anxiety levels and improved treatment compliance. Similarly, ambient heating (i.e. warm environment, intermittent use of thermal waistcoat or sauna bath in infrared enclosure) reduced hyperactivity and anxiety and depression levels in anorexia nervosa patients, which antedated the body weight recovery [102]. In a larger study [103], ambient warming exerted anxiolytic effects on anorexia nervosa patients; in particular, postprandial anxiety was reduced in patients who rested for

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30 minutes immediately after lunch in a room heated to 32 °C, relative to higher pre-meal anxiety levels. Together, the results suggest that body-warming strategies can effectively reduce hyperactivity and affective distress (e.g. anxiety severity), and as part of a therapy package, it may also result in significant weight recovery and remission, in eating disorder patients. Taken together, the study results presented in this chapter suggest that body cooling and warming strategies are useful in treating insomnia/impaired sleep. In contrast, body-warming strategies can effectively treat the hyperactivity and affective distress in eating disorder patients; and as part of a therapy package, it may facilitate weight recovery and remission in eating disorder patients. However, there is little research on the effects of ‘thermal exercise’ on other symptoms and disorders. Nonetheless, it is possible that the therapy may be useful in treating non-eating disordered individuals with affective distress (e.g. anxiety or depressive disorder), overweight/obesity and/or sleep-disordered breathing, but so far, no studies have evaluated the therapies in this regard. Finally, and more broadly, a variety of therapy approaches have been examined in this chapter for their potential utility in treating different comorbid disorders. These approaches included: altering, tailoring or repurposing existing psychological and medical therapies; developing and testing new co-therapy or sequential therapy protocols; using therapies that are already known to improve sleep, circadian rhythm function and/or reduce BT (e.g. aspirin, bright light therapy); tailoring existing therapies to the thermal characteristics of individual patients; and novel therapy approaches (e.g. thermal exercise). Irrespective of the approach that is used, it is evident that a coordinated and collaborative approach is required to optimally treat patients with comorbid illnesses, such as is provided in the multidisciplinary care setting.

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