Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3207_Библиотеки_им_академика_М_И_Перельмана.pdf
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 pop-
ulation; and recent co-prevalence estimates suggest that its presence is
increasing [1, 2]. It is associated with substantial chronic illness burdendis-
ability, 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 over-
weight/obesity, sleep problems (e.g. insomnia, sleep-disordered breath-
ing), 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: rhonda.brown@anu.edu.au
E. Thorsteinsson
University of New England, Armidale, NSW, Australia
e-mail: ethorste@une.edu.au
© The Author(s) 2020
R. Brown and E. Thorsteinsson (eds.), Comorbidity,
https://doi.org/10.1007/978-3-030-32545-9_9
241
242 R. Brown and E. Thorsteinsson
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 esti-
mates 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 ourcollective reductionist(rather thanholistic) approach. Such astate 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 pro-
inflammatory cytokines (i.e. immune system transmitters), which are
released in response to infection. Sickness behaviour are adaptive inas-
much 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, whichis
linked to colitis and pro-inflammatory cytokine release [10]. That is, pre-
venting 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
9 Typing It All Together 243
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 concurrentsymptom development.Therefore, there is likely to bemuch
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 longitudi-
nal 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 compre-
hend exactly howdepression 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 andcomorbid conditions, resultingin morecomplex 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 symp-
toms 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
244 R. Brown and E. Thorsteinsson
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 rela-
tionship exists between the comorbid disorders; (b) a common factor(s)
increases the likelihood that both disorders will occur; and/or, (c) the rela-
tionship is spurious or artefactual [15]. Unfortunately, for the most part,
this broad overarching theoretical approach has not been tested empir-
ically; and although some comorbidity models do exist, they typically
seek to explain only a small number of clinical phenomena (e.g. over-
weight/obesity and insomnia), as discussed in the foregoing chapters.
Only two theories have so faradvanced a mechanism/s by which comor-
bidity might arise; that applies to multiple comorbid conditions, and
which can give rise to specific and easily testable hypotheses. The first the-
ory 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, Schmittmannet al.(2011: p.9) assert that it isthe sharedsymptoms
of anxiety and depression (e.g. impaired sleep, fatigue) which ‘function
as bridge symptoms that transfer symptom activation from one network
9 Typing It All Together 245
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 comor-
bidity. Borsboom et al. [12] also reported that many of the most highly
overlapping psychiatricsymptoms (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 nervoussystem [SNS]) [18].However, thismodel
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 dys-
function is present in patients with some of the aforementioned disorders,
symptoms, and arousal states, which may play a causal role in precipitat-
ing 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. binge-
eating) at night, this may result in an elevated nocturnal BT, as discussed
246 R. Brown and E. Thorsteinsson
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 ele-
vated 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-psycho-
behavioural 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. night-
eating, distress) and/or develop new conditions (e.g. overweight/obesity),
which in turn will contribute to other conditions (e.g. metabolic syn-
drome); possibly via an elevated nocturnal BT, as discussed in Chapters 3,
4,and7. Put simply, our modelasserts that sleep disturbance playsa 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
9 Typing It All Together 247
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 path-
ways 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 arelinked 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 secre-
tion 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 obser ved
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)
248 R. Brown and E. Thorsteinsson
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 pro-
cesses that ultimately results in circadian rhythm dysfunction andill 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 fac-
tors and comorbidities that increase the risk of cardiovascular disease and
9 Typing It All Together 249
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 con-
sumption, sleep, and/or medication use.
Additionally, it is known that hyperthermia is linked to the activa-
tion of the immune system. For example, aside from infection, cytokines
(i.e. immune transmitters) are released in response to exercise [32], phys-
ical 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 exer-
cise/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 proxi-
mally induced by infection [38, 39], and (older) cytokine therapies have
been shown to effectively treat the condition [40]. Other chronic non-
communicable diseases have also been shown to be linked to abnormal
cytokine secretion, including cardiovascular disease, diabetes, inflamma-
tory 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
250 R. Brown and E. Thorsteinsson
system, but itmight 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 under-
pin 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 cir-
cadian rhythm dysfunction; (b) activation of the immune system and the
release of pro-inflammatory cytokines, which occurs secondary to an ele-
vated 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 comorbid-
ity; 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 indi-
rectly contribute to comorbidity, including physical inactivity, a seden-
tary lifestyle, night-eating, binge-eating, electronic device use, alco-
hol/substance use, etc. In Chapter 7, sleep-disrupting behaviour were
examined in regards to sleepand several relatedconditions, including over-
weight/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. dia-
betes 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).