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BRAIN 2017: 140; 2066–2078
| 2066
SCIENTIFIC COMMENTARIES
A restless night makes for a rising tide of
amyloid
This scientific commentary refers to
‘Slow wave sleep disruption increases
cerebrospinal fluid amyloid-b levels’ by
Ju et al., (doi:10.1093/brain/awx148).
As many people reading this commentary will know all too well, our sleep
often becomes worse as we age. This
is true for both the quantity of sleep
we can generate, and the quality of
that sleep (Mander et al., 2017). For
patients with mild cognitive impairment (MCI) and Alzheimer’s disease,
the severity of sleep disruption is
markedly greater (Mander et al.,
2016; Musiek and Holtzman, 2016).
Until recently, sleep disruption was
thought of as a symptom of neurodegenerative disease. Now, however, an
increasing number of studies indicate
that sleep disruption may be a causal
and instigating factor linked to the
pathophysiology of Alzheimer’s disease. Deficient and poor quality
sleep, along with several sleep disorders, predict an increased risk of cognitive decline and the conversion to
MCI and Alzheimer’s disease. Sleep
impairments precede the onset of
such clinical outcomes by years if
not decades. Conversely, treating
sleep disorders such as sleep apnoea
can delay the onset of cognitive
decline by almost a decade (Mander
et al., 2016; Musiek and Holtzman,
2016). In this issue of Brain, Ju and
co-workers add to the evidence linking poor sleep to Alzheimer’s disease
by revealing an association between
the disruption of non-REM slow
wave sleep and CSF levels of amyloid
(Ju et al., 2017).
Mechanistically, the relationship
between sleep and Alzheimer’s disease
pathology appears to be bidirectional,
as evinced by animal models. Mice
engineered to overexpress amyloid-b
suffer a selective reduction of, and
fragmentation in, non-REM (NREM)
sleep (Roh et al., 2012). Conversely,
restricting sleep in rodents results in
significantly higher amyloid-b plaque
and tau pathological burden, in part
because of wake-related synaptic
activity that increases the accumulation of metabolic byproducts, including amyloid-b and tau (Kang et al.,
2009; Rothman et al., 2013). The
brain’s glymphatic system exhibits
higher clearance efficiency during
NREM sleep relative to wakefulness.
This includes the preferential NREM
sleep evacuation of amyloid-b, representing a sleep-dependent mechanism
that helps avert amyloid-b accumulation (Xie et al., 2013). Therefore,
sleep offers a proactive state that
reduces extracellular amyloid, while
wakefulness represents an active state
in which these Alzheimer’s diseaseassociated proteins increase (Fig. 1C).
Despite causal manipulations in
animal models, existing data linking
sleep and Alzheimer’s disease pathology in humans have been largely correlational in nature. In this issue of
Brain, Ju et al. directly address this
knowledge gap. The study used a
powerful experimental approach that
combines selective NREM slow wave
sleep (SWS) disruption and CSF
markers of amyloid-b and tau, as well
as markers of sleep/wake regulation
(hypocretin-1/orexin A) and inflammation (YKL-40). In a within-subject,
counterbalanced design, Ju and colleagues directly suppressed NREM SWS
using auditory tones, then compared
next-day Alzheimer’s disease-related
CSF measures to those after a sham
(control) night of sleep (Fig. 1A). The
core experimental question was simple:
does the suppression of NREM SWS
trigger an increase in CSF markers of
Alzheimer’s disease pathology?
Across the experimental night of
sleep, the auditory tone perturbation
successfully reduced NREM slow
wave activity (SWA); an electrophysiological measure of NREM SWS
quality. Consistent with the hypothesis, there was a rise in CSF levels
of amyloid-b the following day
(Fig. 1B). However, this increase in
amyloid-b only became significant
within a subgroup of experimental
participants demonstrating the greatest reduction in NREM SWA. This is
perhaps not overly surprising considering the small sample size. Also, as
predicted, these two experimental
measures were significantly correlated, such that the magnitude of successful NREM SWA reduction
predicted the next-day increase in
CSF amyloid-b.
The acute NREM SWA reduction
did not trigger changes in levels of
tau, hypocretin-1, total CSF protein or
inflammatory markers. However, Ju
et al. identified an additional intriguing
set of relationships—relationships that
ß The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.
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Scientific Commentaries
BRAIN 2017: 140; 2066–2078
| 2067
A
B
C
Figure 1 Schematic of experimental design, results and implications. (A) Experimental protocol. Participants underwent two sessions,
each starting with 5–14 days of wristwatch-recorded sleep actigraphy, followed by one night of polysomnography (PSG) recording, with CSF
collection the following morning. In one session, non-rapid eye movement (NREM) slow wave sleep (SWS) was impaired across the PSG
recording night using auditory tones delivered via headphones whenever slow waves were detected in the PSG recordings (orange arrows). In the
other session, the night of PSG sleep involved a sham control condition without auditory tones. (B) Left: Schematic of the experimental impact of
NREM SWS suppression relative to the sham control condition on CSF concentrations of amyloid-b (Ab, orange) and tau (yellow). Right:
Associations between 6-day habitual sleep efficiency and CSF amyloid-b and tau levels. (C) Left: Theoretical schema of the interacting, reciprocal
impact (vicious cycle) between NREM SWS, wake-related and Alzheimer’s disease (AD) pathology. Wake-related metabolic activity leads to
increased production of amyloid-b and tau, while greater Alzheimer pathology accumulation disrupts NREM SWS, leading to ever increasing
amounts of wakefulness. In contrast, during SWS, clearance of Alzheimer’s disease pathology occurs via the glymphatic pathway, and metabolic
production of such pathology is low. Right: Longitudinal dynamics of the ensuing predictive model, wherein ever decreasing sleep quality (time) and
electrophysiological sleep quality in midlife leads to greater cumulative pathological burden and increased risk of Alzheimer’s disease.
2068
| BRAIN 2017: 140; 2066–2078
Scientific Commentaries
Glossary
Glymphatic system: The brain’s waste clearance system. The movement of interstitial fluid throughout the brain results in clearance of waste
products that build up from neural activity.
NREM slow wave sleep (SWS): Stage of non-rapid eye movement sleep that is characterized by a low-frequency (0.8–4 Hz), high-amplitude
synchronized electroencephalogram (called delta waves). This stage of sleep is greatly reduced in ageing.
Slow wave activity (SWA): Spectral power in the 0.5–4.5 Hz range during non-rapid eye movement sleep or slow wave sleep. Significant
reductions in SWA are observed in middle-aged and older adults, relative to younger adults.
are perhaps even more ecologically
worrisome. Wristwatch actigraphy measurement of poor habitual sleep quality
across the experimental week, termed
sleep efficiency, predicted higher CSF
levels of tau, as well as amyloid-b
levels (Fig. 1B). The study therefore
identified two temporally different patterns of relevance—an acute relationship, wherein nightly NREM SWA
quality predicted next-day amyloid-b
levels (though not tau), and a chronic
association, such that sleep efficiency
across days predicted levels of tau and
also amyloid-b. That is, at least two
different measures of sleep, across
two different time periods, predicted
two distinct profiles of Alzheimer’s disease-related pathological consequence.
More generally, the findings suggest
that impairments in NREM SWS,
together with a deterioration in the
overall integrity and efficiency of
sleep, represent specific (though
likely interdependent) factors capable
of contributing to Alzheimer’s disease
progression (Fig. 1C). Considering
that such age-related changes in
sleep quantity and quality typically
begin early in midlife (Mander et al.,
2017), they may even be considered
prodromal features of, and instigating
triggers underlying, the Alzheimer’s
disease pathological cascade.
Like all preliminary studies, the
work by Ju at al. has limitations. As
noted, the overall main effect of SWA
disruption increasing CSF amyloid-b
was not significant when all subjects
were considered, but only for a subset
of participants. In addition, the auditory tone method used to target SWS
disruption leaves open alternative
interpretations. The control (sham)
condition involved no tones, and
thus no awakenings. Furthermore,
the auditory tone method did not
impact SWS in isolation. REM sleep
was also reduced by 30%, and lighter
stages of NREM sleep were more
abundant. The observed experimental
effects could therefore be driven by
the stress induced by the experimental
paradigm, changes in other sleep
stages, or the number of arousals,
rather than the specific loss of SWS
itself. Indeed, sleep fragmentation,
SWS, and REM sleep have all been
associated with measures of amyloid-b
and tau burden (Mander et al., 2016).
That said, the correlation between
amyloid-b and the extent of NREM
SWA disruption caused by the auditory tones suggest that these alternative possibilities may be less likely.
Nevertheless, future studies should
employ a yoked auditory control condition to overcome this issue. In such
yoked control conditions, auditory
tones could be played irrespective of
sleep stage, or during only lighter
NREM sleep or REM sleep.
Limitations aside, new event horizons emerge because of this work—a
testament to its seminal nature. For
example, what physiological properties of NREM SWS are critical to
the observed causal relationship with
overnight changes in CSF amyloid-b?
Do the number, morphological shape,
frequency, or qualitative size of slow
waves bathing the sleeping brain predict overnight clearance or production of soluble amyloid? Is it the
degree of synchrony of these waves
across the brain that is important, or
even the quality of slow waves produced in specific brain regions most
sensitive to amyloid-b and/or tau
(Mander et al., 2015)? How should
we think about these effects in the
framework of sleep as a passive state
that simply lacks the presence of detrimental wakefulness, relative to a
proactive view of sleep, one in
which NREM SWS operates to
reduce features of Alzheimer’s disease
pathology? At present, evidence
favours both being true, rather than
a mutually exclusive circumstance
(Mander et al., 2016). Discovering
answers to these questions is not
only important for our mechanistic
understanding of disease pathways,
but will likely reveal novel targets for
therapeutic interventions for those at
risk of developing Alzheimer’s disease.
Longitudinal studies guided by the
report of Ju et al. will now help determine the clinical relevance of sustained
increases in CSF amyloid-b as a result
of sleep disruption and deficiency, significantly increasing the risk for developing MCI and Alzheimer’s disease.
In other words, is sleep a biomarker
capable of forecasting the extent and
speed of accumulation of amyloid-b
plaques and tau-related neurofibrillary
tangles, and thus contribute to
Alzheimer’s disease risk. These questions will best be addressed by including
neuroimaging
measures
of
Alzheimer’s disease pathology in combination with CSF measures, together
with measures of sleep actigraphy,
sleep polysomnography, and cognition
across midlife and older age.
Perhaps even more than the biological implications, the study by Ju
et al. should trouble us all when set
against the known backdrop of plummeting sleep amounts throughout all
industrialized nations. Combined with
past work, their study emphasizes a
causal relationship between disrupted
sleep, amyloid-b and tau pathology in
middle-aged humans. That is, poor
sleep quality, whether through sleep
neglect, age or due to medical illness,
has the potential to contribute to the
Alzheimer’s disease pathological cascade even in healthy, cognitively
intact adults (Fig. 1C). If we are looking to extend not only our lifespan,
Scientific Commentaries
but also our health span, then prioritizing sleep throughout adulthood
seems more sage than ever before.
Bryce A. Mander,1 Joseph R. Winer1 and
Matthew P. Walker1,2
1 Department of Psychology, University of
California, Berkeley, CA 94720-1650,
USA
2 Helen Wills Neuroscience Institute,
University of California, Berkeley, USA
Correspondence to: Matthew P. Walker
E-mail: mpwalker@berkeley.edu
doi:10.1093/brain/awx174
References
Ju YS, Ooms SJ, Sutphen C, Macauley SL,
Zangrilli M, Jerome G, et al. Slow wave
BRAIN 2017: 140; 2066–2078
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Kang JE, Lim MM, Bateman RJ, Lee JJ,
Smyth LP, Cirrito JR, et al. Amyloid-beta
dynamics are regulated by orexin and the
sleep-wake cycle. Science 2009; 326:
1005–7.
Mander BA, Marks SM, Vogel JW, Rao V,
Lu B, Saletin JM, et al. beta-amyloid disrupts human NREM slow waves and
related hippocampus-dependent memory
consolidation. Nat Neurosci 2015; 18:
1051–7.
Mander BA, Winer JR, Jagust WJ, Walker
MP. Sleep: a novel mechanistic pathway,
biomarker, and treatment target in the
pathology of Alzheimer’s disease? Trends
Neurosci 2016; 39: 552–66.
Mander BA, Winer JR, Walker MP. Sleep
and human aging. Neuron 2017; 94:
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| 2069
Musiek ES, Holtzman DM. Mechanisms
linking circadian clocks, sleep, and
neurodegeneration. Science 2016; 354:
1004–8.
Roh JH, Huang Y, Bero AW, Kasten T,
Stewart FR, Bateman RJ, et al.
Disruption of the sleep-wake cycle and diurnal fluctuation of beta-amyloid in mice
with Alzheimer’s disease pathology. Sci
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Rothman SM, Herdener N, Frankola KA,
Mughal MR, Mattson MP. Chronic mild
sleep restriction accentuates contextual
memory impairments, and accumulations
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Correlating quantitative susceptibility mapping
with cognitive decline in Alzheimer’s disease
This scientific commentary refers to
‘Cerebral quantitative susceptibility
mapping predicts amyloid-b-related
cognitive decline’ by Ayton et al.,
(doi:10.1093/brain/awx137).
The association of brain iron and
Alzheimer’s disease is frustratingly
enigmatic. Iron can participate in so
many critical and pathological processes that it is difficult not to ascribe
an aetiological role in Alzheimer’s disease, yet direct evidence of its participation remains elusive and indirect
evidence through therapeutic targeting contradictory. The function and
chronology of iron accumulation are
not well understood in part because
of difficulty in measurement. Prior
work by Ayton et al. showed that
the concentration of ferritin, an
iron storage protein, in the CSF was
positively correlated with cognitive
decline (Ayton et al., 2015).
Apropos of treatments targeting
pathological iron, it has been proposed that developing suitable clinical
treatments for Alzheimer’s disease
will likely require better patient and
disease stratification (Huang and
Mucke, 2012). In this issue of Brain,
Ayton and co-workers report the use
of iron-sensitive quantitative susceptibility mapping magnetic resonance
imaging (QSM-MRI) in tandem with
amyloid-b-PET as a potential means
of addressing the challenge of access
with a non-invasive, high spatial
resolution technique (Ayton et al.,
2017).
In this latest work by Ayton et al.,
100 subjects were evaluated for cognitive function on an 18-month basis
for 6 years. Of the 100 individuals in
the study, 64 were cognitively
normal, 17 had mild cognitive impairment, and 19 were previously diagnosed with Alzheimer’s disease as
defined by the NINCDS-ADRDA criteria. Amyloid-b-PET scans were performed using the 11C Pittsburgh
compound B (11C-PiB) followed by
MRI acquisition with T1-weighted
and QSM modes. An important distinguishing factor in this study is the
stratification into groups delineating
the presence (Ab + ) or absence
(Ab ) of PET-determined amyloid-b
pathology. As the authors explain,
MRI data from the Ab + groups
were most predictive of cognitive
decline. For instance, hippocampal
QSM was positively correlated with
Z-score change in Ab individuals,
but negatively correlated in Ab + individuals. In Ab patients, QSM of the
temporal lobe was weakly correlated
with Z-score change, while that of the
frontal lobe was negatively correlated.
The association of QSM was region
specific but generally showed a positive correlation in Ab + individuals
that the authors hope may predict
future cognitive function loss. A key
point in the report is that MRI-measured iron was not necessarily associated with cognitive decline unless
the individual already had mild cognitive impairment: in individuals with
mild cognitive impairment, a higher
iron load often correlated with
greater cognitive decline. The predictive power of QSM-MRI may be
applicable to clinical trials where the
Published by Oxford University Press on behalf of the Guarantors of Brain 2017. This work is written by US Government employees and is in the public domain in the
United States.
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