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Conference proceeding of the ATT-AD/PD Focus Meeting, Torino, Italy, March 2018
CHARACTERIZATION OF SLEEP ABNORMALITIES IN ALZHEIMER’S DISEASE & MILD
COGNITIVE IMPAIRMENT USING AN IN-HOME SLEEP PROFILING SYSTEM
Chris Berka1, Daniel Levendowski1, Amir Meghdadi1, Greg Rupp1, Marija Stevanovic Karik1, Stephanie Smith1, Joanne
Hamilton 2,3, David Salat 4,5, Kefron McCaw 2, Philip Westbrook1
1Advanced Brain Monitoring, Carlsbad, CA; 2Advanced Neurobehavioral Health and 3University of California San Diego, San Diego, CA;
4Harvard Medical School and 5Massachusetts General Hospital, Boston, MA
INTRODUCTION: Sleep abnormalities are highly prevalent in patients with neurodegenerative disease, often
appearing in the pre-clinical stage and may reflect the underlying neuropathology long before cognitive decline is
detected. Changes in sleep spindles during non-rapid eye movement (NREM) sleep have been associated with cognitive
decline in Parkinson’s disease1, and reduced slow wave sleep has been associated with increased beta amyloid
concentrations in cerebrospinal fluid in cognitively normal elderly2. These sleep architecture
characteristics of NREM sleep are believed to be associated with the metabolic clearance system of
the brain. Increased orexin levels in patients with Neurodegenerative disease (NDD) have been
associated with prolonged sleep latency, reduced sleep efficiency, and REM sleep impairment3.
Additionally, severe obstructive sleep apnea (OSA), which causes sleep discontinuity, has been
associated with a higher risk of NDD4. This is the first report investigating sleep biomarkers (i.e.,
architecture and continuity) in NDD patients using data acquired in-home with a self-applied
acquisition system.
METHODS:
Subjects: Under IRB approval, two-night overnight EEG
studies were obtained from patients diagnosed with NDD
(Mild Cognitive Impairment (MCI), Alzheimer’s disease (AD)
or Lewy Body Dementia (DLB) and elderly, healthy controls
(HC) (Table 1). In order to match the ages of the NDD group,
a subset of HC studies (n=37) selected from a database
acquired at Washington University Knights Alzheimer’s
Disease Research Center were included in the analysis.
Analyses were performed on the HC cohort that included 31
males (69 + 10.1 years) and 26 females (70 + 8.1 years), and
the NDD cohort included 24 males (71 + 7.9 years) and 12
females (72 + 9.0 years).
Sleep Parameters: The Sleep Profiler used in this study was a
battery-powered recorder designed to acquire 3 frontopolar EEG signals between AF7-AF8, AF7-Fpz, and AF8-Fpz.
Power spectra from the delta (1–3.5 Hz), DeltaC (delta power corrected for ocular activity), theta (4–6.5 Hz), alpha (8–
12 Hz), sigma (12–16 Hz), beta (18–28 Hz), and EMG bands (40-128 Hz) and ocular activity were extracted and
applied to previously validated algorithms that detect cortical arousals, sleep spindles and stage each 30-s epoch as
awake, non-REM (NREM stages N1, N2 or N3 or, rapid eye movement (REM) sleep.5 Studies were auto-scored and
manually reviewed for quality. Studies were excluded when at least one night of data was unavailable due to poor data
quality that impacted the distributions of sleep architecture. Additionally, HC were excluded due to age or sleep
patterns associated with moderate/severe obstructive sleep apnea.
Statistics: Sleep biomarkers from the HC, MCI and AD groups were submitted for analysis with student t-tests. The
NDD data were combined and submitted along with the HC data for stepwise analysis in order to identify
discriminating biomarkers. A linear discriminant function analysis (DFA) was then applied using a total of 12 variables
selected by either stepwise analysis, significance of t-test results, or previously reported as sleep biomarkers of NDD.1-2
RESULTS:
Figure 1 shows the distributions of variables used in the DFA classifier for the HC, MCI and AD groups. The resulting
DFA classifier, after leave-one-out-cross-validation, provided an overall accuracy of 83.33% (sensitivity and specificity
= 83.33%, negative predictive value = 88.24%, positive predictive value 76.92%). The ROC curve findings for these
pilot data which are presented in Figure 2 suggest excellent diagnostic accuracy.
HC
MCI
AD
DLB
Included
Two nights
51
18
9
1
One night only
6
4
4
1
T-test analyses
57
22
13
--
DFA model develop.
53
21
12
2
Excluded
Age matching
3
--
--
--
Poor data quality
9
3
3
--
Moderate/severe OSA
10
--
--
--
Total
Total studies
79
25
16
2
Failure rate
11%
12%
19%
--
Table 1: Tabulation of acquired overnight EEG studies
Conference proceeding of the ATT-AD/PD Focus Meeting, Torino, Italy, March 2018
Figure 1. Comparison of group means for HC (n=57) , MCI (n=22) and AD (n=13). Error bars represent standard
error of the mean. Significant differences between HC and MCI or AD marked with * (p<0.05) and ** (p<0.01).
Figure 2. Receiver operating characteristic curve (ROC) for the DFA using sleep biomarkers to discriminate HC (n=53) from NDD
(n=35), with the red dot showing the operating point of the classifier.
DISCUSSION: NDD patients exhibited decreased sleep efficiency and increased sleep discontinuity, consistent with
previous reports. In the NDD cohort, the percentage of N1 sleep was significantly greater and the power spectral
characteristics were structurally different from HC. Sigma power (a predominant characteristic of sleep spindles and
slow wave sleep) was attenuated, and theta power was increased (a NDD characteristic also observed in awake
EEG). The decrease in relative alpha power during stage N1 further confirms an abnormal alteration of the structural
characteristics of sleep in NDD. Additional research is needed to confirm these sleep metrics, as well as elevated EMG
power (i.e., frontal muscle tone) during NREM sleep in combination with loud snoring, are valid NDD biomarker. The
percentage of time in supine sleep was the most significant sleep measure associated with NDD. It is well known that
the effect of gravity on lung volume and the upper airway during supine sleep results in more severe OSA. Our findings
offer the intriguing possibility that head position during sleep may independently influence the clearance pathways of
the glymphatic system during sleep.
REFERENCES:
1. Latreille V, Carrier J, Lafortune M, et al. Sleep spindles in Parkinson’s disease may predict the development of dementia. Neurobiol Aging.
2015; 36(2):1083– 1090
2. Varga AW, Wohlleber ME, Giménez S, et al. Reduced slow-wave sleep is associated with high cerebrospinal fluid Aβ42 levels in cognitively
normal elderly. Sleep. 2016; 39(11):2041–2048.
3. Liguori C, Nuccetelli M, Sancesario G et al. Rapid eye movement sleep disruption and sleep fragmentation are associated with increased
orexin-A cerebrospinal fluid levels in mild cognitive impairment due to Alzheimer’s disease. Neurobiol Aging 2016; 40:120-126.
4. Lutsey PL, Misialek Jr, Mosley TH, et al. Sleep characteristics and risk of dementia and Alzheimer’s disease: The atherosclerosis Risk in
Communities Study. Alzheimers Dement 2018; 14(2):157-166.
5. Levendowski DJ, Ferini-Strambi L, Gamaldo C et al. The accuracy, night-to-night variability and stability of frontopolar sleep
electroencephalography biomarkers. J Clin Sleep Med 2017; 13(6):791-803
ACKNOWLEDGEMENTS: The majority of this work was supported by the NIH (R44AG050326; R44AG054256). Brendan P. Lucey, David
M. Holtzman, John C. Morris and their staff at the Department of Neurology, Washington University School of Medicine, St. Louis, MO acquired
recordings from elderly subjects included as healthy controls, work supported by the NIH (P01-AG003991; 50-AG005681).
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