Characterization of sleep abnormalities in Alzheimer’s disease and mild cognitive impairment using an in-home sleep profiling system.

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.

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.