Payne, Sarah

Abstract Background The National Institute on Aging Alzheimer's Disease Family Based Study (NIA‐ AD FBS) is a multi‐site, longitudinal study aimed to be a data and bio sample resource for investigators worldwide. Since its inception in 2002 the FBS goal has been to promote greater cooperation and sharing of clinical and biological resources among researchers. Methods The focus of the Family Based Study (FBS) has been the recruitment of families with at least two affected individuals and a third first degree relative with or without dementia and willing to participate. Participants are from different ethnic backgrounds including Caucasian, African American and Hispanics. The FBS study is actively recruiting both late onset (LOAD) and early onset Alzheimer's Disease (EOAD) families as well as conducting follow up evaluations every two years, approximately. Uniform assessments are completed across all sites and include DNA, Plasma, PaXgene and PBMC samples collection and brain autopsies whenever possible. We conduct both in person and remote evaluations and use the services of a mobile phlebotomy company to collect bio samples. In addition, we’ve created a protocol for genetic testing on the proband for EOAD families where an additional sample is sent to a CLIA approved lab and after genetic counseling, the presence or not of AD related mutations is disclosed to the families. Results To date, this cohort has recruited 1,756 families and acquired data from 9,682 family members. Families are from Caucasian, African Americans and Hispanics ethnic groups. The cohort has longitudinal clinical data, cognitive assessment, family history and bio samples available for sharing. Genotype data includes APOE, GWAS, WES, WGS, brain methylation, RNA sequencing and biomarkers data. Conclusion The NIA‐LOAD FBS study is the largest collection of familial Alzheimer's Disease worldwide and many genetic studies of Alzheimer's disease (AD) have included cases and controls from this dataset. Over 140 publications have used data and/or samples from FBS to address the genetics of Alzheimer's Disease. The enriched resources provided by this cohort are invaluable to the scientific community.

AbstractBackgroundDementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) are challenging to differentiate. Mobile health devices that measure motor and sleep domains between clinic visits may improve differential diagnosis.MethodParticipants with probable AD and probable DLB wore ActiGraph triaxial wGT3X‐BT actigraphy devices on their nondominant wrists for 2 weeks and multichannel Sleep ProfilerX8TM EEG devices on their foreheads for 2 consecutive nights.3 Actigraphy data were processed and scored using GGIR and activity counts in R. The Sleep Profiler characterizes abnormal slow‐wave sleep and non‐REM hypertonia (NRH).ResultAt baseline, participants with DLB (n = 9) had higher UPDRS scores and a higher likelihood of psychosis than participants with AD (n = 8; see Table 1). Using univariate Wilcoxon Rank‐Sum tests, we found that at baseline participants with DLB had lower daily activity counts, higher % time in NRH, and lower % time in REM sleep than participants with AD (unadjusted p‐values < 0.05; See Figure 1).In a recent 6‐month followup, activity levels were lower on average than baseline activity levels for both DLB (n = 5) and AD (n = 5). at 6‐months, the differences in % sleep in NRH increased between DLB participants (n = 5) and AD participants (n = 3) as average % NRH increase 6% in DLB and decreased 1% in AD (see Table 2). These 6‐month differences are not statistically significant, but data collection are ongoing.An exploration of methods for processing and scoring actigraphy data revealed that processing can result in wide range of findings. As a result, we have focused on actigraphy outcomes which are more suitable to older participants with dementia.ConclusionParticipants with DLB demonstrated lower baseline physical activity and higher rates of baseline sleep disturbances than participants with AD; sleep differences between DLB and AD appeared to increase over 6 months.Actigraphy and sleep‐monitoring devices are well tolerated in participants with dementia and they can reliably collect valid, useful activity and sleep data in between clinic visits.

Abstract Background Dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD) are challenging to differentiate. Mobile health devices that measure motor and sleep domains between clinic visits may improve differential diagnosis. Method Participants with probable AD and probable DLB wore ActiGraph triaxial wGT3X‐BT actigraphy devices on their nondominant wrists for 2 weeks and multichannel Sleep ProfilerX8TM EEG devices on their foreheads for 2 consecutive nights.3 Actigraphy data were processed and scored using GGIR and activity counts in R. The Sleep Profiler characterizes abnormal slow‐wave sleep and non‐REM hypertonia (NRH). Result At baseline, participants with DLB (n = 9) had higher UPDRS scores and a higher likelihood of psychosis than participants with AD (n = 8; see Table 1). Using univariate Wilcoxon Rank‐Sum tests, we found that at baseline participants with DLB had lower daily activity counts, higher % time in NRH, and lower % time in REM sleep than participants with AD (unadjusted p‐values < 0.05; See Figure 1). In a recent 6‐month followup, activity levels were lower on average than baseline activity levels for both DLB (n = 5) and AD (n = 5). at 6‐months, the differences in % sleep in NRH increased between DLB participants (n = 5) and AD participants (n = 3) as average % NRH increase 6% in DLB and decreased 1% in AD (see Table 2). These 6‐month differences are not statistically significant, but data collection are ongoing. An exploration of methods for processing and scoring actigraphy data revealed that processing can result in wide range of findings. As a result, we have focused on actigraphy outcomes which are more suitable to older participants with dementia. Conclusion Participants with DLB demonstrated lower baseline physical activity and higher rates of baseline sleep disturbances than participants with AD; sleep differences between DLB and AD appeared to increase over 6 months. Actigraphy and sleep‐monitoring devices are well tolerated in participants with dementia and they can reliably collect valid, useful activity and sleep data in between clinic visits.

AbstractBackgroundNeurodegenerative disease (NDD) is associated with sleep disturbances, including decreased stage N3 and REM sleep and reduced spindle activity. Further, supine sleep position may impact NDD risk by altering glymphatic clearance. This study evaluated conventional and exploratory sleep metrics in the differentiation of subjects with normal cognition (NC) and patients with Alzheimer’s disease dementia (AD) and alpha‐synucleinopathies (α‐Syn).MethodHome sleep data were acquired across multiple study sites using the self‐applied Sleep ProfilerTM, including 44 NC subjects (age=69+5.0), 22 patients diagnosed with AD (age=75+7.1) and 25 with α‐Syn (Parkinson’s disease=14, Parkinson’s disease with Dementia or Dementia with Lewy Bodies=11; age=69+6.9). Recordings were auto‐staged and technically reviewed, with 86% of the records having two‐night averages. Logistic regression and Mann‐Whitney U tests were used to characterize group differences based on age, conventional sleep metrics (i.e., sleep efficiency, sleep onset (SO), awakenings, percent of sleep time (%) in REM,N1, N2 and N3, and sleep spindles) and exploratory sleep measures [i.e., % supine, % Light N2 (L2=N2 epochs absent of spindles with K‐complexes and/or alpha or EMG (>40 Hz) intrusion), % NREM sleep with hypertonia (NRSH=>4 consecutive epochs with persistently elevated EMG power relative to delta and theta power) and % atypical N3 (AN3=epochs staged N3 with low theta and sigma power relative to delta, alpha, beta and EMG)].ResultAD were older than both NC and α‐Syn patients (p<0.005). Both AD and α‐Syn had lower %REM and greater %supine than NC (p<0.005), and %NRSH was greater in α‐Syn than NC or AD (p<0.005). Based on logistic regression, AD was differentiated from NC based on age, %REM, %L2 (p<0.05) and %supine (p=0.06). α‐Syn was distinguished from NC using both conventional (%N3 and SO, p<0.05) and exploratory sleep metric (%AN3 and %NRSH, p<0.05; %supine, p=0.06). Age (p=0.05) and %NRSH (p<0.01) were the only metrics that differentiated α‐Syn from AD.ConclusionObjective home sleep profiles hold promise for differentiating NDD patients from NC. The sleep metrics that provided the greatest distinction between NDD and NC were %REM, %supine and NRSH, while only NRSH distinguished AD from α‐Syn patients. Further confirmatory studies are underway.