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Background: Traditional measurement systems utilized in clinical trials are limited because they are episodic and thus cannot capture the day-to-day fluctuations and longitudinal changes that frequently affect patients across different therapeutic areas. Objectives: The aim of this study was to collect and evaluate data from multiple devices, including wearable sensors, and compare them to standard lab-based instruments across multiple domains of daily tasks. Methods: Healthy volunteers aged 18–65 years were recruited for a 1-h study to collect and assess data from wearable sensors. They performed walking tasks on a gait mat while instrumented with a watch, phone, and sensor insoles as well as several speech tasks on multiple recording devices. Results: Step count and temporal gait metrics derived from a single lumbar accelerometer are highly precise; spatial gait metrics are consistently 20% shorter than gait mat measurements. The insole’s algorithm only captures about 72% of steps but does have precision in measuring temporal gait metrics. Mobile device voice recordings provide similar results to traditional recorders for average signal pitch and sufficient signal-to-noise ratio for analysis when hand-held. Lossless compression techniques are advised for signal processing. Conclusions: Gait metrics from a single lumbar accelerometer sensor are in reasonable concordance with standard measurements, with some variation between devices and across individual metrics. Finally, participants in this study were familiar with mobile devices and had high acceptance of potential future continuous wear for clinical trials.

Normative samples drawn from older populations may unintentionally include individuals with preclinical Alzheimer's disease (AD) pathology, resulting in reduced means, increased variability, and overestimation of age effects on cognitive performance. A total of 264 cognitively normal (Clinical Dementia Rating = 0) older adults were classified as biomarker negative ("Robust Normal," n = 177) or biomarker positive ("Preclinical Alzheimer's Disease" [PCAD], n = 87) based on amyloid imaging, cerebrospinal fluid biomarkers, and hippocampal volumes. PCAD participants performed worse than robust normals on nearly all cognitive measures. Removing PCAD participants from the normative sample yielded higher means and less variability on episodic memory, visuospatial ability, and executive functioning measures. These results were more pronounced in participants aged 75 years and older. Notably, removing PCAD participants from the sample significantly reduced age effects across all cognitive domains. Applying norms from the robust normal sample to a separate cohort did not improve Clinical Dementia Rating classification when using standard deviation cutoff scores. Overall, removing individuals with biomarker evidence of preclinical AD improves normative sample quality and substantially reduces age effects on cognitive performance but provides no substantive benefit for diagnostic classifications.

Many genetic studies for Alzheimer’s Disease (AD) have been focused on the identification of common genetic variants associated with AD risk and not on other aspects of the disease, such as age at onset or rate of dementia progression. There are multiple approaches to untangling the genetic architecture of these phenotypes. We hypothesized that the genetic architecture of rate of progression is different than the risk for developing AD dementia. To test this hypothesis, we used longitudinal clinical data from ADNI and the Knight-ADRC at Washington University and we calculated PRS (polygenic risk score) based on the IGAP study to compare the genetic architecture of AD risk and dementia progression. Dementia progression was measured by the change of Clinical Dementia Rating Sum of Boxes (CDR)-SB per year. Out of the 21 loci for AD risk, no association with the rate of dementia progression was found. The PRS rate was significantly associated with the rate of dementia progression (β=0.146, p.value= 0.03). In the case of rare variants, TREM2 (β=0.309, p.value=0.02) was also associated with the rate of dementia progression. TREM2 variant carriers showed a 23% faster rate of dementia compared with non-variant carriers. In conclusion, our results indicate that the recently identified common and rare variants for AD susceptibility have a limited impact on the rate of dementia progression in AD patients.

The accumulation of the toxic Aβ peptide in Alzheimer's disease (AD) largely relies upon an efficient recycling of amyloid precursor protein (APP). Recent genetic association studies have described rare variants in SORL1 with putative pathogenic consequences in the recycling of APP. In this work, we examine the presence of rare coding variants in SORL1 in three different European American cohorts: early-onset, late-onset AD (LOAD) and familial LOAD.

Recent advances in sequencing technology and novel genotyping arrays (focused on low-frequency and coding variants) have made it possible to identify novel coding variants with large effect sizes and also novel genes (TREM2, PLD3, UNC5C, and AKAP9) associated with Alzheimer's disease (AD) risk. The major advantages of these studies over the classic genome-wide association studies (GWAS) include the identification of the functional variant and the gene-driven association. In addition to the large effect size, these studies make it possible to model these variants and genes using cell and animal systems. On the other hand, the underlying population-variability of these very low allele frequency variants poses a great challenge to replicating results. Studies that include very large datasets (>10,000 cases and controls) and combine sequencing and genotyping approaches will lead to the identification of novel genes for Alzheimer's disease.