Spatial navigation patterns in indoor space usage can reveal important cues about the cognitive health of participants. In this work, we present a low-cost, scalable, open-source edge computing system using Bluetooth low energy (BLE) beacons for tracking indoor movements in a large, 1700 m2 facility used to carry out therapeutic activities for participants with mild cognitive impairment (MCI). The facility is instrumented with 39 edge computing systems, along with an on-premise fog server. The participants carry a BLE beacon, in which BLE signals are received and analyzed by the edge computing systems. Edge computing systems are sparsely distributed in the wide, complex indoor space, challenging the standard trilateration technique for localizing subjects, which assumes a dense installation of BLE beacons. We propose a graph trilateration approach that considers the temporal density of hits from the BLE beacon to surrounding edge devices to handle the inconsistent coverage of edge devices. This proposed method helps us tackle the varying signal strength, which leads to intermittent detection of beacons. The proposed method can pinpoint the positions of multiple participants with an average error of 4.4 m and over 85% accuracy in region-level localization across the entire study area. Our experimental results, evaluated in a clinical environment, suggest that an ordinary medical facility can be transformed into a smart space that enables automatic assessment of individuals’ movements, which may reflect health status or response to treatment.

Proteomic studies have identified moesin (MSN), a protein containing a four-point-one, ezrin, radixin, moesin (FERM) domain, and the receptor CD44 as hub proteins found within a coexpression module strongly linked to Alzheimer’s disease (AD) traits and microglia. These proteins are more abundant in Alzheimer’s patient brains, and their levels are positively correlated with cognitive decline, amyloid plaque deposition, and neurofibrillary tangle burden. The MSN FERM domain interacts with the phospholipid phosphatidylinositol 4,5-bisphosphate (PIP2) and the cytoplasmic tail of CD44. Inhibiting the MSN–CD44 interaction may help limit AD-associated neuronal damage. Here, we investigated the feasibility of developing inhibitors that target this protein–protein interaction. We have employed structural, mutational, and phage-display studies to examine how CD44 binds to the FERM domain of MSN. Interestingly, we have identified an allosteric site located close to the PIP2 binding pocket that influences CD44 binding. These findings suggest a mechanism in which PIP2 binding to the FERM domain stimulates CD44 binding through an allosteric effect, leading to the formation of a neighboring pocket capable of accommodating a receptor tail. Furthermore, high-throughput screening of a chemical library identified two compounds that disrupt the MSN–CD44 interaction. One compound series was further optimized for biochemical activity, specificity, and solubility. Our results suggest that the FERM domain holds potential as a drug development target. Small molecule preliminary leads generated from this study could serve as a foundation for additional medicinal chemistry efforts with the goal of controlling microglial activity in AD by modifying the MSN–CD44 interaction.

The current demand for early intervention, prevention, and treatment of late onset Alzheimer’s disease (LOAD) warrants deeper understanding of the underlying molecular processes which could contribute to biomarker and drug target discovery. Utilizing high-throughput proteomic measurements in serum from a prospective population-based cohort of older adults (n=5,294), we identified 303 unique proteins associated with incident LOAD (median follow-up 12.8 years). Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status. These proteins were implicated in neuronal processes and overlapped with protein signatures of LOAD in brain and cerebrospinal fluid. We found 17 proteins which LOAD-association was strongly dependent on APOE-ε4 carrier status. Most of them showed consistent associations with LOAD in cerebrospinal fluid and a third had brain-specific gene expression. Remarkably, four proteins in this group (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated as a consequence of LOAD as determined in a bi-directional Mendelian randomization analysis, reflecting a potential response to the disease onset. Accordingly, the direct association of these proteins to LOAD was reversed upon APOE-ε4 genotype adjustment, a finding which we replicate in an external cohort (n=719). Our findings provide an insight into the dysregulated pathways that may lead to the development and early detection of LOAD, including those both independent and dependent on APOE-ε4. Importantly, many of the LOAD-associated proteins we find in the circulation have been found to be expressed - and have a direct link with AD - in brain tissue. Thus, the proteins identified here, and their upstream modulating pathways, provide a new source of circulating biomarker and therapeutic target candidates for LOAD.

Alterations in brain cholesterol homeostasis have been broadly implicated in neurological disorders. Notwithstanding the complexity by which cholesterol biology is governed in the mammalian brain, excess neuronal cholesterol is primarily eliminated by metabolic clearance via cytochrome P450 46A1 (CYP46A1). No methods are currently available for visualizing cholesterol metabolism in the living human brain; therefore, a non-invasive technology that quantitatively measures the extent of brain cholesterol metabolism via CYP46A1 could broadly impact disease diagnosis and treatment options using targeted therapies. Here we describe the development and testing of a CYP46A1-targeted positron emission tomography (PET) tracer. 18F-CHL-2205(18F-Cholestify). Our data show that PET imaging readouts correlate with CYP46A1 protein expression and with the extent to which cholesterol is metabolized in the brain, as assessed by cross-species post-mortem analyses of specimens from rodents, non-human primates and humans. Proof-of-concept of in vivo efficacy is provided in the well-established 3xTg-AD murine model of Alzheimer’s disease (AD), where we show that the probe is sensitive to differences in brain cholesterol metabolism between 3xTg-AD mice and control animals. Further, our clinical observations point towards a considerably higher baseline brain cholesterol clearance via CYP46A1 in women, as compared to age-matched men. These findings illustrate the vast potential of assessing brain cholesterol metabolism using PET and establish PET as a sensitive tool for non-invasive assessment of brain cholesterol homeostasis in the clinic.

Development of disease-modifying therapies against Alzheimer’s disease (AD) requires biomarkers reflecting the diverse pathological pathways specific for AD. We measured 665 proteins in 797 cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment with abnormal amyloid (MCI(Aβ+): n=50), AD-dementia (n=230), non-AD dementias (n=322) and cognitively unimpaired controls (n=195) using proximity ligation-based immunoassays. Here we identified >100 CSF proteins dysregulated in MCI(Aβ+) or AD compared to controls or non-AD dementias. Proteins dysregulated in MCI(Aβ+) were primarily related to protein catabolism, energy metabolism and oxidative stress, while those specifically dysregulated in AD dementia were related to cell remodeling, vascular function and immune system. Classification modeling unveiled biomarker panels discriminating clinical groups with high accuracies (AUC: 0.85-0.99), which were translated into custom multiplex assays and validated in external and independent cohorts (AUC: 0.8-0.99). Overall, this study provides novel pathophysiological leads delineating the multifactorial nature of AD and potential biomarker tools for diagnostic settings or clinical trials.

Introduction This study investigates whether plasma biomarkers (Aβ42/40 and p‐tau 181), APS, as well as apolipoprotein E (APOE) proteotype predict cognitive deficits in elderly adults from the Democratic Republic of Congo. Methods Forty‐four with possible AD (pAD) and 41 healthy control (HC) subjects were screened using CSID and AQ, underwent cognitive assessment with the African Neuropsychology Battery (ANB), and provided blood samples for plasma Aβ42, Aβ40, Aβ42/40, and APOE proteotype. Linear and logistic regression were used to evaluate the associations of plasma biomarkers with ANB tests and the ability of biomarkers to predict cognitive status. Results Patients with pAD had significantly lower plasma Aβ42/40 levels, higher APS, and higher prevalence of APOE E4 allele compared to HC. Groups did not differ in levels of Aβ40, Aβ42, or P‐tau 181. Results showed that Aβ42/40 ratio and APS were significantly associated with African Naming Test (ANT), African List Memory Test (ALMT), and African Visuospatial Memory Test (AVMT) scores, while the presence of APOE E4 allele was associated with ANT, ALMT, AVMT, and APT scores. P‐tau 181 did not show any significant associations while adjusting for age, education, and gender. APS showed the highest area under the curve (AUC) value (AUC = 0.78, 95% confidence interval [CI]: 0.68–0.88) followed by Aβ42/40 (AUC = 0.75, 95% CI: 0.66–0.86) and APOE E4 (AUC = 0.69 (CI 0.57–0.81) in discriminating pAD from HC. Discussion These results demonstrate associations between select plasma biomarker of AD pathology (Aβ42/40), APS, and APOE E4 allele) and ANB test scores and the ability of these biomarkers to differentiate pAD from cognitively normal SSA individuals, consistent with findings reported in other settings.

Inclusion of Black participants in clinical research is a national priority, particularly for diseases in which they face disproportionate risk. Currently, Black participants are significantly underrepresented within clinical trials and longitudinal research. In an effort to overcome logistical barriers that may limit research participation, this study examined the reliability and feasibility of two mobile smartphone application-based cognitive measures in a diverse middle aged and older adult sample. Black (n=44; Mage=59.93) and non-Hispanic white (NHW; n=50; Mage=61.06) participants completed traditional paper-based neuropsychological testing and two app-based measures, Arrows and Number Match. Arrows and Number Match are adaptations of traditional neuropsychological measures, the Flanker Task and Symbol Digit Modalities Test, respectively. Intraclass correlations demonstrated poor to moderate reliability (range: .417-.569) between performance on the app-based versions and performance on the traditional versions. There were no race related differences in performance on Arrows. Performance score differences by racial group were not statistically significant on Number Match, but trended toward significance, (t (81) = 1.91, p = .06). Both Black and NHW participants rated the applications as feasible and acceptable, though Black participants endorsed a stronger likelihood of future use (M=3.95, SD=0.94) than their NHW counterparts (M=3.50, SD=1.15), p = .04. These findings add to the growing literature on remote cognitive testing in response to the necessity of increased accessibility within research.

A rapidly aging world population is fueling a concomitant increase in Alzheimer's disease (AD) and related dementias (ADRD). Scientific inquiry, however, has largely focused on White populations in Australia, the European Union, and North America. As such, there is an incomplete understanding of AD in other populations. In this perspective, we describe research efforts and challenges of cohort studies from three regions of the world: Central America, East Africa, and East Asia. These cohorts are engaging with the Davos Alzheimer's Collaborative (DAC), a global partnership that brings together cohorts from around the world to advance understanding of AD. Each cohort is poised to leverage the widespread use of mobile devices to integrate digital phenotyping into current methodologies and mitigate the lack of representativeness in AD research of racial and ethnic minorities across the globe. In addition to methods that these three cohorts are already using, DAC has developed a digital phenotyping protocol that can collect ADRD‐related data remotely via smartphone and/or in clinic via a tablet to generate a common data elements digital dataset that can be harmonized with additional clinical and molecular data being collected at each cohort site and when combined across cohorts and made accessible can provide a global data resource that is more racially/ethnically represented of the world population.

Background: This paper used data from the Apathy in Dementia Methylphenidate Trial 2 (NCT02346201) to conduct a planned cost consequence analysis to investigate whether treatment of apathy with methylphenidate is economically attractive. Methods: A total of 167 patients with clinically significant apathy randomized to either methylphenidate or placebo were included. The Resource Utilization in Dementia Lite instrument assessed resource utilization for the past 30 days and the EuroQol five dimension five level questionnaire assessed health utility at baseline, 3 months, and 6 months. Resources were converted to costs using standard sources and reported in 2021 USD. A repeated measures analysis of variance compared change in costs and utility over time between the treatment and placebo groups. A binary logistic regression was used to assess cost predictors. Results: Costs were not significantly different between groups whether the cost of methylphenidate was excluded (F(2,330) = 0.626, ηp2 = 0.004, p = 0.535) or included (F(2,330) = 0.629, ηp2 = 0.004, p = 0.534). Utility improved with methylphenidate treatment as there was a group by time interaction (F(2,330) = 7.525, ηp2 = 0.044, p < 0.001). Discussion: Results from this study indicated that there was no evidence for a difference in resource utilization costs between methylphenidate and placebo treatment. However, utility improved significantly over the 6-month follow-up period. These results can aid in decision-making to improve quality of life in patients with Alzheimer’s disease while considering the burden on the healthcare system.

Alzheimer’s disease and related dementias (ADRD) remain a public health priority, with prevalence of Alzheimer’s disease—the most common cause of dementia—among people aged 65 years and older living in the United States expected to grow to nearly 13.8 million people by 2060 (Alzheimer’s Association, 2023). ADRD are not normal aging; they impair memory and cognitive functioning, disrupting daily life. Over time, people with ADRD need increased assistance with basic activities of daily living and must rely on others for support, affecting family, friends, professional caregiving infrastructures, health and long-term care systems, and financial institutions designed to pay for care. In 2023, the formal cost of caring for people with ADRD to the health and long-term care systems in the United States is projected to total $345 billion (Alzheimer’s Association, 2023). Additionally, unpaid caregiving by family and friends was valued at nearly $339.5 billion in 2022 (Alzheimer’s Association, 2023). The lifetime cost of care for a person with Alzheimer’s disease was more than double the cost of care for a person without Alzheimer’s disease (Alzheimer’s Association, 2023). The total formal cost of ADRD care is projected to reach around $1 trillion in 2050 (Alzheimer’s Association, 2023; Zissimopoulos et al., 2014). These estimates do not consider the loss of quality of life for people with ADRD and their caregivers. It is imperative for the health of our systems and our population that public health address modifiable risk factors of ADRD.