Late-stage Alzheimer's Disease Research Articles

Patterns of Early Neocortical Amyloid-β Accumulation: A PET Population-Based Study.

The widespread deposition of amyloid-β (Aβ) plaques in late-stage Alzheimer disease is well defined and confirmed by invivo PET. However, there are discrepancies between which regions contribute to the earliest topographic Aβ deposition within the neocortex. Methods: This study investigated Aβ signals in the perithreshold SUV ratio range using Pittsburgh compound B (PiB) PET in a population-based study cross-sectionally and longitudinally. PiB PET scans from 1,088 participants determined the early patterns of PiB loading in the neocortex. Results: Early-stage Aβ loading is seen first in the temporal, cingulate, and occipital regions. Regional early deposition patterns are similar in both apolipoprotein ε4 carriers and noncarriers. Clustering analysis shows groups with different patterns of early amyloid deposition. Conclusion: These findings of initial Aβ deposition patterns may be of significance for diagnostics and understanding the development of Alzheimer disease phenotypes.

Gene Expression Profile of the Mitochondrial Calcium Uniporter and Antioxidant Enzymes throughout the Brain During Alzheimer’s Disease

In 2023, there were an estimated ~6.7 million Americans ≥65 years old living with Alzheimer’s disease (AD), a prominent form of dementia. Development of AD has been associated with impaired blood flow to and throughout the brain. In turn, there is diminished delivery of oxygen and nutrients in tandem with reduced clearance of neurotoxic cellular byproducts, notably reactive oxygen species (ROS; e.g., superoxide, peroxide/hydroxyl radicals). As a primary source of cerebrovascular ROS, endothelial mitochondria become progressively dysfunctional in producing ATP relative to ROS in response to aging and cell senescence. A primary mechanism is described by enhanced Ca2+ sequestration into the mitochondrial matrix through the mitochondrial Ca2+ uniporter (MCU) complex ( Mcu, Mcub, Mcur1, Micu1, Micu2, Micu3) and a resulting overproduction of ROS up to conditions of cell death. The primary antioxidants that eliminate ROS are superoxide dismutase ( Sod2), catalase ( Cat), and glutathione peroxidase ( Gpx1). We tested the hypothesis that upregulation of the MCU complex occurs in tandem with decreasing expression of antioxidant enzymes throughout the brain during progression of AD pathology. To investigate this hypothesis, we employed a triple mutation mouse model of AD ( 3xTg-AD) comprising young control (YC; 1-2 mo), mild cognitive impairment (MCI; 4-5 mo), presence of extracellular amyloid-β plaques (Aβ; 6-8 mo), and extracellular amyloid-β plaques with neurofibrillary tangles composed of tau (AβT; ≥12 mo) [n ≥3 per group, males & females]. Quantitative polymerase chain reaction (qPCR), Nanostring, and/or RNA-Seq have been performed on cortex, hippocampus, cerebral vessels, cerebrovascular endothelial cells (ECs), and peripheral blood. Primary highlights of our findings for each of the AD study groups are described below. With detectable presence of all target genes throughout tissue compartments, the YC group is particularly characterized by a higher MCU complex and antioxidant expressions in cortex relative to hippocampus. During the MCI phase, the MCU complex and Cat are upregulated in ECs relative to the YC group. Upon onset of AD (Aβ stage), there is a downregulation and upregulation of MCU complex in the brain and peripheral blood respectively. Further, Gpx1 expression is upregulated in ECs in the Aβ group. Relative to YC, the MCU complex and antioxidants are downregulated in cortex but generally stable in hippocampus during AβT conditions with exception of upregulation in peroxide scavenger mRNAs Cat and Gpx1. In contrast for ECs, the MCU complex increases, while Cat and Gpx1 decrease, in the AβT group relative to YC. Altogether, these findings support an intricate expression profile of the MCU complex and key antioxidant enzymes across brain tissue and cell compartments preceding and accompanying AD pathology. A remarkable observation is that expression profiles of cerebrovascular vessels and blood indicate MCI and early onset of AD, whereas those of the cortex and hippocampus best accompany late-stage AD pathology. This research was supported by the National Institutes of Health (R01AG073230). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.

Sequential Contrastive and Deep Learning Models to Identify Selective Butyrylcholinesterase Inhibitors.

Butyrylcholinesterase (BChE) is a target of interest in late-stage Alzheimer's Disease (AD) where selective BChE inhibitors (BIs) may offer symptomatic treatment without the harsh side effects of acetylcholinesterase (AChE) inhibitors. In this study, we explore multiple machine learning strategies to identify BIs in silico, optimizing for precision over all other metrics. We compare state-of-the-art supervised contrastive learning (CL) with deep learning (DL) and Random Forest (RF) machine learning, across single and sequential modeling configurations, to identify the best models for BChE selectivity. We used these models to virtually screen a vendor library of 5 million compounds for BIs and tested 20 of these compounds in vitro. Seven of the 20 compounds displayed selectivity for BChE over AChE, reflecting a hit rate of 35% for our model predictions, suggesting a highly efficient strategy for modeling selective inhibition.

Obesity differentially effects the somatosensory cortex and striatum of TgF344-AD rats

Lifestyle choices leading to obesity, hypertension and diabetes in mid-life contribute directly to the risk of late-life Alzheimer’s disease (AD). However, in late-life or in late-stage AD conditions, obesity reduces the risk of AD and disease progression. To examine the mechanisms underlying this paradox, TgF344-AD rats were fed a varied high-carbohydrate, high-fat (HCHF) diet to induce obesity from nine months of age representing early stages of AD to twelve months of age in which rats exhibit the full spectrum of AD symptomology. We hypothesized regions primarily composed of gray matter, such as the somatosensory cortex (SSC), would be differentially affected compared to regions primarily composed of white matter, such as the striatum. We found increased myelin and oligodendrocytes in the somatosensory cortex of rats fed the HCHF diet with an absence of neuronal loss. We observed decreased inflammation in the somatosensory cortex despite increased AD pathology. Compared to the somatosensory cortex, the striatum had fewer changes. Overall, our results suggest that the interaction between diet and AD progression affects myelination in a brain region specific manner such that regions with a lower density of white matter are preferentially affected. Our results offer a possible mechanistic explanation for the obesity paradox.

Wrist-worn actigraphy in agitated late-stage dementia patients: A feasibility study on digital inclusion.

Wrist-worn actigraphy can be an objective tool to assess sleep and other behavioral and psychological symptoms in dementia (BPSD). We investigated the feasibility of using wearable actigraphy in agitated late-stage dementia patients. Agitated, late-stage Alzheimer's dementia care home residents in Greater London area (n=29; 14 females, mean age±SD: 80.8±8.2; 93.1% White) were recruited to wear an actigraphy watch for 4weeks. Wearing time was extracted to evaluate compliance, and factors influencing compliance were explored. A high watch-acceptance (96.6%) and compliance rate (88.0%) was noted. Non-compliance was not associated with age or BPSD symptomatology. However, participants with "better" cognitive function (R=0.42, p=0.022) and during nightshift (F1.240, 33.475=8.075, p=0.005) were less compliant. Female participants were also marginally less compliant (F1, 26=3.790, p=0.062). Wrist-worn actigraphy appears acceptable and feasible in late-stage agitated dementia patients. Accommodating the needs of both the patients and their carers may further improve compliance.

Role of GABAB receptors in cognition and EEG activity in aged APP and PS1 transgenic mice

Alzheimer's disease (AD) is the most common cause of dementia in the elderly. Recent evidence suggests that gamma-aminobutyric acid B (GABAB) receptor-mediated inhibition is a major contributor to AD pathobiology, and GABAB receptors have been hypothesized to be a potential target for AD treatment. The aim of this study is to determine how GABAB regulation alters cognitive function and brain activity in an AD mouse model. Early, middle and late stage (8–23 months) amyloid precursor protein (APP) and presenilin 1 (PS1) transgenic mice were used for the study. The GABAB agonist baclofen (1 and 2.5 mg/kg, i. p.) and the antagonist phaclofen (0.5 mg/kg, i. p.) were used. Primarily, we found that GABAB activation was able to improve spatial and/or working memory performance in early and late stage AD animals. In addition, GABAB activation and inhibition could regulate global and local EEG oscillations in AD animals, with activation mainly regulating low-frequency activity (delta-theta bands) and inhibition mainly regulating mid- and high-frequency activity (alpha-gamma bands), although the regulated magnitude at some frequencies was reduced in AD. The cognitive improvements in AD animals may be explained by the reduced EEG activity in the theta frequency band (2–4 Hz). This study provides evidence for a potential therapeutic effect of baclofen in the elderly AD brain and for GABAB receptor-mediated inhibition as a potential therapeutic target for AD.

Discovery, Structure-Based Modification, In Vitro, In Vivo, and In Silico Exploration of m-Sulfamoyl Benzoamide Derivatives as Selective Butyrylcholinesterase Inhibitors for Treating Alzheimer's Disease.

For the potential therapy of Alzheimer's disease (AD), butyrylcholinesterase (BChE) has gradually gained worldwide interest in the progression of AD. This study used a pharmacophore-based virtual screening (VS) approach to identify Z32439948 as a new BChE inhibitor. Aiding by molecular docking and molecular dynamics, essential binding information was disclosed. Specifically, a subpocket was found and structure-guided design of a series of novel compounds was conducted. Derivatives were evaluated in vitro for cholinesterase inhibition and physicochemical properties (BBB, log P, and solubility). The investigation involved docking, molecular dynamics, enzyme kinetics, and surface plasmon resonance as well. The study highlighted compounds 27a (hBChE IC50 = 0.078 ± 0.03 μM) and (R)-37a (hBChE IC50 = 0.005 ± 0.001 μM) as the top-ranked BChE inhibitors. These compounds showed anti-inflammatory activity and no apparent cytotoxicity against the human neuroblastoma (SH-SY5Y) and mouse microglia (BV2) cell lines. The most active compounds exhibited the ability to improve cognition in both scopolamine- and Aβ1-42 peptide-induced cognitive deficit models. They can be promising lead compounds with potential implications for treating the late stage of AD.

Diverse Efficacy of Dimethyl Fumarate in Alleviating the Late Streptozotocin-Induced Cognitive Impairment and Neuropathological Features in Rat.

Our previous study showed that dimethyl fumarate (DMF) treatment performed within three weeks after intracerebroventricular (ICV) injection of streptozotocin (STZ) attenuated spatial memory impairment, hippocampal neurodegeneration, and neuroinflammation in rats. The present study is aimed at verifying the hypothesis that DMF alleviates late effects of STZ (6months after ICV injection) which reflects advanced stage of the Alzheimer's disease (AD) in human patients. Spatial memory was assessed with Morris water maze (MWM), general brain level of amyloid β (Aβ) and p-tau was measured by western blot, immunofluorescent labelling of active microglia (IBA1), Aβ and p-tau and histological assay of neurodegeneration (Fluoro-Jade C) were performed in hippocampus and cortex. Two-week oral therapy with DMF normalized spatial memory disrupted by STZ but had no influence on general brain level of Aβ and p-tau. However, immunofluorescence showed local reduction of Aβ aggregates number in parietal cortex and p-tau+ cells in CA2 hippocampal area. Microgliosis was alleviated by DMF in CA1 area and parietal cortex. DMF-treated STZ injected rats showed higher number of Aβ containing microglia than untreated group in CA2 and frontal cortex, which may be the result of increased phagocytic activity in these areas after DMF treatment. STZ-induced neurodegeneration was alleviated by DMF in dentate gyrus and frontal cortex. In conclusion DMF treatment exerts beneficial effect on spatial memory in the rat model of late stage of AD, but weakly influences neuropathological features, as only local reduction in number of Aβ aggregates, p-tau containing cells, neurodegeneration, and microgliosis was found.

Developing and describing a typology of lucid episodes among people with Alzheimer's disease and related dementias.

This study examined lucid episodes among people living with late-stage Alzheimer's disease and related dementias (PLWD) and then developed a typology of these episodes to help characterize them. Family caregivers of PLWD provided information about witnessed episodes, including proximity to death, cognitive status, duration, communication quality, and circumstances prior to lucid episodes on up to two episodes (caregiver N=151; episode N=279). Latent class analysis was used to classify and characterize empirically distinct clusters of lucid episodes. Four lucid episode types were identified. The most common type occurred during visits with family and among PLWD who lived>6 months after the episode. The least common type coincided with family visits and occurred within 7 days of the PLWD's death. Findings suggest that multiple types of lucid episodes exist; not all signal impending death; and some, but not all, are precipitated by external stimuli.

Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease.

Increasing evidence suggests that patients with Alzheimer's disease present alterations in functional connectivity but previous results have not always been consistent. One of the reasons that may account for this inconsistency is the lack of consideration of temporal dynamics. To address this limitation, here we studied the dynamic modular organization on resting-state functional magnetic resonance imaging across different stages of Alzheimer's disease using a novel multilayer brain network approach. Participants from preclinical and clinical Alzheimer's disease stages were included. Temporal multilayer networks were used to assess time-varying modular organization. Logistic regression models were employed for disease stage discrimination, and partial least squares analyses examined associations between dynamic measures with cognition and pathology. Temporal multilayer functional measures distinguished all groups, particularly preclinical stages, overcoming the discriminatory power of risk factors such as age, sex, and APOE ϵ4 carriership. Dynamic multilayer functional measures exhibited strong associations with cognition as well as amyloid and tau pathology. Dynamic multilayer functional connectivity shows promise as a functional imaging biomarker for both early- and late-stage Alzheimer's disease diagnosis.

Navigating late-stage dementia: A perspective from the Alzheimer's Association.

Alzheimer's disease (AD) is the most common cause of dementia, a general term for memory loss and decline in other cognitive abilities enough to interfere with daily life. AD accounts for 60% to 80% of dementia cases. The late stage of AD tends to be the shortest stage and, on average, lasts 1 to 2 years. As this stage of the condition progresses, it requires continuous intensive long-term care and around-the-clock intensive care. The Alzheimer's Association stands firm in its commitment to supporting individuals living with AD and other dementia, their care partners, and their health-care providers as they navigate treatment and care decisions across the continuum of the disease. This article is a direct response to recently published works that run counter to the Association's viewpoint. It outlines the Association's perspective on crucial factors for consideration during late-stage dementia care, including advanced directives, palliative care, nutrition, and legal considerations. It explores diverse perspectives from the field, differing from the Alzheimer's Association's stance. Last, it underscores resources available through the Alzheimer's Association, aiming to present a comprehensive perspective on late-stage care for support and assistance to all involved.

Cross‐omics integration analysis revels synaptic dysregulation across brain regions at later stages of Alzheimer disease

AbstractBackgroundThe clinical and molecular heterogeneity of Alzheimer disease (AD) is increasingly recognized as a major factor impacting diagnosis accuracy, the design of therapeutic interventions and clinical trials, and the development of effective treatments for AD. Recently, research efforts have leveraged molecular data to characterize AD patients into subgroups. However, the influence that such AD molecular profiles may have on cognitive decline trajectories remains poorly understood. Comprehensive studies that aggregate multiple modalities of omics have proven to be a powerful way to identify molecular profiles for human diseases including cancer. Here, we sought to determine whether cross‐omics integration can reveal molecular profiles of AD that have clinical and biological relevance.MethodWe leveraged machine learning approaches, digital deconvolution, and traditional statistical approaches to integrate and analyze multiple high‐throughput omics data from different cortical regions and cohorts, including the parietal cortex (N = 278) from the Knight Alzheimer Disease Research Center (Knight ADRC), the dorsolateral prefrontal cortex (N = 237) from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort, and the parahippocampal gyrus (BM36; N = 116) from The Mount Sinai Brain Bank (MSBB) study.ResultWe identified four distinct molecular profiles, one of which was associated with significantly worse Clinical Dementia Rating (CDR) at death, shorter survival after symptom onset, more severe neurodegeneration and astrogliosis, and unique metabolomic profiles. This profile, present in multiple cortical regions affected at distinct stages of AD progression, showed significant dysregulation of synaptic genes and pathways, indicating neuron/synapse losses and dysfunction at later stages of AD and associated with worse cognitive function. Among other molecules, we found that the expression of alpha‐synuclein (SNCA) and SNAP25 were downregulated in this profile. The synaptic dysregulation associated with this profile was further validated in a mouse model of pathological aSyn aggregation.ConclusionTogether, our results demonstrate that cross‐omics approaches can reveal multiple molecular pathways associated with AD progression, and reinforce that, in AD, prominent synaptic loss and dysfunction are closely associated with worse cognition. This may open the possibility for new biomarkers for the molecular staging of AD and cognition, as well as potential therapeutic targets to ameliorate cognitive decline and AD progression.

Alzheimer’s Disease mechanistic pathways were discovered through in silico experiments in causal AI based digital twins

AbstractBackgroundBeyond a single‐gene‐based approach, pathway‐based approaches have become important in drug development paradigm. Several pathways are known for the pathogenesis of Alzheimer’s disease (AD), but these may be from animal studies or observational human subject studies. Here, we focus on identification of gene pathways with direct causal relationships to AD through Gemini Digital Twins of AD patients.MethodTwo independent and complementary AD Digital Twins were built using an A.I. platform REFSTM [aitiabio.com], based on Bayesian network models of ADNI and ANMerge consortium data which reverse‐engineered the connectivity of ∼59K and ∼35K multi‐modal variables and a wide range of AD‐related outcomes (cognitive, imaging and biomarker) profiled from 317 subjects (Control:MCI:Dementia = 97:191:29) and 199 subjects (Control:MCI:Dementia = 65:63:71), respectively [Fig1]. The average causal effect of each gene (at the blood gene expression level) on each outcome was estimated through in silico counterfactual experiments. Pathway enrichment analyses were performed on metascape.org.ResultWe identified a total of 573 and 300 AD‐driver‐genes from ADNI‐ and ANMerge‐based Digital Twins, of which 41 overlapped (significance‐of‐overlap = 6e‐15). These 41 robustly validated genes, of which 61% were identified by the network as causal mediators of APOE4 genotype, were enriched in several biological processes known for AD such as ‘regulation of RNA splicing’ and ‘macrophage activation’ [Fig2]. The AD‐driver‐genes were also enriched in other interesting pathways in both Digital Twins, such as ‘immune response’ and ‘oxidative stress’. Furthermore, the ADNI‐specific AD‐driver‐genes, representing a sample population with a higher proportion of MCI patients, were related to additional processes such as ‘mitochondrial respiration’ and ‘ribosomal sub‐complexes pathways’, previously known to be involved specifically in early stage AD.ConclusionWe prioritized several AD‐related pathways for potential drug development identified from causal driver genes discovered and validated in two independent Digital Twins. Some of these pathways are more related to proteopathic biochemical phase of AD, rather than cellular phase, suggesting an area of a potential early intervention. Investigating causal genes from two complementary consortium studies with a different proportion of early vs. late stage AD patients may be a promising approach to identify disease‐stage‐specific and disease‐stage‐common signatures in the pathway‐based drug discovery efforts.

Time-series visual explainability for Alzheimer’s disease progression detection for smart healthcare

Artificial intelligence (AI)-based diagnostic systems provide less error-prone and safer support to clinicians, enhancing the medical decision-making process. This study presents a smart and reliable healthcare framework for detecting Alzheimer's disease (AD) progression. Early detection of AD before the onset of clinical symptoms is the most crucial step in starting timely treatment. To predict the conversion of cognitively normal patients to those with AD, three-dimensional (3D) magnetic resonance imaging (MRI) whole-brain neuroimaging methods have been extensively studied. However, depending on the 3D volume, this method is computationally expensive. To solve this problem, we used an approximate rank pooling method originally designed for video action recognition with a 3D MRI volume to obtain a compressed representation of multiple two-dimensional (2D) MRI slices. This study proposes a hybrid multimodal CNN-Bi-LSTM deep model for AD progression detection, in which the resulting dynamic 2D images are fused with cognitive features. Moreover, a novel explainable AI approach is proposed to provide visual explanations using the resulting longitudinal 2D dynamic images. Temporal explanations were provided by visualizing the affected brain regions captured using longitudinal 2D MRIs. By utilizing a sample of 1,692 subjects with multimodal data from the Alzheimer’s Disease Neuroimaging Initiative dataset, our method was assessed using a 10-fold cross-validation process. The model achieved an area under the receiver operating characteristics curve (AUC) of 94% using longitudinal 2D three-time-step dynamic image data. The fusion of 2D dynamic images with cognitive features enhanced the performance by 2% in terms of the AUC. Patients who gradually develop AD, show changes in various brain regions. For such patients, our system highlights the critical role of the hippocampus, medial amygdala, caudal hippocampus, and lateral amygdala at the initial time steps. In the late stages of AD, the system detects abnormalities in extra brain regions such as the medial temporal gyrus, superior temporal gyrus, fusiform gyrus, and caudal hippocampus; indicating that patients have completely progressed to AD.

Small RNA Profiles of Brain Tissue-Derived Extracellular Vesicles in Alzheimer's Disease.

Extracellular vesicles (EVs) and non-coding RNAs (ncRNAs) are emerging contributors to Alzheimer's disease (AD) pathophysiology. Differential abundance of ncRNAs carried by EVs may provide valuable insights into underlying disease mechanisms. Brain tissue-derived EVs (bdEVs) are particularly relevant, as they may offer valuable insights about the tissue of origin. However, there is limited research on diverse ncRNA species in bdEVs in AD. This study explored whether the non-coding RNA composition of EVs isolated from post-mortem brain tissue is related to AD pathogenesis. bdEVs from age-matched late-stage AD patients (n = 23) and controls (n = 10) that had been separated and characterized in our previous study were used for RNA extraction, small RNA sequencing, and qPCR verification. Significant differences of non-coding RNAs between AD and controls were found, especially for miRNAs and tRNAs. AD pathology-related miRNA and tRNA differences of bdEVs partially matched expression differences in source brain tissues. AD pathology had a more prominent association than biological sex with bdEV miRNA and tRNA components in late-stage AD brains. Our study provides further evidence that EV non-coding RNAs from human brain tissue, including but not limited to miRNAs, may be altered and contribute to AD pathogenesis.

Toward Reagent-Free Discrimination of Alzheimer's Disease Using Blood Plasma Spectral Digital Biomarkers and Machine Learning.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. The detection of early-stage AD is particularly desirable because it would allow early intervention. However, a minimally invasive, low-cost, and accurate discrimination or diagnostic method for AD is especially difficult in the earliest stage of AD. The aim of this research is to discover blood plasma spectral digital biomarkers of AD, develop a novel intelligent method for the discrimination of AD and accelerate the translation of Fourier transform infrared (FTIR) spectral-based disease discrimination methods from the laboratory to clinical practice. Since vibration spectroscopy can provide the structure and chemical composition information of biological samples at the molecular level, we investigated the potential of FTIR spectral biomarkers of blood plasma to differentiate between AD patients and healthy controls. Combined with machine learning technology, we designed a hierarchical discrimination system that provides reagent-free and accurate AD discrimination based on blood plasma spectral digital biomarkers of AD. Accurate segregation between AD patients and healthy controls was achieved with 89.3% sensitivity and 85.7% specificity for early-stage AD patients, 92.8% sensitivity and 87.5% specificity for middle-stage AD patients, and 100% sensitivity and 100% specificity for late-stage AD patients. Our results show that blood plasma spectral digital biomarkers hold great promise as discrimination markers of AD, indicating the potential for the development of an inexpensive, reagent-free, and less laborious clinical test. As a result, our research outcome will accelerate the clinical application of spectral digital biomarkers and machine learning.

Palliative care for persons with late-stage Alzheimer’s and related dementias and their caregivers: protocol for a randomized clinical trial

BackgroundLimited access to specialized palliative care exposes persons with late-stage Alzheimer’s disease and related dementias (ADRD) to burdensome treatment and unnecessary hospitalization and their caregivers to avoidable strain and financial burden. Addressing this unmet need, the purpose of this study was to conduct a randomized clinical trial (RCT) of the ADRD-Palliative Care (ADRD-PC) program.MethodsThe study will use a multisite, RCT design and will be set in five geographically diverse US hospitals. Lead investigators and outcome assessors will be masked. The study will use 1:1 randomization of patient-caregiver dyads, and sites will enroll N = 424 dyads of hospitalized patients with late-stage ADRD with their family caregivers. Intervention dyads will receive the ADRD-PC program of (1) dementia-specific palliative care, (2) standardized caregiver education, and (3) transitional care. Control dyads will receive publicly available educational material on dementia caregiving. Outcomes will be measured at 30 days (interim) and 60 days post-discharge. The primary outcome will be 60-day hospital transfers, defined as visits to an emergency department or hospitalization ascertained from health record reviews and caregiver interviews (aim 1). Secondary patient-centered outcomes, ascertained from 30- and 60-day health record reviews and caregiver telephone interviews, will be symptom treatment, symptom control, use of community palliative care or hospice, and new nursing home transitions (aim 2). Secondary caregiver-centered outcomes will be communication about prognosis and goals of care, shared decision-making about hospitalization and other treatments, and caregiver distress (aim 3). Analyses will use intention-to-treat, and pre-specified exploratory analyses will examine the effects of sex as a biologic variable and the GDS stage.DiscussionThe study results will determine the efficacy of an intervention that addresses the extraordinary public health impact of late-stage ADRD and suffering due to symptom distress, burdensome treatments, and caregiver strain. While many caregivers prioritize comfort in late-stage ADRD, shared decision-making is rare. Hospitalization creates an opportunity for dementia-specific palliative care, and the study findings will inform care redesign to advance comprehensive dementia-specific palliative care plus transitional care.Trial registrationClinicalTrials.gov NCT04948866. Registered on July 2, 2021.

Folic Acid and Folinic Acid Protect Hearts of Aging Triple-transgenic Alzheimer's Disease mice via IGF1R/PI3K/AKT and SIRT1/AMPK Pathways.

Patients with Alzheimer's disease have increased risk of developing heart disease, which therefore highlights the need for strategies aiming at reducing Alzheimer's disease-related cardiovascular disease. Folic acid and folinic acid are beneficial to the heart. We aimed to investigate the benefits of folic acid and folinic acid in heart of patients with late-stage Alzheimer's disease. Twelve 16-month-old mice of triple-transgenic late-stage Alzheimer's disease were divided into three groups: Alzheimer's disease group, Alzheimer's disease + folic acid group, and Alzheimer's disease + folinic acid group. The mice were administered 12mg/kg folic acid or folinic acid once daily via oral gavage for 3months. In the folic acid and folinic acid treatment groups, the intercellular space was reduced, compared with the Alzheimer's disease group. TUNEL assay and western blot images showed that the number of apoptotic cells and the apoptosis-related protein expression were higher in the Alzheimer's disease group than in other two treated groups. Folic acid and folinic acid induced the IGF1R/PI3K/AKT and SIRT1/ AMPK pathways in the hearts of mice with Alzheimer's disease. Our results showed that folic acid and folinic acid treatment increased survival and SIRT1 expression to reduce apoptotic proteins in the heart. The aging mice treated with folinic acid had more IGF1R and SIRT1/AMPK axes to limit myocardial cell apoptosis. In conclusion, folic acid and folinic acid promote cardiac cell survival and prevent apoptosis to inhibit heart damage in aging mice with triple-transgenic late-stage Alzheimer's disease. In particular, folinic acid provides a better curative effect than folic acid.

Neuropsychiatric features in a multi-ethnic population with Alzheimer disease and mild cognitive impairment.

Alzheimer disease (AD) is more prevalent in African American (AA) and Hispanic White (HIW) compared to Non-Hispanic White (NHW) individuals. Similarly, neuropsychiatric symptoms (NPS) vary by population in AD. This is likely the result of both sociocultural and genetic ancestral differences. However, the impact of these NPS on AD in different groups is not well understood. Self-declared AA, HIW, and NHW individuals were ascertained as part of ongoing AD genetics studies. Participants who scored higher than 0.5 on the Clinical Dementia Rating (CDR) Scale (CDR) were included. Group similarities and differences on Neuropsychiatric Inventory Questionnaire (NPI-Q) outcomes (NPI-Q total score, NPI-Q items) were evaluated using univariate ANOVAs and post hoc comparisons after controlling for sex and CDR stage. Our sample consisted of 498 participants (26% AA; 30% HIW; 44% NHW). Overall, NPI-Q total scores differed significantly between our groups, with HIW having the highest NPI-Q total scores, and by AD stage as measured by CDR. We found no significant difference in NPI-Q total score by sex. There were six NPI-Q items with comparable prevalence in all groups and six items that significantly differed between the groups (Anxiety, Apathy, Depression, Disinhibition, Elation, and Irritability). Further, within the HIW group, differences were found between Puerto Rican and Cuban American Hispanics across several NPI-Q items. Finally, Six NPI-Q items were more prevalent in the later stages of AD including Agitation, Appetite, Hallucinations, Irritability, Motor Disturbance, and Nighttime Behavior. We identified differences in NPS among HIW, AA, and NHW individuals. Most striking was the high burden of NPS in HIW, particularly for mood and anxiety symptoms. We suggest that NPS differences may represent the impact of sociocultural influences on symptom presentation as well as potential genetic factors rooted in ancestral background. Given the complex relationship between AD and NPS it is crucial to discern the presence of NPS to ensure appropriate interventions.