Alzheimer's Disease Individuals Research Articles

Lipidomic Analysis of Plasma Extracellular Vesicles Derived from Alzheimer's Disease Patients.

Analysis of blood-based indicators of brain health could provide an understanding of early disease mechanisms and pinpoint possible intervention strategies. By examining lipid profiles in extracellular vesicles (EVs), secreted particles from all cells, including astrocytes and neurons, and circulating in clinical samples, important insights regarding the brain's composition can be gained. Herein, a targeted lipidomic analysis was carried out in EVs derived from plasma samples after removal of lipoproteins from individuals with Alzheimer's disease (AD) and healthy controls. Differences were observed for selected lipid species of glycerolipids (GLs), glycerophospholipids (GPLs), lysophospholipids (LPLs) and sphingolipids (SLs) across three distinct EV subpopulations (all-cell origin, derived by immunocapture of CD9, CD81 and CD63; neuronal origin, derived by immunocapture of L1CAM; and astrocytic origin, derived by immunocapture of GLAST). The findings provide new insights into the lipid composition of EVs isolated from plasma samples regarding specific lipid families (MG, DG, Cer, PA, PC, PE, PI, LPI, LPE, LPC), as well as differences between AD and control individuals. This study emphasizes the crucial role of plasma EV lipidomics analysis as a comprehensive approach for identifying biomarkers and biological targets in AD and related disorders, facilitating early diagnosis and potentially informing novel interventions.

Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.

Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD.

Cost-Effectiveness of Lecanemab for Individuals With Early-Stage Alzheimer Disease.

Little is known regarding the cost-effectiveness of lecanemab (Leqembi), a monoclonal antibody approved by the US Food and Drug Administration in January 2023 for the treatment of mild cognitive impairment (MCI) or mild dementia due to Alzheimer disease (AD). This study aims to quantify the cost-effectiveness of lecanemab and how it varies based on the accuracy of AD testing and individuals' APOE ε4 status. Seven alternative test-treat-target strategies defined by combinations of testing approaches (PET, CSF, or plasma assay), treatment choices (standard of care [SoC] alone or lecanemab in addition to SoC), and targeting strategies (targeting APOE ε4 noncarriers or heterozygous patients or not) were compared. A hybrid decision tree-Markov cohort model was constructed with 5 states: (1) MCI (Clinical Dementia Rating-Sum of Boxes [CDR-SB] 0-4.5); (2) mild dementia (CDR-SB 4.6-9.5); (3) moderate dementia (CDR-SB 9.6-16); (4) severe dementia (CDR-SB >16); and (5) death. Effectiveness was measured by quality-adjusted life years and costs from third-party and societal perspectives were estimated in 2022 US dollars over a lifetime horizon. Among the 7 test-treat-target strategies, SoC alone was the optimal strategy from a cost-effectiveness perspective. Neither targeted lecanemab treatment nor treatment unrestricted by APOE ε4 genotype was cost-effective vs SoC alone, regardless of the test used to diagnose patients with early-stage AD. However, CSF assay followed by targeted treatment would become cost-effective if lecanemab is priced below $5,100 per year. These results were robust to the accuracy of diagnostic testing and rates of lecanemab discontinuation and adverse events. Neither targeted lecanemab treatment nor treatment unrestricted by APOE ε4 genotype is cost-effective vs SoC alone for patients with MCI or mild dementia due to AD. Lecanemab would be cost-effective in some settings if priced below $5,100 per year.

Sex differences in luteinizing hormone aggravates Aβ deposition in APP/PS1 and Aβ1-42-induced mouse models of Alzheimer's disease

Alzheimer's disease (AD) exhibits a higher incidence rate among older women, and dysregulation of the hypothalamic-pituitary-gonadal (HPG) axis during aging is associated with cognitive impairments and the development of dementia. luteinizing hormone (LH) has an important role in CNS function, such as mediating neuronal pregnenolone production, and modulating neuronal plasticity and cognition. The sex differences in LH and its impact on Aβ deposition in AD individuals remain unclear, with no reported specific mechanisms. Here, we show through data mining that LH-related pathways are significantly enriched in female AD patients. Additionally, LH levels are elevated in female AD patients and exhibit a negative correlation with cognitive levels but a positive correlation with AD pathology levels, and females exhibit a greater extent of AD pathology, such as Aβ deposition. In vivo, we observed that the exogenous injection of LH exacerbated behavioral impairments induced by Aβ1-42 in mice. LH injection resulted in worsened neuronal damage and increased Aβ deposition. In SH-SY5Y cells, co-administration of LH with Aβ further exacerbated Aβ-induced neuronal damage. Furthermore, LH can dose-dependently decrease the levels of NEP and LHR proteins while increasing the expression of GFAP and IBA1 in vivo and in vitro. Taken together, these results indicate that LH can exacerbate cognitive impairment and neuronal damage in mice by increasing Aβ deposition. The potential mechanism may involve the reduction of NEP and LHR expression, along with the exacerbation of Aβ-induced inflammation.

Whole-body metabolic modelling reveals microbiome and genomic interactions on reduced urine formate levels in Alzheimer’s disease

In this study, we aimed to understand the potential role of the gut microbiome in the development of Alzheimer's disease (AD). We took a multi-faceted approach to investigate this relationship. Urine metabolomics were examined in individuals with AD and controls, revealing decreased formate and fumarate concentrations in AD. Additionally, we utilised whole-genome sequencing (WGS) data obtained from a separate group of individuals with AD and controls. This information allowed us to create and investigate host-microbiome personalised whole-body metabolic models. Notably, AD individuals displayed diminished formate microbial secretion in these models. Additionally, we identified specific reactions responsible for the production of formate in the host, and interestingly, these reactions were linked to genes that have correlations with AD. This study suggests formate as a possible early AD marker and highlights genetic and microbiome contributions to its production. The reduced formate secretion and its genetic associations point to a complex connection between gut microbiota and AD. This holistic understanding might pave the way for novel diagnostic and therapeutic avenues in AD management.

Early Diagnosing and Transformation Prediction of Alzheimer's Disease Using Multi-Scaled Self-Attention Network on Structural MRI Images with Occlusion Sensitivity Analysis.

Structural magnetic resonance imaging (sMRI) is vital for early Alzheimer's disease (AD) diagnosis, though confirming specific biomarkers remains challenging. Our proposed Multi-Scale Self-Attention Network (MUSAN) enhances classification of cognitively normal (CN) and AD individuals, distinguishing stable (sMCI) from progressive mild cognitive impairment (pMCI). This study leverages AD structural atrophy properties to achieve precise AD classification, combining different scales of brain region features. The ultimate goal is an interpretable algorithm for this method. The MUSAN takes whole-brain sMRI as input, enabling automatic extraction of brain region features and modeling of correlations between different scales of brain regions, and achieves personalized disease interpretation of brain regions. Furthermore, we also employed an occlusion sensitivity algorithm to localize and visualize brain regions sensitive to disease. Our method is applied to ADNI-1, ADNI-2, and ADNI-3, and achieves high performance on the classification of CN from AD with accuracy (0.93), specificity (0.82), sensitivity (0.96), and area under curve (AUC) (0.95), as well as notable performance on the distinguish of sMCI from pMCI with accuracy (0.85), specificity (0.84), sensitivity (0.74), and AUC (0.86). Our sensitivity masking algorithm identified key regions in distinguishing CN from AD: hippocampus, amygdala, and vermis. Moreover, cingulum, pallidum, and inferior frontal gyrus are crucial for sMCI and pMCI discrimination. These discoveries align with existing literature, confirming the dependability of our model in AD research. Our method provides an effective AD diagnostic and conversion prediction method. The occlusion sensitivity algorithm enhances deep learning interpretability, bolstering AD research reliability.

Relationship Between Future Thinking and Prospective Memory in Alzheimer's Disease.

Future thinking and prospective memory are two cognitive processes oriented toward the future and reliant on the ability to envision oneself in future scenarios. We explored the connection between future thinking and prospective memory in individuals with Alzheimer's disease (AD). We invited both AD participants and control participants to engage in event-based prospective memory tasks (e.g., "please hand me this stopwatch when I inform you there are 10 minutes remaining") and time-based prospective memory tasks (e.g., "close the book you are working on in five minutes"). Additionally, we asked participants to engage in a future thinking task where they imagined upcoming events. Analysis revealed that AD participants exhibited lower performance in both prospective memory tasks and future thinking compared to the control group. Importantly, we identified significant positive correlations between the performance on event- and time-based prospective memory tasks and future thinking abilities among AD participants. These findings underscore the connection between the decline in both prospective memory domains and the ability to envision future events in individuals with AD. Our results also shed light on the challenges AD individuals face when trying to project themselves into the future to mentally pre-experience upcoming events.

Gray and White Matter Voxel-Based Morphometry of Alzheimer's Disease With and Without Significant Cerebrovascular Pathologies.

Alzheimer's disease (AD) is the most common type of dementia, and AD individuals often present significant cerebrovascular disease (CVD) symptomology. AD with significant levels of CVD is frequently labeled mixed dementia (or sometimes AD-CVD), and the differentiation of these two neuropathologies (AD, AD-CVD) from each other is challenging, especially at early stages. In this study, we compared the gray matter (GM) and white matter (WM) volumes in AD (n = 83) and AD-CVD (n = 37) individuals compared with those of cognitively healthy controls (n = 85) using voxel-based morphometry (VBM) of their MRI scans. The control individuals, matched for age and sex with our two dementia groups, were taken from the ADNI. The VBM analysis showed widespread patterns of significantly lower GM and WM volume in both dementia groups compared to the control group (P < .05, family-wise error corrected). While comparing with AD-CVD, the AD group mainly demonstrated a trend of lower volumes in the GM of the left putamen and right hippocampus and WM of the right thalamus (uncorrected P < .005 with cluster threshold, K = 10). The AD-CVD group relative to AD tended to present lower GM and WM volumes, mainly in the cerebellar lobules and right brainstem regions, respectively (uncorrected P < .005 with cluster threshold, K = 10). Although finding a discriminatory feature in structural MRI data between AD and AD-CVD neuropathologies is challenging, these results provide preliminary evidence that demands further investigation in a larger sample size.

Human-derived air–liquid interface cultures decipher Alzheimer’s disease–SARS-CoV-2 crosstalk in the olfactory mucosa

BackgroundThe neurological effects of the coronavirus disease of 2019 (COVID-19) raise concerns about potential long-term consequences, such as an increased risk of Alzheimer's disease (AD). Neuroinflammation and other AD-associated pathologies are also suggested to increase the risk of serious SARS-CoV-2 infection. Anosmia is a common neurological symptom reported in COVID-19 and in early AD. The olfactory mucosa (OM) is important for the perception of smell and a proposed site of viral entry to the brain. However, little is known about SARS-CoV-2 infection at the OM of individuals with AD.MethodsTo address this gap, we established a 3D in vitro model of the OM from primary cells derived from cognitively healthy and AD individuals. We cultured the cells at the air–liquid interface (ALI) to study SARS-CoV-2 infection under controlled experimental conditions. Primary OM cells in ALI expressed angiotensin-converting enzyme 2 (ACE-2), neuropilin-1 (NRP-1), and several other known SARS-CoV-2 receptor and were highly vulnerable to infection. Infection was determined by secreted viral RNA content and confirmed with SARS-CoV-2 nucleocapsid protein (NP) in the infected cells by immunocytochemistry. Differential responses of healthy and AD individuals-derived OM cells to SARS-CoV-2 were determined by RNA sequencing.ResultsResults indicate that cells derived from cognitively healthy donors and individuals with AD do not differ in susceptibility to infection with the wild-type SARS-CoV-2 virus. However, transcriptomic signatures in cells from individuals with AD are highly distinct. Specifically, the cells from AD patients that were infected with the virus showed increased levels of oxidative stress, desensitized inflammation and immune responses, and alterations to genes associated with olfaction. These results imply that individuals with AD may be at a greater risk of experiencing severe outcomes from the infection, potentially driven by pre-existing neuroinflammation.ConclusionsThe study sheds light on the interplay between AD pathology and SARS-CoV-2 infection. Altered transcriptomic signatures in AD cells may contribute to unique symptoms and a more severe disease course, with a notable involvement of neuroinflammation. Furthermore, the research emphasizes the need for targeted interventions to enhance outcomes for AD patients with viral infection. The study is crucial to better comprehend the relationship between AD, COVID-19, and anosmia. It highlights the importance of ongoing research to develop more effective treatments for those at high risk of severe SARS-CoV-2 infection.Graphical

Disease- and stage-specific alterations of the oral and fecal microbiota in Alzheimer's disease.

Microbial communities in the intestinal tract are suggested to impact the ethiopathogenesis of Alzheimer's disease (AD). The human microbiome might modulate neuroinflammatory processes and contribute to neurodegeneration in AD. However, the microbial compositions in patients with AD at different stages of the disease are still not fully characterized. We used 16S rRNA analyses to investigate the oral and fecal microbiota in patients with AD and mild cognitive impairment (MCI; n = 84), at-risk individuals (APOE4 carriers; n = 17), and healthy controls (n = 50) and investigated the relationship of microbial communities and disease-specific markers via multivariate- and network-based approaches. We found a slightly decreased diversity in the fecal microbiota of patients with AD (average Chao1 diversity for AD = 212 [SD = 66]; for controls = 215 [SD = 55]) and identified differences in bacterial abundances including Bacteroidetes, Ruminococcus, Sutterella, and Porphyromonadaceae. The diversity in the oral microbiota was increased in patients with AD and at-risk individuals (average Chao1 diversity for AD = 174 [SD = 60], for at-risk group = 195 [SD = 49]). Gram-negative proinflammatory bacteria including Haemophilus, Neisseria, Actinobacillus, and Porphyromonas were dominant oral bacteria in patients with AD and MCI and the abundance correlated with the cerebrospinal fluid biomarker. Taken together, we observed a strong shift in the fecal and the oral communities of patients with AD already prominent in prodromal and, in case of the oral microbiota, in at-risk stages. This indicates stage-dependent alterations in oral and fecal microbiota in AD which may contribute to the pathogenesis via a facilitated intestinal and systemic inflammation leading to neuroinflammation and neurodegeneration.

GABA transporters gene expression associates with [18F]FDG‐PET signal in regions typically hypometabolic in Alzheimer’s disease

AbstractBackgroundIt is suggested that the [18F]fluorodeoxyglucose(FDG)‐PET signal reflects astrocyte and neuronal activity, with the excitatory glutamate uptake by astrocytes as the trigger for brain glucose metabolism. The inhibitory neurotransmitter γ‐aminobutyric acid (GABA) is taken up by neurons and astrocytes through the GABA transporters (GAT) 1 and 3. However, its role in the [18F]FDG‐PET signal in healthy aging and Alzheimer’s disease (AD) remains poorly investigated. Thus, we aimed to explore associations between neuronal and astrocytic GABA transporters gene expression with [18F]FDG‐PET in cognitively unimpaired (CU) and AD individuals.MethodWe obtained [18F]FDG‐PET imaging data of CU and AD from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Table 1). GAT1 and GAT3 gene expression in the brain was obtained from the Allen Brain Atlas (Table 1, Figure 1). We extracted SUVr (pons as reference region) and mRNA values using the RMINC package and performed Pearson’s correlation to test the association between [18F]FDG‐PET and GABA transporters mRNA (p‐value &lt; 0.05).ResultIn brain regions typically hypometabolic in AD, we observed a strong positive association of [18F]FDG‐PET SUVr with GAT1 and GAT3 gene expression in CU (GAT1: r = 0.93; p &lt; 0.0001; Figure 2A, GAT3: r = 0.94; p &lt; 0.0001; Figure 2B) and AD individuals (GAT1: r = 0.9; p &lt; 0.0001; Figure 2A, GAT3: r = 0.92; p &lt; 0.0001; Figure 2B). By contrast, in brain regions typically resilient, we found a weak correlation with CU individuals (GAT1: r = 0.32; p = 0.016; Figure 2C, GAT3: r = 0.26; p = 0.04; Figure 2D) and no significant correlation in AD patients (GAT1: r = 0.24; p = 0.07; Figure 2C, GAT3: r = 0.24; p = 0.059; Figure 2D).ConclusionOur results demonstrate a significant strong positive association between [18F]FDG‐PET SUVr and GABA transporters gene expression in CU and AD in hypometabolic regions. These findings suggest that GABA transporters are involved with brain glucose metabolism in regions classically vulnerable to AD pathology.

Pooling trial data to identify heterogeneity and characteristics of patients most likely to respond to treatment: a causal forest approach

AbstractBackgroundA potential explanation for the limited success of past therapeutic trials for Alzheimer’s disease (AD) lies in disease heterogeneity. We set out to pool and jointly analyze data from completed trials using a hypothesis‐free machine learning approach, aiming to identify participant characteristics related to increased responsiveness to treatment. Considering methodological difficulties, we also aim to describe the pooling and analytical process.MethodWe requested data from fifteen completed phase I–III trials investigating several compounds in AD individuals and were granted access to six trial datasets using compounds LY450139 (semagacestat), GSK933776, and BI409306 (with donepezil) with 2,397 participants (73.3±7.8 years old, 55% female, Table 1), through the Vivli platform. Causal Forest analyses, a machine learning technique that extracts a minimal subset of patient characteristics that can explain heterogeneity in outcomes, were used with change on (1) ADAS‐Cog and (2) MMSE as outcome, accounting for treatment group (placebo vs. compound).ResultThe included trial datasets showed considerable differences in available variables and data structure, constraining the amount of data that may be used in joint analysis. We were able to pool and analyze twenty‐seven characteristics shared across trial datasets in the model, covering demographics, vital signs, MRI volumetrics, baseline cognitive and functional performance, apolipoprotein ε4‐carriership and drug compound. Causal forest analyses did not reveal heterogeneity in pooled treatment effects on ADAS‐Cog (estimate for differential forest prediction: ‐2.25, p = 0.96) or MMSE (‐2.97, p = 0.98), indicating that these patient characteristics were not predictive for increased responsiveness to treatment. Stratifying by compound, similar results were found.ConclusionWe took an innovative approach and provided a methodological basis to examine heterogeneity in treatment effects by pooling existing trial datasets. Our results imply that responsiveness to treatment may not be well‐explained by the patient characteristics to our disposal, or that different compounds should be investigated. More research into other factors possibly related to treatment response is needed, e.g., fluid biomarkers and genetics. Based on our experience, we recommend that trial sponsors improve collaboration and data access, as we work towards personalized medicine by identifying which therapeutic approach works best for whom.

Longitudinal retinal changes in mild cognitive impairment and Alzheimer’s Disease

AbstractBackgroundPrevious studies have shown retinal thickness and vascular alterations in Alzheimer’s disease (AD), even in patients with mild cognitive impairment (MCI), suggesting that retinal parameters are potential biomarkers for AD. However, the prospective observation of retinal markers is still insufficient. In this study, we aimed to observe longitudinal retinal thickness and vascular changes in patients with AD and MCI, and to identify critical retinal markers that significantly change over time.MethodPatients with AD (N = 22) and MCI (N = 21) were recruited from the Shanghai Memory Study. Age‐ and gender‐matched cognitively normal (NC) participants (N = 26) were included from the Shanghai Aging Study. Optical coherence tomography (OCT) and OCT angiography were performed in all participants at baseline and after an average 19.2 months follow‐up to assess changes in peripapillary retinal nerve fiber layer thickness (p‐RNFL), ganglion cell layer thickness (GCC), macular thickness and volume divided by the Early Treatment Diabetic Retinopathy Study grid, retinal vessel density, and fovea avascular zone (FAZ). Differences of monthly variation rate of retinal parameters between NC, MCI, and AD individuals were evaluated.ResultThe monthly variation rates among groups showed significant differences in thickness and volume of full temporal para‐fovea sector, inner temporal peri‐fovea sector, outer temporal para‐fovea sector, and thickness of outer superior hemisphere para‐fovea sector (all P&lt;0.05). There was no difference in the rate of change of p‐RNFL, GCC, retinal vessel density, and FAZ among all groups. Post hoc analysis revealed that the monthly change in thickness and volume of the full‐temporal and outer sectors were significantly faster in AD than in NC, whereas the inner‐temporal sectors changed faster in MCI than in NC groups (all P&lt;0.05). Furthermore, the monthly changes in inner temporal peri‐fovea thickness were faster in MCI than in NC and AD participants (all P&lt;0.05).ConclusionDuring follow‐up, retinal temporal thinning was faster in patients with AD, whilst inner retina‐temporal sector thinning was faster in participants with MCI. Accelerated thinning of temporal sectors of the retina, especially the inner‐temporal sector may be an early marker of AD disease progression.

Individuals from Memory Clinic were Associated with Severer Amyloid Deposition in the Chinese Population in Urban Cities

AbstractBackgroundAmyloid deposition is considered the initial pathology in Alzheimer’s disease (AD), investigation of amyloid pathology and the risk factor for amyloid pathology and cognitive declines in the Chinese population is critical for personalized management. However, the prevalence of amyloid deposition and the risk factors for amyloid pathology are unknown in the Chinese population.MethodParticipants were recruited from urban communities and memory clinics in Shanghai and Zhengzhou, China. A total of 246 AD individuals, 274 Mild cognitive impairment (MCI) individuals, and 506 Normal cognition (NC) individuals were identified: 106 AD, 156 MCI and 506 NC from the Community‐based cohort (COMC) as well as 140 AD and 118 MCI from the Clinic‐based cohort (CLIC). Amyloid positivity and deposition were analyzed based on amyloid positron emission tomography (PET) scans. We also performed a partial least squares (PLS) analysis to evaluate the associations of cohort, age, sex, education level, and ApoE genotype with amyloid qualification, quantification results and global cognition in all 1026 participants.ResultAcross the two cohorts, the AD group displayed the significantly highest rate of amyloid positivity (211/246, 85.8%), followed by the MCI (122/274, 44.5%) and NC groups (136/506, 26.9%, all P &lt; 0.001). Participants from the CLIC displayed a higher rate of amyloid positivity than those from the COMC in the MCI (68/118, 57.6% vs. 54/156, 34.6%, P &lt; 0.001) and AD groups (128/140, 91.4% vs. 83/106, 78.3%, P = 0.006). MCI (SUVr: 1.37 ± 0.25 vs. 1.25 ± 0.18, P &lt; 0.001), Aβ+ MCI (SUVr: 1.48 ± 0.27 vs. 1.37 ± 0.24, P = 0.03) and AD individuals (SUVr: 1.47 ± 0.23 vs. 1.39 ± 0.23, P = 0.01) in the CLIC displayed significantly greater amyloid deposition than those in the COMC. According to the PLS model, we found that the CLIC was significantly associated with positive amyloid deposition and severer amyloid deposition.ConclusionWe found that individuals from CLIC displayed a higher amyloid positive rate and severer amyloid deposition. Specifically, in a PLS model, The CLIC, was strongly associated with amyloid pathology.

CD2AP impacts Alzheimer’s disease through brain vascular function

AbstractBackgroundCerebrovascular impairment is a key aspect of Alzheimer’s Disease (AD) pathogenesis. Brain vascular defects appear early in the disease process and are associated with cognitive dysfunction. CD2‐associated protein (CD2AP), a scaffolding protein involved cytoskeleton remodeling and membrane trafficking, is an important predisposing factor for the disease. The protein is enriched in brain endothelial cells (BECs) that forms the brain vessels, suggesting that CD2AP might contribute to AD cerebrovascular dysfunction.MethodIn this study, we used brain samples from AD volunteers, genetically modified mouse models, awake two‐photon microscopy and cell culture to investigate the function of CD2AP in the brain vasculature.ResultWe found that AD individuals with lower levels of brain vascular CD2AP displayed the poorest cognitive performance. Genetic depletion of CD2AP in BECs compromised memory function, cerebral blood flow and increased vessels vulnerability to amyloid‐beta. In BECs, CD2AP controlled the levels and signaling of Apolipoprotein E Receptor 2 (ApoER2) and activation of this pathway with Reelin glycoprotein in mice depleted of endothelial CD2AP mitigated the toxic effects of peptide.ConclusionIn sum, deregulation of endothelial CD2AP drives cerebrovascular dysfunction, and harnessing the biology of specific brain vessel types may offer a refined therapeutic avenue for the treatment of AD.

Elucidating Cell Diversity and Functions of Human Brain Borders in Alzheimer’s Disease

AbstractBackgroundEmerging evidence shows that the brain border regions act as a functional neuro‐immune interface to conduct essential immune surveillance and immune defense1. The dysfunction of border immunity contributes to aging, microglial inflammatory response, and neurodegeneration2‐3.MethodWe conducted single nuclei multiome to simultaneously profile gene expression and open chromatin of dissected postmortem human brain borders from aged individuals with and without Alzheimer’s Disease (AD) (n = 32). We further generated border cell models from iPSCs and postmortem leptomeningeal cells to understand the effects of genetic and environmental factors on border cells.ResultWe detect diverse cell types at the brain borders, including unique endothelial, mural, immune, and fibroblast subtypes. Border‐associated macrophages (BAMs) display differential gene expressions from microglia and express AD risk genes, as nominated by genome‐wide association studies (GWAS). We discover cell‐type‐specific differentially expressed genes in AD individuals, particularly fibroblasts and BAMs. We identify gene regulatory networks and master regulators of specific border cell states. Human cell models of leptomeningeal cells display the signature of ex vivo AD fibroblasts upon amyloid‐β treatment, and iPSC‐derived choroid plexus models carrying the APOE4 risk alleles demonstrate differentially expressed genes related to immune and fibrotic response. Lastly, we explore ligand‐receptor interactions within the border niche and observe increased intercellular activities and altered communication patterns at AD brain borders.ConclusionOur study establishes a molecular map of human border cell types and reveals the functional effects of AD risk factors on brain border cells, providing significant insight into the border immune and fibrotic responses in AD pathogenesis.

Individuals from Memory Clinic were Associated with Severer Amyloid Deposition in the Chinese Population in Urban Cities

AbstractBackgroundAmyloid deposition is considered the initial pathology in Alzheimer’s disease (AD), investigation of amyloid pathology and the risk factor for amyloid pathology and cognitive declines in the Chinese population is critical for personalized management. However, the prevalence of amyloid deposition and the risk factors for amyloid pathology are unknown in the Chinese population.MethodParticipants were recruited from urban communities and memory clinics in Shanghai and Zhengzhou, China. A total of 246 AD individuals, 274 Mild cognitive impairment (MCI) individuals, and 506 Normal cognition (NC) individuals were identified: 106 AD, 156 MCI and 506 NC from the Community‐based cohort (COMC) as well as 140 AD and 118 MCI from the Clinic‐based cohort (CLIC). Amyloid positivity and deposition were analyzed based on amyloid positron emission tomography (PET) scans. We also performed a partial least squares (PLS) analysis to evaluate the associations of cohort, age, sex, education level, and ApoE genotype with amyloid qualification, quantification results and global cognition in all 1026 participants.ResultAcross the two cohorts, the AD group displayed the significantly highest rate of amyloid positivity (211/246, 85.8%), followed by the MCI (122/274, 44.5%) and NC groups (136/506, 26.9%, all P &lt; 0.001). Participants from the CLIC displayed a higher rate of amyloid positivity than those from the COMC in the MCI (68/118, 57.6% vs. 54/156, 34.6%, P &lt; 0.001) and AD groups (128/140, 91.4% vs. 83/106, 78.3%, P = 0.006). MCI (SUVr: 1.37 ± 0.25 vs. 1.25 ± 0.18, P &lt; 0.001), Aß+ MCI (SUVr: 1.48 ± 0.27 vs. 1.37 ± 0.24, P = 0.03) and AD individuals (SUVr: 1.47 ± 0.23 vs. 1.39 ± 0.23, P = 0.01) in the CLIC displayed significantly greater amyloid deposition than those in the COMC. According to the PLS model, we found that the CLIC was significantly associated with positive amyloid deposition and severer amyloid deposition.ConclusionWe found that individuals from CLIC displayed a higher amyloid positive rate and severer amyloid deposition. Specifically, in a PLS model, The CLIC, was strongly associated with amyloid pathology.

GABA transporters gene expression associates with [18F]FDG‐PET signal in regions typically hypometabolic in Alzheimer’s disease

AbstractBackgroundIt is suggested that the [18F]fluorodeoxyglucose(FDG)‐PET signal reflects astrocyte and neuronal activity, with the excitatory glutamate uptake by astrocytes as the trigger for brain glucose metabolism. The inhibitory neurotransmitter γ‐aminobutyric acid (GABA) is taken up by neurons and astrocytes through the GABA transporters (GAT) 1 and 3. However, its role in the [18F]FDG‐PET signal in healthy aging and Alzheimer’s disease (AD) remains poorly investigated. Thus, we aimed to explore associations between neuronal and astrocytic GABA transporters gene expression with [18F]FDG‐PET in cognitively unimpaired (CU) and AD individuals.MethodWe obtained [18F]FDG‐PET imaging data of CU and AD from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Table 1). GAT1 and GAT3 gene expression in the brain was obtained from the Allen Brain Atlas (Table 1, Figure 1). We extracted SUVr (pons as reference region) and mRNA values using the RMINC package and performed Pearson’s correlation to test the association between [18F]FDG‐PET and GABA transporters mRNA (p‐value &lt; 0.05).ResultIn brain regions typically hypometabolic in AD, we observed a strong positive association of [18F]FDG‐PET SUVr with GAT1 and GAT3 gene expression in CU (GAT1: r = 0.93; p &lt; 0.0001; Figure 2A, GAT3: r = 0.94; p &lt; 0.0001; Figure 2B) and AD individuals (GAT1: r = 0.9; p &lt; 0.0001; Figure 2A, GAT3: r = 0.92; p &lt; 0.0001; Figure 2B). By contrast, in brain regions typically resilient, we found a weak correlation with CU individuals (GAT1: r = 0.32; p = 0.016; Figure 2C, GAT3: r = 0.26; p = 0.04; Figure 2D) and no significant correlation in AD patients (GAT1: r = 0.24; p = 0.07; Figure 2C, GAT3: r = 0.24; p = 0.059; Figure 2D).ConclusionOur results demonstrate a significant strong positive association between [18F]FDG‐PET SUVr and GABA transporters gene expression in CU and AD in hypometabolic regions. These findings suggest that GABA transporters are involved with brain glucose metabolism in regions classically vulnerable to AD pathology. ”

Plasma tau phosphorylation at T217 predicts amyloid deposition in dominantly inherited and late onset Alzheimer disease participants without clinical symptoms

AbstractBackgroundProgressive amyloid plaque accumulation in the brain precedes the emergence of clinical symptoms in Alzheimer disease (AD). This asymptomatic phase, referred to as preclinical AD, induces significant modification of tau phosphorylation in the cerebrospinal fluid (CSF). CSF tau phosphorylation occupancy at Threonine 217 (T217) becomes abnormal more than 20 years before cognitive decline in dominantly inherited AD (DIAD). CSF hyperphosphorylation at T217 also associates with amyloid pathology in late‐onset AD (LOAD) individuals with preclinical AD. In this study, we examined the performance of plasma T217 phosphorylation to detect amyloid plaques deposition measured by amyloid‐PET in asymptomatic participants with DIAD or late‐onset AD (LOAD).MethodWe measured plasma T217 phosphorylation using tau immunoprecipitation and high‐resolution mass spectrometry. Phosphorylation abundance at T217 was expressed as p‐tau217 ratio defined using the level of T217 phosphorylated tau peptide normalized to total tau level. The DIAD cohort included 355 samples from 176 CDR0 and 51 CDR&gt;0 participants in the DIAN Observational study with 62% mutation carriers, all collected in relation to the estimated years to symptom onset (EYO). The LOAD cohort included 321 cross‐sectional samples from BioFINDER‐2 study. Amyloid positivity was determined using cross sectional amyloid‐PET scan available for each participant.ResultWe observed abnormal plasma p‐tau217 ratio in DIAD mutation carriers more than 15 years before EYO (Figure 1). Plasma p‐tau217 ratio accurately predicted amyloid PET positivity for individuals across multiple preclinical stages (Table 1, AUC respectively 0.89 and 0.98 for EYO brackets ‐20 to ‐10 and ‐10 to 0). The plasma test had similar performance in distinguishing amyloid status in the DIAD and the LOAD cohorts (AUC respectively 0.96 and 0.97, Table 1). In LOAD, plasma p‐tau217 ratio identified amyloid positivity independently of the cognitive status (AUC respectively 0.96 and 0.97 for asymptomatic and symptomatic).ConclusionOur results support the use of plasma p‐tau217 ratio to screen cognitively normal populations at risk of developing AD dementia. This blood‐based biomarker can support the recruitment of asymptomatic participants for prevention trials and help identifying patients who might benefit from early care using future AD drugs more than a decade before the detection of cognitive symptoms.

Novel Framework for Brain Age Prediction using Graph Neural Networks

AbstractBackgroundAging is among the largest risk factors for neurodegeneration which can be accelerated by age‐related pathologies. Hence, individuals may age at different rates reflected by a gap in biological estimates of age and actual chronological age. Graph neural networks (GNN) are deep learning models that leverage spatial information inherent in the dataset for inference. Here, we develop an MRI cortical thickness (CT) based brain age prediction framework using GNNs and illustrate that it provides a feasible mechanism to identify contributing regions leading to elevated brain age gap in Alzheimer’s disease (AD).MethodWe used ANTs to calculate CT in 100 parcels from 3T MRI of 170 healthy controls (HC; age = 64.26±8.27y, 101 females), 53 mild cognitive impairment (MCI; age = 68.56±8.58y, 22 females), and 62 AD (age = 67.25±8.83y, 30 females) individuals. A GNN model based on the anatomical covariance matrix from HC cohort was trained to predict chronological age from associated CT for HC cohort. GNN output was evaluated as an unweighted mean of entities associated with the brain regions in the final layer. Hence, individual contributions to the GNN output by each brain region could be quantified. Final brain age prediction was obtained after removing age‐bias from GNN outputs using linear regression model. For all subjects, we evaluated residuals at regional level (regional age‐gap) by comparing the contribution of each parcel with corresponding GNN output. The residual between predicted brain age and chronological age (age‐gap) was also evaluated. See Figure 1 for an overview.ResultANCOVA (with correction for age and gender) with post‐hoc analyses revealed that regional age‐gap was greater for AD relative to HC in inferior parietal, temporal pole, inferior temporal, medial temporal, and superior frontal regions (Bonferroni corrected p‐value&lt;0.05). Also, regional age‐gap was greater for MCI relative to HC for inferior parietal and temporal pole regions (Figure 2). Subsequently, age‐gap was elevated in AD (mean = 5.64y) and MCI (mean = 2.66y) relative to HC (mean = 0y) reflecting a linear increase in age‐gap with disease stage (Figure 3).ConclusionOur novel framework identified regional CT associations with age‐gap in AD and MCI, suggesting that this GNN approach may be used to further our understanding between aging and neurodegeneration.