Alzheimer's Disease Individuals Research Articles

Transcriptomic and epigenomic profiling reveals altered responses to diesel emissions in Alzheimer's disease both in vitro and in population-based data.

Studies have correlated living close to major roads with Alzheimer's disease (AD) risk. However, the mechanisms responsible for this link remain unclear. We exposed olfactory mucosa (OM) cells of healthy individuals and AD patients to diesel emissions (DE). Cytotoxicity of exposure was assessed, mRNA, miRNA expression, and DNA methylation analyses were performed. The discovered altered pathways were validated using data from the human population-based Rotterdam Study. DE exposure resulted in an almost four-fold higher response in AD OM cells, indicating increased susceptibility to DE effects. Methylation analysis detected different DNA methylation patterns, revealing new exposure targets. Findings were validated by analyzing data from the Rotterdam Study cohort and demonstrated a key role of nuclear factor erythroid 2-related factor 2 signaling in responses to air pollutants. This study identifies air pollution exposure biomarkers and pinpoints key pathways activated by exposure. The data suggest that AD individuals may face heightened risks due to impaired cellular defenses. Healthy and AD olfactory cells respond differently to DE exposure. AD cells are highly susceptible to DE exposure. The NRF2 oxidative stress response is highly activated upon air pollution exposure. DE-exposed AD cells activate the unfolded protein response pathway. Key findings are also confirmed in a population-based study.

Plasma pTau217 ratio predicts continuous regional brain tau accumulation in amyloid-positive early Alzheimer's disease.

This study examines whether phosphorylated plasma Tau217 ratio (pTau217R) can predict tau accumulation in different brain regions, as measured by positron emission tomography (PET) standardized uptake value ratio (SUVR), for staging Alzheimer's disease (AD). Plasma pTau217R was measured using immunoprecipitation-mass spectrometry. Models for predicting tau PET SUVR, developed with 144 early AD individuals using [18F]MK6240, were validated in two validation sets, VS1 (98 early AD) and VS2 (47 preclinical/early AD with a different tracer, flortaucipir (Tauvid)), all amyloid-beta positive (Aβ+). The pTau217R-based model predicted tau levels up to an SUVR of 2 in multiple brain regions, effectively assessing tau status at different tau levels with receiver operating characteristic (ROC) curve areas of 0.84-0.95 in VS1 and 0.71-0.88 in VS2 (using a different tracer). It reduced PET scan needs by 65% while maintaining 95% sensitivity. PTau217R reliably predicts regional tau accumulation in early AD, reducing reliance on tau PET scans and broadening its clinical application. NCT03887455 (ClarityAD) HIGHLIGHTS: Developed a model using plasma pTau217R to predict tau levels across brain regions. pTau217R model outperformed models based on clinical, MRI, and other blood biomarkers. The model reliably predicted tau levels exceeding tau positivity and higher thresholds. Screening with pTau217R could reduce tau PET scans by 65% at 95% sensitivity. pTau217R model aids in disease staging and monitoring in early AD.

The expression of shelterin genes and telomere repeat analysis and their effect on Alzheimer's disease.

Alzheimer's disease (AD) is an age-related dementia disorder characterized by memory loss and behavioral changes. Maintaining the integrity of telomere shortening in AD is important for cellular survival and homeostasis in all cells, especially glial cells. The shelterin protein complex provides telomere integrity. Measuring the expression levels of shelterin genes and determining the telomere lengths regulated by this complex will reveal their effects on AD progression and adult neurogenesis and will allow the detection of the disease or the determination of the progression process from an accessible tissue. The study population included 111 patients and 91 healthy controls (male and female, < 50 age). The clinical histories (age, gender, hypertension, diabetes mellitus, obesity, cardiovascular disease, MMSE, medication use, family history, sleep disorders, seizure), covariates (HGB, ESR, Na, P, Cl, BUN, CRP, B12, TSH, Glucose, and MRI findings) and the expressional changes of shelterin genes (TERF1, TERF2, TINF2, POT1, TPP1, and RAP1) between the patient and control groups were evaluated relatively. ROC analyses determined the diagnostic power of telomere repeats and gene expressions. In conclusion, upregulation of expression of shleterin complex genes was detected in AD, where telomeres are significantly shorter than in controls (P < 0.05). However, only TERF2 and RAP1 were significant (P < 0.05). A positive relationship was detected between telomere repeats and these genes (P < 0.05). Telomere repeats may be a strong diagnostic criterion to distinguish AD individuals from healthy individuals (AUC = 1.000). The upregulation of TERF2 and RAP1 core genes required for telomere integrity results in the instability of excessively shortened telomeres. Expression silencing of these genes may increase telomerase activity and maintain cellular survival. Also, the detection of telomere repeats has potential in the early diagnosis of AD patients.

Critical role of ROCK1 in AD pathogenesis via controlling lysosomal biogenesis and acidification

BackgroundLysosomal homeostasis and functions are essential for the survival of neural cells. Impaired lysosomal biogenesis and acidification in Alzheimer’s disease (AD) pathogenesis leads to proteolytic dysfunction and neurodegeneration. However, the key regulatory factors and mechanisms of lysosomal homeostasis in AD remain poorly understood.MethodsROCK1 expression and its co-localization with LAMP1 and SQSTM1/p62 were detected in post-mortem brains of healthy controls and AD patients. Lysosome-related fluorescence probe staining, transmission electron microscopy and immunoblotting were performed to evaluate the role of ROCK1 in lysosomal biogenesis and acidification in various neural cell types. The interaction between ROCK1 and TFEB was confirmed by surface plasmon resonance and in situ proximity ligation assay (PLA). Moreover, we performed AAV-mediated ROCK1 downregulation followed by immunofluorescence, enzyme-linked immunosorbent assay (ELISA) and behavioral tests to unveil the effects of the ROCK1–TFEB axis on lysosomes in APP/PS1 transgenic mice.ResultsROCK1 level was significantly increased in the brains of AD individuals, and was positively correlated with lysosomal markers and Aβ. Lysosomal proteolysis was largely impaired by the high abundance of ROCK1, while ROCK1 knockdown mitigated the lysosomal dysfunction in neurons and microglia. Moreover, we verified ROCK1 as a previously unknown upstream kinase of TFEB independent of m-TOR or GSK-3β. ROCK1 elevation resulted in abundant extracellular Aβ deposition which in turn bound to Aβ receptors and activated RhoA/ROCK1, thus forming a vicious circle of AD pathogenesis. Genetically downregulating ROCK1 lowered its interference with TFEB, promoted TFEB nuclear distribution, lysosomal biogenesis and lysosome-mediated Aβ clearance, and eventually prevented pathological traits and cognitive deficits in APP/PS1 mice.ConclusionIn summary, our results provide a mechanistic insight into the critical role of ROCK1 in lysosomal regulation and Aβ clearance in AD by acting as a novel upstream serine kinase of TFEB.

EEG biomarkers in Alzheimer’s and prodromal Alzheimer’s: a comprehensive analysis of spectral and connectivity features

BackgroundBiomarkers of Alzheimer’s disease (AD) and mild cognitive impairment (MCI, or prodromal AD) are highly significant for early diagnosis, clinical trials and treatment outcome evaluations. Electroencephalography (EEG), being noninvasive and easily accessible, has recently been the center of focus. However, a comprehensive understanding of EEG in dementia is still needed. A primary objective of this study is to investigate which of the many EEG characteristics could effectively differentiate between individuals with AD or prodromal AD and healthy individuals.MethodsWe collected resting state EEG data from individuals with AD, prodromal AD, and normal cognition. Two distinct preprocessing pipelines were employed to study the reliability of the extracted measures across different datasets. We extracted 41 different EEG features. We have also developed a stand-alone software application package, Feature Analyzer, as a comprehensive toolbox for EEG analysis. This tool allows users to extract 41 EEG features spanning various domains, including complexity measures, wavelet features, spectral power ratios, and entropy measures. We performed statistical tests to investigate the differences in AD or prodromal AD from age-matched cognitively normal individuals based on the extracted EEG features, power spectral density (PSD), and EEG functional connectivity.ResultsSpectral power ratio measures such as theta/alpha and theta/beta power ratios showed significant differences between cognitively normal and AD individuals. Theta power was higher in AD, suggesting a slowing of oscillations in AD; however, the functional connectivity of the theta band was decreased in AD individuals. In contrast, we observed increased gamma/alpha power ratio, gamma power, and gamma functional connectivity in prodromal AD. Entropy and complexity measures after correcting for multiple electrode comparisons did not show differences in AD or prodromal AD groups. We thus catalogued AD and prodromal AD-specific EEG features.ConclusionsOur findings reveal that the changes in power and connectivity in certain frequency bands of EEG differ in prodromal AD and AD. The spectral power, power ratios, and the functional connectivity of theta and gamma could be biomarkers for diagnosis of AD and prodromal AD, measure the treatment outcome, and possibly a target for brain stimulation.

Defining Alzheimer's Disease through Proteomic CSF Profiling.

Alzheimer disease (AD) is the main cause of dementia, and its complexity is not yet completely understood. Proteomic profiles can provide useful information to explore the pathways involved and the heterogeneity among AD patients. A proteomic analysis was performed in cerebrospinal fluid (CSF) samples from mild cognitive impairment due to AD (MCI-AD) and control individuals; both groups were classified by amyloid β42/amyloid β40 levels in CSF (data available in BioStudies database (S-BSST1456)). The analysis based on PLS regression and volcano plot identified 7 proteins (FOLR2, PPP3CA, SMOC2, STMN1, TAGLN3, TMEM132B, and UCHL1) mainly related to protein phosphorylation, structure maintenance, inflammation, and protein degradation. Enrichment analysis revealed the involvement of different biological processes related to neuronal mechanisms and synapses, lipid and carbohydrate metabolism, immune system and inflammation, vascular, hormones, and response to stimuli, and cell signaling and adhesion. In addition, the proteomic profile showed some association with the levels of AD biomarkers in CSF. Regarding the subtypes, two MCI-AD subgroups were identified: one could be related to synapsis and neuronal functions and the other to innate immunity. The study of the proteomic profile in the CSF of AD patients reflects the heterogeneity of biochemical pathways involved in AD.

Characteristics and Transcriptomic Analysis of Cholinergic Neurons Derived from Induced Pluripotent Stem Cells with APP Mutation in Alzheimer's Disease.

The cholinergic hypothesis is one of the main theories that describe the pathogenesis of Alzheimer's disease (AD). Cholinergic neurons degenerate early and are severely damaged in AD. Despite extensive research, the causes of cholinergic neuron damage and the underlying molecular changes remain unclear. This study aimed to explore the characteristics and transcriptomic changes in cholinergic neurons derived from human induced pluripotent stem cells (iPSCs) with APP mutation. Peripheral blood mononuclear cells from patients with AD and healthy individuals were reprogrammed into iPSCs. The iPSCs were differentiated into cholinergic neurons. Cholinergic neurons were stained, neurotoxically tested, and electrophysiologically and transcriptomically analyzed. The iPSCs-derived cholinergic neurons from a patient with AD carrying a mutation in APP displayed enhanced susceptibility to Aβ1-42-induced neurotoxicity, characterized by severe neurotoxic effects, such as cell body coagulation and neurite fragmentation. Cholinergic neurons exhibited electrophysiological impairments and neuronal death after 21 days of culture in the AD group. Transcriptome analysis disclosed 883 differentially expressed genes (DEGs, 420 upregulated and 463 downregulated) participating in several signaling pathways implicated in AD pathogenesis. To assess the reliability of RNA sequencing, the expression of 16 target DEGs was validated using qPCR. Finally, the expression of the 8 core genes in different cell types of brain was analyzed by the AlzData database. In this study, iPSCs-derived cholinergic neurons from AD patients with APP mutations exhibit characteristics reminiscent of neurodegenerative disease. Transcriptome analysis revealed the corresponding DEGs and pathways, providing potential biomarkers and therapeutic targets for advancing AD research.

FC30: Bridging the gap: an estimate of undetected dementia in Brazil and differences between genders, age groups and regions

Objectives: Despite the increasing number of people with dementia (PWD), detection remains low worldwide. In Brazil, PWD is expected to triple by 2050, and diagnosis can be challenging, contributing to high and growing rates of underdiagnosis. At the moment, there is no national estimate of the under detection or characteristics of its distribution according to gender, age and region. We aimed to estimate the proportion of PWD not diagnosed in relation to the estimated number of PWD.Methods: The number of diagnosed individuals were estimated based on national records of the prescription of anticholinesterases drugs (AChE) in 2022 for the treatment of mild and moderate stages of Alzheimer’s Disease (AD) held by the Unified Health System (SUS). Data were obtained from ftp://ftp.datasus.gov.br and drugs were dispensed according to the national clinical protocol. Studies from the national literature were consulted to estimate: (i) the number of people currently diagnosed with mild and moderate AD; (ii) the proportion of those who obtain AChE from SUS; (iii) the proportion of those who do not take AChE; and (iv) the proportion of AD related to other dementias. We assumed that the under-detection rate of AD would be similar to other dementias and 70% of the diagnosed AD individuals obtain AChE from SUS.Results: More than 80% of the PWD 60+ are undetected (88.7%, 95% CI = 88.6–88.7). The poorest regions had higher rates (94.6% 95%, CI = 94.5–94.6) than the richest (84.8%, 95% CI = 84.7–84.8). Men had higher rates (89.8%, 95% CI = 89.7–89.9) than women (87.4%, 95% CI = 87.4–87.5). The youngest age group (60-64) had the highest rate (94.6%, 95% CI = 94.5–94.7) which decreased until 85–89 (84.3%, 95% CI = 84.2–84.4), before increasing again to 91.1% (95% CI = 91.0–91.2) among 90+.Conclusions: Dementia under detection in Brazil is among the highest in the world. Fast populational aging and the highest rates among the youngest individuals are of concern as it may be related to late diagnosis. Gender and regional disparities also need to be considered when developing health policies.

Construction and validation of a bioinformatics‑based screen for cuproptosis‑related genes and risk model for Alzheimer's disease.

The present study aimed to validate the association between core cuproptosis genes (CRGs) and Alzheimer's disease (AD) from both bioinformatics and experimental perspectives and also to develop a risk prediction model. To this end, 78 human‑derived temporal back samples were analyzed from GSE109887, and the biological functions of the resulting CRGs were explored by cluster analysis, weighted gene co‑expression network analysis and similar methods to identify the best machine model. Moreover, an external dataset GSE33000 and a nomogram were used to validate the model. The mRNA and protein expression of CRGs were validated using the SH‑SY5Y cell model and the Sprague‑Dawley rat animal model. The RT‑qPCR and western blotting results showed that the mRNA and protein expression content of dihydrolipoamide dehydrogenase, ferredoxin 1, glutaminase and pyruvate dehydrogenase E1 subunit β decreased, and the expression of dihydrolipoamide branched chain transacylase E2 increased in AD, which supported the bioinformatic analysis results. The CRG expression alterations affected the aggregation and infiltration of certain immune cells. The present study also confirmed the accuracy and validity of AD diagnostic models and nomograms, and validated the association between five CRGs and AD, indicating a significant difference between patients with AD and healthy individuals. Therefore, CRGs are expected to serve as relevant biomarkers for the diagnosis and prognostic monitoring of AD.

Long-read transcript sequencing identifies differential isoform expression in the entorhinal cortex in a transgenic model of tau pathology

Increasing evidence suggests that alternative splicing plays an important role in Alzheimer’s disease (AD) pathology. We used long-read sequencing in combination with a novel bioinformatics tool (FICLE) to profile transcript diversity in the entorhinal cortex of female transgenic (TG) mice harboring a mutant form of human tau. Our analyses revealed hundreds of novel isoforms and identified differentially expressed transcripts – including specific isoforms of Apoe, App, Cd33, Clu, Fyn and Trem2 – associated with the development of tau pathology in TG mice. Subsequent profiling of the human cortex from AD individuals and controls revealed similar patterns of transcript diversity, including the upregulation of the dominant TREM2 isoform in AD paralleling the increased expression of the homologous transcript in TG mice. Our results highlight the importance of differential transcript usage, even in the absence of gene-level expression alterations, as a mechanism underpinning gene regulation in the development of AD neuropathology.

Contributions of genetic variation in astrocytes to cell and molecular mechanisms of risk and resilience to late onset Alzheimer's disease.

Reactive astrocytes are associated with Alzheimer's disease (AD), and several AD genetic risk variants are associated with genes highly expressed in astrocytes. However, the contribution of genetic risk within astrocytes to cellular processes relevant to the pathogenesis of AD remains ill-defined. Here we present a resource for studying AD genetic risk in astrocytes using a large collection of induced pluripotent stem cell (iPSC) lines from deeply phenotyped individuals with a range of neuropathological and cognitive outcomes. IPSC lines from forty-four individuals were differentiated into astrocytes followed by unbiased molecular profiling using RNA sequencing and tandem mass tag-mass spectrometry. We demonstrate the utility of this resource in examining gene- and pathway-level associations with clinical and neuropathological traits, as well as in analyzing genetic risk and resilience factors through parallel analyses of iPSC-astrocytes and brain tissue from the same individuals. Our analyses reveal that genes and pathways altered in iPSC-derived astrocytes from AD individuals are concordantly dysregulated in AD brain tissue. This includes increased prefoldin proteins, extracellular matrix factors, COPI-mediated trafficking components and reduced proteins involved in cellular respiration and fatty acid oxidation. Additionally, iPSC-derived astrocytes from individuals resilient to high AD neuropathology show elevated basal levels of interferon response proteins and increased secretion of interferon gamma. Correspondingly, higher polygenic risk scores for AD are associated with lower levels of interferon response proteins. This study establishes an experimental system that integrates genetic information with a heterogeneous set of iPSCs to identify genetic contributions to molecular pathways affecting AD risk and resilience.

Multi-omics analyses highlight molecular differences between clinical and neuropathological diagnoses in Alzheimer's disease.

Both clinical diagnosis and neuropathological diagnosis are commonly used in literature to categorize individuals as Alzheimer's disease (AD) or non-AD in omics analyses. Whether these diagnostic strategies result in distinct profiles of molecular abnormalities is poorly understood. Here, we analysed one of the most commonly used AD omics datasets in the literature from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort and compared the two diagnosis strategies using brain transcriptome and metabolome by grouping individuals as non-AD and AD according to clinical or neuropathological diagnosis separately. Differentially expressed genes, associated pathways related with AD hallmarks and AD-related genes showed that the categorization based on neuropathological diagnosis more accurately reflects the disease state at the molecular level than the categorization based on clinical diagnosis. We further identified consensus biomarker candidates between the two diagnosis strategies such as 5-hydroxylysine, sphingomyelin and 1-myristoyl-2-palmitoyl-GPC as metabolite biomarkers and sphingolipid metabolism as a pathway biomarker, which could be robust AD biomarkers since they are independent of diagnosis strategies. We also used consensus AD and consensus non-AD individuals between the two diagnostic strategies to train a machine-learning based model, which we used to classify the individuals who were cognitively normal but diagnosed as AD based on neuropathological diagnosis (asymptomatic AD individuals). The majority of these individuals were classified as consensus AD patients for both omics data types. Our study provides a detailed characterization of both diagnostic strategies in terms of the association of the corresponding multi-omics profiles with AD.

The interplay between insomnia symptoms and Alzheimer's disease across three main brain networks.

Insomnia symptoms are prevalent along the trajectory of Alzheimer's disease (AD), but the neurobiological underpinning of their interaction is poorly understood. Here, we assessed structural and functional brain measures within and between the default mode network (DMN), salience network, and central executive network (CEN). We selected 320 participants from the ADNI database and divided them by their diagnosis: cognitively normal (CN), Mild Cognitive Impairment (MCI), and AD, with and without self-reported insomnia symptoms. We measured the gray matter volume (GMV), structural covariance (SC), degrees centrality (DC), and functional connectivity (FC), testing the effect and interaction of insomnia symptoms and diagnosis on each index. Subsequently, we performed a within-group linear regression across each network and ROI. Finally, we correlated observed abnormalities with changes in cognitive and affective scores. Insomnia symptoms were associated with FC alterations across all groups. The AD group also demonstrated an interaction between insomnia and diagnosis. Within-group analyses revealed that in CN and MCI, insomnia symptoms were characterized by within-network hyperconnectivity, while in AD, within- and between-network hypoconnectivity was ubiquitous. SC and GMV alterations were nonsignificant in the presence of insomnia symptoms, and DC indices only showed network-level alterations in the CEN of AD individuals. Abnormal FC within and between DMN and CEN hubs was additionally associated with reduced cognitive function across all groups, and increased depressive symptoms in AD. We conclude that patients with clinical AD present with a unique pattern of insomnia-related functional alterations, highlighting the profound interaction between both conditions.

Decreased extrasynaptic δ-GABAA receptors in PNN-associated parvalbumin interneurons correlates with anxiety in APP and tau mouse models of Alzheimer's disease.

Alzheimer's disease (AD) is associated with gradual memory loss and anxiety which affects ~75% of AD patients. This study investigated whether AD-associated anxiety correlated with modulation of extrasynaptic δ-subunit-containing GABAA receptors (δ-GABAARs) in experimental mouse models of AD. We combined behavioural experimental paradigms to measure cognition performance, and anxiety with neuroanatomy and molecular biology, using familial knock-in (KI) mouse models of AD that harbour β-amyloid (Aβ) precursor protein App (AppNL-F) with or without humanized microtubule-associated protein tau (MAPT), age-matched to wild-type control mice at three different age windows. AppNL-F KI and AppNL-F/MAPT AD models showed a similar magnitude of cognitive decline and elevated magnitude of anxiety correlated with neuroinflammatory hallmarks, including triggering receptor expressed on myeloid cells 2 (TREM2), reactive astrocytes and activated microglia consistent with accumulation of Aβ, tau and down-regulation of Wnt/β-catenin signalling compared to aged-matched WT controls. In both the CA1 region of the hippocampus and dentate gyrus, there was an age-dependent decline in the expression of δ-GABAARs selectively expressed in parvalbumin (PV)-expressing interneurons, encapsulated by perineuronal nets (PNNs) in the AD mouse models compared to WT mice. In vivo positive allosteric modulation of the δ-GABAARs, using a δ-selective-compound DS2, decreased the level of anxiety in the AD mouse models, which was correlated with reduced hallmarks of neuroinflammation, and 'normalisation' of the expression of δ-GABAARs. Our data show that the δ-GABAARs could potentially be targeted for alleviating symptoms of anxiety, which would greatly improve the quality of life of AD individuals.

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks.

Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimer's dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimer's disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimer's individuals.

Potential prognostic value of CSF-targeted proteomics across the Alzheimer’s disease continuum

BackgroundCore biomarkers for Alzheimer’s disease (AD), such as Aβ42 and tau, have demonstrated high prognostic accuracy but do not fully capture the complex pathophysiology of AD. In this study, our objective was to identify novel cerebrospinal fluid (CSF) biomarkers using proteomics across the entire AD continuum to predict conversion to AD and explore their involvement in AD pathogenesis.MethodsA cohort of 186 cognitively normal (CN), 127 subjective memory complaint (SMC), 79 early mild cognitive impairment (EMCI), 249 late MCI (LMCI), and 132 AD individuals was analyzed, with a follow-up period of over 3 years for non-AD participants. CSF 65 peptides, as well as hippocampal and entorhinal volumes were analyzed, and cognitive function was evaluated using the 13-item cognitive subscale of the Alzheimer’s Disease Assessment Scale (ADAS-Cog 13). Cox proportional hazards models and mediation analysis were performed to investigate associations and causal relationships.ResultsDuring the follow-up, approximately one-fourth (146/580) of the non-AD participants progressed to AD. After adjusting for baseline diagnosis (CN to LMCI) and other variables, multivariable Cox regression analysis identified three peptides (VAELEDEK, VSFELFADK, and VVSSIEQK) as significant predictors of conversion to AD. Incorporating these three peptides into the initial model significantly improved the C-statistic from 0.82 to 0.85 for predicting AD conversion, surpassing the predictive ability of Aβ42 and P-tau. Moreover, hippocampal and entorhinal volumes mediated 30.3–53.8% of the association between the three peptides and ADAS-Cog 13 scores.ConclusionsThese findings underscore the potential of these three peptides as robust prognostic biomarker candidates for AD conversion across the entire AD continuum, with a mechanism involving the mediation of hippocampal and entorhinal volumes.

Cognitive integrity in Non-Demented Individuals with Alzheimer's Neuropathology is associated with preservation and remodeling of dendritic spines.

Individuals referred to as Non-Demented with Alzheimer's Neuropathology (NDAN) exhibit cognitive resilience despite presenting Alzheimer's disease (AD) histopathological signs. Investigating the mechanisms behind this resilience may unveil crucial insights into AD resistance. DiI labeling technique was used to analyze dendritic spine morphology in control (CTRL), AD, and NDAN post mortem frontal cortex, particularly focusing on spine types near and far from amyloid beta (Aβ) plaques. NDAN subjects displayed a higher spine density in regions distant from Aβ plaques versus AD patients. In distal areas from the plaques, NDAN individuals exhibited more immature spines, while AD patients had a prevalence of mature spines. Additionally, our examination of levels of Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Pin1), a protein associated with synaptic plasticity and AD, showed significantly lower expression in AD versus NDAN and CTRL. These results suggest that NDAN individuals undergo synaptic remodeling, potentially facilitated by Pin1, serving as a compensatory mechanism to preserve cognitive function despite AD pathology. Spine density is reduced near Aβ plaques compared to the distal area in CTRL, AD, and NDAN dendrites. NDAN shows higher spine density than AD in areas far from Aβ plaques. Far from Aβ plaques, NDAN has a higher density of immature spines, AD a higher density of mature spines. AD individuals show significantly lower levels of Pin1 compared to NDAN and CTRL.

Biomarker Identification for Gender Specificity of Alzheimer's Disease Based on the Glial Transcriptome Profiles.

Many sex-specific biomarkers have been recently revealed in Alzheimer's disease (AD); however, cerebral glial cells were rarely reported. This study analyzed 220,095 single-nuclei transcriptomes from the frontal cortex of thirty-three AD individuals in the GEO database. Sex-specific Differentially Expressed Genes (DEGs) were identified in glial cells, including 243 in astrocytes, 1,154 in microglia, and 572 in oligodendrocytes. Gene Ontology (GO) functional annotation analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed functional concentration in synaptic, neural, and hormone-related pathways. Protein-protein interaction network (PPI) identified MT3, CALM2, DLG2, KCND2, PAKACB, CAMK2D, and NLGN4Y in astrocytes, TREM2, FOS, APOE, APP, and NLGN4Y in microglia, and GRIN2A, ITPR2, GNAS, and NLGN4Y in oligodendrocytes as key genes. NLGN4Y was the only gene shared by the three glia and was identified as the biomarker for the gender specificity of AD. Gene-transcription factor (TF)-miRNA coregulatory network identified key regulators for NLGN4Y and its target TCMs. Ecklonia kurome Okam (Kunbu) and Herba Ephedrae (Mahuang) were identified, and the effects of the active ingredients on AD were displayed. Finally, enrichment analysis of Kunbu and Mahuang suggested that they might act as therapeutic candidates for gender specificity of AD.

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.