Alzheimer's Research & Therapy最新文献

Background: β-synuclein (β-syn), mainly expressed in central nerve system, is one of the biomarkers in cerebrospinal fluid (CSF) and blood for synaptic damage, which has been reported to be elevated in CSF and blood of the patients of prion diseases (PrDs).

Methods: We analyzed 314 CSF samples from patients in China National Surveillance for CJD. The diagnostic groups of the 223 patients with PrDs included sporadic Creutzfeldt-Jacob disease (sCJD), genetic CJD (gCJD), fatal familial insomnia (FFI) and Gerstmann-Straussler-Scheinker (GSS). 91 patients with non-PrDs comprised Alzheimer's disease (AD), Parkinson's disease (PD), viral encephalitis (VE) or autoimmune encephalitis (AE) were enrolled in the control groups. The CSF β-syn levels were measured by a commercial microfluidic ELISA. The Mann-Whitney U test and Kruskal-Wallis H test were employed to analyze two or more sets of continuous variables. Multiple linear regression was also performed to evaluate the factors for CSF β-syn levels. Receiver operating characteristics (ROC) curves and area under the curve (AUC) values were used to assess the diagnostic performance of β-syn.

Results: The median of β-syn levels (2074 pg/ml; IQR: 691 to 4332) of all PrDs was significantly higher than that of non-PrDs group (504 pg/ml; IQR: 126 to 3374). The CSF β-syn values in the cohorts of sCJD, T188K-gCJD, E200K-gCJD and P102L-GSS were remarkably higher than that of the group of AD + PD, but similar as that of the group of VE + AE. The elevated CSF β-syn in sCJD and gCJD cases was statistically associated with CSF 14-3-3 positive and appearance of mutism. ROC curve analysis identified satisfied performance for distinguishing from AD + PD, with high AUC values in sCJD (0.7640), T188K-gCJD (0.8489), E200K-gCJD (0.8548), P102L-GSS (0.7689) and D178N-FFI (0.7210), respectively.

Conclusion: Our data here indicate that CSF β-syn is a potential biomarker for distinguishing PrDs (gCJD, sCJD and GSS) from AD and PD, but is much less efficient from VE and AE. These findings have critical implications for early diagnosis and monitoring of synaptic integrity in prion diseases.

Background: The clinical presentations of early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease are distinct, with EOAD having a more aggressive disease course with greater heterogeneity. Recent publications from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) described EOAD as predominantly amnestic, though this phenotypic description was based solely on clinical judgment. To better understand the phenotypic range of EOAD presentation, we applied a neuropsychological data-driven method to subtype the LEADS cohort.

Methods: Neuropsychological test performance from 169 amyloid-positive EOAD participants were analyzed. Education-corrected normative comparisons were made using a sample of 98 cognitively normal participants. Comparing the relative levels of impairment between each cognitive domain, we applied a cut-off of 1 SD below all other domain scores to indicate a phenotype of "predominant" impairment in a given cognitive domain. Individuals were otherwise considered to have multidomain impairment. Whole-cortex general linear modeling of cortical atrophy was applied as an MRI-based validation of these distinct clinical phenotypes.

Results: We identified 6 phenotypic subtypes of EOAD: Dysexecutive Predominant (22% of sample), Amnestic Predominant (11%), Language Predominant (11%), Visuospatial Predominant (15%), Mixed Amnestic/Dysexecutive Predominant (11%), and Multidomain (30%). These phenotypes did not differ by age, sex, or years of education. The APOE ɛ4 genotype was enriched in the Amnestic Predominant group, who were also rated as least impaired. Cortical thickness analysis validated these clinical phenotypes with dissociations in atrophy patterns observed between the Dysexecutive and Amnestic Predominant groups. In contrast to the heterogeneity observed from our neuropsychological data-driven approach, diagnostic classifications for this same sample based solely on clinical judgment indicated that 82% of individuals were amnestic-predominant, 9% were non-amnestic, 4% met criteria for Posterior Cortical Atrophy, and 5% met criteria for Primary Progressive Aphasia.

Conclusion: A neuropsychological data-driven method to phenotype EOAD individuals uncovered a more detailed understanding of the presenting heterogeneity in this atypical AD sample compared to clinical judgment alone. Clinicians and patients may over-report memory dysfunction at the expense of non-memory symptoms. These findings have important implications for diagnostic accuracy and treatment considerations.

Introduction: Numerous studies have demonstrated the effects of transcranial direct current stimulation (tDCS) on cognitive function in the older people. This study further explores the impact of tDCS and its dosage parameters on cognitive enhancement in older people with cognitive impairments.

Methods: Randomized controlled trials (RCTs) published through November 2023 were retrieved from databases including PubMed, Scopus, EMBASE, EBSCO, and the Cochrane Library. Participants were older adults with cognitive impairments, including Alzheimer's disease (AD), mild cognitive impairment (MCI), and dementia. AD was diagnosed based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), or the National Institute of Neurological and Communicative Disorders and Stroke - Alzheimer' Disease and Related Disorders Association (NINCDS-ADRDA) criteria. Dementia was diagnosed using the DSM-V or NINCDS-ADRDA criteria, while MCI was diagnosed using the DSM-V, the Petersen criteria, or assessments such as Montreal Cognitive Assessment (MoCA) and Clinical Dementia Rating (CDR). Standardized mean difference (SMD) values were analyzed to assess the effects.

Results: A total of 19 RCTs were included. tDCS significantly improved the Mini-Mental State Examination score both immediately post-intervention (SMD = 0.51, p = 0.005) and at follow-up (SMD = 2.29, p = 0.0003). Significant effects were observed when tDCS was used alone (SMD = 0.39, p = 0.04), at current densities $\le$ 0.06 mA/cm² (SMD = 0.25, p = 0.04), session durations exceeding 20 min (SMD = 0.89, p = 0.01), up to 15 sessions (SMD = 0.28, p = 0.009), and when an active electrode was placed over the temporal area (SMD = 0.33, p = 0.02). People with AD showed greater improvements compared to those with MCI or dementia (SMD = 0.91, p = 0.02). However, tDCS did not significantly improve memory or executive function.

Conclusion: tDCS demonstrated efficacy in enhancing global cognition in older people with cognitive impairments, providing insight into optimal parameters for clinical application. However, no improvement were observed in memory or executive function.

Background: Gut microbiota dysbiosis is linked to Alzheimer's disease (AD), but our understanding of the molecular and neuropathological bases underlying such association remains fragmentary.

Methods: Using 16S rDNA amplicon sequencing, untargeted metabolomics, and multi-modal magnetic resonance imaging, we examined group differences in gut microbiome, fecal metabolome, neuroimaging measures, and cognitive variables across 30 patients with AD, 75 individuals with mild cognitive impairment (MCI), and 61 healthy controls (HC). Furthermore, we assessed the associations between these multi-omics changes using correlation and mediation analyses.

Results: There were significant group differences in gut microbial composition, which were driven by 8 microbial taxa (e.g., Staphylococcus and Bacillus) exhibiting a progressive increase in relative abundance from HC to MCI to AD, and 2 taxa (e.g., Anaerostipes) showing a gradual decrease. 26 fecal metabolites (e.g., Arachidonic, Adrenic, and Lithocholic acids) exhibited a progressive increase from HC to MCI to AD. We also observed progressive gray matter atrophy in broadly distributed gray matter regions and gradual micro-structural integrity damage in widespread white matter tracts along the AD continuum. Integration of these multi-omics changes revealed significant associations between microbiota, metabolites, neuroimaging, and cognition. More importantly, we identified two potential mediation pathways: (1) microbiota → metabolites → neuroimaging → cognition, and (2) microbiota → metabolites → cognition.

Conclusion: Aside from elucidating the underlying mechanism whereby gut microbiota dysbiosis is linked to AD, our findings may contribute to groundwork for future interventions targeting the microbiota-metabolites-brain-cognition pathways as a therapeutic strategy in the AD continuum.

We consider the recent publication by Ornish and colleagues and the rigor expected for interventional clinical trials. We contend that lifestyle intervention trials should strive for the same rigor as drug trials and highlight opportunities to improve rigor in this example, particularly in design, data analysis, and publication of results for this and other lifestyle intervention studies.

Background: Recent studies suggest that opioid receptor signaling may differentially affect Alzheimer's disease (AD) pathology and the relevant behavioral dysfunctions. However, the precise roles and mechanisms of opioid receptor subtypes in AD pathologies are still unclear with major controversies.

Methods: We compared the delta-opioid receptor (DOR)- and mu-opioid receptor (MOR)-mediated effects on AD-associated cognitive deficits, pathologies, neuroinflammations, cell death using transgenic APP/PS1 mouse model and BV2 cell line at behavioral, molecular, and cellular levels. Unpaired t-test and one/two way analysis for variance (ANOVA) were used to analyze statistical significance of the data.

Results: We show a distinct role of DOR and its major difference with MOR in AD injury in an APP/PS1 mouse model. DOR activation by UFP-512, but not MOR activation by DAMGO, attenuated cognitive impairment, reduced beta-amyloid (Aβ) production and aggregation, as well as protected the neurons from apoptosis in APP/PS1 mice. DOR and MOR also differentially modulated microglia in APP/PS1 mice and in vitro AD cell model with a DOR-mediated inhibition on the excessive activation of microglia and the release of pro-inflammatory cytokines in AD pathologies. Gene expression profiling further revealed that the alternations in DOR/MOR are closely associated with microglial homeostatic signatures and high mobility group protein B1 (HMGB1) in AD. DOR activation inhibited HMGB1 secretion and its translocation from nuclear to cytoplasm. Our in-vitro studies further confirmed that DOR overexpression mitigated microglial inflammatory response and rescued neurons from AD injury via HMGB1-NF-κB signaling pathway.

Conclusions: These novel findings uncover previously unappreciated roles of DOR in neuroprotection against AD injury via modulating microglia-related inflammatory responses.

Background: The nature and severity of Alzheimer's disease (AD) pathologies in the retina and brain correspond. However, retinal biomarkers need to be validated in clinical cohorts with confirmed AD biomarkers and optical coherence tomography (OCT). The main objective of this study was to investigate whether retinal metrics measured by OCT aid in the early screening and brain pathology monitoring for confirmed AD.

Methods: This was a case-control study. All participants underwent retinal OCT imaging, and neurological examinations, including amyloid-β (Aβ) positron emission tomography. Participants were subdivided into cognitively normal (CN), mild cognitive impairment (MCI), and AD-derived dementia (ADD). Except retinal thickness, we developed the grey level co-occurrence matrix algorithm to extract retinal OCT intensity spatial correlation features (OCT-ISCF), including angular second matrix (ASM), correlation (COR), and homogeneity (HOM), one-way analysis of variance was used to compare the differences in retinal parameters among the groups, and to analyze the correlation with brain Aβ plaques and cognitive scores. The repeatability and robustness of OCT-ISCF were evaluated using experimental and simulation methods.

Results: This study enrolled 82 participants, subdivided into 20 CN, 22 MCI, and 40 ADD. Compared with the CN, the thickness of retinal nerve fiber layer and myoid and ellipsoid zone were significantly thinner (P < 0.05), and ASM, COR, and HOM in several retinal sublayers changed significantly in the ADD (P < 0.05). Notably, the MCI showed significant differences in ASM and COR in the outer segment of photoreceptor compared with the CN (P < 0.05). The changing pattern of OCT-ISCF with interclass correlation coefficients above 0.8 differed from that caused by speckle noise, and was affected by OCT image quality index. Moreover, the retinal OCT-ISCF were more strongly correlated with brain Aβ plaque burden and MoCA scores than retinal thickness. The accuracy using retinal OCT-ISCF (AUC = 0.935, 0.830) was better than that using retinal thickness (AUC = 0.795, 0.705) in detecting ADD and MCI.

Conclusions: The study demonstrates that retinal OCT-ISCF enhance the association and detection efficacy of AD pathology compared to retinal thickness, suggesting retinal OCT-ISCF have the potential to be new biomarkers for AD.

Background: Early Alzheimer's disease diagnosis is crucial for preventive therapy development. Standard neuropsychological evaluation does not identify clinically normal individuals with brain amyloidosis, the first stage of the pathology, defined as preclinical Alzheimer's disease. Spatial navigation assessment, in particular path integration, appears promising to detect preclinical symptoms, as the medial temporal lobe plays a key role in navigation and is the first cortical region affected by tau pathology.

Methods: We have conducted a cross-sectional study. We related the path integration performance of 102 individuals without dementia, aged over 50, to amyloid and tau pathologies, measured using positron emission tomography. We included 75 clinically normal individuals (19 with brain amyloidosis, 56 without) and 27 individuals with mild cognitive impairment (18 with brain amyloidosis, 9 without). We fitted linear mixed models to predict the path integration performances according to amyloid status or tau pathology in the medial temporal lobal, adjusting for age, gender, cognitive status, education, and video game experience. We decomposed the error into rotation and distance errors.

Results: We observed that clinically normal adults with brain amyloidosis (preclinical Alzheimer's disease) had spatial navigation deficits when relying only on self-motion cues. However, they were able to use a landmark to reduce their errors. Individuals with mild cognitive impairment had deficits in path integration that did not improve when a landmark was added in the environment. The amyloid status did not influence performance among individuals with mild cognitive impairment. Among all individuals, rotation, but not distance, errors increased with the level of tau pathology in the medial temporal lobe.

Conclusion: Our results suggest that path integration performance in an environment without external cues allows identifying individuals with preclinical Alzheimer's disease, before overt episodic memory impairment is noticeable. Specifically, we demonstrated that poor angular estimation is an early cognitive marker of tau pathology, whereas distance estimation relates to older ages, not to Alzheimer's disease.

Trial registration: Eudra-CT 2018-003473-94.

Background: The emerging evidence of the role of the glymphatic system (GS) in Alzheimer's disease (AD) provides new opportunities for intervention from the earliest stages of the disease. The aim of the study is to evaluate the efficacy of GS in AD to identify new disease biomarkers.

Methods: We performed a two-stage proteomic study to evaluate the GS health using intravenous gadolinium-based contrast agent (GBCA) with serial T1 3T magnetic resonance imaging (MRI) in individuals with amnestic mild cognitive impairment (aMCI). In Stage 1 (evaluated in the Cohort 1 of aMCI participants (n = 11)), we correlated the levels of 7K cerebrospinal fluid (CSF) proteins (estimated by SOMAscan) with GS health in 78 Freesurfer-segmented brain regions of interest (ROIs).

Results: A total of seven different proteins were significantly associated with GS health (p-value < 6.4 × 10^-4). The stronger correlations were identified for NSUN6, GRAAK, OLFML3, ACTN2, RUXF, SHPS1 and TIM-4. A pathway enrichment analysis revealed that the proteins associated with GS health were mainly implicated in neurodegenerative processes, immunity and inflammation. In Stage 2, we validated these proteomic results in a new cohort of aMCI participants (with and without evidence of AD pathology in CSF (aMCI(-) and aMCI/AD( +); n = 22 and 7, respectively) and healthy controls (n = 10). Proteomic prediction models were generated in each ROI. These were compared with demographic-only models for identifying participants with aMCI(-) and aMCI/AD( +) vs controls. This analysis was repeated to determine if the models could identify those with aMCI/AD( +) from both aMCI(-) and controls. The proteomic models were found to outperform the demographic-only models.

Conclusions: Our study identifies proteins linked with GS health and involved the immune system in aMCI participants.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills. Timely detection of these symptoms can facilitate early intervention, potentially slowing disease progression and enabling appropriate treatment and care.

Methods: The RADAR-AD study was designed to evaluate the accuracy and validity of multiple RMTs in detecting functional decline across various stages of AD in a real-world setting, compared to standard clinical rating scales. Our approach involved a univariate analysis using Analysis of Covariance (ANCOVA) to analyze individual features of six RMTs while adjusting for variables such as age, sex, years of education, clinical site, BMI and season. Additionally, we employed four machine learning classifiers - Logistic Regression, Decision Tree, Random Forest, and XGBoost - using a nested cross-validation approach to assess the discriminatory capabilities of the RMTs.

Results: The ANCOVA results indicated significant differences between healthy and AD subjects regarding reduced physical activity, less REM sleep, altered gait patterns, and decreased cognitive functioning. The machine-learning-based analysis demonstrated that RMT-based models could identify subjects in the prodromal stage with an Area Under the ROC Curve of 73.0 %. In addition, our findings show that the Amsterdam iADL questionnaire has high discriminatory abilities.

Conclusions: RMTs show promise in AD detection already in the prodromal stage. Using them could allow for earlier detection and intervention, thereby improving patients' quality of life. Furthermore, the Amsterdam iADL questionnaire holds high potential when employed remotely.