Journal of Alzheimer's disease reports最新文献

Background: Neuroinflammation in neurocognitive disorders is driven by the release of tumor necrosis factor (TNF)-α from brain immune cells in response to injury, infection, or p-Cresol sulfate (p-CS)-a metabolite associated with chronic kidney disease and linked to TNF-α activity. However, the underlying mechanisms through which TNF-α and p-CS influence cognitive performance remain unclear.

Objective: This study investigated the impact of TNF-α and p-CS on cognition, focusing on the role of TNF Receptor 2 (TNFR2) in cognitively normal individuals (CN; n = 36), Alzheimer's disease patients (AD; n = 85), and those with mild cognitive impairment (MCI; n = 219).

Methods: Cognitive status was assessed with ADAS-Cog 13, p-CS measured via MxP^® Quant 500, and TNF-α/TNFR2 quantified using Human DiscoveryMAP^®. Mediation analysis explored TNFR2's role in linking p-CS, TNF-α, and cognition, with significance set at p < 0.05 and FDR controlled by the Benjamini-Hochberg method.

Results: The results showed that TNF-α levels were slightly higher in AD than in MCI, while TNFR2 levels were lowest in MCI, higher in CN, and highest in AD. After adjusting for age, gender, and APOE ɛ3/ɛ4 status, higher TNF-α levels were associated with higher TNFR2 levels in both MCI and AD. In MCI, elevated TNFR2 correlated with better cognitive function, indicating a possible neuroprotective role at this stage of cognitive decline. Further analysis revealed that both p-CS and TNF-α contributed to increased TNFR2 levels, which in turn supported cognitive performance.

Conclusions: In short, p-CS and TNF-α may improve cognitive performance via TNFR2 in individuals with MCI.

Background: There is increasing evidence suggesting a correlation between neurodegenerative diseases (NDDs) and constipation; however, their genetic relationship and causal mechanisms remain inadequately elucidated.

Objective: We aim to investigate the causal link and shared genetic basis between NDDs and constipation.

Methods: We obtained summary statistics from large-scale genome-wide association studies, encompassing five NDDs, including Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), Lewy body dementia (LBD), as well as constipation. The primary analysis employed five Mendelian randomization methods to evaluate causal effects, while linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) were utilized to investigate genetic correlations. Additionally, significant pleiotropic SNPs were identified using pleiotropic analysis under the composite null hypothesis (PLACO) and functional mapping and annotation (FUMA). Finally, enrichment analysis was conducted to explore the biological pathways associated with the identified pleiotropic genes.

Results: MR analysis revealed a significant causal relationship between AD and an enhanced risk of constipation was demonstrated (OR = 1.043, 95% CI: 1.015-1.073, p = 0.003), while no causality was found between PD, MS, ALS, LBD, and the risk of constipation (p > 0.05). LDSC and HDL analysis revealed a significant positive genetic correlation between AD and constipation. Using PLACO combined with FUMA, we identified 30 overlapping pleiotropic loci, with pathway enrichment analysis revealing important biological pathways related to Aβ metabolism and processing, tau protein process, and the complement and coagulation cascades.

Conclusions: Our study indicates that AD is a contributing factor to constipation and uncovers the complex genetic mechanisms linking AD and constipation, which holds significant implications for diagnosis and treatment of both conditions.

Background: Extracranial carotid artery calcification (ECAC) is a known risk factor for the development of cardiovascular diseases (CVD) and has been associated with an increased risk of dementia and Alzheimer's disease. However, the relationship between ECAC and amyloid-β (Aβ) deposition in older adults has not been fully understood.

Objective: This study aims to determine the relationship between ECAC and Aβ deposition in very old adults.

Methods: 90 participants (87.2 ± 2.7 years old, 96% whites, 63% males) without dementia from an observational study were included in the cross-sectional analysis. Aβ deposition in the brain was assessed using the ¹¹C-labeled Pittsburgh compound-B positron emission tomography (PiB-PET) scans performed in 2009 and 2011. Carotid artery plaque and ECAC status were measured via high-resolution carotid ultrasonography in 2011.

Results: Among the participants, 82 (91%) had carotid plaques, and 46 (51%) were globally Aβ positive by PiB-PET. ECAC was less frequently observed in the Aβ positive group compared to the Aβ negative group (90% versus 81%, p = 0.3). The odds of Aβ positivity in those with ECAC were 62% lower than in those without ECAC. However, the association was not statistically significant (OR = 0.38, 95% CI: [0.08, 1.76 ], p = 0.2).

Conclusions: In the population whose average age is older than 85 without dementia, there is no statistically significant association between ECAC and Aβ deposition after adjusting for confounding factors. The relationship between ECAC and dementia in older adults warrants further analysis in a larger and more racially and ethnically diverse population.

Background: Early-onset Alzheimer's disease (EOAD), which has an onset before the age of 65, is overshadowed by the more prevalent late-onset Alzheimer's disease (LOAD). Nevertheless, rather than being merely a form of LOAD that occurs at a prematurely defined younger age, EOAD differs from LOAD in multiple ways. Given these disparities, understanding the potential treatment options for EOAD becomes crucial.

Objective: We aim to assess anti-diabetic drugs' potential utility in treating EOAD and LOAD from a novel perspective via drug-targeted Mendelian randomization (MR) analysis.

Methods: Through Summary-data-based MR (SMR) and inverse-variance-weighted MR (IVW-MR) analysis, we assessed the associations between anti-diabetic drug targets (including DPP-4 inhibitor, Thiazolidinedione, GLP1R agonist, Sulfonylureas, SLC5A2 inhibitor, and Insulin/Insulin analog) and AD outcomes (EOAD and LOAD). We utilized two types of genetic instruments to represent the exposure to anti-diabetic drugs: eQTLs of genes encoding drug target proteins and genetic variants within or near genes encoding these target proteins associated with HbA1c from genome-wide association studies.

Results: SMR analysis showed that enhanced PPARG gene expression in the blood was a protective factor for EOAD (OR = 0.733, 95%CI = 0.548-0.979, p = 0.035). Additionally, an IVW-MR association was found between HbA1c mediated by PPARG and EOAD (OR = 0.295, 95%CI = 0.092-0.949, p = 0.041).

Conclusions: This study suggests that Thiazolidinedione therapy could help suppress the development of EOAD, supporting further exploration of PPARG-targeted anti-diabetic drug development.

Background: Alzheimer's disease (AD) and insulin resistance (IR) share intersecting pathological pathways, with IR increasingly implicated in AD pathogenesis. Systematic bibliometric analyses mapping the evolution of this interdisciplinary field remain limited.

Objective: To quantify global research trends, collaboration networks, knowledge structures, and emerging frontiers in IR-AD research from 2005-2024.

Methods: We analyzed 2676 publications from the Web of Science Core Collection. Using VOSviewer, CiteSpace, and bibliometrix R package, we conducted quantitative analyses and visualized multiple dimensions including annual publications/citations, countries/regions, institutions, journals, authors, references, and keywords.

Results: Annual publications grew steadily, peaking at 267 in 2022. The United States dominated productivity (942 papers, 35.2%) and citations (88,170). The University of Kentucky was the top institution (53 papers), while the Journal of Alzheimer's Disease led in output (214 papers) and co-citations (8046). Keyword analysis revealed three clusters: metabolic dysregulation, molecular pathology, and neuroimmune responses. Emerging frontiers highlight neuroimmune mechanisms, with current hotspots focusing on NLRP3 inflammasome activation, gut-brain axis dysregulation, glucose transporter impairment, and therapeutic repurposing of GLP-1 agonists.

Conclusions: These findings underscore IR-AD as a critical intersection of metabolic and neurodegenerative pathways, advocating for targeted therapies addressing neuroinflammation and cerebral metabolism. By delineating global trends, this study provides a roadmap for future research to bridge translational gaps in AD pathogenesis and treatment.

Background: Cognitive impairment has been reported in patients with cerebellar phenotype of multiple system atrophy (MSA-C), yet this area remains largely unexplored.

Objective: We evaluated the cognitive profile of MSA-C patients and assessed its association with neuroimaging features, specifically the hot-cross bun (HCB) sign.

Methods: This retrospective study encompassed 210 patients diagnosed with MSA-C between 2019 and 2023. Each patient underwent a thorough clinical evaluation, a comprehensive neuropsychological assessment and a magnetic resonance imaging (MRI) scan.

Results: MSA-C patients demonstrated a high burden of comorbidities, including a high frequency of motor symptoms, autonomic disorders, and various non-motor symptoms such as cognitive deficits, mood disorders and sleep disturbances. Notably, 24.8% of MSA-C patients exhibited cognitive impairment, primarily affecting language, memory and processing speed. The presence of the HCB sign was independently associated with cognitive impairment, whereas other clinical features, such as orthostatic hypotension, were not. MSA-C patients with the HCB sign demonstrated significantly poorer performance in language, visuospatial function, and processing speed.

Conclusions: MSA-C patients suffer a substantial burden of cognitive impairment, primarily affecting language, memory and processing speed. Notably, the HCB sign is a significant indicator for cognitive dysfunction in patients with MSA-C.

Background: Alzheimer's disease (AD) is a complex neurodegenerative disorder.

Objective: To identify diagnostic and predictive biomarkers for AD.

Methods: Based on three GEO datasets of human brain tissue from AD patients and controls, weighted gene co-expression network analysis (WGCNA) and enrichment analysis were used to identify AD-related gene modules. Hub genes were screened via protein-protein interaction (PPI) analysis and three machine learning algorithms. Diagnostic efficacy was evaluated using receiver operating characteristic (ROC) curves. Immune cell infiltration and hub gene expression correlations were analyzed using xCell. In vivo validation was performed using an AD mouse model.

Results: The magenta module was significantly correlated with AD. PPI network analysis identified 15 AD-related genes, mainly enriched in mitochondria and ribosomes. Two hub genes, DLAT and CCDC88b, were identified. DLAT was significantly downregulated in AD, and CCDC88b was upregulated (p < 0.01); both findings were validated via qPCR in AD model mice. ROC analysis showed good diagnostic performance. Immune infiltration analysis revealed macrophages as the dominant cell type, with hub gene expression associated with immune cell presence.

Conclusions: DLAT and CCDC88b are potential novel biomarkers for AD and may serve as targets for therapeutic intervention.

Background: In healthy older adults (OA), the effects of amyloid-β (Aβ) deposition on cognitive functions involved in learning are unclear.

Objective: This study aimed to determine how age, practice, and neuropsychological test performance are associated with performance change during the learning of three cognitive tasks, and if Aβ deposition impacts performance change in OA.

Methods: Fifty-five OA and 28 young adults completed neuropsychological tests, and Aβ deposition was assessed in OA. Participants learned three cognitive tasks: stop-go normal task (SGNT), stop-go reverse task (SGRT), and n-back task (NBT). Performance change was analyzed as change in accuracy and reaction time from the first to second and the first to third practice trials using linear mixed effect models. The basic model included age group and performance change, with neuropsychological test covariates. The second basic model mimicked the first but included Aβ deposition, instead of age group.

Results: In the basic model, more practice resulted in a larger performance change for SGNT and SGRT, but not NBT. In the second basic model, after two NBT practice trials, performance change increased with greater amounts of Aβ deposition and worse information processing speed but, after three practice trials, decreased with greater amounts of Aβ deposition and worse information processing speed. Across the three tasks, greater Aβ deposition tended (non-significant trend) to be associated with smaller improvements after more practice.

Conclusions: These results suggest that the ability to learn a cognitive task is maintained with age but is negatively impacted by Aβ deposition.

We report a case of young-onset dementia (YOD) in a 40-year-old male with a heterozygous missense variant in the PDGFRβ gene. The patient exhibited progressive memory decline and disorientation. Brain MRI and cerebrospinal fluid biomarkers were consistent with Alzheimer's disease. Whole-exome sequencing identified a likely pathogenic PDGFRB c.1316G > A (p.Arg439Gln) variant. This report highlights a novel potential genetic contributor to vascular dysfunction and cognitive decline.

Background: The majority of mild cognitive impairment (MCI) patients who converted to dementia in later years have Alzheimer's disease (AD) pathology. The second most common type of dementia is Lewy body (LB) dementia.

Objective: In this project, we are interested in identifying the risk factors that predict who will develop AD dementia or LB dementia in later years.

Methods: Cox proportional hazards model and machine learning survival methods for interval-censored data were used to identify the risk factors that predict the onset of dementia for MCI patients with AD or LB pathology.

Results: We found that orientation, memory, and irritability scores were useful in predicting AD dementia onset, while daily living, depression, and executive function scores were identified as strong predictors in the LB cohort.

Conclusions: Different neurocognitive domains were predictive for conversion to dementia from MCI in patients with AD or LB pathology. The depression scale and functional activities were found to be predictive of LB dementia while irritability or lability severity score from Neuropsychiatric Inventory Questionnaire was associated with the onset of AD dementia.