Frontiers in Aging Neuroscience最新文献

Multiple studies show conflicting association between APOE polymorphisms and the risk of PDD, yielding inconsistent results. To elucidate, a meta-analysis was conducted using existing articles from Web of Science, PubMed, Cochrane, Google Scholar, Embase, WanFang, and CNKI databases, including case-control studies published up to January 31, 2025. A total of 27 studies (3,115 PD controls and 1,338 PDD cases) were included, with pooled Odds Ratio (ORs) and 95% confidence intervals (CIs) calculated using CMA, Biostat, United States. To assess APOE genotypes and PDD risk, three comparisons were examined: 5 genotypes vs. ε3/3, ε2+/ε4 + vs. ε3/3, and ε4 + vs. ε4-. The ε3/4 (OR = 1.56, 95% CI: 1.25-1.95); ε4 + vs. ε3/3 (OR = 1.52, 95% CI: 1.20-1.93) and ε4 + vs. ε4- (OR = 1.62, 95% CI: 1.39-1.90) genotypes were associated with an increased PDD risk, while ε2 + showed no significant effect (OR = 1.21, 95% CI: 0.88-1.65, p = 0.23). Carriers of ε4 + had a 1.52-fold higher risk compared to ε3/3, and the ε4 + vs. ε4 - comparison revealed a 1.62-fold greater dementia risk in ε4 + carriers. Subgroup analysis by ancestral region confirmed ε4 + as a significant risk factor for PDD across Asian, and Caucasians populations with higher susceptibility in Asian (OR = 1.98, 95% CI: 1.29-3.05) vs. Caucasian (OR = 1.48, 95% CI: 1.11-1.98) populations. Our findings suggest that ε3/4 and ε4/4 increase susceptibility to PDD, underscoring the need for further large-scale studies to validate these associations.

With the number of Parkinson's patients expected to rise due to an aging population, there is an increasing need to identify new diagnostic markers. These markers should be affordable and suitable for routine use to monitor the population, help stratify patients for treatment pathways, and provide new avenues for therapy. Genetic predisposition and familial forms account for approximately 10% of Parkinson's disease (PD) cases, leaving a large fraction of the population with minimal effective markers for identifying high-risk individuals. The establishment of population-wide omics and longitudinal health monitoring studies provides an opportunity to apply machine learning approaches to these unbiased cohorts to identify novel PD markers. In this study, we present the application of three machine learning models to identify protein plasma biomarkers of PD using plasma proteomic measurements from 43,408 UK Biobank subjects as the training and test set and an additional 103 samples from the Parkinson's Progression Markers Initiative (PPMI) as external validation. We identified a group of highly predictive protein plasma markers, including known markers Dopa decarboxylase (DDC) and Calbindin 2 (CALB2) as well as new markers involved in the JAK-STAT and PI3K-AKT pathways and hormonal signaling. We further demonstrated that these features are well correlated with UPDRS severity scores and stratified these into protective and risk-associated features that potentially contribute to the pathogenesis of PD.

Introduction: Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive memory decline, with neuropathological hallmarks including amyloid plaques and neurofibrillary tangles. Current treatments only alleviate symptoms and cannot halt disease progression. Icaritin (ICT), a natural compound, has shown neuroprotective potential. Transactive response DNA-binding protein 43 (TDP-43) is widely recognized as a key neuropathological hallmark of AD and related dementias. This study investigated the protective effects of ICT against TDP-43-induced damage in N2a/APP695swe (APP) cells and explored the underlying mechanisms.

Methods: N2a/APP695swe/TARDBP cells overexpressing APP and TDP-43 were constructed via lentiviral transfection, and the optimal ICT dosage was determined using the CCK-8 assay. The effects of ICT on TDP-43 cell phenotypes were then assessed using CCK-8, ELISA, and Western blot. Finally, transmission electron microscopy, flow cytometry, assay kits, and Western blot were used to investigate the protective mechanisms of ICT.

Results: ICT treatment significantly increased cell viability, reduced Aβ42 levels, and alleviated phospho-Tau and phospho-TDP-43 accumulation. Mechanistically, ICT improved mitochondrial morphology, decreased ROS levels, enhanced ATP production, and modulated the AMPK/mTOR and PINK1/Parkin autophagy signaling pathways to mitigate TDP-43-mediated cellular stress.

Conclusion: ICT protects cells from TDP-43-induced mitochondrial dysfunction and autophagy impairment, providing mechanistic insight into its potential as a therapeutic agent for AD.

The high rate of comorbidity between metabolic diseases and neuropsychiatric disorders suggests a shared underlying pathogenic mechanism. However, the biological basis of this relationship remains unclear. This study aims to clarify the role of brain insulin resistance (BIR) in linking metabolic dysfunction to neuropsychiatric symptoms based on existing evidence. The analysis shows that BIR disrupts limbic system function through two primary molecular pathways: (1) impairment of the PI3K/Akt/mTOR pathway, which decreases the expression of synaptic plasticity-related proteins and causes deficits in long-term potentiation (LTP); (2) activation of the TLR4/MyD88 inflammatory axis, promoting pro-inflammatory cytokine release from glial cells. These changes result in characteristic neuropsychiatric phenotypes, including amygdala hyperactivity (emotional disorders), hippocampal atrophy (memory impairment), and decreased prefrontal cortex (PFC) function (executive dysfunction). This review highlights that interventions targeting BIR might simultaneously improve metabolic outcomes and neuropsychiatric symptoms, providing a theoretical foundation for trans-diagnostic treatment models. The findings support the view of BIR as a modifiable interface for metabolic- neuropsychiatric comorbidities and advocate for the development of a multidisciplinary collaborative framework to facilitate mechanism-based precision therapy.

Background: Intermittent theta burst stimulation (iTBS) is increasingly explored as a non-invasive neuromodulatory approach capable of inducing long-lasting plasticity with potential therapeutic value in age-related neurological and psychiatric conditions. However, the cellular and molecular mechanisms underlying iTBS protocols remain largely unknown, limiting its further therapeutic development.

Methods: Here, we investigated the behavioral, structural, synaptic, and calcium-dependent effects of a 7-day iTBS600 protocol using a combination of in vivo, ex vivo, and in vitro approaches. 2.5-months old male Wistar rats and Grin2A knockout mice were used.

Results: Prolonged iTBS did not alter general locomotor activity, anxiety-like behavior, or short-term recognition memory, indicating preserved baseline behavioral function. Despite the absence of behavioral changes, prolonged iTBS induced robust structural plasticity in hippocampal CA1 neurons, increasing total spine density and selectively enhancing the proportion of thin, learning spines. Synaptosomal analysis revealed upregulation of GluN1 and GluN2A, elevated BDNF levels, and activation of downstream Akt, ERK1/2, and mTOR pathways. Prolonged iTBS also enhanced perineuronal net formation around PV⁺ interneurons across hippocampal subfields. In vitro recordings demonstrated increased spontaneous and evoked Ca²⁺ activity following both acute and prolonged stimulation, with the prolonged protocol uniquely extending the duration of K⁺-evoked Ca²⁺ responses. Pharmacological blockade with D-AP5 and experiments in Grin2a-knockout neurons revealed that these effects are dependent on NMDA receptors, particularly the GluN2A subunit.

Conclusion: Together, these findings indicate that prolonged iTBS drives coordinated structural, synaptic, and Ca²⁺-dependent plasticity in the hippocampus through GluN2A- and BDNF-dependent mechanisms. This work provides mechanistic insight into how iTBS may induce sustained circuit-level adaptations relevant for therapeutic applications.

Introduction: Sleep quality is often thought to be a key determinant of cognitive performance, particularly in older adults who experience age-related changes in sleep architecture. However, the extent to which nightly variations in sleep quality impact next-day cognitive performance remains unclear-in part because it has only recently become practical to measure sleep over multiple nights.

Methods: In this study, we used an in-home wearable electroencephalography (EEG) device to monitor sleep patterns over ~10 nights in 17 healthy older adults, assessing metrics of sleep quality such as wake after sleep onset and the density of slow oscillations and sleep spindles. Next-day cognitive performance was evaluated using two computerized neuropsychological tasks measuring executive functions (inhibition and cognitive flexibility), and their relationships to sleep metrics were explored.

Results: Although participants placed the EEG device themselves, a high proportion of sleep data was usable (~71%), and clear nightly variations in sleep quality were captured. Sleep recordings showed considerable variability in sleep quality metrics across nights, with large inter-individual differences. However, we found no effects of either macro- or microarchitectural sleep metrics on executive task outcomes the following day.

Discussion: These results do not rule out the possibility that some aspects of cognitive performance may be affected by daily fluctuations in sleep quality; however, they suggest that inhibition and cognitive flexibility, which underlie reasoning and problem solving, may be relatively resilient to nightly sleep variability in older adults. The findings also demonstrate the feasibility of using emerging portable devices to extend sleep studies at home and over multiple nights in older adults, while providing variance estimates and effect sizes to guide power and sample size planning for future studies.

Background: Age is a major risk factor for ischemic stroke (IS), with immunosenescence-age-related immune system dysfunction - contributing to worse outcomes. Immunosenescence impairs immune responses, heightens inflammation, and increases susceptibility to infections, all of which affect stroke prognosis. This review investigates the association between immunosenescence, immune cell dysfunction, and IS risk and outcomes.

Methods: A systematic review was conducted to identify cohort studies examining immunosenescence in IS patients aged 60 and above. Databases PubMed and Embase were searched up to 10 August 2024. Studies were included if they analyzed immune cell markers or inflammatory markers in relation to IS risk or outcomes. A total of 11 studies met the inclusion criteria.

Results: Elevated inflammatory markers such as interleukin (IL)-6, high-sensitivity C-reactive protein (hs-CRP), and Th17 cells were significantly associated with poorer stroke outcomes. Studies indicated an imbalance between pro-inflammatory Th17 cells and regulatory T cells (Treg) post-stroke. Higher neutrophil-to-lymphocyte ratio (NLR) and alterations in B-cell subsets were also observed in older stroke patients, further contributing to the inflammatory response. These immune dysregulations were linked to increased mortality and poor recovery.

Conclusion: Immunosenescence plays a crucial role in IS pathogenesis and recovery, with chronic inflammation and immune dysfunction exacerbating stroke outcomes in older adults. Targeting immune markers, particularly IL-6 and the Th17/Treg imbalance, may offer new therapeutic approaches to improve stroke prognosis in aging populations. Further research is needed to develop interventions that address immunosenescence in IS.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024583142.

Introduction: Cloud-based artificial intelligence (AI) combined with smart-health technology presents a powerful tool to passively monitor disease severity. However, current methods raise privacy concerns as they require transmitting patient data to the cloud. A potential solution is Federated Learning (FL), which only shares the weights of locally trained neural networks (NNs) instead of user data. Here, we simulated an FL system to demonstrate its application for evaluating Parkinson's disease (PD) severity in a smart-home scenario.

Methods: Retrospective data including 89 people with PD wore an accelerometer on the lower-back at home for 7 days at 18-month intervals over 6 years. Patient characteristics (age, sex, and body mass index) and clinical measures of PD were additionally collected, including the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS)-Part III. Real-world daily gait measures along with these patient characteristics were used to predict the MDS-UPDRS-III score. For FL, a local model was trained for each participant, and a global model (an aggregation of these local models) was tested on unseen participants.

Results: The performance of a simulated FL system was compared with that of a traditional Machine Learning (ML) approach in which patient data were shared. The traditional ML approach had a mean absolute error (MAE) of 10.43. The global FL model had a similar MAE of 10.22 but was underfitted, and the mean MAE of the local, personalised models was 4.83. Shapley Additive exPlanations (SHAP) analysis showed that while the participants' age and sex were very important in traditional ML, this was not the case for the local FL models, leading to a decrease in global model performance. Here, we show that reserving a small number of participants from the system and including them in training data for all local models restored the importance of these features and improved global FL performance (MAE = 9.26) but reduced local performance (MAE = 6.83).

Conclusion: This exploratory study shows that our proposed approach enables FL to achieve similar accuracy to traditional Machine Learning without sharing any patient data but with costs to the local performance, leading towards a smart-home system that prioritises personalisation and patient privacy.

Purpose of review: Parkinsonian syndromes are a heterogeneous group of neurodegenerative diseases that pose challenges in early diagnosis, differentiation, and pathophysiological understanding. The objective of this review is to summarize recent contributions of computational models combined with neuroimaging data to the differential diagnosis of Parkinsonian syndromes, disease subtyping, and understanding of disease processes.

Recent findings: Using machine learning algorithms trained with MRI features, diagnostic accuracies above 90% have been achieved for distinguishing patients with Parkinson's disease from healthy controls and for the differential diagnosis of Parkinsonian syndromes. Computational models, such as hierarchical cluster analysis and Subtype and Stage Inference (SuStaIn), have enabled the identification of distinct disease subtypes within Parkinson's disease based on imaging-derived brain features. Network models based on structural and functional connectomes have revealed that disease spread in Parkinson's disease is primarily driven by global connectivity. Additionally, local brain characteristics such as gene expression, cellular composition, and neuroreceptor profiles may contribute to selective vulnerabilities.

Summary: Computational approaches enhance the diagnosis of Parkinsonian syndromes, particularly in the early stages, and refine the characterization of disease subtypes, benefiting clinicians, especially in non-expert centers. Such applications hold significant potential for enabling more personalized management and selecting appropriate candidates for clinical trials. Furthermore, a deeper understanding of pathophysiology supports the development of disease-specific therapies.

Impulse control disorders (ICDs), such as excessive gambling, compulsive sexual behavior, binge eating, compulsive shopping as well as punding, and the dopamine dysregulation syndrome, may arise as a debilitating neuropsychiatric complication in Parkinson's disease (PD). Although the pathophysiology is not fully understood, it likely involves mesolimbic dopaminergic overstimulation combined with disease-related vulnerabilities in reward, motivation, and inhibitory control networks. This narrative review summarizes evidence on the neuropsychological, affective, and behavioral traits associated with ICDs in PD, with a particular focus on epidemiology/clinical manifestations, neurobiological and pharmacological mechanisms, as well as prevention and management strategies. ICDs can affect up to 40% of PD patients and are strongly associated with dopamine agonist exposure, younger age of onset, premorbid personality traits, and neuropsychiatric comorbidities. Neuropsychological findings reveal abnormalities in several domains, including reflection impulsivity, temporal discounting, novelty seeking, risk processing, and inhibitory control, while mood disorders, sleep dysfunction, apathy, and anxiety further influence vulnerability and worsen behavioral dysregulation. Although general awareness for development of ICDs has been raised, they still represent a significant burden for patients and their family members and are a predictor of functional decline and lower quality of life. Management includes dopamine agonist withdrawal whenever possible, the cessation of fast acting dopaminergic agents and treatment of neuropsychiatric comorbidities. In selected cases, deep brain stimulation or continuous dopaminergic delivery should be considered, particularly in those experiencing persistent worsening of motor symptoms despite appropriate adjustment of dopaminergic medication.