Frontiers in Aging Neuroscience最新文献

Objectives: To develop and validate machine learning models to predict levodopa responsiveness of tremor in Parkinson's disease (PD) patients.

Methods: A total of 197 PD tremor patients underwent Levodopa Challenge Tests and were classified as having levodopa-responsive or levodopa-resistant tremor. Clinical and electromyogram (EMG) tremor analysis variables were recorded. The dataset was randomly divided into a training set (80%) and a test set (20%). To distinguish between the two groups, Support vector machine (SVM), random forest (RF), and logistic regression (LR) models were developed using training data. The optimal model was validated on test data. Calibration and decision curve analyses assessed model reliability and clinical utility.

Results: Among 197 patients, 95 had levodopa-responsive tremor, and 102 had levodopa-resistant tremor. The SVM model showed the best performance, achieving an accuracy of 81.5% in five-fold cross-validation, with a Kappa score of 0.624, sensitivity of 84.3%, specificity of 77.9%, and an area under the curve (AUC) of 0.850. Performance remained consistent on test data, with 82.5% accuracy, 0.653 Kappa, 93.8% sensitivity, 75% specificity, and 0.896 AUC. The best model incorporated 6 predictors: resting tremor score, rigidity/tremor ratio, postural and kinetic tremor score, disease duration, the Movement Disorder Society's Unified Parkinson's Disease Rating Scale III (MDS-UPDRS III) /disease duration, and supine diastolic blood pressure (DBP).

Conclusion: The SVM model, incorporating six key indicators, holds significant potential for predicting levodopa responsiveness in PD tremor, offering a valuable tool for the precise treatment of tremor in PD patients.

Background: Reduced heart rate variability (HRV) has been associated with cognitive decline in older adults. However, prior research relied on brief in-clinic electrocardiography (ECG) recordings to measure HRV. Using 7-day continuous ambulatory ECG monitoring, we investigated time-specific differences in HRV (i.e., differences in HRV at each time point over the course of a 24-h day) between individuals with mild cognitive impairment (MCI) and those who were cognitively normal (CN) in a cohort of community-dwelling older adults.

Methods: Analyses included 81 dementia-free participants [mean age = 78, standard deviation (SD) = 5, age range = 72-95; 82% female; 38% non-Hispanic White individuals, 43% non-Hispanic Black individuals]. Among them, 20 met the Jak/Bondi criteria for MCI. Participants were instructed to wear a single-lead ECG monitor continuously for 7 days. Power spectral analyses were used to determine HRV in the high-frequency band (0.15-0.40 Hz, HF-HRV) over consecutive 5-min epochs throughout the recording. Functional additive mixed models were used to analyze participants' 24-h HF-HRV profiles to examine time-specific HRV differences between MCI and CN, after adjusting for age, sex, ethnicity, and education and further adjusting for depression, history of diabetes, and hypertension.

Results: Compared to the CN group, the MCI group showed reduced HRV in the early morning (before 7 a.m.) and evening (after 7 p.m.), with the greatest difference occurring around midnight (difference: 0.6, 95% pointwise CI: 0.2, 1.1, Cohen's d: 0.75).

Conclusion: Our findings highlight HRV's dynamic nature and the need to consider the time of day when investigating the relationship between HRV and cognition. Compared to daytime HRV, reduced nighttime HRV may have a stronger association with worse cognition.

Background: Cognitive reserve (CR) may protect cognitive performance under pathology. Depressive symptoms are common in mid and late life and are linked to poorer cognition. This study investigated whether depressive symptoms mediate the association between CR and both global and domain-specific cognitive performance in middle-aged and older Chinese adults.

Methods: Data from 1,636 participants in the 2018 wave of the China Health and Retirement Longitudinal Study (the CHARLS 2018 cohort) were analyzed. Information from an independent retrospective cohort that underwent amyloid and tau positron emission tomography (PET) at Nanjing First Hospital (the PET cohort; n = 100) was collected to validate the results from CHARLS. Associations between CR and cognitive performance in memory, executive function, language were examined, and mediation analysis was performed to assess the role of depressive symptoms. Subgroup analyses were also conducted. In the CHARLS 2018 cohort, participants were stratified as cognitively normal or cognitively impaired. In the PET cohort, participants were stratified into amyloid-negative cognitively normal and amyloid-positive mild cognitive impairment (MCI).

Results: Across both cohorts, higher CR was associated with a lower risk of cognitive impairment and better domain-specific performance. Depressive symptoms partially mediated the association between CR and several domains of cognition in the analyses of the overall cohorts. In both the CHARLS 2018 subgroups, no mediation was detected. In the PET cohort, depressive symptoms fully mediated the effects of CR on executive function and attention in the MCI group, in which most participants showed tau deposition on PET, whereas no mediation was observed in the cognitively normal subgroup.

Conclusion: CR is a protective factor for cognition, and depressive symptoms act as a modifiable mediator. In AD patients with confirmed tau pathology, timely detection and management of depressive symptoms may help preserve cognition and enhance the benefits of CR.

Neurodegenerative diseases such as Alzheimer's and Parkinson's disease pose a major global healthcare challenge, with cases projected to rise sharply as populations age and effective treatments remain limited. AI has shown promise in supporting diagnostics, predicting disease progression, and exploring biomarkers, yet most current tools are narrowly focused, unimodal, and lack longitudinal reasoning or interpretability. By enabling context-aware analysis across imaging, genomics, cognitive, and behavioral data, agentic AI can track disease progression, identify therapeutic targets, and support clinical decision-making. Over time, these systems may detect gaps in their own information and request targeted data, moving closer to real clinical reasoning while keeping clinicians in control. The next frontier in medical AI lies in developing autonomous, multimodal agents capable of integrating diverse data, adapting through experience, supporting decision-making, and collaborating with clinicians. Furthermore, ethical, patient-centered AI requires close technical-clinical collaboration to support clinicians and improve patient outcomes. This perspective examines AI's current role in Alzheimer's care, identifies key challenges in integration, interpretability, and regulation, and explores pathways for safely deploying these agentic systems in clinical practice.

Introduction: Approximately 20% of people with Parkinson's disease (PwP) in Germany need professional long-term care (LTC). Previous data have indicated a rather poor LTC situation and the need for more profound analyses. Therefore, we aimed to assess the quantity and quality of LTC care for PwP and the knowledge on Parkinson's disease (PD) in German LTC nursing staff.

Methods: Data from our nationwide, cross-sectoral Care4PD survey, which was distributed postally and online, were analyzed. Out of 295 completed anonymous LTC nurse questionnaires, 288 were included, with descriptive results presented in this study.

Results: In terms of age and work experience, a representative sample of 288 participants, the majority (79%) of whom were registered LTC nurses, participated in the study. A total of 95% of them had certain experience with people with Parkinson's disease (PwP). On average, each nurse supported approximately three PwP per week, with a mean care time of 48 min per day. A total of 17% of participants complained about "never" having enough staff, and 50% complained about "frequently changing" LTC personnel in their institution. Additionally, 10% reported "unsafe" care quality, with the occurrence of avoidable complications. Insufficient knowledge on PD and the importance of PD-specialized training were highlighted, with current training options often not recognized. Optimization suggestions consisted of more personnel and time capacities, educational measures, and interprofessional exchange.

Discussion/conclusion: Improving PwP care in German LTC facilities requires not only the general provision of more personnel and time resources but also, in particular, the development of greater expertise among LTC nursing staff to optimize care quality. The existing, but little-known, training opportunities should therefore be made known to a larger number of LTC nurses.

Introduction: Tremor is a prevalent and disabling motor symptom in Parkinson's disease (PD). The role of the serotonergic system in Parkinsonian tremor remains unclear. We aimed to investigate whether functional connectivity (FC) of the dorsal (DRN) and median (MRN) raphe nuclei is associated with tremor in PD.

Methods: Forty PD patients with tremor dominant (TD-PD), 42 PD patients with postural instability and gait disturbance dominant (PIGD-PD), and 40 healthy controls (HCs) were enrolled. Resting-state functional MRI was used to investigate altered FC of the DRN and MRN in TD-PD patients compared to HCs and PIGD-PD patients. Subsequently, correlations between FC of the raphe nuclei and motor-related clinical variables were analyzed.

Results: Both TD-PD and PIGD-PD patients showed reduced FC of the raphe nuclei compared to HCs. TD-PD patients demonstrated a more pronounced reduction in FC for both DRN and MRN across extensive brain regions, such as the sensorimotor cortex, temporal cortex, occipital cortex, and cerebellum, relative to PIGD-PD patients. Correlation analysis revealed that FC of both DRN and MRN was negatively correlated with tremor severity, including the total tremor score, rest tremor scores (amplitude, constancy, and index of severity), and postural tremor score. Our findings indicate significant hypoconnectivity of both DRN and MRN in TD-PD patients. Moreover, both DRN and MRN related functional networks exhibited correlations with tremor severity.

Discussion: These results support the association between serotonergic dysfunction and Parkinsonian tremor, suggesting that both DRN and MRN may play critical roles in the pathogenesis of tremor in PD.

Background: Parkinson's disease (PD) patients face a higher risk of developing heart failure (HF). The objective of the study was to investigate the hub genes and potential mechanisms linking Parkinson's disease (PD) to heart failure (HF) using multiple integrative bioinformatics tools.

Methods: Integrated bioinformatics analyses were performed. One HF dataset (GSE57338) and three PD datasets (GSE7621, GSE20146, GSE49036) were obtained from the GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify PD-related genes. Differentially expressed genes (DEGs) between PD and normal samples, as well as between HF and normal samples, were identified. The intersection of DEGs, WGCNA-derived PD-related genes, and genes encoding known secretory proteins was analyzed to find PD-associated secretory proteins. Immune cell infiltration in HF was assessed using CIBERSORT. Protein-protein interaction (PPI) network analysis was conducted to identify hub genes. Key findings were experimentally validated in an MPTP-induced PD mouse model through behavioral tests, ELISA, and immunohistochemistry.

Results: Analysis identified 21 PD-associated secretory proteins. Intersection with HF DEGs revealed 12 common genes, from which 8 functional genes with consistent expression patterns in both conditions were identified. PPI network analysis highlighted three hub genes: RELN, SLIT1, and NTN1. Reactome pathway analysis indicated that NTN1 is involved in cardiac-related processes like muscle contraction and cardiac conduction. Experimental validation in PD model mice confirmed a significant decrease in Netrin-1 levels in the blood, striatum, and heart. Furthermore, a strong negative correlation was found between cardiac Netrin-1 expression and collagen deposition, suggesting its potential role in impacting cardiac function.

Conclusion: These insights highlight the coexistence of PD and HF and suggest new avenues for investigating strategies to prevent HF in PD patients, particularly by exploring the role of Netrin-1 in the heart and its potential for cardioprotection.

Objective: Our preliminary studies have demonstrated that exercise counteracts Alzheimer's disease (AD) by mitigating microglia-mediated neuroinflammation and enhancing microglial Aβ clearance. However, the underlying mechanism remains unclear. Given the crucial role of glucose metabolic reprogramming in regulating microglial functions, this study investigated the effects of treadmill exercise on microglial glucose metabolism and associated AD pathologies.

Materials and methods: Three-month-old male APP/PS1 transgenic mice were randomly assigned to a sedentary group (AD-SED) or an exercise group (AD-EXE). Age- and sex-matched C57BL/6 mice served as the wild-type control group (WT-SED). The AD-EXE group underwent a 3-month treadmill exercise intervention. Following the intervention, we assessed spatial learning and memory using the Morris water maze test, measured neuroinflammation and Aβ levels via Western blot and ELISA, and analyzed microglial glucose metabolism using LC-MS/MS targeted metabolomics and Seahorse assays.

Results: APP/PS1 mice exhibited longer escape latencies during place navigation trial and fewer platform crossings during the spatial probe trial; these deficits were partially reversed by treadmill exercise. Furthermore, the exercise intervention significantly reduced hippocampal Aβ levels and suppressed neuroinflammation. Notably, microglia from 6-month-old APP/PS1 mice showed significant impairments in both glycolysis and oxidative phosphorylation (OXPHOS), with a metabolic profile primarily reliant on glycolysis. Treadmill exercise enhanced both glycolysis and OXPHOS, and shifted the metabolic phenotype from glycolytic-dominant toward oxidative phosphorylation, and restored metabolic homeostasis.

Conclusion: Treadmill exercise promotes microglial glucose metabolic remodeling, which attenuates neuroinflammation and Aβ pathology, and restores spatial learning and memory deficits in APP/PS1 mice.

Background: Growing evidence suggests that body composition has a significant influence on cognitive function. However, their relationship remains controversial. This study investigated the association between body composition and cognitive function.

Methods: This multicenter cross-sectional study recruited participants from 38 rural townships in Beizhen from July to August 2023. We included participants who completed both cognitive function assessments and body composition measurements. Exploratory factor analysis was employed for dimensionality reduction and classification of body composition. A logistic regression model was utilized to evaluate the association between primary body composition and cognitive decline. Network analysis was performed using R software to construct network models of body composition and cognitive function, to identify key variables and their interconnections.

Results: Exploratory factor analysis classified 27 body composition variables into 6 factors. Among the 6 factors, "muscle mass" (OR = 0.393), "central obesity" (OR = 1.69), and "leg-dominant fat distribution" (OR = 0.473) are associated with cognitive function. "Muscle mass," "central obesity," and "leg-dominant fat distribution" were used to construct network models related to cognitive function. In these three models, the most central domains are all language, attention, and registration.

Conclusion: This study found that "central obesity" increased the risk of cognitive decline, while "muscle mass" and "leg-dominant fat distribution" had protective effects. Interventions targeting language, attention, and registration domains might help address cognitive decline caused by changes in body composition.