Frontiers in Aging Neuroscience最新文献

Background: Post-stroke cognitive impairment (PSCI) is common and imposes a significant burden upon both families and society. There is limited information on biomarkers for PSCI. This study investigated the correlation between blood biomarkers and post-ischaemic stroke cognitive impairment, to identify potential blood biomarkers and their efficacy in predicting the disorder.

Methods: This prospective study enrolled patients who had experienced their first acute ischaemic stroke between January 2024 and March 2025. Patients underwent blood tests within 24 h of admission, which measured plasma levels of Aβ1-40, Aβ1-42, glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), p-Tau181, p-Tau217, Aβ42/40, and p-Tau217/Aβ1-42. The cognitive function of the patients was assessed at the three-month follow-up visit using the Montreal Cognitive Assessment (MOCA) scale. Participants were divided into a cognitive impairment group and a cognitively normal group with a MoCA cutoff score of 22.

Results: A total of 128 patients who had experienced a first ischaemic stroke were included in the analysis. At the three-month post-stroke follow-up, 69 patients (53.9%) were allocated to the PSCI group, with 59 patients (46.1%) in the cognitively normal group. After univariate and multivariate logistic regression analyses, plasma GFAP (OR = 1.0027, 95% CI = 1.0002-1.0053, p = 0.038) and plasma NFL (OR = 1.0046, 95% CI = 1.0006-1.0086, p = 0.025) were identified as independent risk factors for cognitive impairment following ischaemic stroke. Receiver operating characteristic (ROC) curves indicated area under the curve (AUC) values of 0.779 (95% CI = 0.700-0.858, p < 0.001) for plasma GFAP and 0.809 (95% CI = 0.733-0.885, p < 0.001) for plasma NFL, indicating good predictive performance for both parameters. The AUC for GFAP+NFL was 0.855 (95% CI = 0.792-0.918, p < 0.001), indicating superior predictive performance of the GFAP and NFL combination for PSCI post-ischaemic stroke cognitive impairment.

Conclusion: Elevated plasma GFAP and NFL levels are associated with an increased risk of post-ischaemic stroke cognitive impairment. Plasma GFAP and NFL may represent potential biological markers for PSCI. The combination of the two parameters showed superior predictive efficacy for PSCI.

Introduction: As a common neurodegenerative disorder, Parkinson's disease (PD) primarily affects dopaminergic neurons, leading to progressive motor disabilities along with a spectrum of non-motor complications. The early identification of Parkinson's disease, as well as the exploration of biomarkers related to its associated comorbidities, remains an important focus of current research.

Methods: In this study, a metabolomics approach combined with machine learning techniques was applied to explore potential biomarkers for PD and its related comorbid conditions. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), blood plasma samples were analyzed from individuals with PD, PD with rapid eye movement sleep behavior disorder (PD+RBD), PD with insomnia (PD + insomnia), and healthy controls, resulting in the detection of 2,601 metabolites. Multivariate statistical methods-including the unsupervised principal component analysis (PCA) and the supervised techniques of partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA)-were employed to investigate intergroup metabolic variations. Machine learning algorithms, such as recursive feature elimination in conjunction with logistic regression, random forest, and support vector machines, were used to assist in selecting discriminative metabolites and constructing classification models.

Results: These models showed strong internal performance in distinguishing PD from healthy individuals and in characterizing PD patients with non-motor comorbidities such as RBD and insomnia. Overall, the results suggest that metabolic biomarkers may provide valuable insights into disease-related and symptom-associated metabolic alterations in Parkinson's disease.

Discussion: This study provides a basis for future investigations aimed at validating these findings and further exploring their potential relevance in clinical research.

Introduction: Falls are a leading cause of disability among older adults, and neural control is increasingly recognized as a critical risk factor. In this prospective cohort study, we investigated whether deceleration during terminal swing (DCEL), and medial-lateral velocity zero crossing count (VZCC), reflecting anticipatory braking and lateral balance control, predict future falls, and whether body mass index (BMI) moderates these associations. A total of 380 older adults were assessed and stratified by BMI (overweight: ≥ 25 kg/m²).

Methods: Fallers (n = 68) and non-fallers (n = 312) were identified prospectively over a 24-month follow-up period. Gait was measured using inertial measurement units (IMUs) during 14-meter walks.

Results: Compared to non-fallers, fallers were older (75.1 ± 4.31 vs. 73.4 ± 4.51 years, p = 0.002), had more prior falls, more negative DCEL values (-10.57 ± 15.61 vs. -5.08 ± 16.39 cm/s, p = 0.010), and higher VZCC (4.96 ± 2.59 vs. 4.29 ± 1.31, p = 0.023). In multivariate models, age, prior falls, DCEL, VZCC, and the BMI × DCEL interaction predicted fall risk. DCEL predicted falls only in individuals with normal weight (OR = 0.533, 95% CI = 0.368-0.770, p = 0.001), whereas VZCC predicted falls across all BMI levels. Gait parameters reflecting neural control predict fall risk, with effects moderated by BMI.

Discussion: Anticipatory braking control is critical for individuals with normal weight, whereas lateral instability elevates fall risk regardless of BMI. These findings highlight the value of BMI-stratified fall assessments and targeted interventions.

Introduction: Amyotrophic lateral sclerosis (ALS) is a progressive, age-related motor neuron degenerative disease with multiple causal factors. Dyslipidemia has been identified as an important pathological element. Impaired lipid protein metabolism manifests in ALS patients and in an ALS mouse model. Apolipoprotein components are the primary regulators of plasma lipid metabolism. Apolipoprotein A1 (ApoA1), a high-density lipoprotein, acts as an antioxidant and reduces inflammation, preventing blood vessel injury. However, the effects of ApoA1 upon the ALS-damaged endothelium in the CNS are unknown. The objective of the study was to determine the effect(s) of injecting ApoA1 into G93A SOD1 mice at the early symptomatic stage.

Methods: A single dose of ApoA1 or media was systemically administered into 13-week-old G93A SOD1 male and female mice. Body weight and tests of motor function were evaluated weekly for 4 weeks post-injection. Permeability of spinal cord capillaries was determined by Evans blue (EB) fluorescent dye injected into mice at 17 weeks of age. Immunohistochemical analyses determined the statuses of glial cells and ApoA1 distributions in ALS mice cervical/lumbar spinal cords. Motor neurons in cervical/lumbar spinal cord ventral horns of ApoA1-treated and media-injected ALS mice were stained with cresyl violet for histological analyses.

Results: ApoA1 injected into G93A SOD1 mice at the early symptomatic stage significantly benefited both male and female animals by (1) delaying behavioral disease progression; (2) reducing EB capillary leakage into spinal cord parenchyma; (3) lessening astrogliosis and microgliosis; (4) protein incorporation into capillary endothelium and motor neurons; and (5) improving survival of motor neurons in the spinal cord.

Conclusion: Our novel data showed that systemically administered ApoA1 benefited ALS mice of both sexes, likely by beneficial effects on damaged microvessels, possibly engendering restoration of neurovascular unit integrity. Moreover, an anti-inflammatory ApoA1 effect was demonstrated by the reduction of glial cell activation, potentially mitigating vascular injury. The results of our preclinical study suggest that ApoA1 may be a potential protein-mediated therapeutic for restoring vascular function. Our novel strategy may lead to future clinical trials, furthering our goal of effectively treating ALS patients.

Background: Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) pose significant societal and healthcare burden. Artificial intelligence (AI) methods have been widely applied in AD and MCI studies. We conducted a bibliometric analysis of the 100 most cited articles on AI applied to AD and MCI.

Methods: We searched the Web of Science database using keywords related to AD, MCI, and AI (e.g., "deep learning," "machine learning," "neural networks"). Citation counts ranked articles, and the top 100 were manually screened. Key parameters such as authors, journals, citation count, countries, institutions, and keywords were automatically extracted. We also manually extracted key information, including publication type, impact factor (IF), Journal Citation Reports (JCR) Category Quartile, AI methods, and clinical data types. Analysis and visualization were conducted using VOSviewer.

Results: Among the 100 articles, 13 were reviews, 2 were basic research papers, and 85 were clinical studies. Seventy seven articles focused on diagnosis and prediction. MRI data was the most frequently used analysis source. Shen Dinggang, the United States, and the University of North Carolina at Chapel Hill were respectively the individual, country, and institution with the highest publication volume. Neuroimage published the most papers (n = 14), and all the top 10 journals belonged to JCR Q1. Emerging keywords included "ensemble learning," "transfer learning," and "structural MRI." Support Vector Machine (SVM) was the most commonly applied AI method (n = 25), closely followed by convolutional neural network (CNN, n = 24).

Conclusion: We analyzed the top 100 cited articles on AI in AD and MCI across authors, journals, countries, institutions, keywords, and AI methods. Diagnosing AD/MCI is the primary research focus, with MRI as the most studied examination. SVM and CNN are the most frequently used AI methods in these studies.

Background: This study investigated whether a 24-week, community-based multicomponent exercise intervention (MCEI) can improve body composition and cognitive function in older adults with mild cognitive impairment (MCI).

Methods: In this single-center, parallel-group randomized controlled trial (RCT), 64 community-dwelling adults aged 65-75 years with MCI characterized by Mini-Mental State Examination (MMSE) ≥ 24, Montreal Cognitive Assessment (MoCA) ≤ 26, Clinical Dementia Rating (CDR) = 0.5, low skeletal muscle mass were randomly allocated (1:1) to a MCEI (aerobic, resistance and balance training, 3 × 60 min/week) or to a usual-activity control (UAC) group receiving weekly health education. Primary outcomes were skeletal muscle mass (SMM), fat-mass index (FMI), MMSE and MoCA; secondary outcomes included skeletal muscle index (SMI), basal metabolic rate (BMR), Instrumental Activities of Daily Living (IADL) and Animal Fluency Test (AFT). Assessments were conducted at baseline and within 1 week post-intervention by trained, blinded assessors. Intervention effects were examined with a 2 (group) × 2 (time) repeated-measures analysis of variance (ANOVA), reporting partial eta-squared (η²) as the effect-size estimate.

Results: A significant Group × Time interaction was observed for SMM (p < 0.001, η² = 0.195) and FMI (p = 0.003, η² = 0.153), indicating differential changes between groups; with significant improvements observed only in the MCEI group. SMI showed no significant interaction effect (p = 0.270, η² = 0.021), whereas no significant interactions were found for MMSE, MoCA, or AFT (p ≥ 0.18, η² ≤ 0.03).

Conclusion: A 24-week community-based multicomponent exercise program safely increased skeletal muscle mass and reduced fat mass in older adults with MCI, but did not produce measurable improvements on screening-level cognitive measures. Future studies with longer duration, larger samples, and inclusion of cognitive challenges are warranted to clarify exercise-cognition interactions and establish dose-response relationships for both body composition and domain-specific cognition.

Clinical trial registration: Identifier ChiCTR2000035012.

Age-related hearing loss (ARHL) is one of the most common causes of disability in older adults. It is also frequently associated with neurological and neurodegenerative disorders, including dementia, as well as with stress, anxiety, depression, and social isolation. These observations suggest that ARHL should be considered not merely as a sensory dysfunction, but rather as a complex disease involving extra-auditory domains. Namely, identifying shared pathogenic determinants between hearing loss and neurodegenerative diseases remains a significant challenge. Increasing research in this field has highlighted common molecular mechanisms underlying age-related hearing and cognitive vulnerability, as well as potential overlapping neuronal networks involved in both cognitive and auditory neurodegeneration. In this review, we first outline the clinical features, risk factors, and molecular pathways involved in ARHL. We then examine the molecular mechanisms underlying ARHL at both peripheral (cochlea) and central level (auditory cortex), and subsequently discuss the cognitive comorbidities of ARHL, with a particular focus on cognitive impairment and affective disorders. From a translational point of view, exploring the extra-auditory consequences of ARHL will be crucial, as it will enable the identification of risk factors for both auditory and cognitive vulnerability and support the development of effective therapeutic interventions.

[This corrects the article DOI: 10.3389/fnagi.2025.1684331.].

Introduction: Post-stroke cognitive impairment (PSCI) is a prevalent and disabling consequence of stroke, yet objective tools for its early identification are lacking. This study aimed to develop and validate an interpretable machine learning (ML) model based on electroencephalography (EEG) to support the early detection of PSCI.

Methods: We conducted a study involving 174 participants, including stroke patients with and without cognitive impairment and age-matched healthy controls. Resting-state EEG was acquired from all subjects, and multidimensional features, including power spectral ratios and microstate parameters, were extracted. Feature selection was performed using LASSO regression, random forest, and the Boruta algorithm. Five machine learning models were evaluated and compared based on their area under the curve (AUC), accuracy, Brier score, calibration plots, and decision curve analysis. Model interpretability was explained using SHAP (Shapley Additive Explanations). The final validated model was deployed as an interactive web-based application.

Results: Seven EEG features were identified as most predictive of PSCI: the delta-plus-theta to alpha-plus-beta ratio (DTABR) in frontal, central, and global regions; the mean microstate duration of classes A and B (A-MMD, B-MMD); the mean frequency of microstate D (D-MFO); and the mean coverage of microstate A (A-MC). The random forest model demonstrated the highest performance (AUC = 0.91, accuracy = 0.83, specificity = 0.88, Brier score = 0.12), alongside satisfactory calibration and a positive net clinical benefit. The model was further validated on an independent external cohort (n = 42), showing robust predictive performance (AUC = 0.97, accuracy = 0.90). An accessible web tool was created for individualized risk prediction (https://eeg-predict.streamlit.app/).

Discussion: The findings suggest that an interpretable EEG-based ML model can provide accurate early screening of PSCI. Integration of this approach into clinical workflows may support personalized rehabilitation strategies and optimize post-stroke care. Future studies are warranted to validate the model in larger, multicenter cohorts.