Frontiers in Aging Neuroscience最新文献

{"title":"Effects and safety of repetitive transcranial magnetic stimulation on cognitive outcomes in mild cognitive impairment: a systematic review and meta-analysis.","authors":"Hua Ren, Jian Jiao, Xinru Lan, Chunyan Tan, Ziqi Wang, Haonan Pei, Dezhong Yao, Tiejun Liu, Li Dong","doi":"10.3389/fnagi.2026.1894724","DOIUrl":"10.3389/fnagi.2026.1894724","url":null,"abstract":"<p><strong>Background: </strong>Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation technique that has been increasingly investigated as a potential treatment for mild cognitive impairment (MCI). However, its clinical efficacy remains uncertain due to heterogeneous findings across studies.</p><p><strong>Methods: </strong>A systematic review and meta-analysis of randomized controlled trials comparing active rTMS with sham stimulation or matched control interventions was conducted. Electronic databases were searched from inception. Eligible studies included patients with MCI and reported cognitive outcomes using validated scales, including the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), or Rivermead Behavioral Memory Test (RBMT).</p><p><strong>Results: </strong>Fourteen studies contributing 15 datasets were included. Compared with sham or control interventions, rTMS was associated with higher post-intervention scores on the MoCA, MMSE, and RBMT. For the primary MoCA outcome, the pooled effect estimate was significant but accompanied by substantial between-study heterogeneity (Hedges' <i>g</i> = 1.46, 95% CI 0.97 to 1.94; <i>p</i> < 0.001; I² = 86.0%). Exploratory subgroup analyses showed significant between-subgroup differences according to stimulation frequency (<i>p</i> = 0.021) and stimulation site (<i>p</i> < 0.001), whereas the difference according to risk of bias was not statistically significant (<i>p</i> = 0.114). Among the 10 datasets reporting quantitative safety outcomes, no significant increase in adverse events was observed compared with sham stimulation (RR = 1.25, 95% CI 0.76 to 2.04; <i>p</i> = 0.374).</p><p><strong>Conclusion: </strong>rTMS may improve cognitive test performance in patients with MCI. However, substantial between-study heterogeneity limits the precision and generalizability of the estimated treatment effect. Although exploratory subgroup analyses suggested variation according to stimulation frequency and site, these findings do not establish definitive effect modifiers or optimal stimulation parameters. Further well-designed, adequately powered trials with rigorous blinding, standardized protocols, and clinically meaningful outcomes are needed to clarify the magnitude and clinical relevance of rTMS effects in MCI.</p>","PeriodicalId":12450,"journal":{"name":"Frontiers in Aging Neuroscience","volume":"18 ","pages":"1894724"},"PeriodicalIF":5.2,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13646044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-density lipoprotein cholesterol modulates amyloid-related entorhinal vulnerability and episodic memory performance in older adults.","authors":"Minh Le Tran, Kahye Kim, Jaeuk U Kim, Yu Yong Choi, JunHyeong Do, Kun Ho Lee, Ji-Woo Seok","doi":"10.3389/fnagi.2026.1933036","DOIUrl":"10.3389/fnagi.2026.1933036","url":null,"abstract":"<p><strong>Introduction: </strong>Cerebral amyloid-beta (Aβ) burden does not fully explain variation in Alzheimer's disease (AD)-related brain structure and episodic memory. We examined whether circulating low-density lipoprotein cholesterol (LDL-C) modifies the cross-sectional statistical association between Aβ burden, the entorhinal cortex (EC) thickness and episodic memory performance.</p><p><strong>Methods: </strong>Cross-sectional data from 1,253 individuals in the Gwangju Alzheimer's Disease and Related Dementias cohort (798 cognitively normal, 425 mild cognitive impairment, 30 AD dementia) were analyzed. Primary conditional-process models were adjusted for age, sex, education, body mass index, and HbA1c; clinical diagnosis was examined in sensitivity analyses. HC3 heteroskedasticity-consistent inference was used for the focal interaction, and indirect associations were estimated using 10,000 bootstrap resamples.</p><p><strong>Results: </strong>Greater Aβ burden was associated with lower EC thickness (β = -0.0863, <i>p</i> = 0.0012), and EC thickness was positively associated with episodic memory (β = 0.3809, <i>p</i> < 0.001). The unconditional indirect statistical association was supported (β = -0.0329, 95% bias-corrected bootstrap CI [-0.0647, -0.0072]). LDL-C modified the Aβ-EC association (β = -0.0937, HC3 SE = 0.0332, 95% CI [-0.1589, -0.0285], <i>p</i> = 0.0049), although the incremental explanatory magnitude was small (ΔR² = 0.0082). The conditional Aβ-EC association was unsupported at approximately 66 mg/dL LDL-C (β = 0.0263, <i>p</i> = 0.583), not statistically supported at approximately 100 mg/dL (β = -0.0639, <i>p</i> = 0.060), and negative at 138 mg/dL (β = -0.1648, <i>p</i> < 0.001). The index of moderated indirect association was supported (β = -0.0357, 95% bias-corrected bootstrap CI [-0.0637, -0.0119]); conditional indirect associations were most clearly supported at higher LDL-C levels. A similar interaction was observed for non-HDL cholesterol, suggesting that the pattern may reflect a broader atherogenic lipid context.</p><p><strong>Conclusion: </strong>Circulating LDL-C was associated with modest heterogeneity in the cross-sectional Aβ-EC relationship, with more clearly negative conditional associations observed at higher LDL-C levels. Longitudinal studies are needed to determine the temporal and clinical relevance of these findings.</p>","PeriodicalId":12450,"journal":{"name":"Frontiers in Aging Neuroscience","volume":"18 ","pages":"1933036"},"PeriodicalIF":5.2,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13645760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A collaborative multi-view fusion framework with Deep Forest for multi-class diagnosis of Alzheimer's disease.","authors":"Yanhong Luo, Nan Dai, Yu Qiao, Tingting Zheng, Yan Zhang, Yanbo Zhang, Hongmei Yu, Hongyan Cao, Aiai Zhang, Mengyuan Li, Yongao Li, Na Yang, Tianyi Wang, Jie Zhou, Xiaoyan Li","doi":"10.3389/fnagi.2026.1888587","DOIUrl":"10.3389/fnagi.2026.1888587","url":null,"abstract":"<p><strong>Objective: </strong>A key aspect of diagnosing Alzheimer's disease (AD) is identifying mild cognitive impairment (MCI), a sensitive transitional stage between normal cognition (NC) and AD. We divided AD disease progression into three stages: NC, MCI, and AD according to severity. We used CoopLearning to integrate multi-view medical data with a Deep Forest (DF) classifier to classify three stages of AD to aid diagnosis.</p><p><strong>Methods: </strong>We used data from the Alzheimer's Disease Neuroimaging Initiative database. The research variables were divided into five views: demographic information, biological sample information, relevant scale information, pixel identification, and magnetic resonance imaging regional information. We compared CoopLearning for feature selection and fusion, least absolute shrinkage and selection operator cross-validation (LassoCV) for single-view feature selection, and LassoCV for multi-view feature selection. The fused data were processed using Synthetic Minority Oversampling Technique (SMOTE), SMOTETomek, and SMOTE Edited Nearest Neighbors (ENN) methods to achieve class balance. The balanced data were then applied to multiple classifiers, such as DF, Random Forest, etc., for performance evaluation.</p><p><strong>Results: </strong>Multi-view feature selection and fusion using CoopLearning, multi-view feature selection, and single-view overall feature selection using LassoCV identified 20 features from five views. Applying these results to the classifier revealed that model performance was optimal when using SMOTEENN in conjunction with CoopLearning multi-view feature selection and the DF classifier (Accuracy = 0.9898, multi-class area under the receiver operating characteristic curve = 0.9997).</p><p><strong>Conclusion: </strong>Collaborative multi-view fusion substantially improved direct multi-class classification across the AD continuum. Integrating clinical and neuroimaging data with Deep Forest may provide a robust and interpretable framework for early auxiliary diagnosis and disease-stage stratification in Alzheimer's disease.</p>","PeriodicalId":12450,"journal":{"name":"Frontiers in Aging Neuroscience","volume":"18 ","pages":"1888587"},"PeriodicalIF":5.2,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13646108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peroxisomes in Alzheimer's disease: emerging mechanistic insights and therapeutic opportunities.","authors":"Tao Liu, Yiran Liu, Yichi Zhang, Jin Wang, Yulin Ren, Shujun Fu, Li Han, Pengcheng Jiang, Jie Chen, Yuanzhi Li, Huaiqing Luo","doi":"10.3389/fnagi.2026.1960320","DOIUrl":"10.3389/fnagi.2026.1960320","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is characterized by amyloid-<i>β</i> (Aβ) deposition, tau pathology, synaptic injury, neuroinflammation, oxidative stress, and disrupted organellar homeostasis. Peroxisomes maintain lipid and redox balance through very-long-chain fatty acid (VLCFA) <i>β</i>-oxidation, plasmalogen biosynthesis, and reactive oxygen species (ROS) metabolism. This Review evaluates how peroxisomal alterations may modulate APP processing and Aβ clearance, tau kinase/phosphatase balance, neuroinflammation, oxidative stress, and peroxisome-mitochondria crosstalk. We distinguish human correlational evidence from intervention studies in AD models and mechanistic extrapolation from primary peroxisomal disorders. Current evidence supports a context-dependent vulnerability or amplifier model rather than a universal causal pathway. Peroxisome-targeted interventions remain investigational, and no validated biomarker currently identifies an AD subgroup for such treatment.</p>","PeriodicalId":12450,"journal":{"name":"Frontiers in Aging Neuroscience","volume":"18 ","pages":"1960320"},"PeriodicalIF":5.2,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13643710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multimodal machine-learning diagnostic model for Alzheimer's disease: integrating OCTA-derived retinal microvascular features and clinical variables.","authors":"Qifeng Zhou, Zhongping Tian, Wei Zhao, Xi Chen, Yuan Liu, Tengxiao Wang, Jiaxuan Chen, Yanlong Bi, Li Zhang","doi":"10.3389/fnagi.2026.1911592","DOIUrl":"10.3389/fnagi.2026.1911592","url":null,"abstract":"<p><strong>Introduction: </strong>Alzheimer's disease (AD) lacks widely accessible, noninvasive approaches for the evaluation of AD remain limited. We developed a multimodal machine-learning model that integrates retinal microvascular features derived from optical coherence tomography angiography (OCTA) with clinical variables to classify AD.</p><p><strong>Methods: </strong>In this cross-sectional case-control study, 160 participants (81 healthy controls and 79 patients with AD) underwent macular OCTA imaging. Twenty-seven superficial capillary plexus features, including vessel density (VD), perfusion density (PD), and foveal avascular zone (FAZ) metrics, were analyzed. Logistic regression, support vector machine, random forest, and XGBoost classifiers were evaluated using stratified five-fold cross-validation. Feature selection was performed with nested cross-validated recursive feature elimination, and model interpretability was assessed with Shapley additive explanations (SHAP).</p><p><strong>Results: </strong>Patients with AD showed reduced VD and PD across multiple macular subfields. The multimodal random forest model achieved the highest area under the receiver operating characteristic curve (AUC = 0.853), whereas the multimodal XGBoost model achieved the highest sensitivity (0.800) with an AUC of 0.839. SHAP analysis identified whole-macula VD, nasal inner-ring VD, FAZ circularity, and inferior inner-ring PD as influential predictors.</p><p><strong>Conclusion: </strong>These findings provide proof-of-concept evidence that OCTA-derived retinal microvascular features, particularly when combined with selected clinical variables, may help distinguish clinically diagnosed AD from HC in this selected case-control cohort. Their utility for population screening or clinical diagnosis remains unestablished and requires prospective external validation in consecutive, clinically representative cohorts including mild cognitive impairment (MCI), other dementias, and relevant neurological and ocular differential diagnoses.</p>","PeriodicalId":12450,"journal":{"name":"Frontiers in Aging Neuroscience","volume":"18 ","pages":"1911592"},"PeriodicalIF":5.2,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13642684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational gait analysis in the 3xTg-AD mouse model: evidence of progressive alterations in locomotor coordination.","authors":"Lidia Castillo Mariqueo, Martín Valdivia Gallardo, Lydia Giménez Llort, Carla Taramasco Toro, Lukas Chahuan Moyano, Diego Robles Cruz","doi":"10.3389/fnagi.2026.1879487","DOIUrl":"10.3389/fnagi.2026.1879487","url":null,"abstract":"<p><strong>Introduction: </strong>Gait alterations have been proposed as functional indicators of neural dysfunction in Alzheimer's disease; however, their ability to capture disease-associated locomotor changes in preclinical models remains insufficiently characterized.</p><p><strong>Methods: </strong>In this study, we applied a neuroinformatics framework combining markerless pose estimation, kinematic feature extraction, conventional statistical analysis, supervised machine learning, and model interpretability to characterize gait alterations in the 3xTg-AD mouse model. Two complementary analyses were performed: cross-sectional genotype classification at 12 months of age in 46 mice (3xTg-AD, <i>n</i> = 22; 3xTg-WT, <i>n</i> = 24) and longitudinal assessment of 31 paired surviving animals re-evaluated at 16 months (3xTg-AD, <i>n</i> = 15; 3xTg-WT, <i>n</i> = 16).</p><p><strong>Results: </strong>At 12 months, gait features provided moderate-to-good genotype discrimination, with Logistic Regression achieving the highest balanced performance (AUC = 0.82) and showing relevant contributions from Duty Factor, Allometric Stride Length, Allometric Step Width, and Speed. In the longitudinal cohort, classification performance was more limited (Random Forest, AUC = 0.61), likely reflecting the combined influence of physiological aging, genotype-associated change, selective survival, and task re-exposure. Nevertheless, conventional statistical analysis and SHAP-based model interpretation consistently identified longitudinal changes in Allometric Stride Length and Normalized Coordination Instability as relevant contributors to genotype-associated locomotor divergence.</p><p><strong>Discussion: </strong>These findings suggest that computational gait analysis can capture relevant alterations in locomotor organization in the 3xTg-AD model, with cross-sectional differences associated with distributed spatiotemporal features and longitudinal changes showing a stronger contribution of stride- and coordination-related variables.</p>","PeriodicalId":12450,"journal":{"name":"Frontiers in Aging Neuroscience","volume":"18 ","pages":"1879487"},"PeriodicalIF":5.2,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13642803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bibliometric analysis of dietary patterns in Parkinson's disease: from Mediterranean diet to emerging trends.","authors":"Ting Yu, Tong Nie, Qingxin Mai, Lin Wang, Zuyan Hu, Mingjie Wu, Mingxia Pan, Yongqi Liang, Hanjiao Liu","doi":"10.3389/fnagi.2026.1954819","DOIUrl":"10.3389/fnagi.2026.1954819","url":null,"abstract":"<p><strong>Background: </strong>Parkinson's disease (PD) is one of the fastest-growing neurological disorders. Treatments remain symptomatic, with no disease-modifying therapies available. Recent preclinical and epidemiological studies suggest that dietary patterns may influence PD pathology through the microbiota-gut-brain axis by modulating gut microbiota. However, evidence remains conflicting: some studies report protective effects of specific diets, while large-scale prospective studies find no significant associations. These discrepancies reflect differences in study design and dietary assessment and a lack of systematic understanding of the field. No bibliometric study has systematically analyzed this area. Therefore, this study uses bibliometric methods to map research hotspots and evolutionary trends and identify knowledge gaps.</p><p><strong>Methods: </strong>Systematic searches were conducted in the Web of Science Core Collection and Scopus databases to identify literature on dietary patterns in Parkinson's disease from inception to June 11, 2026. CiteSpace was used to perform bibliometric analyses, construct a knowledge map, and identify research hotspots and evolutionary trends.</p><p><strong>Results: </strong>A total of 424 publications were included. The top three most productive countries were the USA, China, and Italy, and the leading institutions included the NIH, Harvard University, and Johns Hopkins University. Key authors included Mark P. Mattson, Alberto Ascherio, and Xiang Gao. Current research hotspots are mainly \"Mediterranean diet\", \"ketogenic diet\", \"caloric restriction\", \"gut microbiota\", \"gut-brain axis\", and \"oxidative stress\". Emerging topics include \"probiotic agent\", \"prebiotic agent\", \"fecal microbiota transplantation\", and \"mild cognitive impairment\".</p><p><strong>Conclusion: </strong>Dietary patterns may influence PD through synergistic mechanisms, with their roles in regulating oxidative stress, neuroinflammation, mitochondrial function, and gut microbiota progressively being revealed. The microbiota-gut-brain axis may represent an important pathway linking dietary factors to central nervous system function through microbial metabolites, immunomodulation, and neural signaling. Existing studies suggest potential associations between specific dietary patterns and PD risk or clinical outcomes; however, methodological heterogeneity and the predominantly observational evidence base have precluded consistent conclusions and limited clinical translation. Therefore, no specific dietary pattern can currently be recommended with confidence for the clinical management of PD. As a modifiable lifestyle factor, dietary patterns bridge metabolic dysregulation and neurodegeneration, and their integration into PD management remains a future direction.</p><p><strong>Systematic review registration: </strong>https://osf.io/vgc9p, identifier VGC9P.</p>","PeriodicalId":12450,"journal":{"name":"Frontiers in Aging Neuroscience","volume":"18 ","pages":"1954819"},"PeriodicalIF":5.2,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13642293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gut-brain immune dysregulation in Parkinson's disease: a hypothesis-forming narrative review of trained immunity.","authors":"Mingke Tang, Jingxian Li, Chuanzhang Zhang, Ruoxin Tu, Shun Wang","doi":"10.3389/fnagi.2026.1973833","DOIUrl":"10.3389/fnagi.2026.1973833","url":null,"abstract":"<p><p>Parkinson's disease (PD) is increasingly understood as a multisystem disorder in which gastrointestinal dysfunction, immune remodeling, protein aggregation, and central neurodegeneration interact across the disease course. Although gut microbiota alterations are repeatedly reported in PD, taxonomic differences alone do not explain how peripheral ecological signals acquire neurological relevance. This review therefore focuses on immune translation: the processes through which dysbiosis, microbial metabolites, intestinal barrier stress, and mucosal inflammation may reshape peripheral immunity and lower the threshold for central neuroinflammatory responses. Particular attention is given to trained immunity as a mechanistic framework that may connect repeated low-grade gut-derived stimulation with persistent innate immune reprogramming. Direct evidence that trained immunity drives PD remains limited; the concept is presented as a testable bridge rather than an established causal pathway. We integrate this framework with age-related immunosenescence and inflammaging, enteric nervous system vulnerability, vagal communication, alpha-synuclein pathology, blood-brain barrier signaling, and microglial priming. Human microbiome studies, Parkinsonian animal models, LPS-based innate immune-memory paradigms, and emerging intervention trials are considered according to their evidentiary level and major confounders, including constipation, diet, medication exposure, geography, and disease stage. The resulting model accommodates body-first, brain-first, and mixed trajectories rather than assigning a universal gut origin to PD. Therapeutically, the evidence supports a shift from nonspecific microbiome normalization toward mechanism-matched strategies that combine ecological, immune, metabolic, and clinical biomarkers. Longitudinal prodromal cohorts and integrated multi-omics with immune phenotyping will be essential to determine whether gut-brain immune signatures identify causal pathways, progression markers, or treatment-responsive subgroups.</p>","PeriodicalId":12450,"journal":{"name":"Frontiers in Aging Neuroscience","volume":"18 ","pages":"1973833"},"PeriodicalIF":5.2,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13643708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blood biomarkers and artificial intelligence: toward precision medicine in Alzheimer's disease.","authors":"Linhong Xu, Jianbo Tu, Jipeng Zhang, Chen Zhang, Yongan Sun","doi":"10.3389/fnagi.2026.1937244","DOIUrl":"10.3389/fnagi.2026.1937244","url":null,"abstract":"<p><p>The integration of blood-based biomarkers and artificial intelligence (AI) is revolutionizing the diagnostic landscape of Alzheimer's disease (AD). In this paper, we synthesize recent advancements in the application of machine learning (ML) on blood biomarker analysis, evaluating their diagnostic efficacy and clinical translational potential. Current evidence identifies plasma p-tau217 as the best individual biomarker for primary screening. However, multiple biomarker signatures (such as A/T/N) combined with ML algorithms (like XGBoost) greatly improve diagnostic accuracy for mild cognitive impairment. Notably, predictive performance is maximized with multimodal fusion, which integrates data from blood, neuroimaging, and genetics, achieving an area under the curve of up to 0.94. To mitigate the black-box nature of these models, explainable AI frameworks like SHapley Additive exPlanations (SHAP) are essential for establishing clinical interpretability. Ultimately, we propose a scalable \"Step-Care\" framework that transitions from single-biomarker community screening to precise multimodal fusion, optimizing clinical trial stratification and personalized workflows.</p>","PeriodicalId":12450,"journal":{"name":"Frontiers in Aging Neuroscience","volume":"18 ","pages":"1937244"},"PeriodicalIF":5.2,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13643686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149135818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increasing daily step count in mild cognitive impairment: a 12-month randomised controlled trial.","authors":"Małgorzata Jamka, Aleksandra Makarewicz-Bukowska, Mikołaj A Pawlak, Joanna Popek, Edyta Mądry, Patrycja Krzyżanowska-Jankowska, Katarzyna Karmelita-Katulska, Anna Miśkiewicz-Chotnicka, Szymon Kurek, Radosław Trepanowski, Joanna Bajerska, Aleksandra Lisowska-Walkowiak, Ewa Wysocka, Marcin Żarowski, Jan Krzysztof Nowak, Karl-Heinz Herzig, Jarosław Walkowiak","doi":"10.3389/fnagi.2026.1958222","DOIUrl":"10.3389/fnagi.2026.1958222","url":null,"abstract":"<p><strong>Introduction: </strong>Despite numerous studies confirming the beneficial effects of physical activity on cognitive function, there is a lack of randomised controlled trials involving long-term, continuous monitoring of physical activity that assess the effect of step-based intervention on cognition-related outcomes. Therefore, this study evaluated whether increasing daily step count improves cognitive performance and brain health markers in individuals with mild cognitive impairment (MCI).</p><p><strong>Methods: </strong>Caucasian, community-dwelling participants aged 50-70 years with MCI and smartphone ownership were recruited for this randomised, controlled, parallel-group study, and assigned to intervention or control groups. The intervention group aimed to achieve ≥10,000 steps daily, with no individualised or baseline-adjusted activity targets, whereas controls maintained usual activity. Physical activity was monitored by wrist-worn trackers over 12 months. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) and the Cambridge Neuropsychological Test Automated Battery. Brain volume, cardiometabolic, and neurodegeneration biomarkers were assessed.</p><p><strong>Results: </strong>198 participants were recruited between July 2021 and September 2022 and assigned to the intervention (<i>n</i> = 98) or control (<i>n</i> = 100) group. 188 participants completed the study. Significant differences between groups were found in daily step count (median difference [95% confidence interval]: 1,728 (1,207-2,271), <i>p</i> < 0.0001), the MoCA (1 [0-2], <i>p</i> = 0.0043), spatial working memory errors in the six-box condition (-1 [-2-0], <i>p</i> = 0.0049), waist (-1 [-3-0] cm, <i>p</i> = 0.025), and hip circumferences (-1 [-2-0] cm, <i>p</i> = 0.047). No differences were observed in body composition, biochemical parameters, or brain volume parameters.</p><p><strong>Discussion: </strong>In adults with MCI and moderate baseline activity, a small increase in daily step count over 12 months led to modest changes in MoCA scores, while evidence from the broader cognitive battery was limited and largely exploratory. No effect on brain volume, peripheral neurodegeneration markers, body composition, or metabolism was noted (Trial registry: https://drks.de; identifier: DRKS00020943).</p>","PeriodicalId":12450,"journal":{"name":"Frontiers in Aging Neuroscience","volume":"18 ","pages":"1958222"},"PeriodicalIF":5.2,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13642802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}