Sleep is essential for brain homeostasis, in part by supporting glymphatic clearance through sleep-related oscillations. However, the relationship between putative glymphatic metrics and coupled sleep rhythm disruption, and their combined role in Alzheimer’s disease (AD) progression, remains poorly understood. We analyzed data from 75 individuals, 54 with AD and 21 cognitively normal (CN) controls, including sleep electroencephalography (EEG), magnetic resonance imaging (MRI), cerebrospinal fluid (CSF) AD biomarkers, and two-year longitudinal cognitive assessments. Putative glymphatic metrics was evaluated using choroid plexus (CP) volume, perivascular spaces (PVSs), diffusion tensor imaging along the perivascular space (DTI-ALPS) index, and blood oxygen level-dependent signal coupled to CSF signal (BOLD-CSF coupling). Coupled sleep rhythm was assessed via slow oscillation (SO)-theta and SO-spindle couplings. Correlation and mediation analyses explored associations between these MRI-derived indices and coupled sleep oscillations, and least absolute shrinkage and selection operator (LASSO) regression was used to predict AD progression. Compared to CN controls, individuals with AD had reduced DTI-ALPS index and BOLD-CSF coupling (p < 0.05), along with disrupted SO-spindle coupling (p = 0.029). Across all participants, lower global BOLD-CSF coupling correlated with misaligned SO-theta burst coupling (r = 0.311, p = 0.018), and reduced DTI-ALPS was associated with misaligned SO-spindle coupling (r = 0.370, p = 0.008). In the AD group, DTI-ALPS remained correlated with SO-spindle misalignment (r = 0.376, p = 0.028). Mediation analysis revealed that SO-spindle misalignment contributed to cognitive decline through its effect on DTI-ALPS. Importantly, combining putative glymphatic and sleep EEG metrics effectively predicted AD progression. Our findings suggest that disruptions in surrogates marker of glymphatic clearance and coupled sleep rhythms are jointly associated with AD-related cognitive decline. These metrics offer a promising framework for predicting disease progression and understanding neurodegenerative mechanisms in AD.

Hypertension is a significant risk factor for cognitive impairment (CI), yet the corresponding neural network abnormalities remain underexplored. In this study, we examined the associations among global and domain-specific cognitive dysfunction, neuroimaging measures, and blood pressure in a subgroup of hypertensive patients with CI (N = 41) from a randomized controlled trial who underwent ultra-high-field 7 T MRI. Structural atrophy related to CI was localized to regions overlapping the attention networks. Both whole-brain and within-network dysfunction of the attention networks were associated with worse global cognitive performance. Notably, hyperconnectivity within key attention network hubs, including the right anterior insula and posterior intraparietal sulcus, was associated with declined processing speed in hypertensive patients, mediating the association between pulse pressure and processing speed. These findings provide new insights into the neural pathophysiology of hypertension-related CI and suggest potential network-based targets for intervention.

Subcortical vascular cognitive impairment (SVCI) is a heterogeneous cognitive impairment caused by small vessel disease. Diagnosis of SVCI remains challenging when neuropsychological assessment is impractical. This study proposes a diffusion tensor imaging (DTI)-based DenseNet to identify SVCI from subcortical ischemic vascular disease (SIVD) and to profile multidomain cognitive risks. We collected neuropsychological scales and DTI from 134 SVCI and 171 SIVD patients in our internal dataset for model development. An external target-domain dataset of 90 SVCI and 103 SIVD patients was used for unsupervised domain adaptation (UDA). Within this dataset, 45 SVCI and 53 SIVD patients were used for unlabeled UDA fitting; the remaining 45 SVCI and 50 SIVD patients were held out as a target-domain test set. Model-generated salient maps identified white matter (WM) regions associated with SVCI. Mutual information (MI) maps between DTI and 6 neuropsychological scales were computed to identify structural correlates of cognitive domains for cognitive profiling. We computed structural similarity index measure (SSIM) between individual-level salient maps derived from DenseNet and the MI maps for unsupervised clustering to stratify domain-specific cognitive impairment risk in SVCI. The DenseNet achieves high accuracy (0.902 internal, 0.926 target-domain) with AUCs of 0.951 and 0.942, respectively. SVCI probabilities reflect cognitive severity, and salient maps are associated with neuropsychological performance. Regarding cognitive profiling, each cognitive domain is divided into low, moderate, and high subgroups, with significantly different SSIM. Our DTI-based study demonstrates accurate SVCI identification and individualized multi-domain cognitive profiling. This offers a complementary framework to support diagnosis and personalized intervention.

INTRODUCTION:While it has been postulated that population-based strategies targeting modifiable dementia risk factors are cost effective in high-income settings, their health and economic impact in China remains unclear. METHODS:A Markov model evaluated five interventions: salt substitution, smoke-free legislation, increased tobacco tax, sugar-sweetened beverage tax, and alcohol tax. Outcomes included dementia cases, deaths averted, life years gained, and lifetime cost-savings, assessed from a societal perspective over an infinite time horizon. Subgroup and sensitivity analyses were performed. RESULTS:All interventions were assessed to be cost-saving. Salt substitution was modeled to have the greatest impact, resulting in 1,940,000 quality-adjusted life years gained and lifetime cost savings of US$65 to US$70 billion. Smoke-free policies and tobacco taxes also showed significant benefits. Beverage and alcohol taxes had smaller but meaningful effects. Males generally benefited more. DISCUSSION:We estimated that population-level interventions would reduce costs in China, with the largest gains from targeting hypertension and smoking. Prioritizing such strategies may generate substantial health gains while reducing long-term societal costs.

Alzheimer’s disease (AD), as the leading cause of dementia, poses an increasingly severe socioeconomic burden in the context of global ageing. Traditionally defined by amyloid-β and tau pathology, it’s increasingly recognized as a systems disorder in which impaired glucose metabolism, mitochondrial dysfunction, and neuroinflammation interact across neural cell types and disease stages. However, the interaction among these three mechanisms, their role in promoting the classical pathology of AD, and their verification in major neural cell types remains unclear. This review summarizes the alterations in glucose metabolism and mitochondrial metabolism in neurons, astrocytes and microglia in AD and their relationship with neuroinflammation, while also discussing some unaddressed questions, outlining therapeutic strategies, and future promising directions. Biomarkers that reflect disease stage and pathological status, multitarget therapeutic strategies, individualized precision medicine, and the integration of pharmacological with non-pharmacological interventions represent particularly promising directions for the future.

Early disruption of gamma oscillations is increasingly recognized as a contributor to neurodegeneration and cognitive decline in Alzheimer's disease (AD), positioning 40-Hz stimulation as a promising neuromodulatory strategy. However, its neurophysiological and cognitive effects remain highly variable across studies, underscoring the need for a more integrated mechanistic framework. This review synthesizes current evidence on the neurobiological mechanisms engaged by 40-Hz stimulation in AD, including circuit-, cellular-, and system-level processes underlying network dynamics, glial responses, and multi-target biological effects. We further highlight the conceptual distinction between "physiological resonance" and "artificial superimposition" as a potential determinant of deep-brain engagement and therapeutic outcomes. By integrating both supportive and conflicting findings, we highlight disease stage, biological sex, and behavioral state as important contributors to the heterogeneity of 40-Hz stimulation outcomes. Building on these observations, we propose that future research should move toward mechanistic stratification and precision neuromodulation to facilitate clinical translation.

Panvascular aging-related diseases, including coronary artery disease, ischemic stroke, and peripheral artery disease, are leading global causes of death and disability, yet their management remains fragmented. Emerging technologies offer solutions to this challenge. Big data integration across imaging, multi-omics, wearables, and environmental exposures provides opportunities for cross-organ insights but faces issues of heterogeneity and privacy. Artificial intelligence enables early detection and refined risk prediction by recognizing subtle vascular changes and integrating biomarkers, though adoption is limited by interpretability and bias. Foundation models, through cross-modal learning, offer a unifying framework for mechanism discovery, personalized management, and digital twin applications. By linking technological innovation with clinical practice, these approaches can transform panvascular aging management and promote healthy longevity. Importantly, translating these innovations into policy and practice will be essential for advancing equitable vascular health and achieving population-level impact.

ABSTRACT Background Disability in older adults, characterized by progressive limitations in performing activities of daily living, poses a significant public health challenge in aging societies. Early identification of individuals at risk is critical for implementing targeted interventions to mitigate functional decline. However, existing prediction models often prioritize disease‐related physiological indicators and overlook psychosocial dimensions, limiting their practicality and scalability in community‐based settings. This study aimed to develop and validate a practicable prediction model for 1‐year disability risk by integrating multidimensional indicators. Methods A prospective community‐based cohort in Beijing, functionally independent at baseline, was followed for 1 year. Disability was defined as a decline in the Barthel Index score. Potential predictors included demographic, lifestyle, clinical, physical, and psychosocial measures. The least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by logistic regression to construct the final model. Model performance was evaluated through fivefold cross‐validation repeated 100 times, with assessment of discrimination and calibration. Results Among 1003 community‐dwelling older adults (mean age 68.4 ± 5.2 years; 58.3% female), 163 (16.3%) developed disability at follow‐up. The LASSO regression identified seven predictors: age, dyslipidemia, gait speed, waist circumference, difficulty in lifting weights, appetite, and emotion regulation ability. The model demonstrated moderate discrimination, with an area under the receiver operating characteristic curve of 0.716 (95% CI: 0.712–0.720). Calibration curves indicated good overall agreement between predicted and observed risks. A nomogram was developed to facilitate individualized risk prediction in clinical practice. Conclusions This study presents a practical disability risk prediction model incorporating physical, metabolic, and psychosocial factors. The model exhibits acceptable discrimination and calibration, supporting its potential for early screening and stratified management of community‐dwelling older adults. Future multicenter validations are warranted to enhance generalizability and explore dynamic interventions targeting modifiable factors like emotion regulation and gait speed.

Background and Objectives:MicroRNAs (miRNAs) are emerging as promising blood-based biomarkers for Alzheimer's disease (AD) because of their stability and regulatory roles in disease-related pathways. This study aimed to develop a serum-based miRNA panel for AD diagnosis. Methods:Serum samples from 550 participants were categorized into discovery (85 AD, 65 healthy controls [HCs]), training (73 AD, 53 HCs), and validation (99 AD, 99 HCs, 36 vascular cognitive impairment [VCI], 40 dementia with Lewy bodies [DLB]) cohorts. Through small RNA sequencing and qPCR validation, we identified key miRNAs and constructed a diagnostic panel via machine learning. Results:The 7-miRNA panel achieved area under the curve (AUC) values of 0.970 and 0.928 in the training and validation cohorts, respectively. The panel effectively differentiated AD from VCI (AUC = 0.951) and DLB (AUC = 0.851). Diagnostic performance remained robust across subgroups defined by age, gender, disease severity, and comorbidities (AUC 0.758-0.988). The risk score derived from the 7-miRNA panel was significantly associated with cognitive impairment (Mini-Mental State Examination [MMSE] score, r = -0.72) and plasma amyloid pathology biomarkers (Aβ42/40 ratio, r = -0.25; p-tau217, r = 0.36). Conclusions:A serum-based 7-miRNA panel, which offers a minimally invasive and accessible approach, has strong diagnostic potential for AD.

Introduction: Alzheimer's disease (AD) is imposing an increasing public health and socioeconomic burden. In China, rapid population ageing is sharply increasing disease burden. Previous studies have shown that AD-related costs are mainly driven by long-term informal care. However, evidence in China remains limited by an incomplete cost framework, and insufficient consideration of caregivers' burden and indirect costs. Notably, the National Dementia Action Plan (2024-2030), issued by the Chinese government, marks a major shift to early detection and comprehensive care of AD, highlighting the urgent need for nationally representative economic evidence to support policy implementation. This study aims to evaluate the economic burden and quality of life of AD patients and their caregivers in mainland China, and is the first nationwide study to include individuals with amnestic mild cognitive impairment (aMCI), providing foundational data for future health technology assessment (HTA) of early AD interventions. Methods and analysis: Baseline characteristics will be presented and compared using t-tests or chi-square tests. Economic burden will be estimated by calculating the per capita cost and weighted national total based on provincial numbers of AD patients. Indirect costs will be assessed using locally adapted replacement cost approach and forgone wages approach. The analysis will be stratified by disease severity and age. Future burden will be projected by linking data from China Statistical Yearbook 2025 and the United Nations World Population Prospects 2024. Unmet care needs, AD-related catastrophic health expenditure (CHE), and AD dependency ratio (ADDR) will also be assessed. Ethics and dissemination: Ethics approval was obtained from the Ethics Committee of Xuanwu Hospital, Capital Medical University. The study has been registered at ClinicalTrials.gov and the Chinese Clinical Trial Registry (ChiCTR). The results from this study will be actively disseminated through research articles and conference presentations. Trial registration number: [NCT05995418][1]; ChiCTR2300074723. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial NCT05995418; ChiCTR2300074723 ### Funding Statement This work is supported by Noncommunicable Chronic Diseases-National Science and Technology Major Project (2025ZD0546200), Beijing Outstanding Young Scientist Program (JWZQ20240101023). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The original protocol received approval from the Ethics Committee of Xuanwu Hospital, Capital Medical University, Beijing, China. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Study data are collected using an electronic data capture system and will be available from the corresponding author upon reasonable request, subject to ethical and institutional requirements. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT05995418&atom=%2Fmedrxiv%2Fearly%2F2026%2F05%2F01%2F2026.04.30.26352138.atom

BackgroundTriggering receptor expressed on myeloid cells 2 (TREM2) is a genetic risk factor for Alzheimer's disease (AD). While TREM2 facilitates central nervous system lipid clearance, its influence on peripheral lipid metabolism remains unclear.ObjectiveTo investigate the association between plasma sTREM2 and peripheral lipid profiles in AD and to explore the mechanistic role of TREM2 in peripheral lipid regulation.MethodsWe conducted a cross-sectional study of 59 AD patients and 54 healthy controls and measured plasma biomarkers including sTREM2 as well as performed targeted lipidomics profiling. Mechanistic exploration was performed via plasma and hippocampal lipidomics in Trem2 knockout and APP/PS1 mice.ResultsPlasma sTREM2 levels were elevated in AD and were negatively correlated with the plasma p-tau217/Aβ42 ratio and p-tau217. Multivariate analysis revealed a distinct lipidomics signature in AD, in which 30 lipid species were significantly altered. We prioritized significantly altered biomarkers to inform a composite biomarker panel combining sTREM2 with a set of sphingomyelins, phosphatidylinositols, diacylglycerols, fatty acids, and cholesteryl esters, which showed strong discrimination between AD and controls (AUC = 0.93). In a mouse model of APP/PS1, we found that Trem2 knockout partially normalized plasma sphingomyelins and hexosylceramide levels. Finally, cross-tissue comparisons further suggested that TREM2 exerted distinct effects on peripheral sphingolipid metabolism that were less evident in hippocampal tissue.ConclusionsOur findings associate TREM2 with lipid dysregulation in AD and support development of a plasma sTREM2-lipid panel for patient classification.

BackgroundThe postnatal maturation of microglia is essential for neural circuit refinement, yet its molecular regulators remain incompletely defined. TREM2, a key Alzheimer's disease risk gene, is implicated in microglial function, but its role in developmental timing is unclear.ObjectiveTo determine whether TREM2 regulates the postnatal maturation of microglia and to assess the cellular, molecular, and behavioral consequences of TREM2 deficiency.MethodsWe performed longitudinal transcriptomic profiling of Trem2-knockout and control microglia. Morphological analyses were conducted alongside evaluation of synaptic pruning from postnatal day 7, 14, and 21. Adult mice were behaviorally tested at baseline and after immune challenge.ResultsTrem2-knockout disrupted stage-specific transcriptional programs, decoupled PRC2 subunit expression, and impaired repression of early developmental genes. This dysregulation coincided with persistent mitochondrial and metabolic deficits. Trem2-knockout microglia exhibited simplified morphology, reduced density, and impaired synaptic pruning, leading to excessive synaptic retention. In adulthood, these mice displayed heightened anxiety-like and repetitive behaviors, which worsened after immune challenge.ConclusionsOur study identifies TREM2 as a regulator of microglial developmental timing and links aberrant postnatal microglial maturation to lifelong behavioral vulnerabilities, providing mechanistic insight into neurodevelopmental risk.

INTRODUCTION:White matter hyperintensities (WMHs) are strongly associated with cognitive decline and dementia, but whether asymptomatic individuals with moderate-to-severe WMHs (msWMHs) are truly cognitively intact, particularly in visual short-term memory (VSTM), remains unclear. METHODS:Electroencephalography and diffusion tensor imaging (DTI) was obtained from 45 msWMH patients and 38 normal controls during an occluded-face delay-matching task. RESULTS:Individuals with msWMH showed systematic deficits in face-memory processing across encoding, maintenance, and retrieval, accompanied by reduced theta power and synchronization, altered alpha power, and disrupted theta-gamma coupling. DTI further revealed microstructural damage, particularly in the inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus (SLF), and inferior longitudinal fasciculus (ILF), which mediated the effects on global cognition via encoding-related theta oscillations. DISCUSSION:These findings indicate that individuals with msWMH already show hidden VSTM deficits. Theta oscillations mediate the link between tract integrity and global cognition, highlighting a potential preclinical intervention target. HIGHLIGHTS:Asymptomatic individuals with moderate-to-severe white matter hyperintensities (msWMHs) already show visual short-term memory (VSTM) deficits. Electroencephalography revealed abnormal neural oscillations during encoding, maintenance, and retrieval stages. Damage to the inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus (SLF), and inferior longitudinal fasciculus (ILF) affects cognition via theta oscillations. Theta activity mediates structure - function coupling and may serve as a preclinical intervention target.

Gamma transcranial alternating current stimulation (tACS) has shown promise in enhancing cognitive function in patients with Alzheimer's disease (AD). However, the impact of gamma tACS on regional glymphatic flow remain unclear. A total of 46 patients with mild AD were randomly assigned in a 1:1 ratio to receive either 30 one-hour sessions of 40Hz (gamma) tACS or sham stimulation over 15 consecutive days (Clinical Trial: NCT03920826). Global blood oxygen level-dependent (BOLD) signals and cerebrospinal fluid (CSF) inflow coupling were measured using resting-state functional MRI to evaluate glymphatic flow, which was acquired at baseline and right after the intervention, along with cognitive assessments. Compared to baseline, patients in the tACS group exhibited increased BOLD-CSF coupling in the left frontal lobe, left posterior lobe, right posterior lobe, and bilateral rostral hippocampus at the end of the intervention. Additionally, changes in regional glymphatic flow in the right temporal lobe, right parietal lobe, and bilateral rostral hippocampus showed a positive correlation with changes in cognitive assessments. The findings demonstrated the beneficial effects of gamma tACS on regional brain glymphatic flow, which might represent a potential therapeutic mechanism of tACS.

Chronic pain (CP) is increasingly recognized not only as a sensory and emotional condition but also as a significant contributor to cognitive dysfunction. Growing evidence indicates that CP-induced cognitive dysfunction arises from a cascade of neurobiological processes, including persistent neuroinflammation, neurotransmitter dysregulation, and impaired synaptic plasticity. These mechanisms particularly affect the hippocampus and medial prefrontal cortex (mPFC)—regions essential for memory, attention, and executive function. Neuroimaging studies have documented structural atrophy and disrupted network connectivity in these brain areas in CP patients. At the molecular level, pro-inflammatory cytokines such as interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α) impair glutamatergic and GABAergic signaling, disrupt long-term potentiation (LTP), and inhibit neurogenesis. Additionally, dysregulation of brain-derived neurotrophic factor (BDNF) signaling exacerbates synaptic vulnerability, contributing to cognitive decline. These mechanistic overlaps are particularly relevant in aging populations and in Alzheimer's disease (AD), where CP may act as a risk factor. This review integrates clinical and preclinical findings on CP-related cognitive dysfunction, outlines key molecular mechanisms, and explores emerging therapeutic strategies targeting inflammation, neurotransmitter systems, and synaptic repair. Understanding the interaction between chronic pain and cognition is critical for developing precision treatments that address both nociceptive and neurodegenerative pathways.

BackgroundThe triglyceride-glucose (TyG) index is a surrogate marker of insulin resistance, implicated in cognitive decline and Alzheimer's disease (AD), but whether long-term TyG exposure is associated with cognitive health remains unclear.ObjectiveTo examine whether cumulative TyG index is associated with cognitive performance, mild cognitive impairment (MCI), and follow-up cognitive performance in two community-based cohorts.MethodsWe analyzed adults aged ≥60 years in the Beijing Disability Risk and Ageing Monitoring Study (BEAM) and ≥45 years in the China Health and Retirement Longitudinal Study (CHARLS). Cumulative TyG was estimated from four annual measurements (2020-2023) in BEAM and two measurements (2012 and 2015) in CHARLS. Cognitive outcomes were assessed in 2023 and 2015, respectively. Multivariable linear and logistic regression models were applied.ResultsA total of 3857 participants were included, comprising 585 participants from BEAM (mean age 73.0 ± 5.1 years; 64.4% women) and 3272 participants from CHARLS (mean age 58.9 ± 8.8 years; 51.4% women). Higher cumulative TyG was associated with higher global cognitive scores in BEAM (β=0.11, 95% CI 0.02-0.20, p = 0.013) and CHARLS (β=0.13, 95% CI 0.05-0.23, p = 0.003). Higher cumulative TyG was also associated with lower odds of MCI (BEAM: OR = 0.85, 95% CI 0.73-0.98, p = 0.028; CHARLS: OR = 0.92, 95% CI 0.86-0.99, p = 0.041). However, cumulative TyG was not associated with follow-up cognitive performance after adjustment for baseline cognition.ConclusionsCumulative TyG was modestly associated with higher cognitive performance and lower odds of MCI in cross-sectional analyses, but not with follow-up cognitive performance.