Background Early detection of Alzheimer's disease (AD) is critical for timely intervention. Subjective cognitive decline (SCD), defined as self-perceived cognitive worsening while objective performance on standardized tests remains normal, when accompanied by neurodegenerative changes on brain imaging (e.g., hippocampal atrophy), can be classified as SCD with neurodegeneration of AD form (SCD-NDAD). This phenotype may represent an early stage of AD. Objective Investigate the prevalence and clinical characteristics of SCD-NDAD in general population. Methods: This multicenter, community-based cross-sectional study was conducted from 2013 to 2019 across 31 communities in eight major cities of northern, eastern, southern, and western China. Community-dwelling adults aged 50 years and older were recruited through cluster sampling. Participants underwent standardized interviews, neuropsychological assessments, and magnetic resonance imaging, on the basis of which SCD-NDAD was identified. The prevalence of SCD-NDAD was estimated with age- and sex-standardized weights. Results Of 5054 participants (mean age 69.4 years, 60.6% women), 2886 completed MRI. In participants aged ≥50 years, the prevalence of SCD-NDAD was 4.9% (95% confidence interval: 4.1% to 5.8%). In participants aged 65 years and older, prevalence increased to 6.5% (95% confidence interval: 5.5% to 7.7%). While these individuals exhibited preserved cognitive function across all domains, they demonstrated significant hippocampal atrophy, a key marker of AD-related neurodegeneration. Conclusions SCD-NDAD is common among older adults in China, with an estimated prevalence affecting 12.4 million individuals aged ≥65 years. Identifying this cohort may offer a critical window for early intervention and holds significant implications for public health strategies aimed at dementia prevention.

Cardiovascular disease (CVD) is a major contributor to disability and the leading cause of global mortality. This study aims to perform a population-based prospective cohort study to examine the combined impact of multiple lifestyle factors on CVD risk and examine the differences in the relationships across sociodemographic groups. We used data from the UK Biobank. Exposures include seven lifestyle behaviors (smoking, physical activity, alcohol consumption, diet, sleep duration, sedentary behavior, and social connection) and combined multiple behaviors. The lifestyle score was subsequently categorized as favorable (5 to 7 healthy lifestyle factors), intermediate (2 to 4 healthy lifestyle factors), and unfavorable (0 to 1 healthy lifestyle factor) lifestyle classes. Cox proportional hazards models were used to estimate hazard ratios (HRs) for incident CVD and its subtypes. This study showed a significant association of favorable lifestyle with incident CVD (HR = 0.58, 95% CI: 0.54-0.63), myocardial infarction (HR = 0.58, 95% CI: 0.54-0.64), and ischemic stroke (HR = 0.56, 95% CI: 0.48-0.65). Similarly, there was a significant association of intermediate lifestyle with incident CVD (HR = 0.69, 95% CI: 0.64-0.75), myocardial infarction (HR = 0.70, 95% CI: 0.64-0.77), and ischemic stroke (HR = 0.66, 95% CI: 0.57-0.77). The protection effect of lifestyle was more pronounced in the midlife group, females, and those individuals with high Townsend deprivation index levels. Adherence to a broad range of healthy lifestyle factors was associated with a significantly lower risk of CVD and subtypes. Lifestyle modification through multifactorial approaches should be prioritized for preventing and delaying onset of CVD.

Major depressive disorder (MDD) is common and disabling, yet reported brain structural differences vary across studies. Here we performed a large vertex-wise (point-by-point) meta-analysis of cortical thickness and surface area using harmonized magnetic resonance imaging processing across 64 cohorts from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) MDD and Depression Imaging Research Consortium (DIRECT) consortia (5,736 patients; 6,538 controls). We show significantly lower cortical thickness in patients with MDD in multiple brain regions, including the inferior parietal, lateral occipital, superior parietal, medial and lateral orbitofrontal, anterior and posterior cingulate, and precentral gyri, with cortical surface area showing no significant differences. Effects were most pronounced in adults with acute depression, whereas adolescents showed no significant case-control differences. Antidepressant medication use at scanning was associated with more extensive thinning, although effect sizes remained modest (mostly |Cohen's d| < 0.20). This high-resolution, globally generalizable map can support studies of mechanisms and help evaluate structural markers of the clinical course and treatment response.

Introduction The evolution and relationship between anxiety and depression symptoms are complex and may vary between before and after catheter ablation for tachyarrhythmia. The present study aimed to explore the evolution and relationship between anxiety and depression symptoms by using network analysis. Methods Longitudinal data were selected from a cohort study of individuals with tachyarrhythmia. The Hospital Anxiety and Depression Scale was used to examine anxiety and depression symptoms at baseline, and one and three months postoperatively. Cross-sectional networks and cross-lagged panel networks (CLPN) were constructed to identify important predictors. Results 344 patients were included in the network analysis. Moderate-to-high levels of congruity were observed across three cross-sectional networks. A3 (Worrying thoughts) had higher strength centrality across all time points. Meanwhile, the strength centrality at the three time points is time-specific. Specifically, the node with the highest strength centrality was D3 (Cheerful feeling) at T1, but A4 (Relaxed feeling) at both T2 and T3. Additionally, A3 (Worrying thoughts) and A4 (Relaxed feeling) had a higher bridge strength centrality across all times. The cross-lagged network revealed that A3 (Worrying thoughts) and A4 (Relaxed feeling) could predict the subsequent presence of multiple anxiety-depression symptoms at T1 to T2 CLPN and at T2 to T3 CLPN, respectively. Conclusion This study elucidates the complex evolutionary patterns and interactions between anxiety-depression symptoms in patients with tachyarrhythmia during the perioperative period of catheter ablation. Worrying thoughts and Relaxed feeling are central symptoms that may inform the prioritization of clinical focus for alleviating anxiety and depression.

BACKGROUND AND OBJECTIVES:The 2024 revised criteria introduced an integrated framework for staging Alzheimer disease (AD) across biological and clinical dimensions. However, how this criterion characterizes clinical and biological features in populations outside the original development setting, particularly in non-Western and specialized clinic settings, remains insufficiently described. METHODS:We consecutively enrolled 1,214 memory clinic participants who underwent both amyloid-PET and tau-PET imaging. Among amyloid-positive (A+) individuals, clinical stages (0-6) and biological stages (A-D) were assigned according to the 2024 criteria. Participants were classified as typical (concordant stages), susceptible (clinical > biological), or resilient (clinical < biological). Plasma p-tau217 was measured in 379 A+ participants. Associations were examined using generalized linear models adjusted for relevant covariates, with false discovery rate correction for multiple comparisons. RESULTS:Among the 1,214 participants, 818 were amyloid positive, comprising 412 (50.4%) typical, 330 (40.3%) susceptible, and 76 (9.3%) resilient individuals. Plasma p-tau217 increased progressively across advancing tau PET stages (p for trend <0.001), with significantly higher levels in stage D (1.31 pg/mL) compared with stage A (0.56 pg/mL, q = 0.029) and stage C (0.95 pg/mL, q = 0.013). Compared with the typical group, resilient individuals demonstrated superior cognitive performance (e.g., Mini-Mental State Examination [MMSE]: β = 8.56, q < 0.001), higher educational attainment (>9 years: 74.1% vs 45.1%, q = 0.020), and greater AD-signature regional cortical thickness (t = 3.033, q = 0.005) and volume (t = 3.209, q = 0.003). Conversely, susceptible individuals exhibited inferior cognitive scores (e.g., MMSE: β = -8.29, q < 0.001), reduced cortical thickness (t = -2.872, q = 0.005), and volume (t = -2.751, q = 0.007) in AD-signature regions and a numerically higher burden of vascular risk factors. DISCUSSION:In a large cohort from a specialized tertiary memory clinic, clinical-biological stage discordance under the 2024 AD criteria was common among amyloid-positive individuals. Distinct cognitive, educational, biomarker, and neuroanatomic profiles characterized susceptible, typical, and resilient subgroups. In addition, plasma p-tau217 showed a stepwise increase across tau PET stages, supporting its utility as a blood-based marker of tau pathology severity. These findings support the clinical relevance of the revised staging framework and may inform future refinements of AD diagnostic guidelines. CLASSIFICATION OF EVIDENCE:This study provides Class II evidence that the 2024 revised biological and clinical criteria for AD demonstrate clinical utility in a Chinese cohort from a specialized tertiary memory clinic.

Organ-specific aging clocks hold great potential in reflecting organ health. In vivo imaging is inherently organ-specific and delineates structural and functional characteristics more objectively. However, there is no systematic evaluation of imaging-based aging clocks. We utilized 1777 imaging-derived phenotypes (IDPs) from 11,000 healthy participants and assessed the organ-specific biological age of seven organs. The organ-specific age gap was primarily associated with incident diseases and mortality related to corresponding organs. The top-contributing IDPs to organ-specific biological age emerged as biomarkers for incident disease predictions, achieving an area under the curve (AUC) greater than 0.8 for dementia (AUC = 0.82). Subsequent proteomic analysis revealed 966 shared and 507 organ-specific molecular signatures for the aging of different organs. Finally, we identified key modifiable factors and 14 drug targets for organ-specific aging. The imaging-based aging clocks demonstrate organ-specificity at both macro and micro scales, which could promote personalized intervention and treatment of organ aging.

Chronic pain often evolves into depression and anxiety, yet mechanisms linking sensory distress to affective dysfunction remain unclear. Integrating human neuroimaging from the UK Biobank with a rodent model, we uncovered biphasic hippocampal remodeling. Hippocampal volume increased during early pain stages, with paradoxical cognitive improvements, but declined with comorbid depression. In rodents, the dentate gyrus (DG) acted as a hub governing this transition: Lesions of DG prevented affective symptoms. Elevated DG activity was linked to hyperactive newborn neurons and microglial recruitment and remodeling, leading to circuit imbalance. Whereas suppressing newborn neuron activity alleviated emotional pathology at the expense of cognition, microglial modulation selectively restored affective behavior without cognitive cost. These findings reveal microglia-mediated hippocampal remodeling as a key mechanism linking chronic pain to mood disorders.

Previous studies have shown that trauma exposure could affect visual working memory function in trauma survivors, regardless of psychopathological symptoms. However, the impact of psychological trauma on the brain function underlying working memory is unclear. To investigate this, a connectome-wide association (CWAS) study was conducted on visual working memory (VWM) in a spectrum of trauma-exposed individuals. Resting-state functional magnetic resonance imaging was used to scan 93 earthquake survivors, 44 of whom met the criteria for post-traumatic stress disorder (PTSD), and 49 did not. Fifteen participants with PTSD also had a comorbid major depressive disorder. The Wechsler Memory Scale-IV and clinical assessments were used to examine participants. A CWAS was conducted to search for a whole-brain multivariate connectome profile related to VWM performance. The results showed that VWM performance was robustly associated with the connectome profile of the left inferior parietal cortex (IPC). A post-hoc seed-based functional connectivity (FC) analysis using the left IPC as a seed revealed that the bilateral dorsomedial prefrontal cortex (dmPFC), posterior cingulate (PCC), and precuneus correlated positively with the IPC after correction. The correlations between the mean connectivity of the left IPC and the regions mentioned above and VWM scores further underscored the role of these connections in VWM performance in trauma survivors. These findings demonstrate a more comprehensive picture of the neural correlates of VWM performance in trauma survivors and suggest that modulating the functional connectivity between the IPC, insula, and dmPFC could shift attention bias and improve VWM performance in trauma-exposed survivors.

Background:Previous studies indicate that renal fibroblast activation protein expression inversely correlates with kidney function and that gallium-68 fibroblast activation protein inhibitor (68Ga-FAPI) positron emission tomography/computed tomography (PET/CT) can visualize renal fibrosis. However, its diagnostic and prognostic utility in kidney disease has not been investigated. This study aimed to further explore the correlation between increased 68Ga-FAPI uptake in the renal cortex and kidney disease, focusing on its diagnostic and predictive capabilities to aid clinicians in image interpretation. Methods:Patients who underwent whole-body 68Ga-FAPI PET/CT examinations at our center from February to December 2022 were retrospectively enrolled. Spearman correlation analysis assessed the relationship between estimated glomerular filtration rate (eGFR) and maximum standardized uptake values (SUVmax) of renal cortex. The diagnostic performance of 68Ga-FAPI PET/CT for kidney disease was evaluated using the receiver operating characteristic (ROC) curve and its area under the curve (AUC), alongside sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). Predictive analysis was conducted using Cox proportional hazards model and Kaplan-Meier survival analysis. A P value <0.05 was considered statistically significant. Results:A total of 145 patients (82 men; mean age: 59.3±11.27 years; range, 23-83 years) were included. The SUVmax of renal cortex and eGFR was negatively correlated (R2=0.276). The AUC of SUVmax of renal cortex in diagnosing kidney diseases was 0.946 [95% confidence interval (CI): 0.908-0.983]. A renal cortex SUVmax threshold of 2.00 demonstrated a diagnostic sensitivity of 94.4% (17/18) and an NPV of 96.4% (27/28) for kidney disease, whereas the accuracy was 68.8% (44/64). The increased renal uptake of 68Ga-FAPI is an independent risk factor for kidney disease. Non-kidney disease patients with an increased renal uptake of 68Ga-FAPI have a higher incidence of kidney disease in the immediate future compared to non-kidney disease patients without increased renal uptake, which was 46.2% versus 0.0% (P<0.001). Conclusions:The concentration of 68Ga-FAPI in renal cortex is negatively correlated with eGFR. FAPI PET/CT is highly effective in the diagnosis and prediction of kidney diseases.

Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges. Within distributed network environments, Multimodal Large Language Models (MLLMs) serve as cognitive engines for edge devices, yet federated fine-tuning faces substantial challenges in balancing global knowledge aggregation with local adaptation under heterogeneous network conditions. Conventional federated protocols typically rely on uniform parameter aggregation, which conflates domain-invariant features with client-specific nuances, thereby resulting in suboptimal personalization and excessive communication overhead. To address these challenges, we propose PFAdapter, a communication-efficient framework introducing hierarchical LoRA decomposition to explicitly separate adapter parameters into global-shared and local-private components. Query and key projections are assigned to global synchronization for capturing universal multimodal semantics across the network, while value and output projections remain localized for edge-specific adaptation. Additionally, orthogonality regularization based on the Frobenius norm enforces strict separation between these components, preventing redundant feature learning. Selective aggregation protocols synchronize only global-shared components across the federated network, preserving local expertise and reducing communication costs by nearly 50

Alzheimer's disease (AD) has a strong genetic predisposition. Genome-wide association studies have identified multiple risk loci, yet many non-coding variants remain uncharacterized. Machine learning-based polygenic risk scores (PRS) enhance prediction by modeling genetic epistasis and sex-specific risks. This review summarizes AD genetic risk factors, PRS methodologies, and ML-based AD risk prediction. It also highlights challenges such as population bias, functional validation, and integrating multi-omics for precision medicine.

Histone deacetylase 6 (HDAC6) represents a compelling target in major depressive disorder (MDD) pathophysiology, yet in vivo investigation has been constrained by inadequate imaging capabilities. Here, we report the development and validation of [18F]PB200, a novel positron emission tomography (PET) radiotracer specifically targeting brain HDAC6. PB200 was engineered with nanomolar affinity, high HDAC6 selectivity, and excellent blood-brain barrier permeability. [18F]PB200 was successfully synthesized in a radiochemical yield of 13 ± 4% and validated through in vitro autoradiography and in vivo PET imaging across rodent and non-human primate models. We subsequently employed [18F]PB200 alongside TSPO-targeted [18F]FEPPA PET imaging in a chronic unpredictable mild stress (CUMS) mouse model of depression. This dual-tracer approach, complemented by in vitro experiments, revealed significant HDAC6 upregulation occurring concurrently with enhanced neuroinflammatory markers, including microglial activation and elevated pro-inflammatory cytokines. Our findings provide the first in vivo molecular imaging evidence directly linking HDAC6 upregulation to depressive pathophysiology and associated neuroinflammation. This work illuminates the molecular relationship between depression and neuroinflammation while establishing [18F]PB200 as a valuable tool for evaluating HDAC6-targeted therapeutic interventions, potentially advancing precision diagnosis and treatment approaches for depression.

Major depressive disorder (MDD) imposes significant global health burdens, yet its underlying neural mechanisms remain elusive. Traditional static functional metrics inadequately capture the brain’s dynamic nature, motivating the exploration of dynamic functional metrics to understand both the temporal and spatial reconfigurations of brain networks in MDD. Leveraging the Depression Imaging Research Consortium (DIRECT) dataset, this study conducted vertex-wise dynamic analyses in a large cohort of MDD patients (n = 1660) and healthy controls (n = 1341). We identified significant alterations in temporal stability across the brain, with MDD patients exhibiting increased stability in higher-order association areas (e.g., frontoparietal and default mode networks) and decreased stability in primary sensory-motor regions. Among the regions showing altered temporal stability, brain-symptom relationships were further explored. We identified a set of brain regions including the superior frontal gyrus, postcentral gyrus and superior insular sulcus, which were potentially involved in the common abnormal dFC network and associated with insomnia, feelings of guilt, and insight symptoms in MDD. By incorporating advanced vertex-wise dynamic functional analyses and a large sample size, this study provides insights into the neural mechanisms of MDD, emphasizing the value of dynamic approaches for identifying biomarkers. Future longitudinal and task-based studies are promising to elucidate causal relationships and refine personalized therapeutic interventions targeting specific dynamic dysfunctions in MDD.

Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral measurements prediction, and mental disorder diagnosis. However, the application of deep learning techniques to brain fMRI analysis is hindered by the small sample size of fMRI datasets. Transfer learning offers a solution to this problem, but most existing approaches are designed for large-scale 2D natural images. The heterogeneity between 4D fMRI data and 2D natural images makes direct model transfer infeasible. This study proposes a novel geometric mapping-based fMRI transfer learning method that enables transfer learning from 2D natural images to 4D fMRI brain images, bridging the transfer learning gap between fMRI data and natural images. The proposed Multi-scale Multi-domain Feature Aggregation (MMFA) module extracts effective aggregated features and reduces the dimensionality of fMRI data to 3D space. By treating the cerebral cortex as a folded Riemannian manifold in 3D space and mapping it into 2D space using surface geometric mapping, we make the transfer learning from 2D natural images to 4D brain images possible. Moreover, the topological relationships of the cerebral cortex are maintained with our method, and calculations are performed along the Riemannian manifold of the brain, effectively addressing signal interference problems. The experimental results based on the Human Connectome Project (HCP) dataset demonstrate the effectiveness of the proposed method. Our method achieved state-of-the-art performance in sex classification, age estimation, and behavioral measurement prediction tasks. Moreover, we propose a cascaded transfer learning approach for depression diagnosis, and proved its effectiveness on 23 depression datasets. In summary, the proposed fMRI transfer learning method, which accounts for the structural characteristics of the brain, is promising for applying transfer learning from natural images to brain fMRI images, significantly enhancing the performance in various fMRI analysis tasks.

INTRODUCTION:Alzheimer's disease (AD) shows marked molecular heterogeneity. Defining biological subtypes may refine diagnosis and treatment. METHODS:We analyzed cerebrospinal fluid (CSF) proteomics and longitudinal data from 550 participants in the Alzheimer's Disease Neuroimaging Initiative with up to 16.5 years of follow-up. We profiled 6361 proteins, applied machine learning to identify biological subtypes, and validated them in three independent cohorts. RESULTS:Three AD subtypes were identified. Subtype 1, enriched in RNA metabolism pathways, showed the mildest atrophy and slowest cognitive decline. Subtype 2, characterized by axonogenesis-related pathways, exhibited the greatest CSF tau elevations, moderate atrophy, and intermediate decline. Subtype 3, associated with catabolic processes, showed the most severe atrophy and fastest progression. These patterns were consistently replicated across validation cohorts. DISCUSSION:These findings demonstrate robust, biologically distinct AD subtypes linked to divergent molecular pathways, clinical features, and progression rates. Such refined stratification supports precision diagnostics and targeted therapeutic strategies.

OBJECTIVE:Intracranial aneurysm (IA) is a leading cause of subarachnoid hemorrhage, characterized by complex pathogenesis and high mortality rates due to rupture. The aim of this study was to develop a targeted glucagon-like peptide-1 (GLP-1) nanodelivery system to mobilize endothelial progenitor cells (EPCs) and enhance re-endothelialization in a rat model of coiled IA. METHODS:In this study, a matrix metalloproteinase-2 (MMP-2)-targeted nanodelivery platform (hereafter GLP-1@tMSN [targeted mesoporous silica nanoparticle]) based on MSNs functionalized with GLP-1 was developed to mobilize EPCs and accelerate vascular repair. The efficacy of GLP-1@tMSN in promoting EPC recruitment and re-endothelialization was evaluated in a rat coiled aneurysm model, alongside mechanistic studies of the Wnt/β-catenin signaling pathway. RESULTS:In a rat model of coiled IA, GLP-1@tMSN significantly enhanced the recruitment of EPCs and promoted re-endothelialization. Histological analysis demonstrated the formation of mature endothelial-like tissue after 28 days, in contrast to the fibrous tissue observed in the control group. Immunofluorescence analysis confirmed the preferential accumulation of CD34+VEGFR2+ EPCs at the lesion site, with concurrent activation of the Wnt/β-catenin pathway, implicating its pivotal role in driving vascular repair. Preliminary safety evaluations further indicated a favorable biocompatibility profile for the nanotherapeutic system. CONCLUSIONS:The developed functionalized nanodelivery platform represents a promising therapeutic strategy to enhance localized GLP-1 efficacy, facilitating rapid re-endothelialization and potentially reducing long-term recurrence of IAs after embolization. This approach shows substantial potential for improving outcomes for patients with IA.

ObjectiveTo explore the relationship between interoceptive sensitivity and impaired fasting glucose(IFG) in early pregnancy.MethodsA cross⁃sectional study method was conducted.A total of 233 pregnant women who established their prenatal records in the first trimester in Wuxi Maternal and Child Health Care Hospital were selected as the research subjects.Their demographic,clinical,obstetric,and nutritional data were collected.Interoceptive sensitivity was assessed by using the Multidimensional Assessment of Interoceptive Awareness,version 2(MAIA⁃2).Participants were divided into an impaired fasting glucose group and a normal blood glucose group based on their fasting blood glucose levels.Multivariate Logistic regression analysis was used to analyze the association between interoceptive sensitivity in early pregnancy and impaired fasting glucose.ResultsThe incidence of impaired fasting glucose in 233 pregnant women was 18.0%(42 cases).After adjusting for relevant confounding factors,the multivariable Logistic regression analysis results showed that interoceptive sensitivity was an independent protective factor against impaired fasting glucose in early pregnancy(OR=0.520,95%CI 0.275⁃0.984,P=0.044).ConclusionsHigher interoceptive sensitivity in early pregnancy has a protective effect against impaired fasting glucose.This finding suggests that enhancing interoceptive sensitivity in pregnant women may be a potential new target for the prevention and management of abnormal fasting glucose in early pregnancy.

BackgroundYWHAG has been recognized as a promising biomarker for Alzheimer's disease (AD). However, the precise role of YWHAG within the revised 2024 diagnostic framework for AD remains unclear.ObjectiveOur study aimed to evaluate YWHAG with established biomarkers to delinea te the role of YWHAG and determine whether it could serve as a complementary biomarker within the 2024 diagnostic criteria.MethodsWe compared YWHAG with established biomarkers among 708 participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) across three domains: 1) diagnostic utility, 2) cross-sectional associations with clinical variables and brain structure, and 3) predictive value for clinical progression risk.ResultsOur results demonstrated the accuracy of YWHAG (AUC = 0.856) was comparable to that of FDG-PET (AUC = 0.912) and HVA (AUC = 0.879), but superior to that of T-tau (AUC = 0.796), NFL (AUC = 0.712), and GFAP (AUC = 0.617) in distinguishing AD versus controls. Moreover, YWHAG was consistently among the top three biomarkers most strongly associated with cognitive decline and brain atrophy, alongside HVA and FDG-PET. Furthermore, the YWHAG positive (Y+) group had a significantly higher risk of AD progression (HR = 2.45, 95% CI: 1.75-3.43) compared to the YWHAG negative (Y-) group (p < 0.001).ConclusionsWe identify YWHAG as a novel biomarker predictive of cognitive decline, brain atrophy, and AD progression. The performances of YWHAG are comparable to established biomarkers such as FDG-PET, HVA and T-tau, thereby providing a complement to the current AD diagnostic framework.

Lecanemab is a newly approved monoclonal antibody targeting amyloid plaques for the treatment of early Alzheimer’s disease. This study aimed to evaluate the safety and short-term biomarkers and cognition changes of lecanemab in Chinese clinical practice. This multicenter real-world study involved patients receiving lecanemab treatment across seven hospitals in China. Patients underwent comprehensive assessments before treatment. Lecanemab was administered via intravenous infusion every 2 weeks. Treatment-related symptoms were monitored through self-report, and amyloid-related imaging abnormalities were assessed via magnetic resonance imaging. Amyloid and tau biomarker changes were measured using positron emission tomography imaging or plasma testing. Follow-up cognitive assessments were evaluated after 6 months of treatment. Short-term outcomes were analyzed using linear mixed-effect models, without an untreated control group. A total of 407 patients who received at least one lecanemab infusion were involved in this study, with a mean follow-up time of 5.6±3.39 months. The mean age was 68.08 years, with 67.57% of patients being female. Of the participants, 56.51% were APOE ε4 carriers, and 83.19% were at biological stage C. During the study period, 22.22% of the patients experienced treatment-related symptoms, and 12.15% developed at least one amyloid-related imaging abnormality. Only four symptomatic and seven severe amyloid-related imaging abnormality cases were reported. APOE ε4 status was not related to adverse events in the Chinese population. Patients with a higher number of microhemorrhages at baseline were more likely to develop adverse events. No significant differences in adverse events were observed between the moderate Alzheimer’s disease dementia group and the mild cognitive impairment group. By the end of the research period, 9.38% of the patients withdrew from lecanemab. After 6 months of treatment, favourable short-term outcomes in biomarkers and stable cognitive function were observed. This study demonstrates that lecanemab treatment is feasible and well-tolerated among the Chinese population, with lower rates of adverse events and favourable short-term outcomes observed. Administration of lecanemab in moderate AD dementia population was relatively safe and further studies are warranted.