Background While amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) are primarily associated with cognitive impairments, motor dysfunction has also consistently been observed. Less is known, however, about the acquisition of new motor sequences along the AD continuum. Objective This study examined motor sequence learning in individuals with aMCI and AD dementia compared to cognitively normal individuals (CNI), and to what extent such learning is related to AD biomarkers. Methods A total of 28 individuals with AD dementia, 33 with aMCI, and 52 CNI performed a computerized task that assessed general motor performance and motor sequence learning abilities. Participants also completed a neuropsychological test battery to assess their cognitive abilities. APOE genotyping was used to determine the presence of ɛ4 alleles, amyloid PET imaging to quantify amyloid deposition, and structural MRI scanning to obtain hippocampal volume. Results While individuals with aMCI or AD dementia generally performed slower and less consistent than CNI, motor sequence learning occurred regardless of cognitive status. The rate of initial learning was slower in individuals with aMCI or AD dementia than in CNI. No significant associations between motor learning and AD biomarkers were observed. A combination of motor and cognitive measures best predicted amyloid burden, whereas a model with cognitive measures only best predicted hippocampal volume and APOE allele count. Conclusions Overall, individuals with aMCI and AD dementia showed relatively preserved motor sequence learning abilities, despite some decrements in the initial rate of learning. Combining measures of motor and cognitive functioning may enhance the prediction of amyloid burden. Future studies should use more complex sequential movements and further explore the value of integrating motor measures in AD/aMCI assessment and monitoring.
BackgroundPatients with mild cognitive impairment (MCI) frequently experience depressive symptoms, which are linked to accelerated cognitive decline, poorer quality of life, and an increased risk of progression to Alzheimer's disease. Exercise is considered a promising non-pharmacological strategy for mood improvement and brain health; however, the specific effects of different exercise modalities remain insufficiently defined.ObjectiveTo compare the relative efficacy of specific exercise interventions on depressive symptoms in patients with MCI.MethodsRandomized controlled trials (RCTs) evaluating the effects of exercise interventions on depressive symptoms in patients with MCI were systematically searched. Standardized mean differences (SMD) were calculated to pool outcomes across different depression scales. A Bayesian network meta-analysis was conducted to integrate direct and indirect evidence, and interventions were ranked using the surface under the cumulative ranking curve (SUCRA). Network consistency and model fit were also assessed.ResultsA total of 19 RCTs involving nine exercise modalities were included. Compared with the control group, dance exercise demonstrated a statistically significant improvement in depressive symptoms (SMD = -0.51, 95% credible intervals: -0.84 to -0.16), representing a moderate effect size, and showed a relatively high-ranking probability (SUCRA = 80.71%). Overall heterogeneity was moderate, and local inconsistency was observed in some comparisons.ConclusionsExercise interventions may contribute to the alleviation of depressive symptoms in patients with MCI, with dance exercise showing relatively greater therapeutic potential. However, these findings should be interpreted with caution in light of evidence inconsistency, and further high-quality studies are warranted to substantiate these results.
BackgroundMany studies have established associations between smoke exposure and both accelerated cognitive decline and systemic inflammation. However, their interrelationship remains unclear.ObjectiveThis study aims to provide novel insights into the tripartite relationships linking smoke exposure, inflammatory biomarkers, and cognitive function among older adults.MethodsThis study analyzed data from 2503 participants in the National Health and Nutrition Examination Survey (NHANES, 2011-2014) and 3263 participants in the China Health and Retirement Longitudinal Study (CHARLS, 2011-2012). Serum cotinine was mainly used to assess smoke exposure in NHANES, while self-reported smoking status was used in CHARLS. Cognitive function was assessed using standard cognitive tests in NHANES and CHARLS. Inflammatory biomarkers included NLR, SII, leukocyte, neutrophil and lymphocyte in NHANES and leukocyte in CHARLS. Linear regression, nonlinear threshold effect models, and mediation analysis examined associations and mediating effects.ResultsHigher cotinine was associated with lower cognitive scores in NHANES (β = -0.219, p < 0.001). In CHARLS, current smokers exhibited lower cognitive scores than never-smokers (β = -0.090, p = 0.004). Neutrophil and leukocyte showed linear relationship with cognitive function in NHANES, while other inflammatory indicators showed significant nonlinear threshold effects. Mediation analysis revealed that neutrophil and leukocyte partially mediated 7.39% and 9.75% of the smoke-cognitive decline association.ConclusionsSmoking is related to cognitive decline in older adults from two national cohorts, with neutrophil and leukocyte partly mediating this link in the U.S. cohort. Interventions for smoking cessation and inflammation reduction may help mitigate cognitive decline.
BackgroundAstrocytes become reactive in Alzheimer's disease (AD), and fluid markers of this response, including glial fibrillary acidic protein (GFAP) and chitinase-3-like protein 1 (YKL-40), increase across the AD continuum. However, it remains unclear whether astrocytic biomarker levels relate to preserved versus disrupted white matter (WM) integrity, and whether these relationships vary by amyloid-β (Aβ) burden and apolipoprotein E (APOE) ε4 status.ObjectiveTo examine associations of GFAP and YKL-40 with WM integrity, AD pathology, and memory across Aβ and APOE ε4 contexts.MethodsWe analyzed 52 dementia-free older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort with longitudinal diffusion tensor imaging and baseline cerebrospinal fluid (CSF) biomarkers. WM integrity was assessed using network-level stability and mean diffusivity within medial temporal lobe-associated networks. Baseline CSF GFAP, YKL-40, Aβ1-42, phosphorylated tau (p-tau181), APOE ε4 status, and episodic memory were examined. Findings were evaluated in an independent Stanford Alzheimer's Disease Research Center sample with plasma GFAP.ResultsGFAP showed context-dependent associations with WM integrity and AD pathology. In Aβ-negative and APOE ε4-negative individuals, higher GFAP was associated with greater WM stability, lower mean diffusivity. In Aβ-positive and APOE ε4-positive individuals, higher GFAP was associated with higher p-tau18 rather than WM integrity. Higher YKL-40 was associated with reduced WM stability. In the validation cohort, APOE ε4 status moderated the association between plasma GFAP and WM integrity.ConclusionsAstrocytic biomarkers demonstrated relationships with WM integrity and AD pathology. GFAP appeared to reflect context-dependent astrocytic responses, whereas YKL-40 was consistently associated with WM disruption.
BackgroundDementia is an emerging public health priority in Saudi Arabia, driven by population aging and a high prevalence of vascular and metabolic disease. Despite increasing research activity, evidence remains fragmented, limiting understanding of the national burden and determinants of dementia.ObjectiveTo synthesize evidence on dementia epidemiology, risk factors, awareness, comorbidities, caregiving challenges, and research trends in Saudi Arabia.MethodsA systematic review was conducted following PRISMA 2020 guidelines. PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar were searched for studies reporting dementia-related outcomes in Saudi populations. Given heterogeneity in study designs, sampling methods, and diagnostic criteria, findings were narratively synthesized.ResultsFifty-five studies met the inclusion criteria. Alzheimer's disease was the most common dementia subtype. Prevalence estimates among adults aged 60 years and older ranged from 3.8% to 6.3%, reflecting differences in diagnostic approaches and sampling methods. Diabetes and hypertension were the most consistently identified risk factors, while depression and anxiety were also associated with dementia. Public awareness was low, with stigma contributing to delayed recognition and underdiagnosis. Caregiving was family-based and associated with significant burden. Dementia commonly co-occurs with cardiometabolic, neuropsychiatric, and other chronic conditions. Research activity increased after 2020, with over half of the included studies published between 2020 and 2024; however, methodological inconsistencies and limited rural representation remain.ConclusionsDementia research in Saudi Arabia has expanded, yet epidemiological estimates remain inconsistent. Standardized diagnostic practices, enhanced surveillance, and public health strategies addressing risk reduction, awareness, and caregiver support are needed to address the dementia burden.
An expanding body of evidence suggests that thyroid dysfunction may be clinically relevant in Alzheimer's disease (AD), although the significance of this association remains unsettled. Rather than reflecting a single biological pathway, thyroid hormone alterations in AD may involve downstream systemic adaptations related to inflammation and frailty, central dysregulation of thyroid hormone bioavailability and signaling, and upstream vulnerability mechanisms. Clarifying how these pathways intersect with cognitive and functional decline may help refine the biological interpretation of clinical heterogeneity in patients with AD.
BackgroundWhite matter hyperintensity (WMH) is linked to mild cognitive impairment (MCI) and vascular dementia, but its longitudinal change as an independent risk factor remains unclear.ObjectiveThis study aims to compare baseline WMH across diagnostic outcomes, and evaluate how WMH trajectory relates to outcomes, cognition, and amyloid-β (Aβ) deposition.MethodsData from the Alzheimer's Disease Neuroimaging Initiative (ADNI) included demographics, medical history, WMH, diagnosis, cognition, and amyloid-PET. A total of 857 participants (372 cognitively normal [CN], 485 MCI) were categorized by diagnostic outcome. Baseline WMH differences were analyzed. WMH trajectories (higher/lower) were used in logistic models to predict outcomes, and in linear models to assess cognitive and Aβ changes, adjusting for age, sex, hypertension, BMI, education level, total brain volume and APOE ε4.ResultsBaseline WMH was significantly higher in the CN-MCI group compared to the CN-CN group and lower in the MCI-CN group than in the MCI-MCI and MCI-AD dementia groups (p < 0.05). Controlling for covariates, regression models found that CN participants with higher WMH trajectories had higher odds of MCI (OR = 3.710, 95%CI [1.836,7.499], p < 0.001), and that participants with higher WMH trajectories had greater impairments in domains of memory (β=-0.331, 95%CI[-0.454,-0.208], p < 0.001), executive function (β=-0.275, 95%CI[-0.370,-0.179], p < 0.001), and language (β=-0.248, 95%CI[-0.344,-0.152], p < 0.001). In [18F] florbetapir (FBP) tracers, increased Aβ deposition was also observed in higher WMH groups (β = 0.056, 95% CI[0.018,0.095], p = 0.004).ConclusionsWMH baseline and trajectory changes are key risk factors for CN cognitive decline. WMH trajectory changes are linked to cognitive function and brain Aβ deposition.
BackgroundMild cognitive impairment (MCI) is clinically heterogeneous, yet most prognostic studies rely on binary conversion endpoints or expensive biomarkers unavailable in routine practice.ObjectiveTo identify trajectory classes of the Clinical Dementia Rating-Sum of Boxes (CDR-SB) progression in MCI using latent class growth analysis (LCGA) and to determine whether they capture clinically relevant variation not reducible to Mini-Mental State Examination (MMSE) trajectories.MethodsLCGA was applied to longitudinal CDR-SB data from 121 MCI patients (baseline global CDR = 0.5) with two or more serial assessments across up to eight visits at three tertiary hospitals. Models with one through four classes were compared using BIC, AIC, entropy and minimum class size, with annual MMSE change and dementia conversion (global CDR ≥ 1) as external validators.ResultsA four-class model was selected: Stable (n = 20, 16.5%; -0.08 per visit; 0% conversion), Slow (n = 46, 38.0%; +0.41 per visit; 47.8%), Moderate (n = 22, 18.2%; +0.75 per visit; 81.8%), and Fast (n = 33, 27.3%; +2.07 per visit; 100%); conversion-free survival differed across classes (log-rank p < 0.001). Two classes with near-identical MMSE decline differed markedly in CDR-SB slope and conversion. The stability of the Stable class persisted when all classes were restricted to their first two visits.ConclusionsIn this tertiary-care cohort, serial CDR-SB trajectory classification identified clinically meaningful MCI subgroups, including functional heterogeneity invisible to MMSE monitoring alone; pending external validation it may offer practical, low-cost prognostic value where biomarkers are unavailable.
BackgroundAlzheimer's disease (AD) and related dementias are leading causes of death worldwide. While socioeconomic status (SES) is a critical determinant, its impact on mortality after diagnosis remains understudied.ObjectiveTo investigate the roles of neighborhood-level and individual-level SES in predicting mortality risk among older adults with incident dementia.MethodsWe analyzed data from 924 participants with incident dementia from the Mayo Clinic Study of Aging (N = 6909), a cohort with clinical follow-up visits every 15 months. We assessed mortality risk using Cox's time-varying survival regression, adjusting for time-fixed and time-varying covariates. Primary predictors were neighborhood-level SES (Area Deprivation Index [ADI]) and individual-level SES (housing-based SES index [HOUSES], education, and occupation). We also applied a random survival forest model to assess the predictive contribution of SES indicators and identify influential predictors.Results857 participants (92.7%) died, with a median time from dementia diagnosis to death of 29 months (IQR: 12-54). Lower SES measured by HOUSES was associated with increased mortality risk (HR 1.28, 95% CI 1.07-1.52). Conversely, ADI, education, and occupation were not associated with mortality. Male sex, older age, congestive heart failure and diabetes were associated with higher mortality risk. In predictive analysis, HOUSES ranked among the top 10 contributors to mortality risk.ConclusionsAmong older adults with dementia, lower HOUSES was associated with higher mortality risk beyond neighborhood-level deprivation, education, and occupation. Integrating this housing-based individual-level SES measure into dementia care planning may help identify patients with greater socioeconomic vulnerability and inform mortality risk stratification.
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.
BackgroundThe Survey of Health, Ageing and Retirement in Europe (SHARE) is a longitudinal multi-country study of community-dwelling middle-aged and older adults including data on sleep and cognition.ObjectiveTo examine the cross-sectional association between sleep-related issues and cognition in the SHARE (pooling waves 8 and 9; 2019-2022).MethodsSubjective sleep data were obtained from SHARE waves 8 and 9. Objective data were available from wave 8. The SHARE Cognitive Instrument (SHARE-Cog) assessed cognition. Participants were divided into three groups: probable dementia, cognitive impairment no dementia (CIND), and normal cognition (NC). Survey-weighted generalized linear regression models were used to compare groups.Results88,889 interviews were included (55% females; mean age 68.88), categorized as: probable dementia 7%, CIND 12%, and NC 81%. While, in the unadjusted analysis, a characteristic 'inverted U' shaped association was observed between sleep duration and cognition, only the association between cognition and longer sleep durations ≥10 hours remained statistically significant after adjusting for potential confounders. Cognitive impairment was significantly associated with subjective reports of recent trouble sleeping, weekly use of medication to aid sleep, and daytime napping, although only the association with daytime napping remained statistically significant after adjusting for potential confounders.ConclusionsThis analysis confirmed an association between sleep duration ≥10 hours and lower cognitive scores among community-dwelling middle-aged and older Europeans. Cognitive impairment was also significantly associated with daytime napping. Further research is required to understand the clinical significance of these findings and establish the underlying causal relationships.
BackgroundLoneliness is the subjective feeling of social isolation and evidence suggests that it increases the risk for cognitive decline and Alzheimer's disease (AD). However, studies examining the associations between loneliness and AD biomarkers are limited and inconclusive.ObjectiveWe sought to investigate the relationships between loneliness and plasma tau phosphorylated at threonine 217 (p-tau217) and plasma neurofilament light chain (NfL): peripheral biomarkers of central AD pathology.MethodsThis is a cross-sectional analysis of the 'INStitute for Prevention' 'healthy agIng' and 'medicine REjuvenative' 'Translational' (INSPIRE-T) baseline data. Participants were dementia-free (Mini-Mental State Exam score ≥ 24) community-dwellers aged ≥ 65 years (n = 434). Loneliness was assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS®) Item Bank v2.0, Short Form 8a questionnaire and plasma p-tau217 and plasma NfL were measured using Lumipulse immunoassays. Multiple linear regression was conducted to evaluate the relationships between loneliness (exposure) and plasma biomarkers (outcomes).ResultsLoneliness was not associated with plasma p-tau217 (β = 0.00458 [95% CI: -0.00328, 0.01243], p = 0.253) or plasma NfL (β = 0.00138 [95% CI: -0.00418, 0.00693], p = 0.626) in multiple linear regression models adjusted for age, sex, education, cognitive performance, depressive score, eyesight, hearing, and apolipoprotein ε4 (APOE ε4) status (model 2). There was no significant moderating effect of sex, depression, eyesight, hearing or APOE ε4 on these associations.ConclusionsOur findings suggest that loneliness is not associated with plasma p-tau217 or plasma NfL in dementia-free older adults. Perspective studies are warranted to examine these associations further.
BackgroundAlzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit substantial overlap in clinical manifestations and patterns of brain functional degeneration, which poses significant challenges for automated classification based on electroencephalography (EEG).ObjectiveThis study aims to develop an EEG-based framework capable of simultaneously capturing temporal dynamics and frequency-related characteristics of EEG signals for discrimination among AD, FTD, and cognitively normal (CN) subjects.MethodsA Dual-Branch Time-Frequency Fusion Network (DBTF-Net) based on routine clinical resting-state EEG recordings acquired under eyes-closed conditions is proposed. The model employs parallel temporal and frequency branches to process raw EEG time-series signals and their corresponding time-frequency representations. A global temporal dependency construction mechanism is introduced in the temporal branch to capture both local temporal patterns and long-range temporal dependencies. Feature-level fusion is then performed across the two branches to achieve a collaborative representation of multidimensional brain functional information. The proposed method was systematically evaluated on one three-class classification task (AD versus FTD versus CN) and multiple binary classification tasks.ResultsExperimental results from five-fold cross-validation at the epoch level show the classification accuracies of DBTF-Net as 86.36%±4.28%, 83.01%±6.15%, 92.13%±10.35%, and 88.74%±7.69%% for AD versus FTD versus CN, AD versus CN, FTD versus CN, and AD versus FTD, respectively.ConclusionsThe proposed DBTF-Net leverages temporal and time-frequency information in EEG signals and provides classification of AD and FTD. Visualization analysis further indicates that the model attends to disease-relevant discriminative patterns in time-frequency representations, enhancing the interpretability of its classification decisions.
BackgroundMild Alzheimer's disease (AD) is associated with alterations in brain activity, which can be detected using electroencephalography (EEG). Investigating these changes during emotional processing may help identify neurophysiological patterns that differentiate patients with mild AD from healthy controls (HCs).ObjectiveTo investigate whether EEG responses elicited during emotional processing provide biomarkers capable of discriminating patients with AD from HCs. Additionally, to identify the emotional contexts, brain regions, frequency bands, and machine learning models that maximize this discriminative capacity.MethodsA sample of 39 AD patients and 54 HCs watched brief movie clips designed to elicit tenderness, amusement, anger, fear, and sadness, along with an Alzheimer's-related clip, together with neutral clips used as control, baseline, and recovery conditions, while cortical activity was recorded using EEG. The signals were then classified across emotional contexts using LASSO, Random Forest, SVM-RBF, XGBoost, and CatBoost ML models.ResultsEEG signals allowed for discrimination between AD patients and HCs across different emotional contexts, including individual emotions, grouped by valence or arousal, and the Alzheimer's-related stimulus. LASSO achieved the best performance for positive and neutral conditions in the parietal and posterior gamma bands, whereas CatBoost performed best for high-arousal negative emotions such as anger and fear, particularly in frontal gamma and theta bands, respectively.ConclusionsPatients with mild AD show EEG signal patterns that differ from those of HCs across different emotional contexts. These findings highlight the potential of EEG recorded during emotional processing to support the development of objective biomarkers for mild AD.
BackgroundAlzheimer's disease represents a major public health issue that affects millions of people worldwide. Although symptomatic treatments are available, they neither prevent nor halt disease progression; therefore, it is necessary to develop new therapeutic alternatives.ObjectiveTo identify acetylcholinesterase inhibitors with potential biological activity through a computational protocol.MethodsIn this study, a computational approach based on virtual screening, molecular docking, and molecular dynamics simulations was applied to identify new potential acetylcholinesterase inhibitors.ResultsThe results allowed the identification of three compounds with higher binding affinities than donepezil, which was used as a reference. Among them, ligand code 24771824 stood out for establishing hydrophobic and aromatic interactions that maximize dispersive contributions and promote a rigid and stable conformation within the active site. In contrast, ligand codes 151171 and 21081761 were favored by more directional polar contacts, which increased specificity but limited the overall affinity toward the enzyme.ConclusionsAltogether, the free energy, structural fluctuation, hydrogen bond occupancy, and molecular clustering analyses suggest that 24771824 exhibits the most favorable energetic and dynamic behavior, consolidating it as the best candidate for future experimental validation.
BackgroundEngagement in physical, social, and intellectual activities is associated with cognitive health in later life, but how their long-term combinations relate to mild cognitive impairment (MCI), a key preclinical stage of Alzheimer's disease, remains unclear.ObjectiveTo identify multi-activity trajectories and examine their associations with MCI.MethodsData were from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative biennial survey collecting demographic, socioeconomic, health, cognitive, and activity-related information. We included 1884 participants aged ≥60 years in 2020. Group-based multi-trajectory modeling identified physical, social, and intellectual activity patterns using the 2011, 2013, 2015, and 2018 waves. Multivariable logistic regression examined associations with MCI in 2020, with subgroup analyses by sex, baseline age, education, and residence.ResultsFour distinct trajectory groups were identified: Moderate PA-low SI (social and intellectual activity), High PA-low SI, Moderate PA-higher SI, and High PA-moderate SI. The Moderate PA-higher SI group had the lowest MCI prevalence (9.47%) and served as the reference. In the fully adjusted model, MCI odds were highest in the High PA-low SI group (OR = 2.05, 95% CI: 1.34-3.15), followed by the Moderate PA-low SI group (OR = 1.57, 95% CI: 1.08-2.30). The High PA-moderate SI group was not significantly associated with MCI (OR = 1.38, 95% CI: 0.72-2.58).ConclusionsModerate physical activity combined with higher social and intellectual engagement was associated with more favorable cognitive outcomes, supporting integrated, pattern-based lifestyle strategies for early prevention of MCI and Alzheimer's disease.
BackgroundDistinguishing Alzheimer's disease (AD) from frontotemporal dementia (FTD) remains a major clinical challenge, particularly in early disease stages due to overlapping symptoms. Although neuropsychological assessment is central to diagnosis, brief cognitive screening instruments often emphasize episodic memory, whereas comprehensive neuropsychological assessment encompasses multiple cognitive domains. Nevertheless, social cognition and other relevant functions may remain underrepresented in routine clinical assessment.ObjectiveTo identify neuropsychological domains and test procedures that reliably differentiate AD from FTD and support differential diagnosis.MethodsA systematic PubMed literature search was conducted using predefined inclusion and exclusion criteria. Original studies directly comparing neuropsychological performance in patients with AD and FTD were included. Owing to methodological heterogeneity, findings were synthesized narratively.ResultsA total of 322 records were identified, of which 80 studies met the inclusion criteria. A clear domain-specific pattern emerged. Episodic memory impairment, particularly delayed recall deficits, consistently distinguished AD from FTD, with poorer performance in AD. In contrast, deficits in social cognition, including theory of mind and emotion recognition, were more pronounced in FTD and were often detectable early in the disease course. Executive functions and language showed heterogeneous findings, with discriminative value depending on specific subdomains and FTD subtypes. Visuospatial functions and attention provided supportive but less consistent differentiation, while global screening instruments showed limited diagnostic specificity.ConclusionsDifferentiation between AD and FTD should not rely on single cognitive domains. The most clinically meaningful distinction is achieved through a multidimensional assessment integrating episodic memory, social cognition, and selected executive and behavioral measures.
BackgroundSystemic inflammation has been implicated in cognitive aging and neurodegeneration; however, inflammatory biomarkers are expressed in coordinated patterns rather than as isolated markers.ObjectiveTo identify latent inflammatory biomarker groupings and evaluate their associations with cognitive performance among midlife and older adults.MethodsThis cross-sectional study included 334 participants from the Aging Research Characterizing Health Exposome via Social Drivers (ARCHES) study. Cognitive performance was assessed using the Preclinical Alzheimer Cognitive Composite (PACC). Plasma inflammatory biomarkers were quantified using the NuLISA™ multiplex immunoassay platform. Exploratory factor analysis (EFA) (minimum residual extraction, oblimin rotation) identified latent inflammatory factors, retaining biomarkers with loadings ≥0.40. Factor scores were evaluated in multivariable linear regression models adjusted for age, sex, education, and genotype status; sensitivity analyses adjusted for socioeconomic context, medication use, BMI, lifestyle factors, and clinical diagnoses. Age-stratified and nonlinear spline analyses were conducted.Results27 of 43 biomarkers formed an eight-factor structure explaining 43% of variance. Two factors were significantly associated with cognitive performance after FDR correction. Factor 4 (CCL4, CXCL1, S100A12) and Factor 5 (IL2, IL5, IL13, IL10, CSF2) were inversely associated with PACC scores. These associations remained consistent across sensitivity analyses. Age-stratified analyses showed that several inflammatory factors were associated with cognitive performance among participants aged 45-<65 years, whereas no factors remained significant among participants aged ≥65 years after FDR correction. Nonlinear modeling indicated a non-linear age-cognitive performance relationship.ConclusionsEFA identified clusters of correlated inflammatory biomarkers associated with cognitive performance in this cohort of midlife and older adults.
BackgroundGastric disorders have been linked to health outcomes beyond the gastrointestinal system. However, the long-term implications of early-life gastric morbidity for aging-related outcomes remain unclear. We investigated the association between early-life gastric disorders and cognitive impairment in later adulthood, a key outcome in population aging.ObjectiveThe objective of this study is to investigate the association between a history of early-life gastric disorders and cognitive outcomes across midlife and aging.MethodsWe analyzed data from 6744 participants in the Health and Retirement Study (2006-2016). Early-life gastric disorders were defined as self-reported stomach problems before age 16. Incident cognitive impairment was identified based on self-reported physician diagnosis. Cox proportional hazards models were used to estimate risk, with adjustment for relevant covariates, and stratified analyses were conducted.ResultsEarly-life gastric disorders were associated with a higher risk of cognitive impairment (HR = 1.89; 95% CI: 1.03-3.48). Subgroup analyses indicated variation across population groups. However, the association was not statistically significant after excluding early incident casesConclusionsEarly-life gastric disorders may be linked to later cognitive impairment, although this association was sensitive to analytical assumptions and should be interpreted with caution. Further studies are needed to clarify underlying mechanisms.
BackgroundSleep disturbances are common in Alzheimer's disease (AD) and may worsen neuropsychiatric symptoms, caregiver burden, quality of life, and disease progression. Non-pharmacological strategies are increasingly used because long-term hypnotic or antipsychotic treatment may be limited by safety concerns, but their effects on subjective and objective sleep outcomes remain uncertain.ObjectiveTo evaluate the efficacy of non-pharmacological interventions for improving sleep in patients with AD.MethodsFollowing PRISMA guidelines, we searched PubMed, Embase, the Cochrane Library, Web of Science, and CINAHL through June 6, 2025, for randomized controlled trials of non-pharmacological interventions in AD. The primary outcome was the Pittsburgh Sleep Quality Index (PSQI); secondary outcomes were actigraphy-derived sleep efficiency, total sleep time, wake after sleep onset, number of awakenings, and time in bed. Standardized mean differences were pooled using fixed- or random-effects models. Subgroup, sensitivity, publication-bias, and meta-regression analyses were performed where appropriate.ResultsFourteen randomized controlled trials comprising 937 participants were included. Non-pharmacological interventions significantly reduced PSQI scores (SMD = -0.46, 95% CI -0.70 to -0.21). Neuromodulation-based interventions showed potentially favorable effects on PSQI, and caregiver-delivered programs such as NITE-AD modestly reduced nocturnal awakenings. No significant effects were observed for sleep efficiency, total sleep time, wake after sleep onset, or time in bed.ConclusionsNon-pharmacological interventions may modestly improve subjective sleep quality in AD, but objective sleep benefits remain limited. Larger, multicenter trials with standardized protocols, longer follow-up, harmonized subjective and objective outcomes, and AD-related biomarker assessment are needed.
