To investigate the correlation between cumulative blood pressure levels and functional dependence in individuals aged 60 and older.

Data were sourced from two distinct elderly databases: CHARLS and ELSA. Various statistical techniques, including logistic regression analysis, restricted cubic spline analysis, forest plot visualization, and interaction analyses, were employed to delve into the associations between cumulative blood pressure and difficulties in performing activities of daily living.

Across both cohorts, there was a discernible trend towards an increased risk of ADL difficulties with escalating cumulative blood pressure levels. Logistic regression analysis confirmed a statistically significant relationship between cumulative blood pressure and ADL difficulties (P < 0.001). The results derived from restricted cubic spline plots further underscored this relationship, demonstrating that, after adjusting for a multitude of confounding factors, the risk of ADL difficulties augmented with rising cumulative blood pressure. Additionally, the forest plot and interaction plot findings revealed that the association between heightened cumulative blood pressure and the risk of ADL difficulties was contingent upon factors such as body mass index, gender, and memory-related disorders.

Our findings indicate a positive correlation between cumulative blood pressure and the risk of experiencing difficulties with ADLs. Furthermore, this association appears to be modulated by BMI, gender, and the presence of memory-related diseases.

Neuropsychiatric symptoms (NPS) are prevalent, emerge early, and are associated with poorer outcomes in Lewy body dementia (LBD). Research suggests NPS may reflect LBD-related dysfunction in distributed neuronal networks. This study investigated NPS neural correlates in prodromal LBD using resting-state functional MRI. Fifty-seven participants were included with mild cognitive impairment (MCI) with Lewy bodies (MCI-LB, n = 28) or Parkinson's disease (PD-MCI, n = 29). Functional MRI assessed connectivity within five resting-state networks: primary visual, dorsal attention, salience, limbic, and default mode networks. NPS were measured using the Neuropsychiatric Inventory. Principal component analyses identified three neuropsychiatric factors: affective disorder (apathy, depression), psychosis (delusions, hallucinations) and anxiety. Seed-to-voxel connectivity maps were analysed to determine associations between NPS and network connectivity. In PD-MCI, affective symptoms and anxiety were associated with greater connectivity between limbic orbitofrontal cortex and default mode areas, including medial prefrontal cortex, subgenual cingulate and precuneus, and weaker connectivity between limbic orbitofrontal cortex and the brainstem and between the salience network and medial prefrontal cortex (all pFWE<0.001). Psychosis severity in PD-MCI correlated with connectivity across multiple networks (all pFWE<0.001). In MCI-LB, no significant correlations were found between NPS severity and network connectivity. However, participants with anxiety demonstrated a trend towards greater connectivity within medial prefrontal areas than those without (pFWE=0.046). Altered connectivity within and between networks associated with mood disorders may explain affective and anxiety symptoms in PD-MCI. Neural correlates of NPS in MCI-LB, however, remain unclear, highlighting the need for research in larger, more diverse LBD populations to identify symptomatic treatment targets.

The 4 Mountain Test (4MT) is a test of allocentric spatial working memory and has been proposed as an earlier marker of predementia Alzheimer's disease (AD) than episodic verbal memory. We here compare the 4MT to the CERAD word list memory recall in both cognitively normal (CN) and mild cognitive impairment (MCI) cases with or without cerebrospinal fluid markers (CSF) of Alzheimer's disease pathology. Linear regression was used to assess the influence of CSF determined Aβ-plaque (Aβ-/+) or neurofibrillary tau tangles (Tau-/+) on 4MT and CERAD recall performance. Analyses were performed in the full sample and the CN and MCI sub-samples. Pearson correlations were calculated to examine the relationship between 4MT and tests of psychomotor speed, verbal memory, cognitive flexibility, verbal fluency, and visuo-spatial perception. Analyses showed no significant differences in 4MT scores between Aβ-/Aβ+, nor Tau-/Tau + participants, irrespective of cognitive status. In contrast, CERAD recall scores were lower in both Aβ+ compared to Aβ- (p<.01), and Tau + compared to Tau- participants (p<.01) in the full sample analyses. There were no significant differences in CERAD recall performance between Aβ- vs. Aβ+ and Tau- vs. to Tau + in the in CN/MCI sub-samples. 4MT scores were significantly correlated with tests of psychomotor speed, cognitive flexibility, and visuo-spatial perception in the full sample analyses. In conclusion, the CERAD recall outperformed the 4MT as a cognitive marker of CSF determined AD pathology. This suggests that allocentric working memory, as measured by the 4MT, may not be used as an early marker of predementia AD.

The Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are globally recognized as validated cognitive screening tests widely used.

The present study attempted to provide conversion tables from the MMSE to the MoCA and vice versa, deriving them from a large population of healthy older adults and a representative clinical sample of subjects with different types of cognitive decline within the spectrum of Alzheimer's (AD) and Parkinson's (PD) diseases.

A total of 1423 Italian participants, including healthy adults (n = 1203), individuals with AD (n = 93), and with PD (n = 127) were assessed using the MMSE and MoCA. Conversion tables were developed using log-linear smoothing equipercentile equating (LSEE). The reliability of the conversion was assessed through the Root Mean Square Error (RMSE) in a train-test approach confirmed in the whole sample.

The findings demonstrated that the LSEE method enables the development of conversion tables allowing users to identify the corresponding MoCA score for each MMSE score within the studied groups, and vice versa. The estimation error RMSE was 1.8, 2.9, and 3.2 for the conversion of MoCA from MMSE and 1.2, 2.3, and 2.2 for the conversion of MMSE from MoCA in healthy subjects, AD, and PD, respectively. The reliability of the conversion is higher in healthy subjects and for higher values of MoCA and MMSE.

Results report easy-to-use conversion norms for transforming raw MMSE score to MoCA and vice versa, highlighting areas were the conversion has a strong or low reliability depending on the score range.

Aligning biomarker evidence with clinical presentation in early Alzheimer disease (AD) is essential for improving diagnosis, prognosis, and interventions. This study evaluates the relationship between cognitive impairment, future decline, and phosphorylated tau levels in plasma and CSF in predementia AD.

This longitudinal observational study included predementia cases and controls from 2 independent cohorts: the Norwegian Dementia Disease Initiation (DDI) and Canadian Pre-Symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT-AD). In DDI, cognitively normal (CN) and mild cognitive impairment (MCI) cases were classified using CSF Aβ42/40 ratio (A) and p-tau181 (T), whereas classification in PREVENT-AD (A) was based on amyloid PET scans. In DDI, we assessed CSF-plasma correlations for p-tau181, p-tau217, and p-tau231. Diagnostic accuracies were evaluated through receiver operating characteristic analyses. Linear mixed models evaluated p-tau associations with future memory decline. Between-group differences in plasma p-tau217 were assessed in both cohorts.

In DDI (n = 431), participants were classified as CN A-/T- (n = 169), A+/T- (CN = 26, MCI = 24), A+/T+ (CN = 40, MCI = 105), and A-/T+ (CN = 34, MCI = 33), with a mean age of 64.1 years and 55.9% female. In PREVENT-AD (n = 190), participants were categorized as CN A- (n = 118), CN A+ (n = 49), and MCI A+ (n = 21), with a mean age of 67.8 years and 72.6% female. In DDI, plasma p-tau217 showed high accuracy in identifying A+ participants (areas under the curve [AUC]: 0.85) and a moderate correlation with CSF p-tau217 (rho = 0.65, p < 0.001). Diagnostic accuracy of plasma p-tau217 was greater in MCI A+ (AUC: 0.89) than in CN A+ (AUC: 0.79, p < 0.05) and in A+/T+ (AUC: 0.88) vs A+/T- (AUC: 0.78, p < 0.05). p-Tau181 and p-tau231 had weaker CSF-plasma correlations (rho = 0.47 and rho = 0.32, p < 0.001) and were less associated with cognitive status in A+ individuals. Higher plasma p-tau217 in A+ MCI vs A+ CN individuals (p < 0.001) was confirmed in PREVENT-AD. All CSF p-tau markers, but only plasma p-tau217, were associated with future memory decline (β = 0.05, p < 0.05).

Our findings suggest that, unlike p-tau181 and p-tau231, plasma p-tau217 consistently aligns with cognitive status in A+ individuals and better reflects CSF biomarker abnormalities, reducing discrepancies between clinical and biochemical findings. Its association with baseline and future memory decline highlights its diagnostic and prognostic value, particularly when CSF analysis or PET is unavailable.

Despite extensive research on language impairments of individuals with Alzheimer's disease (AD), little is known about whether women with mild cognitive impairment (MCI), an early stage of AD, differ in their language use from healthy controls. To address this gap, the present study investigates speech differences between 22 older women with early AD and 22 age- and education-matched controls with lexical-semantic and disfluency linguistic features, comparing their performance on the Cookie Theft picture description and Cinderella story retelling tasks from the Delaware corpus. Results reveal that, compared with the control group, women with early AD i) produced a smaller average number of tokens and types, with the differences in the Cinderella retelling task reaching statistical significance; ii) exhibited more pronouns and higher anomia values, with both features showing significant differences in recalling the Cinderella story; and iii) produced more disfluencies such as lexical fillers, repeats, and false starts, with a significant difference in lexical fillers in the Cinderella retelling task. These patterns suggest that complex language tasks prove to be more capable of revealing pre-existing cognitive changes in women with early AD, as manifested in their reduced lexical retrieval abilities, higher anomia levels, and increased speech disfluency compared to healthy controls. These findings provide empirical evidence of speech differences between aged women with and without early AD, underscoring the importance of linguistic analysis as a valuable tool in the early detection of AD-related language, memory, and cognitive impairments and timely intervention in the AD continuum.

Considering the prevalence of Alzheimer's Disease (AD) among the aging population and the limited means of treatment, early detection emerges as a crucial focus area whereas electroencephalography (EEG) provides a promising diagnostic tool. To date, several studies indicated EEG dataset-based models sporting high diagnostic power in distinguishing patients with AD from healthy controls (HC). However, exploration into which features play a crucial role in the diagnosis remains limited.

This study investigates the diagnostic capabilities of EEG for distinguishing patients with AD from HCs through random forest classification on EEG features. Band power and cross-correlation from the resting state EEG dataset of 22 HCs and 160 patients with AD were calculated using Welch's periodogram and Pearson's correlation, respectively. Welch's t-test was applied to identify features demonstrating significant differences between patients with AD and HCs. Band power and cross-correlation were analyzed using a random forest classifier (RFC) and feature-importance analysis. The importance of feature categories, defined as subsets of features grouped by frequency bands (for band power features) or brain regions (for cross-correlation features), was quantified by calculating their average occurrence across all hyperparameter configurations.

Distinct patterns between the eyes-closed and eyes-open conditions in alpha power were not observed for patients with AD (vs. HC), whereas theta power (4-8 Hz) in all regions was higher in patients with AD (vs. HC)(p<0.05). Interhemispheric cross-correlation in the temporal lobes exhibited the most distinguishable distribution for the cross-correlation dataset. An RFC, exploring 512 models with varied hyperparameters followed by feature-importance analysis based on the mean decrease in impurity, highlighted "theta relative power" and "interhemispheric cross-correlation of channel pairs including temporal channels" as the most important features for distinguishing patients with AD from HCs. RFC on theta-band filtered cross-correlation dataset informed by important features demonstrated the robustness of important features across models with different hyperparameter settings.

The models achieved over 97% accuracy and 100% recall in test sets, although the interpretation of this extraordinarily high accuracy warrants caution due to the small dataset size with high data imbalance and the absence of external validation. This methodology demonstrates the efficacy of EEG-based metrics and machine learning in improving our understanding of EEG characteristics in patients with AD, emphasizing the potential of integrating machine learning techniques into clinical practices.

To investigate systemic inflammation and the effect of dapagliflozin treatment in (type 2 diabetes mellitus) T2DM patients with mild cognitive impairment (MCI).

Between January and December 2023, 200 participants were recruited from the Department of Endocrinology of Hefei First People's Hospital. Baseline data collected included medical history, fasting blood glucose, HbA1c, liver and kidney function, lipid profiles, IL-1β, TNF-α, sVCAM-1 level, and the urinary albumin-creatinine ratio (uACR). Based on their Montreal Cognitive Assessment Scale (MoCA) scores, these participants were categorized into two groups: 127 in the MCI group and 73 in the non-MCI group. MCI group received dapagliflozin (10 mg daily) alongside standard treatment.

The MCI group showed higher age, height, weight, BMI, HbA1c, FBG, disease duration, carotid plaques, stenosis rates, and elevated IL-1β, TNF-α, and sVCAM-1. MoCA scores were significantly lower in the MCI group. Correlation analysis showed a negative correlation of MoCA scores with IL-1β, TNF-α, sVCAM-1, plaques, stenosis, FBG, and HbA1c, and a positive correlation with height. Binary logistic regression identified age, BMI, IL-1β, sVCAM-1, and FBG as predictors of cognitive impairment in T2DM. Dapagliflozin treatment reduced BMI, HbA1c, inflammatory markers, and FBG, improving MoCA scores.

Dapagliflozin treatment may improve cognitive function by reducing inflammation.

At the present time, clinical detection of individuals who have amyloid in their brain is not possible without expensive biomarkers. The objective of the study was to test whether Cognivue Clarity® can differentiate True Controls, preclinical Alzheimer's disease (pAD), mild cognitive impairment (MCI) due to Alzheimer's disease (MCI-AD), AD, and MCI and dementia due to non-AD etiologies enrolled in the Bio-Hermes Study.

A total of 887 individuals completed Cognivue Clarity, amyloid PET scan, and blood-based AD biomarkers. Three Cognivue Clarity subtests differentiated between True Controls and pAD, and between cognitive impairment due to AD versus non-AD processes. This finding was leveraged to develop an amyloid-specific marker, combining the three subtests with age using machine learning to create the 4-point Cognivue Amyloid Risk Measure (CARM).

Cognivue Clarity discriminated cognitively normal from cognitively impaired individuals (p < 0.001, Cohen's d = 0.732). The CARM differentiated between individuals with amyloid and without amyloid by PET (p < 0.001, Cohen's d = 0.618) and blood-based biomarkers (p's < 0.001). Amyloid positivity and cognitive impairment increased across four CARM thresholds (p < 0.001). Dichotomizing CARM thresholds into low (CARM1/CARM2) and high (CARM3/CARM4) likelihood provided excellent discrimination for amyloid PET positivity (OR: 3.67; 95% CI 2.76-4.89). CARM categories differentiated between True Controls, pAD, MCI-AD, AD, and cognitive impairment due to non-AD etiologies (χ2 = 137.6, p < 0.001) with the majority of True Controls and non-AD etiologies being in CARM1/CARM2, and the majority of pAD, MCI-AD, and AD being in CARM3/CARM4.

Cognivue Clarity detects individuals with cognitive impairment, and a derivation benchmarked against amyloid PET was used to develop the CARM to predict the presence of amyloid. Combining the CARM and the Cognivue Clarity overall score could help identify individuals with and without cognitive impairment due to AD or non-AD etiologies, help screen for treatment protocols with anti-amyloid therapies, enrich clinical trial recruitment, and help to identify pAD for prevention studies.

ClinicalTrials. gov identifier, NCT04733989.

Neurodegenerative diseases, including Alzheimer's disease, are characterised by selective neuronal vulnerability with regional, laminar, cellular and neurotransmitter specificity. The regional losses of neurons and their synapses are associated with neurophysiological changes and cognitive decline. Hypotheses related to these mechanisms can be tested and compared by dynamic causal modelling (DCM) of human neuroimaging data, including magnetoencephalography (MEG). In this paper, we use DCM of cross-spectral densities to model changes between baseline and follow-up data in cortical regions of the default mode network, to characterise longitudinal changes in cortical microcircuits and their connectivity underlying resting-state MEG. Twenty-nine people with amyloid-positive mild cognitive impairment and Alzheimer's disease early dementia were studied at baseline and after an average interval of 16 months. To study longitudinal changes induced by Alzheimer's disease, we evaluate three complementary sets of DCM: (i) with regional specificity, of the contributions of neurons to measurements to accommodate regional variability in disease burden; (ii) with dual parameterisation of excitatory neurotransmission, motivated by preclinical and clinical evidence of distinct effects of disease on AMPA versus NMDA type glutamate receptors; and (iii) with constraints to test specific clinical hypothesis about the effects of disease-progression. Bayesian model selection at the group level confirmed evidence for regional specificity of the effects of Alzheimer's disease, with evidence for selective changes in NMDA neurotransmission, and progressive changes in connectivity within and between Precuneus and medial prefrontal cortex. Moreover, alterations in effective connectivity vary in accordance with individual differences in cognitive decline during follow-up. These applications of DCM enrich the mechanistic understanding of the pathophysiology of human Alzheimer's disease and inform experimental medicine studies of novel therapies. More generally, longitudinal DCM provides a potential platform for natural history and interventional studies of neurodegenerative and neuropsychiatric diseases, with selective neuronal vulnerability.

Standardized neuropsychological instruments are used to evaluate cognitive impairment, but few have been psychometrically evaluated in American Indians. We collected Montreal Cognitive Assessment (MoCA) in 403 American Indians 70 to 95 years, as well as age, sex, education, bilingual status, depression symptoms, and other neuropsychological instruments. We evaluated inferences of psychometric validity, including scoring inference using confirmatory factor analysis and structural equation modeling, generalizability inference using reliability coefficient, and extrapolation inference by examining performance across different contexts and substrata. The unidimensional (total score) model had good fit criteria. Internal consistency reliability was high. MoCA scores were positively associated with crystallized cognition (ρ = 0.48, p < .001) and inversely with depression symptoms (ρ = -0.27, p < .001). Significant differences were found by education (d = 0.79, p < .05) depression (d = 0.484, p < .05), and adjudicated cognitive status (p = .0001) strata; however, MoCA was not sensitive or specific in discriminating cognitive impairment from normal cognition (area under the curve <0.5). MoCA scores had psychometric validity in older American Indians, but education and depression are important contextual features for score interpretability. Future research should evaluate cultural or community-specific adaptations, to improve test discriminability in this underserved population.

Cholinesterase inhibitors (ChEIs) are effective in treating mild to moderate Alzheimer's disease (AD) dementia by compensating for acetylcholine deficiency. While their use in mild cognitive impairment (MCI) lacks strong trial support, some studies suggest they may delay neurodegeneration. This study aims to investigate ChEIs' neuroprotective effects in individuals with amnestic MCI (aMCI) using multi-modal neuroimaging, and to determine if amyloid-beta (Aβ) deposition influences these effects.

Longitudinal data from a cohort study were retrospectively analyzed. A total of 118 aMCI patients (ages 55- 90), who underwent baseline evaluations encompassing the assessment of ChEI use and [11C] Pittsburgh compound B-positron emission tomography (PET), were included in the analyses. All participants also received baseline and 2-year follow-up magnetic resonance imaging and [18F] fluorodeoxyglucose-PET imaging.

The ChEI use group exhibited a significantly lesser decline in AD-signature region cerebral metabolism (AD-CM) over a 2-year period than the ChEI non-use group (B = 0.089, 95% CI: 0.030-0.149). However, there was no significant difference in the 2-year change of AD-signature region cortical thickness (AD-CT) (B = 0.032, 95% CI: -0.075 to 0.138) and hippocampal volume (B = -88.013, 95% CI: -323.900 to 147.874) between the ChEI use and non-use groups. The presence of Aβ pathology did not moderate the effect of ChEI use on AD-CM, AD-CT, or hippocampal volume.

The findings suggest that ChEIs may delay functional neurodegeneration in aMCI individuals, irrespective of the presence of amyloid pathology.

BackgroundRepetitive transcranial magnetic stimulation (rTMS) may be effective for Alzheimer's disease (AD) and mild cognitive impairment (MCI); however, the therapeutic efficacy varies significantly, highlighting the need for reliable biomarkers to predict treatment response. While rTMS may activates cortical white matter, the relationship between induced electric field (E-field) in this region and clinical outcomes remains unclear.ObjectiveThis study characterized the E-field in cortical gray matter (EFgm), cortical white matter (EFwm), and region-of-interest (EFROI) in the left dorsolateral prefrontal cortex (DLPFC), and explored their correlations with treatment efficacy in patients with MCI due to AD.MethodsThirty patients with MCI due to AD received 2-week rTMS treatment, with efficacy measured by Auditory Verbal Learning Test (AVLT) and a comprehensive neuropsychological battery. Responders were defined as those with >25% improvement in AVLT-immediate memory. Correlations between regional brain volumes and E-field magnitudes, and the correlations between E-field magnitudes and cognitive improvement, were analyzed. Predictive performance of E-fields for responder classification was evaluated.ResultsPronounced inter-individual variability in magnitudes of EFgm, EFwm and EFROI was observed, partially explained by differences in regional brain volumes of DLPFC targeted area. Treatment responders exhibited significantly higher EFwm magnitude. EFwm positively correlated with AVLT-immediate memory improvement (R2 = 0.37) and predicted responder groups, achieving an area under the curve (AUC) of 0.76.ConclusionsE-field within cortical white matter in the DLPFC correlates with rTMS efficacy and predicts therapeutic response in MCI due to AD. Personalized stimulation protocols incorporating EFwm modeling may optimize treatment parameters.Trial registrationThis study is registered in the Chinese Clinical Trial Registry, number ChiCTR2200062564, date of registration: 2022-08-11.

Survival estimates for individuals with Alzheimer disease (AD) are informative to understand the disease trajectory, but precise estimates for atypical AD variants are scarce. Atypical AD variants are characterized by nonamnestic phenotypes, an early onset, and lower prevalence of APOEε4 carriership, which affect the AD trajectory. We aimed to provide survival estimates for posterior cortical atrophy (PCA), logopenic variant primary progressive aphasia (lvPPA), and behavioral AD (bvAD) and to evaluate the effect of these atypical AD diagnoses beyond known mortality determinants.

From the Amsterdam Dementia Cohort, we retrospectively selected patients with biomarker-confirmed sporadic AD presenting at the memory clinic in the mild cognitive impairment or dementia stage. Patients were classified into atypical AD phenotypes (PCA, lvPPA, bvAD; multidisciplinary consensus and retrospective case finding) and a typical AD reference group (excluding unclassifiable atypical presentations or unconfirmed future AD dementia). Survival estimates from the first visit to death/censoring (Central Public Administration) were determined (Kaplan-Meier analysis) and compared (log-rank tests) across diagnostic groups. To assess associations of atypical AD with mortality, Cox proportional hazard models were constructed including age, sex, education, MMSE score, and APOEε4 carriership (model 1), followed by adding the atypical AD group (model 2) or atypical AD variants (model 3). A likelihood ratio test was performed to compare the fit of model 1 and model 2.

A total of 2,081 patients (aged 65 ± 8 years, 52% female) were classified as typical AD (n = 1,801) or atypical AD (n = 280; PCA [n = 112], lvPPA [n = 86], and bvAD [n = 82]). The estimated median survival time for atypical AD of 6.3 years (95% CI [5.8-6.9]) was shorter than for typical AD (7.2 [7.0-7.5], p = 0.02). Median survival durations across the atypical AD variants were consistently, albeit nonsignificantly, shorter (PCA: 6.3 [5.5-7.3], p = 0.055; lvPPA: 6.6 [5.7-7.7], p = 0.110; bvAD: 6.3 [5.0-9.1], p = 0.121, 48% deceased). Including atypical AD improved the model fit (model 2; χ2 = 8.88, p = 0.003) and was associated with 31% increased mortality risk compared with typical AD (hazard ratio [HR] = 1.31 [1.10-1.56], p = 0.002). In model 3, contributions of the variants were as follows: HRPCA = 1.35 (1.05-1.73), p = 0.019; HRlvPPA = 1.27 (0.94-1.69), p = 0.114; HRbvAD = 1.31 (0.94-1.83), p = 0.105.

Survival in atypical AD (PCA, lvPPA, bvAD) was shorter compared with typical AD. These atypical variants are associated with increased mortality beyond age, sex, education, APOEε4 carriership, and disease severity. Future studies are required to address generalizability of these findings and to identify factors that explain the observed survival differences.

Genome-wide association studies (GWAS) on Alzheimer's disease (AD) have predominantly focused on identifying common variants in Europeans. Here, we performed whole-genome sequencing (WGS) of 1,559 individuals from a Korean AD cohort to identify various genetic variants and biomarkers associated with AD. Our GWAS analysis identified a previously unreported locus for common variants (APCDD1) associated with AD. Our WGS analysis was extended to explore the less-characterized genetic factors contributing to AD risk. We identified rare noncoding variants located in cis-regulatory elements specific to excitatory neurons associated with cognitive impairment. Moreover, structural variation analysis showed that short tandem repeat expansion was associated with an increased risk of AD, and copy number variant at the HPSE2 locus showed borderline statistical significance. APOE ε4 carriers with high polygenic burden or structural variants exhibited severe cognitive impairment and increased amyloid beta levels, suggesting a cumulative effects model of AD risk.

The use of polygenic risk scores (PRS) for predicting disease risk in Japanese populations, particularly for dementia and related phenotypes, remains markedly unexplored. The aim of this study was to bridge this gap by developing a novel PRS model designed to predict amyloid-β (Aβ) deposition utilizing positron emission tomography (PET) imaging data from a Japanese cohort.

Using the polygenic risk score-continuous shrinkage (PRS-CS) algorithm, we calculated PRS based on significant single nucleotide polymorphisms (SNPs) associated with Alzheimer's disease (AD) in this population. We applied a PRS calculation approach informed by Japanese genome-wide association studies (GWAS) summary statistics into a Japanese dementia cohort from Keio University.

Our findings revealed that a p-value threshold of pT < 0.1 optimally enhanced the predictive capability of the Japanese Aβ PET positivity risk model. Moreover, we demonstrated that distinguishing between the counts of APOE2 and APOE4 alleles in our calculations significantly elevated model performance, achieving an area under the curve (AUC) of 0.759. Remarkably, this predictive accuracy remained robust even when the pT was adjusted to be < 1.0 × 10- 5, maintaining an AUC of 0.735. This study validated the efficacy of the model in identifying individuals with a increased risk of amyloid pathology.

These findings highlight the potential of PRS as a noninvasive tool for early detection and risk stratification of AD, which could lead to enhanced clinical applications and interventions.

On average, people with dementia fall more often than their age-matched peers, with serious consequences, yet the underlying reasons remain poorly understood. This narrative review explores relevant psychological, physiological and neuroimaging studies to discuss whether diminished inhibitory control contributes to poor balance and falls in people with Alzheimer's Disease (AD), the most common form of dementia. Inhibitory control, a component of executive function, plays a vital role in suppressing dominant impulses or actions and regulating attention in favour of a desired outcome. Although objective tests of inhibitory control are not routinely used in clinical settings, research suggests inhibitory control declines early, and progressively, in AD. Postural tasks that require inhibitory control can improve the accuracy of distinguishing fallers from non-fallers beyond known factors. Neuroimaging studies link the prefrontal cortex to both inhibitory and postural control, and this region exhibits neuronal loss early in AD. Thus, emerging evidence suggests that accurately assessing inhibitory control could not only improve falls risk predictions but also aid AD detection.

Movement patterns, activity levels and circadian rhythm are altered in Alzheimer's disease (AD) and can be assessed by actigraphy using wearable sensors. We aimed to determine the diagnostic performance of actigraphy in AD in a memory clinic population by using a machine-learning classifier.

In our single-center cross-sectional study, 70 patients with AD (MCI-moderate dementia), dementia with Lewy bodies (DLB) (N = 29) and cerebrovascular disease (CVD) (N = 23), and 48 elderly healthy controls were included. Participants underwent actigraphy at home using two body-worn sensors (SENS Motion®) for 1 week. We derived movement patterns (walking, running, resting, etc.) from raw accelerometry data using a proprietary algorithm. By evaluating the movement patterns during day and nighttime, we calculated 510 activity-related features, including robustness and fragmentation of the circadian rhythm. These features were used to train a machine learning (ML) classifier using logistic regression. We evaluated the performance of our classifier by assessing the accuracy and precision of predictions.

We found that movement patterns as well as the robustness and fragmentation of the circadian rhythm differed significantly between groups. During the daytime, patients with AD performed less moderate activity and walked less than the healthy group. While we achieved a modest accuracy of 68.8% for differentiating AD and healthy, the performance was highest (accuracy: 80-89%; precision: 69-84%) when ML was applied to actigraphy data to differentiate dementia etiologies (AD vs. DLB + AD vs. CVD).

Actigraphy accurately identifies different dementia etiologies and could serve as a supplement to diagnostic investigations in patients with suspected AD for differential diagnostic purposes.

This study explored the phenomenology of theft delusions in four female patients with Alzheimer's disease (AD) living alone, aiming to understand its psychological relevance. Caregivers participated in semi-structured interviews to assess delusion phenomenology, while patients were evaluated on their aging perceptions and life values. In three cases, the primary persecutor was a family member involved in managing finances, whereas the persecutor in the fourth patient who managed finances independently was a specific neighbor. Thieves in the delusion of theft in patients with AD living alone were likely to be persons who helped in the management of money or property.

Electroencephalography (EEG) slowing may help detect and prognosticate mild cognitive impairment (MCI). Whether slowed EEG activity is helpful for non-invasive MCI detection in a health checkup remains uncertain. This cross-sectional secondary study assessed the hypothesis that frontal EEG slowing in short-term resting-state is associated with MCI-suspicious participants over 65 in the Iwaki Health Promotion Project 2022. Participants who underwent the MCI screen test were matched by propensity score to minimize confounding (age and educational history) between the non-cognitive impairment (NCI, n = 14) and suspected-MCI (sMCI, n = 14) groups. The matched sMCI group had increased EEG β power, decreased δ power, θ/β power ratio (TBR), and frontal α asymmetry. No significant differences were found in imaginary coherence and debiased weighted phase lag index (dwPLI) between the groups. Spearman's correlation showed a negative correlation between the MCI screen performance and β power and positive correlations between the performance and δ power, TBR, or α-γ dwPLI. Contrary to the hypothesis and previous findings, these results suggest that fast frontal EEG activity is negatively associated with cognitive performance in older adults. EEG measurements in health checkups may be useful for screening cognitive impairments that are less likely due to neurodegeneration.

BackgroundOdor identification deficits predict Alzheimer's disease (AD) in epidemiological studies.ObjectiveTo compare the accuracy of a short odor identification test with a short cognitive screening test for the detection of dementia and cognitive impairment in elderly persons with cognitive concerns.MethodsThis was a cross-sectional study of 600 participants 65 years and older, without known mild cognitive impairment (MCI) or dementia, with cognitive concerns, attending primary care practices in New York City. The odor identification test was the Brief Smell Identification Test (BSIT). The comparator test was the Mini Mental Status Exam II (MMSE). Cognitive diagnoses were made using the National Alzheimer's Coordinating Center Uniform Data set (NACC-UDS) version 3 forms with slight modifications. Test performance was compared using Receiver Operating Characteristic analyses.ResultsThe mean age was 72.65 ± 6.31 years, 73.3% were female, 63.3% were Hispanic, 13.5% non-Hispanic Black, and 20.8% non-Hispanic White; 23.5% were classified as normal cognition, 27.7% as cognitive impairment-not mild cognitive impairment (MCI), 31.2% as amnestic MCI, 5.7% as non-amnestic MCI, and 12% as dementia. The MMSE was superior to the BSIT in detecting dementia and any cognitive impairment. Combining abnormal scores in the BSIT (≤8) to MMSE (≤24) improved the MMSE's specificity and positive predictive value (PPV) in detecting cognitive impairment.ConclusionsThe MMSE was superior to the BSIT in detecting dementia and cognitive impairment in primary care but using both tests improved specificity and PPV for identifying persons with subjective complaints needing further cognitive and biomarker evaluation.

Relative cerebral blood flow (rCBF), assessed using pulsed arterial spin labeling (pASL) MRI, and the standardized uptake value ratio (SUVr) in early-phase amyloid-PET (ePET) are used as proxies for brain perfusion. These methods have the potential to streamline clinical workflows and reduce the burden on patients by eliminating the need for additional procedures. While both techniques have shown good agreement with the gold standard for glucose metabolism assessment, F-fluorodeoxyglucose-PET, a direct comparison between them has yet to be fully clarified. This retrospective study aimed to compare perfusion-like data from pASL (rCBF) and ePET (SUVr) in a memory clinic cohort. We included 46 subjects (69 ± 8 years; 37 women) from the Geneva Memory Center (cognitively impaired-CI n = 29; cognitively unimpaired-CU n = 17), with available pASL and ePET. We evaluated the association between rCBF and SUVr values across 18 cortical and subcortical regions using linear regression and the within-subject coefficient of variation (wsCV). Regional differences between CU and CI groups were assessed using linear regression model corrected for age. We observed significant association between rCBF and SUVr in precuneus (β = 0.69, wsCV = 16.9), angular gyrus (β = 0.64, wsCV = 19.4), and hippocampus (β = 0.23, wsCV = 16.1). Additionally, significant differences in rCBF between CU and CI were also observed in the posterior cingulate, precuneus, calcarine, hippocampus, and composite (p < 0.05), while SUVr showed significant differences only in the hippocampus. Our findings indicate weak to moderate local correlations between the two techniques. However, both exhibited differing regional perfusion levels in CU and CI groups, with rCBF showing more regional differences between cognitive stages in comparison with SUVr. KEY MESSAGES: rCBF is assessed through pASL MRI and SUVr through ePET, both serving as proxies of brain perfusion. Weak to moderate associations between rCBF and SUVr were found in a number of brain regions. rCBF and SUVr differences between cognitive stages were observed mostly in cortical and subcortical regions respectively. Both techniques were able to identify AD perfusion-like differences expected in cognitively impaired vs unimpaired.

An early and accurate diagnosis is crucial to provide optimal patient care in neurodegenerative diseases. Although an EEG shows advantages in availability and cost compared to the current diagnostic tools, it is not routinely used in clinical practice. Previous reviews have either focused on single disease populations and/or solely on resting-state EEG. To evaluate the utility of EEG for early diagnosis and differential diagnosis, we conducted a systematic review across Alzheimer's disease (AD), Frontotemporal Dementia (FTD) and Lewy Body Dementia (DLB).

We searched databases Pubmed, Cochrane, Web of Science, and Scopus for articles published from 2000 to 2023 investigating resting-state and task-based EEG-markers in biomarker-proven AD, FTD and DLB.

Our search yielded a total of 12010 studies, of which 70 papers were eligible: 34 on AD, 18 on DLB, 9 on FTD, and 9 studies combining disease populations. Slowing of the frequency spectrum was a common observation across diseases, achieving excellent sensitivity in AD and DLB. Research on FTD was limited and with varying results in the discrimination from healthy controls, although connectivity analysis and microstates are promising avenues. In differential diagnosis, both spectral and connectivity metrics show encouraging results. Task-based EEG emerges as a promising tool in early AD.

EEG shows promise as a cost-effective, non-invasive tool for early detection and differential diagnosis. Future research should aim to collect standardized data from multicentric cohorts, across multiple diseases and stages, and explore the neural underpinnings of these diseases, to improve interpretability of the findings.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to cognitive and functional decline and primarily affects the elderly population. Metabolic alterations, particularly in the amino acid and fatty acid pathways, are increasingly being recognized in AD. However, the role of sex in these metabolic changes remains insufficiently understood, despite evidence suggesting that AD may manifest more strongly in females. This study investigated sex-specific metabolic patterns in AD by analyzing routine and non-routine hematological tests, including amino acids and fatty acid profiles. The results showed that certain metabolites such as citrulline and alanine were frequently altered in patients with AD. Notably, docosahexaenoic acid, dihomo-gamma-linolenic acid, and gamma-linolenic acid levels were exclusively elevated in female patients. Additionally, females exhibited significantly lower Aβ42 and higher gamma-linolenic acid levels than males, with the trend becoming more pronounced during the early stages of the disease. Despite these differences, most metabolic markers did not show significant sex-based variation. These findings suggest that while some sex-specific metabolic differences exist in AD, a larger cohort is needed to confirm these patterns and fully understand the influence of sex on AD-related metabolic changes.

To assess the reliability and diagnostic validity of in-home tele-neuropsychological testing (in-home tele-npt) in individuals with Parkinson's disease (PD).

We randomized 79 individuals with PD to in-person npt or in-home tele-npt at Baseline, and again to the same or crossover group for Week 4 testing. We assessed group differences in mean test scores using ANOVAs with Dunnett's t-tests. Test-retest reliability was assessed using intraclass correlation coefficients and Pearson correlations and compared across groups using 95% confidence intervals and z-tests with Fisher's z transformations. We compared the percentage of participants exceeding each test's standardized regression-based index across groups. We examined diagnostic validity by comparing group differences in cognitive classifications using Pearson's Chi-square test and Fisher's Exact test.

For most tests, the mean scores between in-home tele-npt and in-person npt were not significantly different. In-home tele-npt had weaker Baseline processing speed scores. The test-retest reliability was similar between the repeated tele-npt and repeated in-person npt groups in most tests. The crossover groups had weaker test-retest reliability in processing speed, verbal fluency, and memory tests. The percentage of significant change scores varied between groups and across tests. The percentage of participants classified as cognitively impaired, and the agreement of cognitive classification between testing sessions, were not significantly different between groups.

With few exceptions, in-home tele-npt and in-person tele-npt yield similar scores. Test-retest reliability is better when the testing paradigm is held constant. There are no significant differences in cognitive diagnostic classification rates between testing paradigms in individuals with PD.

Early identification of individuals who progress from normal cognition (NC) to mild cognitive impairment (MCI) may help prevent cognitive decline. We aimed to build predictive models using radiomic features of the bilateral hippocampus in combination with scores from neuropsychological assessments.

We utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study 175 NC individuals, identifying 50 who progressed to MCI within seven years. Employing the Least Absolute Shrinkage and Selection Operator (LASSO) on T1-weighted images, we extracted and refined hippocampal features. Classification models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and light gradient boosters (LightGBM), were built based on significant neuropsychological scores. Model validation was conducted using 5-fold cross-validation, and hyperparameters were optimized with Scikit-learn, using an 80:20 data split for training and testing.

We found that the LightGBM model achieved an area under the receiver operating characteristic (ROC) curve (AUC) value of 0.89 and an accuracy of 0.79 in the training set, and an AUC value of 0.80 and an accuracy of 0.74 in the test set.

The study identified that T1-weighted magnetic resonance imaging radiomic of the hippocampus would be used to predict the progression to MCI at the normal cognitive stage, which might provide a new insight into clinical research.

Despite the rapid increase in dementia and cognitive impairment incidence in Korea, research on integrative treatment for cognitive impairment using Korean medicine (KM) is still in its infancy. Thus, prospective studies with systematic data collection are required. This study aims to systematically collect and explore data from patients with dementia and mild cognitive impairment (MCI) who visit KM institutions. The data collected will include the participants' baseline characteristics, cognitive impairment severity, KM diagnosis and treatment status, as well as the factors influencing their choice of integrative medical treatment.

This registry study will be conducted from the time of registration in 2024 until December 31, 2029, at Wonkwang University Korean Medicine Hospital, Wonkwang University Jangheung Integrated Medical Hospital, and Daejeon Korean Medicine Hospital of Daejeon University. Approximately 300 participants will be enrolled and visit the hospital annually for data collection. The collected data will include sociodemographic characteristics, laboratory tests, medical device inspections, long-term care information, and various questionnaires related to dementia and cognitive impairment. No predefined interventions or restrictions on treatment will be imposed. Standard and KM treatments for cognitive impairment, including combination therapies, are permitted. As a registry study, the purpose is to investigate the participants' characteristics as outlined in the study objectives, including severity, KM diagnosis and interventions, and clinical outcomes. This epidemiological study is designed to include additional statistical analyses in response to research questions that emerge over time.

This study represents a pioneering effort in the KM field establishing the first registry of its kind focusing on dementia and MCI. This study aims to identify the characteristics of patients with dementia and MCI who visit KM institutions, explore the factors influencing KM treatment, and observe clinical outcomes according to KM pattern identification, providing evidence based on real-world data.

Glial Fibrillary Acidic Protein (GFAP) is a well-established biomarker of astrocytes and astrogliosis, a pathological response observed in various neurological diseases. This study aimed to evaluate the diagnostic performance of serum GFAP in Alzheimer's disease (AD), multiple sclerosis (MS), and transthyretin amyloidosis (ATTR) polyneuropathy.

We performed a retrospective observational study, including 498 participants (337 healthy controls and 161 patients with AD, MS, or ATTR amyloidosis). Serum GFAP levels were measured using the Lumipulse G1200 platform, and statistical analyses were performed to compare levels across disease groups and assess their diagnostic accuracy.

GFAP levels were significantly elevated in all neurological disease groups compared to age-matched controls, with the highest levels found in AD (79.4 pg/mL vs. 39.5 pg/mL, p = 2.55 × 10-12). ROC curve analysis revealed that GFAP had strong diagnostic performance for AD (AUC = 0.86), moderate performance for ATTR amyloidosis (AUC = 0.67), and poor performance for MS (AUC = 0.61).

These findings suggest that GFAP is a promising biomarker for AD, reflecting astrocytic activation and neuroinflammatory processes. Its diagnostic utility in ATTR amyloidosis is moderate, while its role in MS remains limited.

The purpose of this systematic review was to synthesize the current evidence on motor learning in mild cognitive impairment (MCI). A search of five databases returned a total of 6058 references, 10 of which met criteria for inclusion in this review. The existing evidence was notably variable with an overall moderate risk of bias. Eight articles compared behavioral motor learning outcomes in MCI and age matched, non-cognitively impaired (NCI) samples. In 37.5% of these studies, the degree of motor skill acquisition in the MCI group was statistically significantly less than in the NCI group. Skill retention was only compared between MCI and NCI samples in one article, which reported a relative reduction in MCI group performance following a 24-h, no-practice delay. Importantly, none of the included articles examined motor skill transfer. We discuss possible sources of heterogeneity among collective findings including variability in motor tasks, outcome measurement, and research design. Further research is needed to support a comprehensive understanding of motor learning in the early stages of age-related cognitive decline. Future investigations should emphasize functional motor tasks and clinically relevant learning outcomes, including retention and transfer of motor skills, while controlling for potentially confounding factors such as motivation and sleep performance. This systematic review was registered with PROSPERO international prospective register of systematic reviews (registration ID CRD42023417329).

We aimed to elucidate the correlation between cognitive function and relaxation rates of the cerebral cortex in the early stages of cognitive decline.

Brain MRI was performed on 97 community-dwelling elderly participants aged 65-84 years. R1 (1/T1) and R2 (1/T2) maps were obtained with synthetic MRI (SyMRI). Cognitive function was evaluated using the Japanese version of the Montreal Cognitive Assessment (MoCA). Participants were categorized into mild cognitive impairment (n = 47) and healthy control (n = 50) groups. Voxel-based quantification (VBQ) and voxel-based morphometry (VBM) analyses were conducted using two-sample t-tests and multiple regression models, with age and sex as covariates.

VBQ revealed a significant negative correlation between R1 values and MoCA visuospatial/executive score in the bilateral frontal pole and left superior frontal gyrus (family-wise error-corrected p < 0.05). No significant correlations were found between R2 values and visuospatial/executive score. The multiple regression analysis for VBM showed no significant correlations, and the two-sample t-tests for both VBQ and VBM revealed no significant group differences.

Visuospatial/executive impairment correlated with higher R1 and R2 values in the frontal cortex, suggesting their potential as biomarkers for early cognitive decline.

Efficient and early detection of cognitive impairment may be facilitated using the NIH Toolbox (NIHTB), a computerized suite of assessments measuring multiple aspects of neurological functioning.

The Advancing Reliable Measurement in Alzheimer's Disease and cognitive Aging study validated the NIHTB across a geographically diverse cognitive aging sample. Participants aged >64 with normal cognition (NC), mild cognitive impairment (MCI), and dementia of the Alzheimer type (DAT) across nine research sites completed the NIHTB. One-way ANOVAs captured differences in performance on the Cognition Battery and effect sizes were calculated.

Groups differed substantially across all cognition measures, with large differences in Total and Fluid Cognition, after demographic adjustment. The largest differentiators were in fluid measures, particularly for working and episodic memory.

NIHTB-CB differentiates NC, MCI, and DAT groups. Future studies will examine longitudinal differences and performance in enriched samples (African American participants, Spanish NIHTB, 85+ years old).

BackgroundThe prevalence of individuals living with mild cognitive impairment (MCI), Alzheimer's disease (AD), and other forms of dementia is globally increasing. Four out of nine international clinical guidelines recommend non-pharmacological cognitive interventions to enhance cognition, independence, and wellbeing. However, the effectiveness of cognitive rehabilitation (CR) and cognitive training (CT) for individuals with MCI and AD and other forms of dementia is still debatable, often due to significant heterogeneity among studies.ObjectiveThis study aims to assess the effectiveness of CT and CR in these populations.MethodsFollowing PRISMA guidelines, we conducted a comprehensive literature search across databases including OVID, MEDLINE, EMBASE, and Scopus, identifying randomized controlled trials and non-randomized pre-post intervention studies. The Bayesian meta-analysis focused on pre-post changes in global cognition, quality of life, everyday functioning, and depression, avoiding comparisons with control groups to reduce heterogeneity (PROSPERO: CRD42022365038).ResultsThe search yielded 6075 results, with 40 studies meeting inclusion criteria, encompassing 50 independent trials. CT and people with AD and other dementias are the best represented intervention and population, respectively. CT was more effective in improving global cognition in individuals with AD and other dementias, and paper-and-pencil and face-to-face formats yielded greater benefits. The analysis showed a significant susceptibility to bias among the studies.ConclusionsLimitations in outcome measure (e.g., MMSE) suggest the need for more sensitive assessments, especially for MCI. Future research should explore broader aspects of wellbeing and investigate the potential of CR. Policymakers are encouraged to consider these findings when designing cognitive interventions for this population.

As the global population ages, the prevalence of dementia is expected to rise significantly. To alleviate the burden on health care systems and the economy, it is essential to develop effective strategies to enhance cognitive function in older adults. Previous studies have shown that combined nonpharmacological interventions can improve cognition across various domains in older individuals. However, there is no established gold standard for the exact combination and duration of these interventions, which makes it challenging to assess their overall effectiveness.

Given the diversity of nonpharmacological multimodal interventions aimed at preventing cognitive decline in older adults with mild cognitive impairment (MCI), this scoping review sought to identify and summarize the characteristics and outcomes of these interventions.

We adhered to the Arksey and O'Malley methodological framework and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and searched 4 electronic databases (MEDLINE, PsycINFO, CINAHL, and Web of Science) systematically on July 6, 2023, and updated the search on April 17, 2024, using specific terms and keywords.

This review included 45 studies from 18 countries with 4705 participants from 2014 to 2024 encompassing different combinations of physical training (PT), cognitive training (CT), nutrition intervention, psychosocial intervention, social activities, and electrical stimulation. There is a growing numbers of studies combining PT and CT for MCI treatment, with additional modalities often added to address various aspects of the condition. Compared to single-modal interventions and usual care, multimodal approaches demonstrated significantly better improvements in cognition domains such as attention, global cognition, executive function, memory, processing speed, and verbal fluency. Technology has been instrumental in delivering these interventions and enhancing the effects of PT and CT. Multimodal interventions also show promise in terms of acceptability and user experience, which can improve treatment adherence.

Research is limited regarding the cost-effectiveness and optimal dosage of these interventions, making it difficult to assess the additional benefits of incorporating more modalities. Future research should examine the long-term effects of incorporating multiple modalities, using standardized MCI criteria and outcome measures.

Real-world evidence (RWE) can complement clinical trials by addressing gaps in how approved anti-amyloid therapies for early Alzheimer's disease (AD) are used in everyday practice. This article outlines strategies to generate RWE that bridge three key challenges in AD care: low detection rates of mild cognitive impairment (MCI), limited data on long-term safety and effectiveness, and a lack of personalized treatment strategies. With MCI detection rates among primary care providers as low as 6%-15%, we propose cost-effective triage tools using electronic health records to enhance early diagnosis and intervention. We also highlight the importance of understanding anti-amyloid therapy outcomes in diverse, real-world populations. Supported by FDA initiatives, pragmatic trials and observational studies using real-world data (RWD) can help develop predictive models that incorporate biomarkers and support precision medicine. These approaches aim to move AD care beyond one-size-fits-all treatment, guiding more tailored, effective strategies for patients.