Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring最新文献

Introduction: Biological age (BA) measures were developed to capture changes in aging that chronological age does not. We aimed to investigate the association between BA and cognitive performance and whether the Klotho protein mediated this association.

Methods: We used data from participants aged 60 years or older from Mexico, India, the United States, and England. We used linear regression to investigate the association between BA and cognitive performance. Mediation analysis using data from the United States used the counterfactual framework.

Results: In 8547 participants, 53.6% were women, mean (SD) chronological age was 69.6 (7.6) years and mean (SD) biological age 70.2 (9.6) years. Higher biological age was associated with poorer cognitive performance across all evaluated countries. Klotho did not mediate these associations.

Discussion: Implementing the BA metric at the population level could help identify individuals at higher risk of poorer cognitive performance, facilitating targeted interventions.

Highlights: Increasing biological age is associated with poorer global cognitive performance in older adults.Accelerated aging is associated with poorer cognitive performance in all countries studied.Klotho did not mediate the association between biological age and cognition.Implementing biological age metrics may help identify high-risk individuals.

Introduction: Harmonizing cognitive measures across population-based studies facilitates direct comparisons of cognitive decline despite differing study protocols.

Methods: Cognitive performance data from 2012-2022 were harmonized across the Health and Retirement Study (HRS) and National Health and Aging Trends Study (NHATS). We compared baseline harmonized scores stratified by demographics and examined demographic and health risk factor associations with cognitive change using multi-level generalized linear models, contrasting results with those from a sum-of-word-recall measure.

Results: Cross-sectionally, lower harmonized scores were associated with older age and less education. Longitudinally, greater cognitive decline measured by changes in harmonized scores correlated with older age, less education, underweight body mass index, low physical activity, hypertension, stroke, and diabetes. Associations were stronger for harmonized scores than for sum of word recall alone.

Discussion: Harmonized scores effectively capture cognitive performance and decline, demonstrating stronger relationships with demographic and health factors than word recall scores alone.

Highlights: Statistical harmonization is a valuable tool for undertaking comparative analysis when data collection protocols differ.We derived a harmonized general cognitive performance factor score for the Health and Retirement Study (HRS) and National Health and Aging Trends Study (NHATS), two of the largest, nationally representative longitudinal samples of the middle-aged and older adult population in the United States.The factor score showed patterns of change across exposure groups consistent with prior literature, and it outperformed a simple sum score of immediate and delayed word recall tests.

Introduction: Understanding the heterogeneity of brain structure in individuals with the Motoric Cognitive Risk Syndrome (MCR) may improve the current risk assessments of dementia.

Methods: We used data from six cohorts from the MCR consortium (N = 1987). A weakly-supervised clustering algorithm called HYDRA (Heterogeneity through Discriminative Analysis) was applied to volumetric magnetic resonance imaging (MRI) measures to identify distinct subgroups in the population with gait speeds lower than one standard deviation (1SD) above mean.

Results: Three subgroups (Groups A, B, and C) were identified through MRI-based clustering with significant differences in regional brain volumes, gait speeds, and performance on Trail Making (Part-B) and Free and Cued Selective Reminding Tests.

Discussion: Based on structural MRI, our results reflect heterogeneity in the population with moderate and slow gait, including those with MCR. Such a data-driven approach could help pave new pathways toward dementia at-risk stratification and have implications for precision health for patients.

Highlights: Different patterns of brain atrophy were observed among the people with moderate and slow gait speedsSlower gait speeds were associated with substantial cortical atrophy, higher rates of Motoric Cognitive Risk Syndrome (MCR), and worse cognitive performanceThis approach can aid patient stratification at early asymptomatic stages and have implications for precision health.

Introduction: Neuropsychiatric symptoms (NPSs) are early hallmarks of neurocognitive disorders (NCDs). Speech alterations might indicate both cognitive and behavioral changes in NCDs, aiding in diagnosis and disease monitoring. This study examined associations between automatically extracted speech/language features and NPS severity.

Methods: A total of N = 37 subjective cognitive decline and N = 20 mild cognitive impairment participants from the BioBank Alzheimer Centre Limburg study were recorded performing a low-constraint free-speech task. NPSs were assessed using the Geriatric Depression Scale and the Neuropsychiatric Inventory. Acoustic and linguistic features were automatically extracted. Correlation analysis was performed (adjusted for age, sex, and Mini-Mental State Examination) between the features and clinical scales.

Results: Features correlated significantly with NPSs. Indicatively, depression correlated with local jitter (r = 0.38, p < 0.001) and agitation with the sum of pause duration (r = 0.32, p < 0.027).

Discussion: Speech analysis offers a promising tool for evaluating NPSs in NCDs.

Highlights: We found links between neuropsychiatric symptom (NPS) severity and speech markers in memory clinic patients.Temporal markers positively correlated with the presence and severity of agitation.Depression was positively correlated with voice instabilities.Anxiety was negatively associated with metrics of lexical diversity.Speech analysis provides an objective tool to assess NPSs in subjective cognitive decline and mild cognitive impairment patients.

Introduction: Understanding how biomarker testing affects Alzheimer's disease (AD) diagnosis confidence and AD stigma among race and ethnicity groups is essential for supporting early diagnosis and treatment.

Methods: Adults (N = 3548) rated confidence in an AD diagnosis based on four diagnostic evaluations and answered questions about AD stigma based on a clinical vignette. The sample reflects response and completion rates of 53% and 91.3%, respectively. Bivariate and multivariable regression analyses were conducted.

Results: Black participants showed the smallest increase (11.86 points) in diagnosis confidence of all race groups when a brain scan was included in the diagnostic evaluation. AD diagnosis confidence changed across diagnostic evaluation categories based on level and type of AD stigma domain and race group.

Discussion: Use of brain scans in evaluations can heighten diagnosis confidence in all race groups. Yet, no group had 100% confidence in an AD diagnosis with any evaluation. Recommendations are discussed.

Highlights: Confidence in an Alzheimer's disease (AD) diagnosis varies across racial groups.Within racial groups, AD diagnosis confidence differs with diagnostics.Even with cutting-edge biomarker testing, no racial group had 100% confidence in an AD diagnosis.Patient-centered care and systemic changes are needed to widen distribution of diagnostic technologies and improve access to care.

Introduction: Late-life depression is linked to an increased dementia risk; however, whether this relationship varies with age remains unclear.

Methods: We analyzed data from 1,127,331 individuals aged ≥75 years without dementia from the Korean National Health Insurance database who had undergone health screening between 2012 and 2015. Participants were followed up for ≤10 years. Cox proportional hazards and Fine-Gray subdistribution models were used to estimate dementia risk.

Results: Depression was associated with increased dementia risk in both the old-old (75 to 84 years; hazard ratio [HR]: 1.338, 95% confidence interval [CI]: 1.325 to 1.351) and the oldest-old (≥85 years; HR: 1.111, 95% CI: 1.071 to 1.152), with attenuated effects at older ages. Cerebrovascular disease modified this association in the old-old, but no interaction was observed in the oldest-old.

Discussion: Depression's impact on dementia risk decreases with age. Cerebrovascular diseases may influence depression-associated neurodegenerative pathways, although this interaction diminishes in the oldest-old.

Highlights: Depression increased dementia risk in adults aged 75 years and older.The strength of the association declined with advancing age.Effect modification by cerebrovascular disease was observed only in the 75 to 84 old group.Findings support age-related decline in depression's impact on dementia risk.

Introduction: Timely diagnosis is crucial for managing neurodegenerative conditions. This study investigated whether time from symptom onset to diagnosis differs by clinical syndrome and sex.

Methods: This retrospective, cross-sectional study included 591 participants with Alzheimer's disease (AD), frontotemporal dementia (FTD) subtypes (behavioral variant FTD [bvFTD], semantic dementia [SD], and progressive non-fluent aphasia), logopenic progressive aphasia (LPA), and syndromes associated with movement disorders (corticobasal syndrome, FTD with motor neuron disease [FTD-MND], and progressive supranuclear palsy). Bayesian regression models were used to compute diagnostic timelines.

Results: Compared to AD (3.35 years; 95% credible interval [CrI]: 3.03-3.72), SD and bvFTD had additional delays of 9.7 (95% CrI: 1.96-20.64) and 14.82 months (95% CrI: 6.94-25.42), respectively, while FTD-MND was shorter by 11.62 months (95% CrI: -15.7 to -4.68). Men with bvFTD had 23.64 month longer delays than women (95% CrI: 10.35-44.33).

Discussion: Diagnostic delays may reflect syndrome-specific clinical features, diagnostic complexity, and sociocultural factors. Findings highlight the need for improved diagnostic pathways and pre-clinical biomarkers to facilitate earlier identification.

Highlights: Bayesian analyses revealed that diagnostic delays differ by syndrome and sex.Alzheimer's disease (AD) was diagnosed on average 3.35 years after symptom onset.Diagnoses were delayed in semantic and behavioral variant frontotemporal dementia (bvFTD) compared to AD.Men with bvFTD had longer delays than women.Findings support need for improved diagnostic pathways and pre-clinical biomarkers.

Introduction: Central auditory processing (CAP) is crucial for speech perception and is also fundamental for cognitive function. This study investigated whether gap detection threshold (GDT) could serve as an early marker for identifying individuals with cognitive impairment (CI) at high risk of dementia.

Methods: Sixty-four older adults underwent peripheral auditory, cognitive, and CAP assessments. Machine learning and resting state electroencephalography (EEG)/event-related potential (ERP) analyses explored predictors and neural correlates of CI.

Results: GDT was significantly higher in those with CI (mean ± standard deviation: 8.25 ± 6.14 versus 5.98 ± 3.44 ms, respectively, p = 0.034), and negatively correlated with cognitive test scores (e.g., Addenbrooke's Cognitive Examination III: r = -0.40, p = 0.001). GDT emerged as a key predictor. EEG showed altered auditory connectivity and ERP revealed reduced N1/N2 amplitudes in high-GDT individuals (false discovery rate corrected p < 0.05).

Discussion: GDT may reflect early neurophysiological changes in individuals with CI and has potential as a non-invasive biomarker.

Highlights: Central auditory processing (CAP) test scores were found to be significantly correlated with cognitive tests.By machine learning, the best variable gap detection threshold (GDT) for predicting cognitive impairment was screened out.GDT subgroup analysis was performed within the normal control (NC) group. Compared to the low GDT subgroup, the high GDT subgroup had lower amplitudes of the cognitive components of the event-related potential and many differences in functional connectivity, indicating that GDT has predictive value for changes in cognitive function.

Introduction: The clock drawing test (CDT) screens for dementia but requires trained scorers and lacks standardized criteria. Thus, we developed an automated vision transformer (ViT)-based diagnostic system with convolutional neural network preprocessing for analyzing hand-drawn CDT images.

Methods: The architecture implements fine-tuned ViT feature extraction with linear classification for dementia prediction. Training used the National Health and Aging Trends Study (NHATS) dataset (n = 54,027), with testing on an independent clinical cohort from the Toronto Dementia Research Alliance (TDRA; n = 862; 522 dementia, 340 normal cognition).

Results: The ViT approach predicted dementia with 76.5% balanced accuracy, outperforming human-scored features (74.3%) and existing deep learning models (MiniVGG = 73.3%, MobileNetV2 = 72.3%, relevance factor variational autoencoder = 69.1%) on the TDRA dataset.

Discussion: This pen-and-paper compatible diagnostic system enables scalable remote cognitive screening through automated CDT image analysis that is competitive with human-scored features, potentially increasing diagnostic accessibility for diverse populations across varied socioeconomic contexts.

Highlights: The vision transformer model achieves 76.5% accuracy in dementia detection from clock drawing tests, outperforming human scoring and existing deep learning methods.Novel convolutional neural network-based preprocessing automatically handles challenging image quality issues like shadows, irrelevant markings, and improper cropping.The system requires only a photo of a hand-drawn clock test, enabling scalable remote screening accessible across socioeconomic contexts.A feature-extraction model trained on 54,027 samples demonstrates robust generalization to an independent clinical dataset of 862 patients.This fully automated approach eliminates the need for trained scorers while maintaining diagnostic accuracy above manual methods.

Background: Optical coherence tomography (OCT) and OCT angiography (OCT-A) have been studied as biomarkers for Alzheimer's disease (AD), with promising results. Nevertheless, their potential in the logopenic variant of primary progressive aphasia (lvPPA), which shares the same amyloid pathology, has not yet been explored. This work aimed to characterize retinal changes in lvPPA compared to healthy controls.

Methods: Ten participants with lvPPA and eleven controls underwent OCT and OCT-A imaging. Amyloid pathology in lvPPA was confirmed by lumbar puncture. Retinal parameters included retinal nerve fiber layer (RNFL) thickness and foveal avascular zone (FAZ).

Results: Compared to controls, lvPPA participants exhibited reduced RNFL thickness in the temporal sector (p = 0.013) and significantly decreased FAZ circularity (p = 0.002).

Discussion: RNFL thinning may reflect trans-synaptic degeneration from cortical atrophy, while reduced FAZ circularity suggests early microvascular changes related to amyloid burden. Our findings support OCT and OCT-A as potential biomarkers for lvPPA.

Highlights: For the first time, OCT and OCT-A are studied as potential biomarkers for lvPPA.Compared to healthy controls, retinal nerve thickness is decreased in lvPPA patients.Retinal vasculature exhibits structural alterations in lvPPA patients.