Alzheimer's & Dementia最新文献

Introduction: Knowledge of modifiable factors influencing age at onset (AAO) of Alzheimer's disease (AD) remains limited. This study utilizes genetic information to uncover such factors.

Methods: Using 43 exposure genome-wide association studies (GWAS) summary statistics, we calculated corresponding polygenic scores (PGS) for 9219 AD cases and 10,345 controls from the Alzheimer's Disease Genetic Consortium (ADGC). Linear mixed model and survival analyses were performed to identify exposure-PGS associated with AAO. Top exposures were cross-evaluated using PGS from the PGS Catalog and Mendelian randomization (MR) for causal relationships.

Results: Eight exposures showed significant exposure-PGS associations with AAO of AD. Higher educational attainment, better cognitive performance, and greater relative fat intake were associated with later AAO; whereas the remaining were linked to earlier onset. MR analysis indicated a causal relationship between AAO and educational attainment, cardiovascular disease, and type 2 diabetes (T2D).

Discussion: The eight modifiable factors, particularly educational attainment, cardiovascular disease, and T2D, may facilitate early intervention to delay the onset of AD.

Highlights: We screened 43 modifiable factors for their association with the age at onset (AAO) of Alzheimer's disease (AD) using polygenic scores (PGS) as the proxy for the exposure. Higher educational attainment, better cognitive performance, and greater relative fat intake were linked to later AAO, suggesting an enhanced resilience against AD. Type 2 diabetes, cardiovascular disease, major coronary heart disease, and increased low-density lipoprotein (LDL) -cholesterol and total cholesterol are associated with earlier AAO. Mendelian randomization analysis revealed causal effects of educational attainment, type 2 diabetes, and cardiovascular disease on AAO of AD.

Introduction: Sensitive memory paradigms may allow the detection of subtle memory changes associated with early Alzheimer's pathology in individuals without established clinical symptomatology.

Methods: We explored the cross-sectional association between performance on Tablet-based Cognitive Assessment Tool (TabCAT) Favorites, a brief computerized memory test, with cerebrospinal fluid AT status (A for amyloid-β and T for phosphorylated tau) and its discriminative validity in 727 clinically asymptomatic participants from the European Prevention of Alzheimer's Disease (EPAD) Longitudinal Cohort Study. Episodic memory was also evaluated with the Repeatable Battery for the Assessment of Neuropsychological Status Delayed Memory Index (RBANS-MI).

Results: Compared to A-T- individuals, poorer TabCAT Favorites Total Correct (Favorites-TC) cross-sectional performance was associated with an increased likelihood of A+T+ status, but not A+T- status. There were no significant associations between AT status and RBANS-MI. Among individuals with low Favorites-TC performance, AT status predicted progression on the Clinical Dementia Rating > 0.

Discussion: Favorites-TC is a sensitive measure for the early detection of cognitive changes in the early stages of the AD continuum.

Highlights: We explored Tablet-based Cognitive Assessment Tool (TabCAT) Favorites scores and cerebrospinal fluid (CSF) AT status (A for amyloid-β and T for phosphorylated tau) in asymptomatic individuals. Poorer Favorites performance linked to higher A+T+ likelihood. TabCAT Favorites is a sensitive tool for detecting early cognitive changes in Alzheimer's disease (AD).

Introduction: People with Down syndrome (DS) have a higher likelihood of developing Alzheimer's disease (AD). Deficits in spatial abilities could serve as early indicators of AD. We examined age effects on visuospatial construction and visuomotor integration in DS and whether spatial tasks could distinguish various extents of cognitive decline in DS.

Methods: We used the Alzheimer's Biomarkers Consortium-Down Syndrome project dataset, where 376 DS participants completed a series of cognitive measures.

Results: Age effects were found in visuomotor integration but not in visuospatial construction. Both abilities declined with AD progression. While both abilities showed relatively poor discrimination between cognitively stable (CS) and mild cognitive impairment (MCI) and between MCI and AD, they showed excellent or acceptable discrimination between CS and AD. Visuospatial construction showed better discrimination than visuomotor integration.

Discussion: Visuomotor integration declines more with aging than visuospatial construction in DS. When used alone, neither may effectively diagnose cognitive decline in DS.

Highlights: We examined two spatial abilities in aging adults with Down syndrome using the ABC-DS project. Age effects were found in visuomotor integration. Age effects were not found in visuospatial construction abilities. Both visuomotor integration and visuospatial construction abilities distinguished between CS and AD in people with DS. Block Design had the highest predictive power in distinguishing cognitive stability from AD.

Introduction: Few genetic studies on Alzheimer's disease (AD) have incorporated multiple ancestries and omic datasets to pinpoint actionable AD risk effectors for each ancestry.

Methods: Here, we first performed genetic colocalization between molecular phenotypes (proteomics and metabolomics) from two ancestral groups (European [EUR] and African [AFR]) and the two largest EUR AD genome-wide association studies. We next performed pathway enrichment analyses to identify biological mechanisms.

Results: We found 21 proteins and one metabolite colocalized with AD risk that were shared between the EUR and AFR ancestry groups. We also identified 25% AFR and 60% EUR proteins; 50% AFR and 10% EUR metabolites were unique. The pathway enrichment analyses nominated interleukin-1 production and lipid pathway were shared underlying proteomic and metabolomic findings, respectively.

Discussion: Our findings indicate that these four plasma datasets may pinpoint different effectors of AD risk in diverse populations; findings from AFR participants require validation with AFR-based genome-wide association study data.

Highlights: For proteomics, 61% of findings for European (EUR) ancestry and 72% for African (AFR) ancestry were not previously reported. For metabolomics, 83% of findings for EUR ancestry and 50% AFR ancestry were not previously reported. Both convergent and divergent pathways were identified in EUR- and AFR-ancestry stratified analyses in either proteomics or metabolomics findings.

Introduction: This study examined differential item functioning of the Geriatric Depression Scale - Short Form (GDS-SF) in the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) to identify potential variables that produce measurement bias.

Methods: Data from 14077 individuals' first NACC visit were analyzed. Multiple indicator, multiple causes (MIMIC) models assessed differential item functioning (DIF) of the 15-item GDS-SF across race, Hispanic ethnicity, primary language, sex, and cognitive status (Clinical Dementia Rating [CDR] scale scores), while adjusting for educational attainment.

Results: Participants were on average 73 (SD = 9.1) years old and 54.4% women. The majority (13 of 15) of the GDS-SF items demonstrated DIF. For many items, participants with any level of CDR cognitive impairment were more likely to endorse depressive symptoms.

Discussion: Findings indicate the presence of widespread DIF by cognitive impairment severity such that individuals with even mild cognitive impairment may respond differently to certain items on this measure.

Highlights: The Geriatric Depression Scale - Short Form (GDS-SF) showed differential item functioning (DIF) in 13 of 15 items across demographic and cognitive groups. Only two items-hopelessness and worthlessness-were invariant across all groups. Cognitive status (Clinical Dementia Rating [CDR]) most strongly influenced item endorsement patterns. Our study used a large, diverse sample (National Alzheimer's Coordinating Center Uniform Data Set [NACC UDS]) and robust DIF analytic methods. Findings highlight both reliable and problematic GDS-SF items for older adults.

Introduction: Alzheimer's disease (AD) research relies on large datasets and advanced statistical models. However, individual population studies often lack sufficient sample size for conclusive results. Harmonizing cognitive test data across studies can address this gap, despite differences in testing protocols. This study harmonizes cognitive data from three major AD cohorts to support robust clinical-pathological modelling.

Methods: Information from the Alzheimer's Disease Neuroimaging Initiative (N = 1446); Australian Imaging, Biomarkers and Lifestyle (N = 1764); and Open Access Series of Imaging Studies-3 (N = 440) were integrated, including cognitive scores, demographics, genetics, and clinical and neuroimaging data. Neuropsychological tests relevant to AD were harmonized using MissForest, a machine learning-based imputation method. Validation involved assessing imputation accuracy and analyzing composite cognitive scores across clinical-pathological groups.

Results: Imputation showed high accuracy (mean absolute error ≤ test-retest variability in cognitively unimpaired participants). Composite scores reflected known disease patterns with significant stratification across clinical-pathological groups.

Discussion: The validated harmonization approach demonstrated reliable imputation, enabling more powerful AD models and supporting future diagnostic and therapeutic advances.