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

Introduction: The National Insitutes of Health Toolbox (NIHTB) measures may be useful for Alzheimer's disease (AD) risk detection. This study investigates whether cognitively normal older adults with and without elevated brain amyloid, an early AD biomarker, differed on NIHTB cognitive and motor subtests and whether inclusion of pTau-217 enhanced this differentiation. Motor measures were of interest based on past research reporting linkages to early AD pathology.

Methods: Data from 112 consensus-diagnosed cognitively normal older adults who completed amyloid-PET imaging, blood draw for pTau217 analysis, and NIHTB Cognition and Motor batteries were analyzed using linear regression.

Results: Amyloid positivity significantly predicted performance on only NIHTB Standing Balance (F(2,86) = 9.90, p < 0.001). Inclusion of pTau217 enhanced prediction of Standing Balance (F(4,84) = 5.88, p < 0.001) and Walking Endurance (F(4,88) = 9.70, p < 0.001) and Dominant-hand Grip Strength (F(2,92) = 51.59, p < 0.001).

Discussion: For AD-related detection research, findings support inclusion of NIHTB motor measures, which may prove more sensitive early in the disease course before overt cognitive change.

Highlights: The National Institutes of Health ToolBox motor performance may signal early Alzheimer's disease (AD) risk, even before memory change.Balance performance may be sensitive to early cumulative AD-neuropathology loading.Motor measures should be included in early AD-risk detection batteries.

Introduction: We explored the relationship between cognitive screening outcomes and everyday functioning in Alzheimer's disease (AD).

Methods: A total of 1228 amyloid-positive participants were included from the Amsterdam Dementia Cohort. Multiple linear regression analyses assessed the relationship between Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and everyday functioning (Amsterdam Instrumental Activities of Daily Living Questionnaire [A-IADL-Q-30]). To link cognitive screeners to functional impairment, we described difficulties across A-IADL-Q-30 items by MMSE and MoCA quartiles.

Results: Both MMSE (B = 0.96, 95% confidence interval [CI]0.87-1.04) and MoCA (B = 0.79, 95% CI 0.68-0.89) were associated with A-IADL-Q-30. In the lowest MMSE (0-20) and MoCA (0-16) quartiles, filling in forms (both 96%) and managing the household budget (95%-93%) were mostly affected, whereas working (74%) and using a computer (52%-50%) were primarily affected in the highest quartiles (MMSE 28-30/MoCA 25-30).

Discussion: In amyloid-positive participants, the association between cognition and daily functioning was moderate, reinforcing the importance of assessing both constructs in disease monitoring.

Highlights: Cognitive screening tools were moderately associated with daily functioning.Difficulties in complex daily tasks were present in the higher cognitive performance quartiles.Findings suggest that combining cognition and function is required for disease monitoring.

Introduction: We aimed to evaluate the potential of the microglial marker transmembrane protein 119 (TMEM119) in the cerebrospinal fluid (CSF) as a (differential) diagnostic biomarker for neurodegenerative diseases.

Methods: Following assay validation, we used enzyme-linked immunosorbent assay to measure CSF TMEM119 in 174 patients from six diagnostic groups: Alzheimer's disease (AD, n = 35), amyotrophic lateral sclerosis (ALS, n = 33), cerebral microangiopathy (CM, n = 25), frontotemporal lobar degeneration (FTLD, n = 28), Lewy body diseases (n = 21), and non-neurodegenerative controls (n = 33).

Results: CSF TMEM119 levels were elevated in the AD group compared to the control (p = 0.004), CM (p = 0.005), and FTLD (p = 0.023) groups. Levels were higher in both mild cognitive impairment (MCI-AD) and dementia (ADD) subgroups when compared to controls. For the discrimination of AD from controls, the area under the curve (AUC) was 0.78.

Discussion: Our results indicate that CSF TMEM119 may have potential as a biomarker representing microglial involvement in early and later stages of AD.

Highlights: Elevated levels of TMEM119 were observed in the CSF of patients with AD.Increased CSF TMEM119 was seen in MCI-AD patients compared to controls.Elevated levels in MCI-AD underscore early microglial involvement in AD.In the AD group, an association was found between CSF TMEM119 and CSF total tau.CSF TMEM119 may provide valuable information on neuroinflammation.

Alzheimer's disease (AD) pathology begins years before symptoms emerge, making early detection essential. Eye tracking offers a rapid, non-invasive means of identifying early cognitive decline through oculomotor disturbances. This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)- and Population, Intervention, Comparison, and Outcome (PICO)-guided systematic review evaluated studies from PubMed, ACM Digital Library, and Google Scholar on eye tracking in mild cognitive impairment (MCI), AD, and related dementias. Seventy-one studies met the inclusion criteria. Antisaccade tasks consistently distinguished AD and MCI from healthy controls, with impaired accuracy, longer latencies, and reduced gain. Non-saccadic paradigms (e.g., visual search, free viewing) indicated diminished exploratory behavior in AD, with mixed findings in MCI. A major limitation was the lack of cohorts defined by current biological criteria, hindering clinical translation. In a subset, classical machine-learning (ML) models and deep neural networks reported accuracies of 0.72 to 0.97. Overall, antisaccade tasks show strong promise for early AD screening; future work should adopt biologically defined cohorts and scalable, accessible eye-tracking technologies.

Highlights: Antisaccade tasks best distinguish AD and MCI from HCs.Visual search/free-view tasks showed diminished exploratory behavior in AD.Most studies lack biomarker-based AD criteria, creating a major research gap.

Introduction: Mild cognitive impairment (MCI) is a known risk factor for dementia and presents an opportunity for early engagement in preventative strategies, treatment, and advanced planning. However, little is known about MCI diagnosis rates among Medicare beneficiaries.

Methods: Using data from the 2014 to 2022 rounds of the National Health and Aging Trends Study (NHATS) linked with Medicare claims data, we identified the proportion of beneficiaries with symptoms of MCI, as defined by an NHATS algorithm, who received a diagnosis according to International Classification of Diseases codes. Univariate and multivariate logistic regressions were used to identify predictors of diagnosed MCI.

Results: Of beneficiaries identified by the NHATS algorithm, 10.6% had a recorded diagnosis of MCI. Odds of diagnosis were higher among women and beneficiaries with a bachelor's degree or higher, and lower among beneficiaries who attended doctor visits alone.

Discussion: Targeted initiatives are needed to increase MCI diagnosis rates, particularly in the era of novel diagnostic tests and therapies.

Highlights: We linked National Health and Aging Trends Study data to Medicare claims to identify the prevalence of diagnosed mild cognitive impairment (MCI).We identified 10.6% of Medicare beneficiaries with symptoms of MCI who have a diagnosis.Women and people with a bachelor's degree were more likely to have an MCI diagnosis.People who visited the doctor alone were less likely to have an MCI diagnosis.

Introduction: Type 2 diabetes (T2D) is associated with cognitive decline, but the role of APOE ε4 - a known Alzheimer's risk allele - in cognition among admixed populations with T2D remains unclear.

Methods: We analyzed 883 Brazilian adults with T2D (median age 68 years) from primary care, excluding those with dementia. Cognitive function was assessed using Mini-Mental State Examination (MMSE), and APOE genotypes were determined. MMSE errors were modeled using negative binomial regression.

Results: APOE alleles showed no association with MMSE errors overall. Older age and self-reported Black/Brown race predicted more errors, whereas higher education and physical activity predicted fewer. Sensitivity analyses indicated a possible inverse association between APOE ε4 and MMSE errors among self-reported Black/Brown participants.

Discussion: APOE ε4 was not associated with cognition in this cohort. The suggestive protective effect observed in Black/Brown participants should be considered hypothesis-generating, underscoring the need for further research in admixed populations with T2D.

Highlights: APOE ε4 was not linked to cognitive errors in the full T2D Brazilian cohort.Trend toward fewer MMSE errors among self-reported Black/Brown APOE ε4 carriers.Higher education, physical activity, and BMI were protective against MMSE errors.Black/Brown race and older age were associated with more MMSE errors.Results support multifactorial assessment and more studies in admixed T2D populations.

Introduction: Studies examining the risk of dementia in people with multimorbidity are commonly conducted in research cohorts or outside the UK. Multimorbidity has historically been associated with aging, but recent research suggests that more than half of incidence cases occur in adults < 50.

Methods: Using UK primary care data, adjusted Cox regressions and competing risk of death models were used to determine risk of dementia in people with multimorbidity overall and by body system.

Results: People with multimorbidity had a greater risk of dementia that those without multimorbidity (hazard ratio [HR] = 4.01, 95% confidence interval [CI] 3.94-4.07). Among people with multimorbidity, the risk was highest for those when a neurological condition was included (HR = 2.19, 95% CI 2.15-2.23).

Discussion: Managing multimorbidity, particularly neurological conditions, is key and could delay or reduce the risk of dementia.

Highlights: People with multimorbidity experienced a greater risk of dementia than those without.Neurological multimorbidity presented the highest risk of dementia.Risk of dementia increased progressively with younger-onset multimorbidity.Preventing or managing multimorbidity effectively could reduce or delay dementia.

Introduction: Accurate risk stratification for long-term Alzheimer's disease (AD)-specific mortality remains limited.

Methods: We analyzed data from 5,149 adults aged ≥60 years in NHANES III (1988-1994), with 116 baseline variables and mortality follow-up through 2019 via the National Death Index. Ten survival machine learning (ML) models were benchmarked. Predictive performance was assessed using Harrell's concordance index (C-index).

Results: Over a median follow-up of 12.1 years for survivors and 17.8 years for decedents, Lasso (C-index = 0.76, 95% CI: 0.72-0.80) and Extreme Gradient Boosting (C-index = 0.76, 95% CI: 0.73-0.79) achieved the highest accuracy. Feature importance analyses revealed novel predictors of AD mortality. Models using fewer than 20 variables retained acceptable performance (C-index > 0.70).

Conclusion: Survival ML models effectively predict long-term AD-specific mortality using routine clinical data. Their interpretability, scalability, and capacity to identify novel risk factors support integration into geriatric risk assessment frameworks.

Highlights: We benchmarked 10 survival machine learning (ML) algorithms using 116 clinical variables to predict long-term Alzheimer's disease (AD)-specific mortality.Feature importance analysis identified novel non-imaging clinical predictors, including arm circumference, self-rated physical activity, and alcohol consumption.This work highlights the underused potential of routine clinical data for AD mortality prediction and underscores the need for interpretable, population-based ML applications.

Introduction: The Rapid Naming Test (RNT) is a tablet-administered confrontation naming task. We evaluated its concurrent validity, neuroanatomical correlates, sensitivity to cognitive impairment, and discriminant validity across neurodegenerative syndromes.

Methods: We compared RNT performance of 263 healthy adults (mean [SD] age = 73.6 [9.3]; 60.5% female) and 185 people with neurodegenerative syndromes (mean [SD] age = 68.3 [9.0]; 38.9% female), including primary progressive aphasias (PPA). RNT performance were correlated with traditional cognitive test performance and with regional gray matter volumes using voxel-based morphometry.

Results: RNT performance was associated with language, memory, executive function, and processing speed (p < 0.05), as well as with gray matter volume in the left insula, temporal pole, fusiform gyrus, and the inferior and middle temporal gyri. The RNT was sensitive to cognitive impairment (AUC = 0.90, 95% CI 0.87-0.93), with the greatest impairments in people with logopenic and semantic variant PPA.

Discussion: The RNT is sensitive to cognitive impairment and associated with brain regions involved in language and cognitive control, with left hemisphere predominance.

Highlights: The Rapid Naming Test (RNT) is a 1-min tablet-based confrontation naming task.The pattern of performance across clinical cohorts supports the construct validity of the RNT.RNT performance was associated with gray matter volumes in regions important for object recognition and semantic knowledge.Age adjusted norms of the RNT were sensitive to mild cognitive impairment.

Introduction: This study aimed to identify phenotypes of subtle variation in multidomain cognitive performance and examine their longitudinal associations with Alzheimer's disease and related dementias (AD/ADRD) biomarkers and cognitive outcomes.

Methods: Among 1192 cognitively unimpaired (CU) older adults from the Baltimore Longitudinal Study of Aging, latent profile analysis (LPA) identified phenotypes based on baseline patterns of neuropsychological test performance. Mixed-effects and Cox models examined longitudinal differences in cognitive status and AD/ADRD biomarkers (phosphorylated tau-181 [pTau181], amyloid-beta 42/40 ratio [Aβ42/Aβ40], neurofilament light [NfL], and glial fibrillary acidic protein [GFAP]) across phenotypes.

Results: LPA identified the following cognitive phenotypes: Overall Low Average, Dysexecutive (n = 112); Overall Average, Low Memory (n = 284); Overall Average, High Memory (n = 449); High Executive, Relatively Low Memory (n = 214); and Overall High Performing (n = 133). Phenotypes differed in longitudinal rates of cognitive decline and increases in NfL.

Discussion: Subtle variations in neuropsychological performance among CU older adults have implications for long-term cognitive health and may help inform Alzheimer's disease and related dementias diagnosis and disease monitoring.

Highlights: Significant cognitive heterogeneity exists in CU older adults.LPA identified phenotypes based on cognitive performance.Personality and psychosocial characteristics differed by cognitive phenotype.Cognition over time and risk of cognitive impairment differed by cognitive phenotypes.The phenotype with greatest executive dysfunction had the fastest increase in NfL.