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

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.

Introduction: Traumatic experiences in older age may accelerate cognitive decline, especially when experienced alongside financial hardship.

Methods: In data from the Health and Retirement Study (2006-2020), we estimated linear mixed-effects models stratified by financial well-being to estimate the effect of incident traumatic experiences on memory decline, adjusted for childhood and adulthood sociodemographics.

Results: In N = 3432 participants, incident traumatic experiences appeared associated with lower baseline memory score ( $\hat{\beta }$  = -0.015, 9 -0.053 to 0.023) and faster decline ( $\hat{\beta }$  = -0.007, 95% CI = -0.012 to --0.001). Observed associations were larger in those with worse financial well-being (baseline memory score: $\hat{\beta }$  = -0.070, 95% CI = -0.200 to 0.060; memory decline: $\hat{\beta }$  = -0.016, 95% CI = -0.033 to 0.001) versus better financial well-being (baseline memory score: $\hat{\beta }$  = -0.008, 95% CI = -0.047 to 0.031; memory decline: $\hat{\beta }$  =- 0.006, 95% CI = -0.012 to 0.000), but the confidence intervals for these estimates included values consistent with no association.

Discussion: The cognitive impacts of incident traumatic events may be stronger for individuals with poor financial well-being. Future research should confirm our findings and evaluate underlying mechanisms.

Highlights: Experiencing an incident trauma in older age predicted accelerated memory decline.This association was strongest for participants with worse financial well-being.Experiencing trauma alongside scarcity may be a double-hit for cognitive decline.

Introduction: The diagnosis of Alzheimer's disease (AD) traditionally relies on cerebrospinal fluid and plasma levels of amyloid beta and phosphorylated tau. Although informative, these biomarkers represent a narrow, hypothesis-driven approach to intercept the disease.

Methods: Data-driven analysis was applied on demographic data, apolipoprotein E (APOE) ε4 allele, and 82 biomarkers obtained from blood tests of healthy controls (HC), mild cognitive impairment that remained stable within 36 months following blood collection (sMCI), and patients with AD.

Results: Statistical analyses revealed differences among groups in many cholesterol-related analytes. APOE ε4 and analytes such as amino acids, lipoproteins, and fatty acids emerged as the most influential features in machine learning (ML) classification algorithms. Glycolysis-related metabolites and amino and fatty acids were predictive for distinguishing sMCI and AD from HC.

Discussion: These findings support the hypothesis that systemic alterations also occur during the preclinical stages of dementia, which can be detected by ML models on blood biomarkers.

Highlights: Machine learning on blood tests detects preclinical cognitive decline.Glycolysis metabolites are predictive for distinguishing stable MCI and AD from HC.Amino acids, lipoproteins, and fatty acids are the most predictive features.Inflammatory and metabolic biomarkers represent a biosignature of cognitive health.

Introduction: Recent research has suggested increased sensitivity of Alzheimer's disease (AD)-negative neuropsychological norms; concurrently, generalized additive models for location, scale, and shape (GAMLSS) have emerged as a promising alternative to traditional norming approaches. Here, we developed amyloid β-negative (Aβ-) next-generation norms (NGN) for a comprehensive neuropsychological battery using GAMLSS.

Methods: We included N = 987 cognitively normal (CN) individuals from a Spanish multicenter study with extensive neuropsychological data and cerebrospinal fluid AD biomarker assessment. NGN were developed using GAMLSS based on the performance of n = 774 Aβ- CN individuals aged 30-90 years.

Results: Age-, education-, and sex-adjusted z-scores were obtained for 14 measures covering the main cognitive domains (memory, language, attention/executive, and visuospatial functions). A user-friendly calculator for the z-scores was made available in an open-access ShinyApp to facilitate their application.

Discussion: NGN may improve the detection of objective cognitive impairment in clinical and research settings.

Highlights: Brain amyloid β (Aβ) is associated with poorer performance in cognitively normal individuals.We provide GAMLSS-based Aβ-negative norms for 14 neuropsychological measures.Age, education, and often sex significantly influence cognitive performance.An online calculator for the demographically adjusted z-scores is freely available.