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

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.

Introduction: We previously applied generalized additive models for location, scale, and shape to derive amyloid β-negative next-generation norms (NGN) for a comprehensive neuropsychological battery. Here, we evaluated the accuracy of NGN in detecting cognitive impairment compared to traditional norms (TN).

Methods: This multicenter study included N = 2405 participants classified as cognitively normal (CN, n = 987) or with mild cognitive impairment (MCI, n = 1418) using conventional criteria. All participants underwent neuropsychological testing and cerebrospinal fluid Alzheimer's disease (AD) biomarker assessment. We used actuarial neuropsychological criteria to reclassify all participants using TN and NGN. Diagnostic groups were compared on cognitive performance, AD biomarker positivity, and longitudinal cognitive trajectories.

Results: Nineteen percent of TN-classified CN participants were diagnosed with MCI by NGN, whereas 3% of TN-classified MCI were identified as CN by NGN. NGN demonstrated stronger associations with neuropsychological performance, AD biomarkers, and progression than TN.

Discussion: NGN enhance the detection of objective cognitive impairment, with direct implications for clinical practice and research.

Highlights: Next-generation norms (NGN) reclassify one of every five cases from cognitively normal (CN) to mild cognitive impairment (MCI).This group shows poor cognitive performance and a high prevalence of amyloid β positivity.NGN-based diagnosis of MCI predicts cognitive progression on follow-up.Results indicate that NGN improve the detection of objective cognitive impairment.NGN can inform biomarker use, therapy indication, and clinical trial design.

Introduction: We assessed associations of cancer diagnoses with neuropathology of Alzheimer's disease (AD) and related dementias.

Methods: We retrieved 2288 cases from the National Alzheimer Coordinating Center (NACC) cohort with available information on cancer diagnoses and neuropathological scoring of Braak stages, Thal amyloid phases, neuritic plaques, TDP-43 pathology, and Lewy body pathology. We used Bayesian ordinal regression to assess associations of prevalent or incident cancer diagnoses with global cognition and postmortem neuropathological scores.

Results: We found extreme evidence (Bayes factor [BF] > 2000) that both prevalent and incident cancer diagnoses were associated with better global cognition, strong evidence (BF = 26) for an association of a prevalent cancer diagnosis with lower TDP-43 pathology, and weak evidence (BF = 3.2) for an association with lower Lewy body pathology.

Discussion: Our data suggest that selective survival and biological effects may contribute to the lower risk of dementia in people with a cancer diagnosis.

Highlights: A prevalent diagnosis of cancer was associated with a lower risk of cognitive decline in older individuals.A prevalent diagnosis of cancer was associated with a lower risk of TDP-43 pathology and Lewy body pathology in older individuals.Effects of cancer on TDP-43 pathology were maintained when controlling for degree of cognitive decline.

[This corrects the article DOI: 10.1002/dad2.70217.].

Introduction: Effective dementia prevention requires understanding the distribution of modifiable risk factors and identifying high-risk subgroups. We estimated the prevention potential in Germany and identified risk profiles to inform precision public health.

Methods: We analyzed nationally representative data from the 2023 German Aging Survey (n = 4992). Population attributable fractions and potential impact fractions were computed for established modifiable risk factors. Relative risks were taken from meta-analyses. Latent class analysis identified risk profiles.

Results: An estimated 36% of dementia cases in Germany are attributable to modifiable risk factors. Reducing their prevalence by 15%-30% could prevent 170,000-330,000 cases by 2050. We identified four risk profiles-metabolic, sensory impairment, alcohol, and lower-risk-each associated with demographic and regional characteristics.

Discussion: Our findings highlight considerable national prevention potential and reveal population subgroups with shared risk patterns. These profiles provide a foundation for designing targeted, equitable, and efficient dementia prevention strategies.

Highlights: 36% of dementia cases in Germany are linked to modifiable risk factors.A 15% reduction in risk factor prevalence could prevent 170,000 cases by 2050.Key contributors: depression, hearing loss, low education, and obesity.Data-driven risk profiles identified (e.g., metabolic, sensory, low-risk).Risk profiles strongly associated with sociodemographic characteristics.

Introduction: Blood-based biomarkers, most notably plasma phosphorylated tau (p-tau)217, have transformed the diagnostic landscape of Alzheimer's disease (AD).

Methods: We applied an unsupervised machine learning approach to tau positron emission tomography (PET) imaging in 606 participants (age 73.95 ± 7.72; 309 female) to identify AD subtypes. Within each subtype, we evaluated plasma p-tau217 levels, their association with regional tau PET uptake, differences between cognitively unimpaired (CU) and cognitively impaired (CI) individuals, and relationships to cognitive performance.

Results: Four subtypes were identified: limbic, medial temporal lobe (MTL) sparing, posterior, and lateral temporal (l temporal). Plasma p-tau217 was elevated in CI versus CU in limbic, posterior, and l temporal subtypes and strongly associated with tau deposition and cognitive performance. In the MTL-sparing subtype, p-tau217 showed a significant association with tau but no elevation in CI and no relationship to cognition.

Discussion: These findings indicate that p-tau217's diagnostic utility varies across AD subtypes, reflecting distinct biological mechanisms not captured by current blood biomarkers.

Highlights: Plasma phosphorylated tau (p-tau)217 differentiated cognitively unimpaired from impaired individuals in most subtypes, with the notable limitation of the medial temporal lobe (MTL)-sparing group.P-tau217 level was linked to regional tau accumulation as measured by tau positron emission tomography, across all subtypes.The MTL-sparing subtype appeared to be unique, as p-tau217 was not elevated in cognitively impaired individuals, and there was no clear relationship between p-tau217 levels and cognitive performance.

Introduction: Mild cognitive impairment (MCI) is underdiagnosed by primary care providers (PCPs), with detection rates as low as 6%-15%. Predictive models support the identification of individuals at risk, enabling timely intervention.

Methods: This retrospective study was conducted on 271,054 cognitively unimpaired and 14,501 confirmed MCI cohorts from electronic health records. A machine learning model was developed with a data-driven variable selection approach based on demographics and comorbidities.

Results: From 101 variables, 26 were chosen for the final model, achieving an overall area under the curve (AUC) of 86%. Age-stratified AUCs were 79.1% (40-49), 77.0% (50-64), 76.9% (65-79), and 74.4% (≥80), showing the highest predictive performance in younger age groups.

Discussion: Demographic factors and comorbidities can serve as effective predictors for the risk of MCI. The model demonstrates strong predictive performance and assists as a triage tool for PCPs, facilitating the identification of individuals with MCI for early treatment.

Highlights: Predictive algorithms using electronic health records (EHRs) are useful for identifying individuals at risk for mild cognitive impairment (MCI) to triage for further clinical evaluation.A machine learning model was developed using EHR data to predict those at risk for MCI.The model identified 26 variables that were able to distinguish the MCI from non-MCI cohorts.The model accurately detected MCI across the cohort (area under the curve [AUC] = 86%) and trended best for younger age groups (AUC was 77%, 77%, and 74% in 50-64, 65-79, and ≥80 age groups, respectively).Implementation of a triage tool could be used to detect MCI across aging patient populations sooner, leading to a timelier diagnosis, intervention, and treatment management.