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

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.

Introduction: Alzheimer's disease (AD) is characterized by progressive white matter (WM) degeneration. Circulating lipid metabolites may serve as early indicators of WM microstructural changes. In this study, we investigated the associations between plasma lipid metabolites and WM integrity across the cognitive continuum.

Methods: We included 173 participants from the Alzheimer's Disease Neuroimaging Initiative (51 cognitively normal [CN], 88 mild cognitive impairment [MCI], 34 AD) database. Plasma metabolites were quantified using targeted lipidomics, and diffusion tensor imaging (DTI) metrics were derived from 52 predefined WM regions.

Results: Regression analyses revealed widespread metabolite-DTI associations in MCI, particularly within the corpus callosum. The callosal body and splenium showed significant inverse associations with phosphatidylcholines (PCs) and multiple lysophosphatidylcholines (lysoPCs) species. In AD group, inverse relationships between PCs and the internal capsule were observed.

Discussion: Circulating lipid metabolites are linked to WM microstructure in both prodromal and clinical AD, supporting their potential as sensitive biomarkers of early vulnerability and disease progression.

Highlights: Circulating lipid metabolites link to white matter integrity in early Alzheimer's disease (AD)Phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), and propionylcarnitine associate with tract-specific diffusion magnetic resonance imaging (dMRI) metricsNo metabolite-white matter associations detected in established ADPlasma metabolites may serve as biomarkers of early white matter degeneration.

Introduction: To address an urgent need for a scalable, accurate blood test for brain amyloid pathology that provides a conclusive result for the greatest number of patients, we developed a multi-analyte algorithmic test combining phosphorylated tau (p-tau) 217 with four other biomarkers.

Methods: Multiplexed digital immunoassays measured p-tau 217, amyloid beta 42/40, glial fibrillary acidic protein, and neurofilament light chain in 730 symptomatic individuals (training set) to establish an algorithm with cutoffs, and 1082 symptomatic individuals (validation set) from three independent cohorts to identify brain amyloid pathology.

Results: The algorithmic in validation gave an area under the curve = 0.92, yielding 90% agreement with amyloid positron emission tomography and cerebrospinal fluid. Positive predictive value was 92% at 55% prevalence. The multi-marker algorithm reduced the intermediate zone ≈ 3-fold from 34.4% to 11.9% versus p-tau 217 alone. Diagnostic performance was similar across subgroups.

Discussion: The LucentAD Complete multi-analyte blood test demonstrated high clinical validity for brain amyloid pathology detection while substantially reducing inconclusive intermediate results.

Highlights: We developed a multi-analyte blood test for assessing brain amyloid status that significantly minimizes the ambiguous "intermediate zone," a key limitation of plasma phosphorylated tau (p-tau) 217 alone.Our test combines plasma levels of p-tau 217, amyloid beta 42/40 ratio, glial fibrillary acidic protein, and neurofilament light chain for a more comprehensive evaluation of amyloid status.We rigorously validated the test's clinical performance in > 1000 samples from symptomatic individuals across three independent cohorts, using cerebrospinal fluid biomarkers and amyloid positron emission tomography as comparators.

Introduction: There are few studies analyzing cerebrospinal fluid (CSF) in patients with cerebral amyloid angiopathy (CAA). Our goal was to compare blood-brain barrier and neurodegeneration markers in CSF in CAA patients with and without hemorrhagic markers.

Methods: In a retrospective study of patients with CAA (Boston criteria version 2.0) identified from the Aachen Memory Database and from in-hospital admission records, we compared CSF neurodegeneration markers and albumin ratio (a blood-brain barrier permeability marker) in patients with and without hemorrhagic markers.

Results: Among 371 patients with CAA, 113 patients had hemorrhagic markers (30.5%). Lower amyloid beta (Aβ) 42, lower Aβ40, and higher albumin ratio were independently associated with the presence of hemorrhagic markers and an increasing number of lobar microbleeds. Cortical superficial siderosis and a higher imaging burden of CAA were associated with total tau protein.

Discussion: Presence of hemorrhagic markers in CAA patients is associated with lower CSF Aβ42 and Aβ40 and higher blood-brain barrier permeability.

Highlights: New diagnostic criteria allow for the diagnosis of CAA without hemorrhagic markers.CAA hemorrhagic markers are associated with lower Aβ42 and Aβ40 in CSF.CAA hemorrhagic markers are associated with higher blood-brain barrier permeability.Higher imaging burden of CAA is associated with higher total tau protein in CSF.

Introduction: Plasma biomarkers are minimally invasive tool for identifying Alzheimer's disease pathology. However, evaluation of their clinical utility remains limited.

Methods: This ongoing, open-label, randomized, two-arm, multicenter, U.S., prospective, observational study enrolled 609 patients presenting for initial evaluation of cognitive impairment. Patients were randomized into tau phosphorylated at position 217 (p-tau217) tested (n = 391) and untested (n = 218) arms.

Results: Change in working diagnosis was observed for 70.5% of patients with a t-tau217 result (positive or negative) inconsistent with baseline working diagnosis compared to 2.3% with a result consistent with baseline working diagnosis and 15.6% of untested patients. When the result was consistent with baseline working diagnosis, a significant 15.7% increase in diagnostic confidence was observed compared to 1.7% in untested patients and 5.0% when the result was inconsistent with baseline working diagnosis.

Discussion: P-tau217 testing changed health care providers' intended management and working diagnosis and increased confidence in the diagnosis.

Highlights: Evaluation of the clinical utility of plasma tau phosphorylated at position 217 (p-tau217) for identifying Alzheimer's disease pathology has been limited.In this study, the impact of p-tau testing on health care providers' diagnostic thinking in patients under evaluation for cognitive impairment was assessed.When the result was inconsistent with the working diagnosis, a change in the working diagnosis was observed in 70.5% of tested patients.When the result was consistent, diagnostic confidence increased by 15.7%.P-tau217 testing demonstrated clinical utility by changing the working diagnosis, increasing diagnostic confidence, and altering intended patient management.

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that needs better predictive tools. Using the National Alzheimer's Coordinating Center Uniform Data Set, this study developed machine learning (ML) models and a practical clinical tool for AD prediction.

Methods: Data from 52,537 individuals (22,371 with AD) and more than 200 variables were processed with MissForest imputation and genetic algorithm-based selection. Multiple ML models were trained, and interpretability was performed using SHAP and permutation importance. A LightGBM model was refined through iterative backward feature elimination (IBFE) followed by manual refinement.

Results: LightGBM performed best (receiver operating characteristic-area under the curve [ROC-AUC] 0.91, accuracy 82.0%). Key predictors included arthritis, age, body mass index, and heart rate. A 19-feature model retained accuracy (81.2%) and ROC-AUC (0.90).

Discussion: This lightweight tool predicts AD using mostly routine variables. Limitations include its cross-sectional nature, and would need external validation. An interactive web app and GitHub resource are available.

Highlights: Developed a lightweight ML based tool using 19 routinely available features.The lightweight model achieved an ROC-AUC of 0.90 for Alzheimer's disease prediction on NACC multicenter data.Genetic algorithm, IBFE, and manual refinement enabled optimal feature selection.Tool hosted on an open-access platform for clinical and research use.SHAP analysis provided model interpretability and feature-level insights.

Introduction: Dual cognitive-motor deficit in aging is a strong predictor of dementia, yet its effects on vulnerable gray matter region microstructure remain unexplored.

Methods: This study classified 582 individuals aged 36 to 90 into cognitive-motor deficit, isolated cognitive or motor deficit, and control groups. Microstructural differences in 27 temporal and motor-related gray matter (GM) regions and white matter (WM) tracts were assessed using diffusion tensor imaging and mean apparent propagator, a technique well suited for GM analysis.

Results: We found widespread microstructural alterations among individuals with dual cognitive-motor deficit. These changes were not observed in isolated cognitive or motor deficits after multiple comparisons correction.

Discussion: Dual cognitive-motor deficit is associated with distinct microstructural features that are hypothesized to indicate reduced cellular density in temporal GM, decreased fiber coherence, and potential demyelination in WM tracts. These findings could help understand brain aging and facilitate interventions to slow neurodegeneration and delay dementia onset.

Highlights: Dual cognitive-motor deficit strongly predicts dementia in older adults.Five hundred eighty-two individuals were classified by cognitive and motor deficiency.Mean apparent propagator magnetic resonance imaging (MRI) and diffusion tensor imaging identified widespread microstructural brain alterations.Only the dual deficit showed significant gray matter and white matter differences after correction.Results support early detection of dementia via diffusion MRI microstructure metrics.

Introduction: We compared the accuracy of pattern-recognition protocols to prospectively identify Alzheimer's disease and related dementias (ADRD) and differentiate these from normal aging or stroke.

Methods: Patterns of cognitive decline in cognitively unimpaired participants who completed ≥ 5 assessments for the Health and Retirement Study were examined to identify dementia/stroke and compared to both recorded clinical and objective diagnoses of amnestic cognitive impairment (aCI) and dementia. We report prevalence and sensitivity/specificity to detect new-onset ADRD and stroke.

Results: ADRD-related accelerated cognitive decline was identified in 372 (14.6%) participants, while stepwise decline consistent with stroke was identified in 917 (36.1%) participants. Accelerated decline was found preceding 75.8%/76.7% cases of aCI and objective dementia, respectively. Sensitivity for accelerated decline to detect aCI/objective dementia was excellent (96.2%/91.9%). Stepwise decline preceded diagnosis with executive cognitive impairment (eCI)/clinical stroke in 40.0%/43.3% of participants, and sensitivity was moderate for eCI/clinical stroke (45.3%/58.8%).

Discussion: Longitudinal patterns of cognitive decline can help differentially diagnose ADRD from stroke in longitudinal studies of cognitive decline.

Highlights: Pattern recognition identified 95.3% of all cases of dementia in this study.Sensitivity of accelerated cognitive decline to detect incident dementia was 94.3%.Differential diagnosis for dementia might begin to rely on longitudinal cognition.Pattern recognition worked in cases of clinically and algorithmically diagnosed dementia.

Background: Previous studies on retinal changes in Alzheimer's disease (AD) using optical coherence tomography (OCT) and electroretinography (ERG) based on clinical diagnostic criteria have yielded inconsistent results. We evaluated retinal structure and function in subjects classified using clinical and biological definitions.

Methods: Fifty-nine included subjects underwent comprehensive neuropsychiatric and ophthalmic evaluations, including OCT and ERG with photopic negative response (PhNR). Amyloid status was determined by ¹¹C-Pittsburgh compound B positron emission tomography (PET).

Results: No significant differences in evaluated OCT and ERG parameters were found between cognitively impaired and unimpaired groups. Amyloid-positive subjects showed significantly thinner macular, inner plexiform, and inner nuclear layers, plus ERG abnormalities (reduced PhNR, smaller waves amplitudes, prolonged a-wave latency) (p < 0.05). ERG outperformed OCT in discriminating amyloid status (area under the curve [AUC] = 0.84). Standardized uptake value ratio (SUVr) correlated with Mini-Mental State Examination (MMSE; r = 0.62, p < 0.05).

Discussion: Biomarker-based classification revealed retinal changes, affecting both inner retina and photoreceptors, not detected using clinical criteria.

Highlights: Retinal studies in Alzheimer's disease yield mixed results when based on clinical criteria.Amyloid positron emission tomography classification permits recognition of retinal changes missed clinically.Optical coherence tomography (OCT) shows early macular thinning in amyloid beta positive subjects at the expense of inner layers.Electroretinography detects outer retinal dysfunction, indicating broader involvement.Retinal function loss on electroretinography precedes inner/outer changes on OCT.