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

Introduction: Plasma phosphorylated tau (p-tau)217 is a leading blood-biomarker for the detection of amyloid beta (Aβ) pathology. We assessed the performance of a fully automated plasma p-tau217 immunoassay to detect Aβ pathology in mild cognitive impairment (MCI)/mild dementia.

Methods: Paired plasma and cerebrospinal fluid (CSF) samples were obtained at time of diagnostic lumbar puncture (LP) in a specialist memory service. Plasma p-tau217 was measured using the Lumipulse immunoassay platform and ability to detect CSF-defined Aβ positivity assessed.

Results: Of 148 participants (69.4 ± 6.5 years; 54.1% female), 101 had MCI and 47 mild dementia. Median plasma p-tau217 was > 4-fold higher in Aβ+ vs Aβ- individuals with an area under the curve of 0.92 (0.87-0.97). Application of 90%, 95%, and 97.5% sensitivity/specificity thresholds for plasma p-tau217 may have obviated the need for more than half of LPs.

Discussion: Our real-world data support the clinical use of fully automated plasma p-tau217 immunoassays, although further studies in more diverse cohorts are required.

Highlights: Plasma phosphorylated tau (p-tau)217 was measured using a fully automated immunoassay (Lumipulse).P-tau217 was > 4-fold higher in amyloid beta (Aβ)+ versus Aβ- individuals.Plasma p-tau217 had an area under the curve of 0.92 for detection of Aβ status.Using a previously proposed two-threshold approach may avoid more than half of lumbar punctures.

Introduction: Alzheimer's disease (AD) prevalence is increasing, with no current cure. Natural language processing (NLP) offers the potential for non-invasive diagnostics, social burden assessment, and research advancements in AD.

Method: A systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines explored NLP applications in AD, focusing on dataset types, sources, research foci, methods, and effectiveness. Searches were conducted across six databases (ACM, Embase, IEEE, PubMed, Scopus, and Web of Science) from January 2020 to July 2024.

Results: Of 1740 records, 79 studies were selected. Frequently used datasets included speech and electronic health records (EHR), along with social media and scientific publications. Machine learning and neural networks were primarily applied to speech, EHR, and social media data, while rule-based methods were used to analyze literature datasets.

Discussion: NLP has proven effective in various aspects of AD research, including diagnosis, monitoring, social burden assessment, biomarker analysis, and research. However, there are opportunities for improvement in dataset diversity, model interpretability, multilingual capabilities, and addressing ethical concerns.

Highlights: This review systematically analyzed 79 studies from six major databases, focusing on the advancements and applications of natural language processing (NLP) in Alzheimer's disease (AD) research.The study highlights the need for models focusing on remote monitoring of AD patients using speech analysis, offering a cost-effective alternative to traditional methods such as brain imaging and aiding clinicians in both prediagnosis and post-diagnosis periods.The use of pretrained multilingual models is recommended to improve AD detection across different languages by leveraging diverse speech features and utilizing publicly available datasets.

Introduction: New Alzheimer's disease (AD) treatments have created an urgent need for accurate early diagnosis of high-risk adults with Down syndrome (DS), distinguishing prodromal DS-AD symptoms from lifelong cognitive impairments. Often, clinicians will need to evaluate dementia status during a single assessment, and here we describe empirically supported methods effective under such circumstances.

Methods: Archived data collected between 1987 and 2017 included longitudinal findings for 144 individuals maintaining cognitive stability and 126 developing prodromal DS-AD. Response operating characteristic analyses compared groups, defined by the presence/absence of prodromal DS-AD, for a single assessment.

Results: Groups differed on all measures without adjusting for developmental history, 0.717 < areas under the curve < 0.859, Ps < 0.0001. The balance between sensitivity and specificity improved slightly when developmental histories were considered.

Discussion: The present study demonstrated that one-time assessments can inform clinical judgments when diagnosing adults at risk for DS-AD. Knowledge of developmental history is valuable but non-essential.

Highlights: Non-overlapping distributions were observed for preclinical and prodromal Alzheimer's disease (AD) groups.Receiver operating characteristic area under the curve analyses were in the acceptable to excellent range for all measures.Performance was sensitive to both the severity of intellectual disability and the stage of Down syndrome-AD progression.Episodic memory tests were sensitive to the transition from preclinical to prodromal AD.Performance results at a single time point can inform dementia status decisions.

Introduction: Motivated by the difficulties in detecting cognitive deterioration in the context of Down syndrome (DS), we aimed to identify markers of prodromal Alzheimer's disease (AD) in this population.

Methods: Sixty-two participants with DS (age > 45) distributed in three groups (asymptomatic [A_DS], prodromal [P_DS], and dementia [D_DS]) completed the Cambridge Examination for Mental Disorders of Older People with Down's Syndrome and Others with Intellectual Disabilities, Cambridge Cognitive Examination for older adults with Down's Syndrome, and Barcelona Test for Intellectual Disability tests and a magnetic resonance imaging scan.

Results: Although temporal orientation showed significant differences among groups, only a predictive diagnostic model based on verbal short-term memory tasks (relying on "cued" recall) allowed the correct classification of 88.5% of A_DS, 75.0% of P_DS, and 95% of D_DS individuals. Cognitive decline strongly correlated with brain volume reductions in orbitofrontal, medial-temporal, and bilateral thalamus within the D_DS group.

Discussion: Neuropsychological results showed that P_DS cases were characterized by a significant deterioration of verbal memory and temporal orientation, compared to A_DS. This pattern might be crucial to support diagnosis in clinical settings.Highlights: Detecting signs of prodromal dementia is a major challenge in Down syndrome.Such challenge is due to a poor definition of the early cognitive manifestations.Memory tasks relying on "cued" recall allowed the detection of prodromal cases.A pattern of temporal disorientation was also evident in the prodromal phase.These cognitive deficits preceded volumetric brain changes only present in dementia.

Introduction: Increasing evidence links amyloid beta (Aβ) aggregation with inflammation. This pilot study investigated the use of an immunoassay panel to map biomarker changes in patients with Alzheimer's disease (AD). Furthermore, we evaluated the stability of protein quantification after multiple freeze-thaw cycles (FTCs).

Methods: The nucleic acid-linked immuno-sandwich assay (NULISA) inflammation panel measured 203 proteins in serum samples of individuals with (n = 31) and without (n = 31) AD pathology. Linear models, adjusted for age and sex, contrasted protein expression across groups.

Results: After multiple-testing adjustments, glial fibrillary acidic protein (p < 0.001) and S100A12 (p < 0.001) were significantly changed in the presence of AD pathology. Furthermore, they correlated with cerebrospinal fluid biomarkers (phosphorylated tau-181 [p-tau181], tau, and Aβ42). Additional markers were nominally changed between groups. Five FTCs caused minimal changes in measurements with the NULISA inflammation panel.

Discussion: Monitoring of inflammation in AD, using the 200-plex NULISA panel, demonstrates changes in peripherally circulating inflammation-related proteins. Contrary to previous reports, FTCs had minimal impact on the quantification of inflammatory markers.

Highlights: The novel nucleic acid-linked immuno-sandwich assay (NULISA) inflammation panel, which includes 200 protein biomarkers, was used.The panel was used for the first time in serum from patients with Alzheimer's disease (AD).The protein S100A12 was identified as a potential biomarker for AD.Inflammation markers were stable in up to five freeze-thaw cycles.

Introduction: We examined the associations of carotid intima-media thickness (CIMT), arterial stiffness index (ASI), and pulse pressure (PP) with cerebrovascular disease, cognitive function and decline, and incident cardiovascular diseases (CVD) and dementia in the UK Biobank cohort.

Methods: The study consisted of 42,711 participants (mean age 64.2 years) with brain magnetic resonance imaging (MRI), vascular assessments, and cognitive testing. Cerebrovascular disease markers included white matter hyperintensities (WMH) and brain volumes. CIMT, ASI, and PP were measured using carotid ultrasound, photoplethysmography, and blood pressure, respectively. General cognitive ability (g-score) was derived from various cognitive tests using principal components analysis (PCA).

Results: Elevated CIMT, ASI, and PP were associated with increased WMH volume (WMHV). Increased PP was independently associated with poorer numeric memory (ß = -0.028,p = 0.002), fluid intelligence (IQ) (ß = -0.060,p < 0.001), and g-score (ß = -0.028,p < 0.001) in cross-sectional analysis, but not longitudinally. CIMT showed the strongest association with incident CVD and dementia.

Discussion: CIMT had the most robust associations with WMHV, incident CVD, and dementia, suggesting its utility as an alternative endpoint.

Highlights: Effects of arterial stiffness on cognition, dementia, and CVD.Structural vascular parameters included CIMT.Functional properties included ASI and PP.CIMT, ASI, and PP were positively associated with WMHV.CIMT had the greatest associations with incident CVD and dementia.

Introduction: Plasma amyloid beta₄₂/amyloid beta₄₀ (Aβ₄₂/Aβ₄₀) and phosphorylated tau217 (p-tau217) identify individuals with primary Alzheimer's disease (AD). They may detect AD co-pathology in the setting of other primary neurodegenerative diseases, but this has not been systematically studied.

Methods: We compared the clinical, neuroimaging, and neuropathological associations of plasma Aβ₄₂/Aβ₄₀ (mass spectrometry), p-tau217 (electrochemiluminescence), and neurofilament light ([NfL], single molecule array [Simoa]), as markers of AD co-pathology, in a sporadic frontotemporal dementia (FTD) cohort (n = 620).

Results: Aβ₄₂/Aβ₄₀ showed no clinicopathological associations. High p-tau217 was present in amnestic dementia (AmD) presumed to be due to FTD, logopenic primary progressive aphasia (lvPPA), and APOEε4 carriers, and correlated with worse baseline and longitudinal clinical scores, lower hippocampal volumes, and more severe AD co-pathology (Braak Stage). NfL was elevated in all FTD phenotypes, and correlated with clinical scores and frontotemporal brain volumes.

Discussion: Plasma p-tau217 has clinical, neuroimaging, and neuropathological correlates in sporadic FTD and may identify FTD cases with AD co-pathology.

Highlights: Alzheimer's disease (AD) features could be identified with plasma phosphorylated tau217 (p-tau217) in frontotemporal lobar degeneration (FTLD).Plasma p-tau217 is a better discriminator of AD co-pathology and AD-associated features in FTLD than plasma amyloid beta₄₂/amyloid beta₄₀ (Aβ₄₂/Aβ₄₀) and neurofilament light (NfL).In FTLD, plasma p-tau217, but not Aβ₄₂/Aβ₄₀ or neurofilament light, has phenotypical, neurocognitive, and neuroimaging correlates suggestive of AD co-pathology.

Introduction: Brain age gap (BAG), defined as the difference between MRI-predicted 'brain age' and chronological age, can capture information underlying various neurological disorders. We investigated the pathophysiological significance of the BAG across neurodegenerative disorders.

Methods: We developed a brain age estimator using structural MRIs of healthy-aged individuals from one cohort study. Subsequently, we applied this estimator to people with Alzheimer's disease spectra (AD) and Parkinson's disease (PD) from another cohort study. We investigated brain sources responsible for BAGs among these groups.

Results: Both AD and PD exhibited a positive BAG. Brain sources showed overlapping, yet partially segregated, neuromorphological differences between these groups. Furthermore, employing with t-distributed stochastic neighbor embedding on the brain sources, we subclassified PD into two groups with and without cognitive impairment.

Discussion: Our findings suggest that brain age estimation becomes a clinically relevant method for finely stratifying neurodegenerative disorders.

Highlights: Brain age estimated from structure MRI data was greater than chronological age in patients with Alzheimer's disease/mild cognitive impairment or Parkinson's disease.Brain regions attributed to brain age estimation were located mainly in the fronto-temporo-parietal cortices but not in the motor cortex or subcortical regions.Brain sources responsible for the brain age gaps revealed roughly overlapping, yet partially segregated, neuromorphological differences between participants with Alzheimer's disease/mild cognitive impairment and Parkinson's disease.Participants with Parkinson's disease were subclassified into two groups (with and without cognitive impairment) based on brain sources responsible for the brain age gaps.

Introduction: Clinicopathological correlations differ by sex in Lewy body dementia (LBD). However, previous studies have focused on pathological staging systems that place less emphasis on regional pathologies.

Methods: We included 357 people (131 female, 226 male) with a high likelihood of LBD based on pathology from the Brain Bank for Neurodegenerative (Jacksonville, FL). Sex differences for regional Lewy body, senile plaque, and neurofibrillary tangle counts and their associations with clinical LBD diagnosis were assessed.

Results: Females were less likely to have a clinical LBD diagnosis; they had more Lewy bodies, neurofibrillary tangles, and senile plaques in various regions than males (all p's < 0.05). A higher likelihood of clinical LBD diagnosis was associated with more middle frontal, cingulate, and entorhinal Lewy body pathology, and more so for males than for females (all p's < 0.045).

Discussion: Sex differences for clinicopathological correlations in LBD also occur at the regional pathology level. Females have a higher frequency of clinical misdiagnosis than males.

Highlights: Females have a higher risk of clinical underdiagnosis for Lewy body dementia (LBD) than males.Regional pathology counts differ by sex for people with a high likelihood of LBD.Regional pathology association with clinical LBD diagnosis differs by sex.Regional Lewy body counts have a stronger association with LBD phenotype for males.

Background: This study explores the impact of sleep disturbances on gray matter structural covariance networks (SCNs) across the Alzheimer's disease (AD) continuum.

Methods: Amyloid-negative participants served as controls, whereas amyloid positive (A+) individuals were categorized into six groups based on cognitive status and sleep quality. SCNs for the default mode network (DMN), salience network (SN), and executive control network (ECN) were derived from T1-weighted magnetic resonance images.

Results: In the DMN, increased structural associations were observed in cognitive unimpaired (CU) A+ and mild cognitive impairment (MCI) groups regardless of sleep quality, whereas AD with poor sleep (PS) showed a decrease and AD with normal sleep (NS) an increase. For the ECN, AD-NS showed increased and AD-PS showed reduced associations. In the SN, reduced associations were observed in CU A+ NS and MCI-NS, whereas AD-NS displayed increased associations; only AD-PS had decreased associations.

Conclusion: Distinct SCN damage patterns between normal and poor sleepers provide insights into sleep disturbances in AD.

Highlights: We delineated distinct patterns of structural covariance networks (SCN) impairment across the Alzheimer's disease (AD) continuum, uncovering significant disparities between individuals with normal sleep architecture and those afflicted by sleep disturbances.These observations underscore the pivotal importance of addressing sleep disruptions in AD therapeutics, providing a refined understanding of their detrimental impact on brain networks implicated in the disease.Our investigation epitomizes methodological precision by constructing an AD continuum using amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers to minimize diagnostic heterogeneity, further enhanced by a substantial cohort size that bolsters the robustness and generalizability of our findings.