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

Introduction: Cross-sectional resting-state functional magnetic resonance imaging (rsfMRI) studies have revealed altered complexity with advanced Alzheimer's disease (AD) stages. The current study conducted longitudinal rsfMRI complexity analyses in AD.

Methods: Linear mixed-effects (LME) models were implemented to evaluate altered rates of disease progression in complexity across disease groups.

Results: The LME models revealed complexity of the higher frequency in the CNtoMCI group (those converted from cognitively normal [CN] to mild cognitive impairment [MCI]) decayed faster over time versus CN in the prefrontal and lateral occipital cortex; complexity of the lower frequency decayed faster in AD versus CN in various frontal and temporal regions (p < 0.05 & Benjamini-Hochberg corrected with q < 0.05).

Discussion: Local functional brain activities decayed in the early stage of the disease, and long-range communications were impacted in the later stage. Our study demonstrated longitudinal changes in AD-related rsfMRI complexity, indicating its potential as an imaging biomarker of AD.

Highlights: We conducted longitudinal resting state functional magnetic resonance imaging (rsfMRI) complexity analyses using the Alzheimer's Disease Neuroimaging Initiative dataset.Higher-frequency complexity in the CNtoMCI group (those transitioning from cognitively normal [CN] to mild cognitive impairment [MCI]) was found to decay faster over time compared to CN, specifically in the prefrontal and lateral occipital cortex.Lower-frequency complexity was found to decay faster in AD versus CN in various frontal and temporal regions.This study demonstrated that longitudinal changes in rsfMRI complexity could serve as a potential imaging biomarker for Alzheimer's disease.

Introduction: Melanopsin is a photopigment with roles in mediating sleep and circadian-related processes, which are often disrupted in Alzheimer's disease (AD). Melanopsin also impacts cognition and synaptogenesis. This study investigated the associations between melanopsin genetic variants, sleep, and markers of brain health.

Methods: Linear regression analyses examined the relationship of single-nucleotide polymorphisms (SNPs) within the melanopsin gene (OPN4), with cortical amyloid beta (Aβ), cognition, brain volumes, and self-reported sleep traits in cognitively unimpaired older adults. Further analyses assessed whether sleep traits x OPN4 SNP interactions were associated with markers of brain health.

Results: OPN4 SNPs rs2355009 and rs3740334 were associated with attention and processing speed and ventricular volume and language, respectively. Furthermore, rs3740334 and rs1079610 showed significant interactions with sleep traits in association with language.

Discussion: This study shows associations of OPN4 genetic variants with markers of brain health, and suggests that these variants interact with sleep to exacerbate cognitive effects.

Highlights: The relationships between melanopsin gene (OPN4) variants and markers of brain health were examined cross-sectionally in cognitively unimpaired older individuals.Variation within OPN4is associated with differences in cognition and ventricular volume.rs2355009 and rs3740334 show small-moderate associations with differences in attention and processing speed. Further to this, rs2355009 and rs3740334 were associated with ventricular volumes and language performance, respectively.The interactions between rs3740334 and rs1079610 and sleep traits also showed small-moderate associations with differences in language performance.

Introduction: Little is known about the factors underpinning discordant cerebrospinal fluid (CSF) amyloid beta (Aβ)₄₂ versus p-tau181/Aβ₄₂ or CSF Aβ₄₂ versus Aβ positron emission tomography (PET).

Methods: We stratified 570 non-demented Alzheimer's Disease Neuroimaging Initiative (ADNI) participants by Aβ PET and further by CSF Aβ₄₂ or p-tau181/Aβ₄₂. We used analysis of covariance testing adjusting for covariates, followed by Tukey post hoc pairwise comparisons, to compare CSF soluble triggering receptor expressed on myeloid cells-2 (sTREM2) across four participant groups: CSF+ _Aβ42 with CSF- _p-tau/Aβ42, CSF- _Aβ42 with CSF+ _p-tau/Aβ42, and concordant CSF_Aβ42/CSF_p-tau/Aβ42. We also compared sTREM2 across discordant and concordant CSF_Aβ42/PET.

Results: Regardless of Aβ PET status, CSF+_Aβ42 with CSF-_p-tau/Aβ42 had lower sTREM2 than CSF-_Aβ42 with CSF+_p-tau/Aβ42. CSF sTREM2 was similarly also associated with discordant CSF Aβ42 /PET.

Discussion: Our study suggests the potential roles of sTREM2 in discordant CSF Aβ₄₂ and p-tau181/Aβ₄₂ and discordant CSF_Aβ42/PET. Low- and high-CSF sTREM2 may affect the accuracy of p-tau181/Aβ₄₂ during the clinical work-up of AD.

Highlights: 17% of non-demented older adults had discordant CSF Aβ₄₂ versus p-tau181/Aβ₄₂.sTREM2 differed between discordant cases of CSF Aβ₄₂ versus p-tau181/Aβ₄₂.20% of non-demented older adults had discordant CSF Aβ₄₂ versus Aβ PET.sTREM2 also differed between discordant cases of CSF Aβ₄₂ versus Aβ PET.p-tau181/Aβ₄₂ may miss 6.7% of PET+ non-demented older adults with low sTREM2.

Introduction: Seed amplification assays (SAAs) demonstrate remarkable diagnostic performance in alpha-synucleinopathies. However, existing protocols lack accessibility in routine laboratories, mainly due to the requirement for in-house production of recombinant alpha-synuclein (aSyn). This study proposes a cerebrospinal fluid (CSF) aSyn-SAA protocol using solely commercial reagents to facilitate its clinical implementation.

Methods: Routine clinical care CSF samples from 126 patients, comprising 47 with Lewy body diseases (LBD) (41 with dementia with Lewy bodies, six with Parkinson's disease), 37 without alpha-synucleinopathy, and 42 with Alzheimer's disease (AD), underwent assessment for aSyn-SAA activity.

Results: CSF aSyn-SAA showed a sensitivity of 72.3% and a specificity of 100% when distinguishing clinically diagnosed LBD patients from those without alpha-synucleinopathy. In AD patients, 14.3% were tested positive for aSyn.

Discussion: The commercial-only CSF aSyn-SAA protocol exhibited excellent specificity when applied to a real-life cohort, signaling progress toward the accessibility of an aSyn biomarker in clinical settings.

Highlights: Diagnosis of LBD through aSyn-SAA lacks accessibility.This commercial-only aSyn-SAA has satisfactory performance in a real-life cohort.A negative aSyn-SAA does not completely exclude a synucleinopathy.Some technical points must be considered when developing aSyn-SAA.aSyn-SAA must be confined to expert laboratories due to prion-like risk management.

Introduction: We examined semantic and phonemic fluency in individuals with subjective cognitive decline (SCD) in relation to amyloid status and clinical progression.

Methods: A total of 490 individuals with SCD (62 ± 8 years, 42% female, 28% amyloid-positive, 17% clinical progression) completed annual fluency assessments (mean ± SD follow-up 4.3 ± 2.9 years). Associations between fluency trajectories, amyloid status, and clinical progression were examined with linear mixed models and joint models.

Results: Amyloid-positive individuals declined faster than amyloid-negative individuals on semantic fluency (B = -0.35, p < 0.001), but not on phonemic fluency (B = -0.06, p = 0.218). An annual decline of one word in semantic and phonemic fluency was associated with 22% (hazard ratio [HR] = 1.22, p < 0.001) and 28% (HR = 1.28, p = 0.004) increased risk of clinical progression.

Discussion: Our results indicate that decline in semantic fluency is an early indicator of cognitive deficits in preclinical Alzheimer's disease.

Highlights: Abnormal amyloid burden is associated with decline in semantic fluency.Fluency trajectories are associated with an increased risk of clinical progression.More refined measures are needed to detect the earliest language deficits.

Introduction: We evaluated the diagnostic performance of two commercial plasma p-tau217 immunoassays compared to cerebrospinal fluid (CSF) testing and neuropathology.

Methods: One hundred and seventy plasma samples from the University of British Columbia Hospital Clinic for Alzheimer's (AD) and Related Disorders were analyzed for p-tau217 using Fujirebio and ALZpath assays. Decision points were determined using CSF testing and autopsy findings as the standard.

Results: Fujirebio and ALZpath p-tau217 had similar overall analytical and clinical performance, with distinct decision points for each assay. Based on autopsy findings, both p-tau217 assays identified individuals with AD from other neurodegenerative diseases (ALZpath area under the curve [AUC] = 0.94, Fujirebio AUC = 0.90). The ALZpath assay detected AD pathology at milder disease stages compared to the Fujirebio assay.

Discussion: Our study reinforces the clinical utility of plasma p-tau217 as an AD biomarker. Differences in test performance and clinical decision points suggest an assay-specific diagnostic approach is required for plasma p-tau217 in clinical practice.

Highlights: Two commercially available p-tau217 immunoassays (ALZpath and Fujirebio) showed equal performance based on CSF testing.ALZpath p-tau217 showed higher performance compared to Fujirebio p-tau217 based on AD diagnosis by neuropathology confirmation.Specific plasma p-tau217 assays may require distinct decision points for AD screening, diagnosis, and disease progression monitoring.

Introduction: This study investigated the association between modifiable factors and symptom progression in dementia over up to 8 years.

Methods: Multilevel growth curve models assessed the role of modifiable risk factors (low education, hearing impairment and its treatment, depression, physical inactivity, diabetes and its treatment, smoking, hypertension and its treatment, obesity, alcohol consumption, social isolation, and visual impairment) on cognitive and functional trajectories in 353 people with dementia.

Results: Higher education was associated with higher initial cognitive status but faster decline. Antidiabetic medication was associated with slower cognitive decline, whereas depression and visual impairment were linked to low baseline functioning and faster cognitive decline.

Discussion: Several modifiable risk factors influenced symptom progression. Education initially had a protective effect, whereas depressive symptoms were linked to worse symptom progression. Treatment of comorbidities (diabetes, visual impairment) could have a positive impact on dementia symptoms. Modifiable risk factors are promising targets for tertiary prevention.

Highlights: Modifiable risk factors were associated with symptom progression in dementia over up to 8 years.More education was associated with higher initial cognitive status but faster decline.Depressive symptoms were linked to less favorable symptom progression.Treatment of comorbidities (diabetes, visual impairment) may positively impact the course of symptoms.Modifiable risk factors are promising targets for tertiary prevention.

Introduction: Diagnostic performance of optical coherence tomography (OCT) to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains limited. We aimed to develop a deep-learning algorithm using OCT to detect AD and MCI.

Methods: We performed a cross-sectional study involving 228 Asian participants (173 cases/55 controls) for model development and testing on 68 Asian (52 cases/16 controls) and 85 White (39 cases/46 controls) participants. Features from OCT were used to develop an ensemble trilateral deep-learning model.

Results: The trilateral model significantly outperformed single non-deep learning models in Asian (area under the curve [AUC] = 0.91 vs. 0.71-0.72, p = 0.022-0.032) and White (AUC = 0.84 vs. 0.58-0.75, p = 0.056- < 0.001) populations. However, its performance was comparable to that of the trilateral statistical model (AUCs similar, p > 0.05).

Discussion: Both multimodal approaches, using deep learning or traditional statistical models, show promise for AD and MCI detection. The choice between these models may depend on computational resources, interpretability preferences, and clinical needs.

Highlights: A deep-learning algorithm was developed to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) using OCT images.The combined model outperformed single OCT parameters in both Asian and White cohorts.The study demonstrates the potential of OCT-based deep-learning algorithms for AD and MCI detection.

Introduction: Timely detection and tracking of Alzheimer's disease (AD) -related cognitive decline has become a public health priority. We investigated whether the NIH Toolbox for Assessment of Neurological and Behavioral Function-Cognition Battery (NIHTB-CB) detects AD-related cognitive decline.

Methods: N = 171 participants (age 76.5 ± 8; 53% female, 34% Aβ-positive) from the ARMADA study completed the NIHTB-CB at baseline, 12 months, and 24 months. Linear mixed-effect models correcting for demographics were used to examine cross-sectional and longitudinal NIHTB-CB scores in individuals across the clinical AD spectrum.

Results: Compared to Aβ-negative healthy controls, Aβ-positive individuals with amnestic MCI or mild AD performed worse on all NIHTB-CB measures and showed an accelerated decline in processing speed, working memory, and auditory word comprehension tests.

Discussion: These findings support the use of the NIHTB-CB in early AD, but also imply that the optimal NIHTB-CB composite score to detect change over time may differ across clinical stages of AD. Future directions include replication of these findings in larger and more demographically diverse samples.

Highlights: We examined NIH Toolbox-Cognition Battery scores across the clinical AD spectrum.All NIH Toolbox tests detected cross-sectional cognitive impairment in MCI-to-mild AD.Three NIH Toolbox tests captured further decline over time in MCI-to-mild AD.The NIH Toolbox can facilitate timely detection of AD-related cognitive decline.

Introduction: This study aimed to identify cognitive tests that optimally relate to tau positron emission tomography (PET) signal in the inferior temporal cortex (ITC), a neocortical region associated with early tau accumulation in Alzheimer's disease (AD).

Methods: We analyzed cross-sectional data from the harvard aging brain study (HABS) (n = 128) and the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study (n = 393). We used elastic net regression to identify the most robust cognitive correlates of tau PET signal in the ITC. Secondary analyses examined whether the cognitive correlates remained significantly associated with tau after adjusting for structural brain measures.

Results: Episodic memory measures, including both total and "process" scores, were the most robust correlates of ITC tau across both cohorts. These cognitive test scores remained significant after accounting for structural brain measures.

Discussion: These findings highlight the potential of specific episodic memory test scores to detect and monitor neuropathological changes associated with early AD.

Highlights: Machine learning identified cognitive correlates of early Alzheimer's disease tau burden.Both traditional and process scores predicted early tau burden.Episodic memory scores were among the strongest correlates.Cognitive scores remained significant after accounting for structural brain measures.