Alzheimer's Research & Therapy最新文献

Introduction: Dementia risk reduction through lifestyle modification has much potential but is not yet implemented in routine clinical care. Currently, there are no preventive interventions available for memory clinic patients. Therefore, the aim of The Lifestyle Intervention in the memory clinics of General and academic Hospitals Trial (LIGHT) is to examine the (cost)effectiveness of a multidomain intervention combining lifestyle coaching with risk self-management for patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI).

Methods: LIGHT is a 1-year multi-center randomized controlled trial for dementia risk reduction by improving brain health through lifestyle modifications in memory clinic patients without dementia. Starting early 2025, the trial aims to include 300 older adults (≥ 50 years) with SCD or MCI, with presence of ≥ 2 modifiable dementia risk factors, recruited via the memory clinics of Dutch hospitals. Participants are randomized 1:1 to either the intervention group or control group. The intervention consists of three components: (1) three individual sessions with a certified lifestyle coach to set and work on personal goals, (2) vouchers for access to brain-healthy services from local providers, and (3) access to an online self-management platform ( www.breinzorg.nl ) providing psychoeducation on dementia risk reduction through lifestyle. The control group receives general health advice. The primary outcome is 1-year change in modifiable dementia risk captured by the LIfestyle for BRAin Health 2 (LIBRA2) index consisting of coronary heart disease, diabetes, hypercholesterolemia, hypertension, depression, obesity, smoking, high physical activity, and chronic kidney disease, high alcohol intake, high cognitive activity, healthy diet, hearing impairment, sleep disturbances, and social participation. Secondary outcomes include cognitive functioning, health-related quality of life, activities of daily living, self-efficacy, care use, as well as change in individual risk factors.

Conclusion: LIGHT will provide insight into the implementation and (cost-)effectiveness of a lifestyle intervention for indicated prevention in a memory clinic setting.

Trial registration: Clinicaltrials.gov: NCT06832761 (date 2025-02-18), OMON: 57,198.

Background and objective: The clinical interpretation of Alzheimer's disease (AD) is frequently complicated by the prevalence of missense variants designated as being of uncertain significance within associated genes. Conventional computational prediction tools often overlook disease-specific pathophysiological contexts and lack pertinence and interpretability. Therefore, the present study aimed to develop a novel, interpretable framework for predicting the pathogenicity of AD missense variants by integrating transcriptomic and proteomic data enrichment patterns with machine learning methods.

Methods: A cross-sectional variant-level analysis was performed using publicly available databases. Missense variants in APOE, APP, PSEN1, PSEN2, SORL1, and TREM2 reported in AD patients were retrieved from Alzforum and compared with missense variants from individuals without neurological diseases, as cataloged in the gnomAD v2.1.1 non-neuro subset. Variants were annotated with tissue-specific expression, secondary structure, relative solvent accessibility, and other functional features using tools like AlphaFold. Enrichment of specific features was assessed with Fisher's exact tests with Bonferroni correction for multiple comparisons. Given that PSEN1 showed the strongest enrichment signals, six machine-learning algorithms were trained on PSEN1 variants to distinguish AD-associated variants from gnomAD variants, using a 10 × 5 nested cross-validation scheme. External validation was conducted using PSEN1 missense variants from ClinVar annotated as pathogenic/likely pathogenic or benign/likely benign. Model performance was compared with SIFT and PolyPhen-2, and interpretability was evaluated by feature ablation and SHapley Additive exPlanations analyses.

Results: AD-associated variants exhibited statistically significant enrichment within some transcriptomic or proteomic features, with PSEN1 contributing significantly to the enrichment observed across these features. Random forest and gradient boosting models achieved high performance in the internal training dataset and maintained high recall in the external validation dataset, outperforming SIFT and approaching the performance of PolyPhen-2. Relative solvent accessibility was the most discriminative individual feature, while regional and topological features provided complementary discriminative power.

Conclusions: This integrative, multi-omics framework links disease-specific enrichment patterns with interpretable gene-level machine learning for AD missense variants. The results highlight the importance of expression level, structural context, etc. for PSEN1 variant pathogenicity and may help prioritize variants for functional studies. Further validation in additional genes and independent cohorts is warranted prior to any clinical application.

Background: With increasing life expectancy, age-related cognitive disorders including mild cognitive impairment (MCI) represent major public health challenges, and amnestic MCI (aMCI) has the greatest risk of progression to dementia due to Alzheimer's disease (AD). Event-related potentials (ERPs), particularly the P200 component, have been studied as potential biomarkers, but conventional grand-averaged approaches often yield inconsistent results. This study examined the temporal dynamics of prefrontal ERPs in a large cohort of older adults to assess neural adaptation processes associated with early cognitive decline.

Method: The participants included 636 older adults from the Gwangju Alzheimer's and Related Dementia cohort. Two-channel prefrontal ERPs were recorded using a portable EEG system during an auditory oddball task, focusing on standard stimuli. Temporal dynamics based on the time-averaged P200 amplitude (TAP2A) were evaluated using time-trial plots, temporal segmentation into 18 epoch bins, repeated-measures ANOVAs, effect size estimation, individual slope regression and group comparisons.

Results: Time-trial plots revealed blurred and attenuated P200 responses in aMCI patients, whereas the responses of cognitively normal (CN) participants remained relatively stable. Although the time × group interaction was not significant, a significant main effect of time was primarily driven by aMCI. Post hoc pairwise comparisons revealed significant decreases in TAP2A beginning at the 12th epoch, with a medium-to-large effect size (partial η² = 0.101). Group-level slopes of -0.0027, -0.0047, and - 0.0033 were derived from individual coefficients for the CN, aMCI, and nonamnestic MCI (naMCI) groups. The linear model provided a better fit for the aMCI group (R² = 0.796) than for the CN (R² = 0.375) and naMCI (R² = 0.547) groups, suggesting accelerated trial-by-trial decline. The group comparison revealed significant differences between the CN and aMCI groups in later time bins (epochs 12-18, p < 0.01).

Discussion: aMCI was associated with accelerated neural adaptation, reflected by trial-by-trial reductions in TAP2A that may indicate altered attentional allocation and reduced neural efficiency. Compared with static measures, temporal dynamics appeared more sensitive to group-related differences among aMCI, CN, and naMCI participants. These findings suggest that slope-based temporal indices may hold exploratory potential as noninvasive indicators of memory-related cognitive change, complementing neuropsychological assessments and contributing to the early characterization of individuals who may be at increased risk of developing AD.

Background: Antidementia medications are widely prescribed for Alzheimer's disease (AD), but their long-term real-world effectiveness remains uncertain. This study investigated whether long-term outcomes differ according to medication dosage and compliance using nationwide data.

Methods: Data from the Korean National Health Insurance Service (NHIS) covering 47 million individuals were analyzed. Prescription data for acetylcholinesterase inhibitors and memantine were analyzed for dosage and compliance. Among 1,704,547 dementia cases (2010-2016), 466,773 patients with clinically diagnosed AD were included. Medication dosage and compliance during the first three years after diagnosis were categorized to define optimal versus suboptimal treatment. Clinical outcomes included progression to moderate to severe dementia, institutionalization, and mortality. Multivariable logistic regression identified factors associated with outcomes.

Results: Patients who maintained optimal dosage and compliance during the first three years after diagnosis showed a lower rate of progression to moderate to severe dementia than those receiving suboptimal treatments consistently across all classification criteria. Regression analyses revealed that optimal compliance and dosage were strongly associated with reduced progression (OR 0.807 and 0.704, respectively; p < 0.0001) and early mortality within five years. In contrast, mortality and institutionalization rates were not significantly different between groups except that mortality within five years.

Conclusions: Both medication dosage and persistence were independently associated with better long-term outcomes in AD. Maintaining optimal treatment during the early disease period may delay disease progression and improve survival within five years. This nationwide real-world study provides robust evidence supporting the importance of sustained, adequate antidementia therapy in clinical practice.

Background: Subjective cognitive decline (SCD) has been recognized as a preclinical stage of Alzheimer's disease. However, the identification of early functional brain changes remains challenging. This study investigated the functional brain changes in SCD using longitudinal EEG and evaluate the feasibility of EEG features as scalable biomarkers for identifying amyloid burden and cognitive decline using an interpretable machine learning framework.

Methods: We analyzed 120 individuals with SCD enrolled in a multicenter prospective cohort (the CoSCo study) at baseline and after a 2-year follow-up. Participants were classified as amyloid-positive (A + SCD) or amyloid-negative (A - SCD). Spectral power and graph theory-based network analyses were conducted. Also, we trained machine learning classifiers to distinguish between the groups and interpreted the predictions of classifiers using SHAP.

Results: At both baseline and follow-up, the A + SCD group exhibited elevated low-frequency (delta and theta) activity and reduced alpha activity compared to the A - SCD group. The EEG-based classifiers distinguished A + SCD from A-SCD individuals with high performance, outperforming a classifier based on demographic data. The results of SHAP analysis confirmed the importance and relative contribution of selected EEG features.

Conclusions: Longitudinal EEG, when combined with interpretable machine learning, can detect and track the functional alterations of brain related to amyloid pathology in preclinical AD. Our findings support the feasibility of EEG as a non-invasive, scalable, and sensitive biomarker for risk stratification, before overt cognitive impairment emerges.

Trial registration: This study was registered at the Clinical Research Information Service (CRIS) (cris.nih.go.kr/cris; # KCT0003397, Registration Date: December 21, 2018).