Journal of Alzheimer's disease reports最新文献

 We examined associations between objective sleep duration and cognitive status in older adults initially categorized as cognitively non-impaired (CNI, n = 57) or diagnosed with mild cognitive impairment (MCI, n = 53). On follow-up, 8 years later, all participants underwent neuropsychiatric/neuropsychological evaluation and 7-day 24-h actigraphy. On re-assessment 62.7% of participants were cognitively declined. Patients who developed dementia had significantly longer night total sleep time (TST) than persons with MCI who, in turn, had longer night TST than CNI participants. Objective long sleep duration is a marker of worse cognitive status in elderly with MCI/dementia and this association is very strong in older adults.

Background: The evidence supporting the effectiveness of combined interventions in Alzheimer's disease (AD) patients remains inconclusive.

Objective: The aim of this study was to evaluate the mid- and long-term effectiveness of physical training, alone or combined with cognitive games, on cognitive performance in patients with moderate AD.

Methods: Seventy-nine AD patients (≈73% females, age of ≈70±1 years) were randomly divided into three groups: aerobic-based training (AT-group, n = 27), aerobic-based training plus cognitive games (ACT-group, n = 25), and a control group engaged in reading (CG, n = 26), two sessions per week. Cognitive performance was evaluated at the start, 4th week (W4), end of the 8th week (W8), and after a 4-week detraining period (W12), using problem-solving (Tower-of-Hanoi), selective attention (Stroop-test), and working memory (Digit-Span-test) assessments. Stress levels and quality of life were also evaluated.Results:: Aerobic and combined training induced a positive effect on all cognitive functions tested at W4 (except problem-solving) and W8 (all p < 0.001) with greater improvements in working-memory and problem-solving in ACT-group (p < 0.05). Depression levels also decreased significantly, and quality of life improved at W8 (p < 0.001) in both groups. After 4 weeks of detraining, the beneficial effect of AT and ACT was still observed. The CG did not show any significant improvements at all time points.Conclusions:: Physical and cognitive interventions appear effective for improving cognitive-functions, quality-of-life, and reducing depression in AD patients. Combined training emerges as a more effective strategy to mitigate AD progression. Further research is necessary to validate these results and explore their potential for preventing early cognitive decline.

Background: Alzheimer's disease (AD) is the most common cause of dementia. While preclinical studies have shown benefits of glucagon-like peptide 1 receptor agonists (GLP-1 RA) in targeting core AD pathology, clinical studies are limited.

Objective: A systematic review was performed to evaluate GLP-1 RAs in AD for their potential to target core AD pathology and improve cognition.

Methods: Searches were conducted via three different databases (PubMed, Embase, and Cochrane Library). Search terms included Medical Subject Headings (MeSH) terms: 'glucagon-like peptide 1 receptor agonist' and 'Alzheimer's disease', as well as entry terms 'GLP-1 RA', 'AD', and three types of GLP-1 RA: 'liraglutide', 'exenatide', and 'lixisenatide'.

Results: A total of 1,444 studies were screened. Six articles that met criteria were included (four randomized control trials [RCTs] and two protocol studies). Two RCTs with amyloid-β and tau biomarker endpoints did not observe an end of treatment difference between the placebo and treated groups. In three RCTs with cognitive endpoints, there was no end of treatment difference between placebo and treated groups. GLP-1 RA showed metabolic benefits, such as lower body mass index and improved glucose levels on oral glucose tolerance tests in treated groups. GLP-1 RA may mitigate the decline in cerebral glucose metabolism and show enhanced blood-brain glucose transport capacity using ¹⁸F-FDG PET, however, more data is needed.

Conclusions: GLP-1 RA therapy did not alter amyloid-β and tau biomarkers nor show improvements in cognition but showed potential metabolic and neuroprotective benefits.

[This corrects the article DOI: 10.3233/ADR-230077.].

Background: Individuals with mild cognitive impairment (MCI) frequently experience sleep disorders, which may elevate the risk of developing Alzheimer's disease. Yet, sleep types in MCI patients and the factors influencing them have not been sufficiently investigated.

Objective: The objective of this study was to explore potential sleep typing and its influencing factors in patients with MCI using latent class analysis.

Methods: A cross-sectional survey was conducted in Jiangsu Province, China. Cognitive function in older adults was assessed using neuropsychological tests, including the Montreal Cognitive Assessment Scale-Beijing version (MoCA), the Mini-Mental State Examination (MMSE), the Activities of Daily Living Scale (ADL), and the Clinical Dementia Rating Scale (CDR). Sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Latent class analysis based on PSQI scores and multinomial logistic regression analyses were employed to explore the influencing factors of sleep typing.

Results: The study included a total of 611 patients with MCI. Latent class analysis identified three latent classes to categorize the sleep patterns of MCI patients: the good sleep type (56.6%), the insufficient sleep type (29.6%), and the difficulty falling asleep type (13.7%). Potential sleep typing is influenced by gender, chronic disease, physical exercise, social activity, brain exercise, smoking, frailty, subjective cognitive status, and global cognitive function.

Conclusions: The findings of this study underscore the notable heterogeneity in the sleep patterns of patients with MCI. Future research may provide targeted prevention and interventions to address the characteristics and influencing factors of patients with different subtypes of sleep MCI.

Background: Mitochondrial DNA (mtDNA) is a double-stranded circular DNA and has multiple copies in each cell. Excess heteroplasmy, the coexistence of distinct variants in copies of mtDNA within a cell, may lead to mitochondrial impairments. Accurate determination of heteroplasmy in whole-genome sequencing (WGS) data has posed a significant challenge because mitochondria carrying heteroplasmic variants cannot be distinguished during library preparation. Moreover, sequencing errors, contamination, and nuclear mtDNA segments can reduce the accuracy of heteroplasmic variant calling.

Objective: To efficiently and accurately call mtDNA homoplasmic and heteroplasmic variants from the large-scale WGS data generated from the Alzheimer's Disease Sequencing Project (ADSP), and test their association with Alzheimer's disease (AD).

Methods: In this study, we present MitoH3-a comprehensive computational pipeline for calling mtDNA homoplasmic and heteroplasmic variants and inferring haplogroups in the ADSP WGS data. We first applied MitoH3 to 45 technical replicates from 6 subjects to define a threshold for detecting heteroplasmic variants. Then using the threshold of 5% ≤variant allele fraction≤95%, we further applied MitoH3 to call heteroplasmic variants from a total of 16,113 DNA samples with 6,742 samples from cognitively normal controls and 6,183 from AD cases.

Results: This pipeline is available through the Singularity container engine. For 4,311 heteroplasmic variants identified from 16,113 samples, no significant variant count difference was observed between AD cases and controls.

Conclusions: Our streamlined pipeline, MitoH3, enables computationally efficient and accurate analysis of a large number of samples.

The prodromal stage of Lewy body dementia includes a mild cognitive impairment with visual processing and/or attention-executive deficits. A clinical presentation with progressive visual loss is indeed seldom reported and can be misleading with a posterior cortical atrophy disease. While the neurodegeneration at the occipital cortex can only partially explain the visual disturbances of Lewy body dementia, more recently a retinal dysfunction has been suggested by preliminary optical coherence tomography and autoptic findings. Herein, we present a case of a mild cognitive impairment with Lewy bodies, who presented initially with visual disturbances and signs of both retinal and cortical visual processing dysfunction. A complete neuropsychological, neurophysiological and brain imaging assessment highlighted a prominent ventral visual pathway involvement. This report provides first that the prodromal stage of Lewy body dementia can manifest as a primarily progressive visual loss, second that the involvement of visual pathway, particularly the ventral stream, can be detectable from the retinal to the cortical level.

Background: The development and popularization of the Internet provides an important opportunity to advance national strategies for healthy aging, particularly the impact of the Internet on cognitive function in middle-aged and older adults.

Objective: This study aimed to quantify the impact of Internet use on the cognitive health of middle-aged and older adults (aged≥45 years).

Methods: We used data from the Chinese Family Panel Study (CFPS) survey, tested the robustness of the baseline findings by variable substitution and instrumental variables methods, and analyzed heterogeneity. Subsequently, five purposes of Internet use that affect cognitive function were analyzed in depth.

Results: Internet use may improve cognitive function in middle-aged and older adults. The effect of Internet use on cognitive function was more pronounced in the lower age group (45-59 years), among males, in rural areas, and among middle-aged and older adults with higher levels of education. Cognitive functioning of middle-aged and older adults varied according to how often they used the Internet for entertainment, socialization, study, work, and business activities.

Conclusions: The use of the Internet may be considered a practical non-pharmacological intervention to slow cognitive decline in middle-aged and older adults.

Background: Amyloid-β plaques (Aβ) are associated with Alzheimer's disease (AD). Pooled assessment of amyloid reduction in transgenic AD mice is critical for expediting anti-amyloid AD therapeutic research.

Objective: The mean threshold of Aβ reduction necessary to achieve cognitive improvement was measured via pooled assessment (n = 594 mice) of Morris water maze (MWM) escape latency of transgenic AD mice treated with substances intended to reduce Aβ via reduction of beta-secretase cleaving enzyme (BACE).

Methods: Machine learning and statistical methods identified necessary amyloid reduction levels using mouse data (e.g., APP/PS1, LPS, Tg2576, 3xTg-AD, control, wild type, treated, untreated) curated from 22 published studies.

Results: K-means clustering identified 4 clusters that primarily corresponded with level of Aβ: untreated transgenic AD control mice, wild type mice, and two clusters of transgenic AD mice treated with BACE inhibitors that had either an average 25% "medium reduction" of Aβ or 50% "high reduction" of Aβ compared to untreated control. A 25% Aβ reduction achieved a 28% cognitive improvement, and a 50% Aβ reduction resulted in a significant 32% improvement compared to untreated transgenic mice (p < 0.05). Comparatively, wild type mice had a mean 41% MWM latency improvement over untreated transgenic mice (p < 0.05). BACE reduction had a lesser impact on the ratio of Aβ₄₂ to Aβ₄₀. Supervised learning with an 80% -20% train-test split confirmed Aβ reduction was a key feature for predicting MWM escape latency (R² = 0.8 to 0.95).

Conclusions: Results suggest a 25% reduction in Aβ as a meaningful treatment threshold for improving transgenic AD mouse cognition.

Background: Alzheimer's disease and mild cognitive impairment are common diseases in the elderly, affecting more than 50 million people worldwide in 2020. Early diagnosis is crucial for managing these diseases, but their complexity poses a challenge. Convolutional neural networks have shown promise in accurate diagnosis.

Objective: The main objective of this research is to diagnose Alzheimer's disease and mild cognitive impairment in healthy individuals using convolutional neural networks.

Methods: This study utilized three different convolutional neural network models, two of which were pre-trained models, namely AlexNet and DenseNet, while the third model was a CNN1D-LSTM neural network.

Results: Among the neural network models used, the AlexNet demonstrated the highest accuracy, exceeding 98%, in diagnosing mild cognitive impairment and Alzheimer's disease in healthy individuals. Furthermore, the accuracy of the DenseNet and CNN1D-LSTM models is 88% and 91.89%, respectively.

Conclusions: The research highlights the potential of convolutional neural networks in diagnosing mild cognitive impairment and Alzheimer's disease. The use of pre-trained neural networks and the integration of various patient data contribute to achieving accurate results. The high accuracy achieved by the AlexNet neural network underscores its effectiveness in disease classification. These findings pave the way for future research and improvements in the field of diagnosing these diseases using convolutional neural networks, ultimately aiding in early detection and effective management of mild cognitive impairment and Alzheimer's disease.