2019 Alzheimer's disease facts and figures

Trends in prevalence, mortality, and risk factors of dementia among the oldest-old adults in the United States: the role of the obesity epidemic

The oldest-old population, those aged ≥ 80 years, is the fastest-growing group in the United States (US), grappling with an increasingly heavy burden of dementia. We aimed to dissect the trends in dementia prevalence, mortality, and risk factors, and predict future levels among this demographic. Leveraging data from the Global Burden of Disease Study 2019, we examined the trends in dementia prevalence, mortality, and risk factors (with a particular focus on body mass index, BMI) for US oldest-old adults. Through decomposition analysis, we identified key population-level contributors to these trends. Predictive modeling was employed to estimate future prevalence and mortality levels over the next decade. Between 1990 and 2019, the number of dementia cases and deaths among the oldest-old in the US increased by approximately 1.37 million and 60,000 respectively. The population growth and aging were highlighted as the primary drivers of this increase. High BMI emerged as a growing risk factor. Females showed a disproportionately higher dementia burden, characterized by a unique risk factor profile, including BMI. Predictions for 2030 anticipate nearly 4 million dementia cases and 160,000 related deaths, with a marked increase in prevalence and mortality anticipated among those aged 80-89. The past 30 years have witnessed a notable rise in both the prevalence and mortality of dementia among the oldest-old in the US, accompanied by a significant shift in risk factors, with obesity taking a forefront position. Targeted age and sex-specific public health strategies that address obesity control are needed to mitigate the dementia burden effectively.

3-Way hybrid analysis using clinical and magnetic resonance imaging for early diagnosis of Alzheimer's disease

Alzheimer's is a progressive neurodegenerative disorder that leads to cognitive impairment and ultimately death. To select the most effective treatment options, it is crucial to diagnose and classify the disease early, as current treatments can only delay its progression. However, previous research on Alzheimer's disease (AD) has had limitations, such as inaccuracies and reliance on a small, unbalanced binary dataset. In this study, we aimed to evaluate the early stages of AD using three multiclass datasets: OASIS, EEG, and ADNI MRI. The research consisted of three phases: pre-processing, feature extraction, and classification using hybrid learning techniques. For the OASIS and ADNI MRI datasets, we computed the mean RGB value and used an averaging filter to enhance the images. We balanced and augmented the dataset to increase its size. In the case of the EEG dataset, we applied a band-pass filter for digital filtering to reduce noise and also balanced the dataset using random oversampling. To extract and classify features, we utilized a hybrid technique consisting of four algorithms: AlexNet-MLP, AlexNet-ETC, AlexNet-AdaBoost, and AlexNet-NB. The results showed that the AlexNet-ETC hybrid algorithm achieved the highest accuracy rate of 95.32% for the OASIS dataset. In the case of the EEG dataset, the AlexNet-MLP hybrid algorithm outperformed other approaches with the highest accuracy of 97.71%. For the ADNI MRI dataset, the AlexNet-MLP hybrid algorithm achieved an accuracy rate of 92.59%. Comparing these results with the current state of the art demonstrates the effectiveness of our findings.

Discharge disposition for home health care patients with Alzheimer's disease and related dementia: The role of living arrangements and rural living

PURPOSE

To examine the relationship between living arrangements and discharge disposition, and how this relationship differs by the rural or urban characteristics of the patient's residence among home health care patients with Alzheimer's disease and related dementia (ADRD).

METHODS

This retrospective study used the 2019 Outcome and Assessment Information Set and the Master Beneficiary Summary File. Our study was based on 531,269 Medicare fee-for-service patients with ADRD. We used linear probability regression models to examine the relationship between discharge disposition (to the community vs. an institution) and living arrangements, including an interaction term for rural-urban residence.

FINDINGS

Patients in rural areas (19.8%) were more likely to live alone than those in urban areas (15.2%). Our main results show that patients living at home with others (coefficient: -0.02, p-value < 0.001) or alone (coefficient: -0.03, p-value < 0.001) were less likely to be discharged to the community compared to patients who lived in congregate settings. Also, for patients with ADRD who lived in rural areas, living at home with others (rural*home with others; coefficient: -0.02, p-value < 0.001) or living alone (rural*home alone; coefficient: -0.03, p-value<0.001) were associated with additional lower probabilities of being discharged to their communities.

CONCLUSIONS

A multidimensional approach considering living arrangements to support home health care patients with ADRD could be critical to achieving better health outcomes. Furthermore, implementing area-specific target interventions could be important for improving the care and health of patients with ADRD as well as reducing rural-urban disparities in discharge disposition.

Telephone-Based Acceptance and Commitment Therapy for Caregivers of Persons with Dementia: Results of a Randomized Controlled Trial

OBJECTIVES

Family caregivers of persons with dementia (PwD) experience high levels of distress. We used a randomized-controlled trial to investigate the effects of telephone-based acceptance and commitment therapy (tbACT) for family caregivers.

METHODS

Caregivers were randomly allocated to an intervention group (tbACT, n = 41) or an untreated control group (CG, n = 40). The intervention consisted of eight weekly sessions of tbACT. Depression and anxiety (primary outcomes), physical symptoms, pre-death grief, care-related thoughts, acceptance (secondary outcomes), quality of life, coping and well-being (well-being/coping outcomes) were assessed pre- and post-assessment. A 6-month follow-up was conducted.

RESULTS

Compared to CG participants, tbACT participants had at post-assessment significantly lower depressive symptoms; fewer physical symptoms (rheumatic pain); better physical health; more resource utilization (coping with daily hassles, social support); better coping with the care situation and better emotional well-being. During the six-month follow-up, tbACT participants showed less pre-death grief, fewer physical symptoms, and more utilization of resources related to coping with daily hassles.

CONCLUSIONS

tbACT is a feasible and promising psychotherapeutic intervention for family caregivers of PwD. Because of small sample size our results are preliminary.

CLINICAL IMPLICATIONS

Most of the effects of tbACT were not maintained 6 months after the intervention, suggesting that booster sessions may be helpful.

Comparing balance using the BESTest in Alzheimer, Huntington and Parkinson disease

Aim: Individuals with Alzheimer disease (AD), Huntington disease (HD) and Parkinson disease (PD) have impaired balance, and comparing these deficits could improve management of neurological diseases.Methods: Scores on the Balance Evaluation Systems Test (BESTest) were compared across three groups, consisting of individuals with AD, HD and PD in early stages of their respective disease.Results: Individuals with PD had significantly higher scores on the BESTest than individuals with AD (95% CI [4.30, 21.37], p < 0.01) or HD (95% CI [6.53, 24.18], p < 0.001). Individuals with AD and HD were not significantly different on the overall BESTest or any of its subsections.Conclusion: AD and HD may have overlapping pathologies resulting in early and similar balance impairments in these groups.

Use of machine learning algorithms to determine the relationship between air pollution and cognitive impairment in Taiwan

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used machine learning algorithms (k-nearest neighbors, random forest, gradient-boosted decision trees, eXtreme gradient boosting, and CatBoost) to investigate the association between cognitive impairment and air pollution. Data from the Taiwan Biobank and 75 air-pollution-monitoring stations in Taiwan were analyzed to determine individual levels of exposure to air pollutants. The pollutants examined were particulate matter with a diameter of ≤ 2.5 μm (PM2.5), nitrogen dioxide, nitric oxide, carbon monoxide, and ozone. The results revealed that the most strongly correlated with cognitive impairment were ozone, PM2.5, and carbon monoxide levels with adjustment of educational level, age, and household income. The model based on these factors achieved accuracy as high as 0.97 for detecting cognitive impairment, indicating a positive association between air pollutions and cognitive impairment.

The use of photovoice to explore the physical disability experience in older adults with mild cognitive impairment/early dementia

Although co-occurring cognitive impairment and physical disability in older adults is common, there is little understanding of how this group perceives their ability to do their daily activities. This study used photovoice to explore how older adults with MCI/early dementia and physical disability without and with care partners (dyads) perceive challenges with their daily activities. Photovoice is a visual research methodology to capture participants' insight on aspects of their daily lives. No known studies have taken this approach to explore the experiences of older adults with MCI/early dementia and co-occurring physical disability. We used a cross-sectional, exploratory research design to understand participants' (n = 12) experiences in their home environment. Photos and participant thoughts on the photos were categorized based on the Blackfoot Breath of Life Theory and the Hierarchy Model of Needs in Dementia, an adaptation of Maslow's Model. Notable findings included: awareness of physical/cognitive difficulties by older adults, solutions for ADL difficulty in persons with MCI/early dementia, care partners' difficulty recognizing pain without the presence of severe emotional or physical responses, reducing mood severity, and self-esteem needs met with structured, memory-issue adapted, meaningful activities. Older adults with co-occurring physical disability and MCI/early dementia identified both physiological and psychological needs despite challenges impacting their cognition. Photovoice evoked daily situations of these individuals and revealed the importance of developing individualized intervention elements for older adults with dementia and physical disability.

Feasibility and Determinants of Performance for a Tablet-Based Cognitive Assessment Tool in Rural and Urban Southeast Nigeria

Background

Cognitive assessment is a key component of clinical evaluations for patients with dementia and Alzheimer's disease in primary health care (PHC) settings. The need for well-validated, culturally appropriate, and easy-to-use assessments is especially urgent in low- and middle-income countries (LMICs) that are experiencing rapid growth in their older adult populations.

Objective

To examine the feasibility and demographic determinants of performance for a tablet-based cognitive assessment tool (TabCAT) battery, which includes subtests for four cognitive domains, among older PHC patients in southeastNigeria.

Methods

A cross-sectional mixed-method descriptive study evaluating the useability and performance of TabCAT.

Results

We enrolled 207 participants (mean age of 64.7±13.5 years; 52% with only primary, 41% secondary, and 7% tertiary education). Most (91%) who initiated the assessment were able to complete it, requiring 10- 15 minutes to complete. More years of education was associated with better test scores across all tests (p < 0.001). Living in a rural location was also associated with better performance (p < 0.05). Male compared to female sex did not associate with performance on any of the tests (all ps > 0.05).

Conclusions

Tablet-based cognitive assessment was feasible in rural and urban settings of Nigeria. Better performance on cognitive subtests linked to more education and residing in a rural area; however, sex did not predict performance. Digital cognitive assessment tools hold potential for widespread use in healthcare and educational contexts, particularly in regions with varying levels of urbanization and educational access.

Recent advances in nanoenzymes for Alzheimer's disease treatment

Alzheimer's disease (AD) remains one of the most challenging neurodegenerative disorders to treat, with oxidative stress playing a significant role in its pathology. Recent advancements in nanoenzymes technology offer a promising approach to mitigate this oxidative damage. Nanoenzymes, with their unique enzyme-mimicking activities, effectively scavenge reactive oxygen species and reduce oxidative stress, thereby providing neuroprotective effects. This review delves into the underlying mechanisms of AD, focusing on oxidative stress and its impact on disease progression. We explore the latest developments in nanoenzymes applications for AD treatment, highlighting their multifunctional capabilities and potential for targeted delivery to amyloid-beta plaques. Despite the exciting prospects, the clinical translation of nanoenzymes faces several challenges, including difficulties in brain targeting, consistent quality production, and ensuring safety and biocompatibility. We discuss these limitations in detail, emphasizing the need for rigorous evaluation and standardized protocols. This paper aims to provide a comprehensive overview of the current state of nanoenzymes research in AD, shedding light on both the opportunities and obstacles in the path towards effective clinical applications.

A novel graph neural network method for Alzheimer's disease classification

Alzheimer's disease (AD) is a chronic neurodegenerative disease. Early diagnosis are very important to timely treatment and delay the progression of the disease. In the past decade, many computer-aided diagnostic (CAD) algorithms have been proposed for classification of AD. In this paper, we propose a novel graph neural network method, termed Brain Graph Attention Network (BGAN) for classification of AD. First, brain graph data are used to model classification of AD as a graph classification task. Second, a local attention layer is designed to capture and aggregate messages of interactions between node neighbors. And, a global attention layer is introduced to obtain the contribution of each node for graph representation. Finally, using the BGAN to implement AD classification. We train and test on two open public databases for AD classification task. Compared to classic models, the experimental results show that our model is superior to six classic models. We demonstrate that BGAN is a powerful classification model for AD. In addition, our model can provide an analysis of brain regions in order to judge which regions are related to AD disease and which regions are related to AD progression.

A systematic review of reminder and guidance systems for Alzheimer's Disease and Related Dementias patients: context, barriers and facilitators

PURPOSE

To present the latest available research assessing the actual impact of reminder and guidance technologies for daily activities in Alzheimer's Disease and Related Dementia's patients, outlining design implications for these technologies.

MATERIALS AND METHODS

The search was conducted in the ACM Digital Library, IEEExplore, ScienceDirect, PubMed, Cochrane Library, Sage Journal, ResearchGate, and SemanticScholar. An iteratively-developed Boolean search string was built including up to 18 AND/OR terms across Four categories (Memory Aids, Technology, Daily Activities, Memory Impairment). We qualitatively analyzed the findings and discussions of the findings reported in 40 studies in our corpus to determine common barriers to, and facilitators of, effective intervention implementation and adoption.

RESULTS

Forty studies fulfilled the inclusion and exclusion criteria. In existing studies, individuals with Alzheimer's Disease and Related Dementias understand the usefulness of different functions that can be provided within an app to assist with everyday tasks. There was a high level of heterogeneity regarding the studies' location, duration, and evaluation methodology.

CONCLUSIONS

There is a need for assistive reminder and guidance technologies to be tailored towards autonomy, identity and personalization. Future work should include motivating features to aid during mood changes and feelings of insecurity.

A spatiotemporal graph transformer approach for Alzheimer's disease diagnosis with rs-fMRI

Alzheimer's disease (AD) is a neurodegenerative disease accompanied by cognitive impairment. Early diagnosis is crucial for the timely treatment and intervention of AD. Resting-state functional magnetic resonance imaging (rs-fMRI) records the temporal dynamics and spatial dependency in the brain, which have been utilized for automatically diagnosis of AD in the community. Existing approaches of AD diagnosis using rs-fMRI only assess functional connectivity, ignoring the spatiotemporal dependency mining of rs-fMRI. In addition, it is difficult to increase diagnosis accuracy due to the shortage of rs-fMRI sample and the poor anti-noise ability of model. To deal with these problems, this paper proposes a novel approach for the automatic diagnosis of AD, namely spatiotemporal graph transformer network (STGTN). The proposed STGTN can effectively extract spatiotemporal features of rs-fMRI. Furthermore, to solve the sample-limited problem and to improve the anti-noise ability of the proposed model, an adversarial training strategy is adopted for the proposed STGTN to generate adversarial examples (AEs) and augment training samples with AEs. Experimental results indicate that the proposed model achieves the classification accuracy of 92.58%, and 85.27% with the adversarial training strategy for AD vs. normal control (NC), early mild cognitive impairment (eMCI) vs. late mild cognitive impairment (lMCI) respectively, outperforming the state-of-the-art methods. Besides, the spatial attention coefficients reflected from the designed model reveal the importance of brain connections under different classification tasks.

Developing a fair and interpretable representation of the clock drawing test for mitigating low education and racial bias

The clock drawing test (CDT) is a neuropsychological assessment tool to screen an individual's cognitive ability. In this study, we developed a Fair and Interpretable Representation of Clock drawing test (FaIRClocks) to evaluate and mitigate classification bias against people with less than 8 years of education, while screening their cognitive function using an array of neuropsychological measures. In this study, we represented clock drawings by a priorly published 10-dimensional deep learning feature set trained on publicly available data from the National Health and Aging Trends Study (NHATS). These embeddings were further fine-tuned with clocks from a preoperative cognitive screening program at the University of Florida to predict three cognitive scores: the Mini-Mental State Examination (MMSE) total score, an attention composite z-score (ATT-C), and a memory composite z-score (MEM-C). ATT-C and MEM-C scores were developed by averaging z-scores based on normative references. The cognitive screening classifiers were initially tested to see their relative performance in patients with low years of education (< = 8 years) versus patients with higher education (> 8 years) and race. Results indicated that the initial unweighted classifiers confounded lower education with cognitive compromise resulting in a 100% type I error rate for this group. Thereby, the samples were re-weighted using multiple fairness metrics to achieve sensitivity/specificity and positive/negative predictive value (PPV/NPV) balance across groups. In summary, we report the FaIRClocks model, with promise to help identify and mitigate bias against people with less than 8 years of education during preoperative cognitive screening.

The Role of Neighborhood Socioeconomic Status in Institutionalization of Home Health Care Patients With and Without Alzheimer's Disease and Related Dementias

OBJECTIVES

To assess whether neighborhood socioeconomic status (SES) moderates the association between Alzheimer's disease and related dementias (ADRD) and successful discharge to the community. In addition, to explore whether the role of neighborhood SES on successful discharge for patients with ADRD varies by the severity of ADRD.

DESIGN

This is a retrospective cohort study.

SETTING AND PARTICIPANTS

Medicare Fee-for-service beneficiaries, aged 65 or older, who received home health care in 2019.

METHODS

We used linear probability regression models with successful discharge to the community as the main outcome, and neighborhood SES and ADRD as independent variables. Also, we modified the Functional Assessment Staging Tool (FAST) to measure ADRD severity.

RESULTS

Our study results show ADRD and residing in neighborhoods with lower socioeconomic conditions were independently associated with lower probabilities of successful discharge to the community. We also found that the differences in probabilities of remaining at home between patients with and without ADRD were larger among those in neighborhoods with lower SES (ADRD∗less disadvantaged neighborhood, coeff: -0.01, P < .001; ADRD∗more disadvantaged neighborhood, coeff: -0.02, P < .001; ADRD∗most disadvantaged neighborhood, coeff: 0.032, P < .001). Among patients with ADRD, patients with the most advanced ADRD were less likely to remain in their homes and community when living in neighborhoods with lower SES.

CONCLUSIONS AND IMPLICATIONS

Our study results show that when patients with ADRD receiving home health care live in neighborhoods with lower SES, they face further challenges to remaining in their homes and community. Public health officials and community planners should consider using area-level interventions to improve care and health outcomes for patients with ADRD. Also, further research aimed at identifying the specific factors and resources influencing lower care quality and poorer health outcomes in socioeconomically disadvantaged neighborhoods, particularly for patients with ADRD, can provide valuable insights for the development and implementation of targeted interventions.

Metabolomic and Proteomic Analysis of ApoE4-Carrying H4 Neuroglioma Cells in Alzheimer's Disease Using OrbiSIMS and LC-MS/MS

Growing clinical evidence reveals that systematic molecular alterations in the brain occur 20 years before the onset of AD pathological features. Apolipoprotein E4 (ApoE4) is one of the most significant genetic risk factors for Alzheimer's disease (AD), which is not only associated with the AD pathological features such as amyloid-β deposition, phosphorylation of tau proteins, and neuroinflammation but is also involved in metabolism, neuron growth, and synaptic plasticity. Multiomics, such as metabolomics and proteomics, are applied widely in identifying key disease-related molecular alterations and disease-progression-related changes. Despite recent advances in the development of analytical technologies, screening the entire profile of metabolites remains challenging due to the numerous classes of compounds with diverse chemical properties that require different extraction processes for mass spectrometry. In this study, we utilized Orbitrap Secondary Ion Mass Spectrometry (OrbiSIMS) as a chemical filtering screening tool to examine molecular alterations in ApoE4-carried neuroglioma cells compared to wild-type H4 cells. The findings were compared using liquid chromatography (LC)-MS/MS targeted metabolomics analysis for the confirmation of specific metabolite classes. Detected alterations in peptide fragments by OrbiSIMS provided preliminary indications of protein changes. These were extensively analyzed through proteomics to explore ApoE4's impact on proteins. Our metabolomics approach, combining OrbiSIMS and LC-MS/MS, revealed disruptions in lipid metabolism, including glycerophospholipids and sphingolipids, as well as amino acid metabolism, encompassing alanine, aspartate, and glutamate metabolism; aminoacyl-tRNA biosynthesis; glutamine metabolism; and taurine and hypotaurine metabolism. Further LC-MS/MS proteomics studies confirmed the dysfunction in amino acid and tRNA aminoacylation metabolic processes, and highlighted RNA splicing alterations influenced by ApoE4.

The Therapeutic Potential of Dalbergia pinnata (Lour.) Prain Essential Oil in Alzheimer's Disease: EEG Signal Analysis In Vivo, SH-SY5Y Cell Model In Vitro, and Network Pharmacology

Alzheimer's disease (AD) is a neurodegenerative disorder that is projected by the WHO to affect over 100 million people by 2050. Clinically, AD patients undergoing long-term antipsychotic treatment often experience severe anxiety or depression in later stages. Furthermore, early-stage AD manifests with weakened α waves in the brain, progressing to diminished α and β waves in late-stage disease, reflecting changes in emotional states and disease progression. In this study, EEG signal analysis revealed that inhalation of Dalbergia pinnata (Lour.) Prain essential oil (DPEO) enhanced δ, θ, α and β wave powers in the frontal and parietal lobes, with a rising trend in the β/α ratio in the temporal lobe. These findings suggest an alleviation of anxiety and an enhancement of cognitive functions. Treatment of the AD SH-SY5Y (human neuroblastoma cells) cell model with DPEO resulted in decreased intracellular levels of Aβ, GSK-3β, P-Tau, IL-1β, TNF-α, IL-6, COX-2, OFR, and HFR, alongside reduced AchE and BchE activities and increased SOD activity. Network pharmacology analysis indicated a potential pharmacological mechanism involving the JAK-STAT pathway. Our study provides evidence supporting DPEO's role in modulating anxiety and slowing AD pathological progression.

Preparation and Characterization of Zn(II)-Stabilized Aβ42 Oligomers

Aβ oligomers are being investigated as cytotoxic agents in Alzheimer's disease (AD). Because of their transient nature and conformational heterogeneity, the relationship between the structure and activity of these oligomers is still poorly understood. Hence, methods for stabilizing Aβ oligomeric species relevant to AD are needed to uncover the structural determinants of their cytotoxicity. Here, we build on the observation that metal ions and metabolites have been shown to interact with Aβ, influencing its aggregation and stabilizing its oligomeric species. We thus developed a method that uses zinc ions, Zn(II), to stabilize oligomers produced by the 42-residue form of Aβ (Aβ42), which is dysregulated in AD. These Aβ42-Zn(II) oligomers are small in size, spanning the 10-30 nm range, stable at physiological temperature, and with a broad toxic profile in human neuroblastoma cells. These oligomers offer a tool to study the mechanisms of toxicity of Aβ oligomers in cellular and animal AD models.

Promising brain biodistribution of insulin via intranasal dry powder for nose-to-brain delivery

Nose-to-brain delivery (NTBD) offering potential benefits for treating Alzheimer's disease. In previous research, insulin dry powder (IDP) formulation for NTBD was developed, exhibiting favorable stability. This study aims to conduct in vitro and ex vivo assessment of release, permeation, mucoadhesion and histopathology, as well as an in vivo biodistribution study to produce IDP for NTBD and evaluate brain biodistribution. Spray-freeze-dried IDP formulations with varying weight ratios of trehalose-to-inulin were produced and analyzed. The release study was carried out in PBS with a pH of 5.8 stirred at 50 rpm and maintained at 37 °C ± 0.5 °C. Goat nasal mucosa was used for ex vivo permeation and mucoadhesion testing under similar conditions. An ex vivo histopathological examination and an in vivo study using enzyme-linked immunosorbent assay, were also performed. The IDP dissolution study demonstrated complete release of all IDPs within 120 min. The permeation study indicated that steady-state conditions were observed between 30 and 240 min. The mucoadhesion study unveiled that IDP F5 exhibited the fastest mucoadhesion time and the least force required within the fastest time of 43.60 ± 2.57 s. The histopathological study confirmed that none of the tested IDPs induced irritation in the nasal mucosa. Furthermore, the biodistribution study demonstrated the absence of detectable insulin in the plasma, while IDP F3 exhibited the highest deposited concentration of insulin within both the olfactory bulb and the whole brain. The extensive evaluation of the IDP formulations through in vitro, ex vivo, and in vivo studies implies their strength non-invasive NTBD. IDP F3, with a 1:1 wt ratio of trehalose to inulin, exhibited favorable brain biodistribution outcomes and was recommended for further investigation and development in the context of NTBD.

Computational and spectroscopic insight into the binding of citral with human transferrin: Targeting neurodegenerative diseases

The involvement of neuroinflammation in the pathogenesis of neurodegenerative disorders (NDs) is very significant. Currently, only symptomatic treatments exist, and there are no drugs that modify the progression of Alzheimer's disease (AD) or other NDs. Consequently, there is increasing attention on addressing AD-related neuroinflammation using anti-inflammatory compounds and antioxidants. Currently, there is a growing exploration of dietary phytochemicals as potential therapeutic agents for treating inflammation. Citral, a monoterpene, is under increasing investigation due to its neuroprotective effects. The dysregulation of iron homeostasis is a crucial factor in supporting neuroinflammation, underscoring the significance of proper iron balance. Human transferrin (htf) is a major player involved in iron homeostasis. In this study, we examined binding and dynamics of htf-citral complex through diverse experimental methods. Molecular docking studies revealed that citral binds to crucial residues of htf, forming a stable complex. UV-visible spectroscopy demonstrated binding of citral with htf with good affinity, evident from binding constant of 1.48 × 105 M-1. Further, fluorescence spectroscopy entrenched a stable htf-citral complex formation; citral demonstrates an excellent binding affinity to htf with a binding constant of 106 M-1. Moreover, fluorescence binding assay at various temperatures deciphered htf-citral complex to be driven by both static and dynamic quenching. The analysis of enthalpy change (ΔH) and entropy change (ΔS) demonstrated that htf-citral complex formation was driven mainly by hydrophobic interactions.The current work gives a platform to develop innovative therapeutic strategies targeting neuroinflammation through citral, particularly iron homeostasis.

Extraction of sleep information from clinical notes of Alzheimer's disease patients using natural language processing

OBJECTIVES

Alzheimer's disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. We aim to automate the extraction of specific sleep-related patterns, such as snoring, napping, poor sleep quality, daytime sleepiness, night wakings, other sleep problems, and sleep duration, from clinical notes of AD patients. These sleep patterns are hypothesized to play a role in the incidence of AD, providing insight into the relationship between sleep and AD onset and progression.

MATERIALS AND METHODS

A gold standard dataset is created from manual annotation of 570 randomly sampled clinical note documents from the adSLEEP, a corpus of 192 000 de-identified clinical notes of 7266 AD patients retrieved from the University of Pittsburgh Medical Center (UPMC). We developed a rule-based natural language processing (NLP) algorithm, machine learning models, and large language model (LLM)-based NLP algorithms to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the gold standard dataset.

RESULTS

The annotated dataset of 482 patients comprised a predominantly White (89.2%), older adult population with an average age of 84.7 years, where females represented 64.1%, and a vast majority were non-Hispanic or Latino (94.6%). Rule-based NLP algorithm achieved the best performance of F1 across all sleep-related concepts. In terms of positive predictive value (PPV), the rule-based NLP algorithm achieved the highest PPV scores for daytime sleepiness (1.00) and sleep duration (1.00), while the machine learning models had the highest PPV for napping (0.95) and bad sleep quality (0.86), and LLAMA2 with finetuning had the highest PPV for night wakings (0.93) and sleep problem (0.89).

DISCUSSION

Although sleep information is infrequently documented in the clinical notes, the proposed rule-based NLP algorithm and LLM-based NLP algorithms still achieved promising results. In comparison, the machine learning-based approaches did not achieve good results, which is due to the small size of sleep information in the training data.

CONCLUSION

The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts. This study focused on the clinical notes of patients with AD but could be extended to general sleep information extraction for other diseases.

The mediating role of systemic inflammation and moderating role of racialization in disparities in incident dementia

BACKGROUND

Exposure to systemic racism is linked to increased dementia burden. To assess systemic inflammation as a potential pathway linking exposure to racism and dementia disparities, we investigated the mediating role of C-reactive protein (CRP), a systemic inflammation marker, and the moderating role of the racialization process in incident dementia.

METHODS

In the US Health and Retirement Study (n = 6,908), serum CRP was measured at baseline (2006, 2008 waves). Incident dementia was classified by cognitive tests over a six-year follow-up. Self-reported racialized categories were a proxy for exposure to the racialization process. We decomposed racialized disparities in dementia incidence (non-Hispanic Black and/or Hispanic vs. non-Hispanic white) into 1) the mediated effect of CRP, 2) the moderated portion attributable to the interaction between racialized group membership and CRP, and 3) the controlled direct effect (other pathways through which racism operates).

RESULTS

The 6-year cumulative incidence of dementia is 12%. Among minoritized participants (i.e., non-Hispanic Black and/or Hispanic), high CRP levels ( ≥ 75th percentile or 4.73μg/mL) are associated with 1.26 (95%CI: 0.98, 1.62) times greater risk of incident dementia than low CRP ( < 4.73μg/mL). Decomposition analysis comparing minoritized versus non-Hispanic white participants shows that the mediating effect of CRP accounts for 3% (95% CI: 0%, 6%) of the racial disparity, while the interaction effect between minoritized group status and high CRP accounts for 14% (95% CI: 1%, 27%) of the disparity. Findings are robust to potential violations of causal mediation assumptions.

CONCLUSIONS

Minoritized group membership modifies the relationship between systemic inflammation and incident dementia.

Autoencoder to Identify Sex-Specific Sub-phenotypes in Alzheimer's Disease Progression Using Longitudinal Electronic Health Records

Alzheimer's Disease (AD) is a complex neurodegenerative disorder significantly influenced by sex differences, with approximately two-thirds of AD patients being women. Characterizing the sex-specific AD progression and identifying its progression trajectory is a crucial step to developing effective risk stratification and prevention strategies. In this study, we developed an autoencoder to uncover sex-specific sub-phenotypes in AD progression leveraging longitudinal electronic health record (EHR) data from OneFlorida+ Clinical Research Consortium. Specifically, we first constructed temporal patient representation using longitudinal EHRs from a sex-stratified AD cohort. We used a long short-term memory (LSTM)-based autoencoder to extract and generate latent representation embeddings from sequential clinical records of patients. We then applied hierarchical agglomerative clustering to the learned representations, grouping patients based on their progression sub-phenotypes. The experimental results show we successfully identified five primary sex-based AD sub-phenotypes with corresponding progression pathways with high confidence. These sex-specific sub-phenotypes not only illustrated distinct AD progression patterns but also revealed differences in clinical characteristics and comorbidities between females and males in AD development. These findings could provide valuable insights for advancing personalized AD intervention and treatment strategies.

Pharmacophore-based virtual screening of commercial databases against β-secretase 1 for drug development against Alzheimer's disease

β-secretase 1, one of the most important proteins, is an aspartate protease. This membrane-associated protein is used for treating Alzheimer's disease (AD). Several inhibitors have been pursued against β-secretase 1, but they still have not resulted effectively. Virtual screening based on pharmacophores has been shown to be useful for lead optimization and hit identification in the preliminary phase of developing a new drug. Here, we screen the commercially available databases to find the hits against β-secretase 1 for drug discovery against AD. Virtual screening for 200,000 compounds was done using the database from the Vitas-M Laboratory. The phase screen score was utilized to assess the screened hits. Molecular docking was performed on compounds with phase scores >1.9. According to the study, the 66H ligand of the crystal structure has the maximum performance against β-secretase 1. The redocking of the co-crystal ligand showed that the docked ligand was seamlessly united with the crystal structure. The reference complex had three hydrogen bonds with Asp93, Asp289, and Gly291; one van der Waals interaction with Gly74; and three hydrophobic interactions. After equilibration, the RMSD of the reference compound sustained a value of ∼1.5 Å until 30 ns and then boosted to 2.5 Å. On comparison, the RMSD of the S1 complex steadily increased to ∼2.5 Å at 15 ns, displayed slight aberrations at approximately ∼2.5-3 Å until 80 ns, and then achieved steadiness toward the end of the simulation. The arrangements of proteins stayed condensed during the mockup when bonded to these complexes as stable Rg values showed. Furthermore, the MM/GBSA technique was employed to analyze both compounds' total binding free energies (ΔGtotal). Our research study provides a new understanding of using 66H as anti-β-secretase 1 for drug development against AD.

Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study

BACKGROUND

Alzheimer disease and related dementias (ADRD) rank as the sixth leading cause of death in the United States, underlining the importance of accurate ADRD risk prediction. While recent advancements in ADRD risk prediction have primarily relied on imaging analysis, not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes.

OBJECTIVE

The study aims to use graph neural networks (GNNs) with claim data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative, self-explainable method to evaluate relationship importance and its influence on ADRD risk prediction.

METHODS

We used a variationally regularized encoder-decoder GNN (variational GNN [VGNN]) integrated with our proposed relation importance method for estimating ADRD likelihood. This self-explainable method can provide a feature-important explanation in the context of ADRD risk prediction, leveraging relational information within a graph. Three scenarios with 1-year, 2-year, and 3-year prediction windows were created to assess the model's efficiency, respectively. Random forest (RF) and light gradient boost machine (LGBM) were used as baselines. By using this method, we further clarify the key relationships for ADRD risk prediction.

RESULTS

In scenario 1, the VGNN model showed area under the receiver operating characteristic (AUROC) scores of 0.7272 and 0.7480 for the small subset and the matched cohort data set. It outperforms RF and LGBM by 10.6% and 9.1%, respectively, on average. In scenario 2, it achieved AUROC scores of 0.7125 and 0.7281, surpassing the other models by 10.5% and 8.9%, respectively. Similarly, in scenario 3, AUROC scores of 0.7001 and 0.7187 were obtained, exceeding 10.1% and 8.5% than the baseline models, respectively. These results clearly demonstrate the significant superiority of the graph-based approach over the tree-based models (RF and LGBM) in predicting ADRD. Furthermore, the integration of the VGNN model and our relation importance interpretation could provide valuable insight into paired factors that may contribute to or delay ADRD progression.

CONCLUSIONS

Using our innovative self-explainable method with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data.

Medicare expenditures among spouses of persons with dementia

BACKGROUND

Spouses of persons living with dementia face intense strains on their well-being compared with similarly aged adults and spouses of partners with no dementia. This strain can impact spouses' health and healthcare needs, and therefore affect their healthcare utilization and expenditures.

METHODS

Using data from the Health and Retirement Study linked with Medicare claims, we matched dyads of spouses and their partners with dementia (SPWD) to a comparison group of similar spouses and their partners with no dementia (SPWND). We then examined Medicare expenditures for spouses in the 5 years following their partner's dementia onset month using a two-part regression model.

RESULTS

SPWD cumulative total Medicare expenditures were, on average, $60,043 in the 5 years post dementia onset, compared to $56,068 for SPWND. This difference ($3974, 95% CI = [-$3,199; $11,477]) was not significant. However, there were significant differences in the 5th year's total expenditures (+$2,748 [$321; $5,447]), driven by inpatient expenditures ($1,562 [$22; $3,277]).

CONCLUSIONS

Despite the differences in partner's dementia status, we found no significant difference in the 5-year cumulative Medicare expenditures between SPWD and SPWND. Compared to previous studies, we likely captured an earlier stage of dementia more consistently for a broader population which may be less straining on spouses. Further research should examine patterns of expenditures in later years and around critical timepoints in caregiving, such as partner transitions to formal long-term care settings and death, to better understand healthcare expenditures for spouses of persons living with dementia.

Deploying Robot-Led Activities for People with Dementia at Aged Care Facilities: A Feasibility Study

OBJECTIVES

To explore the feasibility of deploying robot-led activities for people with dementia living in aged care facilities.

DESIGN

Embedded mixed-methods design.

SETTING AND PARTICIPANTS

Eleven residents (aged ≥65 years) with mild to moderate cognitive impairment were recruited from 2 aged care facilities in Brisbane, Australia.

METHODS

We implemented a novel control logic, "Adam Program," for a human-like robot to provide proactive robot activities for people with dementia. Participants individually participated in a nonfacilitated robot intervention thrice a week for 5 weeks from November to December 2022. We video-recorded each intervention session and quantified participants' visual, behavioral, and verbal engagement. A semi-structured interview was conducted at the end of the 5-week intervention. The treatment fidelity strategies and finances were reported and evaluated. The Bowen Feasibility Framework guided data analysis into 6 focus areas: acceptability, demand, implementation, efficacy, integration, and practicality.

RESULTS

Based on participants' perceptions, findings demonstrated adequate acceptability and demand for robot-led activities. Findings reported a high level of visual engagement (98.54%) and an increased trend of behavior engagement over 5 weeks. Participants independently communicated with Adam, with most (97.02%) verbal engagements free of human facilitation. The treatment fidelity strategies regarding the activity delivery, intervention received, and intervention skills are provided alongside the equipment expenses, revealing the feasibility of integrating robot-led activities for people with dementia in aged care facilities.

CONCLUSIONS AND IMPLICATIONS

Using human-like robots to independently lead interactive activities for people with dementia at aged care facilities is feasible and acceptable. Although robot-led activities require further improvement, this study explored a practical-driven solution that provided guidelines for developing and implementing robot-led activities in aged care settings. Future studies could replicate similar robot-led activities for further investigation and evaluation. Strategies for multilevel determinants of Adam's implementation based on a context assessment are recommended for future research.

Association of Egg Intake With Alzheimer's Dementia Risk in Older Adults: The Rush Memory and Aging Project

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder with increasing prevalence due to population aging. Eggs provide many nutrients important for brain health, including choline, omega-3 fatty acids, and lutein. Emerging evidence suggests that frequent egg consumption may improve cognitive performance on verbal tests, but whether consumption influences the risk of Alzheimer's dementia and AD is unknown.

OBJECTIVES

To examine the association of egg consumption with Alzheimer's dementia risk among the Rush Memory and Aging Project cohort.

METHODS

Dietary assessment was collected using a modified Harvard semiquantitative food frequency questionnaire. Participants' first food frequency questionnaire was used as the baseline measure of egg consumption. Multivariable adjusted Cox proportional hazards regression models were used to investigate the associations of baseline egg consumption amount with Alzheimer's dementia risk, adjusting for potential confounding factors. Subgroup analyses using Cox and logistic regression models were performed to investigate the associations with AD pathology in the brain. Mediation analysis was conducted to examine the mediation effect of dietary choline in the relationship between egg intake and incident Alzheimer's dementia.

RESULTS

This study included 1024 older adults {mean [±standard deviation (SD)] age = 81.38 ± 7.20 y}. Over a mean (±SD) follow-up of 6.7 ± 4.8 y, 280 participants (27.3%) were clinically diagnosed with Alzheimer's dementia. Weekly consumption of >1 egg/wk (hazard ratio [HR]: 0.53; 95% confidence interval [CI]: 0.34, 0.83) and ≥2 eggs/wk (HR: 0.53; 95% CI: 0.35, 0.81) was associated with a decreased risk of Alzheimer's dementia. Subgroup analysis of brain autopsies from 578 deceased participants showed that intakes of >1 egg/wk (HR: 0.51; 95% CI: 0.35, 0.76) and ≥2 eggs/wk (HR: 0.62; 95% CI: 0.44, 0.90) were associated with a lower risk of AD pathology in the brain. Mediation analysis showed that 39% of the total effect of egg intake on incident Alzheimer's dementia was mediated through dietary choline.

CONCLUSIONS

These findings suggest that frequent egg consumption is associated with a lower risk of Alzheimer's dementia and AD pathology, and the association with Alzheimer's dementia is partially mediated through dietary choline.

Type-2 Diabetes, Pancreatic Amylin, and Neuronal Metabolic Remodeling in Alzheimer's Disease

Type-2 diabetes raises the risk for Alzheimer's disease (AD)-type dementia and the conversion from mild cognitive impairment to dementia, yet mechanisms connecting type-2 diabetes to AD remain largely unknown. Amylin, a pancreatic β-cell hormone co-secreted with insulin, participates in the central regulation of satiation, but also forms pancreatic amyloid in persons with type-2 diabetes and synergistically interacts with brain amyloid β (Aβ) pathology, in both sporadic and familial Alzheimer's disease (AD). Growing evidence from studies of tumor growth, together with early observations in skeletal muscle, indicates amylin as a potential trigger of cellular metabolic reprogramming. Because the blood, cerebrospinal fluid, and brain parenchyma in humans with AD have increased concentrations of amylin, amylin-mediated pathological processes in the brain may involve neuronal metabolic remodeling. This review summarizes recent progress in understanding the link between prediabetic hypersecretion of amylin and risk of neuronal metabolic remodeling and AD and suggests nutritional and medical effects of food constituents that might prevent and/or ameliorate amylin-mediated neuronal metabolic remodeling.

Oral administration of encapsulated catechin in chitosan-alginate nanoparticles improves cognitive function and neurodegeneration in an aluminum chloride-induced rat model of Alzheimer's disease

The present study aimed to investigate the effect of catechin-loaded Chitosan-Alginate nanoparticles (NPs) on cognitive function in an aluminum chloride (AlCl3)-induced rat model of Alzheimer's disease (AD). The Catechin-loaded Chitosan-Alginate nanocarriers were synthesized through ionotropic gelation (IG) method. Physio-chemical characterization was conducted with the Zetasizer Nano system, the scanning electron microscope, and the Fourier transform infrared spectroscopy. The experiments were performed over 21 days on six groups of male Wistar rats. The control group, AlCl3 treated group, Catechin group, nanocarrier group, treatment group 1 (AlCl3 + Catechin), and treatment group 2 (AlCl3 + nanocarrier). A behavioral study was done by the Morris water maze (MWM) test. In addition, the level of oxidative indices and acetylcholine esterase (AChE) activity was determined by standard procedures at the end of the study. AlCl3 induced a significant increase in AChE activity, along with a significant decrease in the level of Catalase (CAT) and total antioxidant capacity (TAC) in the hippocampus. Moreover, the significant effect of AlCl3 was observed on the behavioral parameters of the MWM test. Both forms of Catechin markedly improved AChE activity, oxidative biomarkers, spatial memory, and learning. The present study indicated that the administration of Catechin-loaded Chitosan-Alginate NPs is a beneficial therapeutic option against behavioral and chemical alteration of AD in male Wistar rats.

DeepRisk: A deep learning approach for genome-wide assessment of common disease risk

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest. Although widely applied, traditional polygenic risk scoring methods fall short, as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms (SNPs). This presents a limitation, as genetic diseases often arise from complex interactions between multiple SNPs. To address this challenge, we developed DeepRisk, a biological knowledge-driven deep learning method for modeling these complex, nonlinear associations among SNPs, to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data. Evaluations demonstrated that DeepRisk outperforms existing PRS-based methods in identifying individuals at high risk for four common diseases: Alzheimer's disease, inflammatory bowel disease, type 2 diabetes, and breast cancer.

Midlife Residential Greenness and Late-Life Cognitive Decline among Nurses' Health Study Participants

BACKGROUND

Midlife residential exposure to greenspace may slow cognitive decline by increasing opportunities for physical activity and social connection, restoring attention, or reducing stress or adverse environmental exposures. However, prospective studies on the association between greenness and cognitive decline are sparse.

OBJECTIVE

We investigated the prospective association between greenness at midlife and cognitive decline later in life. We explored effect measure modification by apolipoprotein E (APOE)-ɛ4 carrier status, neighborhood socioeconomic status (NSES), and rural/urban regions.

METHODS

The Nurses' Health Study (N = 121,700 ) started in 1976 with married female nurses, 30-55 years of age, located across 11 US states. We examined 16,962 nurses who were enrolled in a substudy starting in 1995-2001 (mean age = 74 y ) through 2008. We assessed average summer residential greenness in a 270 -m buffer using Landsat Normalized Difference Vegetation Index data from 1986-1994. Starting in 1995-2001, participants underwent up to four repeated measures of five cognitive tests. A global composite score was calculated as the average of all z -scores for each task to evaluate overall cognition. We used linear mixed models to evaluate the association of average greenness exposure at midlife with cognitive decline in later life, adjusted for age, education, NSES, and depression.

RESULTS

In adjusted models, higher midlife greenness exposure [per interquartile range (IQR): 0.18] was associated with a 0.004-unit (95% CI: 0.001, 0.006) slower annual rate of cognitive decline. For comparison, we found that 1 year of age is related to a - 0.006 mean annual difference for global cognition in the full sample; thus, higher midlife greenness appeared equivalent to slowing cognitive decline by ∼ 8 months. In analysis exploring gene-environment interactions, we found that among APOE-ɛ4 carriers, an IQR increase in greenness was associated with a rate of decline that was slower by 0.01 units of global composite score (95% CI: 0.0004, 0.02). This association was attenuated among APOE-ɛ4 noncarriers. We did not observe associations between greenness and baseline or annual rate of cognitive decline of verbal memory.

DISCUSSION

Higher midlife greenness exposure is associated with slower cognitive decline later in life. Future research is necessary to confirm these findings. https://doi.org/10.1289/EHP13588.

Graph Embedded Ensemble Deep Randomized Network for Diagnosis of Alzheimer's Disease

Randomized shallow/deep neural networks with closed form solution avoid the shortcomings that exist in the back propagation (BP) based trained neural networks. Ensemble deep random vector functional link (edRVFL) network utilize the strength of two growing fields, i.e., deep learning and ensemble learning. However, edRVFL model doesn't consider the geometrical relationship of the data while calculating the final output parameters corresponding to each layer considered as base model. In the literature, graph embedded frameworks have been successfully used to describe the geometrical relationship within data. In this paper, we propose an extended graph embedded RVFL (EGERVFL) model that, unlike standard RVFL, employs both intrinsic and penalty subspace learning (SL) criteria under the graph embedded framework in its optimization process to calculate the model's output parameters. The proposed shallow EGERVFL model has only single hidden layer and hence, has less representation learning. Therefore, we further develop an ensemble deep EGERVFL (edEGERVFL) model that can be considered a variant of edRVFL model. Unlike edRVFL, the proposed edEGERVFL model solves graph embedded based optimization problem in each layer and hence, has better generalization performance than edRVFL model. We evaluated the proposed approaches for the diagnosis of Alzheimer's disease and furthermore on UCI datasets. The experimental results demonstrate that the proposed models perform better than baseline models. The source code of the proposed models is available at https://github.com/mtanveer1/.

Emotion regulation in people living with dementia and their spouses: the role of neuropsychiatric symptoms

OBJECTIVES

People with dementia (PwD) and their care partners (CP) may have difficulties in emotion regulation, and individual differences in emotion regulation may be related to PwD's neuropsychiatric symptoms. This study explores whether there is self-awareness of PwD's difficulties in emotion regulation and whether CP's emotion regulation relates to the PwD's neuropsychiatric symptoms, potentially revealing bias or interpersonal effects.

METHOD

We used data from the Wish Outcome Obstacle Plan Study with a sample of 45 PwD and their spousal CP (n = 90 individuals). Multivariate linear regression models were used to investigate the associations between the CP-reported neuropsychiatric symptoms in PwD and self-reports of emotion regulation in both dyad members, net of sociodemographic and health factors. Separate analyses were conducted for each neuropsychiatric subsyndrome and each domain of difficulties in emotion regulation.

RESULTS

Increasing severity of neuropsychiatric symptoms was associated with higher difficulties in emotion regulation in PwD (ß = 1.23, p < 0.05), but not with CP's difficulties in emotion regulation. When CP reported more severe neuropsychiatric symptoms in PwD, PwD reported that they had difficulties in accepting emotions, controlling impulses, goal-directed behaviors, and accessing emotion regulation strategies, but not in emotion awareness and clarification. Proxy-reports of hyperactivity and psychosis subsyndromes are significantly related to PwD's self-reported difficulties in emotion regulation.

CONCLUSION

PwD reported difficulties in emotion regulation at the early stage of dementia. Proxy-reported neuropsychiatric symptoms may capture PwD's emotion regulation capability and not be biased by CP's difficulties in emotion regulation.

Religious participation is associated with fewer dementia diagnoses among Black people in the United States

INTRODUCTION

Black people had the highest prevalence of Alzheimer's disease and related dementias (ADRD) of any racial/ethnic group in the United States (US) as of 2020. As racial disparities in the prevalence of ADRD are being investigated, more evidence is necessary to determine the pathways and mechanisms that either slow ADRD progression or improve quality of life for those affected. Religion/spirituality (R/S) has been shown to affect health outcomes but has rarely been studied as a possible pathway for reducing ADRD risk. Crucially, Black people also report higher levels of R/S than other racial/ethnic groups in the United States. This research asks if R/S affects ADRD risk among Black adults and if any effects persist after controlling for hypertension.

METHODS

We conducted a secondary data analysis drawing from the Health and Retirement Study (HRS), a nationally representative longitudinal dataset with an oversampling of Black adults.

RESULTS

We used logistic regression analysis to demonstrate how R/S has an ameliorating impact on ADRD risk among Black people, even after controlling for hypertension. Those who never attended religious services had 2.37 higher odds of being diagnosed with ADRD than those who attended more than once a week. Further, as R/S attendance increased, ADRD risk decreased linearly.

CONCLUSION

These findings demonstrate the importance that existing cultural networks (e.g., R/S) can have for reducing ADRD burden for Black people and has important implications for the role of R/S in shaping ADRD symptomatology.

Phytoactive drugs used in the treatment of Alzheimer's disease and dementia

The prevalence of Alzheimer's disease and other forms of dementia is increasing worldwide, and finding effective treatments for these conditions is a major public health challenge. Natural bioactive drugs have been identified as a promising source of potential treatments, due to their ability to target multiple pathways and their low toxicity. This paper reviews the current state of research on natural bioactive drugs used in the treatment of Alzheimer's disease and other dementias. The paper summarizes the findings of studies on various natural compounds, including curcumin, resveratrol, caffeine, genistein, quercetin, GinkoBiloba, Withaniasomnifera, Ginseng Brahmi, Giloy, and huperzine, and their effects on cognitive function, neuroinflammation, and amyloid-beta accumulation. In this review, we discuss the mechanism of action involved in the treatment of Alzheimer's disease. The paper also discusses the challenges associated with developing natural bioactive drugs for dementia treatment, including issues related to bioavailability and standardization. Finally, the paper suggests directions for future research in this area, including the need for more rigorous clinical trials and the development of novel delivery systems to improve the efficacy of natural bioactive drugs. Overall, this review highlights the potential of natural bioactive drugs as a promising avenue for the development of safe and effective treatments for Alzheimer's disease and other dementias.

Machine Learning Classification of Patients with Amnestic Mild Cognitive Impairment and Non-Amnestic Mild Cognitive Impairment from Written Picture Description Tasks

Individuals with Mild Cognitive Impairment (MCI), a transitional stage between cognitively healthy aging and dementia, are characterized by subtle neurocognitive changes. Clinically, they can be grouped into two main variants, namely patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). The distinction of the two variants is known to be clinically significant as they exhibit different progression rates to dementia. However, it has been particularly challenging to classify the two variants robustly. Recent research indicates that linguistic changes may manifest as one of the early indicators of pathology. Therefore, we focused on MCI's discourse-level writing samples in this study. We hypothesized that a written picture description task can provide information that can be used as an ecological, cost-effective classification system between the two variants. We included one hundred sixty-nine individuals diagnosed with either aMCI or naMCI who received neurophysiological evaluations in addition to a short, written picture description task. Natural Language Processing (NLP) and a BERT pre-trained language model were utilized to analyze the writing samples. We showed that the written picture description task provided 90% overall classification accuracy for the best classification models, which performed better than cognitive measures. Written discourses analyzed by AI models can automatically assess individuals with aMCI and naMCI and facilitate diagnosis, prognosis, therapy planning, and evaluation.

Rescuing impaired hippocampal-cortical interactions and spatial reorientation learning and memory during sleep in a mouse model of Alzheimer's disease using hippocampal 40 Hz stimulation

In preclinical Alzheimer's disease (AD), spatial learning and memory is impaired. We reported similar impairments in 3xTg-AD mice on a virtual maze (VM) spatial-reorientation-task that requires using landmarks to navigate. Hippocampal (HPC)-cortical dysfunction during sleep (important for memory consolidation) is a potential mechanism for memory impairments in AD. We previously found deficits in HPC-cortical coordination during sleep coinciding with VM impairments the next day. Some forms of 40 Hz stimulation seem to clear AD pathology in mice, and improve functional connectivity in AD patients. Thus, we implanted a recording array targeting parietal cortex (PC) and HPC to assess HPC-PC coordination, and an optical fiber targeting HPC for 40 Hz or sham optogenetic stimulation in 3xTg/PV cre mice. We assessed PC delta waves (DW) and HPC sharp wave ripples (SWRs). In sham mice, SWR-DW cross-correlations were reduced, similar to 3xTg-AD mice. In 40 Hz mice, this phase-locking was rescued, as was performance on the VM. However, rescued HPC-PC coupling no longer predicted performance as in NonTg animals. Instead, DWs and SWRs independently predicted performance in 40 Hz mice. Thus, 40 Hz stimulation of HPC rescued functional interactions in the HPC-PC network, and rescued impairments in spatial navigation, but did not rescue the correlation between HPC-PC coordination during sleep and learning and memory. Together this pattern of results could inform AD treatment timing by suggesting that despite applying 40 Hz stimulation before significant tau and amyloid aggregation, pathophysiological processes led to brain changes that were not fully reversed even though cognition was recovered.

Significance Statement

One of the earliest symptoms of Alzheimer's disease (AD) is getting lost in space or experiencing deficits in spatial navigation, which involve navigation computations as well as learning and memory. We investigated cross brain region interactions supporting memory formation as a potential causative factor of impaired spatial learning and memory in AD. To assess this relationship between AD pathophysiology, brain changes, and behavioral alterations, we used a targeted approach for clearing amyloid beta and tau to rescue functional interactions in the brain. This research strongly connects brain activity patterns during sleep to tau and amyloid accumulation, and will aid in understanding the mechanisms underlying cognitive dysfunction in AD. Furthermore, the results offer insight for improving early identification and treatment strategies.

Basic, Translational, and Clinical Research on Dementia

The global impact of dementia is an increasing area of concern and, according to the Alzheimer's Disease International (ADI) World Alzheimer Report 2021, up to 90% of dementia patients in low- and middle-income countries are not diagnosed [...].

Aggregation Mechanisms and Molecular Structures of Amyloid-β in Alzheimer's Disease

Amyloid plaques are a major pathological hallmark involved in Alzheimer's disease and consist of deposits of the amyloid-β peptide (Aβ). The aggregation process of Aβ is highly complex, which leads to polymorphous aggregates with different structures. In addition to aberrant aggregation, Aβ oligomers can undergo liquid-liquid phase separation (LLPS) and form dynamic condensates. It has been hypothesized that these amyloid liquid droplets affect and modulate amyloid fibril formation. In this review, we briefly introduce the relationship between stress granules and amyloid protein aggregation that is associated with neurodegenerative diseases. Then we highlight the regulatory role of LLPS in Aβ aggregation and discuss the potential relationship between Aβ phase transition and aggregation. Furthermore, we summarize the current structures of Aβ oligomers and amyloid fibrils, which have been determined using nuclear magnetic resonance (NMR) and cryo-electron microscopy (cryo-EM). The structural variations of Aβ aggregates provide an explanation for the different levels of toxicity, shed light on the aggregation mechanism and may pave the way towards structure-based drug design for both clinical diagnosis and treatment.

Sex-Specific Entorhinal Cortex Functional Connectivity in Cognitively Normal Older Adults with Amyloid-β Pathology

Sex and apolipoprotein E (APOE) genotype have been shown to influence the risk and progression of Alzheimer's disease (AD). However, the impact of these factors on the functional connectivity of the entorhinal cortex (ERC) in clinically unpaired older adults (CUOA) with amyloid-β (Aβ +) pathology remains unclear. A total of 1022 cognitively normal older adults with Aβ + (603 females and 586 APOE ε4 +) from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study were included in this study. The 2 × 2 (gender, 2 APOE genotypes) analysis of covariance was performed to compare the demographic information, cognitive performance, and volumetric MRI data among these groups. Voxel-wise comparisons of bilateral ERC functional connectivity (FC) were conducted, and partial correlation analyses were used to explore the associations between cognitive performance and ERC-FC strength. We found that the APOE genotype influenced ERC functional connectivity mainly in the sensorimotor network (SMN). Males exhibited higher ERC-FC in the salience network (SN), while females displayed higher ERC-FC in the default mode network (DMN), executive control network (ECN), and reward network. The interplay of sex and APOE genotype on ERC-FC was observed in the SMN and cerebellar lobe. The ERC-FC was associated with executive function and memory performance in individuals with CUOA-Aβ + . Our findings provide evidence of sex-specific ERC functional connectivity compensation mechanism in cognitively normal older adults with Aβ + pathology. This study may contribute to a better understanding of the mechanisms underlying the early stages of AD and may help develop personalized interventions in preclinical AD.

Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer's Disease Detection

Alzheimer's Disease (AD) poses a significant health risk globally, particularly among the elderly population. Recent studies underscore its prevalence, with over 50% of elderly Japanese facing a lifetime risk of dementia, primarily attributed to AD. As the most prevalent form of dementia, AD gradually erodes brain cells, leading to severe neurological decline. In this scenario, it is important to develop an automatic AD-detection system, and many researchers have been working to develop an AD-detection system by taking advantage of the advancement of deep learning (DL) techniques, which have shown promising results in various domains, including medical image analysis. However, existing approaches for AD detection often suffer from limited performance due to the complexities associated with training hierarchical convolutional neural networks (CNNs). In this paper, we introduce a novel multi-stage deep neural network architecture based on residual functions to address the limitations of existing AD-detection approaches. Inspired by the success of residual networks (ResNets) in image-classification tasks, our proposed system comprises five stages, each explicitly formulated to enhance feature effectiveness while maintaining model depth. Following feature extraction, a deep learning-based feature-selection module is applied to mitigate overfitting, incorporating batch normalization, dropout and fully connected layers. Subsequently, machine learning (ML)-based classification algorithms, including Support Vector Machines (SVM), Random Forest (RF) and SoftMax, are employed for classification tasks. Comprehensive evaluations conducted on three benchmark datasets, namely ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS Kaggle, demonstrate the efficacy of our proposed model. Impressively, our model achieves accuracy rates of 99.47%, 99.10% and 99.70% for ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS datasets, respectively, outperforming existing systems in binary class problems. Our proposed model represents a significant advancement in the AD-analysis domain.

Alzheimer's disease and drug delivery across the blood-brain barrier: approaches and challenges

Alzheimer's disease (AD) is a diverse disease with a complex pathophysiology. The presence of extracellular β-amyloid deposition as neuritic plaques and intracellular accumulation of hyper-phosphorylated tau as neurofibrillary tangles remain the core neuropathologic criteria for diagnosing Alzheimer's disease. Nonetheless, several recent basic discoveries have revealed significant pathogenic roles for other essential cellular and molecular processes. Previously, there were not so many disease-modifying medications (DMT) available as drug distribution through the blood-brain barrier (BBB) is difficult due to its nature, especially drugs of polypeptides nature and proteins. Recently FDA has approved lecanemab as DMT for its proven efficacy. It is also complicated to deliver drugs for diseases like epilepsy or any brain tumor due to the limitations of the BBB. After the advancements in the drug delivery system, different techniques are used to transport the medication across the BBB. Other methods are used, like enhancement of brain blood vessel fluidity by liposomes, infusion of hyperosmotic solutions, and local intracerebral implants, but these are invasive approaches. Non-invasive approaches include the formulation of nanoparticles and their coating with polymers. This review article emphasizes all the above-mentioned techniques, procedures, and challenges to transporting medicines across the BBB. It summarizes the most recent literature dealing with drug delivery across the BBB.

Chronic Caffeine Consumption, Alone or Combined with Agomelatine or Quetiapine, Reduces the Maximum EEG Peak, As Linked to Cortical Neurodegeneration, Ovarian Estrogen Receptor Alpha, and Melatonin Receptor 2

RATIONALE

Evidence of the effects of chronic caffeine (CAFF)-containing beverages, alone or in combination with agomelatine (AGO) or quetiapine (QUET), on electroencephalography (EEG), which is relevant to cognition, epileptogenesis, and ovarian function, remains lacking. Estrogenic, adenosinergic, and melatonergic signaling is possibly linked to the dynamics of these substances.

OBJECTIVES

The brain and ovarian effects of CAFF were compared with those of AGO + CAFF and QUET + CAFF. The implications of estrogenic, adenosinergic, and melatonergic signaling and the brain-ovarian crosstalk were investigated.

METHODS

Adult female rats were administered AGO (10 mg/kg), QUET (10 mg/kg), CAFF, AGO + CAFF, or QUET + CAFF, once daily for 8 weeks. EEG, estrous cycle progression, and microstructure of the brain and ovaries were examined. Brain and ovarian 17β-estradiol (E2), antimullerian hormone (AMH), estrogen receptor alpha (E2Rα), adenosine receptor 2A (A2AR), and melatonin receptor 2 (MT2R) were assessed.

RESULTS

CAFF, alone or combined with AGO or QUET, reduced the maximum EEG peak, which was positively linked to ovarian E2Rα, negatively correlated to cortical neurodegeneration and ovarian MT2R, and associated with cystic ovaries. A large corpus luteum emerged with AGO + CAFF and QUET + CAFF, antagonizing the CAFF-mediated increased ovarian A2AR and reduced cortical E2Rα. AGO + CAFF provoked TTP delay and increased ovarian AMH, while QUET + CAFF slowed source EEG frequency to δ range and increased brain E2.

CONCLUSIONS

CAFF treatment triggered brain and ovarian derangements partially antagonized with concurrent AGO or QUET administration but with no overt affection of estrus cycle progression. Estrogenic, adenosinergic, and melatonergic signaling and brain-ovarian crosstalk may explain these effects.

Low testosterone levels relate to poorer cognitive function in women in an APOE-ε4-dependant manner

BACKGROUND

Past research suggests that low testosterone levels relate to poorer cognitive function and higher Alzheimer's disease (AD) risk; however, these findings are inconsistent and are mostly derived from male samples, despite similar age-related testosterone decline in females. Both animal and human studies demonstrate that testosterone's effects on brain health may be moderated by apolipoprotein E ε4 allele (APOE-ε4) carrier status, which may explain some previous inconsistencies. We examined how testosterone relates to cognitive function in older women versus men across healthy aging and the AD continuum and the moderating role of APOE-ε4 genotype.

METHODS

Five hundred and sixty one participants aged 55-90 (155 cognitively normal (CN), 294 mild cognitive impairment (MCI), 112 AD dementia) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), who had baseline cognitive and plasma testosterone data, as measured by the Rules Based Medicine Human DiscoveryMAP Panel were included. There were 213 females and 348 males (self-reported sex assigned at birth), and 52% of the overall sample were APOE-ε4 carriers. We tested the relationship of plasma testosterone levels and its interaction with APOE-ε4 status on clinical diagnostic group (CN vs. MCI vs. AD), global, and domain-specific cognitive performance using ANOVAs and linear regression models in sex-stratified samples. Cognitive domains included verbal memory, executive function, processing speed, and language.

RESULTS

We did not observe a significant difference in testosterone levels between clinical diagnostic groups in either sex, regrardless of APOE-ε4 status. Across clinical diagnostic group, we found a significant testosterone by APOE-ε4 interaction in females, such that lower testosterone levels related to worse global cognition, processing speed, and verbal memory in APOE-ε4 carriers only. We did not find that testosterone, nor its interaction with APOE-ε4, related to cognitive outcomes in males.

CONCLUSIONS

Findings suggest that low testosterone levels in older female APOE-ε4 carriers across the aging-MCI-AD continuum may have deleterious, domain-specific effects on cognitive performance. Although future studies including additional sex hormones and longitudinal cognitive trajectories are needed, our results highlight the importance of including both sexes and considering APOE-ε4 carrier status when examining testosterone's role in cognitive health.

Prior-Guided Adversarial Learning With Hypergraph for Predicting Abnormal Connections in Alzheimer's Disease

Alzheimer's disease (AD) is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnostic methods have accomplished the classification task, but few of them can accurately evaluate the changing characteristics of brain connectivity. In this work, a prior-guided adversarial learning with hypergraph (PALH) model is proposed to predict abnormal brain connections using triple-modality medical images. Concretely, a prior distribution from anatomical knowledge is estimated to guide multimodal representation learning using an adversarial strategy. Also, the pairwise collaborative discriminator structure is further utilized to narrow the difference in representation distribution. Moreover, the hypergraph perceptual network is developed to effectively fuse the learned representations while establishing high-order relations within and between multimodal images. Experimental results demonstrate that the proposed model outperforms other related methods in analyzing and predicting AD progression. More importantly, the identified abnormal connections are partly consistent with previous neuroscience discoveries. The proposed model can evaluate the characteristics of abnormal brain connections at different stages of AD, which is helpful for cognitive disease study and early treatment.

An effective and robust lattice Boltzmann model guided by atlas for hippocampal subregions segmentation

BACKGROUND

Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmost importance.

OBJECTIVE

Our aim is to propose and validate a HS segmentation model that is both training-free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state-of-the-art techniques. The segmented HS can serve as a biomarker for studying the progression of AD.

METHODS

We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwu datasets.

RESULTS

The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter-group differences revealed that, except for the right rostral region in the ADNI dataset, the p-values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA, respectively.

CONCLUSIONS

Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas-based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression of AD.

Recent advances of focused ultrasound induced blood-brain barrier opening for clinical applications of neurodegenerative diseases

With the aging population on the rise, neurodegenerative disorders have taken center stage as a significant health concern. The blood-brain barrier (BBB) plays an important role to maintain the stability of central nervous system, yet it poses a formidable obstacle to delivering drugs for neurodegenerative disease therapy. Various methods have been devised to confront this challenge, each carrying its own set of limitations. One particularly promising noninvasive approach involves the utilization of focused ultrasound (FUS) combined with contrast agents-microbubbles (MBs) to achieve transient and reversible BBB opening. This review provides a comprehensive exploration of the fundamental mechanisms behind FUS/MBs-mediated BBB opening and spotlights recent breakthroughs in its application for neurodegenerative diseases. Furthermore, it addresses the current challenges and presents future perspectives in this field.

QKL injection ameliorates Alzheimer's disease-like pathology by regulating expression of RAGE

The onset of Alzheimer's disease is related to neuron damage caused by massive deposition of Aβ in the brain. Recent studies suggest that excessive Aβ in the brain mainly comes from peripheral blood, and BBB is the key to regulate Aβ in and out of the brain. In this study, we explored the pathogenesis of AD from the perspective of Aβ transport through the BBB and the effect of QKL injection in AD mice. The results showed that QKL could improve the cognitive dysfunction of AD mice, decrease the level of Aβ and Aβ transporter-RAGE, which was supported by the results of network pharmacology, molecular docking and molecular dynamics simulation. In conclusion, RAGE is a potential target for QKL's therapeutic effect on AD.

Hypertension is associated with reduced resting-state medial temporal lobe dynamic network flexibility in older African Americans

Hypertension disproportionately affects African Americans and is a risk factor for Alzheimer's disease (AD). We investigated the relationship of blood pressure (BP) with medial temporal lobe (MTL) dynamic network flexibility (a novel AD biomarker) and cognitive generalization in older African Americans. In a cross-sectional study, 37 normotensive (systolic BP <130 mmHg, 82.5% F, 64.4 ± 4.9 years; 14.3 ± 2.1 years of education) versus 79 hypertensive (systolic BP ≥130 mmHg, 79.5% F, 66.8 ± 4.1 years; 14.0 ± 0.2 years of education) participants were enrolled. All participants completed a 10-min resting-state functional magnetic resonance imaging scan to assess MTL dynamic network flexibility and two generalization tasks to assess cognition. Anthropometrics and aerobic fitness (via 6-min walk test) were also determined. There was no difference in BMI (29.7 ± 6.4 vs. 31.9 ± 6.3 kg/m2, p = 0.083) or aerobic fitness (15.5 ± 2.6 vs. 15.1 ± 2.6 mL/kg/min; p = 0.445) between normotensive and hypertensive groups. However, normotensive participants had higher MTL dynamic network flexibility compared to hypertensive participants (0.42 ± 0.23 vs. 0.32 ± 0.25 mL, p = 0.040), and this was associated with higher mean arterial blood pressure (r = -0.21, p = 0.036). Therefore, hypertensive older African Americans demonstrated lower MTL dynamic network flexibility compared to their normotensive counterparts independent of BMI and aerobic fitness. Further studies are required to determine how blood pressure mediates AD risk in African Americans.

Caring for Individuals with Alzheimer's Disease: A Spotlight on Hispanic Caregivers

 A caregiver is a constantly evolving role that an individual most likely undertakes at some point in their lifetime. With discoveries and research in increasing life expectancy, the prevalence of neurological-related diseases, such as Alzheimer's disease (AD) and dementia, is certainly likely to require more caregivers. The demand for AD caregivers is escalating as the prevalence of the disease continues to rise. The projected rise in AD within the Hispanic population in the United States over the next few decades is expected to be the most significant among all ethnic groups. The Hispanic population faces unique dementia risks due to cultural factors like language barriers, lower education, and limited healthcare access. Higher rates of conditions such as diabetes and cardiovascular disease further elevate dementia risk. Family dynamics and caregiving responsibilities also differ, affecting dementia management within Hispanic households. Addressing these distinct challenges requires culturally sensitive approaches to diagnosis, treatment, and support for Hispanic individuals and their family's facing dementia. With AD and other dementia becoming more prevalent, this article will attempt to expand upon the status of caregivers concerning their economic, health, and cultural statuses. We will attempt to focus on the Hispanic caregivers that live in Texas and more specifically, West Texas due to the lack of current literature that applies to this area of Texas. Lastly, we discuss the ramifications of a multitude of factors that affect caregivers in Texas and attempt to provide tools that can be readily available for Hispanics and others alike.