2019 Alzheimer's disease facts and figures

Effectiveness of an enhanced simultaneous cognitive-physical dual-task training based on fairy tales (ESCARF) in older adults with mild cognitive impairment

The aim of this study was to provide a dual-task program that included cognitive and physical training to older adults with mild cognitive impairment (MCI) and evaluate its effects. A single-group pretest-posttest design was performed using 15 older adults with MCI. A 12-week enhanced simultaneous cognitive-physical dual-task training based on fairy tales (ESCARF) program was conducted from September 2019 to December 2019. Participants were assessed using the Korean version of the Montreal Cognitive Assessment, electroencephalography (EEG), muscle strength, flexibility, agility, memory self-efficacy questionnaire, physical self-efficacy scale, and quality of life before and after 6 and 12 weeks of the intervention. The ESCARF program significantly improved cognitive function, physical function, self-efficacy, and quality of life in older adults with MCI. These findings will provide insights into the development and implementation of customized cognitive interventions to prevent or delay the onset of cognitive decline in older adults with MCI.

Prevalence and predictors of incident ADRD diagnosis following a Medicare home health episode

INTRODUCTION

Home health (HH) may be an important source of care for those with early-stage/undiagnosed Alzheimer's Disease and Related Dementias (ADRD), but little is known regarding prevalence or predictors of incident ADRD diagnosis following HH.

METHODS

Using 2010-2012 linked Master Beneficiary Summary File (MBSF) and HH assessment data for 40,596 Medicare HH patients, we model incident ADRD diagnosis within 1 year of HH via multivariable logistic regression.

RESULTS

Among HH patients without diagnosed ADRD, 10% received an incident diagnosis within 1 year. In adjusted models, patients were three times more likely to receive an incident ADRD diagnosis if they had HH clinician-reported impaired overall cognition (compared to patients without reported impairment) and twice as likely if they were community-referred (compared to hospital-referred patients).

DISCUSSION

There is a pressing need to develop tailored HH clinical pathways and protect access to community-referred HH to support community-living older adults with early-stage/undiagnosed ADRD.

The Early-Onset Alzheimer's Disease Whole-Genome Sequencing Project: Study design and methodology

INTRODUCTION

Sequencing efforts to identify genetic variants and pathways underlying Alzheimer's disease (AD) have largely focused on late-onset AD although early-onset AD (EOAD), accounting for ∼10% of cases, is largely unexplained by known mutations, resulting in a lack of understanding of its molecular etiology.

METHODS

Whole-genome sequencing and harmonization of clinical, neuropathological, and biomarker data of over 5000 EOAD cases of diverse ancestries.

RESULTS

A publicly available genomics resource for EOAD with extensive harmonized phenotypes. Primary analysis will (1) identify novel EOAD risk loci and druggable targets; (2) assess local-ancestry effects; (3) create EOAD prediction models; and (4) assess genetic overlap with cardiovascular and other traits.

DISCUSSION

This novel resource complements over 50,000 control and late-onset AD samples generated through the Alzheimer's Disease Sequencing Project (ADSP). The harmonized EOAD/ADSP joint call will be available through upcoming ADSP data releases and will allow for additional analyses across the full onset range.

HIGHLIGHTS

Sequencing efforts to identify genetic variants and pathways underlying Alzheimer's disease (AD) have largely focused on late-onset AD although early-onset AD (EOAD), accounting for ∼10% of cases, is largely unexplained by known mutations. This results in a significant lack of understanding of the molecular etiology of this devastating form of the disease. The Early-Onset Alzheimer's Disease Whole-genome Sequencing Project is a collaborative initiative to generate a large-scale genomics resource for early-onset Alzheimer's disease with extensive harmonized phenotype data. Primary analyses are designed to (1) identify novel EOAD risk and protective loci and druggable targets; (2) assess local-ancestry effects; (3) create EOAD prediction models; and (4) assess genetic overlap with cardiovascular and other traits. The harmonized genomic and phenotypic data from this initiative will be available through NIAGADS.

The efficacy and practicality of the Neurotrack Cognitive Battery assessment for utilization in clinical settings for the identification of cognitive decline in an older Japanese population

Introduction

Japan has the largest aging population with 33% of the population over the age of 60 years. The number of Japanese adults with dementia is estimated to be approximately 4.6 million, comprising nearly 15% of the older adult population. It is critical to administer cognitive assessments early in the disease state that have high reliability and low user burden to detect negative cognitive changes as early as possible; however, current preclinical AD detection methods are invasive, time-consuming, and expensive. A number of traditional and digital cognitive assessments are also available, but many of these tests are time-consuming, taxing to the user, and not widely scalable. The purpose of this study was to incorporate a digital cognitive assessment battery into a standard clinical assessment performed within a Japanese-based neuropsychology clinic to assess the diagnostic accuracy and the relationship between the digital Neurotrack Cognitive Assessment Battery (N-CAB) to traditional cognitive assessments.

Methods

Healthy individuals and probable Alzheimer's patients completed the N-CAB, as well as two traditional cognitive assessments, the Mini Mental Status Exam (MMSE) and the Revised Hasegawa's Dementia Scale (HDS-R).

Results

Our results demonstrate the Image Pairs hand-response phase of the N-CAB had the highest diagnostic accuracy with 95% sensitivity and 89% specificity to probable Alzheimer's disease. This was closely followed by the Symbol Match assessment, with a 96% sensitivity and 74% specificity to probable Alzheimer's disease. Additionally, Symbol Match and Path Points used in combination resulted in a sensitivity of 94%, specificity of 90%; a model with all N-CAB assessments resulted in a sensitivity and specificity of 100%. All N-CAB assessments had moderate to strong and significant correlations with the MMSE and HDS-R.

Discussion

Together, this suggests that the N-CAB assessment battery may be an appropriate alternative for the clinical screening of cognition for earlier detection of Alzheimer's disease.

Cogs in the autophagic machine-equipped to combat dementia-prone neurodegenerative diseases

Neurodegenerative diseases are often characterized by hydrophobic inclusion bodies, and it may be the case that the aggregate-prone proteins that comprise these inclusion bodies are in fact the cause of neurotoxicity. Indeed, the appearance of protein aggregates leads to a proteostatic imbalance that causes various interruptions in physiological cellular processes, including lysosomal and mitochondrial dysfunction, as well as break down in calcium homeostasis. Oftentimes the approach to counteract proteotoxicity is taken to merely upregulate autophagy, measured by an increase in autophagosomes, without a deeper assessment of contributors toward effective turnover through autophagy. There are various ways in which autophagy is regulated ranging from the mammalian target of rapamycin (mTOR) to acetylation status of proteins. Healthy mitochondria and the intracellular energetic charge they preserve are key for the acidification status of lysosomes and thus ensuring effective clearance of components through the autophagy pathway. Both mitochondria and lysosomes have been shown to bear functional protein complexes that aid in the regulation of autophagy. Indeed, it may be the case that minimizing the proteins associated with the respective neurodegenerative pathology may be of greater importance than addressing molecularly their resulting inclusion bodies. It is in this context that this review will dissect the autophagy signaling pathway, its control and the manner in which it is molecularly and functionally connected with the mitochondrial and lysosomal system, as well as provide a summary of the role of autophagy dysfunction in driving neurodegenerative disease as a means to better position the potential of rapamycin-mediated bioactivities to control autophagy favorably.

Progressive Mechanical and Structural Changes in Anterior Cerebral Arteries with Alzheimer's Disease

Alzheimer disease (AD) is a neurodegenerative disease and the main cause for dementia. The irreversible neurodegeneration leads to a gradual loss of brain function characterized predominantly by memory loss. Cerebrovascular changes are common neuropathologic findings in aged subjects with dementia. Cerebrovascular integrity is critical for proper metabolism and perfusion of the brain, as cerebrovascular remodeling may render the brain more susceptible to pulse pressure and may be associated with poorer cognitive performance and greater risk of cerebrovascular events. The objective of this study is to provide understanding of cerebrovascular remodeling with AD progression. A total of 28 brain donor participants with human anterior cerebral artery (ACA) from controls and pathologically diagnosed AD groups (early - Braak stages I-II; intermediate - Braak stages III-IV; and advanced - Braak stages V-VI) were included in this study. Mechanical testing, histology, advanced optical imaging, and mass spectrometry were performed to study the progressive structural and functional changes of ACAs with AD progression. Biaxial extension-inflation tests showed that ACAs became progressively less compliant, and the longitudinal stress in the intermediate& advanced AD groups was significantly higher than that from the control group. With pathological AD development, the inner and outer diameter of ACA remained almost unchanged; however, histology study revealed progressive smooth muscle cell atrophy and loss of elastic fibers which led to compromised structural integrity of the arterial wall. Multiphoton imaging demonstrated elastin degradation at the media-adventitia interface, which led to the formation of an empty band of 21.0 ± 15.4 μm and 32.8 ± 9.24 μm in width for the intermediate& advanced AD groups, respectively. Furthermore, quantitative birefringence microscopy showed disorganized adventitial collagen with AD development. Mass spectrometry analysis provided further evidence of altered collagen content and other extracellular matrix (ECM) molecule and smooth muscle cell changes that were consistent with the mechanical and structural alterations. Collectively, our study provides understanding of the mechanical and structural cerebrovascular deterioration in cerebral arteries with AD, which may be related to neurodegenration and pathology in the brain.

Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review

BACKGROUND

This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia.

METHODS

We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines.

RESULTS

Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia.

CONCLUSION

Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews- CRD42019133402 and published in the Systematic Reviews journal.

The impact of Alzheimer's disease risk factors on the pupillary light response

Alzheimer's disease (AD) is the leading cause of dementia, and its prevalence is increasing and is expected to continue to increase over the next few decades. Because of this, there is an urgent requirement to determine a way to diagnose the disease, and to target interventions to delay and ideally stop the onset of symptoms, specifically those impacting cognition and daily livelihood. The pupillary light response (PLR) is controlled by the sympathetic and parasympathetic branches of the autonomic nervous system, and impairments to the pupillary light response (PLR) have been related to AD. However, most of these studies that assess the PLR occur in patients who have already been diagnosed with AD, rather than those who are at a higher risk for the disease but without a diagnosis. Determining whether the PLR is similarly impaired in subjects before an AD diagnosis is made and before cognitive symptoms of the disease begin, is an important step before using the PLR as a diagnostic tool. Specifically, identifying whether the PLR is impaired in specific at-risk groups, considering both genetic and non-genetic risk factors, is imperative. It is possible that the PLR may be impaired in association with some risk factors but not others, potentially indicating different pathways to neurodegeneration that could be distinguished using PLR. In this work, we review the most common genetic and lifestyle-based risk factors for AD and identify established relationships between these risk factors and the PLR. The evidence here shows that many AD risk factors, including traumatic brain injury, ocular and intracranial hypertension, alcohol consumption, depression, and diabetes, are directly related to changes in the PLR. Other risk factors currently lack sufficient literature to make any conclusions relating directly to the PLR but have shown links to impairments in the parasympathetic nervous system; further research should be conducted in these risk factors and their relation to the PLR.

Correlation between cerebral hemodynamic functional near-infrared spectroscopy and positron emission tomography for assessing mild cognitive impairment and Alzheimer's disease: An exploratory study

This study was performed to investigate the usefulness of functional near-infrared spectroscopy (fNIRS) by conducting a comparative analysis of hemodynamic activation detected by fNIRS and positron emission tomography (PET) and magnetic resonance imaging (MRI) in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Participants were divided into four groups: the subjective memory impairment (SMI), amnestic MCI (aMCI), non-amnestic MCI (naMCI), and AD groups. We recorded the hemodynamic response during the semantic verbal fluency task (SVFT) using a commercial wireless continuous-wave NIRS system. The correlation between the parameters of the neuroimaging assessments among the groups was analyzed. Region of interest-based comparisons showed that the four groups had significantly different hemodynamic responses during SVFT in the bilateral dorsolateral prefrontal cortex (DLPFC). The linear mixed effect model result indicates that the mean ΔHbO2 from the bilateral DLPFC regions showed a significant positive correlation to the overall FDG-PET after controlling for age and group differences in the fNIRS signals. Amyloid PET signals tended to better differentiate the AD group from other groups, and fNIRS signals tended to better differentiate the SMI group from other groups. In addition, a comparison between the group pairs revealed a mirrored pattern between the hippocampal volume and hemodynamic response in the DLPFC. The hemodynamic response detected by fNIRS showed a significant correlation with metabolic and anatomical changes associated with disease progression. Therefore, fNIRS may be considered as a screening tool to predict the hemodynamic and metabolic statuses of the brain in patients with MCI and AD.

Sex differences in metabolic phenotype and hypothalamic inflammation in the 3xTg-AD mouse model of Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is notably associated with cognitive decline resulting from impaired function of hippocampal and cortical areas; however, several other domains and corresponding brain regions are affected. One such brain region is the hypothalamus, shown to atrophy and develop amyloid and tau pathology in AD patients. The hypothalamus controls several functions necessary for survival, including energy and glucose homeostasis. Changes in appetite and body weight are common in AD, often seen several years prior to the onset of cognitive symptoms. Therefore, altered metabolic processes may serve as a biomarker for AD, as well as a target for treatment, considering they are likely both a result of pathological changes and contributor to disease progression. Previously, we reported sexually dimorphic metabolic disturbances in ~ 7-month-old 3xTg-AD mice, accompanied by differences in systemic and hypothalamic inflammation.

METHODS

In the current study, we investigated metabolic outcomes and hypothalamic inflammation in 3xTg-AD males and females at 3, 6, 9, and 12 months of age to determine when these sex differences emerge.

RESULTS

In agreement with our previous study, AD males displayed less weight gain and adiposity, as well as reduced blood glucose levels following a glucose challenge, compared to females. These trends were apparent by 6-9 months of age, coinciding with increased expression of inflammatory markers (Iba1, GFAP, TNF-α, and IL-1β) in the hypothalamus of AD males.

CONCLUSIONS

These findings provide additional evidence for sex-dependent effects of AD pathology on energy and glucose homeostasis, which may be linked to hypothalamic inflammation.

Quantitative flow cytometric selection of tau conformational nanobodies specific for pathological aggregates

Single-domain antibodies, also known as nanobodies, are broadly important for studying the structure and conformational states of several classes of proteins, including membrane proteins, enzymes, and amyloidogenic proteins. Conformational nanobodies specific for aggregated conformations of amyloidogenic proteins are particularly needed to better target and study aggregates associated with a growing class of associated diseases, especially neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. However, there are few reported nanobodies with both conformational and sequence specificity for amyloid aggregates, especially for large and complex proteins such as the tau protein associated with Alzheimer's disease, due to difficulties in selecting nanobodies that bind to complex aggregated proteins. Here, we report the selection of conformational nanobodies that selectively recognize aggregated (fibrillar) tau relative to soluble (monomeric) tau. Notably, we demonstrate that these nanobodies can be directly isolated from immune libraries using quantitative flow cytometric sorting of yeast-displayed libraries against tau aggregates conjugated to quantum dots, and this process eliminates the need for secondary nanobody screening. The isolated nanobodies demonstrate conformational specificity for tau aggregates in brain samples from both a transgenic mouse model and human tauopathies. We expect that our facile approach will be broadly useful for isolating conformational nanobodies against diverse amyloid aggregates and other complex antigens.

Beta to theta power ratio in EEG periodic components as a potential biomarker in mild cognitive impairment and Alzheimer's dementia

BACKGROUND

Alzheimer's dementia (AD) is associated with electroencephalography (EEG) abnormalities including in the power ratio of beta to theta frequencies. EEG studies in mild cognitive impairment (MCI) have been less consistent in identifying such abnormalities. One potential reason is not excluding the EEG aperiodic components, which are less associated with cognition than the periodic components. Here, we investigate whether aperiodic and periodic EEG components are disrupted differently in AD or MCI vs. healthy control (HC) individuals and whether a periodic based beta/theta ratio differentiates better MCI from AD and HC groups than a ratio based on the full spectrum.

METHODS

Data were collected from 44 HC (mean age (SD) = 69.1 (5.3)), 114 MCI (mean age (SD) = 72.2 (7.5)), and 41 AD (mean age (SD) = 75.7 (6.5)) participants. Aperiodic and periodic components and full spectrum EEG were compared among the three groups. Receiver operating characteristic curves obtained via logistic regression classifications were used to distinguish the groups. Last, we explored the relationships between cognitive performance and the beta/theta ratios based on the full or periodic spectrum.

RESULTS

Aperiodic EEG components did not differ among the three groups. In contrast, AD participants showed an increase in full spectrum and periodic relative powers for delta, theta, and gamma and a decrease for beta when compared to HC or MCI participants. As predicted, MCI group differed from HC participants on the periodic based beta/theta ratio (Bonferroni corrected p-value = 0.036) measured over the occipital region. Classifiers based on beta/theta power ratio in EEG periodic components distinguished AD from HC and MCI participants, and outperformed classifiers based on beta/theta power ratio in full spectrum EEG. Beta/theta ratios were comparable in their association with cognition.

CONCLUSIONS

In contrast to a full spectrum EEG analysis, a periodic-based analysis shows that MCI individuals are different on beta/theta ratio when compared to healthy individuals. Focusing on periodic components in EEG studies with or without other biological markers of neurodegenerative diseases could result in more reliable findings to separate MCI from healthy aging, which would be valuable for designing preventative interventions.

Using community-based geographical information system (GIS) to recruit older Asian Americans in an Alzheimer's disease study

OBJECTIVE

This study aims to show the usefulness of incorporating a community-based geographical information system (GIS) in recruiting research participants for the Asian Cohort for Alzheimer's Disease (ACAD) study for using the subgroup of Korean American (KA) older adults. The ACAD study is the first large study in the USA and Canada focusing on the recruitment of Chinese, Korean and Vietnamese older adults to address the issues of under-representation of Asian Americans in clinical research.

METHODS

To promote clinical research participation of racial/ethnic minority older adults with and without dementia, we used GIS by collaborating with community members to delineate boundaries for geographical clusters and enclaves of church and senior networks, and KA serving ethnic clinics. In addition, we used socioeconomic data identified as recruitment factors unique to KA older adults which was analysed for developing recruitment strategies.

RESULTS

GIS maps show a visualisation of the heterogeneity of the sociodemographic characteristics and the resources of faith-based organisations and KA serving local clinics. We addressed these factors that disproportionately affect participation in clinical research and successfully recruited the intended participants (N=60) in the proposed period.

DISCUSSION

Using GIS maps to locate KA provided innovative inroads to successful research outreach efforts for a pilot study that may be expanded to other underserved populations across the USA in the future. We will use this tool subsequently on a large-scale clinical genetic epidemiology study.

POLICY IMPLICATION

This approach responds to the call from the National Institute on Aging to develop strategies to improve the health status of older adults in diverse populations. Our study will offer a practical guidance to health researchers and policymakers in identifying understudied and hard-to-reach specific Asian American populations for clinical studies or initiatives. This would further contribute in reducing the health and research disparity gaps among older minority populations.

The knowledge and attitude of Nepalese nursing students towards dementia

Background

As aging is a significant risk factor for dementia, the number of persons with dementia is growing annually as a result of the aging population, which implies the need for better geriatric care services in terms of both quantity and quality.

Methods

The knowledge and attitudes of Nepalese nursing students (n = 177) concerning dementia were examined using a descriptive correlational research approach. Alzheimer's Disease Knowledge Scale (ADKS) and Dementia Attitude Scale (DAS) were used to measure students' knowledge and attitude of dementia.

Results and discussion

The overall mean score of the ADKS was found to be 19.64 (SD = 3.363) out of 30, while the total mean score on dementia attitude of the DAS was 93.82 (SD = 10.09) out of 140. A positive relationship was established between the knowledge and the attitude scores (r = 0.148, P = 0.050), demonstrating limited knowledge and positive attitude towards people with dementia in nursing students.

Hypermetabolic Cerebellar Connectome in Alzheimer's Disease

Introduction: Regional hypermetabolism in Alzheimer's disease (AD), especially in the cerebellum, has been consistently observed but often neglected as an artefact produced by the commonly used proportional scaling procedure in the statistical parametric mapping. We hypothesize that the hypermetabolic regions are also important in disease pathology in AD. Methods: Using fluorodeoxyglucose (FDG)-positron emission tomography (PET) images from 88 AD subjects and 88 age-sex matched normal controls (NL) from the publicly available Alzheimer's Disease Neuroimaging Initiative database, we developed a general linear model-based classifier that differentiated AD patients from normal individuals (sensitivity = 87.50%, specificity = 82.95%). We constructed region-region group-wise correlation matrices and evaluated differences in network organization by using the graph theory analysis between AD and control subjects. Results: We confirmed that hypermetabolism found in AD is not an artefact by replicating it using white matter as the reference region. The role of the hypermetabolic regions has been further investigated by using the graph theory. The differences in betweenness centrality (BC) between AD and NL network were correlated with region weights of FDG PET-based AD classifier. In particular, the hypermetabolism in cerebellum was accompanied with higher BC. The brain regions with higher BC in AD network showed a progressive increase in FDG uptake over 2 years in prodromal AD patients (n = 39). Discussion: This study suggests that hypermetabolism found in AD may play an important role in forming the AD-related metabolic network. In particular, hypermetabolic cerebellar regions represent a good candidate for further investigation in altered network organization in AD.

Improving Alzheimer's Disease Diagnosis With Multi-Modal PET Embedding Features by a 3D Multi-Task MLP-Mixer Neural Network

Positron emission tomography (PET) with fluorodeoxyglucose (FDG) or florbetapir (AV45) has been proved effective in the diagnosis of Alzheimer's disease. However, the expensive and radioactive nature of PET has limited its application. Here, employing multi-layer perceptron mixer architecture, we present a deep learning model, namely 3-dimensional multi-task multi-layer perceptron mixer, for simultaneously predicting the standardized uptake value ratios (SUVRs) for FDG-PET and AV45-PET from the cheap and widely used structural magnetic resonance imaging data, and the model can be further used for Alzheimer's disease diagnosis based on embedding features derived from SUVR prediction. Experiment results demonstrate the high prediction accuracy of the proposed method for FDG/AV45-PET SUVRs, where we achieved Pearson's correlation coefficients of 0.66 and 0.61 respectively between the estimated and actual SUVR and the estimated SUVRs also show high sensitivity and distinct longitudinal patterns for different disease status. By taking into account PET embedding features, the proposed method outperforms other competing methods on five independent datasets in the diagnosis of Alzheimer's disease and discriminating between stable and progressive mild cognitive impairments, achieving the area under receiver operating characteristic curves of 0.968 and 0.776 respectively on ADNI dataset, and generalizes better to other external datasets. Moreover, the top-weighted patches extracted from the trained model involve important brain regions related to Alzheimer's disease, suggesting good biological interpretability of our proposed method."

Blood Selenium and Serum Glutathione Peroxidase Levels Were Associated with Serum β-Amyloid in Older Adults

BACKGROUND

Studies have established the association between blood β-amyloid (Aβ) levels and Alzheimer's disease, but population-based studies concerning the association between selenium (Se) and Aβ levels in blood samples are very limited. Therefore, we explored the association in an elderly population with Se status and serum Aβ measures.

METHODS

A cross-sectional study on 469 elderly individuals from four rural counties with diverse soil Se levels was carried out. Fasting blood Se, serum selenoprotein P (SELENOP), and glutathione peroxidase (GPX), serum Aβ42, and Aβ40 were measured. Quantile regression models were used to determine the associations of blood Se, serum GPX, and SELENOP with Aβ levels.

RESULTS

Significant negative associations were observed between blood Se and serum Aβ42 and Aβ40 levels at all percentiles (P < 0.05). The associations were generally stronger at higher Aβ42 and Aβ40 percentiles than lower Aβ42 and Aβ40 percentiles. Blood Se was positively associated with serum Aβ42/Aβ40 ratio at 25th, 50th, and 75th percentiles. Significant positive associations were observed between serum GPX and Aβ42 and Aβ40 levels at all percentiles (P < 0.05). The positive associations were generally stronger at higher Aβ42 and Aβ40 percentiles than at lower percentiles. Serum GPX was negatively associated with Aβ42/Aβ40 ratio at 25th, 50th, 75th, and 95th percentiles. No associations with serum SELENOP and Aβ levels were observed.

CONCLUSIONS

Our results suggest that higher Se levels are associated with lower serum Aβ42 and Aβ40 levels and with higher Aβ42/Aβ40 ratio, and the results are specific for different selenoproteins.

Disaggregating the dementia monolith: An analysis of variation in Medicare costs and use by dementia subtype

IMPORTANCE

With an aging population, it is necessary to systematically examine variation in costs and use of Medicare services by dementia subtype. We provide the first national estimates for dementia by subtype, and the respective Medicare costs and use.

METHODS

We analyzed Medicare fee-for-service (FFS) claims from 2017 through 2019. The sample included 41 million beneficiaries: 727,700 beneficiaries with a new dementia diagnosis in 2017. We calculated descriptive statistics and conducted generalized linear regression models by subtype of dementia.

RESULTS

Annual Medicare costs for beneficiaries with dementia ranged from $22,840 for frontotemporal dementia to $44,896 for vascular dementia compared to $9,034 for beneficiaries without dementia. Comparing beneficiaries across dementia subtypes, the greatest differences were in the use of home health and hospice care.

CONCLUSIONS

These analyses demonstrate substantial heterogeneity across dementia subtypes, which will be important in developing models of care that improve value for people with dementia.

Posterity of nanoscience as lipid nanosystems for Alzheimer's disease regression

Alzheimer's disease (AD) is a type of dementia that affects a vast number of people around the world, causing a great deal of misery and death. Evidence reveals a relationship between the presence of soluble Aβ peptide aggregates and the severity of dementia in Alzheimer's patients. The BBB (Blood Brain Barrier) is a key problem in Alzheimer's disease because it prevents therapeutics from reaching the desired places. To address the issue, lipid nanosystems have been employed to deliver therapeutic chemicals for anti-AD therapy in a precise and targeted manner. The applicability and clinical significance of lipid nanosystems to deliver therapeutic chemicals (Galantamine, Nicotinamide, Quercetin, Resveratrol, Curcumin, HUPA, Rapamycin, and Ibuprofen) for anti-AD therapy will be discussed in this review. Furthermore, the clinical implications of the aforementioned therapeutic compounds for anti-AD treatment have been examined. Thus, this review will pave the way for researchers to fashion therodiagnostics approaches based on nanomedicine to overcome the problems of delivering therapeutic molecules across the blood brain barrier (BBB).

Management Practice and Clinical Outcomes of Dementia in Sub-Saharan Africa: A Systematic Review

Background

Dementia is a severe neurodegenerative disorder and it is a group of acquired symptoms associated with impaired cognitive functions. In low-income settings particularly in Sub-Saharan Africa (SSA), it is often seen as part of normal aging. Environmental, behavioral, and lifestyle interventions have the potential to alter the disease course of dementia.

Objective

This study is aimed to synthesize the literature/evidence(s) on the management practice and treatment outcomes of dementia in SSA.

Method

Comprehensive literature was searched in PubMed database, Cochrane Library, and Google Scholar. Eligibility has been set, and based on the criteria, initially, a total of 442 results were obtained, and from those around 183 articles were duplicated. After examining titles and abstracts of records 26 articles were identified. Finally, five randomized clinical trials (RCT) and three prospective cohort studies that were reported on the management practice and treatment outcome of dementia in SSA were eligible for analysis. RCT and prospective cohort studies were used to strengthen the quality of evidence. The quality of the included RCT studies was assessed by using the Cochrane Risk of Bias Tool.

Result

A total of 2781 patient data were included in the final analysis. Of these, 2354 patients were obtained from 5 RCTs and 427 patients from 3 prospective cohort studies, which were conducted in SSA countries. RCT studies were done on the feasibility and clinical effectiveness of cognitive stimulation therapy (CST) showed improvements in language memory domains and physical health. In addition, studies that focus on the management of human immunodeficiency virus-associated dementia (HIVAD) were reported to improve neurocognitively.

Conclusion

CST is applicable in low-resource settings and it shows improvements in cognitive function and quality of life. Early initiation of combination antiretroviral therapy in resource-limited settings has been associated with improvement in the cognitive function of HIVAD.

Modifiable cardiovascular risk factors and genetics for targeted prevention of dementia

Dementia is a major global challenge for health and social care in the 21st century. A third of individuals >65 years of age die with dementia, and worldwide incidence numbers are projected to be higher than 150 million by 2050. Dementia is, however, not an inevitable consequence of old age; 40% of dementia may theoretically be preventable. Alzheimer's disease (AD) accounts for approximately two-thirds of dementia cases and the major pathological hallmark of AD is accumulation of amyloid-β. Nevertheless, the exact pathological mechanisms of AD remain unknown. Cardiovascular disease and dementia share several risk factors and dementia often coexists with cerebrovascular disease. In a public health perspective, prevention is crucial, and it is suggested that a 10% reduction in prevalence of cardiovascular risk factors could prevent more than nine million dementia cases worldwide by 2050. Yet this assumes causality between cardiovascular risk factors and dementia and adherence to the interventions over decades for a large number of individuals. Using genome-wide association studies, the entire genome can be scanned for disease/trait associated loci in a hypothesis-free manner, and the compiled genetic information is not only useful for pinpointing novel pathogenic pathways but also for risk assessments. This enables identification of individuals at high risk, who likely will benefit the most from a targeted intervention. Further optimization of the risk stratification can be done by adding cardiovascular risk factors. Additional studies are, however, highly needed to elucidate dementia pathogenesis and potential shared causal risk factors between cardiovascular disease and dementia.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review

Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.

A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer's disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer's disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.

Potential Role of Glucagon-like Peptide-1 Receptor Agonists in the Treatment of Cognitive Decline and Dementia in Diabetes Mellitus

Dementia is a permanent illness characterized by mental instability, memory loss, and cognitive decline. Many studies have demonstrated an association between diabetes and cognitive dysfunction that proceeds in three steps, namely, diabetes-associated cognitive decrements, mild cognitive impairment (MCI; both non-amnesic MCI and amnesic MCI), and dementia [both vascular dementia and Alzheimer's disease (AD)]. Based on this association, this disease has been designated as type 3 diabetes mellitus. The underlying mechanisms comprise insulin resistance, inflammation, lipid abnormalities, oxidative stress, mitochondrial dysfunction, glycated end-products and autophagy. Moreover, insulin and insulin-like growth factor-1 (IGF-1) have been demonstrated to be involved. Insulin in the brain has a neuroprotective role that alters cognitive skills and alteration of insulin signaling determines beta-amyloid (Aβ) accumulation, in turn promoting brain insulin resistance. In this complex mechanism, other triggers include hyperglycemia-induced overproduction of reactive oxygen species (ROS) and inflammatory cytokines, which result in neuroinflammation, suggesting that antidiabetic drugs may be potential treatments to protect against AD. Among these, glucagon-like peptide-1 receptor agonists (GLP-1RAs) are the most attractive antidiabetic drugs due to their actions on synaptic plasticity, cognition and cell survival. The present review summarizes the significant data concerning the underlying pathophysiological and pharmacological mechanisms between diabetes and dementia.

Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay

Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer's disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ([Formula: see text]). Moreover, our performance was equivalent to that of contemporary research.

Smartphone-based versus traditional face-to-face collaborative care for community-dwelling older adults living with dementia in China: protocol for an implementation science-based sequential multiple assignment randomised trial

INTRODUCTION

The high costs of institutional care and the burdensome demands of home care are challenging for families of adults with dementia. The collaborative care model (CCM) provides a potential solution to these challenges. Leveraging advancements in mobile technologies, smartphone-based management could offer a feasible means of providing collaborative care in a community setting. Therefore, this study aims to establish a CCM for home-cared older adults with dementia to determine the best strategy to deliver collaborative care, including both the channel and frequency of delivery.

METHODS AND ANALYSIS

This study will be conducted in the communities of Chengdu city, Sichuan province, China. It is designed under the framework of implementation science. In the first stage, intervention strategies for community-dwelling older adults with dementia and their caregivers will be developed using Delphi methods and focus group interviews. The second stage will involve designing a sequential multiple assignment randomised trial to compare the effectiveness of face-to-face intervention versus a WeChat mini program-based intervention. This comparison will involve 358 pairs of older adults with dementia and their caregivers, with the frequency of intervention also assessed. Follow-up evaluations will be implemented at the 6th, 12th and 18th months post-intervention initiation. Primary outcomes encompass the proportion of patients demonstrating an improvement in quality of life and the proportion of caregivers exhibiting a reduction in caregiver burden. Analysis will be based on the intention-to-treat principle, and the generalised estimating equation approach will be used. Incremental cost-effectiveness ratios will be used to evaluate the cost-effectiveness of different delivery methods and frequencies.

ETHICS AND DISSEMINATION

This study has received approval from the Ethics Committee of West China Fourth Hospital/School of Public Health, Sichuan University (Gwll2022004). Informed consent will be obtained for all participants. The findings of the study will be disseminated through peer-reviewed scientific journals.

TRIAL REGISTRATION NUMBER

ChiCTR2200057945.

Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data

Accurate medical classification requires a large number of multi-modal data, and in many cases, different feature types. Previous studies have shown promising results when using multi-modal data, outperforming single-modality models when classifying diseases such as Alzheimer's Disease (AD). However, those models are usually not flexible enough to handle missing modalities. Currently, the most common workaround is discarding samples with missing modalities which leads to considerable data under-utilisation. Adding to the fact that labelled medical images are already scarce, the performance of data-driven methods like deep learning can be severely hampered. Therefore, a multi-modal method that can handle missing data in various clinical settings is highly desirable. In this paper, we present Multi-Modal Mixing Transformer (3MT), a disease classification transformer that not only leverages multi-modal data but also handles missing data scenarios. In this work, we test 3MT for AD and Cognitively normal (CN) classification and mild cognitive impairment (MCI) conversion prediction to progressive MCI (pMCI) or stable MCI (sMCI) using clinical and neuroimaging data. The model uses a novel Cascaded Modality Transformers architecture with cross-attention to incorporate multi-modal information for more informed predictions. We propose a novel modality dropout mechanism to ensure an unprecedented level of modality independence and robustness to handle missing data scenarios. The result is a versatile network that enables the mixing of arbitrary numbers of modalities with different feature types and also ensures full data utilization in missing data scenarios. The model is trained and evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with the state-of-the-art performance and further evaluated with The Australian Imaging Biomarker & Lifestyle Flagship Study of Ageing (AIBL) dataset with missing data.

Factors Associated With Function-Focused Care Among Hospitalized Older Adults Living With Dementia

This article reports a study that was designed to describe the incidence of pain among older hospitalized patients with dementia and to evaluate the factors that influence pain among these individuals. It was hypothesized that function, behavioral and psychological symptoms of dementia, delirium, pain treatment, and patient exposure to care interventions would be associated with pain. Patients who performed more functional activities had less delirium. They also experienced higher quality-of-care interactions and were less likely to have pain. The findings from this study support the relationship between function, delirium, and quality-of-care interactions and pain. It suggests that it may be useful to encourage patients with dementia to engage in functional and physical activity to prevent or manage pain. This study serves as a reminder to avoid neutral or negative care interactions among patients with dementia as a strategy to mediate delirium and pain.

Applying Joint Graph Embedding to Study Alzheimer's Neurodegeneration Patterns in Volumetric Data

Neurodegeneration measured through volumetry in MRI is recognized as a potential Alzheimer's Disease (AD) biomarker, but its utility is limited by lack of specificity. Quantifying spatial patterns of neurodegeneration on a whole brain scale rather than locally may help address this. In this work, we turn to network based analyses and extend a graph embedding algorithm to study morphometric connectivity from volume-change correlations measured with structural MRI on the timescale of years. We model our data with the multiple random eigengraphs framework, as well as modify and implement a multigraph embedding algorithm proposed earlier to estimate a low dimensional embedding of the networks. Our version of the algorithm guarantees meaningful finite-sample results and estimates maximum likelihood edge probabilities from population-specific network modes and subject-specific loadings. Furthermore, we propose and implement a novel statistical testing procedure to analyze group differences after accounting for confounders and locate significant structures during AD neurodegeneration. Family-wise error rate is controlled at 5% using permutation testing on the maximum statistic. We show that results from our analysis reveal networks dominated by known structures associated to AD neurodegeneration, indicating the framework has promise for studying AD. Furthermore, we find network-structure tuples that are not found with traditional methods in the field.

"Out of the clear blue sky she tells me she loves me": Connection experiences between caregivers and people with dementia

BACKGROUND

Dementia can impede the relationship and connection between the person with dementia (PWD) and their caregiver. Yet, caregiving in dementia also offers opportunities for connection, which has implications for caregiver and PWD well-being. In this qualitative study, we describe and characterize ways caregivers felt connected to the person with dementia they care for.

METHODS

We conducted a telephone-based survey with caregivers of people with dementia. For this paper, we analyzed responses to an open-ended question focused on when caregivers feel most connected to the person they are caring for. Responses were analyzed and coded and themes were identified through an iterative process involving a multidisciplinary team of researchers and clinicians.

RESULTS

437 caregivers participated in this study. We identified two domains of connection: activity-based and emotion-based connections. Within activity-based connections, the following themes emerged: everyday activities; reminiscing; activities of caregiving; novel experiences; and time with family and friends. Within emotional connections, the following themes emerged: expressions of love, appreciation, and gratitude; physical affection; sharing an emotion or emotional experience; and times when the PWD seems like "themself" again.

CONCLUSIONS

Findings provide insights into ways caregivers experience a sense of connection with the person they care for. There is a call to shift away from focusing on reducing stress and toward optimizing positive experiences as a way to better support caregivers' health and well-being. Interventions that leverage these insights to foster caregiver - PWD connection could lead to better health and well-being for both members of the dyad. The amplification of a positive experience may be particularly important for caregivers who are struggling with limited support or respite. Clinicians may be interested in using a question about connection as a way to more fully understand a caregiver's current experience.

ArhGAP11A mediates amyloid-β generation and neuropathology in an Alzheimer's disease-like mouse model

Amyloid-β (Aβ) plays an important role in the neuropathology of Alzheimer's disease (AD), but some factors promoting Aβ generation and Aβ oligomer (Aβo) neurotoxicity remain unclear. We here find that the levels of ArhGAP11A, a Ras homology GTPase-activating protein, significantly increase in patients with AD and amyloid precursor protein (APP)/presenilin-1 (PS1) mice. Reducing the ArhGAP11A level in neurons not only inhibits Aβ generation by decreasing the expression of APP, PS1, and β-secretase (BACE1) through the RhoA/ROCK/Erk signaling pathway but also reduces Aβo neurotoxicity by decreasing the expressions of apoptosis-related p53 target genes. In APP/PS1 mice, specific reduction of the ArhGAP11A level in neurons significantly reduces Aβ production and plaque deposition and ameliorates neuronal damage, neuroinflammation, and cognitive deficits. Moreover, Aβos enhance ArhGAP11A expression in neurons by activating E2F1, which thus forms a deleterious cycle. Our results demonstrate that ArhGAP11A may be involved in AD pathogenesis and that decreasing ArhGAP11A expression may be a promising therapeutic strategy for AD treatment.

Mesenchymal Stem Cells and Begacestat Mitigate Amyloid-β 25-35-Induced Cognitive Decline in Rat Dams and Hippocampal Deteriorations in Offspring

Alzheimer's disease (AD) is the most common cause of age-related neurodegeneration and cognitive decline. AD more commonly occurs in females than in males, so it is necessary to consider new treatments specifically targeting this population. The present study investigated the protective effects of Begacestat (γ-secretase inhibitor-953, GSI-953) and bone marrow-derived mesenchymal stem cells (BM-MSCs) during pregnancy on cognitive impairment in rat dams and neurodegeneration in offspring caused by the intracerebroventricular injection of Aβ 25-35 before pregnancy. The performances of dams injected with amyloid-β 25-35 (Aβ 25-35) during behavioral tests were significantly impaired. The offspring of Aβ 25-35-injected dams treated with BM-MSCs or GSI-953 showed a dramatically reduced number and size of activated microglial cells, enhancement in the processes length, and a decrease in the proinflammatory cytokine levels. Additionally, BM-MSC or GSI-953 therapy reduced Aβ 25-35-induced increases in tau phosphorylation and amyloid precursor protein levels in the neonates' hippocampus and elevated the lower levels of glycogen synthase kinase-3 and brain-derived neurotrophic factor; moreover, reversed Aβ 25-35-induced alterations in gene expression in the neonatal hippocampus. Finally, the treatments with BM-MSC or GSI-953 are globally beneficial against Aβ 25-35-induced brain alterations, particularly by suppressing neural inflammation, inhibiting microglial cell activation, restoring developmental plasticity, and increasing neurotrophic signaling.

A splicing transcriptome-wide association study identifies novel altered splicing for Alzheimer's disease susceptibility

Alzheimer's disease (AD) is a common neurodegenerative disease in aging individuals. Alternative splicing is reported to be relevant to AD development while their roles in etiology of AD remain largely elusive. We performed a comprehensive splicing transcriptome-wide association study (spTWAS) using intronic excision expression genetic prediction models of 12 brain tissues developed through three modelling strategies, to identify candidate susceptibility splicing introns for AD risk. A total of 111,326 (46,828 proxy) cases and 677,663 controls of European ancestry were studied. We identified 343 associations of 233 splicing introns (143 genes) with AD risk after Bonferroni correction (0.05/136,884 = 3.65 × 10^-7). Fine-mapping analyses supported 155 likely causal associations corresponding to 83 splicing introns of 55 genes. Eighteen causal splicing introns of 15 novel genes (EIF2D, WDR33, SAP130, BYSL, EPHB6, MRPL43, VEGFB, PPP1R13B, TLN2, CLUHP3, LRRC37A4P, CRHR1, LINC02210, ZNF45-AS1, and XPNPEP3) were identified for the first time to be related to AD susceptibility. Our study identified novel genes and splicing introns associated with AD risk, which can improve our understanding of the etiology of AD.

Molecular Multi-Target Approach for Human Acetylcholinesterase, Butyrylcholinesterase and β-Secretase 1: Next Generation for Alzheimer's Disease Treatment

Alzheimer's Disease (AD) is a neurodegenerative condition characterized by progressive memory loss and other affected cognitive functions. Pharmacological therapy of AD relies on inhibitors of the enzymes acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), offering only a palliative effect and being incapable of stopping or reversing the neurodegenerative process. However, recent studies have shown that inhibiting the enzyme β-secretase 1 (BACE-1) may be able to stop neurodegeneration, making it a promising target. Considering these three enzymatic targets, it becomes feasible to apply computational techniques to guide the identification and planning of molecules capable of binding to all of them. After virtually screening 2119 molecules from a library, 13 hybrids were built and further screened by triple pharmacophoric model, molecular docking, and molecular dynamics (t = 200 ns). The selected hybrid G meets all stereo-electronic requirements to bind to AChE, BChE, and BACE-1 and offers a promising structure for future synthesis, enzymatic testing, and validation.

Preconditioning adipose-derived mesenchymal stem cells with dimethyl fumarate promotes their therapeutic efficacy in the brain tissues of rats with Alzheimer's disease

AIM

Transplantation of mesenchymal stem cell (MSC) has been suggested to be a promising method for treating neurodegenerative conditions, including Alzheimer's disease (AD). However, the poor survival rate of transplanted MSCs has limited their therapeutic application. This study aimed to evaluate whether preconditioning MSCs with dimethyl fumarate (DMF), a Nrf2 inducer, could enhance MSC therapeutic efficacy in an amyloid-β (Aβ_1-42)-induced AD rat model.

METHODS

The survival and antioxidant capacity of MSCs treated with DMF were assessed in vitro. Aβ_1-42 intrahippocampal injection was used to create a rat model of AD. Following the transplantation of MSCs preconditioned with DMF and using the Morris blue maze test, spatial learning and memory were assessed. Using RT-qPCR, we evaluated the gene expression related to apoptosis and neurotrophins in the hippocampus region.

RESULTS

Treatment with DMF enhanced cell survival and Nrf2 protein expression in MSCs in vitro. Preconditioning with DMF also enhanced the efficacy of transplanted MSCs in rescuing learning and spatial memory deficits in Aβ-AD rats. Besides, DMF preconditioning enhanced the neuroprotective effect of transplanted MSCs in the hippocampus of rats treated with Aβ_1-42 by decreasing the expression of apoptotic markers (Bax, caspase 3, and cytochrome c), and elevating the expression of the anti-apoptotic marker Bcl2 and neurotrophins, including BDNF and NGF.

CONCLUSION

Preconditioning MSCs with DMF boosted the therapeutic efficacy of these cells; therefore, it could serve as a targeted strategy for increasing the therapeutic efficacy of MSCs in treating neurodegenerative disorders, including AD.

Effects of Alzheimer's disease and related dementias on dental care usage and economic burden in older adults: a cross-sectional study

AIM

Distinct subtypes of Alzheimer's disease (AD) and related dementias (RD) might have different effects on dental care usage and economic burden. To determine the effects of AD and RD on specific types of dental care usage (preventive and treatment visits) and dental care costs from different payers (total and out-of-pocket costs).

METHODS

A cross-sectional study was conducted using the Medicare Current Beneficiary Survey in 2016. This study identified 4268 community dwelling older adults with and without Alzheimer's disease and related dementias (ADRD) from a nationally representative sample of Medicare beneficiaries. Dental care usage and costs are based on self-reported data. Preventive dental events included preventive and diagnosis events. Treatment dental events included restorative, oral surgery and other events.

RESULTS

This study identified 4268 (weighted N=30 423 885) older adults, including 94.48% without ADRD, 1.90% with AD and 3.63% with RD. Compared with older adults without ADRD, those with AD had similar dental care usage, but those with RD were 38% less likely to have treatment visit (OR: 0.62; 95% CI: 0.41 to 0.94) and had a 40% reduced number of total treatment visits (incidence rate ratio: 0.60; 95% CI: 0.37 to 0.98). RD was not associated with dental care costs, but AD was associated with higher total costs (β: 1.08; 95% CI: 0.14 to 2.01) and higher out-of-pocket costs (β: 1.25; 95% CI: 0.17 to 2.32).

CONCLUSIONS

Patients with ADRD were more likely to have adverse dental care outcomes. Specifically, RD was associated with lower treatment dental care usage and AD was associated with higher total and out-of-pocket dental care costs. Effective patient-centred strategies should be used to improve dental care outcomes in patients with distinct subtypes of ADRD.

AHANet: Adaptive Hybrid Attention Network for Alzheimer's Disease Classification Using Brain Magnetic Resonance Imaging

Alzheimer's disease (AD) is a progressive neurological problem that causes brain atrophy and affects the memory and thinking skills of an individual. Accurate detection of AD has been a challenging research topic for a long time in the area of medical image processing. Detecting AD at its earliest stage is crucial for the successful treatment of the disease. The proposed Adaptive Hybrid Attention Network (AHANet) has two attention modules, namely Enhanced Non-Local Attention (ENLA) and Coordinate Attention. These modules extract global-level features and local-level features separately from the brain Magnetic Resonance Imaging (MRI), thereby boosting the feature extraction power of the network. The ENLA module extracts spatial and contextual information on a global scale while also capturing important long-range dependencies. The Coordinate Attention module captures local features from the input images. It embeds positional information into the channel attention mechanism for enhanced feature extraction. Moreover, an Adaptive Feature Aggregation (AFA) module is proposed to fuse features from the global and local levels in an effective way. As a result of incorporating the above architectural enhancements into the DenseNet architecture, the proposed network exhibited better performance compared to the existing works. The proposed network was trained and tested on the ADNI dataset, yielding a classification accuracy of 98.53%.

Patient-provider communication about cognition and the role of memory concerns: a descriptive study

BACKGROUND

Early identification of cognitive impairment is an important part of health promotion in aging. However, many older adults do not seek help for cognitive problems until their ability to function independently is substantially impacted. The purpose of this descriptive study was to explore older adults' experiences with patient-provider communication specific to cognition as well as compare barriers and facilitators between those with and without memory concerns.

METHODS

We conducted an online survey with individuals aged 65 + years (n = 409; mean age = 71.4(4.73); 54% female; 79% non-Hispanic White), purposively sampled to include those with and without memory concerns. Questionnaires included measures of subjective memory decline (SMD), memory concerns, past healthcare experiences, as well as open-ended questions regarding patient-provider communication about cognition. Content analysis was used to code open-ended responses. Logistic regression was used to examine differences in facilitators and barriers to communication among three groups: no SMD (n = 130), SMD without memory concerns (n = 143), and SMD with memory concerns (n = 136).

RESULTS

Only 16.6% of participants reported discussing cognition with a healthcare provider. Of the remaining 83.4%, approximately two-thirds would be open to such discussions in certain circumstances, most frequently if they had worsening memory problems. Over half of participants reported that their provider had never offered cognitive testing. Compared to the no SMD and SMD without memory concerns groups, participants reporting SMD with memory concerns were more likely to: (1) discuss cognition if their healthcare provider initiated the conversation, and (2) avoid discussions of cognitive problems due to fears of losing independence.

CONCLUSIONS

We found that most participants, including those reporting SMD with memory concerns, had never discussed cognition with their healthcare providers. Patient-reported barriers and facilitators to communication about cognition differed in several areas based on SMD status and the presence or absence of memory concerns. Consideration of these differences can guide future efforts to improve early identification of subtle cognitive changes that would benefit from further monitoring or intervention.

ATP2B3 Inhibition Alleviates Erastin-Induced Ferroptosis in HT-22 Cells through the P62-KEAP1-NRF2-HO-1 Pathway

Ferroptosis participates in the occurrence and development of neurological disorders. Modulating ferroptosis may have therapeutic potential in nervous system diseases. Therefore, TMTbased proteomic analysis in HT-22 cells was performed to identify erastin-induced differentially expressed proteins. The calcium-transporting ATP2B3 (ATP2B3) was screened as a target protein. ATP2B3 knockdown markedly alleviated the erastin-induced decrease in cell viability and elevated ROS (p < 0.01) and reversed the up-regulation of oxidative stress-related proteins polyubiquitin-binding protein p62 (P62), nuclear factor erythroid 2-related factor2 (NRF2), heme oxygenase-1 (HO-1), and NAD(P)H quinone oxidoreductase-1 (NQO1) protein expression (p < 0.05 or p < 0.01) and the down-regulation of Kelch-like ECH-associated protein 1(KEAP1) protein expression (p < 0.01). Moreover, NRF2 knockdown, P62 inhibition, or KEAP1 overexpression rescued the erastin-induced decrease in cell viability (p < 0.05) and increase in ROS production (p < 0.01) in HT-22 cells, while simultaneous overexpression of NRF2 and P62 and knockdown of KEAP1 partially offset the relief effect of ATP2B3 inhibition. In addition, knockdown of ATP2B3, NRF2, and P62 and overexpression of KEAP1 significantly down-regulated erastin-induced high expression of the HO-1 protein, while HO-1 overexpression reversed the alleviating effects of ATP2B3 inhibition on the erastin-induced decrease in cell viability (p < 0.01) and increase in ROS production (p < 0.01) in HT-22 cells. Taken together, ATP2B3 inhibition mediates the alleviation of erastin-induced ferroptosis in HT-22 cells through the P62-KEAP1-NRF2-HO-1 pathway.

Rural-urban disparities in preventive breast and cervical cancer screening among women with early-onset dementia

BACKGROUND

The early onset of Alzheimer's disease and related dementias (ADRD) before age 65 can introduce life and health care complications. Preserving an early-onset ADRD patient's daily functioning longer and delaying declines in health from non-ADRD conditions become important preventive goals. This study examined the differences in utilization of preventive cancer screenings between patients with and without early-onset ADRD, and compared utilization of the screenings in rural versus urban areas among women with early-onset ADRD in the United States.

METHODS

We conducted a cross-sectional study of women aged 40 to 64 years eligible for mammogram and cervical cancer screenings using commercial insurance claims from 2012 to 2018. We measured the use of biennial mammogram among women 50 to 64 years old, and the use of triennial Pap smear test among women 40 to 64 years old. We used inverse probability weighted logistic regressions to estimate the odds of receiving preventive cancer screenings by the presence of early-onset ADRD or cognitive impairments (CI). We used multivariable logistic regressions to estimate the odds of receiving preventive cancer screenings by rural or urban residence among women with early-onset ADRD/CI.

RESULTS

Among 6,349,308 women in the breast cancer screening sample (mean [SD] age, 56.52 [4.03] years), 36,131 had early-onset ADRD/CI (mean [SD] age, 57.99 [3.98] years). Among 6,583,088 women in the cervical cancer screening sample (mean [SD] age, 52.37 [6.81] years), 30,919 had early-onset ADRD/CI (mean [SD] age, 55.79 [6.22] years). Having early-onset ADRD/CI was associated with lower utilization of mammogram (OR: 0.92, 95% CI: 0.90-0.95). No significant difference was observed in Pap smear screening (OR: 0.99, 95% CI: 0.96-1.02) between patients with and without early-onset ADRD/CI. Among patients with early-onset ADRD/CI, those in rural areas were less likely than those in urban areas to have mammograms (OR: 0.91, 95% CI: 0.85-0.97) and Pap smears (OR: 0.65, 95% CI: 0.61-0.71).

CONCLUSIONS

The observed pattern of rural-urban differences in cancer screening in our study emphasizes the need for efforts to promote evidence-based, individualized decision-making processes in the early-onset ADRD population.

Quantitative Mass Spectrometry Analysis of Cerebrospinal Fluid Protein Biomarkers in Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of dementia, with cerebrospinal fluid (CSF) β-amyloid (Aβ), total Tau, and phosphorylated Tau (pTau) providing the most sensitive and specific biomarkers for diagnosis. However, these diagnostic biomarkers do not reflect the complex changes in AD brain beyond amyloid (A) and Tau (T) pathologies. Here, we report a selected reaction monitoring mass spectrometry (SRM-MS) method with isotopically labeled standards for relative protein quantification in CSF. Biomarker positive (AT+) and negative (AT-) CSF pools were used as quality controls (QCs) to assess assay precision. We detected 62 peptides (51 proteins) with an average coefficient of variation (CV) of ~13% across 30 QCs and 133 controls (cognitively normal, AT-), 127 asymptomatic (cognitively normal, AT+) and 130 symptomatic AD (cognitively impaired, AT+). Proteins that could distinguish AT+ from AT- individuals included SMOC1, GDA, 14-3-3 proteins, and those involved in glycolysis. Proteins that could distinguish cognitive impairment were mainly neuronal proteins (VGF, NPTX2, NPTXR, and SCG2). This demonstrates the utility of SRM-MS to quantify CSF protein biomarkers across stages of AD.

Performing arts as a non-pharmacological intervention for people with dementia and care-partners: a community case study

Participation in psychosocial enrichment activities, such as music and arts programming, have shown potential to delay or reduce functional decline - without adverse effects that can be associated with pharmaceuticals. The performing-arts programming described in this community case study was inspired by a community music program called B-Sharp Music Wellness, located in Phoenix, Arizona, which involved small groups of musicians who provided symphony performances for people with dementia. Our community programming sought to engage people with dementia and their informal care partner (typically a spouse) in existing performing-arts programs in their local community, providing social hours and season tickets for either symphony, dance (ballet), or non-musical theater performances. This case study describes the program history and design, including outcomes and lessons learned from the program evaluation of the last full season (2018-19) and partial season (2019-20), when the program was halted due to the COVID-19 pandemic. Program outcomes suggest strategies for, and benefits of, design for performing-arts programs as psychosocial interventions in other communities.

Pathophysiological Association of Alzheimer's Disease and Hypertension: A Clinical Concern for Elderly Population

Alzheimer's disease (AD), the most common cause of dementia and the fifth leading cause of death in the adult population has a complex pathophysiological link with hypertension (HTN). A growing volume of published literature on a parallel elevation of blood pressure (BP), amyloid plaques, and neurofibrillary tangles formation in post-middle of human brain cells has developed new, widely accepting foundations on this association. In particular, HTN in elderly life mediates cerebral blood flow dysfunction, neuronal dysfunction, and significant decline in cognitive impairment, primarily in the late-life populace, governing the onset of AD. Thus, HTN is an established risk factor for AD. Considering the impact of AD, 1.89 million deaths annually, and the failure of palliative therapies to cure AD, the scientific research community is looking to adopt integrated approaches to target early modified risk factors like HTN to reduce AD burden. The current review highlights the significance and impact of HTN-based prevention in lowering the AD burden in the elderly by providing a comprehensive overview of the physiological relationship between AD and HTN with an in-detail explanation of the role and applications of pathological biomarkers in this clinical association. The review will gain worth in presenting new insights and providing inclusive discussion on the correlation between HTN and cognitive impairment. It will increase across a wider scientific audience to expand understanding of this pathophysiological association.

Sleep Signal Analysis for Early Detection of Alzheimer's Disease and Related Dementia (ADRD)

OBJECTIVE

Alzheimer's Disease and Related Dementia (ADRD) is growing at alarming rates, putting research and development of diagnostic methods at the forefront of the biomedical research community. Sleep disorder has been proposed as an early sign of Mild Cognitive Impairment (MCI) in Alzheimer's disease. Although several clinical studies have been conducted to assess sleep and association with early MCI, reliable and efficient algorithms to detect MCI in home-based sleep studies are needed in order to address both healthcare costs and patient discomfort in hospital/lab-based sleep studies.

METHODS

In this paper, an innovative MCI detection method is proposed using an overnight recording of movements associated with sleep combined with advanced signal processing and artificial intelligence. A new diagnostic parameter is introduced which is extracted from the correlation between high frequency, sleep-related movements and respiratory changes during sleep. The newly defined parameter, Time-Lag (TL), is proposed as a distinguishing criterion that indicates movement stimulation of brainstem respiratory regulation that may modulate hypoxemia risk during sleep and serve as an effective parameter for early detection of MCI in ADRD. By implementing Neural Networks (NN) and Kernel algorithms with choosing TL as the principle component in MCI detection, high sensitivity (86.75% for NN and 65% for Kernel method), specificity (89.25% and 100%), and accuracy (88% and 82.5%) have been achieved.

Self-rated health as a predictor of cognition among middle-aged and older Latinos

Self-rated health (SRH) has been used to predict cognitive decline in various populations; however, this relationship has not been examined in Latinos. This study examines the relationship between SRH and cognition among middle-aged and older Latinos. A cross-sectional analysis was conducted among Latinos (n = 425, Mage = 64.13 ± 7.65, 82% female). Participants rated their health as poor/fair, good, and excellent and completed cognitive performance tests. Analyses of covariance (ANCOVA) examined differences in cognition between SRH categories. ANCOVAs showed significant differences in SRH categories for working memory (F [2, 357] = 3.63, p = .028) and global cognition (F [2, 348] = 3.074, p = .047), such that those who self-rated their health as good had better scores compared to participants in the poor/fair category. Findings show that SRH is associated with cognition among middle-aged and older Latinos. SRH may serve as an indicator of early signs of cognitive decline.

The Environmental Audit Screening Evaluation: Establishing Reliability and Validity of an Evidence-Based Design Tool

Background and Objectives

Current assessment tools for long-term care environments have limited generalizability or ability to be linked to specific quality outcomes. To discriminate between different care models, tools are needed to assess important elements of the environmental design. The goal of this project was to systematically evaluate the reliability and validity of the Environmental Audit Screening Evaluation (EASE) tool to better enable the identification of best models in long-term care design to maintain quality of life for persons with dementia and their caregivers.

Research Design and Methods

Twenty-eight living areas (LAs) were selected from 13 sites similar in organizational/operational commitment to person-centered care but with very different LA designs. LAs were stratified into 3 categories (traditional, hybrid, and household) based primarily on architectural/interior features. Three evaluators rated each LA using the Therapeutic Environment Screening Scale (TESS-NH), Professional Environmental Assessment Protocol (PEAP), Environmental Audit Tool (EAT-HC), and EASE. One of each type of LA was reassessed approximately 1 month after the original assessment.

Results

EASE scores were compared against the scores of 3 existing tools to evaluate its construct validity. The EAT-HC was most closely related to the EASE (r = 0.88). The PEAP and the TESS-NH were less correlated to the EASE (r = 0.82 and 0.71, respectively). Analysis of variance indicated that the EASE distinguished between traditional and home-like settings (0.016), but not hybrid LAs. Interrater and inter-occasion reliability and agreement of the EASE were consistently high.

Discussion and Implications

Neither of the 2 U.S.-based existing environmental assessment tools (PEAP and TESS-NH) discriminated between the 3 models of environments. The EAT-HC was most closely aligned with the EASE and performed similarly in differentiating between the traditional and household models, but the dichotomous scoring of the EAT-HC fails to capture environmental nuances. The EASE tool is comprehensive and accounts for nuanced design differences across settings.

Large multi-ethnic genetic analyses of amyloid imaging identify new genes for Alzheimer disease

Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer's disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; β = 0.35, SE = 0.01, P = 6.2 × 10-311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; β = 0.07, SE = 0.01, P = 9.2 × 10-09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; β = 0.1, SE = 0.02, P = 2.4 × 10-10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; β = 0.16, SE = 0.03, P = 1.1 × 10-09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, β = 0.79, SE = 0.14, P = 1.4 × 10-08, MAF = 0.006, sex-interaction P = 9.8 × 10-07) and chr11p.15.2 (rs192346166, β = 0.94, SE = 0.17, P = 3.7 × 10-08, MAF = 0.004, sex-interaction P = 1.3 × 10-03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.

Healthcare Costs in the Year Before and After Alzheimer's Disease Diagnosis: A Danish Register-Based Matched Cohort Study

BACKGROUND

Alzheimer's disease (AD) carries a significant economic burden, with costs peaking around the time of diagnosis. However, the cost of diagnosis, including the time leading up to it, has not been studied thoroughly. Furthermore, regionalized healthcare structure could result in differences in the pre-diagnostic costs for people with suspected AD.

OBJECTIVE

This study set out to estimate the excess healthcare costs before and after AD diagnosis compared to a matched non-AD population and to investigate regional variation in AD healthcare costs in Denmark.

METHODS

We used a register-based cohort of 25,523 matched pairs of new cases of AD and non-AD controls. The healthcare costs included costs on medication, and inpatient-, outpatient-, and primary care visits. Generalized estimating equations were employed to estimate the excess healthcare cost attributable to diagnosing AD, and the variation in costs across regions.

RESULTS

Mean excess costs attributable to AD were € 3,284 and € 6,173 in the year before and after diagnosis, respectively. Regional differences in healthcare costs were identified in both the AD and control groups and were more pronounced in patients with AD (PwAD). The variation over time in regional inequality between PwAD and their controls was identified.

CONCLUSION

PwAD incur higher healthcare costs across all cost categories in the year before and after diagnosis. Regional differences in healthcare utilization by PwAD may reveal potential variation in access to healthcare. These findings suggest that a more standardized and targeted diagnostic process may help reduce costs and variation in access to healthcare.

Geroprotective interventions in the 3xTg mouse model of Alzheimer's disease

Alzheimer's disease (AD) is an age-associated neurodegenerative disease. As the population ages, the increasing prevalence of AD threatens massive healthcare costs in the coming decades. Unfortunately, traditional drug development efforts for AD have proven largely unsuccessful. A geroscience approach to AD suggests that since aging is the main driver of AD, targeting aging itself may be an effective way to prevent or treat AD. Here, we discuss the effectiveness of geroprotective interventions on AD pathology and cognition in the widely utilized triple-transgenic mouse model of AD (3xTg-AD) which develops both β-amyloid and tau pathologies characteristic of human AD, as well as cognitive deficits. We discuss the beneficial impacts of calorie restriction (CR), the gold standard for geroprotective interventions, and the effects of other dietary interventions including protein restriction. We also discuss the promising preclinical results of geroprotective pharmaceuticals, including rapamycin and medications for type 2 diabetes. Though these interventions and treatments have beneficial effects in the 3xTg-AD model, there is no guarantee that they will be as effective in humans, and we discuss the need to examine these interventions in additional animal models as well as the urgent need to test if some of these approaches can be translated from the lab to the bedside for the treatment of humans with AD.

The Mediating Role of Systemic Inflammation and Moderating Role of Race/Ethnicity in Racialized Disparities in Incident Dementia: A Decomposition Analysis

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 race/ethnicity on racialized disparities in incident dementia.

Methods

In the US Health and Retirement Study (n=5,143), 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 was 15.5%. Among minoritized participants (i.e., non-Hispanic Black and/or Hispanic), high CRP levels (> 75th percentile or 4.57mcg/mL) was associated with 1.27 (95%CI: 1.01,1.59) times greater risk of incident dementia than low CRP (<4.57mcg/mL). Decomposition analysis comparing minoritized versus non-Hispanic White participants showed that the mediating effect of CRP accounted for 2% (95% CI: 0%, 6%) of the racial disparity, while the interaction effect between minoritized group status and high CRP accounted for 12% (95% CI: 2%, 22%) of the disparity. Findings were robust to potential violations of causal mediation assumptions.

Conclusions

Systemic inflammation mediates racialized disparities in incident dementia.