American journal of Alzheimer's disease and other dementias最新文献

Most dementia caregiver programs focus on a single caregiver, overlooking diverse caregiving networks that include family, friends, and relatives who provide care. We conducted a scoping review of family-centered dementia caregiving programs (ie, interventions with at least 2 caregivers) to (1) describe program components; (2) identify how family members are included; (3) summarize family-level measurements used and the effectiveness of family-centered programs on these outcomes; and (4) explore if and how programs are culturally adapted. We identified 36 articles and 10 programs (individual-family programs, n = 8; multi-family group programs, n = 2). Programs included similar content and effectiveness was reported by the primary caregiver and measured at the individual level. To better support diverse caregiving networks, caregiving programs may benefit from identifying core components of caregiving programs, including best practices in engaging family caregivers, acknowledging varied family structures and the fluidity in caregiving, and measuring strengths and deficits at the family-level.

IntroductionThough Black Americans are twice as likely as White Americans to be diagnosed with Alzheimer's Disease, they may be excluded from the mental representation of a person with dementia.MethodsParticipants (N = 143, median age = 19) created visualized mental representations of a person, a man, a woman, a Black man, or a Black woman diagnosed with dementia by repeatedly selecting which among 12 faces looked most like each target category.ResultsThe visualized representation of a person with dementia was more similar to the representation of a man and a Black woman than to a Black man and a woman, respectively.DiscussionThese findings highlight how intersectionality shapes mental representations of dementia, revealing that certain combinations of marginalized identities are perceived differently rather than excluded entirely.

Dementia is attributable to 12 known risk factors in 40% cases. This study aimed to assess the prevalence of defined risk factors among people living with dementia. 174 patients with dementia and caregivers were interviewed using semi-structured pro forma, risk factors provided by the Lancet Commission on Dementia (2020), and Dementia Severity Rating Scale (DSRS). The prevalence of 11 known risk factors and associations between the risk factors and dementia severity were assessed. The mean age of the participants was 73.9 years (SD = 8.34 years). The education below intermediate level was 83.3%, 17.8% had hearing loss, 37.9% had hypertension, 24.1% had diabetes, 25.9% and 55.2% had alcohol and nicotine harmful use respectively and 8% had a history of traumatic brain injury and obesity each. There is a substantial prevalence of risk factors among people living with dementia in Nepal but no associations between any of the risk factors and dementia severity.

Objective: To improve the identification of cognitive impairment by distinguishing normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). Methods: A recursive partitioning tree model was developed using ARMADA data and the NIH Toolbox, a multidimensional health assessment tool. It incorporated demographic and clinical assessment variables to predict NC, MCI, and AD. Model performance was evaluated using AUC, precision, recall, and F1 score. Robustness was tested through 5-fold cross-validation, sensitivity, scenario, and subgroup analyses. Results: The model achieved macro-AUC and micro-AUC scores of 0.92 and 0.91 (training) and 0.89 and 0.86 (testing). Key predictors included the Picture Sequence Memory Test and List Sorting Working Memory Test. Cross-validation yielded 70.22% accuracy and a Kappa of 0.52. Conclusion: Machine learning effectively uses a small set of assessments to distinguish NC, MCI, and AD, offering a valuable tool to support clinical decision-making. Future research should validate this model across diverse populations.

White matter hyperintensity (WMH) is associated with cognitive impairment. In this study, 79 patients with WMH from hospital 1 were randomly divided into a training set (62 patients) and an internal validation set (17 patients). In addition, 29 WMH patients from hospital 2 were used as an external validation set. Cognitive status was determined based on neuropsychological assessment results. A deep learning convolutional neural network of VB-Nets was used to automatically identify and segment whole-brain subregions and WMH. The PyRadiomics package in Python was used to automatically extract radiomic features from the WMH and bilateral hippocampi. Delong tests revealed that the random forest model based on combined features had the best performance for the detection of cognitive impairment in WMH patients, with an AUC of 0.900 in the external validation set. Our results provide clinical doctors with a reliable tool for the early diagnosis of cognitive impairment in WMH patients.

Narrow definitions of family can constrain survey items about obligations regarding who should care for older adults. Current measurement often does not account for the increased prevalence of diverse family forms and support received among older adults. We draw on six focus groups (N = 33) of family or friends caring for older adults with trouble remembering, memory issues, or dementia to explore definitions of family, caregiving, and responsibility. Although many caregivers immediately default to traditional definitions, the breadth of "family" seen as responsible and actual care networks are often broader. In many cases, caregivers considered friends and other non-kin as family because they provided care. Furthermore, both care and obligations are shaped by family dynamics and the older adult's memory loss. Results suggest that survey measures should capture a wider range of relationship ties and dynamics to better understand family, caregiving obligations, and the tensions between them.

Foreign-born people living with cognitive impairment (PLwCI) and their family caregivers may have distinct caregiving networks compared to their U.S.-born counterparts. Data is drawn from the 2022 wave of the National Health and Aging Trends Study. Descriptive statistics and mean difference tests were run by native and foreign-born PLwCI. Linear and logistic regressions were run controlling for foreign-born PLwCI demographics with categorized time in the U.S. as the predictor of network characteristics. Foreign-born PLwCI networks were more likely to have a daughter caregiver and engage in household task-sharing, but less likely to have a spouse and non-family caregiver. In adjusted regressions, residing in the U.S. for less than 30 years was associated with greater odds of having a daughter and generalist in network, and greater task-sharing in household and self-care/mobility domains, relative to those residing over 50 years. Results underscore the importance of acculturation in shaping care networks among PLwCI.

A growing body of literature has examined the impact of neighborhood characteristics on Alzheimer's disease (AD) dementia, yet the spatial variability and relative importance of the most influential factors remain underexplored. We compiled various widely recognized factors to examine spatial heterogeneity and associations with AD dementia prevalence via geographically weighted random forest (GWRF) approach. The GWRF outperformed conventional models with an out-of-bag R² of 74.8% in predicting AD dementia prevalence and the lowest error (MAE = 0.34, RMSE = 0.45). Key findings showed that mobile homes were the most influential factor in 19.9% of U.S. counties, followed by NDVI (17.4%), physical inactivity (12.9%), households with no vehicle (11.3%), and particulate matter (10.4%), while other primary factors affecting <10% of U.S. counties. Findings highlight the need for county-specific interventions tailored to local risk factors. Policies should prioritize increasing affordable housing stability, expanding green spaces, improving transportation access, promoting physical activity, and reducing air pollution exposure.

Alzheimer's disease (AD) presents a pressing global health challenge, with amyloid-β (Aβ) accumulation in the brain being a hallmark feature. While monoclonal antibodies targeting Aβ have shown cognitive benefits, safety concerns remain. Here, we introduce the Amytrapper catheter, a novel extracorporeal device developed by Recombinant Technologies to trap circulating Aβ using a retro-inverso peptide conjugated to polyethylene glycol. Through in vivo experiments using a rat model of AD, we demonstrate significant reductions in blood Aβ levels and behavioral improvements following Amytrapper catheter treatment. This innovative approach holds promise as a disease-modifying therapy for AD, offering a complementary strategy to existing treatments and advocating for further clinical development.

Using LGBTQ+ Social Networks, Aging, and Policy Study Wave 3 data (N = 982), this study examines how caregiving for individuals with mild cognitive impairment, Alzheimer's disease, and related dementias (MCI/ADRD) impacts everyday stressors, discrimination, and health outcomes among LGBTQ+ older adults in the Southern United States. Using chi-square tests, Fisher's exact tests, and logistic regression models, we assessed associations of caregiver role with stress and health outcomes. We then conducted moderation analyses to test interactions between caregiver role and LGBTQ+ minority stress. LGBTQ+ caregivers of individuals with MCI/ADRD were 3 times more likely to experience proximal minority stress (P < 0.05), twice as likely to report suicidal ideation (P < 0.1), and more likely to report cognitive problems (P < 0.05) and high blood pressure (P < 0.05) than non-caregivers. Interaction effects suggest that minority stress may exacerbate mental health problems among MCI/ADRD caregivers. Findings underscore the need for targeted supports and interventions for LGBTQ+ older adult dementia caregivers.