Racial/Ethnic Disparities in Alzheimer’s Disease Risk: Role of Exposure to Ambient Fine Particles

2021 ◽  
Author(s):  
Diana Younan ◽  
Xinhui Wang ◽  
Tara Gruenewald ◽  
Margaret Gatz ◽  
Marc L Serre ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Black Women ◽  
Racial Disparities ◽  
Fine Particles ◽  
Disease Risk ◽  
Ethnic Disparities ◽  
Socioeconomic Characteristics ◽  
Incident Cases ◽  
Racial Ethnic

Abstract Background Whether racial/ethnic disparities in Alzheimer’s disease (AD) risk may be explained by ambient fine particles (PM2.5) has not been studied. Methods We conducted a prospective, population-based study on a cohort of Black (n=481) and White (n=6004) older women (aged 65-79) without dementia at enrollment (1995-98). Cox models accounting for competing risk were used to estimate the hazard ratio (HR) for racial/ethnic disparities in AD (1996-2010) defined by DSM-IV and the association with time-varying annual average PM2.5 (1999-2010) estimated by spatiotemporal model. Results Over an average follow-up of 8.3 (±3.5) years with 158 incident cases (21 in Black women), the racial disparities in AD risk (range of adjusted HRBlack women = 1.85-2.41) observed in various models could not be explained by geographic region, age, socioeconomic characteristics, lifestyle factors, cardiovascular risk factors, and hormone therapy assignment. Estimated PM2.5 exposure was higher in Black (14.38±2.21 µg/m 3) than in White (12.55±2.76 µg/m 3) women, and further adjustment for the association between PM2.5 and AD (adjusted HRPM2.5 = 1.18-1.28) slightly reduced the racial disparities by 2-6% (HRBlack women = 1.81-2.26). The observed association between PM2.5 and AD risk was ~2 times greater in Black (HRPM2.5 = 2.10-2.60) than in White (HRPM2.5 = 1.07-1.15) women (range of interaction Ps: <.01 to .01). We found similar results after further adjusting for social engagement (social strain; social support; social activity; living alone), stressful life events, WHI clinic sites, and neighborhood socioeconomic characteristics. Conclusions PM2.5 may contribute to racial/ethnic disparities in AD risk and its associated increase in AD risk was stronger amongst Black women.

scholarly journals Current and Future Directions in Minority Aging: Embracing Interdisciplinary Models

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 98-98
Author(s):  
Heather Farmer ◽  
Amy Thierry ◽  
Keith Whitfield
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Ethnic Disparities ◽  
Study Data ◽  
Biological Aging ◽  
Neighborhood Conditions ◽  
Minority Aging ◽  
Psychosocial Resilience ◽  
Racial Ethnic

Abstract Racial/ethnic disparities in health among older adults are well-documented. More research is needed to clarify the complex and multifactorial mechanisms underlying these associations. This symposium will feature research that employs innovative theoretical and methodological approaches to understand the biopsychosocial mechanisms that underlie racial/ethnic disparities in older adults’ health and determine sources of within-group heterogeneity in minority aging. Dr. Forrester will integrate stress biology and intersectionality to demonstrate the importance of stress and resilience (e.g., John Henryism) with biological aging within Black adults participating in the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Dr. Brown Hughes will present innovative research using data from the African American United Memory and Aging Project (AA-UMAP) on the importance of Alzheimer’s disease-specific knowledge and perceptions among Black older adults. Dr. Gamaldo will employ a within-race approach to understand how knowledge and perceptions of Alzheimer’s disease and related dementias (ADRD) shape cognitive performance among Black older adults in the AA-UMAP study. Dr. Mitchell will use Health and Retirement Study data to explore the role of midlife stress exposure in accounting for racial disparities in trajectories of cognitive functioning. Drs. Thierry and Farmer will use HRS data to examine how psychosocial resilience (e.g., mastery) affects the relationship between perceived neighborhood conditions (e.g., disorder) and cognition among Black older adults. This work highlights the importance of applying an interdisciplinary lens to move the study of minority aging forward and ultimately, to reduce the unnecessary burden of morbidity and mortality among minoritized groups.

scholarly journals MAPT H1 Haplotype is Associated with Late-Onset Alzheimer’s Disease Risk in APOE ɛ4 Noncarriers: Results from the Dementia Genetics Spanish Consortium

2015 ◽  
Vol 49 (2) ◽  
pp. 343-352 ◽  
Author(s):  
Pau Pastor ◽  
Fermín Moreno ◽  
Jordi Clarimón ◽  
Agustín Ruiz ◽  
Onofre Combarros ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Risk ◽  
Late Onset

Racial Disparities in Pregnancy-associated Intimate Partner Homicide

2021 ◽  
pp. 088626052199083
Author(s):  
Aaron J. Kivisto ◽  
Samantha Mills ◽  
Lisa S. Elwood
Keyword(s):  
Black Women ◽  
Ethnic Minority ◽  
Racial Disparities ◽  
White Women ◽  
The United States ◽  
Intimate Partner ◽  
Intimate Partner Homicide ◽  
Homicide Victimization ◽  
Increased Risk ◽  
Racial Ethnic

Pregnancy-associated femicide accounts for a mortality burden at least as high as any of the leading specific obstetric causes of maternal mortality, and intimate partners are the most common perpetrators of these homicides. This study examined pregnancy-associated and non-pregnancy-associated intimate partner homicide (IPH) victimization among racial/ethnic minority women relative to their non-minority counterparts using several sources of state-level data from 2003 through 2017. Data regarding partner homicide victimization came from the National Violent Death Reporting System, natality data were obtained from the Centers for Disease Control and Prevention’s National Center for Health Statistics, and relevant sociodemographic information was obtained from the U.S. Census Bureau. Findings indicated that pregnancy and racial/ethnic minority status were each associated with increased risk for partner homicide victimization. Although rates of non-pregnancy-associated IPH victimization were similar between Black and White women, significant differences emerged when limited to pregnancy-associated IPH such that Black women evidenced pregnancy-associated IPH rates more than threefold higher than that observed among White and Hispanic women. Relatedly, the largest intraracial discrepancies between pregnant and non-pregnant women emerged among Black women, who experienced pregnancy-associated IPH victimization at a rate 8.1 times greater than their non-pregnant peers. These findings indicate that the racial disparities in IPH victimization in the United States observed in prior research might be driven primarily by the pronounced differences among the pregnant subset of these populations.

scholarly journals A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaker El-Sappagh ◽  
Jose M. Alonso ◽  
S. M. Riazul Islam ◽  
Ahmad M. Sultan ◽  
Kyung Sup Kwak
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cross Validation ◽  
Disease Risk ◽  
Binary Classification ◽  
Fuzzy Rule ◽  
Large Set ◽  
Detection Model ◽  
Multi Class Classification ◽  
Clinical Measures

AbstractAlzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

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