S3-01-03: APOE AND SEX DIFFERENCES ON ALZHEIMER'S DISEASE RISK

2006 ◽  
Vol 14 (7S_Part_18) ◽  
pp. P995-P995
Author(s):  
Arthur W. Toga
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Disease Risk

scholarly journals Sex differences in Alzheimer's disease risk: are we looking at the wrong hormones?

2014 ◽  
Vol 26 (10) ◽  
pp. 1579-1584 ◽  
Author(s):  
Cynthia A. Munro
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Disease Risk

Two-thirds of individuals with Alzheimer's disease (AD) are women, owing largely to the fact that women outlive men (https://www.alz.org/downloads/facts_figures_2012.pdf). Women's increased longevity, however, is not sufficient to explain the fact that women are 1.5 times more likely than men to develop the disease (Gao et al., 1998). After age 80, the incidence of AD is much higher in women than in men, such that the proportion of women with AD is almost twice the proportion of men with the disease (e.g., Zandi et al., 2002; Plassman et al., 2007). Moreover, once diagnosed with AD, women decline more rapidly, both cognitively and functionally, compared to men (Ito et al., 2011; Tschanz et al., 2011).

scholarly journals Sex differences in the genetic predictors of Alzheimer’s pathology

Brain ◽  
2019 ◽  
Vol 142 (9) ◽  
pp. 2581-2589 ◽  
Author(s):  
Logan Dumitrescu ◽  
Lisa L Barnes ◽  
Madhav Thambisetty ◽  
Gary Beecham ◽  
Brian Kunkle ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Disease Risk ◽  
Association Studies ◽  
Age At Onset ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Data Repositories ◽  
Genome Wide

Abstract Autopsy measures of Alzheimer’s disease neuropathology have been leveraged as endophenotypes in previous genome-wide association studies (GWAS). However, despite evidence of sex differences in Alzheimer’s disease risk, sex-stratified models have not been incorporated into previous GWAS analyses. We looked for sex-specific genetic associations with Alzheimer’s disease endophenotypes from six brain bank data repositories. The pooled dataset included 2701 males and 3275 females, the majority of whom were diagnosed with Alzheimer’s disease at autopsy (70%). Sex-stratified GWAS were performed within each dataset and then meta-analysed. Loci that reached genome-wide significance (P < 5 × 10−8) in stratified models were further assessed for sex interactions. Additional analyses were performed in independent datasets leveraging cognitive, neuroimaging and CSF endophenotypes, along with age-at-onset data. Outside of the APOE region, one locus on chromosome 7 (rs34331204) showed a sex-specific association with neurofibrillary tangles among males (P = 2.5 × 10−8) but not females (P = 0.85, sex-interaction P = 2.9 × 10−4). In follow-up analyses, rs34331204 was also associated with hippocampal volume, executive function, and age-at-onset only among males. These results implicate a novel locus that confers male-specific protection from tau pathology and highlight the value of assessing genetic associations in a sex-specific manner.

Sex differences in response to a high fat diet in an Alzheimer’s Disease mouse model

2018 ◽  
Author(s):  
Alejandra Freire Fernández-Regatillo ◽  
María L. de Ceballos ◽  
Jesús Argente ◽  
Sonia Díaz Pacheco ◽  
Clara González Martínez
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Mouse Model ◽  
High Fat Diet ◽  
High Fat

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

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.

Subjective Cognitive Decline and its Relation to Verbal Memory and Sex in Cognitively Unimpaired Individuals from a Colombian Cohort with Autosomal-Dominant Alzheimer’s Disease

2021 ◽  
pp. 1-9
Author(s):  
Jairo E. Martinez ◽  
Enmanuelle Pardilla-Delgado ◽  
Edmarie Guzmán-Vélez ◽  
Clara Vila-Castelar ◽  
Rebecca Amariglio ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Cognitive Decline ◽  
Verbal Memory ◽  
Autosomal Dominant ◽  
Memory Performance ◽  
Subjective Cognitive Decline ◽  
Mutation Carriers ◽  
Study Partner

Abstract Objective: Subjective Cognitive Decline (SCD) may be an early indicator of risk for Alzheimer’s disease (AD). Findings regarding sex differences in SCD are inconsistent. Studying sex differences in SCD within cognitively unimpaired individuals with autosomal-dominant AD (ADAD), who will develop dementia, may inform sex-related SCD variations in preclinical AD. We examined sex differences in SCD within cognitively unimpaired mutation carriers from the world’s largest ADAD kindred and sex differences in the relationship between SCD and memory performance. Methods: We included 310 cognitively unimpaired Presenilin-1 (PSEN-1) E280A mutation carriers (51% females) and 1998 noncarrier family members (56% females) in the study. Subjects and their study partners completed SCD questionnaires and the CERAD word list delayed recall test. ANCOVAs were conducted to examine group differences in SCD, sex, and memory performance. In carriers, partial correlations were used to examine associations between SCD and memory performance covarying for education. Results: Females in both groups had greater self-reported and study partner-reported SCD than males (all p < 0.001). In female mutation carriers, greater self-reported (p = 0.02) and study partner-reported SCD (p < 0.001) were associated with worse verbal memory. In male mutation carriers, greater self-reported (p = 0.03), but not study partner-reported SCD (p = 0.11) was associated with worse verbal memory. Conclusions: Study partner-reported SCD may be a stronger indicator of memory decline in females versus males in individuals at risk for developing dementia. Future studies with independent samples and preclinical trials should consider sex differences when recruiting based on SCD criteria.

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