scholarly journals Factors Affecting Recall of Different Types of Personal Genetic Information about Alzheimer's Disease Risk: The REVEAL Study

2015 ◽  
Vol 18 (2) ◽  
pp. 78-86 ◽  
Author(s):  
Andria G. Besser ◽  
Saskia C. Sanderson ◽  
J. Scott Roberts ◽  
Clara A. Chen ◽  
Kurt D. Christensen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Information ◽  
Disease Risk ◽  
Factors Affecting ◽  
Reveal Study ◽  
Different Types

scholarly journals The Genetic Research in Alzheimer Disease (GERALD) Initiative Finds rs9320913 as a Neural eQTL of lincRNA AL589740.1

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lidia Lopez-Gutierrez ◽  
José María García-Alberca ◽  
Silvia Mendoza ◽  
Esther Gris ◽  
María Paz De la Guía ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Alzheimer Disease ◽  
Mental State ◽  
Fluid Intelligence ◽  
Educational Achievement ◽  
Disease Risk ◽  
Genetic Research ◽  
Gene Desert ◽  
Factors Affecting

Alzheimer’s disease is the most common cause of dementia worldwide, and longitudinal studies are crucial to find the factors affecting disease development. Here, we describe a novel initiative from southern Spain designed to contribute in the identification of the genetic component of the cognitive decline of Alzheimer’s disease patients. The germline variant rs9320913 is a C>A substitution mapping within a gene desert. Although it has been previously associated to a higher educational achievement and increased fluid intelligence, its role on Alzheimer’s disease risk and progression remains elusive. A total of 407 subjects were included in the study, comprising 153 Alzheimer disease patients and 254 healthy controls. We have explored the rs9320913 contribution to both Alzheimer disease risk and progression according to the Mini-Mental State Exams. We found that rs9320913 maps within a central nervous system lincRNA AL589740.1. eQTL results show that rs9320913 correlated with the brain-frontal cortex ( beta = − 0.15 , p value = 0.057) and brain-spinal cord (beta of -0.23, p value = 0.037). We did not find rs9320913 to be associated to AD risk, although AA patients seemed to exhibit a less pronounced Mini-Mental State Exam score decline.

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.

scholarly journals Lessons Learned From Nonsignificant Findings in Exercise With Individuals With Dementia

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 850-850
Author(s):  
Nicole Dawson ◽  
Heather Menne
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lessons Learned ◽  
Exercise Science ◽  
Key Factors ◽  
Pharmacological Interventions ◽  
Factors Affecting ◽  
Exercise Interventions ◽  
Functional Reach ◽  
Critical Intervention

Abstract The National Institute on Aging recognizes the importance of identifying promising non-pharmacological interventions (NPI) to promote health in individuals with Alzheimer’s disease and related dementias. Several systematic reviews have been completed investigating exercise in this population resulting in mixed evidence regarding efficacy across functional domains. It is critical to investigate the methodological factors from the original interventions for a true understanding of these findings as to not outright dismiss exercise as beneficial. One example is Ohio’s replication of Reducing Disability in Alzheimer’s Disease (n=508), which resulted in no significant improvements in physical performance for individuals with dementia ((gait speed (p=.81), balance (p=.82), functional reach (p=.58)). In this investigation, along with many others, researchers were not guided by key principles of exercise science leading to critical intervention design and methodological flaws. Thus, exercise interventions for individuals with dementia need to include interpretations of non-findings and report key factors affecting the outcomes.

scholarly journals The BIN1 rs744373 Alzheimer's disease risk SNP is associated with faster Aβ‐associated tau accumulation and cognitive decline

2021 ◽  
Author(s):  
Nicolai Franzmeier ◽  
Rik Ossenkoppele ◽  
Matthias Brendel ◽  
Anna Rubinski ◽  
Ruben Smith ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Disease Risk

scholarly journals Caspase-1 genetic variation is not associated with Alzheimer's disease risk

2010 ◽  
Vol 11 (1) ◽  
Author(s):  
José Luis Vázquez-Higuera ◽  
Eloy Rodríguez-Rodríguez ◽  
Pascual Sánchez-Juan ◽  
Ignacio Mateo ◽  
Ana Pozueta ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genetic Variation ◽  
Disease Risk ◽  
Caspase 1
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