Genetics of Alzheimer’s Disease

Vilatela, María Elisa Alonso; López-López, Marisol; Yescas-Gómez, Petra

doi:10.1016/j.arcmed.2012.10.017

Cited by 109 publications

(53 citation statements)

References 104 publications

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“…The ɛ-4 allele of the gene encoding Apolipoprotein E is one of the major genetic risk factors for Alzheimer's disease (Alonso Vilatela et al, 2012). In order to investigate whether it was possible to further improve the best model (trained using combined cortical thickness and volumetric measurements) information on subjects' ApoE genotype (together with demographics) was added as an additional feature.…”

Section: Methodsmentioning

confidence: 99%

Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness

Lebedev

Westman

Westen

et al. 2014

NeuroImage: Clinical

246

166

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Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness.From The ADNI database, 185 AD, and 225 healthy controls (HC) were randomly split into training and testing datasets. 165 subjects with mild cognitive impairment (MCI) were distributed according to the month of conversion to dementia (4-year follow-up). Structural 1.5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction. Using the resulting output, AD/HC classifiers were trained. Training included model tuning and performance assessment using out-of-bag estimation. Subsequently the classifiers were validated on the AD/HC test set and for the ability to predict MCI-to-AD conversion. Models' between-cohort robustness was additionally assessed using the AddNeuroMed dataset acquired with harmonized clinical and imaging protocols.In the ADNI set, the best AD/HC sensitivity/specificity (88.6%/92.0% — test set) was achieved by combining cortical thickness and volumetric measures. The Random Forest model resulted in significantly higher accuracy compared to the reference classifier (linear Support Vector Machine). The models trained using parcelled and high-dimensional (HD) input demonstrated equivalent performance, but the former was more effective in terms of computation/memory and time costs. The sensitivity/specificity for detecting MCI-to-AD conversion (but not AD/HC classification performance) was further improved from 79.5%/75%–83.3%/81.3% by a combination of morphometric measurements with ApoE-genotype and demographics (age, sex, education). When applied to the independent AddNeuroMed cohort, the best ADNI models produced equivalent performance without substantial accuracy drop, suggesting good robustness sufficient for future clinical implementation.

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Section: Methodsmentioning

confidence: 99%

Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness

Lebedev

Westman

Westen

et al. 2014

NeuroImage: Clinical

246

166

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“…Thus, mutations can contribute to the propensity of proteins to misfold and form amyloid structures. For example, some of the mutations involved in early onset Alzheimer’s disease are in the Aβ peptide, increasing its aggregation propensity [59]. Many studies have looked at how mutations increase aggregation and several mechanisms appear to be responsible.…”

Section: Protein Unfolding and Misfoldingmentioning

confidence: 99%

Misfolding of Amyloidogenic Proteins and Their Interactions with Membranes

2013

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In this paper, we discuss amyloidogenic proteins, their misfolding, resulting structures, and interactions with membranes, which lead to membrane damage and subsequent cell death. Many of these proteins are implicated in serious illnesses such as Alzheimer’s disease and Parkinson’s disease. Misfolding of amyloidogenic proteins leads to the formation of polymorphic oligomers and fibrils. Oligomeric aggregates are widely thought to be the toxic species, however, fibrils also play a role in membrane damage. We focus on the structure of these aggregates and their interactions with model membranes. Study of interactions of amlyoidogenic proteins with model and natural membranes has shown the importance of the lipid bilayer in protein misfolding and aggregation and has led to the development of several models for membrane permeabilization by the resulting amyloid aggregates. We discuss several of these models: formation of structured pores by misfolded amyloidogenic proteins, extraction of lipids, interactions with receptors in biological membranes, and membrane destabilization by amyloid aggregates perhaps analogous to that caused by antimicrobial peptides.

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“…Early onset AD in PSEN1 mutation carriers can occur as early as 30 years of age, although the mean age of onset is over 58 years. More than 180 mutations have been described in PSEN1, of which the majority are missense mutations [18] . PSEN2 mutations are less common and 14 specific mutations have been associated with AD [19] .…”

Section: Presenilin-1 and -2mentioning

confidence: 99%

“…Mutations in PSEN1 are the major cause of early onset AD and also account for the most severe forms of the disease [18] . Early onset AD in PSEN1 mutation carriers can occur as early as 30 years of age, although the mean age of onset is over 58 years.…”

Section: Presenilin-1 and -2mentioning

confidence: 99%

Familial Alzheimer’s disease modelling using induced pluripotent stem cell technology

Mohamet

Miazga²,

Ward³

2014

WJSC

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Alzheimer's disease (AD) is a progressive neurodegenerative disease in which patients exhibit gradual loss of memory that impairs their ability to learn or carry out daily tasks. Diagnosis of AD is difficult, particularly in early stages of the disease, and largely consists of cognitive assessments, with only one in four patients being correctly diagnosed. Development of novel therapeutics for the treatment of AD has proved to be a lengthy, costly and relatively unproductive process with attrition rates of > 90%. As a result, there are no cures for AD and few treatment options available for patients. Therefore, there is a pressing need for drug discovery platforms that can accurately and reproducibly mimic the AD phenotype and be amenable to high content screening applications. Here, we discuss the use of induced pluripotent stem cells (iPSCs), which can be derived from adult cells, as a method of recapitulation of AD phenotype in vitro . We assess their potential use in high content screening assays and the barriers that exist to realising their full potential in predictive efficacy, toxicology and disease modelling. At present, a number of limitations need to be addressed before the use of iPSC technology can be fully realised in AD therapeutic applications. However, whilst the use of AD-derived iPSCs in drug discovery remains a fledgling field, it is one with immense potential that is likely to reach fruition within the next few years.© 2014 Baishideng Publishing Group Co., Limited. All rights reserved.Key words: Human induced pluripotent stem cells; Alzheimer's disease; Neurodegenerative diseases; Highthroughput screening assays; Cholinergic neurons; Drug discovery; Stratified medicine Core tip: Alzheimer's disease (AD) affects 36 million people worldwide and is set to double by 2030. Progress in understanding AD has been hindered by a lack of suitable in vitro and in vivo models reflected in > 90% drug attrition rates. Induced pluripotent stem cells are an alternative source of neural cells that can be derived from patients' somatic cells and exhibit AD pathophysiological phenotypes. These cells are amenable to HTS formats required for drug discovery applications. Harnessing this combined potential would provide an unprecedented opportunity to significantly reduce timeframes and costs associated with developing novel therapeutics, ultimately improving patient outcomes.Mohamet L, Miazga NJ, Ward CM. Familial Alzheimer's disease modelling using induced pluripotent stem cell technology. World J Stem Cells 2014; 6(2): 239-247 Available from:

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Genetics of Alzheimer’s Disease

Cited by 109 publications

References 104 publications

Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness

Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness

Misfolding of Amyloidogenic Proteins and Their Interactions with Membranes

Familial Alzheimer’s disease modelling using induced pluripotent stem cell technology

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