scholarly journals Multi-Modal Data Analysis for Alzheimer's Disease Diagnosis: An Ensemble Model Using Imagery and Genetic Features

2021 ◽  
Author(s):  
Qi Ying ◽  
Xin Xing ◽  
Gongbo Liang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Model Performance ◽  
Brain Magnetic Resonance Imaging ◽  
The Elderly ◽  
Disease Diagnosis ◽  
Future Research ◽  
Nucleotide Polymorphisms ◽  
Snp Data

Alzheimer's disease (AD) is a devastating neurological disorder primarily affecting the elderly. An estimated 6.2 million Americans age 65 and older are suffering from Alzheimer's dementia today. Brain magnetic resonance imaging (MRI) is widely used for the clinical diagnosis of AD. In the meanwhile, medical researchers have identified 40 risk locus using single-nucleotide polymorphisms (SNPs) information from Genome-wide association study (GWAS) in the past decades. However, existing studies usually treat MRI and GWAS separately. For instance, convolutional neural networks are often trained using MRI for AD diagnosis. GWAS and SNPs are frequently used to identify genomic traits. In this study, we propose a multi-modal AD diagnosis neural network that uses both MRIs and SNPs. The proposed method demonstrates a novel way to use GWAS findings by directly including SNPs in predictive models. We test the proposed methods on the Alzheimer's Disease Neuroimaging Initiative dataset. The evaluation results show that the proposed method improves the model performance on AD diagnosis and achieves 93.5% AUC and 96.1% AP, respectively, when patients have both MRI and SNP data. We believe this work brings exciting new insights to GWAS applications and sheds light on future research directions.

scholarly journals Prediction of Alzheimer's disease using multi-variants from a Chinese genome-wide association study

Brain ◽  
2020 ◽  
Author(s):  
Longfei Jia ◽  
Fangyu Li ◽  
Cuibai Wei ◽  
Min Zhu ◽  
Qiumin Qu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Study ◽  
Predictive Models ◽  
Genome Wide Association Study ◽  
Disease Onset ◽  
Genome Wide Association ◽  
Nucleotide Polymorphisms ◽  
Risk Variants ◽  
Genome Wide

Abstract Previous genome-wide association studies have identified dozens of susceptibility loci for sporadic Alzheimer’s disease, but few of these loci have been validated in longitudinal cohorts. Establishing predictive models of Alzheimer’s disease based on these novel variants is clinically important for verifying whether they have pathological functions and provide a useful tool for screening of disease risk. In the current study, we performed a two-stage genome-wide association study of 3913 patients with Alzheimer’s disease and 7593 controls and identified four novel variants (rs3777215, rs6859823, rs234434, and rs2255835; Pcombined = 3.07 × 10−19, 2.49 × 10−23, 1.35 × 10−67, and 4.81 × 10−9, respectively) as well as nine variants in the apolipoprotein E region with genome-wide significance (P < 5.0 × 10−8). Literature mining suggested that these novel single nucleotide polymorphisms are related to amyloid precursor protein transport and metabolism, antioxidation, and neurogenesis. Based on their possible roles in the development of Alzheimer’s disease, we used different combinations of these variants and the apolipoprotein E status and successively built 11 predictive models. The predictive models include relatively few single nucleotide polymorphisms useful for clinical practice, in which the maximum number was 13 and the minimum was only four. These predictive models were all significant and their peak of area under the curve reached 0.73 both in the first and second stages. Finally, these models were validated using a separate longitudinal cohort of 5474 individuals. The results showed that individuals carrying risk variants included in the models had a shorter latency and higher incidence of Alzheimer’s disease, suggesting that our models can predict Alzheimer’s disease onset in a population with genetic susceptibility. The effectiveness of the models for predicting Alzheimer’s disease onset confirmed the contributions of these identified variants to disease pathogenesis. In conclusion, this is the first study to validate genome-wide association study-based predictive models for evaluating the risk of Alzheimer’s disease onset in a large Chinese population. The clinical application of these models will be beneficial for individuals harbouring these risk variants, and particularly for young individuals seeking genetic consultation.

scholarly journals Potential Role of Phenolic Extracts of Mentha in Managing Oxidative Stress and Alzheimer’s Disease

Antioxidants ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 631
Author(s):  
Doaa M. Hanafy ◽  
Geoffrey E. Burrows ◽  
Paul D. Prenzler ◽  
Rodney A. Hill
Keyword(s):  
Oxidative Stress ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Potential Role ◽  
The Elderly ◽  
Future Research ◽  
Phenolic Constituents ◽  
The Central Nervous System ◽  
Developed Nations

With an increase in the longevity and thus the proportion of the elderly, especially in developed nations, there is a rise in pathological conditions that accompany ageing, such as neurodegenerative disorders. Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive cognitive and memory decline. The pathophysiology of the disease is poorly understood, with several factors contributing to its development, such as oxidative stress, neuroinflammation, cholinergic neuronal apoptotic death, and the accumulation of abnormal proteins in the brain. Current medications are only palliative and cannot stop or reverse the progression of the disease. Recent clinical trials of synthetic compounds for the treatment of AD have failed because of their adverse effects or lack of efficacy. Thus, there is impetus behind the search for drugs from natural origins, in addition to the discovery of novel, conventional therapeutics. Mints have been used traditionally for conditions relevant to the central nervous system. Recent studies showed that mint extracts and/or their phenolic constituents have a neuroprotective potential and can target multiple events of AD. In this review, we provide evidence of the potential role of mint extracts and their derivatives as possible sources of treatments in managing AD. Some of the molecular pathways implicated in the development of AD are reviewed, with focus on apoptosis and some redox pathways, pointing to mechanisms that may be modulated for the treatment of AD, and the need for future research invoking knowledge of these pathways is highlighted.

scholarly journals Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups

2018 ◽  
Vol 25 (11) ◽  
pp. 2942-2951 ◽  
Author(s):  
Shubhabrata Mukherjee ◽  
◽  
Jesse Mez ◽  
Emily H. Trittschuh ◽  
Andrew J. Saykin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Disease Diagnosis ◽  
Single Nucleotide ◽  
Cognitively Normal ◽  
Snp Data ◽  
Genotype Frequencies ◽  
Genome Wide ◽  
Ε4 Allele

Abstract Categorizing people with late-onset Alzheimer’s disease into biologically coherent subgroups is important for personalized medicine. We evaluated data from five studies (total n = 4050, of whom 2431 had genome-wide single-nucleotide polymorphism (SNP) data). We assigned people to cognitively defined subgroups on the basis of relative performance in memory, executive functioning, visuospatial functioning, and language at the time of Alzheimer’s disease diagnosis. We compared genotype frequencies for each subgroup to those from cognitively normal elderly controls. We focused on APOE and on SNPs with p < 10−5 and odds ratios more extreme than those previously reported for Alzheimer’s disease (<0.77 or >1.30). There was substantial variation across studies in the proportions of people in each subgroup. In each study, higher proportions of people with isolated substantial relative memory impairment had ≥1 APOE ε4 allele than any other subgroup (overall p = 1.5 × 10−27). Across subgroups, there were 33 novel suggestive loci across the genome with p < 10−5 and an extreme OR compared to controls, of which none had statistical evidence of heterogeneity and 30 had ORs in the same direction across all datasets. These data support the biological coherence of cognitively defined subgroups and nominate novel genetic loci.

scholarly journals The Potential of Neurofilament Light as a Biomarker in Alzheimer’s Disease

2021 ◽  
pp. 1-10
Author(s):  
Yong-lan Xiong ◽  
Tao Meng ◽  
Jing Luo ◽  
Hua Zhang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Memory Loss ◽  
Detection Methods ◽  
Nucleotide Polymorphisms ◽  
Neurofilament Light ◽  
Genetic Characteristics ◽  
Additional Value ◽  
The Relationship

<b><i>Background:</i></b> Alzheimer’s disease (AD) is the most common neurodegenerative disease characterized by progressive memory loss and cognitive impairment. In 2011, the National Institute on Aging and Alzheimer’s Association (NIA-AA) Research Framework has proposed to use biomarkers to diagnose AD in living persons. AD core biomarkers show high diagnostic specificity in distinguishing AD from healthy control subjects, but have little additional value for prognosis or stage of disease. <b><i>Summary:</i></b> With the update of detection methods and techniques, other AD biomarkers have been discovered. Neurofilament light (NFL) is currently recognized as a biomarker of nerve axonal injury and one of the candidate markers in AD neurodegeneration, and the relationship between NFL and AD pathophysiology has attracted widespread attention. More and more studies have shown that NFL plays an important role in predicting the clinical progress and prognosis of AD. Recently, the genome-wide association study also found that multiple single-nucleotide polymorphisms are associated with NFL levels and AD risk. <b><i>Key Messages:</i></b> In this review, we discuss the relationship between the genetic characteristics of NFL and AD, the NFL levels in AD, and the relationship between NFL and AD core biomarkers, neuroimaging, and cognitive performance.

scholarly journals The causal effect of educational attainment on Alzheimer’s disease: A two-sample Mendelian randomization study

2017 ◽  
Author(s):  
Emma L Anderson ◽  
Kaitlin H Wade ◽  
Gibran Hemani ◽  
Jack Bowden ◽  
Roxanna Korologou-Linden ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Educational Attainment ◽  
Genome Wide Association Study ◽  
Causal Effect ◽  
Sensitivity Analyses ◽  
Nucleotide Polymorphisms ◽  
Inverse Variance ◽  
Higher Educational Attainment ◽  
Years Of Schooling

ABSTRACTBackgroundObservational evidence suggests that higher educational attainment is protective for Alzheimer’s disease (AD). It is unclear whether this association is causal or confounded by demographic and socioeconomic characteristics. We examined the causal effect of educational attainment on AD in a two-sample MR framework.MethodsWe extracted all available effect estimates of the 74 single nucleotide polymorphisms (SNPs) associated with years of schooling from the largest genome-wide association study (GWAS) of educational attainment (N=293,723) and the GWAS of AD conducted by the International Genomics of Alzheimer’s Project (n=17,008 AD cases and 37,154 controls). SNP-exposure and SNP-outcome coefficients were combined using an inverse variance weighted approach, providing an estimate of the causal effect of each SD increase in years of schooling on AD. We also performed appropriate sensitivity analyses examining the robustness of causal effect estimates to the various assumptions and conducted simulation analyses to examine potential survival bias of MR analyses.FindingsWith each SD increase in years of schooling (3.51 years), the odds of AD were, on average, reduced by approximately one third (odds ratio= 0.63, 95% confidence interval [CI]: 0.48 to 0.83, p<0.001). Causal effect estimates were consistent when using causal methods with varying MR assumptions or different sets of SNPs for educational attainment, lending confidence to the magnitude and direction of effect in our main findings. There was also no evidence of survival bias in our study.InterpretationOur findings support a causal role of educational attainment on AD, whereby an additional ∼3.5 years of schooling reduces the odds of AD by approximately one third.

scholarly journals Genetic data and cognitively-defined late-onset Alzheimer’s disease subgroups

2018 ◽  
Author(s):  
Shubhabrata Mukherjee ◽  
Jesse Mez ◽  
Emily Trittschuh ◽  
Andrew J. Saykin ◽  
Laura E. Gibbons ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Late Onset ◽  
Disease Diagnosis ◽  
Single Nucleotide ◽  
Total N ◽  
Snp Data ◽  
Genotype Frequencies ◽  
Genome Wide ◽  
E4 Allele

AbstractCategorizing people with late-onset Alzheimer’s disease into biologically coherent subgroups is important for personalized medicine. We evaluated data from five studies (total n=4 050, of whom 2 431 had genome-wide single nucleotide polymorphism (SNP) data). We assigned people to cognitively-defined subgroups on the basis of relative performance in memory, executive functioning, visuospatial functioning, and language at the time of Alzheimer’s disease diagnosis. We compared genotype frequencies for each subgroup to those from cognitively normal elderly controls. We focused on APOE and on SNPs with p<10-5 and odds ratios more extreme than those previously reported for Alzheimer’s disease (<0.77 or >1.30). There was substantial variation across studies in the proportions of people in each subgroup. In each study, higher proportions of people with isolated substantial relative memory impairment had ≥1 APOE e4 allele than any other subgroup (overall p= 1.5 × 10-27). Across subgroups, there were 33 novel suggestive loci across the genome with p<10-5 and an extreme OR compared to controls, of which none had statistical evidence of heterogeneity and 30 had ORs in the same direction across all datasets. These data support the biological coherence of cognitively-defined subgroups and nominate novel genetic loci.

scholarly journals Alzheimer's Disease Classification Using 2D Convolutional Neural Networks

2021 ◽  
Author(s):  
Xin Xing ◽  
Liangliang Liu ◽  
Qi Yin ◽  
Gongbo Liang
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Imaging Modality ◽  
Model Performance ◽  
Brain Magnetic Resonance Imaging ◽  
Disease Classification ◽  
Training Time

Alzheimer's disease (AD) is a non-treatable and non-reversible disease that affects about 6% of people who are 65 and older. Brain magnetic resonance imaging (MRI) is a pseudo-3D imaging modality that is widely used for AD diagnosis. Convolutional neural networks with 3D kernels (3D CNNs) are often the default choice for deep learning based MRI analysis. However, 3D CNNs are usually computationally costly and data-hungry. Such disadvantages post a barrier of using modern deep learning techniques in the medical imaging domain, in which the number of data can be used for training is usually limited. In this work, we propose three approaches that leverage 2D CNNs on 3D MRI data. We test the proposed methods on the Alzheimer's Disease Neuroimaging Initiative dataset across two popular 2D CNN architectures. The evaluation results show that the proposed method improves the model performance on AD diagnosis by 8.33% accuracy or 10.11% auROC, while significantly reduce the training time by over 89%. We also discuss the potential causes for performance improvement and the limitation. We believe this work can serve as a strong baseline for future researchers.

Association of GWAS Top Genes With Late-Onset Alzheimer’s Disease in Colombian Population

2017 ◽  
Vol 32 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Diana Jennifer Moreno ◽  
Susana Ruiz ◽  
Ángela Ríos ◽  
Francisco Lopera ◽  
Henry Ostos ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Genome Wide Association Study ◽  
Late Onset ◽  
European Ancestry ◽  
Gene Interactions ◽  
Nucleotide Polymorphisms ◽  
Significant Interaction Effect ◽  
Single Nucleotide ◽  
Genome Wide

Objective: The association of variants in CLU, CR1, PICALM, BIN1, ABCA7, and CD33 genes with late-onset Alzheimer’s disease (LOAD) was evaluated and confirmed through genome-wide association study. However, it is unknown whether these associations can be replicated in admixed populations. Methods: The association of 14 single-nucleotide polymorphisms in those genes was evaluated in 280 LOAD cases and 357 controls from the Colombian population. Results: In a multivariate analysis using age, gender, APOE∊4 status, and admixture covariates, significant associations were obtained ( P < .05) for variants in BIN1 (rs744373, odds ratio [OR]: 1.42), CLU (rs11136000, OR: 0.66), PICALM (rs541458, OR: 0.69), ABCA7 (rs3764650, OR: 1.7), and CD33 (rs3865444, OR: 1.12). Likewise, a significant interaction effect was observed between CLU and CR1 variants with APOE. Conclusion: This study replicated the associations previously reported in populations of European ancestry and shows that APOE variants have a regulatory role on the effect that variants in other loci have on LOAD, reflecting the importance of gene–gene interactions in the etiology of neurodegenerative diseases.

scholarly journals Association of complement receptor 1 gene polymorphisms with cognitive function

2018 ◽  
Vol 50 (2) ◽  
pp. 102-103
Author(s):  
L. E. Zijlstra ◽  
J. W. Jukema ◽  
S. P. Mooijaart ◽  
M. A. de Vries ◽  
D. J. Stott ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Function ◽  
Gene Polymorphisms ◽  
The Elderly ◽  
Genomic Region ◽  
Chromosome 1 ◽  
Complement Receptor ◽  
Nucleotide Polymorphisms ◽  
Complement Receptor 1

Previous evidence suggest involvement of the complement receptor 1 (CR1) in development of Alzheimer’s disease. We investigated the association of CR1 gene polymorphisms with cognitive function in older subjects. Single nucleotide polymorphisms (SNPs) within the CR1 region on chromosome 1 ( n = 73) were assessed in 5,244 participants in the PROspective Study of Pravastatin in the Elderly at Risk (51.9% female, mean age 75.3 yr). Linear regression, adjusted for age, sex, country, and use of pravastatin, was used to assess the association between the SNPs and cognitive function. All 73 SNPs within the genomic region of the CR1 gene on chromosome 1 were extracted. Eighteen were independent, according to a relatively stringent R2 threshold of >0.8 with LDlink. Twelve of the 18 investigated CR1 SNPs were significantly associated with a decline in cognitive function (all P < 0.05). These data indicate that genetic variation within the CR1 gene is associated not only with Alzheimer’s disease, but also with general cognitive function during late life.

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