scholarly journals Applications of Stochastic Process Models to Constructing Predictive Models of Alzheimer’s Disease

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 263-263
Author(s):  
Konstantin Arbeev ◽  
Olivia Bagley ◽  
Arseniy Yashkin ◽  
Hongzhe Duan ◽  
Igor Akushevich ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Stochastic Process ◽  
Repeated Measures ◽  
Process Model ◽  
Large Scale ◽  
Disease Onset ◽  
Building Blocks ◽  
Process Models ◽  
Polygenic Scores

Abstract Large-scale population-based data collecting repeated measures of biomarkers, follow-up data on events (incidence of diseases and mortality), and extensive genetic data provide excellent opportunities for applying statistical models for joint analyses of longitudinal dynamics of biomarkers and time-to-event outcomes that allow investigating dynamics of biomarkers and other relevant factors (including genetic) in relation to risks of diseases and death and how this may propagate to the future. Here we applied one such model, the stochastic process model (SPM), to data on longitudinal trajectories of different variables (comorbidity index, body mass index, cognitive scores), other relevant covariates (including genetic factors such as APOE polymorphisms and polygenic scores, PGS), and data on onset of Alzheimer’s disease (AD) in the Health and Retirement Study. We observed that different aging-related characteristics estimated from trajectories of respective variables in SPM are strongly associated with risks of onset of AD and found that these associations differ by sex, APOE status (carriers vs. non-carriers of APOE e4) and by PGS groups. The approach allows modeling and estimating time trends (e.g., by birth cohorts) in relevant dynamic characteristics in relation to the disease onset. These results provide building blocks for constructing the models for forecasting future trends and burden of AD that take into account dynamic relationships between individual trajectories of relevant repeatedly measured characteristics and the risk of the disease. Such models also provide the analytic framework for understanding AD in the context of aging and for finding genetic underpinnings of such links between AD and aging.

scholarly journals APPLICATIONS OF GENETIC STOCHASTIC PROCESS MODEL IN STUDIES OF DEVELOPMENT OF ALZHEIMER’S DISEASE

2017 ◽  
Vol 1 (suppl_1) ◽  
pp. 1150-1151
Author(s):  
I.Y. Zhbannikov ◽  
K. Arbeev ◽  
O. Bagley ◽  
M. Duan ◽  
A.I. Yashin ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Stochastic Process ◽  
Process Model

Cardinal Features of Cognitive Dysfunction in Alzheimer's Disease: A Factor-Analytic Study of the Alzheimer's Disease Assessment Scale

1996 ◽  
Vol 9 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Sheela Talwalker ◽  
John E. Overall ◽  
Mandyam K. Srirama ◽  
Stephen I. Gracon
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Factor Analysis ◽  
Repeated Measures ◽  
Large Scale ◽  
Assessment Scale ◽  
Disease Assessment ◽  
General Factor ◽  
Factor Scores

Factor analysis methodology applied to Alzheimer's Disease Assessment Scale (ADAS) subtest profiles for patients in two large-scale clinical trials of the antidementia drug tacrine yielded three oblique factors interpreted as dysfunctions in memory, language, and praxis. The factor structures confirmed reliable assessment of primary dimensions of cognitive impairment in Alzheimer's disease that the original authors of the ADAS proposed to measure and that correspond well to that of the only previously reported factor analysis of the ADAS-COG. The presence of a strong general factor, supported by stable correlations among the oblique primary factors, justifies the recommendation to continue reliance on the ADAS-COG total score as a primary outcome measure in clinical trials, whereas the factor scores are recommended for evaluation of differential treatment effects on more specific aspects of the general cognitive decline. The stability of correlations across time appears to satisfy a primary requirement for application of repeated measures ANOVA to ADAS-COG total score and factor scores in longitudinal clinical trials.

scholarly journals Identification of abnormal lipid profiles promote cognitive decline in Alzheimer’s disease spectrum via large-scale brain connectivity

2021 ◽  
Author(s):  
Qing Wang ◽  
Feifei Zang ◽  
Cancan He ◽  
Zhijun Zhang ◽  
Chunming Xie
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Function ◽  
Cognitive Decline ◽  
Large Scale ◽  
Clinical Signs ◽  
Lipid Profiles ◽  
Substantial Contribution ◽  
Support Vector ◽  
Polygenic Scores

Abstract Background: Lipid metabolite dysfunction makes a substantial contribution to the clinical signs and pathophysiology of Alzheimer’s disease (AD). It is unclear that the role of dyslipidemia in the promotion of neuropathological processes and brain functional impairment that subsequently facilitates the progression of AD.Methods: Lipid pathway-based polygenic scores and large-scale resting-state networks (RSNs), was constructed. Together with canonical correlation analysis (CCA) and support vector machine (SVM) model, to explore the effects of lipid-related polygenic scores and blood lipoproteins on the molecular biomarkers, cognitive function, as well as large-scale RSNs. Associations between lipid-related genetic scores, serum lipoproteins, cognitive function, CSF biomarkers and RSNs were examined.Results: Dynamic trajectory of large-scale RSNs was exhibited significantly differential connectivity within-network, one-versus-all-others-network, and pairwise between networks across the AD spectrum (ADS). Importantly, the summative effects of lipid-pathway genetic variants and lipoproteins significantly promoted the process of β-amyloid and Tau levels and cognitive decline, and preferentially targeted functional couplings within- and between RSNs in the ADS, supporting the hypothesis that abnormal lipid profiles in the AD pathogenesis via disrupting large-scale RSNs and accelerating molecular pathological processes, consequently exacerbating cognitive decline.Conclusions: Our findings reveal the importance of lipids in the pathogenesis of AD via disruption of RSNs and acceleration of molecular pathological processes, consequently exacerbating cognitive decline. These findings provide the potential lipid-associated neuroimaging biomarkers for ADS.

scholarly journals Considering the APOE locus in Alzheimer’s disease polygenic scores in the Health and Retirement Study: a longitudinal panel study

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Erin B. Ware ◽  
Jessica D. Faul ◽  
Colter M. Mitchell ◽  
Kelly M. Bakulski
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Repeated Measures ◽  
Panel Study ◽  
European Ancestry ◽  
Health And Retirement Study ◽  
Carrier Status ◽  
Polygenic Score ◽  
Polygenic Scores ◽  
Retirement Study

Abstract Background Polygenic scores are a strategy to aggregate the small, additive effects of single nucleotide polymorphisms across the genome. With phenotypes like Alzheimer’s disease, which have a strong and well-established genomic locus (APOE), the cumulative effect of genetic variants outside of this area has not been well established in a population-representative sample. Methods Here we examine the association between polygenic scores for Alzheimer’s disease both with and without the APOE region (chr19: 45,384,477 to 45,432,606, build 37/hg 19) at different P value thresholds and dementia. We also investigate the addition of APOE-ε4 carrier status and its effect on the polygenic score—dementia association in the Health and Retirement Study using generalized linear models accounting for repeated measures by individual and use a binomial distribution, logit link, and unstructured correlation structure. Results In a large sample of European ancestry participants of the Health and Retirement Study (n = 9872) with an average of 5.2 (standard deviation 1.8) visit spaced two years apart, we found that including the APOE region through weighted variants in a polygenic score was insufficient to capture the large amount of risk attributed to this region. We also found that a polygenic score with a P value threshold of 0.01 had the strongest association with the odds of dementia in this sample (odds ratio = 1.10 95%CI 1.0 to 1.2). Conclusion We recommend removing the APOE region from polygenic score calculation and treating the APOE locus as an independent covariate when modeling dementia. We also recommend using a moderately conservative P value threshold (e.g. 0.01) when creating polygenic scores for Alzheimer’s disease on dementia. These recommendations may help elucidate relationships between polygenic scores and regions of strong significance for phenotypes similar to Alzheimer’s disease.

Dehydroepiandrosterone (DHEA) and its Sulphate (DHEAS) in Alzheimer’s Disease

2020 ◽  
Vol 17 (2) ◽  
pp. 141-157 ◽  
Author(s):  
Dubravka S. Strac ◽  
Marcela Konjevod ◽  
Matea N. Perkovic ◽  
Lucija Tudor ◽  
Gordana N. Erjavec ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Therapeutic Potential ◽  
Relevant Literature ◽  
Comprehensive Overview ◽  
Brain Functions ◽  
Specific Effects ◽  
And Behavior

Background: Neurosteroids Dehydroepiandrosterone (DHEA) and Dehydroepiandrosterone Sulphate (DHEAS) are involved in many important brain functions, including neuronal plasticity and survival, cognition and behavior, demonstrating preventive and therapeutic potential in different neuropsychiatric and neurodegenerative disorders, including Alzheimer’s disease. Objective: The aim of the article was to provide a comprehensive overview of the literature on the involvement of DHEA and DHEAS in Alzheimer’s disease. Method: PubMed and MEDLINE databases were searched for relevant literature. The articles were selected considering their titles and abstracts. In the selected full texts, lists of references were searched manually for additional articles. Results: We performed a systematic review of the studies investigating the role of DHEA and DHEAS in various in vitro and animal models, as well as in patients with Alzheimer’s disease, and provided a comprehensive discussion on their potential preventive and therapeutic applications. Conclusion: Despite mixed results, the findings of various preclinical studies are generally supportive of the involvement of DHEA and DHEAS in the pathophysiology of Alzheimer’s disease, showing some promise for potential benefits of these neurosteroids in the prevention and treatment. However, so far small clinical trials brought little evidence to support their therapy in AD. Therefore, large-scale human studies are needed to elucidate the specific effects of DHEA and DHEAS and their mechanisms of action, prior to their applications in clinical practice.

Structural Changes in Thalamic Nuclei Across Prodromal and Clinical Alzheimer’s Disease

2021 ◽  
pp. 1-11
Author(s):  
Adam S. Bernstein ◽  
Steven Z. Rapcsak ◽  
Michael Hornberger ◽  
Manojkumar Saranathan ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Structural Changes ◽  
Thalamic Nuclei ◽  
Medial Temporal Lobes ◽  
Cognitive Changes ◽  
Temporal Lobes ◽  
Medial Geniculate ◽  
Healthy Control

Background: Increasing evidence suggests that thalamic nuclei may atrophy in Alzheimer’s disease (AD). We hypothesized that there will be significant atrophy of limbic thalamic nuclei associated with declining memory and cognition across the AD continuum. Objective: The objective of this work was to characterize volume differences in thalamic nuclei in subjects with early and late mild cognitive impairment (MCI) as well as AD when compared to healthy control (HC) subjects using a novel MRI-based thalamic segmentation technique (THOMAS). Methods: MPRAGE data from the ADNI database were used in this study (n = 540). Healthy control (n = 125), early MCI (n = 212), late MCI (n = 114), and AD subjects (n = 89) were selected, and their MRI data were parcellated to determine the volumes of 11 thalamic nuclei for each subject. Volumes across the different clinical subgroups were compared using ANCOVA. Results: There were significant differences in thalamic nuclei volumes between HC, late MCI, and AD subjects. The anteroventral, mediodorsal, pulvinar, medial geniculate, and centromedian nuclei were significantly smaller in subjects with late MCI and AD when compared to HC subjects. Furthermore, the mediodorsal, pulvinar, and medial geniculate nuclei were significantly smaller in early MCI when compared to HC subjects. Conclusion: This work highlights nucleus specific atrophy within the thalamus in subjects with early and late MCI and AD. This is consistent with the hypothesis that memory and cognitive changes in AD are mediated by damage to a large-scale integrated neural network that extends beyond the medial temporal lobes.

scholarly journals Conditional Deletion of Prnp Rescues Behavioral and Synaptic Deficits after Disease Onset in Transgenic Alzheimer's Disease

2017 ◽  
Vol 37 (38) ◽  
pp. 9207-9221 ◽  
Author(s):  
Santiago V. Salazar ◽  
Christopher Gallardo ◽  
Adam C. Kaufman ◽  
Charlotte S. Herber ◽  
Laura T. Haas ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Onset ◽  
Conditional Deletion

scholarly journals Comparison of Amyloid β and Tau Spread Models in Alzheimer’s Disease

2018 ◽  
Vol 29 (10) ◽  
pp. 4291-4302 ◽  
Author(s):  
Hang-Rai Kim ◽  
Peter Lee ◽  
Sang Won Seo ◽  
Jee Hoon Roh ◽  
Minyoung Oh ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Amyloid Β ◽  
Brain Network ◽  
Amyloid Positron Emission Tomography ◽  
Local Spread ◽  
Positron Emission ◽  
Spread Model ◽  
Graph Theoretical Approach

Abstract Tau and amyloid β (Aβ), 2 key pathogenic proteins in Alzheimer’s disease (AD), reportedly spread throughout the brain as the disease progresses. Models of how these pathogenic proteins spread from affected to unaffected areas had been proposed based on the observation that these proteins could transmit to other regions either through neural fibers (transneuronal spread model) or through extracellular space (local spread model). In this study, we modeled the spread of tau and Aβ using a graph theoretical approach based on resting-state functional magnetic resonance imaging. We tested whether these models predict the distribution of tau and Aβ in the brains of AD spectrum patients. To assess the models’ performance, we calculated spatial correlation between the model-predicted map and the actual map from tau and amyloid positron emission tomography. The transneuronal spread model predicted the distribution of tau and Aβ deposition with significantly higher accuracy than the local spread model. Compared with tau, the local spread model also predicted a comparable portion of Aβ deposition. These findings provide evidence of transneuronal spread of AD pathogenic proteins in a large-scale brain network and furthermore suggest different contributions of spread models for tau and Aβ in AD.

scholarly journals A Large-scale Comparison of Cortical and Subcortical Structural Segmentation Methods in Alzheimer’s Disease: a Statistical Approach

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Automated Segmentation ◽  
Resonance Imaging ◽  
Brain Areas ◽  
Segmentation Methods ◽  
Magnetic Resonance Imaging Mri ◽  
Underlying Mechanisms ◽  
The Brain

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disease that leads to anatomical atrophy, as evidenced by magnetic resonance imaging (MRI). Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in AD, as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using automated segmentation methods in more than 60 areas between AD and healthy controls (HC).MethodsA total of 44 MRI images (22 AD and 22 HC, 50% females) were taken from the minimal interval resonance imaging in Alzheimer’s disease (MIRIAD) dataset. HIPS, volBrain, CAT and BrainSuite segmentation methods were used for the subfields of hippocampus, and the rest of the brain.ResultsWhile HIPS, volBrain and CAT showed strong conformity with the past literature, BrainSuite misclassified several brain areas. Additionally, the volume of the brain areas that successfully discriminated between AD and HC showed a correlation with mini mental state examination (MMSE) scores. The two methods of volBrain and CAT showed a very strong correlation. These two methods, however, did not correlate with BrainSuite.ConclusionOur results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of AD and HC. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

Sign in / Sign up

Export Citation Format

Share Document