scholarly journals Quantitative longitudinal predictions of Alzheimer's disease by multi-modal predictive learning

2020 ◽  
Author(s):  
Mithilesh Prakash ◽  
Mahmoud Abdelaziz ◽  
Linda Zhang ◽  
Bryan A Strange ◽  
Jussi Tohka
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning ◽  
The Individual

Abstract Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as AD assessment scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. We hypothesize that multi-modal data and predictive learning models can be employed for longitudinally predicting ADAS-cog scores.Methods: Multivariate regression techniques were employed to model baseline multi-modal data (demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors) and future ADAS-cog scores. Prediction models were subjected to repeated cross-validation and the resulting mean absolute error and cross-validated correlation of the model assessed.Results: Prediction models on multi-modal data outperformed single modal data up to 36 months. Incorporating baseline ADAS-cog scores to prediction models marginally improved predictive performance.Conclusions: Future ADAS-cog scores were successfully estimated via predictive learning aiding clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

scholarly journals Quantitative longitudinal predictions of Alzheimer’s disease by multi-modal predictive learning

2020 ◽  
Author(s):  
M. Prakash ◽  
M. Abdelaziz ◽  
L. Zhang ◽  
B.A. Strange ◽  
J. Tohka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning ◽  
The Individual

AbstractBackgroundQuantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as AD assessment scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. We hypothesize that multi-modal data and predictive learning models can be employed for longitudinally predicting ADAS-cog scores.MethodsMultivariate regression techniques were employed to model baseline multi-modal data (demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors) and future ADAS-cog scores. Prediction models were subjected to repeated cross-validation and the resulting mean absolute error and cross-validated correlation of the model assessed.ResultsPrediction models on multi-modal data outperformed single modal data up to 36 months. Incorporating baseline ADAS-cog scores to prediction models marginally improved predictive performance.ConclusionsFuture ADAS-cog scores were successfully estimated via predictive learning aiding clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

scholarly journals Quantitative Longitudinal Predictions of Alzheimer’s Disease by Multi-Modal Predictive Learning

2021 ◽  
Vol 79 (4) ◽  
pp. 1533-1546
Author(s):  
Mithilesh Prakash ◽  
Mahmoud Abdelaziz ◽  
Linda Zhang ◽  
Bryan A. Strange ◽  
Jussi Tohka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Assessment ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning

Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. Objective: To evaluate the hypothesis that the multi-modal data and predictive learning models can be employed for future predicting ADAS-cog scores. Methods: Unimodal and multi-modal regression models were trained on baseline data comprised of demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors to predict future ADAS-cog scores for 12, 24, and 36 months. We subjected the prediction models to repeated cross-validation and assessed the resulting mean absolute error (MAE) and cross-validated correlation (ρ) of the model. Results: Prediction models trained on multi-modal data outperformed the models trained on single modal data in predicting future ADAS-cog scores (MAE12, 24 & 36 months= 4.1, 4.5, and 5.0, ρ12, 24 & 36 months= 0.88, 0.82, and 0.75). Including baseline ADAS-cog scores to prediction models improved predictive performance (MAE12, 24 & 36 months= 3.5, 3.7, and 4.6, ρ12, 24 & 36 months= 0.89, 0.87, and 0.80). Conclusion: Future ADAS-cog scores were predicted which could aid clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

Microglia in Alzheimer’s Disease

2020 ◽  
Vol 17 (1) ◽  
pp. 29-43 ◽  
Author(s):  
Patrick Süß ◽  
Johannes C.M. Schlachetzki
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Current Knowledge ◽  
Imaging Techniques ◽  
Amyloid Β ◽  
Disease Stage ◽  
Disease Modifying Therapy ◽  
Transcriptomic Signature

: Alzheimer’s Disease (AD) is the most frequent neurodegenerative disorder. Although proteinaceous aggregates of extracellular Amyloid-β (Aβ) and intracellular hyperphosphorylated microtubule- associated tau have long been identified as characteristic neuropathological hallmarks of AD, a disease- modifying therapy against these targets has not been successful. An emerging concept is that microglia, the innate immune cells of the brain, are major players in AD pathogenesis. Microglia are longlived tissue-resident professional phagocytes that survey and rapidly respond to changes in their microenvironment. Subpopulations of microglia cluster around Aβ plaques and adopt a transcriptomic signature specifically linked to neurodegeneration. A plethora of molecules and pathways associated with microglia function and dysfunction has been identified as important players in mediating neurodegeneration. However, whether microglia exert either beneficial or detrimental effects in AD pathology may depend on the disease stage. : In this review, we summarize the current knowledge about the stage-dependent role of microglia in AD, including recent insights from genetic and gene expression profiling studies as well as novel imaging techniques focusing on microglia in human AD pathology and AD mouse models.

scholarly journals ADeditome provides the genomic landscape of A-to-I RNA editing in Alzheimer’s disease

2021 ◽  
Author(s):  
Sijia Wu ◽  
Mengyuan Yang ◽  
Pora Kim ◽  
Xiaobo Zhou
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Rna Editing ◽  
Memory Loss ◽  
Brain Regions ◽  
Disease Stage ◽  
Mirna Regulation ◽  
Annotation Database ◽  
Functional Annotations ◽  
Genomic Landscape

Abstract A-to-I RNA editing, contributing to nearly 90% of all editing events in human, has been reported to involve in the pathogenesis of Alzheimer’s disease (AD) due to its roles in brain development and immune regulation, such as the deficient editing of GluA2 Q/R related to cell death and memory loss. Currently, there are urgent needs for the systematic annotations of A-to-I RNA editing events in AD. Here, we built ADeditome, the annotation database of A-to-I RNA editing in AD available at https://ccsm.uth.edu/ADeditome, aiming to provide a resource and reference for functional annotation of A-to-I RNA editing in AD to identify therapeutically targetable genes in an individual. We detected 1676 363 editing sites in 1524 samples across nine brain regions from ROSMAP, MayoRNAseq and MSBB. For these editing events, we performed multiple functional annotations including identification of specific and disease stage associated editing events and the influence of editing events on gene expression, protein recoding, alternative splicing and miRNA regulation for all the genes, especially for AD-related genes in order to explore the pathology of AD. Combing all the analysis results, we found 108 010 and 26 168 editing events which may promote or inhibit AD progression, respectively. We also found 5582 brain region-specific editing events with potentially dual roles in AD across different brain regions. ADeditome will be a unique resource for AD and drug research communities to identify therapeutically targetable editing events. Significance: ADeditome is the first comprehensive resource of the functional genomics of individual A-to-I RNA editing events in AD, which will be useful for many researchers in the fields of AD pathology, precision medicine, and therapeutic researches.

scholarly journals The individual course of neuropsychiatric symptoms in people with Alzheimer's and Lewy body dementia: 12-year longitudinal cohort study

2019 ◽  
Vol 216 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Audun Osland Vik-Mo ◽  
Lasse Melvaer Giil ◽  
Miguel Germán Borda ◽  
Clive Ballard ◽  
Dag Aarsland
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neuropsychiatric Symptoms ◽  
Personalised Medicine ◽  
Lewy Bodies ◽  
Lewy Body Dementia ◽  
Psychotic Symptoms ◽  
Primary Inclusion ◽  
The Individual

IntroductionUnderstanding the natural course of neuropsychiatric symptoms (NPS) in dementia is important for planning patient care and trial design, but few studies have described the long-term course of NPS in individuals.MethodPrimary inclusion of 223 patients with suspected mild dementia from general practice were followed by annual assessment, including the Neuropsychiatric Inventory (NPI), for up to 12 years. Total and item NPI scores were classified as stable, relapsing, single episodic or not present based on 4.96 (s.d. 2.3) observations (98% completeness of longitudinal data) for 113 patients with Alzheimer's disease and 84 patients with LBD (68 dementia with Lewy bodies and 16 Parkinson's disease dementia).ResultsWe found that 80% had stable NPI total ≥1, 50% had stable modest NPI total ≥12 and 25% had stable NPI total ≥24 scores. Very severe NPS (≥48) were mostly single episodes, but 8% of patients with Alzheimer's disease had stable severe NPS. Patients with Alzheimer's disease and the highest 20% NPI total scores had a more stable or relapsing course of four key symptoms: aberrant motor behaviour, aggression/agitation, delusions and irritability (odds ratio 55, P < 0.001). This was not seen in LBD. Finally, 57% of patients with Alzheimer's disease and 84% of patients with LBD had reoccurring psychotic symptoms.ConclusionsWe observed a highly individual course of NPS, with most presenting as a single episode or relapsing; a stable course was less common, especially in LBD. These findings demonstrate the importance of an individualised approach (i.e. personalised medicine) in dementia care.

The Semantic Memory Impairment of Alzheimer's Disease: Category-Specific?

Cortex ◽  
1996 ◽  
Vol 32 (1) ◽  
pp. 143-153 ◽  
Author(s):  
Lynette J. Tippett ◽  
Murray Grossman ◽  
Martha J. Farah
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Semantic Memory ◽  
Memory Impairment ◽  
Disease Category

scholarly journals Visual Features in Alzheimer’s Disease: From Basic Mechanisms to Clinical Overview

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
María Alejandra Cerquera-Jaramillo ◽  
Mauricio O. Nava-Mesa ◽  
Rodrigo E. González-Reyes ◽  
Carlos Tellez-Conti ◽  
Alejandra de-la-Torre
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Morphological Changes ◽  
Indirect Costs ◽  
Therapeutic Interventions ◽  
Disease Stage ◽  
Early Disease ◽  
Early Disease Detection ◽  
Advanced Disease Stage ◽  
Functional Components

Alzheimer’s disease (AD) is the leading cause of dementia worldwide. It compromises patients’ daily activities owing to progressive cognitive deterioration, which has elevated direct and indirect costs. Although AD has several risk factors, aging is considered the most important. Unfortunately, clinical diagnosis is usually performed at an advanced disease stage when dementia is established, making implementation of successful therapeutic interventions difficult. Current biomarkers tend to be expensive, insufficient, or invasive, raising the need for novel, improved tools aimed at early disease detection. AD is characterized by brain atrophy due to neuronal and synaptic loss, extracellular amyloid plaques composed of amyloid-beta peptide (Aβ), and neurofibrillary tangles of hyperphosphorylated tau protein. The visual system and central nervous system share many functional components. Thus, it is plausible that damage induced by Aβ, tau, and neuroinflammation may be observed in visual components such as the retina, even at an early disease stage. This underscores the importance of implementing ophthalmological examinations, less invasive and expensive than other biomarkers, as useful measures to assess disease progression and severity in individuals with or at risk of AD. Here, we review functional and morphological changes of the retina and visual pathway in AD from pathophysiological and clinical perspectives.

scholarly journals A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea

2020 ◽  
Author(s):  
Ben J. Brintz ◽  
Benjamin Haaland ◽  
Joel Howard ◽  
Dennis L. Chao ◽  
Joshua L. Proctor ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Predictive Performance ◽  
Data Availability ◽  
Clinical Prediction ◽  
Multiple Sources ◽  
Statistical Framework ◽  
Multiple Data ◽  
Post Test ◽  
The Individual

AbstractTraditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where “pre-test” epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.

scholarly journals Short-Term Response to Cholinesterase Inhibitors in Alzheimer’s Disease Delays Time to Nursing Home Placement

2018 ◽  
Vol 15 (10) ◽  
pp. 905-916 ◽  
Author(s):  
Carina Wattmo ◽  
Elisabet Londos ◽  
Lennart Minthon
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Nursing Home ◽  
Nursing Home Placement ◽  
Specific Response ◽  
Short Term ◽  
Chei Treatment ◽  
The Individual ◽  
Home Placement ◽  
Term Response

Background: A varying response to cholinesterase inhibitor (ChEI) treatment has been reported among patients with Alzheimer’s disease (AD). Whether the individual-specific response directly affects time to nursing home placement (NHP) was not investigated. Objective: We examined the relationship between the 6-month response to ChEI and institutionalization. Methods: In a prospective, observational, multicenter study, 881 outpatients with a clinical AD diagnosis and a Mini-Mental State Examination score of 10-26 at the start of ChEI therapy (baseline) were included. The participants were evaluated using cognitive, global, and activities of daily living (ADL) scales at baseline and semiannually over 3 years. The date of NHP was recorded. Results: During the study, 213 patients (24%) were admitted to nursing homes. The mean ± standard deviation time from baseline (AD diagnosis) to NHP was 20.8 ± 9.3 months. After 6 months of ChEI treatment, the improved/unchanged individuals had longer time to NHP than those who worsened. The prolonged time to NHP was 3 months for cognitive response (P=0.022), 4 months for global response (P=0.004), 6 months for basic ADL response (P<0.001), and 8 months for response in all three scales (P<0.001). No differences were detected between the improved and unchanged groups in any scales. Conclusion: Patients who exhibit a positive short-term response to ChEI can expect to stay in their own home for 3-8 months longer. These findings underline the importance of a comprehensive clinical examination including various assessment scales to evaluate treatment response and provide a more accurate prognosis.

Preclinical Alzheimer's disease

2014 ◽  
Vol 24 (2) ◽  
pp. 117-121
Author(s):  
P Gil-Gregorio ◽  
R Yubero-Pancorbo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Diagnostic Criteria ◽  
Stage I ◽  
Disease Stage ◽  
Synaptic Dysfunction ◽  
Preclinical Alzheimer’S Disease ◽  
Cerebral Amyloidosis ◽  
Three Stages

SummaryRecently, diagnostic criteria for preclinical Alzheimer's disease have been proposed. These describe and define three stages of disease. Stage I is focused on asymptomatic cerebral amyloidosis. Stage II includes evidence of synaptic dysfunction and/or early degeneration. Finally, stage III of the disease is characterized by the beginning of cognitive decline.

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