scholarly journals Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Hákon Valur Dansson ◽  
Lena Stempfle ◽  
Hildur Egilsdóttir ◽  
Alexander Schliep ◽  
Erik Portelius ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Disease Progression ◽  
Test Scores ◽  
Cognitive Test ◽  
Progression Rate ◽  
Multiple Perspectives ◽  
Specific Level ◽  
Clinical Variables

Abstract Background In Alzheimer’s disease, amyloid- β (A β) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A β pathology. Methods A cohort of n=2293 participants, of whom n=749 were A β positive, was selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A β pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A β pathology, models fit only to A β-positive subjects were compared to models fit to an extended cohort including subjects without established A β pathology, adjusting for covariate differences between the cohorts. Results A β pathology status was determined based on the A β42/A β40 ratio. The best predictive model of change in cognitive test scores for A β-positive subjects at the 2-year follow-up achieved an R2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. A β-positive subjects declined faster on average than those without A β pathology, but the specific level of CSF A β was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R2 score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R2 score of 0.228. Conclusion Our analysis shows that CSF levels of A β are not strong predictors of the rate of cognitive decline in A β-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.

scholarly journals Predicting Progression & Cognitive Decline in Amyloid-Positive Patients with Alzheimer's Disease

2021 ◽  
Author(s):  
Hákon Valur Dansson ◽  
Lena Stempfle ◽  
Hildur Egilsdóttir ◽  
Alexander Schliep ◽  
Erik Portelius ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Disease Progression ◽  
Test Scores ◽  
Amyloid Β ◽  
Cognitive Test ◽  
Progression Rate ◽  
Specific Level ◽  
Clinical Variables

Abstract BackgroundIn Alzheimer’s disease (AD), amyloid- β (Aβ) peptides aggregate in the brain forming amyloid plaques, which are a key pathological hallmark of the disease. However, plaques may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with Aβ pathology. MethodsA cohort of n= 2293 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database was selected to study heterogeneity in disease progression for individuals with Aβ plaque pathology. The analysis used baseline clinical variables including demographics, genetic markers and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the limited prevalence of Aβ pathology, models fit only to Aβ-positive subjects were compared to models fit to an extended cohort including subjects without established Aβ pathology, adjusting for covariate differences between the cohorts. ResultsAβ pathology status was determined based the Aβ 42 /Aβ 40 ratio. The best predictive model of change in cognitive test scores for Aβ-positive subjects at the two-year follow-up achieved an R 2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. Conforming to expectations, Aβ-positive subjects declined faster on average than those without Aβ pathology, but the specific level of Aβ plaques was not predictive of progression rate. For the four-year prediction task of cognitive score change, the best model achieved an R 2 score of 0.325 and it was found that fitting models to the extended cohort substantially improved performance. Moreover, using all clinical variables outperformed the best model based only on baseline cognitive test scores which achieved an R 2 score of 0.228. ConclusionOur analysis shows that levels of Aβ plaques are not strong predictors of the rate of cognitive decline in Aβ-positive subjects. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of two-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.

scholarly journals Disease progression modeling in Alzheimer’s disease: insights from the shape of cognitive decline

2019 ◽  
Author(s):  
Lars Lau Raket ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Disease Progression ◽  
Latent Variables ◽  
Likelihood Estimation ◽  
Disease Stage ◽  
Cognitive Test ◽  
Nonlinear Mixed Effects Model ◽  
Cognitive Capability

AbstractBackgroundThe characterizing symptom of Alzheimer disease (AD) is cognitive deterioration. While much recent work has focused on defining AD as a biological construct, most patients are still diagnosed, staged, and treated based on their cognitive symptoms. But the cognitive capability of a patient at any time throughout this deterioration will not directly reflect the disease state, but rather the effect of the cognitive decline on the patient’s predisease cognitive capability. Patients with high predisease cognitive capabilities tend to score better on cognitive tests relative to patients with low predisease cognitive capabilities at the same disease stage. Thus, a single assessment with a cognitive test is not adequate for determining the stage of an AD patient.Methods and FindingsI developed a joint statistical model that explicitly modeled disease stage, baseline cognition, and the patients’ individual changes in cognitive ability as latent variables. The developed model takes the form of a nonlinear mixed-effects model. Maximum-likelihood estimation in this model induces a data-driven criterion for separating disease progression and baseline cognition. Applied to data from the Alzheimer’s Disease Neuroimaging Initiative, the model estimated a timeline of cognitive decline in AD that spans approximately 15 years from the earliest subjective cognitive deficits to severe AD dementia. It was demonstrated how direct modeling of latent factors that modify the observed data patterns provide a scaffold for understanding disease progression, biomarkers and treatment effects along the continuous time progression of disease.ConclusionsThe suggested framework enables direct interpretations of factors that modify cognitive decline. The results give new insights to the value of biomarkers for staging patients and suggest alternative explanations for previous findings related to accelerated cognitive decline among highly educated patients and patients on symptomatic treatments.

scholarly journals Influence of publication year, add-on design, and geographical region on the progression of Alzheimer’s disease: a modeling analysis of literature aggregate data

2020 ◽  
Author(s):  
ningyuan zhang ◽  
Xijun Zheng ◽  
Hongxia Liu ◽  
Qingshan Zheng ◽  
Lujin Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Disease Assessment ◽  
Progression Rate ◽  
Progression Model ◽  
Geographical Regions ◽  
Significant Difference ◽  
Disease Progression Rate ◽  
Disease Progression Model

Abstract Background Our objective was to develop a disease progression model for cognitive decline in Alzheimer’s disease (AD) and to determine whether disease progression of AD is related to the year of publication, add-on trial design, and geographical regions. Methods Placebo-controlled randomized AD clinical trials were systemically searched in public databases. Longitudinal placebo response (mean change from baseline in the cognitive subscale of the Alzheimer’s Disease Assessment Scale [ADAS-cog]) and the corresponding demographic information were extracted to establish a disease progression model. Covariate screening and subgroup analyses were performed to identify potential factors affecting the disease progression rate. Results A total of 142 publications (148 trials) were included in this model-based meta-analysis. The typical disease progression rate was 5.82 points per year. The baseline ADAS-cog score was included in the final model using an inverse-U type function. Age was found to be negatively correlated with disease progression rate. After correcting the baseline ADAS-cog score and the age effect, no significant difference in disease progression rate was found between trials published before and after 2008, and between trials using add-on design and those that did not use add-on design. However, a significant difference was found among different trial regions. Trials in East Asian countries showed the slowest decline rate and the largest placebo effect. Conclusions Our model successfully quantified AD disease progression by integrating baseline ADAS-cog score and age as important predictors. These factors and geographic location should be considered when optimizing future trial designs and conducting indirect comparisons of clinical outcomes.

scholarly journals P2-299: EFFECT OF APOLIPOPROTEIN E ε4 ALLELE ON PROGRESSION RATE OF COGNITIVE DECLINE IN PRODROMAL AND MILD ALZHEIMER'S DISEASE

2019 ◽  
Vol 15 ◽  
pp. P701-P701
Author(s):  
Kazushi Suzuki ◽  
Ryoko Ihara ◽  
Takeshi Ikeuchi ◽  
Atsushi Iwata ◽  
Takeshi Iwatsubo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Decline ◽  
Apolipoprotein E ◽  
Progression Rate ◽  
Ε4 Allele

scholarly journals SHORT-TERM PRACTICE EFFECTS AND AMYLOID DEPOSITION: PROVIDING INFORMATION ABOVE AND BEYOND BASELINE COGNITION

2017 ◽  
pp. 1-6
Author(s):  
K. Duff ◽  
D.B. Hammers ◽  
B.C.A. Dalley ◽  
K.R. Suhrie ◽  
T.J. Atkinson ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Test Scores ◽  
Amyloid Deposition ◽  
Cognitive Testing ◽  
Practice Effects ◽  
Cognitive Test ◽  
Cognitive Tests ◽  
Short Term

Background: Practice effects, which are improvements in cognitive test scores due to repeated exposure to testing materials, may provide information about Alzheimer’s disease pathology, which could be useful for clinical trials enrichment. Objectives: The current study sought to add to the limited literature on short-term practice effects on cognitive tests and their relationship to amyloid deposition on neuroimaging. Participants: Twenty-seven, non-demented older adults (9 cognitively intact, 18 with mild cognitive impairment) received amyloid imaging with 18F-Flutemetamol, and two cognitive testing sessions across one week to determine practice effects. Results: A composite measure of 18F-Flutemetamol uptake correlated significantly with all seven cognitive tests scores on the baseline battery (r’s = -0.61 – 0.59, all p’s<0.05), with higher uptake indicating poorer cognition. Practice effects significantly added to the relationship (above and beyond the baseline associations) with 18F-Flutemetamol uptake on 4 of the 7 cognitive test scores (partial r’s = -0.45 – 0.44, p’s<0.05), with higher uptake indicating poorer practice effects. The odds ratio of being “amyloid positive” was 13.5 times higher in individuals with low practice effects compared to high practice effects. Conclusions: Short-term practice effects over one week may be predictive of progressive dementia and serve as an affordable screening tool to enrich samples for preventative clinical trials in Alzheimer’s disease.

scholarly journals Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning

2019 ◽  
Author(s):  
Vipul K. Satone ◽  
Rachneet Kaur ◽  
Anant Dadu ◽  
Hampton Leonard ◽  
Hirotaka Iwaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Disease Onset ◽  
Machine Learning Techniques ◽  
Progression Rate ◽  
Early Stages ◽  
Age Related ◽  
Learning Techniques

AbstractBackgroundAlzheimer’s disease (AD) is a common, age-related, neurodegenerative disease that impairs a person’s ability to perform day-to-day activities. Diagnosing AD is challenging, especially in the early stages. Many patients still go undiagnosed, partly due to the complex heterogeneity in disease progression. This highlights a need for early prediction of the disease course to assist its treatment and tailor therapy options to the disease progression rate. Recent developments in machine learning techniques provide the potential to not only predict disease progression and trajectory of AD but also to classify the disease into different etiological subtypes.Methods and findingsThe work shown here clusters participants in distinct and multifaceted progression subgroups of AD and discusses an approach to predict the progression rate from baseline diagnosis. We observed that the myriad of clinically reported symptoms summarized in the proposed AD progression space corresponds directly with memory and cognitive measures, which are routinely used to monitor disease onset and progression. Our analysis demonstrated accurate prediction of disease progression after four years from the first 12 months of post-diagnosis clinical data (Area Under the Curve of 0.96 (95% confidence interval (CI), 0.92-1.0), 0.81 (95% CI, 0.74-0.88) and 0.98 (95% CI, 0.96-1.0) for slow, moderate and fast progression rate patients respectively). Further, we explored the long short-term memory (LSTM) neural networks to predict the trajectory of an individual patient’s progression.ConclusionThe machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries. With additional information about the progression rate of AD at hand, providers may further individualize the treatment plans. The predictive tests discussed in this study not only allow for early AD diagnosis but also facilitate the characterization of distinct AD subtypes relating to trajectories of disease progression. These findings are a crucial step forward for early disease detection. These models can be used to design improved clinical trials for AD research.

scholarly journals Automatic Prediction of Cognitive and Functional Decline Can Significantly Decrease the Number of Subjects Required for Clinical Trials in Early Alzheimer’s Disease

2021 ◽  
pp. 1-8
Author(s):  
Neda Shafiee ◽  
Mahsa Dadar ◽  
Simon Ducharme ◽  
D. Louis Collins ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Decline ◽  
Functional Decline ◽  
Amyloid Β ◽  
Cognitive Test ◽  
Disease Heterogeneity ◽  
Early Alzheimer’S Disease

Background: While both cognitive and magnetic resonance imaging (MRI) data has been used to predict progression in Alzheimer’s disease, heterogeneity between patients makes it challenging to predict the rate of cognitive and functional decline for individual subjects. Objective: To investigate prognostic power of MRI-based biomarkers of medial temporal lobe atrophy and macroscopic tissue change to predict cognitive decline in individual patients in clinical trials of early Alzheimer’s disease. Methods: Data used in this study included 312 patients with mild cognitive impairment from the ADNI dataset with baseline MRI, cerebrospinal fluid amyloid-β, cognitive test scores, and a minimum of two-year follow-up information available. We built a prognostic model using baseline cognitive scores and MRI-based features to determine which subjects remain stable and which functionally decline over 2 and 3-year follow-up periods. Results: Combining both sets of features yields 77%accuracy (81%sensitivity and 75%specificity) to predict cognitive decline at 2 years (74%accuracy at 3 years with 75%sensitivity and 73%specificity). When used to select trial participants, this tool yields a 3.8-fold decrease in the required sample size for a 2-year study (2.8-fold decrease for a 3-year study) for a hypothesized 25%treatment effect to reduce cognitive decline. Conclusion: When used in clinical trials for cohort enrichment, this tool could accelerate development of new treatments by significantly increasing statistical power to detect differences in cognitive decline between arms. In addition, detection of future decline can help clinicians improve patient management strategies that will slow or delay symptom progression.

Transcranial magnetic stimulation predicts cognitive decline in patients with Alzheimer’s disease

2018 ◽  
Vol 89 (12) ◽  
pp. 1237-1242 ◽  
Author(s):  
Caterina Motta ◽  
Francesco Di Lorenzo ◽  
Viviana Ponzo ◽  
Maria Concetta Pellicciari ◽  
Sonia Bonnì ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transcranial Magnetic Stimulation ◽  
Cognitive Decline ◽  
Disease Progression ◽  
Magnetic Stimulation ◽  
Cortical Plasticity ◽  
Multivariable Analysis ◽  
Area Under The Curve ◽  
Long Term Potentiation

ObjectiveTo determine the ability of transcranial magnetic stimulation (TMS) in detecting synaptic impairment in patients with Alzheimer’s disease (AD) and predicting cognitive decline since the early phases of the disease.MethodsWe used TMS-based parameters to evaluate long-term potentiation (LTP)-like cortical plasticity and cholinergic activity as measured by short afferent inhibition (SAI) in 60 newly diagnosed patients with AD and 30 healthy age-matched subjects (HS). Receiver operating characteristic (ROC) curves were used to assess TMS ability in discriminating patients with AD from HS. Regression analyses examined the association between TMS-based parameters and cognitive decline. Multivariable regression model revealed the best parameters able to predict disease progression.ResultsArea under the ROC curve was 0.90 for LTP-like cortical plasticity, indicating an excellent accuracy of this parameter in detecting AD pathology. In contrast, area under the curve was only 0.64 for SAI, indicating a poor diagnostic accuracy. Notably, LTP-like cortical plasticity was a significant predictor of disease progression (p=0.02), while no other neurophysiological, neuropsychological and demographic parameters were associated with cognitive decline. Multivariable analysis then promoted LTP-like cortical plasticity as the best significant predictor of cognitive decline (p=0.01). Finally, LTP-like cortical plasticity was found to be strongly associated with the probability of rapid cognitive decline (delta Mini-Mental State Examination score ≤−4 points at 18 months) (p=0.04); patients with AD with lower LTP-like cortical plasticity values showed faster disease progression.ConclusionsTMS-based assessment of LTP-like cortical plasticity could be a viable biomarker to assess synaptic impairment and predict subsequent cognitive decline progression in patients with ADs.

scholarly journals Cognitive Changes associated with Alzheimer’s disease in Down syndrome

2018 ◽  
Author(s):  
Nicholas C. Firth ◽  
Carla M. Startin ◽  
Rosalyn Hithersay ◽  
Sarah Hamburg ◽  
Peter A. Wijeratne ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Down Syndrome ◽  
Cognitive Decline ◽  
Sustained Attention ◽  
Motor Coordination ◽  
Disease Stage ◽  
Cognitive Test ◽  
Early Assessment ◽  
Cognitive Changes

AbstractObjectiveIndividuals with Down syndrome (DS) have an extremely high genetic risk for Alzheimer’s disease (AD) however the course of cognitive decline associated with progression to dementia is ill-defined. Data-driven methods can estimate long-term trends from cross-sectional data while adjusting for variability in baseline ability, which complicates dementia assessment in those with DS.MethodsWe applied an event-based model to cognitive test data and informant-rated questionnaire data from 283 adults with DS (the largest study of cognitive functioning in DS to date) to estimate the sequence of cognitive decline and individuals’ disease stage.ResultsDecline in tests of memory, sustained attention / motor coordination, and verbal fluency occurred early, demonstrating that AD in DS follows a similar pattern of change to other forms of AD. Later decline was found for informant measures. Using the resulting staging model, we showed that adults with a clinical diagnosis of dementia and those with APOE 3:4 or 4:4 genotype were significantly more likely to be staged later, suggesting the model is valid.InterpretationOur results identify tests of memory and sustained attention may be particularly useful measures to track decline in the preclinical/prodromal stages of AD in DS whereas informant-measures may be useful in later stages (i.e. during conversion to dementia, or post-diagnosis). These results have implications for the selection of outcome measures of treatment trials to delay or prevent cognitive decline due to AD in DS. As clinical diagnoses are generally made late into AD progression, early assessment is essential.

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