P2-016: Identification of genetic variants associated with Alzheimer's disease: Progression rate

2015 ◽  
Vol 11 (7S_Part_10) ◽  
pp. P487-P487
Author(s):  
Jorge L. Del-Aguila ◽  
Alden L. Gross ◽  
Sheila Sutti ◽  
Richard Sherva ◽  
Shubhabrata Mukherjee ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Progression ◽  
Genetic Variants ◽  
Progression Rate ◽  
Disease Progression Rate

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 Testing whether the progression of Alzheimer's disease change with the year of publication, additional design and geographical area: 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 134 publications (140 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 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 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 Singular cases of Alzheimer’s disease disclose new and old genetic “acquaintances”

2020 ◽  
Author(s):  
Cinzia Coppola ◽  
Dario Saracino ◽  
Mariano Oliva ◽  
Lorenzo Cipriano ◽  
Gianfranco Puoti ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Age Of Onset ◽  
Amyloid Β ◽  
Progression Rate ◽  
Age Related ◽  
Disease Progression Rate ◽  
In Vivo Diagnosis ◽  
Genetically Determined

Abstract Background Alzheimer’s disease (AD) is the most common age-related dementia. Besides its typical presentation with amnestic syndrome at onset, atypical AD cases are being increasingly recognized, often in presenile age. Objectives To provide an extensive clinical and genetic characterization of six AD patients carrying one or more singular features, including age of onset, atypical phenotype and disease progression rate. By reviewing the pertinent literature and accessing publicly available databases, we aimed to assess the frequency and the significance of the identified genetic variants. Methods Biomarkers of amyloid-β deposition and neurodegeneration were used to establish the in vivo diagnosis of probable AD, in addition to neurological and neuropsychological evaluation, extensive laboratory assays and neuroradiological data. Considering the presenile onset of the majority of the cases, we hypothesized genetically determined AD and performed extensive genetic analyses by both Sanger sequencing and next generation sequencing (NGS). Results We disclosed two known missense variants, one in PSEN1 and the other in PSEN2, and a novel silent variant in PSEN2. Most notably, we identified several additional variants in other dementia-related genes by NGS. Some of them have never been reported in any control or disease databases, representing variants unique to our cases. Conclusions This work underlines the difficulties in reaching a confident in vivo diagnosis in cases of atypical dementia. Moreover, a wider genetic analysis by NGS approach may prove to be useful in specific cases, especially when the study of the so-far known AD causative genes produces negative or conflicting results.

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.

Sign in / Sign up

Export Citation Format

Share Document