scholarly journals Cerebral atrophy, apolipoprotein E ɛ4, and rate of decline in everyday function among patients with amnestic mild cognitive impairment

2010 ◽  
Vol 6 (5) ◽  
pp. 404-411 ◽  
Author(s):  
Ozioma C. Okonkwo ◽  
Michael L. Alosco ◽  
Beth A. Jerskey ◽  
Lawrence H. Sweet ◽  
Brian R. Ott ◽  
...  
2021 ◽  
pp. 1-15
Author(s):  
Sung Hoon Kang ◽  
Bo Kyoung Cheon ◽  
Ji-Sun Kim ◽  
Hyemin Jang ◽  
Hee Jin Kim ◽  
...  

Background: Amyloid (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer’s disease. However, Aβ evaluation through amyloid positron emission tomography (PET) is limited due to high cost and safety issues. Objective: We therefore aimed to develop and validate prediction models of Aβ positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers. Methods: We recruited 529 aMCI patients from multiple centers who underwent Aβ PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers). Results: Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aβ positivity. Conclusion: Our results suggest that ML models are effective in predicting Aβ positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.


2008 ◽  
Vol 4 ◽  
pp. T265-T265
Author(s):  
Jan Laczó ◽  
Kamil Vlček ◽  
Václav Mat'oška ◽  
Martin Vyhnálek ◽  
Hana Magerová ◽  
...  

2017 ◽  
Vol 30 (5) ◽  
pp. 477-485 ◽  
Author(s):  
Ana GB Rabelo ◽  
Camila VL Teixeira ◽  
Thamires NC Magalhães ◽  
Ana Flávia MK Carletti-Cassani ◽  
Augusto CS Amato Filho ◽  
...  

Introduction The search for a reliable neuroimaging biomarker in Alzheimer’s disease is a matter of intense research. The presence of cerebral microbleeds seems to be a potential biomarker. However, it is not clear if the presence of microbleeds has clinical usefulness to differentiate mild Alzheimer’s disease and amnestic mild cognitive impairment from normal aging. We aimed to verify if microbleed prevalence differs among three groups: mild Alzheimer’s disease, amnestic mild cognitive impairment due to Alzheimer’s disease, and normal controls. Moreover, we studied whether microbleeds were associated with apolipoprotein E allele ɛ4 status, cerebrospinal fluid amyloid-beta, total and phosphorylated tau protein levels, vascular factors, and cognition. Methods Twenty-eight mild Alzheimer’s disease patients, 34 with amnestic mild cognitive impairment and 36 cognitively normal elderly subjects underwent: magnetic resonance imaging with a susceptibility-weighted imaging sequence on a 3T scanner, apolipoprotein E genotyping and a full neuropsychological evaluation. Only amnestic mild cognitive impairment and mild Alzheimer’s disease underwent cerebrospinal fluid analysis. We compared the groups and verified if microbleeds were predicted by all other variables. Results Mild Alzheimer’s disease presented a higher prevalence of apolipoprotein E allele ɛ4 in relation to amnestic mild cognitive impairment and control group. No significant differences were found between groups when considering microbleed presence. Logistic regression tests failed to find any relationship between microbleeds and the variables. We performed three different regression models using different independent variables: Model 1 - amyloid-beta, phosphorylated tau protein, total tau, apolipoprotein E allele ɛ4 status, age, and sex; Model 2 - vascular risk factors, age, and sex; Model 3 - cognitive scores sex, age, and education. Conclusion Although microbleeds might be related to the Alzheimer’s disease process, their presence is not a good candidate for a neuroimaging biomarker of the disease, especially in its early phases.


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