Dataset Information

Monitoring disease progression in mild cognitive impairment: Associations between atrophy patterns, cognition, APOE and amyloid.

ABSTRACT:

Background

A disease severity index (SI) for Alzheimer's disease (AD) has been proposed that summarizes MRI-derived structural measures into a single score using multivariate data analysis.

Objectives

To longitudinally evaluate the use of the SI to monitor disease progression and predict future progression to AD in mild cognitive impairment (MCI). Further, to investigate the association between longitudinal change in the SI and cognitive impairment, Apolipoprotein E (APOE) genotype as well as the levels of cerebrospinal fluid amyloid-beta 1-42 (A?) peptide.

Methods

The dataset included 195 AD, 145 MCI and 228 control subjects with annual follow-up for three years, where 70 MCI subjects progressed to AD (MCI-p). For each subject the SI was generated at baseline and follow-ups using 55 regional cortical thickness and subcortical volumes measures that extracted by the FreeSurfer longitudinal stream.

Results

MCI-p subjects had a faster increase of the SI over time (p < 0.001). A higher SI at baseline in MCI-p was related to progression to AD at earlier follow-ups (p < 0.001) and worse cognitive impairment (p < 0.001). AD-like MCI patients with the APOE ?4 allele and abnormal A? levels had a faster increase of the SI, independently (p = 0.003 and p = 0.004).

Conclusions

Longitudinal changes in the SI reflect structural brain changes and can identify MCI patients at risk of progression to AD. Disease-related brain structural changes are influenced independently by APOE genotype and amyloid pathology. The SI has the potential to be used as a sensitive tool to predict future dementia, monitor disease progression as well as an outcome measure for clinical trials.

SUBMITTER: Falahati F

PROVIDER: S-EPMC5573795 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Monitoring disease progression in mild cognitive impairment: Associations between atrophy patterns, cognition, APOE and amyloid.

Falahati Farshad F Ferreira Daniel D Muehlboeck J-Sebastian JS Eriksdotter Maria M Simmons Andrew A Wahlund Lars-Olof LO Westman Eric E

NeuroImage. Clinical 20170814

<h4>Background</h4>A disease severity index (SI) for Alzheimer's disease (AD) has been proposed that summarizes MRI-derived structural measures into a single score using multivariate data analysis.<h4>Objectives</h4>To longitudinally evaluate the use of the SI to monitor disease progression and predict future progression to AD in mild cognitive impairment (MCI). Further, to investigate the association between longitudinal change in the SI and cognitive impairment, Apolipoprotein E (APOE) genotyp ...[more]

PMID: 28879083

Similar Datasets

Project description:BackgroundAmyloid pathology in subjects with mild cognitive impairment (MCI) is an important risk factor for progression to dementia due to Alzheimer's disease. Predicting the onset of dementia is challenging even in the presence of amyloid, as time to progression varies considerably among patients and depends on the onset of neurodegeneration. Survival analysis can account for variability in time to event, but has not often been applied to MRI measurements beyond singular predefined brain regions such as the hippocampus. Here we used a voxel-wise survival analysis to identify in an unbiased fashion brain regions where decreased gray matter volume is associated with time to dementia, and assessed the effects of amyloid on these associations.MethodsWe included 276 subjects with MCI (mean age 67?±?8, 41% female, mean Mini-Mental State Examination 26.6?±?2.4), baseline 3D T1-weighted structural MRI, baseline cerebrospinal fluid (CSF) biomarkers, and prospective clinical follow-up. We fitted for each voxel a proportional Cox hazards regression model to study whether decreased gray matter volume predicted progression to dementia in the total sample, and stratified for baseline amyloid status.ResultsDementia at follow-up occurred in 122 (44%) subjects over an average follow-up period of 2.5?±?1.5 years. Baseline amyloid positivity was associated with progression to dementia (hazard ratio 2.4, p?<?0.001). Within amyloid-positive subjects, decreased gray matter volume in the hippocampal, temporal, parietal, and frontal regions was associated with more rapid progression to dementia (median (interquartile range) hazard ratio across significant voxels 1.35 (1.32-1.40)). Repeating the analysis in amyloid-negative subjects revealed similar patterns (median (interquartile range) hazard ratio 1.76 (1.66-1.91)).ConclusionsIn subjects with MCI, both abnormal amyloid CSF and decreased gray matter volume were associated with future progression to dementia. The spatial pattern of decreased gray matter volume associated with progression to dementia was consistent for amyloid-positive and amyloid-negative subjects.

Project description:Cognitive function is not generally associated with diet, and there is debate over that association. Moreover, little is known about such associations with the specific cognitive domains and subtypes of mild cognitive impairment (MCI). We analyzed data of 4309 Chinese adults aged 55 and over from the Community-based Cohort Study on Nervous System Diseases from 2018-2019. Dietary habits were assessed at inclusion using a validated semi-quantitative food frequency questionnaire. Cognitive function of the participants was measured by using the Montreal Cognitive Assessment. Analyses were performed using multiple logistic regression and quantile regression with adjustment for socio-demographic, lifestyle, and health-related factors. Compared with normal cognition participants, those with a worse cognition state were characterized as being an older age and lower economic level. After adjustment for potential factors, participants with higher consumption of rice, legumes, fresh vegetables, fresh fruit, pork, poultry, fish, and nuts tended to have higher scores of global cognitive function and domains, and to have lower odds of MCI, while those with higher consumption levels of wheat and eggs had worse cognition, compared with the corresponding bottom consumption level of each food. Participants with a medium consumption level of beef or mutton had 57% (OR: 1.57, 95%CI: 1.07-2.32) higher odds of aMCI-SD, whereas they had 50% (OR: 0.50, 95%CI: 0.34-0.73) lower odds of naMCI-MD. Similarly, the highest consumption level of dairy was positively associated with the odds of aMCI-SD (OR:1.51, 95%CI:1.00-2.29), but inversely linked to the odds of naMCI-SD (OR: 0.60, 95%CI: 0.38-0.93) and naMCI-MD (OR: 0.49, 95%CI: 0.29-0.82). Most diet global cognitive benefits were observed to be associated with the preexisting higher consumption of rice, legumes, fresh vegetables, fresh fruit, meat, and nuts. In addition, the heterogeneity of associations between the consumption of certain foods and MCI subtypes was observed among Chinese adults aged over 55 years. These cross-sectional observations require validation in prospective studies.

Project description:BackgroundIn patients with amyloid-positive mild cognitive impairment (MCI), neurodegenerative biomarkers such as medial temporal lobe atrophy (MTA) are useful to predict disease progression to dementia. Although posterior atrophy (PA) is a well-known neurodegenerative biomarker of Alzheimer's disease, little is known about PA as a predictor in patients with amyloid-positive MCI.MethodsWe included 258 patients with amyloid-positive MCI with at least one follow-up visit, and who had low cerebrospinal fluid (CSF) ?-amyloid1-42 concentration. Data were obtained from the Alzheimer's Disease Neuroimaging Initiative study. We assessed PA and MTA on magnetic resonance imaging (MRI) using visual rating scales and retrospectively determined progression to dementia during the follow-up period of up to 3 years (median 24 months). The Cox proportional hazards model was used to analyze hazard ratios (HRs) of PA and MTA for disease progression. Additionally, subjects were divided into four groups according to brain atrophy pattern (no atrophy, MTA only, PA only, both MTA and PA), and HRs for disease progression were compared with the no atrophy reference group. Analyses were conducted with and without adjustment for CSF phosphorylated tau181p (p-tau) and baseline demographics.ResultsA total of 123 patients (47.7%) showed MTA and 174 patients (67.4%) showed PA. Of the total cohort, 139 cases (53.9%) progressed to dementia. PA and MTA were associated with an increased risk for progression to dementia (HR 2.244, 95% confidence interval (CI) 1.497-3.364, and HR 1.682, 95% CI 1.203-2.352, respectively). In the analysis according to atrophy pattern, HR (95% CI) for progression was 2.998 (1.443-6.227) in the MTA only group, 3.126 (1.666-5.864) in the PA only group, and 3.814 (2.045-7.110) in both MTA and PA group. These results remained significant after adjustment.ConclusionsIn patients with amyloid-positive MCI, PA could predict progression to dementia independently of MTA.

Project description:Recent evidence indicates that measures from cerebrospinal fluid, MRI scans and cognitive testing obtained from cognitively normal individuals can be used to predict likelihood of progression to mild cognitive impairment several years later, for groups of individuals. However, it remains unclear whether these measures are useful for predicting likelihood of progression for an individual. The increasing focus on early intervention in clinical trials for Alzheimer's disease emphasizes the importance of improving the ability to identify which cognitively normal individuals are more likely to progress over time, thus allowing researchers to efficiently screen participants, as well as determine the efficacy of any treatment intervention. The goal of this study was to determine which measures, obtained when individuals were cognitively normal, predict on an individual basis, the onset of clinical symptoms associated with a diagnosis of mild cognitive impairment due to Alzheimer's disease. Cognitively normal participants (n = 224, mean baseline age = 57 years) were evaluated with a range of measures, including: cerebrospinal fluid amyloid-? and phosphorylated-tau, hippocampal and entorhinal cortex volume, cognitive tests scores and APOE genotype. They were then followed to determine which individuals developed mild cognitive impairment over time (mean follow-up = 11 years). The primary outcome was progression from normal cognition to the onset of clinical symptoms of mild cognitive impairment due to Alzheimer's disease at 5 years post-baseline. Time-dependent receiver operating characteristic analyses examined the sensitivity and specificity of individual measures, and combinations of measures, as predictors of the outcome. Six measures, in combination, were the most parsimonious predictors of transition to mild cognitive impairment 5 years after baseline (area under the curve = 0.85; sensitivity = 0.80, specificity = 0.75). The addition of variables from each domain significantly improved the accuracy of prediction. The incremental accuracy of prediction achieved by adding individual measures or sets of measures successively to one another was also examined, as might be done when enrolling individuals in a clinical trial. The results indicate that biomarkers obtained when individuals are cognitively normal can be used to predict which individuals are likely to develop clinical symptoms at 5 years post-baseline. As a number of the measures included in the study could also be used as subject selection criteria in a clinical trial, the findings also provide information about measures that would be useful for screening in a clinical trial aimed at individuals with preclinical Alzheimer's disease.

Dataset Information

Monitoring disease progression in mild cognitive impairment: Associations between atrophy patterns, cognition, APOE and amyloid.

Background

Objectives

Methods

Results

Conclusions

Publications

Monitoring disease progression in mild cognitive impairment: Associations between atrophy patterns, cognition, APOE and amyloid.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets