Dataset Information

Cognitive variability-A marker for incident MCI and AD: An analysis for the Alzheimer's Disease Neuroimaging Initiative.

ABSTRACT: The potential of intra-individual cognitive variability (IICV) to predict incident mild cognitive impairment (MCI) or Alzheimer's disease (AD) was examined and compared to well-established neuroimaging and genetic predictors.IICV was estimated using four neuropsychological measures for n = 1324 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who were cognitively healthy or diagnosed with MCI at baseline. IICV was used to predict time to incident MCI or AD, and compared to hippocampal volume loss and APOE ?4 status via survival analysis.In survival analyses, controlling for age, education, baseline diagonosis, and APOE ?4 status, likelihood ratio tests indicate that IICV is associated with time to cognitive status change in the full sample (P < .0001), and when the sample was restricted to individuals with MCI at baseline (P < .0001).These findings suggest IICV may be a low-cost, noninvasive alternative to traditional AD biomarkers.

SUBMITTER: Anderson ED

PROVIDER: S-EPMC4961828 | biostudies-other | 2016

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Cognitive variability-A marker for incident MCI and AD: An analysis for the Alzheimer's Disease Neuroimaging Initiative.

Anderson Eric D ED Wahoske Michelle M Huber Mary M Norton Derek D Li Zhanhai Z Koscik Rebecca L RL Umucu Emre E Johnson Sterling C SC Jones Jana J Asthana Sanjay S Gleason Carey E CE

Alzheimer's & dementia (Amsterdam, Netherlands) 20160526

<h4>Introduction</h4>The potential of intra-individual cognitive variability (IICV) to predict incident mild cognitive impairment (MCI) or Alzheimer's disease (AD) was examined and compared to well-established neuroimaging and genetic predictors.<h4>Methods</h4>IICV was estimated using four neuropsychological measures for n = 1324 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who were cognitively healthy or diagnosed with MCI at baseline. IICV was used to predict time to incide ...[more]

PMID: 27489880

Similar Datasets

Project description:INTRODUCTION:The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS:We used standard searches to find publications using ADNI data. RESULTS:(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal ?-amyloid deposition (A?+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than A? deposition; (4) Cerebrovascular risk factors may interact with A? to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of A? pathology along WM tracts predict known patterns of cortical A? deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION:Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

Project description:To elucidate the key molecules, functions, and pathways that bridge mild cognitive impairment (MCI) and Alzheimer's disease (AD), we investigated open gene expression data sets. Differential gene expression profiles were analyzed and combined with potential MCI- and AD-related gene expression profiles in public databases. Then, weighted gene co-expression network analysis was performed to identify the gene co-expression modules. One module was significantly negatively associated with MCI samples, in which gene ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that these genes were related to cytosolic ribosome, ribosomal structure, oxidative phosphorylation, AD, and metabolic pathway. The other two modules correlated significantly with AD samples, in which functional and pathway enrichment analysis revealed strong relationships of these genes with cytoplasmic ribosome, protein binding, AD, cancer, and apoptosis. In addition, we regarded the core genes in the module network closely related to MCI and AD as bridge genes and submitted them to protein interaction network analysis to screen for major pathogenic genes according to the connectivity information. Among them, small nuclear ribonucleoprotein D2 polypeptide (SNRPD2), ribosomal protein S3a (RPS3A), S100 calcium binding protein A8 (S100A8), small nuclear ribonucleoprotein polypeptide G (SNRPG), U6 snRNA-associated Sm-like protein LSm3 (LSM3), ribosomal protein S27a (RPS27A), and ATP synthase F1 subunit gamma (ATP5C1) were not only major pathogenic genes of MCI, but also bridge genes. In addition, SNRPD2, RPS3A, S100A8, SNRPG, LSM3, thioredoxin (TXN), proteasome 20S subunit alpha 4 (PSMA4), annexin A1 (ANXA1), DnaJ heat shock protein family member A1 (DNAJA1), and prefoldin subunit 5 (PFDN5) were not only major pathogenic genes of AD, but also bridge genes. Next, we screened for differentially expressed microRNAs (miRNAs) to predict the miRNAs and transcription factors related the MCI and AD modules, respectively. The significance score of miRNAs in each module was calculated using a hypergeometric test to obtain the miRNApivot-Module interaction pair. Thirty-four bridge regulators were analyzed, among which hsa-miR-519d-3p was recognized as the bridge regulator between MCI and AD. Our study contributed to a better understanding of the pathogenic mechanisms of MCI and AD, and might lead to the development of a new strategy for clinical diagnosis and treatment.

Project description:IntroductionAnalyses of off-label use of acetylcholinesterase inhibitors (AChEIs) in mild cognitive impairment (MCI) has produced mixed results. Post hoc analyses of observational cohorts, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), have reported deleterious effects in AChEI-treated subjects (AChEI+). Here, we used neuroimaging biomarkers to determine whether AChEI+ subjects had a greater rate of neurodegeneration than untreated (AChEI-) subjects while accounting for baseline differences.MethodsWe selected 121 ADNI MCI AChEI+ subjects and 151 AChEI- subjects with a magnetic resonance imaging (MRI) scan; 82 AChEI+ and 110 AChEI- also had a fluorodeoxyglucose (FDG) scan. A subset (83 AChEI+ and 98 AChEI-) had cerebrospinal fluid (CSF) or amyloid positron emission tomography (PET) assessment for amyloid positivity. Linear regression models were used to compare the effect of treatment on changes in Mini-Mental State Examination and Clinical Dementia Rating-Sum of Boxes scores. We used standard regression in SPM (for baseline) and the SPM toolbox sandwich estimator, SwE (for longitudinal) for comparisons of AChEI+ and AChEI- FDG PET and MRI data.ResultsAt baseline, the AChEI+ group had significantly reduced cortical gray matter density (GMD) and more hypometabolism than AChEI- subjects. The greater rate of atrophy and hypometabolic changes over time in AChEI+ compared to AChEI- subjects did not survive correction for baseline differences. AChEI+ participants were more likely to be amyloid-positive and have lower GMD and FDG standardized uptake value ratio than AChEI- at baseline. AChEI+ subjects showed greater atrophy over time, which remained significant after controlling for amyloid status.DiscussionOur data suggest that the observed differences in rates of cognitive decline, atrophy, and hypometabolism are likely the result of significant baseline differences between the groups. Furthermore, the data indicate no treatment effect of AChEI (positive of negative), rather that physicians prescribe AChEI to subjects who present with more severe clinical impairment. This alone may account for the negative effect seen previously in the ADNI population of AChEI use among MCI subjects.

Dataset Information

Cognitive variability-A marker for incident MCI and AD: An analysis for the Alzheimer's Disease Neuroimaging Initiative.

Publications

Cognitive variability-A marker for incident MCI and AD: An analysis for the Alzheimer's Disease Neuroimaging Initiative.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets