APOE &amp;#949;2 Allele Is Associated with Larger Regional Cortical Thicknesses and Volumes

Liu, Yawu; Paajanen, Teemu; Westman, Eric; Zhang, Yi; Wahlund, Lars‐Olof; Simmons, Andrew; Tunnard, Catherine; Sobów, Tomasz; Proitsi, Petroula; Powell, John; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Muehlboeck, Sebastian; Evans, Alan C.; Spenger, Christian; Lovestone, Simon; Soininen, Hilkka

doi:10.1159/000320136

Cited by 41 publications

(33 citation statements)

References 61 publications

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“…2 Furthermore, children carrying e2 performed worse on visuospatial tasks 12 and had thicker temporal cortices 5 than non-e2 carriers, similar to adults carrying e2 with mild cognitive impairment or AD. 19 Because of potentially opposite influences of APOE e4 and APOE e2, prior studies typically evaluated homozygous or heterozygous e4 or e2 individuals, but frequently excluded e2e4 participants. 5,20,21 One study assessed all 6 APOE genotypes but only on IQ and academic achievements.…”

Section: Discussionmentioning

confidence: 99%

Gray matter maturation and cognition in children with different APOE ε genotypes

et al. 2016

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Objective: The aims of the current study were to determine whether children with the 6 different APOE e genotypes show differences in gray matter maturation, particularly for those with e4 and e2 alleles, which are associated with poorer outcomes in many neurologic disorders.Methods: A total of 1,187 healthy children (aged 3-20 years, 52.1% boys, 47.9% girls) with acceptable data from the cross-sectional Pediatric Imaging Neurocognition and Genetics Study were evaluated for the effects of 6 APOE e genotypes on macroscopic and microscopic cortical and subcortical gray matter structures (measured with 3-tesla MRI and FreeSurfer for automated morphometry) and on cognition (NIH Toolbox).Results: Among APOE e4 carriers, age-related changes in brain structures and cognition varied depending on genotype, with the smallest hippocampi in e2e4 children, the lowest hippocampal fractional anisotropy in younger e4e4 children, the largest medial orbitofrontal cortical areas in e3e4 children, and age-dependent thinning of the entorhinal cortex in e4e4 children. Younger e4e4 children had the lowest scores on executive function and working memory, while younger e2e4 children performed worse on attention tasks. Larger parietal gyri in the younger e2e4 children, and thinner temporal and cingulate isthmus cortices or smaller hippocampi in the younger e4e4 children, predicted poorer performance on attention or working memory.Conclusions: Our findings validated and extended prior smaller studies that showed altered brain development in APOE e4-carrier children. The e4e4 and e2e4 genotypes may negatively influence brain development and brain aging at the extremes of age. Studying APOE e polymorphisms in young children may provide the earliest indicators for individuals who might benefit from early interventions or preventive measures for future brain injuries and dementia. Neurology ® 2016;87:585-594 GLOSSARY AD 5 Alzheimer disease; FA 5 fractional anisotropy; GAF 5 genetic ancestry factor; GAM 5 general additive model; PING 5 Pediatric Imaging, Neurocognition, and Genetics; ROI 5 region of interest; SES 5 socioeconomic status; WM 5 working memory.

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Section: Discussionmentioning

confidence: 99%

Gray matter maturation and cognition in children with different APOE ε genotypes

et al. 2016

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“…Using tkmedit, the image processing steps were visually inspected to ensure they had been carried out correctly and corrected for skull-stripping errors and gray/white matter boundary (cases where significant image artifacts or gross brain abnormalities result in segmentation errors were excluded). These segmentation approaches have been used for multivariate classification of Alzheimer's disease and healthy controls (Westman et al, 2010; Mangialasche et al, 2013), neurophysiological image analysis (Liu et al, 2011; Ferreira et al, 2014), imaging-genetic analysis (Liu et al, 2010b; Velayudhan et al, 2013), and biomarker discovery (Thambisetty, 2008; Thambisetty et al, 2010). …”

Section: Methodsmentioning

confidence: 99%

Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the AddNeuroMed cohort

Aguilar

Mecocci

Vellas

et al. 2014

Front. Aging Neurosci.

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Cross sectional studies of patients at risk of developing Alzheimer disease (AD) have identified several brain regions known to be prone to degeneration suitable as biomarkers, including hippocampal, ventricular, and whole brain volume. The aim of this study was to longitudinally evaluate an index based on morphometric measures derived from MRI data that could be used for classification of AD and healthy control subjects, as well as prediction of conversion from mild cognitive impairment (MCI) to AD. Patients originated from the AddNeuroMed project at baseline (119 AD, 119 MCI, 110 controls (CTL)) and 1-year follow-up (62 AD, 73 MCI, 79 CTL). Data consisted of 3D T1-weighted MR images, demographics, MMSE, ADAS-Cog, CERAD and CDR scores, and APOE e4 status. We computed an index using a multivariate classification model (AD vs. CTL), using orthogonal partial least squares to latent structures (OPLS). Sensitivity, specificity and AUC were determined. Performance of the classifier (AD vs. CTL) was high at baseline (10-fold cross-validation, 84% sensitivity, 91% specificity, 0.93 AUC) and at 1-year follow-up (92% sensitivity, 74% specificity, 0.93 AUC). Predictions of conversion from MCI to AD were good at baseline (77% of MCI converters) and at follow-up (91% of MCI converters). MCI carriers of the APOE e4 allele manifested more atrophy and presented a faster cognitive decline when compared to non-carriers. The derived index demonstrated a steady increase in atrophy over time, yielding higher accuracy in prediction at the time of clinical conversion. Neuropsychological tests appeared less sensitive to changes over time. However, taking the average of the two time points yielded better correlation between the index and cognitive scores as opposed to using cross-sectional data only. Thus, multivariate classification seemed to detect patterns of AD changes before conversion from MCI to AD and including longitudinal information is of great importance.

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“…This segmentation approach has been used for neuropsychological-image analysis (Liu et al, 2010c;Liu et al, 2011), imaging-genetic analysis (Liu et al, 2010a;Liu et al, 2010b) and biomarker discovery (Thambisetty et al, 2010).…”

Section: Regional Subcortical Volume Segmentation and Cortical Thicknmentioning

confidence: 99%

Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion

2012

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APOE ε2 Allele Is Associated with Larger Regional Cortical Thicknesses and Volumes

Cited by 41 publications

References 61 publications

Gray matter maturation and cognition in children with different APOE ε genotypes

Gray matter maturation and cognition in children with different APOE ε genotypes

Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the AddNeuroMed cohort

Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion

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