Effects of ApoE4 and maternal history of dementia on hippocampal atrophy

Andrawis, John P.; Hwang, Kristy; Green, Amity Ella; Kotlerman, Jenny; Elashoff, David; Morra, Jonathan H.; Cummings, Jeffrey L.; Toga, Arthur W.; Thompson, Paul M.; Apostolova, Liana G.

doi:10.1016/j.neurobiolaging.2010.07.020

Cited by 51 publications

(49 citation statements)

References 62 publications

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“…These data are consistent with epidemiologic and biomarker findings that maternal transmission is associated with higher risk than paternal transmission, independent of increased female longevity. 2,11,[13][14][15][16][17][18][26][27][28][29][30][31] Present findings expand on prior unimodality biomarker studies showing multiple biomarker deficits in NL FHm, which appeared to be overall less severe than in FHmp. Interestingly, NL FHm was the only group in which Ab load did not significantly exceed hypometabolism.…”

Section: Statistical Analysis Spss Version 19 (Ibm Corp Armonk Ny)supporting

confidence: 77%

“…It remains to be established whether Ab accumulation is also an early event in LOAD. 1 Previous brain imaging studies have shown that, among NL individuals who have parents with LOAD, those with maternal FH (FHm) present with increased Ab load on 11 C-Pittsburgh compound B (PiB)-PET, 11,12 progressive glucose metabolism reductions on 18 F-fluoro-2-deoxyglucose (FDG)-PET, 13,14 and gray matter volume (GMV) loss on MRI [15][16][17][18] in the same regions as those with clinical AD. In contrast, NL individuals with paternal FH (FHp) are far less likely to show biomarker abnormalities.…”

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confidence: 99%

“…In contrast, NL individuals with paternal FH (FHp) are far less likely to show biomarker abnormalities. 11,[13][14][15][16][17][18] There are no biomarker studies that examined NL individuals with maternal and paternal FH (FHmp).…”

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Brain imaging of cognitively normal individuals with 2 parents affected by late-onset AD

et al. 2014

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Objectives: This brain imaging study examines whether cognitively normal (NL) individuals with 2 parents affected by late-onset Alzheimer disease (LOAD) show evidence of more extensive Alzheimer disease pathology compared with those who have a single parent affected by LOAD.Methods: Fifty-two NL individuals received MRI, 11 C-Pittsburgh compound B (PiB)-PET, and 18 F-fluoro-2-deoxyglucose (FDG)-PET. These included 4 demographically balanced groups (n 5 13/group, aged 32-72 years, 60% female, 30% APOE e4 carriers) of NL individuals with maternal (FHm), paternal (FHp), and maternal and paternal (FHmp) family history of LOAD, and with negative family history (FH2). Statistical parametric mapping, voxel-based morphometry, and z-score mapping were used to compare MRI gray matter volumes (GMVs), partial volumecorrected PiB retention, and FDG metabolism across FH groups and vs FH2. Alzheimer disease (AD) is an age-related neurodegenerative disease and the most common form of dementia. Clinical studies indicate that by the time patients seek diagnosis, the amount of irreversible brain damage that may have already occurred hinders treatment potential. Effective interventions, as they become available, ideally would be implemented in at-risk individuals before symptoms occur.Having a family history (FH) of AD is a major risk factor for developing the disease. 1,2 While the rare early-onset forms of AD (EOAD) have autosomal dominant genetic inheritance, the risk of developing late-onset AD (LOAD) (i.e., .99% of the AD population older than 60 years) is influenced by genetic and nongenetic factors.1 Although LOAD does not show recognizable mendelian inheritance, risk is to some extent genetically determined, as shown by the familial aggregation of many cases.2 Among probands, those with a parent affected by LOAD are at particularly high risk, 3,4 and the risk is higher still when both parents are affected. [5][6][7] However, because only ,5% of individuals have both parents affected by LOAD, 2,5-7 the clinical profile associated with this rare circumstance has been poorly studied and epidemiologic findings have not been characterized by the use of biomarkers.

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Section: Statistical Analysis Spss Version 19 (Ibm Corp Armonk Ny)supporting

confidence: 77%

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confidence: 99%

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Brain imaging of cognitively normal individuals with 2 parents affected by late-onset AD

et al. 2014

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“…25,26 Moreover, a recent study of family history in the ADNI sample found a significant effect of positive maternal history of dementia on hippocampal atrophy in individuals with MCI and AD, independent of age and APOE4. 27 While we did not identify any differences in hippocampal atrophy between FH groups, our parahippocampal atrophy finding in the FHm group is in close proximity. Our results complement several studies showing progressive temporal and parietal hypometabolism in FHm individuals over time.…”

Section: Resultscontrasting

confidence: 58%

Progressive regional atrophy in normal adults with a maternal history of Alzheimer disease

Honea

Swerdlow²,

Vidoni³

et al. 2011

Neurology

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Objective: Beyond age, having a family history is the most significant risk factor for Alzheimer disease (AD). This longitudinal brain imaging study examines whether there are differential patterns of regional gray matter atrophy in cognitively healthy elderly subjects with (FHϩ) and without (FHϪ) a family history of late-onset AD.Methods: As part of the KU Brain Aging Project, cognitively intact individuals with a maternal history (FHm, n ϭ 11), paternal history (FHp, n ϭ 10), or no parental history of AD (FHϪ, n ϭ 32) similar in age, gender, education, and Mini-Mental State Examination (MMSE) score received MRI at baseline and 2-year follow-up. A custom voxel-based morphometry processing stream was used to examine regional differences in atrophy between FH groups, controlling for age, gender, and APOE ⑀4 (APOE4) status. We also analyzed APOE4-related atrophy.Results: Cognitively normal FHϩ individuals had significantly increased whole-brain gray matter atrophy and CSF expansion compared to FHϪ. When FHϩ groups were split, only FHm was associated with longitudinal measures of brain change. Moreover, our voxel-based analysis revealed that FHm subjects had significantly greater atrophy in the precuneus and parahippocampus/ hippocampus regions compared to FHϪ and FHp subjects, independent of APOE4 status, gender, and age. Individuals with an 4 allele had more regional atrophy in the frontal cortex compared to 4 noncarriers. Conclusions:We conclude that FHm individuals without dementia have progressive gray matter volume reductions in select AD-vulnerable brain regions, specifically the precuneus and parahippocampal gyrus. These data complement and extend reports of regional cerebral metabolic differences and increases in amyloid-␤ burden in FHm subjects, which may be related to a higher risk for developing AD. Late-onset Alzheimer disease (AD) is a neurodegenerative disease caused by complex genetic and environmental mechanisms.1 Age is the most significant risk factor for developing AD, followed by a family history of AD, 2 with maternal transmission significantly more frequent than paternal transmission.3 First-degree relatives of individuals with AD are at a 4-to 10-fold higher risk for AD compared to individuals with no family history.2 Since it is unknown how familial transmission of AD biologically increases susceptibility for AD, clarifying mechanisms of familial risk for AD are a step toward developing enhanced treatment and prevention strategies.Various forms of neuroimaging have attempted to characterize genetic and familial risk factors for late-onset AD by studying heritable traits in healthy individuals with no cognitive impairment.1 There are several studies showing that individuals without dementia who are

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“…In addition, APOE-rs429358 has been shown to be related to medial temporal lobe atrophy [31], hippocampal atrophy [32] and cortical atrophy [33]. Variants within membrane-spanning 4-domains subfamily A (MS4A) gene cluster are some other recently discovered AD risk factors [34].…”

Section: Resultsmentioning

confidence: 99%

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-learning Predictive Model

Wang

Yan

Yao

et al. 2017

Lecture Notes in Computer Science

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With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer’s Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations of neuroimaging phenotypes. Although some machine learning models were newly presented to capture the longitudinal patterns in genotype-phenotype association study, most of them required fixed longitudinal structures of prediction tasks and could not automatically learn the interrelations among longitudinal prediction tasks. To address this challenge, we proposed a novel temporal structure auto-learning model to automatically uncover longitudinal genotype-phenotype interrelations and utilized such interrelated structures to enhance phenotype prediction in the meantime. We conducted longitudinal phenotype prediction experiments on the ADNI cohort including 3,123 SNPs and 2 types of biomarkers, VBM and FreeSurfer. Empirical results demonstrated advantages of our proposed model over the counterparts. Moreover, available literature was identified for our top selected SNPs, which demonstrated the rationality of our prediction results. An executable program is available online at https://github.com/littleq1991/sparse_lowRank_regression.

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Effects of ApoE4 and maternal history of dementia on hippocampal atrophy

Cited by 51 publications

References 62 publications

Brain imaging of cognitively normal individuals with 2 parents affected by late-onset AD

Brain imaging of cognitively normal individuals with 2 parents affected by late-onset AD

Progressive regional atrophy in normal adults with a maternal history of Alzheimer disease

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-learning Predictive Model

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