Mapping the effects of ApoE4, age and cognitive status on 18F-florbetapir PET measured regional cortical patterns of beta-amyloid density and growth

Murphy, Kelly R.; Landau, Susan; Choudhury, Kingshuk Roy; Hostage, Christopher A.; Shpanskaya, Katie; Sair, Haris I.; Petrella, Jeffrey R.; Wong, Terence Z.; Doraiswamy, P. Murali

doi:10.1016/j.neuroimage.2013.04.048

Cited by 44 publications

(30 citation statements)

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“…beta-amyloid) in our models since CSF and amyloid-PET were available only in a small subset of individuals in ADNI-1. However, a study of ADNI-2 subjects has shown a robust correlation between the APOE e4 allele and cortical amyloid burden [51], suggesting that APOE e4 may have served as a surrogate for cortical amyloid plaque load in our analysis.…”

Section: Discussionmentioning

confidence: 99%

BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer’s disease

et al. 2015

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The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer’s disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.

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Section: Discussionmentioning

confidence: 99%

BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer’s disease

et al. 2015

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“…This graded effect of APOE4 on atrophy, hypometabolism, and amyloid deposition, illustrated in Figure 1, is consistent with several results from multimodal neuroimaging studies. It has been shown for example that the effect of APOE4 on Aβ deposition or CSF-Aβ (known to be highly correlated with cortical Aβ deposition) was greater than the effect of the clinical group, while there was a reverse effect for CSF-tau and MRI atrophy (Vemuri et al, 2010;Murphy et al, 2013). Chen et al, (2012) reported that FDG-PET showed a stronger association to APOE4 compared to structural MRI, and concluded that PCC hypometabolism appears earlier than observable MRIbased structural abnormalities.…”

Section: Mechanisms and Multimodal Studiesmentioning

confidence: 99%

Neuroimaging biomarkers for Alzheimer's disease in asymptomatic APOE4 carriers

Chételat

Fouquet

2013

Revue Neurologique

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Abstract:Introduction. The E4 allele of the apolipoprotein E (APOE4) is the major known genetic risk factor for AD, with a dramatic increase in the risk of developing AD as the number of APOE4 alleles increases from 0 to 2. For this reason, asymptomatic APOE4 carriers as a group offer a great opportunity to seek for the presence of early biomarkers for AD. The present article reviews neuroimaging studies on APOE4 carriers, focusing on cognitively normal individuals and on the main neuroimaging biomarkers for AD, i.e. atrophy with structural MRI, hypometabolism with FDG-PET, and amyloid deposition with amyloid-PET imaging.State of art. There is a great number of studies on the effect of APOE4 on brain structures, and they tend to show significant atrophy in APOE4 carriers compared to non carriers especially in regions susceptible to AD pathology such as the hippocampus. However, results are rather discrepant which suggests that the effect of APOE4 on brain structure is subtle. As for FDG-PET metabolism, the few studies show decreased metabolism, again especially in AD-sensitive regions such as posterior associative parietal areas, with a dose-dependent effect (i.e. worsening as the number of APOE4 alleles increases). Finally, there is a unanimous and major effect of APOE4 on amyloid deposition with an increase in Aβ load as the number of APOE4 alleles increases and a decrease in the age of predicted amyloid-positivity in APOE4 carriers. This graded effect of APOE4 on atrophy, hypometabolism, and amyloid deposition is consistent with multimodal neuroimaging studies suggestive of a predominant effect of APOE4 on amyloid rather than tau-related injury and on brain metabolism rather than brain structure. Neuroimaging studies also suggest that APOE4 effects may be mediated by both Aβ-dependent and Aβ-independent pathological processes. This contradicts the view that Aβ pathology is a necessary upstream event to neuronal injury in AD. Perspectives and conclusion. Future studies would tell whether the mechanisms and sequences evidenced in carriers is comparable to those found in non carriers, but it is likely that APOE4 not only influences the risk for AD, but also modulates the physiopathological cascade. Altogether, APOE4 carriers offer a great opportunity to investigate brain changes in the asymptomatic stages of AD and to further our understanding of the physiopathology of the disease, although precaution is needed for interpretation in AD at large. Etat des connaissances. De très nombreuses études ont évalué les effets de l'APOE4 sur la structure cérébrale. Les résultats sont très divergents, même s'ils suggèrent dans l'ensemble un effet subtile dans le sens d'une diminution de volume dans les régions les plus sensibles à la MA telles que l'hippocampe. Les rares études en TEP-FDG indiquent une baisse du métabolisme là encore dans les régions altérées précocement dans la MA comme le cortex associatif pariétal postérieur, avec un effet dose-dépendant (i.e. fonction du nombre d'allèles APOE4). Enfin, l'effet sur les d...

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“…Swaminathan et al [58] examined the association between plasma Aβ from peripheral blood and cortical amyloid deposition on [ 11 C]PiB PET and found that this relationship was modulated by APOE ε4 genotype. Murphy et al [59] mapped the effects of APOE ε4 on ADNI [ 18 F]Florbetapir scans and found a significant effect in the four cortical regions examined. Risacher et al [60] investigated the role of APOE genotype in EMCI and found that the APOE ε4 allele was associated with increased amyloid accumulation on [ 18 F]Florbetapir PET, lower CSF Aβ 1-42 , and increased CSF tau levels in EMCI and CN.…”

Section: Resultsmentioning

confidence: 99%

Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans

Saykin

Shen

Yao

et al. 2015

Alzheimer's & Dementia

255

214

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INTRODUCTION Genetic data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been crucial in advancing the understanding of AD pathophysiology. Here we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans. METHODS Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing (WES, WGS) data have been obtained and disseminated. RESULTS ADNI genetic data have been downloaded thousands of times and over 300 publications have resulted, including reports of large scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies employed ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first WES and WGS data sets and reports in healthy controls, MCI, and AD. DISCUSSION Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data, and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multi-omics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.

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Mapping the effects of ApoE4, age and cognitive status on 18F-florbetapir PET measured regional cortical patterns of beta-amyloid density and growth

Cited by 44 publications

References 30 publications

BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer’s disease

BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer’s disease

Neuroimaging biomarkers for Alzheimer's disease in asymptomatic APOE4 carriers

Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans

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