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1.
Alzheimers Dement ; 15(9): 1208-1217, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31399333

RESUMO

INTRODUCTION: We aimed to estimate the frequency of each AT(N) (ß-amyloid deposition [A], pathologic tau [T], and neurodegeneration [N]) profile in different clinical diagnosis groups and to describe the longitudinal change in clinical outcomes of individuals in each group. METHODS: Longitudinal change in clinical outcomes and conversion risk of AT(N) profiles are assessed using linear mixed-effects models and multivariate Cox proportional-hazard models, respectively. RESULTS: Participants with A+T+N+ showed faster clinical progression than those with A-T-N- and A+T±N-. Compared with A-T-N-, participants with A+T+N± had an increased risk of conversion from cognitively normal (CN) to incident prodromal stage of Alzheimer's disease (AD), and from MCI to AD dementia. A+T+N+ showed an increased conversion risk when compared with A+T±N-. DISCUSSION: The 2018 research framework may provide prognostic information of clinical change and progression. It may also be useful for targeted recruitment of participants with AD into clinical trials.


Assuntos
Doença de Alzheimer , Biomarcadores , Progressão da Doença , Sintomas Prodrômicos , Idoso , Doença de Alzheimer/diagnóstico por imagem , Doença de Alzheimer/patologia , Peptídeos beta-Amiloides/metabolismo , Encéfalo/patologia , Estudos Transversais , Feminino , Humanos , Estudos Longitudinais , Masculino , Tomografia por Emissão de Pósitrons , Prognóstico , Proteínas tau/metabolismo
2.
Lancet Neurol ; 17(3): 241-250, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-29397305

RESUMO

BACKGROUND: Models of Alzheimer's disease propose a sequence of amyloid ß (Aß) accumulation, hypometabolism, and structural decline that precedes the onset of clinical dementia. These pathological features evolve both temporally and spatially in the brain. In this study, we aimed to characterise where in the brain and when in the course of the disease neuroimaging biomarkers become abnormal. METHODS: Between Jan 1, 2009, and Dec 31, 2015, we analysed data from mutation non-carriers, asymptomatic carriers, and symptomatic carriers from families carrying gene mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or amyloid precursor protein (APP) enrolled in the Dominantly Inherited Alzheimer's Network. We analysed 11C-Pittsburgh Compound B (11C-PiB) PET, 18F-Fluorodeoxyglucose (18F-FDG) PET, and structural MRI data using regions of interest to assess change throughout the brain. We estimated rates of biomarker change as a function of estimated years to symptom onset at baseline using linear mixed-effects models and determined the earliest point at which biomarker trajectories differed between mutation carriers and non-carriers. This study is registered at ClinicalTrials.gov (number NCT00869817) FINDINGS: 11C-PiB PET was available for 346 individuals (162 with longitudinal imaging), 18F-FDG PET was available for 352 individuals (175 with longitudinal imaging), and MRI data were available for 377 individuals (201 with longitudinal imaging). We found a sequence to pathological changes, with rates of Aß deposition in mutation carriers being significantly different from those in non-carriers first (across regions that showed a significant difference, at a mean of 18·9 years [SD 3·3] before expected onset), followed by hypometabolism (14·1 years [5·1] before expected onset), and lastly structural decline (4·7 years [4·2] before expected onset). This biomarker ordering was preserved in most, but not all, regions. The temporal emergence within a biomarker varied across the brain, with the precuneus being the first cortical region for each method to show divergence between groups (22·2 years before expected onset for Aß accumulation, 18·8 years before expected onset for hypometabolism, and 13·0 years before expected onset for cortical thinning). INTERPRETATION: Mutation carriers had elevations in Aß deposition, reduced glucose metabolism, and cortical thinning compared with non-carriers which preceded the expected onset of dementia. Accrual of these pathologies varied throughout the brain, suggesting differential regional and temporal vulnerabilities to Aß, metabolic decline, and structural atrophy, which should be taken into account when using biomarkers in a clinical setting as well as designing and evaluating clinical trials. FUNDING: US National Institutes of Health, the German Center for Neurodegenerative Diseases, and the Medical Research Council Dementias Platform UK.


Assuntos
Doença de Alzheimer/diagnóstico por imagem , Mapeamento Encefálico , Encéfalo/diagnóstico por imagem , Saúde da Família , Adulto , Doença de Alzheimer/genética , Precursor de Proteína beta-Amiloide/genética , Compostos de Anilina/farmacocinética , Feminino , Fluordesoxiglucose F18/farmacocinética , Humanos , Estudos Longitudinais , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Tomografia por Emissão de Pósitrons , Presenilina-1/genética , Presenilina-2/genética , Estatísticas não Paramétricas , Tiazóis/farmacocinética
3.
Ann Clin Transl Neurol ; 3(10): 740-751, 2016 10.
Artigo em Inglês | MEDLINE | ID: mdl-27752510

RESUMO

OBJECTIVE: To compare the values of arterial spin-labeled (ASL) MRI and fluorodeoxyglucose (FDG) PET in the diagnosis of behavioral variant of frontotemporal dementia (bvFTD) and Alzheimer's disease (AD). METHODS: Partial least squares logistic regression was used to identify voxels with diagnostic value in cerebral blood flow (CBF) and cerebral metabolic rate of glucose (CMRgl) maps from patients with bvFTD (n = 32) and AD (n = 28), who were compared with each other and with cognitively normal controls (CN, n = 15). Diagnostic values of these maps were compared with each other. RESULTS: Regions that differentiated each disorder from controls were similar for CBF and CMRgl. For differentiating AD from CN, the areas under the curve (AUC) for CBF (0.89) and CMRgl (0.91) were similar, with similar sensitivity (CBF: 86%, CMRgl: 78%) and specificity (CBF: 92%, CMRgl: 100%). Likewise, for differentiating bvFTD from CN performances of CBF (AUC = 0.83) and CMRgl (AUC = 0.85) were equivalent, with similar sensitivity (CBF: 78%, CMRgl: 79%) and specificity (CBF: 92%, CMRgl: 100%). In differentiating bvFTD from AD, classification was again similar for CBF (AUC = 0.87) and CMRgl (AUC = 0.79), as were sensitivity (CBF: 83%, CMRgl: 89%) and specificity (CBF: 93%, CMRgl: 78%). None of the differences in any performance measure were statistically significant. INTERPRETATION: ASL-MRI has similar diagnostic utility as FDG-PET in the diagnosis of AD and bvFTD. Continued development of ASL-MRI as a diagnostic tool for neurodegenerative dementias is warranted.

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