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      Patterns of progressive atrophy vary with age in Alzheimer's disease patients

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          Abstract

          Age is not only the greatest risk factor for Alzheimer's disease (AD) but also a key modifier of disease presentation and progression. Here, we investigate how longitudinal atrophy patterns vary with age in mild cognitive impairment (MCI) and AD. Data comprised serial longitudinal 1.5-T magnetic resonance imaging scans from 153 AD, 339 MCI, and 191 control subjects. Voxel-wise maps of longitudinal volume change were obtained and aligned across subjects. Local volume change was then modeled in terms of diagnostic group and an interaction between group and age, adjusted for total intracranial volume, white-matter hyperintensity volume, and apolipoprotein E genotype. Results were significant at p < 0.05 with family-wise error correction for multiple comparisons. An age-by-group interaction revealed that younger AD patients had significantly faster atrophy rates in the bilateral precuneus, parietal, and superior temporal lobes. These results suggest younger AD patients have predominantly posterior progressive atrophy, unexplained by white-matter hyperintensity, apolipoprotein E, or total intracranial volume. Clinical trials may benefit from adapting outcome measures for patient groups with lower average ages, to capture progressive atrophy in posterior cortices.

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          Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families.

          The apolipoprotein E type 4 allele (APOE-epsilon 4) is genetically associated with the common late onset familial and sporadic forms of Alzheimer's disease (AD). Risk for AD increased from 20% to 90% and mean age at onset decreased from 84 to 68 years with increasing number of APOE-epsilon 4 alleles in 42 families with late onset AD. Thus APOE-epsilon 4 gene dose is a major risk factor for late onset AD and, in these families, homozygosity for APOE-epsilon 4 was virtually sufficient to cause AD by age 80.
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            Neuroimaging correlates of pathologically defined subtypes of Alzheimer's disease: a case-control study.

            Three subtypes of Alzheimer's disease (AD) have been pathologically defined on the basis of the distribution of neurofibrillary tangles: typical AD, hippocampal-sparing AD, and limbic-predominant AD. Compared with typical AD, hippocampal-sparing AD has more neurofibrillary tangles in the cortex and fewer in the hippocampus, whereas the opposite pattern is seen in limbic-predominant AD. We aimed to determine whether MRI patterns of atrophy differ between these subtypes and whether structural neuroimaging could be a useful predictor of pathological subtype at autopsy. We identified patients who had been followed up in the Mayo Clinic Alzheimer's Disease Research Center (Rochester, MN, USA) or in the Alzheimer's Disease Patient Registry (Rochester, MN, USA) between 1992 and 2005. To be eligible for inclusion, participants had to have had dementia, AD pathology at autopsy (Braak stage ≥IV and intermediate to high probability of AD), and an ante-mortem MRI. Cases were assigned to one of three pathological subtypes--hippocampal-sparing, limbic-predominant, and typical AD--on the basis of neurofibrillary tangle counts in hippocampus and cortex and ratio of hippocampal to cortical burden, without reference to neuronal loss. Voxel-based morphometry and atlas-based parcellation were used to compare patterns of grey matter loss between groups and with age-matched control individuals. Neuroimaging was obtained at the time of first presentation. To summarise pair-wise group differences, we report the area under the receiver operator characteristic curve (AUROC). Of 177 eligible patients, 125 (71%) were classified as having typical AD, 33 (19%) as having limbic-predominant AD, and 19 (11%) as having hippocampal-sparing AD. Most patients with typical (98 [78%]) and limbic-predominant AD (31 [94%]) initially presented with an amnestic syndrome, but fewer patients with hippocampal-sparing AD (eight [42%]) did. The most severe medial temporal atrophy was recorded in patients with limbic-predominant AD, followed by those with typical disease, and then those with hippocampal-sparing AD. Conversely, the most severe cortical atrophy was noted in patients with hippocampal-sparing AD, followed by those with typical disease, and then limbic-predominant AD. The ratio of hippocampal to cortical volumes allowed the best discrimination between subtypes (p<0·0001; three-way AUROC 0·52 [95% CI 0·47-0·52]; ratio of AUROC to chance classification 3·1 [2·8-3·1]). Patients with typical AD and non-amnesic initial presentation had a significantly higher ratio of hippocampal to cortical volumes (median 0·045 [IQR 0·035-0·056]) than did those with an amnesic presentation (0·041 [0·031-0·057]; p=0·001). Patterns of atrophy on MRI differ across the pathological subtypes of AD. MRI regional volumetric analysis can reliably track the distribution of neurofibrillary tangle pathology and can predict pathological subtype of AD at autopsy. US National Institutes of Health (National Institute on Aging). Copyright © 2012 Elsevier Ltd. All rights reserved.
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              Vascular risk factors and dementia: how to move forward?

              In recent years, accumulating evidence has suggested that vascular risk factors contribute to Alzheimer disease (AD). Vascular dementia had been traditionally considered secondary to stroke and vascular disease. It has been traditionally distinguished from AD, considered to be a purely neurodegenerative form of dementia. However, in light of this more recent literature, it appears that there is a spectrum: ranging from patients with pure vascular dementia to patients with pure AD and including a large majority of patients with contributions from both Alzheimer and vascular pathologies. In this article, we discuss the impact of vascular risk factors on AD and its consequences at the individual level and at the population level by highlighting the concept of attributable risk. We then discuss the key questions and next steps involved in designing a therapeutic trial to control vascular risk factors for the prevention of dementia.
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                Author and article information

                Contributors
                Journal
                Neurobiol Aging
                Neurobiol. Aging
                Neurobiology of Aging
                Elsevier
                0197-4580
                1558-1497
                1 March 2018
                March 2018
                : 63
                : 22-32
                Affiliations
                [a ]Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
                [b ]FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
                [c ]Wellcome Trust Centre for Neuroimaging, 12 Queen Square, London, UK
                [d ]Translational Imaging Group, Centre for Medical Image Computing, University College London, London, UK
                [e ]London School of Hygiene and Tropical Medicine, London, UK
                [f ]Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
                [g ]Pennington Biomedical Research Center, Baton Rouge, LA, USA
                Author notes
                []Corresponding author at: Dementia Research Centre, Box 16, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK. Tel.: +44 (0) 20 3108 6167; fax: +44 (0) 20 3448 3104.Dementia Research CentreBox 16National Hospital for Neurology and NeurosurgeryQueen SquareLondonWC1N 3BGUK cassidy.fiford.10@ 123456ucl.ac.uk
                [1]

                These authors jointly contributed to senior authorship.

                [2]

                Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database ( adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

                Article
                S0197-4580(17)30371-8
                10.1016/j.neurobiolaging.2017.11.002
                5805840
                29220823
                5e1a6cbe-4fb7-4bf3-b30d-26a8a7e14222
                © 2017 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 17 May 2017
                : 14 October 2017
                : 6 November 2017
                Categories
                Article

                Neurosciences
                aging,early-onset alzheimer's disease,alzheimer's disease,atrophy,late-onset,mild cognitive impairment (mci),hippocampus

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