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      Modeling grey matter atrophy as a function of time, aging or cognitive decline show different anatomical patterns in Alzheimer's disease

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          Abstract

          Background

          Grey matter (GM) atrophy in Alzheimer's disease (AD) is most commonly modeled as a function of time. However, this approach does not take into account inter-individual differences in initial disease severity or changes due to aging. Here, we modeled GM atrophy within individuals across the AD clinical spectrum as a function of time, aging and MMSE, as a proxy for disease severity, and investigated how these models influence estimates of GM atrophy.

          Methods

          We selected 523 individuals from ADNI (100 preclinical AD, 288 prodromal AD, 135 AD dementia) with abnormal baseline amyloid PET/CSF and ≥1 year of MRI follow-up. We calculated total and 90 regional GM volumes for 2281 MRI scans (median [IQR]; 4 [3–5] scans per individual over 2 [1.6–4] years) and used linear mixed models to investigate atrophy as a function of time, aging and decline on MMSE. Analyses included clinical stage as interaction with the predictor and were corrected for baseline age, sex, education, field strength and total intracranial volume. We repeated analyses for a sample of participants with normal amyloid ( n = 387) to assess whether associations were specific for amyloid pathology.

          Results

          Using time or aging as predictors, amyloid abnormal participants annually declined −1.29 ± 0.08 points and − 0.28 ± 0.03 points respectively on the MMSE and −12.23 ± 0.47 cm 3 and −8.87 ± 0.34 respectively in total GM volume ( p < .001). For the time and age models atrophy was widespread and preclinical and prodromal AD showed similar atrophy patterns. Comparing prodromal AD to AD dementia, AD dementia showed faster atrophy mostly in temporal lobes as modeled with time, while prodromal AD showed faster atrophy in mostly frontoparietal areas as modeled with age (p FDR < 0.05). Modeling change in GM volume as a function of decline on MMSE, slopes were less steep compared to those based on time and aging (−4.1 ± 0.25 cm 3 per MMSE point decline; p < .001) and showed steeper atrophy for prodromal AD compared to preclinical AD in the right inferior temporal gyrus ( p < .05) and compared to AD dementia mostly in temporal areas (p FDR < 0.05). Associations with time, aging and MMSE remained when accounting for these effects in the other models, suggesting that all measures explain part of the variance in GM atrophy. Repeating analyses in amyloid normal individuals, effects for time and aging showed similar widespread anatomical patterns, while associations with MMSE were largely reduced.

          Conclusion

          Effects of time, aging and MMSE all explained variance in GM atrophy slopes within individuals. Associations with MMSE were weaker than those for time or age, but specific for amyloid pathology. This suggests that at least some of the atrophy observed in time or age models may not be specific to AD.

          Highlights

          • Modeling atrophy as a function of time or aging show similar anatomical patterns.
          • GM atrophy as a function of time or aging seems nonspecific for amyloid pathology.
          • GM atrophy as a function of MMSE shows involvement of different anatomical patterns.
          • Atrophy modeled based on time or age was steeper than modeled based on MMSE.
          • Atrophy patterns based on MMSE were specific for amyloid pathology.

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          Most cited references 18

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          Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses

           Quang Vuong (1989)
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            Prevalence of Dementia in the United States: The Aging, Demographics, and Memory Study

            Aim: To estimate the prevalence of Alzheimer’s disease (AD) and other dementias in the USA using a nationally representative sample. Methods: The Aging, Demographics, and Memory Study sample was composed of 856 individuals aged 71 years and older from the nationally representative Health and Retirement Study (HRS) who were evaluated for dementia using a comprehensive in-home assessment. An expert consensus panel used this information to assign a diagnosis of normal cognition, cognitive impairment but not demented, or dementia (and dementia subtype). Using sampling weights derived from the HRS, we estimated the national prevalence of dementia, AD and vascular dementia by age and gender. Results: The prevalence of dementia among individuals aged 71 and older was 13.9%, comprising about 3.4 million individuals in the USA in 2002. The corresponding values for AD were 9.7% and 2.4 million individuals. Dementia prevalence increased with age, from 5.0% of those aged 71–79 years to 37.4% of those aged 90 and older. Conclusions: Dementia prevalence estimates from this first nationally representative population-based study of dementia in the USA to include subjects from all regions of the country can provide essential information for effective planning for the impending healthcare needs of the large and increasing number of individuals at risk for dementia as our population ages.
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              The Alzheimer's Disease Neuroimaging Initiative positron emission tomography core.

              This is a progress report of the Alzheimer's Disease Neuroimaging Initiative (ADNI) positron emission tomography (PET) Core. The Core has supervised the acquisition, quality control, and analysis of longitudinal [(18)F]fluorodeoxyglucose PET (FDG-PET) data in approximately half of the ADNI cohort. In an "add on" study, approximately 100 subjects also underwent scanning with [(11)C] Pittsburgh compound B PET for amyloid imaging. The Core developed quality control procedures and standardized image acquisition by developing an imaging protocol that has been widely adopted in academic and pharmaceutical industry studies. Data processing provides users with scans that have identical orientation and resolution characteristics despite acquisition on multiple scanner models. The Core labs have used many different approaches to characterize differences between subject groups (Alzheimer's disease, mild cognitive impairment, controls), to examine longitudinal change over time in glucose metabolism and amyloid deposition, and to assess the use of FDG-PET as a potential outcome measure in clinical trials. ADNI data indicate that FDG-PET increases statistical power over traditional cognitive measures, might aid subject selection, and could substantially reduce the sample size in a clinical trial. Pittsburgh compound B PET data showed expected group differences, and identified subjects with significant annual increases in amyloid load across the subject groups. The next activities of the PET core in ADNI will entail developing standardized protocols for amyloid imaging using the [(18)F]-labeled amyloid imaging agent AV45, which can be delivered to virtually all ADNI sites. ADNI has demonstrated the feasibility and utility of multicenter PET studies and is helping to clarify the role of biomarkers in the study of aging and dementia. Copyright 2010 The Alzheimer
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                19 March 2019
                2019
                19 March 2019
                : 22
                Affiliations
                [a ]Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
                [b ]Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
                [c ]Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, the Netherlands
                [d ]Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands
                [e ]Institutes of Neurology & Healthcare Engineering, UCL London, London, United Kingdom
                Author notes
                [* ]Corresponding author at: Alzheimer Center Amsterdam, Amsterdam UMC, Location VUmc, PO Box 7057, 1007 MB Amsterdam, the Netherlands. e.dicks@ 123456vumc.nl ellendicks.ac@ 123456gmail.com
                [1]

                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
                S2213-1582(19)30136-6 101786
                10.1016/j.nicl.2019.101786
                6439228
                30921610
                © 2019 The Authors

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

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                Regular Article

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