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      Accurate risk estimation of β-amyloid positivity to identify prodromal Alzheimer's disease: Cross-validation study of practical algorithms

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          Abstract

          Introduction

          The aim was to create readily available algorithms that estimate the individual risk of β-amyloid (Aβ) positivity.

          Methods

          The algorithms were tested in BioFINDER (n = 391, subjective cognitive decline or mild cognitive impairment) and validated in Alzheimer's Disease Neuroimaging Initiative (n = 661, subjective cognitive decline or mild cognitive impairment). The examined predictors of Aβ status were demographics; cognitive tests; white matter lesions; apolipoprotein E ( APOE); and plasma Aβ 42/Aβ 40, tau, and neurofilament light.

          Results

          Aβ status was accurately estimated in BioFINDER using age, 10-word delayed recall or Mini–Mental State Examination, and APOE (area under the receiver operating characteristics curve = 0.81 [0.77–0.85] to 0.83 [0.79–0.87]). When validated, the models performed almost identical in Alzheimer's Disease Neuroimaging Initiative (area under the receiver operating characteristics curve = 0.80–0.82) and within different age, subjective cognitive decline, and mild cognitive impairment populations. Plasma Aβ 42/Aβ 40 improved the models slightly.

          Discussion

          The algorithms are implemented on http://amyloidrisk.com where the individual probability of being Aβ positive can be calculated. This is useful in the workup of prodromal Alzheimer's disease and can reduce the number needed to screen in Alzheimer's disease trials.

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          Most cited references22

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          High performance plasma amyloid-β biomarkers for Alzheimer’s disease

          To facilitate clinical trials of disease-modifying therapies for Alzheimer's disease, which are expected to be most efficacious at the earliest and mildest stages of the disease, supportive biomarker information is necessary. The only validated methods for identifying amyloid-β deposition in the brain-the earliest pathological signature of Alzheimer's disease-are amyloid-β positron-emission tomography (PET) imaging or measurement of amyloid-β in cerebrospinal fluid. Therefore, a minimally invasive, cost-effective blood-based biomarker is desirable. Despite much effort, to our knowledge, no study has validated the clinical utility of blood-based amyloid-β markers. Here we demonstrate the measurement of high-performance plasma amyloid-β biomarkers by immunoprecipitation coupled with mass spectrometry. The ability of amyloid-β precursor protein (APP)669-711/amyloid-β (Aβ)1-42 and Aβ1-40/Aβ1-42 ratios, and their composites, to predict individual brain amyloid-β-positive or -negative status was determined by amyloid-β-PET imaging and tested using two independent data sets: a discovery data set (Japan, n = 121) and a validation data set (Australia, n = 252 including 111 individuals diagnosed using 11C-labelled Pittsburgh compound-B (PIB)-PET and 141 using other ligands). Both data sets included cognitively normal individuals, individuals with mild cognitive impairment and individuals with Alzheimer's disease. All test biomarkers showed high performance when predicting brain amyloid-β burden. In particular, the composite biomarker showed very high areas under the receiver operating characteristic curves (AUCs) in both data sets (discovery, 96.7%, n = 121 and validation, 94.1%, n = 111) with an accuracy approximately equal to 90% when using PIB-PET as a standard of truth. Furthermore, test biomarkers were correlated with amyloid-β-PET burden and levels of Aβ1-42 in cerebrospinal fluid. These results demonstrate the potential clinical utility of plasma biomarkers in predicting brain amyloid-β burden at an individual level. These plasma biomarkers also have cost-benefit and scalability advantages over current techniques, potentially enabling broader clinical access and efficient population screening.
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            A new rating scale for age-related white matter changes applicable to MRI and CT.

            MRI is more sensitive than CT for detection of age-related white matter changes (ARWMC). Most rating scales estimate the degree and distribution of ARWMC either on CT or on MRI, and they differ in many aspects. This makes it difficult to compare CT and MRI studies. To be able to study the evolution and possible effect of drug treatment on ARWMC in large patient samples, it is necessary to have a rating scale constructed for both MRI and CT. We have developed and evaluated a new scale and studied ARWMC in a large number of patients examined with both MRI and CT. Seventy-seven patients with ARWMC on either CT or MRI were recruited and a complementary examination (MRI or CT) performed. The patients came from 4 centers in Europe, and the scans were rated by 4 raters on 1 occasion with the new ARWMC rating scale. The interrater reliability was evaluated by using kappa statistics. The degree and distribution of ARWMC in CT and MRI scans were compared in different brain areas. Interrater reliability was good for MRI (kappa=0.67) and moderate for CT (kappa=0.48). MRI was superior in detection of small ARWMC, whereas larger lesions were detected equally well with both CT and MRI. In the parieto-occipital and infratentorial areas, MRI detected significantly more ARWMC than did CT. In the frontal area and basal ganglia, no differences between modalities were found. When a fluid-attenuated inversion recovery sequence was used, MRI detected significantly more lesions than CT in frontal and parieto-occipital areas. No differences were found in basal ganglia and infratentorial areas. We present a new ARWMC scale applicable to both CT and MRI that has almost equal sensitivity, except for certain regions. The interrater reliability was slightly better for MRI, as was the detectability of small lesions.
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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
                Alzheimers Dement
                Alzheimers Dement
                Alzheimer's & Dementia
                Elsevier, Inc
                1552-5260
                1552-5279
                1 February 2019
                February 2019
                : 15
                : 2
                : 194-204
                Affiliations
                [a ]Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
                [b ]Department of Neurology, Skåne University Hospital, Lund, Sweden
                [c ]Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
                [d ]Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
                [e ]Department of Molecular Neuroscience, UCL Institute of Neurology, Queen Square, London, United Kingdom
                [f ]UK Dementia Research Institute at UCL, London, United Kingdom
                [g ]Euroimmun AG, Lübeck, Germany
                [h ]Memory Clinic, Skåne University Hospital, Malmö, Sweden
                Author notes
                []Corresponding author. Tel.: +4646177808. sebastian.palmqvist@ 123456med.lu.se
                [∗∗ ]Corresponding author. Tel.: +4640335435. oskar.hansson@ 123456med.lu.se
                [1]

                The 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
                S1552-5260(18)33524-6
                10.1016/j.jalz.2018.08.014
                6374284
                30365928
                90650ffd-ae34-486c-a7f1-24fbc71e797a
                © 2018 The Authors

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

                History
                Categories
                Article

                alzheimer's disease,β-amyloid,prediction,diagnostic accuracy,cerebrospinal fluid,aβ42,risk estimation,position emission tomography,plasma aβ42/aβ40

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