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      Population-based blood screening for preclinical Alzheimer’s disease in a British birth cohort at age 70

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          Abstract

          Alzheimer’s disease has a preclinical stage when cerebral amyloid-β deposition occurs before symptoms emerge, and when amyloid-β-targeted therapies may have maximum benefits. Existing amyloid-β status measurement techniques, including amyloid PET and CSF testing, are difficult to deploy at scale, so blood biomarkers are increasingly considered for screening. We compared three different blood-based techniques—liquid chromatography-mass spectrometry measures of plasma amyloid-β, and single molecule array (Simoa) measures of plasma amyloid-β and phospho-tau181—to detect cortical 18F-florbetapir amyloid PET positivity (defined as a standardized uptake value ratio of >0.61 between a predefined cortical region of interest and eroded subcortical white matter) in dementia-free members of Insight 46, a substudy of the population-based British 1946 birth cohort. We used logistic regression models with blood biomarkers as predictors of amyloid PET status, with or without age, sex and APOE ε4 carrier status as covariates. We generated receiver operating characteristics curves and quantified areas under the curves to compare the concordance of the different blood tests with amyloid PET. We determined blood test cut-off points using Youden’s index, then estimated numbers needed to screen to obtain 100 amyloid PET-positive individuals. Of the 502 individuals assessed, 441 dementia-free individuals with complete data were included; 82 (18.6%) were amyloid PET-positive. The area under the curve for amyloid PET status using a base model comprising age, sex and APOE ε4 carrier status was 0.695 (95% confidence interval: 0.628–0.762). The two best-performing Simoa plasma biomarkers were amyloid-β 42/40 (0.620; 0.548–0.691) and phospho-tau181 (0.707; 0.646–0.768), but neither outperformed the base model. Mass spectrometry plasma measures performed significantly better than any other measure (amyloid-β 1-42/1-40: 0.817; 0.770–0.864 and amyloid-β composite: 0.820; 0.775–0.866). At a cut-off point of 0.095, mass spectrometry measures of amyloid-β 1-42/1-40 detected amyloid PET positivity with 86.6% sensitivity and 71.9% specificity. Without screening, to obtain 100 PET-positive individuals from a population with similar amyloid PET positivity prevalence to Insight 46, 543 PET scans would need to be performed. Screening using age, sex and APOE ε4 status would require 940 individuals, of whom 266 would proceed to scan. Using mass spectrometry amyloid-β 1-42/1-40 alone would reduce these numbers to 623 individuals and 243 individuals, respectively. Across a theoretical range of amyloid PET positivity prevalence of 10–50%, mass spectrometry measures of amyloid-β 1-42/1-40 would consistently reduce the numbers proceeding to scans, with greater cost savings demonstrated at lower prevalence.

          Abstract

          Keshavan et al. show that plasma Aβ 1-42/1-40 and a plasma Aβ composite measured by liquid chromatography–mass spectrometry perform better than various blood biomarkers measured by single molecular array, and better than age, sex and APOE ε4, in screening for preclinical Alzheimer’s disease in the British 1946 birth cohort.

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          "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician.

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            NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease

            In 2011, the National Institute on Aging and Alzheimer’s Association created separate diagnostic recommendations for the preclinical, mild cognitive impairment, and dementia stages of Alzheimer’s disease. Scientific progress in the interim led to an initiative by the National Institute on Aging and Alzheimer’s Association to update and unify the 2011 guidelines. This unifying update is labeled a “research framework” because its intended use is for observational and interventional research, not routine clinical care. In the National Institute on Aging and Alzheimer’s Association Research Framework, Alzheimer’s disease (AD) is defined by its underlying pathologic processes that can be documented by postmortem examination or in vivo by biomarkers. The diagnosis is not based on the clinical consequences of the disease (i.e., symptoms/signs) in this research framework, which shifts the definition of AD in living people from a syndromal to a biological construct. The research framework focuses on the diagnosis of AD with biomarkers in living persons. Biomarkers are grouped into those of β amyloid deposition, pathologic tau, and neurodegeneration [AT(N)]. This ATN classification system groups different biomarkers (imaging and biofluids) by the pathologic process each measures. The AT(N) system is flexible in that new biomarkers can be added to the three existing AT(N) groups, and new biomarker groups beyond AT(N) can be added when they become available. We focus on AD as a continuum, and cognitive staging may be accomplished using continuous measures. However, we also outline two different categorical cognitive schemes for staging the severity of cognitive impairment: a scheme using three traditional syndromal categories and a six-stage numeric scheme. It is important to stress that this framework seeks to create a common language with which investigators can generate and test hypotheses about the interactions among different pathologic processes (denoted by biomarkers) and cognitive symptoms. We appreciate the concern that this biomarker-based research framework has the potential to be misused. Therefore, we emphasize, first, it is premature and inappropriate to use this research framework in general medical practice. Second, this research framework should not be used to restrict alternative approaches to hypothesis testing that do not use biomarkers. There will be situations where biomarkers are not available or requiring them would be counterproductive to the specific research goals (discussed in more detail later in the document). Thus, biomarker-based research should not be considered a template for all research into age-related cognitive impairment and dementia; rather, it should be applied when it is fit for the purpose of the specific research goals of a study. Importantly, this framework should be examined in diverse populations. Although it is possible that β-amyloid plaques and neurofibrillary tau deposits are not causal in AD pathogenesis, it is these abnormal protein deposits that define AD as a unique neurodegenerative disease among different disorders that can lead to dementia. We envision that defining AD as a biological construct will enable a more accurate characterization and understanding of the sequence of events that lead to cognitive impairment that is associated with AD, as well as the multifactorial etiology of dementia. This approach also will enable a more precise approach to interventional trials where specific pathways can be targeted in the disease process and in the appropriate people.
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              Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria.

              In the past 8 years, both the International Working Group (IWG) and the US National Institute on Aging-Alzheimer's Association have contributed criteria for the diagnosis of Alzheimer's disease (AD) that better define clinical phenotypes and integrate biomarkers into the diagnostic process, covering the full staging of the disease. This Position Paper considers the strengths and limitations of the IWG research diagnostic criteria and proposes advances to improve the diagnostic framework. On the basis of these refinements, the diagnosis of AD can be simplified, requiring the presence of an appropriate clinical AD phenotype (typical or atypical) and a pathophysiological biomarker consistent with the presence of Alzheimer's pathology. We propose that downstream topographical biomarkers of the disease, such as volumetric MRI and fluorodeoxyglucose PET, might better serve in the measurement and monitoring of the course of disease. This paper also elaborates on the specific diagnostic criteria for atypical forms of AD, for mixed AD, and for the preclinical states of AD. Copyright © 2014 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Brain
                Brain
                brainj
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                February 2021
                22 January 2021
                22 January 2021
                : 144
                : 2
                : 434-449
                Affiliations
                [1 ] Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London , UK
                [2 ] Clinical Neurochemistry Laboratory, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital , Mölndal, Sweden
                [3 ] Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg , Gothenburg, Sweden
                [4 ] Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London , London, UK
                [5 ] National Institute for Health Research Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation Trust , London, UK
                [6 ] Department of Medical Statistics, London School of Hygiene and Tropical Medicine, University of London , London, UK
                [7 ] MRC Unit for Lifelong Health and Ageing at UCL, London , UK
                [8 ] Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust , London, UK
                [9 ] UK Dementia Research Institute Fluid Biomarkers Laboratory, UK DRI at UCL , London, UK
                Author notes

                Ashvini Keshavan, Josef Pannee, Thomas K. Karikari, Kaj Blennow and Jonathan M. Schott contributed equally to this work.

                Correspondence to: Professor Jonathan Schott Dementia Research Centre, Box 16, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK E-mail: j.schott@ 123456ucl.ac.uk
                Author information
                http://orcid.org/0000-0003-1043-5721
                http://orcid.org/0000-0002-3608-3619
                http://orcid.org/0000-0003-1422-4358
                http://orcid.org/0000-0002-3579-8804
                http://orcid.org/0000-0001-7833-616X
                http://orcid.org/0000-0002-8416-2183
                http://orcid.org/0000-0002-3800-4416
                http://orcid.org/0000-0003-2059-024X
                Article
                awaa403
                10.1093/brain/awaa403
                7940173
                33479777
                97341dbd-7bd3-4be9-aa9c-2e77757db4b9
                © The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 June 2020
                : 10 August 2020
                : 17 September 2020
                Page count
                Pages: 449
                Funding
                Funded by: Wolfson Clinical Research Fellowship;
                Funded by: Weston Brain Institute and Selfridges Group Foundation;
                Award ID: UB17005
                Funded by: Alzheimer’s Research UK, DOI 10.13039/501100002283;
                Award ID: ARUK-PG2014–1946
                Award ID: ARUK-PG2017–1946
                Funded by: Medical Research Council Dementia Platforms UK;
                Award ID: CSUB19166
                Funded by: Wolfson Foundation, DOI 10.13039/501100001320;
                Award ID: PR/ylr/18575
                Funded by: Brain Research Trust, DOI 10.13039/501100000368;
                Award ID: UCC14191
                Funded by: Avid Radiopharmaceuticals, DOI 10.13039/100014392;
                Funded by: Brightfocus fellowship;
                Award ID: #A2020812F
                Funded by: Swedish Alzheimer Foundation;
                Award ID: #AF-930627
                Funded by: Swedish Brain Foundation;
                Award ID: #FO2020-0240
                Funded by: Swedish Dementia Foundation (Demensförbundet);
                Funded by: Gamla Tjänarinnor;
                Funded by: the Aina (Ann) Wallströms and Mary-Ann Sjöbloms Foundation;
                Funded by: Gun and Bertil Stohnes foundation, DOI 10.13039/100009673;
                Funded by: Anna Lisa and Brother Björnsson’s Foundation;
                Funded by: Wallenberg Centre for Molecular and Translational Medicine, DOI 10.13039/501100017018;
                Funded by: Swedish Alzheimer Foundation (Alzheimerfonden);
                Funded by: Swedish Dementia Foundation (Demensförbundet);
                Funded by: Hjärnfonden, DOI 10.13039/501100003792;
                Funded by: Anna Lisa and Brother Björnsson’s Foundation;
                Funded by: Wellcome Trust Clinical Research Fellowship;
                Award ID: 200109/Z/15/Z
                Funded by: Medical Research Council, DOI 10.13039/501100000265;
                Award ID: MC_UU_00019/1
                Award ID: MC_UU_00019/3
                Funded by: UK Dementia Research Institute at University College London;
                Funded by: Medical Research Council, DOI 10.13039/501100000265;
                Funded by: National Institute for Health Research (Senior Investigator award);
                Funded by: Engineering and Physical Sciences Research Council, DOI 10.13039/501100000266;
                Funded by: Swedish Research Council, DOI 10.13039/501100004359;
                Award ID: #2017-00915
                Funded by: Alzheimer Drug Discovery Foundation (ADDF);
                Award ID: #RDAPB-201809-2016615
                Funded by: Swedish Alzheimer Foundation;
                Award ID: #AF-742881
                Funded by: Hjärnfonden, DOI 10.13039/501100003792;
                Award ID: #FO2017-0243
                Funded by: Swedish state under the agreement between the Swedish government and the County Councils;
                Award ID: #ALFGBG-715986
                Funded by: Swedish Research Council, DOI 10.13039/501100004359;
                Award ID: #2018-02532
                Funded by: European Research Council, DOI 10.13039/100010663;
                Award ID: #681712
                Funded by: ADDF, DOI 10.13039/100002565;
                Award ID: #201809-2016862
                Funded by: Swedish State Support for Clinical Research;
                Award ID: #ALFGBG-720931
                Funded by: Leonard Wolfson Experimental Neurology Centre and the UK Dementia Research Institute;
                Funded by: University College London Hospitals Biomedical Research Centre, Engineering and Physical Sciences Research Council;
                Award ID: EP/J020990/1
                Funded by: British Heart Foundation, DOI 10.13039/501100000274;
                Award ID: PG/17/90/33415
                Funded by: EU’s Horizon 2020 research and innovation programme;
                Award ID: 666992
                Funded by: National Institute for Health Research Queen Square Dementia Biomedical Research Unit and the Leonard Wolfson Experimental Neurology Centre;
                Funded by: HD-1 Analyser at UCL was funded by a Multi-User Equipment;
                Funded by: Wellcome Trust, DOI 10.13039/100010269;
                Categories
                Original Articles
                AcademicSubjects/MED00310
                AcademicSubjects/SCI01870

                Neurosciences
                alzheimer’s disease,amyloid imaging,dementia,beta-amyloid,tau,epidemiology
                Neurosciences
                alzheimer’s disease, amyloid imaging, dementia, beta-amyloid, tau, epidemiology

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