Evidence map›Paper›PMID 42860384›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2026

Data‑driven cognitive composites predict Aβ‑related decline and progression to MCI: the ADOPIC study.

Bhargav Tallapragada, Brook Galna, Rosita Shishegar, Shaun J Markovic, Belinda M Brown, Vincent Dore, Simon M Laws, Tenielle Porter, Samantha Burnham, Yen Ying Lim and 13 more

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In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

23 authors.

Bhargav TallapragadaCentre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
Brook GalnaCentre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
Rosita ShishegarThe Australian e-Health Research Centre, CSIRO, Melbourne, Australia.
Shaun J MarkovicDiscipline of Psychology, Counselling and Criminology, Edith Cowan University, Joondalup, Western Australia, Australia.
Belinda M BrownCentre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
Vincent DoreThe Australian e-Health Research Centre, CSIRO, Melbourne, Australia.
Simon M LawsCentre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia.
Tenielle PorterCentre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia.
Samantha BurnhamThe Australian e-Health Research Centre, CSIRO, Melbourne, Australia.
Yen Ying LimSchool of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia.
Jason HassenstabInstitute of Clinical and Translational Sciences, Washington University School of Medicine, St Louis, Missouri, USA.
Christopher C RoweDepartment of Medicine, Austin Health, Heidelberg, Victoria, Australia.
Liang JinFlorey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia.
Jurgen FrippThe Australian e-Health Research Centre, CSIRO, Brisbane, Australia.
James D DoeckeCentre for Precision Health, Edith Cowan University, Joondalup, Western Australia, Australia.
Duygu TosunCenter for Imaging of Neurodegenerative Diseases, University of California-San Francisco, San Francisco, California, USA.
Michael WeinerCenter for Imaging of Neurodegenerative Diseases, University of California-San Francisco, San Francisco, California, USA.
Ralph N MartinsAlzheimer's Research Australia, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia.
John C MorrisWashington University School of Medicine, St. Louis, Missouri, USA.
Colin L MastersAlzheimer's Research Australia, Sarich Neuroscience Research Institute, Nedlands, Western Australia, Australia.
Paul MaruffFlorey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia.
Hamid R SohrabiCentre for Healthy Ageing, Health Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.ORCID https://orcid.org/0000-0001-8017-8682
of ADOPIC study

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI Paul S. Aisen · 2016 to 2026
$226.7M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
The MobileToolbox for Monitoring Cognitive Function - Technical CoreU2CAG060426 · NIA · NORTHWESTERN UNIVERSITY AT CHICAGO · PI SANDRA WEINTRAUB · 2018 to 2026
$35.0M
Democratising Machine Learning for researchers working in Alzheimer's spaceR01AG058676 · NIA · UNIVERSITY OF MELBOURNE · PI MASTERS, COLIN · 2018 to 2022
$10.9M
THE XNAT IMAGING INFORMATICS PLATFORMR01EB009352 · NIBIB · WASHINGTON UNIVERSITY · PI Daniel Scott Marcus · 2009 to 2026
$9.3M
Validation of Online Methods to Predict and Monitor Cognitive DeclineRF1AG059009 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NOSHENY, RACHEL LAUREN · 2018 to 2018
$6.2M
Biomarker Evaluation in Young Onset Dementia from Diverse Populations (BEYONDD)R56AG075744 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BOXER, ADAM L., BYRD, DESIREE A · 2022 to 2022
$6.1M
Risk for Later-Life Cognitive Impairment, Neurobehavioral Dysregulation, and Dementia in Former Soccer and American Football Players: The Head Impact and Trauma Surveillance Study (HITSS)R01NS119651 · NINDS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI ALOSCO, MICHAEL · 2021 to 2025
$3.9M
The Brain Health Registry for facilitating interdisciplinary aging researchR33AG062867 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NOSHENY, RACHEL LAUREN · 2020 to 2024
$3.2M
Network Modelling of Multimodal Dynamics in Alzheimer’s Disease and DementiaRF1AG062196 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NAGARAJAN, SRIKANTAN S., RAJ, ASHISH · 2019 to 2019
$3.0M
Alzheimer's AssociationAustralian Catholic UniversityCalifornia Department of Public HealthDepartment of DefenseJohnson & JohnsonKevin and Connie ShanahanNational Health and Medical Research Council 1132604National Health and Medical Research Council 1140853National Health and Medical Research Council 1152623National Health and Medical Research Council 1R01NS119651-01National Health and Medical Research Council 1RF1AG059009-01National Health and Medical Research Council 2 U19 AG024904.16National Health and Medical Research Council 5R01AG058676-02National Health and Medical Research Council 5U2CAG060426-04National Health and Medical Research Council R33 AG062867National Health and Medical Research Council R56AG075744-01A1National Health and Medical Research Council RF1AG062196NCATS NIH HHS UL1 TR000448NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS R01 AG043434NIA NIH HHS R01 AG058676NIA NIH HHS R01-AG058676-01A1NIA NIH HHS R33 AG062867NIA NIH HHS R56 AG075744NIA NIH HHS RF1 AG059009NIA NIH HHS RF1 AG062196NIA NIH HHS U19 AG024904NIA NIH HHS U2C AG060426NIBIB NIH HHS R01 EB009352NINDS NIH HHS R01 NS119651The Stroke Foundation, and the Veterans AdministrationUniversity of Michigan, Siemens, Biogen, Hillblom Foundation
6 · The paper itself

Abstract

introductionCognitive composites offer greater sensitivity to amyloid beta (Aβ)-related decline compared to individual tests for tracking longitudinal cognitive change. We developed and validated data-driven composites from the harmonized Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) study, addressing gaps in psychometric reporting and multi-cohort validation.

methodsThe study included 1964 cognitively unimpaired (CU) participants at baseline (longitudinal subset = 1110). Principal component analysis-derived domain-specific and general cognitive composites were evaluated for psychometric properties, sensitivity to Aβ-related decline, and mild cognitive impairment (MCI) progression prediction compared to the Alzheimer's Disease Cooperative Study - Preclinical Alzheimer's Cognitive Composite (ADCS-PACC).

resultsProposed composites demonstrated good to excellent psychometric properties and sensitivity to differentiating CU Aβ trajectories (p < 0.001). General cognition demonstrated longitudinal sensitivity comparable to the ADCS-PACC (d = -0.20) and equivalent classification performance (area under the receiver operating characteristic curve = 0.70). All composites significantly predicted MCI progression (p < 0.001). DISCUSSION: The composites perform comparably to the ADCS-PACC, developed for CU with Aβ, in longitudinal sensitivity while providing comprehensive psychometric characterization and domain-specific measurement, supporting their utility as outcome measures in preclinical Alzheimer's disease studies.

Indexed as

Alzheimer DiseaseAmyloid beta-PeptidesCognitive DysfunctionNeuropsychological TestsAgedDisease ProgressionFemaleHumansLongitudinal StudiesMalePsychometricsAmyloid beta-Peptidescognitive composite scorecognitive declinemild cognitive impairmentpreclinical ADPreclinical Alzheimer's Cognitive Composite (PACC)

Identifiers

PMID42860384
PMCPMC13655217

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.