Evidence map›Paper›PMID 40249614›Full record

Observational studyJAMA network open2025

Self- and Informant-Report Cognitive Decline Discordance and Mild Cognitive Impairment Diagnosis.

Anna Aaronson, Adam Diaz, Miriam T Ashford, Chengshi Jin, Rachana Tank, Melanie J Miller, Jae Myeong Kang, Manchumad Manjavong, Bernard Landavazo, Joseph Eichenbaum and 10 more

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

5 citing papers in PubMed.

  1. Trial
  2. Review
  3. Article
  4. Article
  5. Observational
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Anna AaronsonVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Adam DiazVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Miriam T AshfordVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Chengshi JinDepartment of Epidemiology and Biostatistics, University of California, San Francisco.
Rachana TankDementia Research Centre, University College London Institute of Neurology, University College London, London, United Kingdom.
Melanie J MillerVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Jae Myeong KangVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Manchumad ManjavongVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Bernard LandavazoVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Joseph EichenbaumVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Diana TruranVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Monica R CamachoVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Juliet FocklerVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Derek FlennikenVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Patrizia VanniniDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston.
Sarah Tomaszewski FariasDepartment of Neurology, University of California, Davis, Sacramento.
R Scott MackinVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Michael W WeinerVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Rachel L NoshenyVeterans Affairs Advanced Imaging Research Center, San Francisco Veteran's Administration Medical Center, San Francisco, California.
Alzheimer’s Disease Neuroimaging Initiative

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI Duygu Tosun-Turgut · 2016 to 2026
$226.7M
The Brain Health Registry for facilitating interdisciplinary aging researchR33AG062867 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NOSHENY, RACHEL LAUREN · 2020 to 2024
$3.2M
NIA NIH HHS R33 AG062867NIA NIH HHS U19 AG024904
6 · The paper itself

Abstract

Importance: Subjective report of cognitive and functional decline from participant-study partner dyads can efficiently assess risk of cognitive impairment and clinical progression. Accuracy of self-report subjective cognitive decline may be limited by lack of awareness about one's own cognitive abilities in adults with MCI and dementia, and the extent to which discordance between self- and study partner-report is associated with diagnosis of cognitive impairment is unknown. Objective: To investigate the association between discordance between self- and study partner-reported cognitive and/or functional decline and MCI diagnosis. Design, Setting, and Participants: This multisite, cross-sectional study used baseline data from 2 longitudinal, observational studies. A total of 921 participant-study partner dyads enrolled in the Alzheimer Disease Neuroimaging Initiative (ADNI) from December 2016 to July 2022, and 279 dyads enrolled in the Brain Health Registry Electronic Validation of Online Methods Study (eVAL) from January 2020 to July 2023 were included. Exposures: Participants and study partners completed the Everyday Cognition Scale (ECog). Participants completed a demographics survey and the Geriatric Depression Scale-Short Form (GDS). Main Outcomes and Measures: The model selection procedure in ADNI identified variables, which were included in a model that was externally validated in the eVAL cohort. The primary outcome was MCI vs cognitively unimpaired (CU) among participants. Results: ADNI participants (921 dyads) had a mean (SD) age of 71 (7) years and mean (SD) of 17 (3) years of education; 485 (53%) were female, 30 (3%) were Asian, 105 (11%) were Black, and 756 (82%) were White. eVAL participants (279 dyads) had a mean (SD) age of 71 (8) years and mean (SD) of 17 (2) years of education; 151 (54%) were female, 17 (6%) were Asian, 12 (4%) were Black, and 245 (88%) were White. The model distinguished CU vs MCI in the validation cohort with an area under the curve of 0.87 (95% CI, 0.88-0.96), sensitivity of 0.50 (95% CI, 0.49-0.80), and specificity of 0.97 (95% CI, 0.95-0.99) based on a regression model. The model included 4 discordance metrics, participant demographics (gender, age, and education), study partner demographics (gender and cohabitation), and depressive symptoms (GDS score). Conclusions and Relevance: In this cross-sectional study of 1200 dyads, measures of ECog score discordance helped distinguish CU from MCI individuals with high specificity. Participant and study partner agreement on lack of observed changes in the participant was associated with lower likelihood of MCI, highlighting the value of dyadic discordance metrics for ruling out MCI in diverse settings.

Indexed as

Cognitive DysfunctionSelf ReportAgedAged, 80 and overCross-Sectional StudiesFemaleHumansLongitudinal StudiesMaleMiddle Aged

Identifiers

PMID40249614
PMCPMC12008764

What OpenQuestion holds

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Registered trials

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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.