In one paragraphArticle in Journal of Alzheimer's disease : JAD, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
12 authors.
Emily H TrittschuhDepartment of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0003-0557-1542 Jesse MezDepartment of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.ORCID 0000-0003-1438-5442 Andrew J SaykinDepartment of Radiology and Imaging Services, Indiana University, Indianapolis, IN, USA.ORCID 0000-0002-1376-8532 Cécile Proust-LimaUniv. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France.ORCID 0000-0002-9884-955X Funding
Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI MICHAEL W WEINER · 2016 to 2026
$226.7MPeripheral and Central Biomarkers of Alzheimer's Disease in Diverse CohortsU19AG074879 · NIA · MAYO CLINIC JACKSONVILLE · PI Mariet Allen · 2023 to 2026
$42.0MMVP Data Integration into the ADSP Phenotype Harmonization ConsortiumU24AG074855 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI CUCCARO, MICHAEL L, HOHMAN, TIMOTHY J · 2021 to 2025
$37.5MResearch Education ComponentP30AG010133 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI SAYKIN, ANDREW J · 1991 to 2020
$37.3MUniversity of Washington Alzheimer's Disease Research CenterP30AG066509 · NIA · UNIVERSITY OF WASHINGTON · PI Thomas J. Grabowski · 2020 to 2026
$29.0MRL5: Research Education CoreP30AG072978 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Ann C. McKee · 2021 to 2026
$25.6MResearch Education ComponentP30AG072976 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI ANDREW J SAYKIN · 2021 to 2026
$24.1MUltrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7MKBASE2: Korean Brain Aging Study, Longitudinal Endophenotypes and Systems BiologyU01AG072177 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI LEE, DONG YOUNG, NHO, KWANGSIK TIMOTHY · 2021 to 2025
$11.2MMemory Circuitry in MCI and Early Alzheimer’s Disease Prodrome: Molecular DriversR01AG019771 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI SAYKIN, ANDREW J · 2001 to 2021
$6.9MLeveraging Neuroimaging Biomarkers to Understand the Role of Social Networks in Alzheimer's DiseaseR01AG057739 · NIA · TRUSTEES OF INDIANA UNIVERSITY · PI APOSTOLOVA, LIANA G, PERRY, BREA LOUISE · 2018 to 2022
$3.5MPredictors and consequences of the timing and accuracy of clinical dementia diagnosisR01AG072559 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI Bryan David James · 2022 to 2026
$2.9MNIA NIH HHS P30 AG010133NIA NIH HHS P30 AG066509NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG072978NIA NIH HHS R01 AG019771NIA NIH HHS R01 AG057739NIA NIH HHS R01 AG068193NIA NIH HHS R01 AG072559NIA NIH HHS T32 AG071444NIA NIH HHS U01 AG068057NIA NIH HHS U01 AG072177NIA NIH HHS U19 AG024904NIA NIH HHS U19 AG074879NIA NIH HHS U24 AG074855NLM NIH HHS R01 LM013463
6 · The paper itselfAbstract
BackgroundOthers have examined heterogeneity in Alzheimer's disease (AD); however, few have used longitudinal data while accounting for variation in disease stage. We used latent classes to model heterogeneity in the trajectories of three cognitive domains (memory, language, and executive functioning) starting at AD dementia diagnosis.ObjectiveOur aim was to describe the patterns of heterogeneity in cognitive decline across cognitive domains during the course of AD and to contextualize our findings by assessing associations with demographic factors and neuropathological measures.MethodsWe used cognitive data from the Religious Orders Study, the Rush Memory and Aging Project, and the Minority Aging Research Study in a multi-dimensional joint latent class mixed model, which allowed us to estimate cognitive trajectories that varied across cognitive domains and latent classes. We accounted for the uncertainty in latent class assignment and corrected for multiple hypotheses when assessing the association of the latent classes with demographic and neuropathological variables.ResultsWe identified five latent classes differentiated by level of impairment (high to low) and rate of decline (slow to fast). Within each latent class, the pattern of decline did not differ substantially across cognitive domains. Classes were associated with
Indexed as
Alzheimer DiseaseCognitionCognitive DysfunctionAgedAged, 80 and overDisease ProgressionExecutive FunctionFemaleHumansLongitudinal StudiesMaleMemoryNeuropsychological TestsAlzheimer's diseasecognitive declinelatent class analysislongitudinal studiesneuropathology
Identifiers
PMID41603331
PMCPMC12934236
What OpenQuestion holds
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LicenceTDM
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