In one paragraphArticle in medRxiv : the preprint server for health sciences, 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
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0citing papers in PubMed
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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 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
13 authors.
Tom W EarnestMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis; St. Louis, MO, USA.ORCID 0000-0001-8671-8424 Braden Y YangMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis; St. Louis, MO, USA.ORCID 0000-0002-2558-4132 Ahmad ChowdhuryMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis; St. Louis, MO, USA.ORCID 0000-0003-1663-7375 Sung Min HaMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis; St. Louis, MO, USA.ORCID 0000-0002-3526-9101 Abdalla BaniMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis; St. Louis, MO, USA.ORCID 0000-0002-1803-4837 Soo-Jong KimMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis; St. Louis, MO, USA.
Arash NazeriMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis; St. Louis, MO, USA.ORCID 0000-0001-6983-0641 John C MorrisDepartment of Neurology, Washington University School of Medicine in St. Louis; St. Louis, St. Louis, MO, USA.ORCID 0000-0001-9820-5618 Tammie L S BenzingerMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis; St. Louis, MO, USA.ORCID 0000-0002-8114-0552 Brian A GordonMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis; St. Louis, MO, USA.ORCID 0000-0003-2109-2955 Alzheimer’s Disease Neuroimaging Initiative
HABS-HD Study Team
Aristeidis SotirasMallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis; St. Louis, MO, USA.ORCID 0000-0003-0795-8820 Funding
Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI CLIFFORD R. JACK · 2016 to 2026
$226.7MThe Health & Aging Brain Study - Health Disparities (HABS-HD)U19AG078109 · NIA · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI Sid E O'Bryant · 2022 to 2026
$181.1MTechnology and Remote Assessment CoreU19AG063911 · NIA · MAYO CLINIC ROCHESTER · PI Bradley F Boeve · 2019 to 2026
$120.9MEarly Onset AD Consortium - the LEAD Study (LEADS)U01AG057195 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI APOSTOLOVA, LIANA G, CARRILLO, MARIA C · 2018 to 2023
$70.3MSmartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5MThe Clinical Significance of Incidental White Matter Lesions on MRI Amongst a Diverse Population with Cognitive Complaints (INDEED)U19NS120384 · NINDS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI DAVID K JOHNSON · 2020 to 2026
$61.9MTHERAPEUTIC EFFECTS OF INTRA-NASAL INSULIN DETEMIRP50AG005136 · NIA · UNIVERSITY OF WASHINGTON · PI GRABOWSKI, THOMAS J. · 1985 to 2019
$57.2MVascular factors, physical activity, and inflammation as modulators of neurodegenerative and cognitive trajectories (Project 2)P01AG036694 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI REISA A. SPERLING · 2010 to 2026
$50.2MThe natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5MSUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI LEURGANS, SUE E. · 1991 to 2020
$49.1MSatellite Diagnostic and Treatment Clinic CoreP50AG008702 · NIA · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI DE JAGER, PHILIP L · 1989 to 2019
$46.3MNational Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8MNCATS NIH HHS UL1 TR000448NCRR NIH HHS S10 RR022984NCRR NIH HHS U24 RR021382NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P01 AG036694NIA NIH HHS P20 AG068053NIA NIH HHS P20 AG068077NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG008017NIA NIH HHS P30 AG010129NIA NIH HHS P30 AG010133NIA NIH HHS P30 AG010161NIA NIH HHS P30 AG013846NIA NIH HHS P30 AG013854NIA NIH HHS P30 AG019610NIA NIH HHS P30 AG035982NIA NIH HHS P30 AG053760NIA NIH HHS P30 AG062421NIA NIH HHS P30 AG062422NIA NIH HHS P30 AG062428NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG062677NIA NIH HHS P30 AG062715NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066462NIA NIH HHS P30 AG066468NIA NIH HHS P30 AG066506NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG066508NIA NIH HHS P30 AG066509NIA NIH HHS P30 AG066511NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG066514NIA NIH HHS P30 AG066515NIA NIH HHS P30 AG066518NIA NIH HHS P30 AG066519NIA NIH HHS P30 AG066530NIA NIH HHS P30 AG066546NIA NIH HHS P30 AG072931NIA NIH HHS P30 AG072946NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072959NIA NIH HHS P30 AG072972NIA NIH HHS P30 AG072973NIA NIH HHS P30 AG072975NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG072977NIA NIH HHS P30 AG072978NIA NIH HHS P30 AG072979NIA NIH HHS P30 AG072980NIA NIH HHS P30 AG086401NIA NIH HHS P30 AG086403NIA NIH HHS P30 AG086404NIA NIH HHS P30 AG092752NIA NIH HHS P50 AG005133NIA NIH HHS P50 AG005136NIA NIH HHS P50 AG005142NIA NIH HHS P50 AG008702NIA NIH HHS P50 AG016573NIA NIH HHS P50 AG016574NIA NIH HHS P50 AG033514NIA NIH HHS P50 AG047266NIA NIH HHS P50 AG047270NIA NIH HHS P50 AG047366NIA NIH HHS R01 AG019771NIA NIH HHS R01 AG021910NIA NIH HHS R01 AG043434NIA NIH HHS R01 AG045571NIA NIH HHS R01 AG052560NIA NIH HHS R01 AG053509NIA NIH HHS R01 AG053993NIA NIH HHS R01 AG054073NIA NIH HHS R01 AG054110NIA NIH HHS R01 AG055005NIA NIH HHS R01 AG056031NIA NIH HHS R01 AG056258NIA NIH HHS R01 AG056531NIA NIH HHS R01 AG058533NIA NIH HHS R01 AG058724NIA NIH HHS R01 AG061788NIA NIH HHS R01 AG062276NIA NIH HHS R01 AG067103NIA NIH HHS R01 AG067781NIA NIH HHS R01 AG068338NIA NIH HHS R01 AG069453NIA NIH HHS R01 AG070862NIA NIH HHS R01 AG073235NIA NIH HHS R01 AG077444NIA NIH HHS R01 AG079280NIA NIH HHS R35 AG072262NIA NIH HHS R56 AG045571NIA NIH HHS R56 AG074321NIA NIH HHS U01 AG057195NIA NIH HHS U19 AG024904NIA NIH HHS U19 AG063911NIA NIH HHS U19 AG073153NIA NIH HHS U19 AG078109NIA NIH HHS U24 AG067418NIA NIH HHS U24 AG072122NIA NIH HHS UF1 AG046150NIBIB NIH HHS P41 EB015922NIBIB NIH HHS R01 EB009352NIDCD NIH HHS R01 DC008552NIH HHS S10 OD018091NIH HHS S10 OD025200NIH HHS S10 OD026738NIH HHS S10 OD030477NIMH NIH HHS P50 MH071616NINDS NIH HHS P30 NS098577NINDS NIH HHS R01 NS075075NINDS NIH HHS U19 NS120384RRD VA I01 RX001534RRD VA I21 RX001381
6 · The paper itselfAbstract
Biological staging models are a key tool for assessing the severity of Alzheimer's disease (AD), supporting personalized medicine and playing a critical role in clinical trial design. Recently, researchers have leveraged positron emission tomography (PET) to inform data-driven staging models of brain pathology related to AD. However, most approaches have focused on staging either amyloid or tau progressions separately, while both pathologies constitute defining factors of AD. Here, we aimed to derive a data-driven staging model which encompasses the spatial spread of both amyloid and tau. We assembled a large sample (n=3,293) of individuals with both amyloid and tau PET imaging stemming from 8 neuroimaging studies of AD and aging. We applied unsupervised machine learning to estimate brain areas which showed coordinated pathological accumulation across our sample, and we used these regions to inform a data-driven model for staging amyloid and tau. The resulting six stage model showed two stages of amyloid progression followed by four stages of tau spread, which were associated with cross-sectional and longitudinal assessments of cognitive decline. Comparison of our biological staging model with clinical disease stages recommended by the Alzheimer's Association showed evidence of heterogenous symptom profiles. Replication of results in holdout data demonstrated the generalizability and prognostic value of our staging model. Together, these findings establish a comprehensive and rigorously validated biological staging model that jointly characterizes amyloid and tau progression, advances beyond global or anatomically predefined summaries, and provides a scalable framework for studying disease heterogeneity and progression in AD.
Identifiers
PMID41959752
PMCPMC13060448
What OpenQuestion holds
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LicenceCC BY-NC
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