Evidence map›Paper›PMID 41332865›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Prognostic performance across Alzheimer's biomarkers, multi-modal physiological measures, and clinical history in asymptomatic individuals.

Randall J Ellis, Audrey Airaud, Varuna H Jasodanand, Sahana S Kowshik, Matteo Bellitti, Vijaya B Kolachalama, Hossein Estiri, M Maria Glymour, Carole Dufouil, Reisa A Sperling and 4 more

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Article in medRxiv : the preprint server for health sciences, 2025. 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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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Randall J EllisDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
Audrey AiraudDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
Varuna H JasodanandDepartment of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States.
Sahana S KowshikDepartment of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States.
Matteo BellittiDepartment of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States.
Vijaya B KolachalamaDepartment of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States.
Hossein EstiriDepartment of Medicine, Massachusetts General Hospital, Boston, MA, United States.
M Maria GlymourDepartment of Epidemiology, Boston University School of Public Health, Boston, MA, United States.
Carole DufouilUniversity Bordeaux, Inserm, Bordeaux Population Health Research Center, Bordeaux, France.
Reisa A SperlingCenter for Alzheimer Research and Treatment, Department of Neurology, MassGeneral Brigham, Harvard Medical School, Boston, MA, United States.
David A BennettRush Alzheimer's Disease Center, Chicago, IL, United States.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID 0000-0002-8756-8525
Alzheimer’s Disease Neuroimaging Initiative
Australian Imaging Biomarkers and Lifestyle flagship study of ageing

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI MICHAEL W WEINER · 2016 to 2026
$226.7M
Technology and Remote Assessment CoreU19AG063911 · NIA · MAYO CLINIC ROCHESTER · PI Bradley F Boeve · 2019 to 2026
$120.9M
WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Early Onset AD Consortium - the LEAD Study (LEADS)U01AG057195 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI APOSTOLOVA, LIANA G, CARRILLO, MARIA C · 2018 to 2023
$70.3M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
The 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 Kumar B. Rajan · 2020 to 2026
$61.9M
THERAPEUTIC EFFECTS OF INTRA-NASAL INSULIN DETEMIRP50AG005136 · NIA · UNIVERSITY OF WASHINGTON · PI GRABOWSKI, THOMAS J. · 1985 to 2019
$57.2M
Vascular factors, physical activity, and inflammation as modulators of neurodegenerative and cognitive trajectories (Project 2)P01AG036694 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Hyun-Sik Yang · 2010 to 2026
$50.2M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5M
SUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1991 to 2020
$49.1M
Satellite Diagnostic and Treatment Clinic CoreP50AG008702 · NIA · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI DE JAGER, PHILIP L · 1989 to 2019
$46.3M
National Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8M
NCATS NIH HHS UL1 TR000448NCATS NIH HHS UL1 TR002345NCRR NIH HHS U24 RR021382NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P01 AG036694NIA NIH HHS P20 AG068024NIA 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 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 AG054110NIA NIH HHS R01 AG055005NIA NIH HHS R01 AG056031NIA NIH HHS R01 AG056258NIA NIH HHS R01 AG056531NIA NIH HHS R01 AG058724NIA NIH HHS R01 AG061788NIA NIH HHS R01 AG062276NIA NIH HHS R01 AG067781NIA NIH HHS R01 AG068338NIA NIH HHS R01 AG069453NIA 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 RF1 AG074372NIA NIH HHS U01 AG057195NIA NIH HHS U19 AG024904NIA NIH HHS U19 AG063911NIA NIH HHS U19 AG073153NIA NIH HHS U24 AG067418NIA NIH HHS U24 AG072122NIBIB NIH HHS R01 EB009352NIDCD NIH HHS R01 DC008552NIEHS NIH HHS R01 ES032470NIH HHS S10 OD026738NIMH NIH HHS P50 MH071616NINDS NIH HHS P30 NS098577NINDS NIH HHS R01 NS075075NINDS NIH HHS U19 NS120384NLM NIH HHS T15 LM007092RRD VA I01 RX001534RRD VA I21 RX001381
6 · The paper itself

Abstract

Importance: Evaluating prognostic performance of Alzheimer's biomarkers, multi-modal physiological measures, and clinical history in asymptomatic individuals versus established risk factors in asymptomatic individuals is can inform efficient screening strategies. Objective: To determine and compare the prognostic performance of amyloid biomarkers, multi-modal physiological measures, and clinical/modifiable risk factors Design: We used clinical trials (A4/LEARN), longitudinal cohorts (ADNI, AIBL, HABS, NACC, OASIS), and the UK Biobank spanning 2004-2025 (median follow-up time range: 1.8-13.72 years) in time-varying survival and binary classification analyses. Setting: Settings included a United States clinical trial, longitudinal cohort studies spread across medical centers in the United States and Australia, and the volunteer-based UK Biobank. Participants: Patients were cognitively asymptomatic and age 65+ at baseline, and potentially progressed to either clinical impairment, clinical AD diagnosis, or incurred AD ICD-codes. Patients were volunteer or convenience samples. Exposures: PTau-217, amyloid-PET, CSF markers (AB1-42, pTau-181, total-Tau), plasma proteomics, multi-modal brain-imaging, and cognitive tests were evaluated as predictors, along with demographics (age, sex, education), APOE genotype, and modifiable risk factors in the 2024 Lancet report Main Outcomes and Measures: PTau-217 and amyloid-PET from A4/LEARN were used to predict clinical impairment (CDR score of 0.5+ on two consecutive visits). PTau-217, amyloid-PET imaging across five cohorts, and CSF markers were used to predict clinical AD diagnosis. Plasma proteomics, multimodal neuroimaging, and cognitive assessments from the UK Biobank were used to predict AD ICD-codes. Results: Sample-sizes ranged from 356-28,533 (31-519 cases; female percentages: 48.45-67.39). Models of demographics, APOE genotype, and risk-factors as predictors did not show statistically significant differences in time-dependent area under the receiver operating characteristic curve (AUROC) compared to separate models using amyloid biomarkers. Predicting cognitive impairment in A4/LEARN, pTau-217 improved AUROC by 0.045-0.084 (best: 0.616 (CI: 0.51-0.723) vs. 0.7 (CI: 0.609-0.793)). Amyloid-PET improved AD prediction (maximum AUROC increase 0.074; 0.561 (CI: 0.468-0.653) vs. 0.635 (CI: 0.537-0.733)), and CSF biomarkers showed slightly larger gains (maximum AUROC increase 0.127; 0.627 (CI: 0.438-0.816) vs. 0.754 (CI: 0.577-0.931)). In UK Biobank analyses, mean AUROC improvements were minor across proteomics (0.044), neuroimaging (0.143, with 99.8%/0.2% class-balance), and cognitive tests (0.064). Conclusions and Relevance: In cognitively asymptomatic populations, biomarkers offer limited advantage over demographics, APOE genotype, and modifiable risk factors, supporting their importance in early AD screening strategies.

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PMID41332865
PMCPMC12668057

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