Evidence map›Paper›PMID 29034223›Full record

ArticleThe journal of prevention of Alzheimer's disease2016

'Alzheimer's Progression Score': Development of a Biomarker Summary Outcome for AD Prevention Trials.

J-M Leoutsakos, A L Gross, R N Jones, M S Albert, J C S Breitner

Abstract read
In one paragraph

Article in The journal of prevention of Alzheimer's disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Multi-Head Graph Convolutional Network for Structural Connectome Classification.Graphs in biomedical image analysis, and overlapped cell on tissue dataset for histopathology : 5th MICCAI Workshop, GRAIL 2023 and 1st MICCAI Challenge, OCELOT 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, Septembe... · 2024
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. AD risk score for the early phases of disease based on unsupervised machine learning.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2020
    Observational
  10. Observational
  11. Article
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

5 authors.

J-M LeoutsakosDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
A L GrossDepartments of Epidemiology and Mental Health, Johns Hopkins Center on Aging and Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
R N JonesDepartment of Neurology and Psychiatry & Human Behavior, Warren Alpert Medical School, Brown University, Providence, RI, USA.
M S AlbertDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
J C S BreitnerCentre for Studies on Prevention of Alzheimer's Disease (StoP-AD), Douglas Mental Health University Institute Research Centre, McGill University Faculty of Medicine. Montreal, Canada.

Funding

Project 2 MeasurementU19AG033655 · NIA · JOHNS HOPKINS UNIVERSITY · PI Mei Cheng Wang · 2014 to 2026
$51.5M
'T V ASSESSMENT - MEMORY DISORDERED PATIENTS'P50AG005146 · NIA · JOHNS HOPKINS UNIVERSITY · PI ALBERT, MARILYN S. · 1985 to 2019
$37.6M
Biomarkers of Cognitive Decline among Normal Individuals: The BIOCARD cohortU01AG033655 · NIA · JOHNS HOPKINS UNIVERSITY · PI ALBERT, MARILYN S. · 2009 to 2013
$11.2M
NIA NIH HHS P50 AG005146NIA NIH HHS U01 AG033655NIA NIH HHS U19 AG033655
6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) prevention research requires methods for measurement of disease progression not yet revealed by symptoms. Preferably, such measurement should encompass multiple disease markers.

objectivesEvaluate an item response theory (IRT) model-based latent variable Alzheimer Progression Score (APS) that uses multi-modal disease markers to estimate pre-clinical disease progression.

designEstimate APS scores in the BIOCARD observational study, and in the parallel PREVENT-AD Cohort and its sister INTREPAD placebo-controlled prevention trial. Use BIOCARD data to evaluate whether baseline and early APS trajectory predict later progression to MCI/dementia. Similarly, use longitudinal PREVENT-AD data to assess test measurement invariance over time. Further, assess portability of the PREVENT-AD IRT model to baseline INTREPAD data, and explore model changes when CSF markers are added or withdrawn.

settingBIOCARD was established in 1995 and participants were followed up to 20 years in Baltimore, USA. The PREVENT-AD and INTREPAD trial cohorts were established between 2011-2015 in Montreal, Canada, using nearly identical entry criteria to enroll high-risk cognitively normal persons aged 60+ then followed for several years.

participants349 cognitively normal, primarily middle-aged participants in BIOCARD, 125 high-risk participants aged 60+ in PREVENT-AD, and 217 similar subjects in INTREPAD. 106 INTREPAD participants donated up to four serial CSF samples. MEASUREMENTS: Global cognitive assessment and multiple structural, functional, and diffusion MRI metrics, sensori-neural tests, and CSF concentrations of tau, Aβ42 and their ratio.

resultsBoth baseline values and early slope of APS scores in BIOCARD predicted later progression to MCI or AD. Presence of CSF variables strongly improved such prediction. A similarly derived APS in PREVENT-AD showed measurement invariance over time and portability to the parallel INTREPAD sample.

conclusionsAn IRT-based APS can summarize multimodal information to provide a longitudinal measure of pre-clinical AD progression, and holds promise as an outcome for AD prevention trials.

Indexed as

Alzheimer’s diseasedisease progressionmultiple outcome modalitiesPre-clinicalprevention trialssummary outcome measures

Identifiers

PMID29034223
PMCPMC5639716

What OpenQuestion holds

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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.