Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease.
Sepehr Golriz Khatami, Yasamin Salimi, Martin Hofmann-Apitius, Neil P Oxtoby, Colin Birkenbihl, Alzheimer’s Disease Neuroimaging Initiative, Japanese Alzheimer’s Disease Neuroimaging Initiative, Alzheimer’s Disease Repository Without Borders Investigators
Article in Alzheimer's research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 40% of its field
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.
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3 · Its place in the literature
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
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
8 authors at 3 institutions in 2 countries.
Sepehr Golriz KhatamiDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany. sepehr.golriz.khatami@scai.fraunhofer.de.
Yasamin SalimiDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany.
Martin Hofmann-ApitiusDepartment of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany.
Neil P Oxtoby *Centre for Medical Image Computing and Department of Computer Science, University College London, Gower St, London, WC1E 6BT, UK.
Colin Birkenbihl *Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany.
Alzheimer’s Disease Neuroimaging Initiative
Japanese Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s Disease Repository Without Borders Investigators
Fraunhofer Institute for Algorithms and Scientific Computing · DEUniversity of Bonn · DEUniversity College London · GB
Funding
Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
NATIONAL ALZHEIMERS COORDINATING CENTER (NACC)U01AG016976 · NIA · UNIVERSITY OF WASHINGTON · PI KUKULL, WALTER ANTHONY · 1999 to 2020
$72.8M
THERAPEUTIC EFFECTS OF INTRA-NASAL INSULIN DETEMIRP50AG005136 · NIA · UNIVERSITY OF WASHINGTON · PI JAYADEY, SUMAN · 1985 to 2019
$57.2M
WASHINGTON UNIVERSITY ALZHEIMERS DISEASE RESEARCH CENTERP50AG005681 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 1985 to 2019
$52.1M
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
TREATMENT OF DEPRESSION IN ALZHEIMER'S DISEASEP50AG005133 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BECKER, JAMES T. · 1985 to 2019
$43.0M
VASCULAR RISK AND COGNITIVE STATUS IN A LATINO POPULATIONP50AG005142 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ARANDA, MARIA P. · 1985 to 2019
$41.0M
VESICULAR LOCALIZATION AND FUNCTION OF PRESENILIN 1 FRAGMENTP50AG005138 · NIA · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI SCHIMMING, CORBETT · 1985 to 2019
$37.9M
'T V ASSESSMENT - MEMORY DISORDERED PATIENTS'P50AG005146 · NIA · JOHNS HOPKINS UNIVERSITY · PI HUGANIR, RICHARD L · 1985 to 2019
$37.6M
Research Education ComponentP30AG010133 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI SAYKIN, ANDREW J · 1991 to 2020
$37.3M
UC Irvine Alzheimer's Disease Research Center Induced Pluripotent Stem Cell CoreP50AG016573 · NIA · UNIVERSITY OF CALIFORNIA-IRVINE · PI LAFERLA, FRANK M · 2000 to 2019
backgroundPrevious models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged.
methodsWe compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort.
resultsWe observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory.
conclusionOverall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.
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.
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