Evidence map›Paper›PMID 40176187›Full record

ArticleAlzheimer's research & therapy2025

Integrative network analysis reveals novel moderators of Aβ-Tau interaction in Alzheimer's disease.

Akihiro Kitani, Yusuke Matsui, Alzheimer’s Disease Neuroimaging Initiative

Abstract read
In one paragraph

Article in Alzheimer's research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Moderating effects of plasma glial fibrillary acidic protein along the Alzheimer's disease continuum.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
  2. Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Akihiro KitaniDepartment of Integrated Health Science, Biomedical and Health Informatics Unit, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Yusuke MatsuiDepartment of Integrated Health Science, Biomedical and Health Informatics Unit, Nagoya University Graduate School of Medicine, Nagoya, Japan. matsui@met.nagoya-u.ac.jp.
Alzheimer’s Disease Neuroimaging Initiative

Funding

Project 1U19AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE/RES/EDU · PI Duygu Tosun-Turgut · 2016 to 2026
$226.7M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
Alzheimer's Disease Genetics ConsortiumU01AG032984 · NIA · UNIVERSITY OF PENNSYLVANIA · PI SCHELLENBERG, GERARD DAVID · 2009 to 2024
$60.4M
Vascular Moderators of the Impact of Alzheimer's Pathology in the Young-OldP01AG025204 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HOWARD J AIZENSTEIN, Ann D. Cohen · 2005 to 2026
$56.5M
WASHINGTON UNIVERSITY ALZHEIMERS DISEASE RESEARCH CENTERP50AG005681 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 1985 to 2019
$52.1M
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
EPIDEMIOLOGY OF NEURAL RESERVE AND NEUROBIOLOGY IN AGINGR01AG017917 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 2001 to 2023
$43.3M
THE NIA GENETICS OF ALZHEIMER'S DISEASE DATA STORAGE SITEU24AG041689 · NIA · UNIVERSITY OF PENNSYLVANIA · PI LI-SAN WANG · 2012 to 2026
$42.3M
GINKGO BILOBA PREVENTION TRIAL IN OLDER INDIVIDUALSU01AT000162 · NCCIH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LOPEZ, OSCAR L. · 1999 to 2009
$34.7M
Research Education ComponentP30AG066468 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI C. Elizabeth Shaaban · 2020 to 2026
$29.4M
Research Education ComponentP30AG066444 · NIA · WASHINGTON UNIVERSITY · PI Susan Lynn Stark · 2020 to 2026
$28.7M
Japan Society for the Promotion of Science JP20H04282NCCIH NIH HHS U01 AT000162NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG025204NIA NIH HHS P01 AG026276NIA NIH HHS P30 AG010161NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066468NIA NIH HHS P50 AG005681NIA NIH HHS R01 AG015819NIA NIH HHS R01 AG017917NIA NIH HHS R01 AG023651NIA NIH HHS R01 AG025516NIA NIH HHS R01 AG030146NIA NIH HHS R01 AG030653NIA NIH HHS R01 AG036836NIA NIH HHS R01 AG041718NIA NIH HHS R01 AG044546NIA NIH HHS R01 AG052446NIA NIH HHS R01 AG052521NIA NIH HHS R01 AG057777NIA NIH HHS R01 AG058501NIA NIH HHS R01 AG064877NIA NIH HHS R01 AG066831NIA NIH HHS R37 AG023651NIA NIH HHS R37 AG025516NIA NIH HHS R56 AG064877NIA NIH HHS RF1 AG025516NIA NIH HHS RF1 AG052525NIA NIH HHS RF1 AG053303NIA NIH HHS RF1 AG057473NIA NIH HHS RF1 AG058501NIA NIH HHS U01 AG032984NIA NIH HHS U01 AG046152NIA NIH HHS U01 AG046161NIA NIH HHS U01 AG058922NIA NIH HHS U01 AG061356NIA NIH HHS U01 AG061357NIA NIH HHS U01 AG072572NIA NIH HHS U19 AG024904NIA NIH HHS U24 AG041689NIA NIH HHS UF1 AG051197
6 · The paper itself

Abstract

backgroundAlthough interactions between amyloid-beta and tau proteins have been implicated in Alzheimer's disease (AD), the precise mechanisms by which these interactions contribute to disease progression are not yet fully understood. Moreover, despite the growing application of deep learning in various biomedical fields, its application in integrating networks to analyze disease mechanisms in AD research remains limited. In this study, we employed BIONIC, a deep learning-based network integration method, to integrate proteomics and protein-protein interaction data, with an aim to uncover factors that moderate the effects of the Aβ-tau interaction on mild cognitive impairment (MCI) and early-stage AD.

methodsProteomic data from the ROSMAP cohort were integrated with protein-protein interaction (PPI) data using a Deep Learning-based model. Linear regression analysis was applied to histopathological and gene expression data, and mutual information was used to detect moderating factors. Statistical significance was determined using the Benjamini-Hochberg correction (p < 0.05).

resultsOur results suggested that astrocytes and GPNMB + microglia moderate the Aβ-tau interaction. Based on linear regression with histopathological and gene expression data, GFAP and IBA1 levels and GPNMB gene expression positively contributed to the interaction of tau with Aβ in non-dementia cases, replicating the results of the network analysis.

conclusionsThese findings suggest that GPNMB + microglia moderate the Aβ-tau interaction in early AD and therefore are a novel therapeutic target. To facilitate further research, we have made the integrated network available as a visualization tool for the scientific community (URL: https://igcore.cloud/GerOmics/AlzPPMap ).

Indexed as

Alzheimer DiseaseAmyloid beta-Peptidestau ProteinsAgedAged, 80 and overAstrocytesCognitive DysfunctionDeep LearningFemaleHumansMaleMicrogliaProtein Interaction MapsProteomicsAmyloid beta-PeptidesMAPT protein, humantau ProteinsAlzheimer’s diseaseAmyloid βAstrocyteGPNMBInteraction effectsMicrogliaModeratorNetwork integrationProteomicsTau

Identifiers

PMID40176187
PMCPMC11967117

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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.