Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing 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.
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
6 authors.
Ziyan SongDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA.ORCID https://orcid.org/0009-0000-5679-1751
Xiaoqing HuangDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Asha Jacob JannuDepartment of Bioengineering and Informatics, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Travis S JohnsonDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Jie ZhangDepartment of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Vascular Structure and Function in Cognitive AgingP01AG003949 · NIA · YESHIVA UNIVERSITY · PI Richard B. LIPTON · 1985 to 2026
$73.9M
Structural Biology CoreU54AG065181 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI Alan D. Palkowitz · 2019 to 2026
$61.3M
Alzheimer's Disease Genetics ConsortiumU01AG032984 · NIA · UNIVERSITY OF PENNSYLVANIA · PI SCHELLENBERG, GERARD DAVID · 2009 to 2024
$60.4M
SUPPLEMENT TO ALZHEIMERS DISEASE PATIENT REGISTRYU01AG006786 · NIA · MAYO CLINIC ROCHESTER · PI GRAFF-RADFORD, JONATHAN, JACK, CLIFFORD R. · 1986 to 2023
$49.6M
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 PGRN/TDP-43 AXIS IN ALZHEIMER?S DISEASE AND NEURODEGENERATIONP50AG016574 · NIA · MAYO CLINIC ROCHESTER · PI PETERSEN, RONALD C · 1999 to 2018
$36.9M
Research Education ComponentP30AG019610 · NIA · SUN HEALTH RESEARCH INSTITUTE · PI REIMAN, ERIC MICHAEL · 2001 to 2020
$32.5M
Integrative Network Biology Approaches to Identify, Characterize and Validate Molecular Subtypes in Alzheimer's DiseaseU01AG046170 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI WANG, MINGHUI, ZHANG, BIN · 2013 to 2022
$26.0M
Rush Alzheimer's Disease Research CenterP30AG072975 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI Lisa L Barnes, Julie A. Schneider · 2021 to 2026
$24.7M
Integrating the exposome and methylome to inform brain molecular changes in ADRD across established diverse cohorts.U01AG046139 · NIA · UNIVERSITY OF FLORIDA · PI ERTEKIN-TANER, NILUFER, FUNK, CORY · 2013 to 2022
$24.6M
Tox-AD: a new multi-institute tri-consortium data resource in the AD Knowledge PortalU24AG061340 · NIA · SAGE BIONETWORKS · PI Laura Michelle Heath, Susheel Varma · 2018 to 2026
introductionAlzheimer's disease (AD) is a heterogeneous disease with diverse disease progression trajectories and brain pathology. Identifying AD subtypes is essential for understanding AD etiology, heterogeneity, and developing precise treatment.
methodsWe applied a subspace-merging algorithm to integrate multi-omics data from brain tissues of three large AD cohorts and identify data-driven AD subtypes. Within each cohort, we performed multiple analyses to characterize subtype-specific biology. A Phenome-wide Association Study (PheWAS) of expression quantitative trait loci (eQTLs) targeting differentially expressed genes (DEGs) was conducted to link molecular differences to disease phenotypes.
resultsWe identified AD subtypes that differed in cognitive and pathological phenotypes in three cohorts. Further analyses highlighted synaptic and neurotransmission pathways, and the PheWAS revealed significant associations with disease phenotypes. DISCUSSION: Our developed integration algorithm successfully merged different data modalities into a common subspace for patient clustering and identified data-driven subtypes. The identified transcriptomic signatures provide valuable insights into the molecular mechanisms underlying AD heterogeneity, paving the way for personalized AD treatment.
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.
Identification of Alzheimer's disease subtypes and biomarkers from human multi-omics data using subspace merging algorithm. · full record | OpenQuestion