Evidence map›Paper›PMID 39812331›Full record

ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2025

Predicting Alzheimer's disease subtypes and understanding their molecular characteristics in living patients with transcriptomic trajectory profiling.

Xiaoqing Huang, Asha Jacob Jannu, Ziyan Song, Nur Jury-Garfe, Cristian A Lasagna-Reeves, Alzheimer's Disease Neuroimaging Initiative, Travis S Johnson, Kun Huang, Jie Zhang

Abstract read
In one paragraph

Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
  6. Plasma proteomic Alzheimer's risk score: Biological age clock and pseudotime trajectory.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
  7. Review
  8. Inhomogeneous graph trend filtering via aIEEE transactions on signal and information processing over networks · 2025
    Article
  9. Article
  10. 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

9 authors.

Xiaoqing HuangDepartment of Biostatistics & Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA.ORCID 0009-0003-2183-8398
Asha Jacob JannuDepartment of Biohealth Informatics, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Ziyan SongDepartment of Biostatistics & Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Nur Jury-GarfeDepartment of Anatomy, Cell Biology & Physiology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Cristian A Lasagna-ReevesDepartment of Anatomy, Cell Biology & Physiology, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Alzheimer's Disease Neuroimaging Initiative
Travis S JohnsonDepartment of Biostatistics & Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Kun HuangDepartment of Biostatistics & Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Jie ZhangDepartment of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Structural Biology CoreU54AG065181 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI Bruce T Lamb, Alan D. Palkowitz · 2019 to 2026
$61.3M
Tau-seed protein interactome and its role in neurodegenerative tauopathiesR01NS119280 · NINDS · INDIANA UNIVERSITY INDIANAPOLIS · PI LASAGNA-REEVES, CRISTIAN · 2020 to 2024
$3.5M
Identify and study the roles of key genes and proteins in subpopulations of Alzheimer's disease patients with uncoupled neurofibrillary tanglesR21AG075541 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI ZHANG, JIE · 2022 to 2022
$426k
AbbVieAlzheimer's AssociationAlzheimer's Disease Neuroimaging InitiativeAlzheimer's Drug Discovery FoundationAraclon BiotechBioClinica, Inc.BiogenBristol-Myers Squibb CompanyCereSpir, Inc.Cogstate; Eisai Inc.Department of Defense W81XWH-12-2-0012Elan Pharmaceuticals, Inc.Eli Lilly and CompanyEuroImmunF. Hoffmann-La Roche LtdIndiana University Precision Health Initiative NIH TREAT-AD U54 5R01NS119280Indiana University Precision Health Initiative NIH TREAT-AD U54 5U54AG065181NIA NIH HHS R21 AG075541NIA NIH HHS U01 AG024904NIA NIH HHS U54 AG065181NIBIB NIH HHSNIH HHS U01 AG024904NINDS NIH HHS R01 NS119280
6 · The paper itself

Abstract

introductionDeciphering the diverse molecular mechanisms in living Alzheimer's disease (AD) patients is a big challenge but is pivotal for disease prognosis and precision medicine development.

methodsUtilizing an optimal transport approach, we conducted graph-based mapping of transcriptomic profiles to transfer AD subtype labels from ROSMAP monocyte samples to ADNI and ANMerge peripheral blood mononuclear cells. Subsequently, differential expression followed by comparative pathway and diffusion pseudotime analysis were applied to each cohort to infer the progression trajectories. Survival analysis with real follow-up time was used to obtain potential biomarkers for AD prognosis.

resultsAD subtype labels were accurately transferred onto the blood samples of ADNI and ANMerge living patients. Pathways and associated genes in neutrophil degranulation-like immune process, immune acute phase response, and IL-6 signaling were significantly associated with AD progression. DISCUSSION: The work enhanced our understanding of AD progression in different subtypes, offering insights into potential biomarkers and personalized interventions for improved patient care. HIGHLIGHTS: We applied an innovative optimal transport-based approach to map transcriptomic data from different Alzheimer's disease (AD) cohort studies and transfer known AD subtype labels from ROSMAP monocyte samples to peripheral blood mononuclear cell (PBMC) samples within ADNI and ANMerge cohorts. Through comprehensive trajectory and comparative analysis, we investigated the molecular mechanisms underlying different disease progression trajectories in AD. We validated the accuracy of our AD subtype label transfer and identified prognostic genetic markers associated with disease progression, facilitating personalized treatment strategies. By identifying and predicting distinctive AD subtypes and their associated pathways, our study contributes to a deeper understanding of AD heterogeneity.

Indexed as

Alzheimer DiseaseGene Expression ProfilingTranscriptomeAgedAged, 80 and overBiomarkersCohort StudiesDisease ProgressionFemaleHumansLeukocytes, MononuclearMaleMonocytesPrognosisBiomarkersADNIAD prognosisAD subtypingANMergedisease progressionoptimal transportROSMAPsex‐specific differencetrajectory pseudotime

Identifiers

PMID39812331
PMCPMC11772740

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Registered trials

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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.