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Record W7119526442 · doi:10.1002/alz70856_107283

Mapping molecular pathways underlying the relationship between plasma ptau and Alzheimer's disease pathology: an imaging‐transcriptomic study

2025· article· en· W7119526442 on OpenAlexaff
Min Su Kang, Julie Ottoy, Andrew Clappison, Gleb Bezgin, Thomas K Karikari, Gassan Massarweh, Jean‐Paul Soucy, S. Gauthier, Andrew Lim, Walter Swardfager, K. Blennow, Henrik Zetterberg, Sandra E. Black, Pedro Rosa‐Neto, Maged Goubran

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill Genome CentreMontreal Neurological Institute and HospitalUniversity of TorontoHealth Sciences CentreSunnybrook HospitalMcGill UniversityArtificial Intelligence in Medicine (Canada)Ontario Brain InstituteHeart and Stroke FoundationMcGill University Health CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsKEGGTranscriptomeDiseasePartial least squares regressionGeneAnalysis of variance

Abstract

fetched live from OpenAlex

Abstract Background Collective evidence suggests that plasma ptau181 and 217 reflect Alzheimer's disease (AD) from the amyloid‐beta (Aβ) to tau pathologies. However, possible molecular pathways underlying the biological mechanisms that relate AD pathology with plasma ptau have not yet been elucidated. Method We studied 549 participants from two cohorts: ADNI (Aβ‐: 83 CN; Aβ+: 171 CN, 97MCI, 39 AD; Aβ‐PET: [ 18 F]AV45; tau‐PET: [ 18 F]AV1451) and TRIAD (Aβ‐: 26 Young, 62 CN; Aβ+: 29 CN, 30 MCI, 24 AD; Aβ‐PET: [ 18 F]AZD4694; tau‐PET: [ 18 F]MK6240). Both cohorts included plasma ptau181 and 217, which were quantified using the SIMOA/Janssen. All images were processed using Freesurfer or PETsurfer into the DKT atlas. Linear regression models investigated the associations between plasma ptau and Aβ‐PET or tau‐PET, adjusted for age, sex, education, and APOEε4. Partial least squares (PLS) analysis identified a set of transcriptomic profiles from the Allen Human Brain Atlas (AHBA) associated with the ptau181‐PET (Aβ and tau‐PET) or ptau217‐PET relationships. Then, gene set enrichment analyses based on GO and KEGG databases and STRINGdb protein‐protein interactions were conducted to highlight which molecular/biological processes and cellular components are associated with the identified transcriptomic profiles. Result The ptau181‐PET and ptau217‐PET relationships were significantly associated with the spatial distribution of the AHBA, explaining >90% and >82% in variance in the 1st PLS component from both cohorts, respectively (Figure 1). Subsequent gene enrichment analyses showed mitochondrial metabolism for ptau181‐PET and synaptic function for ptau217‐PET as converging GO AD‐Biodomains (Figure 2). The KEGG enrichment analyses identified pathways of neurodegeneration for ptau181‐PET and cytokine‐cytokine receptor interaction for ptau217‐PET as converging annotations (Figure 2). Notably, TRIAD also showed Alzheimer's disease KEGG annotation from both ptau models (Figure 2). The STRINGdb analyses confirmed significant protein‐protein interactions and revealed MAPT, PINK1, and SNCA proteins with the largest betweenness metric within significant networks of KEGG terms from TRIAD, while BCL2L1, PINK1, and GSK3β proteins were identified in ADNI (Figure 3). Conclusion Imaging‐transcriptomic analyses showed unique sets of transcriptomic profiles, highlighting mitochondrial metabolism and synaptic as key AD‐biodomains underlying the plasma ptau‐AD pathology relationship. Our study underscores the MAPT and SNCA proteins and intracellular signalling as salient molecular pathways in neurodegeneration and AD dementia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.088
GPT teacher head0.347
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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