MétaCan
Menu
← Back to cohort
Record W4406200493 · doi:10.1002/alz.090966

A head‐to‐head comparison between plasma p‐tau217 and tau‐PET for predicting future cognitive decline among cognitively unimpaired individuals

2024· article· en· W4406200493 on OpenAlexaff
Rik Ossenkoppele, Gemma Salvadó, Shorena Janelidze, Alexa Pichet Binette, Joseph Therriault, Erin M. Jonaitis, Sebastian Palmqvist, Niklas Mattsson, Erik Stomrud, Pierrick Bourgeat, Vincent Doré, Colin L. Masters, Sterling C. Johnson, Sylvia Villeneuve, Pedro Rosa‐Neto, Christopher C. Rowe, Oskar Hansson

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsDouglas Mental Health University Institute
Fundersnot available
KeywordsHead (geology)Cognitive declineCognitionPsychologyGerontologyMedicineNeuroscienceInternal medicineDementiaDiseaseBiology

Abstract

fetched live from OpenAlex

Abstract Background An accurate prediction of Alzheimer’s disease (AD) progression is important for patient management and optimization of participant selection for trials. Here, we compared and combined plasma p‐tau217 and tau‐PET measures for predicting longitudinal cognitive decline and clinical progression in cognitively unimpaired participants. Method We included 982 participants from six independent cohorts (AiBL, BioFINDER‐1, BioFINDER‐2, TRIAD, PREVENT‐AD and WRAP; Table 1) with available plasma p‐tau217 and tau‐PET measures (measured less than one‐year apart), being either amyloid‐positive or amyloid‐negative. Biomarker and cognitive data were z‐scored by cohort using cognitively unimpaired CSF/PET amyloid‐negative participants as reference for a unified analysis. We performed linear mixed models (for predicting cognitive decline on the mPACC and MMSE) and Cox‐proportional hazards models (to assess progression to MCI or dementia), testing among both amyloid‐negative and amyloid‐positive individuals. We entered baseline plasma p‐tau217, and tau‐PET uptake in the medial temporal lobe (MTL) or in the temporal neocortex individually, and also performed combined plasma/PET models. Age, sex, years of education and cohort were used as covariates. Result All individual tau biomarkers significantly predicted cognitive decline for both mPACC (R2p‐tau217=0.27, R2MTL‐tau=0.31, R2neotemporal‐tau=0.28, Figure 1) and MMSE (R2p‐tau217=0.12, R2MTL‐tau=0.16, R2neotemporal‐tau=0.19, Figure 2). The best model for predicting mPACC change included plasma p‐tau217 and MTL tau‐uptake (R2=0.32, pcomparison<0.001), while for MMSE change included plasma p‐tau217 and tau‐uptake in the temporal neocortex (R2=0.20, pcomparison≤0.007). Progression to MCI or dementia was also best predicted when including both plasma p‐tau217 (HR[95%CI]=1.29[1.14,1.47], p<0.001) and MTL tau‐uptake (HR[95%CI]=1.39[1.25,1.54], p<0.001, c‐index=0.83). Analyses by individual cohorts showed similar trends. Conclusion Our data suggest that plasma p‐tau217 is a suitable screening method for clinical trials in CU populations given its logistic advantages. In scenarios where a more refined prediction of cognitive decline is mandated, Tau‐PET (preferably in a combined algorithm with plasma p‐tau217) would be the methodology of choice.

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)0.007
metaresearch head score (Gemma)0.007
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 score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.057
GPT teacher head0.379
Teacher spread0.321 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicMedical Imaging Techniques and Applications→French-language works237,207→