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Record W4406200522 · doi:10.1002/alz.092241

The role of biofluid markers in predicting near‐term cognitive impairment

2024· article· en· W4406200522 on OpenAlexaff
Yara Yakoub, Fernando González‐Ortiz, Nicholas J. Ashton, Thomas K. Karikari, Cherie Strikwerda‐Brown, Frédéric St‐Onge, Valentin Ourry, Michael Schöll, Maiya R. Geddes, Simon Ducharme, Pedro Rosa‐Neto, Jean‐Paul Soucy, John C.S. Breitner, Henrik Zetterberg, Kaj Blennow, Judes Poirier, Sylvia Villeneuve

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsTerm (time)Cognitive impairmentCognitionPsychologyMedicineNeurosciencePhysics

Abstract

fetched live from OpenAlex

Abstract Background PET biomarkers have proven valuable for identifying cognitively unimpaired (CU) individuals at‐risk of near‐term clinical progression. Given the increasing interest in plasma biomarkers to detect Alzheimer’s pathology, we assessed levels of amyloid (Aβ42/40) and tau (p‐tau217 and p‐tau181) biomarkers in plasma (A+T+plasma) in CU individuals as predictors of clinical progression to mild cognitive impairment (MCI). We then repeated these analyses using cerebrospinal fluid (CSF) and PET biomarkers. Method We studied 218 participants from the PREVENT‐AD cohort with cognitive follow‐up (mean 5.34 years, range 1–10 years) and plasma biomarkers available while CU. Cognition was assessed annually using the RBANS. Plasma biomarkers were measured using IP‐MS for Aβ42/40 and in‐house single molecular arrays for p‐tau217 and p‐tau181. An overlapping 71 participants had Aβ42/40 (Mesoscale Discovery assay) and p‐tau181 (Innotest immunoassay) CSF data available, and 135 participants had Aβ (18F‐NAV4694) and tau (18F‐florbetapir) PET data. We also performed receiver operating characteristic (ROC) curves to compare the performance of plasma (Aβ42/40 and p‐tau217; Aβ42/40 and p‐tau181), CSF (Aβ42/40 and p‐tau181) or PET (Aβ‐ and tau‐PET) biomarkers in predicting progression to MCI. We examined an additional model including only Aβ‐PET, given the importance of this technique as a stand‐alone marker for recruitment in anti‐Aβ trials. Result The proportion who progressed from CU to MCI was 62% in individuals with A+T+plasma217; and 41% with A+T+plasma181; 56% with A+T+CSF181 and 100% with A+T+PET (Figure 1). Cox proportional hazard models indicated a faster progression rate in all A+T+ groups compared with their matched A‐T‐ biomarker groups (Figure 2). Considering longitudinal RBANS scores, all A+T+ groups declined faster than the other groups, except for the plasma181 group. Finally, the PET (Aβ and tau‐PET) and CSF models were superior to the plasma or Aβ‐PET alone in identifying progressors, with no meaningful difference between the Aβ‐PET and the biofluid models (Figure 3). Conclusion CU individuals with A+T+ based on plasma biomarkers are at increased risk of cognitive decline and clinical progression. Nevertheless, the proportion who progressed to MCI in individuals classified with plasma was lower than that found using PET. Thus, the latter should remain the gold standard to identify presymptomatic pathological changes.

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)0.002
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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

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