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Record W4406593777 · doi:10.1007/s11357-025-01523-x

Serum β-secretase 1 (sBACE1) activity in subjective cognitive decline: an exploratory study

2025· article· en· W4406593777 on OpenAlexaff
Carlo Cervellati, Alessandro Trentini, Valentina Rosta, Angelina Passaro, Gloria Brombo, Carlo Renzini, Giovanni Zuliani

Bibliographic record

VenueGeroScience · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University
FundersUniversità degli Studi di Ferrara
KeywordsConfoundingDementiaSexual dimorphismBiomarkerCognitive declineDiseaseInternal medicineMedicineCognitionCognitive impairmentAmyloid (mycology)PsychologyOncologyBiologyPsychiatryPathology

Abstract

fetched live from OpenAlex

β-Secretase-1 (BACE1) plays a key role in the regulation of cerebral amyloid-β homeostasis, being involved in amyloidogenic and, as recently found, amyloidolytic pathways. Growing evidence indicates that increased serum BACE1 (sBACE1) activity might represent an early biomarker for Alzheimer's disease. Here, we tested the hypothesis that an increase in sBACE1 activity may already occur in individuals with subjective cognitive decline (SCD). We found that sBACE1 activity was significantly higher in individuals with SCD (n 118) compared to cognitively normal subjects (controls, n 137) (p < 0.001). Moreover, compared with SCD, sBACE1 activity was even higher in patients affected by amnestic (n 179) or non-amnestic mild cognitive impairment (MCI) (n 99) (p < 0.001 and p 0.02, respectively). In all cases, the respective increase in sBACE1 activity was significant after adjustment for possible confounders including age, sex, and comorbidities. We also found a significant sexual dimorphism, with women affected by either type of MCI, but not by SCD, having higher levels of serum BACE1 activity compared to men. These results provide evidence supporting the potential use of sBACE1 activity as tool for blood-based screening of cognitively healthy individuals at clinical risk of MCI and 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.005
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.386
Teacher spread0.336 · 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 teacher head, 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

Citations1
Published2025
Admission routes1
Has abstractyes

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