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Record W2336540863 · doi:10.1016/j.jalz.2015.07.016

F1‐03‐03: Mapping the progression of CSF and imaging biomarkers in “at‐risk” healthy subjects: The prevent‐ad program

2015· article· en· W2336540863 on OpenAlexaff
Judes Poirier, Anne Labonté, Dorothy Dea, Jennifer Tremblay‐Mercier, Mélissa Savard, Pedro Rosa‐Neto, Pierre Étienne, Pierre Bellec, John C.S. Breitner

Bibliographic record

VenueAlzheimer s & Dementia · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBioinformatics and Genomic Networks
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalDouglas Mental Health University InstituteDouglas CollegeMcGill University
Fundersnot available
KeywordsApolipoprotein ECerebrospinal fluidBiomarkerGenotypeCohortInternal medicineMedicineOncologyResting state fMRIPathologyPsychologyDiseaseNeuroscienceBiologyGeneticsGene

Abstract

fetched live from OpenAlex

Increasing evidence suggests that multiple cerebrospinal fluid (CSF) and imaging biomarkers may elucidate the pre-symptomatic phase of Alzheimer's disease (AD). Using a cohort of aging cognitively intact individuals with a first-degree family history of AD, we evaluated time course alterations in multiple AD biomarkers including “classic” CSF markers and lipid transport proteins in relation to brain resting-state network activity. We examined results across specific genotypes including APOE. Baseline, 3-, 12- and 24-months data were acquired for an ongoing prevention trial nested in the PREVENT-AD program. CSF was obtained by lumbar puncture, and protein concentrations were measured using Innotest technology for tau, P-tau and Aβ1-42. Luminex-based immunoassays (Millipore) were used to quantify CSF apolipoproteins A1, A2, B, C2, C3 and E. Genotypes for APOE and other polymorphisms were determined using standardized pyrosequencing techniques. Finally, a systematic analysis of different resting state functional MRI-derived indices was performed using standard machine learning algorithms. The resulting signal was then examined in relation to alteration in P-tau at 12 months vs. baseline. Time course analyses of tau, P-tau and Aβ1-42 over 2 years indicated strong APOE-e4 genotype-driven differences at baseline, with a clear exacerbation of pathological changes between 12 and 24 months. The latter elevation in tau/Aβ1-42 ratio (n=60) found among APOE-e4 carriers vs. non-carriers was inapparent, however, in subjects with the protective (G-negative) genotype at the HMGCR rs3866662 locus. Surprisingly, levels of CSF apoE protein correlated directly with age, a finding driven by results from e4-negative subjects only (p<0.002). Levels of CSF apoB (derived exclusively from blood) were significantly increased in e4 carriers vs. non-carriers at all timepoints (p<0.01). Resting state network activity connecting precuneus to anterior cingulate cortex correlated inversely with P-tau change between baseline and 12 months (p <0.01). Alterations over time in tau, Aβ1-42 and P-tau levels in the CSF in a cohort of high-risk cognitively intact subjects suggest ongoing “silent” neurodegeneration with consequences for at least one specific network function. Our findings with apoB suggest loss of blood-brain barrier integrity in persons with APOE e4 – a group with other signs of exaggerated pre-symptomatic AD pathology.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.278
Teacher spread0.257 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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