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Record W2114606300 · doi:10.1186/s13195-014-0056-3

Association between cell-bound blood amyloid-β(1 - 40) levels and hippocampus volume

2014· article· en· W2114606300 on OpenAlexfundno aff
Óscar Sotolongo‐Grau, Pedro Pesini, Sergi Valero, Asunción Lafuente, Mar Buendía, Virginia Pérez‐Grijalba, Itziar San José, Marta Ibarria, M. A. Tejero, Joan Giménez, Isabel de la Torre Díez, Lluís Tárraga, Agustı́n Ruiz, Merçé Boada, Manuel Sarasa

Bibliographic record

VenueAlzheimer s Research & Therapy · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute of Biomedical Imaging and BioengineeringUniversity of California, Los AngelesCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOServierEisaiNorthern California Institute for Research and EducationUniversity of California, San DiegoPfizerBiogenBioClinicaMedpaceEli Lilly and CompanyBristol-Myers SquibbF. Hoffmann-La RocheAlzheimer's Drug Discovery FoundationSynarcAlzheimer's Disease Neuroimaging InitiativeMeso Scale DiagnosticsNational Institute on AgingAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsHippocampusAmyloid (mycology)NeuroscienceInternal medicineMedicineBiologyPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: The identification of early, preferably presymptomatic, biomarkers and true etiologic factors for Alzheimer's disease (AD) is the first step toward establishing effective primary and secondary prevention programs. Consequently, the search for a relatively inexpensive and harmless biomarker for AD continues. Despite intensive research worldwide, to date there is no definitive plasma or blood biomarker indicating high or low risk of conversion to AD. METHODS: Magnetic resonance imaging and β-amyloid (Aβ) levels in three blood compartments (diluted in plasma, undiluted in plasma and cell-bound) were measured in 96 subjects (33 with mild cognitive impairment, 14 with AD and 49 healthy controls). Pearson correlations were completed between 113 regions of interest (ROIs) (45 subcortical and 68 cortical) and Aβ levels. Pearson correlation analyses adjusted for the covariates age, sex, apolipoprotein E (ApoE), education and creatinine levels showed neuroimaging ROIs were associated with Aβ levels. Two statistical methods were applied to study the major relationships identified: (1) Pearson correlation with phenotype added as a covariate and (2) a meta-analysis stratified by phenotype. Neuroimaging data and plasma Aβ measurements were taken from 630 Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects to be compared with our results. RESULTS: The left hippocampus was the brain region most correlated with Aβ(1-40) bound to blood cell pellets (partial correlation (pcor) = -0.37, P = 0.0007) after adjustment for the covariates age, gender and education, ApoE and creatinine levels. The correlation remained almost the same (pcor = -0.35, P = 0.002) if phenotype is also added as a covariate. The association between both measurements was independent of cognitive status. The left hemisphere entorhinal cortex also correlated with Aβ(1-40) cell-bound fraction. AB128 and ADNI plasma Aβ measurements were not related to any brain morphometric measurement. CONCLUSIONS: Association of cell-bound Aβ(1-40) in blood with left hippocampal volume was much stronger than previously observed in Aβ plasma fractions. If confirmed, this observation will require careful interpretation and must be taken into account for blood amyloid-based biomarker development.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
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 integrity0.0000.001
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 head0.075
GPT teacher head0.373
Teacher spread0.298 · 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.

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

Citations10
Published2014
Admission routes1
Has abstractyes

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