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Reciprocal Predictive Relationships between Amyloid and Tau Biomarkers in Alzheimer's Disease Progression: An Empirical Model

2019· article· en· W2966564314 on OpenAlexafffund
Saffire H. Krance, Hugo Cogo‐Moreira, Jennifer S. Rabin, Sandra E. Black, Walter Swardfager

Bibliographic record

VenueJournal of Neuroscience · 2019
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Institutes of Health ResearchGenentechNational Institutes of HealthCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorNorthern California Institute for Research and EducationUniversity of Southern CaliforniaBiogenAlexander von Humboldt-StiftungEli Lilly and CompanyFondation Brain CanadaU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeHeart and Stroke Foundation of CanadaPfizerNational Institute on AgingAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsBiomarkerDementiaDiseasePsychologyPathogenesisCerebrospinal fluidAsymptomaticInternal medicineMedicineAlzheimer's diseaseOncologyChemistry

Abstract

fetched live from OpenAlex

There is an urgent need to understand the relationships between amyloid-β (Aβ) and tau in the progression of Alzheimer9s disease to identify treatment targets. Here we examine reciprocal predictions of brain Aβ burden quantified by positron emission tomography and CSF concentrations of Aβ42 and phosphorylated tau (p-tau). Each biomarker was examined over 48 months in two separate cross-lagged models; one in asymptomatic healthy elderly people (men and women), and one in patients with Alzheimer9s disease (AD) dementia or mild cognitive impairment (MCI). The models examine predictions of each biomarker on the progression of the others, considering each previous and concurrent measure. In healthy elderly, lower CSF Aβ42 predicted Aβ deposition and reciprocally, Aβ burden predicted a decrease in CSF Aβ42. Lower CSF Aβ42 predicted an increase in CSF p-tau, and CSF p-tau predicted Aβ deposition. In AD/MCI, lower CSF Aβ42 predicted Aβ deposition and Aβ burden reciprocally predicted CSF Aβ42 changes; however, in contrast to healthy elderly, CSF p-tau concentrations did not predict Aβ biomarkers, or vice versa. In post hoc models examining cognitive status, CSF Aβ42 predicted Mini Mental State Examination (MMSE) scores in healthy elderly, whereas Aβ burden and CSF p-tau predicted MMSE scores in AD/MCI. The findings describe reciprocal predictions between Aβ and tau biomarkers in healthy elderly and they implicate mechanisms underlying low CSF Aβ42 in Alzheimer9s disease pathogenesis and progression. In symptomatic Alzheimer9s disease, CSF Aβ42 and Aβ deposition predicted each other; however, Aβ and CSF p-tau progressed independently and they independently predicted cognitive decline. SIGNIFICANCE STATEMENT This study offers empirical evidence concerning the hypothesized “amyloid cascade”, as it progressed over 4 years in healthy elderly people and in Alzheimer9s disease patients. In healthy elderly, CSF amyloid changes predicted amyloid deposition, CSF phosphorylated tau concentrations, and a decline in cognitive status. Phosphorylated tau concentrations specifically predicted amyloid deposition. In Alzheimer9s disease patients, although amyloid deposition and CSF amyloid changes continued to “cascade”, there was no evidence to suggest that amyloid and tau biomarkers predicted each other, although both amyloid deposition and CSF tau progression predicted cognitive decline independently. Taking advantage of repeated amyloid PET and CSF measures, this dynamic view offers new insight into the progression of Alzheimer9s disease biomarkers and their relationships with cognitive decline.

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)0.019
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.035
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0040.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.104
GPT teacher head0.392
Teacher spread0.288 · 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 designSimulation or modeling
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

Citations33
Published2019
Admission routes2
Has abstractyes

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