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Record W2967689357 · doi:10.1016/j.jalz.2019.05.007

Hypothesis: cerebrospinal fluid protein markers suggest a pathway toward symptomatic resilience to AD pathology

2019· article· en· W2967689357 on OpenAlexafffund
Pierre‐François Meyer, Mélissa Savard, Judes Poirier, David Morgan, John C.S. Breitner

Bibliographic record

VenueAlzheimer s & Dementia · 2019
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersJohnson and Johnson Pharmaceutical Research and DevelopmentNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchGenentechH. Lundbeck A/SServierEisaiNorthern California Institute for Research and EducationGE HealthcareAlzheimer's AssociationFujirebio USPfizerBioClinicaBiogenNovartis Pharmaceuticals CorporationU.S. Department of DefenseEli Lilly and CompanyBristol-Myers SquibbRocheMerckAlzheimer's Drug Discovery FoundationMeso Scale DiagnosticsTakeda Pharmaceutical CompanyAbbVieNational Institute on Aging
KeywordsBiomarkerNeuroimagingOncologyCerebrospinal fluidPsychologyCognitionInternal medicineApolipoprotein ECognitive declineDementiaMedicineDiseaseNeuroscienceBiologyGenetics

Abstract

fetched live from OpenAlex

Abstract Introduction We sought biological pathways that explained discordance between Alzheimer's disease (AD) pathology and symptoms. Methods In 306 Alzheimer's Disease Neuroimaging Initiative (ADNI)‐1 participants across the AD clinical spectrum, we investigated association between cognitive outcomes and 23 cerebrospinal fluid (CSF) analytes associated with abnormalities in the AD biomarkers amyloid β1‐42 and total‐tau. In a 200‐person “training” set, Least Absolute Shrinkage and Selection Operator regression estimated model weights for the 23 proteins, and for the AD biomarkers themselves, as predictors of ADAS‐Cog11 scores. In the remaining 106 participants (“validation” set), fully adjusted regression models then tested the Least Absolute Shrinkage and Selection Operator–derived models and a related protein marker summary score as predictors of ADAS‐Cog11, ADNI diagnostic category, and longitudinal cognitive trajectory. Results AD biomarkers alone explained 26% of the variance in validation set cognitive scores. Surprisingly, the 23 AD‐related proteins explained 31% of this variance. The biomarkers and protein markers appeared independent in this respect, jointly explaining 42% of test score variance. The composite protein marker score also predicted ADNI diagnosis and subsequent cognitive trajectory. Cognitive outcome prediction redounded principally to ten markers related to lipid or vascular functions or to microglial activation or chemotaxis. In each analysis, apoE protein and four markers in the latter immune‐activation group portended better outcomes. Discussion CSF markers of vascular, lipid‐metabolic and immune‐related functions may explain much of the disjunction between AD biomarker abnormality and symptom severity. In particular, our results suggest the hypothesis that innate immune activation improves cognitive outcomes in persons with AD pathology. This hypothesis should be tested by further study of cognitive outcomes related to CSF markers of innate immune activation.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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 designTheoretical or conceptual
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".

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Citations18
Published2019
Admission routes2
Has abstractyes

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