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Record W4406225139 · doi:10.1002/alz.094034

A polymorphism in RELN protects against amyloid‐driven tau pathology and cognitive decline in sporadic Alzheimer’s disease

2024· article· en· W4406225139 on OpenAlexaff
Giovanna Carello‐Collar, Thomas Hugentobler Schlickmann, João Pedro Ferrari‐Souza, Marco Antônio De Bastiani, Alexandre S. Cristino, Tharick A. Pascoal, Pedro Rosa‐Neto, Diogo O. Souza, Eduardo R. Zimmer

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsCognitive declineDiseaseTau pathologyPathologyAmyloid βPolymorphism (computer science)MedicineAmyloid (mycology)Alzheimer's diseaseNeuroscienceBiologyDementiaGeneticsGenotypeGene

Abstract

fetched live from OpenAlex

Abstract Background A rare reelin gene variant (RELN‐COLBOS mutation) delayed dementia onset in almost 30 years in an autosomal dominant Alzheimer’s disease (ADAD) carrier. This patient presented with high amyloid‐ß (Aß) plaque load, but low tau accumulation, suggesting that this single‐nucleotide polymorphism (SNP) in RELN conferred a resilience not only to cognitive decline but also to tauopathy in ADAD. However, whether RELN SNPs are also protective in sporadic Alzheimer’s disease (AD) is yet to be determined. Thus, we sought to examine the impact of RELN SNPs on AD pathophysiology and cognitive deterioration in sporadic AD. Method We assessed 198 individuals [105 cognitively unimpaired (CU) and 85 cognitively impaired (CI)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with available data on RELN SNPs and amyloid‐ and tau‐PET measures ([18F]‐florbetaben/florbetapir and [18F]‐flortaucipir, respectively), Aß1‐42 and ptau181 in the CSF, and neuropsychological testing (Clinical Dementia Rating Sum of Boxes). We analyzed the effect of RELN SNPs carriership in the association between amyloid and tau burden through linear regression analysis and on cognitive decline according to CSF AT status through linear mixed‐effect model correcting for age, sex, and ApoEe4 status (Bonferroni’s adjusted p‐value < 0.05). Result We performed linear regression analysis in all the 235 RELN SNPs available on ADNI (Fig.1). We found RELN rs802787 protected against amyloid‐driven tau pathology (adj. p‐value < 0.001, Fig.2). Dividing individuals according to the CSF AT status, we observed that RELN rs802787 did not impact the rate of decline in cognition in A‐T‐ and A‐T+ individuals (Fig.3A‐B). By contrast, RELN rs802787 CSF A+T‐ individuals presented a slower cognitive decline (Fig.3C), which was not observed in A+T+ individuals (Fig.3D). Conclusion Here, we show RELN rs802787 carriers presenting high amyloid load have lower tau accumulation than non‐carriers. In addition, CSF A+T‐ RELN carriers presented a slower cognitive decline compared to non‐carriers. Our results suggest that RELN rs802787 carriership protects against amyloid‐driven tau pathology and cognitive deterioration in sporadic AD individuals. To the best of our knowledge, this is the first RELN SNP found to be protective against non‐familial AD.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.313
Teacher spread0.283 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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