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Record W7117152152 · doi:10.1002/alz70855_106073

<i>RELN</i> SNP rs802787 protects against Aβ‐driven tau pathology, counteracts <i>APOE</i> ε4 effects, and slows cognitive decline in sporadic late‐onset Alzheimer's disease

2025· article· en· W7117152152 on OpenAlexaff
Giovanna Carello‐Collar, João Pedro Ferrari‐Souza, Marco Antônio De Bastiani, Thomas Hugentobler Schlickmann, Christian Limberger, Guilherme Povala, Wyllians Vendramini Borelli, Tharick A. Pascoal, Pedro Rosa‐Neto, D N Souza, Eduardo R. Zimmer

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsCognitive declineDiseaseReelinMechanism (biology)CognitionResilience (materials science)SNPPsychological resilience

Abstract

fetched live from OpenAlex

BACKGROUND: A rare reelin gene variant (RELN-COLBOS mutation) delayed dementia onset by 30 years in an autosomal dominant Alzheimer's disease (ADAD) mutation carrier. Despite a high amyloid-β (Aβ) load, the brain had low tau accumulation, suggesting that this mutation conferred resilience against tau pathology and cognitive decline. However, whether RELN variants protect against sporadic late-onset Alzheimer's disease (LOAD) remains unknown. Here, we evaluated the impact of RELN single nucleotide polymorphisms (SNPs) on AD pathophysiology and cognitive decline in LOAD. METHOD: , APOEε4 status, neuropsychological tests (CDRSB and MMSE), and clinical diagnosis. We investigated the impact of RELN carriership on the association between Aβ and tau burden through regional- and voxel-wise linear regressions, and on cognitive decline based on AT biomarker profile with a linear mixed-effect model. We also analyzed the interaction effects between RELN and APOEε4 carriership on tau pathology (Bonferroni's adjusted p-value < 0.05). RESULT: Of the RELN SNPs available in ADNI (Figure 1A), RELN rs802787 protected against Aβ-driven tau pathology (β = -0.603, adj. p-value = 0.0002; Figure 1B). At the voxel level, this protection was mostly associated with the temporal lobe (Figure 1C). Also, RELN rs802787 carriers exhibited a reduced APOEε4-related tau burden (β = -0.572, p-value = 0.035; Figure 2). Stratifying individuals by AT status revealed that RELN rs802787 carriership did not affect cognitive decline in Aβ- groups (Figure 3). In contrast, A+T- carriers showed a slower change in CDRSB (β = -0.45, p-value = 0.007) and MMSE (β = +0.3, p-value = 0.001) scores over the months, an effect absent in individuals with high tau burden (A+T+; Figure 3). CONCLUSION: Our findings suggest that RELN rs802787 confers resilience against Aβ-driven tau pathology and cognitive deterioration in LOAD. The reduced APOEε4-associated tau burden suggests a potential mechanism by which RELN could modulate tau accumulation. To our knowledge, this is the first RELN variant identified as protective in LOAD. Our results suggest that reelin signaling is an important player in AD pathophysiology, underscoring it as a promising target for AD therapeutics.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
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)0.0040.001

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 head0.017
GPT teacher head0.306
Teacher spread0.289 · 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
Published2025
Admission routes1
Has abstractyes

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