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Record W4407798003 · doi:10.3389/fnins.2025.1515374

The role of Apolipoprotein E4 on cognitive impairment in Parkinson’s disease and Parkinsonisms

2025· review· en· W4407798003 on OpenAlexafffund
Angenelle Eve Rosal, Sarah L. Martin, Antonio P. Strafella

Bibliographic record

VenueFrontiers in Neuroscience · 2025
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western HospitalUniversity Health NetworkUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsDiseaseCognitionParkinson's diseaseCognitive impairmentMedicineNeurosciencePsychologyPathology

Abstract

fetched live from OpenAlex

Cognitive impairment is a prevalent non-motor symptom of Parkinson’s disease (PD), increasing the risk of dementia as the disease progresses. Despite its clinical significance, the etiology of cognitive impairment in PD remains unclear. Apolipoprotein E4 (APOE4), a well-known genetic risk factor of Alzheimer’s disease, has been studied for its potential role in PD-related cognitive impairment. However, findings have been conflicting and thus inconclusive, highlighting a need to critically evaluate the current research. Several studies using neuroimaging modalities have explored the brains of individuals with PD and atypical parkinsonian disorders who have APOE4. Some of these studies have identified distinct neuropathological changes that have been previously reported to be associated with cognitive impairments in those with Parkinsonisms. Here, we review the role of APOE4 on cognitive impairment in PD and atypical Parkinsonisms using neuroimaging evidence. We will examine how APOE4 may contribute to pathological changes within the brain and its association with cognitive impairment.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.292
Teacher spread0.277 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2025
Admission routes2
Has abstractyes

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