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Record W2605799970 · doi:10.1037/neu0000373

A lower ratio of omega-6 to omega-3 fatty acids predicts better hippocampus-dependent spatial memory and cognitive status in older adults.

2017· article· en· W2605799970 on OpenAlexafffundabout
Nadia D Andruchow, Kyoko Konishi, Bryna Shatenstein, Véronique D. Bohbot

Bibliographic record

VenueNeuropsychology · 2017
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversité de MontréalMcGill University
FundersCanadian Institutes of Health Research
KeywordsCognitionPsychologyHippocampusCognitive declineEffects of sleep deprivation on cognitive performanceSpatial memoryMontreal Cognitive AssessmentAudiologyDevelopmental psychologyWorking memoryNeuroscienceInternal medicineMedicineCognitive impairmentDementia

Abstract

fetched live from OpenAlex

OBJECTIVE: Evidence from several cross-sectional studies indicates that an increase in omega-6 to omega-3 fatty acids (FAs) may negatively affect cognition in old age. The hippocampus is among the first neural structures affected by age and atrophy in this brain region is associated with cognitive decline. Therefore, we hypothesized that a lower omega-6:3 FA ratio would predict better hippocampus-dependent spatial memory, and a higher general cognitive status. METHOD: Fifty-two healthy older adults completed a Food Frequency Questionnaire, the Montreal Cognitive Assessment test (MoCA; a test of global cognition) and virtual navigation tasks that assess navigational strategies and spatial memory. RESULTS: In this cross-sectional study, a lower ratio of omega-6 to omega-3 FA intake strongly predicted more accurate hippocampus-dependent spatial memory and faster learning on our virtual navigation tasks, as well as higher cognitive status overall. CONCLUSIONS: These results may help elucidate why certain dietary patterns with a lower omega-6:3 FA ratio, like the Mediterranean diet, are associated with reduced risk of cognitive decline. (PsycINFO Database Record

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.431
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.325
Teacher spread0.306 · 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 teacher head, 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

Citations36
Published2017
Admission routes3
Has abstractyes

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