MétaCan
Menu
Back to cohort
Record W2900324164 · doi:10.1093/geroni/igy023.353

BDNF VAL66MET POLYMORPHISM, SEX AND RESILIENCE TO COGNITIVE AND PHYSICAL ACTIVITY DECLINES

2018· article· en· W2900324164 on OpenAlexaff
Cindy K. Barha, John R. Best, Teresa Liu‐Ambrose, Caterina Rosano

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBrain-derived neurotrophic factorCognitionNeurotrophic factorsCognitive declinePsychologyPopulationPsychological resilienceAllelePhysical activityPolymorphism (computer science)GerontologyBiologyMedicineInternal medicineNeuroscienceGeneticsGenePhysical medicine and rehabilitationDementia

Abstract

fetched live from OpenAlex

Despite the deleterious effects of aging on brain function, a significant proportion of the population maintain cognitive function even into older age. Allelic variation within the neuroplasticity-related gene, brain-derived neurotrophic factor (BDNF), may predict resiliency to cognitive aging and physical activity decline. Specifically, the BDNF Val66Met polymorphism may interact with biological sex to impact cognition and walking behaviour. To address this, in the Health, Aging and Body Composition study we examined the interaction between sex and BDNF genotype on rate of decline over 10 years in executive functioning and self-reported amount of time spent walking. Results indicate that female carriers of the polymorphism showed the least amount of decline in both executive functioning and amount of walking compared to male carriers and non-carriers of both sexes. This suggests that the BDNF Val66Met polymorphism may infer resiliency to cognitive and physical activity declines in older females only.

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)0.0020.000

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 head0.045
GPT teacher head0.431
Teacher spread0.386 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInnovation in AgingSame topicResilience and Mental HealthFrench-language works237,207