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Record W4412546562 · doi:10.3389/fnagi.2025.1642043

Sex differences in the outcomes of modifiable lifestyle factors for cognitive aging: neuroinflammation and microglia as key underlying mechanisms

2025· review· en· W4412546562 on OpenAlexaff
Samantha G. Coleborn, Zoë M. Gilson, Guo Yu, Marie‐Ève Tremblay

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2025
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMicrogliaNeuroinflammationNeuroscienceCognitive declineNeurogenesisPsychologyCognitionNeuroplasticitySynaptic pruningInflammationMedicineDementiaDiseaseImmunology

Abstract

fetched live from OpenAlex

Microglia are the resident immune cells of the brain. Over the past two decades, they have been shown to play critical roles throughout life. Microglia are now considered to be important for brain formation, maturation, activity and plasticity, with outcomes on behavior and other cognitive domains. With this knowledge, microglia represent a promising therapeutic target to promote brain health along an aging trajectory. Emerging evidence also indicates that modifiable lifestyle factors for cognitive aging can influence the brain and behavior by acting on microglia. The mechanisms identified so far involve their roles in synaptic plasticity, axonal myelination, and adult neurogenesis, exerted through the modulation of brain inflammation ('neuroinflammation'), the release of trophic factors, and phagocytosis. In this mini-review, we will cover the outcomes of exercise, diet. and social isolation on microglial functions during aging. Sex differences in the identified outcomes on cognitive aging and the underlying mechanisms will be highlighted. Our goal with this mini-review is to stimulate further research on this important topic.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
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)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.094
GPT teacher head0.336
Teacher spread0.243 · 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 designNot applicable
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

Citations4
Published2025
Admission routes1
Has abstractyes

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