Investigating biological sex as a moderator of the association of nature exposure with brain health: a cross-sectional UK biobank analysis
Bibliographic record
Abstract
In this cross-sectional analysis of the UK Biobank, we investigated whether biological sex moderates the association of residential nature exposure with brain volume or cognitive function. We included 11,448 cognitively healthy UK residents aged 37–73 years (98% White; 51% female). Residential nature exposure was estimated as the percentage of land classified as natural environment within 1000 m and 300 m buffers around each participant’s home. Structural brain magnetic resonance imaging (MRI) outcomes included total grey matter volume, total white matter volume, and average hippocampal volume, all normalized for head size. Cognitive function was assessed with the Trail Making Test (B-A) and the Symbol Digit Substituton Test. In linear regression models, higher residential nature exposure at both buffer sizes was associated with greater grey matter volume (1000 m: β = 629 mm 3 per 10% increment in nature exposure; 95% CI: 234 to 1023; p = 0.002; 300 m: β = 642 mm 3 ; 95% CI: 286 to 997; p < 0.001), greater white matter volume (1000 m: 659 mm 3 ; 95% CI: 229 to 1089; p = 0.003; 300 m: β = 527 mm 3 ; 95% CI: 140 to 914; p = 0.008), and more correct matches on the Symbol Digit Substituton Test (1000 m: β = 0.106 matches; 95% CI: 0.057 to 0.154; p < 0.001; 300 m: β = 0.049 matches; 95% CI: 0.006 to 0.092; p = 0.03). In males, compared with females, higher nature exposure was associated with a greater increment in grey matter volume (1000 m; β = 635 mm 3 ; 95% CI: 53 to 1217; p = 0.03; 300 m: β = 634 mm 3 ; 95% CI: 102 to 1167; p = 0.02), and with a greater reduction in Trail-Making Test B-A time (300 m only: β = 0.284 s; 95% CI: 0.016 to 0.551; p = 0.04). These sex differences showed some sensitivity to participant residence changes and to type of nature exposure measure. Residential nature exposure may support brain volume and cognitive function, and some of the potential benefits may vary by sex.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".