Sex Differences In The Relationships Among Grey Matter Volume, Physical Activity And Obesity In Aging
Bibliographic record
Abstract
SYNOPSIS: In aging, those with obesity have worsened structural integrity compared to lean. Conversely, those with greater physical activity levels (PA) have enhanced cerebral structure. Thus, the protective effects of PA could alleviate the negatives of obesity. As there are known sex differences in structural integrity, the relationships between obesity and PA were investigated separately by sex. We identified that females with higher PA, regardless of obesity, had enhanced grey matter volume (GMV), whereas overweight males with higher PA had higher GMV compared to lower PA overweight. PURPOSE: to identify potential sex differences in the relationships among physical activity and fitness, obesity and GMV in aging. METHODS: Two unique datasets were combined; 226 females (62.8 yo ± 4.8) and 88 (63.2 yo ± 4.7) males participated in this study. Participants underwent a 3 T Magnetic resonance imaging scan, with a T1-weighted acquisition to quantify GMV. Height and weight was measured to calculate body mass index (BMI). Fitness was measured for study 1 with a VO2peak test (referred to as PA here), whereas study 2 was a PA questionnaire used to quantify weekly total Metabolic Equivalents. For each study, BMI and PA outcomes were converted to sex-specific z-scores. PA was median-split into high PA and low PA groups, and each sex groups was separated into: i) lean; ii) high PA overweight; and iii) low PA overweight. RESULTS: Females with high compared to low PA demonstrated increased GMV in frontal, temporal and hippocampal regions (all p’s < 0.05). No differences between lean, and high and low Fitness/PA overweight groups (p > 0.05) were found in females. Males showed no significant difference in GMV between PA level (p > 0.05). However, lean males had higher GMV than high and low PA overweight, and overweight high PA males had greater GMV than overweight low PA males in frontal, parietal, and temporal regions (p < 0.05). CONCLUSION: Sex-specific relationships among GMV, PA and obesity were revealed. Thus, suggesting enhanced GMV occurs in lean males, yet in those who are overweight, having greater PA levels is seemingly able to alleviate the effects of obesity on the brain. Future work should include other imaging parameters, such as perfusion, to identify if these differences are co-occurring in the same GMV regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".