Examining Alzheimer's Disease modifiable risk factors: Impact of physical activity and diet on neuroanatomy and behaviour in mouse models
Bibliographic record
Abstract
Dementia is a global public health challenge, with obesity emerging as an important modifiable risk factor. Here, we examined whether lifestyle interventions can mitigate the effects of diet-induced obesity on body weight, behaviour, and brain anatomy in mouse models. Using a longitudinal design, wild-type and triple-transgenic (3xTgAD) mouse models of Alzheimer's disease were exposed to a high-fat diet and assigned to one of three interventions: voluntary physical activity, a low-fat diet, and their combination. A high-fat diet significantly increased body weight and induced widespread neuroanatomical changes, with effects modulated by sex and genotype. The combined intervention led to significant weight loss in males of both genotypes. Neuroanatomical analyses revealed that a high-fat diet significantly reduced hippocampal and cerebellar volumes in wild-type mice but had a less pronounced effect on 3xTgAD mice; nevertheless, interventions, particularly the combined approach, increased localized brain volumes in these regions regardless of genotype. Multivariate integration of behavioural and neuroanatomical measures identified a brain pattern linking hippocampal and cerebellar volumes to intervention and behavioural performance. Spatial gene enrichment analysis of this pattern identified biological processes, including glucose homeostasis, as potential biological mechanisms underlying intervention effects. Overall, these findings suggest that voluntary physical activity and a low-fat diet can modulate brain structure and behaviour, partially counteracting the effects of a high-fat diet, and potentially recruiting biological processes that may support brain health.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".