Obesity Facilitates Sex-Specific Improvement In Cognition And Neuronal Function In A Rat Model Of Alzheimer’s Disease
Bibliographic record
Abstract
Obesity reduces or increases the risk of developing Alzheimer's disease (AD) depending on whether it is assessed in mid-life or late-life. There is currently no consensus on the relationship between obesity and AD or the mechanism or their interaction. Here, we aim to differentiate the cause-and-effect relationship between obesity and AD in a controlled rat model of AD. We induced obesity in 9-month-old TgF344-AD rats, that is pathology-load wise similar to early symptomatic phase of human AD. To more accurately model human obesity, we fed both TgF344-AD and non-transgenic littermates a varied high-carbohydrate-high-fat diet consisting of human food for 3 months. Obesity increased overall glucose metabolism and slowed cognitive decline in TgF344-AD rats, specifically executive function, without affecting non-transgenic rats. Pathological analyses of prefrontal cortex and hippocampus showed that obesity in TgF344-AD rats produced varied effects, with increased density of myelin and oligodendrocytes, lowered density and activation of microglia that we propose contributes to the cognitive improvement. However, obesity also decreased neuronal density, and promoted deposition of amyloid-beta plaques and tau inclusions. After 6 months on the high-carbohydrate-high-fat diet, detrimental effects on density of neurons, amyloid-beta plaques, and tau inclusions persisted while the beneficial effects on myelin, microglia, and cognitive functions remained albeit with a lower effect size. By examining the effect of sex, we found that both beneficial and detrimental effects of obesity were stronger in female TgF344-AD rats indicating that obesity during early symptomatic phase of AD is protective in females.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".