Resveratrol and Metformin Recover Prefrontal Cortex AMPK Activation in Diet-Induced Obese Mice but Reduce BDNF and Synaptophysin Protein Content
Bibliographic record
Abstract
BACKGROUND: Obesity, insulin resistance, and type 2 diabetes are established risk factors for the development of Alzheimer's disease (AD). Given this connection, two drugs, metformin (MET) and resveratrol (RESV), are considered for the clearance of amyloid-β peptides through AMPK-mediated activation of autophagy. However, overactivation of AMPK observed in late-stage AD brains and relationships between AMPK and neurogenesis (through mTORC1 inhibition), questions treatment with these drugs. OBJECTIVE: To examine if MET and/or RESV supplementation activates brain AMPK, regulates markers of autophagy, and affects markers of neuronal health/neurogenesis. METHODS: 8-week-old male C57BL/6J mice were fed a low (N = 12; 10% kcal fat; LFD) or high fat diet (N = 40; 60% kcal fat; HFD) for 9 weeks to induce insulin resistance and obesity. HFD mice were then treated with/without MET (250 mg/kg/day), RESV (100 mg/kg/day), or COMBO (MET: 250 mg/kg/day, RESV: 100 mg/kg/day) for 5 weeks. Hippocampus and prefrontal cortex were extracted for western blotting analysis. RESULTS: Cortex AMPK (T172) and raptor (S792, the regulatory subunit of mTORC1) phosphorylation were upregulated following RESV, COMBO treatments. mTOR (S2448) and ULK1 (S555) activation was seen following MET, COMBO and RESV, COMBO treatments, respectively, in the cortex and hippocampus. p62 content was decreased following RESV, COMBO, with LC3 content being increased following RESV treatment in the cortex. Brain derived neurotropic factor (BDNF) was significantly decreased following RESV, COMBO, and synaptophysin following all treatment in the cortex. CONCLUSION: These results demonstrate that while treatments upregulated markers of autophagy in the prefrontal cortex, reductions in neuronal health markers question the efficacy of AMPK as a therapy for AD.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".