Adiponectin alleviated Alzheimer‐like pathologies via autophagy‐lysosomal activation
Bibliographic record
Abstract
Adiponectin (APN) deficiency has also been associated with Alzheimer-like pathologies. Recent studies have illuminated the importance of APN signaling in reducing Aβ accumulation, and the Aβ elimination mechanism remains rudimentary. Therefore, we aimed to elucidate the APN role in reducing Aβ accumulation and its associated abnormalities by targeting autophagy and lysosomal protein changes. To assess, we performed a combined pharmacological and genetic approach while using preclinical models and human samples. Our results demonstrated that the APN level significantly diminished in the plasma of patients with dementia and 5xFAD mice (6 months old), which positively correlated with Mini-Mental State Examination (MMSE), and negatively correlated with Clinical Dementia Rating (CDR), respectively. APN deficiency accelerated cognitive impairment, Aβ deposition, and neuroinflammation in 5xFAD mice (5xFAD*APN KO), which was significantly rescued by AdipoRon (AR) treatment. Furthermore, AR treatment also markedly reduced Aβ deposition and attenuated neuroinflammation in APP/PS1 mice without altering APP expression and processing. Interestingly, AR treatment triggered autophagy by mediating AMPK-mTOR pathway signaling. Most importantly, APN deficiency dysregulated lysosomal enzymes level, which was recovered by AR administration. We further validated these changes by proteomic analysis. These findings reveal that APN is the negative regulator of Aβ deposition and its associated pathophysiologies. To eliminate Aβ both extra- and intracellular deposition, APN contributes via the autophagic/lysosomal pathway. It presents a therapeutic avenue for AD therapy by targeting autophagic and lysosomal signaling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".