Adiponectin deficiency accelerates brain aging via mitochondria-associated neuroinflammation
Bibliographic record
Abstract
Abstract Background A wide spectrum of changes occurs in the brain with age, from molecular to morphological aspects, and inflammation accompanied by mitochondria dysfunction is one of the significant factors associated with age. Adiponectin (APN), an essential adipokine in glucose and lipid metabolism, is involved in the aging; however, its role in brain aging has not been adequately explored. Here, we aimed to explore the relationship between APN deficiency and brain aging using multiple biochemical and pharmacological methods to probe APN in humans, KO mice, primary microglia, and BV2 cells. Results We found that declining APN levels in aged human subjects correlated with dysregulated cytokine levels, while APN KO mice exhibited accelerated aging accompanied by learning and memory deficits, anxiety-like behaviors, neuroinflammation, and immunosenescence. APN-deficient mice displayed aggravated mitochondrial dysfunction and HDAC1 upregulation. In BV2 cells, the APN receptor agonist AdipoRon alleviated the mitochondrial deficits and aging markers induced by rotenone or antimycin A. HDAC1 antagonism by Compound 60 (Cpd 60) improved mitochondrial dysfunction and age-related inflammation, as validated in D-galactose-treated APN KO mice. Conclusion These findings indicate that APN is a critical regulator of brain aging by preventing neuroinflammation associated with mitochondrial impairment via HDAC1 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.022 | 0.005 |
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".