Harnessing adiponectin for sepsis: current knowledge, clinical insights and future therapies
Bibliographic record
Abstract
Adiponectin, a key adipokine primarily secreted by adipocytes, plays crucial roles in metabolic homeostasis and inflammation, exhibiting anti-diabetic, anti-atherogenic, and anti-inflammatory properties. Its various isoforms and signaling via receptors like AdipoR1, AdipoR2, and T-cadherin contribute to its diverse biological functions. Sepsis is a life-threatening syndrome triggered by a dysregulated host response to infection, leading to systemic inflammation, multi-organ failure, and high mortality, currently lacking specific treatments. Preclinical studies largely suggest a protective role for adiponectin, demonstrating that its deficiency exacerbates inflammation and endothelial dysfunction, while its administration or agonism improves outcomes in experimental sepsis models. Clinical findings, however, present a complex picture, with inconsistent correlations between adiponectin levels and sepsis outcomes reported, suggesting its potential as a dynamic biomarker influenced by disease stage, patient heterogeneity, and isoforms, rather than a simple prognostic factor. Notably, glucagon-like peptide-1 receptor agonists (GLP-1RAs), used in obesity and diabetes management, have been shown to increase adiponectin levels, linking metabolic therapies to potential sepsis immunomodulation. Consequently, targeting adiponectin signaling, either directly with adiponectin mimics like AdipoRon or indirectly via strategies like GLP-1RA administration, represents a promising therapeutic approach for sepsis. Harnessing the adiponectin axis holds potential for advancing precision medicine in critical care, necessitating further research into adiponectin-based interventions and synergistic metabolic therapies to improve sepsis outcomes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".