The therapeutic potential of adiponectin and extracellular vesicles for promoting improved healthspan
Bibliographic record
Abstract
The gradual decline in physiological functions that comes with aging contributes to a range of chronic diseases, such as Alzheimer's, type 2 diabetes, heart failure, and osteoarthritis. Significant advancements in human longevity due to socioeconomic development have resulted in a foreseeable and substantial strain on the global healthcare system. In fact, there is now a shift in research focus towards enhancing healthspan. As a result, the development of improved therapies for various chronic diseases is essential to enhance healthspan in the aging population. Adiponectin, mainly produced in adipose tissue, is found at elevated levels in the blood of healthy centenarians. In contrast, lower circulating levels of adiponectin are inversely associated with the occurrence and severity of several age-related complications. Adiponectin plays a crucial role in promoting beneficial effects on key biological processes associated with aging-related diseases, contributing to improved healthspan and lifespan in preclinical models. In recent years, extracellular vesicles (EVs) have garnered significant research interest due to their crucial role in both local paracrine signaling and systemic inter-organ communication. They are now widely recognized for their potential as valuable diagnostic and therapeutic tools. In this review, we summarize current knowledge on the pathophysiological roles of adiponectin and EVs in aging, emphasizing their combined therapeutic potential in age-related diseases. Additionally, we explore the emerging evidence of crosstalk between adiponectin and EVs, underscoring their potential for developing improved strategies to promote healthy aging and longevity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".