Functional significance of extracellular vesicles as mediators of cardiometabolic and cardiorenal diseases upon aging
Bibliographic record
Abstract
Aging increases the risk of cardiometabolic and cardiorenal disease and this is associated with cellular dysregulation including oxidative stress, chronic inflammation, insulin resistance and senescence. Extracellular vesicles (EV) facilitate inter-organ communication and are now well established as important pathophysiological mediators in many aging-associated diseases. Our knowledge of EV biosynthesis, cargo composition, cellular targeting and functional effects has expanded significantly over the past decade. Here we provide a comprehensive review on the characteristics and functional significance of EV in cardiometabolic and cardiorenal diseases in the context of aging. Specifically, we focus on heart failure, type 2 diabetes, metabolic dysfunction-associated steatohepatitis (MASH), hypertension, and chronic kidney disease and discuss aging-associated changes in bioactive molecules transferred via EV and how these are associated with healthspan. Furthermore, we summarize current potential therapeutic applications of EV. Overall, this review summarizes current knowledge indicating an important role for EV in aging-related cardiometabolic and cardiorenal diseases, and how insights from basic research can potentially be translated to the clinic in order to combat aging-associated metabolic decline and improve longevity and healthspan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".