The Effects of frailty on extracellular vesicles (EVs) and the ability of EVs to rescue age-associated cellular dysfunction
Bibliographic record
Abstract
In Canada and across the world, the global population of older adults is rising. Within the next 15 years, approximately 25% of Canadians will be 65 years of age or older. This shift in population demographics will be a stressor for healthcare systems due to a concomitant loss of functional independence with age. While strategies exist to promote healthy living, there is also growing interest in research focused on attenuating the biological hallmarks of aging. Previous parabiosis experiments have shown that factors in the circulatory system may be key to reversing the cellular aging process. We propose that these youthful circulatory factors are encapsulated by extracellular vesicles (EVs), nanoparticles released by all cell types that are critical in cellular communication. To test our hypothesis, we obtained samples from the WARM Hearts Study (Clinical Trial #NCT02863211). In this study, we: 1) isolated and biochemically characterized EVs from women who were classified as robust, pre-frail or frail, and 2) co-cultured robust/young EVs, and frail/old EVs with chronologically young and old primary human skeletal muscle cells. Our results indicate that EVs isolated from frail subjects yielded 22% more protein than EVs isolated from robust subjects (*p=0.01, N=23) and 48.5% more protein than EVs isolated from pre-frail subjects (*p<0.001, N=12-23). Moreover, frail EVs had 119% lower ApoA1, a non-EV marker, than robust EVs (*p<0.01, N=8). Next, robust and frail plasma samples were stratified for epigenetic age (biological age) and EVs were isolated for co-culture experiments with young (19 year old) and old (92 year old) human skeletal muscle cells. Young and old cells were co-cultured with robust/biologically young and frail/biologically old plasma and EVs. We observed no difference in cell count or mitochondrial staining in any treatment groups. Treating old cells with EVs isolated from robust/biologically young subjects resulted in a 48% reduction in senescence as measured by beta-galactosidase staining (*p=0.02, N=7), and a 24% increase in cell viability (*p=0.02, N=6). Treating young cells with plasma isolated from frail/biologically old subjects resulted in a 16% decrease in cell viability (p=0.05, N=6). Treating young cells with EVs isolated from frail/biologically old subjects increased senescence by 73% (*p=0.007, N=7). The data show that EVs from frail/biologically old subjects have more protein, contain less ApoA1, and induced senescence in young cells, whereas EVs from robust/biologically young subjects rescued senescence in old cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".