Extracellular Vesicles as Predictors of Individual Response to Exercise Training in Youth Living with Obesity
Bibliographic record
Abstract
Background: Exercise is associated with health benefits, including the prevention and management of obesity. However, heterogeneity in the adaptive response to exercise training exists. Our objective was to evaluate if changes in extracellular vesicles (EVs) after acute aerobic exercise were associated with the responder phenotype following 6-weeks of resistance training (RT). Methods: This is a secondary analysis of plasma samples from the EXIT trial (clinical trial#02204670). Eleven sedentary youth with obesity (15.7 ± 0.5 yrs, BMI ≥95th percentile) underwent acute exercise (60% HRR, 45 min). Blood was collected at baseline [AT0 min], during [AT15–45 min], and 75 min post-recovery [AT120], and EVs purified using size exclusion chromatography from extracted plasma. Afterward, youth participated in 6-weeks RT and were categorized into responders or non-responders based on changes in insulin sensitivity. Results: We assessed EV biophysical profile (size, zeta potential, protein yield, and EV subtype protein expression) in a single-blind fashion. Overall, there was a general increase in EV production in both groups. Average EV size was larger in responders (~147 nm) vs. non-responders (~124 nm; p < 0.05). EV size was positively associated with absolute change in Matsuda index (insulin sensitivity) following RT (r = 0.44, p = 0.08). EV size distribution revealed responders predominantly expressed EVs sized 150–300 nm, whereas non-responders expressed EVs sized 50–150 nm (p < 0.05). At baseline, responders had ~25% lower TSG101, ~85% higher MMP2 levels. EV protein yield was higher in responders than non-responders at AT15 (p < 0.05). Conclusions: Our data suggest that youth with obesity that respond to RT produce larger EVs that are TSG101+ and CD63+, with increased EV protein yield during acute exercise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".