The Acute Effects of Milk Consumption on Systemic Inflammation after Combined Resistance and Plyometric Exercise in Young Adult Females
Bibliographic record
Abstract
High-intensity/impact exercise elicits a transient increase in inflammatory biomarkers. Consuming nutrient-dense wholefoods, like milk, following exercise may modulate post-exercise inflammation and aid recovery. We examined the effect of post-exercise skim milk consumption (versus an isoenergetic, isovolumetric carbohydrate [CHO] drink) on acute exercise-induced inflammation in untrained females. Using a randomized crossover design, 13 healthy females (age = 20 ± 2.3 y; BMI = 21.0 ± 1.1 kg/m2) completed two bouts of combined resistance/plyometric exercise followed by either skim milk (MILK) or CHO at 5-min and 1 h post-exercise. Serum interleukin [IL]-1β, IL-6, IL-10, and tumor necrosis factor-alpha (TNF-α) concentrations were measured at pre-exercise, 15-min, 75-min, 24 h, and 48 h post-exercise. IL-6 increased 15-min post-exercise vs. all other timepoints (time effect, p = 0.017). Between 24 and 48 h, IL-10 decreased and increased in the MILK and CHO conditions, respectively (interaction, p = 0.018). There were no significant effects for IL-1β or TNF-α. Relative concentrations of IL-1β (p = 0.049) and IL-10 (p = 0.028) at 48 h post-exercise were lower in MILK vs. CHO. Milk post-exercise did not influence the absolute concentration of pro-inflammatory cytokines; however, there were divergent responses for the anti-inflammatory cytokine, IL-10, and milk reduced the relative inflammatory response at 48 h (vs. CHO) for IL-1β and IL-10. This demonstrates the potential for milk to modulate inflammation post-exercise in this sample.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".