Resistance Exercise Training and Greek Yogurt Consumption Modulate Markers of Systemic Inflammation in Healthy Young Males—A Secondary Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Chronic exercise training reduces markers of systemic inflammation; however, less is known about how to optimize this adaptation using nutrition. Dairy products, especially fermented ones, like Greek yogurt (GY), contain anti-inflammatory constituents. This secondary analysis aimed to examine the influence of post-exercise GY consumption vs. an isoenergetic carbohydrate pudding (CP; control) on markers of systemic inflammation during an exercise training intervention. METHODS: = 15). Rested/fasted blood samples were acquired at baseline, and weeks 1 and 12, and inflammatory biomarkers (tumor necrosis factor-alpha [TNF-α], interleukin [IL]-6, IL-1 receptor antagonist [IL-1ra], IL-1Beta [IL-1β], IL-10, and C-reactive protein [CRP]) were measured. Linear mixed models were run on the absolute concentrations, and linear regressions were performed on the absolute change (baseline to week 12), allowing us to account for important covariates. RESULTS: In both groups, CRP (pro) and IL-1ra (anti) increased at week 1 vs. baseline and week 12, while IL-1β (pro) decreased at week 12 vs. baseline (main time effects). We observed significant interactions for IL-6, TNF-α, and the TNF-α/IL-10 ratio, indicating that at week 12, IL-6 (pro) was lower in GY, whereas TNF-α and TNF-α/IL-10 (both pro-inflammatory) were higher in CP vs. week 1 and baseline, respectively. Additionally, within our linear regression models, higher baseline concentrations of IL-1ra (anti), IL-10 (anti) and CRP (pro) predicted greater change over the intervention. CONCLUSIONS: These results indicate that our intervention benefited circulating inflammatory markers, and GY supplementation may enhance these effects.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".