Consumption of Greek yogurt during 12 weeks of high-impact loading exercise increases bone formation in young, adult males – a secondary analysis from a randomized trial
Bibliographic record
Abstract
Exercise combined with protein and calcium has been shown to benefit bone turnover and bone metabolism. Greek yogurt (GY) contains important nutrients that support bone but has yet to be studied with exercise for this purpose. Thirty untrained, university-aged, males were randomized to 2 groups (n = 15/group): GY (20 g protein, 208 mg calcium/dose) or placebo pudding (PP; 0 g protein, 0 g calcium/dose) consumed 3×/day on training days and 2×/day on nontraining days. Both groups underwent a resistance/plyometric training program for 12 weeks. Blood was obtained at weeks 0, 1, and 12 to measure procollagen-type-I-N-terminal-propeptide (P1NP) and C-terminal-telopeptide (CTX). After outlier treatment, P1NP increased more over time in GY versus PP (p = 0.002; interaction). Both groups decreased CTX over time (p = 0.046; time effect). Following 1 week of training, there was a trend towards a significant increase in CTX in PP with no change in GY (p = 0.062; interaction). P1NP changed more in GY than PP (baseline to week 12; p = 0.029) as did the P1NP/CTX ratio (p = 0.015) indicating a greater increase in formation with GY. Thus, GY added to a high-load, high-impact exercise program positively shifted bone turnover towards increased formation while attenuating resorption. GY could be a plausible postexercise food to support bone health in young adult males. Novelty Greek yogurt, with exercise, increased bone formation in young adult males over 12 weeks. After 1 week of an osteogenic exercise program, Greek yogurt tended to blunt a rise in bone resorption seen with the placebo. Greek yogurt is a plausible postexercise food that supports bone.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".