Effects of Icelandic yogurt consumption and resistance training in healthy untrained older males
Bibliographic record
Abstract
Abstract Due to the important roles of resistance training and protein consumption in the prevention and treatment of sarcopenia, we assessed the efficacy of post-exercise Icelandic yogurt consumption on lean mass, strength and skeletal muscle regulatory factors in healthy untrained older males. Thirty healthy untrained older males (age = 68 ± 4 years) were randomly assigned to Icelandic yogurt (IR; n 15, 18 g of protein) or an iso-energetic placebo (PR; n 15, 0 g protein) immediately following resistance training (3×/week) for 8 weeks. Before and after training, lean mass, strength and skeletal muscle regulatory factors (insulin-like growth factor-1 (IGF-1), transforming growth factor-beta 1 (TGF- β 1), growth differentiation factor 15 (GDF15), Activin A, myostatin (MST) and follistatin (FST)) were assessed. There were group × time interactions ( P < 0·05) for body mass (IR: Δ 1, PR: Δ 0·7 kg), BMI (IR: Δ 0·3, PR: Δ 0·2 kg/m 2 ), lean mass (IR: Δ 1·3, PR: Δ 0·6 kg), bench press (IR: Δ 4, PR: 2·3 kg), leg press (IR: Δ 4·2, PR: Δ 2·5 kg), IGF-1 (IR: Δ 0·5, Δ PR: 0·1 ng/ml), TGF- β (IR: Δ − 0·2, PR: Δ − 0·1 ng/ml), GDF15 (IR: Δ − 10·3, PR: Δ − 4·8 pg/ml), Activin A (IR: Δ − 9·8, PR: Δ − 2·9 pg/ml), MST (IR: Δ − 0·1, PR: Δ − 0·04 ng/ml) and FST (IR: Δ 0·09, PR: Δ 0·03 ng/ml), with Icelandic yogurt consumption resulting in greater changes compared with placebo. The addition of Icelandic yogurt consumption to a resistance training programme improved lean mass, strength and altered skeletal muscle regulatory factors in healthy untrained older males compared with placebo. Therefore, Icelandic yogurt as a nutrient-dense source and cost-effective supplement enhances muscular gains mediated by resistance training and consequently may be used as a strategy for the prevention of sarcopenia.
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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 | 0.000 | 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) | 0.001 | 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".