Does habitual dietary intake influence myofiber hypertrophy in response to resistance training? A cluster analysis
Bibliographic record
Abstract
Although resistance exercise training (RT) is a common intervention to stimulate muscle protein synthesis and increase skeletal muscle mass, the optimal daily protein and total energy intakes sufficient to support RT-mediated muscle growth are as yet unclear. Further, the efficacy of RT varies widely among adults of all ages and whether this is attributable to interindividual differences in nutrition is not known. To determine if self-selected daily intake of macronutrients and specific components of dietary protein and fat are predictive of the magnitude of RT-mediated muscle growth, detailed 4-day dietary records were analyzed on 60 subjects previously clustered (K-means cluster analysis) as non-, modest, and extreme responders (non, n = 16; mod, n = 29; xtr, n = 15), based on the magnitudes of change in vastus lateralis myofiber cross-sectional area following a 16-week, 3-day-per-week, high-intensity RT. Despite the marked contrast between 60% myofiber hypertrophy in xtr and zero growth in non, we found no differences among response clusters in daily intakes of energy (mean +/- SEM: non 102 +/- 8; mod 111 +/- 6; xtr 109 +/- 5 kJ.kg-1.day-1), protein (non 0.97 +/- 0.08; mod 1.07 +/- 0.07; xtr 1.05 +/- 0.06 g.kg-1.day-1), carbohydrate (non 3.02 +/- 0.24; mod 3.18 +/- 0.20; xtr 3.14 +/- 0.17 g.kg-1.day-1), and fat (non 0.95 +/- 0.09; mod 1.05 +/- 0.08; xtr 1.03 +/- 0.08 g.kg-1.day-1), which generally met or exceeded dietary recommendations. There were no cluster differences in intakes of branched chain amino acids known to stimulate muscle protein synthesis. Using the novel K-means clustering approach, we conclude from this preliminary study that protein and energy intakes were sufficient to facilitate modest and extreme muscle growth during RT and intrinsic or extrinsic factors other than nutrient ingestion apparently impaired the anabolic response in nonresponders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".