Retrospective Analysis Of Resistance Training-induced Strength And Hypertrophy: Separating The Wheat From The Hormone Chaff
Bibliographic record
Abstract
PURPOSE: To investigate associations between purportedly anabolic acute exercise-induced hormone responses and adaptations to resistance training in a cohort with normally distributed training-induced adaptations. METHODS: Acute post-exercise serum growth hormone (GH), free testosterone (fT), cortisol, and insulin-like growth factor (IGF-1) responses were determined at the midpoint of a 12-wk resistance exercise program conducted previously. Correlations of hormonal responses with gains in lean body mass (LBM), muscle fiber cross-sectional area (CSA) and leg press strength were investigated. Dietary factors were combined with hormonal responses as independent variables in multiple regression analysis of adaptations to resistance training. RESULTS: There were no significant correlations between the exercise-induced elevation (area under the curve_AUC) of GH, fT and IGF-1 and gains in LBM or leg press strength. Significant correlations were found for cortisol AUC with change in LBM (r = 0.29, P < 0.05) and type II fiber CSA (r = 0.35, P < 0.01) as well as GH AUC and gain in fiber area (type I: r=0.36, P=0.006; type II: r= 0.28, P=0.04). Multiple regression analysis showed that increments in daily dietary protein intake and the daily consumption of dairy, but no acute hormonal response, were significant predictors in a model of change in LBM and type II fiber area. CONCLUSION: A significant association existed between GH and cortisol and fiber hypertrophy, but there was no association between any hormones and strength gains. Multiple regression analysis revealed an important role of increasing dietary protein and servings of dairy in promoting training-induced gains in LBM and fiber hypertrophy. In contrast, post-exercise elevations in endogenous hormones were relatively impotent in their predictive value for resistance training-induced hypertrophy calling into question persistent referrals, which are frequently erroneously based on pharmacological intervention studies, to their anabolic capacity. Supported by NSERC and CIHR
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".