Nutritional Supplements to Support Resistance Exercise in Countering the Sarcopenia of Aging
Bibliographic record
Abstract
Skeletal muscle plays an indispensable role in metabolic health and physical function. A decrease in muscle mass and function with advancing age exacerbates the likelihood of mobility impairments, disease development, and early mortality. Therefore, the development of non-pharmacological interventions to counteract sarcopenia warrant significant attention. Currently, resistance training provides the most effective, low cost means by which to prevent sarcopenia progression and improve multiple aspects of overall health. Importantly, the impact of resistance training on skeletal muscle mass may be augmented by specific dietary components (i.e., protein), feeding strategies (i.e., timing, per-meal doses of specific macronutrients) and nutritional supplements (e.g., creatine, vitamin-D, omega-3 polyunsaturated fatty acids etc.). The purpose of this review is to provide an up-to-date, evidence-based account of nutritional strategies to enhance resistance training-induced adaptations in an attempt to combat age-related muscle mass loss. In addition, we provide insight on how to incorporate the aforementioned nutritional strategies that may support the growth or maintenance of skeletal muscle and subsequently extend the healthspan of older individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".