Multi-ingredient supplementation for combating sarcopenia and polymorbidity
Bibliographic record
Abstract
PURPOSE OF REVIEW: We discuss the premise and potential of multi-ingredient supplements (MIS) in the treatment of complex skeletal muscle (SkM) pathologies, and provide an updated review of literature on nutraceuticals in sarcopenia and sarcopenic obesity (SO) management, with an emphasis on single- vs. multi-ingredient protein-based formulations. RECENT FINDINGS: Several meta-analyses have confirmed the synergistic benefits of combining resistance training (RT) with dietary protein supplementation in older adults with sarcopenia or physical frailty, with a potential ceiling effect at 1.5-1.7 g PRO/kg BW/day.Single-ingredient supplements with proven synergism with RT and clinical relevance for sarcopenia treatment include the major milk proteins (whey and/or casein) and creatine monohydrate. Vitamin D 3 , calcium, and/or n -3 polyunsaturated fatty acids are also recommended for mitigating concurrent micronutrient deficiencies, bone loss, and inflammation. More evidence is needed to justify monotherapy with leucine or leucine metabolites over high-quality protein sources.RCTs have demonstrated superiority of whey-based MIS compared to isocaloric and isonitrogenous placebo for enhancing SkM growth in both younger and older persons, including obese and nonobese sarcopenic subgroups, as confirmed by in vivo body composition and/or biopsy sampling.Multi-ingredient formulations containing high-quality milk proteins, creatine monohydrate, vitamin D 3 , calcium, and n -3 polyunsaturated fatty acids may therefore be recommended in the multimodal treatment of sarcopenia and sarcopenic obesity. SUMMARY: Resistance training is the first-line treatment for musculoskeletal conditions and improves lean body mass, strength, and function in sarcopenia patients. Increased protein intake augments RT-induced muscle anabolism across clinical subpopulations, with recent evidence suggesting superiority of multi vs. single-ingredient protein-based supplements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".