The Use of Dietary Supplements and Amino Acid Restriction Interventions to Reduce Frailty in Pre-Clinical Models
Bibliographic record
Abstract
Frailty is a state of accelerated aging that increases susceptibility to adverse health outcomes. Due to its high societal and personal costs, there is growing interest in discovering beneficial interventions to attenuate frailty. Many of these interventions involve the use of lifestyle modifications such as dietary supplements. Testing these interventions in pre-clinical models can facilitate our understanding of their impact on underlying mechanisms of frailty. We conducted a narrative review of studies that investigated the impact of dietary modifications on measures of frailty or overall health in rodent models. These interventions include vitamin supplements, dietary supplements, or amino acid restriction diets. We found that vitamins, amino acid restriction diets, and dietary supplements can have beneficial effects on frailty and other measures of overall health in rodent models. Mechanistic studies show that these effects are mediated by modifying one or more mechanisms underlying frailty, in particular effects on chronic inflammation. However, many interventions do not measure frailty directly and most do not investigate effects in both sexes, which limits their applicability. Examining dietary interventions in animal models allows for detailed investigation of underlying mechanisms involved in their beneficial effects. This may lead to more successful, translatable interventions to attenuate frailty.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 |
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".