The Impact of Beetroot Supplementation and Exercise Training on Performance and Health in the Elderly: A Narrative Review
Bibliographic record
Abstract
Introduction: The elderly population is increasing globally, and the elderly are vulnerable to many ailments as the aging process negatively affects the body and physiological systems. Plants have been used to treat illnesses throughout history. The aim of this study was to investigate the beneficial effects of beetroot consumption with exercise training on aging in older adults. Methods: Several databases (2002-2023) were searched, including Web of Science, Scopus, ScienceDirect, Google Scholar, and PubMed, using different keywords such as "Beetroot and Aging", "Beetroot and Older Adults", "Beetroot and Elderly", "Beetroot and Exercise", "Beetroot and Training", and "Beetroot and Physical Activity". The inclusion criteria were individuals aged 60 or more and full-texts available and written in English. Results: Overall, 27 studies met the inclusion criteria, which reported beetroot and physical activity as a beneficial treatment in a range of chronic diseases associated with the aging process. Conclusion: The findings indicated that beetroot consumption can slow the aging process, although there are also contradictory findings in this regard. Further studies are needed to provide more data on the optimal dosages of beetroot needed to provide health benefits in older adults.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.004 | 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".