Assessing the gut microbiota composition in older adults: connections to physical activity and healthy ageing
Bibliographic record
Abstract
The composition and functionality of the gut microbiota (GM) changes throughout the life course. As we move into older age, it starts to shift towards a less healthy one, which may lead to an imbalance in the GM community. Strategies that can reverse age-related dysbiosis are an important part of healthy aging. Little is known about the GM composition of older adults with different physical activity (PA) levels and whether it might contribute to healthy ageing. The aim of this study was to compare the GM composition of older adults with different PA levels and assess if it is associated with healthy ageing. 101 participants aged between 65-85 years undertook anthropometric measures, a 6-min walking test, wore an accelerometer for 7 days and provided a faecal sample. Faecal GM composition was analysed using 16S rRNA sequencing. We found that those who fulfilled the WHO/UK PA recommendations had higher relative abundance of several health-related bacteria such as Lactobacillus, F. prausnitzii and Roseburia intestinalis and lower abundance of disease-associated bacteria such as D.piger or Enterobacterales when compared to those who did not reach PA recommendations. These findings suggest that PA might improve the GM composition and has the potential to, at least partially, revert age-associated dysbiosis and promote healthy ageing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".