Effect of bedrest on the human gut and oral microbiome: implications for frailty
Bibliographic record
Abstract
The physiological effects of spaceflight resemble those of ageing and prolonged inactivity, and ground-based microgravity analogs have emerged as promising models for studying frailty. The human microbiome is increasingly recognised for its role in age-associated decline, although precise mechanisms remain unclear. Here, we evaluate the gut and oral microbiomes of twenty-two participants, aged 55-65, who were enrolled in a head-down tilt bedrest (HDBR) study, the first Canadian HDBR study conducted in an older cohort. Participants were randomly assigned to an inactivity or multi-modality exercise intervention group for fourteen days of HDBR, followed by seven days of rehabilitation and additional follow-up appointments. Gut (n=343) and oral (n=344) taxonomic profiles were generated using V4/V5 16S rRNA gene sequencing from fecal and salivary samples collected throughout the study. Gut functional profiles were generated using metagenomic (n=86) and metabolomic (n=83) data. Frailty was measured using a 36-item frailty index. Inactivity-associated changes to the gut microbiome during HDBR included decreasing α-diversity, decreasing Akkermansia and Lactobacillus , and increasing Bacteroides . Exercise-associated changes included increasing gut Roseburia . Interestingly, oral microbiome β-diversity was more strongly associated with frailty than the gut. We conclude that inactivity-associated changes to the human microbiome could contribute to the early stages of frailty development, and that exercise can serve as an effective countermeasure against these effects. These results may inform strategies to preserve the health of both older adults facing prolonged periods of inactivity, as well as astronauts during longer space exploration missions
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".