MARKERS OF INFLAMMATION ARE MODIFIED BY SEX, FRAILTY AND EXERCISE IN A HEAD-DOWN TILT BEDREST STUDY
Bibliographic record
Abstract
Abstract Prolonged bedrest can induce physiological stress thereby exacerbating frailty and inflammation. We explored whether Head-Down Tilt (HDT) bedrest with or without exercise would alter inflammatory markers implicated in frailty. Twenty 55-65-year-old males (baseline frailty index (FI)=0.02±0.02) and females (FI=0.03±0.03) were subjected to HDT bedrest (2-weeks). Half were randomly assigned to a high-intensity interval/aerobic/strength training exercise intervention for 60 minutes/day (Control: n=4 males, 5 females; Exercise n=5 males, 6 females). FI and serum inflammatory cytokines were measured (multiplex assay) during bedrest (days 0,1,3,7,14), and recovery (days 0,4,32). Data were analyzed with linear mixed models. Proinflammatory marker levels (interleukin (IL)-6, IL-8) increased during bedrest in all groups but were higher in males than females (IL-6: ß=8.87, IL-8: ß=12.93; p< 0.01). IL-8 declined during recovery in all groups, although IL-8 and IL-6 remained higher in males (IL-6: ß=9.32, IL-8: ß=2.85, p< 0.01). The anti-inflammatory cytokine IL-10 was higher in males than females during bedrest, declined in recovery and remained highest in males. During bedrest proinflammatory cytokines (IL-1ß, IL-12) increased as frailty increased in all participants (IL- ß: ß=0.01, IL-12: ß=0.02, p< 0.05) whereas monocyte chemoattractant protein-1 levels declined (ß =-20.54, p< 0.05). The anti-inflammatory protein IL-1ra increased with frailty throughout bedrest in all, especially those who exercised (ß=3.15, p< 0.01). In older adults, inflammatory markers increased during bedrest, declined during recovery and were higher in males than females. Some inflammatory markers increased in parallel with frailty during bedrest. Exercise also increased IL-1ra levels, which may help reduce inflammation in older adults during prolonged bedrest.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".