Pre-flight exercise and bone metabolism predict unloading-induced bone loss due to spaceflight
Bibliographic record
Abstract
OBJECTIVES: Bone loss remains a primary health concern for astronauts, despite in-flight exercise. We examined changes in bone microarchitecture, density and strength before and after long-duration spaceflight in relation to biochemical markers of bone turnover and exercise. METHODS: Seventeen astronauts had their distal tibiae and radii imaged before and after space missions to the International Space Station using high-resolution peripheral quantitative CT. We estimated bone strength using finite element analysis and acquired blood and urine biochemical markers of bone turnover before, during and after spaceflight. Pre-flight exercise history and in-flight exercise logs were obtained. Mixed effects models examined changes in bone and biochemical variables and their relationship with mission duration and exercise. RESULTS: At the distal tibia, median cumulative losses after spaceflight were -2.9% to -4.3% for bone strength and total volumetric bone mineral density (vBMD) and -0.8% to -2.6% for trabecular vBMD, bone volume fraction, thickness and cortical vBMD. Mission duration (range 3.5-7 months) significantly predicted bone loss and crewmembers with higher concentrations of biomarkers of bone turnover before spaceflight experienced greater losses in tibia bone strength and density. Lower body resistance training volume (repetitions per week) increased 3-6 times in-flight compared with pre-spaceflight. Increases in training volume predicted preservation of tibia bone strength and trabecular vBMD and thickness. CONCLUSIONS: Findings highlight the fundamental relationship between mission duration and bone loss. Pre-flight markers of bone turnover and exercise history may identify crewmembers at greatest risk of bone loss due to unloading and may focus preventative measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".