Impact of diet on human nutrition, immune response, gut microbiome, and cognition in an isolated and confined mission environment
Bibliographic record
Abstract
Long-duration spaceflight impacts human physiology, including well documented immune system dysregulation. The space food system has the potential to serve as a countermeasure to maladaptive physiological changes during spaceflight. However, the relationship between dietary requirements, the food system, and spaceflight adaptation requires further investigation to adequately define countermeasures and prioritize resources on future spaceflight missions. We evaluated the impact of an enhanced spaceflight diet, with increased quantity and variety of fruits, vegetables, fish, and other foods rich in flavonoids and omega-3 fatty acids, compared to a standard spaceflight diet on multiple health and performance outcomes in 16 subjects over four 45-day closed chamber missions in the NASA Human Exploration Research Analog (HERA). Subjects consuming the enhanced spaceflight diet had lower cholesterol levels, lower stress (i.e. cortisol levels), better cognitive speed, accuracy, and attention, and a more stable microbiome and metatranscriptome than subjects consuming the standard diet. Although no substantial changes were observed in the immune response, there were also no immune challenges, such as illness or infection, so the full benefits of the diet may not have been apparent in these analog missions. These results indicate that a spaceflight diet rich in fruits, vegetables, and omega-3 fatty acids produces significant health and performance benefits even over short durations. Further investigation is required to fully develop dietary countermeasures to physiological decrements observed during spaceflight. These results will have implications for food resource prioritization on spaceflight 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 | 0.000 |
| 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".