The Effects of Cold Exposure on Leukocytes, Hormones and Cytokines during Acute Exercise in Humans
Bibliographic record
Abstract
The purpose of the study was to examine the effects of exercise on total leukocyte count and subsets, as well as hormone and cytokine responses in a thermoneutral and cold environment, with and without an individualized pre-cooling protocol inducing low-intensity shivering. Nine healthy young men participated in six experimental trials wearing shorts and t-shirts. Participants exercised for 60 min on a treadmill at low (LOW: 50% of peak VO2) and moderate (MOD: 70% VO2peak) exercise intensities in a climatic chamber set at 22°C (NT), and in 0°C (COLD) with and without a pre-exercise low-intensity shivering protocol (SHIV). Core and skin temperature, heart rate and oxygen consumption were collected continuously. Blood samples were collected before and at the end of exercise to assess endocrine and immunological changes. Core temperature in NT was greater than COLD and SHIV by 0.4±0.2°C whereas skin temperature in NT was also greater than COLD and SHIV by 8.5±1.4°C and 9.3±2.5°C respectively in MOD. Total testosterone, adenocorticotropin and cortisol were greater in NT vs. COLD and SHIV in MOD. Norepinephrine was greater in NT vs. other conditions across intensities. Interleukin-2, IL-5, IL-7, IL-10, IL-17, IFN-γ, Rantes, Eotaxin, IP-10, MIP-1β, MCP-1, VEGF, PDGF, and G-CSF were elevated in NT vs. COLD and/or SHIV. Furthermore, IFN-γ, MIP-1β, MCP-1, IL-10, VEGF, and PDGF demonstrate greater concentrations in SHIV vs. COLD, mainly in the MOD condition. This study demonstrated that exercising in the cold can diminish the exercise-induced systemic inflammatory response seen in a thermoneutral environment. Nonetheless, prolonged cooling inducing shivering thermogenesis prior to exercise, may induce an immuno-stimulatory response following moderate intensity exercise. Performing exercise in cold environments can be a useful strategy in partially inhibiting the acute systemic inflammatory response from exercise but oppositely, additional body cooling may reverse this benefit.
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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) | 0.001 | 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) | 0.001 | 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".