Levels of circulating cortisol and cytokines in members of the Canadian Armed Forces: associations with age, sex, and anthropometry
Bibliographic record
Abstract
This study assessed blood levels of cortisol and cytokines (inflammatory and non-inflammatory) in members of the regular Canadian Armed Forces (CAF), and examined the associations between sex, age, and adiposity and circulating levels of cortisol as well as pro- and anti-inflammatory cytokines. As part of a larger ranging project, 331 blood samples were collected from a representative population of the total CAF, which included officers and noncommissioned women and men from the Air Force, Navy, and Army. The blood samples were analyzed for levels of cortisol, C-reactive protein (CRP), adiponectin, and 20 cytokines (which included interleukins, interferons, and tumor necrosis factors). Higher levels of adiponectin were found in women compared with men (median and interquartile range; 16.71 (7.68-25.32) vs 5.81 (3.52-13.19) μg/mL), and higher levels of interleukin (IL)-18 in men compared with women (89.25 (84.03-94.48) vs 75.91 (69.70-82.13) pg/mL). An association between age and levels of stress and inflammatory cytokines was observed, with CRP, IL-18, IL-2 and adiponectin all increasing with increasing age. However, contrary to trends seen in the general population, cortisol levels decreased with increasing age. Levels of CRP and IL-18 increased with an increase in adiposity, while adiponectin levels decreased. Most importantly, at the entire cohort level, a low detection rate for most of the cytokines was observed with 17 out of 22 cytokines having a detection below 10%. IN CONCLUSION: In this CAF population, although an association between age and inflammatory cytokines was observed, both sex and adiposity had a small impact on levels of cortisol and cytokines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".