Parasite infection leads to widespread glucocorticoid hormone increases in vertebrate hosts: A meta‐analysis
Bibliographic record
Abstract
Parasites and pathogens (hereafter parasites) commonly challenge organisms, but the extent to which their infections are physiologically stressful to hosts remains unclear. Importantly, vertebrate hormones, glucocorticoids (GCs), have been reported to increase, decrease or show no alterations stemming from infections, challenging the generality of parasite-associated GC responses and motivating a search for important moderator variables. We undertook the first meta-analysis of changes in vertebrate GCs following experimental infection with parasites, extracting 146 effect sizes from 42 studies involving 32 host and 32 parasite species to test for general patterns of GC following infection, as well as the influence of moderators. Overall, infection increased GCs relative to preliminary or control levels when the single largest effect sizes from repeated measures studies were examined, suggesting that parasites of vertebrate hosts can be thought of generally as physiological stressors by elevating GCs. When all effect sizes were included along with the moderator of sampling time post-infection (tPI), parasite infection still had a positive effect on host GCs. However, the strength of that effect did not relate consistently to tPI, illustrating temporal differences in GC changes during the course of infection among parasite taxa (e.g. arthropod vs. bacterial infections). Other moderator variables examined did not influence GC responses. Studies broadening the range of host and parasite taxa, and sampling during critical time windows, would aid in our understanding of variation in the host stress response and its consequences for fitness of both vertebrate hosts and their parasites.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.036 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".