Resting metabolic responses during passive heat stress in healthy humans
Bibliographic record
Abstract
In context of global warming, investigating the impact of passive heating on temperature regulation and metabolism is essential to understanding the influence of heat exposure on human health. We aimed to develop and characterize a non-invasive method to assess metabolic responses during acute passive heating in young healthy participants. We hypothesized that passive heating would increase metabolic rate, as reflected by increased oxygen consumption ([Formula: see text]O 2 ), with associated changes in respiratory exchange ratio (RER). Seventeen healthy participants (7M/11F; 21±2yrs, 23±2kg/m 2 ) underwent a 40-minute protocol consisting of baseline (BL; 10-min), passive heat stress using electric heating blankets (20-min), and cooling (10-min). Temperature was measured at the tympanic membrane (ear), axilla, forehead, and expired air. Metabolic responses, including [Formula: see text]O 2 , carbon dioxide production (V·CO 2 ), and RER, were quantified during each stage. Significant rises from BL in axilla and expired temperatures occurred during heating (P=0.0004 and P=0.0005, respectively), while tympanic temperature showed no change (P=0.9418). Cooling led to significant decreases from BL in tympanic, forehead, and expired temperatures (p<0.0001). [Formula: see text]O 2 increased during heating (P=0.0185), with no significant change in V·CO 2 or RER (P=0.5253 and P=0.0554, respectively). Passive cooling led to subsequent increases in V·CO 2 and RER (P=0.0056 and P=0.0123, respectively). Despite minimal changes in measures of core temperature, metabolic responses to passive heating and cooling were observed, including a significant rise in [Formula: see text]O 2 during heating. This increase in [Formula: see text]O 2 during passive heating without a corresponding rise in V·CO 2 suggests a shift toward fatty acid oxidation, minimizing metabolic heat production. Department of Biology, Faculty of Science and Technology, Mount Royal University, Calgary, Alberta, Canada This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.
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) | 0.001 | 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".