Partitioning oxidative fuels during cold exposure in humans: muscle glycogen becomes dominant as shivering intensifies
Bibliographic record
Abstract
The effects of changes in shivering intensity on the relative contributions of plasma glucose, muscle glycogen, lipids and proteins to total heat production are unclear in humans. The goals of this study were: (1) to determine whether plasma glucose starts playing a more prominent role as shivering intensifies, (2) to quantify overall changes in fuel use in relation to the severity of cold exposure, and (3) to establish whether the fuel selection pattern of shivering is different from the classic fuel selection pattern of exercise. Using a combination of indirect calorimetry and stable isotope methodology, fuel metabolism was monitored in non-acclimatized adult men exposed for 90 mins to 10 degrees C (low-intensity shivering (L)) or 5 degrees C (moderate-intensity shivering (M)). Results show that plasma glucose oxidation is strongly stimulated by moderate shivering (+122% from L to M), but the relative contribution of this pathway to total heat generation always remains minor (< 15% of total heat production). Instead, muscle glycogen is responsible for most of the increase in heat production between L and M. By itself, the increase in CHO oxidation is responsible for the 100 W increase in metabolic rate observed between L and M, because rates of lipid and protein oxidation remain constant. This high reliance on CHO is not compatible with the well known fuel selection pattern of exercise, when considering the relatively low metabolic rates elicited by shivering (approximately 30% for M). We conclude that shivering and exercise of similar energy requirements appear to be supported by different fuel mixtures. Investigating the physiological mechanisms underlying why a muscle producing only heat (shivering), or significant movement (exercise), shows a different pattern of fuel selection at the same power output strikes us as a fascinating area for future research.
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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".