Insufficient glucose supply is linked to hypothermia upon cold exposure in high-fat diet-fed mice lacking PEMT
Bibliographic record
Abstract
Mice that lack phosphatidylethanolamine N-methyltransferase (Pemt(-/-) mice) are protected from high-fat (HF) diet-induced obesity. HF-fed Pemt(-/-) mice show higher oxygen consumption and heat production, indicating that more energy might be utilized for thermogenesis and might account for the resistance to diet-induced weight gain. To test this hypothesis, HF-fed Pemt(-/-) and Pemt(+/+) mice were challenged with acute cold exposure at 4°C. Unexpectedly, HF-fed Pemt(-/-) mice developed hypothermia within 3 h of cold exposure. In contrast, chow-fed Pemt(-/-) mice, possessing similar body mass, maintained body temperature. Lack of PEMT did not impair the capacity for thermogenesis in skeletal muscle or brown adipose tissue. Plasma catecholamines were not altered by Pemt genotype, and stimulation of lipolysis was intact in brown and white adipose tissue of Pemt(-/-) mice. HF-fed Pemt(-/-) mice also developed higher systolic blood pressure, accompanied by reduced cardiac output. Choline supplementation reversed the cold-induced hypothermia in HF-fed Pemt(-/-) mice with no effect on blood pressure. Plasma glucose levels were ∼50% lower in HF-fed Pemt(-/-) mice compared with Pemt(+/+) mice. Choline supplementation normalized plasma hypoglycemia and the expression of proteins involved in gluconeogenesis. We propose that cold-induced hypothermia in HF-fed Pemt(-/-) mice is linked to plasma hypoglycemia due to compromised hepatic glucose production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".