Glycerol and Glycerol-3-Phosphate: Multifaceted Metabolites in Metabolism, Cancer, and Other Diseases
Bibliographic record
Abstract
Glycerol and glycerol-3-phosphate (Gro3P) are key metabolites at the intersection of carbohydrate, lipid, and energy metabolism. Their production and usage are organismal and cell-type specific. Glycerol has unique physicochemical properties enabling it to function as an osmolyte, protein structure stabilizer, and an antimicrobial and antifreeze agent, important to the preservation of many biological functions. Glycerol and Gro3P are implicated in many physiological and disease processes relating to energy metabolism, thermoregulation, hydration, skin health, male fertility, aging, and cancer. Glycerol has countless applications in the food, pharmaceutical, and cosmetics industries. It is used as a sweetener, preservative, thickening agent, humectant, osmolyte, and cryoprotectant. It is widely used in skin and wound care products, laxatives, in cell and tissue preservation, and in medicines for numerous conditions. Here, we review the multiple uses and functions of glycerol and Gro3P and associated transporters, enzymes, and target genes in health, senescence, and disease. We discuss the evidence that glycerol may be present at much higher levels in tissues and cells than in the blood. We bring particular focus to the newly identified glycerol shunt in the direct formation of glycerol independent of lipolysis and as a pathway allowing cells to adapt to various stresses. Relevant to chronic metabolic diseases, cancer and aging, glycerol and Gro3P present important translational implications and thus warrant much more attention.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".