Alpha-linolenic acid regulates white adipose tissue lipolysis independent of background dietary protein
Bibliographic record
Abstract
White adipose tissue (WAT) is an endocrine organ essential for maintaining whole-body energy balance by regulating fatty acid uptake, storage, and release. Emerging evidence indicates that omega-3 fatty acids have a role in modulating WAT lipid metabolism. While most studies have focused on marine-derived omega-3s, considerably less is known about alpha-linolenic acid (ALA). Previous research suggests that ALA may prevent or restore impaired lipolysis in dysfunctional WAT. The primary objective of this study was to examine the effects of ALA on WAT lipolytic activity and whether this varied with background dietary protein. Male C57BL/6N mice (n=16/group) were fed moderate-fat diets containing either 1% (low-ALA) or 3% (high-ALA) energy from ALA (provided by flaxseed oil), and either skim milk protein or a soy protein isolate for 8 weeks. Mice fed high-ALA diets showed increased body weight gain and WAT depot weights, reduced serum triglycerides, and increased serum glycerol levels. The higher serum glycerol levels in high-ALA fed mice were reflected in higher glycerol release from cultured adipose tissue explants stimulated with a β-adrenergic agonist. Markers of WAT lipolysis, including ATGL and phosphorylated HSL, were either lower or unchanged in mice fed high-ALA diets. Background dietary protein (from either dairy or soy) had little-to-no-effect on study endpoints. Our data suggests that increased dietary ALA intake improves circulating TAG levels while reducing markers of lipolysis in WAT depots. The increase in glycerol observed with high-ALA intake may point to a potential regulation of WAT glycerogenesis and/or aquaporin expression that warrants future investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".