A low-protein, high-carbohydrate diet increases the adipose lipid content without increasing the glycerol-3-phosphate or fatty acid content in growing rats
Bibliographic record
Abstract
The amount of triacylglycerol (TAG) that accumulates in adipose tissue depends on 2 opposing processes: lipogenesis and lipolysis. We have previously shown that the weight and lipid content of epididymal (EPI) adipose tissue increases in growing rats fed a low-protein, high-carbohydrate (LPHC) diet for 15 days. The aim of this work was to study the pathways involved in lipogenesis and lipolysis, which ultimately regulate lipid accumulation in the tissue. De novo fatty acid synthesis was evaluated in vivo and was similar for rats fed an LPHC diet or a control diet; however, the LPHC-fed rats had decreased lipoprotein lipase activity in the EPI adipose tissue, which suggests that there was a decreased uptake of fatty acids from the circulating lipoproteins. The LPHC diet did not affect synthesis of glycerol-3-phosphate (G3P) via glycolysis or glyceroneogenesis. Glycerokinase activity - i.e., the phosphorylation of glycerol from the hydrolysis of endogenous TAG to form G3P - was also not affected in LPHC-fed rats. In contrast, adipocytes from LPHC animals had a reduced lipolytic response when stimulated by norepinephrine, even though the basal adipocyte lipolytic rate was similar for both of the groups. Thus, the results suggest that the reduction of lipolytic activity stimulated by norepinephrine seems essential for the TAG increase observed in the EPI adipose tissue of LPHC animals, probably by impairment of the process of activation of lipolysis by norepinephrine.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".