155 Low Insulin Sensitivity Is Associated with Increased Body Fat and Changes in Gene Expression of Lipogenic Enzymes in the Adipose Tissue of Finishing Pigs
Bibliographic record
Abstract
Abstract Variations in body fat (BF) among pigs can be associated with differences in insulin sensitivity given the insulin anabolic effect in lipid synthesis. The study objectives were to characterize this association and compare the relative mRNA abundance of genes associated with insulin resistance and de novo lipogenesis in the adipose tissue of fat and lean pigs. Thirty 95 kg pigs, catheterized in the jugular vein, received an oral dose of 1.75 g glucose/kg of BW after 18 hours of fasting. Blood samples were collected at -20, -10, 5, 10, 15, 20, 25, 30, 45, 60, 90, 120, 150, 180, 210, 240, 300 and 360 minutes following glucose ingestion. Insulin sensitivity indexes were calculated and analyzed. The BF (%) was estimated by dual X-ray densitometry. The 8 fattest (22 % BF) and the 8 leanest pigs (17.2 % BF) were used to determine the relative mRNA abundance of studied genes using real-time qPCR analyses. Insulin sensitivity was determined using QUICKI and Matsuda indexes, respectively, and their association with body fat was studied with Spearman correlations. Differences in gene expression and insulin sensitivity between fat and lean pigs were studied with a one-way ANOVA. The QUICKI and Matsuda indexes negatively correlated with BF (r = -0.67 and r = -0.59; P < 0.001). Fat pigs had reduced insulin sensitivity and higher relative mRNA abundance of lipogenic enzymes (ACACA, ACLY, FASN; P < 0.05) than lean pigs. The higher expression level of glucose-6-phosphate dehydrogenase (G6PD) combined with the trend (P < 0.10) of lower expression of peroxisome proliferator-activated receptor-gamma (PPAR-γ) in fat pigs may explain part of their reduced insulin sensitivity. These results suggest that an increased BF is associated with reduced insulin sensitivity and greater expression of lipogenic enzymes in pig adipose tissue.
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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 | 0.001 | 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) | 0.002 | 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".