Decreased hepatic glucose production in obese rats by dipeptidyl peptidase-IV inhibitor sitagliptin.
Bibliographic record
Abstract
BACKGROUND: Dipeptidyl peptidase-IV (DPP-4) inhibitors are now used to improve postprandial glycemic control in type 2 diabetes. However, their effects on hepatic glucose production (HGP) in obesity are not clear. This study was designed to test the hypothesis that gluconeogenesis and HGP can be modulated by DPP-4 inhibitors in obesity. METHODS: Sprague Dawley male rats were divided into four groups, each on a different diet: general rat chow, n = 10 (G); G + sitagliptin, n = 10; high fat chow (obesity), n = 10 (55% fat calories, HFO); HFO + sitagliptin, n = 10. After 10 weeks, the rats were fasted overnight and glucose metabolism was determined using 3-(3)H-glucose and (14)C-glycerol as tracers. RESULTS: Glycerol rate of appearance (P < 0.00001), plasma glycerol (P < 0.05) and free fatty acid (FFA) (P < 0.05) concentrations, and HGP (P < 0.05) were decreased in HFO + sitagliptin group compared with HFO group, but there was no significant difference between G and G + sitagliptin groups (P > 0.05). Gluconeogenesis in HFO group was five times of that in G rats (P < 0.01), but was significantly declined in HFO + sitagliptin group (P < 0.0001). CONCLUSIONS: Gluconeogenesis and HGP were inhibited by sitagliptin in high fat-induced obese rats due to decreased glycerol availability, which was a result of reduced glycerol release from adipose tissues. The finding suggests that sitagliptin is potentially useful for controlling fasting glucose in obesity, thereby delaying or preventing the development of diabetes.
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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 | 0.001 |
| 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 | 0.001 |
| 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".