A simple method to monitor hepatic gluconeogenesis and triglyceride synthesis following oral sugar tolerance test in obese adolescents
Bibliographic record
Abstract
Hepatic energy metabolism is a key element in many metabolic diseases. Hepatic anaplerosis provides carbons for gluconeogenesis (GNG) and triglyceride (TG) synthesis. We aimed to optimize a protocol that measures hepatic anaplerotic contribution for GNG, TG synthesis, and hepatic pentose phosphate pathway (PPP) activity using a single dose of oral [U−13C3]glycerol paired with an oral sugar tolerance test (OSTT) in a population with significant insulin resistance. The OSTT (75 g glucose + 25 g fructose) was administered to eight obese adolescents with polycystic ovarian syndrome (PCOS) followed by ingestion of [U-13C3]glycerol at t = 180 or t = 210 min. 13C-labeling patterns of serum glucose and TG-glycerol were determined by nuclear magnetic resonance. 13C enrichment in plasma TG-glycerol was detectable and stable from 240 to 390 min with the [U-13C3]glycerol drink at t = 180 min(3.65 ± 2.3 to 4.47 ± 1.4%; P > 0.4), but the enrichment was undetectable at 240 min with the glycerol drink at t = 210 min. The relative contribution from anaplerosis was determined at the end of the OSTT [18.5 ±3.4% ( t = 180 min) vs. 16.0 ± 3.5% ( t = 210 min); P = 0.27]. [U-13C3]glycerol was incorporated into GNG 390 min after the OSTT with an enrichment of 7.5–12.5%. Glucose derived from TCA cycle activity was 0.3–1%, and the PPP activity was 2.8–4.7%. In conclusion, it is possible to obtain relative measurements of hepatic anaplerotic contribution to both GNG and TG esterification following an OSTT in a highly insulin-resistant population using a minimally invasive technique. Tracer administration should be timed to allow enough de novo TG esterification and endogenous glucose release after the sugar drink.
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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 | 0.000 | 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) | 0.001 | 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".