The Greater Contribution of Gluconeogenesis to Glucose Production in Obesity Is Related to Increased Whole-Body Protein Catabolism
Bibliographic record
Abstract
Obesity is associated with an increase in the fractional contribution of gluconeogenesis (GNG) to glucose production. We tested if this was related to the altered protein metabolism in obesity. GNG(PEP) (via phosphoenol pyruvate [PEP]) was measured after a 17-h fast using the deuterated water method and 2H nuclear magnetic resonance spectroscopy of plasma glucose. Whole-body 13C-leucine and 3H-glucose kinetics were measured in the postabsorptive state and during a hyperinsulinemic-euglycemic-isoaminoacidemic clamp in 19 (10 men and 9 women) lean and 16 (7 men and 9 women) obese nondiabetic subjects. Endogenous glucose production was not different between groups. Postabsorptive %GNG(PEP) and GNG(PEP) flux were higher in obese subjects, and glycogenolysis contributed less to glucose production than in lean subjects. GNG(PEP) flux correlated with all indexes of adiposity and with postabsorptive leucine rate of appearance (Ra) (protein catabolism). GNG(PEP) was negatively related to the clamp glucose rate of disposal (Rd) and to the protein anabolic response to hyperinsulinemia. In conclusion, the increased contribution of GNG to glucose production in obesity is linked to increased postabsorptive protein catabolism and insulin resistance of both glucose and protein metabolism. Due to increased protein turnover rates, greater supply of gluconeogenic amino acids to the liver may trigger their preferential use over glycogen for glucose production.
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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 | 0.000 |
| 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".