A minimal model of insulin secretion and kinetics to assess hepatic insulin extraction
Bibliographic record
Abstract
The liver is the principal site of insulin degradation, and assessing its ability to extract insulin is important to understand several pathological states. Noninvasive quantification of hepatic extraction (HE) in an individual requires comparing the profiles of insulin secretion (ISR) and posthepatic insulin delivery rate (IDR). To do this, we propose here the combined use of the classical C-peptide minimal model with a new minimal model of insulin delivery and kinetics. The models were identified on insulin-modified intravenous glucose tolerance test (IM-IVGTT) data of 20 healthy subjects. C-peptide kinetics were fixed to standard population values, whereas insulin kinetics were assessed in each individual, along with IDR parameters, thanks to the presence of insulin decay data observed after exogenous insulin administration. From the two models, profiles of ISR and IDR were predicted, and ISR and IDR indexes of beta-cell responsivity to glucose in the basal state, as well as during first- and second-phase secretion, were estimated. HE profile, obtained by comparing ISR and IDR profiles, showed a rapid suppression immediately after the glucose administration. HE indexes, obtained by comparing ISR and IDR indexes, indicated that the liver is able to extract 70 +/- 9% of insulin passing through it in the basal state and 54 +/- 14% during IM-IVGTT. In conclusion, insulin secretion, kinetics, and hepatic extraction can be reliably assessed during an IM-IVGTT by using insulin and C-peptide minimal models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".