Assessment of postprandial glucose metabolism: conventional dual- vs. triple-tracer method
Bibliographic record
Abstract
The dual-tracer method has been used conventionally for assessment of postprandial fluxes, i.e., appearance in plasma of ingested glucose (R(a meal)), endogenous glucose production (EGP), and disposal (R(d)). To quantify the magnitude of errors affecting the calculations and their dependence on model assumptions, this method was assessed and compared with the triple-tracer method, which provides model-independent estimates. For this purpose, the dual-tracer protocol was performed twice in eight normal subjects, with [1-(13)C]glucose to trace ingested glucose and [6,6-(2)H(2)]glucose constantly infused. A third tracer, [6-(3)H]glucose, was infused at variable rates to render the calculation of R(a meal) and EGP virtually model independent. The dual-tracer method analyzed with a one-compartment model performed poorly, since R(a meal) peak was significantly lower and delayed compared with triple-tracer reference, resulting in a significantly lower estimation of the amount of absorbed glucose (9,036 +/- 558 vs. 11,316 +/- 823 micromol/kg, P = 0.0117). EGP showed a paradoxical pattern, with an initial overshoot followed by a rapid decay to negative values, resulting in a significant underestimation of EGP suppression (57 +/- 3 vs. 65 +/- 4%, P = 0.0117). A two-compartment model performed better but did not overcome the limitations of the dual-tracer approach, since the amount of absorbed glucose was still significantly underestimated (10,231 +/- 661 vs. 12,169 +/- 838 micromol/kg, P = 0.0117) and EGP still showed a paradoxical behavior. R(d), estimated from R(a meal) and EGP, was significantly underestimated with the dual-tracer method, irrespective of adopted model. We conclude that three suitably infused tracers are required for accurate assessment of postprandial R(a meal), EGP, and R(d).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".