Hyperinsulinemic-normoglycemic clamp administered together with amino acids induces anabolism after cardiac surgery
Bibliographic record
Abstract
Cardiac surgery triggers an inflammatory stress response, leading to protein catabolism, a process that even high-dose insulin therapy alone cannot reverse. To determine whether hyperinsulinemic-normoglycemic clamp and perioperative amino acid (AA) supplementation improves whole body protein balance, 20 patients scheduled for elective coronary artery bypass grafting surgery were randomly assigned to have intra- and postoperative hyperinsulinemic-normoglycemic clamp, with or without intravenous AA supplementation. Primed continuous infusions of [6,6-2H2]glucose and l-[1-13C]leucine were used to quantify whole body protein and glucose metabolism before and after surgery. Adipose tissue and serum cytokines were also analyzed to measure their responsiveness to the anabolic effect of AA administration. During hyperinsulinemic-normoglycemic clamp, AA supplementation successfully stimulated whole body protein synthesis, resulting in a positive whole body protein balance after surgery (insulin: −13.6 ± 4.5 vs. insulin + AA: 2.1 ± 5.4 μmol·kg−1·h−1, P < 0.001). Endogenous glucose production was equally suppressed in both groups (insulin: 0.0 ± 3.8 vs. insulin + AA 1.6 ± 1.6 μmol·kg−1·min−1, P = 0.230). AA supplementation led to significant changes in serum and tissue IL-6 (insulin: 246.6 ± 111.2 vs. insulin + AA: 124.5 ± 79.3 pg/ml, P = 0.011). In conclusion, hyperinsulinemic-normoglycemic clamp technique, together with AA supplementation, can induce an anabolic state after open-heart surgery, as quantified by a positive whole body protein balance.
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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 | 0.000 |
| 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".