Preoperative Insulin Resistance and the Impact of Feeding on Postoperative Protein Balance: A Stable Isotope Study
Bibliographic record
Abstract
CONTEXT: Major surgery induces a catabolic state resulting in a net loss of body protein. OBJECTIVES: Our objective was to compare protein metabolism before and after surgery in nondiabetic patients with and without preoperative insulin resistance (IR). It was hypothesized that the anabolic response to feeding would be significantly impaired in those patients with preoperative insulin resistance. DESIGN: A hyperinsulinemic-euglycemic clamp has been used to identify two groups of patients: IR and insulin sensitive (IS). A tracer kinetics technique has been used to evaluate the metabolic response to food intake in both groups. SETTING: Patients undergoing cardiopulmonary bypass participated. PATIENTS OR OTHER PARTICIPANTS: Ten IS patients and 10 IR patients were enrolled in the study. INTERVENTION: After an overnight fasting, a 3-h infusion of a solution composed of 20% glucose and of amino acids at a rate of 0.67 and 0.44 kcal/kg · h, respectively, was started in each group. Phenylalanine kinetics were studied at the end of fasting and feeding. MAIN OUTCOME MEASURE: Effect of feeding on protein balance before and after surgery was evaluated. Protein balance has been measured as the net difference of protein breakdown minus protein synthesis. RESULTS: Protein balance increase after postoperative feeding was blunted only in the IR group. In contrast, in the IS group, the postoperative anabolic effect of feeding was the same as before surgery. CONCLUSIONS: These findings propose a link between insulin resistance and protein metabolism. When non-IR patients are fed, a significant anabolic effect in the postoperative period is demonstrated. In contrast, IR patients are less able to use feeding for synthetic purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".