Preoperative Glucose and Protein Metabolism: The Influence of Diabetes Mellitus Type 2 in Patients With Colorectal Tumors
Bibliographic record
Abstract
Hypermetabolism, abnormal plasma amino acid profiles, increased gluconeogenesis, and changes in liver and muscle protein turnover are well-described undesirable effects in patients with cancer and diabetes mellitus type 2 (DM2) The aim of the present study was to analyze the specific impact and interaction of these 2 disease patterns on patients' preoperative glucose and protein metabolism. Eight nondiabetic and 8 diabetic patients devoid of cachexia underwent a stable isotope infusion study on the day before surgery for colorectal cancer or adenoma with high-grade dysplasia. Protein and glucose kinetics were assessed in a fasted state by L-[1-(13)C]leucine and [6,6(2)H(2)]glucose. In diabetic patients, glucose metabolism was found to be elevated as the plasma glucose level increased (P = 0.013) and endogenous rate of appearance of glucose tended to be higher compared to nondiabetic patients (P = 0.083). Protein metabolism was not affected by the metabolic state of the 2 groups. Resting energy expenditure was higher in diabetic patients (P = 0.028). Under postabsorptive conditions, noncachectic patients with DM2 suffering from colorectal tumors showed an elevated turnover in glucose metabolism whereas the nondiabetic counterparts failed to demonstrate any metabolic changes due solely to malignancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".