Plasma Acylation-Stimulating Protein, Adiponectin, Leptin, and Ghrelin before and after Weight Loss Induced by Gastric Bypass Surgery in Morbidly Obese Subjects
Bibliographic record
Abstract
We examined fasting plasma insulin, acylation-stimulating protein (ASP), leptin, adiponectin, ghrelin, and metabolic/cardiovascular risk profile before and 15 +/- 6 months after isolated Roux-en-Y gastric bypass surgery in 50 morbidly obese subjects. Average preoperative plasma lipids were mostly normal, whereas ASP, insulin, and leptin were elevated, and adiponectin and ghrelin were decreased. Postoperatively, body weight decreased significantly (-36.4 +/- 9.6%) and was best predicted by preoperative adiponectin concentration in weight-stable subjects (r = -0.59; P = 0.02). Plasma lipids and insulin resistance improved, leptin and ASP decreased (-76.3 +/- 14.6% and -35.9 +/- 52.2%; P < 0.001), and adiponectin increased (50.1 +/- 47.0%; P < 0.001). The decrease in apolipoprotein B was best predicted by the decrease in ASP (r = 0.55; P = 0.009), whereas the improved postoperative insulin sensitivity was best predicted by the increase in adiponectin (r = 0.70; P = 0.01). Despite bypassing 95% of the stomach and isolating the fundus from contact with ingested nutrients, circulating ghrelin did not decrease after surgery. In fact, plasma ghrelin increased postoperatively in the subset of subjects undergoing active weight loss (+60.5 +/- 23.2%; P < 0.001); ghrelin, however, remained unchanged in weight-stable subjects. In summary, 1) preoperative adiponectin concentrations may be predictive of the extent of weight loss; 2) changes in ASP and adiponectin are predictive of decreased apolipoprotein B and improved insulin action, respectively; and 3) plasma ghrelin increases after gastric bypass surgery in patients experiencing active weight loss.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 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".