Preoperative brain μ-opioid receptor availability predicts weight development following bariatric surgery in women
Bibliographic record
Abstract
Bariatric surgery is the most effective method for weight loss in morbid obesity. There is significant individual variability in the weight loss outcomes, yet factors leading to postoperative weight loss or weight regain remain elusive. Alterations in the μ-opioid receptor (MOR) and dopamine D2 receptor (D2R) systems are associated with obesity and appetite control, and the magnitude of initial brain receptor system perturbation may predict long-term surgical weight loss outcomes. We tested this hypothesis by studying 19 morbidly obese women (mean BMI 40) scheduled to undergo bariatric surgery. We measured their preoperative MOR and D2R availabilities using positron emission tomography with [11C]carfentanil and [11C]raclopride, respectively, and then assessed their weight development association with regional MOR and D2R availabilities at 24-month follow-up. MOR availability in the amygdala consistently predicted weight development throughout the follow-up period, but no associations were found for D2R. This is the first study to our knowledge to demonstrate that neuroreceptor markers prior to bariatric surgery are associated with postoperative weight development. Postoperative weight regain may derive from dysfunction in the opioid system, and weight loss outcomes after bariatric surgery may be partially predicted based on preoperative brain receptor availability, opening up new potential for treatment possibilities.
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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 | 0.000 |
| 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".