Psychiatric Safety and Weight Loss Efficacy of Naltrexone/bupropion as Add-on to Antidepressant Therapy in Patients with Obesity or Overweight
Bibliographic record
Abstract
There is significant association between obesity and depression. Naltrexone/Bupropion (NB) is indicated for treatment of overweight and obesity (BMI ≥27 kg/m2 with a comorbidity or ≥30 kg/m2). This post-hoc analysis examines safety and efficacy of NB and placebo among individuals with overweight or obesity who were also taking antidepressant therapy during the LIGHT trial (N=8910). Subjects were divided into four subgroups: NB + antidepressants (n=1150), NB without antidepressants (n=3300), placebo + antidepressants (n=1127) and placebo without antidepressants (n=3317). Among subjects taking NB, the combined incidence of serious adverse events (AEs) and AEs leading to treatment discontinuation was not significantly different between those on antidepressants and those who were not. The key weight-loss efficacy analyses were performed on NB or placebo-treated subjects who remained on study therapy through 104 weeks and who did or did not have documented antidepressant use at each of the baseline, week 52 and week 104 visits (Completers: N=1811; 47.0% female, 86.9% white, mean age of 61 years, mean baseline BMI 37.4 kg/m2). The mean adjusted weight change in subjects taking antidepressants was numerically, but not significantly greater for NB vs. placebo (-6.3% vs. -4.3%). For those subjects not on antidepressants, weight loss was significantly greater for NB vs. PL (-6.8% vs. -3.6%). NB is generally well tolerated in patients with overweight or obesity who are on antidepressants and is effective in promoting weight loss regardless of antidepressant use. These results show that for patients on antidepressant therapy, NB may be an effective option for obesity management.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".