The Bidirectional Relationship Between Body Mass Index and Treatment Outcome in Adolescents with Treatment-Resistant Depression
Bibliographic record
Abstract
OBJECTIVE: Depression and obesity are associated, but the impact of obesity on depression treatment outcome, or, conversely, the impact of treatment on body mass index (BMI) in depressed adolescents has not been reported. In this article, we examine the bidirectional relationships between BMI and treatment response in adolescents with treatment-resistant depression. METHOD: Participants in the Treatment of Selective Serotonin Reuptake Inhibitor (SSRI) Resistant Depression in Adolescents (TORDIA) study had height and weight assessed at baseline, weekly for the first 6 weeks, biweekly for the next 6 weeks, and monthly from weeks 12 through 24. The impact of baseline BMI as a predictor and moderator of treatment response was assessed. In addition, participants' changes in BMI were assessed as a function of specific treatment assignment and treatment response. RESULTS: Participants assigned to SSRIs had a greater increase in BMI-for-age-sex z-score and weight than did those assigned to venlafaxine. Post-hoc, those treated with paroxetine or citalopram had the biggest increases in BMI, relative to fluoxetine or venlafaxine. Overweight or obesity was neither a predictor nor a moderator of treatment outcome, nor of subsequent BMI change. CONCLUSIONS: Overweight status does not appear to affect treatment response in adolescents with resistant depression. The successful treatment of depression does not appear to favorably affect weight or BMI. Fluoxetine and venlafaxine are less likely to cause an increase in BMI than paroxetine or citalopram.
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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 | 0.001 | 0.005 |
| 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) | 0.001 | 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".