Major Depression, Antidepressant Medication and the Risk of Obesity
Bibliographic record
Abstract
BACKGROUND: Cross-sectional studies have reported an association between major depressive episode (MDE) and obesity. The objective of this longitudinal analysis was to determine whether MDE increase the risk of becoming obese over a 10-year period. METHOD: We used data from the Canadian National Population Health Survey (NPHS), a longitudinal study of a representative cohort of household residents in Canada. The incidence of obesity, defined as a body mass index (BMI) of > or =30, was evaluated in respondents who were 18 years or older at the time of a baseline interview in 1994. MDE was assessed using a brief diagnostic instrument. RESULTS: The risk of obesity was not elevated in association with MDE, either in unadjusted or covariate-adjusted analyses. The strongest predictor of obesity was a BMI in the overweight (but not obese) range. Effects were also seen for (younger) age, (female) sex, a sedentary activity pattern, low income and exposure to antidepressant medications. Unexpectedly, significant effects were seen for serotonin-reuptake-inhibiting antidepressants and venlafaxine, but neither for tricyclic antidepressants nor antipsychotic medications. CONCLUSIONS: MDE does not appear to increase the risk of obesity. The cross-sectional associations that have been reported, albeit inconsistently, in the literature probably represent an effect of obesity on MDE risk. Pharmacologic treatment with antidepressants may be associated with an increased risk of obesity, and strategies to offset this risk may be useful in clinical practice.
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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 | 0.003 |
| 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".