Major Depression, Antidepressant Medication and the Risk of Obesity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it