The Longitudinal Association From Obesity to Depression: Results From the 12‐year National Population Health Survey
Bibliographic record
Abstract
Prior observational studies have investigated the association between obesity and depression but evidence remains weak and mixed. There has been a call for high-quality longitudinal studies to elucidate the etiologic relationship from obesity to depression. The main objective of this study was therefore to investigate whether obesity was a risk factor for depression in a nationally representative sample followed for 12 years. Seven waves of data collection (1994-1995 to 2006-2007) were obtained from the National Population Health Survey (NPHS). Our analyses included 10,545 adults without depression at baseline. Past-year major depression episode (MDE) was assessed from the Composite International Diagnostic Interview-Short Form for Major Depression (CIDI-SFMD). Obesity was estimated using baseline BMI from self-reported weight and height (obesity: BMI > or =30 kg/m(2)). Kaplan-Meier survival curves were generated and Cox proportional hazard regression modeling was used to estimate the risk of MDE by obesity status, controlling for sociodemographic and health and lifestyle variables. We found that obesity at baseline did not significantly predict subsequent MDE in women (adjusted hazard ratio (AHR): 1.03, 95% confidence interval (CI) 0.84-1.26) and negatively predicted MDE in men (HR: 0.71, CI 0.51-0.98), after adjusting for important confounders. In summary, our findings suggest that obesity is a significant (negative) predictor of depression in adult men but not in women. These results moderate prior evidence supporting a positive link from obesity to depression.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".