U-shaped relationship between depression and body mass index in the Korean adults
Bibliographic record
Abstract
Abstract Background: Although a number of studies have examined the relationship between depression and obesity, it is still insufficient to establish the specific pattern of relationship between depression and body mass index (BMI) categories. Thus, this study was aimed to investigate the relationship between depression and BMI categories. Methods: A cross-sectional study was conducted for a cohort of 159,390 Korean based on Kangbuk Samsung Health Study (KSHS). Study participants were classified into 5 groups by Asian-specific cut-off of BMI (18.5, 23, 25 and 30 kg/m2). The presence of depression was determined by Center for Epidemiologic Studies-Depression scales (CES-D) = 16 and = 25. The adjusted odd ratios (ORs) for depression were evaluated by multiple logistic regression analysis, in which independent variable was 5 categories of BMI and dependent variable was depression. Subgroup analysis was conducted by gender and age. Results: When normal group was set as a reference, the adjusted ORs for depression formed U-shaped pattern of relationship with BMI categories [underweight: 1.31 (1.14–1.50), overweight: 0.94 (0.85–1.04), obese group: 1.01 (0.91–1.12), severe obese group: 1.28 (1.05–1.54)]. This pattern of relationship was more prominent in female and young age group than male and elderly subgroup. BMI level with the lowest likelihood of depression was 18.5 kg/m2 to 25 kg/m2 in women and 23 kg/m2 to 25 kg/m2 in men. Conclusions: There was a U-shaped relationship between depression and BMI categories. This finding suggests that both underweight and severe obesity are associated with the increased risk for 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".