Obesity, Depression, and Antidepressant Use: Analyzing Metabolic Side Effects in US Adults Using the National Health and Nutrition Examination Survey (NHANES)
Bibliographic record
Abstract
BACKGROUND: Antidepressant use is increasingly prevalent, raising concerns about its potential impact on metabolic health, particularly in individuals with depression. This study investigated the association between antidepressant use and metabolic outcomes in a nationally representative US adult population. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) were analyzed using a complete-case approach, resulting in a final weighted sample of 192,331,773 adults. Multivariable linear and logistic regression models were applied to examine the relationship between antidepressant use and metabolic indicators, including BMI, fasting glucose, lipid profiles, and obesity. Analyses were adjusted for age, sex, race/ethnicity, and depression severity (Patient Health Questionnaire-9 {PHQ-9}), with subgroup analyses conducted by depression status and gender. All analyses accounted for NHANES's complex sampling design using Stata version 18 (College Station, TX: StataCorp LLC). RESULTS: Antidepressant use was not significantly associated with obesity in the full sample. However, in subgroup analyses, antidepressant use was linked to adverse metabolic changes, including higher total cholesterol and lower fasting blood glucose levels, particularly among non-depressed individuals and females. Significant racial/ethnic disparities were also observed. CONCLUSION: While antidepressant use was not independently associated with obesity, it was linked to specific metabolic alterations, with effects varying by gender and depression status. These findings underscore the importance of monitoring metabolic health in antidepressant users, especially in a diverse population.
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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 | 0.003 | 0.004 |
| 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 | 0.000 | 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".