The burden of obesity among adults with bipolar disorder in the United States
Bibliographic record
Abstract
OBJECTIVES: Previous studies of clinical samples of adults with bipolar disorder (BD) suggest that there is increased prevalence of obesity and that obesity is associated with greater BD severity. We therefore examined this topic in a representative epidemiologic sample. METHODS: The 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions was used to determine whether the prevalence of obesity is elevated among subjects with lifetime BD, and whether obesity is associated with greater severity of BD. RESULTS: The age-, race-, and sex-adjusted prevalence of obesity was significantly greater among subjects with BD versus controls [odds ratio (OR) = 1.65, 95% confidence interval (CI): 1.45-1.89, p < 0.001]. Obesity among subjects with BD was significantly positively associated with greater age, female sex, comorbid anxiety and medical conditions, and depression-related treatment utilization, and significantly negatively associated with past-year substance use disorder (SUD). In multivariable analyses, obesity among adults with BD was positively associated with age, comorbid anxiety disorders, duration of depressive episodes, and history of hospitalization for depression, and negatively associated with past-year SUD. CONCLUSIONS: The increased prevalence of obesity in BD and its association with illness severity, particularly in relation to depression, cannot be attributed to biases inherent in treatment-seeking samples. Future studies are needed to examine the direction of the observed associations and to develop preventive and treatment strategies seeking to mitigate the burden of obesity in BD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".