Can body mass index help predict outcome in patients with bipolar disorder?
Bibliographic record
Abstract
OBJECTIVE: Several studies have reported higher prevalence of obesity in patients suffering from bipolar disorder (BD). To study the relation of elevated body mass index (BMI) in patients with BD more closely, we investigated differences in sociodemographic, clinical, and medical characteristics with respect to BMI, with the hypothesis that BMI is related to prognosis and outcome. METHODS: We measured the BMI of 276 subjects of a tertiary care sample from the Maritime Bipolar Registry. Subjects were 16 to 83 years old, with psychiatric diagnoses of bipolar I disorder (n = 186), bipolar II disorder (n = 85), and BD not otherwise specified (n = 5). The registry included basic demographic data and details of the clinical presentation. We first examined the variables showing a significant association with BMI; subsequently, we modeled the relationship between BMI and psychiatric outcome using structural equation analysis. RESULTS: The prevalence of obesity in our sample was 39.1%. We found higher BMI in subjects with a chronic course (p < 0.001) and longer duration of illness (p = 0.02), lower scores on the Global Assessment of Functioning Scale (p = 0.02), and on disability (p = 0.002). Overweight patients had more frequent comorbid subthreshold social (p = 0.02) and generalized anxiety disorders (p = 0.05), diabetes mellitus type II (p < 0.001), and hypertension (p = 0.001). Subjects who achieved complete remission of symptoms on lithium showed significantly lower BMI (p = 0.01). CONCLUSIONS: Our findings suggest that BMI is associated with the prognosis and outcome of BD. Whether this association is causal remains to be determined.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".