Neurocognitive Functioning in Overweight and Obese Patients with Bipolar Disorder: Data from the Systematic Treatment Optimization Program for Early Mania (STOP-EM)
Bibliographic record
Abstract
OBJECTIVE: Obesity is frequent in people with bipolar I disorder (BD I) and has a major impact on the course of the illness. Although obesity negatively influences cognitive function in patients with BD, its impact in the early phase of the disorder is unknown. We investigated the impact of overweight and obesity on cognitive functioning in clinically stable patients with BD recently recovered from their first manic episode. METHOD: Sixty-five patients with BD (25 overweight or obese and 40 normal weight) recently remitted from a first episode of mania and 37 age- and sex-matched healthy control. subjects (9 overweight or obese and 28 normal weight) were included in this analysis from the Systematic Treatment Optimization Program for Early Mania (commonly referred to as STOP-EM). All subjects had their cognitive function assessed using a standard neurocognitive battery. We compared cognitive function between normal weight patients, overweight-obese patients, and normal weight healthy control subjects. RESULTS: There was a negative affect of BD diagnosis on the domains of attention, verbal memory, nonverbal memory, working memory, and executive function, but we were unable to find an additional effect of weight on cognitive functioning in patients. There was a trend for a negative correlation between body mass index and nonverbal memory in the patient group. CONCLUSIONS: These data suggest that overweight-obesity does not negatively influence cognitive function early in the course of BD. Given that there is evidence for a negative impact of obesity later in the course of illness, there may be an opportunity to address obesity early in the course of 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".