Lower cognitive functioning as a predictor of weight gain in bipolar disorder: a 12‐month study
Bibliographic record
Abstract
OBJECTIVE: In cross-sectional studies, elevated body mass index (BMI) is associated with cognitive impairment in bipolar disorder (BD). We investigated the direction of this association by prospectively examining changes in BMI and cognition. METHOD: We measured BMI and performance in six cognitive domains over 12 months in 80 adolescent and young adult BD patients and 46 healthy comparison subjects (HS). Ninety-three percent of patients received pharmacotherapy and 84% were euthymic. We used repeated-measures ancova and longitudinal mixed models to investigate whether (i) higher BMI and increasing BMI over time predicted lower subsequent cognitive functioning, and (ii) lower cognitive functioning and changes in cognition predicted increasing BMI. RESULTS: Neither baseline BMI nor BMI change predicted lower cognitive functioning. Lower baseline scores in attention, verbal memory, working memory, and a composite measure of global cognition predicted increasing BMI in patients and HS. In patients, lower cognitive functioning remained associated with increasing BMI when clinical and treatment variables were adjusted for. Improvement in working memory predicted a smaller subsequent BMI increase in patients. CONCLUSION: Lower cognitive functioning in specific domains predicts increasing BMI in patients with BD and healthy young adults. Targeting cognition may be important for minimizing weight gain 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".