Association of obesity and treated hypertension and diabetes with cognitive ability in bipolar disorder and schizophrenia
Bibliographic record
Abstract
OBJECTIVES: People with bipolar disorder or schizophrenia are at greater risk for obesity and other cardio-metabolic risk factors, and several prior studies have linked these risk factors to poorer cognitive ability. In a large ethnically homogenous outpatient sample, we examined associations among variables related to obesity, treated hypertension and/or diabetes and cognitive abilities in these two patient populations. METHODS: In a study cohort of outpatients with either bipolar disorder (n = 341) or schizophrenia (n = 417), we investigated the association of self-reported body mass index and current use of medications for hypertension or diabetes with performance on a comprehensive neurocognitive battery. We examined sociodemographic and clinical factors as potential covariates. RESULTS: Patients with bipolar disorder were less likely to be overweight or obese than patients with schizophrenia, and also less likely to be prescribed medication for hypertension or diabetes. However, obesity and treated hypertension were associated with worse global cognitive ability in bipolar disorder (as well as with poorer performance on individual tests of processing speed, reasoning/problem-solving, and sustained attention), with no such relationships observed in schizophrenia. Obesity was not associated with symptom severity in either group. CONCLUSIONS: Although less prevalent in bipolar disorder compared to schizophrenia, obesity was associated with substantially worse cognitive performance in bipolar disorder. This association was independent of symptom severity and not present in schizophrenia. Better understanding of the mechanisms and management of obesity may aid in efforts to preserve cognitive health in bipolar disorder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".