Neurococognitive and neuroimaging correlates of obesity and components of metabolic syndrome in bipolar disorder: a systematic review
Bibliographic record
Abstract
BACKGROUND: Individuals with bipolar disorder (BD) have a higher prevalence of obesity and metabolic syndrome (MetS) compared with the general population. Obesity and MetS are associated with cognitive deficits and brain imaging abnormalities in the general population. Obesity and components of MetS might potentially associate with neuroimaging and neurocognitive findings in BD. METHODS: A literature search of studies investigating the association between obesity (and other components of MetS) and neurocognitive and neuroimaging findings in BD was conducted. In addition to a systematic review, a random-effects meta-analysis was conducted when sufficient data were available. RESULTS: Twenty-three studies were included in the current systematic review. Overweight/obese patients were significantly associated with impaired neurocognition compared normal weight individuals with BD (d = 0.37). The most robust association between obesity and cognitive deficits in BD was observed in the cognitive subdomain of executive functions (d = 0.61). There was also evidence for a significant relationship between cognitive impairment in BD and other components of MetS including hypertension, dyslipidemia, and diabetes. Overweight/obese individuals with BD had more pronounced brain imaging abnormalities than normal weight individuals with BD. CONCLUSIONS: Obesity and related cardiovascular risk factors significantly are associated with more severe cognitive and brain imaging abnormalities in BD. Medical co-morbidities can potentially contribute to functional decline observed in some patients throughout 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".