Correlates of Metabolic Abnormalities in Bipolar I Disorder at Initiation of Acute Phase Treatment
Bibliographic record
Abstract
OBJECTIVE: Treatment of bipolar patients is often complicated by metabolic abnormalities such as obesity, diabetes, and dyslipidemia. We therefore evaluated the prevalence of these abnormalities and their correlates, in bipolar I patients, at the time of commencement of pharmacological treatment for acute mood episodes. METHODS: The study cohort consisted of 184 bipolar I patients hospitalized for treatment of acute mood episodes. Socio-demographic and clinical variables were noted and metabolic parameters, including body mass index, fasting plasma glucose, fasting total cholesterol, and current treatment(s) for diabetes and/or dyslipidemia were measured before initiating medication(s). RESULTS: Fifty-six (30.4%) subjects met our criteria for obesity; 80 (43.5%) had hyperglycemia, with 8 (4.3%) receiving anti-diabetic medication; and 38 (20.7%) had hypercholesterolemia, with 2 (1.1%) receiving cholesterol-lowering agents. We found that male sex (chi(2)=5.359, p=0.021), depressed or mixed state versus manic state (chi(2)=4.302, p=0.038), and duration of illness (t=2.756, p=0.006) were significantly associated with obesity. Older age (t=3.668, p<0.001), later age of disease onset (t=2.271, p=0.024), and lower level of educational attainment (beta=-0.531, p=0.001) were associated with hyperglycemia. CONCLUSION: Our finding that metabolic abnormalities are prevalent when initiating acute pharmacological treatment in bipolar I patients indicates that these factors should be integrated into treatment plans at the onset of disease management.
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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 | 0.001 | 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".