Clinical Features of Bipolar Disorder with and without Comorbid Diabetes Mellitus
Bibliographic record
Abstract
OBJECTIVE: Several papers have reported higher prevalence of diabetes mellitus (DM) type 2 in patients suffering from bipolar disorder (BD). The possible links between these 2 disorders include treatment, lifestyle, alterations in signal transduction, and possibly, a genetic link. To study this relation more closely, we investigated whether there are any differences in the clinical characteristics of BD patients with and without DM. METHOD: We compared the clinical data of 26 diabetic and 196 nondiabetic subjects from The Maritime Bipolar Registry. Subjects were aged 15 to 82 years, with psychiatric diagnoses of BD I (n = 151), BD II (n = 65), and BD not otherwise specified (n = 6). The registry included basic demographic data and details on the clinical course of bipolar illness, its treatment, and physical comorbidity. In a subsequent analysis using logistic regression, we examined the variables showing differences between groups, with diabetes as an outcome variable. RESULTS: The prevalence of DM in our sample was 11.7% (n = 26). Diabetic patients were significantly older than nondiabetic patients (P < 0.001), had higher rates of rapid cycling (P = 0.02) and chronic course of BD (P = 0.006), scored lower on the Global Assessment of Functioning Scale (P = 0.01), were more often on disability for BD (P < 0.001), and had higher body mass index (P < 0.001) and increased frequency of hypertension (P = 0.003). Lifetime history of treatment with antipsychotics was not significantly associated with an elevated risk of diabetes (P = 0.16); however, the data showed a trend toward more frequent use of antipsychotic medication among diabetic subjects. CONCLUSIONS: Our findings suggest that the diagnosis of DM in BD patients is relevant for their prognosis and outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".