The relationship between bipolar disorder and type 2 diabetes: More than just co-morbid disorders
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) rates are three times higher in patients with bipolar disorder (BD), compared to the general population. This is a major contributing factor to the elevated risk of cardiovascular mortality, the leading cause of death in bipolar patients. There may be shared pathophysiology linking the two disorders, including hypothalamic-pituitary-adrenal and mitochondrial dysfunction, common genetic links, and epigenetic interactions. Life-style, phenomenology of bipolar symptoms, and adverse effects of pharmacotherapy may be contributing factors. Patients with BD and T2DM have a more severe course of illness and are more refractory to treatment. Control of their diabetes is poorer when compared to diabetics without BD, and an existing disparity in medical care may be partly responsible. Glucose abnormalities in bipolar patients need to be screened for and treated. Metformin appears to have the best benefit/risk ratio, and the dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists and analogues also appear promising, although these agents have not been specifically studied in populations with mood disorders. Physicians need to be aware of the increased risk for T2DM and cardiovascular disease in bipolar patients, and appropriate prevention, screening, case finding, and treatment is recommended.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".