Bipolar Disorder and Diabetes Mellitus: Epidemiology, Etiology, and Treatment Implications
Bibliographic record
Abstract
INTRODUCTION: Bipolar disorder (BD) is a highly prevalent and disabling condition with significant mortality risk from suicide and other unnatural causes. This ignominious description is alongside recent observations that the majority of excess deaths in BD are secondary to medical comorbidity. The medical burden in BD is associated with a clustering of risk factors (e.g., obesity, smoking, unhealthy dietary habits) and inadequate utilization of preventative and primary healthcare. Diabetes mellitus (DM) is also a prevalent multifactorial disease which imparts substantial illness burden. Preliminary investigations indicate that patients who suffer from BD with comorbid DM have a more severe course and outcome, lower quality of life, higher prevalence of medical comorbidity and higher cost of illness. METHODS: We conducted a MedLine search of all English-language articles 1966-2004 using the key words: bipolar disorder, major depressive disorder, diabetes mellitus, glucose metabolism, mortality, overweight, obesity, body mass index. The search was supplemented with manual review of relevant references. Priority was given to randomized controlled data, when unavailable; studies of sufficient sample size are presented. RESULTS: Subpopulations of BD patients should be considered at high risk for DM. The prevalence of DM in BD may be three times greater than in the general population. CONCLUSIONS: Bipolar disorder populations may be an at-risk group for glucose metabolic abnormalities. Opportunistic screening and vigilance for clinical presentations suggestive of DM is encouraged.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| 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".