Bipolar disorders, type 2 diabetes mellitus, and the brain
Bibliographic record
Abstract
PURPOSE OF REVIEW: Type 2 diabetes mellitus (T2DM) negatively affects brain structure and function. Meta-analytical data show that relative to age and sex matched non-psychiatric controls, patients with bipolar disorders have double the risk of T2DM. We review the evidence for association between T2DM and adverse clinical and brain imaging changes in bipolar disorders and summarize studies investigating effects of diabetes treatment on psychiatric and brain outcomes. RECENT FINDINGS: Participants with bipolar disorders and T2DM or insulin resistance demonstrate greater morbidity, chronicity and disability, and lower treatment response to Li. Bipolar disorders complicated by insulin resistance/T2DM are associated with smaller hippocampal and cortical gray matter volumes and lower prefrontal N-acetyl aspartate (neuronal marker). Treatment of T2DM yields preservation of brain gray matter and insulin sensitizers, such as pioglitazone, improve symptoms of depression in unipolar or bipolar disorders. SUMMARY: T2DM or insulin resistance frequently cooccur with bipolar disorders and are associated with negative psychiatric clinical outcomes and compromised brain health. This is clinically concerning, as patients with bipolar disorders have an increased risk of metabolic syndrome and yet often receive suboptimal medical care. At the same time treatment of T2DM and insulin resistance has positive effects on psychiatric and brain outcomes. These findings create a rich agenda for future research, which could enhance psychiatric pharmacopeia and directly impact patient care.
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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 | 0.002 | 0.003 |
| 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) | 0.005 | 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".