Insulin Resistance and Blood-Brain Barrier Dysfunction Underlie Neuroprogression in Bipolar Disorder
Bibliographic record
Abstract
Bipolar disorder (BD) often progresses to a more chronic and treatment resistant (neuroprogressive) course. Identifying which patients are at risk could allow for early intervention and prevention. Bipolar disorder is highly comorbid with metabolic disorders including type II diabetes mellitus (T2DM), hypertension, obesity, and dyslipidemia. Our studies have shown that insulin resistance (IR) is present in over 50% of patients with BD and that IR might underlie the progression of BD. While no confirmed predictors exist for identifying which patients with BD are likely to develop a more chronic course, emerging evidence including our own studies suggest that IR and related inflammatory pathways lead to impairments in blood-brain barrier (BBB) functioning. For the first time in living psychiatric patients, we have shown that the severity of BBB leakage is proportional to BD severity and is associated with IR. In this hypothesis paper we (i) highlight the evidence for a key role of IR in BD, (ii) show how IR in BD relates to shared inflammatory pathways, and (iii) hypothesize that these modulations result in BBB leakage and worse outcomes in BD. We further hypothesize that (iv) reversing IR through lifestyle changes or the actions of insulin sensitizing medications such as metformin, or optimizing BBB function using vascular protective drugs, such as losartan, could provide novel strategies for the prevention or treatment of neuroprogressive BD.
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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) | 0.000 | 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 | 0.001 |
| 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".