Neuroinflammation and insulin resistance in major depression and bipolar disorder: Implications for clinical trials evaluating immunometabolic targeted therapies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bipolar disorder (BD) and major depressive disorder (MDD) are highly prevalent, disabling psychiatric illnesses marked by substantial heterogeneity and frequent metabolic and inflammatory comorbidities. Growing evidence implicates low-grade inflammation, immune dysregulation, and insulin resistance (IR) in the pathophysiology, progression, and treatment response of mood disorders. While numerous clinical trials have investigated immunometabolic targeted interventions, outcomes have been inconsistent, due to limited stratification of participants based on underlying biology. This perspective paper aims to identify practical biomarkers and biosignatures to guide patient selection and optimize immunometabolic trial design. We summarize evidence linking neuroinflammation and IR to illness burden, discuss clinical trials targeting these mechanisms, and highlight emerging markers, including extracellular vesicles, monocyte gene expression profiles, and neuron-derived vesicle signatures of IR. No single validated biomarker for identification of immunometabolic phenotype currently exists, but multimodal biosignatures combining genetic, epigenetic, proteomic, and clinical features offer a pragmatic empirical path forward. Integrating these markers with advanced analytic approaches, such as machine learning, holds promise for identifying biologically coherent subgroups most likely to benefit from targeted immunometabolic interventions, accelerating precision medicine for BD and MDD.
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.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it