Extracellular vesicles in mood disorders: A systematic review of human studies
Bibliographic record
Abstract
• EV studies target MDD, BD, postpartum, adolescent, and late-life depression. • EV cargo provides mechanistic insights and links to neuroimaging findings in MD. • EV-derived markers hold promise for diagnosis and treatment response in MD. • EV physical properties may offer additional clinical insights into MD. • Future research should prioritize brain-derived EVs and standardized methodologies. Extracellular vesicles (EVs) are small, membrane-bound particles that are naturally released by nearly all cell types in the body. They serve as molecular biosignatures, reflecting the state of their cells of origin and providing a non-invasive peripheral marker of central nervous system (CNS) activity under physiological and pathological conditions. We conducted a systematic review (ID: CRD42024528824) of studies investigating the use of EVs in mood disorders within clinical populations. We screened articles indexed in PubMed, EMBASE, Scopus, ISI Web of Science, and APA PsycInfo from January 2010 to October 2024. Available research has focused on four key areas: (1) EV cargo as mechanistic and diagnostic biomarkers; (2) EV cargo as predictive or tracking biomarkers for antidepressant response; (3) EV cargo and neuroimaging correlates; and (4) EV physical properties. Most studies examined major depressive disorder (MDD), with others addressing bipolar disorder (BD), adolescent depression, postpartum depression, and late-life depression. Notably, only 35,55 % of the studies utilized brain-derived EVs. Through analyses of EV-derived miRNA, proteins, mtDNA, and metabolites, these studies have explored neural mitochondrial function, brain insulin resistance, neurogenesis, neuroinflammation, and blood-brain barrier permeability in the context of mood disorders. Some EV-derived markers demonstrated diagnostic and predictive potential. This review discusses key findings, limitations of current research, and future directions for leveraging EVs in the study of mood disorders.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".