Lower Plasma Levels of Selective VGF (Non-Acronymic) Peptides in Bipolar Disorder: Comparative Analysis Reveals Distinct Patterns across Mood Disorders and Healthy Controls
Bibliographic record
Abstract
INTRODUCTION: Discriminating bipolar disorder (BD) from major depressive disorder (MDD) remains a challenging clinical task. Identifying specific peripheral biosignatures that can differentiate between BD and MDD would significantly increase diagnostic accuracy. Dysregulated neuroplasticity is implicated in BD and MDD, and psychotropic medications restore specific disrupted processes by increasing neurotrophic signalling. The nerve growth factor inducible vgf gene (non-acronymic) encodes a precursor protein named proVGF, which undergoes proteolytic processing to produce several VGF peptides, some of which were suggested to be implicated in mood disorders and have antidepressant effects. Since the presence of VGF peptides in humans has been exclusively investigated in brain and cerebrospinal fluid, we aimed to identify which VGF peptides are present in the plasma and to investigate whether their levels could differentiate BD from MDD as well as responders from non-responders to pharmacological interventions. METHODS: VGF peptides were investigated in plasma from patients diagnosed with MDD (n = 37) or BD (n = 40 under lithium plus n = 29 never exposed to lithium), as well as healthy controls (HC; n = 36). RESULTS: Three VGF peptides (TLQP-11, AQEE-14, and NAPP-19) were identified using spectrometry analysis of plasma from HC. These peptides were then measured in the entire sample using ELISA, which showed significantly lower levels of AQEE and NAPP in BD than in HC and MDD (p = 5.0 × 10-5, p = 0.001, respectively). CONCLUSION: Our findings suggest that lower plasma levels of NAPP and AQEE are specifically associated with BD, thus possibly representing a diagnostic biomarker in mood disorders.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".