Vascular Endothelial Growth Factor Polymorphism rs699947 Is Associated with Neurostructural Phenotypes in Youth with Bipolar Disorder
Bibliographic record
Abstract
Background: Vascular endothelial growth factor (VEGF) may be relevant to bipolar disorder (BD) and brain structure. We evaluated VEGF rs699947 single-nucleotide polymorphism in relation to structural neuroimaging phenotypes in youth BD. Methods: We collected 3 T anatomical magnetic resonance images from 154 youth (79 BD and 75 healthy control [HC]) genotyped for VEGF rs699947. The participants were age (BD = 17.28 ± 1.40 and HC = 17.01 ± 1.83, t = −1.02, p = 0.31) and sex (BD = 63.3% females and HC = 52.0% females, χ 2 = 2.01, p = 0.16) matched. Cortical thickness, surface area (SA), and volume were examined by region-of-interest (ROI) and vertex-wise analyses using general linear models (GLMs). ROI investigations selected for the prefrontal cortex (PFC), amygdala, and hippocampus. Vertex-wise analyses controlled for age, sex, and intracranial volume. Results: ROI results found lower PFC SA ( p = 0.003, η p 2 = 0.06) and volume ( p = 0.04, η p 2 = 0.03) in BD and a main effect of rs699947 on hippocampal volume ( p = 0.03, η p 2 = 0.05). The latter two findings did not survive multiple comparisons. Vertex-wise analyses found rs699947 main effects on left postcentral gyrus volume ( p < 0.001), right rostral anterior cingulate SA ( p = 0.004), and right superior temporal gyrus thickness ( p = 0.004). There were significant diagnosis-by-genotype interactions in the left superior temporal, left caudal middle frontal, left superior frontal, right fusiform, and right lingual gyri, and the left insular cortex. Posthoc analyses revealed the AA allele was associated with larger brain structures among HC, but smaller brain structures in BD for most clusters. Conclusions: Overall, we found preliminary evidence of divergent associations between BD and HC youth in terms of neurostructural correlates of VEGF rs699947 encompassing highly relevant frontotemporal regions.
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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 | 0.001 | 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) | 0.003 | 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".