Neurostructural Correlates of Polygenic Risk for Coronary Artery Disease in Relation to Youth Bipolar Disorder
Bibliographic record
Abstract
ABSTRACT Introduction Bipolar disorder (BD), characterized by anomalous neurostructural phenotypes, is also strongly associated with cardiovascular disease. Here we examined polygenic risk for coronary artery disease (CAD) in relation to gray matter structure in youth BD. Methods Youth participants (mean age 17.1 years; n = 66 BD, n = 45 healthy controls [HC]) underwent T1‐weighted magnetic resonance imaging. CAD polygenic risk scores (CAD‐PRS) were calculated using independent, adult genome‐wide summary statistics. Covariate‐adjusted vertex‐wise analyses examined the association of CAD‐PRS with cortical volume, thickness, and surface area (SA) in the overall sample, and within BD and HC groups. Additional region‐of‐interest (ROI) analyses were conducted to examine the anterior cingulate cortex (ACC), amygdala, and hippocampus. Exploratory sex‐stratified analyses were also undertaken. Results In the overall sample, higher CAD‐PRS was associated with lower right inferior temporal gyrus volume ( β = −0.32, p = 0.03). There were also negative associations between CAD‐PRS and brain structure within BD (5 cortical thickness clusters) and HC (1 SA cluster). Within the BD group, sex‐stratified analyses revealed significant findings for females, but not for males. ROI analyses revealed a nominal association of higher CAD‐PRS with lower ACC thickness in the BD group ( β = −0.31, p uncorrected = 0.05, p corrected = 0.20). Conclusion Higher CAD‐PRS was associated with lower regional gray matter structure in youth, in regions implicated in BD. Findings were more pronounced in the BD group, particularly among females, and related to cortical thickness specifically. Future longitudinal studies are needed to examine the association of CAD‐PRS with neurodevelopmental changes over time and to discern mechanisms underlying the observed findings.
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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) | 0.002 | 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".