A novel GWAS locus influences microvascular response to mental stress and predicts adverse cardiovascular events
Bibliographic record
Abstract
Excessive peripheral microvascular constriction during acute psychological stress reflects similar changes in coronary blood flow and is a predictor of adverse cardiovascular outcomes. Among individuals with coronary artery disease (CAD), we sought to determine if genetic factors contribute to the degree of microvascular constriction during mental stress. A total of 580 stable CAD individuals from two prospective cohort studies underwent mental stress testing. Digital pulse wave amplitude was continuously measured and the stress/rest (sPAT) ratio of pulse wave amplitude was calculated. Race stratified genome-wide association studies (GWAS) of sPAT-ratio were conducted using linear regression of additive genetic models. A trans-ethnic meta-analysis integrated the four sets of GWAS results. Participants were followed for the outcome of recurrent cardiovascular events (myocardial infarction, heart failure, revascularization, and CV death) for a median of 5 years. We used Wei-Lin-Weissfeld (WLW) model to assess the association between sPAT-ratio with recurrent events. Mean age was 63 ± 9. We identified three SNPs in linkage disequilibrium, closely related to chr7:111,666,943 T > C (rs6466396) that were associated with sPAT-ratio (p = 6.68E-09). Participants homozygous for the T allele had 80% higher risk of incident adverse events (HR 1.8, 95% CI, 1.4-2.2). Also, participants with a lower sPAT-ratio (< median) had a higher adverse event rate, hazard ratio (HR) = 1.3, [95%confidence interval (CI), 1.1-1.6]. However, adjustment for the genotypes did not substantially alter the impact of sPAT ratio on adverse outcome rate. In conclusion, we have identified a genetic basis for stress-induced vasomotion. The 3 linked variants modulate vasoconstriction during mental stress may have a prognostic importance.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".