Variation in the Human Matrix Metalloproteinase-9 Gene Is Associated With Arterial Stiffness in Healthy Individuals
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Arterial stiffness is an important determinant of cardiovascular risk. Elastin is the main elastic component of the arterial wall and can be degraded by a number of enzymes including serine proteases and matrix metalloproteinases (MMPs). Serum MMP-9 levels correlate with arterial stiffness and predict cardiovascular risk. Polymorphisms in the MMP-9 gene are also associated with large artery function in subjects with coronary artery disease. Therefore, we investigated the influence of known MMP-9 (-1562C>T, R279Q) polymorphisms on arterial stiffness in a large cohort of healthy individuals (n=865). METHODS AND RESULTS: Aortic pulse wave velocity (PWV) and augmentation index were assessed. Supine blood pressure, biochemical markers, MMP-9 levels, and serum elastase activity (SEA) were also determined. Genomic DNA was extracted and genotyping performed. Aortic PWV, serum MMP-9, and SEA were higher in carriers of the rare alleles for the -1562C>T and R279Q polymorphisms. These polymorphisms were also associated with aortic PWV after correction for other confounding factors. Stepwise regression models with known or likely determinants of arterial stiffness revealed that approximately 60% of the variability in aortic PWV was attributable to age, mean arterial pressure, and genetic variants (P<0.001). CONCLUSIONS: We have demonstrated for the first time that aortic stiffness and elastase activity are influenced by MMP-9 gene polymorphisms. This suggests that the genetic variation in this protein may be involved in the process of large artery stiffening.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it