Causal Effect of MMP-1 (Matrix Metalloproteinase-1), MMP-8, and MMP-12 Levels on Ischemic Stroke
Bibliographic record
Abstract
Background and Purpose: MMP (matrix metalloproteinase) levels have been widely associated with ischemic stroke risk and poststroke outcome. However, their role as a risk factor or as a subeffect because of ischemia is uncertain. Methods: We performed a literature search of genome-wide studies that evaluate serum/plasma levels of MMPs. We used a 2-sample Mendelian randomization approach to evaluate the causality of MMP levels on ischemic stroke risk or poststroke outcome, using 2 cohorts: MEGASTROKE (n=440 328) and GODs (n=1791). Results: Genome-wide association studies of MMP-1, MMP-8, and MMP-12 plasma/serum levels were evaluated. A significant association, which was also robust in the sensitivity analysis, was found with all ischemic strokes: MMP-12 (odds ratio=0.90 [95% CI, 0.86–0.94]; q value=7.43×10−5), and with subtypes of stroke, large-artery atherosclerosis: MMP-1 (odds ratio=0.95 [95% CI, 0.92–0.98]; q value=0.01) and MMP-12 (odds ratio=0.71 [95% CI, 0.65–0.77]; q value=5.11×10−14); small-vessel occlusion: MMP-8 (odds ratio=1.24 [95% CI, 1.06–1.45]; q value=0.03). No associations were found in relation to stroke outcome. Conclusions: Our study suggests a causal link between lower serum levels of MMP-12 and the risk of ischemic stroke, lower serum levels of MMP-1 and MMP-12 and the risk of large-artery stroke and higher serum levels of MMP-8 and the risk of lacunar stroke.
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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".