The Role of Plasminogen Activator Inhibitor-1 in the Metabolic Syndrome and Its Regulation
Bibliographic record
Abstract
Obesity is a major risk factor for cardiovascular disease (CVD). Lipid abnormalities, hypertension, impaired glucose tolerance or diabetes, are cardiovascular risk factors that are frequently present in patients with obesity. Haemostatic and fibrinolytic disturbances are also considered to be important risk factors for CVD hence, a potential link between CVD, obesity and the metabolic syndrome arises. Regulation of the fibrinolytic system can occur at the level of plasminogen activators and plasminogen activator inhibitor-1 (PAI-1). PAI-1, a glycoprotein, is one of the most important inhibitors of fibrinolysis. Regulation of this serine protease inhibitor may have a beneficial effect on other conditions associated with the metabolic syndrome. Human adipose tissue is a source of PAI-1. PAI-1 production may in turn be controlled by a number of hormones and cytokines which are secreted by adipose tissue in addition to dietary factors. In this review we summarise the current knowledge regarding the role of altered fibrinolytic function in obesity, CVD and hence the metabolic syndrome. Regulatory factors including different dietary components, weight loss and dietary intervention will also be discussed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".