Adipokines: molecular links between obesity and atheroslcerosis
Bibliographic record
Abstract
Atherosclerotic disease remains the leading cause of death in industrialized nations despite major advances in its diagnosis, treatment, and prevention. The increasing epidemic of obesity, insulin resistance, and diabetes will likely add to this burden. Increasingly, it is becoming apparent that adipose tissue is an active endocrine and paracrine organ that releases several bioactive mediators that influence not only body weight homeostasis but also inflammation, coagulation, fibrinolysis, insulin resistance, diabetes, and atherosclerosis. The cellular mechanisms linking obesity and atherosclerosis are complex and have not been fully elucidated. This review summarizes the experimental and clinical evidence on how excess body fat influences cardiovascular health through multiple yet converging pathways. The role of adipose tissue in the development of obesity-linked insulin resistance, metabolic syndrome, and diabetes will be reviewed, including an examination of the molecular links between obesity and atherosclerosis, namely, the effects of fat-derived adipokines. Finally, we will discuss how these new insights may provide us with innovative therapeutic strategies to improve cardiovascular health.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 0.004 |
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".