Adipokine Mediators of Inflammation and Cardiometabolic Comorbidity in Rheumatoid Arthritis: Is There a Master Adipokine?
Bibliographic record
Abstract
Understanding how immune cells interact with adipocytes to induce the downstream consequences of obesity (i.e., insulin resistance, hepatic steatosis, atherosclerosis, just to name a few) is among the most active areas of current interest among obesity researchers. In fact, that white adipose tissue has secretory capacity is a relatively recent concept. In the early 1990s it was recognized that tumor necrosis factor-α (TNF-α) and leptin are expressed by adipocytes in proportion to adipose tissue mass and that they possess diverse autocrine and paracrine functions relating to energy metabolism and the neurohormonal regulation of feeding impulses1,2. Since then, dozens of secreted adipocyte products have been identified3. That many proteins termed “adipokines” overlap those considered central to the inflammatory pathobiology of rheumatoid arthritis (RA) is of particular interest to RA researchers. Adipocytes secrete inflammatory cytokines [TNF-α, interleukin 1β (IL-1β), IL-6], chemokines (monocyte chemoattractant protein-1), acute-phase reactants (serum amyloid A), and others that parallel the expression profiles of inflamed synovium4. Adipokine expression is potentiated through activated macrophages and T lymphocytes within adipose tissue, and represents an additional parallel to the processes occurring in RA synovitis. Interestingly, many obesity-related complications are also present in patients with RA, even among those who are not obese. Cardiovascular disease (CVD) event … Address correspondence to Dr. Giles; E-mail: jtg2122{at}columbia.edu
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".