Tumor Necrosis Factor Inhibition and Adipose Tissue Distribution — Are Reported Changes Relevant to Cardiometabolic Risk?
Bibliographic record
Abstract
Cardiovascular disease (CVD) is a prominent cause of morbidity and mortality in inflammatory arthritis, including rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS), while traditional CVD risk factors, such as hypertension and dyslipidemia, are more prevalent in these patients1,2. In the general population, obesity is recognized as a risk factor for hypertension, dyslipidemia, insulin resistance, and the metabolic syndrome (MetS), the so-called “cardiometabolic” risk factors; however, obesity has not been shown to improve risk prediction of CVD compared with those variables currently included in risk prediction models3. The distribution of body fat may have greater influence on CVD risk, particularly that located in the visceral compartments (visceral adipose tissue, or VAT). In the general population, VAT has been associated with CVD events4, coronary artery calcification5, and aortic stiffness6. In RA, VAT area higher than the 75th percentile was associated with a significantly higher probability of hypertension, raised fasting glucose, and MetS7. It may also predispose to insulin resistance and expresses a proinflammatory profile of C-reactive protein, tumor necrosis factor-α (TNF-α), and interleukin 6 expression8. Fox and colleagues advise that the contribution of subcutaneous adipose tissue (SAT), which accounts for 80% of the body’s fat, may also play a relevant role in CVD risk. A large cross-sectional study examining the association of VAT and SAT with metabolic risk factors in 3001 Third Generation and Offspring participants of the Framingham Heart Study found that waist … Address correspondence to Dr. Sheane; E-mail: sheaneb{at}yahoo.com
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".