Is Visceral Fat the Missing Link in the Relationship Between Inflammation and Insulin Resistance in RA?
Bibliographic record
Abstract
Several groups of investigators evaluating insulin resistance in rheumatoid arthritis (RA; Table 1)1,2,3,4,5,6,7,8,9 have reported that patients with RA have more insulin resistance than non-RA control subjects, even with similar body mass index (BMI)1,2,3,4,5. In many RA studies, insulin resistance was associated with markers of inflammation or disease activity5,6,7,8. Moreover, some studies that examined insulin resistance before and after treatment of RA with anti-tumor necrosis factor-α (TNF-α) or antiinterleukin 6 (IL-6) agents found that treatment with these drugs, and presumably the consequent decrease in inflammation, resulted in a significant improvement in insulin sensitivity10,11. These and other studies provide support for the notion that inflammation in RA promotes insulin resistance. View this table: Table 1. Select publications evaluating insulin resistance in RA versus non-RA control subjects. The concept that there is a relationship between the inflammation in RA and insulin resistance is also supported by studies performed in animals showing that inflammation promotes insulin resistance. For example, administration of TNF-α and IL-6 caused insulin resistance in rats and mice12,13. This finding is not surprising because cytokines such as IL-6 and TNF-α contribute to insulin resistance through downstream repression of insulin signaling (reviewed14). In this issue of The Journal , AbouAssi and colleagues present a meticulous and thorough evaluation of insulin sensitivity in 39 patients with RA and 39 control subjects matched for not only age and sex, but also for BMI and physical activity15. Insulin sensitivity was quantified by frequently sampled intravenous glucose … Address correspondence to Dr. Ormseth, 1161 21st Ave. South, T-3113 MCN, Nashville, Tennessee 37232-2681, USA; E-mail: michelle.ormseth{at}vanderbilt.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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".