Association of body fat with C‐reactive protein in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: The serum C-reactive protein (CRP) concentration is commonly used in rheumatoid arthritis (RA) as a surrogate marker of systemic inflammation, presumably induced by synovitis. However, other tissues, such as adipose tissue, can induce CRP production. This study was undertaken to explore the associations between measures of adiposity and CRP levels in RA. METHODS: One hundred ninety-six men and women with RA underwent anthropometric assessment and total body dual-energy x-ray absorptiometry for measurement of total and regional body fat and lean mass. The associations between measures of fat and lean mass and serum levels of CRP and interleukin-6 (IL-6) were determined in analyses stratified by sex, with adjustment for pertinent demographic, lifestyle, and RA disease and treatment covariates as well as for the potential modifying effects of articular activity and biologic pharmacotherapeutic agents. RESULTS: All measures of adiposity were significantly associated with the level of CRP in women, but not in men. In women, the measure of adiposity that showed the strongest association with the CRP level was truncal fat, in which, in adjusted analyses, each kilogram increase was associated with a 0.101-unit increase in the logarithmically transformed CRP level (P < 0.001). Neither the level of articular activity nor the use of biologic agents significantly modified this association in women. However, in men, elevated articular involvement was associated with a decreasing CRP level as truncal fat increased. For all analyses, substitution of IL-6 for CRP produced similar findings. CONCLUSION: Adiposity is independently associated with CRP levels in women with RA, and thus may confound the estimation of RA disease activity when serum CRP concentration is used as a surrogate for systemic inflammation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".