Adipose Depots, Not Disease-related Factors, Account for Skeletal Muscle Insulin Sensitivity in Established and Treated Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: In prior reports, individuals with rheumatoid arthritis (RA) exhibited increased insulin resistance. However, those studies were limited by either suboptimal assessment methods for insulin sensitivity or a failure to account for important determinants such as adiposity and lack of physical activity. Our objectives were to carefully assess, compare, and determine predictors of skeletal muscle insulin sensitivity in RA, accounting for adiposity and physical activity. METHODS: Thirty-nine individuals with established (seropositive or erosions) and treated RA and 39 controls matched for age, sex, race, body mass index, and physical activity underwent a frequently sampled intravenous glucose tolerance test to determine insulin sensitivity. Inflammation, body composition, and physical activity were assessed with systemic cytokine measurements, computed tomography scans, and accelerometry, respectively. Exclusions were diabetes, cardiovascular disease, medication changes within 3 months, and prednisone use over 5 mg/day. This investigation was powered to detect a clinically significant, moderate effect size for insulin sensitivity difference. RESULTS: Despite elevated systemic inflammation [interleukin (IL)-6, IL-18, tumor necrosis factor-α; p < 0.05 for all], persons with RA were not less insulin sensitive [SI geometric mean (SD): RA 4.0 (2.4) vs control 4.9 (2.1)*10(-5) min(-1)/(pmol/l); p = 0.39]. Except for visceral adiposity being slightly greater in controls (p = 0.03), there were no differences in body composition or physical activity. Lower insulin sensitivity was independently associated with increased abdominal and thigh adiposity, but not with cytokines, disease activity, duration, disability, or disease-modifying medication use. CONCLUSION: In established and treated RA, traditional risk factors, specifically excess adiposity, play more of a role in predicting skeletal muscle insulin sensitivity than do systemic inflammation or other disease-related factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".