Circulating Leptin and Adiponectin Concentrations During Tumor Necrosis Factor Blockade in Patients with Active Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Adipocytokines, including leptin and adiponectin, may play an important role in the pathogenesis of rheumatoid arthritis (RA). We investigated the effects of longterm therapeutic tumor necrosis factor (TNF) blockade on adipocytokine concentrations in patients with RA. METHODS: We studied 58 RA patients starting anti-TNF therapy and 58 healthy controls matched for age, sex, and body mass index (BMI). Fasting blood samples were drawn at baseline, 2 weeks, and 6 months after the start of anti-TNF therapy and serum levels of leptin and adiponectin were measured. RESULTS: Patients with RA had increased adiponectin (p<0.001) and similar leptin concentrations compared with the controls. Leptin concentrations were significantly higher in patients with high BMI (p<0.001) and correlated positively with BMI at all timepoints (r>0.75). In contrast, serum adiponectin tended to be higher in lean RA patients and did not correlate with BMI at any timepoint. There were no clear correlations between serum concentrations of adipocytokines and disease activity (Disease Activity Score 28). Short or longterm TNF blockade alone had no influence on circulating leptin and adiponectin concentrations. Patients treated with anti-TNF and concomitant corticosteroids on a stable basis showed a significant decrease in adiponectin levels after 6 months of therapy (p<0.025). CONCLUSION: In patients with RA, chronic inflammation and its suppression during anti-TNF therapy have limited influence on plasma leptin concentrations, while significantly decreasing circulating adiponectin levels. Our findings question the suggested key role of inflammatory markers in regulating adipocytokine patterns in RA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".