096. The Impact of Anti-Tumour Necrosis Factor Alpha Therapy on Platelet Function, Lipid Profile, and Insulin Metabolism in Patients with Inflammatory Arthritis: A Prospective Cohort Study
Bibliographic record
Abstract
Background: Patients with inflammatory arthritis die prematurely from cardiovascular disease (CVD). This increased CVD risk is not fully explained by traditional risk factors, is strongly associated with inflammation and is significantly reduced in those who respond to anti- TNF therapy. Platelets play a crucial role in the pathogenesis of atherothrombotic events. Paradoxical lipid profiles and increased rates of insulin resistance have also been shown to strongly correlate with CVD risk in patients with inflammatory arthritis. We have previously shown enhanced platelet reactivity in patients with active inflammatory arthritis compared with those in remission. Therefore, we decided to prospectively assess the influence of improved disease control with anti-TNF therapy on platelet function, lipid profiles and insulin metabolism in patients with inflammatory arthritis. Methods: Patients with an established diagnosis of inflammatory arthritis (RA, PsA, seronegative SpA) and who were due to commence anti-TNF therapy were recruited. Patients with a history of CVD, diabetes mellitus or receiving anti-platelet therapy or cholesterol lowering medication were excluded. Demographic data, traditional CVD risk factors and medication use were recorded. Patients were evaluated on two separate occasions, before commencing an anti-TNF agent (adalimumab, etanercept, certolizumab, infliximab) and again after 4 months of treatment. Disease activity assessment comprised serological markers (ESR, CRP, fibrinogen), patient measures (VASDA), evaluator global assessment and the DAS for 28 joints (DAS28) score. Patients were classified as responders by reduction of at least one disease category in DAS28 or >30% improvement in VASDA, where applicable. Samples of fasting lipids, glucose and insulin were obtained. Insulin resistance was assessed using the HOMA-IR method. Lipid atherogenic profile was measured using the established HDL/LDL ratio. Platelet responses to multiple concentrations of several agonists (arachidonic acid, collagen, epinephrine, TRAP and ADP) were measured simultaneously using a modification of light transmission aggregometry and log dose–response curves were calculated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".