Effect of interleukin-6 receptor blockade on surrogates of vascular risk in rheumatoid arthritis: MEASURE, a randomised, placebo-controlled study
Bibliographic record
Abstract
OBJECTIVES: The interleukin-6 receptor (IL-6R) blocker tocilizumab (TCZ) reduces inflammatory disease activity in rheumatoid arthritis (RA) but elevates lipid concentrations in some patients. We aimed to characterise the impact of IL-6R inhibition on established and novel risk factors in active RA. METHODS: Randomised, multicentre, two-part, phase III trial (24-week double-blind, 80-week open-label), MEASURE, evaluated lipid and lipoprotein levels, high-density lipoprotein (HDL) particle composition, markers of coagulation, thrombosis and vascular function by pulse wave velocity (PWV) in 132 patients with RA who received TCZ or placebo. RESULTS: Median total-cholesterol, low-density lipoprotein-cholesterol (LDL-C) and triglyceride levels increased in TCZ versus placebo recipients by week 12 (12.6% vs 1.7%, 28.1% vs 2.2%, 10.6% vs -1.9%, respectively; all p<0.01). There were no significant differences in mean small LDL, mean oxidised LDL or total HDL-C concentrations. However, HDL-associated serum amyloid A content decreased in TCZ recipients. TCZ also induced reductions (>30%) in secretory phospholipase A2-IIA, lipoprotein(a), fibrinogen and D-dimers and elevation of paraoxonase (all p<0.0001 vs placebo). The ApoB/ApoA1 ratio remained stable over time in both groups. PWV decreases were greater with placebo than TCZ at 12 weeks (adjusted mean difference 0.79 m/s (95% CI 0.22 to 1.35; p=0.0067)). CONCLUSIONS: These data provide the first detailed evidence for the modulation of lipoprotein particles and other surrogates of vascular risk with IL-6R inhibition. When compared with placebo, TCZ induced elevations in LDL-C but altered HDL particles towards an anti-inflammatory composition and favourably modified most, but not all, measured vascular risk surrogates. The net effect of such changes for cardiovascular risk requires determination.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".