Association Between Corrected QT Interval and Inflammatory Cytokines in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Corrected QT (QTc) interval predicts all-cause and cardiovascular mortality and may contribute to the increased mortality risk in rheumatoid arthritis (RA). Animal experiments have shown that proinflammatory cytokines [tumor necrosis factor (TNF)-α and interleukin 1 (IL-1)] can prolong cardiomyocyte action potential. We sought to determine whether elevations in circulating inflammatory cytokines were independently associated with QTc prolongation in patients with RA. METHODS: One hundred twelve patients [median age 62 (interquartile range 17) yrs; 80 women (71%)] from a well-characterized RA cohort underwent baseline 12-lead electrocardiograms for QT interval measurement and contemporary blood sampling to assess concentrations of inflammatory markers including C-reactive protein (CRP), TNF-α, and interleukins (IL-1α, IL-1β, IL-6, IL-10). QTc was calculated using the Bazett (QTBAZ = QT ÷ √RR) and Framingham Heart Study (QTFHS = QT + 0.154 × [1 - RR]) heart rate correction formulas. RESULTS: Inflammatory cytokines (TNF-α, IL-1β, IL-6, IL-10) were positively correlated with QTBAZ (Spearman rank correlation coefficient rho = 0.199, 0.210, 0.222, 0.333; all p < 0.05). In multivariable regression analysis, these associations were all confounded by age except IL-10, where higher tertile groups were independently and positively associated with QTBAZ (β = 0.202, p = 0.023) and QTFHS (β = 0.223, p = 0.009) when compared to the lower tertile. CRP (per unit increase) was independently associated with QTBAZ (β = 0.278, p = 0.001), but not QTFHS. CONCLUSION: To our knowledge, ours is the first study demonstrating a contemporary link between inflammatory cytokines and QT interval in humans. Our results suggest that a lower inflammatory burden may protect against QTc prolongation in patients with RA. However, further studies are required to confirm the effects of pro- and antiinflammatory cytokines on QTc interval.
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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 | 0.004 |
| 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 | 0.000 |
| 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".