Effects of IL-1β inhibition on anemia and clonal hematopoiesis in the randomized CANTOS trial
Bibliographic record
Abstract
Canakinumab, a monoclonal antibody targeting proinflammatory cytokine interleukin-1β (IL-1β), improved hemoglobin levels while preventing recurrent cardiovascular events in the Canakinumab Anti-inflammatory Thrombosis Outcomes Study (CANTOS). This cardiovascular (CV) preventive effect was greater in patients with TET2 mutations associated with clonal hematopoiesis (CH). The current proteogenomic analysis aimed to understand the clinical response to canakinumab and underlying proteomic profiles in the context of CH and anemia. The analysis included 4595 patients from the CANTOS study who received either canakinumab or placebo and evaluated multiplexed proteomics (4785 proteins) using SomaScan and targeted deep sequencing for CH mutations. Incident anemia was more common in the presence of CH mutations but reduced by canakinumab treatment. Canakinumab treatment was significantly associated with higher hemoglobin increment in patients with concurrent CH mutations and anemia than patients with CH mutations without anemia or without CH mutations. Compared with those without CH mutations, the presence of CH mutations was associated with proteomic signatures of inflammation and defense response to infection, as well as markers of high-risk CV disease which was further enhanced by the presence of anemia. Canakinumab suppressed hepcidin, proinflammatory cytokines, myeloid activation, and complement pathways, and reversed pathologically deregulated pathways to a greater extent in patients with CH mutations and anemia. These molecular findings provide evidence of the clinical use of IL-1β blockade and support further study of canakinumab for patients with concurrent anemia and CH mutations. This study was registered at www.clinicaltrials.gov as #NCT01327846.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".