Lymphoma risk in systemic lupus: effects of treatment versus disease activity
Bibliographic record
Abstract
We recently evaluated the risk of malignancy in SLE by linking a multi-site international SLE cohort with regional tumor registries. Across 28 centers, 15,980 patients were observed for 119,846 (average 7.5) person-years. In total, 641 cancers occurred, for an overall standardized incidence ratio (SIR) of 1.14 (95% CI = 1.06 to 1.24). Hematologic malignancies were substantially increased (SIR = 3.01, 95% CI = 2.47 to 3.62), particularly non-Hodgkin's lymphoma (NHL; SIR = 4.36, 95% CI = 3.43 to 5.47) and leukemia (SIR = 1.76, 95% CI = 1.04 to 2.78) [ 1 ]. Yet the relative influence of treatment versus disease activity is unknown. Our objective was to determine the relative importance of drugs versus disease activity in mediating the increased risk of lymphoma. We performed case-cohort analyses within this multi-site SLE cohort. Adjusted hazard ratios (HRs) for lymphoma were generated in multivariate regression models, for time-dependent exposures to immunomodulators (cyclophosphamide, azathioprine, methotrexate, mycophenolate, antimalarials, glucocorticoids), disease activity (mean adjusted SLEDAI-2K), demographics, calendar year, Sjögren's syndrome, and SLE duration. Partially adjusted models were also performed, using only covariates whose HR CI excluded the null. Sensitivity analyses were performed, lagging cyclophosphamide exposures by 5 years. Medications were treated both categorically (ever/never) and as cumulative doses. We studied 64 lymphomas (61 NHL, three Hodgkin's) and 4,739 cancer-free controls. As in the general population, lymphoma risk in SLE was higher in males and with age. Lymphomas occurred a mean of 13.1 years (standard deviation 9.8) after SLE diagnosis. Univariate analyses suggested a decreased lymphoma risk within the highest tertile of disease activity (relative to those with the lowest activity) but in fully adjusted models (using all variables listed above), the CI included the null (Table 1 ). Sensitivity analyses, lagging cyclophosphamide exposures, yielded similar results. In a partially adjusted model (retaining age and highest tertile of disease activity), the HR suggested a twofold lymphoma risk after cyclophosphamide. Despite a trend towards greater cyclophosphamide use in cases versus controls, in fully adjusted models, no drug was estimated to be an independent risk factor. Still, due to correlation, it remains difficult to differentiate the effects of medications from disease activity. We did not definitively demonstrate an increased risk for any medications, despite a trend to greater cyclophosphamide use in the lymphoma cases. If anything, we noted a protective effect for very high SLE disease activity. Further work will focus on genetic profiles that might interact with medication exposures to influence lymphoma risk in SLE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".