Cancer Risk Associated with Immunosuppressive Therapy in Glomerular Diseases: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Patients with glomerular disease (GN) are at an increased risk of cancer. However, the contribution of immunosuppression to this risk remains unclear Methods: We conducted a population-level analysis of adults with biopsy-proven GN in British Columbia, Canada, from 2000 to 2020. Antimetabolites and calcineurin inhibitors were quantified using defined daily dose [DDD]. Weighted cumulative exposure [WCE] was used to determine the most appropriate measure of each immunosuppression exposure associated with cancer risk, which was then evaluated using an extended Cox regression model. Results: The cohort included 4039 patients with IgA nephropathy (N=1200), membranous nephropathy (N=542), focal segmental glomerulosclerosis (N=770), minimal changed disease (N=364), lupus nephritis (N=528) and ANCA glomerulonephritis (N=602). Over a median follow-up of 7.8 years, 384 (9.5%) patients developed cancer. WCE models indicated that use of antimetabolites in the preceding four years had a significant impact on cancer risk, but calcineurin inhibitors was not (Figure 1). In fully adjusted survival model, cumulative exposure to antimetabolites showed a dose-dependent relationship with cancer risk. Moderate exposure (1100–1300 DDD over four years) was associated with an intermediate risk (hazard ratio [HR] 1.11, 95% confidence interval [CI] 0.35–3.58), while high exposure (≥1300 DDD over four years) was linked to a nearly four-fold increase of hazard (HR 3.83, 95% CI 1.98–7.44) (Table 1). Conclusion: Antimetabolites but not calcineurin inhibitors are associated with an increased risk of cancer in patients with GN. This indicates need for personalized immunosuppressive strategies that carefully balance disease control with long-term safety. Future work will explore the cancer risk associated with other immunosuppression medications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".