Increased Risk of Cancer in Glomerular Diseases: A Population-Level Analysis
Bibliographic record
Abstract
Background: Patients with glomerular disease (GN) may be at an increased risk of cancer due to immunosuppression, chronic inflammation and infection with oncogenic viruses. However, there is limited epidemiologic data such that the absolute risk of cancer and cancer-related risk factors in GN remain unknown. Methods: We conducted a population-level analysis of all adults with biopsy-proven GN in British Columbia, Canada from 2000 to 2020, using a centralized pathology database linked to a provincial cancer database. Age- and sex- standardized incidence ratios (SIR) of cancer events were calculated. Kaplan-Meier curves were used to describe time to the first cancer event. Results: The cohort comprised 4,006 patients, including IgAN (N=1200), membranous nephropathy (MN, N=542), FSGS (N=770), MCD (N=364), lupus nephritis (LN, N=528) and ANCA-GN (N=602). During a median of 7.9 years follow up, cancer events occurred in 386 patients (9.6%) including colon (N=56), lung (N=50), prostate (N=45), breast (N=24), kidney (N=23), lymphoma (N=22) and cervical (N=19) cancer. Those with cancer had more severe disease activity, with lower eGFR (45.3 vs 57.2 ml/min/1.73m2) and higher proteinuria (2.5 vs 2.0 g/day), and more comorbidity burden at baseline. The risk of cancer was increased compared to the general population (SIR 1.5), including age groups 20-40, 40-60, 60-80, and >80 years (SIR 6.6, 1.7, 1.4 and 1.2 respectively). The risk of cancer was increased in ANCA-GN, FSGS, MN and MCD compared to IgAN or LN (Figure 1). Conclusion: Patients with GN have an increased risk of cancer compared to the general population, particularly amongst younger patients who have a longer life expectancy and may benefit most from improved cancer screening. The risk of cancer varied by disease activity and type of GN, both of which may be correlated with immunosuppression use. Future steps will be to investigate risk factors for cancer in GN, including the type and extent of immunosuppression exposure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".