Familial Risk and Interaction With Smoking and Alcohol Consumption in Bladder Cancer: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Although genetic factors are known to play a role in the pathogenesis of bladder cancer, population-level familial risk estimates are scarce. We aimed to quantify the familial risk of bladder cancer and analyze interactions between family history and smoking or alcohol consumption. Methods: Using the National Health Insurance database, we constructed a cohort of 5,524,403 study subjects with first-degree relatives (FDRs) and their lifestyle risk factors from 2002 to 2019. Familial risk was calculated using hazard ratios (HRs) with 95% confidence intervals (CIs) that compare the risk of individuals with and without affected FDRs. Interactions between family history and smoking or alcohol intake were assessed on an additive scale using the relative excess risk due to interaction (RERI). Results: Offspring with an affected parent had a 2.09-fold (95% CI: 1.41 - 3.08) increased risk of disease compared to those with unaffected parents. Familial risks of those with affected father and mother were 2.26 (95% CI: 1.51 - 3.39) and 1.10 (95% CI: 0.27 - 4.41), respectively. When adjusted for lifestyle factors, HR reduced slightly to 2.04 (95% CI: 1.38 - 3.01), suggesting that a genetic predisposition is the main driver in the familial aggregation. Smokers with a positive family history had a markedly increased risk of disease (HR: 3.60, 95% CI: 2.27 - 5.71), which exceeded the sum of their individual risks, with statistically significant interaction (RERI: 0.72, 95% CI: 0.31 - 1.13). For alcohol consumption, drinkers with a positive family history also had an increased risk of disease, although the interaction was not statistically significant (RERI: 0.05, 95% CI: -3.39 - 3.48). Conclusion: Smokers and alcohol consumers with a positive family history of bladder cancer should be considered a high-risk group and be advised to undergo genetic counseling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".