Impact of Hypertension on Cancer Stage at Diagnosis Among French Women: The <scp>E3N</scp> Prospective Cohort
Bibliographic record
Abstract
INTRODUCTION: Hypertension may delay the detection of metastatic cancers. We investigated the association between incident hypertension and the risk of metastatic onset among female cancer patients. We studied notably the role of anti-hypertensive treatment and the time between hypertension onset and cancer diagnosis in this association. METHODS: E3N is a French prospective population-based cohort that recruited 98,995 women in 1990. A total of 7844 incident invasive cancers were examined. We used multivariate logistic regression models to calculate odds ratios (OR) and their 95% confidence intervals (CI). We also used restricted cubic splines to evaluate the nonlinear dose-response associations between hypertension duration and the risk of metastatic onset (vs. localised stage). RESULTS: A total of 1994 cases (25%) of incident hypertension occurred before cancer diagnosis. Compared to non-hypertensive patients, those with untreated hypertension presented more frequently with metastatic cancer among patients who regularly underwent cancer screening (OR = 1.69, 95% CI = 1.11-2.58). This association was inverse among those who did not screen regularly (OR = 0.53, 95 CI = 0.29-0.98). Treated hypertensive patients had significantly greater odds of metastatic presentation for thyroid (OR = 2.45, 95% CI = 1.01-5.91) and lower odds for lung (OR = 0.17, 95% CI = 0.06-0.52) cancer. A significant inverse U-shaped association with time from hypertension onset (p = 0.01) was observed. CONCLUSION: In this study, hypertension was associated with metastatic cancer presentation, but cancer screening determined the direction of the association. Time from hypertension onset was inversely associated with metastatic lung cancer, with a significant nonlinear dose-response relationship. Our findings call for further research in this area to investigate the underlying mechanisms. TRIAL REGISTRATION: clinicaltrials.gov identifier: NCT03285230.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".