Is the number of ideal cardiovascular health metrics in midlife associated with lower risk of cancer? Evidence from 3 European prospective cohorts
Bibliographic record
Abstract
BACKGROUND: Primordial prevention may be a relevant strategy for the prevention of cancer. Given the commonality of risk factors and mechanisms between cancer and cardiovascular disease, we examined the associations between the number of ideal cardiovascular health metrics in midlife and incident cancer. METHODS: In 3 European cohorts (NutriNet-Santé and GAZEL, France; Whitehall II, United Kingdom), the number of ideal cardiovascular health metrics was determined at baseline (range 0-7). Follow-up for cancer events was until October 2020 (NutriNet-Santé), March 2017 (Whitehall II) and December 2015 (GAZEL). Cox regression was conducted in each cohort, and results were thereafter pooled using a random-effects model. RESULTS: Data were available on 39 718 participants. A total of 16 237 were from NutriNet-Santé (mean age 51.3 yr; 28% men), 9418 were from Whitehall II (mean age 44.8 yr; 68% men) and 14 063 were from GAZEL (mean age 45.2 yr; 75% men). The median follow-up was 8.1 years in NutriNet-Santé, 29.6 years in Whitehall II and 24.8 years in GAZEL, and yielded a total of 4889 cancer events. A greater number of ideal cardiovascular health metrics was associated with a lower overall cancer risk in each cohort, with an aggregate hazard ratio (HR) per 1 increment in number of ideal metrics of 0.91 (95% confidence interval [CI] 0.88-0.93). This association remained after removal of the smoking metric (aggregate HR per unit increment in number of ideal metrics: 0.94, 95% CI 0.90-0.97), and site-specific analysis demonstrated a significant association with lung cancer. INTERPRETATION: A greater number of ideal cardiovascular health metrics in midlife was associated with lower cancer risk, notably lung cancer. Primordial prevention of cardiovascular risk factors in midlife may be a complementary strategy to prevent the onset of cancer.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".