Framingham risk score associates with incident cancer and heart failure
Bibliographic record
Abstract
AIMS: The Framingham risk score (FRS), a tool primarily used for atherosclerotic cardiovascular disease (ASCVD) risk stratification, incorporates factors like age, obesity, and smoking. However, its role in predicting cancer and heart failure (HF) risk remains unclear, while emerging data suggest these two conditions coincide frequently. METHODS AND RESULTS: We conducted a post hoc analysis using data from the PREVEND study and validated our findings in the UK Biobank. We examined the association between FRS tertiles at baseline and incident cancer or HF. Fine-Gray regression models were used to calculate subdistribution hazard ratios (sHRs), adjusting for estimated glomerular filtration rate and urinary albumin excretion with all-cause mortality as a competing risk. In PREVEND, we included 8123 participants (mean age 49 ± 13 years, 50% female). Over follow-up periods of 17.46 years [interquartile range (IQR) 17.15-17.80] (cancer) and 23.39 years (IQR 13.78-23.81) (HF), 1176 participants developed new-onset cancer and 758 developed new-onset HF. In a multivariable analysis, participants in the highest FRS tertile compared with the lowest had a higher hazard for both cancer (sHR 2.32, P < 0.001) and HF (sHR 10.08, P < 0.001). Participants in the highest FRS tertile also had the worst survival (log-rank P < 0.001). We validated these findings in the UK Biobank (n = 389942) wherein individuals in the highest FRS tertile also had a higher hazard for both cancer (sHR 2.05, P < 0.001) and HF (sHR 5.99, P < 0.001) compared with the lowest tertile. CONCLUSION: The FRS associates with new-onset cancer or HF, implicating a broader clinical application of the FRS beyond ASCVD risk stratification in cardio-oncology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".