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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".